diff --git a/.github/ISSUE_TEMPLATE/config.yml b/.github/ISSUE_TEMPLATE/config.yml index fec273756..fc9514dc7 100644 --- a/.github/ISSUE_TEMPLATE/config.yml +++ b/.github/ISSUE_TEMPLATE/config.yml @@ -2,10 +2,10 @@ blank_issues_enabled: false contact_links: - name: Have you read the docs? - url: https://llama-stack.readthedocs.io/en/latest/index.html + url: https://llamastack.github.io/latest/providers/external/index.html about: Much help can be found in the docs - name: Start a discussion - url: https://github.com/meta-llama/llama-stack/discussions/new + url: https://github.com/llamastack/llama-stack/discussions/new/ about: Start a discussion on a topic - name: Chat on Discord url: https://discord.gg/llama-stack diff --git a/.github/actions/run-and-record-tests/action.yml b/.github/actions/run-and-record-tests/action.yml index 60550cfdc..a3eb31d9f 100644 --- a/.github/actions/run-and-record-tests/action.yml +++ b/.github/actions/run-and-record-tests/action.yml @@ -2,26 +2,28 @@ name: 'Run and Record Tests' description: 'Run integration tests and handle recording/artifact upload' inputs: - test-subdirs: - description: 'Comma-separated list of test subdirectories to run' - required: true - test-pattern: - description: 'Regex pattern to pass to pytest -k' - required: false - default: '' stack-config: description: 'Stack configuration to use' required: true - provider: - description: 'Provider to use for tests' - required: true + setup: + description: 'Setup to use for tests (e.g., ollama, gpt, vllm)' + required: false + default: '' inference-mode: description: 'Inference mode (record or replay)' required: true - run-vision-tests: - description: 'Whether to run vision tests' + suite: + description: 'Test suite to use: base, responses, vision, etc.' required: false - default: 'false' + default: '' + subdirs: + description: 'Comma-separated list of test subdirectories to run; overrides suite' + required: false + default: '' + pattern: + description: 'Regex pattern to pass to pytest -k' + required: false + default: '' runs: using: 'composite' @@ -36,14 +38,23 @@ runs: - name: Run Integration Tests shell: bash run: | - uv run --no-sync ./scripts/integration-tests.sh \ - --stack-config '${{ inputs.stack-config }}' \ - --provider '${{ inputs.provider }}' \ - --test-subdirs '${{ inputs.test-subdirs }}' \ - --test-pattern '${{ inputs.test-pattern }}' \ - --inference-mode '${{ inputs.inference-mode }}' \ - ${{ inputs.run-vision-tests == 'true' && '--run-vision-tests' || '' }} \ - | tee pytest-${{ inputs.inference-mode }}.log + SCRIPT_ARGS="--stack-config ${{ inputs.stack-config }} --inference-mode ${{ inputs.inference-mode }}" + + # Add optional arguments only if they are provided + if [ -n '${{ inputs.setup }}' ]; then + SCRIPT_ARGS="$SCRIPT_ARGS --setup ${{ inputs.setup }}" + fi + if [ -n '${{ inputs.suite }}' ]; then + SCRIPT_ARGS="$SCRIPT_ARGS --suite ${{ inputs.suite }}" + fi + if [ -n '${{ inputs.subdirs }}' ]; then + SCRIPT_ARGS="$SCRIPT_ARGS --subdirs ${{ inputs.subdirs }}" + fi + if [ -n '${{ inputs.pattern }}' ]; then + SCRIPT_ARGS="$SCRIPT_ARGS --pattern ${{ inputs.pattern }}" + fi + + uv run --no-sync ./scripts/integration-tests.sh $SCRIPT_ARGS | tee pytest-${{ inputs.inference-mode }}.log - name: Commit and push recordings @@ -57,12 +68,7 @@ runs: echo "New recordings detected, committing and pushing" git add tests/integration/recordings/ - if [ "${{ inputs.run-vision-tests }}" == "true" ]; then - git commit -m "Recordings update from CI (vision)" - else - git commit -m "Recordings update from CI" - fi - + git commit -m "Recordings update from CI (suite: ${{ inputs.suite }})" git fetch origin ${{ github.ref_name }} git rebase origin/${{ github.ref_name }} echo "Rebased successfully" diff --git a/.github/actions/setup-ollama/action.yml b/.github/actions/setup-ollama/action.yml index e57876cb0..5c95d131d 100644 --- a/.github/actions/setup-ollama/action.yml +++ b/.github/actions/setup-ollama/action.yml @@ -1,17 +1,17 @@ name: Setup Ollama description: Start Ollama inputs: - run-vision-tests: - description: 'Run vision tests: "true" or "false"' + suite: + description: 'Test suite to use: base, responses, vision, etc.' required: false - default: 'false' + default: '' runs: using: "composite" steps: - name: Start Ollama shell: bash run: | - if [ "${{ inputs.run-vision-tests }}" == "true" ]; then + if [ "${{ inputs.suite }}" == "vision" ]; then image="ollama-with-vision-model" else image="ollama-with-models" diff --git a/.github/actions/setup-test-environment/action.yml b/.github/actions/setup-test-environment/action.yml index d830e3d13..478e8f598 100644 --- a/.github/actions/setup-test-environment/action.yml +++ b/.github/actions/setup-test-environment/action.yml @@ -8,14 +8,14 @@ inputs: client-version: description: 'Client version (latest or published)' required: true - provider: - description: 'Provider to setup (ollama or vllm)' - required: true - default: 'ollama' - run-vision-tests: - description: 'Whether to setup provider for vision tests' + setup: + description: 'Setup to configure (ollama, vllm, gpt, etc.)' required: false - default: 'false' + default: 'ollama' + suite: + description: 'Test suite to use: base, responses, vision, etc.' + required: false + default: '' inference-mode: description: 'Inference mode (record or replay)' required: true @@ -30,13 +30,13 @@ runs: client-version: ${{ inputs.client-version }} - name: Setup ollama - if: ${{ inputs.provider == 'ollama' && inputs.inference-mode == 'record' }} + if: ${{ (inputs.setup == 'ollama' || inputs.setup == 'ollama-vision') && inputs.inference-mode == 'record' }} uses: ./.github/actions/setup-ollama with: - run-vision-tests: ${{ inputs.run-vision-tests }} + suite: ${{ inputs.suite }} - name: Setup vllm - if: ${{ inputs.provider == 'vllm' && inputs.inference-mode == 'record' }} + if: ${{ inputs.setup == 'vllm' && inputs.inference-mode == 'record' }} uses: ./.github/actions/setup-vllm - name: Build Llama Stack diff --git a/.github/workflows/README.md b/.github/workflows/README.md index 8344d12a4..7c9d2bffd 100644 --- a/.github/workflows/README.md +++ b/.github/workflows/README.md @@ -5,10 +5,11 @@ Llama Stack uses GitHub Actions for Continuous Integration (CI). Below is a tabl | Name | File | Purpose | | ---- | ---- | ------- | | Update Changelog | [changelog.yml](changelog.yml) | Creates PR for updating the CHANGELOG.md | +| API Conformance Tests | [conformance.yml](conformance.yml) | Run the API Conformance test suite on the changes. | | Installer CI | [install-script-ci.yml](install-script-ci.yml) | Test the installation script | | Integration Auth Tests | [integration-auth-tests.yml](integration-auth-tests.yml) | Run the integration test suite with Kubernetes authentication | | SqlStore Integration Tests | [integration-sql-store-tests.yml](integration-sql-store-tests.yml) | Run the integration test suite with SqlStore | -| Integration Tests (Replay) | [integration-tests.yml](integration-tests.yml) | Run the integration test suite from tests/integration in replay mode | +| Integration Tests (Replay) | [integration-tests.yml](integration-tests.yml) | Run the integration test suites from tests/integration in replay mode | | Vector IO Integration Tests | [integration-vector-io-tests.yml](integration-vector-io-tests.yml) | Run the integration test suite with various VectorIO providers | | Pre-commit | [pre-commit.yml](pre-commit.yml) | Run pre-commit checks | | Test Llama Stack Build | [providers-build.yml](providers-build.yml) | Test llama stack build | @@ -20,4 +21,3 @@ Llama Stack uses GitHub Actions for Continuous Integration (CI). Below is a tabl | Test External API and Providers | [test-external.yml](test-external.yml) | Test the External API and Provider mechanisms | | UI Tests | [ui-unit-tests.yml](ui-unit-tests.yml) | Run the UI test suite | | Unit Tests | [unit-tests.yml](unit-tests.yml) | Run the unit test suite | -| Update ReadTheDocs | [update-readthedocs.yml](update-readthedocs.yml) | Update the Llama Stack ReadTheDocs site | diff --git a/.github/workflows/conformance.yml b/.github/workflows/conformance.yml new file mode 100644 index 000000000..9383476f5 --- /dev/null +++ b/.github/workflows/conformance.yml @@ -0,0 +1,71 @@ +# API Conformance Tests +# This workflow ensures that API changes maintain backward compatibility and don't break existing integrations +# It runs schema validation and OpenAPI diff checks to catch breaking changes early + +name: API Conformance Tests + +run-name: Run the API Conformance test suite on the changes. + +on: + push: + branches: [ main ] + pull_request: + branches: [ main ] + types: [opened, synchronize, reopened] + paths: + - 'docs/static/llama-stack-spec.yaml' + - 'docs/static/llama-stack-spec.html' + - '.github/workflows/conformance.yml' # This workflow itself + +concurrency: + group: ${{ github.workflow }}-${{ github.ref == 'refs/heads/main' && github.run_id || github.ref }} + # Cancel in-progress runs when new commits are pushed to avoid wasting CI resources + cancel-in-progress: true + +jobs: + # Job to check if API schema changes maintain backward compatibility + check-schema-compatibility: + runs-on: ubuntu-latest + steps: + # Using specific version 4.1.7 because 5.0.0 fails when trying to run this locally using `act` + # This ensures consistent behavior between local testing and CI + - name: Checkout PR Code + uses: actions/checkout@08c6903cd8c0fde910a37f88322edcfb5dd907a8 # v5.0.0 + + # Checkout the base branch to compare against (usually main) + # This allows us to diff the current changes against the previous state + - name: Checkout Base Branch + uses: actions/checkout@08c6903cd8c0fde910a37f88322edcfb5dd907a8 # v5.0.0 + with: + ref: ${{ github.event.pull_request.base.ref }} + path: 'base' + + # Cache oasdiff to avoid checksum failures and speed up builds + - name: Cache oasdiff + id: cache-oasdiff + uses: actions/cache@0400d5f644dc74513175e3cd8d07132dd4860809 + with: + path: ~/oasdiff + key: oasdiff-${{ runner.os }} + + # Install oasdiff: https://github.com/oasdiff/oasdiff, a tool for detecting breaking changes in OpenAPI specs. + - name: Install oasdiff + if: steps.cache-oasdiff.outputs.cache-hit != 'true' + run: | + curl -fsSL https://raw.githubusercontent.com/oasdiff/oasdiff/main/install.sh | sh + cp /usr/local/bin/oasdiff ~/oasdiff + + # Setup cached oasdiff + - name: Setup cached oasdiff + if: steps.cache-oasdiff.outputs.cache-hit == 'true' + run: | + sudo cp ~/oasdiff /usr/local/bin/oasdiff + sudo chmod +x /usr/local/bin/oasdiff + + # Run oasdiff to detect breaking changes in the API specification + # This step will fail if incompatible changes are detected, preventing breaking changes from being merged + - name: Run OpenAPI Breaking Change Diff + run: | + oasdiff breaking --fail-on ERR base/docs/static/llama-stack-spec.yaml docs/static/llama-stack-spec.yaml --match-path '^/v1/openai/v1' \ + --match-path '^/v1/vector-io' \ + --match-path '^/v1/vector-dbs' diff --git a/.github/workflows/integration-tests.yml b/.github/workflows/integration-tests.yml index 57e582b20..711eccd9e 100644 --- a/.github/workflows/integration-tests.yml +++ b/.github/workflows/integration-tests.yml @@ -1,6 +1,6 @@ name: Integration Tests (Replay) -run-name: Run the integration test suite from tests/integration in replay mode +run-name: Run the integration test suites from tests/integration in replay mode on: push: @@ -28,18 +28,10 @@ on: description: 'Test against both the latest and published versions' type: boolean default: false - test-provider: - description: 'Test against a specific provider' + test-setup: + description: 'Test against a specific setup' type: string default: 'ollama' - test-subdirs: - description: 'Comma-separated list of test subdirectories to run' - type: string - default: '' - test-pattern: - description: 'Regex pattern to pass to pytest -k' - type: string - default: '' concurrency: # Skip concurrency for pushes to main - each commit should be tested independently @@ -50,18 +42,18 @@ jobs: run-replay-mode-tests: runs-on: ubuntu-latest - name: ${{ format('Integration Tests ({0}, {1}, {2}, client={3}, vision={4})', matrix.client-type, matrix.provider, matrix.python-version, matrix.client-version, matrix.run-vision-tests) }} + name: ${{ format('Integration Tests ({0}, {1}, {2}, client={3}, {4})', matrix.client-type, matrix.setup, matrix.python-version, matrix.client-version, matrix.suite) }} strategy: fail-fast: false matrix: client-type: [library, server] - # Use vllm on weekly schedule, otherwise use test-provider input (defaults to ollama) - provider: ${{ (github.event.schedule == '1 0 * * 0') && fromJSON('["vllm"]') || fromJSON(format('["{0}"]', github.event.inputs.test-provider || 'ollama')) }} + # Use vllm on weekly schedule, otherwise use test-setup input (defaults to ollama) + setup: ${{ (github.event.schedule == '1 0 * * 0') && fromJSON('["vllm"]') || fromJSON(format('["{0}"]', github.event.inputs.test-setup || 'ollama')) }} # Use Python 3.13 only on nightly schedule (daily latest client test), otherwise use 3.12 python-version: ${{ github.event.schedule == '0 0 * * *' && fromJSON('["3.12", "3.13"]') || fromJSON('["3.12"]') }} client-version: ${{ (github.event.schedule == '0 0 * * *' || github.event.inputs.test-all-client-versions == 'true') && fromJSON('["published", "latest"]') || fromJSON('["latest"]') }} - run-vision-tests: [true, false] + suite: [base, vision] steps: - name: Checkout repository @@ -72,16 +64,14 @@ jobs: with: python-version: ${{ matrix.python-version }} client-version: ${{ matrix.client-version }} - provider: ${{ matrix.provider }} - run-vision-tests: ${{ matrix.run-vision-tests }} + setup: ${{ matrix.setup }} + suite: ${{ matrix.suite }} inference-mode: 'replay' - name: Run tests uses: ./.github/actions/run-and-record-tests with: - test-subdirs: ${{ inputs.test-subdirs }} - test-pattern: ${{ inputs.test-pattern }} stack-config: ${{ matrix.client-type == 'library' && 'ci-tests' || 'server:ci-tests' }} - provider: ${{ matrix.provider }} + setup: ${{ matrix.setup }} inference-mode: 'replay' - run-vision-tests: ${{ matrix.run-vision-tests }} + suite: ${{ matrix.suite }} diff --git a/.github/workflows/pre-commit.yml b/.github/workflows/pre-commit.yml index 5f13620f7..b5845be53 100644 --- a/.github/workflows/pre-commit.yml +++ b/.github/workflows/pre-commit.yml @@ -28,7 +28,7 @@ jobs: fetch-depth: ${{ github.actor == 'dependabot[bot]' && 0 || 1 }} - name: Set up Python - uses: actions/setup-python@a26af69be951a213d495a4c3e4e4022e16d87065 # v5.6.0 + uses: actions/setup-python@e797f83bcb11b83ae66e0230d6156d7c80228e7c # v6.0.0 with: python-version: '3.12' cache: pip @@ -37,7 +37,7 @@ jobs: .pre-commit-config.yaml - name: Set up Node.js - uses: actions/setup-node@49933ea5288caeca8642d1e84afbd3f7d6820020 # v4.4.0 + uses: actions/setup-node@a0853c24544627f65ddf259abe73b1d18a591444 # v5.0.0 with: node-version: '20' cache: 'npm' @@ -47,12 +47,21 @@ jobs: run: npm ci working-directory: llama_stack/ui - - uses: pre-commit/action@2c7b3805fd2a0fd8c1884dcaebf91fc102a13ecd # v3.0.1 + - name: Run pre-commit + id: precommit + uses: pre-commit/action@2c7b3805fd2a0fd8c1884dcaebf91fc102a13ecd # v3.0.1 continue-on-error: true env: SKIP: no-commit-to-branch RUFF_OUTPUT_FORMAT: github + - name: Check pre-commit results + if: steps.precommit.outcome == 'failure' + run: | + echo "::error::Pre-commit hooks failed. Please run 'pre-commit run --all-files' locally and commit the fixes." + echo "::warning::Some pre-commit hooks failed. Check the output above for details." + exit 1 + - name: Debug run: | echo "github.ref: ${{ github.ref }}" @@ -80,17 +89,23 @@ jobs: echo "No changes to commit" fi - - name: Verify if there are any diff files after pre-commit + - name: Verify no uncommitted changes if: github.actor != 'dependabot[bot]' run: | - git diff --exit-code || (echo "There are uncommitted changes, run pre-commit locally and commit again" && exit 1) + if ! git diff --exit-code; then + echo "::error::There are uncommitted changes after pre-commit. Please run 'pre-commit run --all-files' locally and commit the fixes." + echo "::warning::Files with changes:" + git diff --name-status + exit 1 + fi - name: Verify if there are any new files after pre-commit if: github.actor != 'dependabot[bot]' run: | unstaged_files=$(git ls-files --others --exclude-standard) if [ -n "$unstaged_files" ]; then - echo "There are uncommitted new files, run pre-commit locally and commit again" + echo "::error::There are new untracked files after pre-commit. Please run 'pre-commit run --all-files' locally and commit the fixes." + echo "::warning::New files:" echo "$unstaged_files" exit 1 fi diff --git a/.github/workflows/python-build-test.yml b/.github/workflows/python-build-test.yml index bf9a3e057..ea8e6a66a 100644 --- a/.github/workflows/python-build-test.yml +++ b/.github/workflows/python-build-test.yml @@ -24,7 +24,7 @@ jobs: uses: actions/checkout@08c6903cd8c0fde910a37f88322edcfb5dd907a8 # v5.0.0 - name: Install uv - uses: astral-sh/setup-uv@4959332f0f014c5280e7eac8b70c90cb574c9f9b # v6.6.0 + uses: astral-sh/setup-uv@b75a909f75acd358c2196fb9a5f1299a9a8868a4 # v6.7.0 with: python-version: ${{ matrix.python-version }} activate-environment: true diff --git a/.github/workflows/record-integration-tests.yml b/.github/workflows/record-integration-tests.yml index d4f5586e2..65a04f125 100644 --- a/.github/workflows/record-integration-tests.yml +++ b/.github/workflows/record-integration-tests.yml @@ -10,19 +10,19 @@ run-name: Run the integration test suite from tests/integration on: workflow_dispatch: inputs: - test-subdirs: - description: 'Comma-separated list of test subdirectories to run' - type: string - default: '' - test-provider: - description: 'Test against a specific provider' + test-setup: + description: 'Test against a specific setup' type: string default: 'ollama' - run-vision-tests: - description: 'Whether to run vision tests' - type: boolean - default: false - test-pattern: + suite: + description: 'Test suite to use: base, responses, vision, etc.' + type: string + default: '' + subdirs: + description: 'Comma-separated list of test subdirectories to run; overrides suite' + type: string + default: '' + pattern: description: 'Regex pattern to pass to pytest -k' type: string default: '' @@ -38,11 +38,11 @@ jobs: - name: Echo workflow inputs run: | echo "::group::Workflow Inputs" - echo "test-subdirs: ${{ inputs.test-subdirs }}" - echo "test-provider: ${{ inputs.test-provider }}" - echo "run-vision-tests: ${{ inputs.run-vision-tests }}" - echo "test-pattern: ${{ inputs.test-pattern }}" echo "branch: ${{ github.ref_name }}" + echo "test-setup: ${{ inputs.test-setup }}" + echo "suite: ${{ inputs.suite }}" + echo "subdirs: ${{ inputs.subdirs }}" + echo "pattern: ${{ inputs.pattern }}" echo "::endgroup::" - name: Checkout repository @@ -55,16 +55,16 @@ jobs: with: python-version: "3.12" # Use single Python version for recording client-version: "latest" - provider: ${{ inputs.test-provider || 'ollama' }} - run-vision-tests: ${{ inputs.run-vision-tests }} + setup: ${{ inputs.test-setup || 'ollama' }} + suite: ${{ inputs.suite }} inference-mode: 'record' - name: Run and record tests uses: ./.github/actions/run-and-record-tests with: - test-pattern: ${{ inputs.test-pattern }} - test-subdirs: ${{ inputs.test-subdirs }} stack-config: 'server:ci-tests' # recording must be done with server since more tests are run - provider: ${{ inputs.test-provider || 'ollama' }} + setup: ${{ inputs.test-setup || 'ollama' }} inference-mode: 'record' - run-vision-tests: ${{ inputs.run-vision-tests }} + suite: ${{ inputs.suite }} + subdirs: ${{ inputs.subdirs }} + pattern: ${{ inputs.pattern }} diff --git a/.github/workflows/stale_bot.yml b/.github/workflows/stale_bot.yml index 087df72d7..502a78f8e 100644 --- a/.github/workflows/stale_bot.yml +++ b/.github/workflows/stale_bot.yml @@ -24,7 +24,7 @@ jobs: runs-on: ubuntu-latest steps: - name: Stale Action - uses: actions/stale@5bef64f19d7facfb25b37b414482c7164d639639 # v9.1.0 + uses: actions/stale@3a9db7e6a41a89f618792c92c0e97cc736e1b13f # v10.0.0 with: stale-issue-label: 'stale' stale-issue-message: > diff --git a/.github/workflows/ui-unit-tests.yml b/.github/workflows/ui-unit-tests.yml index 2afb92bee..c16f512d1 100644 --- a/.github/workflows/ui-unit-tests.yml +++ b/.github/workflows/ui-unit-tests.yml @@ -29,7 +29,7 @@ jobs: uses: actions/checkout@08c6903cd8c0fde910a37f88322edcfb5dd907a8 # v5.0.0 - name: Setup Node.js - uses: actions/setup-node@49933ea5288caeca8642d1e84afbd3f7d6820020 # v4.4.0 + uses: actions/setup-node@a0853c24544627f65ddf259abe73b1d18a591444 # v5.0.0 with: node-version: ${{ matrix.node-version }} cache: 'npm' diff --git a/.github/workflows/update-readthedocs.yml b/.github/workflows/update-readthedocs.yml deleted file mode 100644 index e12f0adf8..000000000 --- a/.github/workflows/update-readthedocs.yml +++ /dev/null @@ -1,70 +0,0 @@ -name: Update ReadTheDocs - -run-name: Update the Llama Stack ReadTheDocs site - -on: - workflow_dispatch: - inputs: - branch: - description: 'RTD version to update' - required: false - default: 'latest' - push: - branches: - - main - paths: - - 'docs/**' - - 'pyproject.toml' - - '.github/workflows/update-readthedocs.yml' - tags: - - '*' - pull_request: - branches: - - main - paths: - - 'docs/**' - - 'pyproject.toml' - - '.github/workflows/update-readthedocs.yml' - -concurrency: - group: ${{ github.workflow }}-${{ github.ref == 'refs/heads/main' && github.run_id || github.ref }} - cancel-in-progress: true - -jobs: - update-readthedocs: - runs-on: ubuntu-latest - env: - TOKEN: ${{ secrets.READTHEDOCS_TOKEN }} - steps: - - name: Checkout repository - uses: actions/checkout@08c6903cd8c0fde910a37f88322edcfb5dd907a8 # v5.0.0 - - - name: Install dependencies - uses: ./.github/actions/setup-runner - - - name: Build HTML - run: | - cd docs - uv run make html - - - name: Trigger ReadTheDocs build - if: github.event_name != 'pull_request' - run: | - if [ -z "$TOKEN" ]; then - echo "READTHEDOCS_TOKEN is not set" - exit 1 - fi - - response=$(curl -X POST \ - -H "Content-Type: application/json" \ - -d "{ - \"token\": \"$TOKEN\", - \"version\": \"$GITHUB_REF_NAME\" - }" \ - https://readthedocs.org/api/v2/webhook/llama-stack/289768/) - - echo "Response: $response" - if [ $(echo $response | jq -r '.build_triggered') != 'true' ]; then - echo "Failed to trigger ReadTheDocs build" - exit 1 - fi diff --git a/.gitignore b/.gitignore index f3831f29c..ca210db9a 100644 --- a/.gitignore +++ b/.gitignore @@ -18,7 +18,6 @@ Package.resolved .venv/ .vscode _build -docs/src # Sample tool-calling datasets generated by NVIDIA notebooks docs/notebooks/nvidia/tool_calling/sample_data/ pyrightconfig.json @@ -26,5 +25,9 @@ venv/ pytest-report.xml .coverage .python-version +AGENTS.md +server.log CLAUDE.md .claude/ +docs/.docusaurus/ +docs/node_modules/ diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 514fe6d2e..b7880a9fc 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -86,7 +86,7 @@ repos: language: python pass_filenames: false require_serial: true - files: ^llama_stack/templates/.*$|^llama_stack/providers/.*/inference/.*/models\.py$ + files: ^llama_stack/distributions/.*$|^llama_stack/providers/.*/inference/.*/models\.py$ - id: provider-codegen name: Provider Codegen additional_dependencies: diff --git a/.readthedocs.yaml b/.readthedocs.yaml deleted file mode 100644 index 461977a6c..000000000 --- a/.readthedocs.yaml +++ /dev/null @@ -1,25 +0,0 @@ -# .readthedocs.yaml -# Read the Docs configuration file -# See https://docs.readthedocs.io/en/stable/config-file/v2.html for details - -# Required -version: 2 - -# Build documentation in the "docs/" directory with Sphinx -sphinx: - configuration: docs/source/conf.py - -# Set the OS, Python version and other tools you might need -build: - os: ubuntu-22.04 - tools: - python: "3.12" - jobs: - pre_create_environment: - - asdf plugin add uv - - asdf install uv latest - - asdf global uv latest - create_environment: - - uv venv "${READTHEDOCS_VIRTUALENV_PATH}" - install: - - UV_PROJECT_ENVIRONMENT="${READTHEDOCS_VIRTUALENV_PATH}" uv sync --frozen --group docs diff --git a/CHANGELOG.md b/CHANGELOG.md index 2f47c3ae3..c51a1b2aa 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -1,5 +1,103 @@ # Changelog +# v0.2.20 +Published on: 2025-08-29T22:25:32Z + +Here are some key changes that are coming as part of this release. + +### Build and Environment + +- Environment improvements: fixed env var replacement to preserve types. +- Docker stability: fixed container startup failures for Fireworks AI provider. +- Removed absolute paths in build for better portability. + +### Features + +- UI Enhancements: Implemented file upload and VectorDB creation/configuration directly in UI. +- Vector Store Improvements: Added keyword, vector, and hybrid search inside vector store. +- Added S3 authorization support for file providers. +- SQL Store: Added inequality support to where clause. + +### Documentation + +- Fixed post-training docs. +- Added Contributor Guidelines for creating Internal vs. External providers. + +### Fixes + +- Removed unsupported bfcl scoring function. +- Multiple reliability and configuration fixes for providers and environment handling. + +### Engineering / Chores + +- Cleaner internal development setup with consistent paths. +- Incremental improvements to provider integration and vector store behavior. + + +### New Contributors +- @omertuc made their first contribution in #3270 +- @r3v5 made their first contribution in vector store hybrid search + +--- + +# v0.2.19 +Published on: 2025-08-26T22:06:55Z + +## Highlights +* feat: Add CORS configuration support for server by @skamenan7 in https://github.com/llamastack/llama-stack/pull/3201 +* feat(api): introduce /rerank by @ehhuang in https://github.com/llamastack/llama-stack/pull/2940 +* feat: Add S3 Files Provider by @mattf in https://github.com/llamastack/llama-stack/pull/3202 + + +--- + +# v0.2.18 +Published on: 2025-08-20T01:09:27Z + +## Highlights +* Add moderations create API +* Hybrid search in Milvus +* Numerous Responses API improvements +* Documentation updates + + +--- + +# v0.2.17 +Published on: 2025-08-05T01:51:14Z + +## Highlights + +* feat(tests): introduce inference record/replay to increase test reliability by @ashwinb in https://github.com/meta-llama/llama-stack/pull/2941 +* fix(library_client): improve initialization error handling and prevent AttributeError by @mattf in https://github.com/meta-llama/llama-stack/pull/2944 +* fix: use OLLAMA_URL to activate Ollama provider in starter by @ashwinb in https://github.com/meta-llama/llama-stack/pull/2963 +* feat(UI): adding MVP playground UI by @franciscojavierarceo in https://github.com/meta-llama/llama-stack/pull/2828 +* Standardization of errors (@nathan-weinberg) +* feat: Enable DPO training with HuggingFace inline provider by @Nehanth in https://github.com/meta-llama/llama-stack/pull/2825 +* chore: rename templates to distributions by @ashwinb in https://github.com/meta-llama/llama-stack/pull/3035 + + +--- + +# v0.2.16 +Published on: 2025-07-28T23:35:23Z + +## Highlights + +* Automatic model registration for self-hosted providers (ollama and vllm currently). No need for `INFERENCE_MODEL` environment variables which need to be updated, etc. +* Much simplified starter distribution. Most `ENABLE_` env variables are now gone. When you set `VLLM_URL`, the `vllm` provider is auto-enabled. Similar for `MILVUS_URL`, `PGVECTOR_DB`, etc. Check the [run.yaml](https://github.com/meta-llama/llama-stack/blob/main/llama_stack/templates/starter/run.yaml) for more details. +* All tests migrated to pytest now (thanks @Elbehery) +* DPO implementation in the post-training provider (thanks @Nehanth) +* (Huge!) Support for external APIs and providers thereof (thanks @leseb, @cdoern and others). This is a really big deal -- you can now add more APIs completely out of tree and experiment with them before (optionally) wanting to contribute back. +* `inline::vllm` provider is gone thank you very much +* several improvements to OpenAI inference implementations and LiteLLM backend (thanks @mattf) +* Chroma now supports Vector Store API (thanks @franciscojavierarceo). +* Authorization improvements: Vector Store/File APIs now supports access control (thanks @franciscojavierarceo); Telemetry read APIs are gated according to logged-in user's roles. + + + +--- + # v0.2.15 Published on: 2025-07-16T03:30:01Z diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index c81e9e7b1..da0ba5717 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -187,14 +187,16 @@ Note that the provider "description" field will be used to generate the provider ### Building the Documentation -If you are making changes to the documentation at [https://llama-stack.readthedocs.io/en/latest/](https://llama-stack.readthedocs.io/en/latest/), you can use the following command to build the documentation and preview your changes. You will need [Sphinx](https://www.sphinx-doc.org/en/master/) and the readthedocs theme. +If you are making changes to the documentation at [https://llamastack.github.io/](https://llamastack.github.io/), you can use the following command to build the documentation and preview your changes. ```bash -# This rebuilds the documentation pages. -uv run --group docs make -C docs/ html +# This rebuilds the documentation pages and the OpenAPI spec. +npm install +npm run gen-api-docs all +npm run build -# This will start a local server (usually at http://127.0.0.1:8000) that automatically rebuilds and refreshes when you make changes to the documentation. -uv run --group docs sphinx-autobuild docs/source docs/build/html --write-all +# This will start a local server (usually at http://127.0.0.1:3000). +npm run serve ``` ### Update API Documentation @@ -205,4 +207,4 @@ If you modify or add new API endpoints, update the API documentation accordingly uv run ./docs/openapi_generator/run_openapi_generator.sh ``` -The generated API documentation will be available in `docs/_static/`. Make sure to review the changes before committing. \ No newline at end of file +The generated API schema will be available in `docs/static/`. Make sure to review the changes before committing. diff --git a/README.md b/README.md index 4df4a5372..d6c5b4138 100644 --- a/README.md +++ b/README.md @@ -7,7 +7,7 @@ [![Unit Tests](https://github.com/meta-llama/llama-stack/actions/workflows/unit-tests.yml/badge.svg?branch=main)](https://github.com/meta-llama/llama-stack/actions/workflows/unit-tests.yml?query=branch%3Amain) [![Integration Tests](https://github.com/meta-llama/llama-stack/actions/workflows/integration-tests.yml/badge.svg?branch=main)](https://github.com/meta-llama/llama-stack/actions/workflows/integration-tests.yml?query=branch%3Amain) -[**Quick Start**](https://llama-stack.readthedocs.io/en/latest/getting_started/index.html) | [**Documentation**](https://llama-stack.readthedocs.io/en/latest/index.html) | [**Colab Notebook**](./docs/getting_started.ipynb) | [**Discord**](https://discord.gg/llama-stack) +[**Quick Start**](https://llamastack.github.io/latest/getting_started/index.html) | [**Documentation**](https://llamastack.github.io/latest/index.html) | [**Colab Notebook**](./docs/getting_started.ipynb) | [**Discord**](https://discord.gg/llama-stack) ### ✨🎉 Llama 4 Support 🎉✨ @@ -109,7 +109,7 @@ By reducing friction and complexity, Llama Stack empowers developers to focus on ### API Providers Here is a list of the various API providers and available distributions that can help developers get started easily with Llama Stack. -Please checkout for [full list](https://llama-stack.readthedocs.io/en/latest/providers/index.html) +Please checkout for [full list](https://llamastack.github.io/latest/providers/index.html) | API Provider Builder | Environments | Agents | Inference | VectorIO | Safety | Telemetry | Post Training | Eval | DatasetIO | |:--------------------:|:------------:|:------:|:---------:|:--------:|:------:|:---------:|:-------------:|:----:|:--------:| @@ -140,7 +140,7 @@ Please checkout for [full list](https://llama-stack.readthedocs.io/en/latest/pro | NVIDIA NEMO | Hosted | | ✅ | ✅ | | | ✅ | ✅ | ✅ | | NVIDIA | Hosted | | | | | | ✅ | ✅ | ✅ | -> **Note**: Additional providers are available through external packages. See [External Providers](https://llama-stack.readthedocs.io/en/latest/providers/external.html) documentation. +> **Note**: Additional providers are available through external packages. See [External Providers](https://llamastack.github.io/latest/providers/external/index.html) documentation. ### Distributions @@ -149,24 +149,24 @@ Here are some of the distributions we support: | **Distribution** | **Llama Stack Docker** | Start This Distribution | |:---------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------------------:| -| Starter Distribution | [llamastack/distribution-starter](https://hub.docker.com/repository/docker/llamastack/distribution-starter/general) | [Guide](https://llama-stack.readthedocs.io/en/latest/distributions/self_hosted_distro/starter.html) | -| Meta Reference | [llamastack/distribution-meta-reference-gpu](https://hub.docker.com/repository/docker/llamastack/distribution-meta-reference-gpu/general) | [Guide](https://llama-stack.readthedocs.io/en/latest/distributions/self_hosted_distro/meta-reference-gpu.html) | +| Starter Distribution | [llamastack/distribution-starter](https://hub.docker.com/repository/docker/llamastack/distribution-starter/general) | [Guide](https://llamastack.github.io/latest/distributions/self_hosted_distro/starter.html) | +| Meta Reference | [llamastack/distribution-meta-reference-gpu](https://hub.docker.com/repository/docker/llamastack/distribution-meta-reference-gpu/general) | [Guide](https://llamastack.github.io/latest/distributions/self_hosted_distro/meta-reference-gpu.html) | | PostgreSQL | [llamastack/distribution-postgres-demo](https://hub.docker.com/repository/docker/llamastack/distribution-postgres-demo/general) | | ### Documentation -Please checkout our [Documentation](https://llama-stack.readthedocs.io/en/latest/index.html) page for more details. +Please checkout our [Documentation](https://llamastack.github.io/latest/index.html) page for more details. * CLI references - * [llama (server-side) CLI Reference](https://llama-stack.readthedocs.io/en/latest/references/llama_cli_reference/index.html): Guide for using the `llama` CLI to work with Llama models (download, study prompts), and building/starting a Llama Stack distribution. - * [llama (client-side) CLI Reference](https://llama-stack.readthedocs.io/en/latest/references/llama_stack_client_cli_reference.html): Guide for using the `llama-stack-client` CLI, which allows you to query information about the distribution. + * [llama (server-side) CLI Reference](https://llamastack.github.io/latest/references/llama_cli_reference/index.html): Guide for using the `llama` CLI to work with Llama models (download, study prompts), and building/starting a Llama Stack distribution. + * [llama (client-side) CLI Reference](https://llamastack.github.io/latest/references/llama_stack_client_cli_reference.html): Guide for using the `llama-stack-client` CLI, which allows you to query information about the distribution. * Getting Started - * [Quick guide to start a Llama Stack server](https://llama-stack.readthedocs.io/en/latest/getting_started/index.html). + * [Quick guide to start a Llama Stack server](https://llamastack.github.io/latest/getting_started/index.html). * [Jupyter notebook](./docs/getting_started.ipynb) to walk-through how to use simple text and vision inference llama_stack_client APIs * The complete Llama Stack lesson [Colab notebook](https://colab.research.google.com/drive/1dtVmxotBsI4cGZQNsJRYPrLiDeT0Wnwt) of the new [Llama 3.2 course on Deeplearning.ai](https://learn.deeplearning.ai/courses/introducing-multimodal-llama-3-2/lesson/8/llama-stack). * A [Zero-to-Hero Guide](https://github.com/meta-llama/llama-stack/tree/main/docs/zero_to_hero_guide) that guide you through all the key components of llama stack with code samples. * [Contributing](CONTRIBUTING.md) - * [Adding a new API Provider](https://llama-stack.readthedocs.io/en/latest/contributing/new_api_provider.html) to walk-through how to add a new API provider. + * [Adding a new API Provider](https://llamastack.github.io/latest/contributing/new_api_provider.html) to walk-through how to add a new API provider. ### Llama Stack Client SDKs @@ -193,4 +193,4 @@ Thanks to all of our amazing contributors! - \ No newline at end of file + diff --git a/benchmarking/k8s-benchmark/README.md b/benchmarking/k8s-benchmark/README.md new file mode 100644 index 000000000..9b5e140f0 --- /dev/null +++ b/benchmarking/k8s-benchmark/README.md @@ -0,0 +1,229 @@ +# Llama Stack Benchmark Suite on Kubernetes + +## Motivation + +Performance benchmarking is critical for understanding the overhead and characteristics of the Llama Stack abstraction layer compared to direct inference engines like vLLM. + +### Why This Benchmark Suite Exists + +**Performance Validation**: The Llama Stack provides a unified API layer across multiple inference providers, but this abstraction introduces potential overhead. This benchmark suite quantifies the performance impact by comparing: +- Llama Stack inference (with vLLM backend) +- Direct vLLM inference calls +- Both under identical Kubernetes deployment conditions + +**Production Readiness Assessment**: Real-world deployments require understanding performance characteristics under load. This suite simulates concurrent user scenarios with configurable parameters (duration, concurrency, request patterns) to validate production readiness. + +**Regression Detection (TODO)**: As the Llama Stack evolves, this benchmark provides automated regression detection for performance changes. CI/CD pipelines can leverage these benchmarks to catch performance degradations before production deployments. + +**Resource Planning**: By measuring throughput, latency percentiles, and resource utilization patterns, teams can make informed decisions about: +- Kubernetes resource allocation (CPU, memory, GPU) +- Auto-scaling configurations +- Cost optimization strategies + +### Key Metrics Captured + +The benchmark suite measures critical performance indicators: +- **Throughput**: Requests per second under sustained load +- **Latency Distribution**: P50, P95, P99 response times +- **Time to First Token (TTFT)**: Critical for streaming applications +- **Inter-Token Latency (ITL)**: Token generation speed for streaming +- **Error Rates**: Request failures and timeout analysis + +This data enables data-driven architectural decisions and performance optimization efforts. + +## Setup + +**1. Deploy base k8s infrastructure:** +```bash +cd ../../docs/source/distributions/k8s +./apply.sh +``` + +**2. Deploy benchmark components:** +```bash +./apply.sh +``` + +**3. Verify deployment:** +```bash +kubectl get pods +# Should see: llama-stack-benchmark-server, vllm-server, etc. +``` + +## Benchmark Results + +We use [GuideLLM](https://github.com/neuralmagic/guidellm) against our k8s deployment for comprehensive performance testing. + + +### Performance - 1 vLLM Replica + +We vary the number of Llama Stack replicas with 1 vLLM replica and compare performance below. + +![Performance - 1 vLLM Replica](results/vllm_replica1_benchmark_results.png) + + +For full results see the `benchmarking/k8s-benchmark/results/` directory. + + +## Quick Start + +Follow the instructions below to run benchmarks similar to the ones above. + +### Comprehensive Benchmark Suite + +**Run all benchmarks with different cluster configurations:** +```bash +./scripts/run-all-benchmarks.sh +``` + +This script will automatically: +- Scale deployments to different configurations +- Run benchmarks for each setup +- Generate output files with meaningful names that include setup information + +### Individual Benchmarks + +**Benchmark Llama Stack (runs against current cluster setup):** +```bash +./scripts/run-guidellm-benchmark.sh --target stack +``` + +**Benchmark vLLM direct (runs against current cluster setup):** +```bash +./scripts/run-guidellm-benchmark.sh --target vllm +``` + +**Benchmark with custom parameters:** +```bash +./scripts/run-guidellm-benchmark.sh --target stack --max-seconds 120 --prompt-tokens 1024 --output-tokens 512 +``` + +**Benchmark with custom output file:** +```bash +./scripts/run-guidellm-benchmark.sh --target stack --output-file results/my-custom-benchmark.txt +``` + +### Generating Charts + +Once the benchmarks are run, you can generate performance charts from benchmark results: + +```bash +uv run ./scripts/generate_charts.py +``` + +This loads runs in the `results/` directory and creates visualizations comparing different configurations and replica counts. + +## Benchmark Workflow + +The benchmark suite is organized into two main scripts with distinct responsibilities: + +### 1. `run-all-benchmarks.sh` - Orchestration & Scaling +- **Purpose**: Manages different cluster configurations and orchestrates benchmark runs +- **Responsibilities**: + - Scales Kubernetes deployments (vLLM replicas, Stack replicas, worker counts) + - Runs benchmarks for each configuration + - Generates meaningful output filenames with setup information +- **Use case**: Running comprehensive performance testing across multiple configurations + +### 2. `run-guidellm-benchmark.sh` - Single Benchmark Execution +- **Purpose**: Executes a single benchmark against the current cluster state +- **Responsibilities**: + - Runs GuideLLM benchmark with configurable parameters + - Accepts custom output file paths + - No cluster scaling - benchmarks current deployment state +- **Use case**: Testing specific configurations or custom scenarios + +### Typical Workflow +1. **Comprehensive Testing**: Use `run-all-benchmarks.sh` to automatically test multiple configurations +2. **Custom Testing**: Use `run-guidellm-benchmark.sh` for specific parameter testing or manual cluster configurations +3. **Analysis**: Use `generate_charts.py` to visualize results from either approach + +## Command Reference + +### run-all-benchmarks.sh + +Orchestrates multiple benchmark runs with different cluster configurations. This script: +- Automatically scales deployments before each benchmark +- Runs benchmarks against the configured cluster setup +- Generates meaningfully named output files + +```bash +./scripts/run-all-benchmarks.sh +``` + +**Configuration**: Edit the `configs` array in the script to customize benchmark configurations: +```bash +# Each line: (target, stack_replicas, vllm_replicas, stack_workers) +configs=( + "stack 1 1 1" + "stack 1 1 2" + "stack 1 1 4" + "vllm 1 1 -" +) +``` + +**Output files**: Generated with setup information in filename: +- Stack: `guidellm-benchmark-stack-s{replicas}-sw{workers}-v{vllm_replicas}-{timestamp}.txt` +- vLLM: `guidellm-benchmark-vllm-v{vllm_replicas}-{timestamp}.txt` + +### run-guidellm-benchmark.sh Options + +Runs a single benchmark against the current cluster setup (no scaling). + +```bash +./scripts/run-guidellm-benchmark.sh [options] + +Options: + -t, --target Target to benchmark (default: stack) + -s, --max-seconds Maximum duration in seconds (default: 60) + -p, --prompt-tokens Number of prompt tokens (default: 512) + -o, --output-tokens Number of output tokens (default: 256) + -r, --rate-type Rate type (default: concurrent) + -c, --rate Rate (default: 1,2,4,8,16,32,64,128) + --output-file Output file path (default: auto-generated) + --stack-deployment Name of the stack deployment (default: llama-stack-benchmark-server) + --vllm-deployment Name of the vllm deployment (default: vllm-server) + --stack-url URL of the stack service (default: http://llama-stack-benchmark-service:8323/v1/openai) + -h, --help Show help message + +Examples: + ./scripts/run-guidellm-benchmark.sh --target vllm # Benchmark vLLM direct + ./scripts/run-guidellm-benchmark.sh --target stack # Benchmark Llama Stack (default) + ./scripts/run-guidellm-benchmark.sh -t vllm -s 60 -p 512 -o 256 # vLLM with custom parameters + ./scripts/run-guidellm-benchmark.sh --output-file results/my-benchmark.txt # Specify custom output file + ./scripts/run-guidellm-benchmark.sh --stack-deployment my-stack-server # Use custom stack deployment name +``` + +## Local Testing + +### Running Benchmark Locally + +For local development without Kubernetes: + +**1. (Optional) Start Mock OpenAI server:** + +There is a simple mock OpenAI server if you don't have an inference provider available. +The `openai-mock-server.py` provides: +- **OpenAI-compatible API** for testing without real models +- **Configurable streaming delay** via `STREAM_DELAY_SECONDS` env var +- **Consistent responses** for reproducible benchmarks +- **Lightweight testing** without GPU requirements + +```bash +uv run python openai-mock-server.py --port 8080 +``` + +**2. Start Stack server:** +```bash +LLAMA_STACK_CONFIG=benchmarking/k8s-benchmark/stack_run_config.yaml uv run uvicorn llama_stack.core.server.server:create_app --port 8321 --workers 4 --factory +``` + +**3. Run GuideLLM benchmark:** +```bash +GUIDELLM__PREFERRED_ROUTE="chat_completions" uv run guidellm benchmark run \ + --target "http://localhost:8321/v1/openai/v1" \ + --model "meta-llama/Llama-3.2-3B-Instruct" \ + --rate-type sweep \ + --max-seconds 60 \ + --data "prompt_tokens=256,output_tokens=128" --output-path='output.html' +``` diff --git a/docs/source/distributions/k8s-benchmark/apply.sh b/benchmarking/k8s-benchmark/apply.sh similarity index 91% rename from docs/source/distributions/k8s-benchmark/apply.sh rename to benchmarking/k8s-benchmark/apply.sh index 4f2270da8..6e6607663 100755 --- a/docs/source/distributions/k8s-benchmark/apply.sh +++ b/benchmarking/k8s-benchmark/apply.sh @@ -17,11 +17,8 @@ export POSTGRES_PASSWORD=llamastack export INFERENCE_MODEL=meta-llama/Llama-3.2-3B-Instruct export SAFETY_MODEL=meta-llama/Llama-Guard-3-1B -export MOCK_INFERENCE_MODEL=mock-inference - -export MOCK_INFERENCE_URL=openai-mock-service:8080 - export BENCHMARK_INFERENCE_MODEL=$INFERENCE_MODEL +export LLAMA_STACK_WORKERS=4 set -euo pipefail set -x diff --git a/docs/source/distributions/k8s-benchmark/openai-mock-server.py b/benchmarking/k8s-benchmark/openai-mock-server.py similarity index 60% rename from docs/source/distributions/k8s-benchmark/openai-mock-server.py rename to benchmarking/k8s-benchmark/openai-mock-server.py index de0680842..9e898af8e 100755 --- a/docs/source/distributions/k8s-benchmark/openai-mock-server.py +++ b/benchmarking/k8s-benchmark/openai-mock-server.py @@ -11,180 +11,192 @@ OpenAI-compatible mock server that returns: - Valid OpenAI-formatted chat completion responses with dynamic content """ -from flask import Flask, request, jsonify, Response -import time -import random -import uuid -import json import argparse +import json import os +import random +import time +import uuid + +from flask import Flask, Response, jsonify, request app = Flask(__name__) + # Models from environment variables def get_models(): models_str = os.getenv("MOCK_MODELS", "meta-llama/Llama-3.2-3B-Instruct") model_ids = [m.strip() for m in models_str.split(",") if m.strip()] - + return { "object": "list", "data": [ - { - "id": model_id, - "object": "model", - "created": 1234567890, - "owned_by": "vllm" - } - for model_id in model_ids - ] + {"id": model_id, "object": "model", "created": 1234567890, "owned_by": "vllm"} for model_id in model_ids + ], } + def generate_random_text(length=50): """Generate random but coherent text for responses.""" words = [ - "Hello", "there", "I'm", "an", "AI", "assistant", "ready", "to", "help", "you", - "with", "your", "questions", "and", "tasks", "today", "Let", "me","know", "what", - "you'd", "like", "to", "discuss", "or", "explore", "together", "I", "can", "assist", - "with", "various", "topics", "including", "coding", "writing", "analysis", "and", "more" + "Hello", + "there", + "I'm", + "an", + "AI", + "assistant", + "ready", + "to", + "help", + "you", + "with", + "your", + "questions", + "and", + "tasks", + "today", + "Let", + "me", + "know", + "what", + "you'd", + "like", + "to", + "discuss", + "or", + "explore", + "together", + "I", + "can", + "assist", + "with", + "various", + "topics", + "including", + "coding", + "writing", + "analysis", + "and", + "more", ] return " ".join(random.choices(words, k=length)) -@app.route('/v1/models', methods=['GET']) + +@app.route("/v1/models", methods=["GET"]) def list_models(): models = get_models() print(f"[MOCK] Returning models: {[m['id'] for m in models['data']]}") return jsonify(models) -@app.route('/v1/chat/completions', methods=['POST']) + +@app.route("/v1/chat/completions", methods=["POST"]) def chat_completions(): """Return OpenAI-formatted chat completion responses.""" data = request.get_json() - default_model = get_models()['data'][0]['id'] - model = data.get('model', default_model) - messages = data.get('messages', []) - stream = data.get('stream', False) - + default_model = get_models()["data"][0]["id"] + model = data.get("model", default_model) + messages = data.get("messages", []) + stream = data.get("stream", False) + print(f"[MOCK] Chat completion request - model: {model}, stream: {stream}") - + if stream: return handle_streaming_completion(model, messages) else: return handle_non_streaming_completion(model, messages) + def handle_non_streaming_completion(model, messages): response_text = generate_random_text(random.randint(20, 80)) - + # Calculate realistic token counts - prompt_tokens = sum(len(str(msg.get('content', '')).split()) for msg in messages) + prompt_tokens = sum(len(str(msg.get("content", "")).split()) for msg in messages) completion_tokens = len(response_text.split()) - + response = { "id": f"chatcmpl-{uuid.uuid4().hex[:8]}", "object": "chat.completion", "created": int(time.time()), "model": model, - "choices": [ - { - "index": 0, - "message": { - "role": "assistant", - "content": response_text - }, - "finish_reason": "stop" - } - ], + "choices": [{"index": 0, "message": {"role": "assistant", "content": response_text}, "finish_reason": "stop"}], "usage": { "prompt_tokens": prompt_tokens, "completion_tokens": completion_tokens, - "total_tokens": prompt_tokens + completion_tokens - } + "total_tokens": prompt_tokens + completion_tokens, + }, } - + return jsonify(response) + def handle_streaming_completion(model, messages): def generate_stream(): # Generate response text full_response = generate_random_text(random.randint(30, 100)) words = full_response.split() - + # Send initial chunk initial_chunk = { "id": f"chatcmpl-{uuid.uuid4().hex[:8]}", "object": "chat.completion.chunk", "created": int(time.time()), "model": model, - "choices": [ - { - "index": 0, - "delta": {"role": "assistant", "content": ""} - } - ] + "choices": [{"index": 0, "delta": {"role": "assistant", "content": ""}}], } yield f"data: {json.dumps(initial_chunk)}\n\n" - + # Send word by word for i, word in enumerate(words): chunk = { "id": f"chatcmpl-{uuid.uuid4().hex[:8]}", - "object": "chat.completion.chunk", + "object": "chat.completion.chunk", "created": int(time.time()), "model": model, - "choices": [ - { - "index": 0, - "delta": {"content": f"{word} " if i < len(words) - 1 else word} - } - ] + "choices": [{"index": 0, "delta": {"content": f"{word} " if i < len(words) - 1 else word}}], } yield f"data: {json.dumps(chunk)}\n\n" # Configurable delay to simulate realistic streaming stream_delay = float(os.getenv("STREAM_DELAY_SECONDS", "0.005")) time.sleep(stream_delay) - + # Send final chunk final_chunk = { "id": f"chatcmpl-{uuid.uuid4().hex[:8]}", "object": "chat.completion.chunk", "created": int(time.time()), "model": model, - "choices": [ - { - "index": 0, - "delta": {"content": ""}, - "finish_reason": "stop" - } - ] + "choices": [{"index": 0, "delta": {"content": ""}, "finish_reason": "stop"}], } yield f"data: {json.dumps(final_chunk)}\n\n" yield "data: [DONE]\n\n" - + return Response( generate_stream(), - mimetype='text/event-stream', + mimetype="text/event-stream", headers={ - 'Cache-Control': 'no-cache', - 'Connection': 'keep-alive', - 'Access-Control-Allow-Origin': '*', - } + "Cache-Control": "no-cache", + "Connection": "keep-alive", + "Access-Control-Allow-Origin": "*", + }, ) -@app.route('/health', methods=['GET']) + +@app.route("/health", methods=["GET"]) def health(): return jsonify({"status": "healthy", "type": "openai-mock"}) -if __name__ == '__main__': - parser = argparse.ArgumentParser(description='OpenAI-compatible mock server') - parser.add_argument('--port', type=int, default=8081, - help='Port to run the server on (default: 8081)') + +if __name__ == "__main__": + parser = argparse.ArgumentParser(description="OpenAI-compatible mock server") + parser.add_argument("--port", type=int, default=8081, help="Port to run the server on (default: 8081)") args = parser.parse_args() - + port = args.port - + models = get_models() print("Starting OpenAI-compatible mock server...") print(f"- /models endpoint with: {[m['id'] for m in models['data']]}") print("- OpenAI-formatted chat/completion responses with dynamic content") print("- Streaming support with valid SSE format") print(f"- Listening on: http://0.0.0.0:{port}") - app.run(host='0.0.0.0', port=port, debug=False) + app.run(host="0.0.0.0", port=port, debug=False) diff --git a/benchmarking/k8s-benchmark/results/guidellm-benchmark-stack-s1-sw1-v1-20250922-103408.txt b/benchmarking/k8s-benchmark/results/guidellm-benchmark-stack-s1-sw1-v1-20250922-103408.txt new file mode 100644 index 000000000..0f707a968 --- /dev/null +++ b/benchmarking/k8s-benchmark/results/guidellm-benchmark-stack-s1-sw1-v1-20250922-103408.txt @@ -0,0 +1,171 @@ +Collecting uv + Downloading uv-0.8.19-py3-none-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (11 kB) +Downloading uv-0.8.19-py3-none-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (20.9 MB) + ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 20.9/20.9 MB 144.3 MB/s eta 0:00:00 +Installing collected packages: uv +Successfully installed uv-0.8.19 +WARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv + +[notice] A new release of pip is available: 24.0 -> 25.2 +[notice] To update, run: pip install --upgrade pip +Using Python 3.11.13 environment at: /usr/local +Resolved 61 packages in 551ms +Downloading pillow (6.3MiB) +Downloading hf-xet (3.0MiB) +Downloading tokenizers (3.1MiB) +Downloading pygments (1.2MiB) +Downloading pandas (11.8MiB) +Downloading aiohttp (1.7MiB) +Downloading pydantic-core (1.9MiB) +Downloading numpy (16.2MiB) +Downloading transformers (11.1MiB) +Downloading pyarrow (40.8MiB) + Downloading pydantic-core + Downloading aiohttp + Downloading tokenizers + Downloading hf-xet + Downloading pygments + Downloading pillow + Downloading numpy + Downloading pandas + Downloading transformers + Downloading pyarrow +Prepared 61 packages in 1.23s +Installed 61 packages in 114ms + + aiohappyeyeballs==2.6.1 + + aiohttp==3.12.15 + + aiosignal==1.4.0 + + annotated-types==0.7.0 + + anyio==4.10.0 + + attrs==25.3.0 + + certifi==2025.8.3 + + charset-normalizer==3.4.3 + + click==8.1.8 + + datasets==4.1.1 + + dill==0.4.0 + + filelock==3.19.1 + + frozenlist==1.7.0 + + fsspec==2025.9.0 + + ftfy==6.3.1 + + guidellm==0.3.0 + + h11==0.16.0 + + h2==4.3.0 + + hf-xet==1.1.10 + + hpack==4.1.0 + + httpcore==1.0.9 + + httpx==0.28.1 + + huggingface-hub==0.35.0 + + hyperframe==6.1.0 + + idna==3.10 + + loguru==0.7.3 + + markdown-it-py==4.0.0 + + mdurl==0.1.2 + + multidict==6.6.4 + + multiprocess==0.70.16 + + numpy==2.3.3 + + packaging==25.0 + + pandas==2.3.2 + + pillow==11.3.0 + + propcache==0.3.2 + + protobuf==6.32.1 + + pyarrow==21.0.0 + + pydantic==2.11.9 + + pydantic-core==2.33.2 + + pydantic-settings==2.10.1 + + pygments==2.19.2 + + python-dateutil==2.9.0.post0 + + python-dotenv==1.1.1 + + pytz==2025.2 + + pyyaml==6.0.2 + + regex==2025.9.18 + + requests==2.32.5 + + rich==14.1.0 + + safetensors==0.6.2 + + six==1.17.0 + + sniffio==1.3.1 + + tokenizers==0.22.1 + + tqdm==4.67.1 + + transformers==4.56.2 + + typing-extensions==4.15.0 + + typing-inspection==0.4.1 + + tzdata==2025.2 + + urllib3==2.5.0 + + wcwidth==0.2.14 + + xxhash==3.5.0 + + yarl==1.20.1 +Using Python 3.11.13 environment at: /usr/local +Audited 1 package in 3ms +Note: Environment variable`HF_TOKEN` is set and is the current active token independently from the token you've just configured. +Creating backend... +Backend openai_http connected to http://llama-stack-benchmark-service:8323/v1/openai for model meta-llama/Llama-3.2-3B-Instruct. +Creating request loader... +Created loader with 1000 unique requests from prompt_tokens=512,output_tokens=256. + + +╭─ Benchmarks ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮ +│ [17:34:30] ⠋ 100% concurrent@1 (complete) Req: 0.3 req/s, 3.32s Lat, 1.0 Conc, 18 Comp, 1 Inc, 0 Err │ +│ Tok: 74.0 gen/s, 238.6 tot/s, 40.2ms TTFT, 13.4ms ITL, 546 Prompt, 246 Gen │ +│ [17:35:35] ⠋ 100% concurrent@2 (complete) Req: 0.6 req/s, 3.46s Lat, 2.0 Conc, 34 Comp, 2 Inc, 0 Err │ +│ Tok: 139.6 gen/s, 454.0 tot/s, 48.0ms TTFT, 14.1ms ITL, 546 Prompt, 243 Gen │ +│ [17:36:40] ⠋ 100% concurrent@4 (complete) Req: 1.1 req/s, 3.44s Lat, 3.9 Conc, 68 Comp, 4 Inc, 0 Err │ +│ Tok: 273.2 gen/s, 900.4 tot/s, 50.7ms TTFT, 14.3ms ITL, 546 Prompt, 238 Gen │ +│ [17:37:45] ⠋ 100% concurrent@8 (complete) Req: 2.2 req/s, 3.55s Lat, 7.7 Conc, 129 Comp, 8 Inc, 0 Err │ +│ Tok: 519.1 gen/s, 1699.8 tot/s, 66.0ms TTFT, 14.6ms ITL, 547 Prompt, 240 Gen │ +│ [17:38:50] ⠋ 100% concurrent@16 (complete) Req: 4.1 req/s, 3.76s Lat, 15.5 Conc, 247 Comp, 16 Inc, 0 Err │ +│ Tok: 1005.5 gen/s, 3256.7 tot/s, 101.0ms TTFT, 15.0ms ITL, 547 Prompt, 244 Gen │ +│ [17:39:56] ⠋ 100% concurrent@32 (complete) Req: 8.1 req/s, 3.84s Lat, 30.9 Conc, 483 Comp, 32 Inc, 0 Err │ +│ Tok: 1926.3 gen/s, 6327.2 tot/s, 295.7ms TTFT, 14.8ms ITL, 547 Prompt, 239 Gen │ +│ [17:41:03] ⠋ 100% concurrent@64 (complete) Req: 9.9 req/s, 6.05s Lat, 59.7 Conc, 576 Comp, 58 Inc, 0 Err │ +│ Tok: 2381.0 gen/s, 7774.5 tot/s, 1196.2ms TTFT, 20.2ms ITL, 547 Prompt, 241 Gen │ +│ [17:42:10] ⠋ 100% concurrent@128 (complete) Req: 9.2 req/s, 11.59s Lat, 107.2 Conc, 514 Comp, 117 Inc, 0 Err │ +│ Tok: 2233.4 gen/s, 7286.3 tot/s, 2403.9ms TTFT, 38.2ms ITL, 547 Prompt, 242 Gen │ +╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯ +Generating... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ (8/8) [ 0:08:41 < 0:00:00 ] + +Benchmarks Metadata: + Run id:511a14fd-ba11-4ffa-92ef-7cc23db4dd38 + Duration:528.5 seconds + Profile:type=concurrent, strategies=['concurrent', 'concurrent', 'concurrent', 'concurrent', 'concurrent', 'concurrent', 'concurrent', 'concurrent'], streams=[1, 2, 4, 8, 16, 32, 64, 128] + Args:max_number=None, max_duration=60.0, warmup_number=None, warmup_duration=3.0, cooldown_number=None, cooldown_duration=None + Worker:type_='generative_requests_worker' backend_type='openai_http' backend_target='http://llama-stack-benchmark-service:8323/v1/openai' backend_model='meta-llama/Llama-3.2-3B-Instruct' + backend_info={'max_output_tokens': 16384, 'timeout': 300, 'http2': True, 'follow_redirects': True, 'headers': {}, 'text_completions_path': '/v1/completions', 'chat_completions_path': + '/v1/chat/completions'} + Request Loader:type_='generative_request_loader' data='prompt_tokens=512,output_tokens=256' data_args=None processor='meta-llama/Llama-3.2-3B-Instruct' processor_args=None + Extras:None + + +Benchmarks Info: +=================================================================================================================================================== +Metadata |||| Requests Made ||| Prompt Tok/Req ||| Output Tok/Req ||| Prompt Tok Total||| Output Tok Total|| + Benchmark| Start Time| End Time| Duration (s)| Comp| Inc| Err| Comp| Inc| Err| Comp| Inc| Err| Comp| Inc| Err| Comp| Inc| Err +--------------|-----------|---------|-------------|------|-----|-----|------|------|----|------|------|----|-------|------|----|-------|------|---- + concurrent@1| 17:34:35| 17:35:35| 60.0| 18| 1| 0| 546.4| 512.0| 0.0| 246.0| 14.0| 0.0| 9835| 512| 0| 4428| 14| 0 + concurrent@2| 17:35:40| 17:36:40| 60.0| 34| 2| 0| 546.4| 512.0| 0.0| 242.7| 80.0| 0.0| 18577| 1024| 0| 8253| 160| 0 + concurrent@4| 17:36:45| 17:37:45| 60.0| 68| 4| 0| 546.4| 512.0| 0.0| 238.1| 103.2| 0.0| 37156| 2048| 0| 16188| 413| 0 + concurrent@8| 17:37:50| 17:38:50| 60.0| 129| 8| 0| 546.7| 512.0| 0.0| 240.3| 180.0| 0.0| 70518| 4096| 0| 31001| 1440| 0 + concurrent@16| 17:38:55| 17:39:55| 60.0| 247| 16| 0| 546.6| 512.0| 0.0| 244.1| 142.6| 0.0| 135002| 8192| 0| 60300| 2281| 0 + concurrent@32| 17:40:01| 17:41:01| 60.0| 483| 32| 0| 546.5| 512.0| 0.0| 239.2| 123.2| 0.0| 263972| 16384| 0| 115540| 3944| 0 + concurrent@64| 17:41:08| 17:42:08| 60.0| 576| 58| 0| 546.6| 512.0| 0.0| 241.3| 13.9| 0.0| 314817| 29696| 0| 138976| 807| 0 +concurrent@128| 17:42:15| 17:43:15| 60.0| 514| 117| 0| 546.5| 512.0| 0.0| 241.6| 143.9| 0.0| 280911| 59904| 0| 124160| 16832| 0 +=================================================================================================================================================== + + +Benchmarks Stats: +======================================================================================================================================================= +Metadata | Request Stats || Out Tok/sec| Tot Tok/sec| Req Latency (sec) ||| TTFT (ms) ||| ITL (ms) ||| TPOT (ms) || + Benchmark| Per Second| Concurrency| mean| mean| mean| median| p99| mean| median| p99| mean| median| p99| mean| median| p99 +--------------|-----------|------------|------------|------------|------|-------|------|-------|-------|-------|-----|-------|-----|-----|-------|----- + concurrent@1| 0.30| 1.00| 74.0| 238.6| 3.32| 3.43| 3.61| 40.2| 39.3| 51.2| 13.4| 13.3| 14.0| 13.3| 13.2| 13.9 + concurrent@2| 0.58| 1.99| 139.6| 454.0| 3.46| 3.64| 3.74| 48.0| 45.8| 72.0| 14.1| 14.1| 14.5| 14.0| 14.0| 14.4 + concurrent@4| 1.15| 3.95| 273.2| 900.4| 3.44| 3.69| 3.74| 50.7| 47.2| 118.6| 14.3| 14.3| 14.4| 14.2| 14.2| 14.4 + concurrent@8| 2.16| 7.67| 519.1| 1699.8| 3.55| 3.76| 3.87| 66.0| 48.8| 208.2| 14.6| 14.5| 14.8| 14.5| 14.5| 14.8 + concurrent@16| 4.12| 15.48| 1005.5| 3256.7| 3.76| 3.90| 4.18| 101.0| 65.6| 396.7| 15.0| 15.0| 15.9| 15.0| 15.0| 15.9 + concurrent@32| 8.05| 30.89| 1926.3| 6327.2| 3.84| 4.04| 4.39| 295.7| 265.6| 720.4| 14.8| 14.9| 15.5| 14.8| 14.8| 15.3 + concurrent@64| 9.87| 59.74| 2381.0| 7774.5| 6.05| 6.18| 9.94| 1196.2| 1122.5| 4295.3| 20.2| 20.0| 25.8| 20.1| 19.9| 25.8 +concurrent@128| 9.25| 107.16| 2233.4| 7286.3| 11.59| 12.04| 14.46| 2403.9| 2322.3| 4001.5| 38.2| 38.5| 53.0| 38.0| 38.3| 52.7 +======================================================================================================================================================= + +Saving benchmarks report... +Benchmarks report saved to /benchmarks.json + +Benchmarking complete. diff --git a/benchmarking/k8s-benchmark/results/guidellm-benchmark-stack-s1-sw2-v1-20250922-104457.txt b/benchmarking/k8s-benchmark/results/guidellm-benchmark-stack-s1-sw2-v1-20250922-104457.txt new file mode 100644 index 000000000..21f1ef425 --- /dev/null +++ b/benchmarking/k8s-benchmark/results/guidellm-benchmark-stack-s1-sw2-v1-20250922-104457.txt @@ -0,0 +1,171 @@ +Collecting uv + Downloading uv-0.8.19-py3-none-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (11 kB) +Downloading uv-0.8.19-py3-none-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (20.9 MB) + ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 20.9/20.9 MB 149.3 MB/s eta 0:00:00 +Installing collected packages: uv +Successfully installed uv-0.8.19 +WARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv + +[notice] A new release of pip is available: 24.0 -> 25.2 +[notice] To update, run: pip install --upgrade pip +Using Python 3.11.13 environment at: /usr/local +Resolved 61 packages in 494ms +Downloading pandas (11.8MiB) +Downloading tokenizers (3.1MiB) +Downloading pygments (1.2MiB) +Downloading aiohttp (1.7MiB) +Downloading transformers (11.1MiB) +Downloading numpy (16.2MiB) +Downloading pillow (6.3MiB) +Downloading pydantic-core (1.9MiB) +Downloading hf-xet (3.0MiB) +Downloading pyarrow (40.8MiB) + Downloading pydantic-core + Downloading aiohttp + Downloading tokenizers + Downloading hf-xet + Downloading pillow + Downloading pygments + Downloading numpy + Downloading pandas + Downloading pyarrow + Downloading transformers +Prepared 61 packages in 1.24s +Installed 61 packages in 126ms + + aiohappyeyeballs==2.6.1 + + aiohttp==3.12.15 + + aiosignal==1.4.0 + + annotated-types==0.7.0 + + anyio==4.10.0 + + attrs==25.3.0 + + certifi==2025.8.3 + + charset-normalizer==3.4.3 + + click==8.1.8 + + datasets==4.1.1 + + dill==0.4.0 + + filelock==3.19.1 + + frozenlist==1.7.0 + + fsspec==2025.9.0 + + ftfy==6.3.1 + + guidellm==0.3.0 + + h11==0.16.0 + + h2==4.3.0 + + hf-xet==1.1.10 + + hpack==4.1.0 + + httpcore==1.0.9 + + httpx==0.28.1 + + huggingface-hub==0.35.0 + + hyperframe==6.1.0 + + idna==3.10 + + loguru==0.7.3 + + markdown-it-py==4.0.0 + + mdurl==0.1.2 + + multidict==6.6.4 + + multiprocess==0.70.16 + + numpy==2.3.3 + + packaging==25.0 + + pandas==2.3.2 + + pillow==11.3.0 + + propcache==0.3.2 + + protobuf==6.32.1 + + pyarrow==21.0.0 + + pydantic==2.11.9 + + pydantic-core==2.33.2 + + pydantic-settings==2.10.1 + + pygments==2.19.2 + + python-dateutil==2.9.0.post0 + + python-dotenv==1.1.1 + + pytz==2025.2 + + pyyaml==6.0.2 + + regex==2025.9.18 + + requests==2.32.5 + + rich==14.1.0 + + safetensors==0.6.2 + + six==1.17.0 + + sniffio==1.3.1 + + tokenizers==0.22.1 + + tqdm==4.67.1 + + transformers==4.56.2 + + typing-extensions==4.15.0 + + typing-inspection==0.4.1 + + tzdata==2025.2 + + urllib3==2.5.0 + + wcwidth==0.2.14 + + xxhash==3.5.0 + + yarl==1.20.1 +Using Python 3.11.13 environment at: /usr/local +Audited 1 package in 3ms +Note: Environment variable`HF_TOKEN` is set and is the current active token independently from the token you've just configured. +Creating backend... +Backend openai_http connected to http://llama-stack-benchmark-service:8323/v1/openai for model meta-llama/Llama-3.2-3B-Instruct. +Creating request loader... +Created loader with 1000 unique requests from prompt_tokens=512,output_tokens=256. + + +╭─ Benchmarks ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮ +│ [17:45:18] ⠋ 100% concurrent@1 (complete) Req: 0.3 req/s, 3.42s Lat, 1.0 Conc, 17 Comp, 1 Inc, 0 Err │ +│ Tok: 73.9 gen/s, 233.7 tot/s, 50.2ms TTFT, 13.4ms ITL, 547 Prompt, 253 Gen │ +│ [17:46:23] ⠋ 100% concurrent@2 (complete) Req: 0.6 req/s, 3.42s Lat, 2.0 Conc, 34 Comp, 2 Inc, 0 Err │ +│ Tok: 134.7 gen/s, 447.4 tot/s, 50.8ms TTFT, 14.3ms ITL, 546 Prompt, 235 Gen │ +│ [17:47:28] ⠋ 100% concurrent@4 (complete) Req: 1.1 req/s, 3.55s Lat, 3.9 Conc, 66 Comp, 4 Inc, 0 Err │ +│ Tok: 268.7 gen/s, 873.1 tot/s, 54.9ms TTFT, 14.4ms ITL, 547 Prompt, 243 Gen │ +│ [17:48:33] ⠋ 100% concurrent@8 (complete) Req: 2.2 req/s, 3.56s Lat, 7.8 Conc, 130 Comp, 8 Inc, 0 Err │ +│ Tok: 526.1 gen/s, 1728.4 tot/s, 60.6ms TTFT, 14.7ms ITL, 547 Prompt, 239 Gen │ +│ [17:49:38] ⠋ 100% concurrent@16 (complete) Req: 4.1 req/s, 3.79s Lat, 15.7 Conc, 246 Comp, 16 Inc, 0 Err │ +│ Tok: 1006.9 gen/s, 3268.6 tot/s, 74.8ms TTFT, 15.3ms ITL, 547 Prompt, 243 Gen │ +│ [17:50:44] ⠋ 100% concurrent@32 (complete) Req: 7.8 req/s, 3.95s Lat, 30.9 Conc, 467 Comp, 32 Inc, 0 Err │ +│ Tok: 1912.0 gen/s, 6191.6 tot/s, 119.1ms TTFT, 15.7ms ITL, 547 Prompt, 244 Gen │ +│ [17:51:50] ⠋ 100% concurrent@64 (complete) Req: 13.0 req/s, 4.75s Lat, 61.8 Conc, 776 Comp, 64 Inc, 0 Err │ +│ Tok: 3154.3 gen/s, 10273.3 tot/s, 339.1ms TTFT, 18.3ms ITL, 547 Prompt, 242 Gen │ +│ [17:52:58] ⠋ 100% concurrent@128 (complete) Req: 15.1 req/s, 7.82s Lat, 117.7 Conc, 898 Comp, 127 Inc, 0 Err │ +│ Tok: 3617.4 gen/s, 11843.9 tot/s, 1393.8ms TTFT, 26.8ms ITL, 547 Prompt, 240 Gen │ +╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯ +Generating... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ (8/8) [ 0:08:41 < 0:00:00 ] + +Benchmarks Metadata: + Run id:f73d408e-256a-4c32-aa40-05e8d7098b66 + Duration:529.2 seconds + Profile:type=concurrent, strategies=['concurrent', 'concurrent', 'concurrent', 'concurrent', 'concurrent', 'concurrent', 'concurrent', 'concurrent'], streams=[1, 2, 4, 8, 16, 32, 64, 128] + Args:max_number=None, max_duration=60.0, warmup_number=None, warmup_duration=3.0, cooldown_number=None, cooldown_duration=None + Worker:type_='generative_requests_worker' backend_type='openai_http' backend_target='http://llama-stack-benchmark-service:8323/v1/openai' backend_model='meta-llama/Llama-3.2-3B-Instruct' + backend_info={'max_output_tokens': 16384, 'timeout': 300, 'http2': True, 'follow_redirects': True, 'headers': {}, 'text_completions_path': '/v1/completions', 'chat_completions_path': + '/v1/chat/completions'} + Request Loader:type_='generative_request_loader' data='prompt_tokens=512,output_tokens=256' data_args=None processor='meta-llama/Llama-3.2-3B-Instruct' processor_args=None + Extras:None + + +Benchmarks Info: +===================================================================================================================================================== +Metadata |||| Requests Made ||| Prompt Tok/Req ||| Output Tok/Req ||| Prompt Tok Total||| Output Tok Total || + Benchmark| Start Time| End Time| Duration (s)| Comp| Inc| Err| Comp| Inc| Err| Comp| Inc| Err| Comp| Inc| Err| Comp| Inc| Err +--------------|-----------|---------|-------------|------|-----|-----|------|------|----|------|------|----|-------|------|----|--------|------|----- + concurrent@1| 17:45:23| 17:46:23| 60.0| 17| 1| 0| 546.6| 512.0| 0.0| 252.8| 136.0| 0.0| 9292| 512| 0| 4298| 136| 0 + concurrent@2| 17:46:28| 17:47:28| 60.0| 34| 2| 0| 546.4| 512.0| 0.0| 235.4| 130.0| 0.0| 18577| 1024| 0| 8003| 260| 0 + concurrent@4| 17:47:33| 17:48:33| 60.0| 66| 4| 0| 546.5| 512.0| 0.0| 243.0| 97.5| 0.0| 36072| 2048| 0| 16035| 390| 0 + concurrent@8| 17:48:38| 17:49:38| 60.0| 130| 8| 0| 546.6| 512.0| 0.0| 239.2| 146.0| 0.0| 71052| 4096| 0| 31090| 1168| 0 + concurrent@16| 17:49:43| 17:50:43| 60.0| 246| 16| 0| 546.6| 512.0| 0.0| 243.3| 112.3| 0.0| 134456| 8192| 0| 59862| 1797| 0 + concurrent@32| 17:50:49| 17:51:49| 60.0| 467| 32| 0| 546.6| 512.0| 0.0| 244.2| 147.3| 0.0| 255242| 16384| 0| 114038| 4714| 0 + concurrent@64| 17:51:55| 17:52:55| 60.0| 776| 64| 0| 546.5| 512.0| 0.0| 242.2| 106.1| 0.0| 424115| 32768| 0| 187916| 6788| 0 +concurrent@128| 17:53:03| 17:54:03| 60.0| 898| 127| 0| 546.5| 512.0| 0.0| 240.3| 69.8| 0.0| 490789| 65024| 0| 215810| 8864| 0 +===================================================================================================================================================== + + +Benchmarks Stats: +====================================================================================================================================================== +Metadata | Request Stats || Out Tok/sec| Tot Tok/sec| Req Latency (sec)||| TTFT (ms) ||| ITL (ms) ||| TPOT (ms) || + Benchmark| Per Second| Concurrency| mean| mean| mean| median| p99| mean| median| p99| mean| median| p99| mean| median| p99 +--------------|-----------|------------|------------|------------|-----|-------|------|-------|-------|-------|-----|-------|-----|-----|-------|----- + concurrent@1| 0.29| 1.00| 73.9| 233.7| 3.42| 3.45| 3.50| 50.2| 50.9| 62.5| 13.4| 13.4| 13.5| 13.3| 13.3| 13.5 + concurrent@2| 0.57| 1.96| 134.7| 447.4| 3.42| 3.67| 4.12| 50.8| 49.2| 79.8| 14.3| 14.2| 15.9| 14.3| 14.2| 15.9 + concurrent@4| 1.11| 3.92| 268.7| 873.1| 3.55| 3.72| 3.80| 54.9| 51.7| 101.3| 14.4| 14.4| 14.5| 14.4| 14.4| 14.5 + concurrent@8| 2.20| 7.82| 526.1| 1728.4| 3.56| 3.78| 3.93| 60.6| 49.8| 189.5| 14.7| 14.7| 14.8| 14.6| 14.6| 14.8 + concurrent@16| 4.14| 15.66| 1006.9| 3268.6| 3.79| 3.94| 4.25| 74.8| 54.3| 328.4| 15.3| 15.3| 16.1| 15.2| 15.2| 16.0 + concurrent@32| 7.83| 30.91| 1912.0| 6191.6| 3.95| 4.07| 4.53| 119.1| 80.5| 674.0| 15.7| 15.6| 17.4| 15.7| 15.6| 17.3 + concurrent@64| 13.03| 61.85| 3154.3| 10273.3| 4.75| 4.93| 5.43| 339.1| 321.1| 1146.6| 18.3| 18.4| 19.3| 18.2| 18.3| 19.2 +concurrent@128| 15.05| 117.71| 3617.4| 11843.9| 7.82| 8.58| 13.35| 1393.8| 1453.0| 5232.2| 26.8| 26.7| 36.0| 26.7| 26.6| 35.9 +====================================================================================================================================================== + +Saving benchmarks report... +Benchmarks report saved to /benchmarks.json + +Benchmarking complete. diff --git a/benchmarking/k8s-benchmark/results/guidellm-benchmark-stack-s1-sw4-v1-20250922-105539.txt b/benchmarking/k8s-benchmark/results/guidellm-benchmark-stack-s1-sw4-v1-20250922-105539.txt new file mode 100644 index 000000000..a192f0ba3 --- /dev/null +++ b/benchmarking/k8s-benchmark/results/guidellm-benchmark-stack-s1-sw4-v1-20250922-105539.txt @@ -0,0 +1,171 @@ +Collecting uv + Downloading uv-0.8.19-py3-none-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (11 kB) +Downloading uv-0.8.19-py3-none-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (20.9 MB) + ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 20.9/20.9 MB 156.8 MB/s eta 0:00:00 +Installing collected packages: uv +Successfully installed uv-0.8.19 +WARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv + +[notice] A new release of pip is available: 24.0 -> 25.2 +[notice] To update, run: pip install --upgrade pip +Using Python 3.11.13 environment at: /usr/local +Resolved 61 packages in 480ms +Downloading pillow (6.3MiB) +Downloading pydantic-core (1.9MiB) +Downloading pyarrow (40.8MiB) +Downloading aiohttp (1.7MiB) +Downloading numpy (16.2MiB) +Downloading pygments (1.2MiB) +Downloading transformers (11.1MiB) +Downloading pandas (11.8MiB) +Downloading tokenizers (3.1MiB) +Downloading hf-xet (3.0MiB) + Downloading pydantic-core + Downloading aiohttp + Downloading tokenizers + Downloading hf-xet + Downloading pygments + Downloading pillow + Downloading numpy + Downloading pandas + Downloading pyarrow + Downloading transformers +Prepared 61 packages in 1.25s +Installed 61 packages in 126ms + + aiohappyeyeballs==2.6.1 + + aiohttp==3.12.15 + + aiosignal==1.4.0 + + annotated-types==0.7.0 + + anyio==4.10.0 + + attrs==25.3.0 + + certifi==2025.8.3 + + charset-normalizer==3.4.3 + + click==8.1.8 + + datasets==4.1.1 + + dill==0.4.0 + + filelock==3.19.1 + + frozenlist==1.7.0 + + fsspec==2025.9.0 + + ftfy==6.3.1 + + guidellm==0.3.0 + + h11==0.16.0 + + h2==4.3.0 + + hf-xet==1.1.10 + + hpack==4.1.0 + + httpcore==1.0.9 + + httpx==0.28.1 + + huggingface-hub==0.35.0 + + hyperframe==6.1.0 + + idna==3.10 + + loguru==0.7.3 + + markdown-it-py==4.0.0 + + mdurl==0.1.2 + + multidict==6.6.4 + + multiprocess==0.70.16 + + numpy==2.3.3 + + packaging==25.0 + + pandas==2.3.2 + + pillow==11.3.0 + + propcache==0.3.2 + + protobuf==6.32.1 + + pyarrow==21.0.0 + + pydantic==2.11.9 + + pydantic-core==2.33.2 + + pydantic-settings==2.10.1 + + pygments==2.19.2 + + python-dateutil==2.9.0.post0 + + python-dotenv==1.1.1 + + pytz==2025.2 + + pyyaml==6.0.2 + + regex==2025.9.18 + + requests==2.32.5 + + rich==14.1.0 + + safetensors==0.6.2 + + six==1.17.0 + + sniffio==1.3.1 + + tokenizers==0.22.1 + + tqdm==4.67.1 + + transformers==4.56.2 + + typing-extensions==4.15.0 + + typing-inspection==0.4.1 + + tzdata==2025.2 + + urllib3==2.5.0 + + wcwidth==0.2.14 + + xxhash==3.5.0 + + yarl==1.20.1 +Using Python 3.11.13 environment at: /usr/local +Audited 1 package in 4ms +Note: Environment variable`HF_TOKEN` is set and is the current active token independently from the token you've just configured. +Creating backend... +Backend openai_http connected to http://llama-stack-benchmark-service:8323/v1/openai for model meta-llama/Llama-3.2-3B-Instruct. +Creating request loader... +Created loader with 1000 unique requests from prompt_tokens=512,output_tokens=256. + + +╭─ Benchmarks ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮ +│ [17:55:59] ⠋ 100% concurrent@1 (complete) Req: 0.3 req/s, 3.33s Lat, 1.0 Conc, 18 Comp, 1 Inc, 0 Err │ +│ Tok: 74.0 gen/s, 238.0 tot/s, 49.6ms TTFT, 13.4ms ITL, 546 Prompt, 246 Gen │ +│ [17:57:04] ⠋ 100% concurrent@2 (complete) Req: 0.6 req/s, 3.32s Lat, 1.9 Conc, 35 Comp, 2 Inc, 0 Err │ +│ Tok: 137.1 gen/s, 457.5 tot/s, 50.6ms TTFT, 14.0ms ITL, 546 Prompt, 234 Gen │ +│ [17:58:09] ⠋ 100% concurrent@4 (complete) Req: 1.2 req/s, 3.42s Lat, 4.0 Conc, 69 Comp, 4 Inc, 0 Err │ +│ Tok: 276.7 gen/s, 907.2 tot/s, 52.7ms TTFT, 14.1ms ITL, 547 Prompt, 240 Gen │ +│ [17:59:14] ⠋ 100% concurrent@8 (complete) Req: 2.3 req/s, 3.47s Lat, 7.8 Conc, 134 Comp, 8 Inc, 0 Err │ +│ Tok: 541.4 gen/s, 1775.4 tot/s, 57.3ms TTFT, 14.3ms ITL, 547 Prompt, 240 Gen │ +│ [18:00:19] ⠋ 100% concurrent@16 (complete) Req: 4.3 req/s, 3.60s Lat, 15.6 Conc, 259 Comp, 16 Inc, 0 Err │ +│ Tok: 1034.8 gen/s, 3401.7 tot/s, 72.3ms TTFT, 14.8ms ITL, 547 Prompt, 239 Gen │ +│ [18:01:25] ⠋ 100% concurrent@32 (complete) Req: 8.4 req/s, 3.69s Lat, 31.1 Conc, 505 Comp, 32 Inc, 0 Err │ +│ Tok: 2029.7 gen/s, 6641.5 tot/s, 91.6ms TTFT, 15.0ms ITL, 547 Prompt, 241 Gen │ +│ [18:02:31] ⠋ 100% concurrent@64 (complete) Req: 13.6 req/s, 4.50s Lat, 61.4 Conc, 818 Comp, 64 Inc, 0 Err │ +│ Tok: 3333.9 gen/s, 10787.0 tot/s, 171.3ms TTFT, 17.8ms ITL, 547 Prompt, 244 Gen │ +│ [18:03:40] ⠋ 100% concurrent@128 (complete) Req: 16.1 req/s, 7.43s Lat, 119.5 Conc, 964 Comp, 122 Inc, 0 Err │ +│ Tok: 3897.0 gen/s, 12679.4 tot/s, 446.4ms TTFT, 28.9ms ITL, 547 Prompt, 243 Gen │ +╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯ +Generating... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ (8/8) [ 0:08:41 < 0:00:00 ] + +Benchmarks Metadata: + Run id:5393e64f-d9f8-4548-95d8-da320bba1c24 + Duration:530.1 seconds + Profile:type=concurrent, strategies=['concurrent', 'concurrent', 'concurrent', 'concurrent', 'concurrent', 'concurrent', 'concurrent', 'concurrent'], streams=[1, 2, 4, 8, 16, 32, 64, 128] + Args:max_number=None, max_duration=60.0, warmup_number=None, warmup_duration=3.0, cooldown_number=None, cooldown_duration=None + Worker:type_='generative_requests_worker' backend_type='openai_http' backend_target='http://llama-stack-benchmark-service:8323/v1/openai' backend_model='meta-llama/Llama-3.2-3B-Instruct' + backend_info={'max_output_tokens': 16384, 'timeout': 300, 'http2': True, 'follow_redirects': True, 'headers': {}, 'text_completions_path': '/v1/completions', 'chat_completions_path': + '/v1/chat/completions'} + Request Loader:type_='generative_request_loader' data='prompt_tokens=512,output_tokens=256' data_args=None processor='meta-llama/Llama-3.2-3B-Instruct' processor_args=None + Extras:None + + +Benchmarks Info: +=================================================================================================================================================== +Metadata |||| Requests Made ||| Prompt Tok/Req ||| Output Tok/Req ||| Prompt Tok Total||| Output Tok Total|| + Benchmark| Start Time| End Time| Duration (s)| Comp| Inc| Err| Comp| Inc| Err| Comp| Inc| Err| Comp| Inc| Err| Comp| Inc| Err +--------------|-----------|---------|-------------|------|-----|-----|------|------|----|------|------|----|-------|------|----|-------|------|---- + concurrent@1| 17:56:04| 17:57:04| 60.0| 18| 1| 0| 546.4| 512.0| 0.0| 246.4| 256.0| 0.0| 9836| 512| 0| 4436| 256| 0 + concurrent@2| 17:57:09| 17:58:09| 60.0| 35| 2| 0| 546.4| 512.0| 0.0| 233.9| 132.0| 0.0| 19124| 1024| 0| 8188| 264| 0 + concurrent@4| 17:58:14| 17:59:14| 60.0| 69| 4| 0| 546.6| 512.0| 0.0| 239.9| 60.5| 0.0| 37715| 2048| 0| 16553| 242| 0 + concurrent@8| 17:59:19| 18:00:19| 60.0| 134| 8| 0| 546.6| 512.0| 0.0| 239.8| 126.6| 0.0| 73243| 4096| 0| 32135| 1013| 0 + concurrent@16| 18:00:24| 18:01:24| 60.0| 259| 16| 0| 546.6| 512.0| 0.0| 239.0| 115.7| 0.0| 141561| 8192| 0| 61889| 1851| 0 + concurrent@32| 18:01:30| 18:02:30| 60.0| 505| 32| 0| 546.5| 512.0| 0.0| 240.5| 113.2| 0.0| 275988| 16384| 0| 121466| 3623| 0 + concurrent@64| 18:02:37| 18:03:37| 60.0| 818| 64| 0| 546.6| 512.0| 0.0| 244.5| 132.4| 0.0| 447087| 32768| 0| 199988| 8475| 0 +concurrent@128| 18:03:45| 18:04:45| 60.0| 964| 122| 0| 546.5| 512.0| 0.0| 242.5| 133.1| 0.0| 526866| 62464| 0| 233789| 16241| 0 +=================================================================================================================================================== + + +Benchmarks Stats: +======================================================================================================================================================= +Metadata | Request Stats || Out Tok/sec| Tot Tok/sec| Req Latency (sec) ||| TTFT (ms) ||| ITL (ms) ||| TPOT (ms) || + Benchmark| Per Second| Concurrency| mean| mean| mean| median| p99| mean| median| p99| mean| median| p99| mean| median| p99 +--------------|-----------|------------|------------|------------|------|--------|------|------|-------|-------|-----|-------|-----|-----|-------|----- + concurrent@1| 0.30| 1.00| 74.0| 238.0| 3.33| 3.44| 3.63| 49.6| 47.2| 66.1| 13.4| 13.3| 14.0| 13.3| 13.3| 14.0 + concurrent@2| 0.59| 1.95| 137.1| 457.5| 3.32| 3.61| 3.67| 50.6| 48.6| 80.4| 14.0| 14.0| 14.2| 13.9| 13.9| 14.1 + concurrent@4| 1.15| 3.95| 276.7| 907.2| 3.42| 3.61| 3.77| 52.7| 49.7| 106.9| 14.1| 14.0| 14.6| 14.0| 13.9| 14.5 + concurrent@8| 2.26| 7.83| 541.4| 1775.4| 3.47| 3.70| 3.79| 57.3| 50.9| 171.3| 14.3| 14.3| 14.4| 14.2| 14.2| 14.4 + concurrent@16| 4.33| 15.57| 1034.8| 3401.7| 3.60| 3.81| 4.22| 72.3| 52.0| 292.9| 14.8| 14.7| 16.3| 14.7| 14.7| 16.3 + concurrent@32| 8.44| 31.12| 2029.7| 6641.5| 3.69| 3.89| 4.24| 91.6| 62.6| 504.6| 15.0| 15.0| 15.4| 14.9| 14.9| 15.4 + concurrent@64| 13.64| 61.40| 3333.9| 10787.0| 4.50| 4.61| 5.67| 171.3| 101.2| 1165.6| 17.8| 17.7| 19.2| 17.7| 17.6| 19.1 +concurrent@128| 16.07| 119.45| 3897.0| 12679.4| 7.43| 7.63| 9.74| 446.4| 195.8| 2533.1| 28.9| 28.9| 31.0| 28.8| 28.8| 30.9 +======================================================================================================================================================= + +Saving benchmarks report... +Benchmarks report saved to /benchmarks.json + +Benchmarking complete. diff --git a/benchmarking/k8s-benchmark/results/guidellm-benchmark-vllm-v1-20250922-111127.txt b/benchmarking/k8s-benchmark/results/guidellm-benchmark-vllm-v1-20250922-111127.txt new file mode 100644 index 000000000..8bee7d905 --- /dev/null +++ b/benchmarking/k8s-benchmark/results/guidellm-benchmark-vllm-v1-20250922-111127.txt @@ -0,0 +1,170 @@ +Collecting uv + Downloading uv-0.8.19-py3-none-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (11 kB) +Downloading uv-0.8.19-py3-none-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (20.9 MB) + ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 20.9/20.9 MB 126.9 MB/s eta 0:00:00 +Installing collected packages: uv +Successfully installed uv-0.8.19 +WARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv + +[notice] A new release of pip is available: 24.0 -> 25.2 +[notice] To update, run: pip install --upgrade pip +Using Python 3.11.13 environment at: /usr/local +Resolved 61 packages in 561ms +Downloading hf-xet (3.0MiB) +Downloading pillow (6.3MiB) +Downloading transformers (11.1MiB) +Downloading pyarrow (40.8MiB) +Downloading numpy (16.2MiB) +Downloading pandas (11.8MiB) +Downloading tokenizers (3.1MiB) +Downloading pydantic-core (1.9MiB) +Downloading pygments (1.2MiB) +Downloading aiohttp (1.7MiB) + Downloading pydantic-core + Downloading aiohttp + Downloading tokenizers + Downloading hf-xet + Downloading pygments + Downloading pillow + Downloading numpy + Downloading pandas + Downloading transformers + Downloading pyarrow +Prepared 61 packages in 1.25s +Installed 61 packages in 114ms + + aiohappyeyeballs==2.6.1 + + aiohttp==3.12.15 + + aiosignal==1.4.0 + + annotated-types==0.7.0 + + anyio==4.10.0 + + attrs==25.3.0 + + certifi==2025.8.3 + + charset-normalizer==3.4.3 + + click==8.1.8 + + datasets==4.1.1 + + dill==0.4.0 + + filelock==3.19.1 + + frozenlist==1.7.0 + + fsspec==2025.9.0 + + ftfy==6.3.1 + + guidellm==0.3.0 + + h11==0.16.0 + + h2==4.3.0 + + hf-xet==1.1.10 + + hpack==4.1.0 + + httpcore==1.0.9 + + httpx==0.28.1 + + huggingface-hub==0.35.0 + + hyperframe==6.1.0 + + idna==3.10 + + loguru==0.7.3 + + markdown-it-py==4.0.0 + + mdurl==0.1.2 + + multidict==6.6.4 + + multiprocess==0.70.16 + + numpy==2.3.3 + + packaging==25.0 + + pandas==2.3.2 + + pillow==11.3.0 + + propcache==0.3.2 + + protobuf==6.32.1 + + pyarrow==21.0.0 + + pydantic==2.11.9 + + pydantic-core==2.33.2 + + pydantic-settings==2.10.1 + + pygments==2.19.2 + + python-dateutil==2.9.0.post0 + + python-dotenv==1.1.1 + + pytz==2025.2 + + pyyaml==6.0.2 + + regex==2025.9.18 + + requests==2.32.5 + + rich==14.1.0 + + safetensors==0.6.2 + + six==1.17.0 + + sniffio==1.3.1 + + tokenizers==0.22.1 + + tqdm==4.67.1 + + transformers==4.56.2 + + typing-extensions==4.15.0 + + typing-inspection==0.4.1 + + tzdata==2025.2 + + urllib3==2.5.0 + + wcwidth==0.2.14 + + xxhash==3.5.0 + + yarl==1.20.1 +Using Python 3.11.13 environment at: /usr/local +Audited 1 package in 3ms +Note: Environment variable`HF_TOKEN` is set and is the current active token independently from the token you've just configured. +Creating backend... +Backend openai_http connected to http://vllm-server:8000 for model meta-llama/Llama-3.2-3B-Instruct. +Creating request loader... +Created loader with 1000 unique requests from prompt_tokens=512,output_tokens=256. + + +╭─ Benchmarks ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮ +│ [18:11:47] ⠋ 100% concurrent@1 (complete) Req: 0.3 req/s, 3.35s Lat, 1.0 Conc, 17 Comp, 1 Inc, 0 Err │ +│ Tok: 76.4 gen/s, 239.4 tot/s, 29.6ms TTFT, 13.0ms ITL, 547 Prompt, 256 Gen │ +│ [18:12:52] ⠋ 100% concurrent@2 (complete) Req: 0.6 req/s, 3.53s Lat, 2.0 Conc, 32 Comp, 2 Inc, 0 Err │ +│ Tok: 145.0 gen/s, 454.5 tot/s, 36.9ms TTFT, 13.7ms ITL, 546 Prompt, 256 Gen │ +│ [18:13:57] ⠋ 100% concurrent@4 (complete) Req: 1.1 req/s, 3.59s Lat, 4.0 Conc, 64 Comp, 4 Inc, 0 Err │ +│ Tok: 284.8 gen/s, 892.7 tot/s, 59.0ms TTFT, 13.9ms ITL, 546 Prompt, 256 Gen │ +│ [18:15:02] ⠋ 100% concurrent@8 (complete) Req: 2.2 req/s, 3.70s Lat, 8.0 Conc, 128 Comp, 7 Inc, 0 Err │ +│ Tok: 553.5 gen/s, 1735.2 tot/s, 79.8ms TTFT, 14.2ms ITL, 547 Prompt, 256 Gen │ +│ [18:16:08] ⠋ 100% concurrent@16 (complete) Req: 4.2 req/s, 3.83s Lat, 16.0 Conc, 240 Comp, 16 Inc, 0 Err │ +│ Tok: 1066.9 gen/s, 3344.6 tot/s, 97.5ms TTFT, 14.6ms ITL, 547 Prompt, 256 Gen │ +│ [18:17:13] ⠋ 100% concurrent@32 (complete) Req: 8.1 req/s, 3.94s Lat, 31.8 Conc, 480 Comp, 31 Inc, 0 Err │ +│ Tok: 2069.7 gen/s, 6488.4 tot/s, 120.8ms TTFT, 15.0ms ITL, 547 Prompt, 256 Gen │ +│ [18:18:20] ⠋ 100% concurrent@64 (complete) Req: 13.6 req/s, 4.60s Lat, 62.3 Conc, 813 Comp, 57 Inc, 0 Err │ +│ Tok: 3472.1 gen/s, 10884.9 tot/s, 190.9ms TTFT, 17.3ms ITL, 547 Prompt, 256 Gen │ +│ [18:19:28] ⠋ 100% concurrent@128 (complete) Req: 16.8 req/s, 7.37s Lat, 123.5 Conc, 1005 Comp, 126 Inc, 0 Err │ +│ Tok: 4289.1 gen/s, 13445.8 tot/s, 356.4ms TTFT, 27.5ms ITL, 547 Prompt, 256 Gen │ +╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯ +Generating... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ (8/8) [ 0:08:43 < 0:00:00 ] + +Benchmarks Metadata: + Run id:8ccb6da1-83f4-4624-8d84-07c723b0b2a5 + Duration:530.4 seconds + Profile:type=concurrent, strategies=['concurrent', 'concurrent', 'concurrent', 'concurrent', 'concurrent', 'concurrent', 'concurrent', 'concurrent'], streams=[1, 2, 4, 8, 16, 32, 64, 128] + Args:max_number=None, max_duration=60.0, warmup_number=None, warmup_duration=3.0, cooldown_number=None, cooldown_duration=None + Worker:type_='generative_requests_worker' backend_type='openai_http' backend_target='http://vllm-server:8000' backend_model='meta-llama/Llama-3.2-3B-Instruct' backend_info={'max_output_tokens': + 16384, 'timeout': 300, 'http2': True, 'follow_redirects': True, 'headers': {}, 'text_completions_path': '/v1/completions', 'chat_completions_path': '/v1/chat/completions'} + Request Loader:type_='generative_request_loader' data='prompt_tokens=512,output_tokens=256' data_args=None processor='meta-llama/Llama-3.2-3B-Instruct' processor_args=None + Extras:None + + +Benchmarks Info: +===================================================================================================================================================== +Metadata |||| Requests Made ||| Prompt Tok/Req ||| Output Tok/Req ||| Prompt Tok Total||| Output Tok Total || + Benchmark| Start Time| End Time| Duration (s)| Comp| Inc| Err| Comp| Inc| Err| Comp| Inc| Err| Comp| Inc| Err| Comp| Inc| Err +--------------|-----------|---------|-------------|------|-----|-----|------|------|----|------|------|----|-------|------|----|--------|------|----- + concurrent@1| 18:11:52| 18:12:52| 60.0| 17| 1| 0| 546.5| 512.0| 0.0| 256.0| 231.0| 0.0| 9291| 512| 0| 4352| 231| 0 + concurrent@2| 18:12:57| 18:13:57| 60.0| 32| 2| 0| 546.5| 512.0| 0.0| 256.0| 251.0| 0.0| 17488| 1024| 0| 8192| 502| 0 + concurrent@4| 18:14:02| 18:15:02| 60.0| 64| 4| 0| 546.4| 512.0| 0.0| 256.0| 175.2| 0.0| 34972| 2048| 0| 16384| 701| 0 + concurrent@8| 18:15:07| 18:16:07| 60.0| 128| 7| 0| 546.6| 512.0| 0.0| 256.0| 50.7| 0.0| 69966| 3584| 0| 32768| 355| 0 + concurrent@16| 18:16:13| 18:17:13| 60.0| 240| 16| 0| 546.5| 512.0| 0.0| 256.0| 166.0| 0.0| 131170| 8192| 0| 61440| 2656| 0 + concurrent@32| 18:17:18| 18:18:18| 60.0| 480| 31| 0| 546.5| 512.0| 0.0| 256.0| 47.4| 0.0| 262339| 15872| 0| 122880| 1468| 0 + concurrent@64| 18:18:25| 18:19:25| 60.0| 813| 57| 0| 546.5| 512.0| 0.0| 256.0| 110.7| 0.0| 444341| 29184| 0| 208128| 6311| 0 +concurrent@128| 18:19:33| 18:20:33| 60.0| 1005| 126| 0| 546.5| 512.0| 0.0| 256.0| 65.8| 0.0| 549264| 64512| 0| 257280| 8296| 0 +===================================================================================================================================================== + + +Benchmarks Stats: +======================================================================================================================================================= +Metadata | Request Stats || Out Tok/sec| Tot Tok/sec| Req Latency (sec) ||| TTFT (ms) ||| ITL (ms) ||| TPOT (ms) || + Benchmark| Per Second| Concurrency| mean| mean| mean| median| p99| mean| median| p99| mean| median| p99| mean| median| p99 +--------------|-----------|------------|------------|------------|------|--------|------|------|-------|-------|-----|-------|-----|-----|-------|----- + concurrent@1| 0.30| 1.00| 76.4| 239.4| 3.35| 3.35| 3.38| 29.6| 29.0| 38.9| 13.0| 13.0| 13.1| 13.0| 13.0| 13.0 + concurrent@2| 0.57| 2.00| 145.0| 454.5| 3.53| 3.53| 3.55| 36.9| 39.0| 59.6| 13.7| 13.7| 13.8| 13.6| 13.7| 13.7 + concurrent@4| 1.11| 4.00| 284.8| 892.7| 3.59| 3.59| 3.65| 59.0| 65.7| 88.2| 13.9| 13.8| 14.1| 13.8| 13.8| 14.0 + concurrent@8| 2.16| 7.99| 553.5| 1735.2| 3.70| 3.69| 3.76| 79.8| 80.7| 152.6| 14.2| 14.2| 14.5| 14.1| 14.1| 14.4 + concurrent@16| 4.17| 15.97| 1066.9| 3344.6| 3.83| 3.82| 3.99| 97.5| 96.3| 283.9| 14.6| 14.6| 14.9| 14.6| 14.6| 14.8 + concurrent@32| 8.08| 31.84| 2069.7| 6488.4| 3.94| 3.90| 4.31| 120.8| 101.7| 564.3| 15.0| 14.9| 15.9| 14.9| 14.8| 15.9 + concurrent@64| 13.56| 62.34| 3472.1| 10884.9| 4.60| 4.54| 5.43| 190.9| 133.9| 1113.2| 17.3| 17.2| 18.2| 17.2| 17.2| 18.2 +concurrent@128| 16.75| 123.45| 4289.1| 13445.8| 7.37| 7.21| 9.21| 356.4| 161.9| 2319.9| 27.5| 27.5| 28.8| 27.4| 27.4| 28.7 +======================================================================================================================================================= + +Saving benchmarks report... +Benchmarks report saved to /benchmarks.json + +Benchmarking complete. diff --git a/benchmarking/k8s-benchmark/results/vllm_replica1_benchmark_results.png b/benchmarking/k8s-benchmark/results/vllm_replica1_benchmark_results.png new file mode 100644 index 000000000..86c6c046e Binary files /dev/null and b/benchmarking/k8s-benchmark/results/vllm_replica1_benchmark_results.png differ diff --git a/benchmarking/k8s-benchmark/scripts/generate_charts.py b/benchmarking/k8s-benchmark/scripts/generate_charts.py new file mode 100755 index 000000000..7b920fc04 --- /dev/null +++ b/benchmarking/k8s-benchmark/scripts/generate_charts.py @@ -0,0 +1,294 @@ +#!/usr/bin/env python3 +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the terms described in the LICENSE file in +# the root directory of this source tree. + +# /// script +# dependencies = [ +# "matplotlib", +# ] +# /// +""" +Script to generate benchmark charts from guidellm text results. +Creates 2x2 grid charts with RPS, Request Latency, TTFT, and ITL metrics against concurrent@x values. +Outputs one chart file per vLLM replica group, with each line representing one benchmark run. +""" + +import glob +import os +import re + +import matplotlib.pyplot as plt + + +def extract_setup_name(filename: str) -> str: + """Extract setup name from filename and format legend appropriately.""" + basename = os.path.basename(filename) + + # Try new pattern: guidellm-benchmark-stack-s{stack_replicas}-sw{workers}-v{vllm_replicas}-{timestamp}.txt + match = re.search(r"guidellm-benchmark-stack-s(\d+)-sw(\d+)-v(\d+)-(\d{8})-(\d{6})\.txt", basename) + if match: + stack_replicas = match.group(1) + workers = match.group(2) + vllm_replicas = match.group(3) + date = match.group(4) + time = match.group(5) + return f"stack-s{stack_replicas}-sw{workers}-v{vllm_replicas}" + + # Try new vLLM pattern: guidellm-benchmark-vllm-v{vllm_replicas}-{timestamp}.txt + match = re.search(r"guidellm-benchmark-vllm-v(\d+)-(\d{8})-(\d{6})\.txt", basename) + if match: + vllm_replicas = match.group(1) + date = match.group(2) + time = match.group(3) + return f"vllm-v{vllm_replicas}" + + # Fall back to old pattern: guidellm-benchmark-{target}-{stack_replicas}-w{workers}-{vllm_replicas}-{timestamp}.txt + match = re.search(r"guidellm-benchmark-([^-]+)-(\d+)-w(\d+)-(\d+)-(\d+)-(\d+)\.txt", basename) + if match: + target = match.group(1) + stack_replicas = match.group(2) + workers = match.group(3) + vllm_replicas = match.group(4) + date = match.group(5) + time = match.group(6) + + if target == "vllm": + return f"vllm-{vllm_replicas}-w{workers}-{vllm_replicas}" + else: + return f"stack-replicas{stack_replicas}-w{workers}-vllm-replicas{vllm_replicas}-{date}-{time}" + + # Fall back to older pattern: guidellm-benchmark-{target}-{stack_replicas}-{vllm_replicas}-{timestamp}.txt + match = re.search(r"guidellm-benchmark-([^-]+)-(\d+)-(\d+)-(\d+)-(\d+)\.txt", basename) + if match: + target = match.group(1) + stack_replicas = match.group(2) + vllm_replicas = match.group(3) + date = match.group(4) + time = match.group(5) + + if target == "vllm": + return f"vllm-{vllm_replicas}-w1-{vllm_replicas}" + else: + return f"stack-replicas{stack_replicas}-vllm-replicas{vllm_replicas}-{date}-{time}" + + return basename.replace("guidellm-benchmark-", "").replace(".txt", "") + + +def parse_txt_file(filepath: str) -> list[tuple[float, float, float, float, float, str]]: + """ + Parse a text benchmark file and extract concurrent@x, RPS, TTFT, ITL, and request latency data. + Returns list of (concurrency, rps_mean, ttft_mean, itl_mean, req_latency_mean, setup_name) tuples. + """ + setup_name = extract_setup_name(filepath) + data_points = [] + + try: + with open(filepath) as f: + content = f.read() + + # Find the benchmark stats table + lines = content.split("\n") + in_stats_table = False + header_lines_seen = 0 + + for line in lines: + line_stripped = line.strip() + + # Look for the start of the stats table + if "Benchmarks Stats:" in line: + in_stats_table = True + continue + + if in_stats_table: + # Skip the first few separator/header lines + if line_stripped.startswith("=") or line_stripped.startswith("-"): + header_lines_seen += 1 + if header_lines_seen >= 3: # After seeing multiple header lines, look for concurrent@ data + if line_stripped.startswith("=") and "concurrent@" not in line_stripped: + break + continue + + # Parse concurrent@ lines in the stats table (may have leading spaces) + if in_stats_table and "concurrent@" in line: + parts = [part.strip() for part in line.split("|")] + + if len(parts) >= 12: # Make sure we have enough columns for new format + try: + # Extract concurrency from benchmark name (e.g., concurrent@1 -> 1) + concurrent_match = re.search(r"concurrent@(\d+)", parts[0]) + if not concurrent_match: + continue + concurrency = float(concurrent_match.group(1)) + + # Extract metrics from the new table format + # From your image, the table has these columns with | separators: + # Benchmark | Per Second | Concurrency | Out Tok/sec | Tot Tok/sec | Req Latency (sec) | TTFT (ms) | ITL (ms) | TPOT (ms) + # Looking at the mean/median/p99 structure, need to find the mean columns + # The structure shows: mean | median | p99 for each metric + rps_mean = float(parts[1]) # Per Second (RPS) + req_latency_mean = float(parts[6]) * 1000 # Request latency mean (convert from sec to ms) + ttft_mean = float(parts[9]) # TTFT mean column + itl_mean = float(parts[12]) # ITL mean column + + data_points.append((concurrency, rps_mean, ttft_mean, itl_mean, req_latency_mean, setup_name)) + + except (ValueError, IndexError) as e: + print(f"Warning: Could not parse line '{line}' in {filepath}: {e}") + continue + + except (OSError, FileNotFoundError) as e: + print(f"Error reading {filepath}: {e}") + + return data_points + + +def generate_charts(benchmark_dir: str = "results"): + """Generate 2x2 grid charts (RPS, Request Latency, TTFT, ITL) from benchmark text files.""" + # Find all text result files instead of JSON + txt_pattern = os.path.join(benchmark_dir, "guidellm-benchmark-*.txt") + txt_files = glob.glob(txt_pattern) + + if not txt_files: + print(f"No text files found matching pattern: {txt_pattern}") + return + + print(f"Found {len(txt_files)} text files") + + # Parse all files and collect data + all_data = {} # setup_name -> [(concurrency, rps, ttft, itl, req_latency), ...] + + for txt_file in txt_files: + print(f"Processing {txt_file}") + data_points = parse_txt_file(txt_file) + + for concurrency, rps, ttft, itl, req_latency, setup_name in data_points: + if setup_name not in all_data: + all_data[setup_name] = [] + all_data[setup_name].append((concurrency, rps, ttft, itl, req_latency)) + + if not all_data: + print("No data found to plot") + return + + # Sort data points by concurrency for each setup + for setup_name in all_data: + all_data[setup_name].sort(key=lambda x: x[0]) # Sort by concurrency + + # Group setups by vLLM replica number (original approach) + replica_groups = {} # vllm_replica_count -> {setup_name: points} + + for setup_name, points in all_data.items(): + # Extract vLLM replica number from setup name + # Expected formats: + # - New stack format: "stack-s{X}-sw{W}-v{Y}" + # - New vLLM format: "vllm-v{Y}" + # - Old formats: "stack-replicas{X}-w{W}-vllm-replicas{Y}" or "vllm-{Y}-w{W}-{Y}" + + # Try new formats first + vllm_match = re.search(r"-v(\d+)$", setup_name) # Matches both "stack-s1-sw2-v3" and "vllm-v1" + if not vllm_match: + # Try old stack format + vllm_match = re.search(r"vllm-replicas(\d+)", setup_name) + if not vllm_match: + # Try old vLLM format: "vllm-{Y}-w{W}-{Y}" + vllm_match = re.search(r"vllm-(\d+)-w\d+-\d+", setup_name) + + if vllm_match: + vllm_replica_num = int(vllm_match.group(1)) + if vllm_replica_num not in replica_groups: + replica_groups[vllm_replica_num] = {} + replica_groups[vllm_replica_num][setup_name] = points + else: + print(f"Warning: Could not extract vLLM replica count from setup name: {setup_name}") + + def create_charts(data_dict, prefix, title_prefix): + """Create a 2x2 grid with RPS, Request Latency, TTFT, and ITL charts.""" + if not data_dict: + print(f"No data found for {prefix}") + return + + # Create 2x2 subplot grid + fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, figsize=(16, 12)) + fig.suptitle(f"{title_prefix} Benchmark Results", fontsize=16, fontweight="bold") + + # Collect all unique concurrency values for tick setting + all_concurrency_values = set() + for points in data_dict.values(): + all_concurrency_values.update([p[0] for p in points]) + all_concurrency_values = sorted(all_concurrency_values) + + # Plot data for each setup in alphabetical order + for setup_name in sorted(data_dict.keys()): + points = data_dict[setup_name] + if not points: + continue + + concurrency_values = [p[0] for p in points] + rps_values = [p[1] for p in points] + ttft_values = [p[2] for p in points] + itl_values = [p[3] for p in points] + req_latency_values = [p[4] for p in points] + + # RPS chart (top-left) + ax1.plot(concurrency_values, rps_values, marker="o", label=setup_name, linewidth=2, markersize=6) + + # Request Latency chart (top-right) + ax2.plot(concurrency_values, req_latency_values, marker="o", label=setup_name, linewidth=2, markersize=6) + + # TTFT chart (bottom-left) + ax3.plot(concurrency_values, ttft_values, marker="o", label=setup_name, linewidth=2, markersize=6) + + # ITL chart (bottom-right) + ax4.plot(concurrency_values, itl_values, marker="o", label=setup_name, linewidth=2, markersize=6) + + # Configure all charts after plotting data + axes = [ax1, ax2, ax3, ax4] + titles = ["RPS", "Request Latency", "TTFT", "ITL"] + ylabels = [ + "Requests Per Second (RPS)", + "Request Latency (ms)", + "Time to First Token (ms)", + "Inter Token Latency (ms)", + ] + + for ax, title, ylabel in zip(axes, titles, ylabels, strict=False): + ax.set_xlabel("Concurrency", fontsize=12) + ax.set_ylabel(ylabel, fontsize=12) + ax.set_title(title, fontsize=14, fontweight="bold") + ax.set_xscale("log", base=2) + ax.set_xticks(all_concurrency_values) + ax.set_xticklabels([str(int(x)) for x in all_concurrency_values]) + ax.grid(True, alpha=0.3) + + # Add legend to the right-most subplot (top-right) + ax2.legend(bbox_to_anchor=(1.05, 1), loc="upper left") + + plt.tight_layout() + + # Save the combined chart + combined_filename = os.path.join(benchmark_dir, f"{prefix}_benchmark_results.png") + plt.savefig(combined_filename, dpi=300, bbox_inches="tight") + plt.close() + print(f"Combined benchmark chart saved to {combined_filename}") + + # Print grouping information + for replica_count, data_dict in replica_groups.items(): + print(f"vLLM Replica {replica_count} setups: {list(data_dict.keys())}") + + # Create separate charts for each replica group + for replica_count, data_dict in replica_groups.items(): + prefix = f"vllm_replica{replica_count}" + title = f"vLLM Replicas={replica_count}" + create_charts(data_dict, prefix, title) + + # Print summary + print("\nSummary:") + for setup_name, points in all_data.items(): + print(f"{setup_name}: {len(points)} data points") + + +if __name__ == "__main__": + generate_charts() diff --git a/benchmarking/k8s-benchmark/scripts/run-all-benchmarks.sh b/benchmarking/k8s-benchmark/scripts/run-all-benchmarks.sh new file mode 100755 index 000000000..0a4a774c7 --- /dev/null +++ b/benchmarking/k8s-benchmark/scripts/run-all-benchmarks.sh @@ -0,0 +1,103 @@ +#!/usr/bin/env bash + +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the terms described in the LICENSE file in +# the root directory of this source tree. + +# Define benchmark configurations: (target, stack_replicas, vllm_replicas, stack_workers) +configs=( + "stack 1 1 1" + "stack 1 1 2" + "stack 1 1 4" + "vllm 1 1 -" +) + +set -euo pipefail + +# Get the directory where this script is located +SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)" + +echo "Running comprehensive GuideLL benchmark suite..." +echo "Start time: $(date)" + +# Default deployment names +STACK_DEPLOYMENT="llama-stack-benchmark-server" +VLLM_DEPLOYMENT="vllm-server" + +# Scaling function +scale_deployments() { + local stack_replicas=$1 + local vllm_replicas=$2 + local workers=$3 + + echo "Scaling deployments..." + + if [[ "$vllm_replicas" != "-" ]]; then + echo "Scaling $VLLM_DEPLOYMENT to $vllm_replicas replicas..." + kubectl scale deployment $VLLM_DEPLOYMENT --replicas=$vllm_replicas + kubectl rollout status deployment $VLLM_DEPLOYMENT --timeout=600s + fi + + if [[ "$target" == "stack" ]]; then + if [[ "$stack_replicas" != "-" ]]; then + echo "Scaling $STACK_DEPLOYMENT to $stack_replicas replicas..." + kubectl scale deployment $STACK_DEPLOYMENT --replicas=$stack_replicas + kubectl rollout status deployment $STACK_DEPLOYMENT --timeout=600s + fi + + if [[ "$workers" != "-" ]]; then + echo "Updating $STACK_DEPLOYMENT to use $workers workers..." + kubectl set env deployment/$STACK_DEPLOYMENT LLAMA_STACK_WORKERS=$workers + kubectl rollout status deployment $STACK_DEPLOYMENT --timeout=600s + fi + fi + + echo "All scaling operations completed. Waiting additional 30s for services to stabilize..." + sleep 30 +} + + +for config in "${configs[@]}"; do + read -r target stack_replicas vllm_replicas workers <<< "$config" + + echo "" + echo "==========================================" + if [[ "$workers" != "-" ]]; then + echo "Running benchmark: $target (stack=$stack_replicas, vllm=$vllm_replicas, workers=$workers)" + else + echo "Running benchmark: $target (stack=$stack_replicas, vllm=$vllm_replicas)" + fi + echo "Start: $(date)" + echo "==========================================" + + # Scale deployments before running benchmark + scale_deployments "$stack_replicas" "$vllm_replicas" "$workers" + + # Generate output filename with setup info + TIMESTAMP=$(date +%Y%m%d-%H%M%S) + if [[ "$target" == "stack" ]]; then + OUTPUT_FILE="results/guidellm-benchmark-${target}-s${stack_replicas}-sw${workers}-v${vllm_replicas}-${TIMESTAMP}.txt" + else + OUTPUT_FILE="results/guidellm-benchmark-${target}-v${vllm_replicas}-${TIMESTAMP}.txt" + fi + + # Run the benchmark with the cluster as configured + "$SCRIPT_DIR/run-guidellm-benchmark.sh" \ + --target "$target" \ + --output-file "$OUTPUT_FILE" + + echo "Completed: $(date)" + echo "Waiting 30 seconds before next benchmark..." + sleep 30 +done + +echo "" +echo "==========================================" +echo "All benchmarks completed!" +echo "End time: $(date)" +echo "==========================================" +echo "" +echo "Results files generated:" +ls -la results/guidellm-*.txt results/guidellm-*.json 2>/dev/null || echo "No result files found" diff --git a/benchmarking/k8s-benchmark/scripts/run-guidellm-benchmark.sh b/benchmarking/k8s-benchmark/scripts/run-guidellm-benchmark.sh new file mode 100755 index 000000000..746eff391 --- /dev/null +++ b/benchmarking/k8s-benchmark/scripts/run-guidellm-benchmark.sh @@ -0,0 +1,219 @@ +#!/usr/bin/env bash + +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the terms described in the LICENSE file in +# the root directory of this source tree. + +set -euo pipefail + +# Default values +TARGET="stack" +MAX_SECONDS=60 +PROMPT_TOKENS=512 +OUTPUT_TOKENS=256 +RATE_TYPE="concurrent" +RATE="1,2,4,8,16,32,64,128" +STACK_DEPLOYMENT="llama-stack-benchmark-server" +STACK_URL="http://llama-stack-benchmark-service:8323/v1/openai" +VLLM_DEPLOYMENT="vllm-server" +OUTPUT_FILE="" + +# Parse command line arguments +usage() { + echo "Usage: $0 [options]" + echo "Options:" + echo " -t, --target Target to benchmark (default: stack)" + echo " -s, --max-seconds Maximum duration in seconds (default: 60)" + echo " -p, --prompt-tokens Number of prompt tokens (default: 512)" + echo " -o, --output-tokens Number of output tokens (default: 256)" + echo " -r, --rate-type Rate type (default: concurrent)" + echo " -c, --rate Rate (default: 1,2,4,8,16,32,64,128)" + echo " --output-file Output file path (default: auto-generated)" + echo " --stack-deployment Name of the stack deployment (default: llama-stack-benchmark-server)" + echo " --vllm-deployment Name of the vllm deployment (default: vllm-server)" + echo " --stack-url URL of the stack service (default: http://llama-stack-benchmark-service:8323/v1/openai)" + echo " -h, --help Show this help message" + echo "" + echo "Examples:" + echo " $0 --target vllm # Benchmark vLLM direct" + echo " $0 --target stack # Benchmark Llama Stack (default)" + echo " $0 -t vllm -s 60 -p 512 -o 256 # vLLM with custom parameters" + echo " $0 --output-file results/my-benchmark.txt # Specify custom output file" + echo " $0 --stack-deployment my-stack-server # Use custom stack deployment name" +} + +while [[ $# -gt 0 ]]; do + case $1 in + -t|--target) + TARGET="$2" + shift 2 + ;; + -s|--max-seconds) + MAX_SECONDS="$2" + shift 2 + ;; + -p|--prompt-tokens) + PROMPT_TOKENS="$2" + shift 2 + ;; + -o|--output-tokens) + OUTPUT_TOKENS="$2" + shift 2 + ;; + -r|--rate-type) + RATE_TYPE="$2" + shift 2 + ;; + -c|--rate) + RATE="$2" + shift 2 + ;; + --output-file) + OUTPUT_FILE="$2" + shift 2 + ;; + --stack-deployment) + STACK_DEPLOYMENT="$2" + shift 2 + ;; + --vllm-deployment) + VLLM_DEPLOYMENT="$2" + shift 2 + ;; + --stack-url) + STACK_URL="$2" + shift 2 + ;; + -h|--help) + usage + exit 0 + ;; + *) + echo "Unknown option: $1" + usage + exit 1 + ;; + esac +done + +# Validate target +if [[ "$TARGET" != "stack" && "$TARGET" != "vllm" ]]; then + echo "Error: Target must be 'stack' or 'vllm'" + usage + exit 1 +fi + +# Set configuration based on target +if [[ "$TARGET" == "vllm" ]]; then + BASE_URL="http://${VLLM_DEPLOYMENT}:8000" + JOB_NAME="guidellm-vllm-benchmark-job" + echo "Benchmarking vLLM direct with GuideLLM..." +else + BASE_URL="$STACK_URL" + JOB_NAME="guidellm-stack-benchmark-job" + echo "Benchmarking Llama Stack with GuideLLM..." +fi + + +echo "Configuration:" +echo " Target: $TARGET" +echo " Base URL: $BASE_URL" +echo " Max seconds: ${MAX_SECONDS}s" +echo " Prompt tokens: $PROMPT_TOKENS" +echo " Output tokens: $OUTPUT_TOKENS" +echo " Rate type: $RATE_TYPE" +if [[ "$TARGET" == "vllm" ]]; then + echo " vLLM deployment: $VLLM_DEPLOYMENT" +else + echo " Stack deployment: $STACK_DEPLOYMENT" +fi +echo "" + +# Create temporary job yaml +TEMP_YAML="/tmp/guidellm-benchmark-job-temp-$(date +%s).yaml" +cat > "$TEMP_YAML" << EOF +apiVersion: batch/v1 +kind: Job +metadata: + name: $JOB_NAME + namespace: default +spec: + template: + spec: + containers: + - name: guidellm-benchmark + image: python:3.11-slim + command: ["/bin/bash"] + args: + - "-c" + - | + # Install uv and guidellm + pip install uv && + uv pip install --system guidellm && + + # Login to HuggingFace + uv pip install --system huggingface_hub && + python -c "from huggingface_hub import login; login(token='\$HF_TOKEN')" && + + # Run GuideLLM benchmark and save output + export COLUMNS=200 + GUIDELLM__PREFERRED_ROUTE="chat_completions" uv run guidellm benchmark run \\ + --target "$BASE_URL" \\ + --rate-type "$RATE_TYPE" \\ + --max-seconds $MAX_SECONDS \\ + --data "prompt_tokens=$PROMPT_TOKENS,output_tokens=$OUTPUT_TOKENS" \\ + --model "$INFERENCE_MODEL" \\ + --rate "$RATE" \\ + --warmup-percent 0.05 \\ + 2>&1 + env: + - name: INFERENCE_MODEL + value: "meta-llama/Llama-3.2-3B-Instruct" + - name: HF_TOKEN + valueFrom: + secretKeyRef: + name: hf-token-secret + key: token + resources: + requests: + memory: "4Gi" + cpu: "500m" + limits: + memory: "8Gi" + cpu: "2000m" + restartPolicy: Never + backoffLimit: 3 +EOF + +echo "Cleaning up any existing GuideLLM benchmark job..." +kubectl delete job $JOB_NAME 2>/dev/null || true + +echo "Deploying GuideLLM benchmark Job..." +kubectl apply -f "$TEMP_YAML" + +echo "Waiting for job to start..." +kubectl wait --for=condition=Ready pod -l job-name=$JOB_NAME --timeout=120s + +# Prepare file names and create results directory +mkdir -p results +if [[ -z "$OUTPUT_FILE" ]]; then + TIMESTAMP=$(date +%Y%m%d-%H%M%S) + OUTPUT_FILE="results/guidellm-benchmark-${TARGET}-${TIMESTAMP}.txt" +fi + +echo "Following GuideLLM benchmark logs..." +kubectl logs -f job/$JOB_NAME + +echo "Job completed. Checking final status..." +kubectl get job $JOB_NAME + +# Save benchmark results using kubectl logs +echo "Saving benchmark results..." +kubectl logs job/$JOB_NAME > "$OUTPUT_FILE" + +echo "Benchmark output saved to: $OUTPUT_FILE" + +# Clean up temporary file +rm -f "$TEMP_YAML" diff --git a/docs/source/distributions/k8s-benchmark/stack-configmap.yaml b/benchmarking/k8s-benchmark/stack-configmap.yaml similarity index 85% rename from docs/source/distributions/k8s-benchmark/stack-configmap.yaml rename to benchmarking/k8s-benchmark/stack-configmap.yaml index edf4ebd75..286ba5f77 100644 --- a/docs/source/distributions/k8s-benchmark/stack-configmap.yaml +++ b/benchmarking/k8s-benchmark/stack-configmap.yaml @@ -5,7 +5,9 @@ data: image_name: kubernetes-benchmark-demo apis: - agents + - files - inference + - files - safety - telemetry - tool_runtime @@ -19,16 +21,17 @@ data: max_tokens: ${env.VLLM_MAX_TOKENS:=4096} api_token: ${env.VLLM_API_TOKEN:=fake} tls_verify: ${env.VLLM_TLS_VERIFY:=true} - - provider_id: vllm-safety - provider_type: remote::vllm - config: - url: ${env.VLLM_SAFETY_URL:=http://localhost:8000/v1} - max_tokens: ${env.VLLM_MAX_TOKENS:=4096} - api_token: ${env.VLLM_API_TOKEN:=fake} - tls_verify: ${env.VLLM_TLS_VERIFY:=true} - provider_id: sentence-transformers provider_type: inline::sentence-transformers config: {} + files: + - provider_id: meta-reference-files + provider_type: inline::localfs + config: + storage_dir: ${env.FILES_STORAGE_DIR:=~/.llama/distributions/starter/files} + metadata_store: + type: sqlite + db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/starter}/files_metadata.db vector_io: - provider_id: ${env.ENABLE_CHROMADB:+chromadb} provider_type: remote::chromadb @@ -41,6 +44,14 @@ data: db: ${env.POSTGRES_DB:=llamastack} user: ${env.POSTGRES_USER:=llamastack} password: ${env.POSTGRES_PASSWORD:=llamastack} + files: + - provider_id: meta-reference-files + provider_type: inline::localfs + config: + storage_dir: ${env.FILES_STORAGE_DIR:=~/.llama/distributions/starter/files} + metadata_store: + type: sqlite + db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/starter}/files_metadata.db safety: - provider_id: llama-guard provider_type: inline::llama-guard @@ -111,9 +122,6 @@ data: - model_id: ${env.INFERENCE_MODEL} provider_id: vllm-inference model_type: llm - - model_id: ${env.SAFETY_MODEL} - provider_id: vllm-safety - model_type: llm shields: - shield_id: ${env.SAFETY_MODEL:=meta-llama/Llama-Guard-3-1B} vector_dbs: [] diff --git a/docs/source/distributions/k8s-benchmark/stack-k8s.yaml.template b/benchmarking/k8s-benchmark/stack-k8s.yaml.template similarity index 81% rename from docs/source/distributions/k8s-benchmark/stack-k8s.yaml.template rename to benchmarking/k8s-benchmark/stack-k8s.yaml.template index 9cb1e5be3..54eeadcad 100644 --- a/docs/source/distributions/k8s-benchmark/stack-k8s.yaml.template +++ b/benchmarking/k8s-benchmark/stack-k8s.yaml.template @@ -52,9 +52,20 @@ spec: value: http://vllm-server-safety.default.svc.cluster.local:8001/v1 - name: VLLM_TLS_VERIFY value: "false" - command: ["python", "-m", "llama_stack.core.server.server", "/etc/config/stack_run_config.yaml", "--port", "8323"] + - name: LLAMA_STACK_LOGGING + value: "all=WARNING" + - name: LLAMA_STACK_CONFIG + value: "/etc/config/stack_run_config.yaml" + - name: LLAMA_STACK_WORKERS + value: "${LLAMA_STACK_WORKERS}" + command: ["uvicorn", "llama_stack.core.server.server:create_app", "--host", "0.0.0.0", "--port", "8323", "--workers", "$(LLAMA_STACK_WORKERS)", "--factory"] ports: - containerPort: 8323 + resources: + requests: + cpu: "4" + limits: + cpu: "4" volumeMounts: - name: llama-storage mountPath: /root/.llama diff --git a/docs/source/distributions/k8s-benchmark/stack_run_config.yaml b/benchmarking/k8s-benchmark/stack_run_config.yaml similarity index 79% rename from docs/source/distributions/k8s-benchmark/stack_run_config.yaml rename to benchmarking/k8s-benchmark/stack_run_config.yaml index ceb1ba2d9..5a9e2ae4f 100644 --- a/docs/source/distributions/k8s-benchmark/stack_run_config.yaml +++ b/benchmarking/k8s-benchmark/stack_run_config.yaml @@ -2,7 +2,10 @@ version: '2' image_name: kubernetes-benchmark-demo apis: - agents +- files - inference +- files +- safety - telemetry - tool_runtime - vector_io @@ -18,6 +21,14 @@ providers: - provider_id: sentence-transformers provider_type: inline::sentence-transformers config: {} + files: + - provider_id: meta-reference-files + provider_type: inline::localfs + config: + storage_dir: ${env.FILES_STORAGE_DIR:=~/.llama/distributions/starter/files} + metadata_store: + type: sqlite + db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/starter}/files_metadata.db vector_io: - provider_id: ${env.ENABLE_CHROMADB:+chromadb} provider_type: remote::chromadb @@ -30,6 +41,19 @@ providers: db: ${env.POSTGRES_DB:=llamastack} user: ${env.POSTGRES_USER:=llamastack} password: ${env.POSTGRES_PASSWORD:=llamastack} + files: + - provider_id: meta-reference-files + provider_type: inline::localfs + config: + storage_dir: ${env.FILES_STORAGE_DIR:=~/.llama/distributions/starter/files} + metadata_store: + type: sqlite + db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/starter}/files_metadata.db + safety: + - provider_id: llama-guard + provider_type: inline::llama-guard + config: + excluded_categories: [] agents: - provider_id: meta-reference provider_type: inline::meta-reference @@ -95,6 +119,8 @@ models: - model_id: ${env.INFERENCE_MODEL} provider_id: vllm-inference model_type: llm +shields: +- shield_id: ${env.SAFETY_MODEL:=meta-llama/Llama-Guard-3-1B} vector_dbs: [] datasets: [] scoring_fns: [] diff --git a/docs/Makefile b/docs/Makefile deleted file mode 100644 index 92dd33a1a..000000000 --- a/docs/Makefile +++ /dev/null @@ -1,20 +0,0 @@ -# Minimal makefile for Sphinx documentation -# - -# You can set these variables from the command line, and also -# from the environment for the first two. -SPHINXOPTS ?= -SPHINXBUILD ?= sphinx-build -SOURCEDIR = source -BUILDDIR = _build - -# Put it first so that "make" without argument is like "make help". -help: - @$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) - -.PHONY: help Makefile - -# Catch-all target: route all unknown targets to Sphinx using the new -# "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS). -%: Makefile - @$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) diff --git a/docs/README.md b/docs/README.md index c238c4720..1847e49d8 100644 --- a/docs/README.md +++ b/docs/README.md @@ -1,14 +1,17 @@ # Llama Stack Documentation -Here's a collection of comprehensive guides, examples, and resources for building AI applications with Llama Stack. For the complete documentation, visit our [ReadTheDocs page](https://llama-stack.readthedocs.io/en/latest/index.html). +Here's a collection of comprehensive guides, examples, and resources for building AI applications with Llama Stack. For the complete documentation, visit our [Github page](https://llamastack.github.io/getting_started/quickstart). ## Render locally -From the llama-stack root directory, run the following command to render the docs locally: +From the llama-stack `docs/` directory, run the following commands to render the docs locally: ```bash -uv run --group docs sphinx-autobuild docs/source docs/build/html --write-all +npm install +npm run gen-api-docs all +npm run build +npm run serve ``` -You can open up the docs in your browser at http://localhost:8000 +You can open up the docs in your browser at http://localhost:3000 ## Content diff --git a/docs/_static/css/my_theme.css b/docs/_static/css/my_theme.css deleted file mode 100644 index d078ec057..000000000 --- a/docs/_static/css/my_theme.css +++ /dev/null @@ -1,35 +0,0 @@ -@import url("theme.css"); - -.wy-nav-content { - max-width: 90%; -} - -.wy-nav-side { - /* background: linear-gradient(45deg, #2980B9, #16A085); */ - background: linear-gradient(90deg, #332735, #1b263c); -} - -.wy-side-nav-search { - background-color: transparent !important; -} - -.hide-title h1 { - display: none; -} - -h2, h3, h4 { - font-weight: normal; -} -html[data-theme="dark"] .rst-content div[class^="highlight"] { - background-color: #0b0b0b; -} -pre { - white-space: pre-wrap !important; - word-break: break-all; -} - -[data-theme="dark"] .mermaid { - background-color: #f4f4f6 !important; - border-radius: 6px; - padding: 0.5em; - } diff --git a/docs/_static/js/detect_theme.js b/docs/_static/js/detect_theme.js deleted file mode 100644 index 712565ef7..000000000 --- a/docs/_static/js/detect_theme.js +++ /dev/null @@ -1,32 +0,0 @@ -document.addEventListener("DOMContentLoaded", function () { - const prefersDark = window.matchMedia("(prefers-color-scheme: dark)").matches; - const htmlElement = document.documentElement; - - // Check if theme is saved in localStorage - const savedTheme = localStorage.getItem("sphinx-rtd-theme"); - - if (savedTheme) { - // Use the saved theme preference - htmlElement.setAttribute("data-theme", savedTheme); - document.body.classList.toggle("dark", savedTheme === "dark"); - } else { - // Fall back to system preference - const theme = prefersDark ? "dark" : "light"; - htmlElement.setAttribute("data-theme", theme); - document.body.classList.toggle("dark", theme === "dark"); - // Save initial preference - localStorage.setItem("sphinx-rtd-theme", theme); - } - - // Listen for theme changes from the existing toggle - const observer = new MutationObserver(function(mutations) { - mutations.forEach(function(mutation) { - if (mutation.attributeName === "data-theme") { - const currentTheme = htmlElement.getAttribute("data-theme"); - localStorage.setItem("sphinx-rtd-theme", currentTheme); - } - }); - }); - - observer.observe(htmlElement, { attributes: true }); -}); diff --git a/docs/_static/js/keyboard_shortcuts.js b/docs/_static/js/keyboard_shortcuts.js deleted file mode 100644 index 81d0b7c65..000000000 --- a/docs/_static/js/keyboard_shortcuts.js +++ /dev/null @@ -1,14 +0,0 @@ -document.addEventListener('keydown', function(event) { - // command+K or ctrl+K - if ((event.metaKey || event.ctrlKey) && event.key === 'k') { - event.preventDefault(); - document.querySelector('.search-input, .search-field, input[name="q"]').focus(); - } - - // forward slash - if (event.key === '/' && - !event.target.matches('input, textarea, select')) { - event.preventDefault(); - document.querySelector('.search-input, .search-field, input[name="q"]').focus(); - } -}); diff --git a/docs/_static/llama-stack-logo.png b/docs/_static/llama-stack-logo.png deleted file mode 100644 index 1899a0fc7..000000000 Binary files a/docs/_static/llama-stack-logo.png and /dev/null differ diff --git a/docs/_static/llama-stack-spec.html b/docs/_static/llama-stack-spec.html deleted file mode 100644 index 8106a54dc..000000000 --- a/docs/_static/llama-stack-spec.html +++ /dev/null @@ -1,17367 +0,0 @@ - - - - - - - OpenAPI specification - - - - - - - - - - - - - diff --git a/docs/_static/llama-stack-spec.yaml b/docs/_static/llama-stack-spec.yaml deleted file mode 100644 index f10af5e44..000000000 --- a/docs/_static/llama-stack-spec.yaml +++ /dev/null @@ -1,12862 +0,0 @@ -openapi: 3.1.0 -info: - title: Llama Stack Specification - version: v1 - description: >- - This is the specification of the Llama Stack that provides - a set of endpoints and their corresponding interfaces that are - tailored to - best leverage Llama Models. -servers: - - url: http://any-hosted-llama-stack.com -paths: - /v1/datasetio/append-rows/{dataset_id}: - post: - responses: - '200': - description: OK - '400': - $ref: '#/components/responses/BadRequest400' - '429': - $ref: >- - #/components/responses/TooManyRequests429 - '500': - $ref: >- - #/components/responses/InternalServerError500 - default: - $ref: '#/components/responses/DefaultError' - tags: - - DatasetIO - description: Append rows to a dataset. - parameters: - - name: dataset_id - in: path - description: >- - The ID of the dataset to append the rows to. - required: true - schema: - type: string - requestBody: - content: - application/json: - schema: - $ref: '#/components/schemas/AppendRowsRequest' - required: true - /v1/post-training/job/cancel: - post: - responses: - '200': - description: OK - '400': - $ref: '#/components/responses/BadRequest400' - '429': - $ref: >- - #/components/responses/TooManyRequests429 - '500': - $ref: >- - #/components/responses/InternalServerError500 - default: - $ref: '#/components/responses/DefaultError' - tags: - - PostTraining (Coming Soon) - description: Cancel a training job. - parameters: [] - requestBody: - content: - application/json: - schema: - $ref: '#/components/schemas/CancelTrainingJobRequest' - required: true - /v1/inference/chat-completion: - post: - responses: - '200': - description: >- - If stream=False, returns a ChatCompletionResponse with the full completion. - If stream=True, returns an SSE event stream of ChatCompletionResponseStreamChunk. - content: - application/json: - schema: - $ref: '#/components/schemas/ChatCompletionResponse' - text/event-stream: - schema: - $ref: '#/components/schemas/ChatCompletionResponseStreamChunk' - '400': - $ref: '#/components/responses/BadRequest400' - '429': - $ref: >- - #/components/responses/TooManyRequests429 - '500': - $ref: >- - #/components/responses/InternalServerError500 - default: - $ref: '#/components/responses/DefaultError' - tags: - - Inference - description: >- - Generate a chat completion for the given messages using the specified model. - parameters: [] - requestBody: - content: - application/json: - schema: - $ref: '#/components/schemas/ChatCompletionRequest' - required: true - /v1/inference/completion: - post: - responses: - '200': - description: >- - If stream=False, returns a CompletionResponse with the full completion. - If stream=True, returns an SSE event stream of CompletionResponseStreamChunk. - content: - application/json: - schema: - $ref: '#/components/schemas/CompletionResponse' - text/event-stream: - schema: - $ref: '#/components/schemas/CompletionResponseStreamChunk' - '400': - $ref: '#/components/responses/BadRequest400' - '429': - $ref: >- - #/components/responses/TooManyRequests429 - '500': - $ref: >- - #/components/responses/InternalServerError500 - default: - $ref: '#/components/responses/DefaultError' - tags: - - Inference - description: >- - Generate a completion for the given content using the specified model. - parameters: [] - requestBody: - content: - application/json: - schema: - $ref: '#/components/schemas/CompletionRequest' - required: true - /v1/agents: - get: - responses: - '200': - description: A PaginatedResponse. - content: - application/json: - schema: - $ref: '#/components/schemas/PaginatedResponse' - '400': - $ref: '#/components/responses/BadRequest400' - '429': - $ref: >- - #/components/responses/TooManyRequests429 - '500': - $ref: >- - #/components/responses/InternalServerError500 - default: - $ref: '#/components/responses/DefaultError' - tags: - - Agents - description: List all agents. - parameters: - - name: start_index - in: query - description: The index to start the pagination from. - required: false - schema: - type: integer - - name: limit - in: query - description: The number of agents to return. - required: false - schema: - type: integer - post: - responses: - '200': - description: >- - An AgentCreateResponse with the agent ID. - content: - application/json: - schema: - $ref: '#/components/schemas/AgentCreateResponse' - '400': - $ref: '#/components/responses/BadRequest400' - '429': - $ref: >- - #/components/responses/TooManyRequests429 - '500': - $ref: >- - #/components/responses/InternalServerError500 - default: - $ref: '#/components/responses/DefaultError' - tags: - - Agents - description: >- - Create an agent with the given configuration. - parameters: [] - requestBody: - content: - application/json: - schema: - $ref: '#/components/schemas/CreateAgentRequest' - required: true - /v1/agents/{agent_id}/session: - post: - responses: - '200': - description: An AgentSessionCreateResponse. - content: - application/json: - schema: - $ref: '#/components/schemas/AgentSessionCreateResponse' - '400': - $ref: '#/components/responses/BadRequest400' - '429': - $ref: >- - #/components/responses/TooManyRequests429 - '500': - $ref: >- - #/components/responses/InternalServerError500 - default: - $ref: '#/components/responses/DefaultError' - tags: - - Agents - description: Create a new session for an agent. - parameters: - - name: agent_id - in: path - description: >- - The ID of the agent to create the session for. - required: true - schema: - type: string - requestBody: - content: - application/json: - schema: - $ref: '#/components/schemas/CreateAgentSessionRequest' - required: true - /v1/agents/{agent_id}/session/{session_id}/turn: - post: - responses: - '200': - description: >- - If stream=False, returns a Turn object. If stream=True, returns an SSE - event stream of AgentTurnResponseStreamChunk. - content: - application/json: - schema: - $ref: '#/components/schemas/Turn' - text/event-stream: - schema: - $ref: '#/components/schemas/AgentTurnResponseStreamChunk' - '400': - $ref: '#/components/responses/BadRequest400' - '429': - $ref: >- - #/components/responses/TooManyRequests429 - '500': - $ref: >- - #/components/responses/InternalServerError500 - default: - $ref: '#/components/responses/DefaultError' - tags: - - Agents - description: Create a new turn for an agent. - parameters: - - name: agent_id - in: path - description: >- - The ID of the agent to create the turn for. - required: true - schema: - type: string - - name: session_id - in: path - description: >- - The ID of the session to create the turn for. - required: true - schema: - type: string - requestBody: - content: - application/json: - schema: - $ref: '#/components/schemas/CreateAgentTurnRequest' - required: true - /v1/openai/v1/responses: - get: - responses: - '200': - description: A ListOpenAIResponseObject. - content: - application/json: - schema: - $ref: '#/components/schemas/ListOpenAIResponseObject' - '400': - $ref: '#/components/responses/BadRequest400' - '429': - $ref: >- - #/components/responses/TooManyRequests429 - '500': - $ref: >- - #/components/responses/InternalServerError500 - default: - $ref: '#/components/responses/DefaultError' - tags: - - Agents - description: List all OpenAI responses. - parameters: - - name: after - in: query - description: The ID of the last response to return. - required: false - schema: - type: string - - name: limit - in: query - description: The number of responses to return. - required: false - schema: - type: integer - - name: model - in: query - description: The model to filter responses by. - required: false - schema: - type: string - - name: order - in: query - description: >- - The order to sort responses by when sorted by created_at ('asc' or 'desc'). - required: false - schema: - $ref: '#/components/schemas/Order' - post: - responses: - '200': - description: An OpenAIResponseObject. - content: - application/json: - schema: - $ref: '#/components/schemas/OpenAIResponseObject' - text/event-stream: - schema: - $ref: '#/components/schemas/OpenAIResponseObjectStream' - '400': - $ref: '#/components/responses/BadRequest400' - '429': - $ref: >- - #/components/responses/TooManyRequests429 - '500': - $ref: >- - #/components/responses/InternalServerError500 - default: - $ref: '#/components/responses/DefaultError' - tags: - - Agents - description: Create a new OpenAI response. - parameters: [] - requestBody: - content: - application/json: - schema: - $ref: '#/components/schemas/CreateOpenaiResponseRequest' - required: true - /v1/agents/{agent_id}: - get: - responses: - '200': - description: An Agent of the agent. - content: - application/json: - schema: - $ref: '#/components/schemas/Agent' - '400': - $ref: '#/components/responses/BadRequest400' - '429': - $ref: >- - #/components/responses/TooManyRequests429 - '500': - $ref: >- - #/components/responses/InternalServerError500 - default: - $ref: '#/components/responses/DefaultError' - tags: - - Agents - description: Describe an agent by its ID. - parameters: - - name: agent_id - in: path - description: ID of the agent. - required: true - schema: - type: string - delete: - responses: - '200': - description: OK - '400': - $ref: '#/components/responses/BadRequest400' - '429': - $ref: >- - #/components/responses/TooManyRequests429 - '500': - $ref: >- - #/components/responses/InternalServerError500 - default: - $ref: '#/components/responses/DefaultError' - tags: - - Agents - description: >- - Delete an agent by its ID and its associated sessions and turns. - parameters: - - name: agent_id - in: path - description: The ID of the agent to delete. - required: true - schema: - type: string - /v1/agents/{agent_id}/session/{session_id}: - get: - responses: - '200': - description: A Session. - content: - application/json: - schema: - $ref: '#/components/schemas/Session' - '400': - $ref: '#/components/responses/BadRequest400' - '429': - $ref: >- - #/components/responses/TooManyRequests429 - '500': - $ref: >- - #/components/responses/InternalServerError500 - default: - $ref: '#/components/responses/DefaultError' - tags: - - Agents - description: Retrieve an agent session by its ID. - parameters: - - name: session_id - in: path - description: The ID of the session to get. - required: true - schema: - type: string - - name: agent_id - in: path - description: >- - The ID of the agent to get the session for. - required: true - schema: - type: string - - name: turn_ids - in: query - description: >- - (Optional) List of turn IDs to filter the session by. - required: false - schema: - type: array - items: - type: string - delete: - responses: - '200': - description: OK - '400': - $ref: '#/components/responses/BadRequest400' - '429': - $ref: >- - #/components/responses/TooManyRequests429 - '500': - $ref: >- - #/components/responses/InternalServerError500 - default: - $ref: '#/components/responses/DefaultError' - tags: - - Agents - description: >- - Delete an agent session by its ID and its associated turns. - parameters: - - name: session_id - in: path - description: The ID of the session to delete. - required: true - schema: - type: string - - name: agent_id - in: path - description: >- - The ID of the agent to delete the session for. - required: true - schema: - type: string - /v1/openai/v1/responses/{response_id}: - get: - responses: - '200': - description: An OpenAIResponseObject. - content: - application/json: - schema: - $ref: '#/components/schemas/OpenAIResponseObject' - '400': - $ref: '#/components/responses/BadRequest400' - '429': - $ref: >- - #/components/responses/TooManyRequests429 - '500': - $ref: >- - #/components/responses/InternalServerError500 - default: - $ref: '#/components/responses/DefaultError' - tags: - - Agents - description: Retrieve an OpenAI response by its ID. - parameters: - - name: response_id - in: path - description: >- - The ID of the OpenAI response to retrieve. - required: true - schema: - type: string - delete: - responses: - '200': - description: An OpenAIDeleteResponseObject - content: - application/json: - schema: - $ref: '#/components/schemas/OpenAIDeleteResponseObject' - '400': - $ref: '#/components/responses/BadRequest400' - '429': - $ref: >- - #/components/responses/TooManyRequests429 - '500': - $ref: >- - #/components/responses/InternalServerError500 - default: - $ref: '#/components/responses/DefaultError' - tags: - - Agents - description: Delete an OpenAI response by its ID. - parameters: - - name: response_id - in: path - description: The ID of the OpenAI response to delete. - required: true - schema: - type: string - /v1/inference/embeddings: - post: - responses: - '200': - description: >- - An array of embeddings, one for each content. Each embedding is a list - of floats. The dimensionality of the embedding is model-specific; you - can check model metadata using /models/{model_id}. - content: - application/json: - schema: - $ref: '#/components/schemas/EmbeddingsResponse' - '400': - $ref: '#/components/responses/BadRequest400' - '429': - $ref: >- - #/components/responses/TooManyRequests429 - '500': - $ref: >- - #/components/responses/InternalServerError500 - default: - $ref: '#/components/responses/DefaultError' - tags: - - Inference - description: >- - Generate embeddings for content pieces using the specified model. - parameters: [] - requestBody: - content: - application/json: - schema: - $ref: '#/components/schemas/EmbeddingsRequest' - required: true - /v1/eval/benchmarks/{benchmark_id}/evaluations: - post: - responses: - '200': - description: >- - EvaluateResponse object containing generations and scores. - content: - application/json: - schema: - $ref: '#/components/schemas/EvaluateResponse' - '400': - $ref: '#/components/responses/BadRequest400' - '429': - $ref: >- - #/components/responses/TooManyRequests429 - '500': - $ref: >- - #/components/responses/InternalServerError500 - default: - $ref: '#/components/responses/DefaultError' - tags: - - Eval - description: Evaluate a list of rows on a benchmark. - parameters: - - name: benchmark_id - in: path - description: >- - The ID of the benchmark to run the evaluation on. - required: true - schema: - type: string - requestBody: - content: - application/json: - schema: - $ref: '#/components/schemas/EvaluateRowsRequest' - required: true - /v1/agents/{agent_id}/session/{session_id}/turn/{turn_id}/step/{step_id}: - get: - responses: - '200': - description: An AgentStepResponse. - content: - application/json: - schema: - $ref: '#/components/schemas/AgentStepResponse' - '400': - $ref: '#/components/responses/BadRequest400' - '429': - $ref: >- - #/components/responses/TooManyRequests429 - '500': - $ref: >- - #/components/responses/InternalServerError500 - default: - $ref: '#/components/responses/DefaultError' - tags: - - Agents - description: Retrieve an agent step by its ID. - parameters: - - name: agent_id - in: path - description: The ID of the agent to get the step for. - required: true - schema: - type: string - - name: session_id - in: path - description: >- - The ID of the session to get the step for. - required: true - schema: - type: string - - name: turn_id - in: path - description: The ID of the turn to get the step for. - required: true - schema: - type: string - - name: step_id - in: path - description: The ID of the step to get. - required: true - schema: - type: string - /v1/agents/{agent_id}/session/{session_id}/turn/{turn_id}: - get: - responses: - '200': - description: A Turn. - content: - application/json: - schema: - $ref: '#/components/schemas/Turn' - '400': - $ref: '#/components/responses/BadRequest400' - '429': - $ref: >- - #/components/responses/TooManyRequests429 - '500': - $ref: >- - #/components/responses/InternalServerError500 - default: - $ref: '#/components/responses/DefaultError' - tags: - - Agents - description: Retrieve an agent turn by its ID. - parameters: - - name: agent_id - in: path - description: The ID of the agent to get the turn for. - required: true - schema: - type: string - - name: session_id - in: path - description: >- - The ID of the session to get the turn for. - required: true - schema: - type: string - - name: turn_id - in: path - description: The ID of the turn to get. - required: true - schema: - type: string - /v1/eval/benchmarks/{benchmark_id}: - get: - responses: - '200': - description: A Benchmark. - content: - application/json: - schema: - $ref: '#/components/schemas/Benchmark' - '400': - $ref: '#/components/responses/BadRequest400' - '429': - $ref: >- - #/components/responses/TooManyRequests429 - '500': - $ref: >- - #/components/responses/InternalServerError500 - default: - $ref: '#/components/responses/DefaultError' - tags: - - Benchmarks - description: Get a benchmark by its ID. - parameters: - - name: benchmark_id - in: path - description: The ID of the benchmark to get. - required: true - schema: - type: string - /v1/openai/v1/chat/completions/{completion_id}: - get: - responses: - '200': - description: A OpenAICompletionWithInputMessages. - content: - application/json: - schema: - $ref: '#/components/schemas/OpenAICompletionWithInputMessages' - '400': - $ref: '#/components/responses/BadRequest400' - '429': - $ref: >- - #/components/responses/TooManyRequests429 - '500': - $ref: >- - #/components/responses/InternalServerError500 - default: - $ref: '#/components/responses/DefaultError' - tags: - - Inference - description: Describe a chat completion by its ID. - parameters: - - name: completion_id - in: path - description: ID of the chat completion. - required: true - schema: - type: string - /v1/datasets/{dataset_id}: - get: - responses: - '200': - description: A Dataset. - content: - application/json: - schema: - $ref: '#/components/schemas/Dataset' - '400': - $ref: '#/components/responses/BadRequest400' - '429': - $ref: >- - #/components/responses/TooManyRequests429 - '500': - $ref: >- - #/components/responses/InternalServerError500 - default: - $ref: '#/components/responses/DefaultError' - tags: - - Datasets - description: Get a dataset by its ID. - parameters: - - name: dataset_id - in: path - description: The ID of the dataset to get. - required: true - schema: - type: string - delete: - responses: - '200': - description: OK - '400': - $ref: '#/components/responses/BadRequest400' - '429': - $ref: >- - #/components/responses/TooManyRequests429 - '500': - $ref: >- - #/components/responses/InternalServerError500 - default: - $ref: '#/components/responses/DefaultError' - tags: - - Datasets - description: Unregister a dataset by its ID. - parameters: - - name: dataset_id - in: path - description: The ID of the dataset to unregister. - required: true - schema: - type: string - /v1/models/{model_id}: - get: - responses: - '200': - description: A Model. - content: - application/json: - schema: - $ref: '#/components/schemas/Model' - '400': - $ref: '#/components/responses/BadRequest400' - '429': - $ref: >- - #/components/responses/TooManyRequests429 - '500': - $ref: >- - #/components/responses/InternalServerError500 - default: - $ref: '#/components/responses/DefaultError' - tags: - - Models - description: Get a model by its identifier. - parameters: - - name: model_id - in: path - description: The identifier of the model to get. - required: true - schema: - type: string - delete: - responses: - '200': - description: OK - '400': - $ref: '#/components/responses/BadRequest400' - '429': - $ref: >- - #/components/responses/TooManyRequests429 - '500': - $ref: >- - #/components/responses/InternalServerError500 - default: - $ref: '#/components/responses/DefaultError' - tags: - - Models - description: Unregister a model. - parameters: - - name: model_id - in: path - description: >- - The identifier of the model to unregister. - required: true - schema: - type: string - /v1/scoring-functions/{scoring_fn_id}: - get: - responses: - '200': - description: A ScoringFn. - content: - application/json: - schema: - $ref: '#/components/schemas/ScoringFn' - '400': - $ref: '#/components/responses/BadRequest400' - '429': - $ref: >- - #/components/responses/TooManyRequests429 - '500': - $ref: >- - #/components/responses/InternalServerError500 - default: - $ref: '#/components/responses/DefaultError' - tags: - - ScoringFunctions - description: Get a scoring function by its ID. - parameters: - - name: scoring_fn_id - in: path - description: The ID of the scoring function to get. - required: true - schema: - type: string - /v1/shields/{identifier}: - get: - responses: - '200': - description: A Shield. - content: - application/json: - schema: - $ref: '#/components/schemas/Shield' - '400': - $ref: '#/components/responses/BadRequest400' - '429': - $ref: >- - #/components/responses/TooManyRequests429 - '500': - $ref: >- - #/components/responses/InternalServerError500 - default: - $ref: '#/components/responses/DefaultError' - tags: - - Shields - description: Get a shield by its identifier. - parameters: - - name: identifier - in: path - description: The identifier of the shield to get. - required: true - schema: - type: string - delete: - responses: - '200': - description: OK - '400': - $ref: '#/components/responses/BadRequest400' - '429': - $ref: >- - #/components/responses/TooManyRequests429 - '500': - $ref: >- - #/components/responses/InternalServerError500 - default: - $ref: '#/components/responses/DefaultError' - tags: - - Shields - description: Unregister a shield. - parameters: - - name: identifier - in: path - description: >- - The identifier of the shield to unregister. - required: true - schema: - type: string - /v1/telemetry/traces/{trace_id}/spans/{span_id}: - get: - responses: - '200': - description: A Span. - content: - application/json: - schema: - $ref: '#/components/schemas/Span' - '400': - $ref: '#/components/responses/BadRequest400' - '429': - $ref: >- - #/components/responses/TooManyRequests429 - '500': - $ref: >- - #/components/responses/InternalServerError500 - default: - $ref: '#/components/responses/DefaultError' - tags: - - Telemetry - description: Get a span by its ID. - parameters: - - name: trace_id - in: path - description: >- - The ID of the trace to get the span from. - required: true - schema: - type: string - - name: span_id - in: path - description: The ID of the span to get. - required: true - schema: - type: string - /v1/telemetry/spans/{span_id}/tree: - post: - responses: - '200': - description: A QuerySpanTreeResponse. - content: - application/json: - schema: - $ref: '#/components/schemas/QuerySpanTreeResponse' - '400': - $ref: '#/components/responses/BadRequest400' - '429': - $ref: >- - #/components/responses/TooManyRequests429 - '500': - $ref: >- - #/components/responses/InternalServerError500 - default: - $ref: '#/components/responses/DefaultError' - tags: - - Telemetry - description: Get a span tree by its ID. - parameters: - - name: span_id - in: path - description: The ID of the span to get the tree from. - required: true - schema: - type: string - requestBody: - content: - application/json: - schema: - $ref: '#/components/schemas/GetSpanTreeRequest' - required: true - /v1/tools/{tool_name}: - get: - responses: - '200': - description: A Tool. - content: - application/json: - schema: - $ref: '#/components/schemas/Tool' - '400': - $ref: '#/components/responses/BadRequest400' - '429': - $ref: >- - #/components/responses/TooManyRequests429 - '500': - $ref: >- - #/components/responses/InternalServerError500 - default: - $ref: '#/components/responses/DefaultError' - tags: - - ToolGroups - description: Get a tool by its name. - parameters: - - name: tool_name - in: path - description: The name of the tool to get. - required: true - schema: - type: string - /v1/toolgroups/{toolgroup_id}: - get: - responses: - '200': - description: A ToolGroup. - content: - application/json: - schema: - $ref: '#/components/schemas/ToolGroup' - '400': - $ref: '#/components/responses/BadRequest400' - '429': - $ref: >- - #/components/responses/TooManyRequests429 - '500': - $ref: >- - #/components/responses/InternalServerError500 - default: - $ref: '#/components/responses/DefaultError' - tags: - - ToolGroups - description: Get a tool group by its ID. - parameters: - - name: toolgroup_id - in: path - description: The ID of the tool group to get. - required: true - schema: - type: string - delete: - responses: - '200': - description: OK - '400': - $ref: '#/components/responses/BadRequest400' - '429': - $ref: >- - #/components/responses/TooManyRequests429 - '500': - $ref: >- - #/components/responses/InternalServerError500 - default: - $ref: '#/components/responses/DefaultError' - tags: - - ToolGroups - description: Unregister a tool group. - parameters: - - name: toolgroup_id - in: path - description: The ID of the tool group to unregister. - required: true - schema: - type: string - /v1/telemetry/traces/{trace_id}: - get: - responses: - '200': - description: A Trace. - content: - application/json: - schema: - $ref: '#/components/schemas/Trace' - '400': - $ref: '#/components/responses/BadRequest400' - '429': - $ref: >- - #/components/responses/TooManyRequests429 - '500': - $ref: >- - #/components/responses/InternalServerError500 - default: - $ref: '#/components/responses/DefaultError' - tags: - - Telemetry - description: Get a trace by its ID. - parameters: - - name: trace_id - in: path - description: The ID of the trace to get. - required: true - schema: - type: string - /v1/post-training/job/artifacts: - get: - responses: - '200': - description: A PostTrainingJobArtifactsResponse. - content: - application/json: - schema: - $ref: '#/components/schemas/PostTrainingJobArtifactsResponse' - '400': - $ref: '#/components/responses/BadRequest400' - '429': - $ref: >- - #/components/responses/TooManyRequests429 - '500': - $ref: >- - #/components/responses/InternalServerError500 - default: - $ref: '#/components/responses/DefaultError' - tags: - - PostTraining (Coming Soon) - description: Get the artifacts of a training job. - parameters: - - name: job_uuid - in: query - description: >- - The UUID of the job to get the artifacts of. - required: true - schema: - type: string - /v1/post-training/job/status: - get: - responses: - '200': - description: A PostTrainingJobStatusResponse. - content: - application/json: - schema: - $ref: '#/components/schemas/PostTrainingJobStatusResponse' - '400': - $ref: '#/components/responses/BadRequest400' - '429': - $ref: >- - #/components/responses/TooManyRequests429 - '500': - $ref: >- - #/components/responses/InternalServerError500 - default: - $ref: '#/components/responses/DefaultError' - tags: - - PostTraining (Coming Soon) - description: Get the status of a training job. - parameters: - - name: job_uuid - in: query - description: >- - The UUID of the job to get the status of. - required: true - schema: - type: string - /v1/post-training/jobs: - get: - responses: - '200': - description: A ListPostTrainingJobsResponse. - content: - application/json: - schema: - $ref: '#/components/schemas/ListPostTrainingJobsResponse' - '400': - $ref: '#/components/responses/BadRequest400' - '429': - $ref: >- - #/components/responses/TooManyRequests429 - '500': - $ref: >- - #/components/responses/InternalServerError500 - default: - $ref: '#/components/responses/DefaultError' - tags: - - PostTraining (Coming Soon) - description: Get all training jobs. - parameters: [] - /v1/vector-dbs/{vector_db_id}: - get: - responses: - '200': - description: A VectorDB. - content: - application/json: - schema: - $ref: '#/components/schemas/VectorDB' - '400': - $ref: '#/components/responses/BadRequest400' - '429': - $ref: >- - #/components/responses/TooManyRequests429 - '500': - $ref: >- - #/components/responses/InternalServerError500 - default: - $ref: '#/components/responses/DefaultError' - tags: - - VectorDBs - description: Get a vector database by its identifier. - parameters: - - name: vector_db_id - in: path - description: >- - The identifier of the vector database to get. - required: true - schema: - type: string - delete: - responses: - '200': - description: OK - '400': - $ref: '#/components/responses/BadRequest400' - '429': - $ref: >- - #/components/responses/TooManyRequests429 - '500': - $ref: >- - #/components/responses/InternalServerError500 - default: - $ref: '#/components/responses/DefaultError' - tags: - - VectorDBs - description: Unregister a vector database. - parameters: - - name: vector_db_id - in: path - description: >- - The identifier of the vector database to unregister. - required: true - schema: - type: string - /v1/health: - get: - responses: - '200': - description: >- - Health information indicating if the service is operational. - content: - application/json: - schema: - $ref: '#/components/schemas/HealthInfo' - '400': - $ref: '#/components/responses/BadRequest400' - '429': - $ref: >- - #/components/responses/TooManyRequests429 - '500': - $ref: >- - #/components/responses/InternalServerError500 - default: - $ref: '#/components/responses/DefaultError' - tags: - - Inspect - description: >- - Get the current health status of the service. - parameters: [] - /v1/tool-runtime/rag-tool/insert: - post: - responses: - '200': - description: OK - '400': - $ref: '#/components/responses/BadRequest400' - '429': - $ref: >- - #/components/responses/TooManyRequests429 - '500': - $ref: >- - #/components/responses/InternalServerError500 - default: - $ref: '#/components/responses/DefaultError' - tags: - - ToolRuntime - description: >- - Index documents so they can be used by the RAG system. - parameters: [] - requestBody: - content: - application/json: - schema: - $ref: '#/components/schemas/InsertRequest' - required: true - /v1/vector-io/insert: - post: - responses: - '200': - description: OK - '400': - $ref: '#/components/responses/BadRequest400' - '429': - $ref: >- - #/components/responses/TooManyRequests429 - '500': - $ref: >- - #/components/responses/InternalServerError500 - default: - $ref: '#/components/responses/DefaultError' - tags: - - VectorIO - description: Insert chunks into a vector database. - parameters: [] - requestBody: - content: - application/json: - schema: - $ref: '#/components/schemas/InsertChunksRequest' - required: true - /v1/providers/{provider_id}: - get: - responses: - '200': - description: >- - A ProviderInfo object containing the provider's details. - content: - application/json: - schema: - $ref: '#/components/schemas/ProviderInfo' - '400': - $ref: '#/components/responses/BadRequest400' - '429': - $ref: >- - #/components/responses/TooManyRequests429 - '500': - $ref: >- - #/components/responses/InternalServerError500 - default: - $ref: '#/components/responses/DefaultError' - tags: - - Providers - description: >- - Get detailed information about a specific provider. - parameters: - - name: provider_id - in: path - description: The ID of the provider to inspect. - required: true - schema: - type: string - /v1/tool-runtime/invoke: - post: - responses: - '200': - description: A ToolInvocationResult. - content: - application/json: - schema: - $ref: '#/components/schemas/ToolInvocationResult' - '400': - $ref: '#/components/responses/BadRequest400' - '429': - $ref: >- - #/components/responses/TooManyRequests429 - '500': - $ref: >- - #/components/responses/InternalServerError500 - default: - $ref: '#/components/responses/DefaultError' - tags: - - ToolRuntime - description: Run a tool with the given arguments. - parameters: [] - requestBody: - content: - application/json: - schema: - $ref: '#/components/schemas/InvokeToolRequest' - required: true - /v1/datasetio/iterrows/{dataset_id}: - get: - responses: - '200': - description: A PaginatedResponse. - content: - application/json: - schema: - $ref: '#/components/schemas/PaginatedResponse' - '400': - $ref: '#/components/responses/BadRequest400' - '429': - $ref: >- - #/components/responses/TooManyRequests429 - '500': - $ref: >- - #/components/responses/InternalServerError500 - default: - $ref: '#/components/responses/DefaultError' - tags: - - DatasetIO - description: >- - Get a paginated list of rows from a dataset. - - Uses offset-based pagination where: - - - start_index: The starting index (0-based). If None, starts from beginning. - - - limit: Number of items to return. If None or -1, returns all items. - - - The response includes: - - - data: List of items for the current page. - - - has_more: Whether there are more items available after this set. - parameters: - - name: dataset_id - in: path - description: >- - The ID of the dataset to get the rows from. - required: true - schema: - type: string - - name: start_index - in: query - description: >- - Index into dataset for the first row to get. Get all rows if None. - required: false - schema: - type: integer - - name: limit - in: query - description: The number of rows to get. - required: false - schema: - type: integer - /v1/eval/benchmarks/{benchmark_id}/jobs/{job_id}: - get: - responses: - '200': - description: The status of the evaluation job. - content: - application/json: - schema: - $ref: '#/components/schemas/Job' - '400': - $ref: '#/components/responses/BadRequest400' - '429': - $ref: >- - #/components/responses/TooManyRequests429 - '500': - $ref: >- - #/components/responses/InternalServerError500 - default: - $ref: '#/components/responses/DefaultError' - tags: - - Eval - description: Get the status of a job. - parameters: - - name: benchmark_id - in: path - description: >- - The ID of the benchmark to run the evaluation on. - required: true - schema: - type: string - - name: job_id - in: path - description: The ID of the job to get the status of. - required: true - schema: - type: string - delete: - responses: - '200': - description: OK - '400': - $ref: '#/components/responses/BadRequest400' - '429': - $ref: >- - #/components/responses/TooManyRequests429 - '500': - $ref: >- - #/components/responses/InternalServerError500 - default: - $ref: '#/components/responses/DefaultError' - tags: - - Eval - description: Cancel a job. - parameters: - - name: benchmark_id - in: path - description: >- - The ID of the benchmark to run the evaluation on. - required: true - schema: - type: string - - name: job_id - in: path - description: The ID of the job to cancel. - required: true - schema: - type: string - /v1/eval/benchmarks/{benchmark_id}/jobs/{job_id}/result: - get: - responses: - '200': - description: The result of the job. - content: - application/json: - schema: - $ref: '#/components/schemas/EvaluateResponse' - '400': - $ref: '#/components/responses/BadRequest400' - '429': - $ref: >- - #/components/responses/TooManyRequests429 - '500': - $ref: >- - #/components/responses/InternalServerError500 - default: - $ref: '#/components/responses/DefaultError' - tags: - - Eval - description: Get the result of a job. - parameters: - - name: benchmark_id - in: path - description: >- - The ID of the benchmark to run the evaluation on. - required: true - schema: - type: string - - name: job_id - in: path - description: The ID of the job to get the result of. - required: true - schema: - type: string - /v1/agents/{agent_id}/sessions: - get: - responses: - '200': - description: A PaginatedResponse. - content: - application/json: - schema: - $ref: '#/components/schemas/PaginatedResponse' - '400': - $ref: '#/components/responses/BadRequest400' - '429': - $ref: >- - #/components/responses/TooManyRequests429 - '500': - $ref: >- - #/components/responses/InternalServerError500 - default: - $ref: '#/components/responses/DefaultError' - tags: - - Agents - description: List all session(s) of a given agent. - parameters: - - name: agent_id - in: path - description: >- - The ID of the agent to list sessions for. - required: true - schema: - type: string - - name: start_index - in: query - description: The index to start the pagination from. - required: false - schema: - type: integer - - name: limit - in: query - description: The number of sessions to return. - required: false - schema: - type: integer - /v1/eval/benchmarks: - get: - responses: - '200': - description: A ListBenchmarksResponse. - content: - application/json: - schema: - $ref: '#/components/schemas/ListBenchmarksResponse' - '400': - $ref: '#/components/responses/BadRequest400' - '429': - $ref: >- - #/components/responses/TooManyRequests429 - '500': - $ref: >- - #/components/responses/InternalServerError500 - default: - $ref: '#/components/responses/DefaultError' - tags: - - Benchmarks - description: List all benchmarks. - parameters: [] - post: - responses: - '200': - description: OK - '400': - $ref: '#/components/responses/BadRequest400' - '429': - $ref: >- - #/components/responses/TooManyRequests429 - '500': - $ref: >- - #/components/responses/InternalServerError500 - default: - $ref: '#/components/responses/DefaultError' - tags: - - Benchmarks - description: Register a benchmark. - parameters: [] - requestBody: - content: - application/json: - schema: - $ref: '#/components/schemas/RegisterBenchmarkRequest' - required: true - /v1/openai/v1/chat/completions: - get: - responses: - '200': - description: A ListOpenAIChatCompletionResponse. - content: - application/json: - schema: - $ref: '#/components/schemas/ListOpenAIChatCompletionResponse' - '400': - $ref: '#/components/responses/BadRequest400' - '429': - $ref: >- - #/components/responses/TooManyRequests429 - '500': - $ref: >- - #/components/responses/InternalServerError500 - default: - $ref: '#/components/responses/DefaultError' - tags: - - Inference - description: List all chat completions. - parameters: - - name: after - in: query - description: >- - The ID of the last chat completion to return. - required: false - schema: - type: string - - name: limit - in: query - description: >- - The maximum number of chat completions to return. - required: false - schema: - type: integer - - name: model - in: query - description: The model to filter by. - required: false - schema: - type: string - - name: order - in: query - description: >- - The order to sort the chat completions by: "asc" or "desc". Defaults to - "desc". - required: false - schema: - $ref: '#/components/schemas/Order' - post: - responses: - '200': - description: An OpenAIChatCompletion. - content: - application/json: - schema: - oneOf: - - $ref: '#/components/schemas/OpenAIChatCompletion' - - $ref: '#/components/schemas/OpenAIChatCompletionChunk' - '400': - $ref: '#/components/responses/BadRequest400' - '429': - $ref: >- - #/components/responses/TooManyRequests429 - '500': - $ref: >- - #/components/responses/InternalServerError500 - default: - $ref: '#/components/responses/DefaultError' - tags: - - Inference - description: >- - Generate an OpenAI-compatible chat completion for the given messages using - the specified model. - parameters: [] - requestBody: - content: - application/json: - schema: - $ref: '#/components/schemas/OpenaiChatCompletionRequest' - required: true - /v1/datasets: - get: - responses: - '200': - description: A ListDatasetsResponse. - content: - application/json: - schema: - $ref: '#/components/schemas/ListDatasetsResponse' - '400': - $ref: '#/components/responses/BadRequest400' - '429': - $ref: >- - #/components/responses/TooManyRequests429 - '500': - $ref: >- - #/components/responses/InternalServerError500 - default: - $ref: '#/components/responses/DefaultError' - tags: - - Datasets - description: List all datasets. - parameters: [] - post: - responses: - '200': - description: A Dataset. - content: - application/json: - schema: - $ref: '#/components/schemas/Dataset' - '400': - $ref: '#/components/responses/BadRequest400' - '429': - $ref: >- - #/components/responses/TooManyRequests429 - '500': - $ref: >- - #/components/responses/InternalServerError500 - default: - $ref: '#/components/responses/DefaultError' - tags: - - Datasets - description: Register a new dataset. - parameters: [] - requestBody: - content: - application/json: - schema: - $ref: '#/components/schemas/RegisterDatasetRequest' - required: true - /v1/models: - get: - responses: - '200': - description: A ListModelsResponse. - content: - application/json: - schema: - $ref: '#/components/schemas/ListModelsResponse' - '400': - $ref: '#/components/responses/BadRequest400' - '429': - $ref: >- - #/components/responses/TooManyRequests429 - '500': - $ref: >- - #/components/responses/InternalServerError500 - default: - $ref: '#/components/responses/DefaultError' - tags: - - Models - description: List all models. - parameters: [] - post: - responses: - '200': - description: A Model. - content: - application/json: - schema: - $ref: '#/components/schemas/Model' - '400': - $ref: '#/components/responses/BadRequest400' - '429': - $ref: >- - #/components/responses/TooManyRequests429 - '500': - $ref: >- - #/components/responses/InternalServerError500 - default: - $ref: '#/components/responses/DefaultError' - tags: - - Models - description: Register a model. - parameters: [] - requestBody: - content: - application/json: - schema: - $ref: '#/components/schemas/RegisterModelRequest' - required: true - /v1/openai/v1/responses/{response_id}/input_items: - get: - responses: - '200': - description: An ListOpenAIResponseInputItem. - content: - application/json: - schema: - $ref: '#/components/schemas/ListOpenAIResponseInputItem' - '400': - $ref: '#/components/responses/BadRequest400' - '429': - $ref: >- - #/components/responses/TooManyRequests429 - '500': - $ref: >- - #/components/responses/InternalServerError500 - default: - $ref: '#/components/responses/DefaultError' - tags: - - Agents - description: >- - List input items for a given OpenAI response. - parameters: - - name: response_id - in: path - description: >- - The ID of the response to retrieve input items for. - required: true - schema: - type: string - - name: after - in: query - description: >- - An item ID to list items after, used for pagination. - required: false - schema: - type: string - - name: before - in: query - description: >- - An item ID to list items before, used for pagination. - required: false - schema: - type: string - - name: include - in: query - description: >- - Additional fields to include in the response. - required: false - schema: - type: array - items: - type: string - - name: limit - in: query - description: >- - A limit on the number of objects to be returned. Limit can range between - 1 and 100, and the default is 20. - required: false - schema: - type: integer - - name: order - in: query - description: >- - The order to return the input items in. Default is desc. - required: false - schema: - $ref: '#/components/schemas/Order' - /v1/providers: - get: - responses: - '200': - description: >- - A ListProvidersResponse containing information about all providers. - content: - application/json: - schema: - $ref: '#/components/schemas/ListProvidersResponse' - '400': - $ref: '#/components/responses/BadRequest400' - '429': - $ref: >- - #/components/responses/TooManyRequests429 - '500': - $ref: >- - #/components/responses/InternalServerError500 - default: - $ref: '#/components/responses/DefaultError' - tags: - - Providers - description: List all available providers. - parameters: [] - /v1/inspect/routes: - get: - responses: - '200': - description: >- - Response containing information about all available routes. - content: - application/json: - schema: - $ref: '#/components/schemas/ListRoutesResponse' - '400': - $ref: '#/components/responses/BadRequest400' - '429': - $ref: >- - #/components/responses/TooManyRequests429 - '500': - $ref: >- - #/components/responses/InternalServerError500 - default: - $ref: '#/components/responses/DefaultError' - tags: - - Inspect - description: >- - List all available API routes with their methods and implementing providers. - parameters: [] - /v1/tool-runtime/list-tools: - get: - responses: - '200': - description: A ListToolDefsResponse. - content: - application/json: - schema: - $ref: '#/components/schemas/ListToolDefsResponse' - '400': - $ref: '#/components/responses/BadRequest400' - '429': - $ref: >- - #/components/responses/TooManyRequests429 - '500': - $ref: >- - #/components/responses/InternalServerError500 - default: - $ref: '#/components/responses/DefaultError' - tags: - - ToolRuntime - description: List all tools in the runtime. - parameters: - - name: tool_group_id - in: query - description: >- - The ID of the tool group to list tools for. - required: false - schema: - type: string - - name: mcp_endpoint - in: query - description: >- - The MCP endpoint to use for the tool group. - required: false - schema: - $ref: '#/components/schemas/URL' - /v1/scoring-functions: - get: - responses: - '200': - description: A ListScoringFunctionsResponse. - content: - application/json: - schema: - $ref: '#/components/schemas/ListScoringFunctionsResponse' - '400': - $ref: '#/components/responses/BadRequest400' - '429': - $ref: >- - #/components/responses/TooManyRequests429 - '500': - $ref: >- - #/components/responses/InternalServerError500 - default: - $ref: '#/components/responses/DefaultError' - tags: - - ScoringFunctions - description: List all scoring functions. - parameters: [] - post: - responses: - '200': - description: OK - '400': - $ref: '#/components/responses/BadRequest400' - '429': - $ref: >- - #/components/responses/TooManyRequests429 - '500': - $ref: >- - #/components/responses/InternalServerError500 - default: - $ref: '#/components/responses/DefaultError' - tags: - - ScoringFunctions - description: Register a scoring function. - parameters: [] - requestBody: - content: - application/json: - schema: - $ref: '#/components/schemas/RegisterScoringFunctionRequest' - required: true - /v1/shields: - get: - responses: - '200': - description: A ListShieldsResponse. - content: - application/json: - schema: - $ref: '#/components/schemas/ListShieldsResponse' - '400': - $ref: '#/components/responses/BadRequest400' - '429': - $ref: >- - #/components/responses/TooManyRequests429 - '500': - $ref: >- - #/components/responses/InternalServerError500 - default: - $ref: '#/components/responses/DefaultError' - tags: - - Shields - description: List all shields. - parameters: [] - post: - responses: - '200': - description: A Shield. - content: - application/json: - schema: - $ref: '#/components/schemas/Shield' - '400': - $ref: '#/components/responses/BadRequest400' - '429': - $ref: >- - #/components/responses/TooManyRequests429 - '500': - $ref: >- - #/components/responses/InternalServerError500 - default: - $ref: '#/components/responses/DefaultError' - tags: - - Shields - description: Register a shield. - parameters: [] - requestBody: - content: - application/json: - schema: - $ref: '#/components/schemas/RegisterShieldRequest' - required: true - /v1/toolgroups: - get: - responses: - '200': - description: A ListToolGroupsResponse. - content: - application/json: - schema: - $ref: '#/components/schemas/ListToolGroupsResponse' - '400': - $ref: '#/components/responses/BadRequest400' - '429': - $ref: >- - #/components/responses/TooManyRequests429 - '500': - $ref: >- - #/components/responses/InternalServerError500 - default: - $ref: '#/components/responses/DefaultError' - tags: - - ToolGroups - description: List tool groups with optional provider. - parameters: [] - post: - responses: - '200': - description: OK - '400': - $ref: '#/components/responses/BadRequest400' - '429': - $ref: >- - #/components/responses/TooManyRequests429 - '500': - $ref: >- - #/components/responses/InternalServerError500 - default: - $ref: '#/components/responses/DefaultError' - tags: - - ToolGroups - description: Register a tool group. - parameters: [] - requestBody: - content: - application/json: - schema: - $ref: '#/components/schemas/RegisterToolGroupRequest' - required: true - /v1/tools: - get: - responses: - '200': - description: A ListToolsResponse. - content: - application/json: - schema: - $ref: '#/components/schemas/ListToolsResponse' - '400': - $ref: '#/components/responses/BadRequest400' - '429': - $ref: >- - #/components/responses/TooManyRequests429 - '500': - $ref: >- - #/components/responses/InternalServerError500 - default: - $ref: '#/components/responses/DefaultError' - tags: - - ToolGroups - description: List tools with optional tool group. - parameters: - - name: toolgroup_id - in: query - description: >- - The ID of the tool group to list tools for. - required: false - schema: - type: string - /v1/vector-dbs: - get: - responses: - '200': - description: A ListVectorDBsResponse. - content: - application/json: - schema: - $ref: '#/components/schemas/ListVectorDBsResponse' - '400': - $ref: '#/components/responses/BadRequest400' - '429': - $ref: >- - #/components/responses/TooManyRequests429 - '500': - $ref: >- - #/components/responses/InternalServerError500 - default: - $ref: '#/components/responses/DefaultError' - tags: - - VectorDBs - description: List all vector databases. - parameters: [] - post: - responses: - '200': - description: A VectorDB. - content: - application/json: - schema: - $ref: '#/components/schemas/VectorDB' - '400': - $ref: '#/components/responses/BadRequest400' - '429': - $ref: >- - #/components/responses/TooManyRequests429 - '500': - $ref: >- - #/components/responses/InternalServerError500 - default: - $ref: '#/components/responses/DefaultError' - tags: - - VectorDBs - description: Register a vector database. - parameters: [] - requestBody: - content: - application/json: - schema: - $ref: '#/components/schemas/RegisterVectorDbRequest' - required: true - /v1/telemetry/events: - post: - responses: - '200': - description: OK - '400': - $ref: '#/components/responses/BadRequest400' - '429': - $ref: >- - #/components/responses/TooManyRequests429 - '500': - $ref: >- - #/components/responses/InternalServerError500 - default: - $ref: '#/components/responses/DefaultError' - tags: - - Telemetry - description: Log an event. - parameters: [] - requestBody: - content: - application/json: - schema: - $ref: '#/components/schemas/LogEventRequest' - required: true - /v1/openai/v1/vector_stores/{vector_store_id}/files: - get: - responses: - '200': - description: >- - A VectorStoreListFilesResponse containing the list of files. - content: - application/json: - schema: - $ref: '#/components/schemas/VectorStoreListFilesResponse' - '400': - $ref: '#/components/responses/BadRequest400' - '429': - $ref: >- - #/components/responses/TooManyRequests429 - '500': - $ref: >- - #/components/responses/InternalServerError500 - default: - $ref: '#/components/responses/DefaultError' - tags: - - VectorIO - description: List files in a vector store. - parameters: - - name: vector_store_id - in: path - description: >- - The ID of the vector store to list files from. - required: true - schema: - type: string - - name: limit - in: query - description: >- - (Optional) A limit on the number of objects to be returned. Limit can - range between 1 and 100, and the default is 20. - required: false - schema: - type: integer - - name: order - in: query - description: >- - (Optional) Sort order by the `created_at` timestamp of the objects. `asc` - for ascending order and `desc` for descending order. - required: false - schema: - type: string - - name: after - in: query - description: >- - (Optional) A cursor for use in pagination. `after` is an object ID that - defines your place in the list. - required: false - schema: - type: string - - name: before - in: query - description: >- - (Optional) A cursor for use in pagination. `before` is an object ID that - defines your place in the list. - required: false - schema: - type: string - - name: filter - in: query - description: >- - (Optional) Filter by file status to only return files with the specified - status. - required: false - schema: - $ref: '#/components/schemas/VectorStoreFileStatus' - post: - responses: - '200': - description: >- - A VectorStoreFileObject representing the attached file. - content: - application/json: - schema: - $ref: '#/components/schemas/VectorStoreFileObject' - '400': - $ref: '#/components/responses/BadRequest400' - '429': - $ref: >- - #/components/responses/TooManyRequests429 - '500': - $ref: >- - #/components/responses/InternalServerError500 - default: - $ref: '#/components/responses/DefaultError' - tags: - - VectorIO - description: Attach a file to a vector store. - parameters: - - name: vector_store_id - in: path - description: >- - The ID of the vector store to attach the file to. - required: true - schema: - type: string - requestBody: - content: - application/json: - schema: - $ref: '#/components/schemas/OpenaiAttachFileToVectorStoreRequest' - required: true - /v1/openai/v1/completions: - post: - responses: - '200': - description: An OpenAICompletion. - content: - application/json: - schema: - $ref: '#/components/schemas/OpenAICompletion' - '400': - $ref: '#/components/responses/BadRequest400' - '429': - $ref: >- - #/components/responses/TooManyRequests429 - '500': - $ref: >- - #/components/responses/InternalServerError500 - default: - $ref: '#/components/responses/DefaultError' - tags: - - Inference - description: >- - Generate an OpenAI-compatible completion for the given prompt using the specified - model. - parameters: [] - requestBody: - content: - application/json: - schema: - $ref: '#/components/schemas/OpenaiCompletionRequest' - required: true - /v1/openai/v1/vector_stores: - get: - responses: - '200': - description: >- - A VectorStoreListResponse containing the list of vector stores. - content: - application/json: - schema: - $ref: '#/components/schemas/VectorStoreListResponse' - '400': - $ref: '#/components/responses/BadRequest400' - '429': - $ref: >- - #/components/responses/TooManyRequests429 - '500': - $ref: >- - #/components/responses/InternalServerError500 - default: - $ref: '#/components/responses/DefaultError' - tags: - - VectorIO - description: Returns a list of vector stores. - parameters: - - name: limit - in: query - description: >- - A limit on the number of objects to be returned. Limit can range between - 1 and 100, and the default is 20. - required: false - schema: - type: integer - - name: order - in: query - description: >- - Sort order by the `created_at` timestamp of the objects. `asc` for ascending - order and `desc` for descending order. - required: false - schema: - type: string - - name: after - in: query - description: >- - A cursor for use in pagination. `after` is an object ID that defines your - place in the list. - required: false - schema: - type: string - - name: before - in: query - description: >- - A cursor for use in pagination. `before` is an object ID that defines - your place in the list. - required: false - schema: - type: string - post: - responses: - '200': - description: >- - A VectorStoreObject representing the created vector store. - content: - application/json: - schema: - $ref: '#/components/schemas/VectorStoreObject' - '400': - $ref: '#/components/responses/BadRequest400' - '429': - $ref: >- - #/components/responses/TooManyRequests429 - '500': - $ref: >- - #/components/responses/InternalServerError500 - default: - $ref: '#/components/responses/DefaultError' - tags: - - VectorIO - description: Creates a vector store. - parameters: [] - requestBody: - content: - application/json: - schema: - $ref: '#/components/schemas/OpenaiCreateVectorStoreRequest' - required: true - /v1/openai/v1/files/{file_id}: - get: - responses: - '200': - description: >- - An OpenAIFileObject containing file information. - content: - application/json: - schema: - $ref: '#/components/schemas/OpenAIFileObject' - '400': - $ref: '#/components/responses/BadRequest400' - '429': - $ref: >- - #/components/responses/TooManyRequests429 - '500': - $ref: >- - #/components/responses/InternalServerError500 - default: - $ref: '#/components/responses/DefaultError' - tags: - - Files - description: >- - Returns information about a specific file. - parameters: - - name: file_id - in: path - description: >- - The ID of the file to use for this request. - required: true - schema: - type: string - delete: - responses: - '200': - description: >- - An OpenAIFileDeleteResponse indicating successful deletion. - content: - application/json: - schema: - $ref: '#/components/schemas/OpenAIFileDeleteResponse' - '400': - $ref: '#/components/responses/BadRequest400' - '429': - $ref: >- - #/components/responses/TooManyRequests429 - '500': - $ref: >- - #/components/responses/InternalServerError500 - default: - $ref: '#/components/responses/DefaultError' - tags: - - Files - description: Delete a file. - parameters: - - name: file_id - in: path - description: >- - The ID of the file to use for this request. - required: true - schema: - type: string - /v1/openai/v1/vector_stores/{vector_store_id}: - get: - responses: - '200': - description: >- - A VectorStoreObject representing the vector store. - content: - application/json: - schema: - $ref: '#/components/schemas/VectorStoreObject' - '400': - $ref: '#/components/responses/BadRequest400' - '429': - $ref: >- - #/components/responses/TooManyRequests429 - '500': - $ref: >- - #/components/responses/InternalServerError500 - default: - $ref: '#/components/responses/DefaultError' - tags: - - VectorIO - description: Retrieves a vector store. - parameters: - - name: vector_store_id - in: path - description: The ID of the vector store to retrieve. - required: true - schema: - type: string - post: - responses: - '200': - description: >- - A VectorStoreObject representing the updated vector store. - content: - application/json: - schema: - $ref: '#/components/schemas/VectorStoreObject' - '400': - $ref: '#/components/responses/BadRequest400' - '429': - $ref: >- - #/components/responses/TooManyRequests429 - '500': - $ref: >- - #/components/responses/InternalServerError500 - default: - $ref: '#/components/responses/DefaultError' - tags: - - VectorIO - description: Updates a vector store. - parameters: - - name: vector_store_id - in: path - description: The ID of the vector store to update. - required: true - schema: - type: string - requestBody: - content: - application/json: - schema: - $ref: '#/components/schemas/OpenaiUpdateVectorStoreRequest' - required: true - delete: - responses: - '200': - description: >- - A VectorStoreDeleteResponse indicating the deletion status. - content: - application/json: - schema: - $ref: '#/components/schemas/VectorStoreDeleteResponse' - '400': - $ref: '#/components/responses/BadRequest400' - '429': - $ref: >- - #/components/responses/TooManyRequests429 - '500': - $ref: >- - #/components/responses/InternalServerError500 - default: - $ref: '#/components/responses/DefaultError' - tags: - - VectorIO - description: Delete a vector store. - parameters: - - name: vector_store_id - in: path - description: The ID of the vector store to delete. - required: true - schema: - type: string - /v1/openai/v1/vector_stores/{vector_store_id}/files/{file_id}: - get: - responses: - '200': - description: >- - A VectorStoreFileObject representing the file. - content: - application/json: - schema: - $ref: '#/components/schemas/VectorStoreFileObject' - '400': - $ref: '#/components/responses/BadRequest400' - '429': - $ref: >- - #/components/responses/TooManyRequests429 - '500': - $ref: >- - #/components/responses/InternalServerError500 - default: - $ref: '#/components/responses/DefaultError' - tags: - - VectorIO - description: Retrieves a vector store file. - parameters: - - name: vector_store_id - in: path - description: >- - The ID of the vector store containing the file to retrieve. - required: true - schema: - type: string - - name: file_id - in: path - description: The ID of the file to retrieve. - required: true - schema: - type: string - post: - responses: - '200': - description: >- - A VectorStoreFileObject representing the updated file. - content: - application/json: - schema: - $ref: '#/components/schemas/VectorStoreFileObject' - '400': - $ref: '#/components/responses/BadRequest400' - '429': - $ref: >- - #/components/responses/TooManyRequests429 - '500': - $ref: >- - #/components/responses/InternalServerError500 - default: - $ref: '#/components/responses/DefaultError' - tags: - - VectorIO - description: Updates a vector store file. - parameters: - - name: vector_store_id - in: path - description: >- - The ID of the vector store containing the file to update. - required: true - schema: - type: string - - name: file_id - in: path - description: The ID of the file to update. - required: true - schema: - type: string - requestBody: - content: - application/json: - schema: - $ref: '#/components/schemas/OpenaiUpdateVectorStoreFileRequest' - required: true - delete: - responses: - '200': - description: >- - A VectorStoreFileDeleteResponse indicating the deletion status. - content: - application/json: - schema: - $ref: '#/components/schemas/VectorStoreFileDeleteResponse' - '400': - $ref: '#/components/responses/BadRequest400' - '429': - $ref: >- - #/components/responses/TooManyRequests429 - '500': - $ref: >- - #/components/responses/InternalServerError500 - default: - $ref: '#/components/responses/DefaultError' - tags: - - VectorIO - description: Delete a vector store file. - parameters: - - name: vector_store_id - in: path - description: >- - The ID of the vector store containing the file to delete. - required: true - schema: - type: string - - name: file_id - in: path - description: The ID of the file to delete. - required: true - schema: - type: string - /v1/openai/v1/embeddings: - post: - responses: - '200': - description: >- - An OpenAIEmbeddingsResponse containing the embeddings. - content: - application/json: - schema: - $ref: '#/components/schemas/OpenAIEmbeddingsResponse' - '400': - $ref: '#/components/responses/BadRequest400' - '429': - $ref: >- - #/components/responses/TooManyRequests429 - '500': - $ref: >- - #/components/responses/InternalServerError500 - default: - $ref: '#/components/responses/DefaultError' - tags: - - Inference - description: >- - Generate OpenAI-compatible embeddings for the given input using the specified - model. - parameters: [] - requestBody: - content: - application/json: - schema: - $ref: '#/components/schemas/OpenaiEmbeddingsRequest' - required: true - /v1/openai/v1/files: - get: - responses: - '200': - description: >- - An ListOpenAIFileResponse containing the list of files. - content: - application/json: - schema: - $ref: '#/components/schemas/ListOpenAIFileResponse' - '400': - $ref: '#/components/responses/BadRequest400' - '429': - $ref: >- - #/components/responses/TooManyRequests429 - '500': - $ref: >- - #/components/responses/InternalServerError500 - default: - $ref: '#/components/responses/DefaultError' - tags: - - Files - description: >- - Returns a list of files that belong to the user's organization. - parameters: - - name: after - in: query - description: >- - A cursor for use in pagination. `after` is an object ID that defines your - place in the list. For instance, if you make a list request and receive - 100 objects, ending with obj_foo, your subsequent call can include after=obj_foo - in order to fetch the next page of the list. - required: false - schema: - type: string - - name: limit - in: query - description: >- - A limit on the number of objects to be returned. Limit can range between - 1 and 10,000, and the default is 10,000. - required: false - schema: - type: integer - - name: order - in: query - description: >- - Sort order by the `created_at` timestamp of the objects. `asc` for ascending - order and `desc` for descending order. - required: false - schema: - $ref: '#/components/schemas/Order' - - name: purpose - in: query - description: >- - Only return files with the given purpose. - required: false - schema: - $ref: '#/components/schemas/OpenAIFilePurpose' - post: - responses: - '200': - description: >- - An OpenAIFileObject representing the uploaded file. - content: - application/json: - schema: - $ref: '#/components/schemas/OpenAIFileObject' - '400': - $ref: '#/components/responses/BadRequest400' - '429': - $ref: >- - #/components/responses/TooManyRequests429 - '500': - $ref: >- - #/components/responses/InternalServerError500 - default: - $ref: '#/components/responses/DefaultError' - tags: - - Files - description: >- - Upload a file that can be used across various endpoints. - - The file upload should be a multipart form request with: - - - file: The File object (not file name) to be uploaded. - - - purpose: The intended purpose of the uploaded file. - parameters: [] - requestBody: - content: - multipart/form-data: - schema: - type: object - properties: - file: - type: string - format: binary - purpose: - $ref: '#/components/schemas/OpenAIFilePurpose' - required: - - file - - purpose - required: true - /v1/openai/v1/models: - get: - responses: - '200': - description: A OpenAIListModelsResponse. - content: - application/json: - schema: - $ref: '#/components/schemas/OpenAIListModelsResponse' - '400': - $ref: '#/components/responses/BadRequest400' - '429': - $ref: >- - #/components/responses/TooManyRequests429 - '500': - $ref: >- - #/components/responses/InternalServerError500 - default: - $ref: '#/components/responses/DefaultError' - tags: - - Models - description: List models using the OpenAI API. - parameters: [] - /v1/openai/v1/files/{file_id}/content: - get: - responses: - '200': - description: >- - The raw file content as a binary response. - content: - application/json: - schema: - $ref: '#/components/schemas/Response' - '400': - $ref: '#/components/responses/BadRequest400' - '429': - $ref: >- - #/components/responses/TooManyRequests429 - '500': - $ref: >- - #/components/responses/InternalServerError500 - default: - $ref: '#/components/responses/DefaultError' - tags: - - Files - description: >- - Returns the contents of the specified file. - parameters: - - name: file_id - in: path - description: >- - The ID of the file to use for this request. - required: true - schema: - type: string - /v1/openai/v1/vector_stores/{vector_store_id}/files/{file_id}/content: - get: - responses: - '200': - description: >- - A list of InterleavedContent representing the file contents. - content: - application/json: - schema: - $ref: '#/components/schemas/VectorStoreFileContentsResponse' - '400': - $ref: '#/components/responses/BadRequest400' - '429': - $ref: >- - #/components/responses/TooManyRequests429 - '500': - $ref: >- - #/components/responses/InternalServerError500 - default: - $ref: '#/components/responses/DefaultError' - tags: - - VectorIO - description: >- - Retrieves the contents of a vector store file. - parameters: - - name: vector_store_id - in: path - description: >- - The ID of the vector store containing the file to retrieve. - required: true - schema: - type: string - - name: file_id - in: path - description: The ID of the file to retrieve. - required: true - schema: - type: string - /v1/openai/v1/vector_stores/{vector_store_id}/search: - post: - responses: - '200': - description: >- - A VectorStoreSearchResponse containing the search results. - content: - application/json: - schema: - $ref: '#/components/schemas/VectorStoreSearchResponsePage' - '400': - $ref: '#/components/responses/BadRequest400' - '429': - $ref: >- - #/components/responses/TooManyRequests429 - '500': - $ref: >- - #/components/responses/InternalServerError500 - default: - $ref: '#/components/responses/DefaultError' - tags: - - VectorIO - description: >- - Search for chunks in a vector store. - - Searches a vector store for relevant chunks based on a query and optional - file attribute filters. - parameters: - - name: vector_store_id - in: path - description: The ID of the vector store to search. - required: true - schema: - type: string - requestBody: - content: - application/json: - schema: - $ref: '#/components/schemas/OpenaiSearchVectorStoreRequest' - required: true - /v1/post-training/preference-optimize: - post: - responses: - '200': - description: A PostTrainingJob. - content: - application/json: - schema: - $ref: '#/components/schemas/PostTrainingJob' - '400': - $ref: '#/components/responses/BadRequest400' - '429': - $ref: >- - #/components/responses/TooManyRequests429 - '500': - $ref: >- - #/components/responses/InternalServerError500 - default: - $ref: '#/components/responses/DefaultError' - tags: - - PostTraining (Coming Soon) - description: Run preference optimization of a model. - parameters: [] - requestBody: - content: - application/json: - schema: - $ref: '#/components/schemas/PreferenceOptimizeRequest' - required: true - /v1/tool-runtime/rag-tool/query: - post: - responses: - '200': - description: >- - RAGQueryResult containing the retrieved content and metadata - content: - application/json: - schema: - $ref: '#/components/schemas/RAGQueryResult' - '400': - $ref: '#/components/responses/BadRequest400' - '429': - $ref: >- - #/components/responses/TooManyRequests429 - '500': - $ref: >- - #/components/responses/InternalServerError500 - default: - $ref: '#/components/responses/DefaultError' - tags: - - ToolRuntime - description: >- - Query the RAG system for context; typically invoked by the agent. - parameters: [] - requestBody: - content: - application/json: - schema: - $ref: '#/components/schemas/QueryRequest' - required: true - /v1/vector-io/query: - post: - responses: - '200': - description: A QueryChunksResponse. - content: - application/json: - schema: - $ref: '#/components/schemas/QueryChunksResponse' - '400': - $ref: '#/components/responses/BadRequest400' - '429': - $ref: >- - #/components/responses/TooManyRequests429 - '500': - $ref: >- - #/components/responses/InternalServerError500 - default: - $ref: '#/components/responses/DefaultError' - tags: - - VectorIO - description: Query chunks from a vector database. - parameters: [] - requestBody: - content: - application/json: - schema: - $ref: '#/components/schemas/QueryChunksRequest' - required: true - /v1/telemetry/metrics/{metric_name}: - post: - responses: - '200': - description: A QueryMetricsResponse. - content: - application/json: - schema: - $ref: '#/components/schemas/QueryMetricsResponse' - '400': - $ref: '#/components/responses/BadRequest400' - '429': - $ref: >- - #/components/responses/TooManyRequests429 - '500': - $ref: >- - #/components/responses/InternalServerError500 - default: - $ref: '#/components/responses/DefaultError' - tags: - - Telemetry - description: Query metrics. - parameters: - - name: metric_name - in: path - description: The name of the metric to query. - required: true - schema: - type: string - requestBody: - content: - application/json: - schema: - $ref: '#/components/schemas/QueryMetricsRequest' - required: true - /v1/telemetry/spans: - post: - responses: - '200': - description: A QuerySpansResponse. - content: - application/json: - schema: - $ref: '#/components/schemas/QuerySpansResponse' - '400': - $ref: '#/components/responses/BadRequest400' - '429': - $ref: >- - #/components/responses/TooManyRequests429 - '500': - $ref: >- - #/components/responses/InternalServerError500 - default: - $ref: '#/components/responses/DefaultError' - tags: - - Telemetry - description: Query spans. - parameters: [] - requestBody: - content: - application/json: - schema: - $ref: '#/components/schemas/QuerySpansRequest' - required: true - /v1/telemetry/traces: - post: - responses: - '200': - description: A QueryTracesResponse. - content: - application/json: - schema: - $ref: '#/components/schemas/QueryTracesResponse' - '400': - $ref: '#/components/responses/BadRequest400' - '429': - $ref: >- - #/components/responses/TooManyRequests429 - '500': - $ref: >- - #/components/responses/InternalServerError500 - default: - $ref: '#/components/responses/DefaultError' - tags: - - Telemetry - description: Query traces. - parameters: [] - requestBody: - content: - application/json: - schema: - $ref: '#/components/schemas/QueryTracesRequest' - required: true - /v1/inference/rerank: - post: - responses: - '200': - description: >- - RerankResponse with indices sorted by relevance score (descending). - content: - application/json: - schema: - $ref: '#/components/schemas/RerankResponse' - '400': - $ref: '#/components/responses/BadRequest400' - '429': - $ref: >- - #/components/responses/TooManyRequests429 - '500': - $ref: >- - #/components/responses/InternalServerError500 - default: - $ref: '#/components/responses/DefaultError' - tags: - - Inference - description: >- - Rerank a list of documents based on their relevance to a query. - parameters: [] - requestBody: - content: - application/json: - schema: - $ref: '#/components/schemas/RerankRequest' - required: true - /v1/agents/{agent_id}/session/{session_id}/turn/{turn_id}/resume: - post: - responses: - '200': - description: >- - A Turn object if stream is False, otherwise an AsyncIterator of AgentTurnResponseStreamChunk - objects. - content: - application/json: - schema: - $ref: '#/components/schemas/Turn' - text/event-stream: - schema: - $ref: '#/components/schemas/AgentTurnResponseStreamChunk' - '400': - $ref: '#/components/responses/BadRequest400' - '429': - $ref: >- - #/components/responses/TooManyRequests429 - '500': - $ref: >- - #/components/responses/InternalServerError500 - default: - $ref: '#/components/responses/DefaultError' - tags: - - Agents - description: >- - Resume an agent turn with executed tool call responses. - - When a Turn has the status `awaiting_input` due to pending input from client - side tool calls, this endpoint can be used to submit the outputs from the - tool calls once they are ready. - parameters: - - name: agent_id - in: path - description: The ID of the agent to resume. - required: true - schema: - type: string - - name: session_id - in: path - description: The ID of the session to resume. - required: true - schema: - type: string - - name: turn_id - in: path - description: The ID of the turn to resume. - required: true - schema: - type: string - requestBody: - content: - application/json: - schema: - $ref: '#/components/schemas/ResumeAgentTurnRequest' - required: true - /v1/eval/benchmarks/{benchmark_id}/jobs: - post: - responses: - '200': - description: >- - The job that was created to run the evaluation. - content: - application/json: - schema: - $ref: '#/components/schemas/Job' - '400': - $ref: '#/components/responses/BadRequest400' - '429': - $ref: >- - #/components/responses/TooManyRequests429 - '500': - $ref: >- - #/components/responses/InternalServerError500 - default: - $ref: '#/components/responses/DefaultError' - tags: - - Eval - description: Run an evaluation on a benchmark. - parameters: - - name: benchmark_id - in: path - description: >- - The ID of the benchmark to run the evaluation on. - required: true - schema: - type: string - requestBody: - content: - application/json: - schema: - $ref: '#/components/schemas/RunEvalRequest' - required: true - /v1/openai/v1/moderations: - post: - responses: - '200': - description: A moderation object. - content: - application/json: - schema: - $ref: '#/components/schemas/ModerationObject' - '400': - $ref: '#/components/responses/BadRequest400' - '429': - $ref: >- - #/components/responses/TooManyRequests429 - '500': - $ref: >- - #/components/responses/InternalServerError500 - default: - $ref: '#/components/responses/DefaultError' - tags: - - Safety - description: >- - Classifies if text and/or image inputs are potentially harmful. - parameters: [] - requestBody: - content: - application/json: - schema: - $ref: '#/components/schemas/RunModerationRequest' - required: true - /v1/safety/run-shield: - post: - responses: - '200': - description: A RunShieldResponse. - content: - application/json: - schema: - $ref: '#/components/schemas/RunShieldResponse' - '400': - $ref: '#/components/responses/BadRequest400' - '429': - $ref: >- - #/components/responses/TooManyRequests429 - '500': - $ref: >- - #/components/responses/InternalServerError500 - default: - $ref: '#/components/responses/DefaultError' - tags: - - Safety - description: Run a shield. - parameters: [] - requestBody: - content: - application/json: - schema: - $ref: '#/components/schemas/RunShieldRequest' - required: true - /v1/telemetry/spans/export: - post: - responses: - '200': - description: OK - '400': - $ref: '#/components/responses/BadRequest400' - '429': - $ref: >- - #/components/responses/TooManyRequests429 - '500': - $ref: >- - #/components/responses/InternalServerError500 - default: - $ref: '#/components/responses/DefaultError' - tags: - - Telemetry - description: Save spans to a dataset. - parameters: [] - requestBody: - content: - application/json: - schema: - $ref: '#/components/schemas/SaveSpansToDatasetRequest' - required: true - /v1/scoring/score: - post: - responses: - '200': - description: >- - A ScoreResponse object containing rows and aggregated results. - content: - application/json: - schema: - $ref: '#/components/schemas/ScoreResponse' - '400': - $ref: '#/components/responses/BadRequest400' - '429': - $ref: >- - #/components/responses/TooManyRequests429 - '500': - $ref: >- - #/components/responses/InternalServerError500 - default: - $ref: '#/components/responses/DefaultError' - tags: - - Scoring - description: Score a list of rows. - parameters: [] - requestBody: - content: - application/json: - schema: - $ref: '#/components/schemas/ScoreRequest' - required: true - /v1/scoring/score-batch: - post: - responses: - '200': - description: A ScoreBatchResponse. - content: - application/json: - schema: - $ref: '#/components/schemas/ScoreBatchResponse' - '400': - $ref: '#/components/responses/BadRequest400' - '429': - $ref: >- - #/components/responses/TooManyRequests429 - '500': - $ref: >- - #/components/responses/InternalServerError500 - default: - $ref: '#/components/responses/DefaultError' - tags: - - Scoring - description: Score a batch of rows. - parameters: [] - requestBody: - content: - application/json: - schema: - $ref: '#/components/schemas/ScoreBatchRequest' - required: true - /v1/post-training/supervised-fine-tune: - post: - responses: - '200': - description: A PostTrainingJob. - content: - application/json: - schema: - $ref: '#/components/schemas/PostTrainingJob' - '400': - $ref: '#/components/responses/BadRequest400' - '429': - $ref: >- - #/components/responses/TooManyRequests429 - '500': - $ref: >- - #/components/responses/InternalServerError500 - default: - $ref: '#/components/responses/DefaultError' - tags: - - PostTraining (Coming Soon) - description: Run supervised fine-tuning of a model. - parameters: [] - requestBody: - content: - application/json: - schema: - $ref: '#/components/schemas/SupervisedFineTuneRequest' - required: true - /v1/synthetic-data-generation/generate: - post: - responses: - '200': - description: >- - Response containing filtered synthetic data samples and optional statistics - content: - application/json: - schema: - $ref: '#/components/schemas/SyntheticDataGenerationResponse' - '400': - $ref: '#/components/responses/BadRequest400' - '429': - $ref: >- - #/components/responses/TooManyRequests429 - '500': - $ref: >- - #/components/responses/InternalServerError500 - default: - $ref: '#/components/responses/DefaultError' - tags: - - SyntheticDataGeneration (Coming Soon) - description: >- - Generate synthetic data based on input dialogs and apply filtering. - parameters: [] - requestBody: - content: - application/json: - schema: - $ref: '#/components/schemas/SyntheticDataGenerateRequest' - required: true - /v1/version: - get: - responses: - '200': - description: >- - Version information containing the service version number. - content: - application/json: - schema: - $ref: '#/components/schemas/VersionInfo' - '400': - $ref: '#/components/responses/BadRequest400' - '429': - $ref: >- - #/components/responses/TooManyRequests429 - '500': - $ref: >- - #/components/responses/InternalServerError500 - default: - $ref: '#/components/responses/DefaultError' - tags: - - Inspect - description: Get the version of the service. - parameters: [] -jsonSchemaDialect: >- - https://json-schema.org/draft/2020-12/schema -components: - schemas: - Error: - type: object - properties: - status: - type: integer - description: HTTP status code - title: - type: string - description: >- - Error title, a short summary of the error which is invariant for an error - type - detail: - type: string - description: >- - Error detail, a longer human-readable description of the error - instance: - type: string - description: >- - (Optional) A URL which can be used to retrieve more information about - the specific occurrence of the error - additionalProperties: false - required: - - status - - title - - detail - title: Error - description: >- - Error response from the API. Roughly follows RFC 7807. - AppendRowsRequest: - type: object - properties: - rows: - type: array - items: - type: object - additionalProperties: - oneOf: - - type: 'null' - - type: boolean - - type: number - - type: string - - type: array - - type: object - description: The rows to append to the dataset. - additionalProperties: false - required: - - rows - title: AppendRowsRequest - CancelTrainingJobRequest: - type: object - properties: - job_uuid: - type: string - description: The UUID of the job to cancel. - additionalProperties: false - required: - - job_uuid - title: CancelTrainingJobRequest - CompletionMessage: - type: object - properties: - role: - type: string - const: assistant - default: assistant - description: >- - Must be "assistant" to identify this as the model's response - content: - $ref: '#/components/schemas/InterleavedContent' - description: The content of the model's response - stop_reason: - type: string - enum: - - end_of_turn - - end_of_message - - out_of_tokens - description: >- - Reason why the model stopped generating. Options are: - `StopReason.end_of_turn`: - The model finished generating the entire response. - `StopReason.end_of_message`: - The model finished generating but generated a partial response -- usually, - a tool call. The user may call the tool and continue the conversation - with the tool's response. - `StopReason.out_of_tokens`: The model ran - out of token budget. - tool_calls: - type: array - items: - $ref: '#/components/schemas/ToolCall' - description: >- - List of tool calls. Each tool call is a ToolCall object. - additionalProperties: false - required: - - role - - content - - stop_reason - title: CompletionMessage - description: >- - A message containing the model's (assistant) response in a chat conversation. - GrammarResponseFormat: - type: object - properties: - type: - type: string - enum: - - json_schema - - grammar - description: >- - Must be "grammar" to identify this format type - const: grammar - default: grammar - bnf: - type: object - additionalProperties: - oneOf: - - type: 'null' - - type: boolean - - type: number - - type: string - - type: array - - type: object - description: >- - The BNF grammar specification the response should conform to - additionalProperties: false - required: - - type - - bnf - title: GrammarResponseFormat - description: >- - Configuration for grammar-guided response generation. - GreedySamplingStrategy: - type: object - properties: - type: - type: string - const: greedy - default: greedy - description: >- - Must be "greedy" to identify this sampling strategy - additionalProperties: false - required: - - type - title: GreedySamplingStrategy - description: >- - Greedy sampling strategy that selects the highest probability token at each - step. - ImageContentItem: - type: object - properties: - type: - type: string - const: image - default: image - description: >- - Discriminator type of the content item. Always "image" - image: - type: object - properties: - url: - $ref: '#/components/schemas/URL' - description: >- - A URL of the image or data URL in the format of data:image/{type};base64,{data}. - Note that URL could have length limits. - data: - type: string - contentEncoding: base64 - description: base64 encoded image data as string - additionalProperties: false - description: >- - Image as a base64 encoded string or an URL - additionalProperties: false - required: - - type - - image - title: ImageContentItem - description: A image content item - InterleavedContent: - oneOf: - - type: string - - $ref: '#/components/schemas/InterleavedContentItem' - - type: array - items: - $ref: '#/components/schemas/InterleavedContentItem' - InterleavedContentItem: - oneOf: - - $ref: '#/components/schemas/ImageContentItem' - - $ref: '#/components/schemas/TextContentItem' - discriminator: - propertyName: type - mapping: - image: '#/components/schemas/ImageContentItem' - text: '#/components/schemas/TextContentItem' - JsonSchemaResponseFormat: - type: object - properties: - type: - type: string - enum: - - json_schema - - grammar - description: >- - Must be "json_schema" to identify this format type - const: json_schema - default: json_schema - json_schema: - type: object - additionalProperties: - oneOf: - - type: 'null' - - type: boolean - - type: number - - type: string - - type: array - - type: object - description: >- - The JSON schema the response should conform to. In a Python SDK, this - is often a `pydantic` model. - additionalProperties: false - required: - - type - - json_schema - title: JsonSchemaResponseFormat - description: >- - Configuration for JSON schema-guided response generation. - Message: - oneOf: - - $ref: '#/components/schemas/UserMessage' - - $ref: '#/components/schemas/SystemMessage' - - $ref: '#/components/schemas/ToolResponseMessage' - - $ref: '#/components/schemas/CompletionMessage' - discriminator: - propertyName: role - mapping: - user: '#/components/schemas/UserMessage' - system: '#/components/schemas/SystemMessage' - tool: '#/components/schemas/ToolResponseMessage' - assistant: '#/components/schemas/CompletionMessage' - ResponseFormat: - oneOf: - - $ref: '#/components/schemas/JsonSchemaResponseFormat' - - $ref: '#/components/schemas/GrammarResponseFormat' - discriminator: - propertyName: type - mapping: - json_schema: '#/components/schemas/JsonSchemaResponseFormat' - grammar: '#/components/schemas/GrammarResponseFormat' - SamplingParams: - type: object - properties: - strategy: - $ref: '#/components/schemas/SamplingStrategy' - description: The sampling strategy. - max_tokens: - type: integer - default: 0 - description: >- - The maximum number of tokens that can be generated in the completion. - The token count of your prompt plus max_tokens cannot exceed the model's - context length. - repetition_penalty: - type: number - default: 1.0 - description: >- - Number between -2.0 and 2.0. Positive values penalize new tokens based - on whether they appear in the text so far, increasing the model's likelihood - to talk about new topics. - stop: - type: array - items: - type: string - description: >- - Up to 4 sequences where the API will stop generating further tokens. The - returned text will not contain the stop sequence. - additionalProperties: false - required: - - strategy - title: SamplingParams - description: Sampling parameters. - SamplingStrategy: - oneOf: - - $ref: '#/components/schemas/GreedySamplingStrategy' - - $ref: '#/components/schemas/TopPSamplingStrategy' - - $ref: '#/components/schemas/TopKSamplingStrategy' - discriminator: - propertyName: type - mapping: - greedy: '#/components/schemas/GreedySamplingStrategy' - top_p: '#/components/schemas/TopPSamplingStrategy' - top_k: '#/components/schemas/TopKSamplingStrategy' - SystemMessage: - type: object - properties: - role: - type: string - const: system - default: system - description: >- - Must be "system" to identify this as a system message - content: - $ref: '#/components/schemas/InterleavedContent' - description: >- - The content of the "system prompt". If multiple system messages are provided, - they are concatenated. The underlying Llama Stack code may also add other - system messages (for example, for formatting tool definitions). - additionalProperties: false - required: - - role - - content - title: SystemMessage - description: >- - A system message providing instructions or context to the model. - TextContentItem: - type: object - properties: - type: - type: string - const: text - default: text - description: >- - Discriminator type of the content item. Always "text" - text: - type: string - description: Text content - additionalProperties: false - required: - - type - - text - title: TextContentItem - description: A text content item - ToolCall: - type: object - properties: - call_id: - type: string - tool_name: - oneOf: - - type: string - enum: - - brave_search - - wolfram_alpha - - photogen - - code_interpreter - title: BuiltinTool - - type: string - arguments: - oneOf: - - type: string - - type: object - additionalProperties: - oneOf: - - type: string - - type: integer - - type: number - - type: boolean - - type: 'null' - - type: array - items: - oneOf: - - type: string - - type: integer - - type: number - - type: boolean - - type: 'null' - - type: object - additionalProperties: - oneOf: - - type: string - - type: integer - - type: number - - type: boolean - - type: 'null' - arguments_json: - type: string - additionalProperties: false - required: - - call_id - - tool_name - - arguments - title: ToolCall - ToolConfig: - type: object - properties: - tool_choice: - oneOf: - - type: string - enum: - - auto - - required - - none - title: ToolChoice - description: >- - Whether tool use is required or automatic. This is a hint to the model - which may not be followed. It depends on the Instruction Following - capabilities of the model. - - type: string - default: auto - description: >- - (Optional) Whether tool use is automatic, required, or none. Can also - specify a tool name to use a specific tool. Defaults to ToolChoice.auto. - tool_prompt_format: - type: string - enum: - - json - - function_tag - - python_list - description: >- - (Optional) Instructs the model how to format tool calls. By default, Llama - Stack will attempt to use a format that is best adapted to the model. - - `ToolPromptFormat.json`: The tool calls are formatted as a JSON object. - - `ToolPromptFormat.function_tag`: The tool calls are enclosed in a - tag. - `ToolPromptFormat.python_list`: The tool calls are output as Python - syntax -- a list of function calls. - system_message_behavior: - type: string - enum: - - append - - replace - description: >- - (Optional) Config for how to override the default system prompt. - `SystemMessageBehavior.append`: - Appends the provided system message to the default system prompt. - `SystemMessageBehavior.replace`: - Replaces the default system prompt with the provided system message. The - system message can include the string '{{function_definitions}}' to indicate - where the function definitions should be inserted. - default: append - additionalProperties: false - title: ToolConfig - description: Configuration for tool use. - ToolDefinition: - type: object - properties: - tool_name: - oneOf: - - type: string - enum: - - brave_search - - wolfram_alpha - - photogen - - code_interpreter - title: BuiltinTool - - type: string - description: - type: string - parameters: - type: object - additionalProperties: - $ref: '#/components/schemas/ToolParamDefinition' - additionalProperties: false - required: - - tool_name - title: ToolDefinition - ToolParamDefinition: - type: object - properties: - param_type: - type: string - description: - type: string - required: - type: boolean - default: true - default: - oneOf: - - type: 'null' - - type: boolean - - type: number - - type: string - - type: array - - type: object - additionalProperties: false - required: - - param_type - title: ToolParamDefinition - ToolResponseMessage: - type: object - properties: - role: - type: string - const: tool - default: tool - description: >- - Must be "tool" to identify this as a tool response - call_id: - type: string - description: >- - Unique identifier for the tool call this response is for - content: - $ref: '#/components/schemas/InterleavedContent' - description: The response content from the tool - additionalProperties: false - required: - - role - - call_id - - content - title: ToolResponseMessage - description: >- - A message representing the result of a tool invocation. - TopKSamplingStrategy: - type: object - properties: - type: - type: string - const: top_k - default: top_k - description: >- - Must be "top_k" to identify this sampling strategy - top_k: - type: integer - description: >- - Number of top tokens to consider for sampling. Must be at least 1 - additionalProperties: false - required: - - type - - top_k - title: TopKSamplingStrategy - description: >- - Top-k sampling strategy that restricts sampling to the k most likely tokens. - TopPSamplingStrategy: - type: object - properties: - type: - type: string - const: top_p - default: top_p - description: >- - Must be "top_p" to identify this sampling strategy - temperature: - type: number - description: >- - Controls randomness in sampling. Higher values increase randomness - top_p: - type: number - default: 0.95 - description: >- - Cumulative probability threshold for nucleus sampling. Defaults to 0.95 - additionalProperties: false - required: - - type - title: TopPSamplingStrategy - description: >- - Top-p (nucleus) sampling strategy that samples from the smallest set of tokens - with cumulative probability >= p. - URL: - type: object - properties: - uri: - type: string - description: The URL string pointing to the resource - additionalProperties: false - required: - - uri - title: URL - description: A URL reference to external content. - UserMessage: - type: object - properties: - role: - type: string - const: user - default: user - description: >- - Must be "user" to identify this as a user message - content: - $ref: '#/components/schemas/InterleavedContent' - description: >- - The content of the message, which can include text and other media - context: - $ref: '#/components/schemas/InterleavedContent' - description: >- - (Optional) This field is used internally by Llama Stack to pass RAG context. - This field may be removed in the API in the future. - additionalProperties: false - required: - - role - - content - title: UserMessage - description: >- - A message from the user in a chat conversation. - ChatCompletionRequest: - type: object - properties: - model_id: - type: string - description: >- - The identifier of the model to use. The model must be registered with - Llama Stack and available via the /models endpoint. - messages: - type: array - items: - $ref: '#/components/schemas/Message' - description: List of messages in the conversation. - sampling_params: - $ref: '#/components/schemas/SamplingParams' - description: >- - Parameters to control the sampling strategy. - tools: - type: array - items: - $ref: '#/components/schemas/ToolDefinition' - description: >- - (Optional) List of tool definitions available to the model. - tool_choice: - type: string - enum: - - auto - - required - - none - description: >- - (Optional) Whether tool use is required or automatic. Defaults to ToolChoice.auto. - .. deprecated:: Use tool_config instead. - tool_prompt_format: - type: string - enum: - - json - - function_tag - - python_list - description: >- - (Optional) Instructs the model how to format tool calls. By default, Llama - Stack will attempt to use a format that is best adapted to the model. - - `ToolPromptFormat.json`: The tool calls are formatted as a JSON object. - - `ToolPromptFormat.function_tag`: The tool calls are enclosed in a - tag. - `ToolPromptFormat.python_list`: The tool calls are output as Python - syntax -- a list of function calls. .. deprecated:: Use tool_config instead. - response_format: - $ref: '#/components/schemas/ResponseFormat' - description: >- - (Optional) Grammar specification for guided (structured) decoding. There - are two options: - `ResponseFormat.json_schema`: The grammar is a JSON - schema. Most providers support this format. - `ResponseFormat.grammar`: - The grammar is a BNF grammar. This format is more flexible, but not all - providers support it. - stream: - type: boolean - description: >- - (Optional) If True, generate an SSE event stream of the response. Defaults - to False. - logprobs: - type: object - properties: - top_k: - type: integer - default: 0 - description: >- - How many tokens (for each position) to return log probabilities for. - additionalProperties: false - description: >- - (Optional) If specified, log probabilities for each token position will - be returned. - tool_config: - $ref: '#/components/schemas/ToolConfig' - description: (Optional) Configuration for tool use. - additionalProperties: false - required: - - model_id - - messages - title: ChatCompletionRequest - ChatCompletionResponse: - type: object - properties: - metrics: - type: array - items: - $ref: '#/components/schemas/MetricInResponse' - description: >- - (Optional) List of metrics associated with the API response - completion_message: - $ref: '#/components/schemas/CompletionMessage' - description: The complete response message - logprobs: - type: array - items: - $ref: '#/components/schemas/TokenLogProbs' - description: >- - Optional log probabilities for generated tokens - additionalProperties: false - required: - - completion_message - title: ChatCompletionResponse - description: Response from a chat completion request. - MetricInResponse: - type: object - properties: - metric: - type: string - description: The name of the metric - value: - oneOf: - - type: integer - - type: number - description: The numeric value of the metric - unit: - type: string - description: >- - (Optional) The unit of measurement for the metric value - additionalProperties: false - required: - - metric - - value - title: MetricInResponse - description: >- - A metric value included in API responses. - TokenLogProbs: - type: object - properties: - logprobs_by_token: - type: object - additionalProperties: - type: number - description: >- - Dictionary mapping tokens to their log probabilities - additionalProperties: false - required: - - logprobs_by_token - title: TokenLogProbs - description: Log probabilities for generated tokens. - ChatCompletionResponseEvent: - type: object - properties: - event_type: - type: string - enum: - - start - - complete - - progress - description: Type of the event - delta: - $ref: '#/components/schemas/ContentDelta' - description: >- - Content generated since last event. This can be one or more tokens, or - a tool call. - logprobs: - type: array - items: - $ref: '#/components/schemas/TokenLogProbs' - description: >- - Optional log probabilities for generated tokens - stop_reason: - type: string - enum: - - end_of_turn - - end_of_message - - out_of_tokens - description: >- - Optional reason why generation stopped, if complete - additionalProperties: false - required: - - event_type - - delta - title: ChatCompletionResponseEvent - description: >- - An event during chat completion generation. - ChatCompletionResponseStreamChunk: - type: object - properties: - metrics: - type: array - items: - $ref: '#/components/schemas/MetricInResponse' - description: >- - (Optional) List of metrics associated with the API response - event: - $ref: '#/components/schemas/ChatCompletionResponseEvent' - description: The event containing the new content - additionalProperties: false - required: - - event - title: ChatCompletionResponseStreamChunk - description: >- - A chunk of a streamed chat completion response. - ContentDelta: - oneOf: - - $ref: '#/components/schemas/TextDelta' - - $ref: '#/components/schemas/ImageDelta' - - $ref: '#/components/schemas/ToolCallDelta' - discriminator: - propertyName: type - mapping: - text: '#/components/schemas/TextDelta' - image: '#/components/schemas/ImageDelta' - tool_call: '#/components/schemas/ToolCallDelta' - ImageDelta: - type: object - properties: - type: - type: string - const: image - default: image - description: >- - Discriminator type of the delta. Always "image" - image: - type: string - contentEncoding: base64 - description: The incremental image data as bytes - additionalProperties: false - required: - - type - - image - title: ImageDelta - description: >- - An image content delta for streaming responses. - TextDelta: - type: object - properties: - type: - type: string - const: text - default: text - description: >- - Discriminator type of the delta. Always "text" - text: - type: string - description: The incremental text content - additionalProperties: false - required: - - type - - text - title: TextDelta - description: >- - A text content delta for streaming responses. - ToolCallDelta: - type: object - properties: - type: - type: string - const: tool_call - default: tool_call - description: >- - Discriminator type of the delta. Always "tool_call" - tool_call: - oneOf: - - type: string - - $ref: '#/components/schemas/ToolCall' - description: >- - Either an in-progress tool call string or the final parsed tool call - parse_status: - type: string - enum: - - started - - in_progress - - failed - - succeeded - description: Current parsing status of the tool call - additionalProperties: false - required: - - type - - tool_call - - parse_status - title: ToolCallDelta - description: >- - A tool call content delta for streaming responses. - CompletionRequest: - type: object - properties: - model_id: - type: string - description: >- - The identifier of the model to use. The model must be registered with - Llama Stack and available via the /models endpoint. - content: - $ref: '#/components/schemas/InterleavedContent' - description: >- - The content to generate a completion for. - sampling_params: - $ref: '#/components/schemas/SamplingParams' - description: >- - (Optional) Parameters to control the sampling strategy. - response_format: - $ref: '#/components/schemas/ResponseFormat' - description: >- - (Optional) Grammar specification for guided (structured) decoding. - stream: - type: boolean - description: >- - (Optional) If True, generate an SSE event stream of the response. Defaults - to False. - logprobs: - type: object - properties: - top_k: - type: integer - default: 0 - description: >- - How many tokens (for each position) to return log probabilities for. - additionalProperties: false - description: >- - (Optional) If specified, log probabilities for each token position will - be returned. - additionalProperties: false - required: - - model_id - - content - title: CompletionRequest - CompletionResponse: - type: object - properties: - metrics: - type: array - items: - $ref: '#/components/schemas/MetricInResponse' - description: >- - (Optional) List of metrics associated with the API response - content: - type: string - description: The generated completion text - stop_reason: - type: string - enum: - - end_of_turn - - end_of_message - - out_of_tokens - description: Reason why generation stopped - logprobs: - type: array - items: - $ref: '#/components/schemas/TokenLogProbs' - description: >- - Optional log probabilities for generated tokens - additionalProperties: false - required: - - content - - stop_reason - title: CompletionResponse - description: Response from a completion request. - CompletionResponseStreamChunk: - type: object - properties: - metrics: - type: array - items: - $ref: '#/components/schemas/MetricInResponse' - description: >- - (Optional) List of metrics associated with the API response - delta: - type: string - description: >- - New content generated since last chunk. This can be one or more tokens. - stop_reason: - type: string - enum: - - end_of_turn - - end_of_message - - out_of_tokens - description: >- - Optional reason why generation stopped, if complete - logprobs: - type: array - items: - $ref: '#/components/schemas/TokenLogProbs' - description: >- - Optional log probabilities for generated tokens - additionalProperties: false - required: - - delta - title: CompletionResponseStreamChunk - description: >- - A chunk of a streamed completion response. - AgentConfig: - type: object - properties: - sampling_params: - $ref: '#/components/schemas/SamplingParams' - input_shields: - type: array - items: - type: string - output_shields: - type: array - items: - type: string - toolgroups: - type: array - items: - $ref: '#/components/schemas/AgentTool' - client_tools: - type: array - items: - $ref: '#/components/schemas/ToolDef' - tool_choice: - type: string - enum: - - auto - - required - - none - title: ToolChoice - description: >- - Whether tool use is required or automatic. This is a hint to the model - which may not be followed. It depends on the Instruction Following capabilities - of the model. - deprecated: true - tool_prompt_format: - type: string - enum: - - json - - function_tag - - python_list - title: ToolPromptFormat - description: >- - Prompt format for calling custom / zero shot tools. - deprecated: true - tool_config: - $ref: '#/components/schemas/ToolConfig' - max_infer_iters: - type: integer - default: 10 - model: - type: string - description: >- - The model identifier to use for the agent - instructions: - type: string - description: The system instructions for the agent - name: - type: string - description: >- - Optional name for the agent, used in telemetry and identification - enable_session_persistence: - type: boolean - default: false - description: >- - Optional flag indicating whether session data has to be persisted - response_format: - $ref: '#/components/schemas/ResponseFormat' - description: Optional response format configuration - additionalProperties: false - required: - - model - - instructions - title: AgentConfig - description: Configuration for an agent. - AgentTool: - oneOf: - - type: string - - type: object - properties: - name: - type: string - args: - type: object - additionalProperties: - oneOf: - - type: 'null' - - type: boolean - - type: number - - type: string - - type: array - - type: object - additionalProperties: false - required: - - name - - args - title: AgentToolGroupWithArgs - ToolDef: - type: object - properties: - name: - type: string - description: Name of the tool - description: - type: string - description: >- - (Optional) Human-readable description of what the tool does - parameters: - type: array - items: - $ref: '#/components/schemas/ToolParameter' - description: >- - (Optional) List of parameters this tool accepts - metadata: - type: object - additionalProperties: - oneOf: - - type: 'null' - - type: boolean - - type: number - - type: string - - type: array - - type: object - description: >- - (Optional) Additional metadata about the tool - additionalProperties: false - required: - - name - title: ToolDef - description: >- - Tool definition used in runtime contexts. - ToolParameter: - type: object - properties: - name: - type: string - description: Name of the parameter - parameter_type: - type: string - description: >- - Type of the parameter (e.g., string, integer) - description: - type: string - description: >- - Human-readable description of what the parameter does - required: - type: boolean - default: true - description: >- - Whether this parameter is required for tool invocation - default: - oneOf: - - type: 'null' - - type: boolean - - type: number - - type: string - - type: array - - type: object - description: >- - (Optional) Default value for the parameter if not provided - additionalProperties: false - required: - - name - - parameter_type - - description - - required - title: ToolParameter - description: Parameter definition for a tool. - CreateAgentRequest: - type: object - properties: - agent_config: - $ref: '#/components/schemas/AgentConfig' - description: The configuration for the agent. - additionalProperties: false - required: - - agent_config - title: CreateAgentRequest - AgentCreateResponse: - type: object - properties: - agent_id: - type: string - description: Unique identifier for the created agent - additionalProperties: false - required: - - agent_id - title: AgentCreateResponse - description: >- - Response returned when creating a new agent. - CreateAgentSessionRequest: - type: object - properties: - session_name: - type: string - description: The name of the session to create. - additionalProperties: false - required: - - session_name - title: CreateAgentSessionRequest - AgentSessionCreateResponse: - type: object - properties: - session_id: - type: string - description: >- - Unique identifier for the created session - additionalProperties: false - required: - - session_id - title: AgentSessionCreateResponse - description: >- - Response returned when creating a new agent session. - CreateAgentTurnRequest: - type: object - properties: - messages: - type: array - items: - oneOf: - - $ref: '#/components/schemas/UserMessage' - - $ref: '#/components/schemas/ToolResponseMessage' - description: List of messages to start the turn with. - stream: - type: boolean - description: >- - (Optional) If True, generate an SSE event stream of the response. Defaults - to False. - documents: - type: array - items: - type: object - properties: - content: - oneOf: - - type: string - - $ref: '#/components/schemas/InterleavedContentItem' - - type: array - items: - $ref: '#/components/schemas/InterleavedContentItem' - - $ref: '#/components/schemas/URL' - description: The content of the document. - mime_type: - type: string - description: The MIME type of the document. - additionalProperties: false - required: - - content - - mime_type - title: Document - description: A document to be used by an agent. - description: >- - (Optional) List of documents to create the turn with. - toolgroups: - type: array - items: - $ref: '#/components/schemas/AgentTool' - description: >- - (Optional) List of toolgroups to create the turn with, will be used in - addition to the agent's config toolgroups for the request. - tool_config: - $ref: '#/components/schemas/ToolConfig' - description: >- - (Optional) The tool configuration to create the turn with, will be used - to override the agent's tool_config. - additionalProperties: false - required: - - messages - title: CreateAgentTurnRequest - InferenceStep: - type: object - properties: - turn_id: - type: string - description: The ID of the turn. - step_id: - type: string - description: The ID of the step. - started_at: - type: string - format: date-time - description: The time the step started. - completed_at: - type: string - format: date-time - description: The time the step completed. - step_type: - type: string - enum: - - inference - - tool_execution - - shield_call - - memory_retrieval - title: StepType - description: Type of the step in an agent turn. - const: inference - default: inference - model_response: - $ref: '#/components/schemas/CompletionMessage' - description: The response from the LLM. - additionalProperties: false - required: - - turn_id - - step_id - - step_type - - model_response - title: InferenceStep - description: An inference step in an agent turn. - MemoryRetrievalStep: - type: object - properties: - turn_id: - type: string - description: The ID of the turn. - step_id: - type: string - description: The ID of the step. - started_at: - type: string - format: date-time - description: The time the step started. - completed_at: - type: string - format: date-time - description: The time the step completed. - step_type: - type: string - enum: - - inference - - tool_execution - - shield_call - - memory_retrieval - title: StepType - description: Type of the step in an agent turn. - const: memory_retrieval - default: memory_retrieval - vector_db_ids: - type: string - description: >- - The IDs of the vector databases to retrieve context from. - inserted_context: - $ref: '#/components/schemas/InterleavedContent' - description: >- - The context retrieved from the vector databases. - additionalProperties: false - required: - - turn_id - - step_id - - step_type - - vector_db_ids - - inserted_context - title: MemoryRetrievalStep - description: >- - A memory retrieval step in an agent turn. - SafetyViolation: - type: object - properties: - violation_level: - $ref: '#/components/schemas/ViolationLevel' - description: Severity level of the violation - user_message: - type: string - description: >- - (Optional) Message to convey to the user about the violation - metadata: - type: object - additionalProperties: - oneOf: - - type: 'null' - - type: boolean - - type: number - - type: string - - type: array - - type: object - description: >- - Additional metadata including specific violation codes for debugging and - telemetry - additionalProperties: false - required: - - violation_level - - metadata - title: SafetyViolation - description: >- - Details of a safety violation detected by content moderation. - ShieldCallStep: - type: object - properties: - turn_id: - type: string - description: The ID of the turn. - step_id: - type: string - description: The ID of the step. - started_at: - type: string - format: date-time - description: The time the step started. - completed_at: - type: string - format: date-time - description: The time the step completed. - step_type: - type: string - enum: - - inference - - tool_execution - - shield_call - - memory_retrieval - title: StepType - description: Type of the step in an agent turn. - const: shield_call - default: shield_call - violation: - $ref: '#/components/schemas/SafetyViolation' - description: The violation from the shield call. - additionalProperties: false - required: - - turn_id - - step_id - - step_type - title: ShieldCallStep - description: A shield call step in an agent turn. - ToolExecutionStep: - type: object - properties: - turn_id: - type: string - description: The ID of the turn. - step_id: - type: string - description: The ID of the step. - started_at: - type: string - format: date-time - description: The time the step started. - completed_at: - type: string - format: date-time - description: The time the step completed. - step_type: - type: string - enum: - - inference - - tool_execution - - shield_call - - memory_retrieval - title: StepType - description: Type of the step in an agent turn. - const: tool_execution - default: tool_execution - tool_calls: - type: array - items: - $ref: '#/components/schemas/ToolCall' - description: The tool calls to execute. - tool_responses: - type: array - items: - $ref: '#/components/schemas/ToolResponse' - description: The tool responses from the tool calls. - additionalProperties: false - required: - - turn_id - - step_id - - step_type - - tool_calls - - tool_responses - title: ToolExecutionStep - description: A tool execution step in an agent turn. - ToolResponse: - type: object - properties: - call_id: - type: string - description: >- - Unique identifier for the tool call this response is for - tool_name: - oneOf: - - type: string - enum: - - brave_search - - wolfram_alpha - - photogen - - code_interpreter - title: BuiltinTool - - type: string - description: Name of the tool that was invoked - content: - $ref: '#/components/schemas/InterleavedContent' - description: The response content from the tool - metadata: - type: object - additionalProperties: - oneOf: - - type: 'null' - - type: boolean - - type: number - - type: string - - type: array - - type: object - description: >- - (Optional) Additional metadata about the tool response - additionalProperties: false - required: - - call_id - - tool_name - - content - title: ToolResponse - description: Response from a tool invocation. - Turn: - type: object - properties: - turn_id: - type: string - description: >- - Unique identifier for the turn within a session - session_id: - type: string - description: >- - Unique identifier for the conversation session - input_messages: - type: array - items: - oneOf: - - $ref: '#/components/schemas/UserMessage' - - $ref: '#/components/schemas/ToolResponseMessage' - description: >- - List of messages that initiated this turn - steps: - type: array - items: - oneOf: - - $ref: '#/components/schemas/InferenceStep' - - $ref: '#/components/schemas/ToolExecutionStep' - - $ref: '#/components/schemas/ShieldCallStep' - - $ref: '#/components/schemas/MemoryRetrievalStep' - discriminator: - propertyName: step_type - mapping: - inference: '#/components/schemas/InferenceStep' - tool_execution: '#/components/schemas/ToolExecutionStep' - shield_call: '#/components/schemas/ShieldCallStep' - memory_retrieval: '#/components/schemas/MemoryRetrievalStep' - description: >- - Ordered list of processing steps executed during this turn - output_message: - $ref: '#/components/schemas/CompletionMessage' - description: >- - The model's generated response containing content and metadata - output_attachments: - type: array - items: - type: object - properties: - content: - oneOf: - - type: string - - $ref: '#/components/schemas/InterleavedContentItem' - - type: array - items: - $ref: '#/components/schemas/InterleavedContentItem' - - $ref: '#/components/schemas/URL' - description: The content of the attachment. - mime_type: - type: string - description: The MIME type of the attachment. - additionalProperties: false - required: - - content - - mime_type - title: Attachment - description: An attachment to an agent turn. - description: >- - (Optional) Files or media attached to the agent's response - started_at: - type: string - format: date-time - description: Timestamp when the turn began - completed_at: - type: string - format: date-time - description: >- - (Optional) Timestamp when the turn finished, if completed - additionalProperties: false - required: - - turn_id - - session_id - - input_messages - - steps - - output_message - - started_at - title: Turn - description: >- - A single turn in an interaction with an Agentic System. - ViolationLevel: - type: string - enum: - - info - - warn - - error - title: ViolationLevel - description: Severity level of a safety violation. - AgentTurnResponseEvent: - type: object - properties: - payload: - $ref: '#/components/schemas/AgentTurnResponseEventPayload' - description: >- - Event-specific payload containing event data - additionalProperties: false - required: - - payload - title: AgentTurnResponseEvent - description: >- - An event in an agent turn response stream. - AgentTurnResponseEventPayload: - oneOf: - - $ref: '#/components/schemas/AgentTurnResponseStepStartPayload' - - $ref: '#/components/schemas/AgentTurnResponseStepProgressPayload' - - $ref: '#/components/schemas/AgentTurnResponseStepCompletePayload' - - $ref: '#/components/schemas/AgentTurnResponseTurnStartPayload' - - $ref: '#/components/schemas/AgentTurnResponseTurnCompletePayload' - - $ref: '#/components/schemas/AgentTurnResponseTurnAwaitingInputPayload' - discriminator: - propertyName: event_type - mapping: - step_start: '#/components/schemas/AgentTurnResponseStepStartPayload' - step_progress: '#/components/schemas/AgentTurnResponseStepProgressPayload' - step_complete: '#/components/schemas/AgentTurnResponseStepCompletePayload' - turn_start: '#/components/schemas/AgentTurnResponseTurnStartPayload' - turn_complete: '#/components/schemas/AgentTurnResponseTurnCompletePayload' - turn_awaiting_input: '#/components/schemas/AgentTurnResponseTurnAwaitingInputPayload' - AgentTurnResponseStepCompletePayload: - type: object - properties: - event_type: - type: string - enum: - - step_start - - step_complete - - step_progress - - turn_start - - turn_complete - - turn_awaiting_input - const: step_complete - default: step_complete - description: Type of event being reported - step_type: - type: string - enum: - - inference - - tool_execution - - shield_call - - memory_retrieval - description: Type of step being executed - step_id: - type: string - description: >- - Unique identifier for the step within a turn - step_details: - oneOf: - - $ref: '#/components/schemas/InferenceStep' - - $ref: '#/components/schemas/ToolExecutionStep' - - $ref: '#/components/schemas/ShieldCallStep' - - $ref: '#/components/schemas/MemoryRetrievalStep' - discriminator: - propertyName: step_type - mapping: - inference: '#/components/schemas/InferenceStep' - tool_execution: '#/components/schemas/ToolExecutionStep' - shield_call: '#/components/schemas/ShieldCallStep' - memory_retrieval: '#/components/schemas/MemoryRetrievalStep' - description: Complete details of the executed step - additionalProperties: false - required: - - event_type - - step_type - - step_id - - step_details - title: AgentTurnResponseStepCompletePayload - description: >- - Payload for step completion events in agent turn responses. - AgentTurnResponseStepProgressPayload: - type: object - properties: - event_type: - type: string - enum: - - step_start - - step_complete - - step_progress - - turn_start - - turn_complete - - turn_awaiting_input - const: step_progress - default: step_progress - description: Type of event being reported - step_type: - type: string - enum: - - inference - - tool_execution - - shield_call - - memory_retrieval - description: Type of step being executed - step_id: - type: string - description: >- - Unique identifier for the step within a turn - delta: - $ref: '#/components/schemas/ContentDelta' - description: >- - Incremental content changes during step execution - additionalProperties: false - required: - - event_type - - step_type - - step_id - - delta - title: AgentTurnResponseStepProgressPayload - description: >- - Payload for step progress events in agent turn responses. - AgentTurnResponseStepStartPayload: - type: object - properties: - event_type: - type: string - enum: - - step_start - - step_complete - - step_progress - - turn_start - - turn_complete - - turn_awaiting_input - const: step_start - default: step_start - description: Type of event being reported - step_type: - type: string - enum: - - inference - - tool_execution - - shield_call - - memory_retrieval - description: Type of step being executed - step_id: - type: string - description: >- - Unique identifier for the step within a turn - metadata: - type: object - additionalProperties: - oneOf: - - type: 'null' - - type: boolean - - type: number - - type: string - - type: array - - type: object - description: >- - (Optional) Additional metadata for the step - additionalProperties: false - required: - - event_type - - step_type - - step_id - title: AgentTurnResponseStepStartPayload - description: >- - Payload for step start events in agent turn responses. - AgentTurnResponseStreamChunk: - type: object - properties: - event: - $ref: '#/components/schemas/AgentTurnResponseEvent' - description: >- - Individual event in the agent turn response stream - additionalProperties: false - required: - - event - title: AgentTurnResponseStreamChunk - description: Streamed agent turn completion response. - "AgentTurnResponseTurnAwaitingInputPayload": - type: object - properties: - event_type: - type: string - enum: - - step_start - - step_complete - - step_progress - - turn_start - - turn_complete - - turn_awaiting_input - const: turn_awaiting_input - default: turn_awaiting_input - description: Type of event being reported - turn: - $ref: '#/components/schemas/Turn' - description: >- - Turn data when waiting for external tool responses - additionalProperties: false - required: - - event_type - - turn - title: >- - AgentTurnResponseTurnAwaitingInputPayload - description: >- - Payload for turn awaiting input events in agent turn responses. - AgentTurnResponseTurnCompletePayload: - type: object - properties: - event_type: - type: string - enum: - - step_start - - step_complete - - step_progress - - turn_start - - turn_complete - - turn_awaiting_input - const: turn_complete - default: turn_complete - description: Type of event being reported - turn: - $ref: '#/components/schemas/Turn' - description: >- - Complete turn data including all steps and results - additionalProperties: false - required: - - event_type - - turn - title: AgentTurnResponseTurnCompletePayload - description: >- - Payload for turn completion events in agent turn responses. - AgentTurnResponseTurnStartPayload: - type: object - properties: - event_type: - type: string - enum: - - step_start - - step_complete - - step_progress - - turn_start - - turn_complete - - turn_awaiting_input - const: turn_start - default: turn_start - description: Type of event being reported - turn_id: - type: string - description: >- - Unique identifier for the turn within a session - additionalProperties: false - required: - - event_type - - turn_id - title: AgentTurnResponseTurnStartPayload - description: >- - Payload for turn start events in agent turn responses. - OpenAIResponseAnnotationCitation: - type: object - properties: - type: - type: string - const: url_citation - default: url_citation - description: >- - Annotation type identifier, always "url_citation" - end_index: - type: integer - description: >- - End position of the citation span in the content - start_index: - type: integer - description: >- - Start position of the citation span in the content - title: - type: string - description: Title of the referenced web resource - url: - type: string - description: URL of the referenced web resource - additionalProperties: false - required: - - type - - end_index - - start_index - - title - - url - title: OpenAIResponseAnnotationCitation - description: >- - URL citation annotation for referencing external web resources. - "OpenAIResponseAnnotationContainerFileCitation": - type: object - properties: - type: - type: string - const: container_file_citation - default: container_file_citation - container_id: - type: string - end_index: - type: integer - file_id: - type: string - filename: - type: string - start_index: - type: integer - additionalProperties: false - required: - - type - - container_id - - end_index - - file_id - - filename - - start_index - title: >- - OpenAIResponseAnnotationContainerFileCitation - OpenAIResponseAnnotationFileCitation: - type: object - properties: - type: - type: string - const: file_citation - default: file_citation - description: >- - Annotation type identifier, always "file_citation" - file_id: - type: string - description: Unique identifier of the referenced file - filename: - type: string - description: Name of the referenced file - index: - type: integer - description: >- - Position index of the citation within the content - additionalProperties: false - required: - - type - - file_id - - filename - - index - title: OpenAIResponseAnnotationFileCitation - description: >- - File citation annotation for referencing specific files in response content. - OpenAIResponseAnnotationFilePath: - type: object - properties: - type: - type: string - const: file_path - default: file_path - file_id: - type: string - index: - type: integer - additionalProperties: false - required: - - type - - file_id - - index - title: OpenAIResponseAnnotationFilePath - OpenAIResponseAnnotations: - oneOf: - - $ref: '#/components/schemas/OpenAIResponseAnnotationFileCitation' - - $ref: '#/components/schemas/OpenAIResponseAnnotationCitation' - - $ref: '#/components/schemas/OpenAIResponseAnnotationContainerFileCitation' - - $ref: '#/components/schemas/OpenAIResponseAnnotationFilePath' - discriminator: - propertyName: type - mapping: - file_citation: '#/components/schemas/OpenAIResponseAnnotationFileCitation' - url_citation: '#/components/schemas/OpenAIResponseAnnotationCitation' - container_file_citation: '#/components/schemas/OpenAIResponseAnnotationContainerFileCitation' - file_path: '#/components/schemas/OpenAIResponseAnnotationFilePath' - OpenAIResponseInput: - oneOf: - - $ref: '#/components/schemas/OpenAIResponseOutputMessageWebSearchToolCall' - - $ref: '#/components/schemas/OpenAIResponseOutputMessageFileSearchToolCall' - - $ref: '#/components/schemas/OpenAIResponseOutputMessageFunctionToolCall' - - $ref: '#/components/schemas/OpenAIResponseInputFunctionToolCallOutput' - - $ref: '#/components/schemas/OpenAIResponseMessage' - "OpenAIResponseInputFunctionToolCallOutput": - type: object - properties: - call_id: - type: string - output: - type: string - type: - type: string - const: function_call_output - default: function_call_output - id: - type: string - status: - type: string - additionalProperties: false - required: - - call_id - - output - - type - title: >- - OpenAIResponseInputFunctionToolCallOutput - description: >- - This represents the output of a function call that gets passed back to the - model. - OpenAIResponseInputMessageContent: - oneOf: - - $ref: '#/components/schemas/OpenAIResponseInputMessageContentText' - - $ref: '#/components/schemas/OpenAIResponseInputMessageContentImage' - discriminator: - propertyName: type - mapping: - input_text: '#/components/schemas/OpenAIResponseInputMessageContentText' - input_image: '#/components/schemas/OpenAIResponseInputMessageContentImage' - OpenAIResponseInputMessageContentImage: - type: object - properties: - detail: - oneOf: - - type: string - const: low - - type: string - const: high - - type: string - const: auto - default: auto - description: >- - Level of detail for image processing, can be "low", "high", or "auto" - type: - type: string - const: input_image - default: input_image - description: >- - Content type identifier, always "input_image" - image_url: - type: string - description: (Optional) URL of the image content - additionalProperties: false - required: - - detail - - type - title: OpenAIResponseInputMessageContentImage - description: >- - Image content for input messages in OpenAI response format. - OpenAIResponseInputMessageContentText: - type: object - properties: - text: - type: string - description: The text content of the input message - type: - type: string - const: input_text - default: input_text - description: >- - Content type identifier, always "input_text" - additionalProperties: false - required: - - text - - type - title: OpenAIResponseInputMessageContentText - description: >- - Text content for input messages in OpenAI response format. - OpenAIResponseInputTool: - oneOf: - - $ref: '#/components/schemas/OpenAIResponseInputToolWebSearch' - - $ref: '#/components/schemas/OpenAIResponseInputToolFileSearch' - - $ref: '#/components/schemas/OpenAIResponseInputToolFunction' - - $ref: '#/components/schemas/OpenAIResponseInputToolMCP' - discriminator: - propertyName: type - mapping: - web_search: '#/components/schemas/OpenAIResponseInputToolWebSearch' - file_search: '#/components/schemas/OpenAIResponseInputToolFileSearch' - function: '#/components/schemas/OpenAIResponseInputToolFunction' - mcp: '#/components/schemas/OpenAIResponseInputToolMCP' - OpenAIResponseInputToolFileSearch: - type: object - properties: - type: - type: string - const: file_search - default: file_search - description: >- - Tool type identifier, always "file_search" - vector_store_ids: - type: array - items: - type: string - description: >- - List of vector store identifiers to search within - filters: - type: object - additionalProperties: - oneOf: - - type: 'null' - - type: boolean - - type: number - - type: string - - type: array - - type: object - description: >- - (Optional) Additional filters to apply to the search - max_num_results: - type: integer - default: 10 - description: >- - (Optional) Maximum number of search results to return (1-50) - ranking_options: - type: object - properties: - ranker: - type: string - description: >- - (Optional) Name of the ranking algorithm to use - score_threshold: - type: number - default: 0.0 - description: >- - (Optional) Minimum relevance score threshold for results - additionalProperties: false - description: >- - (Optional) Options for ranking and scoring search results - additionalProperties: false - required: - - type - - vector_store_ids - title: OpenAIResponseInputToolFileSearch - description: >- - File search tool configuration for OpenAI response inputs. - OpenAIResponseInputToolFunction: - type: object - properties: - type: - type: string - const: function - default: function - description: Tool type identifier, always "function" - name: - type: string - description: Name of the function that can be called - description: - type: string - description: >- - (Optional) Description of what the function does - parameters: - type: object - additionalProperties: - oneOf: - - type: 'null' - - type: boolean - - type: number - - type: string - - type: array - - type: object - description: >- - (Optional) JSON schema defining the function's parameters - strict: - type: boolean - description: >- - (Optional) Whether to enforce strict parameter validation - additionalProperties: false - required: - - type - - name - title: OpenAIResponseInputToolFunction - description: >- - Function tool configuration for OpenAI response inputs. - OpenAIResponseInputToolMCP: - type: object - properties: - type: - type: string - const: mcp - default: mcp - description: Tool type identifier, always "mcp" - server_label: - type: string - description: Label to identify this MCP server - server_url: - type: string - description: URL endpoint of the MCP server - headers: - type: object - additionalProperties: - oneOf: - - type: 'null' - - type: boolean - - type: number - - type: string - - type: array - - type: object - description: >- - (Optional) HTTP headers to include when connecting to the server - require_approval: - oneOf: - - type: string - const: always - - type: string - const: never - - type: object - properties: - always: - type: array - items: - type: string - description: >- - (Optional) List of tool names that always require approval - never: - type: array - items: - type: string - description: >- - (Optional) List of tool names that never require approval - additionalProperties: false - title: ApprovalFilter - description: >- - Filter configuration for MCP tool approval requirements. - default: never - description: >- - Approval requirement for tool calls ("always", "never", or filter) - allowed_tools: - oneOf: - - type: array - items: - type: string - - type: object - properties: - tool_names: - type: array - items: - type: string - description: >- - (Optional) List of specific tool names that are allowed - additionalProperties: false - title: AllowedToolsFilter - description: >- - Filter configuration for restricting which MCP tools can be used. - description: >- - (Optional) Restriction on which tools can be used from this server - additionalProperties: false - required: - - type - - server_label - - server_url - - require_approval - title: OpenAIResponseInputToolMCP - description: >- - Model Context Protocol (MCP) tool configuration for OpenAI response inputs. - OpenAIResponseInputToolWebSearch: - type: object - properties: - type: - oneOf: - - type: string - const: web_search - - type: string - const: web_search_preview - - type: string - const: web_search_preview_2025_03_11 - default: web_search - description: Web search tool type variant to use - search_context_size: - type: string - default: medium - description: >- - (Optional) Size of search context, must be "low", "medium", or "high" - additionalProperties: false - required: - - type - title: OpenAIResponseInputToolWebSearch - description: >- - Web search tool configuration for OpenAI response inputs. - OpenAIResponseMessage: - type: object - properties: - content: - oneOf: - - type: string - - type: array - items: - $ref: '#/components/schemas/OpenAIResponseInputMessageContent' - - type: array - items: - $ref: '#/components/schemas/OpenAIResponseOutputMessageContent' - role: - oneOf: - - type: string - const: system - - type: string - const: developer - - type: string - const: user - - type: string - const: assistant - type: - type: string - const: message - default: message - id: - type: string - status: - type: string - additionalProperties: false - required: - - content - - role - - type - title: OpenAIResponseMessage - description: >- - Corresponds to the various Message types in the Responses API. They are all - under one type because the Responses API gives them all the same "type" value, - and there is no way to tell them apart in certain scenarios. - OpenAIResponseOutputMessageContent: - type: object - properties: - text: - type: string - type: - type: string - const: output_text - default: output_text - annotations: - type: array - items: - $ref: '#/components/schemas/OpenAIResponseAnnotations' - additionalProperties: false - required: - - text - - type - - annotations - title: >- - OpenAIResponseOutputMessageContentOutputText - "OpenAIResponseOutputMessageFileSearchToolCall": - type: object - properties: - id: - type: string - description: Unique identifier for this tool call - queries: - type: array - items: - type: string - description: List of search queries executed - status: - type: string - description: >- - Current status of the file search operation - type: - type: string - const: file_search_call - default: file_search_call - description: >- - Tool call type identifier, always "file_search_call" - results: - type: array - items: - type: object - properties: - attributes: - type: object - additionalProperties: - oneOf: - - type: 'null' - - type: boolean - - type: number - - type: string - - type: array - - type: object - description: >- - (Optional) Key-value attributes associated with the file - file_id: - type: string - description: >- - Unique identifier of the file containing the result - filename: - type: string - description: Name of the file containing the result - score: - type: number - description: >- - Relevance score for this search result (between 0 and 1) - text: - type: string - description: Text content of the search result - additionalProperties: false - required: - - attributes - - file_id - - filename - - score - - text - title: >- - OpenAIResponseOutputMessageFileSearchToolCallResults - description: >- - Search results returned by the file search operation. - description: >- - (Optional) Search results returned by the file search operation - additionalProperties: false - required: - - id - - queries - - status - - type - title: >- - OpenAIResponseOutputMessageFileSearchToolCall - description: >- - File search tool call output message for OpenAI responses. - "OpenAIResponseOutputMessageFunctionToolCall": - type: object - properties: - call_id: - type: string - description: Unique identifier for the function call - name: - type: string - description: Name of the function being called - arguments: - type: string - description: >- - JSON string containing the function arguments - type: - type: string - const: function_call - default: function_call - description: >- - Tool call type identifier, always "function_call" - id: - type: string - description: >- - (Optional) Additional identifier for the tool call - status: - type: string - description: >- - (Optional) Current status of the function call execution - additionalProperties: false - required: - - call_id - - name - - arguments - - type - title: >- - OpenAIResponseOutputMessageFunctionToolCall - description: >- - Function tool call output message for OpenAI responses. - "OpenAIResponseOutputMessageWebSearchToolCall": - type: object - properties: - id: - type: string - description: Unique identifier for this tool call - status: - type: string - description: >- - Current status of the web search operation - type: - type: string - const: web_search_call - default: web_search_call - description: >- - Tool call type identifier, always "web_search_call" - additionalProperties: false - required: - - id - - status - - type - title: >- - OpenAIResponseOutputMessageWebSearchToolCall - description: >- - Web search tool call output message for OpenAI responses. - OpenAIResponseText: - type: object - properties: - format: - type: object - properties: - type: - oneOf: - - type: string - const: text - - type: string - const: json_schema - - type: string - const: json_object - description: >- - Must be "text", "json_schema", or "json_object" to identify the format - type - name: - type: string - description: >- - The name of the response format. Only used for json_schema. - schema: - type: object - additionalProperties: - oneOf: - - type: 'null' - - type: boolean - - type: number - - type: string - - type: array - - type: object - description: >- - The JSON schema the response should conform to. In a Python SDK, this - is often a `pydantic` model. Only used for json_schema. - description: - type: string - description: >- - (Optional) A description of the response format. Only used for json_schema. - strict: - type: boolean - description: >- - (Optional) Whether to strictly enforce the JSON schema. If true, the - response must match the schema exactly. Only used for json_schema. - additionalProperties: false - required: - - type - description: >- - (Optional) Text format configuration specifying output format requirements - additionalProperties: false - title: OpenAIResponseText - description: >- - Text response configuration for OpenAI responses. - CreateOpenaiResponseRequest: - type: object - properties: - input: - oneOf: - - type: string - - type: array - items: - $ref: '#/components/schemas/OpenAIResponseInput' - description: Input message(s) to create the response. - model: - type: string - description: The underlying LLM used for completions. - instructions: - type: string - previous_response_id: - type: string - description: >- - (Optional) if specified, the new response will be a continuation of the - previous response. This can be used to easily fork-off new responses from - existing responses. - store: - type: boolean - stream: - type: boolean - temperature: - type: number - text: - $ref: '#/components/schemas/OpenAIResponseText' - tools: - type: array - items: - $ref: '#/components/schemas/OpenAIResponseInputTool' - include: - type: array - items: - type: string - description: >- - (Optional) Additional fields to include in the response. - max_infer_iters: - type: integer - additionalProperties: false - required: - - input - - model - title: CreateOpenaiResponseRequest - OpenAIResponseError: - type: object - properties: - code: - type: string - description: >- - Error code identifying the type of failure - message: - type: string - description: >- - Human-readable error message describing the failure - additionalProperties: false - required: - - code - - message - title: OpenAIResponseError - description: >- - Error details for failed OpenAI response requests. - OpenAIResponseObject: - type: object - properties: - created_at: - type: integer - description: >- - Unix timestamp when the response was created - error: - $ref: '#/components/schemas/OpenAIResponseError' - description: >- - (Optional) Error details if the response generation failed - id: - type: string - description: Unique identifier for this response - model: - type: string - description: Model identifier used for generation - object: - type: string - const: response - default: response - description: >- - Object type identifier, always "response" - output: - type: array - items: - $ref: '#/components/schemas/OpenAIResponseOutput' - description: >- - List of generated output items (messages, tool calls, etc.) - parallel_tool_calls: - type: boolean - default: false - description: >- - Whether tool calls can be executed in parallel - previous_response_id: - type: string - description: >- - (Optional) ID of the previous response in a conversation - status: - type: string - description: >- - Current status of the response generation - temperature: - type: number - description: >- - (Optional) Sampling temperature used for generation - text: - $ref: '#/components/schemas/OpenAIResponseText' - description: >- - Text formatting configuration for the response - top_p: - type: number - description: >- - (Optional) Nucleus sampling parameter used for generation - truncation: - type: string - description: >- - (Optional) Truncation strategy applied to the response - user: - type: string - description: >- - (Optional) User identifier associated with the request - additionalProperties: false - required: - - created_at - - id - - model - - object - - output - - parallel_tool_calls - - status - - text - title: OpenAIResponseObject - description: >- - Complete OpenAI response object containing generation results and metadata. - OpenAIResponseOutput: - oneOf: - - $ref: '#/components/schemas/OpenAIResponseMessage' - - $ref: '#/components/schemas/OpenAIResponseOutputMessageWebSearchToolCall' - - $ref: '#/components/schemas/OpenAIResponseOutputMessageFileSearchToolCall' - - $ref: '#/components/schemas/OpenAIResponseOutputMessageFunctionToolCall' - - $ref: '#/components/schemas/OpenAIResponseOutputMessageMCPCall' - - $ref: '#/components/schemas/OpenAIResponseOutputMessageMCPListTools' - discriminator: - propertyName: type - mapping: - message: '#/components/schemas/OpenAIResponseMessage' - web_search_call: '#/components/schemas/OpenAIResponseOutputMessageWebSearchToolCall' - file_search_call: '#/components/schemas/OpenAIResponseOutputMessageFileSearchToolCall' - function_call: '#/components/schemas/OpenAIResponseOutputMessageFunctionToolCall' - mcp_call: '#/components/schemas/OpenAIResponseOutputMessageMCPCall' - mcp_list_tools: '#/components/schemas/OpenAIResponseOutputMessageMCPListTools' - OpenAIResponseOutputMessageMCPCall: - type: object - properties: - id: - type: string - description: Unique identifier for this MCP call - type: - type: string - const: mcp_call - default: mcp_call - description: >- - Tool call type identifier, always "mcp_call" - arguments: - type: string - description: >- - JSON string containing the MCP call arguments - name: - type: string - description: Name of the MCP method being called - server_label: - type: string - description: >- - Label identifying the MCP server handling the call - error: - type: string - description: >- - (Optional) Error message if the MCP call failed - output: - type: string - description: >- - (Optional) Output result from the successful MCP call - additionalProperties: false - required: - - id - - type - - arguments - - name - - server_label - title: OpenAIResponseOutputMessageMCPCall - description: >- - Model Context Protocol (MCP) call output message for OpenAI responses. - OpenAIResponseOutputMessageMCPListTools: - type: object - properties: - id: - type: string - description: >- - Unique identifier for this MCP list tools operation - type: - type: string - const: mcp_list_tools - default: mcp_list_tools - description: >- - Tool call type identifier, always "mcp_list_tools" - server_label: - type: string - description: >- - Label identifying the MCP server providing the tools - tools: - type: array - items: - type: object - properties: - input_schema: - type: object - additionalProperties: - oneOf: - - type: 'null' - - type: boolean - - type: number - - type: string - - type: array - - type: object - description: >- - JSON schema defining the tool's input parameters - name: - type: string - description: Name of the tool - description: - type: string - description: >- - (Optional) Description of what the tool does - additionalProperties: false - required: - - input_schema - - name - title: MCPListToolsTool - description: >- - Tool definition returned by MCP list tools operation. - description: >- - List of available tools provided by the MCP server - additionalProperties: false - required: - - id - - type - - server_label - - tools - title: OpenAIResponseOutputMessageMCPListTools - description: >- - MCP list tools output message containing available tools from an MCP server. - OpenAIResponseContentPart: - oneOf: - - $ref: '#/components/schemas/OpenAIResponseContentPartOutputText' - - $ref: '#/components/schemas/OpenAIResponseContentPartRefusal' - discriminator: - propertyName: type - mapping: - output_text: '#/components/schemas/OpenAIResponseContentPartOutputText' - refusal: '#/components/schemas/OpenAIResponseContentPartRefusal' - OpenAIResponseContentPartOutputText: - type: object - properties: - type: - type: string - const: output_text - default: output_text - text: - type: string - additionalProperties: false - required: - - type - - text - title: OpenAIResponseContentPartOutputText - OpenAIResponseContentPartRefusal: - type: object - properties: - type: - type: string - const: refusal - default: refusal - refusal: - type: string - additionalProperties: false - required: - - type - - refusal - title: OpenAIResponseContentPartRefusal - OpenAIResponseObjectStream: - oneOf: - - $ref: '#/components/schemas/OpenAIResponseObjectStreamResponseCreated' - - $ref: '#/components/schemas/OpenAIResponseObjectStreamResponseOutputItemAdded' - - $ref: '#/components/schemas/OpenAIResponseObjectStreamResponseOutputItemDone' - - $ref: '#/components/schemas/OpenAIResponseObjectStreamResponseOutputTextDelta' - - $ref: '#/components/schemas/OpenAIResponseObjectStreamResponseOutputTextDone' - - $ref: '#/components/schemas/OpenAIResponseObjectStreamResponseFunctionCallArgumentsDelta' - - $ref: '#/components/schemas/OpenAIResponseObjectStreamResponseFunctionCallArgumentsDone' - - $ref: '#/components/schemas/OpenAIResponseObjectStreamResponseWebSearchCallInProgress' - - $ref: '#/components/schemas/OpenAIResponseObjectStreamResponseWebSearchCallSearching' - - $ref: '#/components/schemas/OpenAIResponseObjectStreamResponseWebSearchCallCompleted' - - $ref: '#/components/schemas/OpenAIResponseObjectStreamResponseMcpListToolsInProgress' - - $ref: '#/components/schemas/OpenAIResponseObjectStreamResponseMcpListToolsFailed' - - $ref: '#/components/schemas/OpenAIResponseObjectStreamResponseMcpListToolsCompleted' - - $ref: '#/components/schemas/OpenAIResponseObjectStreamResponseMcpCallArgumentsDelta' - - $ref: '#/components/schemas/OpenAIResponseObjectStreamResponseMcpCallArgumentsDone' - - $ref: '#/components/schemas/OpenAIResponseObjectStreamResponseMcpCallInProgress' - - $ref: '#/components/schemas/OpenAIResponseObjectStreamResponseMcpCallFailed' - - $ref: '#/components/schemas/OpenAIResponseObjectStreamResponseMcpCallCompleted' - - $ref: '#/components/schemas/OpenAIResponseObjectStreamResponseContentPartAdded' - - $ref: '#/components/schemas/OpenAIResponseObjectStreamResponseContentPartDone' - - $ref: '#/components/schemas/OpenAIResponseObjectStreamResponseCompleted' - discriminator: - propertyName: type - mapping: - response.created: '#/components/schemas/OpenAIResponseObjectStreamResponseCreated' - response.output_item.added: '#/components/schemas/OpenAIResponseObjectStreamResponseOutputItemAdded' - response.output_item.done: '#/components/schemas/OpenAIResponseObjectStreamResponseOutputItemDone' - response.output_text.delta: '#/components/schemas/OpenAIResponseObjectStreamResponseOutputTextDelta' - response.output_text.done: '#/components/schemas/OpenAIResponseObjectStreamResponseOutputTextDone' - response.function_call_arguments.delta: '#/components/schemas/OpenAIResponseObjectStreamResponseFunctionCallArgumentsDelta' - response.function_call_arguments.done: '#/components/schemas/OpenAIResponseObjectStreamResponseFunctionCallArgumentsDone' - response.web_search_call.in_progress: '#/components/schemas/OpenAIResponseObjectStreamResponseWebSearchCallInProgress' - response.web_search_call.searching: '#/components/schemas/OpenAIResponseObjectStreamResponseWebSearchCallSearching' - response.web_search_call.completed: '#/components/schemas/OpenAIResponseObjectStreamResponseWebSearchCallCompleted' - response.mcp_list_tools.in_progress: '#/components/schemas/OpenAIResponseObjectStreamResponseMcpListToolsInProgress' - response.mcp_list_tools.failed: '#/components/schemas/OpenAIResponseObjectStreamResponseMcpListToolsFailed' - response.mcp_list_tools.completed: '#/components/schemas/OpenAIResponseObjectStreamResponseMcpListToolsCompleted' - response.mcp_call.arguments.delta: '#/components/schemas/OpenAIResponseObjectStreamResponseMcpCallArgumentsDelta' - response.mcp_call.arguments.done: '#/components/schemas/OpenAIResponseObjectStreamResponseMcpCallArgumentsDone' - response.mcp_call.in_progress: '#/components/schemas/OpenAIResponseObjectStreamResponseMcpCallInProgress' - response.mcp_call.failed: '#/components/schemas/OpenAIResponseObjectStreamResponseMcpCallFailed' - response.mcp_call.completed: '#/components/schemas/OpenAIResponseObjectStreamResponseMcpCallCompleted' - response.content_part.added: '#/components/schemas/OpenAIResponseObjectStreamResponseContentPartAdded' - response.content_part.done: '#/components/schemas/OpenAIResponseObjectStreamResponseContentPartDone' - response.completed: '#/components/schemas/OpenAIResponseObjectStreamResponseCompleted' - "OpenAIResponseObjectStreamResponseCompleted": - type: object - properties: - response: - $ref: '#/components/schemas/OpenAIResponseObject' - description: The completed response object - type: - type: string - const: response.completed - default: response.completed - description: >- - Event type identifier, always "response.completed" - additionalProperties: false - required: - - response - - type - title: >- - OpenAIResponseObjectStreamResponseCompleted - description: >- - Streaming event indicating a response has been completed. - "OpenAIResponseObjectStreamResponseContentPartAdded": - type: object - properties: - response_id: - type: string - description: >- - Unique identifier of the response containing this content - item_id: - type: string - description: >- - Unique identifier of the output item containing this content part - part: - $ref: '#/components/schemas/OpenAIResponseContentPart' - description: The content part that was added - sequence_number: - type: integer - description: >- - Sequential number for ordering streaming events - type: - type: string - const: response.content_part.added - default: response.content_part.added - description: >- - Event type identifier, always "response.content_part.added" - additionalProperties: false - required: - - response_id - - item_id - - part - - sequence_number - - type - title: >- - OpenAIResponseObjectStreamResponseContentPartAdded - description: >- - Streaming event for when a new content part is added to a response item. - "OpenAIResponseObjectStreamResponseContentPartDone": - type: object - properties: - response_id: - type: string - description: >- - Unique identifier of the response containing this content - item_id: - type: string - description: >- - Unique identifier of the output item containing this content part - part: - $ref: '#/components/schemas/OpenAIResponseContentPart' - description: The completed content part - sequence_number: - type: integer - description: >- - Sequential number for ordering streaming events - type: - type: string - const: response.content_part.done - default: response.content_part.done - description: >- - Event type identifier, always "response.content_part.done" - additionalProperties: false - required: - - response_id - - item_id - - part - - sequence_number - - type - title: >- - OpenAIResponseObjectStreamResponseContentPartDone - description: >- - Streaming event for when a content part is completed. - "OpenAIResponseObjectStreamResponseCreated": - type: object - properties: - response: - $ref: '#/components/schemas/OpenAIResponseObject' - description: The newly created response object - type: - type: string - const: response.created - default: response.created - description: >- - Event type identifier, always "response.created" - additionalProperties: false - required: - - response - - type - title: >- - OpenAIResponseObjectStreamResponseCreated - description: >- - Streaming event indicating a new response has been created. - "OpenAIResponseObjectStreamResponseFunctionCallArgumentsDelta": - type: object - properties: - delta: - type: string - description: >- - Incremental function call arguments being added - item_id: - type: string - description: >- - Unique identifier of the function call being updated - output_index: - type: integer - description: >- - Index position of the item in the output list - sequence_number: - type: integer - description: >- - Sequential number for ordering streaming events - type: - type: string - const: response.function_call_arguments.delta - default: response.function_call_arguments.delta - description: >- - Event type identifier, always "response.function_call_arguments.delta" - additionalProperties: false - required: - - delta - - item_id - - output_index - - sequence_number - - type - title: >- - OpenAIResponseObjectStreamResponseFunctionCallArgumentsDelta - description: >- - Streaming event for incremental function call argument updates. - "OpenAIResponseObjectStreamResponseFunctionCallArgumentsDone": - type: object - properties: - arguments: - type: string - description: >- - Final complete arguments JSON string for the function call - item_id: - type: string - description: >- - Unique identifier of the completed function call - output_index: - type: integer - description: >- - Index position of the item in the output list - sequence_number: - type: integer - description: >- - Sequential number for ordering streaming events - type: - type: string - const: response.function_call_arguments.done - default: response.function_call_arguments.done - description: >- - Event type identifier, always "response.function_call_arguments.done" - additionalProperties: false - required: - - arguments - - item_id - - output_index - - sequence_number - - type - title: >- - OpenAIResponseObjectStreamResponseFunctionCallArgumentsDone - description: >- - Streaming event for when function call arguments are completed. - "OpenAIResponseObjectStreamResponseMcpCallArgumentsDelta": - type: object - properties: - delta: - type: string - item_id: - type: string - output_index: - type: integer - sequence_number: - type: integer - type: - type: string - const: response.mcp_call.arguments.delta - default: response.mcp_call.arguments.delta - additionalProperties: false - required: - - delta - - item_id - - output_index - - sequence_number - - type - title: >- - OpenAIResponseObjectStreamResponseMcpCallArgumentsDelta - "OpenAIResponseObjectStreamResponseMcpCallArgumentsDone": - type: object - properties: - arguments: - type: string - item_id: - type: string - output_index: - type: integer - sequence_number: - type: integer - type: - type: string - const: response.mcp_call.arguments.done - default: response.mcp_call.arguments.done - additionalProperties: false - required: - - arguments - - item_id - - output_index - - sequence_number - - type - title: >- - OpenAIResponseObjectStreamResponseMcpCallArgumentsDone - "OpenAIResponseObjectStreamResponseMcpCallCompleted": - type: object - properties: - sequence_number: - type: integer - description: >- - Sequential number for ordering streaming events - type: - type: string - const: response.mcp_call.completed - default: response.mcp_call.completed - description: >- - Event type identifier, always "response.mcp_call.completed" - additionalProperties: false - required: - - sequence_number - - type - title: >- - OpenAIResponseObjectStreamResponseMcpCallCompleted - description: Streaming event for completed MCP calls. - "OpenAIResponseObjectStreamResponseMcpCallFailed": - type: object - properties: - sequence_number: - type: integer - description: >- - Sequential number for ordering streaming events - type: - type: string - const: response.mcp_call.failed - default: response.mcp_call.failed - description: >- - Event type identifier, always "response.mcp_call.failed" - additionalProperties: false - required: - - sequence_number - - type - title: >- - OpenAIResponseObjectStreamResponseMcpCallFailed - description: Streaming event for failed MCP calls. - "OpenAIResponseObjectStreamResponseMcpCallInProgress": - type: object - properties: - item_id: - type: string - description: Unique identifier of the MCP call - output_index: - type: integer - description: >- - Index position of the item in the output list - sequence_number: - type: integer - description: >- - Sequential number for ordering streaming events - type: - type: string - const: response.mcp_call.in_progress - default: response.mcp_call.in_progress - description: >- - Event type identifier, always "response.mcp_call.in_progress" - additionalProperties: false - required: - - item_id - - output_index - - sequence_number - - type - title: >- - OpenAIResponseObjectStreamResponseMcpCallInProgress - description: >- - Streaming event for MCP calls in progress. - "OpenAIResponseObjectStreamResponseMcpListToolsCompleted": - type: object - properties: - sequence_number: - type: integer - type: - type: string - const: response.mcp_list_tools.completed - default: response.mcp_list_tools.completed - additionalProperties: false - required: - - sequence_number - - type - title: >- - OpenAIResponseObjectStreamResponseMcpListToolsCompleted - "OpenAIResponseObjectStreamResponseMcpListToolsFailed": - type: object - properties: - sequence_number: - type: integer - type: - type: string - const: response.mcp_list_tools.failed - default: response.mcp_list_tools.failed - additionalProperties: false - required: - - sequence_number - - type - title: >- - OpenAIResponseObjectStreamResponseMcpListToolsFailed - "OpenAIResponseObjectStreamResponseMcpListToolsInProgress": - type: object - properties: - sequence_number: - type: integer - type: - type: string - const: response.mcp_list_tools.in_progress - default: response.mcp_list_tools.in_progress - additionalProperties: false - required: - - sequence_number - - type - title: >- - OpenAIResponseObjectStreamResponseMcpListToolsInProgress - "OpenAIResponseObjectStreamResponseOutputItemAdded": - type: object - properties: - response_id: - type: string - description: >- - Unique identifier of the response containing this output - item: - $ref: '#/components/schemas/OpenAIResponseOutput' - description: >- - The output item that was added (message, tool call, etc.) - output_index: - type: integer - description: >- - Index position of this item in the output list - sequence_number: - type: integer - description: >- - Sequential number for ordering streaming events - type: - type: string - const: response.output_item.added - default: response.output_item.added - description: >- - Event type identifier, always "response.output_item.added" - additionalProperties: false - required: - - response_id - - item - - output_index - - sequence_number - - type - title: >- - OpenAIResponseObjectStreamResponseOutputItemAdded - description: >- - Streaming event for when a new output item is added to the response. - "OpenAIResponseObjectStreamResponseOutputItemDone": - type: object - properties: - response_id: - type: string - description: >- - Unique identifier of the response containing this output - item: - $ref: '#/components/schemas/OpenAIResponseOutput' - description: >- - The completed output item (message, tool call, etc.) - output_index: - type: integer - description: >- - Index position of this item in the output list - sequence_number: - type: integer - description: >- - Sequential number for ordering streaming events - type: - type: string - const: response.output_item.done - default: response.output_item.done - description: >- - Event type identifier, always "response.output_item.done" - additionalProperties: false - required: - - response_id - - item - - output_index - - sequence_number - - type - title: >- - OpenAIResponseObjectStreamResponseOutputItemDone - description: >- - Streaming event for when an output item is completed. - "OpenAIResponseObjectStreamResponseOutputTextDelta": - type: object - properties: - content_index: - type: integer - description: Index position within the text content - delta: - type: string - description: Incremental text content being added - item_id: - type: string - description: >- - Unique identifier of the output item being updated - output_index: - type: integer - description: >- - Index position of the item in the output list - sequence_number: - type: integer - description: >- - Sequential number for ordering streaming events - type: - type: string - const: response.output_text.delta - default: response.output_text.delta - description: >- - Event type identifier, always "response.output_text.delta" - additionalProperties: false - required: - - content_index - - delta - - item_id - - output_index - - sequence_number - - type - title: >- - OpenAIResponseObjectStreamResponseOutputTextDelta - description: >- - Streaming event for incremental text content updates. - "OpenAIResponseObjectStreamResponseOutputTextDone": - type: object - properties: - content_index: - type: integer - description: Index position within the text content - text: - type: string - description: >- - Final complete text content of the output item - item_id: - type: string - description: >- - Unique identifier of the completed output item - output_index: - type: integer - description: >- - Index position of the item in the output list - sequence_number: - type: integer - description: >- - Sequential number for ordering streaming events - type: - type: string - const: response.output_text.done - default: response.output_text.done - description: >- - Event type identifier, always "response.output_text.done" - additionalProperties: false - required: - - content_index - - text - - item_id - - output_index - - sequence_number - - type - title: >- - OpenAIResponseObjectStreamResponseOutputTextDone - description: >- - Streaming event for when text output is completed. - "OpenAIResponseObjectStreamResponseWebSearchCallCompleted": - type: object - properties: - item_id: - type: string - description: >- - Unique identifier of the completed web search call - output_index: - type: integer - description: >- - Index position of the item in the output list - sequence_number: - type: integer - description: >- - Sequential number for ordering streaming events - type: - type: string - const: response.web_search_call.completed - default: response.web_search_call.completed - description: >- - Event type identifier, always "response.web_search_call.completed" - additionalProperties: false - required: - - item_id - - output_index - - sequence_number - - type - title: >- - OpenAIResponseObjectStreamResponseWebSearchCallCompleted - description: >- - Streaming event for completed web search calls. - "OpenAIResponseObjectStreamResponseWebSearchCallInProgress": - type: object - properties: - item_id: - type: string - description: Unique identifier of the web search call - output_index: - type: integer - description: >- - Index position of the item in the output list - sequence_number: - type: integer - description: >- - Sequential number for ordering streaming events - type: - type: string - const: response.web_search_call.in_progress - default: response.web_search_call.in_progress - description: >- - Event type identifier, always "response.web_search_call.in_progress" - additionalProperties: false - required: - - item_id - - output_index - - sequence_number - - type - title: >- - OpenAIResponseObjectStreamResponseWebSearchCallInProgress - description: >- - Streaming event for web search calls in progress. - "OpenAIResponseObjectStreamResponseWebSearchCallSearching": - type: object - properties: - item_id: - type: string - output_index: - type: integer - sequence_number: - type: integer - type: - type: string - const: response.web_search_call.searching - default: response.web_search_call.searching - additionalProperties: false - required: - - item_id - - output_index - - sequence_number - - type - title: >- - OpenAIResponseObjectStreamResponseWebSearchCallSearching - OpenAIDeleteResponseObject: - type: object - properties: - id: - type: string - description: >- - Unique identifier of the deleted response - object: - type: string - const: response - default: response - description: >- - Object type identifier, always "response" - deleted: - type: boolean - default: true - description: Deletion confirmation flag, always True - additionalProperties: false - required: - - id - - object - - deleted - title: OpenAIDeleteResponseObject - description: >- - Response object confirming deletion of an OpenAI response. - EmbeddingsRequest: - type: object - properties: - model_id: - type: string - description: >- - The identifier of the model to use. The model must be an embedding model - registered with Llama Stack and available via the /models endpoint. - contents: - oneOf: - - type: array - items: - type: string - - type: array - items: - $ref: '#/components/schemas/InterleavedContentItem' - description: >- - List of contents to generate embeddings for. Each content can be a string - or an InterleavedContentItem (and hence can be multimodal). The behavior - depends on the model and provider. Some models may only support text. - text_truncation: - type: string - enum: - - none - - start - - end - description: >- - (Optional) Config for how to truncate text for embedding when text is - longer than the model's max sequence length. - output_dimension: - type: integer - description: >- - (Optional) Output dimensionality for the embeddings. Only supported by - Matryoshka models. - task_type: - type: string - enum: - - query - - document - description: >- - (Optional) How is the embedding being used? This is only supported by - asymmetric embedding models. - additionalProperties: false - required: - - model_id - - contents - title: EmbeddingsRequest - EmbeddingsResponse: - type: object - properties: - embeddings: - type: array - items: - type: array - items: - type: number - description: >- - List of embedding vectors, one per input content. Each embedding is a - list of floats. The dimensionality of the embedding is model-specific; - you can check model metadata using /models/{model_id} - additionalProperties: false - required: - - embeddings - title: EmbeddingsResponse - description: >- - Response containing generated embeddings. - AgentCandidate: - type: object - properties: - type: - type: string - const: agent - default: agent - config: - $ref: '#/components/schemas/AgentConfig' - description: >- - The configuration for the agent candidate. - additionalProperties: false - required: - - type - - config - title: AgentCandidate - description: An agent candidate for evaluation. - AggregationFunctionType: - type: string - enum: - - average - - weighted_average - - median - - categorical_count - - accuracy - title: AggregationFunctionType - description: >- - Types of aggregation functions for scoring results. - BasicScoringFnParams: - type: object - properties: - type: - $ref: '#/components/schemas/ScoringFnParamsType' - const: basic - default: basic - description: >- - The type of scoring function parameters, always basic - aggregation_functions: - type: array - items: - $ref: '#/components/schemas/AggregationFunctionType' - description: >- - Aggregation functions to apply to the scores of each row - additionalProperties: false - required: - - type - - aggregation_functions - title: BasicScoringFnParams - description: >- - Parameters for basic scoring function configuration. - BenchmarkConfig: - type: object - properties: - eval_candidate: - $ref: '#/components/schemas/EvalCandidate' - description: The candidate to evaluate. - scoring_params: - type: object - additionalProperties: - $ref: '#/components/schemas/ScoringFnParams' - description: >- - Map between scoring function id and parameters for each scoring function - you want to run - num_examples: - type: integer - description: >- - (Optional) The number of examples to evaluate. If not provided, all examples - in the dataset will be evaluated - additionalProperties: false - required: - - eval_candidate - - scoring_params - title: BenchmarkConfig - description: >- - A benchmark configuration for evaluation. - EvalCandidate: - oneOf: - - $ref: '#/components/schemas/ModelCandidate' - - $ref: '#/components/schemas/AgentCandidate' - discriminator: - propertyName: type - mapping: - model: '#/components/schemas/ModelCandidate' - agent: '#/components/schemas/AgentCandidate' - LLMAsJudgeScoringFnParams: - type: object - properties: - type: - $ref: '#/components/schemas/ScoringFnParamsType' - const: llm_as_judge - default: llm_as_judge - description: >- - The type of scoring function parameters, always llm_as_judge - judge_model: - type: string - description: >- - Identifier of the LLM model to use as a judge for scoring - prompt_template: - type: string - description: >- - (Optional) Custom prompt template for the judge model - judge_score_regexes: - type: array - items: - type: string - description: >- - Regexes to extract the answer from generated response - aggregation_functions: - type: array - items: - $ref: '#/components/schemas/AggregationFunctionType' - description: >- - Aggregation functions to apply to the scores of each row - additionalProperties: false - required: - - type - - judge_model - - judge_score_regexes - - aggregation_functions - title: LLMAsJudgeScoringFnParams - description: >- - Parameters for LLM-as-judge scoring function configuration. - ModelCandidate: - type: object - properties: - type: - type: string - const: model - default: model - model: - type: string - description: The model ID to evaluate. - sampling_params: - $ref: '#/components/schemas/SamplingParams' - description: The sampling parameters for the model. - system_message: - $ref: '#/components/schemas/SystemMessage' - description: >- - (Optional) The system message providing instructions or context to the - model. - additionalProperties: false - required: - - type - - model - - sampling_params - title: ModelCandidate - description: A model candidate for evaluation. - RegexParserScoringFnParams: - type: object - properties: - type: - $ref: '#/components/schemas/ScoringFnParamsType' - const: regex_parser - default: regex_parser - description: >- - The type of scoring function parameters, always regex_parser - parsing_regexes: - type: array - items: - type: string - description: >- - Regex to extract the answer from generated response - aggregation_functions: - type: array - items: - $ref: '#/components/schemas/AggregationFunctionType' - description: >- - Aggregation functions to apply to the scores of each row - additionalProperties: false - required: - - type - - parsing_regexes - - aggregation_functions - title: RegexParserScoringFnParams - description: >- - Parameters for regex parser scoring function configuration. - ScoringFnParams: - oneOf: - - $ref: '#/components/schemas/LLMAsJudgeScoringFnParams' - - $ref: '#/components/schemas/RegexParserScoringFnParams' - - $ref: '#/components/schemas/BasicScoringFnParams' - discriminator: - propertyName: type - mapping: - llm_as_judge: '#/components/schemas/LLMAsJudgeScoringFnParams' - regex_parser: '#/components/schemas/RegexParserScoringFnParams' - basic: '#/components/schemas/BasicScoringFnParams' - ScoringFnParamsType: - type: string - enum: - - llm_as_judge - - regex_parser - - basic - title: ScoringFnParamsType - description: >- - Types of scoring function parameter configurations. - EvaluateRowsRequest: - type: object - properties: - input_rows: - type: array - items: - type: object - additionalProperties: - oneOf: - - type: 'null' - - type: boolean - - type: number - - type: string - - type: array - - type: object - description: The rows to evaluate. - scoring_functions: - type: array - items: - type: string - description: >- - The scoring functions to use for the evaluation. - benchmark_config: - $ref: '#/components/schemas/BenchmarkConfig' - description: The configuration for the benchmark. - additionalProperties: false - required: - - input_rows - - scoring_functions - - benchmark_config - title: EvaluateRowsRequest - EvaluateResponse: - type: object - properties: - generations: - type: array - items: - type: object - additionalProperties: - oneOf: - - type: 'null' - - type: boolean - - type: number - - type: string - - type: array - - type: object - description: The generations from the evaluation. - scores: - type: object - additionalProperties: - $ref: '#/components/schemas/ScoringResult' - description: The scores from the evaluation. - additionalProperties: false - required: - - generations - - scores - title: EvaluateResponse - description: The response from an evaluation. - ScoringResult: - type: object - properties: - score_rows: - type: array - items: - type: object - additionalProperties: - oneOf: - - type: 'null' - - type: boolean - - type: number - - type: string - - type: array - - type: object - description: >- - The scoring result for each row. Each row is a map of column name to value. - aggregated_results: - type: object - additionalProperties: - oneOf: - - type: 'null' - - type: boolean - - type: number - - type: string - - type: array - - type: object - description: Map of metric name to aggregated value - additionalProperties: false - required: - - score_rows - - aggregated_results - title: ScoringResult - description: A scoring result for a single row. - Agent: - type: object - properties: - agent_id: - type: string - description: Unique identifier for the agent - agent_config: - $ref: '#/components/schemas/AgentConfig' - description: Configuration settings for the agent - created_at: - type: string - format: date-time - description: Timestamp when the agent was created - additionalProperties: false - required: - - agent_id - - agent_config - - created_at - title: Agent - description: >- - An agent instance with configuration and metadata. - Session: - type: object - properties: - session_id: - type: string - description: >- - Unique identifier for the conversation session - session_name: - type: string - description: Human-readable name for the session - turns: - type: array - items: - $ref: '#/components/schemas/Turn' - description: >- - List of all turns that have occurred in this session - started_at: - type: string - format: date-time - description: Timestamp when the session was created - additionalProperties: false - required: - - session_id - - session_name - - turns - - started_at - title: Session - description: >- - A single session of an interaction with an Agentic System. - AgentStepResponse: - type: object - properties: - step: - oneOf: - - $ref: '#/components/schemas/InferenceStep' - - $ref: '#/components/schemas/ToolExecutionStep' - - $ref: '#/components/schemas/ShieldCallStep' - - $ref: '#/components/schemas/MemoryRetrievalStep' - discriminator: - propertyName: step_type - mapping: - inference: '#/components/schemas/InferenceStep' - tool_execution: '#/components/schemas/ToolExecutionStep' - shield_call: '#/components/schemas/ShieldCallStep' - memory_retrieval: '#/components/schemas/MemoryRetrievalStep' - description: >- - The complete step data and execution details - additionalProperties: false - required: - - step - title: AgentStepResponse - description: >- - Response containing details of a specific agent step. - Benchmark: - type: object - properties: - identifier: - type: string - provider_resource_id: - type: string - provider_id: - type: string - type: - type: string - enum: - - model - - shield - - vector_db - - dataset - - scoring_function - - benchmark - - tool - - tool_group - const: benchmark - default: benchmark - description: The resource type, always benchmark - dataset_id: - type: string - description: >- - Identifier of the dataset to use for the benchmark evaluation - scoring_functions: - type: array - items: - type: string - description: >- - List of scoring function identifiers to apply during evaluation - metadata: - type: object - additionalProperties: - oneOf: - - type: 'null' - - type: boolean - - type: number - - type: string - - type: array - - type: object - description: Metadata for this evaluation task - additionalProperties: false - required: - - identifier - - provider_id - - type - - dataset_id - - scoring_functions - - metadata - title: Benchmark - description: >- - A benchmark resource for evaluating model performance. - OpenAIAssistantMessageParam: - type: object - properties: - role: - type: string - const: assistant - default: assistant - description: >- - Must be "assistant" to identify this as the model's response - content: - oneOf: - - type: string - - type: array - items: - $ref: '#/components/schemas/OpenAIChatCompletionContentPartTextParam' - description: The content of the model's response - name: - type: string - description: >- - (Optional) The name of the assistant message participant. - tool_calls: - type: array - items: - $ref: '#/components/schemas/OpenAIChatCompletionToolCall' - description: >- - List of tool calls. Each tool call is an OpenAIChatCompletionToolCall - object. - additionalProperties: false - required: - - role - title: OpenAIAssistantMessageParam - description: >- - A message containing the model's (assistant) response in an OpenAI-compatible - chat completion request. - "OpenAIChatCompletionContentPartImageParam": - type: object - properties: - type: - type: string - const: image_url - default: image_url - description: >- - Must be "image_url" to identify this as image content - image_url: - $ref: '#/components/schemas/OpenAIImageURL' - description: >- - Image URL specification and processing details - additionalProperties: false - required: - - type - - image_url - title: >- - OpenAIChatCompletionContentPartImageParam - description: >- - Image content part for OpenAI-compatible chat completion messages. - OpenAIChatCompletionContentPartParam: - oneOf: - - $ref: '#/components/schemas/OpenAIChatCompletionContentPartTextParam' - - $ref: '#/components/schemas/OpenAIChatCompletionContentPartImageParam' - - $ref: '#/components/schemas/OpenAIFile' - discriminator: - propertyName: type - mapping: - text: '#/components/schemas/OpenAIChatCompletionContentPartTextParam' - image_url: '#/components/schemas/OpenAIChatCompletionContentPartImageParam' - file: '#/components/schemas/OpenAIFile' - OpenAIChatCompletionContentPartTextParam: - type: object - properties: - type: - type: string - const: text - default: text - description: >- - Must be "text" to identify this as text content - text: - type: string - description: The text content of the message - additionalProperties: false - required: - - type - - text - title: OpenAIChatCompletionContentPartTextParam - description: >- - Text content part for OpenAI-compatible chat completion messages. - OpenAIChatCompletionToolCall: - type: object - properties: - index: - type: integer - description: >- - (Optional) Index of the tool call in the list - id: - type: string - description: >- - (Optional) Unique identifier for the tool call - type: - type: string - const: function - default: function - description: >- - Must be "function" to identify this as a function call - function: - $ref: '#/components/schemas/OpenAIChatCompletionToolCallFunction' - description: (Optional) Function call details - additionalProperties: false - required: - - type - title: OpenAIChatCompletionToolCall - description: >- - Tool call specification for OpenAI-compatible chat completion responses. - OpenAIChatCompletionToolCallFunction: - type: object - properties: - name: - type: string - description: (Optional) Name of the function to call - arguments: - type: string - description: >- - (Optional) Arguments to pass to the function as a JSON string - additionalProperties: false - title: OpenAIChatCompletionToolCallFunction - description: >- - Function call details for OpenAI-compatible tool calls. - OpenAIChoice: - type: object - properties: - message: - $ref: '#/components/schemas/OpenAIMessageParam' - description: The message from the model - finish_reason: - type: string - description: The reason the model stopped generating - index: - type: integer - description: The index of the choice - logprobs: - $ref: '#/components/schemas/OpenAIChoiceLogprobs' - description: >- - (Optional) The log probabilities for the tokens in the message - additionalProperties: false - required: - - message - - finish_reason - - index - title: OpenAIChoice - description: >- - A choice from an OpenAI-compatible chat completion response. - OpenAIChoiceLogprobs: - type: object - properties: - content: - type: array - items: - $ref: '#/components/schemas/OpenAITokenLogProb' - description: >- - (Optional) The log probabilities for the tokens in the message - refusal: - type: array - items: - $ref: '#/components/schemas/OpenAITokenLogProb' - description: >- - (Optional) The log probabilities for the tokens in the message - additionalProperties: false - title: OpenAIChoiceLogprobs - description: >- - The log probabilities for the tokens in the message from an OpenAI-compatible - chat completion response. - OpenAIDeveloperMessageParam: - type: object - properties: - role: - type: string - const: developer - default: developer - description: >- - Must be "developer" to identify this as a developer message - content: - oneOf: - - type: string - - type: array - items: - $ref: '#/components/schemas/OpenAIChatCompletionContentPartTextParam' - description: The content of the developer message - name: - type: string - description: >- - (Optional) The name of the developer message participant. - additionalProperties: false - required: - - role - - content - title: OpenAIDeveloperMessageParam - description: >- - A message from the developer in an OpenAI-compatible chat completion request. - OpenAIFile: - type: object - properties: - type: - type: string - const: file - default: file - file: - $ref: '#/components/schemas/OpenAIFileFile' - additionalProperties: false - required: - - type - - file - title: OpenAIFile - OpenAIFileFile: - type: object - properties: - file_data: - type: string - file_id: - type: string - filename: - type: string - additionalProperties: false - title: OpenAIFileFile - OpenAIImageURL: - type: object - properties: - url: - type: string - description: >- - URL of the image to include in the message - detail: - type: string - description: >- - (Optional) Level of detail for image processing. Can be "low", "high", - or "auto" - additionalProperties: false - required: - - url - title: OpenAIImageURL - description: >- - Image URL specification for OpenAI-compatible chat completion messages. - OpenAIMessageParam: - oneOf: - - $ref: '#/components/schemas/OpenAIUserMessageParam' - - $ref: '#/components/schemas/OpenAISystemMessageParam' - - $ref: '#/components/schemas/OpenAIAssistantMessageParam' - - $ref: '#/components/schemas/OpenAIToolMessageParam' - - $ref: '#/components/schemas/OpenAIDeveloperMessageParam' - discriminator: - propertyName: role - mapping: - user: '#/components/schemas/OpenAIUserMessageParam' - system: '#/components/schemas/OpenAISystemMessageParam' - assistant: '#/components/schemas/OpenAIAssistantMessageParam' - tool: '#/components/schemas/OpenAIToolMessageParam' - developer: '#/components/schemas/OpenAIDeveloperMessageParam' - OpenAISystemMessageParam: - type: object - properties: - role: - type: string - const: system - default: system - description: >- - Must be "system" to identify this as a system message - content: - oneOf: - - type: string - - type: array - items: - $ref: '#/components/schemas/OpenAIChatCompletionContentPartTextParam' - description: >- - The content of the "system prompt". If multiple system messages are provided, - they are concatenated. The underlying Llama Stack code may also add other - system messages (for example, for formatting tool definitions). - name: - type: string - description: >- - (Optional) The name of the system message participant. - additionalProperties: false - required: - - role - - content - title: OpenAISystemMessageParam - description: >- - A system message providing instructions or context to the model. - OpenAITokenLogProb: - type: object - properties: - token: - type: string - bytes: - type: array - items: - type: integer - logprob: - type: number - top_logprobs: - type: array - items: - $ref: '#/components/schemas/OpenAITopLogProb' - additionalProperties: false - required: - - token - - logprob - - top_logprobs - title: OpenAITokenLogProb - description: >- - The log probability for a token from an OpenAI-compatible chat completion - response. - OpenAIToolMessageParam: - type: object - properties: - role: - type: string - const: tool - default: tool - description: >- - Must be "tool" to identify this as a tool response - tool_call_id: - type: string - description: >- - Unique identifier for the tool call this response is for - content: - oneOf: - - type: string - - type: array - items: - $ref: '#/components/schemas/OpenAIChatCompletionContentPartTextParam' - description: The response content from the tool - additionalProperties: false - required: - - role - - tool_call_id - - content - title: OpenAIToolMessageParam - description: >- - A message representing the result of a tool invocation in an OpenAI-compatible - chat completion request. - OpenAITopLogProb: - type: object - properties: - token: - type: string - bytes: - type: array - items: - type: integer - logprob: - type: number - additionalProperties: false - required: - - token - - logprob - title: OpenAITopLogProb - description: >- - The top log probability for a token from an OpenAI-compatible chat completion - response. - OpenAIUserMessageParam: - type: object - properties: - role: - type: string - const: user - default: user - description: >- - Must be "user" to identify this as a user message - content: - oneOf: - - type: string - - type: array - items: - $ref: '#/components/schemas/OpenAIChatCompletionContentPartParam' - description: >- - The content of the message, which can include text and other media - name: - type: string - description: >- - (Optional) The name of the user message participant. - additionalProperties: false - required: - - role - - content - title: OpenAIUserMessageParam - description: >- - A message from the user in an OpenAI-compatible chat completion request. - OpenAICompletionWithInputMessages: - type: object - properties: - id: - type: string - description: The ID of the chat completion - choices: - type: array - items: - $ref: '#/components/schemas/OpenAIChoice' - description: List of choices - object: - type: string - const: chat.completion - default: chat.completion - description: >- - The object type, which will be "chat.completion" - created: - type: integer - description: >- - The Unix timestamp in seconds when the chat completion was created - model: - type: string - description: >- - The model that was used to generate the chat completion - input_messages: - type: array - items: - $ref: '#/components/schemas/OpenAIMessageParam' - additionalProperties: false - required: - - id - - choices - - object - - created - - model - - input_messages - title: OpenAICompletionWithInputMessages - DataSource: - oneOf: - - $ref: '#/components/schemas/URIDataSource' - - $ref: '#/components/schemas/RowsDataSource' - discriminator: - propertyName: type - mapping: - uri: '#/components/schemas/URIDataSource' - rows: '#/components/schemas/RowsDataSource' - Dataset: - type: object - properties: - identifier: - type: string - provider_resource_id: - type: string - provider_id: - type: string - type: - type: string - enum: - - model - - shield - - vector_db - - dataset - - scoring_function - - benchmark - - tool - - tool_group - const: dataset - default: dataset - description: >- - Type of resource, always 'dataset' for datasets - purpose: - type: string - enum: - - post-training/messages - - eval/question-answer - - eval/messages-answer - description: >- - Purpose of the dataset indicating its intended use - source: - $ref: '#/components/schemas/DataSource' - description: >- - Data source configuration for the dataset - metadata: - type: object - additionalProperties: - oneOf: - - type: 'null' - - type: boolean - - type: number - - type: string - - type: array - - type: object - description: Additional metadata for the dataset - additionalProperties: false - required: - - identifier - - provider_id - - type - - purpose - - source - - metadata - title: Dataset - description: >- - Dataset resource for storing and accessing training or evaluation data. - RowsDataSource: - type: object - properties: - type: - type: string - const: rows - default: rows - rows: - type: array - items: - type: object - additionalProperties: - oneOf: - - type: 'null' - - type: boolean - - type: number - - type: string - - type: array - - type: object - description: >- - The dataset is stored in rows. E.g. - [ {"messages": [{"role": "user", - "content": "Hello, world!"}, {"role": "assistant", "content": "Hello, - world!"}]} ] - additionalProperties: false - required: - - type - - rows - title: RowsDataSource - description: A dataset stored in rows. - URIDataSource: - type: object - properties: - type: - type: string - const: uri - default: uri - uri: - type: string - description: >- - The dataset can be obtained from a URI. E.g. - "https://mywebsite.com/mydata.jsonl" - - "lsfs://mydata.jsonl" - "data:csv;base64,{base64_content}" - additionalProperties: false - required: - - type - - uri - title: URIDataSource - description: >- - A dataset that can be obtained from a URI. - Model: - type: object - properties: - identifier: - type: string - description: >- - Unique identifier for this resource in llama stack - provider_resource_id: - type: string - description: >- - Unique identifier for this resource in the provider - provider_id: - type: string - description: >- - ID of the provider that owns this resource - type: - type: string - enum: - - model - - shield - - vector_db - - dataset - - scoring_function - - benchmark - - tool - - tool_group - const: model - default: model - description: >- - The resource type, always 'model' for model resources - metadata: - type: object - additionalProperties: - oneOf: - - type: 'null' - - type: boolean - - type: number - - type: string - - type: array - - type: object - description: Any additional metadata for this model - model_type: - $ref: '#/components/schemas/ModelType' - default: llm - description: >- - The type of model (LLM or embedding model) - additionalProperties: false - required: - - identifier - - provider_id - - type - - metadata - - model_type - title: Model - description: >- - A model resource representing an AI model registered in Llama Stack. - ModelType: - type: string - enum: - - llm - - embedding - title: ModelType - description: >- - Enumeration of supported model types in Llama Stack. - AgentTurnInputType: - type: object - properties: - type: - type: string - const: agent_turn_input - default: agent_turn_input - description: >- - Discriminator type. Always "agent_turn_input" - additionalProperties: false - required: - - type - title: AgentTurnInputType - description: Parameter type for agent turn input. - ArrayType: - type: object - properties: - type: - type: string - const: array - default: array - description: Discriminator type. Always "array" - additionalProperties: false - required: - - type - title: ArrayType - description: Parameter type for array values. - BooleanType: - type: object - properties: - type: - type: string - const: boolean - default: boolean - description: Discriminator type. Always "boolean" - additionalProperties: false - required: - - type - title: BooleanType - description: Parameter type for boolean values. - ChatCompletionInputType: - type: object - properties: - type: - type: string - const: chat_completion_input - default: chat_completion_input - description: >- - Discriminator type. Always "chat_completion_input" - additionalProperties: false - required: - - type - title: ChatCompletionInputType - description: >- - Parameter type for chat completion input. - CompletionInputType: - type: object - properties: - type: - type: string - const: completion_input - default: completion_input - description: >- - Discriminator type. Always "completion_input" - additionalProperties: false - required: - - type - title: CompletionInputType - description: Parameter type for completion input. - JsonType: - type: object - properties: - type: - type: string - const: json - default: json - description: Discriminator type. Always "json" - additionalProperties: false - required: - - type - title: JsonType - description: Parameter type for JSON values. - NumberType: - type: object - properties: - type: - type: string - const: number - default: number - description: Discriminator type. Always "number" - additionalProperties: false - required: - - type - title: NumberType - description: Parameter type for numeric values. - ObjectType: - type: object - properties: - type: - type: string - const: object - default: object - description: Discriminator type. Always "object" - additionalProperties: false - required: - - type - title: ObjectType - description: Parameter type for object values. - ParamType: - oneOf: - - $ref: '#/components/schemas/StringType' - - $ref: '#/components/schemas/NumberType' - - $ref: '#/components/schemas/BooleanType' - - $ref: '#/components/schemas/ArrayType' - - $ref: '#/components/schemas/ObjectType' - - $ref: '#/components/schemas/JsonType' - - $ref: '#/components/schemas/UnionType' - - $ref: '#/components/schemas/ChatCompletionInputType' - - $ref: '#/components/schemas/CompletionInputType' - - $ref: '#/components/schemas/AgentTurnInputType' - discriminator: - propertyName: type - mapping: - string: '#/components/schemas/StringType' - number: '#/components/schemas/NumberType' - boolean: '#/components/schemas/BooleanType' - array: '#/components/schemas/ArrayType' - object: '#/components/schemas/ObjectType' - json: '#/components/schemas/JsonType' - union: '#/components/schemas/UnionType' - chat_completion_input: '#/components/schemas/ChatCompletionInputType' - completion_input: '#/components/schemas/CompletionInputType' - agent_turn_input: '#/components/schemas/AgentTurnInputType' - ScoringFn: - type: object - properties: - identifier: - type: string - provider_resource_id: - type: string - provider_id: - type: string - type: - type: string - enum: - - model - - shield - - vector_db - - dataset - - scoring_function - - benchmark - - tool - - tool_group - const: scoring_function - default: scoring_function - description: >- - The resource type, always scoring_function - description: - type: string - metadata: - type: object - additionalProperties: - oneOf: - - type: 'null' - - type: boolean - - type: number - - type: string - - type: array - - type: object - return_type: - $ref: '#/components/schemas/ParamType' - params: - $ref: '#/components/schemas/ScoringFnParams' - additionalProperties: false - required: - - identifier - - provider_id - - type - - metadata - - return_type - title: ScoringFn - description: >- - A scoring function resource for evaluating model outputs. - StringType: - type: object - properties: - type: - type: string - const: string - default: string - description: Discriminator type. Always "string" - additionalProperties: false - required: - - type - title: StringType - description: Parameter type for string values. - UnionType: - type: object - properties: - type: - type: string - const: union - default: union - description: Discriminator type. Always "union" - additionalProperties: false - required: - - type - title: UnionType - description: Parameter type for union values. - Shield: - type: object - properties: - identifier: - type: string - provider_resource_id: - type: string - provider_id: - type: string - type: - type: string - enum: - - model - - shield - - vector_db - - dataset - - scoring_function - - benchmark - - tool - - tool_group - const: shield - default: shield - description: The resource type, always shield - params: - type: object - additionalProperties: - oneOf: - - type: 'null' - - type: boolean - - type: number - - type: string - - type: array - - type: object - description: >- - (Optional) Configuration parameters for the shield - additionalProperties: false - required: - - identifier - - provider_id - - type - title: Shield - description: >- - A safety shield resource that can be used to check content. - Span: - type: object - properties: - span_id: - type: string - description: Unique identifier for the span - trace_id: - type: string - description: >- - Unique identifier for the trace this span belongs to - parent_span_id: - type: string - description: >- - (Optional) Unique identifier for the parent span, if this is a child span - name: - type: string - description: >- - Human-readable name describing the operation this span represents - start_time: - type: string - format: date-time - description: Timestamp when the operation began - end_time: - type: string - format: date-time - description: >- - (Optional) Timestamp when the operation finished, if completed - attributes: - type: object - additionalProperties: - oneOf: - - type: 'null' - - type: boolean - - type: number - - type: string - - type: array - - type: object - description: >- - (Optional) Key-value pairs containing additional metadata about the span - additionalProperties: false - required: - - span_id - - trace_id - - name - - start_time - title: Span - description: >- - A span representing a single operation within a trace. - GetSpanTreeRequest: - type: object - properties: - attributes_to_return: - type: array - items: - type: string - description: The attributes to return in the tree. - max_depth: - type: integer - description: The maximum depth of the tree. - additionalProperties: false - title: GetSpanTreeRequest - SpanStatus: - type: string - enum: - - ok - - error - title: SpanStatus - description: >- - The status of a span indicating whether it completed successfully or with - an error. - SpanWithStatus: - type: object - properties: - span_id: - type: string - description: Unique identifier for the span - trace_id: - type: string - description: >- - Unique identifier for the trace this span belongs to - parent_span_id: - type: string - description: >- - (Optional) Unique identifier for the parent span, if this is a child span - name: - type: string - description: >- - Human-readable name describing the operation this span represents - start_time: - type: string - format: date-time - description: Timestamp when the operation began - end_time: - type: string - format: date-time - description: >- - (Optional) Timestamp when the operation finished, if completed - attributes: - type: object - additionalProperties: - oneOf: - - type: 'null' - - type: boolean - - type: number - - type: string - - type: array - - type: object - description: >- - (Optional) Key-value pairs containing additional metadata about the span - status: - $ref: '#/components/schemas/SpanStatus' - description: >- - (Optional) The current status of the span - additionalProperties: false - required: - - span_id - - trace_id - - name - - start_time - title: SpanWithStatus - description: A span that includes status information. - QuerySpanTreeResponse: - type: object - properties: - data: - type: object - additionalProperties: - $ref: '#/components/schemas/SpanWithStatus' - description: >- - Dictionary mapping span IDs to spans with status information - additionalProperties: false - required: - - data - title: QuerySpanTreeResponse - description: >- - Response containing a tree structure of spans. - Tool: - type: object - properties: - identifier: - type: string - provider_resource_id: - type: string - provider_id: - type: string - type: - type: string - enum: - - model - - shield - - vector_db - - dataset - - scoring_function - - benchmark - - tool - - tool_group - const: tool - default: tool - description: Type of resource, always 'tool' - toolgroup_id: - type: string - description: >- - ID of the tool group this tool belongs to - description: - type: string - description: >- - Human-readable description of what the tool does - parameters: - type: array - items: - $ref: '#/components/schemas/ToolParameter' - description: List of parameters this tool accepts - metadata: - type: object - additionalProperties: - oneOf: - - type: 'null' - - type: boolean - - type: number - - type: string - - type: array - - type: object - description: >- - (Optional) Additional metadata about the tool - additionalProperties: false - required: - - identifier - - provider_id - - type - - toolgroup_id - - description - - parameters - title: Tool - description: A tool that can be invoked by agents. - ToolGroup: - type: object - properties: - identifier: - type: string - provider_resource_id: - type: string - provider_id: - type: string - type: - type: string - enum: - - model - - shield - - vector_db - - dataset - - scoring_function - - benchmark - - tool - - tool_group - const: tool_group - default: tool_group - description: Type of resource, always 'tool_group' - mcp_endpoint: - $ref: '#/components/schemas/URL' - description: >- - (Optional) Model Context Protocol endpoint for remote tools - args: - type: object - additionalProperties: - oneOf: - - type: 'null' - - type: boolean - - type: number - - type: string - - type: array - - type: object - description: >- - (Optional) Additional arguments for the tool group - additionalProperties: false - required: - - identifier - - provider_id - - type - title: ToolGroup - description: >- - A group of related tools managed together. - Trace: - type: object - properties: - trace_id: - type: string - description: Unique identifier for the trace - root_span_id: - type: string - description: >- - Unique identifier for the root span that started this trace - start_time: - type: string - format: date-time - description: Timestamp when the trace began - end_time: - type: string - format: date-time - description: >- - (Optional) Timestamp when the trace finished, if completed - additionalProperties: false - required: - - trace_id - - root_span_id - - start_time - title: Trace - description: >- - A trace representing the complete execution path of a request across multiple - operations. - Checkpoint: - type: object - properties: - identifier: - type: string - description: Unique identifier for the checkpoint - created_at: - type: string - format: date-time - description: >- - Timestamp when the checkpoint was created - epoch: - type: integer - description: >- - Training epoch when the checkpoint was saved - post_training_job_id: - type: string - description: >- - Identifier of the training job that created this checkpoint - path: - type: string - description: >- - File system path where the checkpoint is stored - training_metrics: - $ref: '#/components/schemas/PostTrainingMetric' - description: >- - (Optional) Training metrics associated with this checkpoint - additionalProperties: false - required: - - identifier - - created_at - - epoch - - post_training_job_id - - path - title: Checkpoint - description: Checkpoint created during training runs. - PostTrainingJobArtifactsResponse: - type: object - properties: - job_uuid: - type: string - description: Unique identifier for the training job - checkpoints: - type: array - items: - $ref: '#/components/schemas/Checkpoint' - description: >- - List of model checkpoints created during training - additionalProperties: false - required: - - job_uuid - - checkpoints - title: PostTrainingJobArtifactsResponse - description: Artifacts of a finetuning job. - PostTrainingMetric: - type: object - properties: - epoch: - type: integer - description: Training epoch number - train_loss: - type: number - description: Loss value on the training dataset - validation_loss: - type: number - description: Loss value on the validation dataset - perplexity: - type: number - description: >- - Perplexity metric indicating model confidence - additionalProperties: false - required: - - epoch - - train_loss - - validation_loss - - perplexity - title: PostTrainingMetric - description: >- - Training metrics captured during post-training jobs. - PostTrainingJobStatusResponse: - type: object - properties: - job_uuid: - type: string - description: Unique identifier for the training job - status: - type: string - enum: - - completed - - in_progress - - failed - - scheduled - - cancelled - description: Current status of the training job - scheduled_at: - type: string - format: date-time - description: >- - (Optional) Timestamp when the job was scheduled - started_at: - type: string - format: date-time - description: >- - (Optional) Timestamp when the job execution began - completed_at: - type: string - format: date-time - description: >- - (Optional) Timestamp when the job finished, if completed - resources_allocated: - type: object - additionalProperties: - oneOf: - - type: 'null' - - type: boolean - - type: number - - type: string - - type: array - - type: object - description: >- - (Optional) Information about computational resources allocated to the - job - checkpoints: - type: array - items: - $ref: '#/components/schemas/Checkpoint' - description: >- - List of model checkpoints created during training - additionalProperties: false - required: - - job_uuid - - status - - checkpoints - title: PostTrainingJobStatusResponse - description: Status of a finetuning job. - ListPostTrainingJobsResponse: - type: object - properties: - data: - type: array - items: - type: object - properties: - job_uuid: - type: string - additionalProperties: false - required: - - job_uuid - title: PostTrainingJob - additionalProperties: false - required: - - data - title: ListPostTrainingJobsResponse - VectorDB: - type: object - properties: - identifier: - type: string - provider_resource_id: - type: string - provider_id: - type: string - type: - type: string - enum: - - model - - shield - - vector_db - - dataset - - scoring_function - - benchmark - - tool - - tool_group - const: vector_db - default: vector_db - description: >- - Type of resource, always 'vector_db' for vector databases - embedding_model: - type: string - description: >- - Name of the embedding model to use for vector generation - embedding_dimension: - type: integer - description: Dimension of the embedding vectors - vector_db_name: - type: string - additionalProperties: false - required: - - identifier - - provider_id - - type - - embedding_model - - embedding_dimension - title: VectorDB - description: >- - Vector database resource for storing and querying vector embeddings. - HealthInfo: - type: object - properties: - status: - type: string - enum: - - OK - - Error - - Not Implemented - description: Current health status of the service - additionalProperties: false - required: - - status - title: HealthInfo - description: >- - Health status information for the service. - RAGDocument: - type: object - properties: - document_id: - type: string - description: The unique identifier for the document. - content: - oneOf: - - type: string - - $ref: '#/components/schemas/InterleavedContentItem' - - type: array - items: - $ref: '#/components/schemas/InterleavedContentItem' - - $ref: '#/components/schemas/URL' - description: The content of the document. - mime_type: - type: string - description: The MIME type of the document. - metadata: - type: object - additionalProperties: - oneOf: - - type: 'null' - - type: boolean - - type: number - - type: string - - type: array - - type: object - description: Additional metadata for the document. - additionalProperties: false - required: - - document_id - - content - - metadata - title: RAGDocument - description: >- - A document to be used for document ingestion in the RAG Tool. - InsertRequest: - type: object - properties: - documents: - type: array - items: - $ref: '#/components/schemas/RAGDocument' - description: >- - List of documents to index in the RAG system - vector_db_id: - type: string - description: >- - ID of the vector database to store the document embeddings - chunk_size_in_tokens: - type: integer - description: >- - (Optional) Size in tokens for document chunking during indexing - additionalProperties: false - required: - - documents - - vector_db_id - - chunk_size_in_tokens - title: InsertRequest - Chunk: - type: object - properties: - content: - $ref: '#/components/schemas/InterleavedContent' - description: >- - The content of the chunk, which can be interleaved text, images, or other - types. - metadata: - type: object - additionalProperties: - oneOf: - - type: 'null' - - type: boolean - - type: number - - type: string - - type: array - - type: object - description: >- - Metadata associated with the chunk that will be used in the model context - during inference. - embedding: - type: array - items: - type: number - description: >- - Optional embedding for the chunk. If not provided, it will be computed - later. - stored_chunk_id: - type: string - description: >- - The chunk ID that is stored in the vector database. Used for backend functionality. - chunk_metadata: - $ref: '#/components/schemas/ChunkMetadata' - description: >- - Metadata for the chunk that will NOT be used in the context during inference. - The `chunk_metadata` is required backend functionality. - additionalProperties: false - required: - - content - - metadata - title: Chunk - description: >- - A chunk of content that can be inserted into a vector database. - ChunkMetadata: - type: object - properties: - chunk_id: - type: string - description: >- - The ID of the chunk. If not set, it will be generated based on the document - ID and content. - document_id: - type: string - description: >- - The ID of the document this chunk belongs to. - source: - type: string - description: >- - The source of the content, such as a URL, file path, or other identifier. - created_timestamp: - type: integer - description: >- - An optional timestamp indicating when the chunk was created. - updated_timestamp: - type: integer - description: >- - An optional timestamp indicating when the chunk was last updated. - chunk_window: - type: string - description: >- - The window of the chunk, which can be used to group related chunks together. - chunk_tokenizer: - type: string - description: >- - The tokenizer used to create the chunk. Default is Tiktoken. - chunk_embedding_model: - type: string - description: >- - The embedding model used to create the chunk's embedding. - chunk_embedding_dimension: - type: integer - description: >- - The dimension of the embedding vector for the chunk. - content_token_count: - type: integer - description: >- - The number of tokens in the content of the chunk. - metadata_token_count: - type: integer - description: >- - The number of tokens in the metadata of the chunk. - additionalProperties: false - title: ChunkMetadata - description: >- - `ChunkMetadata` is backend metadata for a `Chunk` that is used to store additional - information about the chunk that will not be used in the context during - inference, but is required for backend functionality. The `ChunkMetadata` is - set during chunk creation in `MemoryToolRuntimeImpl().insert()`and is not - expected to change after. Use `Chunk.metadata` for metadata that will - be used in the context during inference. - InsertChunksRequest: - type: object - properties: - vector_db_id: - type: string - description: >- - The identifier of the vector database to insert the chunks into. - chunks: - type: array - items: - $ref: '#/components/schemas/Chunk' - description: >- - The chunks to insert. Each `Chunk` should contain content which can be - interleaved text, images, or other types. `metadata`: `dict[str, Any]` - and `embedding`: `List[float]` are optional. If `metadata` is provided, - you configure how Llama Stack formats the chunk during generation. If - `embedding` is not provided, it will be computed later. - ttl_seconds: - type: integer - description: The time to live of the chunks. - additionalProperties: false - required: - - vector_db_id - - chunks - title: InsertChunksRequest - ProviderInfo: - type: object - properties: - api: - type: string - description: The API name this provider implements - provider_id: - type: string - description: Unique identifier for the provider - provider_type: - type: string - description: The type of provider implementation - config: - type: object - additionalProperties: - oneOf: - - type: 'null' - - type: boolean - - type: number - - type: string - - type: array - - type: object - description: >- - Configuration parameters for the provider - health: - type: object - additionalProperties: - oneOf: - - type: 'null' - - type: boolean - - type: number - - type: string - - type: array - - type: object - description: Current health status of the provider - additionalProperties: false - required: - - api - - provider_id - - provider_type - - config - - health - title: ProviderInfo - description: >- - Information about a registered provider including its configuration and health - status. - InvokeToolRequest: - type: object - properties: - tool_name: - type: string - description: The name of the tool to invoke. - kwargs: - type: object - additionalProperties: - oneOf: - - type: 'null' - - type: boolean - - type: number - - type: string - - type: array - - type: object - description: >- - A dictionary of arguments to pass to the tool. - additionalProperties: false - required: - - tool_name - - kwargs - title: InvokeToolRequest - ToolInvocationResult: - type: object - properties: - content: - $ref: '#/components/schemas/InterleavedContent' - description: >- - (Optional) The output content from the tool execution - error_message: - type: string - description: >- - (Optional) Error message if the tool execution failed - error_code: - type: integer - description: >- - (Optional) Numeric error code if the tool execution failed - metadata: - type: object - additionalProperties: - oneOf: - - type: 'null' - - type: boolean - - type: number - - type: string - - type: array - - type: object - description: >- - (Optional) Additional metadata about the tool execution - additionalProperties: false - title: ToolInvocationResult - description: Result of a tool invocation. - PaginatedResponse: - type: object - properties: - data: - type: array - items: - type: object - additionalProperties: - oneOf: - - type: 'null' - - type: boolean - - type: number - - type: string - - type: array - - type: object - description: The list of items for the current page - has_more: - type: boolean - description: >- - Whether there are more items available after this set - url: - type: string - description: The URL for accessing this list - additionalProperties: false - required: - - data - - has_more - title: PaginatedResponse - description: >- - A generic paginated response that follows a simple format. - Job: - type: object - properties: - job_id: - type: string - description: Unique identifier for the job - status: - type: string - enum: - - completed - - in_progress - - failed - - scheduled - - cancelled - description: Current execution status of the job - additionalProperties: false - required: - - job_id - - status - title: Job - description: >- - A job execution instance with status tracking. - ListBenchmarksResponse: - type: object - properties: - data: - type: array - items: - $ref: '#/components/schemas/Benchmark' - additionalProperties: false - required: - - data - title: ListBenchmarksResponse - Order: - type: string - enum: - - asc - - desc - title: Order - description: Sort order for paginated responses. - ListOpenAIChatCompletionResponse: - type: object - properties: - data: - type: array - items: - type: object - properties: - id: - type: string - description: The ID of the chat completion - choices: - type: array - items: - $ref: '#/components/schemas/OpenAIChoice' - description: List of choices - object: - type: string - const: chat.completion - default: chat.completion - description: >- - The object type, which will be "chat.completion" - created: - type: integer - description: >- - The Unix timestamp in seconds when the chat completion was created - model: - type: string - description: >- - The model that was used to generate the chat completion - input_messages: - type: array - items: - $ref: '#/components/schemas/OpenAIMessageParam' - additionalProperties: false - required: - - id - - choices - - object - - created - - model - - input_messages - title: OpenAICompletionWithInputMessages - description: >- - List of chat completion objects with their input messages - has_more: - type: boolean - description: >- - Whether there are more completions available beyond this list - first_id: - type: string - description: ID of the first completion in this list - last_id: - type: string - description: ID of the last completion in this list - object: - type: string - const: list - default: list - description: >- - Must be "list" to identify this as a list response - additionalProperties: false - required: - - data - - has_more - - first_id - - last_id - - object - title: ListOpenAIChatCompletionResponse - description: >- - Response from listing OpenAI-compatible chat completions. - ListDatasetsResponse: - type: object - properties: - data: - type: array - items: - $ref: '#/components/schemas/Dataset' - description: List of datasets - additionalProperties: false - required: - - data - title: ListDatasetsResponse - description: Response from listing datasets. - ListModelsResponse: - type: object - properties: - data: - type: array - items: - $ref: '#/components/schemas/Model' - additionalProperties: false - required: - - data - title: ListModelsResponse - ListOpenAIResponseInputItem: - type: object - properties: - data: - type: array - items: - $ref: '#/components/schemas/OpenAIResponseInput' - description: List of input items - object: - type: string - const: list - default: list - description: Object type identifier, always "list" - additionalProperties: false - required: - - data - - object - title: ListOpenAIResponseInputItem - description: >- - List container for OpenAI response input items. - ListOpenAIResponseObject: - type: object - properties: - data: - type: array - items: - $ref: '#/components/schemas/OpenAIResponseObjectWithInput' - description: >- - List of response objects with their input context - has_more: - type: boolean - description: >- - Whether there are more results available beyond this page - first_id: - type: string - description: >- - Identifier of the first item in this page - last_id: - type: string - description: Identifier of the last item in this page - object: - type: string - const: list - default: list - description: Object type identifier, always "list" - additionalProperties: false - required: - - data - - has_more - - first_id - - last_id - - object - title: ListOpenAIResponseObject - description: >- - Paginated list of OpenAI response objects with navigation metadata. - OpenAIResponseObjectWithInput: - type: object - properties: - created_at: - type: integer - description: >- - Unix timestamp when the response was created - error: - $ref: '#/components/schemas/OpenAIResponseError' - description: >- - (Optional) Error details if the response generation failed - id: - type: string - description: Unique identifier for this response - model: - type: string - description: Model identifier used for generation - object: - type: string - const: response - default: response - description: >- - Object type identifier, always "response" - output: - type: array - items: - $ref: '#/components/schemas/OpenAIResponseOutput' - description: >- - List of generated output items (messages, tool calls, etc.) - parallel_tool_calls: - type: boolean - default: false - description: >- - Whether tool calls can be executed in parallel - previous_response_id: - type: string - description: >- - (Optional) ID of the previous response in a conversation - status: - type: string - description: >- - Current status of the response generation - temperature: - type: number - description: >- - (Optional) Sampling temperature used for generation - text: - $ref: '#/components/schemas/OpenAIResponseText' - description: >- - Text formatting configuration for the response - top_p: - type: number - description: >- - (Optional) Nucleus sampling parameter used for generation - truncation: - type: string - description: >- - (Optional) Truncation strategy applied to the response - user: - type: string - description: >- - (Optional) User identifier associated with the request - input: - type: array - items: - $ref: '#/components/schemas/OpenAIResponseInput' - description: >- - List of input items that led to this response - additionalProperties: false - required: - - created_at - - id - - model - - object - - output - - parallel_tool_calls - - status - - text - - input - title: OpenAIResponseObjectWithInput - description: >- - OpenAI response object extended with input context information. - ListProvidersResponse: - type: object - properties: - data: - type: array - items: - $ref: '#/components/schemas/ProviderInfo' - description: List of provider information objects - additionalProperties: false - required: - - data - title: ListProvidersResponse - description: >- - Response containing a list of all available providers. - RouteInfo: - type: object - properties: - route: - type: string - description: The API endpoint path - method: - type: string - description: HTTP method for the route - provider_types: - type: array - items: - type: string - description: >- - List of provider types that implement this route - additionalProperties: false - required: - - route - - method - - provider_types - title: RouteInfo - description: >- - Information about an API route including its path, method, and implementing - providers. - ListRoutesResponse: - type: object - properties: - data: - type: array - items: - $ref: '#/components/schemas/RouteInfo' - description: >- - List of available route information objects - additionalProperties: false - required: - - data - title: ListRoutesResponse - description: >- - Response containing a list of all available API routes. - ListToolDefsResponse: - type: object - properties: - data: - type: array - items: - $ref: '#/components/schemas/ToolDef' - description: List of tool definitions - additionalProperties: false - required: - - data - title: ListToolDefsResponse - description: >- - Response containing a list of tool definitions. - ListScoringFunctionsResponse: - type: object - properties: - data: - type: array - items: - $ref: '#/components/schemas/ScoringFn' - additionalProperties: false - required: - - data - title: ListScoringFunctionsResponse - ListShieldsResponse: - type: object - properties: - data: - type: array - items: - $ref: '#/components/schemas/Shield' - additionalProperties: false - required: - - data - title: ListShieldsResponse - ListToolGroupsResponse: - type: object - properties: - data: - type: array - items: - $ref: '#/components/schemas/ToolGroup' - description: List of tool groups - additionalProperties: false - required: - - data - title: ListToolGroupsResponse - description: >- - Response containing a list of tool groups. - ListToolsResponse: - type: object - properties: - data: - type: array - items: - $ref: '#/components/schemas/Tool' - description: List of tools - additionalProperties: false - required: - - data - title: ListToolsResponse - description: Response containing a list of tools. - ListVectorDBsResponse: - type: object - properties: - data: - type: array - items: - $ref: '#/components/schemas/VectorDB' - description: List of vector databases - additionalProperties: false - required: - - data - title: ListVectorDBsResponse - description: Response from listing vector databases. - Event: - oneOf: - - $ref: '#/components/schemas/UnstructuredLogEvent' - - $ref: '#/components/schemas/MetricEvent' - - $ref: '#/components/schemas/StructuredLogEvent' - discriminator: - propertyName: type - mapping: - unstructured_log: '#/components/schemas/UnstructuredLogEvent' - metric: '#/components/schemas/MetricEvent' - structured_log: '#/components/schemas/StructuredLogEvent' - EventType: - type: string - enum: - - unstructured_log - - structured_log - - metric - title: EventType - description: >- - The type of telemetry event being logged. - LogSeverity: - type: string - enum: - - verbose - - debug - - info - - warn - - error - - critical - title: LogSeverity - description: The severity level of a log message. - MetricEvent: - type: object - properties: - trace_id: - type: string - description: >- - Unique identifier for the trace this event belongs to - span_id: - type: string - description: >- - Unique identifier for the span this event belongs to - timestamp: - type: string - format: date-time - description: Timestamp when the event occurred - attributes: - type: object - additionalProperties: - oneOf: - - type: string - - type: integer - - type: number - - type: boolean - - type: 'null' - description: >- - (Optional) Key-value pairs containing additional metadata about the event - type: - $ref: '#/components/schemas/EventType' - const: metric - default: metric - description: Event type identifier set to METRIC - metric: - type: string - description: The name of the metric being measured - value: - oneOf: - - type: integer - - type: number - description: >- - The numeric value of the metric measurement - unit: - type: string - description: >- - The unit of measurement for the metric value - additionalProperties: false - required: - - trace_id - - span_id - - timestamp - - type - - metric - - value - - unit - title: MetricEvent - description: >- - A metric event containing a measured value. - SpanEndPayload: - type: object - properties: - type: - $ref: '#/components/schemas/StructuredLogType' - const: span_end - default: span_end - description: Payload type identifier set to SPAN_END - status: - $ref: '#/components/schemas/SpanStatus' - description: >- - The final status of the span indicating success or failure - additionalProperties: false - required: - - type - - status - title: SpanEndPayload - description: Payload for a span end event. - SpanStartPayload: - type: object - properties: - type: - $ref: '#/components/schemas/StructuredLogType' - const: span_start - default: span_start - description: >- - Payload type identifier set to SPAN_START - name: - type: string - description: >- - Human-readable name describing the operation this span represents - parent_span_id: - type: string - description: >- - (Optional) Unique identifier for the parent span, if this is a child span - additionalProperties: false - required: - - type - - name - title: SpanStartPayload - description: Payload for a span start event. - StructuredLogEvent: - type: object - properties: - trace_id: - type: string - description: >- - Unique identifier for the trace this event belongs to - span_id: - type: string - description: >- - Unique identifier for the span this event belongs to - timestamp: - type: string - format: date-time - description: Timestamp when the event occurred - attributes: - type: object - additionalProperties: - oneOf: - - type: string - - type: integer - - type: number - - type: boolean - - type: 'null' - description: >- - (Optional) Key-value pairs containing additional metadata about the event - type: - $ref: '#/components/schemas/EventType' - const: structured_log - default: structured_log - description: >- - Event type identifier set to STRUCTURED_LOG - payload: - $ref: '#/components/schemas/StructuredLogPayload' - description: >- - The structured payload data for the log event - additionalProperties: false - required: - - trace_id - - span_id - - timestamp - - type - - payload - title: StructuredLogEvent - description: >- - A structured log event containing typed payload data. - StructuredLogPayload: - oneOf: - - $ref: '#/components/schemas/SpanStartPayload' - - $ref: '#/components/schemas/SpanEndPayload' - discriminator: - propertyName: type - mapping: - span_start: '#/components/schemas/SpanStartPayload' - span_end: '#/components/schemas/SpanEndPayload' - StructuredLogType: - type: string - enum: - - span_start - - span_end - title: StructuredLogType - description: >- - The type of structured log event payload. - UnstructuredLogEvent: - type: object - properties: - trace_id: - type: string - description: >- - Unique identifier for the trace this event belongs to - span_id: - type: string - description: >- - Unique identifier for the span this event belongs to - timestamp: - type: string - format: date-time - description: Timestamp when the event occurred - attributes: - type: object - additionalProperties: - oneOf: - - type: string - - type: integer - - type: number - - type: boolean - - type: 'null' - description: >- - (Optional) Key-value pairs containing additional metadata about the event - type: - $ref: '#/components/schemas/EventType' - const: unstructured_log - default: unstructured_log - description: >- - Event type identifier set to UNSTRUCTURED_LOG - message: - type: string - description: The log message text - severity: - $ref: '#/components/schemas/LogSeverity' - description: The severity level of the log message - additionalProperties: false - required: - - trace_id - - span_id - - timestamp - - type - - message - - severity - title: UnstructuredLogEvent - description: >- - An unstructured log event containing a simple text message. - LogEventRequest: - type: object - properties: - event: - $ref: '#/components/schemas/Event' - description: The event to log. - ttl_seconds: - type: integer - description: The time to live of the event. - additionalProperties: false - required: - - event - - ttl_seconds - title: LogEventRequest - VectorStoreChunkingStrategy: - oneOf: - - $ref: '#/components/schemas/VectorStoreChunkingStrategyAuto' - - $ref: '#/components/schemas/VectorStoreChunkingStrategyStatic' - discriminator: - propertyName: type - mapping: - auto: '#/components/schemas/VectorStoreChunkingStrategyAuto' - static: '#/components/schemas/VectorStoreChunkingStrategyStatic' - VectorStoreChunkingStrategyAuto: - type: object - properties: - type: - type: string - const: auto - default: auto - description: >- - Strategy type, always "auto" for automatic chunking - additionalProperties: false - required: - - type - title: VectorStoreChunkingStrategyAuto - description: >- - Automatic chunking strategy for vector store files. - VectorStoreChunkingStrategyStatic: - type: object - properties: - type: - type: string - const: static - default: static - description: >- - Strategy type, always "static" for static chunking - static: - $ref: '#/components/schemas/VectorStoreChunkingStrategyStaticConfig' - description: >- - Configuration parameters for the static chunking strategy - additionalProperties: false - required: - - type - - static - title: VectorStoreChunkingStrategyStatic - description: >- - Static chunking strategy with configurable parameters. - VectorStoreChunkingStrategyStaticConfig: - type: object - properties: - chunk_overlap_tokens: - type: integer - default: 400 - description: >- - Number of tokens to overlap between adjacent chunks - max_chunk_size_tokens: - type: integer - default: 800 - description: >- - Maximum number of tokens per chunk, must be between 100 and 4096 - additionalProperties: false - required: - - chunk_overlap_tokens - - max_chunk_size_tokens - title: VectorStoreChunkingStrategyStaticConfig - description: >- - Configuration for static chunking strategy. - OpenaiAttachFileToVectorStoreRequest: - type: object - properties: - file_id: - type: string - description: >- - The ID of the file to attach to the vector store. - attributes: - type: object - additionalProperties: - oneOf: - - type: 'null' - - type: boolean - - type: number - - type: string - - type: array - - type: object - description: >- - The key-value attributes stored with the file, which can be used for filtering. - chunking_strategy: - $ref: '#/components/schemas/VectorStoreChunkingStrategy' - description: >- - The chunking strategy to use for the file. - additionalProperties: false - required: - - file_id - title: OpenaiAttachFileToVectorStoreRequest - VectorStoreFileLastError: - type: object - properties: - code: - oneOf: - - type: string - const: server_error - - type: string - const: rate_limit_exceeded - description: >- - Error code indicating the type of failure - message: - type: string - description: >- - Human-readable error message describing the failure - additionalProperties: false - required: - - code - - message - title: VectorStoreFileLastError - description: >- - Error information for failed vector store file processing. - VectorStoreFileObject: - type: object - properties: - id: - type: string - description: Unique identifier for the file - object: - type: string - default: vector_store.file - description: >- - Object type identifier, always "vector_store.file" - attributes: - type: object - additionalProperties: - oneOf: - - type: 'null' - - type: boolean - - type: number - - type: string - - type: array - - type: object - description: >- - Key-value attributes associated with the file - chunking_strategy: - $ref: '#/components/schemas/VectorStoreChunkingStrategy' - description: >- - Strategy used for splitting the file into chunks - created_at: - type: integer - description: >- - Timestamp when the file was added to the vector store - last_error: - $ref: '#/components/schemas/VectorStoreFileLastError' - description: >- - (Optional) Error information if file processing failed - status: - $ref: '#/components/schemas/VectorStoreFileStatus' - description: Current processing status of the file - usage_bytes: - type: integer - default: 0 - description: Storage space used by this file in bytes - vector_store_id: - type: string - description: >- - ID of the vector store containing this file - additionalProperties: false - required: - - id - - object - - attributes - - chunking_strategy - - created_at - - status - - usage_bytes - - vector_store_id - title: VectorStoreFileObject - description: OpenAI Vector Store File object. - VectorStoreFileStatus: - oneOf: - - type: string - const: completed - - type: string - const: in_progress - - type: string - const: cancelled - - type: string - const: failed - OpenAIJSONSchema: - type: object - properties: - name: - type: string - description: Name of the schema - description: - type: string - description: (Optional) Description of the schema - strict: - type: boolean - description: >- - (Optional) Whether to enforce strict adherence to the schema - schema: - type: object - additionalProperties: - oneOf: - - type: 'null' - - type: boolean - - type: number - - type: string - - type: array - - type: object - description: (Optional) The JSON schema definition - additionalProperties: false - required: - - name - title: OpenAIJSONSchema - description: >- - JSON schema specification for OpenAI-compatible structured response format. - OpenAIResponseFormatJSONObject: - type: object - properties: - type: - type: string - const: json_object - default: json_object - description: >- - Must be "json_object" to indicate generic JSON object response format - additionalProperties: false - required: - - type - title: OpenAIResponseFormatJSONObject - description: >- - JSON object response format for OpenAI-compatible chat completion requests. - OpenAIResponseFormatJSONSchema: - type: object - properties: - type: - type: string - const: json_schema - default: json_schema - description: >- - Must be "json_schema" to indicate structured JSON response format - json_schema: - $ref: '#/components/schemas/OpenAIJSONSchema' - description: >- - The JSON schema specification for the response - additionalProperties: false - required: - - type - - json_schema - title: OpenAIResponseFormatJSONSchema - description: >- - JSON schema response format for OpenAI-compatible chat completion requests. - OpenAIResponseFormatParam: - oneOf: - - $ref: '#/components/schemas/OpenAIResponseFormatText' - - $ref: '#/components/schemas/OpenAIResponseFormatJSONSchema' - - $ref: '#/components/schemas/OpenAIResponseFormatJSONObject' - discriminator: - propertyName: type - mapping: - text: '#/components/schemas/OpenAIResponseFormatText' - json_schema: '#/components/schemas/OpenAIResponseFormatJSONSchema' - json_object: '#/components/schemas/OpenAIResponseFormatJSONObject' - OpenAIResponseFormatText: - type: object - properties: - type: - type: string - const: text - default: text - description: >- - Must be "text" to indicate plain text response format - additionalProperties: false - required: - - type - title: OpenAIResponseFormatText - description: >- - Text response format for OpenAI-compatible chat completion requests. - OpenaiChatCompletionRequest: - type: object - properties: - model: - type: string - description: >- - The identifier of the model to use. The model must be registered with - Llama Stack and available via the /models endpoint. - messages: - type: array - items: - $ref: '#/components/schemas/OpenAIMessageParam' - description: List of messages in the conversation. - frequency_penalty: - type: number - description: >- - (Optional) The penalty for repeated tokens. - function_call: - oneOf: - - type: string - - type: object - additionalProperties: - oneOf: - - type: 'null' - - type: boolean - - type: number - - type: string - - type: array - - type: object - description: (Optional) The function call to use. - functions: - type: array - items: - type: object - additionalProperties: - oneOf: - - type: 'null' - - type: boolean - - type: number - - type: string - - type: array - - type: object - description: (Optional) List of functions to use. - logit_bias: - type: object - additionalProperties: - type: number - description: (Optional) The logit bias to use. - logprobs: - type: boolean - description: (Optional) The log probabilities to use. - max_completion_tokens: - type: integer - description: >- - (Optional) The maximum number of tokens to generate. - max_tokens: - type: integer - description: >- - (Optional) The maximum number of tokens to generate. - n: - type: integer - description: >- - (Optional) The number of completions to generate. - parallel_tool_calls: - type: boolean - description: >- - (Optional) Whether to parallelize tool calls. - presence_penalty: - type: number - description: >- - (Optional) The penalty for repeated tokens. - response_format: - $ref: '#/components/schemas/OpenAIResponseFormatParam' - description: (Optional) The response format to use. - seed: - type: integer - description: (Optional) The seed to use. - stop: - oneOf: - - type: string - - type: array - items: - type: string - description: (Optional) The stop tokens to use. - stream: - type: boolean - description: >- - (Optional) Whether to stream the response. - stream_options: - type: object - additionalProperties: - oneOf: - - type: 'null' - - type: boolean - - type: number - - type: string - - type: array - - type: object - description: (Optional) The stream options to use. - temperature: - type: number - description: (Optional) The temperature to use. - tool_choice: - oneOf: - - type: string - - type: object - additionalProperties: - oneOf: - - type: 'null' - - type: boolean - - type: number - - type: string - - type: array - - type: object - description: (Optional) The tool choice to use. - tools: - type: array - items: - type: object - additionalProperties: - oneOf: - - type: 'null' - - type: boolean - - type: number - - type: string - - type: array - - type: object - description: (Optional) The tools to use. - top_logprobs: - type: integer - description: >- - (Optional) The top log probabilities to use. - top_p: - type: number - description: (Optional) The top p to use. - user: - type: string - description: (Optional) The user to use. - additionalProperties: false - required: - - model - - messages - title: OpenaiChatCompletionRequest - OpenAIChatCompletion: - type: object - properties: - id: - type: string - description: The ID of the chat completion - choices: - type: array - items: - $ref: '#/components/schemas/OpenAIChoice' - description: List of choices - object: - type: string - const: chat.completion - default: chat.completion - description: >- - The object type, which will be "chat.completion" - created: - type: integer - description: >- - The Unix timestamp in seconds when the chat completion was created - model: - type: string - description: >- - The model that was used to generate the chat completion - additionalProperties: false - required: - - id - - choices - - object - - created - - model - title: OpenAIChatCompletion - description: >- - Response from an OpenAI-compatible chat completion request. - OpenAIChatCompletionChunk: - type: object - properties: - id: - type: string - description: The ID of the chat completion - choices: - type: array - items: - $ref: '#/components/schemas/OpenAIChunkChoice' - description: List of choices - object: - type: string - const: chat.completion.chunk - default: chat.completion.chunk - description: >- - The object type, which will be "chat.completion.chunk" - created: - type: integer - description: >- - The Unix timestamp in seconds when the chat completion was created - model: - type: string - description: >- - The model that was used to generate the chat completion - additionalProperties: false - required: - - id - - choices - - object - - created - - model - title: OpenAIChatCompletionChunk - description: >- - Chunk from a streaming response to an OpenAI-compatible chat completion request. - OpenAIChoiceDelta: - type: object - properties: - content: - type: string - description: (Optional) The content of the delta - refusal: - type: string - description: (Optional) The refusal of the delta - role: - type: string - description: (Optional) The role of the delta - tool_calls: - type: array - items: - $ref: '#/components/schemas/OpenAIChatCompletionToolCall' - description: (Optional) The tool calls of the delta - additionalProperties: false - title: OpenAIChoiceDelta - description: >- - A delta from an OpenAI-compatible chat completion streaming response. - OpenAIChunkChoice: - type: object - properties: - delta: - $ref: '#/components/schemas/OpenAIChoiceDelta' - description: The delta from the chunk - finish_reason: - type: string - description: The reason the model stopped generating - index: - type: integer - description: The index of the choice - logprobs: - $ref: '#/components/schemas/OpenAIChoiceLogprobs' - description: >- - (Optional) The log probabilities for the tokens in the message - additionalProperties: false - required: - - delta - - finish_reason - - index - title: OpenAIChunkChoice - description: >- - A chunk choice from an OpenAI-compatible chat completion streaming response. - OpenaiCompletionRequest: - type: object - properties: - model: - type: string - description: >- - The identifier of the model to use. The model must be registered with - Llama Stack and available via the /models endpoint. - prompt: - oneOf: - - type: string - - type: array - items: - type: string - - type: array - items: - type: integer - - type: array - items: - type: array - items: - type: integer - description: The prompt to generate a completion for. - best_of: - type: integer - description: >- - (Optional) The number of completions to generate. - echo: - type: boolean - description: (Optional) Whether to echo the prompt. - frequency_penalty: - type: number - description: >- - (Optional) The penalty for repeated tokens. - logit_bias: - type: object - additionalProperties: - type: number - description: (Optional) The logit bias to use. - logprobs: - type: boolean - description: (Optional) The log probabilities to use. - max_tokens: - type: integer - description: >- - (Optional) The maximum number of tokens to generate. - n: - type: integer - description: >- - (Optional) The number of completions to generate. - presence_penalty: - type: number - description: >- - (Optional) The penalty for repeated tokens. - seed: - type: integer - description: (Optional) The seed to use. - stop: - oneOf: - - type: string - - type: array - items: - type: string - description: (Optional) The stop tokens to use. - stream: - type: boolean - description: >- - (Optional) Whether to stream the response. - stream_options: - type: object - additionalProperties: - oneOf: - - type: 'null' - - type: boolean - - type: number - - type: string - - type: array - - type: object - description: (Optional) The stream options to use. - temperature: - type: number - description: (Optional) The temperature to use. - top_p: - type: number - description: (Optional) The top p to use. - user: - type: string - description: (Optional) The user to use. - guided_choice: - type: array - items: - type: string - prompt_logprobs: - type: integer - suffix: - type: string - description: >- - (Optional) The suffix that should be appended to the completion. - additionalProperties: false - required: - - model - - prompt - title: OpenaiCompletionRequest - OpenAICompletion: - type: object - properties: - id: - type: string - choices: - type: array - items: - $ref: '#/components/schemas/OpenAICompletionChoice' - created: - type: integer - model: - type: string - object: - type: string - const: text_completion - default: text_completion - additionalProperties: false - required: - - id - - choices - - created - - model - - object - title: OpenAICompletion - description: >- - Response from an OpenAI-compatible completion request. - OpenAICompletionChoice: - type: object - properties: - finish_reason: - type: string - text: - type: string - index: - type: integer - logprobs: - $ref: '#/components/schemas/OpenAIChoiceLogprobs' - additionalProperties: false - required: - - finish_reason - - text - - index - title: OpenAICompletionChoice - description: >- - A choice from an OpenAI-compatible completion response. - OpenaiCreateVectorStoreRequest: - type: object - properties: - name: - type: string - description: A name for the vector store. - file_ids: - type: array - items: - type: string - description: >- - A list of File IDs that the vector store should use. Useful for tools - like `file_search` that can access files. - expires_after: - type: object - additionalProperties: - oneOf: - - type: 'null' - - type: boolean - - type: number - - type: string - - type: array - - type: object - description: >- - The expiration policy for a vector store. - chunking_strategy: - type: object - additionalProperties: - oneOf: - - type: 'null' - - type: boolean - - type: number - - type: string - - type: array - - type: object - description: >- - The chunking strategy used to chunk the file(s). If not set, will use - the `auto` strategy. - metadata: - type: object - additionalProperties: - oneOf: - - type: 'null' - - type: boolean - - type: number - - type: string - - type: array - - type: object - description: >- - Set of 16 key-value pairs that can be attached to an object. - embedding_model: - type: string - description: >- - The embedding model to use for this vector store. - embedding_dimension: - type: integer - description: >- - The dimension of the embedding vectors (default: 384). - provider_id: - type: string - description: >- - The ID of the provider to use for this vector store. - additionalProperties: false - title: OpenaiCreateVectorStoreRequest - VectorStoreFileCounts: - type: object - properties: - completed: - type: integer - description: >- - Number of files that have been successfully processed - cancelled: - type: integer - description: >- - Number of files that had their processing cancelled - failed: - type: integer - description: Number of files that failed to process - in_progress: - type: integer - description: >- - Number of files currently being processed - total: - type: integer - description: >- - Total number of files in the vector store - additionalProperties: false - required: - - completed - - cancelled - - failed - - in_progress - - total - title: VectorStoreFileCounts - description: >- - File processing status counts for a vector store. - VectorStoreObject: - type: object - properties: - id: - type: string - description: Unique identifier for the vector store - object: - type: string - default: vector_store - description: >- - Object type identifier, always "vector_store" - created_at: - type: integer - description: >- - Timestamp when the vector store was created - name: - type: string - description: (Optional) Name of the vector store - usage_bytes: - type: integer - default: 0 - description: >- - Storage space used by the vector store in bytes - file_counts: - $ref: '#/components/schemas/VectorStoreFileCounts' - description: >- - File processing status counts for the vector store - status: - type: string - default: completed - description: Current status of the vector store - expires_after: - type: object - additionalProperties: - oneOf: - - type: 'null' - - type: boolean - - type: number - - type: string - - type: array - - type: object - description: >- - (Optional) Expiration policy for the vector store - expires_at: - type: integer - description: >- - (Optional) Timestamp when the vector store will expire - last_active_at: - type: integer - description: >- - (Optional) Timestamp of last activity on the vector store - metadata: - type: object - additionalProperties: - oneOf: - - type: 'null' - - type: boolean - - type: number - - type: string - - type: array - - type: object - description: >- - Set of key-value pairs that can be attached to the vector store - additionalProperties: false - required: - - id - - object - - created_at - - usage_bytes - - file_counts - - status - - metadata - title: VectorStoreObject - description: OpenAI Vector Store object. - OpenAIFileDeleteResponse: - type: object - properties: - id: - type: string - description: The file identifier that was deleted - object: - type: string - const: file - default: file - description: The object type, which is always "file" - deleted: - type: boolean - description: >- - Whether the file was successfully deleted - additionalProperties: false - required: - - id - - object - - deleted - title: OpenAIFileDeleteResponse - description: >- - Response for deleting a file in OpenAI Files API. - VectorStoreDeleteResponse: - type: object - properties: - id: - type: string - description: >- - Unique identifier of the deleted vector store - object: - type: string - default: vector_store.deleted - description: >- - Object type identifier for the deletion response - deleted: - type: boolean - default: true - description: >- - Whether the deletion operation was successful - additionalProperties: false - required: - - id - - object - - deleted - title: VectorStoreDeleteResponse - description: Response from deleting a vector store. - VectorStoreFileDeleteResponse: - type: object - properties: - id: - type: string - description: Unique identifier of the deleted file - object: - type: string - default: vector_store.file.deleted - description: >- - Object type identifier for the deletion response - deleted: - type: boolean - default: true - description: >- - Whether the deletion operation was successful - additionalProperties: false - required: - - id - - object - - deleted - title: VectorStoreFileDeleteResponse - description: >- - Response from deleting a vector store file. - OpenaiEmbeddingsRequest: - type: object - properties: - model: - type: string - description: >- - The identifier of the model to use. The model must be an embedding model - registered with Llama Stack and available via the /models endpoint. - input: - oneOf: - - type: string - - type: array - items: - type: string - description: >- - Input text to embed, encoded as a string or array of strings. To embed - multiple inputs in a single request, pass an array of strings. - encoding_format: - type: string - description: >- - (Optional) The format to return the embeddings in. Can be either "float" - or "base64". Defaults to "float". - dimensions: - type: integer - description: >- - (Optional) The number of dimensions the resulting output embeddings should - have. Only supported in text-embedding-3 and later models. - user: - type: string - description: >- - (Optional) A unique identifier representing your end-user, which can help - OpenAI to monitor and detect abuse. - additionalProperties: false - required: - - model - - input - title: OpenaiEmbeddingsRequest - OpenAIEmbeddingData: - type: object - properties: - object: - type: string - const: embedding - default: embedding - description: >- - The object type, which will be "embedding" - embedding: - oneOf: - - type: array - items: - type: number - - type: string - description: >- - The embedding vector as a list of floats (when encoding_format="float") - or as a base64-encoded string (when encoding_format="base64") - index: - type: integer - description: >- - The index of the embedding in the input list - additionalProperties: false - required: - - object - - embedding - - index - title: OpenAIEmbeddingData - description: >- - A single embedding data object from an OpenAI-compatible embeddings response. - OpenAIEmbeddingUsage: - type: object - properties: - prompt_tokens: - type: integer - description: The number of tokens in the input - total_tokens: - type: integer - description: The total number of tokens used - additionalProperties: false - required: - - prompt_tokens - - total_tokens - title: OpenAIEmbeddingUsage - description: >- - Usage information for an OpenAI-compatible embeddings response. - OpenAIEmbeddingsResponse: - type: object - properties: - object: - type: string - const: list - default: list - description: The object type, which will be "list" - data: - type: array - items: - $ref: '#/components/schemas/OpenAIEmbeddingData' - description: List of embedding data objects - model: - type: string - description: >- - The model that was used to generate the embeddings - usage: - $ref: '#/components/schemas/OpenAIEmbeddingUsage' - description: Usage information - additionalProperties: false - required: - - object - - data - - model - - usage - title: OpenAIEmbeddingsResponse - description: >- - Response from an OpenAI-compatible embeddings request. - OpenAIFilePurpose: - type: string - enum: - - assistants - - batch - title: OpenAIFilePurpose - description: >- - Valid purpose values for OpenAI Files API. - ListOpenAIFileResponse: - type: object - properties: - data: - type: array - items: - $ref: '#/components/schemas/OpenAIFileObject' - description: List of file objects - has_more: - type: boolean - description: >- - Whether there are more files available beyond this page - first_id: - type: string - description: >- - ID of the first file in the list for pagination - last_id: - type: string - description: >- - ID of the last file in the list for pagination - object: - type: string - const: list - default: list - description: The object type, which is always "list" - additionalProperties: false - required: - - data - - has_more - - first_id - - last_id - - object - title: ListOpenAIFileResponse - description: >- - Response for listing files in OpenAI Files API. - OpenAIFileObject: - type: object - properties: - object: - type: string - const: file - default: file - description: The object type, which is always "file" - id: - type: string - description: >- - The file identifier, which can be referenced in the API endpoints - bytes: - type: integer - description: The size of the file, in bytes - created_at: - type: integer - description: >- - The Unix timestamp (in seconds) for when the file was created - expires_at: - type: integer - description: >- - The Unix timestamp (in seconds) for when the file expires - filename: - type: string - description: The name of the file - purpose: - type: string - enum: - - assistants - - batch - description: The intended purpose of the file - additionalProperties: false - required: - - object - - id - - bytes - - created_at - - expires_at - - filename - - purpose - title: OpenAIFileObject - description: >- - OpenAI File object as defined in the OpenAI Files API. - VectorStoreListFilesResponse: - type: object - properties: - object: - type: string - default: list - description: Object type identifier, always "list" - data: - type: array - items: - $ref: '#/components/schemas/VectorStoreFileObject' - description: List of vector store file objects - first_id: - type: string - description: >- - (Optional) ID of the first file in the list for pagination - last_id: - type: string - description: >- - (Optional) ID of the last file in the list for pagination - has_more: - type: boolean - default: false - description: >- - Whether there are more files available beyond this page - additionalProperties: false - required: - - object - - data - - has_more - title: VectorStoreListFilesResponse - description: >- - Response from listing files in a vector store. - OpenAIModel: - type: object - properties: - id: - type: string - object: - type: string - const: model - default: model - created: - type: integer - owned_by: - type: string - additionalProperties: false - required: - - id - - object - - created - - owned_by - title: OpenAIModel - description: A model from OpenAI. - OpenAIListModelsResponse: - type: object - properties: - data: - type: array - items: - $ref: '#/components/schemas/OpenAIModel' - additionalProperties: false - required: - - data - title: OpenAIListModelsResponse - VectorStoreListResponse: - type: object - properties: - object: - type: string - default: list - description: Object type identifier, always "list" - data: - type: array - items: - $ref: '#/components/schemas/VectorStoreObject' - description: List of vector store objects - first_id: - type: string - description: >- - (Optional) ID of the first vector store in the list for pagination - last_id: - type: string - description: >- - (Optional) ID of the last vector store in the list for pagination - has_more: - type: boolean - default: false - description: >- - Whether there are more vector stores available beyond this page - additionalProperties: false - required: - - object - - data - - has_more - title: VectorStoreListResponse - description: Response from listing vector stores. - Response: - type: object - title: Response - VectorStoreContent: - type: object - properties: - type: - type: string - const: text - description: >- - Content type, currently only "text" is supported - text: - type: string - description: The actual text content - additionalProperties: false - required: - - type - - text - title: VectorStoreContent - description: >- - Content item from a vector store file or search result. - VectorStoreFileContentsResponse: - type: object - properties: - file_id: - type: string - description: Unique identifier for the file - filename: - type: string - description: Name of the file - attributes: - type: object - additionalProperties: - oneOf: - - type: 'null' - - type: boolean - - type: number - - type: string - - type: array - - type: object - description: >- - Key-value attributes associated with the file - content: - type: array - items: - $ref: '#/components/schemas/VectorStoreContent' - description: List of content items from the file - additionalProperties: false - required: - - file_id - - filename - - attributes - - content - title: VectorStoreFileContentsResponse - description: >- - Response from retrieving the contents of a vector store file. - OpenaiSearchVectorStoreRequest: - type: object - properties: - query: - oneOf: - - type: string - - type: array - items: - type: string - description: >- - The query string or array for performing the search. - filters: - type: object - additionalProperties: - oneOf: - - type: 'null' - - type: boolean - - type: number - - type: string - - type: array - - type: object - description: >- - Filters based on file attributes to narrow the search results. - max_num_results: - type: integer - description: >- - Maximum number of results to return (1 to 50 inclusive, default 10). - ranking_options: - type: object - properties: - ranker: - type: string - description: >- - (Optional) Name of the ranking algorithm to use - score_threshold: - type: number - default: 0.0 - description: >- - (Optional) Minimum relevance score threshold for results - additionalProperties: false - description: >- - Ranking options for fine-tuning the search results. - rewrite_query: - type: boolean - description: >- - Whether to rewrite the natural language query for vector search (default - false) - search_mode: - type: string - description: >- - The search mode to use - "keyword", "vector", or "hybrid" (default "vector") - additionalProperties: false - required: - - query - title: OpenaiSearchVectorStoreRequest - VectorStoreSearchResponse: - type: object - properties: - file_id: - type: string - description: >- - Unique identifier of the file containing the result - filename: - type: string - description: Name of the file containing the result - score: - type: number - description: Relevance score for this search result - attributes: - type: object - additionalProperties: - oneOf: - - type: string - - type: number - - type: boolean - description: >- - (Optional) Key-value attributes associated with the file - content: - type: array - items: - $ref: '#/components/schemas/VectorStoreContent' - description: >- - List of content items matching the search query - additionalProperties: false - required: - - file_id - - filename - - score - - content - title: VectorStoreSearchResponse - description: Response from searching a vector store. - VectorStoreSearchResponsePage: - type: object - properties: - object: - type: string - default: vector_store.search_results.page - description: >- - Object type identifier for the search results page - search_query: - type: string - description: >- - The original search query that was executed - data: - type: array - items: - $ref: '#/components/schemas/VectorStoreSearchResponse' - description: List of search result objects - has_more: - type: boolean - default: false - description: >- - Whether there are more results available beyond this page - next_page: - type: string - description: >- - (Optional) Token for retrieving the next page of results - additionalProperties: false - required: - - object - - search_query - - data - - has_more - title: VectorStoreSearchResponsePage - description: >- - Paginated response from searching a vector store. - OpenaiUpdateVectorStoreRequest: - type: object - properties: - name: - type: string - description: The name of the vector store. - expires_after: - type: object - additionalProperties: - oneOf: - - type: 'null' - - type: boolean - - type: number - - type: string - - type: array - - type: object - description: >- - The expiration policy for a vector store. - metadata: - type: object - additionalProperties: - oneOf: - - type: 'null' - - type: boolean - - type: number - - type: string - - type: array - - type: object - description: >- - Set of 16 key-value pairs that can be attached to an object. - additionalProperties: false - title: OpenaiUpdateVectorStoreRequest - OpenaiUpdateVectorStoreFileRequest: - type: object - properties: - attributes: - type: object - additionalProperties: - oneOf: - - type: 'null' - - type: boolean - - type: number - - type: string - - type: array - - type: object - description: >- - The updated key-value attributes to store with the file. - additionalProperties: false - required: - - attributes - title: OpenaiUpdateVectorStoreFileRequest - DPOAlignmentConfig: - type: object - properties: - beta: - type: number - description: Temperature parameter for the DPO loss - loss_type: - $ref: '#/components/schemas/DPOLossType' - default: sigmoid - description: The type of loss function to use for DPO - additionalProperties: false - required: - - beta - - loss_type - title: DPOAlignmentConfig - description: >- - Configuration for Direct Preference Optimization (DPO) alignment. - DPOLossType: - type: string - enum: - - sigmoid - - hinge - - ipo - - kto_pair - title: DPOLossType - DataConfig: - type: object - properties: - dataset_id: - type: string - description: >- - Unique identifier for the training dataset - batch_size: - type: integer - description: Number of samples per training batch - shuffle: - type: boolean - description: >- - Whether to shuffle the dataset during training - data_format: - $ref: '#/components/schemas/DatasetFormat' - description: >- - Format of the dataset (instruct or dialog) - validation_dataset_id: - type: string - description: >- - (Optional) Unique identifier for the validation dataset - packed: - type: boolean - default: false - description: >- - (Optional) Whether to pack multiple samples into a single sequence for - efficiency - train_on_input: - type: boolean - default: false - description: >- - (Optional) Whether to compute loss on input tokens as well as output tokens - additionalProperties: false - required: - - dataset_id - - batch_size - - shuffle - - data_format - title: DataConfig - description: >- - Configuration for training data and data loading. - DatasetFormat: - type: string - enum: - - instruct - - dialog - title: DatasetFormat - description: Format of the training dataset. - EfficiencyConfig: - type: object - properties: - enable_activation_checkpointing: - type: boolean - default: false - description: >- - (Optional) Whether to use activation checkpointing to reduce memory usage - enable_activation_offloading: - type: boolean - default: false - description: >- - (Optional) Whether to offload activations to CPU to save GPU memory - memory_efficient_fsdp_wrap: - type: boolean - default: false - description: >- - (Optional) Whether to use memory-efficient FSDP wrapping - fsdp_cpu_offload: - type: boolean - default: false - description: >- - (Optional) Whether to offload FSDP parameters to CPU - additionalProperties: false - title: EfficiencyConfig - description: >- - Configuration for memory and compute efficiency optimizations. - OptimizerConfig: - type: object - properties: - optimizer_type: - $ref: '#/components/schemas/OptimizerType' - description: >- - Type of optimizer to use (adam, adamw, or sgd) - lr: - type: number - description: Learning rate for the optimizer - weight_decay: - type: number - description: >- - Weight decay coefficient for regularization - num_warmup_steps: - type: integer - description: Number of steps for learning rate warmup - additionalProperties: false - required: - - optimizer_type - - lr - - weight_decay - - num_warmup_steps - title: OptimizerConfig - description: >- - Configuration parameters for the optimization algorithm. - OptimizerType: - type: string - enum: - - adam - - adamw - - sgd - title: OptimizerType - description: >- - Available optimizer algorithms for training. - TrainingConfig: - type: object - properties: - n_epochs: - type: integer - description: Number of training epochs to run - max_steps_per_epoch: - type: integer - default: 1 - description: Maximum number of steps to run per epoch - gradient_accumulation_steps: - type: integer - default: 1 - description: >- - Number of steps to accumulate gradients before updating - max_validation_steps: - type: integer - default: 1 - description: >- - (Optional) Maximum number of validation steps per epoch - data_config: - $ref: '#/components/schemas/DataConfig' - description: >- - (Optional) Configuration for data loading and formatting - optimizer_config: - $ref: '#/components/schemas/OptimizerConfig' - description: >- - (Optional) Configuration for the optimization algorithm - efficiency_config: - $ref: '#/components/schemas/EfficiencyConfig' - description: >- - (Optional) Configuration for memory and compute optimizations - dtype: - type: string - default: bf16 - description: >- - (Optional) Data type for model parameters (bf16, fp16, fp32) - additionalProperties: false - required: - - n_epochs - - max_steps_per_epoch - - gradient_accumulation_steps - title: TrainingConfig - description: >- - Comprehensive configuration for the training process. - PreferenceOptimizeRequest: - type: object - properties: - job_uuid: - type: string - description: The UUID of the job to create. - finetuned_model: - type: string - description: The model to fine-tune. - algorithm_config: - $ref: '#/components/schemas/DPOAlignmentConfig' - description: The algorithm configuration. - training_config: - $ref: '#/components/schemas/TrainingConfig' - description: The training configuration. - hyperparam_search_config: - type: object - additionalProperties: - oneOf: - - type: 'null' - - type: boolean - - type: number - - type: string - - type: array - - type: object - description: The hyperparam search configuration. - logger_config: - type: object - additionalProperties: - oneOf: - - type: 'null' - - type: boolean - - type: number - - type: string - - type: array - - type: object - description: The logger configuration. - additionalProperties: false - required: - - job_uuid - - finetuned_model - - algorithm_config - - training_config - - hyperparam_search_config - - logger_config - title: PreferenceOptimizeRequest - PostTrainingJob: - type: object - properties: - job_uuid: - type: string - additionalProperties: false - required: - - job_uuid - title: PostTrainingJob - DefaultRAGQueryGeneratorConfig: - type: object - properties: - type: - type: string - const: default - default: default - description: >- - Type of query generator, always 'default' - separator: - type: string - default: ' ' - description: >- - String separator used to join query terms - additionalProperties: false - required: - - type - - separator - title: DefaultRAGQueryGeneratorConfig - description: >- - Configuration for the default RAG query generator. - LLMRAGQueryGeneratorConfig: - type: object - properties: - type: - type: string - const: llm - default: llm - description: Type of query generator, always 'llm' - model: - type: string - description: >- - Name of the language model to use for query generation - template: - type: string - description: >- - Template string for formatting the query generation prompt - additionalProperties: false - required: - - type - - model - - template - title: LLMRAGQueryGeneratorConfig - description: >- - Configuration for the LLM-based RAG query generator. - RAGQueryConfig: - type: object - properties: - query_generator_config: - $ref: '#/components/schemas/RAGQueryGeneratorConfig' - description: Configuration for the query generator. - max_tokens_in_context: - type: integer - default: 4096 - description: Maximum number of tokens in the context. - max_chunks: - type: integer - default: 5 - description: Maximum number of chunks to retrieve. - chunk_template: - type: string - default: > - Result {index} - - Content: {chunk.content} - - Metadata: {metadata} - description: >- - Template for formatting each retrieved chunk in the context. Available - placeholders: {index} (1-based chunk ordinal), {chunk.content} (chunk - content string), {metadata} (chunk metadata dict). Default: "Result {index}\nContent: - {chunk.content}\nMetadata: {metadata}\n" - mode: - $ref: '#/components/schemas/RAGSearchMode' - default: vector - description: >- - Search mode for retrieval—either "vector", "keyword", or "hybrid". Default - "vector". - ranker: - $ref: '#/components/schemas/Ranker' - description: >- - Configuration for the ranker to use in hybrid search. Defaults to RRF - ranker. - additionalProperties: false - required: - - query_generator_config - - max_tokens_in_context - - max_chunks - - chunk_template - title: RAGQueryConfig - description: >- - Configuration for the RAG query generation. - RAGQueryGeneratorConfig: - oneOf: - - $ref: '#/components/schemas/DefaultRAGQueryGeneratorConfig' - - $ref: '#/components/schemas/LLMRAGQueryGeneratorConfig' - discriminator: - propertyName: type - mapping: - default: '#/components/schemas/DefaultRAGQueryGeneratorConfig' - llm: '#/components/schemas/LLMRAGQueryGeneratorConfig' - RAGSearchMode: - type: string - enum: - - vector - - keyword - - hybrid - title: RAGSearchMode - description: >- - Search modes for RAG query retrieval: - VECTOR: Uses vector similarity search - for semantic matching - KEYWORD: Uses keyword-based search for exact matching - - HYBRID: Combines both vector and keyword search for better results - RRFRanker: - type: object - properties: - type: - type: string - const: rrf - default: rrf - description: The type of ranker, always "rrf" - impact_factor: - type: number - default: 60.0 - description: >- - The impact factor for RRF scoring. Higher values give more weight to higher-ranked - results. Must be greater than 0 - additionalProperties: false - required: - - type - - impact_factor - title: RRFRanker - description: >- - Reciprocal Rank Fusion (RRF) ranker configuration. - Ranker: - oneOf: - - $ref: '#/components/schemas/RRFRanker' - - $ref: '#/components/schemas/WeightedRanker' - discriminator: - propertyName: type - mapping: - rrf: '#/components/schemas/RRFRanker' - weighted: '#/components/schemas/WeightedRanker' - WeightedRanker: - type: object - properties: - type: - type: string - const: weighted - default: weighted - description: The type of ranker, always "weighted" - alpha: - type: number - default: 0.5 - description: >- - Weight factor between 0 and 1. 0 means only use keyword scores, 1 means - only use vector scores, values in between blend both scores. - additionalProperties: false - required: - - type - - alpha - title: WeightedRanker - description: >- - Weighted ranker configuration that combines vector and keyword scores. - QueryRequest: - type: object - properties: - content: - $ref: '#/components/schemas/InterleavedContent' - description: >- - The query content to search for in the indexed documents - vector_db_ids: - type: array - items: - type: string - description: >- - List of vector database IDs to search within - query_config: - $ref: '#/components/schemas/RAGQueryConfig' - description: >- - (Optional) Configuration parameters for the query operation - additionalProperties: false - required: - - content - - vector_db_ids - title: QueryRequest - RAGQueryResult: - type: object - properties: - content: - $ref: '#/components/schemas/InterleavedContent' - description: >- - (Optional) The retrieved content from the query - metadata: - type: object - additionalProperties: - oneOf: - - type: 'null' - - type: boolean - - type: number - - type: string - - type: array - - type: object - description: >- - Additional metadata about the query result - additionalProperties: false - required: - - metadata - title: RAGQueryResult - description: >- - Result of a RAG query containing retrieved content and metadata. - QueryChunksRequest: - type: object - properties: - vector_db_id: - type: string - description: >- - The identifier of the vector database to query. - query: - $ref: '#/components/schemas/InterleavedContent' - description: The query to search for. - params: - type: object - additionalProperties: - oneOf: - - type: 'null' - - type: boolean - - type: number - - type: string - - type: array - - type: object - description: The parameters of the query. - additionalProperties: false - required: - - vector_db_id - - query - title: QueryChunksRequest - QueryChunksResponse: - type: object - properties: - chunks: - type: array - items: - $ref: '#/components/schemas/Chunk' - description: >- - List of content chunks returned from the query - scores: - type: array - items: - type: number - description: >- - Relevance scores corresponding to each returned chunk - additionalProperties: false - required: - - chunks - - scores - title: QueryChunksResponse - description: >- - Response from querying chunks in a vector database. - QueryMetricsRequest: - type: object - properties: - start_time: - type: integer - description: The start time of the metric to query. - end_time: - type: integer - description: The end time of the metric to query. - granularity: - type: string - description: The granularity of the metric to query. - query_type: - type: string - enum: - - range - - instant - description: The type of query to perform. - label_matchers: - type: array - items: - type: object - properties: - name: - type: string - description: The name of the label to match - value: - type: string - description: The value to match against - operator: - type: string - enum: - - '=' - - '!=' - - =~ - - '!~' - description: >- - The comparison operator to use for matching - default: '=' - additionalProperties: false - required: - - name - - value - - operator - title: MetricLabelMatcher - description: >- - A matcher for filtering metrics by label values. - description: >- - The label matchers to apply to the metric. - additionalProperties: false - required: - - start_time - - query_type - title: QueryMetricsRequest - MetricDataPoint: - type: object - properties: - timestamp: - type: integer - description: >- - Unix timestamp when the metric value was recorded - value: - type: number - description: >- - The numeric value of the metric at this timestamp - unit: - type: string - additionalProperties: false - required: - - timestamp - - value - - unit - title: MetricDataPoint - description: >- - A single data point in a metric time series. - MetricLabel: - type: object - properties: - name: - type: string - description: The name of the label - value: - type: string - description: The value of the label - additionalProperties: false - required: - - name - - value - title: MetricLabel - description: A label associated with a metric. - MetricSeries: - type: object - properties: - metric: - type: string - description: The name of the metric - labels: - type: array - items: - $ref: '#/components/schemas/MetricLabel' - description: >- - List of labels associated with this metric series - values: - type: array - items: - $ref: '#/components/schemas/MetricDataPoint' - description: >- - List of data points in chronological order - additionalProperties: false - required: - - metric - - labels - - values - title: MetricSeries - description: A time series of metric data points. - QueryMetricsResponse: - type: object - properties: - data: - type: array - items: - $ref: '#/components/schemas/MetricSeries' - description: >- - List of metric series matching the query criteria - additionalProperties: false - required: - - data - title: QueryMetricsResponse - description: >- - Response containing metric time series data. - QueryCondition: - type: object - properties: - key: - type: string - description: The attribute key to filter on - op: - $ref: '#/components/schemas/QueryConditionOp' - description: The comparison operator to apply - value: - oneOf: - - type: 'null' - - type: boolean - - type: number - - type: string - - type: array - - type: object - description: The value to compare against - additionalProperties: false - required: - - key - - op - - value - title: QueryCondition - description: A condition for filtering query results. - QueryConditionOp: - type: string - enum: - - eq - - ne - - gt - - lt - title: QueryConditionOp - description: >- - Comparison operators for query conditions. - QuerySpansRequest: - type: object - properties: - attribute_filters: - type: array - items: - $ref: '#/components/schemas/QueryCondition' - description: >- - The attribute filters to apply to the spans. - attributes_to_return: - type: array - items: - type: string - description: The attributes to return in the spans. - max_depth: - type: integer - description: The maximum depth of the tree. - additionalProperties: false - required: - - attribute_filters - - attributes_to_return - title: QuerySpansRequest - QuerySpansResponse: - type: object - properties: - data: - type: array - items: - $ref: '#/components/schemas/Span' - description: >- - List of spans matching the query criteria - additionalProperties: false - required: - - data - title: QuerySpansResponse - description: Response containing a list of spans. - QueryTracesRequest: - type: object - properties: - attribute_filters: - type: array - items: - $ref: '#/components/schemas/QueryCondition' - description: >- - The attribute filters to apply to the traces. - limit: - type: integer - description: The limit of traces to return. - offset: - type: integer - description: The offset of the traces to return. - order_by: - type: array - items: - type: string - description: The order by of the traces to return. - additionalProperties: false - title: QueryTracesRequest - QueryTracesResponse: - type: object - properties: - data: - type: array - items: - $ref: '#/components/schemas/Trace' - description: >- - List of traces matching the query criteria - additionalProperties: false - required: - - data - title: QueryTracesResponse - description: Response containing a list of traces. - RegisterBenchmarkRequest: - type: object - properties: - benchmark_id: - type: string - description: The ID of the benchmark to register. - dataset_id: - type: string - description: >- - The ID of the dataset to use for the benchmark. - scoring_functions: - type: array - items: - type: string - description: >- - The scoring functions to use for the benchmark. - provider_benchmark_id: - type: string - description: >- - The ID of the provider benchmark to use for the benchmark. - provider_id: - type: string - description: >- - The ID of the provider to use for the benchmark. - metadata: - type: object - additionalProperties: - oneOf: - - type: 'null' - - type: boolean - - type: number - - type: string - - type: array - - type: object - description: The metadata to use for the benchmark. - additionalProperties: false - required: - - benchmark_id - - dataset_id - - scoring_functions - title: RegisterBenchmarkRequest - RegisterDatasetRequest: - type: object - properties: - purpose: - type: string - enum: - - post-training/messages - - eval/question-answer - - eval/messages-answer - description: >- - The purpose of the dataset. One of: - "post-training/messages": The dataset - contains a messages column with list of messages for post-training. { - "messages": [ {"role": "user", "content": "Hello, world!"}, {"role": "assistant", - "content": "Hello, world!"}, ] } - "eval/question-answer": The dataset - contains a question column and an answer column for evaluation. { "question": - "What is the capital of France?", "answer": "Paris" } - "eval/messages-answer": - The dataset contains a messages column with list of messages and an answer - column for evaluation. { "messages": [ {"role": "user", "content": "Hello, - my name is John Doe."}, {"role": "assistant", "content": "Hello, John - Doe. How can I help you today?"}, {"role": "user", "content": "What's - my name?"}, ], "answer": "John Doe" } - source: - $ref: '#/components/schemas/DataSource' - description: >- - The data source of the dataset. Ensure that the data source schema is - compatible with the purpose of the dataset. Examples: - { "type": "uri", - "uri": "https://mywebsite.com/mydata.jsonl" } - { "type": "uri", "uri": - "lsfs://mydata.jsonl" } - { "type": "uri", "uri": "data:csv;base64,{base64_content}" - } - { "type": "uri", "uri": "huggingface://llamastack/simpleqa?split=train" - } - { "type": "rows", "rows": [ { "messages": [ {"role": "user", "content": - "Hello, world!"}, {"role": "assistant", "content": "Hello, world!"}, ] - } ] } - metadata: - type: object - additionalProperties: - oneOf: - - type: 'null' - - type: boolean - - type: number - - type: string - - type: array - - type: object - description: >- - The metadata for the dataset. - E.g. {"description": "My dataset"}. - dataset_id: - type: string - description: >- - The ID of the dataset. If not provided, an ID will be generated. - additionalProperties: false - required: - - purpose - - source - title: RegisterDatasetRequest - RegisterModelRequest: - type: object - properties: - model_id: - type: string - description: The identifier of the model to register. - provider_model_id: - type: string - description: >- - The identifier of the model in the provider. - provider_id: - type: string - description: The identifier of the provider. - metadata: - type: object - additionalProperties: - oneOf: - - type: 'null' - - type: boolean - - type: number - - type: string - - type: array - - type: object - description: Any additional metadata for this model. - model_type: - $ref: '#/components/schemas/ModelType' - description: The type of model to register. - additionalProperties: false - required: - - model_id - title: RegisterModelRequest - RegisterScoringFunctionRequest: - type: object - properties: - scoring_fn_id: - type: string - description: >- - The ID of the scoring function to register. - description: - type: string - description: The description of the scoring function. - return_type: - $ref: '#/components/schemas/ParamType' - description: The return type of the scoring function. - provider_scoring_fn_id: - type: string - description: >- - The ID of the provider scoring function to use for the scoring function. - provider_id: - type: string - description: >- - The ID of the provider to use for the scoring function. - params: - $ref: '#/components/schemas/ScoringFnParams' - description: >- - The parameters for the scoring function for benchmark eval, these can - be overridden for app eval. - additionalProperties: false - required: - - scoring_fn_id - - description - - return_type - title: RegisterScoringFunctionRequest - RegisterShieldRequest: - type: object - properties: - shield_id: - type: string - description: >- - The identifier of the shield to register. - provider_shield_id: - type: string - description: >- - The identifier of the shield in the provider. - provider_id: - type: string - description: The identifier of the provider. - params: - type: object - additionalProperties: - oneOf: - - type: 'null' - - type: boolean - - type: number - - type: string - - type: array - - type: object - description: The parameters of the shield. - additionalProperties: false - required: - - shield_id - title: RegisterShieldRequest - RegisterToolGroupRequest: - type: object - properties: - toolgroup_id: - type: string - description: The ID of the tool group to register. - provider_id: - type: string - description: >- - The ID of the provider to use for the tool group. - mcp_endpoint: - $ref: '#/components/schemas/URL' - description: >- - The MCP endpoint to use for the tool group. - args: - type: object - additionalProperties: - oneOf: - - type: 'null' - - type: boolean - - type: number - - type: string - - type: array - - type: object - description: >- - A dictionary of arguments to pass to the tool group. - additionalProperties: false - required: - - toolgroup_id - - provider_id - title: RegisterToolGroupRequest - RegisterVectorDbRequest: - type: object - properties: - vector_db_id: - type: string - description: >- - The identifier of the vector database to register. - embedding_model: - type: string - description: The embedding model to use. - embedding_dimension: - type: integer - description: The dimension of the embedding model. - provider_id: - type: string - description: The identifier of the provider. - vector_db_name: - type: string - description: The name of the vector database. - provider_vector_db_id: - type: string - description: >- - The identifier of the vector database in the provider. - additionalProperties: false - required: - - vector_db_id - - embedding_model - title: RegisterVectorDbRequest - RerankRequest: - type: object - properties: - model: - type: string - description: >- - The identifier of the reranking model to use. - query: - oneOf: - - type: string - - $ref: '#/components/schemas/OpenAIChatCompletionContentPartTextParam' - - $ref: '#/components/schemas/OpenAIChatCompletionContentPartImageParam' - description: >- - The search query to rank items against. Can be a string, text content - part, or image content part. The input must not exceed the model's max - input token length. - items: - type: array - items: - oneOf: - - type: string - - $ref: '#/components/schemas/OpenAIChatCompletionContentPartTextParam' - - $ref: '#/components/schemas/OpenAIChatCompletionContentPartImageParam' - description: >- - List of items to rerank. Each item can be a string, text content part, - or image content part. Each input must not exceed the model's max input - token length. - max_num_results: - type: integer - description: >- - (Optional) Maximum number of results to return. Default: returns all. - additionalProperties: false - required: - - model - - query - - items - title: RerankRequest - RerankData: - type: object - properties: - index: - type: integer - description: >- - The original index of the document in the input list - relevance_score: - type: number - description: >- - The relevance score from the model output. Values are inverted when applicable - so that higher scores indicate greater relevance. - additionalProperties: false - required: - - index - - relevance_score - title: RerankData - description: >- - A single rerank result from a reranking response. - RerankResponse: - type: object - properties: - data: - type: array - items: - $ref: '#/components/schemas/RerankData' - description: >- - List of rerank result objects, sorted by relevance score (descending) - additionalProperties: false - required: - - data - title: RerankResponse - description: Response from a reranking request. - ResumeAgentTurnRequest: - type: object - properties: - tool_responses: - type: array - items: - $ref: '#/components/schemas/ToolResponse' - description: >- - The tool call responses to resume the turn with. - stream: - type: boolean - description: Whether to stream the response. - additionalProperties: false - required: - - tool_responses - title: ResumeAgentTurnRequest - RunEvalRequest: - type: object - properties: - benchmark_config: - $ref: '#/components/schemas/BenchmarkConfig' - description: The configuration for the benchmark. - additionalProperties: false - required: - - benchmark_config - title: RunEvalRequest - RunModerationRequest: - type: object - properties: - input: - oneOf: - - type: string - - type: array - items: - type: string - description: >- - Input (or inputs) to classify. Can be a single string, an array of strings, - or an array of multi-modal input objects similar to other models. - model: - type: string - description: >- - The content moderation model you would like to use. - additionalProperties: false - required: - - input - - model - title: RunModerationRequest - ModerationObject: - type: object - properties: - id: - type: string - description: >- - The unique identifier for the moderation request. - model: - type: string - description: >- - The model used to generate the moderation results. - results: - type: array - items: - $ref: '#/components/schemas/ModerationObjectResults' - description: A list of moderation objects - additionalProperties: false - required: - - id - - model - - results - title: ModerationObject - description: A moderation object. - ModerationObjectResults: - type: object - properties: - flagged: - type: boolean - description: >- - Whether any of the below categories are flagged. - categories: - type: object - additionalProperties: - type: boolean - description: >- - A list of the categories, and whether they are flagged or not. - category_applied_input_types: - type: object - additionalProperties: - type: array - items: - type: string - description: >- - A list of the categories along with the input type(s) that the score applies - to. - category_scores: - type: object - additionalProperties: - type: number - description: >- - A list of the categories along with their scores as predicted by model. - user_message: - type: string - metadata: - type: object - additionalProperties: - oneOf: - - type: 'null' - - type: boolean - - type: number - - type: string - - type: array - - type: object - additionalProperties: false - required: - - flagged - - metadata - title: ModerationObjectResults - description: A moderation object. - RunShieldRequest: - type: object - properties: - shield_id: - type: string - description: The identifier of the shield to run. - messages: - type: array - items: - $ref: '#/components/schemas/Message' - description: The messages to run the shield on. - params: - type: object - additionalProperties: - oneOf: - - type: 'null' - - type: boolean - - type: number - - type: string - - type: array - - type: object - description: The parameters of the shield. - additionalProperties: false - required: - - shield_id - - messages - - params - title: RunShieldRequest - RunShieldResponse: - type: object - properties: - violation: - $ref: '#/components/schemas/SafetyViolation' - description: >- - (Optional) Safety violation detected by the shield, if any - additionalProperties: false - title: RunShieldResponse - description: Response from running a safety shield. - SaveSpansToDatasetRequest: - type: object - properties: - attribute_filters: - type: array - items: - $ref: '#/components/schemas/QueryCondition' - description: >- - The attribute filters to apply to the spans. - attributes_to_save: - type: array - items: - type: string - description: The attributes to save to the dataset. - dataset_id: - type: string - description: >- - The ID of the dataset to save the spans to. - max_depth: - type: integer - description: The maximum depth of the tree. - additionalProperties: false - required: - - attribute_filters - - attributes_to_save - - dataset_id - title: SaveSpansToDatasetRequest - ScoreRequest: - type: object - properties: - input_rows: - type: array - items: - type: object - additionalProperties: - oneOf: - - type: 'null' - - type: boolean - - type: number - - type: string - - type: array - - type: object - description: The rows to score. - scoring_functions: - type: object - additionalProperties: - oneOf: - - $ref: '#/components/schemas/ScoringFnParams' - - type: 'null' - description: >- - The scoring functions to use for the scoring. - additionalProperties: false - required: - - input_rows - - scoring_functions - title: ScoreRequest - ScoreResponse: - type: object - properties: - results: - type: object - additionalProperties: - $ref: '#/components/schemas/ScoringResult' - description: >- - A map of scoring function name to ScoringResult. - additionalProperties: false - required: - - results - title: ScoreResponse - description: The response from scoring. - ScoreBatchRequest: - type: object - properties: - dataset_id: - type: string - description: The ID of the dataset to score. - scoring_functions: - type: object - additionalProperties: - oneOf: - - $ref: '#/components/schemas/ScoringFnParams' - - type: 'null' - description: >- - The scoring functions to use for the scoring. - save_results_dataset: - type: boolean - description: >- - Whether to save the results to a dataset. - additionalProperties: false - required: - - dataset_id - - scoring_functions - - save_results_dataset - title: ScoreBatchRequest - ScoreBatchResponse: - type: object - properties: - dataset_id: - type: string - description: >- - (Optional) The identifier of the dataset that was scored - results: - type: object - additionalProperties: - $ref: '#/components/schemas/ScoringResult' - description: >- - A map of scoring function name to ScoringResult - additionalProperties: false - required: - - results - title: ScoreBatchResponse - description: >- - Response from batch scoring operations on datasets. - AlgorithmConfig: - oneOf: - - $ref: '#/components/schemas/LoraFinetuningConfig' - - $ref: '#/components/schemas/QATFinetuningConfig' - discriminator: - propertyName: type - mapping: - LoRA: '#/components/schemas/LoraFinetuningConfig' - QAT: '#/components/schemas/QATFinetuningConfig' - LoraFinetuningConfig: - type: object - properties: - type: - type: string - const: LoRA - default: LoRA - description: Algorithm type identifier, always "LoRA" - lora_attn_modules: - type: array - items: - type: string - description: >- - List of attention module names to apply LoRA to - apply_lora_to_mlp: - type: boolean - description: Whether to apply LoRA to MLP layers - apply_lora_to_output: - type: boolean - description: >- - Whether to apply LoRA to output projection layers - rank: - type: integer - description: >- - Rank of the LoRA adaptation (lower rank = fewer parameters) - alpha: - type: integer - description: >- - LoRA scaling parameter that controls adaptation strength - use_dora: - type: boolean - default: false - description: >- - (Optional) Whether to use DoRA (Weight-Decomposed Low-Rank Adaptation) - quantize_base: - type: boolean - default: false - description: >- - (Optional) Whether to quantize the base model weights - additionalProperties: false - required: - - type - - lora_attn_modules - - apply_lora_to_mlp - - apply_lora_to_output - - rank - - alpha - title: LoraFinetuningConfig - description: >- - Configuration for Low-Rank Adaptation (LoRA) fine-tuning. - QATFinetuningConfig: - type: object - properties: - type: - type: string - const: QAT - default: QAT - description: Algorithm type identifier, always "QAT" - quantizer_name: - type: string - description: >- - Name of the quantization algorithm to use - group_size: - type: integer - description: Size of groups for grouped quantization - additionalProperties: false - required: - - type - - quantizer_name - - group_size - title: QATFinetuningConfig - description: >- - Configuration for Quantization-Aware Training (QAT) fine-tuning. - SupervisedFineTuneRequest: - type: object - properties: - job_uuid: - type: string - description: The UUID of the job to create. - training_config: - $ref: '#/components/schemas/TrainingConfig' - description: The training configuration. - hyperparam_search_config: - type: object - additionalProperties: - oneOf: - - type: 'null' - - type: boolean - - type: number - - type: string - - type: array - - type: object - description: The hyperparam search configuration. - logger_config: - type: object - additionalProperties: - oneOf: - - type: 'null' - - type: boolean - - type: number - - type: string - - type: array - - type: object - description: The logger configuration. - model: - type: string - description: The model to fine-tune. - checkpoint_dir: - type: string - description: The directory to save checkpoint(s) to. - algorithm_config: - $ref: '#/components/schemas/AlgorithmConfig' - description: The algorithm configuration. - additionalProperties: false - required: - - job_uuid - - training_config - - hyperparam_search_config - - logger_config - title: SupervisedFineTuneRequest - SyntheticDataGenerateRequest: - type: object - properties: - dialogs: - type: array - items: - $ref: '#/components/schemas/Message' - description: >- - List of conversation messages to use as input for synthetic data generation - filtering_function: - type: string - enum: - - none - - random - - top_k - - top_p - - top_k_top_p - - sigmoid - description: >- - Type of filtering to apply to generated synthetic data samples - model: - type: string - description: >- - (Optional) The identifier of the model to use. The model must be registered - with Llama Stack and available via the /models endpoint - additionalProperties: false - required: - - dialogs - - filtering_function - title: SyntheticDataGenerateRequest - SyntheticDataGenerationResponse: - type: object - properties: - synthetic_data: - type: array - items: - type: object - additionalProperties: - oneOf: - - type: 'null' - - type: boolean - - type: number - - type: string - - type: array - - type: object - description: >- - List of generated synthetic data samples that passed the filtering criteria - statistics: - type: object - additionalProperties: - oneOf: - - type: 'null' - - type: boolean - - type: number - - type: string - - type: array - - type: object - description: >- - (Optional) Statistical information about the generation process and filtering - results - additionalProperties: false - required: - - synthetic_data - title: SyntheticDataGenerationResponse - description: >- - Response from the synthetic data generation. Batch of (prompt, response, score) - tuples that pass the threshold. - VersionInfo: - type: object - properties: - version: - type: string - description: Version number of the service - additionalProperties: false - required: - - version - title: VersionInfo - description: Version information for the service. - responses: - BadRequest400: - description: The request was invalid or malformed - content: - application/json: - schema: - $ref: '#/components/schemas/Error' - example: - status: 400 - title: Bad Request - detail: The request was invalid or malformed - TooManyRequests429: - description: >- - The client has sent too many requests in a given amount of time - content: - application/json: - schema: - $ref: '#/components/schemas/Error' - example: - status: 429 - title: Too Many Requests - detail: >- - You have exceeded the rate limit. Please try again later. - InternalServerError500: - description: >- - The server encountered an unexpected error - content: - application/json: - schema: - $ref: '#/components/schemas/Error' - example: - status: 500 - title: Internal Server Error - detail: >- - An unexpected error occurred. Our team has been notified. - DefaultError: - description: An unexpected error occurred - content: - application/json: - schema: - $ref: '#/components/schemas/Error' - example: - status: 0 - title: Error - detail: An unexpected error occurred -security: - - Default: [] -tags: - - name: Agents - description: >- - Main functionalities provided by this API: - - - Create agents with specific instructions and ability to use tools. - - - Interactions with agents are grouped into sessions ("threads"), and each interaction - is called a "turn". - - - Agents can be provided with various tools (see the ToolGroups and ToolRuntime - APIs for more details). - - - Agents can be provided with various shields (see the Safety API for more details). - - - Agents can also use Memory to retrieve information from knowledge bases. See - the RAG Tool and Vector IO APIs for more details. - x-displayName: >- - Agents API for creating and interacting with agentic systems. - - name: Benchmarks - - name: DatasetIO - - name: Datasets - - name: Eval - x-displayName: >- - Llama Stack Evaluation API for running evaluations on model and agent candidates. - - name: Files - - name: Inference - description: >- - This API provides the raw interface to the underlying models. Two kinds of models - are supported: - - - LLM models: these models generate "raw" and "chat" (conversational) completions. - - - Embedding models: these models generate embeddings to be used for semantic - search. - x-displayName: >- - Llama Stack Inference API for generating completions, chat completions, and - embeddings. - - name: Inspect - - name: Models - - name: PostTraining (Coming Soon) - - name: Providers - x-displayName: >- - Providers API for inspecting, listing, and modifying providers and their configurations. - - name: Safety - - name: Scoring - - name: ScoringFunctions - - name: Shields - - name: SyntheticDataGeneration (Coming Soon) - - name: Telemetry - - name: ToolGroups - - name: ToolRuntime - - name: VectorDBs - - name: VectorIO -x-tagGroups: - - name: Operations - tags: - - Agents - - Benchmarks - - DatasetIO - - Datasets - - Eval - - Files - - Inference - - Inspect - - Models - - PostTraining (Coming Soon) - - Providers - - Safety - - Scoring - - ScoringFunctions - - Shields - - SyntheticDataGeneration (Coming Soon) - - Telemetry - - ToolGroups - - ToolRuntime - - VectorDBs - - VectorIO diff --git a/docs/conftest.py b/docs/conftest.py deleted file mode 100644 index ab4d7e998..000000000 --- a/docs/conftest.py +++ /dev/null @@ -1,24 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# All rights reserved. -# -# This source code is licensed under the terms described in the LICENSE file in -# the root directory of this source tree. - -import os -import time - - -def pytest_collection_modifyitems(items): - for item in items: - item.name = item.name.replace(' ', '_') - - -def pytest_runtest_teardown(item): - interval_seconds = os.getenv("LLAMA_STACK_TEST_INTERVAL_SECONDS") - if interval_seconds: - time.sleep(float(interval_seconds)) - - -def pytest_configure(config): - config.option.tbstyle = "short" - config.option.disable_warnings = True diff --git a/docs/docs/advanced_apis/evaluation.mdx b/docs/docs/advanced_apis/evaluation.mdx new file mode 100644 index 000000000..1efaa4c5c --- /dev/null +++ b/docs/docs/advanced_apis/evaluation.mdx @@ -0,0 +1,163 @@ +# Evaluation + +## Evaluation Concepts + +The Llama Stack Evaluation flow allows you to run evaluations on your GenAI application datasets or pre-registered benchmarks. + +We introduce a set of APIs in Llama Stack for supporting running evaluations of LLM applications: +- `/datasetio` + `/datasets` API +- `/scoring` + `/scoring_functions` API +- `/eval` + `/benchmarks` API + +This guide goes over the sets of APIs and developer experience flow of using Llama Stack to run evaluations for different use cases. Checkout our Colab notebook on working examples with evaluations [here](https://colab.research.google.com/drive/10CHyykee9j2OigaIcRv47BKG9mrNm0tJ?usp=sharing). + +The Evaluation APIs are associated with a set of Resources. Please visit the Resources section in our [Core Concepts](../concepts/index.mdx) guide for better high-level understanding. + +- **DatasetIO**: defines interface with datasets and data loaders. + - Associated with `Dataset` resource. +- **Scoring**: evaluate outputs of the system. + - Associated with `ScoringFunction` resource. We provide a suite of out-of-the box scoring functions and also the ability for you to add custom evaluators. These scoring functions are the core part of defining an evaluation task to output evaluation metrics. +- **Eval**: generate outputs (via Inference or Agents) and perform scoring. + - Associated with `Benchmark` resource. + +## Evaluation Providers + +Llama Stack provides multiple evaluation providers: + +- **Meta Reference** (`inline::meta-reference`) - Meta's reference implementation with multi-language support +- **NVIDIA** (`remote::nvidia`) - NVIDIA's evaluation platform integration + +### Meta Reference + +Meta's reference implementation of evaluation tasks with support for multiple languages and evaluation metrics. + +#### Configuration + +| Field | Type | Required | Default | Description | +|-------|------|----------|---------|-------------| +| `kvstore` | `RedisKVStoreConfig \| SqliteKVStoreConfig \| PostgresKVStoreConfig \| MongoDBKVStoreConfig` | No | sqlite | Key-value store configuration | + +#### Sample Configuration + +```yaml +kvstore: + type: sqlite + db_path: ${env.SQLITE_STORE_DIR:=~/.llama/dummy}/meta_reference_eval.db +``` + +#### Features + +- Multi-language evaluation support +- Comprehensive evaluation metrics +- Integration with various key-value stores (SQLite, Redis, PostgreSQL, MongoDB) +- Built-in support for popular benchmarks + +### NVIDIA + +NVIDIA's evaluation provider for running evaluation tasks on NVIDIA's platform. + +#### Configuration + +| Field | Type | Required | Default | Description | +|-------|------|----------|---------|-------------| +| `evaluator_url` | `str` | No | http://0.0.0.0:7331 | The url for accessing the evaluator service | + +#### Sample Configuration + +```yaml +evaluator_url: ${env.NVIDIA_EVALUATOR_URL:=http://localhost:7331} +``` + +#### Features + +- Integration with NVIDIA's evaluation platform +- Remote evaluation capabilities +- Scalable evaluation processing + +## Open-benchmark Eval + +### List of open-benchmarks Llama Stack support + +Llama stack pre-registers several popular open-benchmarks to easily evaluate model performance via CLI. + +The list of open-benchmarks we currently support: +- [MMLU-COT](https://arxiv.org/abs/2009.03300) (Measuring Massive Multitask Language Understanding): Benchmark designed to comprehensively evaluate the breadth and depth of a model's academic and professional understanding +- [GPQA-COT](https://arxiv.org/abs/2311.12022) (A Graduate-Level Google-Proof Q&A Benchmark): A challenging benchmark of 448 multiple-choice questions written by domain experts in biology, physics, and chemistry. +- [SimpleQA](https://openai.com/index/introducing-simpleqa/): Benchmark designed to access models to answer short, fact-seeking questions. +- [MMMU](https://arxiv.org/abs/2311.16502) (A Massive Multi-discipline Multimodal Understanding and Reasoning Benchmark for Expert AGI): Benchmark designed to evaluate multimodal models. + +You can follow this [contributing guide](../references/evals_reference/index.mdx#open-benchmark-contributing-guide) to add more open-benchmarks to Llama Stack + +### Run evaluation on open-benchmarks via CLI + +We have built-in functionality to run the supported open-benchmarks using llama-stack-client CLI + +#### Spin up Llama Stack server + +Spin up llama stack server with 'open-benchmark' template +``` +llama stack run llama_stack/distributions/open-benchmark/run.yaml + +``` + +#### Run eval CLI +There are 3 necessary inputs to run a benchmark eval +- `list of benchmark_ids`: The list of benchmark ids to run evaluation on +- `model-id`: The model id to evaluate on +- `output_dir`: Path to store the evaluate results +``` +llama-stack-client eval run-benchmark ... \ +--model_id \ +--output_dir +``` + +You can run +``` +llama-stack-client eval run-benchmark help +``` +to see the description of all the flags that eval run-benchmark has + +In the output log, you can find the file path that has your evaluation results. Open that file and you can see you aggregate evaluation results over there. + +## Usage Example + +Here's a basic example of using the evaluation API: + +```python +from llama_stack_client import LlamaStackClient + +client = LlamaStackClient(base_url="http://localhost:8321") + +# Register a dataset for evaluation +client.datasets.register( + purpose="evaluation", + source={ + "type": "uri", + "uri": "huggingface://datasets/llamastack/evaluation_dataset" + }, + dataset_id="my_eval_dataset" +) + +# Run evaluation +eval_result = client.eval.run_evaluation( + dataset_id="my_eval_dataset", + scoring_functions=["accuracy", "bleu"], + model_id="my_model" +) + +print(f"Evaluation completed: {eval_result}") +``` + +## Best Practices + +- **Choose appropriate providers**: Use Meta Reference for comprehensive evaluation, NVIDIA for platform-specific needs +- **Configure storage properly**: Ensure your key-value store configuration matches your performance requirements +- **Monitor evaluation progress**: Large evaluations can take time - implement proper monitoring +- **Use appropriate scoring functions**: Select scoring metrics that align with your evaluation goals + +## What's Next? + +- Check out our Colab notebook on working examples with running benchmark evaluations [here](https://colab.research.google.com/github/meta-llama/llama-stack/blob/main/docs/notebooks/Llama_Stack_Benchmark_Evals.ipynb#scrollTo=mxLCsP4MvFqP). +- Check out our [Building Applications - Evaluation](../building_applications/evals.mdx) guide for more details on how to use the Evaluation APIs to evaluate your applications. +- Check out our [Evaluation Reference](../references/evals_reference/index.mdx) for more details on the APIs. +- Explore the [Scoring](./scoring.mdx) documentation for available scoring functions. diff --git a/docs/docs/advanced_apis/post_training.mdx b/docs/docs/advanced_apis/post_training.mdx new file mode 100644 index 000000000..516ac07e1 --- /dev/null +++ b/docs/docs/advanced_apis/post_training.mdx @@ -0,0 +1,305 @@ +# Post-Training + +Post-training in Llama Stack allows you to fine-tune models using various providers and frameworks. This section covers all available post-training providers and how to use them effectively. + +## Overview + +Llama Stack provides multiple post-training providers: + +- **HuggingFace SFTTrainer** (`inline::huggingface`) - Fine-tuning using HuggingFace ecosystem +- **TorchTune** (`inline::torchtune`) - Fine-tuning using Meta's TorchTune framework +- **NVIDIA** (`remote::nvidia`) - Fine-tuning using NVIDIA's platform + +## HuggingFace SFTTrainer + +[HuggingFace SFTTrainer](https://huggingface.co/docs/trl/en/sft_trainer) is an inline post training provider for Llama Stack. It allows you to run supervised fine tuning on a variety of models using many datasets. + +### Features + +- Simple access through the post_training API +- Fully integrated with Llama Stack +- GPU support, CPU support, and MPS support (MacOS Metal Performance Shaders) + +### Configuration + +| Field | Type | Required | Default | Description | +|-------|------|----------|---------|-------------| +| `device` | `str` | No | cuda | | +| `distributed_backend` | `Literal['fsdp', 'deepspeed']` | No | | | +| `checkpoint_format` | `Literal['full_state', 'huggingface']` | No | huggingface | | +| `chat_template` | `str` | No | | +| `model_specific_config` | `dict` | No | `{'trust_remote_code': True, 'attn_implementation': 'sdpa'}` | | +| `max_seq_length` | `int` | No | 2048 | | +| `gradient_checkpointing` | `bool` | No | False | | +| `save_total_limit` | `int` | No | 3 | | +| `logging_steps` | `int` | No | 10 | | +| `warmup_ratio` | `float` | No | 0.1 | | +| `weight_decay` | `float` | No | 0.01 | | +| `dataloader_num_workers` | `int` | No | 4 | | +| `dataloader_pin_memory` | `bool` | No | True | | + +### Sample Configuration + +```yaml +checkpoint_format: huggingface +distributed_backend: null +device: cpu +``` + +### Setup + +You can access the HuggingFace trainer via the `starter` distribution: + +```bash +llama stack build --distro starter --image-type venv +llama stack run --image-type venv ~/.llama/distributions/starter/starter-run.yaml +``` + +### Usage Example + +```python +import time +import uuid + +from llama_stack_client.types import ( + post_training_supervised_fine_tune_params, + algorithm_config_param, +) + +def create_http_client(): + from llama_stack_client import LlamaStackClient + return LlamaStackClient(base_url="http://localhost:8321") + +client = create_http_client() + +# Example Dataset +client.datasets.register( + purpose="post-training/messages", + source={ + "type": "uri", + "uri": "huggingface://datasets/llamastack/simpleqa?split=train", + }, + dataset_id="simpleqa", +) + +training_config = post_training_supervised_fine_tune_params.TrainingConfig( + data_config=post_training_supervised_fine_tune_params.TrainingConfigDataConfig( + batch_size=32, + data_format="instruct", + dataset_id="simpleqa", + shuffle=True, + ), + gradient_accumulation_steps=1, + max_steps_per_epoch=0, + max_validation_steps=1, + n_epochs=4, +) + +algorithm_config = algorithm_config_param.LoraFinetuningConfig( + alpha=1, + apply_lora_to_mlp=True, + apply_lora_to_output=False, + lora_attn_modules=["q_proj"], + rank=1, + type="LoRA", +) + +job_uuid = f"test-job{uuid.uuid4()}" + +# Example Model +training_model = "ibm-granite/granite-3.3-8b-instruct" + +start_time = time.time() +response = client.post_training.supervised_fine_tune( + job_uuid=job_uuid, + logger_config={}, + model=training_model, + hyperparam_search_config={}, + training_config=training_config, + algorithm_config=algorithm_config, + checkpoint_dir="output", +) +print("Job: ", job_uuid) + +# Wait for the job to complete! +while True: + status = client.post_training.job.status(job_uuid=job_uuid) + if not status: + print("Job not found") + break + + print(status) + if status.status == "completed": + break + + print("Waiting for job to complete...") + time.sleep(5) + +end_time = time.time() +print("Job completed in", end_time - start_time, "seconds!") + +print("Artifacts:") +print(client.post_training.job.artifacts(job_uuid=job_uuid)) +``` + +## TorchTune + +[TorchTune](https://github.com/pytorch/torchtune) is an inline post training provider for Llama Stack. It provides a simple and efficient way to fine-tune language models using PyTorch. + +### Features + +- Simple access through the post_training API +- Fully integrated with Llama Stack +- GPU support and single device capabilities +- Support for LoRA + +### Configuration + +| Field | Type | Required | Default | Description | +|-------|------|----------|---------|-------------| +| `torch_seed` | `int \| None` | No | | | +| `checkpoint_format` | `Literal['meta', 'huggingface']` | No | meta | | + +### Sample Configuration + +```yaml +checkpoint_format: meta +``` + +### Setup + +You can access the TorchTune trainer by writing your own yaml pointing to the provider: + +```yaml +post_training: + - provider_id: torchtune + provider_type: inline::torchtune + config: {} +``` + +You can then build and run your own stack with this provider. + +### Usage Example + +```python +import time +import uuid + +from llama_stack_client.types import ( + post_training_supervised_fine_tune_params, + algorithm_config_param, +) + +def create_http_client(): + from llama_stack_client import LlamaStackClient + return LlamaStackClient(base_url="http://localhost:8321") + +client = create_http_client() + +# Example Dataset +client.datasets.register( + purpose="post-training/messages", + source={ + "type": "uri", + "uri": "huggingface://datasets/llamastack/simpleqa?split=train", + }, + dataset_id="simpleqa", +) + +training_config = post_training_supervised_fine_tune_params.TrainingConfig( + data_config=post_training_supervised_fine_tune_params.TrainingConfigDataConfig( + batch_size=32, + data_format="instruct", + dataset_id="simpleqa", + shuffle=True, + ), + gradient_accumulation_steps=1, + max_steps_per_epoch=0, + max_validation_steps=1, + n_epochs=4, +) + +algorithm_config = algorithm_config_param.LoraFinetuningConfig( + alpha=1, + apply_lora_to_mlp=True, + apply_lora_to_output=False, + lora_attn_modules=["q_proj"], + rank=1, + type="LoRA", +) + +job_uuid = f"test-job{uuid.uuid4()}" + +# Example Model +training_model = "meta-llama/Llama-2-7b-hf" + +start_time = time.time() +response = client.post_training.supervised_fine_tune( + job_uuid=job_uuid, + logger_config={}, + model=training_model, + hyperparam_search_config={}, + training_config=training_config, + algorithm_config=algorithm_config, + checkpoint_dir="output", +) +print("Job: ", job_uuid) + +# Wait for the job to complete! +while True: + status = client.post_training.job.status(job_uuid=job_uuid) + if not status: + print("Job not found") + break + + print(status) + if status.status == "completed": + break + + print("Waiting for job to complete...") + time.sleep(5) + +end_time = time.time() +print("Job completed in", end_time - start_time, "seconds!") + +print("Artifacts:") +print(client.post_training.job.artifacts(job_uuid=job_uuid)) +``` + +## NVIDIA + +NVIDIA's post-training provider for fine-tuning models on NVIDIA's platform. + +### Configuration + +| Field | Type | Required | Default | Description | +|-------|------|----------|---------|-------------| +| `api_key` | `str \| None` | No | | The NVIDIA API key. | +| `dataset_namespace` | `str \| None` | No | default | The NVIDIA dataset namespace. | +| `project_id` | `str \| None` | No | test-example-model@v1 | The NVIDIA project ID. | +| `customizer_url` | `str \| None` | No | | Base URL for the NeMo Customizer API | +| `timeout` | `int` | No | 300 | Timeout for the NVIDIA Post Training API | +| `max_retries` | `int` | No | 3 | Maximum number of retries for the NVIDIA Post Training API | +| `output_model_dir` | `str` | No | test-example-model@v1 | Directory to save the output model | + +### Sample Configuration + +```yaml +api_key: ${env.NVIDIA_API_KEY:=} +dataset_namespace: ${env.NVIDIA_DATASET_NAMESPACE:=default} +project_id: ${env.NVIDIA_PROJECT_ID:=test-project} +customizer_url: ${env.NVIDIA_CUSTOMIZER_URL:=http://nemo.test} +``` + +## Best Practices + +- **Choose the right provider**: Use HuggingFace for broader compatibility, TorchTune for Meta models, or NVIDIA for their ecosystem +- **Configure hardware appropriately**: Ensure your configuration matches your available hardware (CPU, GPU, MPS) +- **Monitor jobs**: Always monitor job status and handle completion appropriately +- **Use appropriate datasets**: Ensure your dataset format matches the expected input format for your chosen provider + +## Next Steps + +- Check out the [Building Applications - Fine-tuning](../building_applications/index.mdx) guide for application-level examples +- See the [Providers](../providers/post_training/index.mdx) section for detailed provider documentation +- Review the [API Reference](../advanced_apis/post_training.mdx) for complete API documentation diff --git a/docs/docs/advanced_apis/scoring.mdx b/docs/docs/advanced_apis/scoring.mdx new file mode 100644 index 000000000..0ce787e80 --- /dev/null +++ b/docs/docs/advanced_apis/scoring.mdx @@ -0,0 +1,193 @@ +# Scoring + +The Scoring API in Llama Stack allows you to evaluate outputs of your GenAI system using various scoring functions and metrics. This section covers all available scoring providers and their configuration. + +## Overview + +Llama Stack provides multiple scoring providers: + +- **Basic** (`inline::basic`) - Simple evaluation metrics and scoring functions +- **Braintrust** (`inline::braintrust`) - Advanced evaluation using the Braintrust platform +- **LLM-as-Judge** (`inline::llm-as-judge`) - Uses language models to evaluate responses + +The Scoring API is associated with `ScoringFunction` resources and provides a suite of out-of-the-box scoring functions. You can also add custom evaluators to meet specific evaluation needs. + +## Basic Scoring + +Basic scoring provider for simple evaluation metrics and scoring functions. This provider offers fundamental scoring capabilities without external dependencies. + +### Configuration + +No configuration required - this provider works out of the box. + +```yaml +{} +``` + +### Features + +- Simple evaluation metrics (accuracy, precision, recall, F1-score) +- String matching and similarity metrics +- Basic statistical scoring functions +- No external dependencies required +- Fast execution for standard metrics + +### Use Cases + +- Quick evaluation of basic accuracy metrics +- String similarity comparisons +- Statistical analysis of model outputs +- Development and testing scenarios + +## Braintrust + +Braintrust scoring provider for evaluation and scoring using the [Braintrust platform](https://braintrustdata.com/). Braintrust provides advanced evaluation capabilities and experiment tracking. + +### Configuration + +| Field | Type | Required | Default | Description | +|-------|------|----------|---------|-------------| +| `openai_api_key` | `str \| None` | No | | The OpenAI API Key for LLM-powered evaluations | + +### Sample Configuration + +```yaml +openai_api_key: ${env.OPENAI_API_KEY:=} +``` + +### Features + +- Advanced evaluation metrics +- Experiment tracking and comparison +- LLM-powered evaluation functions +- Integration with Braintrust's evaluation suite +- Detailed scoring analytics and insights + +### Use Cases + +- Production evaluation pipelines +- A/B testing of model versions +- Advanced scoring with custom metrics +- Detailed evaluation reporting and analysis + +## LLM-as-Judge + +LLM-as-judge scoring provider that uses language models to evaluate and score responses. This approach leverages the reasoning capabilities of large language models to assess quality, relevance, and other subjective metrics. + +### Configuration + +No configuration required - this provider works out of the box. + +```yaml +{} +``` + +### Features + +- Subjective quality evaluation using LLMs +- Flexible evaluation criteria definition +- Natural language evaluation explanations +- Support for complex evaluation scenarios +- Contextual understanding of responses + +### Use Cases + +- Evaluating response quality and relevance +- Assessing creativity and coherence +- Subjective metric evaluation +- Human-like judgment for complex tasks + +## Usage Examples + +### Basic Scoring Example + +```python +from llama_stack_client import LlamaStackClient + +client = LlamaStackClient(base_url="http://localhost:8321") + +# Register a basic accuracy scoring function +client.scoring_functions.register( + scoring_function_id="basic_accuracy", + provider_id="basic", + provider_scoring_function_id="accuracy" +) + +# Use the scoring function +result = client.scoring.score( + input_rows=[ + {"expected": "Paris", "actual": "Paris"}, + {"expected": "London", "actual": "Paris"} + ], + scoring_function_id="basic_accuracy" +) +print(f"Accuracy: {result.results[0].score}") +``` + +### LLM-as-Judge Example + +```python +# Register an LLM-as-judge scoring function +client.scoring_functions.register( + scoring_function_id="quality_judge", + provider_id="llm_judge", + provider_scoring_function_id="response_quality", + params={ + "criteria": "Evaluate response quality, relevance, and helpfulness", + "scale": "1-10" + } +) + +# Score responses using LLM judgment +result = client.scoring.score( + input_rows=[{ + "query": "What is machine learning?", + "response": "Machine learning is a subset of AI that enables computers to learn patterns from data..." + }], + scoring_function_id="quality_judge" +) +``` + +### Braintrust Integration Example + +```python +# Register a Braintrust scoring function +client.scoring_functions.register( + scoring_function_id="braintrust_eval", + provider_id="braintrust", + provider_scoring_function_id="semantic_similarity" +) + +# Run evaluation with Braintrust +result = client.scoring.score( + input_rows=[{ + "reference": "The capital of France is Paris", + "candidate": "Paris is the capital city of France" + }], + scoring_function_id="braintrust_eval" +) +``` + +## Best Practices + +- **Choose appropriate providers**: Use Basic for simple metrics, Braintrust for advanced analytics, LLM-as-Judge for subjective evaluation +- **Define clear criteria**: When using LLM-as-Judge, provide specific evaluation criteria and scales +- **Validate scoring functions**: Test your scoring functions with known examples before production use +- **Monitor performance**: Track scoring performance and adjust thresholds based on results +- **Combine multiple metrics**: Use different scoring providers together for comprehensive evaluation + +## Integration with Evaluation + +The Scoring API works closely with the [Evaluation](./evaluation.mdx) API to provide comprehensive evaluation workflows: + +1. **Datasets** are loaded via the DatasetIO API +2. **Evaluation** generates model outputs using the Eval API +3. **Scoring** evaluates the quality of outputs using various scoring functions +4. **Results** are aggregated and reported for analysis + +## Next Steps + +- Check out the [Evaluation](./evaluation.mdx) guide for running complete evaluations +- See the [Building Applications - Evaluation](../building_applications/evals.mdx) guide for application examples +- Review the [Evaluation Reference](../references/evals_reference/) for comprehensive scoring function usage +- Explore the [Evaluation Concepts](../concepts/evaluation_concepts) for detailed conceptual information diff --git a/docs/source/building_applications/agent.md b/docs/docs/building_applications/agent.mdx similarity index 70% rename from docs/source/building_applications/agent.md rename to docs/docs/building_applications/agent.mdx index 6fcc46152..33e98ea8d 100644 --- a/docs/source/building_applications/agent.md +++ b/docs/docs/building_applications/agent.mdx @@ -1,9 +1,18 @@ +--- +title: Agents +description: Build powerful AI applications with the Llama Stack agent framework +sidebar_label: Agents +sidebar_position: 3 +--- + +import Tabs from '@theme/Tabs'; +import TabItem from '@theme/TabItem'; + # Agents An Agent in Llama Stack is a powerful abstraction that allows you to build complex AI applications. -The Llama Stack agent framework is built on a modular architecture that allows for flexible and powerful AI -applications. This document explains the key components and how they work together. +The Llama Stack agent framework is built on a modular architecture that allows for flexible and powerful AI applications. This document explains the key components and how they work together. ## Core Concepts @@ -19,7 +28,6 @@ Agents are configured using the `AgentConfig` class, which includes: ```python from llama_stack_client import Agent - # Create the agent agent = Agent( llama_stack_client, @@ -46,6 +54,9 @@ Each interaction with an agent is called a "turn" and consists of: - **Steps**: The agent's internal processing (inference, tool execution, etc.) - **Output Message**: The agent's response + + + ```python from llama_stack_client import AgentEventLogger @@ -57,9 +68,9 @@ turn_response = agent.create_turn( for log in AgentEventLogger().log(turn_response): log.print() ``` -### Non-Streaming - + + ```python from rich.pretty import pprint @@ -78,6 +89,9 @@ print("Steps:") pprint(response.steps) ``` + + + ### 4. Steps Each turn consists of multiple steps that represent the agent's thought process: @@ -88,5 +102,11 @@ Each turn consists of multiple steps that represent the agent's thought process: ## Agent Execution Loop +Refer to the [Agent Execution Loop](./agent_execution_loop) for more details on what happens within an agent turn. -Refer to the [Agent Execution Loop](agent_execution_loop) for more details on what happens within an agent turn. +## Related Resources + +- **[Agent Execution Loop](./agent_execution_loop)** - Understanding the internal processing flow +- **[RAG (Retrieval Augmented Generation)](./rag)** - Building knowledge-enhanced agents +- **[Tools Integration](./tools)** - Extending agent capabilities with external tools +- **[Safety Guardrails](./safety)** - Implementing responsible AI practices diff --git a/docs/source/building_applications/agent_execution_loop.md b/docs/docs/building_applications/agent_execution_loop.mdx similarity index 72% rename from docs/source/building_applications/agent_execution_loop.md rename to docs/docs/building_applications/agent_execution_loop.mdx index d66448449..458e997da 100644 --- a/docs/source/building_applications/agent_execution_loop.md +++ b/docs/docs/building_applications/agent_execution_loop.mdx @@ -1,10 +1,18 @@ -## Agent Execution Loop +--- +title: Agent Execution Loop +description: Understanding the internal processing flow of Llama Stack agents +sidebar_label: Agent Execution Loop +sidebar_position: 4 +--- -Agents are the heart of Llama Stack applications. They combine inference, memory, safety, and tool usage into coherent -workflows. At its core, an agent follows a sophisticated execution loop that enables multi-step reasoning, tool usage, -and safety checks. +import Tabs from '@theme/Tabs'; +import TabItem from '@theme/TabItem'; -### Steps in the Agent Workflow +# Agent Execution Loop + +Agents are the heart of Llama Stack applications. They combine inference, memory, safety, and tool usage into coherent workflows. At its core, an agent follows a sophisticated execution loop that enables multi-step reasoning, tool usage, and safety checks. + +## Steps in the Agent Workflow Each agent turn follows these key steps: @@ -17,7 +25,7 @@ Each agent turn follows these key steps: 3. **Inference Loop**: The agent enters its main execution loop: - The LLM receives a user prompt (with previous tool outputs) - - The LLM generates a response, potentially with [tool calls](tools) + - The LLM generates a response, potentially with [tool calls](./tools) - If tool calls are present: - Tool inputs are safety-checked - Tools are executed (e.g., web search, code execution) @@ -29,7 +37,9 @@ Each agent turn follows these key steps: 4. **Final Safety Check**: The agent's final response is screened through safety shields -```{mermaid} +## Execution Flow Diagram + +```mermaid sequenceDiagram participant U as User participant E as Executor @@ -70,12 +80,15 @@ sequenceDiagram Each step in this process can be monitored and controlled through configurations. -### Agent Execution Loop Example +## Agent Execution Example + Here's an example that demonstrates monitoring the agent's execution: + + + ```python from llama_stack_client import LlamaStackClient, Agent, AgentEventLogger -from rich.pretty import pprint # Replace host and port client = LlamaStackClient(base_url=f"http://{HOST}:{PORT}") @@ -120,6 +133,13 @@ response = agent.create_turn( # Monitor each step of execution for log in AgentEventLogger().log(response): log.print() +``` + + + + +```python +from rich.pretty import pprint # Using non-streaming API, the response contains input, steps, and output. response = agent.create_turn( @@ -131,9 +151,35 @@ response = agent.create_turn( } ], session_id=session_id, + stream=False, ) pprint(f"Input: {response.input_messages}") pprint(f"Output: {response.output_message.content}") pprint(f"Steps: {response.steps}") ``` + + + + +## Key Configuration Options + +### Loop Control +- **max_infer_iters**: Maximum number of inference iterations (default: 5) +- **max_tokens**: Token limit for responses +- **temperature**: Controls response randomness + +### Safety Configuration +- **input_shields**: Safety checks for user input +- **output_shields**: Safety checks for agent responses + +### Tool Integration +- **tools**: List of available tools for the agent +- **tool_choice**: Control over when tools are used + +## Related Resources + +- **[Agents](./agent)** - Understanding agent fundamentals +- **[Tools Integration](./tools)** - Adding capabilities to agents +- **[Safety Guardrails](./safety)** - Implementing safety measures +- **[RAG (Retrieval Augmented Generation)](./rag)** - Building knowledge-enhanced workflows diff --git a/docs/docs/building_applications/evals.mdx b/docs/docs/building_applications/evals.mdx new file mode 100644 index 000000000..d2eb0bd31 --- /dev/null +++ b/docs/docs/building_applications/evals.mdx @@ -0,0 +1,256 @@ +--- +title: Evaluations +description: Evaluate LLM applications with Llama Stack's comprehensive evaluation framework +sidebar_label: Evaluations +sidebar_position: 7 +--- + +import Tabs from '@theme/Tabs'; +import TabItem from '@theme/TabItem'; + +This guide walks you through the process of evaluating an LLM application built using Llama Stack. For detailed API reference, check out the [Evaluation Reference](../references/evals_reference/) guide that covers the complete set of APIs and developer experience flow. + +:::tip[Interactive Examples] +Check out our [Colab notebook](https://colab.research.google.com/drive/10CHyykee9j2OigaIcRv47BKG9mrNm0tJ?usp=sharing) for working examples with evaluations, or try the [Getting Started notebook](https://colab.research.google.com/github/meta-llama/llama-stack/blob/main/docs/getting_started.ipynb). +::: + +## Application Evaluation Example + +[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/meta-llama/llama-stack/blob/main/docs/getting_started.ipynb) + +Llama Stack offers a library of scoring functions and the `/scoring` API, allowing you to run evaluations on your pre-annotated AI application datasets. + +In this example, we will show you how to: +1. **Build an Agent** with Llama Stack +2. **Query the agent's sessions, turns, and steps** to analyze execution +3. **Evaluate the results** using scoring functions + +## Step-by-Step Evaluation Process + +### 1. Building a Search Agent + +First, let's create an agent that can search the web to answer questions: + +```python +from llama_stack_client import LlamaStackClient, Agent, AgentEventLogger + +client = LlamaStackClient(base_url=f"http://{HOST}:{PORT}") + +agent = Agent( + client, + model="meta-llama/Llama-3.3-70B-Instruct", + instructions="You are a helpful assistant. Use search tool to answer the questions.", + tools=["builtin::websearch"], +) + +# Test prompts for evaluation +user_prompts = [ + "Which teams played in the NBA Western Conference Finals of 2024. Search the web for the answer.", + "In which episode and season of South Park does Bill Cosby (BSM-471) first appear? Give me the number and title. Search the web for the answer.", + "What is the British-American kickboxer Andrew Tate's kickboxing name? Search the web for the answer.", +] + +session_id = agent.create_session("test-session") + +# Execute all prompts in the session +for prompt in user_prompts: + response = agent.create_turn( + messages=[ + { + "role": "user", + "content": prompt, + } + ], + session_id=session_id, + ) + + for log in AgentEventLogger().log(response): + log.print() +``` + +### 2. Query Agent Execution Steps + +Now, let's analyze the agent's execution steps to understand its performance: + + + + +```python +from rich.pretty import pprint + +# Query the agent's session to get detailed execution data +session_response = client.agents.session.retrieve( + session_id=session_id, + agent_id=agent.agent_id, +) + +pprint(session_response) +``` + + + + +```python +# Sanity check: Verify that all user prompts are followed by tool calls +num_tool_call = 0 +for turn in session_response.turns: + for step in turn.steps: + if ( + step.step_type == "tool_execution" + and step.tool_calls[0].tool_name == "brave_search" + ): + num_tool_call += 1 + +print( + f"{num_tool_call}/{len(session_response.turns)} user prompts are followed by a tool call to `brave_search`" +) +``` + + + + +### 3. Evaluate Agent Responses + +Now we'll evaluate the agent's responses using Llama Stack's scoring API: + + + + +```python +# Process agent execution history into evaluation rows +eval_rows = [] + +# Define expected answers for our test prompts +expected_answers = [ + "Dallas Mavericks and the Minnesota Timberwolves", + "Season 4, Episode 12", + "King Cobra", +] + +# Create evaluation dataset from agent responses +for i, turn in enumerate(session_response.turns): + eval_rows.append( + { + "input_query": turn.input_messages[0].content, + "generated_answer": turn.output_message.content, + "expected_answer": expected_answers[i], + } + ) + +pprint(eval_rows) +``` + + + + +```python +# Configure scoring parameters +scoring_params = { + "basic::subset_of": None, # Check if generated answer contains expected answer +} + +# Run evaluation using Llama Stack's scoring API +scoring_response = client.scoring.score( + input_rows=eval_rows, + scoring_functions=scoring_params +) + +pprint(scoring_response) + +# Analyze results +for i, result in enumerate(scoring_response.results): + print(f"Query {i+1}: {result.score}") + print(f" Generated: {eval_rows[i]['generated_answer'][:100]}...") + print(f" Expected: {expected_answers[i]}") + print(f" Score: {result.score}") + print() +``` + + + + +## Available Scoring Functions + +Llama Stack provides several built-in scoring functions: + +### Basic Scoring Functions +- **`basic::subset_of`**: Checks if the expected answer is contained in the generated response +- **`basic::exact_match`**: Performs exact string matching between expected and generated answers +- **`basic::regex_match`**: Uses regular expressions to match patterns in responses + +### Advanced Scoring Functions +- **`llm_as_judge::accuracy`**: Uses an LLM to judge response accuracy +- **`llm_as_judge::helpfulness`**: Evaluates how helpful the response is +- **`llm_as_judge::safety`**: Assesses response safety and appropriateness + +### Custom Scoring Functions +You can also create custom scoring functions for domain-specific evaluation needs. + +## Evaluation Workflow Best Practices + +### 🎯 **Dataset Preparation** +- Use diverse test cases that cover edge cases and common scenarios +- Include clear expected answers or success criteria +- Balance your dataset across different difficulty levels + +### 📊 **Metrics Selection** +- Choose appropriate scoring functions for your use case +- Combine multiple metrics for comprehensive evaluation +- Consider both automated and human evaluation metrics + +### 🔄 **Iterative Improvement** +- Run evaluations regularly during development +- Use evaluation results to identify areas for improvement +- Track performance changes over time + +### 📈 **Analysis & Reporting** +- Analyze failures to understand model limitations +- Generate comprehensive evaluation reports +- Share results with stakeholders for informed decision-making + +## Advanced Evaluation Scenarios + +### Batch Evaluation +For evaluating large datasets efficiently: + +```python +# Prepare large evaluation dataset +large_eval_dataset = [ + {"input_query": query, "expected_answer": answer} + for query, answer in zip(queries, expected_answers) +] + +# Run batch evaluation +batch_results = client.scoring.score( + input_rows=large_eval_dataset, + scoring_functions={ + "basic::subset_of": None, + "llm_as_judge::accuracy": {"judge_model": "meta-llama/Llama-3.3-70B-Instruct"}, + } +) +``` + +### Multi-Metric Evaluation +Combining different scoring approaches: + +```python +comprehensive_scoring = { + "exact_match": "basic::exact_match", + "subset_match": "basic::subset_of", + "llm_judge": "llm_as_judge::accuracy", + "safety_check": "llm_as_judge::safety", +} + +results = client.scoring.score( + input_rows=eval_rows, + scoring_functions=comprehensive_scoring +) +``` + +## Related Resources + +- **[Agents](./agent)** - Building agents for evaluation +- **[Tools Integration](./tools)** - Using tools in evaluated agents +- **[Evaluation Reference](../references/evals_reference/)** - Complete API reference for evaluations +- **[Getting Started Notebook](https://colab.research.google.com/github/meta-llama/llama-stack/blob/main/docs/getting_started.ipynb)** - Interactive examples +- **[Evaluation Examples](https://colab.research.google.com/drive/10CHyykee9j2OigaIcRv47BKG9mrNm0tJ?usp=sharing)** - Additional evaluation scenarios diff --git a/docs/docs/building_applications/index.mdx b/docs/docs/building_applications/index.mdx new file mode 100644 index 000000000..a4b71efd7 --- /dev/null +++ b/docs/docs/building_applications/index.mdx @@ -0,0 +1,83 @@ +--- +title: Building Applications +description: Comprehensive guides for building AI applications with Llama Stack +sidebar_label: Overview +sidebar_position: 5 +--- + +# AI Application Examples + +Llama Stack provides all the building blocks needed to create sophisticated AI applications. + +## Getting Started + +The best way to get started is to look at this comprehensive notebook which walks through the various APIs (from basic inference, to RAG agents) and how to use them. + +**📓 [Building AI Applications Notebook](https://github.com/meta-llama/llama-stack/blob/main/docs/getting_started.ipynb)** + +## Core Topics + +Here are the key topics that will help you build effective AI applications: + +### 🤖 **Agent Development** +- **[Agent Framework](./agent.mdx)** - Understand the components and design patterns of the Llama Stack agent framework +- **[Agent Execution Loop](./agent_execution_loop.mdx)** - How agents process information, make decisions, and execute actions +- **[Agents vs Responses API](./responses_vs_agents.mdx)** - Learn when to use each API for different use cases + +### 📚 **Knowledge Integration** +- **[RAG (Retrieval-Augmented Generation)](./rag.mdx)** - Enhance your agents with external knowledge through retrieval mechanisms + +### 🛠️ **Capabilities & Extensions** +- **[Tools](./tools.mdx)** - Extend your agents' capabilities by integrating with external tools and APIs + +### 📊 **Quality & Monitoring** +- **[Evaluations](./evals.mdx)** - Evaluate your agents' effectiveness and identify areas for improvement +- **[Telemetry](./telemetry.mdx)** - Monitor and analyze your agents' performance and behavior +- **[Safety](./safety.mdx)** - Implement guardrails and safety measures to ensure responsible AI behavior + +### 🎮 **Interactive Development** +- **[Playground](./playground.mdx)** - Interactive environment for testing and developing applications + +## Application Patterns + +### 🤖 **Conversational Agents** +Build intelligent chatbots and assistants that can: +- Maintain context across conversations +- Access external knowledge bases +- Execute actions through tool integrations +- Apply safety filters and guardrails + +### 📖 **RAG Applications** +Create knowledge-augmented applications that: +- Retrieve relevant information from documents +- Generate contextually accurate responses +- Handle large knowledge bases efficiently +- Provide source attribution + +### 🔧 **Tool-Enhanced Systems** +Develop applications that can: +- Search the web for real-time information +- Interact with databases and APIs +- Perform calculations and analysis +- Execute complex multi-step workflows + +### 🛡️ **Enterprise Applications** +Build production-ready systems with: +- Comprehensive safety measures +- Performance monitoring and analytics +- Scalable deployment configurations +- Evaluation and quality assurance + +## Next Steps + +1. **📖 Start with the Notebook** - Work through the complete tutorial +2. **🎯 Choose Your Pattern** - Pick the application type that matches your needs +3. **🏗️ Build Your Foundation** - Set up your [providers](/docs/providers/) and [distributions](/docs/distributions/) +4. **🚀 Deploy & Monitor** - Use our [deployment guides](/docs/deploying/) for production + +## Related Resources + +- **[Getting Started](/docs/getting_started/quickstart)** - Basic setup and concepts +- **[Providers](/docs/providers/)** - Available AI service providers +- **[Distributions](/docs/distributions/)** - Pre-configured deployment packages +- **[API Reference](/docs/api/llama-stack-specification)** - Complete API documentation diff --git a/docs/docs/building_applications/playground.mdx b/docs/docs/building_applications/playground.mdx new file mode 100644 index 000000000..b2aa1b4a5 --- /dev/null +++ b/docs/docs/building_applications/playground.mdx @@ -0,0 +1,299 @@ +--- +title: Llama Stack Playground +description: Interactive interface to explore and experiment with Llama Stack capabilities +sidebar_label: Playground +sidebar_position: 10 +--- + +import Tabs from '@theme/Tabs'; +import TabItem from '@theme/TabItem'; + +# Llama Stack Playground + +:::note[Experimental Feature] +The Llama Stack Playground is currently experimental and subject to change. We welcome feedback and contributions to help improve it. +::: + +The Llama Stack Playground is a simple interface that aims to: +- **Showcase capabilities and concepts** of Llama Stack in an interactive environment +- **Demo end-to-end application code** to help users get started building their own applications +- **Provide a UI** to help users inspect and understand Llama Stack API providers and resources + +## Key Features + +### Interactive Playground Pages + +The playground provides interactive pages for users to explore Llama Stack API capabilities: + +#### Chatbot Interface + + + + + + +**Simple Chat Interface** +- Chat directly with Llama models through an intuitive interface +- Uses the `/inference/chat-completion` streaming API under the hood +- Real-time message streaming for responsive interactions +- Perfect for testing model capabilities and prompt engineering + + + + +**Document-Aware Conversations** +- Upload documents to create memory banks +- Chat with a RAG-enabled agent that can query your documents +- Uses Llama Stack's `/agents` API to create and manage RAG sessions +- Ideal for exploring knowledge-enhanced AI applications + + + + +#### Evaluation Interface + + + + + + +**Custom Dataset Evaluation** +- Upload your own evaluation datasets +- Run evaluations using available scoring functions +- Uses Llama Stack's `/scoring` API for flexible evaluation workflows +- Great for testing application performance on custom metrics + + + + + + +**Pre-registered Evaluation Tasks** +- Evaluate models or agents on pre-defined tasks +- Uses Llama Stack's `/eval` API for comprehensive evaluation +- Combines datasets and scoring functions for standardized testing + +**Setup Requirements:** +Register evaluation datasets and benchmarks first: + +```bash +# Register evaluation dataset +llama-stack-client datasets register \ + --dataset-id "mmlu" \ + --provider-id "huggingface" \ + --url "https://huggingface.co/datasets/llamastack/evals" \ + --metadata '{"path": "llamastack/evals", "name": "evals__mmlu__details", "split": "train"}' \ + --schema '{"input_query": {"type": "string"}, "expected_answer": {"type": "string"}, "chat_completion_input": {"type": "string"}}' + +# Register benchmark task +llama-stack-client benchmarks register \ + --eval-task-id meta-reference-mmlu \ + --provider-id meta-reference \ + --dataset-id mmlu \ + --scoring-functions basic::regex_parser_multiple_choice_answer +``` + + + + +#### Inspection Interface + + + + + + +**Provider Management** +- Inspect available Llama Stack API providers +- View provider configurations and capabilities +- Uses the `/providers` API for real-time provider information +- Essential for understanding your deployment's capabilities + + + + +**Resource Exploration** +- Inspect Llama Stack API resources including: + - **Models**: Available language models + - **Datasets**: Registered evaluation datasets + - **Memory Banks**: Vector databases and knowledge stores + - **Benchmarks**: Evaluation tasks and scoring functions + - **Shields**: Safety and content moderation tools +- Uses `//list` APIs for comprehensive resource visibility +- For detailed information about resources, see [Core Concepts](/docs/concepts) + + + + +## Getting Started + +### Quick Start Guide + + + + +**1. Start the Llama Stack API Server** + +```bash +# Build and run a distribution (example: together) +llama stack build --distro together --image-type venv +llama stack run together +``` + +**2. Start the Streamlit UI** + +```bash +# Launch the playground interface +uv run --with ".[ui]" streamlit run llama_stack.core/ui/app.py +``` + + + + +**Making the Most of the Playground:** + +- **Start with Chat**: Test basic model interactions and prompt engineering +- **Explore RAG**: Upload sample documents to see knowledge-enhanced responses +- **Try Evaluations**: Use the scoring interface to understand evaluation metrics +- **Inspect Resources**: Check what providers and resources are available +- **Experiment with Settings**: Adjust parameters to see how they affect results + + + + +### Available Distributions + +The playground works with any Llama Stack distribution. Popular options include: + + + + +```bash +llama stack build --distro together --image-type venv +llama stack run together +``` + +**Features:** +- Cloud-hosted models +- Fast inference +- Multiple model options + + + + +```bash +llama stack build --distro ollama --image-type venv +llama stack run ollama +``` + +**Features:** +- Local model execution +- Privacy-focused +- No internet required + + + + +```bash +llama stack build --distro meta-reference --image-type venv +llama stack run meta-reference +``` + +**Features:** +- Reference implementation +- All API features available +- Best for development + + + + +## Use Cases & Examples + +### Educational Use Cases +- **Learning Llama Stack**: Hands-on exploration of API capabilities +- **Prompt Engineering**: Interactive testing of different prompting strategies +- **RAG Experimentation**: Understanding how document retrieval affects responses +- **Evaluation Understanding**: See how different metrics evaluate model performance + +### Development Use Cases +- **Prototype Testing**: Quick validation of application concepts +- **API Exploration**: Understanding available endpoints and parameters +- **Integration Planning**: Seeing how different components work together +- **Demo Creation**: Showcasing Llama Stack capabilities to stakeholders + +### Research Use Cases +- **Model Comparison**: Side-by-side testing of different models +- **Evaluation Design**: Understanding how scoring functions work +- **Safety Testing**: Exploring shield effectiveness with different inputs +- **Performance Analysis**: Measuring model behavior across different scenarios + +## Best Practices + +### 🚀 **Getting Started** +- Begin with simple chat interactions to understand basic functionality +- Gradually explore more advanced features like RAG and evaluations +- Use the inspection tools to understand your deployment's capabilities + +### 🔧 **Development Workflow** +- Use the playground to prototype before writing application code +- Test different parameter settings interactively +- Validate evaluation approaches before implementing them programmatically + +### 📊 **Evaluation & Testing** +- Start with simple scoring functions before trying complex evaluations +- Use the playground to understand evaluation results before automation +- Test safety features with various input types + +### 🎯 **Production Preparation** +- Use playground insights to inform your production API usage +- Test edge cases and error conditions interactively +- Validate resource configurations before deployment + +## Related Resources + +- **[Getting Started Guide](../getting_started/quickstart)** - Complete setup and introduction +- **[Core Concepts](/docs/concepts)** - Understanding Llama Stack fundamentals +- **[Agents](./agent)** - Building intelligent agents +- **[RAG (Retrieval Augmented Generation)](./rag)** - Knowledge-enhanced applications +- **[Evaluations](./evals)** - Comprehensive evaluation framework +- **[API Reference](/docs/api/llama-stack-specification)** - Complete API documentation diff --git a/docs/source/building_applications/rag.md b/docs/docs/building_applications/rag.mdx similarity index 54% rename from docs/source/building_applications/rag.md rename to docs/docs/building_applications/rag.mdx index 289c38991..5212616d2 100644 --- a/docs/source/building_applications/rag.md +++ b/docs/docs/building_applications/rag.mdx @@ -1,36 +1,49 @@ -## Retrieval Augmented Generation (RAG) +--- +title: Retrieval Augmented Generation (RAG) +description: Build knowledge-enhanced AI applications with external document retrieval +sidebar_label: RAG (Retrieval Augmented Generation) +sidebar_position: 2 +--- + +import Tabs from '@theme/Tabs'; +import TabItem from '@theme/TabItem'; + +# Retrieval Augmented Generation (RAG) RAG enables your applications to reference and recall information from previous interactions or external documents. -Llama Stack organizes the APIs that enable RAG into three layers: -1. The lowermost APIs deal with raw storage and retrieval. These include Vector IO, KeyValue IO (coming soon) and Relational IO (also coming soon.). -2. The next is the "Rag Tool", a first-class tool as part of the [Tools API](tools.md) that allows you to ingest documents (from URLs, files, etc) with various chunking strategies and query them smartly. -3. Finally, it all comes together with the top-level ["Agents" API](agent.md) that allows you to create agents that can use the tools to answer questions, perform tasks, and more. +## Architecture Overview -RAG System +Llama Stack organizes the APIs that enable RAG into three layers: + +1. **Lower-Level APIs**: Deal with raw storage and retrieval. These include Vector IO, KeyValue IO (coming soon) and Relational IO (also coming soon) +2. **RAG Tool**: A first-class tool as part of the [Tools API](./tools) that allows you to ingest documents (from URLs, files, etc) with various chunking strategies and query them smartly +3. **Agents API**: The top-level [Agents API](./agent) that allows you to create agents that can use the tools to answer questions, perform tasks, and more + +![RAG System Architecture](/img/rag.png) The RAG system uses lower-level storage for different types of data: -* **Vector IO**: For semantic search and retrieval -* **Key-Value and Relational IO**: For structured data storage +- **Vector IO**: For semantic search and retrieval +- **Key-Value and Relational IO**: For structured data storage +:::info[Future Storage Types] We may add more storage types like Graph IO in the future. +::: -### Setting up Vector DBs +## Setting up Vector Databases -For this guide, we will use [Ollama](https://ollama.com/) as the inference provider. -Ollama is an LLM runtime that allows you to run Llama models locally. +For this guide, we will use [Ollama](https://ollama.com/) as the inference provider. Ollama is an LLM runtime that allows you to run Llama models locally. Here's how to set up a vector database for RAG: ```python -# Create http client +# Create HTTP client import os from llama_stack_client import LlamaStackClient client = LlamaStackClient(base_url=f"http://localhost:{os.environ['LLAMA_STACK_PORT']}") - -# Register a vector db +# Register a vector database vector_db_id = "my_documents" response = client.vector_dbs.register( vector_db_id=vector_db_id, @@ -40,9 +53,15 @@ response = client.vector_dbs.register( ) ``` -### Ingesting Documents -You can ingest documents into the vector database using two methods: directly inserting pre-chunked -documents or using the RAG Tool. +## Document Ingestion + +You can ingest documents into the vector database using two methods: directly inserting pre-chunked documents or using the RAG Tool. + +### Direct Document Insertion + + + + ```python # You can insert a pre-chunked document directly into the vector db chunks = [ @@ -58,10 +77,11 @@ chunks = [ client.vector_io.insert(vector_db_id=vector_db_id, chunks=chunks) ``` -#### Using Precomputed Embeddings -If you decide to precompute embeddings for your documents, you can insert them directly into the vector database by -including the embedding vectors in the chunk data. This is useful if you have a separate embedding service or if you -want to customize the ingestion process. + + + +If you decide to precompute embeddings for your documents, you can insert them directly into the vector database by including the embedding vectors in the chunk data. This is useful if you have a separate embedding service or if you want to customize the ingestion process. + ```python chunks_with_embeddings = [ { @@ -79,23 +99,53 @@ chunks_with_embeddings = [ ] client.vector_io.insert(vector_db_id=vector_db_id, chunks=chunks_with_embeddings) ``` -When providing precomputed embeddings, ensure the embedding dimension matches the embedding_dimension specified when -registering the vector database. -### Retrieval +:::warning[Embedding Dimensions] +When providing precomputed embeddings, ensure the embedding dimension matches the `embedding_dimension` specified when registering the vector database. +::: + + + + +### Document Retrieval + You can query the vector database to retrieve documents based on their embeddings. + ```python # You can then query for these chunks chunks_response = client.vector_io.query( - vector_db_id=vector_db_id, query="What do you know about..." + vector_db_id=vector_db_id, + query="What do you know about..." ) ``` -### Using the RAG Tool +## Using the RAG Tool -A better way to ingest documents is to use the RAG Tool. This tool allows you to ingest documents from URLs, files, etc. -and automatically chunks them into smaller pieces. More examples for how to format a RAGDocument can be found in the -[appendix](#more-ragdocument-examples). +:::danger[Deprecation Notice] +The RAG Tool is being deprecated in favor of directly using the OpenAI-compatible Search API. We recommend migrating to the OpenAI APIs for better compatibility and future support. +::: + +A better way to ingest documents is to use the RAG Tool. This tool allows you to ingest documents from URLs, files, etc. and automatically chunks them into smaller pieces. More examples for how to format a RAGDocument can be found in the [appendix](#more-ragdocument-examples). + +### OpenAI API Integration & Migration + +The RAG tool has been updated to use OpenAI-compatible APIs. This provides several benefits: + +- **Files API Integration**: Documents are now uploaded using OpenAI's file upload endpoints +- **Vector Stores API**: Vector storage operations use OpenAI's vector store format with configurable chunking strategies +- **Error Resilience**: When processing multiple documents, individual failures are logged but don't crash the operation. Failed documents are skipped while successful ones continue processing. + +### Migration Path + +We recommend migrating to the OpenAI-compatible Search API for: + +1. **Better OpenAI Ecosystem Integration**: Direct compatibility with OpenAI tools and workflows including the Responses API +2. **Future-Proof**: Continued support and feature development +3. **Full OpenAI Compatibility**: Vector Stores, Files, and Search APIs are fully compatible with OpenAI's Responses API + +The OpenAI APIs are used under the hood, so you can continue to use your existing RAG Tool code with minimal changes. However, we recommend updating your code to use the new OpenAI-compatible APIs for better long-term support. If any documents fail to process, they will be logged in the response but will not cause the entire operation to fail. + +### RAG Tool Example ```python from llama_stack_client import RAGDocument @@ -124,9 +174,12 @@ results = client.tool_runtime.rag_tool.query( ) ``` -You can configure how the RAG tool adds metadata to the context if you find it useful for your application. Simply add: +### Custom Context Configuration + +You can configure how the RAG tool adds metadata to the context if you find it useful for your application: + ```python -# Query documents +# Query documents with custom template results = client.tool_runtime.rag_tool.query( vector_db_ids=[vector_db_id], content="What do you know about...", @@ -135,10 +188,13 @@ results = client.tool_runtime.rag_tool.query( }, ) ``` -### Building RAG-Enhanced Agents + +## Building RAG-Enhanced Agents One of the most powerful patterns is combining agents with RAG capabilities. Here's a complete example: +### Agent with Knowledge Search + ```python from llama_stack_client import Agent @@ -164,7 +220,6 @@ agent = Agent( ) session_id = agent.create_session("rag_session") - # Ask questions about documents in the vector db, and the agent will query the db to answer the question. response = agent.create_turn( messages=[{"role": "user", "content": "How to optimize memory in PyTorch?"}], @@ -172,10 +227,14 @@ response = agent.create_turn( ) ``` -> **NOTE:** the `instructions` field in the `AgentConfig` can be used to guide the agent's behavior. It is important to experiment with different instructions to see what works best for your use case. +:::tip[Agent Instructions] +The `instructions` field in the `AgentConfig` can be used to guide the agent's behavior. It is important to experiment with different instructions to see what works best for your use case. +::: +### Document-Aware Conversations + +You can also pass documents along with the user's message and ask questions about them: -You can also pass documents along with the user's message and ask questions about them. ```python # Initial document ingestion response = agent.create_turn( @@ -198,7 +257,10 @@ response = agent.create_turn( ) ``` -You can print the response with below. +### Viewing Agent Responses + +You can print the response with the following: + ```python from llama_stack_client import AgentEventLogger @@ -206,32 +268,74 @@ for log in AgentEventLogger().log(response): log.print() ``` +## Vector Database Management + ### Unregistering Vector DBs If you need to clean up and unregister vector databases, you can do so as follows: + + + ```python # Unregister a specified vector database vector_db_id = "my_vector_db_id" print(f"Unregistering vector database: {vector_db_id}") client.vector_dbs.unregister(vector_db_id=vector_db_id) +``` + + +```python # Unregister all vector databases for vector_db_id in client.vector_dbs.list(): print(f"Unregistering vector database: {vector_db_id.identifier}") client.vector_dbs.unregister(vector_db_id=vector_db_id.identifier) ``` -### Appendix + + + +## Best Practices + +### 🎯 **Document Chunking** +- Use appropriate chunk sizes (512 tokens is often a good starting point) +- Consider overlap between chunks for better context preservation +- Experiment with different chunking strategies for your content type + +### 🔍 **Embedding Strategy** +- Choose embedding models that match your domain +- Consider the trade-off between embedding dimension and performance +- Test different embedding models for your specific use case + +### 📊 **Query Optimization** +- Use specific, well-formed queries for better retrieval +- Experiment with different search strategies +- Consider hybrid approaches (keyword + semantic search) + +### 🛡️ **Error Handling** +- Implement proper error handling for failed document processing +- Monitor ingestion success rates +- Have fallback strategies for retrieval failures + +## Appendix + +### More RAGDocument Examples + +Here are various ways to create RAGDocument objects for different content types: -#### More RAGDocument Examples ```python from llama_stack_client import RAGDocument import base64 +# File URI RAGDocument(document_id="num-0", content={"uri": "file://path/to/file"}) + +# Plain text RAGDocument(document_id="num-1", content="plain text") + +# Explicit text input RAGDocument( document_id="num-2", content={ @@ -239,6 +343,8 @@ RAGDocument( "text": "plain text input", }, # for inputs that should be treated as text explicitly ) + +# Image from URL RAGDocument( document_id="num-3", content={ @@ -246,14 +352,16 @@ RAGDocument( "image": {"url": {"uri": "https://mywebsite.com/image.jpg"}}, }, ) + +# Base64 encoded image B64_ENCODED_IMAGE = base64.b64encode( requests.get( "https://raw.githubusercontent.com/meta-llama/llama-stack/refs/heads/main/docs/_static/llama-stack.png" ).content ) -RAGDocuemnt( +RAGDocument( document_id="num-4", content={"type": "image", "image": {"data": B64_ENCODED_IMAGE}}, ) ``` -for more strongly typed interaction use the typed dicts found [here](https://github.com/meta-llama/llama-stack-client-python/blob/38cd91c9e396f2be0bec1ee96a19771582ba6f17/src/llama_stack_client/types/shared_params/document.py). +For more strongly typed interaction use the typed dicts found [here](https://github.com/meta-llama/llama-stack-client-python/blob/38cd91c9e396f2be0bec1ee96a19771582ba6f17/src/llama_stack_client/types/shared_params/document.py). diff --git a/docs/source/building_applications/responses_vs_agents.md b/docs/docs/building_applications/responses_vs_agents.mdx similarity index 80% rename from docs/source/building_applications/responses_vs_agents.md rename to docs/docs/building_applications/responses_vs_agents.mdx index 5abe951d6..4cf7620e7 100644 --- a/docs/source/building_applications/responses_vs_agents.md +++ b/docs/docs/building_applications/responses_vs_agents.mdx @@ -1,10 +1,20 @@ +--- +title: Agents vs OpenAI Responses API +description: Compare the Agents API and OpenAI Responses API for building AI applications with tool calling capabilities +sidebar_label: Agents vs Responses API +sidebar_position: 5 +--- + +import Tabs from '@theme/Tabs'; +import TabItem from '@theme/TabItem'; + # Agents vs OpenAI Responses API Llama Stack (LLS) provides two different APIs for building AI applications with tool calling capabilities: the **Agents API** and the **OpenAI Responses API**. While both enable AI systems to use tools, and maintain full conversation history, they serve different use cases and have distinct characteristics. -```{note} -For simple and basic inferencing, you may want to use the [Chat Completions API](https://llama-stack.readthedocs.io/en/latest/providers/index.html#chat-completions) directly, before progressing to Agents or Responses API. -``` +:::note +**Note:** For simple and basic inferencing, you may want to use the [Chat Completions API](../providers/openai#chat-completions) directly, before progressing to Agents or Responses API. +::: ## Overview @@ -21,6 +31,8 @@ Additionally, Agents let you specify input/output shields whereas Responses do n Today the Agents and Responses APIs can be used independently depending on the use case. But, it is also productive to treat the APIs as complementary. It is not currently supported, but it is planned for the LLS Agents API to alternatively use the Responses API as its backend instead of the default Chat Completions API, i.e., enabling a combination of the safety features of Agents with the dynamic configuration and branching capabilities of Responses. +## Feature Comparison + | Feature | LLS Agents API | OpenAI Responses API | |---------|------------|---------------------| | **Conversation Management** | Linear persistent sessions | Can branch from any previous response ID | @@ -34,7 +46,10 @@ Let's compare how both APIs handle a research task where we need to: 2. Access different information sources dynamically 3. Continue the conversation based on search results -### Agents API: Session-based configuration with safety shields + + + +### Session-based Configuration with Safety Shields ```python # Create agent with static session configuration @@ -85,7 +100,10 @@ print(f"First result: {response1.output_message.content}") print(f"Optimization: {response2.output_message.content}") ``` -### Responses API: Dynamic per-call configuration with branching + + + +### Dynamic Per-call Configuration with Branching ```python # First response: Use web search for latest algorithms @@ -130,50 +148,74 @@ print(f"File search results: {response2.output_message.content}") print(f"Alternative web search: {response3.output_message.content}") ``` + + + Both APIs demonstrate distinct strengths that make them valuable on their own for different scenarios. The Agents API excels in providing structured, safety-conscious workflows with persistent session management, while the Responses API offers flexibility through dynamic configuration and OpenAI compatible tool patterns. ## Use Case Examples -### 1. **Research and Analysis with Safety Controls** +### 1. Research and Analysis with Safety Controls **Best Choice: Agents API** **Scenario:** You're building a research assistant for a financial institution that needs to analyze market data, execute code to process financial models, and search through internal compliance documents. The system must ensure all interactions are logged for regulatory compliance and protected by safety shields to prevent malicious code execution or data leaks. **Why Agents API?** The Agents API provides persistent session management for iterative research workflows, built-in safety shields to protect against malicious code in financial models, and structured execution logs (session/turn/step) required for regulatory compliance. The static tool configuration ensures consistent access to your knowledge base and code interpreter throughout the entire research session. -### 2. **Dynamic Information Gathering with Branching Exploration** +### 2. Dynamic Information Gathering with Branching Exploration **Best Choice: Responses API** **Scenario:** You're building a competitive intelligence tool that helps businesses research market trends. Users need to dynamically switch between web search for current market data and file search through uploaded industry reports. They also want to branch conversations to explore different market segments simultaneously and experiment with different models for various analysis types. **Why Responses API?** The Responses API's branching capability lets users explore multiple market segments from any research point. Dynamic per-call configuration allows switching between web search and file search as needed, while experimenting with different models (faster models for quick searches, more powerful models for deep analysis). The OpenAI-compatible tool patterns make integration straightforward. -### 3. **OpenAI Migration with Advanced Tool Capabilities** +### 3. OpenAI Migration with Advanced Tool Capabilities **Best Choice: Responses API** **Scenario:** You have an existing application built with OpenAI's Assistants API that uses file search and web search capabilities. You want to migrate to Llama Stack for better performance and cost control while maintaining the same tool calling patterns and adding new capabilities like dynamic vector store selection. **Why Responses API?** The Responses API provides full OpenAI tool compatibility (`web_search`, `file_search`) with identical syntax, making migration seamless. The dynamic per-call configuration enables advanced features like switching vector stores per query or changing models based on query complexity - capabilities that extend beyond basic OpenAI functionality while maintaining compatibility. -### 4. **Educational Programming Tutor** +### 4. Educational Programming Tutor **Best Choice: Agents API** **Scenario:** You're building a programming tutor that maintains student context across multiple sessions, safely executes code exercises, and tracks learning progress with audit trails for educators. **Why Agents API?** Persistent sessions remember student progress across multiple interactions, safety shields prevent malicious code execution while allowing legitimate programming exercises, and structured execution logs help educators track learning patterns. -### 5. **Advanced Software Debugging Assistant** +### 5. Advanced Software Debugging Assistant **Best Choice: Agents API with Responses Backend** **Scenario:** You're building a debugging assistant that helps developers troubleshoot complex issues. It needs to maintain context throughout a debugging session, safely execute diagnostic code, switch between different analysis tools dynamically, and branch conversations to explore multiple potential causes simultaneously. **Why Agents + Responses?** The Agent provides safety shields for code execution and session management for the overall debugging workflow. The underlying Responses API enables dynamic model selection and flexible tool configuration per query, while branching lets you explore different theories (memory leak vs. concurrency issue) from the same debugging point and compare results. -> **Note:** The ability to use Responses API as the backend for Agents is not yet implemented but is planned for a future release. Currently, Agents use Chat Completions API as their backend by default. +:::info[Future Enhancement] +The ability to use Responses API as the backend for Agents is not yet implemented but is planned for a future release. Currently, Agents use Chat Completions API as their backend by default. +::: -## For More Information +## Decision Framework -- **LLS Agents API**: For detailed information on creating and managing agents, see the [Agents documentation](https://llama-stack.readthedocs.io/en/latest/building_applications/agent.html) -- **OpenAI Responses API**: For information on using the OpenAI-compatible responses API, see the [OpenAI API documentation](https://platform.openai.com/docs/api-reference/responses) -- **Chat Completions API**: For the default backend API used by Agents, see the [Chat Completions providers documentation](https://llama-stack.readthedocs.io/en/latest/providers/index.html#chat-completions) -- **Agent Execution Loop**: For understanding how agents process turns and steps in their execution, see the [Agent Execution Loop documentation](https://llama-stack.readthedocs.io/en/latest/building_applications/agent_execution_loop.html) +Use this framework to choose the right API for your use case: + +### Choose Agents API when: +- ✅ You need **safety shields** for input/output validation +- ✅ Your application requires **linear conversation flow** with persistent context +- ✅ You need **audit trails** and structured execution logs +- ✅ Your tool configuration is **static** throughout the session +- ✅ You're building **educational, financial, or enterprise** applications with compliance requirements + +### Choose Responses API when: +- ✅ You need **conversation branching** to explore multiple paths +- ✅ You want **dynamic per-call configuration** (models, tools, vector stores) +- ✅ You're **migrating from OpenAI** and want familiar tool patterns +- ✅ You need **OpenAI compatibility** for existing workflows +- ✅ Your application benefits from **flexible, experimental** interactions + +## Related Resources + +- **[Agents](./agent)** - Understanding the Agents API fundamentals +- **[Agent Execution Loop](./agent_execution_loop)** - How agents process turns and steps +- **[Tools Integration](./tools)** - Adding capabilities to both APIs +- **[OpenAI Compatibility](../providers/openai)** - Using OpenAI-compatible endpoints +- **[Safety Guardrails](./safety)** - Implementing safety measures in agents diff --git a/docs/docs/building_applications/safety.mdx b/docs/docs/building_applications/safety.mdx new file mode 100644 index 000000000..16fe5f6f8 --- /dev/null +++ b/docs/docs/building_applications/safety.mdx @@ -0,0 +1,395 @@ +--- +title: Safety Guardrails +description: Implement safety measures and content moderation in Llama Stack applications +sidebar_label: Safety +sidebar_position: 9 +--- + +import Tabs from '@theme/Tabs'; +import TabItem from '@theme/TabItem'; + +# Safety Guardrails + +Safety is a critical component of any AI application. Llama Stack provides a comprehensive Shield system that can be applied at multiple touchpoints to ensure responsible AI behavior and content moderation. + +## Shield System Overview + +The Shield system in Llama Stack provides: +- **Content filtering** for both input and output messages +- **Multi-touchpoint protection** across your application flow +- **Configurable safety policies** tailored to your use case +- **Integration with agents** for automated safety enforcement + +## Basic Shield Usage + +### Registering a Safety Shield + + + + +```python +# Register a safety shield +shield_id = "content_safety" +client.shields.register( + shield_id=shield_id, + provider_shield_id="llama-guard-basic" +) +``` + + + + +```python +# Run content through shield manually +response = client.safety.run_shield( + shield_id=shield_id, + messages=[{"role": "user", "content": "User message here"}] +) + +if response.violation: + print(f"Safety violation detected: {response.violation.user_message}") + # Handle violation appropriately +else: + print("Content passed safety checks") +``` + + + + +## Agent Integration + +Shields can be automatically applied to agent interactions for seamless safety enforcement: + + + + +```python +from llama_stack_client import Agent + +# Create agent with input safety shields +agent = Agent( + client, + model="meta-llama/Llama-3.2-3B-Instruct", + instructions="You are a helpful assistant", + input_shields=["content_safety"], # Shield user inputs + tools=["builtin::websearch"], +) + +session_id = agent.create_session("safe_session") + +# All user inputs will be automatically screened +response = agent.create_turn( + messages=[{"role": "user", "content": "Tell me about AI safety"}], + session_id=session_id, +) +``` + + + + +```python +# Create agent with output safety shields +agent = Agent( + client, + model="meta-llama/Llama-3.2-3B-Instruct", + instructions="You are a helpful assistant", + output_shields=["content_safety"], # Shield agent outputs + tools=["builtin::websearch"], +) + +session_id = agent.create_session("safe_session") + +# All agent responses will be automatically screened +response = agent.create_turn( + messages=[{"role": "user", "content": "Help me with my research"}], + session_id=session_id, +) +``` + + + + +```python +# Create agent with comprehensive safety coverage +agent = Agent( + client, + model="meta-llama/Llama-3.2-3B-Instruct", + instructions="You are a helpful assistant", + input_shields=["content_safety"], # Screen user inputs + output_shields=["content_safety"], # Screen agent outputs + tools=["builtin::websearch"], +) + +session_id = agent.create_session("fully_protected_session") + +# Both input and output are automatically protected +response = agent.create_turn( + messages=[{"role": "user", "content": "Research question here"}], + session_id=session_id, +) +``` + + + + +## Available Shield Types + +### Llama Guard Shields + +Llama Guard provides state-of-the-art content safety classification: + + + + +```python +# Basic Llama Guard for general content safety +client.shields.register( + shield_id="llama_guard_basic", + provider_shield_id="llama-guard-basic" +) +``` + +**Use Cases:** +- General content moderation +- Harmful content detection +- Basic safety compliance + + + + +```python +# Advanced Llama Guard with custom categories +client.shields.register( + shield_id="llama_guard_advanced", + provider_shield_id="llama-guard-advanced", + config={ + "categories": [ + "violence", "hate_speech", "sexual_content", + "self_harm", "illegal_activity" + ], + "threshold": 0.8 + } +) +``` + +**Use Cases:** +- Fine-tuned safety policies +- Domain-specific content filtering +- Enterprise compliance requirements + + + + +### Custom Safety Shields + +Create domain-specific safety shields for specialized use cases: + +```python +# Register custom safety shield +client.shields.register( + shield_id="financial_compliance", + provider_shield_id="custom-financial-shield", + config={ + "detect_pii": True, + "financial_advice_warning": True, + "regulatory_compliance": "FINRA" + } +) +``` + +## Safety Response Handling + +When safety violations are detected, handle them appropriately: + + + + +```python +response = client.safety.run_shield( + shield_id="content_safety", + messages=[{"role": "user", "content": "Potentially harmful content"}] +) + +if response.violation: + violation = response.violation + print(f"Violation Type: {violation.violation_type}") + print(f"User Message: {violation.user_message}") + print(f"Metadata: {violation.metadata}") + + # Log the violation for audit purposes + logger.warning(f"Safety violation detected: {violation.violation_type}") + + # Provide appropriate user feedback + return "I can't help with that request. Please try asking something else." +``` + + + + +```python +def handle_safety_response(safety_response, user_message): + """Advanced safety response handling with logging and user feedback""" + + if not safety_response.violation: + return {"safe": True, "message": "Content passed safety checks"} + + violation = safety_response.violation + + # Log violation details + audit_log = { + "timestamp": datetime.now().isoformat(), + "violation_type": violation.violation_type, + "original_message": user_message, + "shield_response": violation.user_message, + "metadata": violation.metadata + } + logger.warning(f"Safety violation: {audit_log}") + + # Determine appropriate response based on violation type + if violation.violation_type == "hate_speech": + user_feedback = "I can't engage with content that contains hate speech. Let's keep our conversation respectful." + elif violation.violation_type == "violence": + user_feedback = "I can't provide information that could promote violence. How else can I help you today?" + else: + user_feedback = "I can't help with that request. Please try asking something else." + + return { + "safe": False, + "user_feedback": user_feedback, + "violation_details": audit_log + } + +# Usage +safety_result = handle_safety_response(response, user_input) +if not safety_result["safe"]: + return safety_result["user_feedback"] +``` + + + + +## Safety Configuration Best Practices + +### 🛡️ **Multi-Layer Protection** +- Use both input and output shields for comprehensive coverage +- Combine multiple shield types for different threat categories +- Implement fallback mechanisms when shields fail + +### 📊 **Monitoring & Auditing** +- Log all safety violations for compliance and analysis +- Monitor false positive rates to tune shield sensitivity +- Track safety metrics across different use cases + +### ⚙️ **Configuration Management** +- Use environment-specific safety configurations +- Implement A/B testing for shield effectiveness +- Regularly update shield models and policies + +### 🔧 **Integration Patterns** +- Integrate shields early in the development process +- Test safety measures with adversarial inputs +- Provide clear user feedback for violations + +## Advanced Safety Scenarios + +### Context-Aware Safety + +```python +# Safety shields that consider conversation context +agent = Agent( + client, + model="meta-llama/Llama-3.2-3B-Instruct", + instructions="You are a healthcare assistant", + input_shields=["medical_safety"], + output_shields=["medical_safety"], + # Context helps shields make better decisions + safety_context={ + "domain": "healthcare", + "user_type": "patient", + "compliance_level": "HIPAA" + } +) +``` + +### Dynamic Shield Selection + +```python +def select_shield_for_user(user_profile): + """Select appropriate safety shield based on user context""" + if user_profile.age < 18: + return "child_safety_shield" + elif user_profile.context == "enterprise": + return "enterprise_compliance_shield" + else: + return "general_safety_shield" + +# Use dynamic shield selection +shield_id = select_shield_for_user(current_user) +response = client.safety.run_shield( + shield_id=shield_id, + messages=messages +) +``` + +## Compliance and Regulations + +### Industry-Specific Safety + + + + +```python +# Healthcare-specific safety configuration +client.shields.register( + shield_id="hipaa_compliance", + provider_shield_id="healthcare-safety-shield", + config={ + "detect_phi": True, # Protected Health Information + "medical_advice_warning": True, + "regulatory_framework": "HIPAA" + } +) +``` + + + + +```python +# Financial services safety configuration +client.shields.register( + shield_id="finra_compliance", + provider_shield_id="financial-safety-shield", + config={ + "detect_financial_advice": True, + "investment_disclaimers": True, + "regulatory_framework": "FINRA" + } +) +``` + + + + +```python +# Educational platform safety for minors +client.shields.register( + shield_id="coppa_compliance", + provider_shield_id="educational-safety-shield", + config={ + "child_protection": True, + "educational_content_only": True, + "regulatory_framework": "COPPA" + } +) +``` + + + + +## Related Resources + +- **[Agents](./agent)** - Integrating safety shields with intelligent agents +- **[Agent Execution Loop](./agent_execution_loop)** - Understanding safety in the execution flow +- **[Evaluations](./evals)** - Evaluating safety shield effectiveness +- **[Telemetry](./telemetry)** - Monitoring safety violations and metrics +- **[Llama Guard Documentation](https://github.com/meta-llama/PurpleLlama/tree/main/Llama-Guard3)** - Advanced safety model details diff --git a/docs/docs/building_applications/telemetry.mdx b/docs/docs/building_applications/telemetry.mdx new file mode 100644 index 000000000..6a255e702 --- /dev/null +++ b/docs/docs/building_applications/telemetry.mdx @@ -0,0 +1,342 @@ +--- +title: Telemetry +description: Monitor and observe Llama Stack applications with comprehensive telemetry capabilities +sidebar_label: Telemetry +sidebar_position: 8 +--- + +import Tabs from '@theme/Tabs'; +import TabItem from '@theme/TabItem'; + +# Telemetry + +The Llama Stack telemetry system provides comprehensive tracing, metrics, and logging capabilities. It supports multiple sink types including OpenTelemetry, SQLite, and Console output for complete observability of your AI applications. + +## Event Types + +The telemetry system supports three main types of events: + + + + +Free-form log messages with severity levels for general application logging: + +```python +unstructured_log_event = UnstructuredLogEvent( + message="This is a log message", + severity=LogSeverity.INFO +) +``` + + + + +Numerical measurements with units for tracking performance and usage: + +```python +metric_event = MetricEvent( + metric="my_metric", + value=10, + unit="count" +) +``` + + + + +System events like span start/end that provide structured operation tracking: + +```python +structured_log_event = SpanStartPayload( + name="my_span", + parent_span_id="parent_span_id" +) +``` + + + + +## Spans and Traces + +- **Spans**: Represent individual operations with timing information and hierarchical relationships +- **Traces**: Collections of related spans that form a complete request flow across your application + +This hierarchical structure allows you to understand the complete execution path of requests through your Llama Stack application. + +## Automatic Metrics Generation + +Llama Stack automatically generates metrics during inference operations. These metrics are aggregated at the **inference request level** and provide insights into token usage and model performance. + +### Available Metrics + +The following metrics are automatically generated for each inference request: + +| Metric Name | Type | Unit | Description | Labels | +|-------------|------|------|-------------|--------| +| `llama_stack_prompt_tokens_total` | Counter | `tokens` | Number of tokens in the input prompt | `model_id`, `provider_id` | +| `llama_stack_completion_tokens_total` | Counter | `tokens` | Number of tokens in the generated response | `model_id`, `provider_id` | +| `llama_stack_tokens_total` | Counter | `tokens` | Total tokens used (prompt + completion) | `model_id`, `provider_id` | + +### Metric Generation Flow + +1. **Token Counting**: During inference operations (chat completion, completion, etc.), the system counts tokens in both input prompts and generated responses +2. **Metric Construction**: For each request, `MetricEvent` objects are created with the token counts +3. **Telemetry Logging**: Metrics are sent to the configured telemetry sinks +4. **OpenTelemetry Export**: When OpenTelemetry is enabled, metrics are exposed as standard OpenTelemetry counters + +### Metric Aggregation Level + +All metrics are generated and aggregated at the **inference request level**. This means: + +- Each individual inference request generates its own set of metrics +- Metrics are not pre-aggregated across multiple requests +- Aggregation (sums, averages, etc.) can be performed by your observability tools (Prometheus, Grafana, etc.) +- Each metric includes labels for `model_id` and `provider_id` to enable filtering and grouping + +### Example Metric Event + +```python +MetricEvent( + trace_id="1234567890abcdef", + span_id="abcdef1234567890", + metric="total_tokens", + value=150, + timestamp=1703123456.789, + unit="tokens", + attributes={ + "model_id": "meta-llama/Llama-3.2-3B-Instruct", + "provider_id": "tgi" + }, +) +``` + +## Telemetry Sinks + +Choose from multiple sink types based on your observability needs: + + + + +Send events to an OpenTelemetry Collector for integration with observability platforms: + +**Use Cases:** +- Visualizing traces in tools like Jaeger +- Collecting metrics for Prometheus +- Integration with enterprise observability stacks + +**Features:** +- Standard OpenTelemetry format +- Compatible with all OpenTelemetry collectors +- Supports both traces and metrics + + + + +Store events in a local SQLite database for direct querying: + +**Use Cases:** +- Local development and debugging +- Custom analytics and reporting +- Offline analysis of application behavior + +**Features:** +- Direct SQL querying capabilities +- Persistent local storage +- No external dependencies + + + + +Print events to the console for immediate debugging: + +**Use Cases:** +- Development and testing +- Quick debugging sessions +- Simple logging without external tools + +**Features:** +- Immediate output visibility +- No setup required +- Human-readable format + + + + +## Configuration + +### Meta-Reference Provider + +Currently, only the meta-reference provider is implemented. It can be configured to send events to multiple sink types: + +```yaml +telemetry: + - provider_id: meta-reference + provider_type: inline::meta-reference + config: + service_name: "llama-stack-service" + sinks: ['console', 'sqlite', 'otel_trace', 'otel_metric'] + otel_exporter_otlp_endpoint: "http://localhost:4318" + sqlite_db_path: "/path/to/telemetry.db" +``` + +### Environment Variables + +Configure telemetry behavior using environment variables: + +- **`OTEL_EXPORTER_OTLP_ENDPOINT`**: OpenTelemetry Collector endpoint (default: `http://localhost:4318`) +- **`OTEL_SERVICE_NAME`**: Service name for telemetry (default: empty string) +- **`TELEMETRY_SINKS`**: Comma-separated list of sinks (default: `console,sqlite`) + +## Visualization with Jaeger + +The `otel_trace` sink works with any service compatible with the OpenTelemetry collector. Traces and metrics use separate endpoints but can share the same collector. + +### Starting Jaeger + +Start a Jaeger instance with OTLP HTTP endpoint at 4318 and the Jaeger UI at 16686: + +```bash +docker run --pull always --rm --name jaeger \ + -p 16686:16686 -p 4318:4318 \ + jaegertracing/jaeger:2.1.0 +``` + +Once running, you can visualize traces by navigating to [http://localhost:16686/](http://localhost:16686/). + +## Querying Metrics + +When using the OpenTelemetry sink, metrics are exposed in standard format and can be queried through various tools: + + + + +Example Prometheus queries for analyzing token usage: + +```promql +# Total tokens used across all models +sum(llama_stack_tokens_total) + +# Tokens per model +sum by (model_id) (llama_stack_tokens_total) + +# Average tokens per request over 5 minutes +rate(llama_stack_tokens_total[5m]) + +# Token usage by provider +sum by (provider_id) (llama_stack_tokens_total) +``` + + + + +Create dashboards using Prometheus as a data source: + +- **Token Usage Over Time**: Line charts showing token consumption trends +- **Model Performance**: Comparison of different models by token efficiency +- **Provider Analysis**: Breakdown of usage across different providers +- **Request Patterns**: Understanding peak usage times and patterns + + + + +Forward metrics to other observability systems: + +- Export to multiple backends simultaneously +- Apply transformations and filtering +- Integrate with existing monitoring infrastructure + + + + +## SQLite Querying + +The `sqlite` sink allows you to query traces without an external system. This is particularly useful for development and custom analytics. + +### Example Queries + +```sql +-- Query recent traces +SELECT * FROM traces WHERE timestamp > datetime('now', '-1 hour'); + +-- Analyze span durations +SELECT name, AVG(duration_ms) as avg_duration +FROM spans +GROUP BY name +ORDER BY avg_duration DESC; + +-- Find slow operations +SELECT * FROM spans +WHERE duration_ms > 1000 +ORDER BY duration_ms DESC; +``` + +:::tip[Advanced Analytics] +Refer to the [Getting Started notebook](https://github.com/meta-llama/llama-stack/blob/main/docs/getting_started.ipynb) for more examples on querying traces and spans programmatically. +::: + +## Best Practices + +### 🔍 **Monitoring Strategy** +- Use OpenTelemetry for production environments +- Combine multiple sinks for development (console + SQLite) +- Set up alerts on key metrics like token usage and error rates + +### 📊 **Metrics Analysis** +- Track token usage trends to optimize costs +- Monitor response times across different models +- Analyze usage patterns to improve resource allocation + +### 🚨 **Alerting & Debugging** +- Set up alerts for unusual token consumption spikes +- Use trace data to debug performance issues +- Monitor error rates and failure patterns + +### 🔧 **Configuration Management** +- Use environment variables for flexible deployment +- Configure appropriate retention policies for SQLite +- Ensure proper network access to OpenTelemetry collectors + +## Integration Examples + +### Basic Telemetry Setup + +```python +from llama_stack_client import LlamaStackClient + +# Client with telemetry headers +client = LlamaStackClient( + base_url="http://localhost:8000", + extra_headers={ + "X-Telemetry-Service": "my-ai-app", + "X-Telemetry-Version": "1.0.0" + } +) + +# All API calls will be automatically traced +response = client.inference.chat_completion( + model="meta-llama/Llama-3.2-3B-Instruct", + messages=[{"role": "user", "content": "Hello!"}] +) +``` + +### Custom Telemetry Context + +```python +# Add custom span attributes for better tracking +with tracer.start_as_current_span("custom_operation") as span: + span.set_attribute("user_id", "user123") + span.set_attribute("operation_type", "chat_completion") + + response = client.inference.chat_completion( + model="meta-llama/Llama-3.2-3B-Instruct", + messages=[{"role": "user", "content": "Hello!"}] + ) +``` + +## Related Resources + +- **[Agents](./agent)** - Monitoring agent execution with telemetry +- **[Evaluations](./evals)** - Using telemetry data for performance evaluation +- **[Getting Started Notebook](https://github.com/meta-llama/llama-stack/blob/main/docs/getting_started.ipynb)** - Telemetry examples and queries +- **[OpenTelemetry Documentation](https://opentelemetry.io/)** - Comprehensive observability framework +- **[Jaeger Documentation](https://www.jaegertracing.io/)** - Distributed tracing visualization diff --git a/docs/source/building_applications/tools.md b/docs/docs/building_applications/tools.mdx similarity index 66% rename from docs/source/building_applications/tools.md rename to docs/docs/building_applications/tools.mdx index 8a54290ed..be60a1639 100644 --- a/docs/source/building_applications/tools.md +++ b/docs/docs/building_applications/tools.mdx @@ -1,6 +1,17 @@ +--- +title: Tools +description: Extend agent capabilities with external tools and function calling +sidebar_label: Tools +sidebar_position: 6 +--- + +import Tabs from '@theme/Tabs'; +import TabItem from '@theme/TabItem'; + # Tools Tools are functions that can be invoked by an agent to perform tasks. They are organized into tool groups and registered with specific providers. Each tool group represents a collection of related tools from a single provider. They are organized into groups so that state can be externalized: the collection operates on the same state typically. + An example of this would be a "db_access" tool group that contains tools for interacting with a database. "list_tables", "query_table", "insert_row" could be examples of tools in this group. Tools are treated as any other resource in llama stack like models. You can register them, have providers for them etc. @@ -9,18 +20,15 @@ When instantiating an agent, you can provide it a list of tool groups that it ha Refer to the [Building AI Applications](https://github.com/meta-llama/llama-stack/blob/main/docs/getting_started.ipynb) notebook for more examples on how to use tools. -## Server-side vs. client-side tool execution +## Server-side vs. Client-side Tool Execution -Llama Stack allows you to use both server-side and client-side tools. With server-side tools, `agent.create_turn` can perform execution of the tool calls emitted by the model -transparently giving the user the final answer desired. If client-side tools are provided, the tool call is sent back to the user for execution -and optional continuation using the `agent.resume_turn` method. +Llama Stack allows you to use both server-side and client-side tools. With server-side tools, `agent.create_turn` can perform execution of the tool calls emitted by the model transparently giving the user the final answer desired. If client-side tools are provided, the tool call is sent back to the user for execution and optional continuation using the `agent.resume_turn` method. - -### Server-side tools +## Server-side Tools Llama Stack provides built-in providers for some common tools. These include web search, math, and RAG capabilities. -#### Web Search +### Web Search You have three providers to execute the web search tool calls generated by a model: Brave Search, Bing Search, and Tavily Search. @@ -39,25 +47,26 @@ The tool requires an API key which can be provided either in the configuration o {"_api_key": } ``` - -#### Math +### Math The WolframAlpha tool provides access to computational knowledge through the WolframAlpha API. ```python client.toolgroups.register( - toolgroup_id="builtin::wolfram_alpha", provider_id="wolfram-alpha" + toolgroup_id="builtin::wolfram_alpha", + provider_id="wolfram-alpha" ) ``` Example usage: ```python result = client.tool_runtime.invoke_tool( - tool_name="wolfram_alpha", args={"query": "solve x^2 + 2x + 1 = 0"} + tool_name="wolfram_alpha", + args={"query": "solve x^2 + 2x + 1 = 0"} ) ``` -#### RAG +### RAG The RAG tool enables retrieval of context from various types of memory banks (vector, key-value, keyword, and graph). @@ -75,16 +84,13 @@ Features: - Configurable query generation - Context retrieval with token limits - -```{note} +:::note[Default Configuration] By default, llama stack run.yaml defines toolgroups for web search, wolfram alpha and rag, that are provided by tavily-search, wolfram-alpha and rag providers. -``` +::: ## Model Context Protocol (MCP) -[MCP](https://github.com/modelcontextprotocol) is an upcoming, popular standard for tool discovery and execution. It is a protocol that allows tools to be dynamically discovered -from an MCP endpoint and can be used to extend the agent's capabilities. - +[MCP](https://github.com/modelcontextprotocol) is an upcoming, popular standard for tool discovery and execution. It is a protocol that allows tools to be dynamically discovered from an MCP endpoint and can be used to extend the agent's capabilities. ### Using Remote MCP Servers @@ -98,8 +104,7 @@ client.toolgroups.register( ) ``` -Note that most of the more useful MCP servers need you to authenticate with them. Many of them use OAuth2.0 for authentication. You can provide authorization headers to send to the MCP server -using the "Provider Data" abstraction provided by Llama Stack. When making an agent call, +Note that most of the more useful MCP servers need you to authenticate with them. Many of them use OAuth2.0 for authentication. You can provide authorization headers to send to the MCP server using the "Provider Data" abstraction provided by Llama Stack. When making an agent call, ```python agent = Agent( @@ -120,20 +125,26 @@ agent = Agent( agent.create_turn(...) ``` -### Running your own MCP server +### Running Your Own MCP Server Here's an example of how to run a simple MCP server that exposes a File System as a set of tools to the Llama Stack agent. + + + ```shell -# start your MCP server +# Start your MCP server mkdir /tmp/content touch /tmp/content/foo touch /tmp/content/bar npx -y supergateway --port 8000 --stdio 'npx -y @modelcontextprotocol/server-filesystem /tmp/content' ``` -Then register the MCP server as a tool group, + + + ```python +# Register the MCP server as a tool group client.toolgroups.register( toolgroup_id="mcp::filesystem", provider_id="model-context-protocol", @@ -141,12 +152,12 @@ client.toolgroups.register( ) ``` - + + ## Adding Custom (Client-side) Tools -When you want to use tools other than the built-in tools, you just need to implement a python function with a docstring. The content of the docstring will be used to describe the tool and the parameters and passed -along to the generative model. +When you want to use tools other than the built-in tools, you just need to implement a python function with a docstring. The content of the docstring will be used to describe the tool and the parameters and passed along to the generative model. ```python # Example tool definition @@ -158,9 +169,13 @@ def my_tool(input: int) -> int: """ return input * 2 ``` -> **NOTE:** We employ python docstrings to describe the tool and the parameters. It is important to document the tool and the parameters so that the model can use the tool correctly. It is recommended to experiment with different docstrings to see how they affect the model's behavior. + +:::tip[Documentation Best Practices] +We employ python docstrings to describe the tool and the parameters. It is important to document the tool and the parameters so that the model can use the tool correctly. It is recommended to experiment with different docstrings to see how they affect the model's behavior. +::: Once defined, simply pass the tool to the agent config. `Agent` will take care of the rest (calling the model with the tool definition, executing the tool, and returning the result to the model for the next iteration). + ```python # Example agent config with client provided tools agent = Agent(client, ..., tools=[my_tool]) @@ -168,14 +183,14 @@ agent = Agent(client, ..., tools=[my_tool]) Refer to [llama-stack-apps](https://github.com/meta-llama/llama-stack-apps/blob/main/examples/agents/e2e_loop_with_client_tools.py) for an example of how to use client provided tools. - ## Tool Invocation Tools can be invoked using the `invoke_tool` method: ```python result = client.tool_runtime.invoke_tool( - tool_name="web_search", kwargs={"query": "What is the capital of France?"} + tool_name="web_search", + kwargs={"query": "What is the capital of France?"} ) ``` @@ -196,7 +211,13 @@ all_tools = client.tools.list_tools() group_tools = client.tools.list_tools(toolgroup_id="search_tools") ``` -## Simple Example 2: Using an Agent with the Web Search Tool +## Complete Examples + +### Web Search Agent + + + + 1. Start by registering a Tavily API key at [Tavily](https://tavily.com/). 2. [Optional] Provide the API key directly to the Llama Stack server ```bash @@ -205,7 +226,10 @@ export TAVILY_SEARCH_API_KEY="your key" ```bash --env TAVILY_SEARCH_API_KEY=${TAVILY_SEARCH_API_KEY} ``` -3. Run the following script. + + + + ```python from llama_stack_client.lib.agents.agent import Agent from llama_stack_client.types.agent_create_params import AgentConfig @@ -240,7 +264,14 @@ for log in EventLogger().log(response): log.print() ``` -## Simple Example3: Using an Agent with the WolframAlpha Tool + + + +### WolframAlpha Math Agent + + + + 1. Start by registering for a WolframAlpha API key at [WolframAlpha Developer Portal](https://developer.wolframalpha.com/access). 2. Provide the API key either when starting the Llama Stack server: ```bash @@ -253,12 +284,57 @@ for log in EventLogger().log(response): provider_data={"wolfram_alpha_api_key": wolfram_api_key}, ) ``` -3. Configure the tools in the Agent by setting `tools=["builtin::wolfram_alpha"]`. -4. Example user query: - ```python - response = agent.create_turn( - messages=[{"role": "user", "content": "Solve x^2 + 2x + 1 = 0 using WolframAlpha"}], - session_id=session_id, - ) - ``` + + + + +```python +# Configure the tools in the Agent by setting tools=["builtin::wolfram_alpha"] +agent = Agent( + client, + model="meta-llama/Llama-3.2-3B-Instruct", + instructions="You are a mathematical assistant that can solve complex equations.", + tools=["builtin::wolfram_alpha"], +) + +session_id = agent.create_session("math-session") + +# Example user query +response = agent.create_turn( + messages=[{"role": "user", "content": "Solve x^2 + 2x + 1 = 0 using WolframAlpha"}], + session_id=session_id, +) ``` + + + + +## Best Practices + +### 🛠️ **Tool Selection** +- Use **server-side tools** for production applications requiring reliability and security +- Use **client-side tools** for development, prototyping, or specialized integrations +- Combine multiple tool types for comprehensive functionality + +### 📝 **Documentation** +- Write clear, detailed docstrings for custom tools +- Include parameter descriptions and expected return types +- Test tool descriptions with the model to ensure proper usage + +### 🔐 **Security** +- Store API keys securely using environment variables or secure configuration +- Use the `X-LlamaStack-Provider-Data` header for dynamic authentication +- Validate tool inputs and outputs for security + +### 🔄 **Error Handling** +- Implement proper error handling in custom tools +- Use structured error responses with meaningful messages +- Monitor tool performance and reliability + +## Related Resources + +- **[Agents](./agent)** - Building intelligent agents with tools +- **[RAG (Retrieval Augmented Generation)](./rag)** - Using knowledge retrieval tools +- **[Agent Execution Loop](./agent_execution_loop)** - Understanding tool execution flow +- **[Building AI Applications Notebook](https://github.com/meta-llama/llama-stack/blob/main/docs/getting_started.ipynb)** - Comprehensive examples +- **[Llama Stack Apps Examples](https://github.com/meta-llama/llama-stack-apps)** - Real-world tool implementations diff --git a/docs/docs/concepts/apis/api_leveling.mdx b/docs/docs/concepts/apis/api_leveling.mdx new file mode 100644 index 000000000..e3e118d0f --- /dev/null +++ b/docs/docs/concepts/apis/api_leveling.mdx @@ -0,0 +1,101 @@ +--- +title: API Stability Leveling +description: Understanding API stability levels and versioning in Llama Stack +sidebar_label: API Stability +sidebar_position: 4 +--- + +# Llama Stack API Stability Leveling + +In order to provide a stable experience in Llama Stack, the various APIs need different stability levels indicating the level of support, backwards compatability, and overall production readiness. + +## Different Levels + +### v1alpha + +- Little to no expectation of support between versions +- Breaking changes are permitted +- Datatypes and parameters can break +- Routes can be added and removed + +#### Graduation Criteria + +- an API can graduate from `v1alpha` to `v1beta` if the team has identified the extent of the non-optional routes and the shape of their parameters/return types for the API eg. `/v1/openai/chat/completions`. Optional types can change. +- CRUD must stay stable once in `v1beta`. This is a commitment to backward compatibility, guaranteeing that most code you write against the v1beta version will not break during future updates. We may make additive changes (like adding a new, optional field to a response), but we will not make breaking changes (like renaming an existing "modelName" field to "name", changing an ID's data type from an integer to a string, or altering an endpoint URL). +- for OpenAI APIs, a comparison to the OpenAI spec for the specific API can be done to ensure completeness. + +### v1beta + +- API routes remain consistent between versions +- Parameters and return types are not ensured between versions +- API, besides minor fixes and adjustments, should be _almost_ v1. Changes should not be drastic. + +#### Graduation Criteria + +- an API can graduate from `v1beta` to `v1` if the API surface and datatypes are complete as identified by the team. The parameters and return types that are mandatory for each route are stable. All aspects of graduating from `v1alpha1` to `v1beta` apply as well. +- Optional parameters, routes, or parts of the return type can be added after graduating to `v1` + +### v1 (stable) + +- Considered stable +- Backwards compatible between Z-streams + - Y-stream breaking changes must go through the proper approval and announcement process. +- Datatypes for a route and its return types cannot change between Z-streams + - Y-stream datatype changes should be sparing, unless the changes are additional net-new parameters +- Must have proper conformance testing as outlined in https://github.com/llamastack/llama-stack/issues/3237 + +### v2+ (Major Versions) + +Introducing a new major version like `/v2` is a significant and disruptive event that should be treated as a last resort. It is reserved for essential changes to a stable `/v1` API that are fundamentally backward-incompatible and cannot be implemented through additive, non-breaking changes or breaking changes across X/Y-Stream releases (x.y.z). + +If a `/v2` version is deemed absolutely necessary, it must adhere to the following protocol to ensure a sane and predictable transition for users: + +#### Lifecycle Progression + + A new major version must follow the same stability lifecycle as `/v1`. It will be introduced as `/v2alpha`, mature to `/v2beta`, and finally become stable as `/v2`. + +#### Coexistence: + +The new `/v2` API must be introduced alongside the existing `/v1` API and run in parallel. It must not replace the `/v1` API immediately. + +#### Deprecation Policy: + +When a `/v2` API is introduced, a clear and generous deprecation policy for the `/v1` API must be published simultaneously. This policy must outline the timeline for the eventual removal of the `/v1` API, giving users ample time to migrate. + +### API Stability vs. Provider Stability + +The leveling introduced in this document relates to the stability of the API and not specifically the providers within the API. + +Providers can iterate as much as they want on functionality as long as they work within the bounds of an API. If they need to change the API, then the API should not be `/v1`, or those breaking changes can only happen on a y-stream release basis. + +### Approval and Announcement Process for Breaking Changes + +- **PR Labeling**: Any pull request that introduces a breaking API change must be clearly labeled with `breaking-change`. +- **PR Title/Commit**: Any pull request that introduces a breaking API change must contain `BREAKING CHANGE` in the title and commit footer. Alternatively, the commit can include `!`, eg. `feat(api)!: title goes here` This is outlined in the [conventional commits documentation](https://www.conventionalcommits.org/en/v1.0.0/#specification) +- **Maintainer Review**: At least one maintainer must explicitly acknowledge the breaking change during review by applying the `breaking-change` label. An approval must come with this label or the acknowledgement this label has already been applied. +- **Announcement**: Breaking changes require inclusion in release notes and, if applicable, a separate communication (e.g., Discord, Github Issues, or GitHub Discussions) prior to release. + +If a PR has proper approvals, labels, and commit/title hygiene, the failing API conformance tests will be bypassed. + + +## Enforcement + +### Migration of API routes under `/v1alpha`, `/v1beta`, and `/v1` + +Instead of placing every API under `/v1`, any API that is not fully stable or complete should go under `/v1alpha` or `/v1beta`. For example, at the time of this writing, `post_training` belongs here, as well as any OpenAI-compatible API whose surface does not exactly match the upstream OpenAI API it mimics. + +This migration is crucial as we get Llama Stack in the hands of users who intend to productize various APIs. A clear view of what is stable and what is actively being developed will enable users to pick and choose various APIs to build their products on. + +This migration will be a breaking change for any API moving out of `/v1`. Ideally, this should happen before 0.3.0 and especially 1.0.0. + +### `x-stability` tags in the OpenAPI spec for oasdiff + +`x-stability` tags allow tools like oasdiff to enforce different rules for different stability levels; these tags should match the routes: [oasdiff stability](https://github.com/oasdiff/oasdiff/blob/main/docs/STABILITY.md) + +### Testing + +The testing of each stable API is already outlined in [issue #3237](https://github.com/llamastack/llama-stack/issues/3237) and is being worked on. These sorts of conformance tests should apply primarily to `/v1` APIs only, with `/v1alpha` and `/v1beta` having any tests the maintainers see fit as well as basic testing to ensure the routing works properly. + +### New APIs going forward + +Any subsequently introduced APIs should be introduced as `/v1alpha` diff --git a/docs/source/concepts/api_providers.md b/docs/docs/concepts/apis/api_providers.mdx similarity index 85% rename from docs/source/concepts/api_providers.md rename to docs/docs/concepts/apis/api_providers.mdx index 6e6502c0c..5f0fe2ac7 100644 --- a/docs/source/concepts/api_providers.md +++ b/docs/docs/concepts/apis/api_providers.mdx @@ -1,4 +1,11 @@ -## API Providers +--- +title: API Providers +description: Understanding remote vs inline provider implementations +sidebar_label: API Providers +sidebar_position: 2 +--- + +# API Providers The goal of Llama Stack is to build an ecosystem where users can easily swap out different implementations for the same API. Examples for these include: - LLM inference providers (e.g., Fireworks, Together, AWS Bedrock, Groq, Cerebras, SambaNova, vLLM, etc.), diff --git a/docs/source/apis/external.md b/docs/docs/concepts/apis/external.mdx similarity index 98% rename from docs/source/apis/external.md rename to docs/docs/concepts/apis/external.mdx index 5831990b0..7b4a3e8d5 100644 --- a/docs/source/apis/external.md +++ b/docs/docs/concepts/apis/external.mdx @@ -1,3 +1,9 @@ +--- +title: External APIs +description: Understanding external APIs in Llama Stack +sidebar_label: External APIs +sidebar_position: 3 +--- # External APIs Llama Stack supports external APIs that live outside of the main codebase. This allows you to: diff --git a/docs/source/concepts/apis.md b/docs/docs/concepts/apis/index.mdx similarity index 89% rename from docs/source/concepts/apis.md rename to docs/docs/concepts/apis/index.mdx index f8f73a928..6e699d137 100644 --- a/docs/source/concepts/apis.md +++ b/docs/docs/concepts/apis/index.mdx @@ -1,4 +1,11 @@ -## APIs +--- +title: APIs +description: Available REST APIs and planned capabilities in Llama Stack +sidebar_label: APIs +sidebar_position: 1 +--- + +# APIs A Llama Stack API is described as a collection of REST endpoints. We currently support the following APIs: diff --git a/docs/source/concepts/architecture.md b/docs/docs/concepts/architecture.mdx similarity index 85% rename from docs/source/concepts/architecture.md rename to docs/docs/concepts/architecture.mdx index 50cc62c7c..8e9738416 100644 --- a/docs/source/concepts/architecture.md +++ b/docs/docs/concepts/architecture.mdx @@ -1,15 +1,19 @@ -## Llama Stack architecture +--- +title: Llama Stack Architecture +description: Understanding Llama Stack's service-oriented design and benefits +sidebar_label: Architecture +sidebar_position: 2 +--- + +# Llama Stack architecture Llama Stack allows you to build different layers of distributions for your AI workloads using various SDKs and API providers. -```{image} ../../_static/llama-stack.png -:alt: Llama Stack -:width: 400px -``` +Llama Stack -### Benefits of Llama stack +## Benefits of Llama stack -#### Current challenges in custom AI applications +### Current challenges in custom AI applications Building production AI applications today requires solving multiple challenges: @@ -32,7 +36,7 @@ Building production AI applications today requires solving multiple challenges: - Different providers have different APIs and abstractions. - Changing providers requires significant code changes. -#### Our Solution: A Universal Stack +### Our Solution: A Universal Stack Llama Stack addresses these challenges through a service-oriented, API-first approach: @@ -59,7 +63,7 @@ Llama Stack addresses these challenges through a service-oriented, API-first app - Ecosystem offers tailored infrastructure, software, and services for deploying a variety of models. -### Our Philosophy +## Our Philosophy - **Service-Oriented**: REST APIs enforce clean interfaces and enable seamless transitions across different environments. - **Composability**: Every component is independent but works together seamlessly @@ -67,4 +71,4 @@ Llama Stack addresses these challenges through a service-oriented, API-first app - **Turnkey Solutions**: Easy to deploy built in solutions for popular deployment scenarios -With Llama Stack, you can focus on building your application while we handle the infrastructure complexity, essential capabilities, and provider integrations. \ No newline at end of file +With Llama Stack, you can focus on building your application while we handle the infrastructure complexity, essential capabilities, and provider integrations. diff --git a/docs/source/concepts/distributions.md b/docs/docs/concepts/distributions.mdx similarity index 81% rename from docs/source/concepts/distributions.md rename to docs/docs/concepts/distributions.mdx index c3be12d93..568099664 100644 --- a/docs/source/concepts/distributions.md +++ b/docs/docs/concepts/distributions.mdx @@ -1,4 +1,11 @@ -## Distributions +--- +title: Distributions +description: Pre-packaged provider configurations for different deployment scenarios +sidebar_label: Distributions +sidebar_position: 3 +--- + +# Distributions While there is a lot of flexibility to mix-and-match providers, often users will work with a specific set of providers (hardware support, contractual obligations, etc.) We therefore need to provide a _convenient shorthand_ for such collections. We call this shorthand a **Llama Stack Distribution** or a **Distro**. One can think of it as specific pre-packaged versions of the Llama Stack. Here are some examples: @@ -6,4 +13,4 @@ While there is a lot of flexibility to mix-and-match providers, often users will **Locally Hosted Distro**: You may want to run Llama Stack on your own hardware. Typically though, you still need to use Inference via an external service. You can use providers like HuggingFace TGI, Fireworks, Together, etc. for this purpose. Or you may have access to GPUs and can run a [vLLM](https://github.com/vllm-project/vllm) or [NVIDIA NIM](https://build.nvidia.com/nim?filters=nimType%3Anim_type_run_anywhere&q=llama) instance. If you "just" have a regular desktop machine, you can use [Ollama](https://ollama.com/) for inference. To provide convenient quick access to these options, we provide a number of such pre-configured locally-hosted Distros. -**On-device Distro**: To run Llama Stack directly on an edge device (mobile phone or a tablet), we provide Distros for [iOS](https://llama-stack.readthedocs.io/en/latest/distributions/ondevice_distro/ios_sdk.html) and [Android](https://llama-stack.readthedocs.io/en/latest/distributions/ondevice_distro/android_sdk.html) +**On-device Distro**: To run Llama Stack directly on an edge device (mobile phone or a tablet), we provide Distros for [iOS](/docs/distributions/ondevice_distro/ios_sdk) and [Android](/docs/distributions/ondevice_distro/android_sdk) diff --git a/docs/source/advanced_apis/evaluation_concepts.md b/docs/docs/concepts/evaluation_concepts.mdx similarity index 80% rename from docs/source/advanced_apis/evaluation_concepts.md rename to docs/docs/concepts/evaluation_concepts.mdx index c26ec8f5e..c7a13fd70 100644 --- a/docs/source/advanced_apis/evaluation_concepts.md +++ b/docs/docs/concepts/evaluation_concepts.mdx @@ -1,16 +1,22 @@ -## Evaluation Concepts +--- +title: Evaluation Concepts +description: Running evaluations on Llama Stack +sidebar_label: Evaluation Concepts +sidebar_position: 5 +--- + +# Evaluation Concepts The Llama Stack Evaluation flow allows you to run evaluations on your GenAI application datasets or pre-registered benchmarks. -We introduce a set of APIs in Llama Stack for supporting running evaluations of LLM applications. +We introduce a set of APIs in Llama Stack for supporting running evaluations of LLM applications: - `/datasetio` + `/datasets` API - `/scoring` + `/scoring_functions` API - `/eval` + `/benchmarks` API This guide goes over the sets of APIs and developer experience flow of using Llama Stack to run evaluations for different use cases. Checkout our Colab notebook on working examples with evaluations [here](https://colab.research.google.com/drive/10CHyykee9j2OigaIcRv47BKG9mrNm0tJ?usp=sharing). - -The Evaluation APIs are associated with a set of Resources. Please visit the Resources section in our [Core Concepts](../concepts/index.md) guide for better high-level understanding. +The Evaluation APIs are associated with a set of Resources. Please visit the Resources section in our [Core Concepts](./index.mdx) guide for better high-level understanding. - **DatasetIO**: defines interface with datasets and data loaders. - Associated with `Dataset` resource. @@ -19,10 +25,9 @@ The Evaluation APIs are associated with a set of Resources. Please visit the Res - **Eval**: generate outputs (via Inference or Agents) and perform scoring. - Associated with `Benchmark` resource. +## Open-benchmark Eval -### Open-benchmark Eval - -#### List of open-benchmarks Llama Stack support +### List of open-benchmarks Llama Stack support Llama stack pre-registers several popular open-benchmarks to easily evaluate model perfomance via CLI. @@ -32,19 +37,17 @@ The list of open-benchmarks we currently support: - [SimpleQA](https://openai.com/index/introducing-simpleqa/): Benchmark designed to access models to answer short, fact-seeking questions. - [MMMU](https://arxiv.org/abs/2311.16502) (A Massive Multi-discipline Multimodal Understanding and Reasoning Benchmark for Expert AGI)]: Benchmark designed to evaluate multimodal models. +You can follow this [contributing guide](../references/evals_reference/#open-benchmark-contributing-guide) to add more open-benchmarks to Llama Stack -You can follow this [contributing guide](https://llama-stack.readthedocs.io/en/latest/references/evals_reference/index.html#open-benchmark-contributing-guide) to add more open-benchmarks to Llama Stack - -#### Run evaluation on open-benchmarks via CLI +### Run evaluation on open-benchmarks via CLI We have built-in functionality to run the supported open-benckmarks using llama-stack-client CLI #### Spin up Llama Stack server Spin up llama stack server with 'open-benchmark' template -``` +```bash llama stack run llama_stack/distributions/open-benchmark/run.yaml - ``` #### Run eval CLI @@ -52,26 +55,24 @@ There are 3 necessary inputs to run a benchmark eval - `list of benchmark_ids`: The list of benchmark ids to run evaluation on - `model-id`: The model id to evaluate on - `output_dir`: Path to store the evaluate results -``` + +```bash llama-stack-client eval run-benchmark ... \ --model_id \ ---output_dir \ +--output_dir ``` You can run -``` +```bash llama-stack-client eval run-benchmark help ``` to see the description of all the flags that eval run-benchmark has - In the output log, you can find the file path that has your evaluation results. Open that file and you can see you aggregate evaluation results over there. - - -#### What's Next? +## What's Next? - Check out our Colab notebook on working examples with running benchmark evaluations [here](https://colab.research.google.com/github/meta-llama/llama-stack/blob/main/docs/notebooks/Llama_Stack_Benchmark_Evals.ipynb#scrollTo=mxLCsP4MvFqP). -- Check out our [Building Applications - Evaluation](../building_applications/evals.md) guide for more details on how to use the Evaluation APIs to evaluate your applications. -- Check out our [Evaluation Reference](../references/evals_reference/index.md) for more details on the APIs. +- Check out our [Building Applications - Evaluation](../building_applications/evals.mdx) guide for more details on how to use the Evaluation APIs to evaluate your applications. +- Check out our [Evaluation Reference](../references/evals_reference/) for more details on the APIs. diff --git a/docs/docs/concepts/index.mdx b/docs/docs/concepts/index.mdx new file mode 100644 index 000000000..1278ef98e --- /dev/null +++ b/docs/docs/concepts/index.mdx @@ -0,0 +1,31 @@ +--- +title: Core Concepts +description: Understanding Llama Stack's service-oriented philosophy and key concepts +sidebar_label: Overview +sidebar_position: 1 +--- + +Given Llama Stack's service-oriented philosophy, a few concepts and workflows arise which may not feel completely natural in the LLM landscape, especially if you are coming with a background in other frameworks. + +## Documentation Structure + +This section covers the fundamental concepts of Llama Stack: + +- **[Architecture](architecture.mdx)** - Learn about Llama Stack's architectural design and principles +- **[APIs](/docs/concepts/apis/)** - Understanding the core APIs and their stability levels + - [API Overview](apis/index.mdx) - Core APIs available in Llama Stack + - [API Providers](apis/api_providers.mdx) - How providers implement APIs + - [External APIs](apis/external.mdx) - External APIs available in Llama Stack + - [API Stability Leveling](apis/api_leveling.mdx) - API stability and versioning +- **[Distributions](distributions.mdx)** - Pre-configured deployment packages +- **[Resources](resources.mdx)** - Understanding Llama Stack resources and their lifecycle + +## Getting Started + +If you're new to Llama Stack, we recommend starting with: + +1. **[Architecture](architecture.mdx)** - Understand the overall system design +2. **[APIs](apis/index.mdx)** - Learn about the available APIs and their purpose +3. **[Distributions](distributions.mdx)** - Choose a pre-configured setup for your use case + +Each concept builds upon the previous ones to give you a comprehensive understanding of how Llama Stack works and how to use it effectively. diff --git a/docs/source/concepts/resources.md b/docs/docs/concepts/resources.mdx similarity index 85% rename from docs/source/concepts/resources.md rename to docs/docs/concepts/resources.mdx index 0cdc9a227..8d1bd221b 100644 --- a/docs/source/concepts/resources.md +++ b/docs/docs/concepts/resources.mdx @@ -1,4 +1,11 @@ -## Resources +--- +title: Resources +description: Resource federation and registration in Llama Stack +sidebar_label: Resources +sidebar_position: 4 +--- + +# Resources Some of these APIs are associated with a set of **Resources**. Here is the mapping of APIs to resources: @@ -12,8 +19,8 @@ Some of these APIs are associated with a set of **Resources**. Here is the mappi Furthermore, we allow these resources to be **federated** across multiple providers. For example, you may have some Llama models served by Fireworks while others are served by AWS Bedrock. Regardless, they will all work seamlessly with the same uniform Inference API provided by Llama Stack. -```{admonition} Registering Resources -:class: tip +:::tip Registering Resources Given this architecture, it is necessary for the Stack to know which provider to use for a given resource. This means you need to explicitly _register_ resources (including models) before you can use them with the associated APIs. -``` + +::: diff --git a/docs/docs/contributing/index.mdx b/docs/docs/contributing/index.mdx new file mode 100644 index 000000000..263900ecc --- /dev/null +++ b/docs/docs/contributing/index.mdx @@ -0,0 +1,233 @@ +# Contributing to Llama Stack +We want to make contributing to this project as easy and transparent as +possible. + +## Set up your development environment + +We use [uv](https://github.com/astral-sh/uv) to manage python dependencies and virtual environments. +You can install `uv` by following this [guide](https://docs.astral.sh/uv/getting-started/installation/). + +You can install the dependencies by running: + +```bash +cd llama-stack +uv sync --group dev +uv pip install -e . +source .venv/bin/activate +``` + +```{note} +You can use a specific version of Python with `uv` by adding the `--python ` flag (e.g. `--python 3.12`). +Otherwise, `uv` will automatically select a Python version according to the `requires-python` section of the `pyproject.toml`. +For more info, see the [uv docs around Python versions](https://docs.astral.sh/uv/concepts/python-versions/). +``` + +Note that you can create a dotenv file `.env` that includes necessary environment variables: +``` +LLAMA_STACK_BASE_URL=http://localhost:8321 +LLAMA_STACK_CLIENT_LOG=debug +LLAMA_STACK_PORT=8321 +LLAMA_STACK_CONFIG= +TAVILY_SEARCH_API_KEY= +BRAVE_SEARCH_API_KEY= +``` + +And then use this dotenv file when running client SDK tests via the following: +```bash +uv run --env-file .env -- pytest -v tests/integration/inference/test_text_inference.py --text-model=meta-llama/Llama-3.1-8B-Instruct +``` + +### Pre-commit Hooks + +We use [pre-commit](https://pre-commit.com/) to run linting and formatting checks on your code. You can install the pre-commit hooks by running: + +```bash +uv run pre-commit install +``` + +After that, pre-commit hooks will run automatically before each commit. + +Alternatively, if you don't want to install the pre-commit hooks, you can run the checks manually by running: + +```bash +uv run pre-commit run --all-files +``` + +```{caution} +Before pushing your changes, make sure that the pre-commit hooks have passed successfully. +``` + +## Discussions -> Issues -> Pull Requests + +We actively welcome your pull requests. However, please read the following. This is heavily inspired by [Ghostty](https://github.com/ghostty-org/ghostty/blob/main/CONTRIBUTING.md). + +If in doubt, please open a [discussion](https://github.com/meta-llama/llama-stack/discussions); we can always convert that to an issue later. + +### Issues +We use GitHub issues to track public bugs. Please ensure your description is +clear and has sufficient instructions to be able to reproduce the issue. + +Meta has a [bounty program](http://facebook.com/whitehat/info) for the safe +disclosure of security bugs. In those cases, please go through the process +outlined on that page and do not file a public issue. + +### Contributor License Agreement ("CLA") +In order to accept your pull request, we need you to submit a CLA. You only need +to do this once to work on any of Meta's open source projects. + +Complete your CLA here: [https://code.facebook.com/cla](https://code.facebook.com/cla) + +**I'd like to contribute!** + +If you are new to the project, start by looking at the issues tagged with "good first issue". If you're interested +leave a comment on the issue and a triager will assign it to you. + +Please avoid picking up too many issues at once. This helps you stay focused and ensures that others in the community also have opportunities to contribute. +- Try to work on only 1–2 issues at a time, especially if you’re still getting familiar with the codebase. +- Before taking an issue, check if it’s already assigned or being actively discussed. +- If you’re blocked or can’t continue with an issue, feel free to unassign yourself or leave a comment so others can step in. + +**I have a bug!** + +1. Search the issue tracker and discussions for similar issues. +2. If you don't have steps to reproduce, open a discussion. +3. If you have steps to reproduce, open an issue. + +**I have an idea for a feature!** + +1. Open a discussion. + +**I've implemented a feature!** + +1. If there is an issue for the feature, open a pull request. +2. If there is no issue, open a discussion and link to your branch. + +**I have a question!** + +1. Open a discussion or use [Discord](https://discord.gg/llama-stack). + + +**Opening a Pull Request** + +1. Fork the repo and create your branch from `main`. +2. If you've changed APIs, update the documentation. +3. Ensure the test suite passes. +4. Make sure your code lints using `pre-commit`. +5. If you haven't already, complete the Contributor License Agreement ("CLA"). +6. Ensure your pull request follows the [conventional commits format](https://www.conventionalcommits.org/en/v1.0.0/). +7. Ensure your pull request follows the [coding style](#coding-style). + + +Please keep pull requests (PRs) small and focused. If you have a large set of changes, consider splitting them into logically grouped, smaller PRs to facilitate review and testing. + +```{tip} +As a general guideline: +- Experienced contributors should try to keep no more than 5 open PRs at a time. +- New contributors are encouraged to have only one open PR at a time until they’re familiar with the codebase and process. +``` + +## Repository guidelines + +### Coding Style + +* Comments should provide meaningful insights into the code. Avoid filler comments that simply + describe the next step, as they create unnecessary clutter, same goes for docstrings. +* Prefer comments to clarify surprising behavior and/or relationships between parts of the code + rather than explain what the next line of code does. +* Catching exceptions, prefer using a specific exception type rather than a broad catch-all like + `Exception`. +* Error messages should be prefixed with "Failed to ..." +* 4 spaces for indentation rather than tab +* When using `# noqa` to suppress a style or linter warning, include a comment explaining the + justification for bypassing the check. +* When using `# type: ignore` to suppress a mypy warning, include a comment explaining the + justification for bypassing the check. +* Don't use unicode characters in the codebase. ASCII-only is preferred for compatibility or + readability reasons. +* Providers configuration class should be Pydantic Field class. It should have a `description` field + that describes the configuration. These descriptions will be used to generate the provider + documentation. +* When possible, use keyword arguments only when calling functions. +* Llama Stack utilizes custom Exception classes for certain Resources that should be used where applicable. + +### License +By contributing to Llama, you agree that your contributions will be licensed +under the LICENSE file in the root directory of this source tree. + +## Common Tasks + +Some tips about common tasks you work on while contributing to Llama Stack: + +### Using `llama stack build` + +Building a stack image will use the production version of the `llama-stack` and `llama-stack-client` packages. If you are developing with a llama-stack repository checked out and need your code to be reflected in the stack image, set `LLAMA_STACK_DIR` and `LLAMA_STACK_CLIENT_DIR` to the appropriate checked out directories when running any of the `llama` CLI commands. + +Example: +```bash +cd work/ +git clone https://github.com/meta-llama/llama-stack.git +git clone https://github.com/meta-llama/llama-stack-client-python.git +cd llama-stack +LLAMA_STACK_DIR=$(pwd) LLAMA_STACK_CLIENT_DIR=../llama-stack-client-python llama stack build --distro <...> +``` + +### Updating distribution configurations + +If you have made changes to a provider's configuration in any form (introducing a new config key, or +changing models, etc.), you should run `./scripts/distro_codegen.py` to re-generate various YAML +files as well as the documentation. You should not change `docs/source/.../distributions/` files +manually as they are auto-generated. + +### Updating the provider documentation + +If you have made changes to a provider's configuration, you should run `./scripts/provider_codegen.py` +to re-generate the documentation. You should not change `docs/source/.../providers/` files manually +as they are auto-generated. +Note that the provider "description" field will be used to generate the provider documentation. + +### Building the Documentation + +If you are making changes to the documentation at [https://llamastack.github.io/](https://llamastack.github.io/), you can use the following command to build the documentation and preview your changes. + +```bash +# This rebuilds the documentation pages and the OpenAPI spec. +npm install +npm run gen-api-docs all +npm run build + +# This will start a local server (usually at http://127.0.0.1:3000). +npm run serve +``` + +### Update API Documentation + +If you modify or add new API endpoints, update the API documentation accordingly. You can do this by running the following command: + +```bash +uv run ./docs/openapi_generator/run_openapi_generator.sh +``` + +The generated API schema will be available in `docs/static/`. Make sure to review the changes before committing. + +## Adding a New Provider + +See: +- [Adding a New API Provider Page](./new_api_provider.mdx) which describes how to add new API providers to the Stack. +- [Vector Database Page](./new_vector_database.mdx) which describes how to add a new vector databases with Llama Stack. +- [External Provider Page](/docs/providers/external/) which describes how to add external providers to the Stack. + + +## Testing + + +See the [Testing README](https://github.com/meta-llama/llama-stack/blob/main/tests/README.md) for detailed testing information. + +## Advanced Topics + +For developers who need deeper understanding of the testing system internals: + +- [Record-Replay Testing](./testing/record-replay.mdx) + +### Benchmarking + +See the [Benchmarking README](https://github.com/meta-llama/llama-stack/blob/main/benchmarking/k8s-benchmark/README.md) for benchmarking information. diff --git a/docs/docs/contributing/new_api_provider.mdx b/docs/docs/contributing/new_api_provider.mdx new file mode 100644 index 000000000..4ae6d5e72 --- /dev/null +++ b/docs/docs/contributing/new_api_provider.mdx @@ -0,0 +1,98 @@ +--- +title: Adding a New API Provider +description: Guide for adding new API providers to Llama Stack +sidebar_label: New API Provider +sidebar_position: 2 +--- + +import Tabs from '@theme/Tabs'; +import TabItem from '@theme/TabItem'; + +This guide will walk you through the process of adding a new API provider to Llama Stack. + + +- Begin by reviewing the [core concepts](../concepts/) of Llama Stack and choose the API your provider belongs to (Inference, Safety, VectorIO, etc.) +- Determine the provider type ([Remote](https://github.com/meta-llama/llama-stack/tree/main/llama_stack/providers/remote) or [Inline](https://github.com/meta-llama/llama-stack/tree/main/llama_stack/providers/inline)). Remote providers make requests to external services, while inline providers execute implementation locally. +- Add your provider to the appropriate [Registry](https://github.com/meta-llama/llama-stack/tree/main/llama_stack/providers/registry/). Specify pip dependencies necessary. +- Update any distribution [Templates](https://github.com/meta-llama/llama-stack/tree/main/llama_stack/distributions/) `build.yaml` and `run.yaml` files if they should include your provider by default. Run [./scripts/distro_codegen.py](https://github.com/meta-llama/llama-stack/blob/main/scripts/distro_codegen.py) if necessary. Note that `distro_codegen.py` will fail if the new provider causes any distribution template to attempt to import provider-specific dependencies. This usually means the distribution's `get_distribution_template()` code path should only import any necessary Config or model alias definitions from each provider and not the provider's actual implementation. + + +Here are some example PRs to help you get started: + - [Grok Inference Implementation](https://github.com/meta-llama/llama-stack/pull/609) + - [Nvidia Inference Implementation](https://github.com/meta-llama/llama-stack/pull/355) + - [Model context protocol Tool Runtime](https://github.com/meta-llama/llama-stack/pull/665) + +## Guidelines for creating Internal or External Providers + +|**Type** |Internal (In-tree) |External (out-of-tree) +|---------|-------------------|---------------------| +|**Description** |A provider that is directly in the Llama Stack code|A provider that is outside of the Llama stack core codebase but is still accessible and usable by Llama Stack. +|**Benefits** |Ability to interact with the provider with minimal additional configurations or installations| Contributors do not have to add directly to the code to create providers accessible on Llama Stack. Keep provider-specific code separate from the core Llama Stack code. + +## Inference Provider Patterns + +When implementing Inference providers for OpenAI-compatible APIs, Llama Stack provides several mixin classes to simplify development and ensure consistent behavior across providers. + +### OpenAIMixin + +The `OpenAIMixin` class provides direct OpenAI API functionality for providers that work with OpenAI-compatible endpoints. It includes: + +#### Direct API Methods +- **`openai_completion()`**: Legacy text completion API with full parameter support +- **`openai_chat_completion()`**: Chat completion API supporting streaming, tools, and function calling +- **`openai_embeddings()`**: Text embeddings generation with customizable encoding and dimensions + +#### Model Management +- **`check_model_availability()`**: Queries the API endpoint to verify if a model exists and is accessible + +#### Client Management +- **`client` property**: Automatically creates and configures AsyncOpenAI client instances using your provider's credentials + +#### Required Implementation + +To use `OpenAIMixin`, your provider must implement these abstract methods: + +```python +@abstractmethod +def get_api_key(self) -> str: + """Return the API key for authentication""" + pass + + +@abstractmethod +def get_base_url(self) -> str: + """Return the OpenAI-compatible API base URL""" + pass +``` + +## Testing the Provider + +Before running tests, you must have required dependencies installed. This depends on the providers or distributions you are testing. For example, if you are testing the `together` distribution, you should install dependencies via `llama stack build --distro together`. + +### 1. Integration Testing + +Integration tests are located in [tests/integration](https://github.com/meta-llama/llama-stack/tree/main/tests/integration). These tests use the python client-SDK APIs (from the `llama_stack_client` package) to test functionality. Since these tests use client APIs, they can be run either by pointing to an instance of the Llama Stack server or "inline" by using `LlamaStackAsLibraryClient`. + +Consult [tests/integration/README.md](https://github.com/meta-llama/llama-stack/blob/main/tests/integration/README.md) for more details on how to run the tests. + +Note that each provider's `sample_run_config()` method (in the configuration class for that provider) + typically references some environment variables for specifying API keys and the like. You can set these in the environment or pass these via the `--env` flag to the test command. + + +### 2. Unit Testing + +Unit tests are located in [tests/unit](https://github.com/meta-llama/llama-stack/tree/main/tests/unit). Provider-specific unit tests are located in [tests/unit/providers](https://github.com/meta-llama/llama-stack/tree/main/tests/unit/providers). These tests are all run automatically as part of the CI process. + +Consult [tests/unit/README.md](https://github.com/meta-llama/llama-stack/blob/main/tests/unit/README.md) for more details on how to run the tests manually. + +### 3. Additional end-to-end testing + +1. Start a Llama Stack server with your new provider +2. Verify compatibility with existing client scripts in the [llama-stack-apps](https://github.com/meta-llama/llama-stack-apps/tree/main) repository +3. Document which scripts are compatible with your provider + +## Submitting Your PR + +1. Ensure all tests pass +2. Include a comprehensive test plan in your PR summary +3. Document any known limitations or considerations diff --git a/docs/source/contributing/new_vector_database.md b/docs/docs/contributing/new_vector_database.mdx similarity index 82% rename from docs/source/contributing/new_vector_database.md rename to docs/docs/contributing/new_vector_database.mdx index 83c0f55bc..044e2f672 100644 --- a/docs/source/contributing/new_vector_database.md +++ b/docs/docs/contributing/new_vector_database.mdx @@ -1,4 +1,12 @@ -# Adding a New Vector Database +--- +title: Adding a New Vector Database +description: Guide for adding new vector database providers to Llama Stack +sidebar_label: New Vector Database +sidebar_position: 3 +--- + +import Tabs from '@theme/Tabs'; +import TabItem from '@theme/TabItem'; This guide will walk you through the process of adding a new vector database to Llama Stack. @@ -31,7 +39,7 @@ filtering, sorting, and aggregating vectors. - `YourVectorIOAdapter.query_chunks()` - `YourVectorIOAdapter.delete_chunks()` 3. **Add to Registry**: Register your provider in the appropriate registry file. - - Update {repopath}`llama_stack/providers/registry/vector_io.py` to include your new provider. + - Update [llama_stack/providers/registry/vector_io.py](https://github.com/meta-llama/llama-stack/blob/main/llama_stack/providers/registry/vector_io.py) to include your new provider. ```python from llama_stack.providers.registry.specs import InlineProviderSpec from llama_stack.providers.registry.api import Api @@ -57,7 +65,7 @@ InlineProviderSpec( 5. Add your provider to the `vector_io_providers` fixture dictionary. - Please follow the naming convention of `your_vectorprovider_index` and `your_vectorprovider_adapter` as the tests require this to execute properly. - Integration Tests - - Integration tests are located in {repopath}`tests/integration`. These tests use the python client-SDK APIs (from the `llama_stack_client` package) to test functionality. + - Integration tests are located in [tests/integration](https://github.com/meta-llama/llama-stack/tree/main/tests/integration). These tests use the python client-SDK APIs (from the `llama_stack_client` package) to test functionality. - The two set of integration tests are: - `tests/integration/vector_io/test_vector_io.py`: This file tests registration, insertion, and retrieval. - `tests/integration/vector_io/test_openai_vector_stores.py`: These tests are for OpenAI-compatible vector stores and test the OpenAI API compatibility. @@ -71,5 +79,5 @@ InlineProviderSpec( - If you are adding tests for the `remote` provider you will have to update the `test` group, which is used in the GitHub CI for integration tests. - `uv add new_pip_package --group test` 5. **Update Documentation**: Please update the documentation for end users - - Generate the provider documentation by running {repopath}`./scripts/provider_codegen.py`. - - Update the autogenerated content in the registry/vector_io.py file with information about your provider. Please see other providers for examples. \ No newline at end of file + - Generate the provider documentation by running [./scripts/provider_codegen.py](https://github.com/meta-llama/llama-stack/blob/main/scripts/provider_codegen.py). + - Update the autogenerated content in the registry/vector_io.py file with information about your provider. Please see other providers for examples. diff --git a/docs/source/contributing/testing/record-replay.md b/docs/docs/contributing/testing/record-replay.mdx similarity index 91% rename from docs/source/contributing/testing/record-replay.md rename to docs/docs/contributing/testing/record-replay.mdx index 3049d333c..47803c150 100644 --- a/docs/source/contributing/testing/record-replay.md +++ b/docs/docs/contributing/testing/record-replay.mdx @@ -1,3 +1,13 @@ +--- +title: Record-Replay Testing System +description: Understanding how Llama Stack captures and replays API interactions for testing +sidebar_label: Record-Replay System +sidebar_position: 4 +--- + +import Tabs from '@theme/Tabs'; +import TabItem from '@theme/TabItem'; + # Record-Replay System Understanding how Llama Stack captures and replays API interactions for testing. @@ -40,18 +50,15 @@ The system patches OpenAI and Ollama client methods to intercept calls before th ### Storage Architecture -Recordings use a two-tier storage system optimized for both speed and debuggability: +Recordings are stored as JSON files in the recording directory. They are looked up by their request hash. ``` recordings/ -├── index.sqlite # Fast lookup by request hash └── responses/ ├── abc123def456.json # Individual response files └── def789ghi012.json ``` -**SQLite index** enables O(log n) hash lookups and metadata queries without loading response bodies. - **JSON files** store complete request/response pairs in human-readable format for debugging. ## Recording Modes @@ -166,8 +173,8 @@ This preserves type safety - when replayed, you get the same Pydantic objects wi Control recording behavior globally: ```bash -export LLAMA_STACK_TEST_INFERENCE_MODE=replay -export LLAMA_STACK_TEST_RECORDING_DIR=/path/to/recordings +export LLAMA_STACK_TEST_INFERENCE_MODE=replay # this is the default +export LLAMA_STACK_TEST_RECORDING_DIR=/path/to/recordings # default is tests/integration/recordings pytest tests/integration/ ``` @@ -231,4 +238,4 @@ Loose hashing (normalizing whitespace, rounding floats) seems convenient but hid - **SQLite** - Fast indexed lookups without loading response bodies - **Hybrid** - Best of both worlds for different use cases -This system provides reliable, fast testing against real AI APIs while maintaining the ability to debug issues when they arise. \ No newline at end of file +This system provides reliable, fast testing against real AI APIs while maintaining the ability to debug issues when they arise. diff --git a/docs/docs/deploying/aws_eks_deployment.mdx b/docs/docs/deploying/aws_eks_deployment.mdx new file mode 100644 index 000000000..fa107ea9c --- /dev/null +++ b/docs/docs/deploying/aws_eks_deployment.mdx @@ -0,0 +1,30 @@ +--- +title: AWS EKS Deployment Guide +description: Deploy Llama Stack on AWS EKS +sidebar_label: AWS EKS Deployment +sidebar_position: 3 +--- + +## AWS EKS Deployment + +### Prerequisites + +- Set up an [EKS cluster](https://docs.aws.amazon.com/eks/latest/userguide/getting-started.html) +- Create a [GitHub OAuth app](https://docs.github.com/en/apps/oauth-apps/building-oauth-apps/creating-an-oauth-app) +- Set authorization callback URL to `http:///api/auth/callback/` + +### Automated Deployment + +```bash +export HF_TOKEN= +export GITHUB_CLIENT_ID= +export GITHUB_CLIENT_SECRET= +export LLAMA_STACK_UI_URL= + +cd docs/source/distributions/eks +./apply.sh +``` + +This script will: +- Set up default storage class for AWS EKS +- Deploy Llama Stack server in Kubernetes pods and services diff --git a/docs/docs/deploying/index.mdx b/docs/docs/deploying/index.mdx new file mode 100644 index 000000000..eaa0e2612 --- /dev/null +++ b/docs/docs/deploying/index.mdx @@ -0,0 +1,14 @@ +--- +title: Deploying Llama Stack +description: Production deployment guides for Llama Stack in various environments +sidebar_label: Overview +sidebar_position: 1 +--- + +import Tabs from '@theme/Tabs'; +import TabItem from '@theme/TabItem'; + +# Deploying Llama Stack + +[**→ Kubernetes Deployment Guide**](./kubernetes_deployment.mdx) +[**→ AWS EKS Deployment Guide**](./aws_eks_deployment.mdx) diff --git a/docs/source/deploying/kubernetes_deployment.md b/docs/docs/deploying/kubernetes_deployment.mdx similarity index 51% rename from docs/source/deploying/kubernetes_deployment.md rename to docs/docs/deploying/kubernetes_deployment.mdx index 4bdd87b24..a937ce355 100644 --- a/docs/source/deploying/kubernetes_deployment.md +++ b/docs/docs/deploying/kubernetes_deployment.mdx @@ -1,27 +1,39 @@ -## Kubernetes Deployment Guide +--- +title: Kubernetes Deployment Guide +description: Deploy Llama Stack on Kubernetes clusters with vLLM inference service +sidebar_label: Kubernetes +sidebar_position: 2 +--- -Instead of starting the Llama Stack and vLLM servers locally. We can deploy them in a Kubernetes cluster. +import Tabs from '@theme/Tabs'; +import TabItem from '@theme/TabItem'; -### Prerequisites -In this guide, we'll use a local [Kind](https://kind.sigs.k8s.io/) cluster and a vLLM inference service in the same cluster for demonstration purposes. +# Kubernetes Deployment Guide -Note: You can also deploy the Llama Stack server in an AWS EKS cluster. See [Deploying Llama Stack Server in AWS EKS](#deploying-llama-stack-server-in-aws-eks) for more details. +Deploy Llama Stack and vLLM servers in a Kubernetes cluster instead of running them locally. This guide covers both local development with Kind and production deployment on AWS EKS. -First, create a local Kubernetes cluster via Kind: +## Prerequisites -``` +### Local Kubernetes Setup + +Create a local Kubernetes cluster via Kind: + +```bash kind create cluster --image kindest/node:v1.32.0 --name llama-stack-test ``` -First set your hugging face token as an environment variable. -``` +Set your Hugging Face token: + +```bash export HF_TOKEN=$(echo -n "your-hf-token" | base64) ``` -Now create a Kubernetes PVC and Secret for downloading and storing Hugging Face model: +## Quick Deployment -``` -cat <$tmp_dir/Containerfile.llama-stack-run-k8s </api/auth/callback/` - - -Run the following script to deploy the Llama Stack server: -``` -export HF_TOKEN= -export GITHUB_CLIENT_ID= -export GITHUB_CLIENT_SECRET= -export LLAMA_STACK_UI_URL= - -cd docs/source/distributions/eks -./apply.sh +**Check pod status:** +```bash +kubectl get pods -l app.kubernetes.io/name=vllm +kubectl logs -l app.kubernetes.io/name=vllm ``` -This script will: +**Test service connectivity:** +```bash +kubectl run -it --rm debug --image=curlimages/curl --restart=Never -- curl http://vllm-server:8000/v1/models +``` -- Set up a default storage class for AWS EKS -- Deploy the Llama Stack server in a Kubernetes Pod and Service \ No newline at end of file +## Related Resources + +- **[Deployment Overview](/docs/deploying/)** - Overview of deployment options +- **[Distributions](/docs/distributions)** - Understanding Llama Stack distributions +- **[Configuration](/docs/distributions/configuration)** - Detailed configuration options diff --git a/docs/source/distributions/building_distro.md b/docs/docs/distributions/building_distro.mdx similarity index 96% rename from docs/source/distributions/building_distro.md rename to docs/docs/distributions/building_distro.mdx index 24098708f..5b65b7f16 100644 --- a/docs/source/distributions/building_distro.md +++ b/docs/docs/distributions/building_distro.mdx @@ -1,5 +1,9 @@ -# Build your own Distribution - +--- +title: Building Custom Distributions +description: Building a Llama Stack distribution from scratch +sidebar_label: Build your own Distribution +sidebar_position: 3 +--- This guide will walk you through the steps to get started with building a Llama Stack distribution from scratch with your choice of API providers. @@ -82,8 +86,11 @@ options: After this step is complete, a file named `-build.yaml` and template file `-run.yaml` will be generated and saved at the output file path specified at the end of the command. -::::{tab-set} -:::{tab-item} Building from a template +import Tabs from '@theme/Tabs'; +import TabItem from '@theme/TabItem'; + + + To build from alternative API providers, we provide distribution templates for users to get started building a distribution backed by different providers. The following command will allow you to see the available templates and their corresponding providers. @@ -156,8 +163,8 @@ You can now edit ~/.llama/distributions/llamastack-starter/starter-run.yaml and ```{tip} The generated `run.yaml` file is a starting point for your configuration. For comprehensive guidance on customizing it for your specific needs, infrastructure, and deployment scenarios, see [Customizing Your run.yaml Configuration](customizing_run_yaml.md). ``` -::: -:::{tab-item} Building from Scratch + + If the provided templates do not fit your use case, you could start off with running `llama stack build` which will allow you to a interactively enter wizard where you will be prompted to enter build configurations. @@ -186,9 +193,8 @@ Tip: use to see options for the providers. You can now edit ~/.llama/distributions/llamastack-my-local-stack/my-local-stack-run.yaml and run `llama stack run ~/.llama/distributions/llamastack-my-local-stack/my-local-stack-run.yaml` ``` -::: - -:::{tab-item} Building from a pre-existing build config file + + - In addition to templates, you may customize the build to your liking through editing config files and build from config files with the following command. - The config file will be of contents like the ones in `llama_stack/distributions/*build.yaml`. @@ -196,9 +202,8 @@ You can now edit ~/.llama/distributions/llamastack-my-local-stack/my-local-stack ``` llama stack build --config llama_stack/distributions/starter/build.yaml ``` -::: - -:::{tab-item} Building with External Providers + + Llama Stack supports external providers that live outside of the main codebase. This allows you to create and maintain your own providers independently or use community-provided providers. @@ -246,16 +251,13 @@ directory or a git repository (git must be installed on the build environment). llama stack build --config my-external-stack.yaml ``` -For more information on external providers, including directory structure, provider types, and implementation requirements, see the [External Providers documentation](../providers/external.md). -::: - -:::{tab-item} Building Container - -```{admonition} Podman Alternative -:class: tip +For more information on external providers, including directory structure, provider types, and implementation requirements, see the [External Providers documentation](../providers/external/). + + +:::tip Podman Alternative Podman is supported as an alternative to Docker. Set `CONTAINER_BINARY` to `podman` in your environment to use Podman. -``` +::: To build a container image, you may start off from a template and use the `--image-type container` flag to specify `container` as the build image type. @@ -274,7 +276,8 @@ You can now edit ~/meta-llama/llama-stack/tmp/configs/ollama-run.yaml and run `l ``` Now set some environment variables for the inference model ID and Llama Stack Port and create a local directory to mount into the container's file system. -``` + +```bash export INFERENCE_MODEL="llama3.2:3b" export LLAMA_STACK_PORT=8321 mkdir -p ~/.llama @@ -308,9 +311,8 @@ Here are the docker flags and their uses: * `--env OLLAMA_URL=http://host.docker.internal:11434`: Configures the URL for the Ollama service -::: - -:::: + + ### Running your Stack server diff --git a/docs/source/distributions/configuration.md b/docs/docs/distributions/configuration.mdx similarity index 92% rename from docs/source/distributions/configuration.md rename to docs/docs/distributions/configuration.mdx index c9677b3b6..d87c7f64b 100644 --- a/docs/source/distributions/configuration.md +++ b/docs/docs/distributions/configuration.mdx @@ -1,3 +1,9 @@ +--- +title: Configuring a "Stack" +description: Configuring a "Stack" +sidebar_label: Configuring a "Stack" +sidebar_position: 6 +--- # Configuring a "Stack" The Llama Stack runtime configuration is specified as a YAML file. Here is a simplified version of an example configuration file for the Ollama distribution: @@ -200,7 +206,7 @@ models: provider_model_id: null model_type: llm ``` -A Model is an instance of a "Resource" (see [Concepts](../concepts/index)) and is associated with a specific inference provider (in this case, the provider with identifier `ollama`). This is an instance of a "pre-registered" model. While we always encourage the clients to register models before using them, some Stack servers may come up a list of "already known and available" models. +A Model is an instance of a "Resource" (see [Concepts](../concepts/)) and is associated with a specific inference provider (in this case, the provider with identifier `ollama`). This is an instance of a "pre-registered" model. While we always encourage the clients to register models before using them, some Stack servers may come up a list of "already known and available" models. What's with the `provider_model_id` field? This is an identifier for the model inside the provider's model catalog. Contrast it with `model_id` which is the identifier for the same model for Llama Stack's purposes. For example, you may want to name "llama3.2:vision-11b" as "image_captioning_model" when you use it in your Stack interactions. When omitted, the server will set `provider_model_id` to be the same as `model_id`. @@ -354,6 +360,47 @@ You can easily validate a request by running: curl -s -L -H "Authorization: Bearer $(cat llama-stack-auth-token)" http://127.0.0.1:8321/v1/providers ``` +#### Kubernetes Authentication Provider + +The server can be configured to use Kubernetes SelfSubjectReview API to validate tokens directly against the Kubernetes API server: + +```yaml +server: + auth: + provider_config: + type: "kubernetes" + api_server_url: "https://kubernetes.default.svc" + claims_mapping: + username: "roles" + groups: "roles" + uid: "uid_attr" + verify_tls: true + tls_cafile: "/path/to/ca.crt" +``` + +Configuration options: +- `api_server_url`: The Kubernetes API server URL (e.g., https://kubernetes.default.svc:6443) +- `verify_tls`: Whether to verify TLS certificates (default: true) +- `tls_cafile`: Path to CA certificate file for TLS verification +- `claims_mapping`: Mapping of Kubernetes user claims to access attributes + +The provider validates tokens by sending a SelfSubjectReview request to the Kubernetes API server at `/apis/authentication.k8s.io/v1/selfsubjectreviews`. The provider extracts user information from the response: +- Username from the `userInfo.username` field +- Groups from the `userInfo.groups` field +- UID from the `userInfo.uid` field + +To obtain a token for testing: +```bash +kubectl create namespace llama-stack +kubectl create serviceaccount llama-stack-auth -n llama-stack +kubectl create token llama-stack-auth -n llama-stack > llama-stack-auth-token +``` + +You can validate a request by running: +```bash +curl -s -L -H "Authorization: Bearer $(cat llama-stack-auth-token)" http://127.0.0.1:8321/v1/providers +``` + #### GitHub Token Provider Validates GitHub personal access tokens or OAuth tokens directly: ```yaml @@ -431,12 +478,12 @@ A rule may also specify a condition, either a 'when' or an 'unless', with additional constraints as to where the rule applies. The constraints supported at present are: - - 'user with in ' - - 'user with not in ' + - 'user with `` in ``' + - 'user with `` not in ``' - 'user is owner' - 'user is not owner' - - 'user in owners ' - - 'user not in owners ' + - 'user in owners ``' + - 'user not in owners ``' The attributes defined for a user will depend on how the auth configuration is defined. diff --git a/docs/source/distributions/customizing_run_yaml.md b/docs/docs/distributions/customizing_run_yaml.mdx similarity index 88% rename from docs/source/distributions/customizing_run_yaml.md rename to docs/docs/distributions/customizing_run_yaml.mdx index 10067bab7..513712f81 100644 --- a/docs/source/distributions/customizing_run_yaml.md +++ b/docs/docs/distributions/customizing_run_yaml.mdx @@ -1,3 +1,9 @@ +--- +title: Customizing run.yaml +description: Customizing run.yaml files for Llama Stack templates +sidebar_label: Customizing run.yaml +sidebar_position: 4 +--- # Customizing run.yaml Files The `run.yaml` files generated by Llama Stack templates are **starting points** designed to be customized for your specific needs. They are not meant to be used as-is in production environments. @@ -37,4 +43,4 @@ your-project/ └── README.md ``` -The goal is to take the generated template and adapt it to your specific infrastructure and operational needs. \ No newline at end of file +The goal is to take the generated template and adapt it to your specific infrastructure and operational needs. diff --git a/docs/source/distributions/eks/apply.sh b/docs/docs/distributions/eks/apply.sh similarity index 100% rename from docs/source/distributions/eks/apply.sh rename to docs/docs/distributions/eks/apply.sh diff --git a/docs/source/distributions/eks/gp3-topology-aware.yaml b/docs/docs/distributions/eks/gp3-topology-aware.yaml similarity index 100% rename from docs/source/distributions/eks/gp3-topology-aware.yaml rename to docs/docs/distributions/eks/gp3-topology-aware.yaml diff --git a/docs/source/distributions/importing_as_library.md b/docs/docs/distributions/importing_as_library.mdx similarity index 77% rename from docs/source/distributions/importing_as_library.md rename to docs/docs/distributions/importing_as_library.mdx index b9b4b065a..122e5220f 100644 --- a/docs/source/distributions/importing_as_library.md +++ b/docs/docs/distributions/importing_as_library.mdx @@ -1,3 +1,9 @@ +--- +title: Using Llama Stack as a Library +description: How to use Llama Stack as a Python library instead of running a server +sidebar_label: Importing as Library +sidebar_position: 5 +--- # Using Llama Stack as a Library ## Setup Llama Stack without a Server @@ -27,7 +33,7 @@ Then, you can access the APIs like `models` and `inference` on the client and ca response = client.models.list() ``` -If you've created a [custom distribution](https://llama-stack.readthedocs.io/en/latest/distributions/building_distro.html), you can also use the run.yaml configuration file directly: +If you've created a [custom distribution](./building_distro), you can also use the run.yaml configuration file directly: ```python client = LlamaStackAsLibraryClient(config_path) diff --git a/docs/docs/distributions/index.mdx b/docs/docs/distributions/index.mdx new file mode 100644 index 000000000..0149f143f --- /dev/null +++ b/docs/docs/distributions/index.mdx @@ -0,0 +1,21 @@ +--- +title: Distributions Overview +description: Pre-packaged sets of Llama Stack components for different deployment scenarios +sidebar_label: Overview +sidebar_position: 1 +--- + +# Distributions Overview + +A distribution is a pre-packaged set of Llama Stack components that can be deployed together. + +This section provides an overview of the distributions available in Llama Stack. + +## Distribution Guides + +- **[Available Distributions](./list_of_distributions.mdx)** - Complete list and comparison of all distributions +- **[Building Custom Distributions](./building_distro.mdx)** - Create your own distribution from scratch +- **[Customizing Configuration](./customizing_run_yaml.mdx)** - Customize run.yaml for your needs +- **[Starting Llama Stack Server](./starting_llama_stack_server.mdx)** - How to run distributions +- **[Importing as Library](./importing_as_library.mdx)** - Use distributions in your code +- **[Configuration Reference](./configuration.mdx)** - Configuration file format details diff --git a/docs/source/distributions/k8s/apply.sh b/docs/docs/distributions/k8s/apply.sh similarity index 83% rename from docs/source/distributions/k8s/apply.sh rename to docs/docs/distributions/k8s/apply.sh index 3356da53e..1b5b26863 100755 --- a/docs/source/distributions/k8s/apply.sh +++ b/docs/docs/distributions/k8s/apply.sh @@ -22,17 +22,17 @@ else fi if [ -z "${GITHUB_CLIENT_ID:-}" ]; then - echo "ERROR: GITHUB_CLIENT_ID not set. You need it for Github login to work. Refer to https://llama-stack.readthedocs.io/en/latest/deploying/index.html#kubernetes-deployment-guide" + echo "ERROR: GITHUB_CLIENT_ID not set. You need it for Github login to work. See the Kubernetes Deployment Guide in the Llama Stack documentation." exit 1 fi if [ -z "${GITHUB_CLIENT_SECRET:-}" ]; then - echo "ERROR: GITHUB_CLIENT_SECRET not set. You need it for Github login to work. Refer to https://llama-stack.readthedocs.io/en/latest/deploying/index.html#kubernetes-deployment-guide" + echo "ERROR: GITHUB_CLIENT_SECRET not set. You need it for Github login to work. See the Kubernetes Deployment Guide in the Llama Stack documentation." exit 1 fi if [ -z "${LLAMA_STACK_UI_URL:-}" ]; then - echo "ERROR: LLAMA_STACK_UI_URL not set. Should be set to the external URL of the UI (excluding port). You need it for Github login to work. Refer to https://llama-stack.readthedocs.io/en/latest/deploying/index.html#kubernetes-deployment-guide" + echo "ERROR: LLAMA_STACK_UI_URL not set. Should be set to the external URL of the UI (excluding port). You need it for Github login to work. See the Kubernetes Deployment Guide in the Llama Stack documentation." exit 1 fi diff --git a/docs/source/distributions/k8s/chroma-k8s.yaml.template b/docs/docs/distributions/k8s/chroma-k8s.yaml.template similarity index 100% rename from docs/source/distributions/k8s/chroma-k8s.yaml.template rename to docs/docs/distributions/k8s/chroma-k8s.yaml.template diff --git a/docs/source/distributions/k8s/hf-token-secret.yaml.template b/docs/docs/distributions/k8s/hf-token-secret.yaml.template similarity index 100% rename from docs/source/distributions/k8s/hf-token-secret.yaml.template rename to docs/docs/distributions/k8s/hf-token-secret.yaml.template diff --git a/docs/source/distributions/k8s/ingress-k8s.yaml.template b/docs/docs/distributions/k8s/ingress-k8s.yaml.template similarity index 100% rename from docs/source/distributions/k8s/ingress-k8s.yaml.template rename to docs/docs/distributions/k8s/ingress-k8s.yaml.template diff --git a/docs/source/distributions/k8s/postgres-k8s.yaml.template b/docs/docs/distributions/k8s/postgres-k8s.yaml.template similarity index 100% rename from docs/source/distributions/k8s/postgres-k8s.yaml.template rename to docs/docs/distributions/k8s/postgres-k8s.yaml.template diff --git a/docs/docs/distributions/k8s/stack-configmap.yaml b/docs/docs/distributions/k8s/stack-configmap.yaml new file mode 100644 index 000000000..3dbb0da97 --- /dev/null +++ b/docs/docs/distributions/k8s/stack-configmap.yaml @@ -0,0 +1,56 @@ +apiVersion: v1 +data: + stack_run_config.yaml: "version: '2'\nimage_name: kubernetes-demo\napis:\n- agents\n- + inference\n- files\n- safety\n- telemetry\n- tool_runtime\n- vector_io\nproviders:\n + \ inference:\n - provider_id: vllm-inference\n provider_type: remote::vllm\n + \ config:\n url: ${env.VLLM_URL:=http://localhost:8000/v1}\n max_tokens: + ${env.VLLM_MAX_TOKENS:=4096}\n api_token: ${env.VLLM_API_TOKEN:=fake}\n tls_verify: + ${env.VLLM_TLS_VERIFY:=true}\n - provider_id: vllm-safety\n provider_type: + remote::vllm\n config:\n url: ${env.VLLM_SAFETY_URL:=http://localhost:8000/v1}\n + \ max_tokens: ${env.VLLM_MAX_TOKENS:=4096}\n api_token: ${env.VLLM_API_TOKEN:=fake}\n + \ tls_verify: ${env.VLLM_TLS_VERIFY:=true}\n - provider_id: sentence-transformers\n + \ provider_type: inline::sentence-transformers\n config: {}\n vector_io:\n + \ - provider_id: ${env.ENABLE_CHROMADB:+chromadb}\n provider_type: remote::chromadb\n + \ config:\n url: ${env.CHROMADB_URL:=}\n kvstore:\n type: postgres\n + \ host: ${env.POSTGRES_HOST:=localhost}\n port: ${env.POSTGRES_PORT:=5432}\n + \ db: ${env.POSTGRES_DB:=llamastack}\n user: ${env.POSTGRES_USER:=llamastack}\n + \ password: ${env.POSTGRES_PASSWORD:=llamastack}\n files:\n - provider_id: + meta-reference-files\n provider_type: inline::localfs\n config:\n storage_dir: + ${env.FILES_STORAGE_DIR:=~/.llama/distributions/starter/files}\n metadata_store:\n + \ type: sqlite\n db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/starter}/files_metadata.db + \ \n safety:\n - provider_id: llama-guard\n provider_type: inline::llama-guard\n + \ config:\n excluded_categories: []\n agents:\n - provider_id: meta-reference\n + \ provider_type: inline::meta-reference\n config:\n persistence_store:\n + \ type: postgres\n host: ${env.POSTGRES_HOST:=localhost}\n port: + ${env.POSTGRES_PORT:=5432}\n db: ${env.POSTGRES_DB:=llamastack}\n user: + ${env.POSTGRES_USER:=llamastack}\n password: ${env.POSTGRES_PASSWORD:=llamastack}\n + \ responses_store:\n type: postgres\n host: ${env.POSTGRES_HOST:=localhost}\n + \ port: ${env.POSTGRES_PORT:=5432}\n db: ${env.POSTGRES_DB:=llamastack}\n + \ user: ${env.POSTGRES_USER:=llamastack}\n password: ${env.POSTGRES_PASSWORD:=llamastack}\n + \ telemetry:\n - provider_id: meta-reference\n provider_type: inline::meta-reference\n + \ config:\n service_name: \"${env.OTEL_SERVICE_NAME:=\\u200B}\"\n sinks: + ${env.TELEMETRY_SINKS:=console}\n tool_runtime:\n - provider_id: brave-search\n + \ provider_type: remote::brave-search\n config:\n api_key: ${env.BRAVE_SEARCH_API_KEY:+}\n + \ max_results: 3\n - provider_id: tavily-search\n provider_type: remote::tavily-search\n + \ config:\n api_key: ${env.TAVILY_SEARCH_API_KEY:+}\n max_results: + 3\n - provider_id: rag-runtime\n provider_type: inline::rag-runtime\n config: + {}\n - provider_id: model-context-protocol\n provider_type: remote::model-context-protocol\n + \ config: {}\nmetadata_store:\n type: postgres\n host: ${env.POSTGRES_HOST:=localhost}\n + \ port: ${env.POSTGRES_PORT:=5432}\n db: ${env.POSTGRES_DB:=llamastack}\n user: + ${env.POSTGRES_USER:=llamastack}\n password: ${env.POSTGRES_PASSWORD:=llamastack}\n + \ table_name: llamastack_kvstore\ninference_store:\n type: postgres\n host: + ${env.POSTGRES_HOST:=localhost}\n port: ${env.POSTGRES_PORT:=5432}\n db: ${env.POSTGRES_DB:=llamastack}\n + \ user: ${env.POSTGRES_USER:=llamastack}\n password: ${env.POSTGRES_PASSWORD:=llamastack}\nmodels:\n- + metadata:\n embedding_dimension: 384\n model_id: all-MiniLM-L6-v2\n provider_id: + sentence-transformers\n model_type: embedding\n- metadata: {}\n model_id: ${env.INFERENCE_MODEL}\n + \ provider_id: vllm-inference\n model_type: llm\n- metadata: {}\n model_id: + ${env.SAFETY_MODEL:=meta-llama/Llama-Guard-3-1B}\n provider_id: vllm-safety\n + \ model_type: llm\nshields:\n- shield_id: ${env.SAFETY_MODEL:=meta-llama/Llama-Guard-3-1B}\nvector_dbs: + []\ndatasets: []\nscoring_fns: []\nbenchmarks: []\ntool_groups:\n- toolgroup_id: + builtin::websearch\n provider_id: tavily-search\n- toolgroup_id: builtin::rag\n + \ provider_id: rag-runtime\nserver:\n port: 8321\n auth:\n provider_config:\n + \ type: github_token\n" +kind: ConfigMap +metadata: + creationTimestamp: null + name: llama-stack-config diff --git a/docs/source/distributions/k8s/stack-k8s.yaml.template b/docs/docs/distributions/k8s/stack-k8s.yaml.template similarity index 100% rename from docs/source/distributions/k8s/stack-k8s.yaml.template rename to docs/docs/distributions/k8s/stack-k8s.yaml.template diff --git a/docs/source/distributions/k8s/stack_run_config.yaml b/docs/docs/distributions/k8s/stack_run_config.yaml similarity index 92% rename from docs/source/distributions/k8s/stack_run_config.yaml rename to docs/docs/distributions/k8s/stack_run_config.yaml index a2d65e1a9..b841ab977 100644 --- a/docs/source/distributions/k8s/stack_run_config.yaml +++ b/docs/docs/distributions/k8s/stack_run_config.yaml @@ -3,6 +3,7 @@ image_name: kubernetes-demo apis: - agents - inference +- files - safety - telemetry - tool_runtime @@ -38,6 +39,14 @@ providers: db: ${env.POSTGRES_DB:=llamastack} user: ${env.POSTGRES_USER:=llamastack} password: ${env.POSTGRES_PASSWORD:=llamastack} + files: + - provider_id: meta-reference-files + provider_type: inline::localfs + config: + storage_dir: ${env.FILES_STORAGE_DIR:=~/.llama/distributions/starter/files} + metadata_store: + type: sqlite + db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/starter}/files_metadata.db safety: - provider_id: llama-guard provider_type: inline::llama-guard diff --git a/docs/source/distributions/k8s/ui-k8s.yaml.template b/docs/docs/distributions/k8s/ui-k8s.yaml.template similarity index 100% rename from docs/source/distributions/k8s/ui-k8s.yaml.template rename to docs/docs/distributions/k8s/ui-k8s.yaml.template diff --git a/docs/source/distributions/k8s/vllm-k8s.yaml.template b/docs/docs/distributions/k8s/vllm-k8s.yaml.template similarity index 100% rename from docs/source/distributions/k8s/vllm-k8s.yaml.template rename to docs/docs/distributions/k8s/vllm-k8s.yaml.template diff --git a/docs/source/distributions/k8s/vllm-safety-k8s.yaml.template b/docs/docs/distributions/k8s/vllm-safety-k8s.yaml.template similarity index 100% rename from docs/source/distributions/k8s/vllm-safety-k8s.yaml.template rename to docs/docs/distributions/k8s/vllm-safety-k8s.yaml.template diff --git a/docs/source/distributions/list_of_distributions.md b/docs/docs/distributions/list_of_distributions.mdx similarity index 89% rename from docs/source/distributions/list_of_distributions.md rename to docs/docs/distributions/list_of_distributions.mdx index ee01c92c4..813d3c721 100644 --- a/docs/source/distributions/list_of_distributions.md +++ b/docs/docs/distributions/list_of_distributions.mdx @@ -1,3 +1,10 @@ +--- +title: Available Distributions +description: List of available distributions for Llama Stack +sidebar_label: Available Distributions +sidebar_position: 2 +--- + # Available Distributions Llama Stack provides several pre-configured distributions to help you get started quickly. Choose the distribution that best fits your hardware and use case. @@ -55,7 +62,7 @@ docker pull llama-stack/distribution-meta-reference-gpu **Partners:** [Fireworks.ai](https://fireworks.ai) and [Together.xyz](https://together.xyz) -**Guides:** [Remote-Hosted Endpoints](remote_hosted_distro/index) +**Guides:** [Remote-Hosted Endpoints](./remote_hosted_distro/) ### 📱 Mobile Development @@ -74,7 +81,7 @@ docker pull llama-stack/distribution-meta-reference-gpu - You need custom configurations - You want to optimize for your specific use case -**Guides:** [Building Custom Distributions](building_distro.md) +**Guides:** [Building Custom Distributions](./building_distro) ## Detailed Documentation @@ -124,4 +131,4 @@ graph TD 3. **Configure your providers** with API keys or local models 4. **Start building** with Llama Stack! -For help choosing or troubleshooting, check our [Getting Started Guide](../getting_started/index.md) or [Community Support](https://github.com/llama-stack/llama-stack/discussions). +For help choosing or troubleshooting, check our [Getting Started Guide](/docs/getting_started/quickstart) or [Community Support](https://github.com/llama-stack/llama-stack/discussions). diff --git a/docs/source/distributions/ondevice_distro/android_sdk.md b/docs/docs/distributions/ondevice_distro/android_sdk.md similarity index 98% rename from docs/source/distributions/ondevice_distro/android_sdk.md rename to docs/docs/distributions/ondevice_distro/android_sdk.md index 9d16d07d7..bfa294e45 100644 --- a/docs/source/distributions/ondevice_distro/android_sdk.md +++ b/docs/docs/distributions/ondevice_distro/android_sdk.md @@ -66,7 +66,7 @@ llama stack run starter --port 5050 Ensure the Llama Stack server version is the same as the Kotlin SDK Library for maximum compatibility. -Other inference providers: [Table](https://llama-stack.readthedocs.io/en/latest/index.html#supported-llama-stack-implementations) +Other inference providers: [Table](/docs/) How to set remote localhost in Demo App: [Settings](https://github.com/meta-llama/llama-stack-client-kotlin/tree/latest-release/examples/android_app#settings) diff --git a/docs/source/distributions/ondevice_distro/ios_sdk.md b/docs/docs/distributions/ondevice_distro/ios_sdk.md similarity index 100% rename from docs/source/distributions/ondevice_distro/ios_sdk.md rename to docs/docs/distributions/ondevice_distro/ios_sdk.md diff --git a/docs/source/distributions/remote_hosted_distro/index.md b/docs/docs/distributions/remote_hosted_distro/index.mdx similarity index 100% rename from docs/source/distributions/remote_hosted_distro/index.md rename to docs/docs/distributions/remote_hosted_distro/index.mdx diff --git a/docs/source/distributions/remote_hosted_distro/watsonx.md b/docs/docs/distributions/remote_hosted_distro/watsonx.md similarity index 100% rename from docs/source/distributions/remote_hosted_distro/watsonx.md rename to docs/docs/distributions/remote_hosted_distro/watsonx.md diff --git a/docs/source/distributions/self_hosted_distro/dell-tgi.md b/docs/docs/distributions/self_hosted_distro/dell-tgi.md similarity index 100% rename from docs/source/distributions/self_hosted_distro/dell-tgi.md rename to docs/docs/distributions/self_hosted_distro/dell-tgi.md diff --git a/docs/source/distributions/self_hosted_distro/dell.md b/docs/docs/distributions/self_hosted_distro/dell.md similarity index 100% rename from docs/source/distributions/self_hosted_distro/dell.md rename to docs/docs/distributions/self_hosted_distro/dell.md diff --git a/docs/source/distributions/self_hosted_distro/meta-reference-gpu.md b/docs/docs/distributions/self_hosted_distro/meta-reference-gpu.md similarity index 95% rename from docs/source/distributions/self_hosted_distro/meta-reference-gpu.md rename to docs/docs/distributions/self_hosted_distro/meta-reference-gpu.md index 7e50a4161..84b85b91c 100644 --- a/docs/source/distributions/self_hosted_distro/meta-reference-gpu.md +++ b/docs/docs/distributions/self_hosted_distro/meta-reference-gpu.md @@ -2,7 +2,7 @@ orphan: true --- -# Meta Reference Distribution +# Meta Reference GPU Distribution ```{toctree} :maxdepth: 2 @@ -41,7 +41,7 @@ The following environment variables can be configured: ## Prerequisite: Downloading Models -Please use `llama model list --downloaded` to check that you have llama model checkpoints downloaded in `~/.llama` before proceeding. See [installation guide](https://llama-stack.readthedocs.io/en/latest/references/llama_cli_reference/download_models.html) here to download the models. Run `llama model list` to see the available models to download, and `llama model download` to download the checkpoints. +Please use `llama model list --downloaded` to check that you have llama model checkpoints downloaded in `~/.llama` before proceeding. See [installation guide](../../references/llama_cli_reference/download_models.md) here to download the models. Run `llama model list` to see the available models to download, and `llama model download` to download the checkpoints. ``` $ llama model list --downloaded diff --git a/docs/source/distributions/self_hosted_distro/nvidia.md b/docs/docs/distributions/self_hosted_distro/nvidia.md similarity index 82% rename from docs/source/distributions/self_hosted_distro/nvidia.md rename to docs/docs/distributions/self_hosted_distro/nvidia.md index e845c3c48..1e52797db 100644 --- a/docs/source/distributions/self_hosted_distro/nvidia.md +++ b/docs/docs/distributions/self_hosted_distro/nvidia.md @@ -11,6 +11,7 @@ The `llamastack/distribution-nvidia` distribution consists of the following prov | agents | `inline::meta-reference` | | datasetio | `inline::localfs`, `remote::nvidia` | | eval | `remote::nvidia` | +| files | `inline::localfs` | | inference | `remote::nvidia` | | post_training | `remote::nvidia` | | safety | `remote::nvidia` | @@ -36,24 +37,6 @@ The following environment variables can be configured: - `INFERENCE_MODEL`: Inference model (default: `Llama3.1-8B-Instruct`) - `SAFETY_MODEL`: Name of the model to use for safety (default: `meta/llama-3.1-8b-instruct`) -### Models - -The following models are available by default: - -- `meta/llama3-8b-instruct ` -- `meta/llama3-70b-instruct ` -- `meta/llama-3.1-8b-instruct ` -- `meta/llama-3.1-70b-instruct ` -- `meta/llama-3.1-405b-instruct ` -- `meta/llama-3.2-1b-instruct ` -- `meta/llama-3.2-3b-instruct ` -- `meta/llama-3.2-11b-vision-instruct ` -- `meta/llama-3.2-90b-vision-instruct ` -- `meta/llama-3.3-70b-instruct ` -- `nvidia/llama-3.2-nv-embedqa-1b-v2 ` -- `nvidia/nv-embedqa-e5-v5 ` -- `nvidia/nv-embedqa-mistral-7b-v2 ` -- `snowflake/arctic-embed-l ` ## Prerequisites @@ -77,22 +60,22 @@ The deployed platform includes the NIM Proxy microservice, which is the service ### Datasetio API: NeMo Data Store The NeMo Data Store microservice serves as the default file storage solution for the NeMo microservices platform. It exposts APIs compatible with the Hugging Face Hub client (`HfApi`), so you can use the client to interact with Data Store. The `NVIDIA_DATASETS_URL` environment variable should point to your NeMo Data Store endpoint. -See the {repopath}`NVIDIA Datasetio docs::llama_stack/providers/remote/datasetio/nvidia/README.md` for supported features and example usage. +See the [NVIDIA Datasetio docs](https://github.com/meta-llama/llama-stack/blob/main/llama_stack/providers/remote/datasetio/nvidia/README.md) for supported features and example usage. ### Eval API: NeMo Evaluator The NeMo Evaluator microservice supports evaluation of LLMs. Launching an Evaluation job with NeMo Evaluator requires an Evaluation Config (an object that contains metadata needed by the job). A Llama Stack Benchmark maps to an Evaluation Config, so registering a Benchmark creates an Evaluation Config in NeMo Evaluator. The `NVIDIA_EVALUATOR_URL` environment variable should point to your NeMo Microservices endpoint. -See the {repopath}`NVIDIA Eval docs::llama_stack/providers/remote/eval/nvidia/README.md` for supported features and example usage. +See the [NVIDIA Eval docs](https://github.com/meta-llama/llama-stack/blob/main/llama_stack/providers/remote/eval/nvidia/README.md) for supported features and example usage. ### Post-Training API: NeMo Customizer -The NeMo Customizer microservice supports fine-tuning models. You can reference {repopath}`this list of supported models::llama_stack/providers/remote/post_training/nvidia/models.py` that can be fine-tuned using Llama Stack. The `NVIDIA_CUSTOMIZER_URL` environment variable should point to your NeMo Microservices endpoint. +The NeMo Customizer microservice supports fine-tuning models. You can reference [this list of supported models](https://github.com/meta-llama/llama-stack/blob/main/llama_stack/providers/remote/post_training/nvidia/models.py) that can be fine-tuned using Llama Stack. The `NVIDIA_CUSTOMIZER_URL` environment variable should point to your NeMo Microservices endpoint. -See the {repopath}`NVIDIA Post-Training docs::llama_stack/providers/remote/post_training/nvidia/README.md` for supported features and example usage. +See the [NVIDIA Post-Training docs](https://github.com/meta-llama/llama-stack/blob/main/llama_stack/providers/remote/post_training/nvidia/README.md) for supported features and example usage. ### Safety API: NeMo Guardrails The NeMo Guardrails microservice sits between your application and the LLM, and adds checks and content moderation to a model. The `GUARDRAILS_SERVICE_URL` environment variable should point to your NeMo Microservices endpoint. -See the {repopath}`NVIDIA Safety docs::llama_stack/providers/remote/safety/nvidia/README.md` for supported features and example usage. +See the [NVIDIA Safety docs](https://github.com/meta-llama/llama-stack/blob/main/llama_stack/providers/remote/safety/nvidia/README.md) for supported features and example usage. ## Deploying models In order to use a registered model with the Llama Stack APIs, ensure the corresponding NIM is deployed to your environment. For example, you can use the NIM Proxy microservice to deploy `meta/llama-3.2-1b-instruct`. @@ -166,4 +149,4 @@ llama stack run ./run.yaml \ ``` ## Example Notebooks -For examples of how to use the NVIDIA Distribution to run inference, fine-tune, evaluate, and run safety checks on your LLMs, you can reference the example notebooks in {repopath}`docs/notebooks/nvidia`. +For examples of how to use the NVIDIA Distribution to run inference, fine-tune, evaluate, and run safety checks on your LLMs, you can reference the example notebooks in [docs/notebooks/nvidia](https://github.com/meta-llama/llama-stack/tree/main/docs/notebooks/nvidia). diff --git a/docs/source/distributions/self_hosted_distro/passthrough.md b/docs/docs/distributions/self_hosted_distro/passthrough.md similarity index 100% rename from docs/source/distributions/self_hosted_distro/passthrough.md rename to docs/docs/distributions/self_hosted_distro/passthrough.md diff --git a/docs/source/distributions/self_hosted_distro/starter.md b/docs/docs/distributions/self_hosted_distro/starter.md similarity index 95% rename from docs/source/distributions/self_hosted_distro/starter.md rename to docs/docs/distributions/self_hosted_distro/starter.md index 9218f7f81..faa82bcfa 100644 --- a/docs/source/distributions/self_hosted_distro/starter.md +++ b/docs/docs/distributions/self_hosted_distro/starter.md @@ -36,25 +36,25 @@ The starter distribution includes a comprehensive set of inference providers: ### Hosted Providers - **[OpenAI](https://openai.com/api/)**: GPT-4, GPT-3.5, O1, O3, O4 models and text embeddings - - provider ID: `openai` - reference documentation: [openai](../../providers/inference/remote_openai.md) + provider ID: `openai` - reference documentation: [openai](../../providers/inference/remote_openai) - **[Fireworks](https://fireworks.ai/)**: Llama 3.1, 3.2, 3.3, 4 Scout, 4 Maverick models and - embeddings - provider ID: `fireworks` - reference documentation: [fireworks](../../providers/inference/remote_fireworks.md) + embeddings - provider ID: `fireworks` - reference documentation: [fireworks](../../providers/inference/remote_fireworks) - **[Together](https://together.ai/)**: Llama 3.1, 3.2, 3.3, 4 Scout, 4 Maverick models and - embeddings - provider ID: `together` - reference documentation: [together](../../providers/inference/remote_together.md) -- **[Anthropic](https://www.anthropic.com/)**: Claude 3.5 Sonnet, Claude 3.7 Sonnet, Claude 3.5 Haiku, and Voyage embeddings - provider ID: `anthropic` - reference documentation: [anthropic](../../providers/inference/remote_anthropic.md) -- **[Gemini](https://gemini.google.com/)**: Gemini 1.5, 2.0, 2.5 models and text embeddings - provider ID: `gemini` - reference documentation: [gemini](../../providers/inference/remote_gemini.md) -- **[Groq](https://groq.com/)**: Fast Llama models (3.1, 3.2, 3.3, 4 Scout, 4 Maverick) - provider ID: `groq` - reference documentation: [groq](../../providers/inference/remote_groq.md) -- **[SambaNova](https://www.sambanova.ai/)**: Llama 3.1, 3.2, 3.3, 4 Scout, 4 Maverick models - provider ID: `sambanova` - reference documentation: [sambanova](../../providers/inference/remote_sambanova.md) -- **[Cerebras](https://www.cerebras.ai/)**: Cerebras AI models - provider ID: `cerebras` - reference documentation: [cerebras](../../providers/inference/remote_cerebras.md) -- **[NVIDIA](https://www.nvidia.com/)**: NVIDIA NIM - provider ID: `nvidia` - reference documentation: [nvidia](../../providers/inference/remote_nvidia.md) -- **[HuggingFace](https://huggingface.co/)**: Serverless and endpoint models - provider ID: `hf::serverless` and `hf::endpoint` - reference documentation: [huggingface-serverless](../../providers/inference/remote_hf_serverless.md) and [huggingface-endpoint](../../providers/inference/remote_hf_endpoint.md) -- **[Bedrock](https://aws.amazon.com/bedrock/)**: AWS Bedrock models - provider ID: `bedrock` - reference documentation: [bedrock](../../providers/inference/remote_bedrock.md) + embeddings - provider ID: `together` - reference documentation: [together](../../providers/inference/remote_together) +- **[Anthropic](https://www.anthropic.com/)**: Claude 3.5 Sonnet, Claude 3.7 Sonnet, Claude 3.5 Haiku, and Voyage embeddings - provider ID: `anthropic` - reference documentation: [anthropic](../../providers/inference/remote_anthropic) +- **[Gemini](https://gemini.google.com/)**: Gemini 1.5, 2.0, 2.5 models and text embeddings - provider ID: `gemini` - reference documentation: [gemini](../../providers/inference/remote_gemini) +- **[Groq](https://groq.com/)**: Fast Llama models (3.1, 3.2, 3.3, 4 Scout, 4 Maverick) - provider ID: `groq` - reference documentation: [groq](../../providers/inference/remote_groq) +- **[SambaNova](https://www.sambanova.ai/)**: Llama 3.1, 3.2, 3.3, 4 Scout, 4 Maverick models - provider ID: `sambanova` - reference documentation: [sambanova](../../providers/inference/remote_sambanova) +- **[Cerebras](https://www.cerebras.ai/)**: Cerebras AI models - provider ID: `cerebras` - reference documentation: [cerebras](../../providers/inference/remote_cerebras) +- **[NVIDIA](https://www.nvidia.com/)**: NVIDIA NIM - provider ID: `nvidia` - reference documentation: [nvidia](../../providers/inference/remote_nvidia) +- **[HuggingFace](https://huggingface.co/)**: Serverless and endpoint models - provider ID: `hf::serverless` and `hf::endpoint` - reference documentation: [huggingface-serverless](../../providers/inference/remote_hf_serverless) and [huggingface-endpoint](../../providers/inference/remote_hf_endpoint) +- **[Bedrock](https://aws.amazon.com/bedrock/)**: AWS Bedrock models - provider ID: `bedrock` - reference documentation: [bedrock](../../providers/inference/remote_bedrock) ### Local/Remote Providers -- **[Ollama](https://ollama.ai/)**: Local Ollama models - provider ID: `ollama` - reference documentation: [ollama](../../providers/inference/remote_ollama.md) -- **[vLLM](https://docs.vllm.ai/en/latest/)**: Local or remote vLLM server - provider ID: `vllm` - reference documentation: [vllm](../../providers/inference/remote_vllm.md) -- **[TGI](https://github.com/huggingface/text-generation-inference)**: Text Generation Inference server - Dell Enterprise Hub's custom TGI container too (use `DEH_URL`) - provider ID: `tgi` - reference documentation: [tgi](../../providers/inference/remote_tgi.md) -- **[Sentence Transformers](https://www.sbert.net/)**: Local embedding models - provider ID: `sentence-transformers` - reference documentation: [sentence-transformers](../../providers/inference/inline_sentence-transformers.md) +- **[Ollama](https://ollama.ai/)**: Local Ollama models - provider ID: `ollama` - reference documentation: [ollama](../../providers/inference/remote_ollama) +- **[vLLM](https://docs.vllm.ai/en/latest/)**: Local or remote vLLM server - provider ID: `vllm` - reference documentation: [vllm](../../providers/inference/remote_vllm) +- **[TGI](https://github.com/huggingface/text-generation-inference)**: Text Generation Inference server - Dell Enterprise Hub's custom TGI container too (use `DEH_URL`) - provider ID: `tgi` - reference documentation: [tgi](../../providers/inference/remote_tgi) +- **[Sentence Transformers](https://www.sbert.net/)**: Local embedding models - provider ID: `sentence-transformers` - reference documentation: [sentence-transformers](../../providers/inference/inline_sentence-transformers) All providers are disabled by default. So you need to enable them by setting the environment variables. diff --git a/docs/source/distributions/starting_llama_stack_server.md b/docs/docs/distributions/starting_llama_stack_server.mdx similarity index 74% rename from docs/source/distributions/starting_llama_stack_server.md rename to docs/docs/distributions/starting_llama_stack_server.mdx index 1a26694a6..0260692b3 100644 --- a/docs/source/distributions/starting_llama_stack_server.md +++ b/docs/docs/distributions/starting_llama_stack_server.mdx @@ -1,3 +1,10 @@ +--- +title: Starting a Llama Stack Server +description: Different ways to run Llama Stack servers - as library, container, or Kubernetes deployment +sidebar_label: Starting Llama Stack Server +sidebar_position: 7 +--- + # Starting a Llama Stack Server You can run a Llama Stack server in one of the following ways: @@ -9,11 +16,11 @@ This is the simplest way to get started. Using Llama Stack as a library means yo ## Container: -Another simple way to start interacting with Llama Stack is to just spin up a container (via Docker or Podman) which is pre-built with all the providers you need. We provide a number of pre-built images so you can start a Llama Stack server instantly. You can also build your own custom container. Which distribution to choose depends on the hardware you have. See [Selection of a Distribution](selection) for more details. +Another simple way to start interacting with Llama Stack is to just spin up a container (via Docker or Podman) which is pre-built with all the providers you need. We provide a number of pre-built images so you can start a Llama Stack server instantly. You can also build your own custom container. Which distribution to choose depends on the hardware you have. See [Selection of a Distribution](./list_of_distributions) for more details. ## Kubernetes: -If you have built a container image and want to deploy it in a Kubernetes cluster instead of starting the Llama Stack server locally. See [Kubernetes Deployment Guide](kubernetes_deployment) for more details. +If you have built a container image and want to deploy it in a Kubernetes cluster instead of starting the Llama Stack server locally. See [Kubernetes Deployment Guide](../deploying/kubernetes_deployment) for more details. ```{toctree} diff --git a/docs/source/getting_started/demo_script.py b/docs/docs/getting_started/demo_script.py similarity index 94% rename from docs/source/getting_started/demo_script.py rename to docs/docs/getting_started/demo_script.py index 777fc78c2..2ea67739f 100644 --- a/docs/source/getting_started/demo_script.py +++ b/docs/docs/getting_started/demo_script.py @@ -18,12 +18,13 @@ embedding_model_id = ( ).identifier embedding_dimension = em.metadata["embedding_dimension"] -_ = client.vector_dbs.register( +vector_db = client.vector_dbs.register( vector_db_id=vector_db_id, embedding_model=embedding_model_id, embedding_dimension=embedding_dimension, provider_id="faiss", ) +vector_db_id = vector_db.identifier source = "https://www.paulgraham.com/greatwork.html" print("rag_tool> Ingesting document:", source) document = RAGDocument( @@ -35,7 +36,7 @@ document = RAGDocument( client.tool_runtime.rag_tool.insert( documents=[document], vector_db_id=vector_db_id, - chunk_size_in_tokens=50, + chunk_size_in_tokens=100, ) agent = Agent( client, diff --git a/docs/source/getting_started/detailed_tutorial.md b/docs/docs/getting_started/detailed_tutorial.mdx similarity index 94% rename from docs/source/getting_started/detailed_tutorial.md rename to docs/docs/getting_started/detailed_tutorial.mdx index 14f888628..33786ac0e 100644 --- a/docs/source/getting_started/detailed_tutorial.md +++ b/docs/docs/getting_started/detailed_tutorial.mdx @@ -1,3 +1,13 @@ +--- +title: Detailed Tutorial +description: Complete guide to using Llama Stack server and client SDK to build AI agents +sidebar_label: Detailed Tutorial +sidebar_position: 3 +--- + +import Tabs from '@theme/Tabs'; +import TabItem from '@theme/TabItem'; + ## Detailed Tutorial In this guide, we'll walk through how you can use the Llama Stack (server and client SDK) to test a simple agent. @@ -8,7 +18,7 @@ In Llama Stack, we provide a server exposing multiple APIs. These APIs are backe Llama Stack is a stateful service with REST APIs to support seamless transition of AI applications across different environments. The server can be run in a variety of ways, including as a standalone binary, Docker container, or hosted service. You can build and test using a local server first and deploy to a hosted endpoint for production. In this guide, we'll walk through how to build a RAG agent locally using Llama Stack with [Ollama](https://ollama.com/) -as the inference [provider](../providers/index.md#inference) for a Llama Model. +as the inference [provider](/docs/providers/inference/) for a Llama Model. ### Step 1: Installation and Setup @@ -21,23 +31,21 @@ ollama run llama3.2:3b --keepalive 60m Install [uv](https://docs.astral.sh/uv/) to setup your virtual environment -::::{tab-set} - -:::{tab-item} macOS and Linux + + Use `curl` to download the script and execute it with `sh`: ```console curl -LsSf https://astral.sh/uv/install.sh | sh ``` -::: - -:::{tab-item} Windows + + Use `irm` to download the script and execute it with `iex`: ```console powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex" ``` -::: -:::: + + Setup your virtual environment. @@ -48,36 +56,24 @@ source .venv/bin/activate ### Step 2: Run Llama Stack Llama Stack is a server that exposes multiple APIs, you connect with it using the Llama Stack client SDK. -::::{tab-set} - -:::{tab-item} Using `venv` + + You can use Python to build and run the Llama Stack server, which is useful for testing and development. -Llama Stack uses a [YAML configuration file](../distributions/configuration.md) to specify the stack setup, -which defines the providers and their settings. The generated configuration serves as a starting point that you can [customize for your specific needs](../distributions/customizing_run_yaml.md). +Llama Stack uses a [YAML configuration file](../distributions/configuration) to specify the stack setup, +which defines the providers and their settings. The generated configuration serves as a starting point that you can [customize for your specific needs](../distributions/customizing_run_yaml). Now let's build and run the Llama Stack config for Ollama. We use `starter` as template. By default all providers are disabled, this requires enable ollama by passing environment variables. ```bash llama stack build --distro starter --image-type venv --run ``` -::: -:::{tab-item} Using `venv` -You can use Python to build and run the Llama Stack server, which is useful for testing and development. - -Llama Stack uses a [YAML configuration file](../distributions/configuration.md) to specify the stack setup, -which defines the providers and their settings. -Now let's build and run the Llama Stack config for Ollama. - -```bash -llama stack build --distro starter --image-type venv --run -``` -::: -:::{tab-item} Using a Container + + You can use a container image to run the Llama Stack server. We provide several container images for the server component that works with different inference providers out of the box. For this guide, we will use `llamastack/distribution-starter` as the container image. If you'd like to build your own image or customize the -configurations, please check out [this guide](../distributions/building_distro.md). +configurations, please check out [this guide](../distributions/building_distro). First lets setup some environment variables and create a local directory to mount into the container’s file system. ```bash export LLAMA_STACK_PORT=8321 @@ -100,9 +96,8 @@ with `host.containers.internal`. The configuration YAML for the Ollama distribution is available at `distributions/ollama/run.yaml`. -```{tip} - -Docker containers run in their own isolated network namespaces on Linux. To allow the container to communicate with services running on the host via `localhost`, you need `--network=host`. This makes the container use the host’s network directly so it can connect to Ollama running on `localhost:11434`. +:::tip +Docker containers run in their own isolated network namespaces on Linux. To allow the container to communicate with services running on the host via `localhost`, you need `--network=host`. This makes the container use the host's network directly so it can connect to Ollama running on `localhost:11434`. Linux users having issues running the above command should instead try the following: ```bash @@ -116,7 +111,6 @@ docker run -it \ --env OLLAMA_URL=http://localhost:11434 ``` ::: -:::: You will see output like below: ``` INFO: Application startup complete. @@ -127,33 +121,31 @@ Now you can use the Llama Stack client to run inference and build agents! You can reuse the server setup or use the [Llama Stack Client](https://github.com/meta-llama/llama-stack-client-python/). Note that the client package is already included in the `llama-stack` package. + + ### Step 3: Run Client CLI Open a new terminal and navigate to the same directory you started the server from. Then set up a new or activate your existing server virtual environment. -::::{tab-set} - -:::{tab-item} Reuse Server `venv` + + ```bash # The client is included in the llama-stack package so we just activate the server venv source .venv/bin/activate ``` -::: - -:::{tab-item} Install with `venv` + + ```bash uv venv client --python 3.12 source client/bin/activate pip install llama-stack-client ``` -::: + + - -:::: - -Now let's use the `llama-stack-client` [CLI](../references/llama_stack_client_cli_reference.md) to check the +Now let's use the `llama-stack-client` [CLI](../references/llama_stack_client_cli_reference) to check the connectivity to the server. ```bash @@ -224,12 +216,11 @@ OpenAIChatCompletion( ### Step 4: Run the Demos -Note that these demos show the [Python Client SDK](../references/python_sdk_reference/index.md). -Other SDKs are also available, please refer to the [Client SDK](../index.md#client-sdks) list for the complete options. +Note that these demos show the [Python Client SDK](../references/python_sdk_reference/). +Other SDKs are also available, please refer to the [Client SDK](/docs/) list for the complete options. -::::{tab-set} - -:::{tab-item} Basic Inference + + Now you can run inference using the Llama Stack client SDK. #### i. Create the Script @@ -269,9 +260,8 @@ Which will output: Model: ollama/llama3.2:3b OpenAIChatCompletion(id='chatcmpl-30cd0f28-a2ad-4b6d-934b-13707fc60ebf', choices=[OpenAIChatCompletionChoice(finish_reason='stop', index=0, message=OpenAIChatCompletionChoiceMessageOpenAIAssistantMessageParam(role='assistant', content="Lines of code unfold\nAlgorithms dance with ease\nLogic's gentle kiss", name=None, tool_calls=None, refusal=None, annotations=None, audio=None, function_call=None), logprobs=None)], created=1751732480, model='llama3.2:3b', object='chat.completion', service_tier=None, system_fingerprint='fp_ollama', usage={'completion_tokens': 16, 'prompt_tokens': 37, 'total_tokens': 53, 'completion_tokens_details': None, 'prompt_tokens_details': None}) ``` -::: - -:::{tab-item} Build a Simple Agent + + Next we can move beyond simple inference and build an agent that can perform tasks using the Llama Stack server. #### i. Create the Script Create a file `agent.py` and add the following code: @@ -439,9 +429,8 @@ uv run python agent.py So, that's me in a nutshell! ``` -::: - -:::{tab-item} Build a RAG Agent + + For our last demo, we can build a RAG agent that can answer questions about the Torchtune project using the documents in a vector database. @@ -460,10 +449,12 @@ client = LlamaStackClient(base_url="http://localhost:8321") embed_lm = next(m for m in client.models.list() if m.model_type == "embedding") embedding_model = embed_lm.identifier vector_db_id = f"v{uuid.uuid4().hex}" -client.vector_dbs.register( +# The VectorDB API is deprecated; the server now returns its own authoritative ID. +# We capture the correct ID from the response's .identifier attribute. +vector_db_id = client.vector_dbs.register( vector_db_id=vector_db_id, embedding_model=embedding_model, -) +).identifier # Create Documents urls = [ @@ -542,10 +533,9 @@ uv run python rag_agent.py ... Overall, DORA is a powerful reinforcement learning algorithm that can learn complex tasks from human demonstrations. However, it requires careful consideration of the challenges and limitations to achieve optimal results. ``` -::: - -:::: + + **You're Ready to Build Your Own Apps!** -Congrats! 🥳 Now you're ready to [build your own Llama Stack applications](../building_applications/index)! 🚀 +Congrats! 🥳 Now you're ready to [build your own Llama Stack applications](../building_applications/)! 🚀 diff --git a/docs/source/getting_started/libraries.md b/docs/docs/getting_started/libraries.mdx similarity index 85% rename from docs/source/getting_started/libraries.md rename to docs/docs/getting_started/libraries.mdx index a54a9b8d3..7cbb792b4 100644 --- a/docs/source/getting_started/libraries.md +++ b/docs/docs/getting_started/libraries.mdx @@ -1,3 +1,9 @@ +--- +description: We have a number of client-side SDKs available for different languages. +sidebar_label: Libraries +sidebar_position: 2 +title: Libraries (SDKs) +--- ## Libraries (SDKs) We have a number of client-side SDKs available for different languages. @@ -7,4 +13,4 @@ We have a number of client-side SDKs available for different languages. | Python | [llama-stack-client-python](https://github.com/meta-llama/llama-stack-client-python) | [![PyPI version](https://img.shields.io/pypi/v/llama_stack_client.svg)](https://pypi.org/project/llama_stack_client/) | Swift | [llama-stack-client-swift](https://github.com/meta-llama/llama-stack-client-swift/tree/latest-release) | [![Swift Package Index](https://img.shields.io/endpoint?url=https%3A%2F%2Fswiftpackageindex.com%2Fapi%2Fpackages%2Fmeta-llama%2Fllama-stack-client-swift%2Fbadge%3Ftype%3Dswift-versions)](https://swiftpackageindex.com/meta-llama/llama-stack-client-swift) | Node | [llama-stack-client-node](https://github.com/meta-llama/llama-stack-client-node) | [![NPM version](https://img.shields.io/npm/v/llama-stack-client.svg)](https://npmjs.org/package/llama-stack-client) -| Kotlin | [llama-stack-client-kotlin](https://github.com/meta-llama/llama-stack-client-kotlin/tree/latest-release) | [![Maven version](https://img.shields.io/maven-central/v/com.llama.llamastack/llama-stack-client-kotlin)](https://central.sonatype.com/artifact/com.llama.llamastack/llama-stack-client-kotlin) \ No newline at end of file +| Kotlin | [llama-stack-client-kotlin](https://github.com/meta-llama/llama-stack-client-kotlin/tree/latest-release) | [![Maven version](https://img.shields.io/maven-central/v/com.llama.llamastack/llama-stack-client-kotlin)](https://central.sonatype.com/artifact/com.llama.llamastack/llama-stack-client-kotlin) diff --git a/docs/source/getting_started/quickstart.md b/docs/docs/getting_started/quickstart.mdx similarity index 66% rename from docs/source/getting_started/quickstart.md rename to docs/docs/getting_started/quickstart.mdx index 0136a7fba..b885f3c66 100644 --- a/docs/source/getting_started/quickstart.md +++ b/docs/docs/getting_started/quickstart.mdx @@ -1,4 +1,9 @@ -## Quickstart +--- +description: environments. +sidebar_label: Quickstart +sidebar_position: 1 +title: Quickstart +--- Get started with Llama Stack in minutes! @@ -6,7 +11,7 @@ Llama Stack is a stateful service with REST APIs to support the seamless transit environments. You can build and test using a local server first and deploy to a hosted endpoint for production. In this guide, we'll walk through how to build a RAG application locally using Llama Stack with [Ollama](https://ollama.com/) -as the inference [provider](../providers/inference/index) for a Llama Model. +as the inference [provider](/docs/providers/inference) for a Llama Model. **💡 Notebook Version:** You can also follow this quickstart guide in a Jupyter notebook format: [quick_start.ipynb](https://github.com/meta-llama/llama-stack/blob/main/docs/quick_start.ipynb) @@ -27,8 +32,75 @@ OLLAMA_URL=http://localhost:11434 \ #### Step 3: Run the demo Now open up a new terminal and copy the following script into a file named `demo_script.py`. -```{literalinclude} ./demo_script.py -:language: python +```python title="demo_script.py" +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the terms described in the LICENSE file in +# the root directory of this source tree. + +from llama_stack_client import Agent, AgentEventLogger, RAGDocument, LlamaStackClient + +vector_db_id = "my_demo_vector_db" +client = LlamaStackClient(base_url="http://localhost:8321") + +models = client.models.list() + +# Select the first LLM and first embedding models +model_id = next(m for m in models if m.model_type == "llm").identifier +embedding_model_id = ( + em := next(m for m in models if m.model_type == "embedding") +).identifier +embedding_dimension = em.metadata["embedding_dimension"] + +vector_db = client.vector_dbs.register( + vector_db_id=vector_db_id, + embedding_model=embedding_model_id, + embedding_dimension=embedding_dimension, + provider_id="faiss", +) +vector_db_id = vector_db.identifier +source = "https://www.paulgraham.com/greatwork.html" +print("rag_tool> Ingesting document:", source) +document = RAGDocument( + document_id="document_1", + content=source, + mime_type="text/html", + metadata={}, +) +client.tool_runtime.rag_tool.insert( + documents=[document], + vector_db_id=vector_db_id, + chunk_size_in_tokens=100, +) +agent = Agent( + client, + model=model_id, + instructions="You are a helpful assistant", + tools=[ + { + "name": "builtin::rag/knowledge_search", + "args": {"vector_db_ids": [vector_db_id]}, + } + ], +) + +prompt = "How do you do great work?" +print("prompt>", prompt) + +use_stream = True +response = agent.create_turn( + messages=[{"role": "user", "content": prompt}], + session_id=agent.create_session("rag_session"), + stream=use_stream, +) + +# Only call `AgentEventLogger().log(response)` for streaming responses. +if use_stream: + for log in AgentEventLogger().log(response): + log.print() +else: + print(response) ``` We will use `uv` to run the script ``` @@ -59,19 +131,19 @@ Ultimately, great work is about making a meaningful contribution and leaving a l ``` Congratulations! You've successfully built your first RAG application using Llama Stack! 🎉🥳 -```{admonition} HuggingFace access -:class: tip +:::tip HuggingFace access If you are getting a **401 Client Error** from HuggingFace for the **all-MiniLM-L6-v2** model, try setting **HF_TOKEN** to a valid HuggingFace token in your environment -``` + +::: ### Next Steps Now you're ready to dive deeper into Llama Stack! -- Explore the [Detailed Tutorial](./detailed_tutorial.md). +- Explore the [Detailed Tutorial](./detailed_tutorial). - Try the [Getting Started Notebook](https://github.com/meta-llama/llama-stack/blob/main/docs/getting_started.ipynb). - Browse more [Notebooks on GitHub](https://github.com/meta-llama/llama-stack/tree/main/docs/notebooks). -- Learn about Llama Stack [Concepts](../concepts/index.md). -- Discover how to [Build Llama Stacks](../distributions/index.md). -- Refer to our [References](../references/index.md) for details on the Llama CLI and Python SDK. +- Learn about Llama Stack [Concepts](/docs/concepts). +- Discover how to [Build Llama Stacks](/docs/distributions). +- Refer to our [References](/docs/references) for details on the Llama CLI and Python SDK. - Check out the [llama-stack-apps](https://github.com/meta-llama/llama-stack-apps/tree/main/examples) repository for example applications and tutorials. diff --git a/docs/docs/index.mdx b/docs/docs/index.mdx new file mode 100644 index 000000000..bed931fe7 --- /dev/null +++ b/docs/docs/index.mdx @@ -0,0 +1,101 @@ +--- +sidebar_position: 1 +title: Welcome to Llama Stack +description: Llama Stack is the open-source framework for building generative AI applications +sidebar_label: Intro +tags: + - getting-started + - overview +--- + +# Welcome to Llama Stack + +Llama Stack is the open-source framework for building generative AI applications. + +:::tip Llama 4 is here! + +Check out [Getting Started with Llama 4](https://colab.research.google.com/github/meta-llama/llama-stack/blob/main/docs/getting_started_llama4.ipynb) + +::: + +:::tip News + +Llama Stack is now available! See the [release notes](https://github.com/meta-llama/llama-stack/releases) for more details. + +::: + + +## What is Llama Stack? + +Llama Stack defines and standardizes the core building blocks needed to bring generative AI applications to market. It provides a unified set of APIs with implementations from leading service providers, enabling seamless transitions between development and production environments. More specifically, it provides: + +- **Unified API layer** for Inference, RAG, Agents, Tools, Safety, Evals, and Telemetry. +- **Plugin architecture** to support the rich ecosystem of implementations of the different APIs in different environments like local development, on-premises, cloud, and mobile. +- **Prepackaged verified distributions** which offer a one-stop solution for developers to get started quickly and reliably in any environment +- **Multiple developer interfaces** like CLI and SDKs for Python, Node, iOS, and Android +- **Standalone applications** as examples for how to build production-grade AI applications with Llama Stack + +Llama Stack + +Our goal is to provide pre-packaged implementations (aka "distributions") which can be run in a variety of deployment environments. LlamaStack can assist you in your entire app development lifecycle - start iterating on local, mobile or desktop and seamlessly transition to on-prem or public cloud deployments. At every point in this transition, the same set of APIs and the same developer experience is available. + +## How does Llama Stack work? + +Llama Stack consists of a server (with multiple pluggable API providers) and Client SDKs meant to be used in your applications. The server can be run in a variety of environments, including local (inline) development, on-premises, and cloud. The client SDKs are available for Python, Swift, Node, and Kotlin. + +## Quick Links + +- Ready to build? Check out the [Getting Started Guide](https://llama-stack.github.io/getting_started/quickstart) to get started. +- Want to contribute? See the [Contributing Guide](https://github.com/llamastack/llama-stack/blob/main/CONTRIBUTING.md). +- Explore [Example Applications](https://github.com/llamastack/llama-stack-apps) built with Llama Stack. + +## Rich Ecosystem Support + +Llama Stack provides adapters for popular providers across all API categories: + +- **Inference**: Meta Reference, Ollama, Fireworks, Together, NVIDIA, vLLM, AWS Bedrock, OpenAI, Anthropic, and more +- **Vector Databases**: FAISS, Chroma, Milvus, Postgres, Weaviate, Qdrant, and others +- **Safety**: Llama Guard, Prompt Guard, Code Scanner, AWS Bedrock +- **Training & Evaluation**: HuggingFace, TorchTune, NVIDIA NEMO + +:::info Provider Details +For complete provider compatibility and setup instructions, see our [Providers Documentation](https://llamastack.github.io/providers/). +::: + +## Get Started Today + + diff --git a/docs/source/providers/agents/index.md b/docs/docs/providers/agents/index.mdx similarity index 51% rename from docs/source/providers/agents/index.md rename to docs/docs/providers/agents/index.mdx index a2c48d4b9..5cd37776d 100644 --- a/docs/source/providers/agents/index.md +++ b/docs/docs/providers/agents/index.mdx @@ -1,3 +1,16 @@ +--- +description: "Agents API for creating and interacting with agentic systems. + + Main functionalities provided by this API: + - Create agents with specific instructions and ability to use tools. + - Interactions with agents are grouped into sessions (\"threads\"), and each interaction is called a \"turn\". + - Agents can be provided with various tools (see the ToolGroups and ToolRuntime APIs for more details). + - Agents can be provided with various shields (see the Safety API for more details). + - Agents can also use Memory to retrieve information from knowledge bases. See the RAG Tool and Vector IO APIs for more details." +sidebar_label: Agents +title: Agents +--- + # Agents ## Overview @@ -12,11 +25,3 @@ Agents API for creating and interacting with agentic systems. - Agents can also use Memory to retrieve information from knowledge bases. See the RAG Tool and Vector IO APIs for more details. This section contains documentation for all available providers for the **agents** API. - -## Providers - -```{toctree} -:maxdepth: 1 - -inline_meta-reference -``` diff --git a/docs/source/providers/agents/inline_meta-reference.md b/docs/docs/providers/agents/inline_meta-reference.mdx similarity index 80% rename from docs/source/providers/agents/inline_meta-reference.md rename to docs/docs/providers/agents/inline_meta-reference.mdx index 5f64f79e1..fd961745f 100644 --- a/docs/source/providers/agents/inline_meta-reference.md +++ b/docs/docs/providers/agents/inline_meta-reference.mdx @@ -1,3 +1,9 @@ +--- +description: "Meta's reference implementation of an agent system that can use tools, access vector databases, and perform complex reasoning tasks." +sidebar_label: Meta-Reference +title: inline::meta-reference +--- + # inline::meta-reference ## Description @@ -20,6 +26,4 @@ persistence_store: responses_store: type: sqlite db_path: ${env.SQLITE_STORE_DIR:=~/.llama/dummy}/responses_store.db - ``` - diff --git a/docs/source/providers/batches/index.md b/docs/docs/providers/batches/index.mdx similarity index 52% rename from docs/source/providers/batches/index.md rename to docs/docs/providers/batches/index.mdx index d6d2fa9a3..2c64b277f 100644 --- a/docs/source/providers/batches/index.md +++ b/docs/docs/providers/batches/index.mdx @@ -1,3 +1,18 @@ +--- +description: "The Batches API enables efficient processing of multiple requests in a single operation, + particularly useful for processing large datasets, batch evaluation workflows, and + cost-effective inference at scale. + + The API is designed to allow use of openai client libraries for seamless integration. + + This API provides the following extensions: + - idempotent batch creation + + Note: This API is currently under active development and may undergo changes." +sidebar_label: Batches +title: Batches +--- + # Batches ## Overview @@ -14,11 +29,3 @@ The Batches API enables efficient processing of multiple requests in a single op Note: This API is currently under active development and may undergo changes. This section contains documentation for all available providers for the **batches** API. - -## Providers - -```{toctree} -:maxdepth: 1 - -inline_reference -``` diff --git a/docs/source/providers/batches/inline_reference.md b/docs/docs/providers/batches/inline_reference.mdx similarity index 86% rename from docs/source/providers/batches/inline_reference.md rename to docs/docs/providers/batches/inline_reference.mdx index a58e5124d..f43800555 100644 --- a/docs/source/providers/batches/inline_reference.md +++ b/docs/docs/providers/batches/inline_reference.mdx @@ -1,3 +1,9 @@ +--- +description: "Reference implementation of batches API with KVStore persistence." +sidebar_label: Reference +title: inline::reference +--- + # inline::reference ## Description @@ -18,6 +24,4 @@ Reference implementation of batches API with KVStore persistence. kvstore: type: sqlite db_path: ${env.SQLITE_STORE_DIR:=~/.llama/dummy}/batches.db - ``` - diff --git a/docs/source/providers/datasetio/index.md b/docs/docs/providers/datasetio/index.mdx similarity index 55% rename from docs/source/providers/datasetio/index.md rename to docs/docs/providers/datasetio/index.mdx index 94a97e2ed..aeeb01980 100644 --- a/docs/source/providers/datasetio/index.md +++ b/docs/docs/providers/datasetio/index.mdx @@ -1,15 +1,10 @@ +--- +sidebar_label: Datasetio +title: Datasetio +--- + # Datasetio ## Overview This section contains documentation for all available providers for the **datasetio** API. - -## Providers - -```{toctree} -:maxdepth: 1 - -inline_localfs -remote_huggingface -remote_nvidia -``` diff --git a/docs/source/providers/datasetio/inline_localfs.md b/docs/docs/providers/datasetio/inline_localfs.mdx similarity index 78% rename from docs/source/providers/datasetio/inline_localfs.md rename to docs/docs/providers/datasetio/inline_localfs.mdx index 87a0c795c..b02a3a3bd 100644 --- a/docs/source/providers/datasetio/inline_localfs.md +++ b/docs/docs/providers/datasetio/inline_localfs.mdx @@ -1,3 +1,9 @@ +--- +description: "Local filesystem-based dataset I/O provider for reading and writing datasets to local storage." +sidebar_label: Localfs +title: inline::localfs +--- + # inline::localfs ## Description @@ -16,6 +22,4 @@ Local filesystem-based dataset I/O provider for reading and writing datasets to kvstore: type: sqlite db_path: ${env.SQLITE_STORE_DIR:=~/.llama/dummy}/localfs_datasetio.db - ``` - diff --git a/docs/source/providers/datasetio/remote_huggingface.md b/docs/docs/providers/datasetio/remote_huggingface.mdx similarity index 77% rename from docs/source/providers/datasetio/remote_huggingface.md rename to docs/docs/providers/datasetio/remote_huggingface.mdx index 3711f7396..82597d999 100644 --- a/docs/source/providers/datasetio/remote_huggingface.md +++ b/docs/docs/providers/datasetio/remote_huggingface.mdx @@ -1,3 +1,9 @@ +--- +description: "HuggingFace datasets provider for accessing and managing datasets from the HuggingFace Hub." +sidebar_label: Remote - Huggingface +title: remote::huggingface +--- + # remote::huggingface ## Description @@ -16,6 +22,4 @@ HuggingFace datasets provider for accessing and managing datasets from the Huggi kvstore: type: sqlite db_path: ${env.SQLITE_STORE_DIR:=~/.llama/dummy}/huggingface_datasetio.db - ``` - diff --git a/docs/source/providers/datasetio/remote_nvidia.md b/docs/docs/providers/datasetio/remote_nvidia.mdx similarity index 83% rename from docs/source/providers/datasetio/remote_nvidia.md rename to docs/docs/providers/datasetio/remote_nvidia.mdx index 1ad1cdb32..35a7dacee 100644 --- a/docs/source/providers/datasetio/remote_nvidia.md +++ b/docs/docs/providers/datasetio/remote_nvidia.mdx @@ -1,3 +1,9 @@ +--- +description: "NVIDIA's dataset I/O provider for accessing datasets from NVIDIA's data platform." +sidebar_label: Remote - Nvidia +title: remote::nvidia +--- + # remote::nvidia ## Description @@ -20,6 +26,4 @@ api_key: ${env.NVIDIA_API_KEY:=} dataset_namespace: ${env.NVIDIA_DATASET_NAMESPACE:=default} project_id: ${env.NVIDIA_PROJECT_ID:=test-project} datasets_url: ${env.NVIDIA_DATASETS_URL:=http://nemo.test} - ``` - diff --git a/docs/source/providers/eval/index.md b/docs/docs/providers/eval/index.mdx similarity index 58% rename from docs/source/providers/eval/index.md rename to docs/docs/providers/eval/index.mdx index a14fada1d..73b0b89aa 100644 --- a/docs/source/providers/eval/index.md +++ b/docs/docs/providers/eval/index.mdx @@ -1,3 +1,9 @@ +--- +description: "Llama Stack Evaluation API for running evaluations on model and agent candidates." +sidebar_label: Eval +title: Eval +--- + # Eval ## Overview @@ -5,12 +11,3 @@ Llama Stack Evaluation API for running evaluations on model and agent candidates. This section contains documentation for all available providers for the **eval** API. - -## Providers - -```{toctree} -:maxdepth: 1 - -inline_meta-reference -remote_nvidia -``` diff --git a/docs/source/advanced_apis/eval/inline_meta-reference.md b/docs/docs/providers/eval/inline_meta-reference.mdx similarity index 77% rename from docs/source/advanced_apis/eval/inline_meta-reference.md rename to docs/docs/providers/eval/inline_meta-reference.mdx index 5bec89cfc..b0eb589e0 100644 --- a/docs/source/advanced_apis/eval/inline_meta-reference.md +++ b/docs/docs/providers/eval/inline_meta-reference.mdx @@ -1,5 +1,7 @@ --- -orphan: true +description: "Meta's reference implementation of evaluation tasks with support for multiple languages and evaluation metrics." +sidebar_label: Meta-Reference +title: inline::meta-reference --- # inline::meta-reference @@ -20,6 +22,4 @@ Meta's reference implementation of evaluation tasks with support for multiple la kvstore: type: sqlite db_path: ${env.SQLITE_STORE_DIR:=~/.llama/dummy}/meta_reference_eval.db - ``` - diff --git a/docs/source/advanced_apis/eval/remote_nvidia.md b/docs/docs/providers/eval/remote_nvidia.mdx similarity index 75% rename from docs/source/advanced_apis/eval/remote_nvidia.md rename to docs/docs/providers/eval/remote_nvidia.mdx index ab91767d6..36bb4726b 100644 --- a/docs/source/advanced_apis/eval/remote_nvidia.md +++ b/docs/docs/providers/eval/remote_nvidia.mdx @@ -1,5 +1,7 @@ --- -orphan: true +description: "NVIDIA's evaluation provider for running evaluation tasks on NVIDIA's platform." +sidebar_label: Remote - Nvidia +title: remote::nvidia --- # remote::nvidia @@ -18,6 +20,4 @@ NVIDIA's evaluation provider for running evaluation tasks on NVIDIA's platform. ```yaml evaluator_url: ${env.NVIDIA_EVALUATOR_URL:=http://localhost:7331} - ``` - diff --git a/docs/source/providers/external/external-providers-guide.md b/docs/docs/providers/external/external-providers-guide.mdx similarity index 99% rename from docs/source/providers/external/external-providers-guide.md rename to docs/docs/providers/external/external-providers-guide.mdx index e2d4ebea9..eb30afd93 100644 --- a/docs/source/providers/external/external-providers-guide.md +++ b/docs/docs/providers/external/external-providers-guide.mdx @@ -283,4 +283,4 @@ additional_pip_packages: No other steps are required other than `llama stack build` and `llama stack run`. The build process will use `module` to install all of the provider dependencies, retrieve the spec, etc. -The provider will now be available in Llama Stack with the type `remote::ramalama`. \ No newline at end of file +The provider will now be available in Llama Stack with the type `remote::ramalama`. diff --git a/docs/source/providers/external/external-providers-list.md b/docs/docs/providers/external/external-providers-list.mdx similarity index 81% rename from docs/source/providers/external/external-providers-list.md rename to docs/docs/providers/external/external-providers-list.mdx index 49f49076b..45fcc50fb 100644 --- a/docs/source/providers/external/external-providers-list.md +++ b/docs/docs/providers/external/external-providers-list.mdx @@ -7,4 +7,5 @@ Here's a list of known external providers that you can use with Llama Stack: | KubeFlow Training | Train models with KubeFlow | Post Training | Remote | [llama-stack-provider-kft](https://github.com/opendatahub-io/llama-stack-provider-kft) | | KubeFlow Pipelines | Train models with KubeFlow Pipelines | Post Training | Inline **and** Remote | [llama-stack-provider-kfp-trainer](https://github.com/opendatahub-io/llama-stack-provider-kfp-trainer) | | RamaLama | Inference models with RamaLama | Inference | Remote | [ramalama-stack](https://github.com/containers/ramalama-stack) | -| TrustyAI LM-Eval | Evaluate models with TrustyAI LM-Eval | Eval | Remote | [llama-stack-provider-lmeval](https://github.com/trustyai-explainability/llama-stack-provider-lmeval) | \ No newline at end of file +| TrustyAI LM-Eval | Evaluate models with TrustyAI LM-Eval | Eval | Remote | [llama-stack-provider-lmeval](https://github.com/trustyai-explainability/llama-stack-provider-lmeval) | +| MongoDB | VectorIO with MongoDB | Vector_IO | Remote | [mongodb-llama-stack](https://github.com/mongodb-partners/mongodb-llama-stack) | diff --git a/docs/source/providers/external/index.md b/docs/docs/providers/external/index.mdx similarity index 66% rename from docs/source/providers/external/index.md rename to docs/docs/providers/external/index.mdx index 989a7f5b8..28a9a1147 100644 --- a/docs/source/providers/external/index.md +++ b/docs/docs/providers/external/index.mdx @@ -5,9 +5,7 @@ Llama Stack supports external providers that live outside of the main codebase. - Share providers with others without contributing to the main codebase - Keep provider-specific code separate from the core Llama Stack code -```{toctree} -:maxdepth: 1 +## External Provider Documentation -external-providers-list -external-providers-guide -``` \ No newline at end of file +- [Known External Providers](./external-providers-list.mdx) +- [Creating External Providers](./external-providers-guide.mdx) diff --git a/docs/source/providers/files/index.md b/docs/docs/providers/files/index.mdx similarity index 61% rename from docs/source/providers/files/index.md rename to docs/docs/providers/files/index.mdx index 128953223..7d729d90f 100644 --- a/docs/source/providers/files/index.md +++ b/docs/docs/providers/files/index.mdx @@ -1,14 +1,10 @@ +--- +sidebar_label: Files +title: Files +--- + # Files ## Overview This section contains documentation for all available providers for the **files** API. - -## Providers - -```{toctree} -:maxdepth: 1 - -inline_localfs -remote_s3 -``` diff --git a/docs/source/providers/files/inline_localfs.md b/docs/docs/providers/files/inline_localfs.mdx similarity index 82% rename from docs/source/providers/files/inline_localfs.md rename to docs/docs/providers/files/inline_localfs.mdx index 09267b7d8..86d141f93 100644 --- a/docs/source/providers/files/inline_localfs.md +++ b/docs/docs/providers/files/inline_localfs.mdx @@ -1,3 +1,9 @@ +--- +description: "Local filesystem-based file storage provider for managing files and documents locally." +sidebar_label: Localfs +title: inline::localfs +--- + # inline::localfs ## Description @@ -19,6 +25,4 @@ storage_dir: ${env.FILES_STORAGE_DIR:=~/.llama/dummy/files} metadata_store: type: sqlite db_path: ${env.SQLITE_STORE_DIR:=~/.llama/dummy}/files_metadata.db - ``` - diff --git a/docs/source/providers/files/remote_s3.md b/docs/docs/providers/files/remote_s3.mdx similarity index 89% rename from docs/source/providers/files/remote_s3.md rename to docs/docs/providers/files/remote_s3.mdx index 2e3cebabd..353cedbfb 100644 --- a/docs/source/providers/files/remote_s3.md +++ b/docs/docs/providers/files/remote_s3.mdx @@ -1,3 +1,9 @@ +--- +description: "AWS S3-based file storage provider for scalable cloud file management with metadata persistence." +sidebar_label: Remote - S3 +title: remote::s3 +--- + # remote::s3 ## Description @@ -28,6 +34,4 @@ auto_create_bucket: ${env.S3_AUTO_CREATE_BUCKET:=false} metadata_store: type: sqlite db_path: ${env.SQLITE_STORE_DIR:=~/.llama/dummy}/s3_files_metadata.db - ``` - diff --git a/docs/source/providers/index.md b/docs/docs/providers/index.mdx similarity index 55% rename from docs/source/providers/index.md rename to docs/docs/providers/index.mdx index 3f66ecd0c..9c560fe32 100644 --- a/docs/source/providers/index.md +++ b/docs/docs/providers/index.mdx @@ -1,3 +1,10 @@ +--- +title: API Providers +description: Ecosystem of providers for swapping implementations across the same API +sidebar_label: Overview +sidebar_position: 1 +--- + # API Providers The goal of Llama Stack is to build an ecosystem where users can easily swap out different implementations for the same API. Examples for these include: @@ -12,17 +19,15 @@ Providers come in two flavors: Importantly, Llama Stack always strives to provide at least one fully inline provider for each API so you can iterate on a fully featured environment locally. -```{toctree} -:maxdepth: 1 +## Provider Categories -external/index -openai -inference/index -agents/index -datasetio/index -safety/index -telemetry/index -vector_io/index -tool_runtime/index -files/index -``` +- **[External Providers](external/index.mdx)** - Guide for building and using external providers +- **[OpenAI Compatibility](./openai.mdx)** - OpenAI API compatibility layer +- **[Inference](inference/index.mdx)** - LLM and embedding model providers +- **[Agents](agents/index.mdx)** - Agentic system providers +- **[DatasetIO](datasetio/index.mdx)** - Dataset and data loader providers +- **[Safety](safety/index.mdx)** - Content moderation and safety providers +- **[Telemetry](telemetry/index.mdx)** - Monitoring and observability providers +- **[Vector IO](vector_io/index.mdx)** - Vector database providers +- **[Tool Runtime](tool_runtime/index.mdx)** - Tool and protocol providers +- **[Files](files/index.mdx)** - File system and storage providers diff --git a/docs/source/providers/inference/index.md b/docs/docs/providers/inference/index.mdx similarity index 52% rename from docs/source/providers/inference/index.md rename to docs/docs/providers/inference/index.mdx index b6d215474..ebbaf1be1 100644 --- a/docs/source/providers/inference/index.md +++ b/docs/docs/providers/inference/index.mdx @@ -1,3 +1,13 @@ +--- +description: "Llama Stack Inference API for generating completions, chat completions, and embeddings. + + This API provides the raw interface to the underlying models. Two kinds of models are supported: + - LLM models: these models generate \"raw\" and \"chat\" (conversational) completions. + - Embedding models: these models generate embeddings to be used for semantic search." +sidebar_label: Inference +title: Inference +--- + # Inference ## Overview @@ -9,33 +19,3 @@ Llama Stack Inference API for generating completions, chat completions, and embe - Embedding models: these models generate embeddings to be used for semantic search. This section contains documentation for all available providers for the **inference** API. - -## Providers - -```{toctree} -:maxdepth: 1 - -inline_meta-reference -inline_sentence-transformers -remote_anthropic -remote_bedrock -remote_cerebras -remote_databricks -remote_fireworks -remote_gemini -remote_groq -remote_hf_endpoint -remote_hf_serverless -remote_llama-openai-compat -remote_nvidia -remote_ollama -remote_openai -remote_passthrough -remote_runpod -remote_sambanova -remote_tgi -remote_together -remote_vertexai -remote_vllm -remote_watsonx -``` diff --git a/docs/source/providers/inference/inline_meta-reference.md b/docs/docs/providers/inference/inline_meta-reference.mdx similarity index 84% rename from docs/source/providers/inference/inline_meta-reference.md rename to docs/docs/providers/inference/inline_meta-reference.mdx index eca12a839..328586f9a 100644 --- a/docs/source/providers/inference/inline_meta-reference.md +++ b/docs/docs/providers/inference/inline_meta-reference.mdx @@ -1,3 +1,9 @@ +--- +description: "Meta's reference implementation of inference with support for various model formats and optimization techniques." +sidebar_label: Meta-Reference +title: inline::meta-reference +--- + # inline::meta-reference ## Description @@ -27,6 +33,4 @@ quantization: model_parallel_size: ${env.MODEL_PARALLEL_SIZE:=0} max_batch_size: ${env.MAX_BATCH_SIZE:=1} max_seq_len: ${env.MAX_SEQ_LEN:=4096} - ``` - diff --git a/docs/docs/providers/inference/inline_sentence-transformers.mdx b/docs/docs/providers/inference/inline_sentence-transformers.mdx new file mode 100644 index 000000000..0e207bbdb --- /dev/null +++ b/docs/docs/providers/inference/inline_sentence-transformers.mdx @@ -0,0 +1,17 @@ +--- +description: "Sentence Transformers inference provider for text embeddings and similarity search." +sidebar_label: Sentence-Transformers +title: inline::sentence-transformers +--- + +# inline::sentence-transformers + +## Description + +Sentence Transformers inference provider for text embeddings and similarity search. + +## Sample Configuration + +```yaml +{} +``` diff --git a/docs/source/providers/inference/remote_anthropic.md b/docs/docs/providers/inference/remote_anthropic.mdx similarity index 69% rename from docs/source/providers/inference/remote_anthropic.md rename to docs/docs/providers/inference/remote_anthropic.mdx index 4680608b1..6bd636c92 100644 --- a/docs/source/providers/inference/remote_anthropic.md +++ b/docs/docs/providers/inference/remote_anthropic.mdx @@ -1,3 +1,9 @@ +--- +description: "Anthropic inference provider for accessing Claude models and Anthropic's AI services." +sidebar_label: Remote - Anthropic +title: remote::anthropic +--- + # remote::anthropic ## Description @@ -14,6 +20,4 @@ Anthropic inference provider for accessing Claude models and Anthropic's AI serv ```yaml api_key: ${env.ANTHROPIC_API_KEY:=} - ``` - diff --git a/docs/docs/providers/inference/remote_azure.mdx b/docs/docs/providers/inference/remote_azure.mdx new file mode 100644 index 000000000..0eb0ea755 --- /dev/null +++ b/docs/docs/providers/inference/remote_azure.mdx @@ -0,0 +1,36 @@ +--- +description: | + Azure OpenAI inference provider for accessing GPT models and other Azure services. + Provider documentation + https://learn.microsoft.com/en-us/azure/ai-foundry/openai/overview +sidebar_label: Remote - Azure +title: remote::azure +--- + +# remote::azure + +## Description + + +Azure OpenAI inference provider for accessing GPT models and other Azure services. +Provider documentation +https://learn.microsoft.com/en-us/azure/ai-foundry/openai/overview + + +## Configuration + +| Field | Type | Required | Default | Description | +|-------|------|----------|---------|-------------| +| `api_key` | `` | No | | Azure API key for Azure | +| `api_base` | `` | No | | Azure API base for Azure (e.g., https://your-resource-name.openai.azure.com) | +| `api_version` | `str \| None` | No | | Azure API version for Azure (e.g., 2024-12-01-preview) | +| `api_type` | `str \| None` | No | azure | Azure API type for Azure (e.g., azure) | + +## Sample Configuration + +```yaml +api_key: ${env.AZURE_API_KEY:=} +api_base: ${env.AZURE_API_BASE:=} +api_version: ${env.AZURE_API_VERSION:=} +api_type: ${env.AZURE_API_TYPE:=} +``` diff --git a/docs/source/providers/inference/remote_bedrock.md b/docs/docs/providers/inference/remote_bedrock.mdx similarity index 74% rename from docs/source/providers/inference/remote_bedrock.md rename to docs/docs/providers/inference/remote_bedrock.mdx index 1454c54c2..04c2154a9 100644 --- a/docs/source/providers/inference/remote_bedrock.md +++ b/docs/docs/providers/inference/remote_bedrock.mdx @@ -1,3 +1,9 @@ +--- +description: "AWS Bedrock inference provider for accessing various AI models through AWS's managed service." +sidebar_label: Remote - Bedrock +title: remote::bedrock +--- + # remote::bedrock ## Description @@ -15,14 +21,12 @@ AWS Bedrock inference provider for accessing various AI models through AWS's man | `profile_name` | `str \| None` | No | | The profile name that contains credentials to use.Default use environment variable: AWS_PROFILE | | `total_max_attempts` | `int \| None` | No | | An integer representing the maximum number of attempts that will be made for a single request, including the initial attempt. Default use environment variable: AWS_MAX_ATTEMPTS | | `retry_mode` | `str \| None` | No | | A string representing the type of retries Boto3 will perform.Default use environment variable: AWS_RETRY_MODE | -| `connect_timeout` | `float \| None` | No | 60 | The time in seconds till a timeout exception is thrown when attempting to make a connection. The default is 60 seconds. | -| `read_timeout` | `float \| None` | No | 60 | The time in seconds till a timeout exception is thrown when attempting to read from a connection.The default is 60 seconds. | +| `connect_timeout` | `float \| None` | No | 60.0 | The time in seconds till a timeout exception is thrown when attempting to make a connection. The default is 60 seconds. | +| `read_timeout` | `float \| None` | No | 60.0 | The time in seconds till a timeout exception is thrown when attempting to read from a connection.The default is 60 seconds. | | `session_ttl` | `int \| None` | No | 3600 | The time in seconds till a session expires. The default is 3600 seconds (1 hour). | ## Sample Configuration ```yaml {} - ``` - diff --git a/docs/source/providers/inference/remote_cerebras.md b/docs/docs/providers/inference/remote_cerebras.mdx similarity index 65% rename from docs/source/providers/inference/remote_cerebras.md rename to docs/docs/providers/inference/remote_cerebras.mdx index 7aa03dd0b..d9cc93aef 100644 --- a/docs/source/providers/inference/remote_cerebras.md +++ b/docs/docs/providers/inference/remote_cerebras.mdx @@ -1,3 +1,9 @@ +--- +description: "Cerebras inference provider for running models on Cerebras Cloud platform." +sidebar_label: Remote - Cerebras +title: remote::cerebras +--- + # remote::cerebras ## Description @@ -9,13 +15,11 @@ Cerebras inference provider for running models on Cerebras Cloud platform. | Field | Type | Required | Default | Description | |-------|------|----------|---------|-------------| | `base_url` | `` | No | https://api.cerebras.ai | Base URL for the Cerebras API | -| `api_key` | `pydantic.types.SecretStr \| None` | No | | Cerebras API Key | +| `api_key` | `` | No | | Cerebras API Key | ## Sample Configuration ```yaml base_url: https://api.cerebras.ai api_key: ${env.CEREBRAS_API_KEY:=} - ``` - diff --git a/docs/source/providers/inference/remote_databricks.md b/docs/docs/providers/inference/remote_databricks.mdx similarity index 53% rename from docs/source/providers/inference/remote_databricks.md rename to docs/docs/providers/inference/remote_databricks.mdx index d0ac89055..7f736db9d 100644 --- a/docs/source/providers/inference/remote_databricks.md +++ b/docs/docs/providers/inference/remote_databricks.mdx @@ -1,3 +1,9 @@ +--- +description: "Databricks inference provider for running models on Databricks' unified analytics platform." +sidebar_label: Remote - Databricks +title: remote::databricks +--- + # remote::databricks ## Description @@ -9,13 +15,11 @@ Databricks inference provider for running models on Databricks' unified analytic | Field | Type | Required | Default | Description | |-------|------|----------|---------|-------------| | `url` | `` | No | | The URL for the Databricks model serving endpoint | -| `api_token` | `` | No | | The Databricks API token | +| `api_token` | `` | No | | The Databricks API token | ## Sample Configuration ```yaml -url: ${env.DATABRICKS_URL:=} -api_token: ${env.DATABRICKS_API_TOKEN:=} - +url: ${env.DATABRICKS_HOST:=} +api_token: ${env.DATABRICKS_TOKEN:=} ``` - diff --git a/docs/source/providers/inference/remote_fireworks.md b/docs/docs/providers/inference/remote_fireworks.mdx similarity index 80% rename from docs/source/providers/inference/remote_fireworks.md rename to docs/docs/providers/inference/remote_fireworks.mdx index 28dbf1d3f..d2c3a664e 100644 --- a/docs/source/providers/inference/remote_fireworks.md +++ b/docs/docs/providers/inference/remote_fireworks.mdx @@ -1,3 +1,9 @@ +--- +description: "Fireworks AI inference provider for Llama models and other AI models on the Fireworks platform." +sidebar_label: Remote - Fireworks +title: remote::fireworks +--- + # remote::fireworks ## Description @@ -17,6 +23,4 @@ Fireworks AI inference provider for Llama models and other AI models on the Fire ```yaml url: https://api.fireworks.ai/inference/v1 api_key: ${env.FIREWORKS_API_KEY:=} - ``` - diff --git a/docs/source/providers/inference/remote_gemini.md b/docs/docs/providers/inference/remote_gemini.mdx similarity index 70% rename from docs/source/providers/inference/remote_gemini.md rename to docs/docs/providers/inference/remote_gemini.mdx index 14b3223f2..0505c69da 100644 --- a/docs/source/providers/inference/remote_gemini.md +++ b/docs/docs/providers/inference/remote_gemini.mdx @@ -1,3 +1,9 @@ +--- +description: "Google Gemini inference provider for accessing Gemini models and Google's AI services." +sidebar_label: Remote - Gemini +title: remote::gemini +--- + # remote::gemini ## Description @@ -14,6 +20,4 @@ Google Gemini inference provider for accessing Gemini models and Google's AI ser ```yaml api_key: ${env.GEMINI_API_KEY:=} - ``` - diff --git a/docs/source/providers/inference/remote_groq.md b/docs/docs/providers/inference/remote_groq.mdx similarity index 76% rename from docs/source/providers/inference/remote_groq.md rename to docs/docs/providers/inference/remote_groq.mdx index 68bd4d5b3..1797035c1 100644 --- a/docs/source/providers/inference/remote_groq.md +++ b/docs/docs/providers/inference/remote_groq.mdx @@ -1,3 +1,9 @@ +--- +description: "Groq inference provider for ultra-fast inference using Groq's LPU technology." +sidebar_label: Remote - Groq +title: remote::groq +--- + # remote::groq ## Description @@ -16,6 +22,4 @@ Groq inference provider for ultra-fast inference using Groq's LPU technology. ```yaml url: https://api.groq.com api_key: ${env.GROQ_API_KEY:=} - ``` - diff --git a/docs/source/providers/inference/remote_hf_endpoint.md b/docs/docs/providers/inference/remote_hf_endpoint.mdx similarity index 59% rename from docs/source/providers/inference/remote_hf_endpoint.md rename to docs/docs/providers/inference/remote_hf_endpoint.mdx index 8aaf13476..771b24f8d 100644 --- a/docs/source/providers/inference/remote_hf_endpoint.md +++ b/docs/docs/providers/inference/remote_hf_endpoint.mdx @@ -1,3 +1,9 @@ +--- +description: "HuggingFace Inference Endpoints provider for dedicated model serving." +sidebar_label: Remote - Hf - Endpoint +title: remote::hf::endpoint +--- + # remote::hf::endpoint ## Description @@ -8,7 +14,7 @@ HuggingFace Inference Endpoints provider for dedicated model serving. | Field | Type | Required | Default | Description | |-------|------|----------|---------|-------------| -| `endpoint_name` | `` | No | | The name of the Hugging Face Inference Endpoint in the format of '{namespace}/{endpoint_name}' (e.g. 'my-cool-org/meta-llama-3-1-8b-instruct-rce'). Namespace is optional and will default to the user account if not provided. | +| `endpoint_name` | `` | No | | The name of the Hugging Face Inference Endpoint in the format of '{namespace}/{endpoint_name}' (e.g. 'my-cool-org/meta-llama-3-1-8b-instruct-rce'). Namespace is optional and will default to the user account if not provided. | | `api_token` | `pydantic.types.SecretStr \| None` | No | | Your Hugging Face user access token (will default to locally saved token if not provided) | ## Sample Configuration @@ -16,6 +22,4 @@ HuggingFace Inference Endpoints provider for dedicated model serving. ```yaml endpoint_name: ${env.INFERENCE_ENDPOINT_NAME} api_token: ${env.HF_API_TOKEN} - ``` - diff --git a/docs/source/providers/inference/remote_hf_serverless.md b/docs/docs/providers/inference/remote_hf_serverless.mdx similarity index 79% rename from docs/source/providers/inference/remote_hf_serverless.md rename to docs/docs/providers/inference/remote_hf_serverless.mdx index 6764590b8..1a89b8e3e 100644 --- a/docs/source/providers/inference/remote_hf_serverless.md +++ b/docs/docs/providers/inference/remote_hf_serverless.mdx @@ -1,3 +1,9 @@ +--- +description: "HuggingFace Inference API serverless provider for on-demand model inference." +sidebar_label: Remote - Hf - Serverless +title: remote::hf::serverless +--- + # remote::hf::serverless ## Description @@ -16,6 +22,4 @@ HuggingFace Inference API serverless provider for on-demand model inference. ```yaml huggingface_repo: ${env.INFERENCE_MODEL} api_token: ${env.HF_API_TOKEN} - ``` - diff --git a/docs/source/providers/inference/remote_llama-openai-compat.md b/docs/docs/providers/inference/remote_llama-openai-compat.mdx similarity index 75% rename from docs/source/providers/inference/remote_llama-openai-compat.md rename to docs/docs/providers/inference/remote_llama-openai-compat.mdx index 5c97aebc3..cb624ad87 100644 --- a/docs/source/providers/inference/remote_llama-openai-compat.md +++ b/docs/docs/providers/inference/remote_llama-openai-compat.mdx @@ -1,3 +1,9 @@ +--- +description: "Llama OpenAI-compatible provider for using Llama models with OpenAI API format." +sidebar_label: Remote - Llama-Openai-Compat +title: remote::llama-openai-compat +--- + # remote::llama-openai-compat ## Description @@ -16,6 +22,4 @@ Llama OpenAI-compatible provider for using Llama models with OpenAI API format. ```yaml openai_compat_api_base: https://api.llama.com/compat/v1/ api_key: ${env.LLAMA_API_KEY} - ``` - diff --git a/docs/source/providers/inference/remote_nvidia.md b/docs/docs/providers/inference/remote_nvidia.mdx similarity index 85% rename from docs/source/providers/inference/remote_nvidia.md rename to docs/docs/providers/inference/remote_nvidia.mdx index 1b12839df..4a8be5d03 100644 --- a/docs/source/providers/inference/remote_nvidia.md +++ b/docs/docs/providers/inference/remote_nvidia.mdx @@ -1,3 +1,9 @@ +--- +description: "NVIDIA inference provider for accessing NVIDIA NIM models and AI services." +sidebar_label: Remote - Nvidia +title: remote::nvidia +--- + # remote::nvidia ## Description @@ -19,6 +25,4 @@ NVIDIA inference provider for accessing NVIDIA NIM models and AI services. url: ${env.NVIDIA_BASE_URL:=https://integrate.api.nvidia.com} api_key: ${env.NVIDIA_API_KEY:=} append_api_version: ${env.NVIDIA_APPEND_API_VERSION:=True} - ``` - diff --git a/docs/source/providers/inference/remote_ollama.md b/docs/docs/providers/inference/remote_ollama.mdx similarity index 75% rename from docs/source/providers/inference/remote_ollama.md rename to docs/docs/providers/inference/remote_ollama.mdx index f9f0a7622..5d9a4ad6c 100644 --- a/docs/source/providers/inference/remote_ollama.md +++ b/docs/docs/providers/inference/remote_ollama.mdx @@ -1,3 +1,9 @@ +--- +description: "Ollama inference provider for running local models through the Ollama runtime." +sidebar_label: Remote - Ollama +title: remote::ollama +--- + # remote::ollama ## Description @@ -15,6 +21,4 @@ Ollama inference provider for running local models through the Ollama runtime. ```yaml url: ${env.OLLAMA_URL:=http://localhost:11434} - ``` - diff --git a/docs/source/providers/inference/remote_openai.md b/docs/docs/providers/inference/remote_openai.mdx similarity index 77% rename from docs/source/providers/inference/remote_openai.md rename to docs/docs/providers/inference/remote_openai.mdx index 18a74caea..56ca94233 100644 --- a/docs/source/providers/inference/remote_openai.md +++ b/docs/docs/providers/inference/remote_openai.mdx @@ -1,3 +1,9 @@ +--- +description: "OpenAI inference provider for accessing GPT models and other OpenAI services." +sidebar_label: Remote - Openai +title: remote::openai +--- + # remote::openai ## Description @@ -16,6 +22,4 @@ OpenAI inference provider for accessing GPT models and other OpenAI services. ```yaml api_key: ${env.OPENAI_API_KEY:=} base_url: ${env.OPENAI_BASE_URL:=https://api.openai.com/v1} - ``` - diff --git a/docs/source/providers/inference/remote_passthrough.md b/docs/docs/providers/inference/remote_passthrough.mdx similarity index 74% rename from docs/source/providers/inference/remote_passthrough.md rename to docs/docs/providers/inference/remote_passthrough.mdx index 9005e5339..972cc2a08 100644 --- a/docs/source/providers/inference/remote_passthrough.md +++ b/docs/docs/providers/inference/remote_passthrough.mdx @@ -1,3 +1,9 @@ +--- +description: "Passthrough inference provider for connecting to any external inference service not directly supported." +sidebar_label: Remote - Passthrough +title: remote::passthrough +--- + # remote::passthrough ## Description @@ -16,6 +22,4 @@ Passthrough inference provider for connecting to any external inference service ```yaml url: ${env.PASSTHROUGH_URL} api_key: ${env.PASSTHROUGH_API_KEY} - ``` - diff --git a/docs/source/providers/inference/remote_runpod.md b/docs/docs/providers/inference/remote_runpod.mdx similarity index 75% rename from docs/source/providers/inference/remote_runpod.md rename to docs/docs/providers/inference/remote_runpod.mdx index ff1c0bcb6..2e8847dc5 100644 --- a/docs/source/providers/inference/remote_runpod.md +++ b/docs/docs/providers/inference/remote_runpod.mdx @@ -1,3 +1,9 @@ +--- +description: "RunPod inference provider for running models on RunPod's cloud GPU platform." +sidebar_label: Remote - Runpod +title: remote::runpod +--- + # remote::runpod ## Description @@ -16,6 +22,4 @@ RunPod inference provider for running models on RunPod's cloud GPU platform. ```yaml url: ${env.RUNPOD_URL:=} api_token: ${env.RUNPOD_API_TOKEN} - ``` - diff --git a/docs/source/providers/inference/remote_sambanova-openai-compat.md b/docs/docs/providers/inference/remote_sambanova-openai-compat.mdx similarity index 99% rename from docs/source/providers/inference/remote_sambanova-openai-compat.md rename to docs/docs/providers/inference/remote_sambanova-openai-compat.mdx index 3074a5885..9b4716d7e 100644 --- a/docs/source/providers/inference/remote_sambanova-openai-compat.md +++ b/docs/docs/providers/inference/remote_sambanova-openai-compat.mdx @@ -18,4 +18,3 @@ openai_compat_api_base: https://api.sambanova.ai/v1 api_key: ${env.SAMBANOVA_API_KEY:=} ``` - diff --git a/docs/source/providers/inference/remote_sambanova.md b/docs/docs/providers/inference/remote_sambanova.mdx similarity index 76% rename from docs/source/providers/inference/remote_sambanova.md rename to docs/docs/providers/inference/remote_sambanova.mdx index 9d15c97d5..6ee28b400 100644 --- a/docs/source/providers/inference/remote_sambanova.md +++ b/docs/docs/providers/inference/remote_sambanova.mdx @@ -1,3 +1,9 @@ +--- +description: "SambaNova inference provider for running models on SambaNova's dataflow architecture." +sidebar_label: Remote - Sambanova +title: remote::sambanova +--- + # remote::sambanova ## Description @@ -16,6 +22,4 @@ SambaNova inference provider for running models on SambaNova's dataflow architec ```yaml url: https://api.sambanova.ai/v1 api_key: ${env.SAMBANOVA_API_KEY:=} - ``` - diff --git a/docs/source/providers/inference/remote_tgi.md b/docs/docs/providers/inference/remote_tgi.mdx similarity index 71% rename from docs/source/providers/inference/remote_tgi.md rename to docs/docs/providers/inference/remote_tgi.mdx index 104bb4aab..3a348056f 100644 --- a/docs/source/providers/inference/remote_tgi.md +++ b/docs/docs/providers/inference/remote_tgi.mdx @@ -1,3 +1,9 @@ +--- +description: "Text Generation Inference (TGI) provider for HuggingFace model serving." +sidebar_label: Remote - Tgi +title: remote::tgi +--- + # remote::tgi ## Description @@ -14,6 +20,4 @@ Text Generation Inference (TGI) provider for HuggingFace model serving. ```yaml url: ${env.TGI_URL:=} - ``` - diff --git a/docs/source/providers/inference/remote_together.md b/docs/docs/providers/inference/remote_together.mdx similarity index 80% rename from docs/source/providers/inference/remote_together.md rename to docs/docs/providers/inference/remote_together.mdx index be764e635..da232a45b 100644 --- a/docs/source/providers/inference/remote_together.md +++ b/docs/docs/providers/inference/remote_together.mdx @@ -1,3 +1,9 @@ +--- +description: "Together AI inference provider for open-source models and collaborative AI development." +sidebar_label: Remote - Together +title: remote::together +--- + # remote::together ## Description @@ -17,6 +23,4 @@ Together AI inference provider for open-source models and collaborative AI devel ```yaml url: https://api.together.xyz/v1 api_key: ${env.TOGETHER_API_KEY:=} - ``` - diff --git a/docs/source/providers/inference/remote_vertexai.md b/docs/docs/providers/inference/remote_vertexai.mdx similarity index 56% rename from docs/source/providers/inference/remote_vertexai.md rename to docs/docs/providers/inference/remote_vertexai.mdx index 962bbd76f..13a910d43 100644 --- a/docs/source/providers/inference/remote_vertexai.md +++ b/docs/docs/providers/inference/remote_vertexai.mdx @@ -1,3 +1,29 @@ +--- +description: | + Google Vertex AI inference provider enables you to use Google's Gemini models through Google Cloud's Vertex AI platform, providing several advantages: + + • Enterprise-grade security: Uses Google Cloud's security controls and IAM + • Better integration: Seamless integration with other Google Cloud services + • Advanced features: Access to additional Vertex AI features like model tuning and monitoring + • Authentication: Uses Google Cloud Application Default Credentials (ADC) instead of API keys + + Configuration: + - Set VERTEX_AI_PROJECT environment variable (required) + - Set VERTEX_AI_LOCATION environment variable (optional, defaults to us-central1) + - Use Google Cloud Application Default Credentials or service account key + + Authentication Setup: + Option 1 (Recommended): gcloud auth application-default login + Option 2: Set GOOGLE_APPLICATION_CREDENTIALS to service account key path + + Available Models: + - vertex_ai/gemini-2.0-flash + - vertex_ai/gemini-2.5-flash + - vertex_ai/gemini-2.5-pro +sidebar_label: Remote - Vertexai +title: remote::vertexai +--- + # remote::vertexai ## Description @@ -35,6 +61,4 @@ Available Models: ```yaml project: ${env.VERTEX_AI_PROJECT:=} location: ${env.VERTEX_AI_LOCATION:=us-central1} - ``` - diff --git a/docs/source/providers/inference/remote_vllm.md b/docs/docs/providers/inference/remote_vllm.mdx similarity index 86% rename from docs/source/providers/inference/remote_vllm.md rename to docs/docs/providers/inference/remote_vllm.mdx index 172d35873..77b8e1355 100644 --- a/docs/source/providers/inference/remote_vllm.md +++ b/docs/docs/providers/inference/remote_vllm.mdx @@ -1,3 +1,9 @@ +--- +description: "Remote vLLM inference provider for connecting to vLLM servers." +sidebar_label: Remote - Vllm +title: remote::vllm +--- + # remote::vllm ## Description @@ -21,6 +27,4 @@ url: ${env.VLLM_URL:=} max_tokens: ${env.VLLM_MAX_TOKENS:=4096} api_token: ${env.VLLM_API_TOKEN:=fake} tls_verify: ${env.VLLM_TLS_VERIFY:=true} - ``` - diff --git a/docs/source/providers/inference/remote_watsonx.md b/docs/docs/providers/inference/remote_watsonx.mdx similarity index 73% rename from docs/source/providers/inference/remote_watsonx.md rename to docs/docs/providers/inference/remote_watsonx.mdx index 0eb8a6fc4..1ceccc3ed 100644 --- a/docs/source/providers/inference/remote_watsonx.md +++ b/docs/docs/providers/inference/remote_watsonx.mdx @@ -1,3 +1,9 @@ +--- +description: "IBM WatsonX inference provider for accessing AI models on IBM's WatsonX platform." +sidebar_label: Remote - Watsonx +title: remote::watsonx +--- + # remote::watsonx ## Description @@ -9,8 +15,8 @@ IBM WatsonX inference provider for accessing AI models on IBM's WatsonX platform | Field | Type | Required | Default | Description | |-------|------|----------|---------|-------------| | `url` | `` | No | https://us-south.ml.cloud.ibm.com | A base url for accessing the watsonx.ai | -| `api_key` | `pydantic.types.SecretStr \| None` | No | | The watsonx API key, only needed of using the hosted service | -| `project_id` | `str \| None` | No | | The Project ID key, only needed of using the hosted service | +| `api_key` | `pydantic.types.SecretStr \| None` | No | | The watsonx API key | +| `project_id` | `str \| None` | No | | The Project ID key | | `timeout` | `` | No | 60 | Timeout for the HTTP requests | ## Sample Configuration @@ -19,6 +25,4 @@ IBM WatsonX inference provider for accessing AI models on IBM's WatsonX platform url: ${env.WATSONX_BASE_URL:=https://us-south.ml.cloud.ibm.com} api_key: ${env.WATSONX_API_KEY:=} project_id: ${env.WATSONX_PROJECT_ID:=} - ``` - diff --git a/docs/source/providers/openai.md b/docs/docs/providers/openai.mdx similarity index 88% rename from docs/source/providers/openai.md rename to docs/docs/providers/openai.mdx index 44a615456..bcff5873c 100644 --- a/docs/source/providers/openai.md +++ b/docs/docs/providers/openai.mdx @@ -1,3 +1,8 @@ +title: OpenAI Compatibility +description: OpenAI API Compatibility +sidebar_label: OpenAI Compatibility +sidebar_position: 1 +--- ## OpenAI API Compatibility ### Server path @@ -42,9 +47,7 @@ models = client.models.list() #### Responses -:::{note} -The Responses API implementation is still in active development. While it is quite usable, there are still unimplemented parts of the API. We'd love feedback on any use-cases you try that do not work to help prioritize the pieces left to implement. Please open issues in the [meta-llama/llama-stack](https://github.com/meta-llama/llama-stack) GitHub repository with details of anything that does not work. -::: +> **Note:** The Responses API implementation is still in active development. While it is quite usable, there are still unimplemented parts of the API. We'd love feedback on any use-cases you try that do not work to help prioritize the pieces left to implement. Please open issues in the [meta-llama/llama-stack](https://github.com/meta-llama/llama-stack) GitHub repository with details of anything that does not work. ##### Simple inference diff --git a/docs/source/providers/post_training/index.md b/docs/docs/providers/post_training/index.mdx similarity index 50% rename from docs/source/providers/post_training/index.md rename to docs/docs/providers/post_training/index.mdx index e69f2a45a..e3c8ba0e8 100644 --- a/docs/source/providers/post_training/index.md +++ b/docs/docs/providers/post_training/index.mdx @@ -1,16 +1,10 @@ +--- +sidebar_label: Post Training +title: Post_Training +--- + # Post_Training ## Overview This section contains documentation for all available providers for the **post_training** API. - -## Providers - -```{toctree} -:maxdepth: 1 - -inline_huggingface-gpu -inline_torchtune-cpu -inline_torchtune-gpu -remote_nvidia -``` diff --git a/docs/source/providers/post_training/inline_huggingface-cpu.md b/docs/docs/providers/post_training/inline_huggingface-cpu.mdx similarity index 84% rename from docs/source/providers/post_training/inline_huggingface-cpu.md rename to docs/docs/providers/post_training/inline_huggingface-cpu.mdx index e663fe8f8..4e64d571a 100644 --- a/docs/source/providers/post_training/inline_huggingface-cpu.md +++ b/docs/docs/providers/post_training/inline_huggingface-cpu.mdx @@ -11,11 +11,8 @@ HuggingFace-based post-training provider for fine-tuning models using the Huggin | `device` | `` | No | cuda | | | `distributed_backend` | `Literal['fsdp', 'deepspeed'` | No | | | | `checkpoint_format` | `Literal['full_state', 'huggingface'` | No | huggingface | | -| `chat_template` | `` | No | <|user|> -{input} -<|assistant|> -{output} | | -| `model_specific_config` | `` | No | {'trust_remote_code': True, 'attn_implementation': 'sdpa'} | | +| `chat_template` | `` | No | `<|user|>`
`{input}`
`<|assistant|>`
`{output}` | | +| `model_specific_config` | `` | No | `{'trust_remote_code': True, 'attn_implementation': 'sdpa'}` | | | `max_seq_length` | `` | No | 2048 | | | `gradient_checkpointing` | `` | No | False | | | `save_total_limit` | `` | No | 3 | | @@ -38,4 +35,3 @@ device: cpu dpo_output_dir: ~/.llama/dummy/dpo_output ``` - diff --git a/docs/source/providers/post_training/inline_huggingface-gpu.md b/docs/docs/providers/post_training/inline_huggingface-gpu.mdx similarity index 75% rename from docs/source/providers/post_training/inline_huggingface-gpu.md rename to docs/docs/providers/post_training/inline_huggingface-gpu.mdx index 21bf965fe..ac7644de7 100644 --- a/docs/source/providers/post_training/inline_huggingface-gpu.md +++ b/docs/docs/providers/post_training/inline_huggingface-gpu.mdx @@ -1,3 +1,9 @@ +--- +description: "HuggingFace-based post-training provider for fine-tuning models using the HuggingFace ecosystem." +sidebar_label: Huggingface-Gpu +title: inline::huggingface-gpu +--- + # inline::huggingface-gpu ## Description @@ -11,11 +17,8 @@ HuggingFace-based post-training provider for fine-tuning models using the Huggin | `device` | `` | No | cuda | | | `distributed_backend` | `Literal['fsdp', 'deepspeed'` | No | | | | `checkpoint_format` | `Literal['full_state', 'huggingface'` | No | huggingface | | -| `chat_template` | `` | No | <|user|> -{input} -<|assistant|> -{output} | | -| `model_specific_config` | `` | No | {'trust_remote_code': True, 'attn_implementation': 'sdpa'} | | +| `chat_template` | `` | No | `<|user|>`
`{input}`
`<|assistant|>`
`{output}` | | +| `model_specific_config` | `` | No | `{'trust_remote_code': True, 'attn_implementation': 'sdpa'}` | | | `max_seq_length` | `` | No | 2048 | | | `gradient_checkpointing` | `` | No | False | | | `save_total_limit` | `` | No | 3 | | @@ -36,6 +39,4 @@ checkpoint_format: huggingface distributed_backend: null device: cpu dpo_output_dir: ~/.llama/dummy/dpo_output - ``` - diff --git a/docs/source/providers/post_training/inline_huggingface.md b/docs/docs/providers/post_training/inline_huggingface.mdx similarity index 84% rename from docs/source/providers/post_training/inline_huggingface.md rename to docs/docs/providers/post_training/inline_huggingface.mdx index 8b10fe79c..870ff6ec5 100644 --- a/docs/source/providers/post_training/inline_huggingface.md +++ b/docs/docs/providers/post_training/inline_huggingface.mdx @@ -11,11 +11,8 @@ HuggingFace-based post-training provider for fine-tuning models using the Huggin | `device` | `` | No | cuda | | | `distributed_backend` | `Literal['fsdp', 'deepspeed'` | No | | | | `checkpoint_format` | `Literal['full_state', 'huggingface'` | No | huggingface | | -| `chat_template` | `` | No | <|user|> -{input} -<|assistant|> -{output} | | -| `model_specific_config` | `` | No | {'trust_remote_code': True, 'attn_implementation': 'sdpa'} | | +| `chat_template` | `` | No | `<|user|>`
`{input}`
`<|assistant|>`
`{output}` | | +| `model_specific_config` | `` | No | `{'trust_remote_code': True, 'attn_implementation': 'sdpa'}` | | | `max_seq_length` | `` | No | 2048 | | | `gradient_checkpointing` | `` | No | False | | | `save_total_limit` | `` | No | 3 | | @@ -38,4 +35,3 @@ device: cpu dpo_output_dir: ~/.llama/dummy/dpo_output ``` - diff --git a/docs/source/providers/post_training/inline_torchtune-cpu.md b/docs/docs/providers/post_training/inline_torchtune-cpu.mdx similarity index 70% rename from docs/source/providers/post_training/inline_torchtune-cpu.md rename to docs/docs/providers/post_training/inline_torchtune-cpu.mdx index 7204e56e8..f789392fc 100644 --- a/docs/source/providers/post_training/inline_torchtune-cpu.md +++ b/docs/docs/providers/post_training/inline_torchtune-cpu.mdx @@ -1,3 +1,9 @@ +--- +description: "TorchTune-based post-training provider for fine-tuning and optimizing models using Meta's TorchTune framework." +sidebar_label: Torchtune-Cpu +title: inline::torchtune-cpu +--- + # inline::torchtune-cpu ## Description @@ -15,6 +21,4 @@ TorchTune-based post-training provider for fine-tuning and optimizing models usi ```yaml checkpoint_format: meta - ``` - diff --git a/docs/source/providers/post_training/inline_torchtune-gpu.md b/docs/docs/providers/post_training/inline_torchtune-gpu.mdx similarity index 70% rename from docs/source/providers/post_training/inline_torchtune-gpu.md rename to docs/docs/providers/post_training/inline_torchtune-gpu.mdx index 98b94f6f6..bd87797af 100644 --- a/docs/source/providers/post_training/inline_torchtune-gpu.md +++ b/docs/docs/providers/post_training/inline_torchtune-gpu.mdx @@ -1,3 +1,9 @@ +--- +description: "TorchTune-based post-training provider for fine-tuning and optimizing models using Meta's TorchTune framework." +sidebar_label: Torchtune-Gpu +title: inline::torchtune-gpu +--- + # inline::torchtune-gpu ## Description @@ -15,6 +21,4 @@ TorchTune-based post-training provider for fine-tuning and optimizing models usi ```yaml checkpoint_format: meta - ``` - diff --git a/docs/source/providers/post_training/inline_torchtune.md b/docs/docs/providers/post_training/inline_torchtune.md similarity index 100% rename from docs/source/providers/post_training/inline_torchtune.md rename to docs/docs/providers/post_training/inline_torchtune.md diff --git a/docs/source/advanced_apis/post_training/remote_nvidia.md b/docs/docs/providers/post_training/remote_nvidia.mdx similarity index 88% rename from docs/source/advanced_apis/post_training/remote_nvidia.md rename to docs/docs/providers/post_training/remote_nvidia.mdx index 9840fa3c4..448ac4c75 100644 --- a/docs/source/advanced_apis/post_training/remote_nvidia.md +++ b/docs/docs/providers/post_training/remote_nvidia.mdx @@ -1,5 +1,7 @@ --- -orphan: true +description: "NVIDIA's post-training provider for fine-tuning models on NVIDIA's platform." +sidebar_label: Remote - Nvidia +title: remote::nvidia --- # remote::nvidia @@ -27,6 +29,4 @@ api_key: ${env.NVIDIA_API_KEY:=} dataset_namespace: ${env.NVIDIA_DATASET_NAMESPACE:=default} project_id: ${env.NVIDIA_PROJECT_ID:=test-project} customizer_url: ${env.NVIDIA_CUSTOMIZER_URL:=http://nemo.test} - ``` - diff --git a/docs/docs/providers/safety/index.mdx b/docs/docs/providers/safety/index.mdx new file mode 100644 index 000000000..3445b17e6 --- /dev/null +++ b/docs/docs/providers/safety/index.mdx @@ -0,0 +1,10 @@ +--- +sidebar_label: Safety +title: Safety +--- + +# Safety + +## Overview + +This section contains documentation for all available providers for the **safety** API. diff --git a/docs/source/providers/safety/inline_code-scanner.md b/docs/docs/providers/safety/inline_code-scanner.mdx similarity index 50% rename from docs/source/providers/safety/inline_code-scanner.md rename to docs/docs/providers/safety/inline_code-scanner.mdx index 3a3e90b3d..3fc3c38a4 100644 --- a/docs/source/providers/safety/inline_code-scanner.md +++ b/docs/docs/providers/safety/inline_code-scanner.mdx @@ -1,3 +1,9 @@ +--- +description: "Code Scanner safety provider for detecting security vulnerabilities and unsafe code patterns." +sidebar_label: Code-Scanner +title: inline::code-scanner +--- + # inline::code-scanner ## Description @@ -8,6 +14,4 @@ Code Scanner safety provider for detecting security vulnerabilities and unsafe c ```yaml {} - ``` - diff --git a/docs/source/providers/safety/inline_llama-guard.md b/docs/docs/providers/safety/inline_llama-guard.mdx similarity index 67% rename from docs/source/providers/safety/inline_llama-guard.md rename to docs/docs/providers/safety/inline_llama-guard.mdx index 4f57898ec..65866c9b2 100644 --- a/docs/source/providers/safety/inline_llama-guard.md +++ b/docs/docs/providers/safety/inline_llama-guard.mdx @@ -1,3 +1,9 @@ +--- +description: "Llama Guard safety provider for content moderation and safety filtering using Meta's Llama Guard model." +sidebar_label: Llama-Guard +title: inline::llama-guard +--- + # inline::llama-guard ## Description @@ -14,6 +20,4 @@ Llama Guard safety provider for content moderation and safety filtering using Me ```yaml excluded_categories: [] - ``` - diff --git a/docs/source/providers/safety/inline_prompt-guard.md b/docs/docs/providers/safety/inline_prompt-guard.mdx similarity index 68% rename from docs/source/providers/safety/inline_prompt-guard.md rename to docs/docs/providers/safety/inline_prompt-guard.mdx index 10a6b8d3f..c52e03e4b 100644 --- a/docs/source/providers/safety/inline_prompt-guard.md +++ b/docs/docs/providers/safety/inline_prompt-guard.mdx @@ -1,3 +1,9 @@ +--- +description: "Prompt Guard safety provider for detecting and filtering unsafe prompts and content." +sidebar_label: Prompt-Guard +title: inline::prompt-guard +--- + # inline::prompt-guard ## Description @@ -14,6 +20,4 @@ Prompt Guard safety provider for detecting and filtering unsafe prompts and cont ```yaml guard_type: injection - ``` - diff --git a/docs/source/providers/safety/remote_bedrock.md b/docs/docs/providers/safety/remote_bedrock.mdx similarity index 74% rename from docs/source/providers/safety/remote_bedrock.md rename to docs/docs/providers/safety/remote_bedrock.mdx index 3c1d6bcb0..5461d7cdc 100644 --- a/docs/source/providers/safety/remote_bedrock.md +++ b/docs/docs/providers/safety/remote_bedrock.mdx @@ -1,3 +1,9 @@ +--- +description: "AWS Bedrock safety provider for content moderation using AWS's safety services." +sidebar_label: Remote - Bedrock +title: remote::bedrock +--- + # remote::bedrock ## Description @@ -15,14 +21,12 @@ AWS Bedrock safety provider for content moderation using AWS's safety services. | `profile_name` | `str \| None` | No | | The profile name that contains credentials to use.Default use environment variable: AWS_PROFILE | | `total_max_attempts` | `int \| None` | No | | An integer representing the maximum number of attempts that will be made for a single request, including the initial attempt. Default use environment variable: AWS_MAX_ATTEMPTS | | `retry_mode` | `str \| None` | No | | A string representing the type of retries Boto3 will perform.Default use environment variable: AWS_RETRY_MODE | -| `connect_timeout` | `float \| None` | No | 60 | The time in seconds till a timeout exception is thrown when attempting to make a connection. The default is 60 seconds. | -| `read_timeout` | `float \| None` | No | 60 | The time in seconds till a timeout exception is thrown when attempting to read from a connection.The default is 60 seconds. | +| `connect_timeout` | `float \| None` | No | 60.0 | The time in seconds till a timeout exception is thrown when attempting to make a connection. The default is 60 seconds. | +| `read_timeout` | `float \| None` | No | 60.0 | The time in seconds till a timeout exception is thrown when attempting to read from a connection.The default is 60 seconds. | | `session_ttl` | `int \| None` | No | 3600 | The time in seconds till a session expires. The default is 3600 seconds (1 hour). | ## Sample Configuration ```yaml {} - ``` - diff --git a/docs/source/providers/safety/remote_nvidia.md b/docs/docs/providers/safety/remote_nvidia.mdx similarity index 81% rename from docs/source/providers/safety/remote_nvidia.md rename to docs/docs/providers/safety/remote_nvidia.mdx index 40ae744a4..0f665e60a 100644 --- a/docs/source/providers/safety/remote_nvidia.md +++ b/docs/docs/providers/safety/remote_nvidia.mdx @@ -1,3 +1,9 @@ +--- +description: "NVIDIA's safety provider for content moderation and safety filtering." +sidebar_label: Remote - Nvidia +title: remote::nvidia +--- + # remote::nvidia ## Description @@ -16,6 +22,4 @@ NVIDIA's safety provider for content moderation and safety filtering. ```yaml guardrails_service_url: ${env.GUARDRAILS_SERVICE_URL:=http://localhost:7331} config_id: ${env.NVIDIA_GUARDRAILS_CONFIG_ID:=self-check} - ``` - diff --git a/docs/source/providers/safety/remote_sambanova.md b/docs/docs/providers/safety/remote_sambanova.mdx similarity index 77% rename from docs/source/providers/safety/remote_sambanova.md rename to docs/docs/providers/safety/remote_sambanova.mdx index 7e608f1b7..da70fce6c 100644 --- a/docs/source/providers/safety/remote_sambanova.md +++ b/docs/docs/providers/safety/remote_sambanova.mdx @@ -1,3 +1,9 @@ +--- +description: "SambaNova's safety provider for content moderation and safety filtering." +sidebar_label: Remote - Sambanova +title: remote::sambanova +--- + # remote::sambanova ## Description @@ -16,6 +22,4 @@ SambaNova's safety provider for content moderation and safety filtering. ```yaml url: https://api.sambanova.ai/v1 api_key: ${env.SAMBANOVA_API_KEY:=} - ``` - diff --git a/docs/source/providers/scoring/index.md b/docs/docs/providers/scoring/index.mdx similarity index 54% rename from docs/source/providers/scoring/index.md rename to docs/docs/providers/scoring/index.mdx index f3bd48eb0..41d63b4ad 100644 --- a/docs/source/providers/scoring/index.md +++ b/docs/docs/providers/scoring/index.mdx @@ -1,15 +1,10 @@ +--- +sidebar_label: Scoring +title: Scoring +--- + # Scoring ## Overview This section contains documentation for all available providers for the **scoring** API. - -## Providers - -```{toctree} -:maxdepth: 1 - -inline_basic -inline_braintrust -inline_llm-as-judge -``` diff --git a/docs/source/advanced_apis/scoring/inline_basic.md b/docs/docs/providers/scoring/inline_basic.mdx similarity index 54% rename from docs/source/advanced_apis/scoring/inline_basic.md rename to docs/docs/providers/scoring/inline_basic.mdx index b56b36013..cbafbc40c 100644 --- a/docs/source/advanced_apis/scoring/inline_basic.md +++ b/docs/docs/providers/scoring/inline_basic.mdx @@ -1,5 +1,7 @@ --- -orphan: true +description: "Basic scoring provider for simple evaluation metrics and scoring functions." +sidebar_label: Basic +title: inline::basic --- # inline::basic @@ -12,6 +14,4 @@ Basic scoring provider for simple evaluation metrics and scoring functions. ```yaml {} - ``` - diff --git a/docs/source/advanced_apis/scoring/inline_braintrust.md b/docs/docs/providers/scoring/inline_braintrust.mdx similarity index 72% rename from docs/source/advanced_apis/scoring/inline_braintrust.md rename to docs/docs/providers/scoring/inline_braintrust.mdx index d1278217c..d12f9de25 100644 --- a/docs/source/advanced_apis/scoring/inline_braintrust.md +++ b/docs/docs/providers/scoring/inline_braintrust.mdx @@ -1,5 +1,7 @@ --- -orphan: true +description: "Braintrust scoring provider for evaluation and scoring using the Braintrust platform." +sidebar_label: Braintrust +title: inline::braintrust --- # inline::braintrust @@ -18,6 +20,4 @@ Braintrust scoring provider for evaluation and scoring using the Braintrust plat ```yaml openai_api_key: ${env.OPENAI_API_KEY:=} - ``` - diff --git a/docs/source/advanced_apis/scoring/inline_llm-as-judge.md b/docs/docs/providers/scoring/inline_llm-as-judge.mdx similarity index 52% rename from docs/source/advanced_apis/scoring/inline_llm-as-judge.md rename to docs/docs/providers/scoring/inline_llm-as-judge.mdx index c7fcddf37..22f326623 100644 --- a/docs/source/advanced_apis/scoring/inline_llm-as-judge.md +++ b/docs/docs/providers/scoring/inline_llm-as-judge.mdx @@ -1,5 +1,7 @@ --- -orphan: true +description: "LLM-as-judge scoring provider that uses language models to evaluate and score responses." +sidebar_label: Llm-As-Judge +title: inline::llm-as-judge --- # inline::llm-as-judge @@ -12,6 +14,4 @@ LLM-as-judge scoring provider that uses language models to evaluate and score re ```yaml {} - ``` - diff --git a/docs/source/providers/telemetry/index.md b/docs/docs/providers/telemetry/index.mdx similarity index 63% rename from docs/source/providers/telemetry/index.md rename to docs/docs/providers/telemetry/index.mdx index c7fbfed73..07190d625 100644 --- a/docs/source/providers/telemetry/index.md +++ b/docs/docs/providers/telemetry/index.mdx @@ -1,13 +1,10 @@ +--- +sidebar_label: Telemetry +title: Telemetry +--- + # Telemetry ## Overview This section contains documentation for all available providers for the **telemetry** API. - -## Providers - -```{toctree} -:maxdepth: 1 - -inline_meta-reference -``` diff --git a/docs/source/providers/telemetry/inline_meta-reference.md b/docs/docs/providers/telemetry/inline_meta-reference.mdx similarity index 73% rename from docs/source/providers/telemetry/inline_meta-reference.md rename to docs/docs/providers/telemetry/inline_meta-reference.mdx index 3e5f4b842..13fab87f3 100644 --- a/docs/source/providers/telemetry/inline_meta-reference.md +++ b/docs/docs/providers/telemetry/inline_meta-reference.mdx @@ -1,3 +1,9 @@ +--- +description: "Meta's reference implementation of telemetry and observability using OpenTelemetry." +sidebar_label: Meta-Reference +title: inline::meta-reference +--- + # inline::meta-reference ## Description @@ -10,7 +16,7 @@ Meta's reference implementation of telemetry and observability using OpenTelemet |-------|------|----------|---------|-------------| | `otel_exporter_otlp_endpoint` | `str \| None` | No | | The OpenTelemetry collector endpoint URL (base URL for traces, metrics, and logs). If not set, the SDK will use OTEL_EXPORTER_OTLP_ENDPOINT environment variable. | | `service_name` | `` | No | ​ | The service name to use for telemetry | -| `sinks` | `list[inline.telemetry.meta_reference.config.TelemetrySink` | No | [, ] | List of telemetry sinks to enable (possible values: otel_trace, otel_metric, sqlite, console) | +| `sinks` | `list[inline.telemetry.meta_reference.config.TelemetrySink` | No | [<TelemetrySink.CONSOLE: 'console'>, <TelemetrySink.SQLITE: 'sqlite'>] | List of telemetry sinks to enable (possible values: otel_trace, otel_metric, sqlite, console) | | `sqlite_db_path` | `` | No | ~/.llama/runtime/trace_store.db | The path to the SQLite database to use for storing traces | ## Sample Configuration @@ -20,6 +26,4 @@ service_name: "${env.OTEL_SERVICE_NAME:=\u200B}" sinks: ${env.TELEMETRY_SINKS:=console,sqlite} sqlite_db_path: ${env.SQLITE_STORE_DIR:=~/.llama/dummy}/trace_store.db otel_exporter_otlp_endpoint: ${env.OTEL_EXPORTER_OTLP_ENDPOINT:=} - ``` - diff --git a/docs/docs/providers/tool_runtime/index.mdx b/docs/docs/providers/tool_runtime/index.mdx new file mode 100644 index 000000000..ab5050952 --- /dev/null +++ b/docs/docs/providers/tool_runtime/index.mdx @@ -0,0 +1,10 @@ +--- +sidebar_label: Tool Runtime +title: Tool_Runtime +--- + +# Tool_Runtime + +## Overview + +This section contains documentation for all available providers for the **tool_runtime** API. diff --git a/docs/source/providers/tool_runtime/inline_rag-runtime.md b/docs/docs/providers/tool_runtime/inline_rag-runtime.mdx similarity index 50% rename from docs/source/providers/tool_runtime/inline_rag-runtime.md rename to docs/docs/providers/tool_runtime/inline_rag-runtime.mdx index 784b4fdad..97428c2e3 100644 --- a/docs/source/providers/tool_runtime/inline_rag-runtime.md +++ b/docs/docs/providers/tool_runtime/inline_rag-runtime.mdx @@ -1,3 +1,9 @@ +--- +description: "RAG (Retrieval-Augmented Generation) tool runtime for document ingestion, chunking, and semantic search." +sidebar_label: Rag-Runtime +title: inline::rag-runtime +--- + # inline::rag-runtime ## Description @@ -8,6 +14,4 @@ RAG (Retrieval-Augmented Generation) tool runtime for document ingestion, chunki ```yaml {} - ``` - diff --git a/docs/source/providers/tool_runtime/remote_bing-search.md b/docs/docs/providers/tool_runtime/remote_bing-search.mdx similarity index 70% rename from docs/source/providers/tool_runtime/remote_bing-search.md rename to docs/docs/providers/tool_runtime/remote_bing-search.mdx index 0d5df7679..ec06bc20f 100644 --- a/docs/source/providers/tool_runtime/remote_bing-search.md +++ b/docs/docs/providers/tool_runtime/remote_bing-search.mdx @@ -1,3 +1,9 @@ +--- +description: "Bing Search tool for web search capabilities using Microsoft's search engine." +sidebar_label: Remote - Bing-Search +title: remote::bing-search +--- + # remote::bing-search ## Description @@ -15,6 +21,4 @@ Bing Search tool for web search capabilities using Microsoft's search engine. ```yaml api_key: ${env.BING_API_KEY:} - ``` - diff --git a/docs/source/providers/tool_runtime/remote_brave-search.md b/docs/docs/providers/tool_runtime/remote_brave-search.mdx similarity index 74% rename from docs/source/providers/tool_runtime/remote_brave-search.md rename to docs/docs/providers/tool_runtime/remote_brave-search.mdx index 26bc4010d..3aeed67d5 100644 --- a/docs/source/providers/tool_runtime/remote_brave-search.md +++ b/docs/docs/providers/tool_runtime/remote_brave-search.mdx @@ -1,3 +1,9 @@ +--- +description: "Brave Search tool for web search capabilities with privacy-focused results." +sidebar_label: Remote - Brave-Search +title: remote::brave-search +--- + # remote::brave-search ## Description @@ -16,6 +22,4 @@ Brave Search tool for web search capabilities with privacy-focused results. ```yaml api_key: ${env.BRAVE_SEARCH_API_KEY:=} max_results: 3 - ``` - diff --git a/docs/docs/providers/tool_runtime/remote_model-context-protocol.mdx b/docs/docs/providers/tool_runtime/remote_model-context-protocol.mdx new file mode 100644 index 000000000..869ca275a --- /dev/null +++ b/docs/docs/providers/tool_runtime/remote_model-context-protocol.mdx @@ -0,0 +1,17 @@ +--- +description: "Model Context Protocol (MCP) tool for standardized tool calling and context management." +sidebar_label: Remote - Model-Context-Protocol +title: remote::model-context-protocol +--- + +# remote::model-context-protocol + +## Description + +Model Context Protocol (MCP) tool for standardized tool calling and context management. + +## Sample Configuration + +```yaml +{} +``` diff --git a/docs/source/providers/tool_runtime/remote_tavily-search.md b/docs/docs/providers/tool_runtime/remote_tavily-search.mdx similarity index 74% rename from docs/source/providers/tool_runtime/remote_tavily-search.md rename to docs/docs/providers/tool_runtime/remote_tavily-search.mdx index 3dc31534d..fdca31bbe 100644 --- a/docs/source/providers/tool_runtime/remote_tavily-search.md +++ b/docs/docs/providers/tool_runtime/remote_tavily-search.mdx @@ -1,3 +1,9 @@ +--- +description: "Tavily Search tool for AI-optimized web search with structured results." +sidebar_label: Remote - Tavily-Search +title: remote::tavily-search +--- + # remote::tavily-search ## Description @@ -16,6 +22,4 @@ Tavily Search tool for AI-optimized web search with structured results. ```yaml api_key: ${env.TAVILY_SEARCH_API_KEY:=} max_results: 3 - ``` - diff --git a/docs/source/providers/tool_runtime/remote_wolfram-alpha.md b/docs/docs/providers/tool_runtime/remote_wolfram-alpha.mdx similarity index 68% rename from docs/source/providers/tool_runtime/remote_wolfram-alpha.md rename to docs/docs/providers/tool_runtime/remote_wolfram-alpha.mdx index 325c189fd..96bc41789 100644 --- a/docs/source/providers/tool_runtime/remote_wolfram-alpha.md +++ b/docs/docs/providers/tool_runtime/remote_wolfram-alpha.mdx @@ -1,3 +1,9 @@ +--- +description: "Wolfram Alpha tool for computational knowledge and mathematical calculations." +sidebar_label: Remote - Wolfram-Alpha +title: remote::wolfram-alpha +--- + # remote::wolfram-alpha ## Description @@ -14,6 +20,4 @@ Wolfram Alpha tool for computational knowledge and mathematical calculations. ```yaml api_key: ${env.WOLFRAM_ALPHA_API_KEY:=} - ``` - diff --git a/docs/docs/providers/vector_io/index.mdx b/docs/docs/providers/vector_io/index.mdx new file mode 100644 index 000000000..4c4c81ef8 --- /dev/null +++ b/docs/docs/providers/vector_io/index.mdx @@ -0,0 +1,10 @@ +--- +sidebar_label: Vector Io +title: Vector_Io +--- + +# Vector_Io + +## Overview + +This section contains documentation for all available providers for the **vector_io** API. diff --git a/docs/source/providers/vector_io/inline_chromadb.md b/docs/docs/providers/vector_io/inline_chromadb.mdx similarity index 60% rename from docs/source/providers/vector_io/inline_chromadb.md rename to docs/docs/providers/vector_io/inline_chromadb.mdx index 518e3f689..a1858eacc 100644 --- a/docs/source/providers/vector_io/inline_chromadb.md +++ b/docs/docs/providers/vector_io/inline_chromadb.mdx @@ -1,3 +1,40 @@ +--- +description: | + [Chroma](https://www.trychroma.com/) is an inline and remote vector + database provider for Llama Stack. It allows you to store and query vectors directly within a Chroma database. + That means you're not limited to storing vectors in memory or in a separate service. + + ## Features + Chroma supports: + - Store embeddings and their metadata + - Vector search + - Full-text search + - Document storage + - Metadata filtering + - Multi-modal retrieval + + ## Usage + + To use Chrome in your Llama Stack project, follow these steps: + + 1. Install the necessary dependencies. + 2. Configure your Llama Stack project to use chroma. + 3. Start storing and querying vectors. + + ## Installation + + You can install chroma using pip: + + ```bash + pip install chromadb + ``` + + ## Documentation + See [Chroma's documentation](https://docs.trychroma.com/docs/overview/introduction) for more details about Chroma in general. +sidebar_label: Chromadb +title: inline::chromadb +--- + # inline::chromadb ## Description @@ -51,6 +88,4 @@ db_path: ${env.CHROMADB_PATH} kvstore: type: sqlite db_path: ${env.SQLITE_STORE_DIR:=~/.llama/dummy}/chroma_inline_registry.db - ``` - diff --git a/docs/source/providers/vector_io/inline_faiss.md b/docs/docs/providers/vector_io/inline_faiss.mdx similarity index 55% rename from docs/source/providers/vector_io/inline_faiss.md rename to docs/docs/providers/vector_io/inline_faiss.mdx index cfa18a839..03bc2a928 100644 --- a/docs/source/providers/vector_io/inline_faiss.md +++ b/docs/docs/providers/vector_io/inline_faiss.mdx @@ -1,3 +1,49 @@ +--- +description: | + [Faiss](https://github.com/facebookresearch/faiss) is an inline vector database provider for Llama Stack. It + allows you to store and query vectors directly in memory. + That means you'll get fast and efficient vector retrieval. + + ## Features + + - Lightweight and easy to use + - Fully integrated with Llama Stack + - GPU support + - **Vector search** - FAISS supports pure vector similarity search using embeddings + + ## Search Modes + + **Supported:** + - **Vector Search** (`mode="vector"`): Performs vector similarity search using embeddings + + **Not Supported:** + - **Keyword Search** (`mode="keyword"`): Not supported by FAISS + - **Hybrid Search** (`mode="hybrid"`): Not supported by FAISS + + > **Note**: FAISS is designed as a pure vector similarity search library. See the [FAISS GitHub repository](https://github.com/facebookresearch/faiss) for more details about FAISS's core functionality. + + ## Usage + + To use Faiss in your Llama Stack project, follow these steps: + + 1. Install the necessary dependencies. + 2. Configure your Llama Stack project to use Faiss. + 3. Start storing and querying vectors. + + ## Installation + + You can install Faiss using pip: + + ```bash + pip install faiss-cpu + ``` + ## Documentation + See [Faiss' documentation](https://faiss.ai/) or the [Faiss Wiki](https://github.com/facebookresearch/faiss/wiki) for + more details about Faiss in general. +sidebar_label: Faiss +title: inline::faiss +--- + # inline::faiss ## Description @@ -57,6 +103,4 @@ more details about Faiss in general. kvstore: type: sqlite db_path: ${env.SQLITE_STORE_DIR:=~/.llama/dummy}/faiss_store.db - ``` - diff --git a/docs/source/providers/vector_io/inline_meta-reference.md b/docs/docs/providers/vector_io/inline_meta-reference.mdx similarity index 80% rename from docs/source/providers/vector_io/inline_meta-reference.md rename to docs/docs/providers/vector_io/inline_meta-reference.mdx index 6f269c441..bcad86750 100644 --- a/docs/source/providers/vector_io/inline_meta-reference.md +++ b/docs/docs/providers/vector_io/inline_meta-reference.mdx @@ -1,3 +1,9 @@ +--- +description: "Meta's reference implementation of a vector database." +sidebar_label: Meta-Reference +title: inline::meta-reference +--- + # inline::meta-reference ## Description @@ -16,12 +22,9 @@ Meta's reference implementation of a vector database. kvstore: type: sqlite db_path: ${env.SQLITE_STORE_DIR:=~/.llama/dummy}/faiss_store.db - ``` - ## Deprecation Notice -```{warning} +:::warning Please use the `inline::faiss` provider instead. -``` - +::: diff --git a/docs/source/providers/vector_io/inline_milvus.md b/docs/docs/providers/vector_io/inline_milvus.mdx similarity index 87% rename from docs/source/providers/vector_io/inline_milvus.md rename to docs/docs/providers/vector_io/inline_milvus.mdx index 33ea4d179..7e6f15c81 100644 --- a/docs/source/providers/vector_io/inline_milvus.md +++ b/docs/docs/providers/vector_io/inline_milvus.mdx @@ -1,3 +1,9 @@ +--- +description: "Please refer to the remote provider documentation." +sidebar_label: Milvus +title: inline::milvus +--- + # inline::milvus ## Description @@ -21,6 +27,4 @@ db_path: ${env.MILVUS_DB_PATH:=~/.llama/dummy}/milvus.db kvstore: type: sqlite db_path: ${env.SQLITE_STORE_DIR:=~/.llama/dummy}/milvus_registry.db - ``` - diff --git a/docs/source/providers/vector_io/inline_qdrant.md b/docs/docs/providers/vector_io/inline_qdrant.mdx similarity index 56% rename from docs/source/providers/vector_io/inline_qdrant.md rename to docs/docs/providers/vector_io/inline_qdrant.mdx index b5072d220..5c9ab10f2 100644 --- a/docs/source/providers/vector_io/inline_qdrant.md +++ b/docs/docs/providers/vector_io/inline_qdrant.mdx @@ -1,3 +1,50 @@ +--- +description: | + [Qdrant](https://qdrant.tech/documentation/) is an inline and remote vector database provider for Llama Stack. It + allows you to store and query vectors directly in memory. + That means you'll get fast and efficient vector retrieval. + + > By default, Qdrant stores vectors in RAM, delivering incredibly fast access for datasets that fit comfortably in + > memory. But when your dataset exceeds RAM capacity, Qdrant offers Memmap as an alternative. + > + > \[[An Introduction to Vector Databases](https://qdrant.tech/articles/what-is-a-vector-database/)\] + + + + ## Features + + - Lightweight and easy to use + - Fully integrated with Llama Stack + - Apache 2.0 license terms + - Store embeddings and their metadata + - Supports search by + [Keyword](https://qdrant.tech/articles/qdrant-introduces-full-text-filters-and-indexes/) + and [Hybrid](https://qdrant.tech/articles/hybrid-search/#building-a-hybrid-search-system-in-qdrant) search + - [Multilingual and Multimodal retrieval](https://qdrant.tech/documentation/multimodal-search/) + - [Medatata filtering](https://qdrant.tech/articles/vector-search-filtering/) + - [GPU support](https://qdrant.tech/documentation/guides/running-with-gpu/) + + ## Usage + + To use Qdrant in your Llama Stack project, follow these steps: + + 1. Install the necessary dependencies. + 2. Configure your Llama Stack project to use Qdrant. + 3. Start storing and querying vectors. + + ## Installation + + You can install Qdrant using docker: + + ```bash + docker pull qdrant/qdrant + ``` + ## Documentation + See the [Qdrant documentation](https://qdrant.tech/documentation/) for more details about Qdrant in general. +sidebar_label: Qdrant +title: inline::qdrant +--- + # inline::qdrant ## Description @@ -60,6 +107,4 @@ path: ${env.QDRANT_PATH:=~/.llama/~/.llama/dummy}/qdrant.db kvstore: type: sqlite db_path: ${env.SQLITE_STORE_DIR:=~/.llama/dummy}/qdrant_registry.db - ``` - diff --git a/docs/source/providers/vector_io/inline_sqlite-vec.md b/docs/docs/providers/vector_io/inline_sqlite-vec.mdx similarity index 51% rename from docs/source/providers/vector_io/inline_sqlite-vec.md rename to docs/docs/providers/vector_io/inline_sqlite-vec.mdx index 854bb9d08..aa6992a56 100644 --- a/docs/source/providers/vector_io/inline_sqlite-vec.md +++ b/docs/docs/providers/vector_io/inline_sqlite-vec.mdx @@ -1,3 +1,205 @@ +--- +description: | + [SQLite-Vec](https://github.com/asg017/sqlite-vec) is an inline vector database provider for Llama Stack. It + allows you to store and query vectors directly within an SQLite database. + That means you're not limited to storing vectors in memory or in a separate service. + + ## Features + + - Lightweight and easy to use + - Fully integrated with Llama Stacks + - Uses disk-based storage for persistence, allowing for larger vector storage + + ### Comparison to Faiss + + The choice between Faiss and sqlite-vec should be made based on the needs of your application, + as they have different strengths. + + #### Choosing the Right Provider + + Scenario | Recommended Tool | Reason + -- |-----------------| -- + Online Analytical Processing (OLAP) | Faiss | Fast, in-memory searches + Online Transaction Processing (OLTP) | sqlite-vec | Frequent writes and reads + Frequent writes | sqlite-vec | Efficient disk-based storage and incremental indexing + Large datasets | sqlite-vec | Disk-based storage for larger vector storage + Datasets that can fit in memory, frequent reads | Faiss | Optimized for speed, indexing, and GPU acceleration + + #### Empirical Example + + Consider the histogram below in which 10,000 randomly generated strings were inserted + in batches of 100 into both Faiss and sqlite-vec using `client.tool_runtime.rag_tool.insert()`. + + ```{image} ../../../../_static/providers/vector_io/write_time_comparison_sqlite-vec-faiss.png + :alt: Comparison of SQLite-Vec and Faiss write times + :width: 400px + ``` + + You will notice that the average write time for `sqlite-vec` was 788ms, compared to + 47,640ms for Faiss. While the number is jarring, if you look at the distribution, you can see that it is rather + uniformly spread across the [1500, 100000] interval. + + Looking at each individual write in the order that the documents are inserted you'll see the increase in + write speed as Faiss reindexes the vectors after each write. + ```{image} ../../../../_static/providers/vector_io/write_time_sequence_sqlite-vec-faiss.png + :alt: Comparison of SQLite-Vec and Faiss write times + :width: 400px + ``` + + In comparison, the read times for Faiss was on average 10% faster than sqlite-vec. + The modes of the two distributions highlight the differences much further where Faiss + will likely yield faster read performance. + + ```{image} ../../../../_static/providers/vector_io/read_time_comparison_sqlite-vec-faiss.png + :alt: Comparison of SQLite-Vec and Faiss read times + :width: 400px + ``` + + ## Usage + + To use sqlite-vec in your Llama Stack project, follow these steps: + + 1. Install the necessary dependencies. + 2. Configure your Llama Stack project to use SQLite-Vec. + 3. Start storing and querying vectors. + + The SQLite-vec provider supports three search modes: + + 1. **Vector Search** (`mode="vector"`): Performs pure vector similarity search using the embeddings. + 2. **Keyword Search** (`mode="keyword"`): Performs full-text search using SQLite's FTS5. + 3. **Hybrid Search** (`mode="hybrid"`): Combines both vector and keyword search for better results. First performs keyword search to get candidate matches, then applies vector similarity search on those candidates. + + Example with hybrid search: + ```python + response = await vector_io.query_chunks( + vector_db_id="my_db", + query="your query here", + params={"mode": "hybrid", "max_chunks": 3, "score_threshold": 0.7}, + ) + + # Using RRF ranker + response = await vector_io.query_chunks( + vector_db_id="my_db", + query="your query here", + params={ + "mode": "hybrid", + "max_chunks": 3, + "score_threshold": 0.7, + "ranker": {"type": "rrf", "impact_factor": 60.0}, + }, + ) + + # Using weighted ranker + response = await vector_io.query_chunks( + vector_db_id="my_db", + query="your query here", + params={ + "mode": "hybrid", + "max_chunks": 3, + "score_threshold": 0.7, + "ranker": {"type": "weighted", "alpha": 0.7}, # 70% vector, 30% keyword + }, + ) + ``` + + Example with explicit vector search: + ```python + response = await vector_io.query_chunks( + vector_db_id="my_db", + query="your query here", + params={"mode": "vector", "max_chunks": 3, "score_threshold": 0.7}, + ) + ``` + + Example with keyword search: + ```python + response = await vector_io.query_chunks( + vector_db_id="my_db", + query="your query here", + params={"mode": "keyword", "max_chunks": 3, "score_threshold": 0.7}, + ) + ``` + + ## Supported Search Modes + + The SQLite vector store supports three search modes: + + 1. **Vector Search** (`mode="vector"`): Uses vector similarity to find relevant chunks + 2. **Keyword Search** (`mode="keyword"`): Uses keyword matching to find relevant chunks + 3. **Hybrid Search** (`mode="hybrid"`): Combines both vector and keyword scores using a ranker + + ### Hybrid Search + + Hybrid search combines the strengths of both vector and keyword search by: + - Computing vector similarity scores + - Computing keyword match scores + - Using a ranker to combine these scores + + Two ranker types are supported: + + 1. **RRF (Reciprocal Rank Fusion)**: + - Combines ranks from both vector and keyword results + - Uses an impact factor (default: 60.0) to control the weight of higher-ranked results + - Good for balancing between vector and keyword results + - The default impact factor of 60.0 comes from the original RRF paper by Cormack et al. (2009) [^1], which found this value to provide optimal performance across various retrieval tasks + + 2. **Weighted**: + - Linearly combines normalized vector and keyword scores + - Uses an alpha parameter (0-1) to control the blend: + - alpha=0: Only use keyword scores + - alpha=1: Only use vector scores + - alpha=0.5: Equal weight to both (default) + + Example using RAGQueryConfig with different search modes: + + ```python + from llama_stack.apis.tools import RAGQueryConfig, RRFRanker, WeightedRanker + + # Vector search + config = RAGQueryConfig(mode="vector", max_chunks=5) + + # Keyword search + config = RAGQueryConfig(mode="keyword", max_chunks=5) + + # Hybrid search with custom RRF ranker + config = RAGQueryConfig( + mode="hybrid", + max_chunks=5, + ranker=RRFRanker(impact_factor=50.0), # Custom impact factor + ) + + # Hybrid search with weighted ranker + config = RAGQueryConfig( + mode="hybrid", + max_chunks=5, + ranker=WeightedRanker(alpha=0.7), # 70% vector, 30% keyword + ) + + # Hybrid search with default RRF ranker + config = RAGQueryConfig( + mode="hybrid", max_chunks=5 + ) # Will use RRF with impact_factor=60.0 + ``` + + Note: The ranker configuration is only used in hybrid mode. For vector or keyword modes, the ranker parameter is ignored. + + ## Installation + + You can install SQLite-Vec using pip: + + ```bash + pip install sqlite-vec + ``` + + ## Documentation + + See [sqlite-vec's GitHub repo](https://github.com/asg017/sqlite-vec/tree/main) for more details about sqlite-vec in general. + + [^1]: Cormack, G. V., Clarke, C. L., & Buettcher, S. (2009). [Reciprocal rank fusion outperforms condorcet and individual rank learning methods](https://dl.acm.org/doi/10.1145/1571941.1572114). In Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval (pp. 758-759). +sidebar_label: Sqlite-Vec +title: inline::sqlite-vec +--- + # inline::sqlite-vec ## Description @@ -215,6 +417,4 @@ db_path: ${env.SQLITE_STORE_DIR:=~/.llama/dummy}/sqlite_vec.db kvstore: type: sqlite db_path: ${env.SQLITE_STORE_DIR:=~/.llama/dummy}/sqlite_vec_registry.db - ``` - diff --git a/docs/source/providers/vector_io/inline_sqlite_vec.md b/docs/docs/providers/vector_io/inline_sqlite_vec.mdx similarity index 85% rename from docs/source/providers/vector_io/inline_sqlite_vec.md rename to docs/docs/providers/vector_io/inline_sqlite_vec.mdx index 9e5654a50..7f69f617d 100644 --- a/docs/source/providers/vector_io/inline_sqlite_vec.md +++ b/docs/docs/providers/vector_io/inline_sqlite_vec.mdx @@ -1,3 +1,9 @@ +--- +description: "Please refer to the sqlite-vec provider documentation." +sidebar_label: Sqlite Vec +title: inline::sqlite_vec +--- + # inline::sqlite_vec ## Description @@ -20,12 +26,9 @@ db_path: ${env.SQLITE_STORE_DIR:=~/.llama/dummy}/sqlite_vec.db kvstore: type: sqlite db_path: ${env.SQLITE_STORE_DIR:=~/.llama/dummy}/sqlite_vec_registry.db - ``` - ## Deprecation Notice -```{warning} +:::warning Please use the `inline::sqlite-vec` provider (notice the hyphen instead of underscore) instead. -``` - +::: diff --git a/docs/source/providers/vector_io/remote_chromadb.md b/docs/docs/providers/vector_io/remote_chromadb.mdx similarity index 59% rename from docs/source/providers/vector_io/remote_chromadb.md rename to docs/docs/providers/vector_io/remote_chromadb.mdx index badfebe90..807771003 100644 --- a/docs/source/providers/vector_io/remote_chromadb.md +++ b/docs/docs/providers/vector_io/remote_chromadb.mdx @@ -1,3 +1,40 @@ +--- +description: | + [Chroma](https://www.trychroma.com/) is an inline and remote vector + database provider for Llama Stack. It allows you to store and query vectors directly within a Chroma database. + That means you're not limited to storing vectors in memory or in a separate service. + + ## Features + Chroma supports: + - Store embeddings and their metadata + - Vector search + - Full-text search + - Document storage + - Metadata filtering + - Multi-modal retrieval + + ## Usage + + To use Chrome in your Llama Stack project, follow these steps: + + 1. Install the necessary dependencies. + 2. Configure your Llama Stack project to use chroma. + 3. Start storing and querying vectors. + + ## Installation + + You can install chroma using pip: + + ```bash + pip install chromadb + ``` + + ## Documentation + See [Chroma's documentation](https://docs.trychroma.com/docs/overview/introduction) for more details about Chroma in general. +sidebar_label: Remote - Chromadb +title: remote::chromadb +--- + # remote::chromadb ## Description @@ -50,6 +87,4 @@ url: ${env.CHROMADB_URL} kvstore: type: sqlite db_path: ${env.SQLITE_STORE_DIR:=~/.llama/dummy}/chroma_remote_registry.db - ``` - diff --git a/docs/docs/providers/vector_io/remote_milvus.mdx b/docs/docs/providers/vector_io/remote_milvus.mdx new file mode 100644 index 000000000..7f7c08122 --- /dev/null +++ b/docs/docs/providers/vector_io/remote_milvus.mdx @@ -0,0 +1,426 @@ +--- +description: | + [Milvus](https://milvus.io/) is an inline and remote vector database provider for Llama Stack. It + allows you to store and query vectors directly within a Milvus database. + That means you're not limited to storing vectors in memory or in a separate service. + + ## Features + + - Easy to use + - Fully integrated with Llama Stack + - Supports all search modes: vector, keyword, and hybrid search (both inline and remote configurations) + + ## Usage + + To use Milvus in your Llama Stack project, follow these steps: + + 1. Install the necessary dependencies. + 2. Configure your Llama Stack project to use Milvus. + 3. Start storing and querying vectors. + + ## Installation + + If you want to use inline Milvus, you can install: + + ```bash + pip install pymilvus[milvus-lite] + ``` + + If you want to use remote Milvus, you can install: + + ```bash + pip install pymilvus + ``` + + ## Configuration + + In Llama Stack, Milvus can be configured in two ways: + - **Inline (Local) Configuration** - Uses Milvus-Lite for local storage + - **Remote Configuration** - Connects to a remote Milvus server + + ### Inline (Local) Configuration + + The simplest method is local configuration, which requires setting `db_path`, a path for locally storing Milvus-Lite files: + + ```yaml + vector_io: + - provider_id: milvus + provider_type: inline::milvus + config: + db_path: ~/.llama/distributions/together/milvus_store.db + ``` + + ### Remote Configuration + + Remote configuration is suitable for larger data storage requirements: + + #### Standard Remote Connection + + ```yaml + vector_io: + - provider_id: milvus + provider_type: remote::milvus + config: + uri: "http://:" + token: ":" + ``` + + #### TLS-Enabled Remote Connection (One-way TLS) + + For connections to Milvus instances with one-way TLS enabled: + + ```yaml + vector_io: + - provider_id: milvus + provider_type: remote::milvus + config: + uri: "https://:" + token: ":" + secure: True + server_pem_path: "/path/to/server.pem" + ``` + + #### Mutual TLS (mTLS) Remote Connection + + For connections to Milvus instances with mutual TLS (mTLS) enabled: + + ```yaml + vector_io: + - provider_id: milvus + provider_type: remote::milvus + config: + uri: "https://:" + token: ":" + secure: True + ca_pem_path: "/path/to/ca.pem" + client_pem_path: "/path/to/client.pem" + client_key_path: "/path/to/client.key" + ``` + + #### Key Parameters for TLS Configuration + + - **`secure`**: Enables TLS encryption when set to `true`. Defaults to `false`. + - **`server_pem_path`**: Path to the **server certificate** for verifying the server's identity (used in one-way TLS). + - **`ca_pem_path`**: Path to the **Certificate Authority (CA) certificate** for validating the server certificate (required in mTLS). + - **`client_pem_path`**: Path to the **client certificate** file (required for mTLS). + - **`client_key_path`**: Path to the **client private key** file (required for mTLS). + + ## Search Modes + + Milvus supports three different search modes for both inline and remote configurations: + + ### Vector Search + Vector search uses semantic similarity to find the most relevant chunks based on embedding vectors. This is the default search mode and works well for finding conceptually similar content. + + ```python + # Vector search example + search_response = client.vector_stores.search( + vector_store_id=vector_store.id, + query="What is machine learning?", + search_mode="vector", + max_num_results=5, + ) + ``` + + ### Keyword Search + Keyword search uses traditional text-based matching to find chunks containing specific terms or phrases. This is useful when you need exact term matches. + + ```python + # Keyword search example + search_response = client.vector_stores.search( + vector_store_id=vector_store.id, + query="Python programming language", + search_mode="keyword", + max_num_results=5, + ) + ``` + + ### Hybrid Search + Hybrid search combines both vector and keyword search methods to provide more comprehensive results. It leverages the strengths of both semantic similarity and exact term matching. + + #### Basic Hybrid Search + ```python + # Basic hybrid search example (uses RRF ranker with default impact_factor=60.0) + search_response = client.vector_stores.search( + vector_store_id=vector_store.id, + query="neural networks in Python", + search_mode="hybrid", + max_num_results=5, + ) + ``` + + **Note**: The default `impact_factor` value of 60.0 was empirically determined to be optimal in the original RRF research paper: ["Reciprocal Rank Fusion outperforms Condorcet and individual Rank Learning Methods"](https://plg.uwaterloo.ca/~gvcormac/cormacksigir09-rrf.pdf) (Cormack et al., 2009). + + #### Hybrid Search with RRF (Reciprocal Rank Fusion) Ranker + RRF combines rankings from vector and keyword search by using reciprocal ranks. The impact factor controls how much weight is given to higher-ranked results. + + ```python + # Hybrid search with custom RRF parameters + search_response = client.vector_stores.search( + vector_store_id=vector_store.id, + query="neural networks in Python", + search_mode="hybrid", + max_num_results=5, + ranking_options={ + "ranker": { + "type": "rrf", + "impact_factor": 100.0, # Higher values give more weight to top-ranked results + } + }, + ) + ``` + + #### Hybrid Search with Weighted Ranker + Weighted ranker linearly combines normalized scores from vector and keyword search. The alpha parameter controls the balance between the two search methods. + + ```python + # Hybrid search with weighted ranker + search_response = client.vector_stores.search( + vector_store_id=vector_store.id, + query="neural networks in Python", + search_mode="hybrid", + max_num_results=5, + ranking_options={ + "ranker": { + "type": "weighted", + "alpha": 0.7, # 70% vector search, 30% keyword search + } + }, + ) + ``` + + For detailed documentation on RRF and Weighted rankers, please refer to the [Milvus Reranking Guide](https://milvus.io/docs/reranking.md). + + ## Documentation + See the [Milvus documentation](https://milvus.io/docs/install-overview.md) for more details about Milvus in general. + + For more details on TLS configuration, refer to the [TLS setup guide](https://milvus.io/docs/tls.md). +sidebar_label: Remote - Milvus +title: remote::milvus +--- + +# remote::milvus + +## Description + + +[Milvus](https://milvus.io/) is an inline and remote vector database provider for Llama Stack. It +allows you to store and query vectors directly within a Milvus database. +That means you're not limited to storing vectors in memory or in a separate service. + +## Features + +- Easy to use +- Fully integrated with Llama Stack +- Supports all search modes: vector, keyword, and hybrid search (both inline and remote configurations) + +## Usage + +To use Milvus in your Llama Stack project, follow these steps: + +1. Install the necessary dependencies. +2. Configure your Llama Stack project to use Milvus. +3. Start storing and querying vectors. + +## Installation + +If you want to use inline Milvus, you can install: + +```bash +pip install pymilvus[milvus-lite] +``` + +If you want to use remote Milvus, you can install: + +```bash +pip install pymilvus +``` + +## Configuration + +In Llama Stack, Milvus can be configured in two ways: +- **Inline (Local) Configuration** - Uses Milvus-Lite for local storage +- **Remote Configuration** - Connects to a remote Milvus server + +### Inline (Local) Configuration + +The simplest method is local configuration, which requires setting `db_path`, a path for locally storing Milvus-Lite files: + +```yaml +vector_io: + - provider_id: milvus + provider_type: inline::milvus + config: + db_path: ~/.llama/distributions/together/milvus_store.db +``` + +### Remote Configuration + +Remote configuration is suitable for larger data storage requirements: + +#### Standard Remote Connection + +```yaml +vector_io: + - provider_id: milvus + provider_type: remote::milvus + config: + uri: "http://:" + token: ":" +``` + +#### TLS-Enabled Remote Connection (One-way TLS) + +For connections to Milvus instances with one-way TLS enabled: + +```yaml +vector_io: + - provider_id: milvus + provider_type: remote::milvus + config: + uri: "https://:" + token: ":" + secure: True + server_pem_path: "/path/to/server.pem" +``` + +#### Mutual TLS (mTLS) Remote Connection + +For connections to Milvus instances with mutual TLS (mTLS) enabled: + +```yaml +vector_io: + - provider_id: milvus + provider_type: remote::milvus + config: + uri: "https://:" + token: ":" + secure: True + ca_pem_path: "/path/to/ca.pem" + client_pem_path: "/path/to/client.pem" + client_key_path: "/path/to/client.key" +``` + +#### Key Parameters for TLS Configuration + +- **`secure`**: Enables TLS encryption when set to `true`. Defaults to `false`. +- **`server_pem_path`**: Path to the **server certificate** for verifying the server's identity (used in one-way TLS). +- **`ca_pem_path`**: Path to the **Certificate Authority (CA) certificate** for validating the server certificate (required in mTLS). +- **`client_pem_path`**: Path to the **client certificate** file (required for mTLS). +- **`client_key_path`**: Path to the **client private key** file (required for mTLS). + +## Search Modes + +Milvus supports three different search modes for both inline and remote configurations: + +### Vector Search +Vector search uses semantic similarity to find the most relevant chunks based on embedding vectors. This is the default search mode and works well for finding conceptually similar content. + +```python +# Vector search example +search_response = client.vector_stores.search( + vector_store_id=vector_store.id, + query="What is machine learning?", + search_mode="vector", + max_num_results=5, +) +``` + +### Keyword Search +Keyword search uses traditional text-based matching to find chunks containing specific terms or phrases. This is useful when you need exact term matches. + +```python +# Keyword search example +search_response = client.vector_stores.search( + vector_store_id=vector_store.id, + query="Python programming language", + search_mode="keyword", + max_num_results=5, +) +``` + +### Hybrid Search +Hybrid search combines both vector and keyword search methods to provide more comprehensive results. It leverages the strengths of both semantic similarity and exact term matching. + +#### Basic Hybrid Search +```python +# Basic hybrid search example (uses RRF ranker with default impact_factor=60.0) +search_response = client.vector_stores.search( + vector_store_id=vector_store.id, + query="neural networks in Python", + search_mode="hybrid", + max_num_results=5, +) +``` + +**Note**: The default `impact_factor` value of 60.0 was empirically determined to be optimal in the original RRF research paper: ["Reciprocal Rank Fusion outperforms Condorcet and individual Rank Learning Methods"](https://plg.uwaterloo.ca/~gvcormac/cormacksigir09-rrf.pdf) (Cormack et al., 2009). + +#### Hybrid Search with RRF (Reciprocal Rank Fusion) Ranker +RRF combines rankings from vector and keyword search by using reciprocal ranks. The impact factor controls how much weight is given to higher-ranked results. + +```python +# Hybrid search with custom RRF parameters +search_response = client.vector_stores.search( + vector_store_id=vector_store.id, + query="neural networks in Python", + search_mode="hybrid", + max_num_results=5, + ranking_options={ + "ranker": { + "type": "rrf", + "impact_factor": 100.0, # Higher values give more weight to top-ranked results + } + }, +) +``` + +#### Hybrid Search with Weighted Ranker +Weighted ranker linearly combines normalized scores from vector and keyword search. The alpha parameter controls the balance between the two search methods. + +```python +# Hybrid search with weighted ranker +search_response = client.vector_stores.search( + vector_store_id=vector_store.id, + query="neural networks in Python", + search_mode="hybrid", + max_num_results=5, + ranking_options={ + "ranker": { + "type": "weighted", + "alpha": 0.7, # 70% vector search, 30% keyword search + } + }, +) +``` + +For detailed documentation on RRF and Weighted rankers, please refer to the [Milvus Reranking Guide](https://milvus.io/docs/reranking.md). + +## Documentation +See the [Milvus documentation](https://milvus.io/docs/install-overview.md) for more details about Milvus in general. + +For more details on TLS configuration, refer to the [TLS setup guide](https://milvus.io/docs/tls.md). + + +## Configuration + +| Field | Type | Required | Default | Description | +|-------|------|----------|---------|-------------| +| `uri` | `` | No | | The URI of the Milvus server | +| `token` | `str \| None` | No | | The token of the Milvus server | +| `consistency_level` | `` | No | Strong | The consistency level of the Milvus server | +| `kvstore` | `utils.kvstore.config.RedisKVStoreConfig \| utils.kvstore.config.SqliteKVStoreConfig \| utils.kvstore.config.PostgresKVStoreConfig \| utils.kvstore.config.MongoDBKVStoreConfig` | No | sqlite | Config for KV store backend | +| `config` | `dict` | No | `{}` | This configuration allows additional fields to be passed through to the underlying Milvus client. See the [Milvus](https://milvus.io/docs/install-overview.md) documentation for more details about Milvus in general. | + +:::note +This configuration class accepts additional fields beyond those listed above. You can pass any additional configuration options that will be forwarded to the underlying provider. +::: + +## Sample Configuration + +```yaml +uri: ${env.MILVUS_ENDPOINT} +token: ${env.MILVUS_TOKEN} +kvstore: + type: sqlite + db_path: ${env.SQLITE_STORE_DIR:=~/.llama/dummy}/milvus_remote_registry.db +``` diff --git a/docs/docs/providers/vector_io/remote_pgvector.mdx b/docs/docs/providers/vector_io/remote_pgvector.mdx new file mode 100644 index 000000000..d21810c68 --- /dev/null +++ b/docs/docs/providers/vector_io/remote_pgvector.mdx @@ -0,0 +1,234 @@ +--- +description: | + [PGVector](https://github.com/pgvector/pgvector) is a remote vector database provider for Llama Stack. It + allows you to store and query vectors directly in memory. + That means you'll get fast and efficient vector retrieval. + + ## Features + + - Easy to use + - Fully integrated with Llama Stack + + There are three implementations of search for PGVectoIndex available: + + 1. Vector Search: + - How it works: + - Uses PostgreSQL's vector extension (pgvector) to perform similarity search + - Compares query embeddings against stored embeddings using Cosine distance or other distance metrics + - Eg. SQL query: SELECT document, embedding <=> %s::vector AS distance FROM table ORDER BY distance + + -Characteristics: + - Semantic understanding - finds documents similar in meaning even if they don't share keywords + - Works with high-dimensional vector embeddings (typically 768, 1024, or higher dimensions) + - Best for: Finding conceptually related content, handling synonyms, cross-language search + + 2. Keyword Search + - How it works: + - Uses PostgreSQL's full-text search capabilities with tsvector and ts_rank + - Converts text to searchable tokens using to_tsvector('english', text). Default language is English. + - Eg. SQL query: SELECT document, ts_rank(tokenized_content, plainto_tsquery('english', %s)) AS score + + - Characteristics: + - Lexical matching - finds exact keyword matches and variations + - Uses GIN (Generalized Inverted Index) for fast text search performance + - Scoring: Uses PostgreSQL's ts_rank function for relevance scoring + - Best for: Exact term matching, proper names, technical terms, Boolean-style queries + + 3. Hybrid Search + - How it works: + - Combines both vector and keyword search results + - Runs both searches independently, then merges results using configurable reranking + + - Two reranking strategies available: + - Reciprocal Rank Fusion (RRF) - (default: 60.0) + - Weighted Average - (default: 0.5) + + - Characteristics: + - Best of both worlds: semantic understanding + exact matching + - Documents appearing in both searches get boosted scores + - Configurable balance between semantic and lexical matching + - Best for: General-purpose search where you want both precision and recall + + 4. Database Schema + The PGVector implementation stores data optimized for all three search types: + CREATE TABLE vector_store_xxx ( + id TEXT PRIMARY KEY, + document JSONB, -- Original document + embedding vector(dimension), -- For vector search + content_text TEXT, -- Raw text content + tokenized_content TSVECTOR -- For keyword search + ); + + -- Indexes for performance + CREATE INDEX content_gin_idx ON table USING GIN(tokenized_content); -- Keyword search + -- Vector index created automatically by pgvector + + ## Usage + + To use PGVector in your Llama Stack project, follow these steps: + + 1. Install the necessary dependencies. + 2. Configure your Llama Stack project to use pgvector. (e.g. remote::pgvector). + 3. Start storing and querying vectors. + + ## This is an example how you can set up your environment for using PGVector + + 1. Export env vars: + ```bash + export ENABLE_PGVECTOR=true + export PGVECTOR_HOST=localhost + export PGVECTOR_PORT=5432 + export PGVECTOR_DB=llamastack + export PGVECTOR_USER=llamastack + export PGVECTOR_PASSWORD=llamastack + ``` + + 2. Create DB: + ```bash + psql -h localhost -U postgres -c "CREATE ROLE llamastack LOGIN PASSWORD 'llamastack';" + psql -h localhost -U postgres -c "CREATE DATABASE llamastack OWNER llamastack;" + psql -h localhost -U llamastack -d llamastack -c "CREATE EXTENSION IF NOT EXISTS vector;" + ``` + + ## Installation + + You can install PGVector using docker: + + ```bash + docker pull pgvector/pgvector:pg17 + ``` + ## Documentation + See [PGVector's documentation](https://github.com/pgvector/pgvector) for more details about PGVector in general. +sidebar_label: Remote - Pgvector +title: remote::pgvector +--- + +# remote::pgvector + +## Description + + +[PGVector](https://github.com/pgvector/pgvector) is a remote vector database provider for Llama Stack. It +allows you to store and query vectors directly in memory. +That means you'll get fast and efficient vector retrieval. + +## Features + +- Easy to use +- Fully integrated with Llama Stack + +There are three implementations of search for PGVectoIndex available: + +1. Vector Search: +- How it works: + - Uses PostgreSQL's vector extension (pgvector) to perform similarity search + - Compares query embeddings against stored embeddings using Cosine distance or other distance metrics + - Eg. SQL query: SELECT document, embedding <=> %s::vector AS distance FROM table ORDER BY distance + +-Characteristics: + - Semantic understanding - finds documents similar in meaning even if they don't share keywords + - Works with high-dimensional vector embeddings (typically 768, 1024, or higher dimensions) + - Best for: Finding conceptually related content, handling synonyms, cross-language search + +2. Keyword Search +- How it works: + - Uses PostgreSQL's full-text search capabilities with tsvector and ts_rank + - Converts text to searchable tokens using to_tsvector('english', text). Default language is English. + - Eg. SQL query: SELECT document, ts_rank(tokenized_content, plainto_tsquery('english', %s)) AS score + +- Characteristics: + - Lexical matching - finds exact keyword matches and variations + - Uses GIN (Generalized Inverted Index) for fast text search performance + - Scoring: Uses PostgreSQL's ts_rank function for relevance scoring + - Best for: Exact term matching, proper names, technical terms, Boolean-style queries + +3. Hybrid Search +- How it works: + - Combines both vector and keyword search results + - Runs both searches independently, then merges results using configurable reranking + +- Two reranking strategies available: + - Reciprocal Rank Fusion (RRF) - (default: 60.0) + - Weighted Average - (default: 0.5) + +- Characteristics: + - Best of both worlds: semantic understanding + exact matching + - Documents appearing in both searches get boosted scores + - Configurable balance between semantic and lexical matching + - Best for: General-purpose search where you want both precision and recall + +4. Database Schema +The PGVector implementation stores data optimized for all three search types: +CREATE TABLE vector_store_xxx ( + id TEXT PRIMARY KEY, + document JSONB, -- Original document + embedding vector(dimension), -- For vector search + content_text TEXT, -- Raw text content + tokenized_content TSVECTOR -- For keyword search +); + +-- Indexes for performance +CREATE INDEX content_gin_idx ON table USING GIN(tokenized_content); -- Keyword search +-- Vector index created automatically by pgvector + +## Usage + +To use PGVector in your Llama Stack project, follow these steps: + +1. Install the necessary dependencies. +2. Configure your Llama Stack project to use pgvector. (e.g. remote::pgvector). +3. Start storing and querying vectors. + +## This is an example how you can set up your environment for using PGVector + +1. Export env vars: +```bash +export ENABLE_PGVECTOR=true +export PGVECTOR_HOST=localhost +export PGVECTOR_PORT=5432 +export PGVECTOR_DB=llamastack +export PGVECTOR_USER=llamastack +export PGVECTOR_PASSWORD=llamastack +``` + +2. Create DB: +```bash +psql -h localhost -U postgres -c "CREATE ROLE llamastack LOGIN PASSWORD 'llamastack';" +psql -h localhost -U postgres -c "CREATE DATABASE llamastack OWNER llamastack;" +psql -h localhost -U llamastack -d llamastack -c "CREATE EXTENSION IF NOT EXISTS vector;" +``` + +## Installation + +You can install PGVector using docker: + +```bash +docker pull pgvector/pgvector:pg17 +``` +## Documentation +See [PGVector's documentation](https://github.com/pgvector/pgvector) for more details about PGVector in general. + + +## Configuration + +| Field | Type | Required | Default | Description | +|-------|------|----------|---------|-------------| +| `host` | `str \| None` | No | localhost | | +| `port` | `int \| None` | No | 5432 | | +| `db` | `str \| None` | No | postgres | | +| `user` | `str \| None` | No | postgres | | +| `password` | `str \| None` | No | mysecretpassword | | +| `kvstore` | `utils.kvstore.config.RedisKVStoreConfig \| utils.kvstore.config.SqliteKVStoreConfig \| utils.kvstore.config.PostgresKVStoreConfig \| utils.kvstore.config.MongoDBKVStoreConfig, annotation=NoneType, required=False, default='sqlite', discriminator='type'` | No | | Config for KV store backend (SQLite only for now) | + +## Sample Configuration + +```yaml +host: ${env.PGVECTOR_HOST:=localhost} +port: ${env.PGVECTOR_PORT:=5432} +db: ${env.PGVECTOR_DB} +user: ${env.PGVECTOR_USER} +password: ${env.PGVECTOR_PASSWORD} +kvstore: + type: sqlite + db_path: ${env.SQLITE_STORE_DIR:=~/.llama/dummy}/pgvector_registry.db +``` diff --git a/docs/source/providers/vector_io/remote_qdrant.md b/docs/docs/providers/vector_io/remote_qdrant.mdx similarity index 88% rename from docs/source/providers/vector_io/remote_qdrant.md rename to docs/docs/providers/vector_io/remote_qdrant.mdx index 043141007..c44a2b937 100644 --- a/docs/source/providers/vector_io/remote_qdrant.md +++ b/docs/docs/providers/vector_io/remote_qdrant.mdx @@ -1,3 +1,9 @@ +--- +description: "Please refer to the inline provider documentation." +sidebar_label: Remote - Qdrant +title: remote::qdrant +--- + # remote::qdrant ## Description @@ -29,6 +35,4 @@ api_key: ${env.QDRANT_API_KEY:=} kvstore: type: sqlite db_path: ${env.SQLITE_STORE_DIR:=~/.llama/dummy}/qdrant_registry.db - ``` - diff --git a/docs/source/providers/vector_io/remote_weaviate.md b/docs/docs/providers/vector_io/remote_weaviate.mdx similarity index 63% rename from docs/source/providers/vector_io/remote_weaviate.md rename to docs/docs/providers/vector_io/remote_weaviate.mdx index c59487cf6..3f1e36422 100644 --- a/docs/source/providers/vector_io/remote_weaviate.md +++ b/docs/docs/providers/vector_io/remote_weaviate.mdx @@ -1,3 +1,38 @@ +--- +description: | + [Weaviate](https://weaviate.io/) is a vector database provider for Llama Stack. + It allows you to store and query vectors directly within a Weaviate database. + That means you're not limited to storing vectors in memory or in a separate service. + + ## Features + Weaviate supports: + - Store embeddings and their metadata + - Vector search + - Full-text search + - Hybrid search + - Document storage + - Metadata filtering + - Multi-modal retrieval + + + ## Usage + + To use Weaviate in your Llama Stack project, follow these steps: + + 1. Install the necessary dependencies. + 2. Configure your Llama Stack project to use chroma. + 3. Start storing and querying vectors. + + ## Installation + + To install Weaviate see the [Weaviate quickstart documentation](https://weaviate.io/developers/weaviate/quickstart). + + ## Documentation + See [Weaviate's documentation](https://weaviate.io/developers/weaviate) for more details about Weaviate in general. +sidebar_label: Remote - Weaviate +title: remote::weaviate +--- + # remote::weaviate ## Description @@ -17,6 +52,7 @@ Weaviate supports: - Metadata filtering - Multi-modal retrieval + ## Usage To use Weaviate in your Llama Stack project, follow these steps: @@ -49,6 +85,4 @@ weaviate_cluster_url: ${env.WEAVIATE_CLUSTER_URL:=localhost:8080} kvstore: type: sqlite db_path: ${env.SQLITE_STORE_DIR:=~/.llama/dummy}/weaviate_registry.db - ``` - diff --git a/docs/source/references/evals_reference/index.md b/docs/docs/references/evals_reference/index.mdx similarity index 92% rename from docs/source/references/evals_reference/index.md rename to docs/docs/references/evals_reference/index.mdx index 054a0b809..0ec555e66 100644 --- a/docs/source/references/evals_reference/index.md +++ b/docs/docs/references/evals_reference/index.mdx @@ -9,12 +9,11 @@ We introduce a set of APIs in Llama Stack for supporting running evaluations of This guide goes over the sets of APIs and developer experience flow of using Llama Stack to run evaluations for different use cases. Checkout our Colab notebook on working examples with evaluations [here](https://colab.research.google.com/drive/10CHyykee9j2OigaIcRv47BKG9mrNm0tJ?usp=sharing). - ## Evaluation Concepts -The Evaluation APIs are associated with a set of Resources as shown in the following diagram. Please visit the Resources section in our [Core Concepts](../../concepts/index.md) guide for better high-level understanding. +The Evaluation APIs are associated with a set of Resources as shown in the following diagram. Please visit the Resources section in our [Core Concepts](../concepts/) guide for better high-level understanding. -![Eval Concepts](./resources/eval-concept.png) +![Eval Concepts](/img/eval-concept.png) - **DatasetIO**: defines interface with datasets and data loaders. - Associated with `Dataset` resource. @@ -23,7 +22,6 @@ The Evaluation APIs are associated with a set of Resources as shown in the follo - **Eval**: generate outputs (via Inference or Agents) and perform scoring. - Associated with `Benchmark` resource. - ## Evaluation Examples Walkthrough [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/meta-llama/llama-stack/blob/main/docs/notebooks/Llama_Stack_Benchmark_Evals.ipynb) @@ -156,7 +154,6 @@ response = client.eval.evaluate_rows( pprint(response) ``` - ### 2. Agentic Evaluation - In this example, we will demonstrate how to evaluate a agent candidate served by Llama Stack via `/agent` API. - We will continue to use the SimpleQA dataset we used in previous example. @@ -202,7 +199,7 @@ pprint(response) Llama Stack offers a library of scoring functions and the `/scoring` API, allowing you to run evaluations on your pre-annotated AI application datasets. -In this example, we will work with an example RAG dataset you have built previously, label with an annotation, and use LLM-As-Judge with custom judge prompt for scoring. Please checkout our [Llama Stack Playground](https://llama-stack.readthedocs.io/en/latest/playground/index.html) for an interactive interface to upload datasets and run scorings. +In this example, we will work with an example RAG dataset you have built previously, label with an annotation, and use LLM-As-Judge with custom judge prompt for scoring. Please checkout our [Llama Stack Playground](../building_applications/playground) for an interactive interface to upload datasets and run scorings. ```python judge_model_id = "meta-llama/Llama-3.1-405B-Instruct-FP8" @@ -268,29 +265,27 @@ response = client.scoring.score( ## Running Evaluations via CLI The following examples give the quick steps to start running evaluations using the llama-stack-client CLI. -#### Benchmark Evaluation CLI +### Benchmark Evaluation CLI There are 3 necessary input for running a benchmark eval - `list of benchmark_ids`: The list of benchmark ids to run evaluation on - `model-id`: The model id to evaluate on -- `utput_dir`: Path to store the evaluate results -``` +- `output_dir`: Path to store the evaluate results + +```bash llama-stack-client eval run-benchmark ... \ --model_id \ --output_dir \ ``` You can run -``` +```bash llama-stack-client eval run-benchmark help ``` -to see the description of all the flags to run benckmark eval +to see the description of all the flags to run benchmark eval +In the output log, you can find the path to the file that has your evaluation results. Open that file and you can see your aggregate evaluation results over there. -In the output log, you can find the path to the file that has your evaluation results. Open that file and you can see you aggrgate -evaluation results over there. - - -#### Application Evaluation CLI +### Application Evaluation CLI Usage: For running application evals, you will already have available datasets in hand from your application. You will need to specify: - `scoring-fn-id`: List of ScoringFunction identifiers you wish to use to run on your application. - `Dataset` used for evaluation: @@ -298,21 +293,19 @@ Usage: For running application evals, you will already have available datasets i - (2) `--dataset-id`: pre-registered dataset in Llama Stack - (Optional) `--scoring-params-config`: optionally parameterize scoring functions with custom params (e.g. `judge_prompt`, `judge_model`, `parsing_regexes`). - -``` +```bash llama-stack-client eval run_scoring ... --dataset-path \ --output-dir ./ ``` -#### Defining BenchmarkConfig +### Defining BenchmarkConfig The `BenchmarkConfig` are user specified config to define: 1. `EvalCandidate` to run generation on: - `ModelCandidate`: The model will be used for generation through LlamaStack /inference API. - `AgentCandidate`: The agentic system specified by AgentConfig will be used for generation through LlamaStack /agents API. 2. Optionally scoring function params to allow customization of scoring function behaviour. This is useful to parameterize generic scoring functions such as LLMAsJudge with custom `judge_model` / `judge_prompt`. - **Example BenchmarkConfig** ```json { @@ -340,29 +333,25 @@ The `BenchmarkConfig` are user specified config to define: } ``` - ## Open-benchmark Contributing Guide ### Create the new dataset for your new benchmark An eval open-benchmark essentially contains 2 parts: - `raw data`: The raw dataset associated with the benchmark. You typically need to search the original paper that introduces the benchmark and find the canonical dataset (usually hosted on huggingface) -- `prompt template`: How to ask the candidate model to generate the answer (prompt template plays a critical role to the evaluation results). Tyically, you can find the reference prompt template associated with the benchmark in benchmarks author's repo ([exmaple](https://github.com/idavidrein/gpqa/blob/main/prompts/chain_of_thought.txt)) or some other popular open source repos ([example](https://github.com/openai/simple-evals/blob/0a6e8f62e52bc5ae915f752466be3af596caf392/common.py#L14)) +- `prompt template`: How to ask the candidate model to generate the answer (prompt template plays a critical role to the evaluation results). Typically, you can find the reference prompt template associated with the benchmark in benchmarks author's repo ([example](https://github.com/idavidrein/gpqa/blob/main/prompts/chain_of_thought.txt)) or some other popular open source repos ([example](https://github.com/openai/simple-evals/blob/0a6e8f62e52bc5ae915f752466be3af596caf392/common.py#L14)) -To create new open-benmark in llama stack, you need to combine the prompt template and the raw data into the `chat_completion_input` column in the evaluation dataset. +To create new open-benchmark in llama stack, you need to combine the prompt template and the raw data into the `chat_completion_input` column in the evaluation dataset. -Llama stack enforeces the evaluate dataset schema to contain at least 3 columns: +Llama stack enforces the evaluate dataset schema to contain at least 3 columns: - `chat_completion_input`: The actual input to the model to run the generation for eval - `input_query`: The raw input from the raw dataset without the prompt template -- `expected_answer`: The ground truth for scoring functions to calcalate the score from. - +- `expected_answer`: The ground truth for scoring functions to calculate the score from. You need to write a script [example convert script](https://gist.github.com/yanxi0830/118e9c560227d27132a7fd10e2c92840) to convert the benchmark raw dataset to llama stack format eval dataset and update the dataset to huggingface [example benchmark dataset](https://huggingface.co/datasets/llamastack/mmmu) - ### Find scoring function for your new benchmark The purpose of scoring function is to calculate the score for each example based on candidate model generation result and expected_answer. It also aggregates the scores from all the examples and generate the final evaluate results. - Firstly, you can see if the existing [llama stack scoring functions](https://github.com/meta-llama/llama-stack/tree/main/llama_stack/providers/inline/scoring) can fulfill your need. If not, you need to write a new scoring function based on what benchmark author / other open source repo describe. ### Add new benchmark into template @@ -373,17 +362,15 @@ Secondly, you need to add the new benchmark you just created under the `benchmar - `dataset_id`: identifier of the dataset associated with your benchmark - `scoring_functions`: scoring function to calculate the score based on generation results and expected_answer - ### Test the new benchmark Spin up llama stack server with 'open-benchmark' templates -``` +```bash llama stack run llama_stack/distributions/open-benchmark/run.yaml - ``` Run eval benchmark CLI with your new benchmark id -``` +```bash llama-stack-client eval run-benchmark \ --model_id \ --output_dir \ diff --git a/docs/docs/references/index.mdx b/docs/docs/references/index.mdx new file mode 100644 index 000000000..dd6ab21cf --- /dev/null +++ b/docs/docs/references/index.mdx @@ -0,0 +1,12 @@ +--- +title: References +description: Reference documentation for Llama Stack +sidebar_label: Overview +sidebar_position: 1 +--- + +# References + +- [Python SDK Reference](/docs/references/python_sdk_reference/) +- [Llama CLI](/docs/references/llama_cli_reference/) for building and running your Llama Stack server +- [Llama Stack Client CLI](./llama_stack_client_cli_reference.md) for interacting with your Llama Stack server diff --git a/docs/source/references/llama_cli_reference/download_models.md b/docs/docs/references/llama_cli_reference/download_models.md similarity index 100% rename from docs/source/references/llama_cli_reference/download_models.md rename to docs/docs/references/llama_cli_reference/download_models.md diff --git a/docs/source/references/llama_cli_reference/index.md b/docs/docs/references/llama_cli_reference/index.md similarity index 99% rename from docs/source/references/llama_cli_reference/index.md rename to docs/docs/references/llama_cli_reference/index.md index 09a8b7177..fe3aa51ab 100644 --- a/docs/source/references/llama_cli_reference/index.md +++ b/docs/docs/references/llama_cli_reference/index.md @@ -29,7 +29,7 @@ You have two ways to install Llama Stack: ## `llama` subcommands 1. `download`: Supports downloading models from Meta or Hugging Face. [Downloading models](#downloading-models) 2. `model`: Lists available models and their properties. [Understanding models](#understand-the-models) -3. `stack`: Allows you to build a stack using the `llama stack` distribution and run a Llama Stack server. You can read more about how to build a Llama Stack distribution in the [Build your own Distribution](../../distributions/building_distro) documentation. +3. `stack`: Allows you to build a stack using the `llama stack` distribution and run a Llama Stack server. You can read more about how to build a Llama Stack distribution in the [Build your own Distribution](../distributions/building_distro) documentation. ### Sample Usage diff --git a/docs/source/references/llama_stack_client_cli_reference.md b/docs/docs/references/llama_stack_client_cli_reference.md similarity index 99% rename from docs/source/references/llama_stack_client_cli_reference.md rename to docs/docs/references/llama_stack_client_cli_reference.md index 2d386dbfa..d4d79cea1 100644 --- a/docs/source/references/llama_stack_client_cli_reference.md +++ b/docs/docs/references/llama_stack_client_cli_reference.md @@ -478,7 +478,6 @@ llama-stack-client scoring_functions list ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━┓ ┃ identifier ┃ provider_id ┃ description ┃ type ┃ ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━┩ -│ basic::bfcl │ basic │ BFCL complex scoring │ scoring_function │ │ basic::docvqa │ basic │ DocVQA Visual Question & Answer scoring function │ scoring_function │ │ basic::equality │ basic │ Returns 1.0 if the input is equal to the target, 0.0 │ scoring_function │ │ │ │ otherwise. │ │ diff --git a/docs/source/references/python_sdk_reference/index.md b/docs/docs/references/python_sdk_reference/index.md similarity index 100% rename from docs/source/references/python_sdk_reference/index.md rename to docs/docs/references/python_sdk_reference/index.md diff --git a/docs/docusaurus.config.ts b/docs/docusaurus.config.ts new file mode 100644 index 000000000..937aa4ddf --- /dev/null +++ b/docs/docusaurus.config.ts @@ -0,0 +1,225 @@ +// @ts-check +// Note: type annotations allow type checking and IDEs autocompletion + +import type * as Preset from "@docusaurus/preset-classic"; +import type { Config } from "@docusaurus/types"; +import type * as Plugin from "@docusaurus/types/src/plugin"; +import type * as OpenApiPlugin from "docusaurus-plugin-openapi-docs"; + +const config: Config = { + title: 'Llama Stack', + tagline: 'The open-source framework for building generative AI applications', + url: 'https://llamastack.github.io', + baseUrl: '/', + onBrokenLinks: "warn", + onBrokenMarkdownLinks: "warn", + favicon: "img/favicon.ico", + + // GitHub pages deployment config. + organizationName: 'reluctantfuturist', + projectName: 'llama-stack', + trailingSlash: false, + + presets: [ + [ + "classic", + { + docs: { + sidebarPath: require.resolve("./sidebars.ts"), + // Please change this to your repo. + // Remove this to remove the "edit this page" links. + editUrl: 'https://github.com/meta-llama/llama-stack/tree/main/docs/', + docItemComponent: "@theme/ApiItem", // Derived from docusaurus-theme-openapi + }, + blog: false, + theme: { + customCss: require.resolve("./src/css/custom.css"), + }, + } satisfies Preset.Options, + ], + ], + + themeConfig: { + image: 'img/llama-stack.png', + navbar: { + title: 'Llama Stack', + logo: { + alt: 'Llama Stack Logo', + src: 'img/llama-stack-logo.png', + }, + items: [ + { + type: 'docSidebar', + sidebarId: 'tutorialSidebar', + position: 'left', + label: 'Docs', + }, + { + type: 'docSidebar', + sidebarId: 'apiSidebar', + position: 'left', + label: 'API Reference', + }, + { + href: 'https://github.com/llamastack/llama-stack', + label: 'GitHub', + position: 'right', + }, + ], + }, + footer: { + style: 'dark', + links: [ + { + title: 'Docs', + items: [ + { + label: 'Getting Started', + to: '/docs/getting_started/quickstart', + }, + { + label: 'Concepts', + to: '/docs/concepts', + }, + { + label: 'API Reference', + to: '/docs/api/llama-stack-specification', + }, + ], + }, + { + title: 'Community', + items: [ + { + label: 'Discord', + href: 'https://discord.gg/llama-stack', + }, + { + label: 'GitHub Discussions', + href: 'https://github.com/llamastack/llama-stack/discussions', + }, + { + label: 'Issues', + href: 'https://github.com/llamastack/llama-stack/issues', + }, + ], + }, + { + title: 'More', + items: [ + { + label: 'GitHub', + href: 'https://github.com/llamastack/llama-stack', + }, + { + label: 'PyPI', + href: 'https://pypi.org/project/llama-stack/', + }, + ], + }, + ], + copyright: `Copyright © ${new Date().getFullYear()} Meta Platforms, Inc. Built with Docusaurus.`, + }, + prism: { + additionalLanguages: [ + 'ruby', + 'csharp', + 'php', + 'java', + 'powershell', + 'json', + 'bash', + 'python', + 'yaml', + ], + }, + docs: { + sidebar: { + hideable: true, + }, + }, + // Language tabs for API documentation + languageTabs: [ + { + highlight: "python", + language: "python", + logoClass: "python", + }, + { + highlight: "bash", + language: "curl", + logoClass: "curl", + }, + { + highlight: "javascript", + language: "nodejs", + logoClass: "nodejs", + }, + { + highlight: "java", + language: "java", + logoClass: "java", + }, + ], + } satisfies Preset.ThemeConfig, + + plugins: [ + [ + "docusaurus-plugin-openapi-docs", + { + id: "openapi", + docsPluginId: "classic", + config: { + llamastack: { + specPath: "static/llama-stack-spec.yaml", + outputDir: "docs/api", + downloadUrl: "https://raw.githubusercontent.com/meta-llama/llama-stack/main/docs/static/llama-stack-spec.yaml", + sidebarOptions: { + groupPathsBy: "tag", + categoryLinkSource: "tag", + }, + } satisfies OpenApiPlugin.Options, + } satisfies Plugin.PluginOptions, + }, + ], + ], + + themes: [ + "docusaurus-theme-openapi-docs", + [ + require.resolve("@easyops-cn/docusaurus-search-local"), + { + // Optimization for production + hashed: true, + + // Language settings + language: ["en"], + + // Content indexing settings + indexDocs: true, + indexBlog: false, // No blog in Llama Stack + indexPages: true, + + // Route configuration + docsRouteBasePath: '/docs', + + // Search behavior optimization for technical docs + searchResultLimits: 8, + searchResultContextMaxLength: 50, + explicitSearchResultPath: true, + + // User experience enhancements + searchBarShortcut: true, + searchBarShortcutHint: true, + searchBarPosition: "right", + + // Performance optimizations + ignoreFiles: [ + "node_modules/**/*", + ], + }, + ], + ], +}; + +export default config; diff --git a/docs/getting_started.ipynb b/docs/getting_started.ipynb index eeebf12d9..641cf4224 100644 --- a/docs/getting_started.ipynb +++ b/docs/getting_started.ipynb @@ -11,11 +11,11 @@ "\n", "# Llama Stack - Building AI Applications\n", "\n", - "\"drawing\"\n", + "\"drawing\"\n", "\n", "[Llama Stack](https://github.com/meta-llama/llama-stack) defines and standardizes the set of core building blocks needed to bring generative AI applications to market. These building blocks are presented in the form of interoperable APIs with a broad set of Service Providers providing their implementations.\n", "\n", - "Read more about the project here: https://llama-stack.readthedocs.io/en/latest/index.html\n", + "Read more about the project here: https://llamastack.github.io\n", "\n", "In this guide, we will showcase how you can build LLM-powered agentic applications using Llama Stack.\n", "\n", @@ -75,7 +75,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "id": "J2kGed0R5PSf", "metadata": { "colab": { @@ -113,17 +113,17 @@ } ], "source": [ - "import os \n", + "import os\n", "import subprocess\n", "import time\n", "\n", - "!pip install uv \n", + "!pip install uv\n", "\n", "if \"UV_SYSTEM_PYTHON\" in os.environ:\n", " del os.environ[\"UV_SYSTEM_PYTHON\"]\n", "\n", "# this command installs all the dependencies needed for the llama stack server with the together inference provider\n", - "!uv run --with llama-stack llama stack build --distro together --image-type venv \n", + "!uv run --with llama-stack llama stack build --distro together --image-type venv\n", "\n", "def run_llama_stack_server_background():\n", " log_file = open(\"llama_stack_server.log\", \"w\")\n", @@ -134,7 +134,7 @@ " stderr=log_file,\n", " text=True\n", " )\n", - " \n", + "\n", " print(f\"Starting Llama Stack server with PID: {process.pid}\")\n", " return process\n", "\n", @@ -142,11 +142,11 @@ " import requests\n", " from requests.exceptions import ConnectionError\n", " import time\n", - " \n", + "\n", " url = \"http://0.0.0.0:8321/v1/health\"\n", " max_retries = 30\n", " retry_interval = 1\n", - " \n", + "\n", " print(\"Waiting for server to start\", end=\"\")\n", " for _ in range(max_retries):\n", " try:\n", @@ -157,12 +157,12 @@ " except ConnectionError:\n", " print(\".\", end=\"\", flush=True)\n", " time.sleep(retry_interval)\n", - " \n", + "\n", " print(\"\\nServer failed to start after\", max_retries * retry_interval, \"seconds\")\n", " return False\n", "\n", "\n", - "# use this helper if needed to kill the server \n", + "# use this helper if needed to kill the server\n", "def kill_llama_stack_server():\n", " # Kill any existing llama stack server processes\n", " os.system(\"ps aux | grep -v grep | grep llama_stack.core.server.server | awk '{print $2}' | xargs kill -9\")\n" @@ -242,7 +242,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "id": "E1UFuJC570Tk", "metadata": { "colab": { @@ -407,9 +407,9 @@ "from llama_stack_client import LlamaStackClient\n", "\n", "client = LlamaStackClient(\n", - " base_url=\"http://0.0.0.0:8321\", \n", + " base_url=\"http://0.0.0.0:8321\",\n", " provider_data = {\n", - " \"tavily_search_api_key\": os.environ['TAVILY_SEARCH_API_KEY'], \n", + " \"tavily_search_api_key\": os.environ['TAVILY_SEARCH_API_KEY'],\n", " \"together_api_key\": os.environ['TOGETHER_API_KEY']\n", " }\n", ")" @@ -1177,7 +1177,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "id": "WS8Gu5b0APHs", "metadata": { "colab": { @@ -1207,7 +1207,7 @@ "from termcolor import cprint\n", "\n", "agent = Agent(\n", - " client, \n", + " client,\n", " model=\"meta-llama/Llama-3.3-70B-Instruct\",\n", " instructions=\"You are a helpful assistant. Use websearch tool to help answer questions.\",\n", " tools=[\"builtin::websearch\"],\n", @@ -1249,7 +1249,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "id": "GvLWltzZCNkg", "metadata": { "colab": { @@ -1367,7 +1367,7 @@ " chunk_size_in_tokens=512,\n", ")\n", "rag_agent = Agent(\n", - " client, \n", + " client,\n", " model=model_id,\n", " instructions=\"You are a helpful assistant\",\n", " tools = [\n", @@ -2154,7 +2154,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": null, "id": "vttLbj_YO01f", "metadata": { "colab": { @@ -2217,7 +2217,7 @@ "from termcolor import cprint\n", "\n", "agent = Agent(\n", - " client, \n", + " client,\n", " model=model_id,\n", " instructions=\"You are a helpful assistant\",\n", " tools=[\"mcp::filesystem\"],\n", @@ -2283,7 +2283,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": null, "id": "4iCO59kP20Zs", "metadata": { "colab": { @@ -2317,7 +2317,7 @@ "from llama_stack_client import Agent, AgentEventLogger\n", "\n", "agent = Agent(\n", - " client, \n", + " client,\n", " model=\"meta-llama/Llama-3.3-70B-Instruct\",\n", " instructions=\"You are a helpful assistant. Use web_search tool to answer the questions.\",\n", " tools=[\"builtin::websearch\"],\n", @@ -2846,7 +2846,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": null, "id": "44e05e16", "metadata": {}, "outputs": [ @@ -2880,8 +2880,7 @@ "!curl -O https://raw.githubusercontent.com/meta-llama/llama-models/refs/heads/main/Llama_Repo.jpeg\n", "\n", "from IPython.display import Image\n", - "Image(\"Llama_Repo.jpeg\", width=256, height=256)\n", - "\n" + "Image(\"Llama_Repo.jpeg\", width=256, height=256)\n" ] }, { diff --git a/docs/getting_started_llama4.ipynb b/docs/getting_started_llama4.ipynb index 1913330fe..648f4bbef 100644 --- a/docs/getting_started_llama4.ipynb +++ b/docs/getting_started_llama4.ipynb @@ -11,11 +11,11 @@ "\n", "# Getting Started with Llama 4 in Llama Stack\n", "\n", - "\"drawing\"\n", + "\"drawing\"\n", "\n", "[Llama Stack](https://github.com/meta-llama/llama-stack) defines and standardizes the set of core building blocks needed to bring generative AI applications to market. These building blocks are presented in the form of interoperable APIs with a broad set of Service Providers providing their implementations.\n", "\n", - "Read more about the project here: https://llama-stack.readthedocs.io/en/latest/index.html\n", + "Read more about the project here: https://llamastack.github.io/latest/index.html\n", "\n", "In this guide, we will showcase how you can get started with using Llama 4 in Llama Stack.\n", "\n", @@ -51,7 +51,7 @@ "metadata": {}, "outputs": [], "source": [ - "!pip install uv \n", + "!pip install uv\n", "\n", "MODEL=\"Llama-4-Scout-17B-16E-Instruct\"\n", "# get meta url from llama.com\n", @@ -223,7 +223,7 @@ } ], "source": [ - "import os \n", + "import os\n", "import subprocess\n", "import time\n", "\n", @@ -232,8 +232,8 @@ "if \"UV_SYSTEM_PYTHON\" in os.environ:\n", " del os.environ[\"UV_SYSTEM_PYTHON\"]\n", "\n", - "# this command installs all the dependencies needed for the llama stack server \n", - "!uv run --with llama-stack llama stack build --distro meta-reference-gpu --image-type venv \n", + "# this command installs all the dependencies needed for the llama stack server\n", + "!uv run --with llama-stack llama stack build --distro meta-reference-gpu --image-type venv\n", "\n", "def run_llama_stack_server_background():\n", " log_file = open(\"llama_stack_server.log\", \"w\")\n", @@ -244,7 +244,7 @@ " stderr=log_file,\n", " text=True\n", " )\n", - " \n", + "\n", " print(f\"Starting Llama Stack server with PID: {process.pid}\")\n", " return process\n", "\n", @@ -252,11 +252,11 @@ " import requests\n", " from requests.exceptions import ConnectionError\n", " import time\n", - " \n", + "\n", " url = \"http://0.0.0.0:8321/v1/health\"\n", " max_retries = 30\n", " retry_interval = 1\n", - " \n", + "\n", " print(\"Waiting for server to start\", end=\"\")\n", " for _ in range(max_retries):\n", " try:\n", @@ -267,12 +267,12 @@ " except ConnectionError:\n", " print(\".\", end=\"\", flush=True)\n", " time.sleep(retry_interval)\n", - " \n", + "\n", " print(\"\\nServer failed to start after\", max_retries * retry_interval, \"seconds\")\n", " return False\n", "\n", "\n", - "# use this helper if needed to kill the server \n", + "# use this helper if needed to kill the server\n", "def kill_llama_stack_server():\n", " # Kill any existing llama stack server processes\n", " os.system(\"ps aux | grep -v grep | grep llama_stack.core.server.server | awk '{print $2}' | xargs kill -9\")\n" diff --git a/docs/getting_started_llama_api.ipynb b/docs/getting_started_llama_api.ipynb index 5a4283117..f6a170980 100644 --- a/docs/getting_started_llama_api.ipynb +++ b/docs/getting_started_llama_api.ipynb @@ -1,909 +1,909 @@ { - "cells": [ - { - "cell_type": "markdown", - "id": "c1e7571c", - "metadata": { - "id": "c1e7571c" - }, - "source": [ - "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/meta-llama/llama-stack/blob/main/docs/getting_started.ipynb)\n", - "\n", - "# Getting Started with Llama 4 in Llama Stack\n", - "\n", - "\"drawing\"\n", - "\n", - "[Llama Stack](https://github.com/meta-llama/llama-stack) defines and standardizes the set of core building blocks needed to bring generative AI applications to market. These building blocks are presented in the form of interoperable APIs with a broad set of Service Providers providing their implementations.\n", - "\n", - "Read more about the project here: https://llama-stack.readthedocs.io/en/latest/index.html\n", - "\n", - "In this guide, we will showcase how you can get started with using Llama 4 in Llama Stack.\n", - "\n", - "**💡 Quick Start Option:** If you want a simpler and faster way to test out Llama Stack, check out the [quick_start.ipynb](quick_start.ipynb) notebook instead. It provides a streamlined experience for getting up and running in just a few steps.\n" - ] + "cells": [ + { + "cell_type": "markdown", + "id": "c1e7571c", + "metadata": { + "id": "c1e7571c" }, - { - "cell_type": "markdown", - "id": "4CV1Q19BDMVw", - "metadata": { - "id": "4CV1Q19BDMVw" - }, - "source": [ - "## 1. Getting started with Llama Stack" - ] - }, - { - "cell_type": "markdown", - "id": "K4AvfUAJZOeS", - "metadata": { - "id": "K4AvfUAJZOeS" - }, - "source": [ - "### 1.1. Create Llama API account\n", - "\n", - "In this showcase, we will use [Llama API](https://llama.developer.meta.com/) as the inference provider. So, you would first get an API key from Llama API if you don't have one already.\n", - "\n", - "\n", - "\n", - "> **Note:** Set the API Key in the Secrets of this notebook\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "id": "oDUB7M_qe-Gs", - "metadata": { - "id": "oDUB7M_qe-Gs" - }, - "source": [ - "### 1.2. Setup and Running a Llama Stack server\n", - "\n", - "Llama Stack is architected as a collection of APIs that provide developers with the building blocks to build AI applications. \n", - "\n", - "Llama stack is typically available as a server with an endpoint that you can make calls to. Partners like Together and Fireworks offer their own Llama Stack compatible endpoints.\n", - "\n", - "In this showcase, we will start a Llama Stack server that is running locally.\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "J2kGed0R5PSf", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "collapsed": true, - "id": "J2kGed0R5PSf", - "outputId": "2478ea60-8d35-48a1-b011-f233831740c5" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Requirement already satisfied: uv in /opt/homebrew/Caskroom/miniconda/base/envs/l4/lib/python3.10/site-packages (0.6.12)\n", - "\u001b[2mUsing Python 3.10.16 environment at: /opt/homebrew/Caskroom/miniconda/base/envs/l4\u001b[0m\n", - "\u001b[2mAudited \u001b[1m1 package\u001b[0m \u001b[2min 83ms\u001b[0m\u001b[0m\n", - "Environment '/Users/erichuang/projects/internal-llama-stack/.venv' already exists, re-using it.\n", - "Virtual environment /Users/erichuang/projects/internal-llama-stack/.venv is already active\n", - "\u001b[2mUsing Python 3.11.11 environment at: /Users/erichuang/projects/internal-llama-stack/.venv\u001b[0m\n", - "\u001b[2mAudited \u001b[1m1 package\u001b[0m \u001b[2min 387ms\u001b[0m\u001b[0m\n", - "Installing pip dependencies\n", - "\u001b[2mUsing Python 3.11.11 environment at: /Users/erichuang/projects/internal-llama-stack/.venv\u001b[0m\n", - "\u001b[2K\u001b[2mResolved \u001b[1m123 packages\u001b[0m \u001b[2min 1.13s\u001b[0m\u001b[0m \u001b[0m\n", - "\u001b[2K\u001b[37m⠙\u001b[0m \u001b[2mPreparing packages...\u001b[0m (0/6) \n", - "\u001b[2K\u001b[1A\u001b[37m⠙\u001b[0m \u001b[2mPreparing packages...\u001b[0m (0/6)-----\u001b[0m\u001b[0m 0 B/9.53 KiB \u001b[1A\n", - "\u001b[2K\u001b[1A\u001b[37m⠙\u001b[0m \u001b[2mPreparing packages...\u001b[0m (0/6)-\u001b[2m\u001b[0m\u001b[0m 9.53 KiB/9.53 KiB \u001b[1A\n", - "\u001b[2mshellingham\u001b[0m \u001b[32m------------------------------\u001b[2m\u001b[0m\u001b[0m 9.53 KiB/9.53 KiB\n", - "\u001b[2K\u001b[2A\u001b[37m⠙\u001b[0m \u001b[2mPreparing packages...\u001b[0m (0/6)----\u001b[0m\u001b[0m 0 B/44.00 KiB \u001b[2A\n", - 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"\u001b[2mtogether \u001b[0m \u001b[32m------------\u001b[2m------------------\u001b[0m\u001b[0m 32.00 KiB/85.81 KiB\n", - "\u001b[2K\u001b[3A\u001b[37m⠹\u001b[0m \u001b[2mPreparing packages...\u001b[0m (3/6)----\u001b[0m\u001b[0m 2.62 MiB/3.08 MiB \u001b[3A\n", - "\u001b[2mtogether \u001b[0m \u001b[32m------------\u001b[2m------------------\u001b[0m\u001b[0m 32.00 KiB/85.81 KiB\n", - "\u001b[2K\u001b[2A\u001b[37m⠹\u001b[0m \u001b[2mPreparing packages...\u001b[0m (3/6)2m--\u001b[0m\u001b[0m 2.80 MiB/3.08 MiB \u001b[2A\n", - "\u001b[2mtogether \u001b[0m \u001b[32m-----------------\u001b[2m-------------\u001b[0m\u001b[0m 48.00 KiB/85.81 KiB\n", - "\u001b[2K\u001b[2A\u001b[37m⠹\u001b[0m \u001b[2mPreparing packages...\u001b[0m (3/6)2m--\u001b[0m\u001b[0m 2.81 MiB/3.08 MiB \u001b[2A\n", - "\u001b[2K\u001b[1A\u001b[37m⠹\u001b[0m \u001b[2mPreparing packages...\u001b[0m (3/6)----\u001b[0m\u001b[0m 48.00 KiB/85.81 KiB \u001b[1A\n", - "\u001b[2K\u001b[1A\u001b[37m⠹\u001b[0m \u001b[2mPreparing packages...\u001b[0m (3/6)2m--\u001b[0m\u001b[0m 80.00 KiB/85.81 KiB \u001b[1A\n", - "\u001b[2K\u001b[2mPrepared \u001b[1m6 packages\u001b[0m \u001b[2min 365ms\u001b[0m\u001b[0m \u001b[1A\n", - "\u001b[2K\u001b[2mInstalled \u001b[1m6 packages\u001b[0m \u001b[2min 50ms\u001b[0m\u001b[0m \u001b[0m\n", - " \u001b[32m+\u001b[39m \u001b[1meval-type-backport\u001b[0m\u001b[2m==0.2.2\u001b[0m\n", - " \u001b[32m+\u001b[39m \u001b[1mfaiss-cpu\u001b[0m\u001b[2m==1.10.0\u001b[0m\n", - " \u001b[32m+\u001b[39m \u001b[1mshellingham\u001b[0m\u001b[2m==1.5.4\u001b[0m\n", - " \u001b[32m+\u001b[39m \u001b[1mtabulate\u001b[0m\u001b[2m==0.9.0\u001b[0m\n", - " \u001b[32m+\u001b[39m \u001b[1mtogether\u001b[0m\u001b[2m==1.5.5\u001b[0m\n", - " \u001b[32m+\u001b[39m \u001b[1mtyper\u001b[0m\u001b[2m==0.15.2\u001b[0m\n", - "torch torchvision --index-url https://download.pytorch.org/whl/cpu\n", - "\u001b[2mUsing Python 3.11.11 environment at: /Users/erichuang/projects/internal-llama-stack/.venv\u001b[0m\n", - "\u001b[2mAudited \u001b[1m2 packages\u001b[0m \u001b[2min 32ms\u001b[0m\u001b[0m\n", - "sentence-transformers --no-deps\n", - "\u001b[2mUsing Python 3.11.11 environment at: /Users/erichuang/projects/internal-llama-stack/.venv\u001b[0m\n", - "\u001b[2mAudited \u001b[1m1 package\u001b[0m \u001b[2min 63ms\u001b[0m\u001b[0m\n", - "\u001b[32mBuild Successful!\u001b[0m\n" - ] - } - ], - "source": [ - "import os \n", - "import subprocess\n", - "import time\n", - "\n", - "!pip install uv \n", - "!uv pip install requests\n", - "\n", - "if \"UV_SYSTEM_PYTHON\" in os.environ:\n", - " del os.environ[\"UV_SYSTEM_PYTHON\"]\n", - "\n", - "# this command installs all the dependencies needed for the llama stack server \n", - "!uv run --with llama-stack llama stack build --distro llama_api --image-type venv \n", - "\n", - "def run_llama_stack_server_background():\n", - " log_file = open(\"llama_stack_server.log\", \"w\")\n", - " process = subprocess.Popen(\n", - " \"uv run --with llama-stack llama stack run llama_api --image-type venv\",\n", - " shell=True,\n", - " stdout=log_file,\n", - " stderr=log_file,\n", - " text=True\n", - " )\n", - " \n", - " print(f\"Starting Llama Stack server with PID: {process.pid}\")\n", - " return process\n", - "\n", - "def wait_for_server_to_start():\n", - " import requests\n", - " from requests.exceptions import ConnectionError\n", - " import time\n", - " \n", - " url = \"http://0.0.0.0:8321/v1/health\"\n", - " max_retries = 30\n", - " retry_interval = 1\n", - " \n", - " print(\"Waiting for server to start\", end=\"\")\n", - " for _ in range(max_retries):\n", - " try:\n", - " response = requests.get(url)\n", - " if response.status_code == 200:\n", - " print(\"\\nServer is ready!\")\n", - " return True\n", - " except ConnectionError:\n", - " print(\".\", end=\"\", flush=True)\n", - " time.sleep(retry_interval)\n", - " \n", - " print(\"\\nServer failed to start after\", max_retries * retry_interval, \"seconds\")\n", - " return False\n", - "\n", - "\n", - "# use this helper if needed to kill the server \n", - "def kill_llama_stack_server():\n", - " # Kill any existing llama stack server processes\n", - " os.system(\"ps aux | grep -v grep | grep llama_stack.core.server.server | awk '{print $2}' | xargs kill -9\")\n" - ] - }, - { - "cell_type": "markdown", - "id": "c40e9efd", - "metadata": {}, - "source": [ - "### 1.3 Starting the Llama Stack Server" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f779283d", - "metadata": {}, - "outputs": [], - "source": [ - "server_process = run_llama_stack_server_background()\n", - "assert wait_for_server_to_start()" - ] - }, - { - "cell_type": "markdown", - "id": "90eb721b", - "metadata": {}, - "source": [ - "### 1.4 Install and Configure the Client\n", - "\n", - "Now that we have our Llama Stack server running locally, we need to install the client package to interact with it. The `llama-stack-client` provides a simple Python interface to access all the functionality of Llama Stack, including:\n", - "\n", - "- Chat Completions ( text and multimodal )\n", - "- Safety Shields \n", - "- Agent capabilities with tools like web search, RAG with Telemetry\n", - "- Evaluation and scoring frameworks\n", - "\n", - "The client handles all the API communication with our local server, making it easy to integrate Llama Stack's capabilities into your applications.\n", - "\n", - "In the next cells, we'll:\n", - "\n", - "1. Install the client package\n", - "2. Set up API keys for external services (Together AI and Tavily Search)\n", - "3. Initialize the client to connect to our local server\n" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "2e68e32a", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[2mUsing Python 3.10.16 environment at: /opt/homebrew/Caskroom/miniconda/base/envs/stack\u001b[0m\n", - "\u001b[2K\u001b[2mResolved \u001b[1m31 packages\u001b[0m \u001b[2min 284ms\u001b[0m\u001b[0m \u001b[0m\n", - "\u001b[2mAudited \u001b[1m31 packages\u001b[0m \u001b[2min 0.04ms\u001b[0m\u001b[0m\n" - ] - } - ], - "source": [ - "!pip install -U llama-stack-client" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "E1UFuJC570Tk", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 1000, - "referenced_widgets": [ - "75307e3dee604d30aa44713e6e293e64", - "5ce87402a79342af995df41ac3940d55", - "fbbcc19886cc43b38424fbb184162c61", - "29212208db6b432eb4f708cd64258954", - "50dd8994a4cf486ebbec5ffd4322992a", - "f9b768c703494dd198f2978aff4892e8", - 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"254ce460ce244c99a5afe39d5d51f6b7", - "4cf1dc345ace4da59f978f661487f975", - "8f30fca71bf24e5ca26e17c2321f893c", - "dd85d37dd1d14c7ea4592f8e11b2d2c8", - "3cb06377e4454f009d6b2aa7aa6ff0a9", - "4502477db4d948e693012364c2dcb370", - "52fe404ec9c14db2a7279b4c154eef3d" - ] - }, - "collapsed": true, - "id": "E1UFuJC570Tk", - "outputId": "aebb69d4-c167-4de5-eb8a-dd19dd538f63" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Not in Google Colab environment\n" - ] - } - ], - "source": [ - "import os\n", - "\n", - "try:\n", - " from google.colab import userdata\n", - " os.environ['LLAMA_API_KEY'] = userdata.get('LLAMA_API_KEY')\n", - "except ImportError:\n", - " print(\"Not in Google Colab environment\")\n", - "\n", - "for key in ['LLAMA_API_KEY']:\n", - " try:\n", - " api_key = os.environ[key]\n", - " if not api_key:\n", - " raise ValueError(f\"{key} environment variable is empty\")\n", - " except KeyError:\n", - " api_key = input(f\"{key} environment variable is not set. Please enter your API key: \")\n", - " os.environ[key] = api_key\n", - "\n", - "from llama_stack_client import LlamaStackClient\n", - "\n", - "client = LlamaStackClient(\n", - " base_url=\"http://0.0.0.0:8321\", \n", - " provider_data = {\n", - " \"llama_api_key\": os.environ['LLAMA_API_KEY']\n", - " }\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "635a7a6f", - "metadata": {}, - "source": [ - "Now that we have completed the setup and configuration, let's start exploring the capabilities of Llama 4!\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "id": "0fc75d73", - "metadata": {}, - "source": [ - "## 2. Running Llama 4" - ] - }, - { - "cell_type": "markdown", - "id": "7dacaa2d-94e9-42e9-82a0-73522dfc7010", - "metadata": { - "id": "7dacaa2d-94e9-42e9-82a0-73522dfc7010" - }, - "source": [ - "### 2.1 Check available models\n", - "\n", - "All the models available are programmatically accessible via the client." - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "ruO9jQna_t_S", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "collapsed": true, - "id": "ruO9jQna_t_S", - "outputId": "ab1722a7-62ab-43bb-9cab-4e45bf62068a" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Available models:\n", - "- Llama-3.1-8B-Instruct\n", - "- meta-llama/Llama-3.1-8B-Instruct\n", - "- Llama-3.2-11B-Vision-Instruct\n", - "- meta-llama/Llama-3.2-11B-Vision-Instruct\n", - "- Llama-3.3-70B-Instruct\n", - "- meta-llama/Llama-3.3-70B-Instruct\n", - "- Llama-4-Maverick-17B-128E-Instruct-FP8\n", - "- meta-llama/Llama-4-Maverick-17B-128E-Instruct\n", - "- all-MiniLM-L6-v2\n" - ] - } - ], - "source": [ - "from rich.pretty import pprint\n", - "\n", - "print(\"Available models:\")\n", - "for m in client.models.list():\n", - " print(f\"- {m.identifier}\")\n" - ] - }, - { - "cell_type": "markdown", - "id": "86366383", - "metadata": { - "id": "86366383" - }, - "source": [ - "### 2.2 Run a simple chat completion with one of the models\n", - "\n", - "We will test the client by doing a simple chat completion." - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "77c29dba", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "77c29dba", - "outputId": "4857974f-4c70-4bc4-f90a-6ae49dc9c41e" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Here is a two-sentence poem about a llama:\n", - "\n", - "With soft fur and gentle eyes, the llama roams with gentle surprise, a peaceful presence in the Andean skies. Its calm demeanor and soft humming song bring serenity to all who belong.\n" - ] - } - ], - "source": [ - "# TODO: update this with a vision model\n", - "model_id = \"meta-llama/Llama-4-Maverick-17B-128E-Instruct\"\n", - "\n", - "response = client.inference.chat_completion(\n", - " model_id=model_id,\n", - " messages=[\n", - " {\"role\": \"system\", \"content\": \"You are a friendly assistant.\"},\n", - " {\"role\": \"user\", \"content\": \"Write a two-sentence poem about llama.\"},\n", - " ],\n", - ")\n", - "\n", - "print(response.completion_message.content)\n" - ] - }, - { - "cell_type": "markdown", - "id": "7737cd41", - "metadata": {}, - "source": [ - "### 2.3 Running multimodal inference" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "e7b1baa7", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " % Total % Received % Xferd Average Speed Time Time Time Current\n", - " Dload Upload Total Spent Left Speed\n", - "100 275k 100 275k 0 0 847k 0 --:--:-- --:--:-- --:--:-- 845k--:--:-- --:--:-- 0\n" - ] - }, - { - "data": { - "image/jpeg": 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", - "text/plain": [ - "" - ] - }, - "execution_count": 15, - "metadata": { - "image/jpeg": { - "height": 256, - "width": 256 - } - }, - "output_type": "execute_result" - } - ], - "source": [ - "!curl -O https://raw.githubusercontent.com/meta-llama/llama-models/refs/heads/main/Llama_Repo.jpeg\n", - "\n", - "from IPython.display import Image\n", - "Image(\"Llama_Repo.jpeg\", width=256, height=256)" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "e1450ecc", - "metadata": {}, - "outputs": [], - "source": [ - "import base64\n", - "def encode_image(image_path):\n", - " with open(image_path, \"rb\") as image_file:\n", - " base64_string = base64.b64encode(image_file.read()).decode(\"utf-8\")\n", - " base64_url = f\"data:image/png;base64,{base64_string}\"\n", - " return base64_url" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "d7914894", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The image features three llamas, each with a distinct color. The llama on the left is white, the middle one is purple, and the one on the right is also white but wears a blue party hat.\n", - "\n", - "To determine the number of different colors present, we can count the unique hues:\n", - "\n", - "1. White (two llamas)\n", - "2. Purple (one llama)\n", - "3. Blue (party hat)\n", - "\n", - "Therefore, there are 3 different colors visible in the image: white, purple, and blue.\n" - ] - } - ], - "source": [ - "response = client.inference.chat_completion(\n", - " messages=[\n", - " {\n", - " \"role\": \"user\",\n", - " \"content\": [\n", - " {\n", - " \"type\": \"image\",\n", - " \"image\": {\n", - " \"url\": {\n", - " \"uri\": encode_image(\"Llama_Repo.jpeg\")\n", - " }\n", - " }\n", - " },\n", - " {\n", - " \"type\": \"text\",\n", - " \"text\": \"How many different colors are those llamas? What are those colors?\",\n", - " }\n", - " ]\n", - " }\n", - " ],\n", - " model_id=model_id,\n", - " stream=False,\n", - ")\n", - "\n", - "print(response.completion_message.content)" - ] - }, - { - "cell_type": "markdown", - "id": "8cf0d555", - "metadata": { - "id": "8cf0d555" - }, - "source": [ - "### 2.4 Have a conversation\n", - "\n", - "Maintaining a conversation history allows the model to retain context from previous interactions. Use a list to accumulate messages, enabling continuity throughout the chat session." - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "3fdf9df6", - "metadata": { - "id": "3fdf9df6" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[36m> Response: The most famous Prime Minister of England during World War 2 was Winston Churchill. He served as the Prime Minister of the United Kingdom from 1940 to 1945, and again from 1951 to 1955. Churchill is widely regarded as one of the greatest wartime leaders in history, known for his leadership, oratory skills, and unwavering resolve during the war.\n", - "\n", - "Churchill played a crucial role in rallying the British people during the war, and his speeches, such as the \"We shall fight on the beaches\" and \"Their finest hour\" speeches, are still remembered and celebrated today. He worked closely with other Allied leaders, including US President Franklin D. Roosevelt and Soviet leader Joseph Stalin, to coordinate the war effort and ultimately secure the defeat of Nazi Germany.\n", - "\n", - "Churchill's leadership and legacy have endured long after the war, and he remains one of the most iconic and influential figures in British history.\u001b[0m\n", - "\u001b[36m> Response: Winston Churchill was known for his many memorable quotes, but one of his most famous is:\n", - "\n", - "**\"We shall fight on the beaches, we shall fight on the landing grounds, we shall fight in the fields and in the streets, we shall fight in the hills; we shall never surrender.\"**\n", - "\n", - "This quote is from his speech to the House of Commons on June 4, 1940, during the early stages of World War II, when Nazi Germany was threatening to invade Britain. The speech is known as the \"We Shall Fight on the Beaches\" speech, and it's considered one of the greatest speeches of the 20th century.\n", - "\n", - "However, if I had to pick a single, even more concise quote, it would be:\n", - "\n", - "**\"Blood, toil, tears, and sweat.\"**\n", - "\n", - "This was the opening phrase of his first speech as Prime Minister to the House of Commons on May 13, 1940, in which he said:\n", - "\n", - "\"I say to the House as I said to those who have joined this Government, I have nothing to offer but blood, toil, tears, and sweat. We have before us an ordeal of the most grievous kind.\"\n", - "\n", - "This quote has become synonymous with Churchill's leadership and resolve during the war.\u001b[0m\n" - ] - } - ], - "source": [ - "from termcolor import cprint\n", - "\n", - "questions = [\n", - " \"Who was the most famous PM of England during world war 2 ?\",\n", - " \"What was his most famous quote ?\"\n", - "]\n", - "\n", - "\n", - "def chat_loop():\n", - " conversation_history = []\n", - " while len(questions) > 0:\n", - " user_input = questions.pop(0)\n", - " if user_input.lower() in [\"exit\", \"quit\", \"bye\"]:\n", - " cprint(\"Ending conversation. Goodbye!\", \"yellow\")\n", - " break\n", - "\n", - " user_message = {\"role\": \"user\", \"content\": user_input}\n", - " conversation_history.append(user_message)\n", - "\n", - " response = client.inference.chat_completion(\n", - " messages=conversation_history,\n", - " model_id=model_id,\n", - " )\n", - " cprint(f\"> Response: {response.completion_message.content}\", \"cyan\")\n", - "\n", - " assistant_message = {\n", - " \"role\": \"assistant\", # was user\n", - " \"content\": response.completion_message.content,\n", - " \"stop_reason\": response.completion_message.stop_reason,\n", - " }\n", - " conversation_history.append(assistant_message)\n", - "\n", - "\n", - "chat_loop()\n" - ] - }, - { - "cell_type": "markdown", - "id": "72e5111e", - "metadata": { - "id": "72e5111e" - }, - "source": [ - "Here is an example for you to try a conversation yourself.\n", - "Remember to type `quit` or `exit` after you are done chatting." - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "id": "9496f75c", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "9496f75c", - "outputId": "7d93a4cf-a5d4-4741-b6eb-6bce3a27ff66" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[36m> Response: Hello! How are you today? Is there something I can help you with or would you like to chat?\u001b[0m\n", - "\u001b[33mEnding conversation. Goodbye!\u001b[0m\n" - ] - } - ], - "source": [ - "# NBVAL_SKIP\n", - "from termcolor import cprint\n", - "\n", - "def chat_loop():\n", - " conversation_history = []\n", - " while True:\n", - " user_input = input(\"User> \")\n", - " if user_input.lower() in [\"exit\", \"quit\", \"bye\"]:\n", - " cprint(\"Ending conversation. Goodbye!\", \"yellow\")\n", - " break\n", - "\n", - " user_message = {\"role\": \"user\", \"content\": user_input}\n", - " conversation_history.append(user_message)\n", - "\n", - " response = client.inference.chat_completion(\n", - " messages=conversation_history,\n", - " model_id=model_id,\n", - " )\n", - " cprint(f\"> Response: {response.completion_message.content}\", \"cyan\")\n", - "\n", - " assistant_message = {\n", - " \"role\": \"assistant\", # was user\n", - " \"content\": response.completion_message.content,\n", - " \"stop_reason\": response.completion_message.stop_reason,\n", - " }\n", - " conversation_history.append(assistant_message)\n", - "\n", - "\n", - "chat_loop()\n" - ] - } - ], - "metadata": { - "accelerator": "GPU", - "colab": { - "gpuType": "T4", - "provenance": [] - }, - "kernelspec": { - "display_name": "l4", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.16" - } + "source": [ + "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/meta-llama/llama-stack/blob/main/docs/getting_started.ipynb)\n", + "\n", + "# Getting Started with Llama 4 in Llama Stack\n", + "\n", + "\"drawing\"\n", + "\n", + "[Llama Stack](https://github.com/meta-llama/llama-stack) defines and standardizes the set of core building blocks needed to bring generative AI applications to market. These building blocks are presented in the form of interoperable APIs with a broad set of Service Providers providing their implementations.\n", + "\n", + "Read more about the project here: https://llamastack.github.io/latest/\n", + "\n", + "In this guide, we will showcase how you can get started with using Llama 4 in Llama Stack.\n", + "\n", + "**💡 Quick Start Option:** If you want a simpler and faster way to test out Llama Stack, check out the [quick_start.ipynb](quick_start.ipynb) notebook instead. It provides a streamlined experience for getting up and running in just a few steps.\n" + ] }, - "nbformat": 4, - "nbformat_minor": 5 - } + { + "cell_type": "markdown", + "id": "4CV1Q19BDMVw", + "metadata": { + "id": "4CV1Q19BDMVw" + }, + "source": [ + "## 1. Getting started with Llama Stack" + ] + }, + { + "cell_type": "markdown", + "id": "K4AvfUAJZOeS", + "metadata": { + "id": "K4AvfUAJZOeS" + }, + "source": [ + "### 1.1. Create Llama API account\n", + "\n", + "In this showcase, we will use [Llama API](https://llama.developer.meta.com/) as the inference provider. So, you would first get an API key from Llama API if you don't have one already.\n", + "\n", + "\n", + "\n", + "> **Note:** Set the API Key in the Secrets of this notebook\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "oDUB7M_qe-Gs", + "metadata": { + "id": "oDUB7M_qe-Gs" + }, + "source": [ + "### 1.2. Setup and Running a Llama Stack server\n", + "\n", + "Llama Stack is architected as a collection of APIs that provide developers with the building blocks to build AI applications. \n", + "\n", + "Llama stack is typically available as a server with an endpoint that you can make calls to. Partners like Together and Fireworks offer their own Llama Stack compatible endpoints.\n", + "\n", + "In this showcase, we will start a Llama Stack server that is running locally.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "J2kGed0R5PSf", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "collapsed": true, + "id": "J2kGed0R5PSf", + "outputId": "2478ea60-8d35-48a1-b011-f233831740c5" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Requirement already satisfied: uv in /opt/homebrew/Caskroom/miniconda/base/envs/l4/lib/python3.10/site-packages (0.6.12)\n", + "\u001b[2mUsing Python 3.10.16 environment at: /opt/homebrew/Caskroom/miniconda/base/envs/l4\u001b[0m\n", + "\u001b[2mAudited \u001b[1m1 package\u001b[0m \u001b[2min 83ms\u001b[0m\u001b[0m\n", + "Environment '/Users/erichuang/projects/internal-llama-stack/.venv' already exists, re-using it.\n", + "Virtual environment 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(0/6)----\u001b[0m\u001b[0m 78.91 KiB/3.08 MiB \u001b[4A\n", + "\u001b[2mshellingham\u001b[0m \u001b[32m------------------------------\u001b[2m\u001b[0m\u001b[0m 9.53 KiB/9.53 KiB\n", + "\u001b[2mtyper \u001b[0m \u001b[32m-----------\u001b[2m-------------------\u001b[0m\u001b[0m 14.88 KiB/44.00 KiB\n", + "\u001b[2mtogether \u001b[0m \u001b[32m------\u001b[2m------------------------\u001b[0m\u001b[0m 16.00 KiB/85.81 KiB\n", + "\u001b[2K\u001b[4A\u001b[37m⠙\u001b[0m \u001b[2mPreparing packages...\u001b[0m (0/6)----\u001b[0m\u001b[0m 94.91 KiB/3.08 MiB \u001b[4A\n", + "\u001b[2mshellingham\u001b[0m \u001b[32m------------------------------\u001b[2m\u001b[0m\u001b[0m 9.53 KiB/9.53 KiB\n", + "\u001b[2mtyper \u001b[0m \u001b[32m-----------\u001b[2m-------------------\u001b[0m\u001b[0m 14.88 KiB/44.00 KiB\n", + "\u001b[2mtogether \u001b[0m \u001b[32m------------\u001b[2m------------------\u001b[0m\u001b[0m 32.00 KiB/85.81 KiB\n", + "\u001b[2K\u001b[4A\u001b[37m⠙\u001b[0m \u001b[2mPreparing packages...\u001b[0m (0/6)----\u001b[0m\u001b[0m 2.62 MiB/3.08 MiB \u001b[4A\n", + "\u001b[2mtyper \u001b[0m \u001b[32m----------------------\u001b[2m--------\u001b[0m\u001b[0m 30.88 KiB/44.00 KiB\n", + "\u001b[2mtogether \u001b[0m \u001b[32m------------\u001b[2m------------------\u001b[0m\u001b[0m 32.00 KiB/85.81 KiB\n", + "\u001b[2K\u001b[3A\u001b[37m⠹\u001b[0m \u001b[2mPreparing packages...\u001b[0m (3/6)----\u001b[0m\u001b[0m 2.62 MiB/3.08 MiB \u001b[3A\n", + "\u001b[2mtyper \u001b[0m \u001b[32m------------------------------\u001b[2m\u001b[0m\u001b[0m 44.00 KiB/44.00 KiB\n", + "\u001b[2mtogether \u001b[0m \u001b[32m------------\u001b[2m------------------\u001b[0m\u001b[0m 32.00 KiB/85.81 KiB\n", + "\u001b[2K\u001b[3A\u001b[37m⠹\u001b[0m \u001b[2mPreparing packages...\u001b[0m (3/6)----\u001b[0m\u001b[0m 2.62 MiB/3.08 MiB \u001b[3A\n", + "\u001b[2mtogether \u001b[0m \u001b[32m------------\u001b[2m------------------\u001b[0m\u001b[0m 32.00 KiB/85.81 KiB\n", + "\u001b[2K\u001b[2A\u001b[37m⠹\u001b[0m \u001b[2mPreparing packages...\u001b[0m (3/6)2m--\u001b[0m\u001b[0m 2.80 MiB/3.08 MiB \u001b[2A\n", + "\u001b[2mtogether \u001b[0m \u001b[32m-----------------\u001b[2m-------------\u001b[0m\u001b[0m 48.00 KiB/85.81 KiB\n", + "\u001b[2K\u001b[2A\u001b[37m⠹\u001b[0m \u001b[2mPreparing packages...\u001b[0m (3/6)2m--\u001b[0m\u001b[0m 2.81 MiB/3.08 MiB \u001b[2A\n", + "\u001b[2K\u001b[1A\u001b[37m⠹\u001b[0m \u001b[2mPreparing packages...\u001b[0m (3/6)----\u001b[0m\u001b[0m 48.00 KiB/85.81 KiB \u001b[1A\n", + "\u001b[2K\u001b[1A\u001b[37m⠹\u001b[0m \u001b[2mPreparing packages...\u001b[0m (3/6)2m--\u001b[0m\u001b[0m 80.00 KiB/85.81 KiB \u001b[1A\n", + "\u001b[2K\u001b[2mPrepared \u001b[1m6 packages\u001b[0m \u001b[2min 365ms\u001b[0m\u001b[0m \u001b[1A\n", + "\u001b[2K\u001b[2mInstalled \u001b[1m6 packages\u001b[0m \u001b[2min 50ms\u001b[0m\u001b[0m \u001b[0m\n", + " \u001b[32m+\u001b[39m \u001b[1meval-type-backport\u001b[0m\u001b[2m==0.2.2\u001b[0m\n", + " \u001b[32m+\u001b[39m \u001b[1mfaiss-cpu\u001b[0m\u001b[2m==1.10.0\u001b[0m\n", + " \u001b[32m+\u001b[39m \u001b[1mshellingham\u001b[0m\u001b[2m==1.5.4\u001b[0m\n", + " \u001b[32m+\u001b[39m \u001b[1mtabulate\u001b[0m\u001b[2m==0.9.0\u001b[0m\n", + " \u001b[32m+\u001b[39m \u001b[1mtogether\u001b[0m\u001b[2m==1.5.5\u001b[0m\n", + " \u001b[32m+\u001b[39m \u001b[1mtyper\u001b[0m\u001b[2m==0.15.2\u001b[0m\n", + "torch torchvision --index-url https://download.pytorch.org/whl/cpu\n", + "\u001b[2mUsing Python 3.11.11 environment at: /Users/erichuang/projects/internal-llama-stack/.venv\u001b[0m\n", + "\u001b[2mAudited \u001b[1m2 packages\u001b[0m \u001b[2min 32ms\u001b[0m\u001b[0m\n", + "sentence-transformers --no-deps\n", + "\u001b[2mUsing Python 3.11.11 environment at: /Users/erichuang/projects/internal-llama-stack/.venv\u001b[0m\n", + "\u001b[2mAudited \u001b[1m1 package\u001b[0m \u001b[2min 63ms\u001b[0m\u001b[0m\n", + "\u001b[32mBuild Successful!\u001b[0m\n" + ] + } + ], + "source": [ + "import os\n", + "import subprocess\n", + "import time\n", + "\n", + "!pip install uv\n", + "!uv pip install requests\n", + "\n", + "if \"UV_SYSTEM_PYTHON\" in os.environ:\n", + " del os.environ[\"UV_SYSTEM_PYTHON\"]\n", + "\n", + "# this command installs all the dependencies needed for the llama stack server\n", + "!uv run --with llama-stack llama stack build --distro llama_api --image-type venv\n", + "\n", + "def run_llama_stack_server_background():\n", + " log_file = open(\"llama_stack_server.log\", \"w\")\n", + " process = subprocess.Popen(\n", + " \"uv run --with llama-stack llama stack run llama_api --image-type venv\",\n", + " shell=True,\n", + " stdout=log_file,\n", + " stderr=log_file,\n", + " text=True\n", + " )\n", + "\n", + " print(f\"Starting Llama Stack server with PID: {process.pid}\")\n", + " return process\n", + "\n", + "def wait_for_server_to_start():\n", + " import requests\n", + " from requests.exceptions import ConnectionError\n", + " import time\n", + "\n", + " url = \"http://0.0.0.0:8321/v1/health\"\n", + " max_retries = 30\n", + " retry_interval = 1\n", + "\n", + " print(\"Waiting for server to start\", end=\"\")\n", + " for _ in range(max_retries):\n", + " try:\n", + " response = requests.get(url)\n", + " if response.status_code == 200:\n", + " print(\"\\nServer is ready!\")\n", + " return True\n", + " except ConnectionError:\n", + " print(\".\", end=\"\", flush=True)\n", + " time.sleep(retry_interval)\n", + "\n", + " print(\"\\nServer failed to start after\", max_retries * retry_interval, \"seconds\")\n", + " return False\n", + "\n", + "\n", + "# use this helper if needed to kill the server\n", + "def kill_llama_stack_server():\n", + " # Kill any existing llama stack server processes\n", + " os.system(\"ps aux | grep -v grep | grep llama_stack.core.server.server | awk '{print $2}' | xargs kill -9\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "c40e9efd", + "metadata": {}, + "source": [ + "### 1.3 Starting the Llama Stack Server" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f779283d", + "metadata": {}, + "outputs": [], + "source": [ + "server_process = run_llama_stack_server_background()\n", + "assert wait_for_server_to_start()" + ] + }, + { + "cell_type": "markdown", + "id": "90eb721b", + "metadata": {}, + "source": [ + "### 1.4 Install and Configure the Client\n", + "\n", + "Now that we have our Llama Stack server running locally, we need to install the client package to interact with it. The `llama-stack-client` provides a simple Python interface to access all the functionality of Llama Stack, including:\n", + "\n", + "- Chat Completions ( text and multimodal )\n", + "- Safety Shields \n", + "- Agent capabilities with tools like web search, RAG with Telemetry\n", + "- Evaluation and scoring frameworks\n", + "\n", + "The client handles all the API communication with our local server, making it easy to integrate Llama Stack's capabilities into your applications.\n", + "\n", + "In the next cells, we'll:\n", + "\n", + "1. Install the client package\n", + "2. Set up API keys for external services (Together AI and Tavily Search)\n", + "3. Initialize the client to connect to our local server\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "2e68e32a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[2mUsing Python 3.10.16 environment at: /opt/homebrew/Caskroom/miniconda/base/envs/stack\u001b[0m\n", + "\u001b[2K\u001b[2mResolved \u001b[1m31 packages\u001b[0m \u001b[2min 284ms\u001b[0m\u001b[0m \u001b[0m\n", + "\u001b[2mAudited \u001b[1m31 packages\u001b[0m \u001b[2min 0.04ms\u001b[0m\u001b[0m\n" + ] + } + ], + "source": [ + "!pip install -U llama-stack-client" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "E1UFuJC570Tk", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000, + "referenced_widgets": [ + "75307e3dee604d30aa44713e6e293e64", + "5ce87402a79342af995df41ac3940d55", + "fbbcc19886cc43b38424fbb184162c61", + "29212208db6b432eb4f708cd64258954", + "50dd8994a4cf486ebbec5ffd4322992a", 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api_key = os.environ[key]\n", + " if not api_key:\n", + " raise ValueError(f\"{key} environment variable is empty\")\n", + " except KeyError:\n", + " api_key = input(f\"{key} environment variable is not set. Please enter your API key: \")\n", + " os.environ[key] = api_key\n", + "\n", + "from llama_stack_client import LlamaStackClient\n", + "\n", + "client = LlamaStackClient(\n", + " base_url=\"http://0.0.0.0:8321\",\n", + " provider_data = {\n", + " \"llama_api_key\": os.environ['LLAMA_API_KEY']\n", + " }\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "635a7a6f", + "metadata": {}, + "source": [ + "Now that we have completed the setup and configuration, let's start exploring the capabilities of Llama 4!\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "0fc75d73", + "metadata": {}, + "source": [ + "## 2. Running Llama 4" + ] + }, + { + "cell_type": "markdown", + "id": "7dacaa2d-94e9-42e9-82a0-73522dfc7010", + "metadata": { + "id": "7dacaa2d-94e9-42e9-82a0-73522dfc7010" + }, + "source": [ + "### 2.1 Check available models\n", + "\n", + "All the models available are programmatically accessible via the client." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "ruO9jQna_t_S", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "collapsed": true, + "id": "ruO9jQna_t_S", + "outputId": "ab1722a7-62ab-43bb-9cab-4e45bf62068a" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Available models:\n", + "- Llama-3.1-8B-Instruct\n", + "- meta-llama/Llama-3.1-8B-Instruct\n", + "- Llama-3.2-11B-Vision-Instruct\n", + "- meta-llama/Llama-3.2-11B-Vision-Instruct\n", + "- Llama-3.3-70B-Instruct\n", + "- meta-llama/Llama-3.3-70B-Instruct\n", + "- Llama-4-Maverick-17B-128E-Instruct-FP8\n", + "- meta-llama/Llama-4-Maverick-17B-128E-Instruct\n", + "- all-MiniLM-L6-v2\n" + ] + } + ], + "source": [ + "from rich.pretty import pprint\n", + "\n", + "print(\"Available models:\")\n", + "for m in client.models.list():\n", + " print(f\"- {m.identifier}\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "86366383", + "metadata": { + "id": "86366383" + }, + "source": [ + "### 2.2 Run a simple chat completion with one of the models\n", + "\n", + "We will test the client by doing a simple chat completion." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "77c29dba", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "77c29dba", + "outputId": "4857974f-4c70-4bc4-f90a-6ae49dc9c41e" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Here is a two-sentence poem about a llama:\n", + "\n", + "With soft fur and gentle eyes, the llama roams with gentle surprise, a peaceful presence in the Andean skies. Its calm demeanor and soft humming song bring serenity to all who belong.\n" + ] + } + ], + "source": [ + "# TODO: update this with a vision model\n", + "model_id = \"meta-llama/Llama-4-Maverick-17B-128E-Instruct\"\n", + "\n", + "response = client.inference.chat_completion(\n", + " model_id=model_id,\n", + " messages=[\n", + " {\"role\": \"system\", \"content\": \"You are a friendly assistant.\"},\n", + " {\"role\": \"user\", \"content\": \"Write a two-sentence poem about llama.\"},\n", + " ],\n", + ")\n", + "\n", + "print(response.completion_message.content)\n" + ] + }, + { + "cell_type": "markdown", + "id": "7737cd41", + "metadata": {}, + "source": [ + "### 2.3 Running multimodal inference" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "e7b1baa7", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " % Total % Received % Xferd Average Speed Time Time Time Current\n", + " Dload Upload Total Spent Left Speed\n", + "100 275k 100 275k 0 0 847k 0 --:--:-- --:--:-- --:--:-- 845k--:--:-- --:--:-- 0\n" + ] + }, + { + "data": { + "image/jpeg": 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", + "text/plain": [ + "" + ] + }, + "execution_count": 15, + "metadata": { + "image/jpeg": { + "height": 256, + "width": 256 + } + }, + "output_type": "execute_result" + } + ], + "source": [ + "!curl -O https://raw.githubusercontent.com/meta-llama/llama-models/refs/heads/main/Llama_Repo.jpeg\n", + "\n", + "from IPython.display import Image\n", + "Image(\"Llama_Repo.jpeg\", width=256, height=256)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "e1450ecc", + "metadata": {}, + "outputs": [], + "source": [ + "import base64\n", + "def encode_image(image_path):\n", + " with open(image_path, \"rb\") as image_file:\n", + " base64_string = base64.b64encode(image_file.read()).decode(\"utf-8\")\n", + " base64_url = f\"data:image/png;base64,{base64_string}\"\n", + " return base64_url" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "d7914894", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The image features three llamas, each with a distinct color. The llama on the left is white, the middle one is purple, and the one on the right is also white but wears a blue party hat.\n", + "\n", + "To determine the number of different colors present, we can count the unique hues:\n", + "\n", + "1. White (two llamas)\n", + "2. Purple (one llama)\n", + "3. Blue (party hat)\n", + "\n", + "Therefore, there are 3 different colors visible in the image: white, purple, and blue.\n" + ] + } + ], + "source": [ + "response = client.inference.chat_completion(\n", + " messages=[\n", + " {\n", + " \"role\": \"user\",\n", + " \"content\": [\n", + " {\n", + " \"type\": \"image\",\n", + " \"image\": {\n", + " \"url\": {\n", + " \"uri\": encode_image(\"Llama_Repo.jpeg\")\n", + " }\n", + " }\n", + " },\n", + " {\n", + " \"type\": \"text\",\n", + " \"text\": \"How many different colors are those llamas? What are those colors?\",\n", + " }\n", + " ]\n", + " }\n", + " ],\n", + " model_id=model_id,\n", + " stream=False,\n", + ")\n", + "\n", + "print(response.completion_message.content)" + ] + }, + { + "cell_type": "markdown", + "id": "8cf0d555", + "metadata": { + "id": "8cf0d555" + }, + "source": [ + "### 2.4 Have a conversation\n", + "\n", + "Maintaining a conversation history allows the model to retain context from previous interactions. Use a list to accumulate messages, enabling continuity throughout the chat session." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "3fdf9df6", + "metadata": { + "id": "3fdf9df6" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[36m> Response: The most famous Prime Minister of England during World War 2 was Winston Churchill. He served as the Prime Minister of the United Kingdom from 1940 to 1945, and again from 1951 to 1955. Churchill is widely regarded as one of the greatest wartime leaders in history, known for his leadership, oratory skills, and unwavering resolve during the war.\n", + "\n", + "Churchill played a crucial role in rallying the British people during the war, and his speeches, such as the \"We shall fight on the beaches\" and \"Their finest hour\" speeches, are still remembered and celebrated today. He worked closely with other Allied leaders, including US President Franklin D. Roosevelt and Soviet leader Joseph Stalin, to coordinate the war effort and ultimately secure the defeat of Nazi Germany.\n", + "\n", + "Churchill's leadership and legacy have endured long after the war, and he remains one of the most iconic and influential figures in British history.\u001b[0m\n", + "\u001b[36m> Response: Winston Churchill was known for his many memorable quotes, but one of his most famous is:\n", + "\n", + "**\"We shall fight on the beaches, we shall fight on the landing grounds, we shall fight in the fields and in the streets, we shall fight in the hills; we shall never surrender.\"**\n", + "\n", + "This quote is from his speech to the House of Commons on June 4, 1940, during the early stages of World War II, when Nazi Germany was threatening to invade Britain. The speech is known as the \"We Shall Fight on the Beaches\" speech, and it's considered one of the greatest speeches of the 20th century.\n", + "\n", + "However, if I had to pick a single, even more concise quote, it would be:\n", + "\n", + "**\"Blood, toil, tears, and sweat.\"**\n", + "\n", + "This was the opening phrase of his first speech as Prime Minister to the House of Commons on May 13, 1940, in which he said:\n", + "\n", + "\"I say to the House as I said to those who have joined this Government, I have nothing to offer but blood, toil, tears, and sweat. We have before us an ordeal of the most grievous kind.\"\n", + "\n", + "This quote has become synonymous with Churchill's leadership and resolve during the war.\u001b[0m\n" + ] + } + ], + "source": [ + "from termcolor import cprint\n", + "\n", + "questions = [\n", + " \"Who was the most famous PM of England during world war 2 ?\",\n", + " \"What was his most famous quote ?\"\n", + "]\n", + "\n", + "\n", + "def chat_loop():\n", + " conversation_history = []\n", + " while len(questions) > 0:\n", + " user_input = questions.pop(0)\n", + " if user_input.lower() in [\"exit\", \"quit\", \"bye\"]:\n", + " cprint(\"Ending conversation. Goodbye!\", \"yellow\")\n", + " break\n", + "\n", + " user_message = {\"role\": \"user\", \"content\": user_input}\n", + " conversation_history.append(user_message)\n", + "\n", + " response = client.inference.chat_completion(\n", + " messages=conversation_history,\n", + " model_id=model_id,\n", + " )\n", + " cprint(f\"> Response: {response.completion_message.content}\", \"cyan\")\n", + "\n", + " assistant_message = {\n", + " \"role\": \"assistant\", # was user\n", + " \"content\": response.completion_message.content,\n", + " \"stop_reason\": response.completion_message.stop_reason,\n", + " }\n", + " conversation_history.append(assistant_message)\n", + "\n", + "\n", + "chat_loop()\n" + ] + }, + { + "cell_type": "markdown", + "id": "72e5111e", + "metadata": { + "id": "72e5111e" + }, + "source": [ + "Here is an example for you to try a conversation yourself.\n", + "Remember to type `quit` or `exit` after you are done chatting." + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "9496f75c", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "9496f75c", + "outputId": "7d93a4cf-a5d4-4741-b6eb-6bce3a27ff66" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[36m> Response: Hello! How are you today? Is there something I can help you with or would you like to chat?\u001b[0m\n", + "\u001b[33mEnding conversation. Goodbye!\u001b[0m\n" + ] + } + ], + "source": [ + "# NBVAL_SKIP\n", + "from termcolor import cprint\n", + "\n", + "def chat_loop():\n", + " conversation_history = []\n", + " while True:\n", + " user_input = input(\"User> \")\n", + " if user_input.lower() in [\"exit\", \"quit\", \"bye\"]:\n", + " cprint(\"Ending conversation. Goodbye!\", \"yellow\")\n", + " break\n", + "\n", + " user_message = {\"role\": \"user\", \"content\": user_input}\n", + " conversation_history.append(user_message)\n", + "\n", + " response = client.inference.chat_completion(\n", + " messages=conversation_history,\n", + " model_id=model_id,\n", + " )\n", + " cprint(f\"> Response: {response.completion_message.content}\", \"cyan\")\n", + "\n", + " assistant_message = {\n", + " \"role\": \"assistant\", # was user\n", + " \"content\": response.completion_message.content,\n", + " \"stop_reason\": response.completion_message.stop_reason,\n", + " }\n", + " conversation_history.append(assistant_message)\n", + "\n", + "\n", + "chat_loop()\n" + ] + } + ], + "metadata": { + "accelerator": "GPU", + "colab": { + "gpuType": "T4", + "provenance": [] + }, + "kernelspec": { + "display_name": "l4", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.16" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/make.bat b/docs/make.bat deleted file mode 100644 index 954237b9b..000000000 --- a/docs/make.bat +++ /dev/null @@ -1,35 +0,0 @@ -@ECHO OFF - -pushd %~dp0 - -REM Command file for Sphinx documentation - -if "%SPHINXBUILD%" == "" ( - set SPHINXBUILD=sphinx-build -) -set SOURCEDIR=. -set BUILDDIR=_build - -%SPHINXBUILD% >NUL 2>NUL -if errorlevel 9009 ( - echo. - echo.The 'sphinx-build' command was not found. Make sure you have Sphinx - echo.installed, then set the SPHINXBUILD environment variable to point - echo.to the full path of the 'sphinx-build' executable. Alternatively you - echo.may add the Sphinx directory to PATH. - echo. - echo.If you don't have Sphinx installed, grab it from - echo.https://www.sphinx-doc.org/ - exit /b 1 -) - -if "%1" == "" goto help - -%SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O% -goto end - -:help -%SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O% - -:end -popd diff --git a/docs/notebooks/Alpha_Llama_Stack_Post_Training.ipynb b/docs/notebooks/Alpha_Llama_Stack_Post_Training.ipynb index 9b1893f9d..b5fe0d8d9 100644 --- a/docs/notebooks/Alpha_Llama_Stack_Post_Training.ipynb +++ b/docs/notebooks/Alpha_Llama_Stack_Post_Training.ipynb @@ -14,7 +14,7 @@ "We will also showcase how to leverage existing Llama stack [inference APIs](https://github.com/meta-llama/llama-stack/blob/main/llama_stack/apis/inference/inference.py) (ollama as provider) to get the new model's output and the [eval APIs](https://github.com/meta-llama/llama-stack/blob/main/llama_stack/apis/eval/eval.py) to help you better measure the new model performance. We hope the flywheel of post-training -> eval -> inference can greatly empower agentic apps development.\n", "\n", "\n", - "- Read more about Llama Stack: https://llama-stack.readthedocs.io/en/latest/introduction/index.html\n", + "- Read more about Llama Stack: https://llamastack.github.io/\n", "- Read more about post training APIs definition: https://github.com/meta-llama/llama-stack/blob/main/llama_stack/apis/post_training/post_training.py\n", "\n", "\n", @@ -3632,7 +3632,7 @@ }, "source": [ "#### 1.2. Kick-off eval job\n", - "- More details on Llama-stack eval: https://llama-stack.readthedocs.io/en/latest/benchmark_evaluations/index.html\n", + "- More details on Llama-stack eval: https://llamastack.github.io/latest/references/evals_reference/index.html\n", " - Define an EvalCandidate\n", " - Run evaluate on datasets (we choose brainstrust's answer-similarity as scoring function with OpenAI's model as judge model)\n", "\n", diff --git a/docs/notebooks/Llama_Stack_Benchmark_Evals.ipynb b/docs/notebooks/Llama_Stack_Benchmark_Evals.ipynb index 6e7d37cf2..2acb79e5f 100644 --- a/docs/notebooks/Llama_Stack_Benchmark_Evals.ipynb +++ b/docs/notebooks/Llama_Stack_Benchmark_Evals.ipynb @@ -12,7 +12,7 @@ "\n", "This notebook will walk you through the main sets of APIs we offer with Llama Stack for supporting running benchmark evaluations of your with working examples to explore the possibilities that Llama Stack opens up for you.\n", "\n", - "Read more about Llama Stack: https://llama-stack.readthedocs.io/en/latest/index.html" + "Read more about Llama Stack: https://llamastack.github.io/latest/index.html" ] }, { diff --git a/docs/notebooks/langchain/Llama_Stack_LangChain.ipynb b/docs/notebooks/langchain/Llama_Stack_LangChain.ipynb new file mode 100644 index 000000000..d44ac6994 --- /dev/null +++ b/docs/notebooks/langchain/Llama_Stack_LangChain.ipynb @@ -0,0 +1,701 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "1ztegmwm4sp", + "metadata": {}, + "source": [ + "## LlamaStack + LangChain Integration Tutorial\n", + "\n", + "This notebook demonstrates how to integrate **LlamaStack** with **LangChain** to build a complete RAG (Retrieval-Augmented Generation) system.\n", + "\n", + "### Overview\n", + "\n", + "- **LlamaStack**: Provides the infrastructure for running LLMs and Open AI Compatible Vector Stores\n", + "- **LangChain**: Provides the framework for chaining operations and prompt templates\n", + "- **Integration**: Uses LlamaStack's OpenAI-compatible API with LangChain\n", + "\n", + "### What You'll See\n", + "\n", + "1. Setting up LlamaStack server with Fireworks AI provider\n", + "2. Creating and Querying Vector Stores\n", + "3. Building RAG chains with LangChain + LLAMAStack\n", + "4. Querying the chain for relevant information\n", + "\n", + "### Prerequisites\n", + "\n", + "- Fireworks API key\n", + "\n", + "---\n", + "\n", + "### 1. Installation and Setup" + ] + }, + { + "cell_type": "markdown", + "id": "2ktr5ls2cas", + "metadata": {}, + "source": [ + "#### Install Required Dependencies\n", + "\n", + "First, we install all the necessary packages for LangChain and FastAPI integration." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "5b6a6a17-b931-4bea-8273-0d6e5563637a", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Requirement already satisfied: uv in /Users/swapna942/miniconda3/lib/python3.12/site-packages (0.7.20)\n", + "\u001b[2mUsing Python 3.12.11 environment at: /Users/swapna942/miniconda3\u001b[0m\n", + "\u001b[2mAudited \u001b[1m7 packages\u001b[0m \u001b[2min 42ms\u001b[0m\u001b[0m\n" + ] + } + ], + "source": [ + "!pip install uv\n", + "!uv pip install fastapi uvicorn \"langchain>=0.2\" langchain-openai \\\n", + " langchain-community langchain-text-splitters \\\n", + " faiss-cpu" + ] + }, + { + "cell_type": "markdown", + "id": "wmt9jvqzh7n", + "metadata": {}, + "source": [ + "### 2. LlamaStack Server Setup\n", + "\n", + "#### Build and Start LlamaStack Server\n", + "\n", + "This section sets up the LlamaStack server with:\n", + "- **Fireworks AI** as the inference provider\n", + "- **Sentence Transformers** for embeddings\n", + "\n", + "The server runs on `localhost:8321` and provides OpenAI-compatible endpoints." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "dd2dacf3-ec8b-4cc7-8ff4-b5b6ea4a6e9e", + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "import os\n", + "import subprocess\n", + "import time\n", + "\n", + "# Remove UV_SYSTEM_PYTHON to ensure uv creates a proper virtual environment\n", + "# instead of trying to use system Python globally, which could cause permission issues\n", + "# and package conflicts with the system's Python installation\n", + "if \"UV_SYSTEM_PYTHON\" in os.environ:\n", + " del os.environ[\"UV_SYSTEM_PYTHON\"]\n", + "\n", + "def run_llama_stack_server_background():\n", + " \"\"\"Build and run LlamaStack server in one step using --run flag\"\"\"\n", + " log_file = open(\"llama_stack_server.log\", \"w\")\n", + " process = subprocess.Popen(\n", + " \"uv run --with llama-stack llama stack build --distro starter --image-type venv --run\",\n", + " shell=True,\n", + " stdout=log_file,\n", + " stderr=log_file,\n", + " text=True,\n", + " )\n", + "\n", + " print(f\"Building and starting Llama Stack server with PID: {process.pid}\")\n", + " return process\n", + "\n", + "\n", + "def wait_for_server_to_start():\n", + " import requests\n", + " from requests.exceptions import ConnectionError\n", + "\n", + " url = \"http://0.0.0.0:8321/v1/health\"\n", + " max_retries = 30\n", + " retry_interval = 1\n", + "\n", + " print(\"Waiting for server to start\", end=\"\")\n", + " for _ in range(max_retries):\n", + " try:\n", + " response = requests.get(url)\n", + " if response.status_code == 200:\n", + " print(\"\\nServer is ready!\")\n", + " return True\n", + " except ConnectionError:\n", + " print(\".\", end=\"\", flush=True)\n", + " time.sleep(retry_interval)\n", + "\n", + " print(\"\\nServer failed to start after\", max_retries * retry_interval, \"seconds\")\n", + " return False\n", + "\n", + "\n", + "def kill_llama_stack_server():\n", + " # Kill any existing llama stack server processes using pkill command\n", + " os.system(\"pkill -f llama_stack.core.server.server\")" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "28bd8dbd-4576-4e76-813f-21ab94db44a2", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Building and starting Llama Stack server with PID: 19747\n", + "Waiting for server to start....\n", + "Server is ready!\n" + ] + } + ], + "source": [ + "server_process = run_llama_stack_server_background()\n", + "assert wait_for_server_to_start()" + ] + }, + { + "cell_type": "markdown", + "id": "gr9cdcg4r7n", + "metadata": {}, + "source": [ + "#### Install LlamaStack Client\n", + "\n", + "Install the client library to interact with the LlamaStack server." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "487d2dbc-d071-400e-b4f0-dcee58f8dc95", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[2mUsing Python 3.12.11 environment at: /Users/swapna942/miniconda3\u001b[0m\n", + "\u001b[2mAudited \u001b[1m1 package\u001b[0m \u001b[2min 27ms\u001b[0m\u001b[0m\n" + ] + } + ], + "source": [ + "!uv pip install llama_stack_client" + ] + }, + { + "cell_type": "markdown", + "id": "0j5hag7l9x89", + "metadata": {}, + "source": [ + "### 3. Initialize LlamaStack Client\n", + "\n", + "Create a client connection to the LlamaStack server with API keys for different providers:\n", + "\n", + "- **Fireworks API Key**: For Fireworks models\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "ab4eff97-4565-4c73-b1b3-0020a4c7e2a5", + "metadata": {}, + "outputs": [], + "source": [ + "from llama_stack_client import LlamaStackClient\n", + "\n", + "client = LlamaStackClient(\n", + " base_url=\"http://0.0.0.0:8321\",\n", + " provider_data={\"fireworks_api_key\": \"***\"},\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "vwhexjy1e8o", + "metadata": {}, + "source": [ + "#### Explore Available Models and Safety Features\n", + "\n", + "Check what models and safety shields are available through your LlamaStack instance." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "880443ef-ac3c-48b1-a80a-7dab5b25ac61", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:httpx:HTTP Request: GET http://0.0.0.0:8321/v1/models \"HTTP/1.1 200 OK\"\n", + "INFO:httpx:HTTP Request: GET http://0.0.0.0:8321/v1/shields \"HTTP/1.1 200 OK\"\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Available Fireworks models:\n", + "- fireworks/accounts/fireworks/models/llama-v3p1-8b-instruct\n", + "- fireworks/accounts/fireworks/models/llama-v3p1-70b-instruct\n", + "- fireworks/accounts/fireworks/models/llama-v3p1-405b-instruct\n", + "- fireworks/accounts/fireworks/models/llama-v3p2-3b-instruct\n", + "- fireworks/accounts/fireworks/models/llama-v3p2-11b-vision-instruct\n", + "- fireworks/accounts/fireworks/models/llama-v3p2-90b-vision-instruct\n", + "- fireworks/accounts/fireworks/models/llama-v3p3-70b-instruct\n", + "- fireworks/accounts/fireworks/models/llama4-scout-instruct-basic\n", + "- fireworks/accounts/fireworks/models/llama4-maverick-instruct-basic\n", + "- fireworks/nomic-ai/nomic-embed-text-v1.5\n", + "- fireworks/accounts/fireworks/models/llama-guard-3-8b\n", + "- fireworks/accounts/fireworks/models/llama-guard-3-11b-vision\n", + "----\n", + "Available shields (safety models):\n", + "code-scanner\n", + "llama-guard\n", + "nemo-guardrail\n", + "----\n" + ] + } + ], + "source": [ + "print(\"Available Fireworks models:\")\n", + "for m in client.models.list():\n", + " if m.identifier.startswith(\"fireworks/\"):\n", + " print(f\"- {m.identifier}\")\n", + "\n", + "print(\"----\")\n", + "print(\"Available shields (safety models):\")\n", + "for s in client.shields.list():\n", + " print(s.identifier)\n", + "print(\"----\")" + ] + }, + { + "cell_type": "markdown", + "id": "gojp7at31ht", + "metadata": {}, + "source": [ + "### 4. Vector Store Setup\n", + "\n", + "#### Create a Vector Store with File Upload\n", + "\n", + "Create a vector store using the OpenAI-compatible vector stores API:\n", + "\n", + "- **Vector Store**: OpenAI-compatible vector store for document storage\n", + "- **File Upload**: Automatic chunking and embedding of uploaded files \n", + "- **Embedding Model**: Sentence Transformers model for text embeddings\n", + "- **Dimensions**: 384-dimensional embeddings" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "be2c2899-ea53-4e5f-b6b8-ed425f5d6572", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:httpx:HTTP Request: POST http://0.0.0.0:8321/v1/openai/v1/files \"HTTP/1.1 200 OK\"\n", + "INFO:httpx:HTTP Request: POST http://0.0.0.0:8321/v1/openai/v1/files \"HTTP/1.1 200 OK\"\n", + "INFO:httpx:HTTP Request: POST http://0.0.0.0:8321/v1/openai/v1/files \"HTTP/1.1 200 OK\"\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "File(id='file-54652c95c56c4c34918a97d7ff8a4320', bytes=41, created_at=1757442621, expires_at=1788978621, filename='shipping_policy.txt', object='file', purpose='assistants')\n", + "File(id='file-fb1227c1d1854da1bd774d21e5b7e41c', bytes=48, created_at=1757442621, expires_at=1788978621, filename='returns_policy.txt', object='file', purpose='assistants')\n", + "File(id='file-673f874852fe42798675a13d06a256e2', bytes=45, created_at=1757442621, expires_at=1788978621, filename='support.txt', object='file', purpose='assistants')\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:httpx:HTTP Request: POST http://0.0.0.0:8321/v1/openai/v1/vector_stores \"HTTP/1.1 200 OK\"\n" + ] + } + ], + "source": [ + "from io import BytesIO\n", + "\n", + "docs = [\n", + " (\"Acme ships globally in 3-5 business days.\", {\"title\": \"Shipping Policy\"}),\n", + " (\"Returns are accepted within 30 days of purchase.\", {\"title\": \"Returns Policy\"}),\n", + " (\"Support is available 24/7 via chat and email.\", {\"title\": \"Support\"}),\n", + "]\n", + "\n", + "file_ids = []\n", + "for content, metadata in docs:\n", + " with BytesIO(content.encode()) as file_buffer:\n", + " file_buffer.name = f\"{metadata['title'].replace(' ', '_').lower()}.txt\"\n", + " create_file_response = client.files.create(file=file_buffer, purpose=\"assistants\")\n", + " print(create_file_response)\n", + " file_ids.append(create_file_response.id)\n", + "\n", + "# Create vector store with files\n", + "vector_store = client.vector_stores.create(\n", + " name=\"acme_docs\",\n", + " file_ids=file_ids,\n", + " embedding_model=\"sentence-transformers/all-MiniLM-L6-v2\",\n", + " embedding_dimension=384,\n", + " provider_id=\"faiss\"\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "9061tmi1zpq", + "metadata": {}, + "source": [ + "#### Test Vector Store Search\n", + "\n", + "Query the vector store. This performs semantic search to find relevant documents based on the query." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "ba9d1901-bd5e-4216-b3e6-19dc74551cc6", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:httpx:HTTP Request: POST http://0.0.0.0:8321/v1/openai/v1/vector_stores/vs_708c060b-45da-423e-8354-68529b4fd1a6/search \"HTTP/1.1 200 OK\"\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Acme ships globally in 3-5 business days.\n", + "Returns are accepted within 30 days of purchase.\n" + ] + } + ], + "source": [ + "search_response = client.vector_stores.search(\n", + " vector_store_id=vector_store.id,\n", + " query=\"How long does shipping take?\",\n", + " max_num_results=2\n", + ")\n", + "for result in search_response.data:\n", + " content = result.content[0].text\n", + " print(content)" + ] + }, + { + "cell_type": "markdown", + "id": "usne6mbspms", + "metadata": {}, + "source": [ + "### 5. LangChain Integration\n", + "\n", + "#### Configure LangChain with LlamaStack\n", + "\n", + "Set up LangChain to use LlamaStack's OpenAI-compatible API:\n", + "\n", + "- **Base URL**: Points to LlamaStack's OpenAI endpoint\n", + "- **Headers**: Include Fireworks API key for model access\n", + "- **Model**: Use Meta Llama v3p1 8b instruct model for inference" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "c378bd10-09c2-417c-bdfc-1e0a2dd19084", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "\n", + "from langchain_openai import ChatOpenAI\n", + "\n", + "# Point LangChain to Llamastack Server\n", + "llm = ChatOpenAI(\n", + " base_url=\"http://0.0.0.0:8321/v1/openai/v1\",\n", + " api_key=\"dummy\",\n", + " model=\"fireworks/accounts/fireworks/models/llama-v3p1-8b-instruct\",\n", + " default_headers={\"X-LlamaStack-Provider-Data\": '{\"fireworks_api_key\": \"***\"}'},\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "5a4ddpcuk3l", + "metadata": {}, + "source": [ + "#### Test LLM Connection\n", + "\n", + "Verify that LangChain can successfully communicate with the LlamaStack server." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "f88ffb5a-657b-4916-9375-c6ddc156c25e", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:httpx:HTTP Request: POST http://0.0.0.0:8321/v1/openai/v1/chat/completions \"HTTP/1.1 200 OK\"\n" + ] + }, + { + "data": { + "text/plain": [ + "AIMessage(content=\"A llama's gentle eyes shine bright,\\nIn the Andes, it roams through morning light.\", additional_kwargs={'refusal': None}, response_metadata={'token_usage': None, 'model_name': 'fireworks/accounts/fireworks/models/llama-v3p1-8b-instruct', 'system_fingerprint': None, 'id': 'chatcmpl-602b5967-82a3-476b-9cd2-7d3b29b76ee8', 'service_tier': None, 'finish_reason': 'stop', 'logprobs': None}, id='run--0933c465-ff4d-4a7b-b7fb-fd97dd8244f3-0')" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Test llm with simple message\n", + "messages = [\n", + " {\"role\": \"system\", \"content\": \"You are a friendly assistant.\"},\n", + " {\"role\": \"user\", \"content\": \"Write a two-sentence poem about llama.\"},\n", + "]\n", + "llm.invoke(messages)" + ] + }, + { + "cell_type": "markdown", + "id": "0xh0jg6a0l4a", + "metadata": {}, + "source": [ + "### 6. Building the RAG Chain\n", + "\n", + "#### Create a Complete RAG Pipeline\n", + "\n", + "Build a LangChain pipeline that combines:\n", + "\n", + "1. **Vector Search**: Query LlamaStack's Open AI compatible Vector Store\n", + "2. **Context Assembly**: Format retrieved documents\n", + "3. **Prompt Template**: Structure the input for the LLM\n", + "4. **LLM Generation**: Generate answers using context\n", + "5. **Output Parsing**: Extract the final response\n", + "\n", + "**Chain Flow**: `Query → Vector Search → Context + Question → LLM → Response`" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "9684427d-dcc7-4544-9af5-8b110d014c42", + "metadata": {}, + "outputs": [], + "source": [ + "# LangChain for prompt template and chaining + LLAMA Stack Client Vector DB and LLM chat completion\n", + "from langchain_core.output_parsers import StrOutputParser\n", + "from langchain_core.prompts import ChatPromptTemplate\n", + "from langchain_core.runnables import RunnableLambda, RunnablePassthrough\n", + "\n", + "\n", + "def join_docs(docs):\n", + " return \"\\n\\n\".join([f\"[{d.filename}] {d.content[0].text}\" for d in docs.data])\n", + "\n", + "PROMPT = ChatPromptTemplate.from_messages(\n", + " [\n", + " (\"system\", \"You are a helpful assistant. Use the following context to answer.\"),\n", + " (\"user\", \"Question: {question}\\n\\nContext:\\n{context}\"),\n", + " ]\n", + ")\n", + "\n", + "vector_step = RunnableLambda(\n", + " lambda x: client.vector_stores.search(\n", + " vector_store_id=vector_store.id,\n", + " query=x,\n", + " max_num_results=2\n", + " )\n", + " )\n", + "\n", + "chain = (\n", + " {\"context\": vector_step | RunnableLambda(join_docs), \"question\": RunnablePassthrough()}\n", + " | PROMPT\n", + " | llm\n", + " | StrOutputParser()\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "0onu6rhphlra", + "metadata": {}, + "source": [ + "### 7. Testing the RAG System\n", + "\n", + "#### Example 1: Shipping Query" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "03322188-9509-446a-a4a8-ce3bb83ec87c", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:httpx:HTTP Request: POST http://0.0.0.0:8321/v1/openai/v1/vector_stores/vs_708c060b-45da-423e-8354-68529b4fd1a6/search \"HTTP/1.1 200 OK\"\n", + "INFO:httpx:HTTP Request: POST http://0.0.0.0:8321/v1/openai/v1/chat/completions \"HTTP/1.1 200 OK\"\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "❓ How long does shipping take?\n", + "💡 Acme ships globally in 3-5 business days. This means that shipping typically takes between 3 to 5 working days from the date of dispatch or order fulfillment.\n" + ] + } + ], + "source": [ + "query = \"How long does shipping take?\"\n", + "response = chain.invoke(query)\n", + "print(\"❓\", query)\n", + "print(\"💡\", response)" + ] + }, + { + "cell_type": "markdown", + "id": "b7krhqj88ku", + "metadata": {}, + "source": [ + "#### Example 2: Returns Policy Query" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "61995550-bb0b-46a8-a5d0-023207475d60", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:httpx:HTTP Request: POST http://0.0.0.0:8321/v1/openai/v1/vector_stores/vs_708c060b-45da-423e-8354-68529b4fd1a6/search \"HTTP/1.1 200 OK\"\n", + "INFO:httpx:HTTP Request: POST http://0.0.0.0:8321/v1/openai/v1/chat/completions \"HTTP/1.1 200 OK\"\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "❓ Can I return a product after 40 days?\n", + "💡 Based on the provided context, you cannot return a product after 40 days. The return window is limited to 30 days from the date of purchase.\n" + ] + } + ], + "source": [ + "query = \"Can I return a product after 40 days?\"\n", + "response = chain.invoke(query)\n", + "print(\"❓\", query)\n", + "print(\"💡\", response)" + ] + }, + { + "cell_type": "markdown", + "id": "h4w24fadvjs", + "metadata": {}, + "source": [ + "---\n", + "We have successfully built a RAG system that combines:\n", + "\n", + "- **LlamaStack** for infrastructure (LLM serving + Vector Store)\n", + "- **LangChain** for orchestration (prompts + chains)\n", + "- **Fireworks** for high-quality language models\n", + "\n", + "### Key Benefits\n", + "\n", + "1. **Unified Infrastructure**: Single server for LLMs and Vector Store\n", + "2. **OpenAI Compatibility**: Easy integration with existing LangChain code\n", + "3. **Multi-Provider Support**: Switch between different LLM providers\n", + "4. **Production Ready**: Built-in safety shields and monitoring\n", + "\n", + "### Next Steps\n", + "\n", + "- Add more sophisticated document processing\n", + "- Implement conversation memory\n", + "- Add safety filtering and monitoring\n", + "- Scale to larger document collections\n", + "- Integrate with web frameworks like FastAPI or Streamlit\n", + "\n", + "---\n", + "\n", + "##### 🔧 Cleanup\n", + "\n", + "Don't forget to stop the LlamaStack server when you're done:\n", + "\n", + "```python\n", + "kill_llama_stack_server()\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "15647c46-22ce-4698-af3f-8161329d8e3a", + "metadata": {}, + "outputs": [], + "source": [ + "kill_llama_stack_server()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.7" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/notebooks/nvidia/tool_calling/2_finetuning_and_inference.ipynb b/docs/notebooks/nvidia/tool_calling/2_finetuning_and_inference.ipynb index a80720a5f..0e69cafd5 100644 --- a/docs/notebooks/nvidia/tool_calling/2_finetuning_and_inference.ipynb +++ b/docs/notebooks/nvidia/tool_calling/2_finetuning_and_inference.ipynb @@ -373,7 +373,7 @@ " metadata={\n", " \"format\": \"json\",\n", " \"description\": \"Tool calling xLAM dataset in OpenAI ChatCompletions format\",\n", - " \"provider\": \"nvidia\"\n", + " \"provider_id\": \"nvidia\"\n", " }\n", ")\n", "print(response)" diff --git a/docs/openapi_generator/generate.py b/docs/openapi_generator/generate.py index c27bc6440..54031d839 100644 --- a/docs/openapi_generator/generate.py +++ b/docs/openapi_generator/generate.py @@ -16,7 +16,7 @@ import sys import fire import ruamel.yaml as yaml -from llama_stack.apis.version import LLAMA_STACK_API_VERSION # noqa: E402 +from llama_stack.apis.version import LLAMA_STACK_API_V1 # noqa: E402 from llama_stack.core.stack import LlamaStack # noqa: E402 from .pyopenapi.options import Options # noqa: E402 @@ -25,7 +25,7 @@ from .pyopenapi.utility import Specification, validate_api # noqa: E402 def str_presenter(dumper, data): - if data.startswith(f"/{LLAMA_STACK_API_VERSION}") or data.startswith( + if data.startswith(f"/{LLAMA_STACK_API_V1}") or data.startswith( "#/components/schemas/" ): style = None @@ -58,7 +58,7 @@ def main(output_dir: str): server=Server(url="http://any-hosted-llama-stack.com"), info=Info( title="Llama Stack Specification", - version=LLAMA_STACK_API_VERSION, + version=LLAMA_STACK_API_V1, description="""This is the specification of the Llama Stack that provides a set of endpoints and their corresponding interfaces that are tailored to best leverage Llama Models.""", diff --git a/docs/openapi_generator/pyopenapi/generator.py b/docs/openapi_generator/pyopenapi/generator.py index 144eb00f7..758fe7e8f 100644 --- a/docs/openapi_generator/pyopenapi/generator.py +++ b/docs/openapi_generator/pyopenapi/generator.py @@ -9,7 +9,9 @@ import ipaddress import types import typing from dataclasses import make_dataclass -from typing import Any, Dict, Set, Union +from typing import Annotated, Any, Dict, get_args, get_origin, Set, Union + +from fastapi import UploadFile from llama_stack.apis.datatypes import Error from llama_stack.strong_typing.core import JsonType @@ -30,9 +32,6 @@ from llama_stack.strong_typing.schema import ( Schema, SchemaOptions, ) -from typing import get_origin, get_args -from typing import Annotated -from fastapi import UploadFile from llama_stack.strong_typing.serialization import json_dump_string, object_to_json from .operations import ( @@ -623,11 +622,11 @@ class Generator: # data passed in request body as multipart/form-data elif op.multipart_params: builder = ContentBuilder(self.schema_builder) - + # Create schema properties for multipart form fields properties = {} required_fields = [] - + for name, param_type in op.multipart_params: if get_origin(param_type) is Annotated: base_type = get_args(param_type)[0] @@ -635,28 +634,21 @@ class Generator: base_type = param_type if base_type is UploadFile: # File upload - properties[name] = { - "type": "string", - "format": "binary" - } + properties[name] = {"type": "string", "format": "binary"} else: # Form field properties[name] = self.schema_builder.classdef_to_ref(base_type) - + required_fields.append(name) - + multipart_schema = { "type": "object", "properties": properties, - "required": required_fields + "required": required_fields, } - + requestBody = RequestBody( - content={ - "multipart/form-data": { - "schema": multipart_schema - } - }, + content={"multipart/form-data": {"schema": multipart_schema}}, required=True, ) # data passed in payload as JSON and mapped to request parameters @@ -800,9 +792,10 @@ class Generator: ) return Operation( - tags=[getattr(op.defining_class, "API_NAMESPACE", op.defining_class.__name__)], - summary=None, - # summary=doc_string.short_description, + tags=[ + getattr(op.defining_class, "API_NAMESPACE", op.defining_class.__name__) + ], + summary=doc_string.short_description, description=description, parameters=parameters, requestBody=requestBody, @@ -835,7 +828,7 @@ class Generator: else: raise NotImplementedError(f"unknown HTTP method: {op.http_method}") - route = op.get_route() + route = op.get_route(op.webmethod) route = route.replace(":path", "") print(f"route: {route}") if route in paths: diff --git a/docs/openapi_generator/pyopenapi/operations.py b/docs/openapi_generator/pyopenapi/operations.py index 045e33848..ce33d3bb9 100644 --- a/docs/openapi_generator/pyopenapi/operations.py +++ b/docs/openapi_generator/pyopenapi/operations.py @@ -11,7 +11,7 @@ import typing from dataclasses import dataclass from typing import Any, Callable, Dict, Iterable, Iterator, List, Optional, Tuple, Union -from llama_stack.apis.version import LLAMA_STACK_API_VERSION +from llama_stack.apis.version import LLAMA_STACK_API_V1, LLAMA_STACK_API_V1BETA, LLAMA_STACK_API_V1ALPHA from termcolor import colored @@ -113,11 +113,13 @@ class EndpointOperation: request_examples: Optional[List[Any]] = None response_examples: Optional[List[Any]] = None - def get_route(self) -> str: - if self.route is not None: - return "/".join(["", LLAMA_STACK_API_VERSION, self.route.lstrip("/")]) + def get_route(self, webmethod) -> str: + api_level = webmethod.level - route_parts = ["", LLAMA_STACK_API_VERSION, self.name] + if self.route is not None: + return "/".join(["", api_level, self.route.lstrip("/")]) + + route_parts = ["", api_level, self.name] for param_name, _ in self.path_params: route_parts.append("{" + param_name + "}") return "/".join(route_parts) @@ -152,33 +154,39 @@ def _get_endpoint_functions( functions = inspect.getmembers(endpoint, inspect.isfunction) for func_name, func_ref in functions: - webmethod = getattr(func_ref, "__webmethod__", None) - if not webmethod: + webmethods = [] + + # Check for multiple webmethods (stacked decorators) + if hasattr(func_ref, "__webmethods__"): + webmethods = func_ref.__webmethods__ + + if not webmethods: continue - print(f"Processing {colored(func_name, 'white')}...") - operation_name = func_name - - if webmethod.method == "GET": - prefix = "get" - elif webmethod.method == "DELETE": - prefix = "delete" - elif webmethod.method == "POST": - prefix = "post" - elif operation_name.startswith("get_") or operation_name.endswith("/get"): - prefix = "get" - elif ( - operation_name.startswith("delete_") - or operation_name.startswith("remove_") - or operation_name.endswith("/delete") - or operation_name.endswith("/remove") - ): - prefix = "delete" - else: - # by default everything else is a POST - prefix = "post" + for webmethod in webmethods: + print(f"Processing {colored(func_name, 'white')}...") + operation_name = func_name + + if webmethod.method == "GET": + prefix = "get" + elif webmethod.method == "DELETE": + prefix = "delete" + elif webmethod.method == "POST": + prefix = "post" + elif operation_name.startswith("get_") or operation_name.endswith("/get"): + prefix = "get" + elif ( + operation_name.startswith("delete_") + or operation_name.startswith("remove_") + or operation_name.endswith("/delete") + or operation_name.endswith("/remove") + ): + prefix = "delete" + else: + # by default everything else is a POST + prefix = "post" - yield prefix, operation_name, func_name, func_ref + yield prefix, operation_name, func_name, func_ref def _get_defining_class(member_fn: str, derived_cls: type) -> type: @@ -239,105 +247,101 @@ def get_endpoint_operations( "update", ], ): - # extract routing information from function metadata - webmethod = getattr(func_ref, "__webmethod__", None) - if webmethod is not None: + # Get all webmethods for this function + webmethods = getattr(func_ref, "__webmethods__", []) + + # Create one EndpointOperation for each webmethod + for webmethod in webmethods: route = webmethod.route route_params = _get_route_parameters(route) if route is not None else None public = webmethod.public request_examples = webmethod.request_examples response_examples = webmethod.response_examples - else: - route = None - route_params = None - public = False - request_examples = None - response_examples = None - # inspect function signature for path and query parameters, and request/response payload type - signature = get_signature(func_ref) + # inspect function signature for path and query parameters, and request/response payload type + signature = get_signature(func_ref) - path_params = [] - query_params = [] - request_params = [] - multipart_params = [] + path_params = [] + query_params = [] + request_params = [] + multipart_params = [] - for param_name, parameter in signature.parameters.items(): - param_type = _get_annotation_type(parameter.annotation, func_ref) + for param_name, parameter in signature.parameters.items(): + param_type = _get_annotation_type(parameter.annotation, func_ref) - # omit "self" for instance methods - if param_name == "self" and param_type is inspect.Parameter.empty: - continue + # omit "self" for instance methods + if param_name == "self" and param_type is inspect.Parameter.empty: + continue - # check if all parameters have explicit type - if parameter.annotation is inspect.Parameter.empty: + # check if all parameters have explicit type + if parameter.annotation is inspect.Parameter.empty: + raise ValidationError( + f"parameter '{param_name}' in function '{func_name}' has no type annotation" + ) + + is_multipart = _is_multipart_param(param_type) + + if prefix in ["get", "delete"]: + if route_params is not None and param_name in route_params: + path_params.append((param_name, param_type)) + else: + query_params.append((param_name, param_type)) + else: + if route_params is not None and param_name in route_params: + path_params.append((param_name, param_type)) + elif is_multipart: + multipart_params.append((param_name, param_type)) + else: + request_params.append((param_name, param_type)) + + # check if function has explicit return type + if signature.return_annotation is inspect.Signature.empty: raise ValidationError( - f"parameter '{param_name}' in function '{func_name}' has no type annotation" + f"function '{func_name}' has no return type annotation" ) - is_multipart = _is_multipart_param(param_type) - - if prefix in ["get", "delete"]: - if route_params is not None and param_name in route_params: - path_params.append((param_name, param_type)) - else: - query_params.append((param_name, param_type)) + return_type = _get_annotation_type(signature.return_annotation, func_ref) + + # operations that produce events are labeled as Generator[YieldType, SendType, ReturnType] + # where YieldType is the event type, SendType is None, and ReturnType is the immediate response type to the request + if typing.get_origin(return_type) is collections.abc.Generator: + event_type, send_type, response_type = typing.get_args(return_type) + if send_type is not type(None): + raise ValidationError( + f"function '{func_name}' has a return type Generator[Y,S,R] and therefore looks like an event but has an explicit send type" + ) else: - if route_params is not None and param_name in route_params: - path_params.append((param_name, param_type)) - elif is_multipart: - multipart_params.append((param_name, param_type)) + event_type = None + + def process_type(t): + if typing.get_origin(t) is collections.abc.AsyncIterator: + # NOTE(ashwin): this is SSE and there is no way to represent it. either we make it a List + # or the item type. I am choosing it to be the latter + args = typing.get_args(t) + return args[0] + elif typing.get_origin(t) is typing.Union: + types = [process_type(a) for a in typing.get_args(t)] + return typing._UnionGenericAlias(typing.Union, tuple(types)) + else: + return t + + response_type = process_type(return_type) + + if prefix in ["delete", "remove"]: + http_method = HTTPMethod.DELETE + elif prefix == "post": + http_method = HTTPMethod.POST + elif prefix == "get": + http_method = HTTPMethod.GET + elif prefix == "set": + http_method = HTTPMethod.PUT + elif prefix == "update": + http_method = HTTPMethod.PATCH else: - request_params.append((param_name, param_type)) + raise ValidationError(f"unknown prefix {prefix}") - # check if function has explicit return type - if signature.return_annotation is inspect.Signature.empty: - raise ValidationError( - f"function '{func_name}' has no return type annotation" - ) - - return_type = _get_annotation_type(signature.return_annotation, func_ref) - - # operations that produce events are labeled as Generator[YieldType, SendType, ReturnType] - # where YieldType is the event type, SendType is None, and ReturnType is the immediate response type to the request - if typing.get_origin(return_type) is collections.abc.Generator: - event_type, send_type, response_type = typing.get_args(return_type) - if send_type is not type(None): - raise ValidationError( - f"function '{func_name}' has a return type Generator[Y,S,R] and therefore looks like an event but has an explicit send type" - ) - else: - event_type = None - - def process_type(t): - if typing.get_origin(t) is collections.abc.AsyncIterator: - # NOTE(ashwin): this is SSE and there is no way to represent it. either we make it a List - # or the item type. I am choosing it to be the latter - args = typing.get_args(t) - return args[0] - elif typing.get_origin(t) is typing.Union: - types = [process_type(a) for a in typing.get_args(t)] - return typing._UnionGenericAlias(typing.Union, tuple(types)) - else: - return t - - response_type = process_type(return_type) - - if prefix in ["delete", "remove"]: - http_method = HTTPMethod.DELETE - elif prefix == "post": - http_method = HTTPMethod.POST - elif prefix == "get": - http_method = HTTPMethod.GET - elif prefix == "set": - http_method = HTTPMethod.PUT - elif prefix == "update": - http_method = HTTPMethod.PATCH - else: - raise ValidationError(f"unknown prefix {prefix}") - - result.append( - EndpointOperation( + # Create an EndpointOperation for this specific webmethod + operation = EndpointOperation( defining_class=_get_defining_class(func_name, endpoint), name=operation_name, func_name=func_name, @@ -354,7 +358,10 @@ def get_endpoint_operations( request_examples=request_examples if use_examples else None, response_examples=response_examples if use_examples else None, ) - ) + + # Store the specific webmethod with this operation + operation.webmethod = webmethod + result.append(operation) if not result: raise ValidationError(f"no eligible endpoint operations in type {endpoint}") diff --git a/docs/openapi_generator/run_openapi_generator.sh b/docs/openapi_generator/run_openapi_generator.sh index 22532ffe7..45d00d6e7 100755 --- a/docs/openapi_generator/run_openapi_generator.sh +++ b/docs/openapi_generator/run_openapi_generator.sh @@ -29,4 +29,4 @@ fi stack_dir=$(dirname $(dirname $THIS_DIR)) PYTHONPATH=$PYTHONPATH:$stack_dir \ - python -m docs.openapi_generator.generate $(dirname $THIS_DIR)/_static + python -m docs.openapi_generator.generate $(dirname $THIS_DIR)/static diff --git a/docs/package-lock.json b/docs/package-lock.json new file mode 100644 index 000000000..aa133c935 --- /dev/null +++ b/docs/package-lock.json @@ -0,0 +1,22087 @@ +{ + "name": 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"gen-api-docs:version": "docusaurus gen-api-docs:version", + "clean-api-docs:version": "docusaurus clean-api-docs:version" + }, + "dependencies": { + "@docusaurus/core": "3.8.1", + "@docusaurus/preset-classic": "3.8.1", + "@easyops-cn/docusaurus-search-local": "^0.52.1", + "@mdx-js/react": "^3.0.0", + "clsx": "^2.0.0", + "docusaurus-plugin-openapi-docs": "4.3.7", + "docusaurus-theme-openapi-docs": "4.3.7", + "prism-react-renderer": "^2.3.0", + "react": "^19.0.0", + "react-dom": "^19.0.0" + }, + "browserslist": { + "production": [ + ">0.5%", + "not dead", + "not op_mini all" + ], + "development": [ + "last 1 chrome version", + "last 1 firefox version", + "last 1 safari version" + ] + } +} diff --git a/docs/quick_start.ipynb b/docs/quick_start.ipynb index 757824578..c194a901d 100644 --- a/docs/quick_start.ipynb +++ b/docs/quick_start.ipynb @@ -11,7 +11,7 @@ "\n", "# Llama Stack - Building AI Applications\n", "\n", - "\"drawing\"\n", + "\"drawing\"\n", "\n", "Get started with Llama Stack in minutes!\n", "\n", @@ -138,7 +138,7 @@ }, "outputs": [], "source": [ - "import os \n", + "import os\n", "import subprocess\n", "\n", "if \"UV_SYSTEM_PYTHON\" in os.environ:\n", @@ -150,13 +150,13 @@ "def run_llama_stack_server_background():\n", " log_file = open(\"llama_stack_server.log\", \"w\")\n", " process = subprocess.Popen(\n", - " f\"OLLAMA_URL=http://localhost:11434 uv run --with llama-stack llama stack run starter --image-type venv", + " f\"OLLAMA_URL=http://localhost:11434 uv run --with llama-stack llama stack run starter --image-type venv\n", " shell=True,\n", " stdout=log_file,\n", " stderr=log_file,\n", " text=True\n", " )\n", - " \n", + "\n", " print(f\"Starting Llama Stack server with PID: {process.pid}\")\n", " return process\n", "\n", @@ -164,11 +164,11 @@ " import requests\n", " from requests.exceptions import ConnectionError\n", " import time\n", - " \n", + "\n", " url = \"http://0.0.0.0:8321/v1/health\"\n", " max_retries = 30\n", " retry_interval = 1\n", - " \n", + "\n", " print(\"Waiting for server to start\", end=\"\")\n", " for _ in range(max_retries):\n", " try:\n", @@ -179,12 +179,12 @@ " except ConnectionError:\n", " print(\".\", end=\"\", flush=True)\n", " time.sleep(retry_interval)\n", - " \n", + "\n", " print(\"\\nServer failed to start after\", max_retries * retry_interval, \"seconds\")\n", " return False\n", "\n", "\n", - "# use this helper if needed to kill the server \n", + "# use this helper if needed to kill the server\n", "def kill_llama_stack_server():\n", " # Kill any existing llama stack server processes\n", " os.system(\"ps aux | grep -v grep | grep llama_stack.core.server.server | awk '{print $2}' | xargs kill -9\")\n" diff --git a/docs/sidebars.ts b/docs/sidebars.ts new file mode 100644 index 000000000..01c1390c1 --- /dev/null +++ b/docs/sidebars.ts @@ -0,0 +1,342 @@ +import type {SidebarsConfig} from '@docusaurus/plugin-content-docs'; + +/** + * Creating a sidebar enables you to: + - create an ordered group of docs + - render a sidebar for each doc of that group + - provide next/previous navigation + + The sidebars can be generated from the filesystem, or explicitly defined here. + + Create as many sidebars as you want. + */ +const sidebars: SidebarsConfig = { + tutorialSidebar: [ + 'index', + { + type: 'category', + label: 'Getting Started', + collapsed: false, + items: [ + 'getting_started/quickstart', + 'getting_started/detailed_tutorial', + 'getting_started/libraries', + ], + }, + { + type: 'category', + label: 'Concepts', + collapsed: false, + items: [ + 'concepts/index', + 'concepts/architecture', + { + type: 'category', + label: 'APIs', + collapsed: true, + items: [ + 'concepts/apis/index', + 'concepts/apis/api_providers', + 'concepts/apis/external', + 'concepts/apis/api_leveling', + ], + }, + 'concepts/distributions', + 'concepts/resources', + ], + }, + { + type: 'category', + label: 'Distributions', + collapsed: false, + items: [ + 'distributions/index', + 'distributions/list_of_distributions', + 'distributions/building_distro', + 'distributions/customizing_run_yaml', + 'distributions/importing_as_library', + 'distributions/configuration', + 'distributions/starting_llama_stack_server', + { + type: 'category', + label: 'Self-Hosted Distributions', + collapsed: true, + items: [ + 'distributions/self_hosted_distro/starter', + 'distributions/self_hosted_distro/dell', + 'distributions/self_hosted_distro/dell-tgi', + 'distributions/self_hosted_distro/meta-reference-gpu', + 'distributions/self_hosted_distro/nvidia', + 'distributions/self_hosted_distro/passthrough', + ], + }, + { + type: 'category', + label: 'Remote-Hosted Distributions', + collapsed: true, + items: [ + 'distributions/remote_hosted_distro/index', + 'distributions/remote_hosted_distro/watsonx', + ], + }, + { + type: 'category', + label: 'On-Device Distributions', + collapsed: true, + items: [ + 'distributions/ondevice_distro/ios_sdk', + 'distributions/ondevice_distro/android_sdk', + ], + }, + ], + }, + { + type: 'category', + label: 'Providers', + collapsed: false, + items: [ + 'providers/index', + { + type: 'category', + label: 'Inference', + collapsed: true, + items: [ + 'providers/inference/index', + 'providers/inference/inline_meta-reference', + 'providers/inference/inline_sentence-transformers', + 'providers/inference/remote_anthropic', + 'providers/inference/remote_azure', + 'providers/inference/remote_bedrock', + 'providers/inference/remote_cerebras', + 'providers/inference/remote_databricks', + 'providers/inference/remote_fireworks', + 'providers/inference/remote_gemini', + 'providers/inference/remote_groq', + 'providers/inference/remote_hf_endpoint', + 'providers/inference/remote_hf_serverless', + 'providers/inference/remote_llama-openai-compat', + 'providers/inference/remote_nvidia', + 'providers/inference/remote_ollama', + 'providers/inference/remote_openai', + 'providers/inference/remote_passthrough', + 'providers/inference/remote_runpod', + 'providers/inference/remote_sambanova', + 'providers/inference/remote_sambanova-openai-compat', + 'providers/inference/remote_tgi', + 'providers/inference/remote_together', + 'providers/inference/remote_vertexai', + 'providers/inference/remote_vllm', + 'providers/inference/remote_watsonx' + ], + }, + { + type: 'category', + label: 'Safety', + collapsed: true, + items: [ + 'providers/safety/index', + 'providers/safety/inline_code-scanner', + 'providers/safety/inline_llama-guard', + 'providers/safety/inline_prompt-guard', + 'providers/safety/remote_bedrock', + 'providers/safety/remote_nvidia', + 'providers/safety/remote_sambanova' + ], + }, + { + type: 'category', + label: 'Vector IO', + collapsed: true, + items: [ + 'providers/vector_io/index', + 'providers/vector_io/inline_chromadb', + 'providers/vector_io/inline_faiss', + 'providers/vector_io/inline_meta-reference', + 'providers/vector_io/inline_milvus', + 'providers/vector_io/inline_qdrant', + 'providers/vector_io/inline_sqlite-vec', + 'providers/vector_io/remote_chromadb', + 'providers/vector_io/remote_milvus', + 'providers/vector_io/remote_pgvector', + 'providers/vector_io/remote_qdrant', + 'providers/vector_io/remote_weaviate' + ], + }, + { + type: 'category', + label: 'Tool Runtime', + collapsed: true, + items: [ + 'providers/tool_runtime/index', + 'providers/tool_runtime/inline_rag-runtime', + 'providers/tool_runtime/remote_bing-search', + 'providers/tool_runtime/remote_brave-search', + 'providers/tool_runtime/remote_model-context-protocol', + 'providers/tool_runtime/remote_tavily-search', + 'providers/tool_runtime/remote_wolfram-alpha' + ], + }, + { + type: 'category', + label: 'Agents', + collapsed: true, + items: [ + 'providers/agents/index', + 'providers/agents/inline_meta-reference' + ], + }, + { + type: 'category', + label: 'Post Training', + collapsed: true, + items: [ + 'providers/post_training/index', + 'providers/post_training/inline_huggingface', + 'providers/post_training/inline_huggingface-cpu', + 'providers/post_training/inline_huggingface-gpu', + 'providers/post_training/inline_torchtune', + 'providers/post_training/inline_torchtune-cpu', + 'providers/post_training/inline_torchtune-gpu', + 'providers/post_training/remote_nvidia' + ], + }, + { + type: 'category', + label: 'DatasetIO', + collapsed: true, + items: [ + 'providers/datasetio/index', + 'providers/datasetio/inline_localfs', + 'providers/datasetio/remote_huggingface', + 'providers/datasetio/remote_nvidia' + ], + }, + { + type: 'category', + label: 'Scoring', + collapsed: true, + items: [ + 'providers/scoring/index', + 'providers/scoring/inline_basic', + 'providers/scoring/inline_braintrust', + 'providers/scoring/inline_llm-as-judge' + ], + }, + { + type: 'category', + label: 'Files', + collapsed: true, + items: [ + 'providers/files/index', + 'providers/files/inline_localfs', + 'providers/files/remote_s3' + ], + }, + { + type: 'category', + label: 'Eval', + collapsed: true, + items: [ + 'providers/eval/index', + 'providers/eval/inline_meta-reference', + 'providers/eval/remote_nvidia' + ], + }, + { + type: 'category', + label: 'Telemetry', + collapsed: true, + items: [ + 'providers/telemetry/index', + 'providers/telemetry/inline_meta-reference' + ], + }, + { + type: 'category', + label: 'Batches', + collapsed: true, + items: [ + 'providers/batches/index', + 'providers/batches/inline_reference' + ], + }, + { + type: 'category', + label: 'External Providers', + collapsed: true, + items: [ + 'providers/external/index', + 'providers/external/external-providers-guide', + 'providers/external/external-providers-list' + ], + }, + 'providers/openai' + ], + }, + { + type: 'category', + label: 'Building Applications', + collapsed: false, + items: [ + 'building_applications/index', + 'building_applications/rag', + 'building_applications/agent', + 'building_applications/agent_execution_loop', + 'building_applications/responses_vs_agents', + 'building_applications/tools', + 'building_applications/evals', + 'building_applications/telemetry', + 'building_applications/safety', + 'building_applications/playground', + ], + }, + { + type: 'category', + label: 'Advanced APIs', + collapsed: false, + items: [ + 'advanced_apis/post_training', + 'advanced_apis/evaluation', + 'advanced_apis/scoring', + ], + }, + { + type: 'category', + label: 'Deploying', + collapsed: false, + items: [ + 'deploying/index', + 'deploying/kubernetes_deployment', + 'deploying/aws_eks_deployment', + ], + }, + { + type: 'category', + label: 'Contributing', + collapsed: false, + items: [ + 'contributing/index', + 'contributing/new_api_provider', + 'contributing/new_vector_database', + 'contributing/testing/record-replay', + ], + }, + { + type: 'category', + label: 'References', + collapsed: false, + items: [ + 'references/index', + 'references/llama_cli_reference/index', + 'references/llama_stack_client_cli_reference', + 'references/python_sdk_reference/index', + 'references/evals_reference/index', + ], + }, + ], + + // API Reference sidebar - use plugin-generated sidebar + apiSidebar: require('./docs/api/sidebar.ts').default, +}; + +export default sidebars; diff --git a/docs/source/advanced_apis/eval/index.md b/docs/source/advanced_apis/eval/index.md deleted file mode 100644 index 330380670..000000000 --- a/docs/source/advanced_apis/eval/index.md +++ /dev/null @@ -1,6 +0,0 @@ -# Eval Providers - -This section contains documentation for all available providers for the **eval** API. - -- [inline::meta-reference](inline_meta-reference.md) -- [remote::nvidia](remote_nvidia.md) \ No newline at end of file diff --git a/docs/source/advanced_apis/index.md b/docs/source/advanced_apis/index.md deleted file mode 100644 index b10672c29..000000000 --- a/docs/source/advanced_apis/index.md +++ /dev/null @@ -1,33 +0,0 @@ -# Advanced APIs - -## Post-training -Fine-tunes a model. - -```{toctree} -:maxdepth: 1 - -post_training/index -``` - -## Eval -Generates outputs (via Inference or Agents) and perform scoring. - -```{toctree} -:maxdepth: 1 - -eval/index -``` - -```{include} evaluation_concepts.md -:start-after: ## Evaluation Concepts -``` - -## Scoring -Evaluates the outputs of the system. - -```{toctree} -:maxdepth: 1 - -scoring/index -``` - diff --git a/docs/source/advanced_apis/post_training/huggingface.md b/docs/source/advanced_apis/post_training/huggingface.md deleted file mode 100644 index a7609d6da..000000000 --- a/docs/source/advanced_apis/post_training/huggingface.md +++ /dev/null @@ -1,122 +0,0 @@ ---- -orphan: true ---- -# HuggingFace SFTTrainer - -[HuggingFace SFTTrainer](https://huggingface.co/docs/trl/en/sft_trainer) is an inline post training provider for Llama Stack. It allows you to run supervised fine tuning on a variety of models using many datasets - -## Features - -- Simple access through the post_training API -- Fully integrated with Llama Stack -- GPU support, CPU support, and MPS support (MacOS Metal Performance Shaders) - -## Usage - -To use the HF SFTTrainer in your Llama Stack project, follow these steps: - -1. Configure your Llama Stack project to use this provider. -2. Kick off a SFT job using the Llama Stack post_training API. - -## Setup - -You can access the HuggingFace trainer via the `ollama` distribution: - -```bash -llama stack build --distro starter --image-type venv -llama stack run --image-type venv ~/.llama/distributions/ollama/ollama-run.yaml -``` - -## Run Training - -You can access the provider and the `supervised_fine_tune` method via the post_training API: - -```python -import time -import uuid - - -from llama_stack_client.types import ( - post_training_supervised_fine_tune_params, - algorithm_config_param, -) - - -def create_http_client(): - from llama_stack_client import LlamaStackClient - - return LlamaStackClient(base_url="http://localhost:8321") - - -client = create_http_client() - -# Example Dataset -client.datasets.register( - purpose="post-training/messages", - source={ - "type": "uri", - "uri": "huggingface://datasets/llamastack/simpleqa?split=train", - }, - dataset_id="simpleqa", -) - -training_config = post_training_supervised_fine_tune_params.TrainingConfig( - data_config=post_training_supervised_fine_tune_params.TrainingConfigDataConfig( - batch_size=32, - data_format="instruct", - dataset_id="simpleqa", - shuffle=True, - ), - gradient_accumulation_steps=1, - max_steps_per_epoch=0, - max_validation_steps=1, - n_epochs=4, -) - -algorithm_config = algorithm_config_param.LoraFinetuningConfig( # this config is also currently mandatory but should not be - alpha=1, - apply_lora_to_mlp=True, - apply_lora_to_output=False, - lora_attn_modules=["q_proj"], - rank=1, - type="LoRA", -) - -job_uuid = f"test-job{uuid.uuid4()}" - -# Example Model -training_model = "ibm-granite/granite-3.3-8b-instruct" - -start_time = time.time() -response = client.post_training.supervised_fine_tune( - job_uuid=job_uuid, - logger_config={}, - model=training_model, - hyperparam_search_config={}, - training_config=training_config, - algorithm_config=algorithm_config, - checkpoint_dir="output", -) -print("Job: ", job_uuid) - - -# Wait for the job to complete! -while True: - status = client.post_training.job.status(job_uuid=job_uuid) - if not status: - print("Job not found") - break - - print(status) - if status.status == "completed": - break - - print("Waiting for job to complete...") - time.sleep(5) - -end_time = time.time() -print("Job completed in", end_time - start_time, "seconds!") - -print("Artifacts:") -print(client.post_training.job.artifacts(job_uuid=job_uuid)) -``` diff --git a/docs/source/advanced_apis/post_training/index.md b/docs/source/advanced_apis/post_training/index.md deleted file mode 100644 index 35d10d14b..000000000 --- a/docs/source/advanced_apis/post_training/index.md +++ /dev/null @@ -1,7 +0,0 @@ -# Post_Training Providers - -This section contains documentation for all available providers for the **post_training** API. - -- [inline::huggingface](inline_huggingface.md) -- [inline::torchtune](inline_torchtune.md) -- [remote::nvidia](remote_nvidia.md) \ No newline at end of file diff --git a/docs/source/advanced_apis/post_training/inline_huggingface.md b/docs/source/advanced_apis/post_training/inline_huggingface.md deleted file mode 100644 index 4d2201c99..000000000 --- a/docs/source/advanced_apis/post_training/inline_huggingface.md +++ /dev/null @@ -1,37 +0,0 @@ ---- -orphan: true ---- - -# inline::huggingface - -## Description - -HuggingFace-based post-training provider for fine-tuning models using the HuggingFace ecosystem. - -## Configuration - -| Field | Type | Required | Default | Description | -|-------|------|----------|---------|-------------| -| `device` | `` | No | cuda | | -| `distributed_backend` | `Literal['fsdp', 'deepspeed'` | No | | | -| `checkpoint_format` | `Literal['full_state', 'huggingface'` | No | huggingface | | -| `chat_template` | `` | No | | -| `model_specific_config` | `` | No | {'trust_remote_code': True, 'attn_implementation': 'sdpa'} | | -| `max_seq_length` | `` | No | 2048 | | -| `gradient_checkpointing` | `` | No | False | | -| `save_total_limit` | `` | No | 3 | | -| `logging_steps` | `` | No | 10 | | -| `warmup_ratio` | `` | No | 0.1 | | -| `weight_decay` | `` | No | 0.01 | | -| `dataloader_num_workers` | `` | No | 4 | | -| `dataloader_pin_memory` | `` | No | True | | - -## Sample Configuration - -```yaml -checkpoint_format: huggingface -distributed_backend: null -device: cpu - -``` - diff --git a/docs/source/advanced_apis/post_training/inline_torchtune.md b/docs/source/advanced_apis/post_training/inline_torchtune.md deleted file mode 100644 index 6684c99ac..000000000 --- a/docs/source/advanced_apis/post_training/inline_torchtune.md +++ /dev/null @@ -1,24 +0,0 @@ ---- -orphan: true ---- - -# inline::torchtune - -## Description - -TorchTune-based post-training provider for fine-tuning and optimizing models using Meta's TorchTune framework. - -## Configuration - -| Field | Type | Required | Default | Description | -|-------|------|----------|---------|-------------| -| `torch_seed` | `int \| None` | No | | | -| `checkpoint_format` | `Literal['meta', 'huggingface'` | No | meta | | - -## Sample Configuration - -```yaml -checkpoint_format: meta - -``` - diff --git a/docs/source/advanced_apis/post_training/nvidia_nemo.md b/docs/source/advanced_apis/post_training/nvidia_nemo.md deleted file mode 100644 index 1a7adbe16..000000000 --- a/docs/source/advanced_apis/post_training/nvidia_nemo.md +++ /dev/null @@ -1,163 +0,0 @@ ---- -orphan: true ---- -# NVIDIA NEMO - -[NVIDIA NEMO](https://developer.nvidia.com/nemo-framework) is a remote post training provider for Llama Stack. It provides enterprise-grade fine-tuning capabilities through NVIDIA's NeMo Customizer service. - -## Features - -- Enterprise-grade fine-tuning capabilities -- Support for LoRA and SFT fine-tuning -- Integration with NVIDIA's NeMo Customizer service -- Support for various NVIDIA-optimized models -- Efficient training with NVIDIA hardware acceleration - -## Usage - -To use NVIDIA NEMO in your Llama Stack project, follow these steps: - -1. Configure your Llama Stack project to use this provider. -2. Set up your NVIDIA API credentials. -3. Kick off a fine-tuning job using the Llama Stack post_training API. - -## Setup - -You'll need to set the following environment variables: - -```bash -export NVIDIA_API_KEY="your-api-key" -export NVIDIA_DATASET_NAMESPACE="default" -export NVIDIA_CUSTOMIZER_URL="your-customizer-url" -export NVIDIA_PROJECT_ID="your-project-id" -export NVIDIA_OUTPUT_MODEL_DIR="your-output-model-dir" -``` - -## Run Training - -You can access the provider and the `supervised_fine_tune` method via the post_training API: - -```python -import time -import uuid - -from llama_stack_client.types import ( - post_training_supervised_fine_tune_params, - algorithm_config_param, -) - - -def create_http_client(): - from llama_stack_client import LlamaStackClient - - return LlamaStackClient(base_url="http://localhost:8321") - - -client = create_http_client() - -# Example Dataset -client.datasets.register( - purpose="post-training/messages", - source={ - "type": "uri", - "uri": "huggingface://datasets/llamastack/simpleqa?split=train", - }, - dataset_id="simpleqa", -) - -training_config = post_training_supervised_fine_tune_params.TrainingConfig( - data_config=post_training_supervised_fine_tune_params.TrainingConfigDataConfig( - batch_size=8, # Default batch size for NEMO - data_format="instruct", - dataset_id="simpleqa", - shuffle=True, - ), - n_epochs=50, # Default epochs for NEMO - optimizer_config=post_training_supervised_fine_tune_params.TrainingConfigOptimizerConfig( - lr=0.0001, # Default learning rate - weight_decay=0.01, # NEMO-specific parameter - ), - # NEMO-specific parameters - log_every_n_steps=None, - val_check_interval=0.25, - sequence_packing_enabled=False, - hidden_dropout=None, - attention_dropout=None, - ffn_dropout=None, -) - -algorithm_config = algorithm_config_param.LoraFinetuningConfig( - alpha=16, # Default alpha for NEMO - type="LoRA", -) - -job_uuid = f"test-job{uuid.uuid4()}" - -# Example Model - must be a supported NEMO model -training_model = "meta/llama-3.1-8b-instruct" - -start_time = time.time() -response = client.post_training.supervised_fine_tune( - job_uuid=job_uuid, - logger_config={}, - model=training_model, - hyperparam_search_config={}, - training_config=training_config, - algorithm_config=algorithm_config, - checkpoint_dir="output", -) -print("Job: ", job_uuid) - -# Wait for the job to complete! -while True: - status = client.post_training.job.status(job_uuid=job_uuid) - if not status: - print("Job not found") - break - - print(status) - if status.status == "completed": - break - - print("Waiting for job to complete...") - time.sleep(5) - -end_time = time.time() -print("Job completed in", end_time - start_time, "seconds!") - -print("Artifacts:") -print(client.post_training.job.artifacts(job_uuid=job_uuid)) -``` - -## Supported Models - -Currently supports the following models: -- meta/llama-3.1-8b-instruct -- meta/llama-3.2-1b-instruct - -## Supported Parameters - -### TrainingConfig -- n_epochs (default: 50) -- data_config -- optimizer_config -- log_every_n_steps -- val_check_interval (default: 0.25) -- sequence_packing_enabled (default: False) -- hidden_dropout (0.0-1.0) -- attention_dropout (0.0-1.0) -- ffn_dropout (0.0-1.0) - -### DataConfig -- dataset_id -- batch_size (default: 8) - -### OptimizerConfig -- lr (default: 0.0001) -- weight_decay (default: 0.01) - -### LoRA Config -- alpha (default: 16) -- type (must be "LoRA") - -Note: Some parameters from the standard Llama Stack API are not supported and will be ignored with a warning. diff --git a/docs/source/advanced_apis/post_training/torchtune.md b/docs/source/advanced_apis/post_training/torchtune.md deleted file mode 100644 index ef72505b1..000000000 --- a/docs/source/advanced_apis/post_training/torchtune.md +++ /dev/null @@ -1,125 +0,0 @@ ---- -orphan: true ---- -# TorchTune - -[TorchTune](https://github.com/pytorch/torchtune) is an inline post training provider for Llama Stack. It provides a simple and efficient way to fine-tune language models using PyTorch. - -## Features - -- Simple access through the post_training API -- Fully integrated with Llama Stack -- GPU support and single device capabilities. -- Support for LoRA - -## Usage - -To use TorchTune in your Llama Stack project, follow these steps: - -1. Configure your Llama Stack project to use this provider. -2. Kick off a fine-tuning job using the Llama Stack post_training API. - -## Setup - -You can access the TorchTune trainer by writing your own yaml pointing to the provider: - -```yaml -post_training: - - provider_id: torchtune - provider_type: inline::torchtune - config: {} -``` - -you can then build and run your own stack with this provider. - -## Run Training - -You can access the provider and the `supervised_fine_tune` method via the post_training API: - -```python -import time -import uuid - -from llama_stack_client.types import ( - post_training_supervised_fine_tune_params, - algorithm_config_param, -) - - -def create_http_client(): - from llama_stack_client import LlamaStackClient - - return LlamaStackClient(base_url="http://localhost:8321") - - -client = create_http_client() - -# Example Dataset -client.datasets.register( - purpose="post-training/messages", - source={ - "type": "uri", - "uri": "huggingface://datasets/llamastack/simpleqa?split=train", - }, - dataset_id="simpleqa", -) - -training_config = post_training_supervised_fine_tune_params.TrainingConfig( - data_config=post_training_supervised_fine_tune_params.TrainingConfigDataConfig( - batch_size=32, - data_format="instruct", - dataset_id="simpleqa", - shuffle=True, - ), - gradient_accumulation_steps=1, - max_steps_per_epoch=0, - max_validation_steps=1, - n_epochs=4, -) - -algorithm_config = algorithm_config_param.LoraFinetuningConfig( - alpha=1, - apply_lora_to_mlp=True, - apply_lora_to_output=False, - lora_attn_modules=["q_proj"], - rank=1, - type="LoRA", -) - -job_uuid = f"test-job{uuid.uuid4()}" - -# Example Model -training_model = "meta-llama/Llama-2-7b-hf" - -start_time = time.time() -response = client.post_training.supervised_fine_tune( - job_uuid=job_uuid, - logger_config={}, - model=training_model, - hyperparam_search_config={}, - training_config=training_config, - algorithm_config=algorithm_config, - checkpoint_dir="output", -) -print("Job: ", job_uuid) - -# Wait for the job to complete! -while True: - status = client.post_training.job.status(job_uuid=job_uuid) - if not status: - print("Job not found") - break - - print(status) - if status.status == "completed": - break - - print("Waiting for job to complete...") - time.sleep(5) - -end_time = time.time() -print("Job completed in", end_time - start_time, "seconds!") - -print("Artifacts:") -print(client.post_training.job.artifacts(job_uuid=job_uuid)) -``` diff --git a/docs/source/advanced_apis/scoring/index.md b/docs/source/advanced_apis/scoring/index.md deleted file mode 100644 index 3cf7af537..000000000 --- a/docs/source/advanced_apis/scoring/index.md +++ /dev/null @@ -1,7 +0,0 @@ -# Scoring Providers - -This section contains documentation for all available providers for the **scoring** API. - -- [inline::basic](inline_basic.md) -- [inline::braintrust](inline_braintrust.md) -- [inline::llm-as-judge](inline_llm-as-judge.md) \ No newline at end of file diff --git a/docs/source/building_applications/evals.md b/docs/source/building_applications/evals.md deleted file mode 100644 index ded62cebb..000000000 --- a/docs/source/building_applications/evals.md +++ /dev/null @@ -1,125 +0,0 @@ -# Evaluations - -The Llama Stack provides a set of APIs in Llama Stack for supporting running evaluations of LLM applications. -- `/datasetio` + `/datasets` API -- `/scoring` + `/scoring_functions` API -- `/eval` + `/benchmarks` API - - - -This guides walks you through the process of evaluating an LLM application built using Llama Stack. Checkout the [Evaluation Reference](../references/evals_reference/index.md) guide goes over the sets of APIs and developer experience flow of using Llama Stack to run evaluations for benchmark and application use cases. Checkout our Colab notebook on working examples with evaluations [here](https://colab.research.google.com/drive/10CHyykee9j2OigaIcRv47BKG9mrNm0tJ?usp=sharing). - - -## Application Evaluation - -[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/meta-llama/llama-stack/blob/main/docs/getting_started.ipynb) - -Llama Stack offers a library of scoring functions and the `/scoring` API, allowing you to run evaluations on your pre-annotated AI application datasets. - -In this example, we will show you how to: -1. Build an Agent with Llama Stack -2. Query the agent's sessions, turns, and steps -3. Evaluate the results. - -##### Building a Search Agent -```python -from llama_stack_client import LlamaStackClient, Agent, AgentEventLogger - -client = LlamaStackClient(base_url=f"http://{HOST}:{PORT}") - -agent = Agent( - client, - model="meta-llama/Llama-3.3-70B-Instruct", - instructions="You are a helpful assistant. Use search tool to answer the questions. ", - tools=["builtin::websearch"], -) -user_prompts = [ - "Which teams played in the NBA Western Conference Finals of 2024. Search the web for the answer.", - "In which episode and season of South Park does Bill Cosby (BSM-471) first appear? Give me the number and title. Search the web for the answer.", - "What is the British-American kickboxer Andrew Tate's kickboxing name? Search the web for the answer.", -] - -session_id = agent.create_session("test-session") - -for prompt in user_prompts: - response = agent.create_turn( - messages=[ - { - "role": "user", - "content": prompt, - } - ], - session_id=session_id, - ) - - for log in AgentEventLogger().log(response): - log.print() -``` - - -##### Query Agent Execution Steps - -Now, let's look deeper into the agent's execution steps and see if how well our agent performs. -```python -# query the agents session -from rich.pretty import pprint - -session_response = client.agents.session.retrieve( - session_id=session_id, - agent_id=agent.agent_id, -) - -pprint(session_response) -``` - -As a sanity check, we will first check if all user prompts is followed by a tool call to `brave_search`. -```python -num_tool_call = 0 -for turn in session_response.turns: - for step in turn.steps: - if ( - step.step_type == "tool_execution" - and step.tool_calls[0].tool_name == "brave_search" - ): - num_tool_call += 1 - -print( - f"{num_tool_call}/{len(session_response.turns)} user prompts are followed by a tool call to `brave_search`" -) -``` - -##### Evaluate Agent Responses -Now, we want to evaluate the agent's responses to the user prompts. - -1. First, we will process the agent's execution history into a list of rows that can be used for evaluation. -2. Next, we will label the rows with the expected answer. -3. Finally, we will use the `/scoring` API to score the agent's responses. - -```python -eval_rows = [] - -expected_answers = [ - "Dallas Mavericks and the Minnesota Timberwolves", - "Season 4, Episode 12", - "King Cobra", -] - -for i, turn in enumerate(session_response.turns): - eval_rows.append( - { - "input_query": turn.input_messages[0].content, - "generated_answer": turn.output_message.content, - "expected_answer": expected_answers[i], - } - ) - -pprint(eval_rows) - -scoring_params = { - "basic::subset_of": None, -} -scoring_response = client.scoring.score( - input_rows=eval_rows, scoring_functions=scoring_params -) -pprint(scoring_response) -``` diff --git a/docs/source/building_applications/index.md b/docs/source/building_applications/index.md deleted file mode 100644 index fddd957ed..000000000 --- a/docs/source/building_applications/index.md +++ /dev/null @@ -1,33 +0,0 @@ -# AI Application Examples - -Llama Stack provides all the building blocks needed to create sophisticated AI applications. - -The best way to get started is to look at this notebook which walks through the various APIs (from basic inference, to RAG agents) and how to use them. - -**Notebook**: [Building AI Applications](https://github.com/meta-llama/llama-stack/blob/main/docs/getting_started.ipynb) - -Here are some key topics that will help you build effective agents: - -- **[RAG (Retrieval-Augmented Generation)](rag)**: Learn how to enhance your agents with external knowledge through retrieval mechanisms. -- **[Agent](agent)**: Understand the components and design patterns of the Llama Stack agent framework. -- **[Agent Execution Loop](agent_execution_loop)**: Understand how agents process information, make decisions, and execute actions in a continuous loop. -- **[Agents vs Responses API](responses_vs_agents)**: Learn the differences between the Agents API and Responses API, and when to use each one. -- **[Tools](tools)**: Extend your agents' capabilities by integrating with external tools and APIs. -- **[Evals](evals)**: Evaluate your agents' effectiveness and identify areas for improvement. -- **[Telemetry](telemetry)**: Monitor and analyze your agents' performance and behavior. -- **[Safety](safety)**: Implement guardrails and safety measures to ensure responsible AI behavior. - -```{toctree} -:hidden: -:maxdepth: 1 - -rag -agent -agent_execution_loop -responses_vs_agents -tools -evals -telemetry -safety -playground/index -``` \ No newline at end of file diff --git a/docs/source/building_applications/playground/index.md b/docs/source/building_applications/playground/index.md deleted file mode 100644 index fd2b92434..000000000 --- a/docs/source/building_applications/playground/index.md +++ /dev/null @@ -1,107 +0,0 @@ -## Llama Stack Playground - -```{note} -The Llama Stack Playground is currently experimental and subject to change. We welcome feedback and contributions to help improve it. -``` - -The Llama Stack Playground is an simple interface which aims to: -- Showcase **capabilities** and **concepts** of Llama Stack in an interactive environment -- Demo **end-to-end** application code to help users get started to build their own applications -- Provide an **UI** to help users inspect and understand Llama Stack API providers and resources - -### Key Features - -#### Playground -Interactive pages for users to play with and explore Llama Stack API capabilities. - -##### Chatbot -```{eval-rst} -.. video:: https://github.com/user-attachments/assets/8d2ef802-5812-4a28-96e1-316038c84cbf - :autoplay: - :playsinline: - :muted: - :loop: - :width: 100% -``` -- **Chat**: Chat with Llama models. - - This page is a simple chatbot that allows you to chat with Llama models. Under the hood, it uses the `/inference/chat-completion` streaming API to send messages to the model and receive responses. -- **RAG**: Uploading documents to memory_banks and chat with RAG agent - - This page allows you to upload documents as a `memory_bank` and then chat with a RAG agent to query information about the uploaded documents. - - Under the hood, it uses Llama Stack's `/agents` API to define and create a RAG agent and chat with it in a session. - -##### Evaluations -```{eval-rst} -.. video:: https://github.com/user-attachments/assets/6cc1659f-eba4-49ca-a0a5-7c243557b4f5 - :autoplay: - :playsinline: - :muted: - :loop: - :width: 100% -``` -- **Evaluations (Scoring)**: Run evaluations on your AI application datasets. - - This page demonstrates the flow evaluation API to run evaluations on your custom AI application datasets. You may upload your own evaluation datasets and run evaluations using available scoring functions. - - Under the hood, it uses Llama Stack's `/scoring` API to run evaluations on selected scoring functions. - -```{eval-rst} -.. video:: https://github.com/user-attachments/assets/345845c7-2a2b-4095-960a-9ae40f6a93cf - :autoplay: - :playsinline: - :muted: - :loop: - :width: 100% -``` -- **Evaluations (Generation + Scoring)**: Use pre-registered evaluation tasks to evaluate an model or agent candidate - - This page demonstrates the flow for evaluation API to evaluate an model or agent candidate on pre-defined evaluation tasks. An evaluation task is a combination of dataset and scoring functions. - - Under the hood, it uses Llama Stack's `/eval` API to run generations and scorings on specified evaluation configs. - - In order to run this page, you may need to register evaluation tasks and datasets as resources first through the following commands. - ```bash - $ llama-stack-client datasets register \ - --dataset-id "mmlu" \ - --provider-id "huggingface" \ - --url "https://huggingface.co/datasets/llamastack/evals" \ - --metadata '{"path": "llamastack/evals", "name": "evals__mmlu__details", "split": "train"}' \ - --schema '{"input_query": {"type": "string"}, "expected_answer": {"type": "string"}, "chat_completion_input": {"type": "string"}}' - ``` - - ```bash - $ llama-stack-client benchmarks register \ - --eval-task-id meta-reference-mmlu \ - --provider-id meta-reference \ - --dataset-id mmlu \ - --scoring-functions basic::regex_parser_multiple_choice_answer - ``` - - -##### Inspect -```{eval-rst} -.. video:: https://github.com/user-attachments/assets/01d52b2d-92af-4e3a-b623-a9b8ba22ba99 - :autoplay: - :playsinline: - :muted: - :loop: - :width: 100% -``` -- **API Providers**: Inspect Llama Stack API providers - - This page allows you to inspect Llama Stack API providers and resources. - - Under the hood, it uses Llama Stack's `/providers` API to get information about the providers. - -- **API Resources**: Inspect Llama Stack API resources - - This page allows you to inspect Llama Stack API resources (`models`, `datasets`, `memory_banks`, `benchmarks`, `shields`). - - Under the hood, it uses Llama Stack's `//list` API to get information about each resources. - - Please visit [Core Concepts](https://llama-stack.readthedocs.io/en/latest/concepts/index.html) for more details about the resources. - -### Starting the Llama Stack Playground - -To start the Llama Stack Playground, run the following commands: - -1. Start up the Llama Stack API server - -```bash -llama stack build --distro together --image-type venv -llama stack run together -``` - -2. Start Streamlit UI -```bash -uv run --with ".[ui]" streamlit run llama_stack.core/ui/app.py -``` diff --git a/docs/source/building_applications/safety.md b/docs/source/building_applications/safety.md deleted file mode 100644 index 30afe7ad2..000000000 --- a/docs/source/building_applications/safety.md +++ /dev/null @@ -1,17 +0,0 @@ -## Safety Guardrails - -Safety is a critical component of any AI application. Llama Stack provides a Shield system that can be applied at multiple touchpoints: - -```python -# Register a safety shield -shield_id = "content_safety" -client.shields.register(shield_id=shield_id, provider_shield_id="llama-guard-basic") - -# Run content through shield -response = client.safety.run_shield( - shield_id=shield_id, messages=[{"role": "user", "content": "User message here"}] -) - -if response.violation: - print(f"Safety violation detected: {response.violation.user_message}") -``` diff --git a/docs/source/building_applications/telemetry.md b/docs/source/building_applications/telemetry.md deleted file mode 100644 index d93242f75..000000000 --- a/docs/source/building_applications/telemetry.md +++ /dev/null @@ -1,143 +0,0 @@ -## Telemetry - -The Llama Stack telemetry system provides comprehensive tracing, metrics, and logging capabilities. It supports multiple sink types including OpenTelemetry, SQLite, and Console output. - -### Events -The telemetry system supports three main types of events: - -- **Unstructured Log Events**: Free-form log messages with severity levels -```python -unstructured_log_event = UnstructuredLogEvent( - message="This is a log message", severity=LogSeverity.INFO -) -``` -- **Metric Events**: Numerical measurements with units -```python -metric_event = MetricEvent(metric="my_metric", value=10, unit="count") -``` -- **Structured Log Events**: System events like span start/end. Extensible to add more structured log types. -```python -structured_log_event = SpanStartPayload(name="my_span", parent_span_id="parent_span_id") -``` - -### Spans and Traces -- **Spans**: Represent operations with timing and hierarchical relationships -- **Traces**: Collection of related spans forming a complete request flow - -### Metrics - -Llama Stack automatically generates metrics during inference operations. These metrics are aggregated at the **inference request level** and provide insights into token usage and model performance. - -#### Available Metrics - -The following metrics are automatically generated for each inference request: - -| Metric Name | Type | Unit | Description | Labels | -|-------------|------|------|-------------|--------| -| `llama_stack_prompt_tokens_total` | Counter | `tokens` | Number of tokens in the input prompt | `model_id`, `provider_id` | -| `llama_stack_completion_tokens_total` | Counter | `tokens` | Number of tokens in the generated response | `model_id`, `provider_id` | -| `llama_stack_tokens_total` | Counter | `tokens` | Total tokens used (prompt + completion) | `model_id`, `provider_id` | - -#### Metric Generation Flow - -1. **Token Counting**: During inference operations (chat completion, completion, etc.), the system counts tokens in both input prompts and generated responses -2. **Metric Construction**: For each request, `MetricEvent` objects are created with the token counts -3. **Telemetry Logging**: Metrics are sent to the configured telemetry sinks -4. **OpenTelemetry Export**: When OpenTelemetry is enabled, metrics are exposed as standard OpenTelemetry counters - -#### Metric Aggregation Level - -All metrics are generated and aggregated at the **inference request level**. This means: - -- Each individual inference request generates its own set of metrics -- Metrics are not pre-aggregated across multiple requests -- Aggregation (sums, averages, etc.) can be performed by your observability tools (Prometheus, Grafana, etc.) -- Each metric includes labels for `model_id` and `provider_id` to enable filtering and grouping - -#### Example Metric Event - -```python -MetricEvent( - trace_id="1234567890abcdef", - span_id="abcdef1234567890", - metric="total_tokens", - value=150, - timestamp=1703123456.789, - unit="tokens", - attributes={"model_id": "meta-llama/Llama-3.2-3B-Instruct", "provider_id": "tgi"}, -) -``` - -#### Querying Metrics - -When using the OpenTelemetry sink, metrics are exposed in standard OpenTelemetry format and can be queried through: - -- **Prometheus**: Scrape metrics from the OpenTelemetry Collector's metrics endpoint -- **Grafana**: Create dashboards using Prometheus as a data source -- **OpenTelemetry Collector**: Forward metrics to other observability systems - -Example Prometheus queries: -```promql -# Total tokens used across all models -sum(llama_stack_tokens_total) - -# Tokens per model -sum by (model_id) (llama_stack_tokens_total) - -# Average tokens per request -rate(llama_stack_tokens_total[5m]) -``` - -### Sinks -- **OpenTelemetry**: Send events to an OpenTelemetry Collector. This is useful for visualizing traces in a tool like Jaeger and collecting metrics for Prometheus. -- **SQLite**: Store events in a local SQLite database. This is needed if you want to query the events later through the Llama Stack API. -- **Console**: Print events to the console. - -### Providers - -#### Meta-Reference Provider -Currently, only the meta-reference provider is implemented. It can be configured to send events to multiple sink types: -1) OpenTelemetry Collector (traces and metrics) -2) SQLite (traces only) -3) Console (all events) - -#### Configuration - -Here's an example that sends telemetry signals to all sink types. Your configuration might use only one or a subset. - -```yaml - telemetry: - - provider_id: meta-reference - provider_type: inline::meta-reference - config: - service_name: "llama-stack-service" - sinks: ['console', 'sqlite', 'otel_trace', 'otel_metric'] - otel_exporter_otlp_endpoint: "http://localhost:4318" - sqlite_db_path: "/path/to/telemetry.db" -``` - -**Environment Variables:** -- `OTEL_EXPORTER_OTLP_ENDPOINT`: OpenTelemetry Collector endpoint (default: `http://localhost:4318`) -- `OTEL_SERVICE_NAME`: Service name for telemetry (default: empty string) -- `TELEMETRY_SINKS`: Comma-separated list of sinks (default: `console,sqlite`) - -### Jaeger to visualize traces - -The `otel_trace` sink works with any service compatible with the OpenTelemetry collector. Traces and metrics use separate endpoints but can share the same collector. - -Start a Jaeger instance with the OTLP HTTP endpoint at 4318 and the Jaeger UI at 16686 using the following command: - -```bash -$ docker run --pull always --rm --name jaeger \ - -p 16686:16686 -p 4318:4318 \ - jaegertracing/jaeger:2.1.0 -``` - -Once the Jaeger instance is running, you can visualize traces by navigating to http://localhost:16686/. - -### Querying Traces Stored in SQLite - -The `sqlite` sink allows you to query traces without an external system. Here are some example -queries. Refer to the notebook at [Llama Stack Building AI -Applications](https://github.com/meta-llama/llama-stack/blob/main/docs/getting_started.ipynb) for -more examples on how to query traces and spans. diff --git a/docs/source/concepts/index.md b/docs/source/concepts/index.md deleted file mode 100644 index a483132b8..000000000 --- a/docs/source/concepts/index.md +++ /dev/null @@ -1,23 +0,0 @@ -# Core Concepts - -Given Llama Stack's service-oriented philosophy, a few concepts and workflows arise which may not feel completely natural in the LLM landscape, especially if you are coming with a background in other frameworks. - -```{include} architecture.md -:start-after: ## Llama Stack architecture -``` - -```{include} apis.md -:start-after: ## APIs -``` - -```{include} api_providers.md -:start-after: ## API Providers -``` - -```{include} distributions.md -:start-after: ## Distributions -``` - -```{include} resources.md -:start-after: ## Resources -``` diff --git a/docs/source/conf.py b/docs/source/conf.py deleted file mode 100644 index 3f84d1310..000000000 --- a/docs/source/conf.py +++ /dev/null @@ -1,155 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# All rights reserved. -# -# This source code is licensed under the terms described in the LICENSE file in -# the root directory of this source tree. - -# Configuration file for the Sphinx documentation builder. -# -# For the full list of built-in configuration values, see the documentation: -# https://www.sphinx-doc.org/en/master/usage/configuration.html - -# -- Project information ----------------------------------------------------- -# https://www.sphinx-doc.org/en/master/usage/configuration.html#project-information - -import json -from datetime import datetime -from pathlib import Path - -import requests -from docutils import nodes - -# Read version from pyproject.toml -with Path(__file__).parent.parent.parent.joinpath("pyproject.toml").open("rb") as f: - pypi_url = "https://pypi.org/pypi/llama-stack/json" - headers = { - 'User-Agent': 'pip/23.0.1 (python 3.11)', # Mimic pip's user agent - 'Accept': 'application/json' - } - version_tag = json.loads(requests.get(pypi_url, headers=headers).text)["info"]["version"] - print(f"{version_tag=}") - - # generate the full link including text and url here - llama_stack_version_url = ( - f"https://github.com/meta-llama/llama-stack/releases/tag/v{version_tag}" - ) - llama_stack_version_link = f"release notes" - -project = "llama-stack" -copyright = f"{datetime.now().year}, Meta" -author = "Meta" - -# -- General configuration --------------------------------------------------- -# https://www.sphinx-doc.org/en/master/usage/configuration.html#general-configuration - -extensions = [ - "myst_parser", - "sphinx_copybutton", - "sphinx_design", - "sphinx_rtd_theme", - "sphinx_rtd_dark_mode", - "sphinx_tabs.tabs", - "sphinxcontrib.redoc", - "sphinxcontrib.mermaid", - "sphinxcontrib.video", - "sphinx_reredirects" -] - -redirects = { - "providers/post_training/index": "../../advanced_apis/post_training/index.html", - "providers/eval/index": "../../advanced_apis/eval/index.html", - "providers/scoring/index": "../../advanced_apis/scoring/index.html", - "playground/index": "../../building_applications/playground/index.html", - "openai/index": "../../providers/index.html#openai-api-compatibility", - "introduction/index": "../concepts/index.html#llama-stack-architecture" -} - -myst_enable_extensions = ["colon_fence"] - -html_theme = "sphinx_rtd_theme" -html_use_relative_paths = True -templates_path = ["_templates"] -exclude_patterns = ["_build", "Thumbs.db", ".DS_Store"] - -myst_enable_extensions = [ - "amsmath", - "attrs_inline", - "attrs_block", - "colon_fence", - "deflist", - "dollarmath", - "fieldlist", - "html_admonition", - "html_image", - # "linkify", - "replacements", - "smartquotes", - "strikethrough", - "substitution", - "tasklist", -] - -myst_substitutions = { - "docker_hub": "https://hub.docker.com/repository/docker/llamastack", - "llama_stack_version": version_tag, - "llama_stack_version_link": llama_stack_version_link, -} - -suppress_warnings = ["myst.header"] - -# Copy button settings -copybutton_prompt_text = "$ " # for bash prompts -copybutton_prompt_is_regexp = True -copybutton_remove_prompts = True -copybutton_line_continuation_character = "\\" - -# Source suffix -source_suffix = { - ".rst": "restructuredtext", - ".md": "markdown", -} - -# -- Options for HTML output ------------------------------------------------- -# https://www.sphinx-doc.org/en/master/usage/configuration.html#options-for-html-output - -# html_theme = "alabaster" -html_theme_options = { - "canonical_url": "https://github.com/meta-llama/llama-stack", - "collapse_navigation": False, - # "style_nav_header_background": "#c3c9d4", - 'display_version': True, - 'version_selector': True, -} - -default_dark_mode = False - -html_static_path = ["../_static"] -# html_logo = "../_static/llama-stack-logo.png" -# html_style = "../_static/css/my_theme.css" - - -def setup(app): - app.add_css_file("css/my_theme.css") - app.add_js_file("js/detect_theme.js") - app.add_js_file("js/keyboard_shortcuts.js") - - def dockerhub_role(name, rawtext, text, lineno, inliner, options={}, content=[]): - url = f"https://hub.docker.com/r/llamastack/{text}" - node = nodes.reference(rawtext, text, refuri=url, **options) - return [node], [] - - def repopath_role(name, rawtext, text, lineno, inliner, options={}, content=[]): - parts = text.split("::") - if len(parts) == 2: - link_text = parts[0] - url_path = parts[1] - else: - link_text = text - url_path = text - - url = f"https://github.com/meta-llama/llama-stack/tree/main/{url_path}" - node = nodes.reference(rawtext, link_text, refuri=url, **options) - return [node], [] - - app.add_role("dockerhub", dockerhub_role) - app.add_role("repopath", repopath_role) diff --git a/docs/source/contributing/index.md b/docs/source/contributing/index.md deleted file mode 100644 index 1846f4d97..000000000 --- a/docs/source/contributing/index.md +++ /dev/null @@ -1,39 +0,0 @@ - -```{include} ../../../CONTRIBUTING.md -``` - -## Adding a New Provider - -See: -- [Adding a New API Provider Page](new_api_provider.md) which describes how to add new API providers to the Stack. -- [Vector Database Page](new_vector_database.md) which describes how to add a new vector databases with Llama Stack. -- [External Provider Page](../providers/external/index.md) which describes how to add external providers to the Stack. - -```{toctree} -:maxdepth: 1 -:hidden: - -new_api_provider -new_vector_database -``` - -## Testing - - -```{include} ../../../tests/README.md -``` - -## Advanced Topics - -For developers who need deeper understanding of the testing system internals: - -```{toctree} -:maxdepth: 1 - -testing/record-replay -``` - -### Benchmarking - -```{include} ../../../docs/source/distributions/k8s-benchmark/README.md -``` diff --git a/docs/source/contributing/new_api_provider.md b/docs/source/contributing/new_api_provider.md deleted file mode 100644 index 6f8f59a47..000000000 --- a/docs/source/contributing/new_api_provider.md +++ /dev/null @@ -1,83 +0,0 @@ -# Adding a New API Provider - -This guide will walk you through the process of adding a new API provider to Llama Stack. - - -- Begin by reviewing the [core concepts](../concepts/index.md) of Llama Stack and choose the API your provider belongs to (Inference, Safety, VectorIO, etc.) -- Determine the provider type ({repopath}`Remote::llama_stack/providers/remote` or {repopath}`Inline::llama_stack/providers/inline`). Remote providers make requests to external services, while inline providers execute implementation locally. -- Add your provider to the appropriate {repopath}`Registry::llama_stack/providers/registry/`. Specify pip dependencies necessary. -- Update any distribution {repopath}`Templates::llama_stack/distributions/` `build.yaml` and `run.yaml` files if they should include your provider by default. Run {repopath}`./scripts/distro_codegen.py` if necessary. Note that `distro_codegen.py` will fail if the new provider causes any distribution template to attempt to import provider-specific dependencies. This usually means the distribution's `get_distribution_template()` code path should only import any necessary Config or model alias definitions from each provider and not the provider's actual implementation. - - -Here are some example PRs to help you get started: - - [Grok Inference Implementation](https://github.com/meta-llama/llama-stack/pull/609) - - [Nvidia Inference Implementation](https://github.com/meta-llama/llama-stack/pull/355) - - [Model context protocol Tool Runtime](https://github.com/meta-llama/llama-stack/pull/665) - -## Inference Provider Patterns - -When implementing Inference providers for OpenAI-compatible APIs, Llama Stack provides several mixin classes to simplify development and ensure consistent behavior across providers. - -### OpenAIMixin - -The `OpenAIMixin` class provides direct OpenAI API functionality for providers that work with OpenAI-compatible endpoints. It includes: - -#### Direct API Methods -- **`openai_completion()`**: Legacy text completion API with full parameter support -- **`openai_chat_completion()`**: Chat completion API supporting streaming, tools, and function calling -- **`openai_embeddings()`**: Text embeddings generation with customizable encoding and dimensions - -#### Model Management -- **`check_model_availability()`**: Queries the API endpoint to verify if a model exists and is accessible - -#### Client Management -- **`client` property**: Automatically creates and configures AsyncOpenAI client instances using your provider's credentials - -#### Required Implementation - -To use `OpenAIMixin`, your provider must implement these abstract methods: - -```python -@abstractmethod -def get_api_key(self) -> str: - """Return the API key for authentication""" - pass - - -@abstractmethod -def get_base_url(self) -> str: - """Return the OpenAI-compatible API base URL""" - pass -``` - -## Testing the Provider - -Before running tests, you must have required dependencies installed. This depends on the providers or distributions you are testing. For example, if you are testing the `together` distribution, you should install dependencies via `llama stack build --distro together`. - -### 1. Integration Testing - -Integration tests are located in {repopath}`tests/integration`. These tests use the python client-SDK APIs (from the `llama_stack_client` package) to test functionality. Since these tests use client APIs, they can be run either by pointing to an instance of the Llama Stack server or "inline" by using `LlamaStackAsLibraryClient`. - -Consult {repopath}`tests/integration/README.md` for more details on how to run the tests. - -Note that each provider's `sample_run_config()` method (in the configuration class for that provider) - typically references some environment variables for specifying API keys and the like. You can set these in the environment or pass these via the `--env` flag to the test command. - - -### 2. Unit Testing - -Unit tests are located in {repopath}`tests/unit`. Provider-specific unit tests are located in {repopath}`tests/unit/providers`. These tests are all run automatically as part of the CI process. - -Consult {repopath}`tests/unit/README.md` for more details on how to run the tests manually. - -### 3. Additional end-to-end testing - -1. Start a Llama Stack server with your new provider -2. Verify compatibility with existing client scripts in the [llama-stack-apps](https://github.com/meta-llama/llama-stack-apps/tree/main) repository -3. Document which scripts are compatible with your provider - -## Submitting Your PR - -1. Ensure all tests pass -2. Include a comprehensive test plan in your PR summary -3. Document any known limitations or considerations diff --git a/docs/source/deploying/index.md b/docs/source/deploying/index.md deleted file mode 100644 index 73b5bf4f5..000000000 --- a/docs/source/deploying/index.md +++ /dev/null @@ -1,4 +0,0 @@ -# Deployment Examples - -```{include} kubernetes_deployment.md -``` \ No newline at end of file diff --git a/docs/source/distributions/index.md b/docs/source/distributions/index.md deleted file mode 100644 index 2a702c282..000000000 --- a/docs/source/distributions/index.md +++ /dev/null @@ -1,15 +0,0 @@ -# Distributions Overview - -A distribution is a pre-packaged set of Llama Stack components that can be deployed together. - -This section provides an overview of the distributions available in Llama Stack. - -```{toctree} -:maxdepth: 3 -list_of_distributions -building_distro -customizing_run_yaml -starting_llama_stack_server -importing_as_library -configuration -``` diff --git a/docs/source/distributions/k8s-benchmark/README.md b/docs/source/distributions/k8s-benchmark/README.md deleted file mode 100644 index 42da4d466..000000000 --- a/docs/source/distributions/k8s-benchmark/README.md +++ /dev/null @@ -1,156 +0,0 @@ -# Llama Stack Benchmark Suite on Kubernetes - -## Motivation - -Performance benchmarking is critical for understanding the overhead and characteristics of the Llama Stack abstraction layer compared to direct inference engines like vLLM. - -### Why This Benchmark Suite Exists - -**Performance Validation**: The Llama Stack provides a unified API layer across multiple inference providers, but this abstraction introduces potential overhead. This benchmark suite quantifies the performance impact by comparing: -- Llama Stack inference (with vLLM backend) -- Direct vLLM inference calls -- Both under identical Kubernetes deployment conditions - -**Production Readiness Assessment**: Real-world deployments require understanding performance characteristics under load. This suite simulates concurrent user scenarios with configurable parameters (duration, concurrency, request patterns) to validate production readiness. - -**Regression Detection (TODO)**: As the Llama Stack evolves, this benchmark provides automated regression detection for performance changes. CI/CD pipelines can leverage these benchmarks to catch performance degradations before production deployments. - -**Resource Planning**: By measuring throughput, latency percentiles, and resource utilization patterns, teams can make informed decisions about: -- Kubernetes resource allocation (CPU, memory, GPU) -- Auto-scaling configurations -- Cost optimization strategies - -### Key Metrics Captured - -The benchmark suite measures critical performance indicators: -- **Throughput**: Requests per second under sustained load -- **Latency Distribution**: P50, P95, P99 response times -- **Time to First Token (TTFT)**: Critical for streaming applications -- **Error Rates**: Request failures and timeout analysis - -This data enables data-driven architectural decisions and performance optimization efforts. - -## Setup - -**1. Deploy base k8s infrastructure:** -```bash -cd ../k8s -./apply.sh -``` - -**2. Deploy benchmark components:** -```bash -cd ../k8s-benchmark -./apply.sh -``` - -**3. Verify deployment:** -```bash -kubectl get pods -# Should see: llama-stack-benchmark-server, vllm-server, etc. -``` - -## Quick Start - -### Basic Benchmarks - -**Benchmark Llama Stack (default):** -```bash -cd docs/source/distributions/k8s-benchmark/ -./run-benchmark.sh -``` - -**Benchmark vLLM direct:** -```bash -./run-benchmark.sh --target vllm -``` - -### Custom Configuration - -**Extended benchmark with high concurrency:** -```bash -./run-benchmark.sh --target vllm --duration 120 --concurrent 20 -``` - -**Short test run:** -```bash -./run-benchmark.sh --target stack --duration 30 --concurrent 5 -``` - -## Command Reference - -### run-benchmark.sh Options - -```bash -./run-benchmark.sh [options] - -Options: - -t, --target Target to benchmark (default: stack) - -d, --duration Duration in seconds (default: 60) - -c, --concurrent Number of concurrent users (default: 10) - -h, --help Show help message - -Examples: - ./run-benchmark.sh --target vllm # Benchmark vLLM direct - ./run-benchmark.sh --target stack # Benchmark Llama Stack - ./run-benchmark.sh -t vllm -d 120 -c 20 # vLLM with 120s, 20 users -``` - -## Local Testing - -### Running Benchmark Locally - -For local development without Kubernetes: - -**1. Start OpenAI mock server:** -```bash -uv run python openai-mock-server.py --port 8080 -``` - -**2. Run benchmark against mock server:** -```bash -uv run python benchmark.py \ - --base-url http://localhost:8080/v1 \ - --model mock-inference \ - --duration 30 \ - --concurrent 5 -``` - -**3. Test against local vLLM server:** -```bash -# If you have vLLM running locally on port 8000 -uv run python benchmark.py \ - --base-url http://localhost:8000/v1 \ - --model meta-llama/Llama-3.2-3B-Instruct \ - --duration 30 \ - --concurrent 5 -``` - -**4. Profile the running server:** -```bash -./profile_running_server.sh -``` - - - -### OpenAI Mock Server - -The `openai-mock-server.py` provides: -- **OpenAI-compatible API** for testing without real models -- **Configurable streaming delay** via `STREAM_DELAY_SECONDS` env var -- **Consistent responses** for reproducible benchmarks -- **Lightweight testing** without GPU requirements - -**Mock server usage:** -```bash -uv run python openai-mock-server.py --port 8080 -``` - -The mock server is also deployed in k8s as `openai-mock-service:8080` and can be used by changing the Llama Stack configuration to use the `mock-vllm-inference` provider. - -## Files in this Directory - -- `benchmark.py` - Core benchmark script with async streaming support -- `run-benchmark.sh` - Main script with target selection and configuration -- `openai-mock-server.py` - Mock OpenAI API server for local testing -- `README.md` - This documentation file diff --git a/docs/source/distributions/k8s-benchmark/benchmark.py b/docs/source/distributions/k8s-benchmark/benchmark.py deleted file mode 100644 index 3d0d18150..000000000 --- a/docs/source/distributions/k8s-benchmark/benchmark.py +++ /dev/null @@ -1,267 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# All rights reserved. -# -# This source code is licensed under the terms described in the LICENSE file in -# the root directory of this source tree. - -""" -Simple benchmark script for Llama Stack with OpenAI API compatibility. -""" - -import argparse -import asyncio -import os -import random -import statistics -import time -from typing import Tuple -import aiohttp - - -class BenchmarkStats: - def __init__(self): - self.response_times = [] - self.ttft_times = [] - self.chunks_received = [] - self.errors = [] - self.success_count = 0 - self.total_requests = 0 - self.concurrent_users = 0 - self.start_time = None - self.end_time = None - self._lock = asyncio.Lock() - - async def add_result(self, response_time: float, chunks: int, ttft: float = None, error: str = None): - async with self._lock: - self.total_requests += 1 - if error: - self.errors.append(error) - else: - self.success_count += 1 - self.response_times.append(response_time) - self.chunks_received.append(chunks) - if ttft is not None: - self.ttft_times.append(ttft) - - def print_summary(self): - if not self.response_times: - print("No successful requests to report") - if self.errors: - print(f"Total errors: {len(self.errors)}") - print("First 5 errors:") - for error in self.errors[:5]: - print(f" {error}") - return - - total_time = self.end_time - self.start_time - success_rate = (self.success_count / self.total_requests) * 100 - - print(f"\n{'='*60}") - print(f"BENCHMARK RESULTS") - print(f"{'='*60}") - print(f"Total time: {total_time:.2f}s") - print(f"Concurrent users: {self.concurrent_users}") - print(f"Total requests: {self.total_requests}") - print(f"Successful requests: {self.success_count}") - print(f"Failed requests: {len(self.errors)}") - print(f"Success rate: {success_rate:.1f}%") - print(f"Requests per second: {self.success_count / total_time:.2f}") - - print(f"\nResponse Time Statistics:") - print(f" Mean: {statistics.mean(self.response_times):.3f}s") - print(f" Median: {statistics.median(self.response_times):.3f}s") - print(f" Min: {min(self.response_times):.3f}s") - print(f" Max: {max(self.response_times):.3f}s") - - if len(self.response_times) > 1: - print(f" Std Dev: {statistics.stdev(self.response_times):.3f}s") - - percentiles = [50, 90, 95, 99] - sorted_times = sorted(self.response_times) - print(f"\nPercentiles:") - for p in percentiles: - idx = int(len(sorted_times) * p / 100) - 1 - idx = max(0, min(idx, len(sorted_times) - 1)) - print(f" P{p}: {sorted_times[idx]:.3f}s") - - if self.ttft_times: - print(f"\nTime to First Token (TTFT) Statistics:") - print(f" Mean: {statistics.mean(self.ttft_times):.3f}s") - print(f" Median: {statistics.median(self.ttft_times):.3f}s") - print(f" Min: {min(self.ttft_times):.3f}s") - print(f" Max: {max(self.ttft_times):.3f}s") - - if len(self.ttft_times) > 1: - print(f" Std Dev: {statistics.stdev(self.ttft_times):.3f}s") - - sorted_ttft = sorted(self.ttft_times) - print(f"\nTTFT Percentiles:") - for p in percentiles: - idx = int(len(sorted_ttft) * p / 100) - 1 - idx = max(0, min(idx, len(sorted_ttft) - 1)) - print(f" P{p}: {sorted_ttft[idx]:.3f}s") - - if self.chunks_received: - print(f"\nStreaming Statistics:") - print(f" Mean chunks per response: {statistics.mean(self.chunks_received):.1f}") - print(f" Total chunks received: {sum(self.chunks_received)}") - - if self.errors: - print(f"\nErrors (showing first 5):") - for error in self.errors[:5]: - print(f" {error}") - - -class LlamaStackBenchmark: - def __init__(self, base_url: str, model_id: str): - self.base_url = base_url.rstrip('/') - self.model_id = model_id - self.headers = {"Content-Type": "application/json"} - self.test_messages = [ - [{"role": "user", "content": "Hi"}], - [{"role": "user", "content": "What is the capital of France?"}], - [{"role": "user", "content": "Explain quantum physics in simple terms."}], - [{"role": "user", "content": "Write a short story about a robot learning to paint."}], - [ - {"role": "user", "content": "What is machine learning?"}, - {"role": "assistant", "content": "Machine learning is a subset of AI..."}, - {"role": "user", "content": "Can you give me a practical example?"} - ] - ] - - - async def make_async_streaming_request(self) -> Tuple[float, int, float | None, str | None]: - """Make a single async streaming chat completion request.""" - messages = random.choice(self.test_messages) - payload = { - "model": self.model_id, - "messages": messages, - "stream": True, - "max_tokens": 100 - } - - start_time = time.time() - chunks_received = 0 - ttft = None - error = None - - session = aiohttp.ClientSession() - - try: - async with session.post( - f"{self.base_url}/chat/completions", - headers=self.headers, - json=payload, - timeout=aiohttp.ClientTimeout(total=30) - ) as response: - if response.status == 200: - async for line in response.content: - if line: - line_str = line.decode('utf-8').strip() - if line_str.startswith('data: '): - chunks_received += 1 - if ttft is None: - ttft = time.time() - start_time - if line_str == 'data: [DONE]': - break - - if chunks_received == 0: - error = "No streaming chunks received" - else: - text = await response.text() - error = f"HTTP {response.status}: {text[:100]}" - - except Exception as e: - error = f"Request error: {str(e)}" - finally: - await session.close() - - response_time = time.time() - start_time - return response_time, chunks_received, ttft, error - - - async def run_benchmark(self, duration: int, concurrent_users: int) -> BenchmarkStats: - """Run benchmark using async requests for specified duration.""" - stats = BenchmarkStats() - stats.concurrent_users = concurrent_users - stats.start_time = time.time() - - print(f"Starting benchmark: {duration}s duration, {concurrent_users} concurrent users") - print(f"Target URL: {self.base_url}/chat/completions") - print(f"Model: {self.model_id}") - - connector = aiohttp.TCPConnector(limit=concurrent_users) - async with aiohttp.ClientSession(connector=connector) as session: - - async def worker(worker_id: int): - """Worker that sends requests sequentially until canceled.""" - request_count = 0 - while True: - try: - response_time, chunks, ttft, error = await self.make_async_streaming_request() - await stats.add_result(response_time, chunks, ttft, error) - request_count += 1 - - except asyncio.CancelledError: - break - except Exception as e: - await stats.add_result(0, 0, None, f"Worker {worker_id} error: {str(e)}") - - # Progress reporting task - async def progress_reporter(): - last_report_time = time.time() - while True: - try: - await asyncio.sleep(1) # Report every second - if time.time() >= last_report_time + 10: # Report every 10 seconds - elapsed = time.time() - stats.start_time - print(f"Completed: {stats.total_requests} requests in {elapsed:.1f}s") - last_report_time = time.time() - except asyncio.CancelledError: - break - - # Spawn concurrent workers - tasks = [asyncio.create_task(worker(i)) for i in range(concurrent_users)] - progress_task = asyncio.create_task(progress_reporter()) - tasks.append(progress_task) - - # Wait for duration then cancel all tasks - await asyncio.sleep(duration) - - for task in tasks: - task.cancel() - - # Wait for all tasks to complete - await asyncio.gather(*tasks, return_exceptions=True) - - stats.end_time = time.time() - return stats - - -def main(): - parser = argparse.ArgumentParser(description="Llama Stack Benchmark Tool") - parser.add_argument("--base-url", default=os.getenv("BENCHMARK_BASE_URL", "http://localhost:8000/v1/openai/v1"), - help="Base URL for the API (default: http://localhost:8000/v1/openai/v1)") - parser.add_argument("--model", default=os.getenv("INFERENCE_MODEL", "test-model"), - help="Model ID to use for requests") - parser.add_argument("--duration", type=int, default=60, - help="Duration in seconds to run benchmark (default: 60)") - parser.add_argument("--concurrent", type=int, default=10, - help="Number of concurrent users (default: 10)") - - args = parser.parse_args() - - benchmark = LlamaStackBenchmark(args.base_url, args.model) - - try: - stats = asyncio.run(benchmark.run_benchmark(args.duration, args.concurrent)) - stats.print_summary() - - except KeyboardInterrupt: - print("\nBenchmark interrupted by user") - except Exception as e: - print(f"Benchmark failed: {e}") - - -if __name__ == "__main__": - main() diff --git a/docs/source/distributions/k8s-benchmark/profile_running_server.sh b/docs/source/distributions/k8s-benchmark/profile_running_server.sh deleted file mode 100755 index 65d620583..000000000 --- a/docs/source/distributions/k8s-benchmark/profile_running_server.sh +++ /dev/null @@ -1,52 +0,0 @@ -#!/bin/bash - -# Copyright (c) Meta Platforms, Inc. and affiliates. -# All rights reserved. -# -# This source code is licensed under the terms described in the LICENSE file in -# the root directory of this source tree. - -# Script to profile an already running Llama Stack server -# Usage: ./profile_running_server.sh [duration_seconds] [output_file] - -DURATION=${1:-60} # Default 60 seconds -OUTPUT_FILE=${2:-"llama_stack_profile"} # Default output file - -echo "Looking for running Llama Stack server..." - -# Find the server PID -SERVER_PID=$(ps aux | grep "llama_stack.core.server.server" | grep -v grep | awk '{print $2}' | head -1) - - -if [ -z "$SERVER_PID" ]; then - echo "Error: No running Llama Stack server found" - echo "Please start your server first with:" - echo "LLAMA_STACK_LOGGING=\"all=ERROR\" MOCK_INFERENCE_URL=http://localhost:8080 SAFETY_MODEL=llama-guard3:1b uv run --with llama-stack python -m llama_stack.core.server.server docs/source/distributions/k8s-benchmark/stack_run_config.yaml" - exit 1 -fi - -echo "Found Llama Stack server with PID: $SERVER_PID" - -# Start py-spy profiling -echo "Starting py-spy profiling for ${DURATION} seconds..." -echo "Output will be saved to: ${OUTPUT_FILE}.svg" -echo "" -echo "You can now run your load test..." -echo "" - -# Get the full path to py-spy -PYSPY_PATH=$(which py-spy) - -# Check if running as root, if not, use sudo -if [ "$EUID" -ne 0 ]; then - echo "py-spy requires root permissions on macOS. Running with sudo..." - sudo "$PYSPY_PATH" record -o "${OUTPUT_FILE}.svg" -d ${DURATION} -p $SERVER_PID -else - "$PYSPY_PATH" record -o "${OUTPUT_FILE}.svg" -d ${DURATION} -p $SERVER_PID -fi - -echo "" -echo "Profiling completed! Results saved to: ${OUTPUT_FILE}.svg" -echo "" -echo "To view the flame graph:" -echo "open ${OUTPUT_FILE}.svg" diff --git a/docs/source/distributions/k8s-benchmark/run-benchmark.sh b/docs/source/distributions/k8s-benchmark/run-benchmark.sh deleted file mode 100755 index e1c826143..000000000 --- a/docs/source/distributions/k8s-benchmark/run-benchmark.sh +++ /dev/null @@ -1,148 +0,0 @@ -#!/usr/bin/env bash - -# Copyright (c) Meta Platforms, Inc. and affiliates. -# All rights reserved. -# -# This source code is licensed under the terms described in the LICENSE file in -# the root directory of this source tree. - -set -euo pipefail - -# Default values -TARGET="stack" -DURATION=60 -CONCURRENT=10 - -# Parse command line arguments -usage() { - echo "Usage: $0 [options]" - echo "Options:" - echo " -t, --target Target to benchmark (default: stack)" - echo " -d, --duration Duration in seconds (default: 60)" - echo " -c, --concurrent Number of concurrent users (default: 10)" - echo " -h, --help Show this help message" - echo "" - echo "Examples:" - echo " $0 --target vllm # Benchmark vLLM direct" - echo " $0 --target stack # Benchmark Llama Stack (default)" - echo " $0 -t vllm -d 120 -c 20 # vLLM with 120s duration, 20 users" -} - -while [[ $# -gt 0 ]]; do - case $1 in - -t|--target) - TARGET="$2" - shift 2 - ;; - -d|--duration) - DURATION="$2" - shift 2 - ;; - -c|--concurrent) - CONCURRENT="$2" - shift 2 - ;; - -h|--help) - usage - exit 0 - ;; - *) - echo "Unknown option: $1" - usage - exit 1 - ;; - esac -done - -# Validate target -if [[ "$TARGET" != "stack" && "$TARGET" != "vllm" ]]; then - echo "Error: Target must be 'stack' or 'vllm'" - usage - exit 1 -fi - -# Set configuration based on target -if [[ "$TARGET" == "vllm" ]]; then - BASE_URL="http://vllm-server:8000/v1" - JOB_NAME="vllm-benchmark-job" - echo "Benchmarking vLLM direct..." -else - BASE_URL="http://llama-stack-benchmark-service:8323/v1/openai/v1" - JOB_NAME="stack-benchmark-job" - echo "Benchmarking Llama Stack..." -fi - -echo "Configuration:" -echo " Target: $TARGET" -echo " Base URL: $BASE_URL" -echo " Duration: ${DURATION}s" -echo " Concurrent users: $CONCURRENT" -echo "" - -# Create temporary job yaml -TEMP_YAML="/tmp/benchmark-job-temp-$(date +%s).yaml" -cat > "$TEMP_YAML" << EOF -apiVersion: batch/v1 -kind: Job -metadata: - name: $JOB_NAME - namespace: default -spec: - template: - spec: - containers: - - name: benchmark - image: python:3.11-slim - command: ["/bin/bash"] - args: - - "-c" - - | - pip install aiohttp && - python3 /benchmark/benchmark.py \\ - --base-url $BASE_URL \\ - --model \${INFERENCE_MODEL} \\ - --duration $DURATION \\ - --concurrent $CONCURRENT - env: - - name: INFERENCE_MODEL - value: "meta-llama/Llama-3.2-3B-Instruct" - volumeMounts: - - name: benchmark-script - mountPath: /benchmark - resources: - requests: - memory: "256Mi" - cpu: "250m" - limits: - memory: "512Mi" - cpu: "500m" - volumes: - - name: benchmark-script - configMap: - name: benchmark-script - restartPolicy: Never - backoffLimit: 3 -EOF - -echo "Creating benchmark ConfigMap..." -kubectl create configmap benchmark-script \ - --from-file=benchmark.py=benchmark.py \ - --dry-run=client -o yaml | kubectl apply -f - - -echo "Cleaning up any existing benchmark job..." -kubectl delete job $JOB_NAME 2>/dev/null || true - -echo "Deploying benchmark Job..." -kubectl apply -f "$TEMP_YAML" - -echo "Waiting for job to start..." -kubectl wait --for=condition=Ready pod -l job-name=$JOB_NAME --timeout=60s - -echo "Following benchmark logs..." -kubectl logs -f job/$JOB_NAME - -echo "Job completed. Checking final status..." -kubectl get job $JOB_NAME - -# Clean up temporary file -rm -f "$TEMP_YAML" diff --git a/docs/source/distributions/k8s/stack-configmap.yaml b/docs/source/distributions/k8s/stack-configmap.yaml deleted file mode 100644 index 4f95554e3..000000000 --- a/docs/source/distributions/k8s/stack-configmap.yaml +++ /dev/null @@ -1,138 +0,0 @@ -apiVersion: v1 -data: - stack_run_config.yaml: | - version: '2' - image_name: kubernetes-demo - apis: - - agents - - inference - - safety - - telemetry - - tool_runtime - - vector_io - providers: - inference: - - provider_id: vllm-inference - provider_type: remote::vllm - config: - url: ${env.VLLM_URL:=http://localhost:8000/v1} - max_tokens: ${env.VLLM_MAX_TOKENS:=4096} - api_token: ${env.VLLM_API_TOKEN:=fake} - tls_verify: ${env.VLLM_TLS_VERIFY:=true} - - provider_id: vllm-safety - provider_type: remote::vllm - config: - url: ${env.VLLM_SAFETY_URL:=http://localhost:8000/v1} - max_tokens: ${env.VLLM_MAX_TOKENS:=4096} - api_token: ${env.VLLM_API_TOKEN:=fake} - tls_verify: ${env.VLLM_TLS_VERIFY:=true} - - provider_id: sentence-transformers - provider_type: inline::sentence-transformers - config: {} - vector_io: - - provider_id: ${env.ENABLE_CHROMADB:+chromadb} - provider_type: remote::chromadb - config: - url: ${env.CHROMADB_URL:=} - kvstore: - type: postgres - host: ${env.POSTGRES_HOST:=localhost} - port: ${env.POSTGRES_PORT:=5432} - db: ${env.POSTGRES_DB:=llamastack} - user: ${env.POSTGRES_USER:=llamastack} - password: ${env.POSTGRES_PASSWORD:=llamastack} - safety: - - provider_id: llama-guard - provider_type: inline::llama-guard - config: - excluded_categories: [] - agents: - - provider_id: meta-reference - provider_type: inline::meta-reference - config: - persistence_store: - type: postgres - host: ${env.POSTGRES_HOST:=localhost} - port: ${env.POSTGRES_PORT:=5432} - db: ${env.POSTGRES_DB:=llamastack} - user: ${env.POSTGRES_USER:=llamastack} - password: ${env.POSTGRES_PASSWORD:=llamastack} - responses_store: - type: postgres - host: ${env.POSTGRES_HOST:=localhost} - port: ${env.POSTGRES_PORT:=5432} - db: ${env.POSTGRES_DB:=llamastack} - user: ${env.POSTGRES_USER:=llamastack} - password: ${env.POSTGRES_PASSWORD:=llamastack} - telemetry: - - provider_id: meta-reference - provider_type: inline::meta-reference - config: - service_name: "${env.OTEL_SERVICE_NAME:=\u200B}" - sinks: ${env.TELEMETRY_SINKS:=console} - tool_runtime: - - provider_id: brave-search - provider_type: remote::brave-search - config: - api_key: ${env.BRAVE_SEARCH_API_KEY:+} - max_results: 3 - - provider_id: tavily-search - provider_type: remote::tavily-search - config: - api_key: ${env.TAVILY_SEARCH_API_KEY:+} - max_results: 3 - - provider_id: rag-runtime - provider_type: inline::rag-runtime - config: {} - - provider_id: model-context-protocol - provider_type: remote::model-context-protocol - config: {} - metadata_store: - type: postgres - host: ${env.POSTGRES_HOST:=localhost} - port: ${env.POSTGRES_PORT:=5432} - db: ${env.POSTGRES_DB:=llamastack} - user: ${env.POSTGRES_USER:=llamastack} - password: ${env.POSTGRES_PASSWORD:=llamastack} - table_name: llamastack_kvstore - inference_store: - type: postgres - host: ${env.POSTGRES_HOST:=localhost} - port: ${env.POSTGRES_PORT:=5432} - db: ${env.POSTGRES_DB:=llamastack} - user: ${env.POSTGRES_USER:=llamastack} - password: ${env.POSTGRES_PASSWORD:=llamastack} - models: - - metadata: - embedding_dimension: 384 - model_id: all-MiniLM-L6-v2 - provider_id: sentence-transformers - model_type: embedding - - metadata: {} - model_id: ${env.INFERENCE_MODEL} - provider_id: vllm-inference - model_type: llm - - metadata: {} - model_id: ${env.SAFETY_MODEL:=meta-llama/Llama-Guard-3-1B} - provider_id: vllm-safety - model_type: llm - shields: - - shield_id: ${env.SAFETY_MODEL:=meta-llama/Llama-Guard-3-1B} - vector_dbs: [] - datasets: [] - scoring_fns: [] - benchmarks: [] - tool_groups: - - toolgroup_id: builtin::websearch - provider_id: tavily-search - - toolgroup_id: builtin::rag - provider_id: rag-runtime - server: - port: 8321 - auth: - provider_config: - type: github_token -kind: ConfigMap -metadata: - creationTimestamp: null - name: llama-stack-config diff --git a/docs/source/getting_started/index.md b/docs/source/getting_started/index.md deleted file mode 100644 index e941534c2..000000000 --- a/docs/source/getting_started/index.md +++ /dev/null @@ -1,13 +0,0 @@ -# Getting Started - -```{include} quickstart.md -:start-after: ## Quickstart -``` - -```{include} libraries.md -:start-after: ## Libraries (SDKs) -``` - -```{include} detailed_tutorial.md -:start-after: ## Detailed Tutorial -``` diff --git a/docs/source/index.md b/docs/source/index.md deleted file mode 100644 index c824ce94a..000000000 --- a/docs/source/index.md +++ /dev/null @@ -1,133 +0,0 @@ -# Llama Stack -Welcome to Llama Stack, the open-source framework for building generative AI applications. -```{admonition} Llama 4 is here! -:class: tip - -Check out [Getting Started with Llama 4](https://colab.research.google.com/github/meta-llama/llama-stack/blob/main/docs/getting_started_llama4.ipynb) -``` -```{admonition} News -:class: tip - -Llama Stack {{ llama_stack_version }} is now available! See the {{ llama_stack_version_link }} for more details. -``` - - -## What is Llama Stack? - -Llama Stack defines and standardizes the core building blocks needed to bring generative AI applications to market. It provides a unified set of APIs with implementations from leading service providers, enabling seamless transitions between development and production environments. More specifically, it provides - -- **Unified API layer** for Inference, RAG, Agents, Tools, Safety, Evals, and Telemetry. -- **Plugin architecture** to support the rich ecosystem of implementations of the different APIs in different environments like local development, on-premises, cloud, and mobile. -- **Prepackaged verified distributions** which offer a one-stop solution for developers to get started quickly and reliably in any environment -- **Multiple developer interfaces** like CLI and SDKs for Python, Node, iOS, and Android -- **Standalone applications** as examples for how to build production-grade AI applications with Llama Stack - -```{image} ../_static/llama-stack.png -:alt: Llama Stack -:width: 400px -``` - -Our goal is to provide pre-packaged implementations (aka "distributions") which can be run in a variety of deployment environments. LlamaStack can assist you in your entire app development lifecycle - start iterating on local, mobile or desktop and seamlessly transition to on-prem or public cloud deployments. At every point in this transition, the same set of APIs and the same developer experience is available. - -## How does Llama Stack work? -Llama Stack consists of a [server](./distributions/index.md) (with multiple pluggable API [providers](./providers/index.md)) and Client SDKs (see below) meant to -be used in your applications. The server can be run in a variety of environments, including local (inline) -development, on-premises, and cloud. The client SDKs are available for Python, Swift, Node, and -Kotlin. - -## Quick Links - -- Ready to build? Check out the [Quick Start](getting_started/index) to get started. -- Want to contribute? See the [Contributing](contributing/index) guide. - -## Supported Llama Stack Implementations - -A number of "adapters" are available for some popular Inference and Vector Store providers. For other APIs (particularly Safety and Agents), we provide *reference implementations* you can use to get started. We expect this list to grow over time. We are slowly onboarding more providers to the ecosystem as we get more confidence in the APIs. - -**Inference API** -| **Provider** | **Environments** | -| :----: | :----: | -| Meta Reference | Single Node | -| Ollama | Single Node | -| Fireworks | Hosted | -| Together | Hosted | -| NVIDIA NIM | Hosted and Single Node | -| vLLM | Hosted and Single Node | -| TGI | Hosted and Single Node | -| AWS Bedrock | Hosted | -| Cerebras | Hosted | -| Groq | Hosted | -| SambaNova | Hosted | -| PyTorch ExecuTorch | On-device iOS, Android | -| OpenAI | Hosted | -| Anthropic | Hosted | -| Gemini | Hosted | -| WatsonX | Hosted | - -**Agents API** -| **Provider** | **Environments** | -| :----: | :----: | -| Meta Reference | Single Node | -| Fireworks | Hosted | -| Together | Hosted | -| PyTorch ExecuTorch | On-device iOS | - -**Vector IO API** -| **Provider** | **Environments** | -| :----: | :----: | -| FAISS | Single Node | -| SQLite-Vec | Single Node | -| Chroma | Hosted and Single Node | -| Milvus | Hosted and Single Node | -| Postgres (PGVector) | Hosted and Single Node | -| Weaviate | Hosted | -| Qdrant | Hosted and Single Node | - -**Safety API** -| **Provider** | **Environments** | -| :----: | :----: | -| Llama Guard | Depends on Inference Provider | -| Prompt Guard | Single Node | -| Code Scanner | Single Node | -| AWS Bedrock | Hosted | - -**Post Training API** -| **Provider** | **Environments** | -| :----: | :----: | -| Meta Reference | Single Node | -| HuggingFace | Single Node | -| TorchTune | Single Node | -| NVIDIA NEMO | Hosted | - -**Eval API** -| **Provider** | **Environments** | -| :----: | :----: | -| Meta Reference | Single Node | -| NVIDIA NEMO | Hosted | - -**Telemetry API** -| **Provider** | **Environments** | -| :----: | :----: | -| Meta Reference | Single Node | - -**Tool Runtime API** -| **Provider** | **Environments** | -| :----: | :----: | -| Brave Search | Hosted | -| RAG Runtime | Single Node | - -```{toctree} -:hidden: -:maxdepth: 3 - -self -getting_started/index -concepts/index -providers/index -distributions/index -advanced_apis/index -building_applications/index -deploying/index -contributing/index -references/index -``` diff --git a/docs/source/providers/eval/inline_meta-reference.md b/docs/source/providers/eval/inline_meta-reference.md deleted file mode 100644 index 606883c72..000000000 --- a/docs/source/providers/eval/inline_meta-reference.md +++ /dev/null @@ -1,21 +0,0 @@ -# inline::meta-reference - -## Description - -Meta's reference implementation of evaluation tasks with support for multiple languages and evaluation metrics. - -## Configuration - -| Field | Type | Required | Default | Description | -|-------|------|----------|---------|-------------| -| `kvstore` | `utils.kvstore.config.RedisKVStoreConfig \| utils.kvstore.config.SqliteKVStoreConfig \| utils.kvstore.config.PostgresKVStoreConfig \| utils.kvstore.config.MongoDBKVStoreConfig` | No | sqlite | | - -## Sample Configuration - -```yaml -kvstore: - type: sqlite - db_path: ${env.SQLITE_STORE_DIR:=~/.llama/dummy}/meta_reference_eval.db - -``` - diff --git a/docs/source/providers/eval/remote_nvidia.md b/docs/source/providers/eval/remote_nvidia.md deleted file mode 100644 index cb764b511..000000000 --- a/docs/source/providers/eval/remote_nvidia.md +++ /dev/null @@ -1,19 +0,0 @@ -# remote::nvidia - -## Description - -NVIDIA's evaluation provider for running evaluation tasks on NVIDIA's platform. - -## Configuration - -| Field | Type | Required | Default | Description | -|-------|------|----------|---------|-------------| -| `evaluator_url` | `` | No | http://0.0.0.0:7331 | The url for accessing the evaluator service | - -## Sample Configuration - -```yaml -evaluator_url: ${env.NVIDIA_EVALUATOR_URL:=http://localhost:7331} - -``` - diff --git a/docs/source/providers/inference/inline_sentence-transformers.md b/docs/source/providers/inference/inline_sentence-transformers.md deleted file mode 100644 index 57ec7f7d0..000000000 --- a/docs/source/providers/inference/inline_sentence-transformers.md +++ /dev/null @@ -1,13 +0,0 @@ -# inline::sentence-transformers - -## Description - -Sentence Transformers inference provider for text embeddings and similarity search. - -## Sample Configuration - -```yaml -{} - -``` - diff --git a/docs/source/providers/post_training/remote_nvidia.md b/docs/source/providers/post_training/remote_nvidia.md deleted file mode 100644 index 9a381d872..000000000 --- a/docs/source/providers/post_training/remote_nvidia.md +++ /dev/null @@ -1,28 +0,0 @@ -# remote::nvidia - -## Description - -NVIDIA's post-training provider for fine-tuning models on NVIDIA's platform. - -## Configuration - -| Field | Type | Required | Default | Description | -|-------|------|----------|---------|-------------| -| `api_key` | `str \| None` | No | | The NVIDIA API key. | -| `dataset_namespace` | `str \| None` | No | default | The NVIDIA dataset namespace. | -| `project_id` | `str \| None` | No | test-example-model@v1 | The NVIDIA project ID. | -| `customizer_url` | `str \| None` | No | | Base URL for the NeMo Customizer API | -| `timeout` | `` | No | 300 | Timeout for the NVIDIA Post Training API | -| `max_retries` | `` | No | 3 | Maximum number of retries for the NVIDIA Post Training API | -| `output_model_dir` | `` | No | test-example-model@v1 | Directory to save the output model | - -## Sample Configuration - -```yaml -api_key: ${env.NVIDIA_API_KEY:=} -dataset_namespace: ${env.NVIDIA_DATASET_NAMESPACE:=default} -project_id: ${env.NVIDIA_PROJECT_ID:=test-project} -customizer_url: ${env.NVIDIA_CUSTOMIZER_URL:=http://nemo.test} - -``` - diff --git a/docs/source/providers/safety/index.md b/docs/source/providers/safety/index.md deleted file mode 100644 index 5ddda2242..000000000 --- a/docs/source/providers/safety/index.md +++ /dev/null @@ -1,18 +0,0 @@ -# Safety - -## Overview - -This section contains documentation for all available providers for the **safety** API. - -## Providers - -```{toctree} -:maxdepth: 1 - -inline_code-scanner -inline_llama-guard -inline_prompt-guard -remote_bedrock -remote_nvidia -remote_sambanova -``` diff --git a/docs/source/providers/scoring/inline_basic.md b/docs/source/providers/scoring/inline_basic.md deleted file mode 100644 index e9e50cff4..000000000 --- a/docs/source/providers/scoring/inline_basic.md +++ /dev/null @@ -1,13 +0,0 @@ -# inline::basic - -## Description - -Basic scoring provider for simple evaluation metrics and scoring functions. - -## Sample Configuration - -```yaml -{} - -``` - diff --git a/docs/source/providers/scoring/inline_braintrust.md b/docs/source/providers/scoring/inline_braintrust.md deleted file mode 100644 index 70a6a1e26..000000000 --- a/docs/source/providers/scoring/inline_braintrust.md +++ /dev/null @@ -1,19 +0,0 @@ -# inline::braintrust - -## Description - -Braintrust scoring provider for evaluation and scoring using the Braintrust platform. - -## Configuration - -| Field | Type | Required | Default | Description | -|-------|------|----------|---------|-------------| -| `openai_api_key` | `str \| None` | No | | The OpenAI API Key | - -## Sample Configuration - -```yaml -openai_api_key: ${env.OPENAI_API_KEY:=} - -``` - diff --git a/docs/source/providers/scoring/inline_llm-as-judge.md b/docs/source/providers/scoring/inline_llm-as-judge.md deleted file mode 100644 index 971e02897..000000000 --- a/docs/source/providers/scoring/inline_llm-as-judge.md +++ /dev/null @@ -1,13 +0,0 @@ -# inline::llm-as-judge - -## Description - -LLM-as-judge scoring provider that uses language models to evaluate and score responses. - -## Sample Configuration - -```yaml -{} - -``` - diff --git a/docs/source/providers/tool_runtime/index.md b/docs/source/providers/tool_runtime/index.md deleted file mode 100644 index 8d29aed43..000000000 --- a/docs/source/providers/tool_runtime/index.md +++ /dev/null @@ -1,18 +0,0 @@ -# Tool_Runtime - -## Overview - -This section contains documentation for all available providers for the **tool_runtime** API. - -## Providers - -```{toctree} -:maxdepth: 1 - -inline_rag-runtime -remote_bing-search -remote_brave-search -remote_model-context-protocol -remote_tavily-search -remote_wolfram-alpha -``` diff --git a/docs/source/providers/tool_runtime/remote_model-context-protocol.md b/docs/source/providers/tool_runtime/remote_model-context-protocol.md deleted file mode 100644 index cf9401c2c..000000000 --- a/docs/source/providers/tool_runtime/remote_model-context-protocol.md +++ /dev/null @@ -1,13 +0,0 @@ -# remote::model-context-protocol - -## Description - -Model Context Protocol (MCP) tool for standardized tool calling and context management. - -## Sample Configuration - -```yaml -{} - -``` - diff --git a/docs/source/providers/vector_io/index.md b/docs/source/providers/vector_io/index.md deleted file mode 100644 index 28ae523d7..000000000 --- a/docs/source/providers/vector_io/index.md +++ /dev/null @@ -1,24 +0,0 @@ -# Vector_Io - -## Overview - -This section contains documentation for all available providers for the **vector_io** API. - -## Providers - -```{toctree} -:maxdepth: 1 - -inline_chromadb -inline_faiss -inline_meta-reference -inline_milvus -inline_qdrant -inline_sqlite-vec -inline_sqlite_vec -remote_chromadb -remote_milvus -remote_pgvector -remote_qdrant -remote_weaviate -``` diff --git a/docs/source/providers/vector_io/remote_milvus.md b/docs/source/providers/vector_io/remote_milvus.md deleted file mode 100644 index 075423d04..000000000 --- a/docs/source/providers/vector_io/remote_milvus.md +++ /dev/null @@ -1,222 +0,0 @@ -# remote::milvus - -## Description - - -[Milvus](https://milvus.io/) is an inline and remote vector database provider for Llama Stack. It -allows you to store and query vectors directly within a Milvus database. -That means you're not limited to storing vectors in memory or in a separate service. - -## Features - -- Easy to use -- Fully integrated with Llama Stack -- Supports all search modes: vector, keyword, and hybrid search (both inline and remote configurations) - -## Usage - -To use Milvus in your Llama Stack project, follow these steps: - -1. Install the necessary dependencies. -2. Configure your Llama Stack project to use Milvus. -3. Start storing and querying vectors. - -## Installation - -You can install Milvus using pymilvus: - -```bash -pip install pymilvus -``` - -## Configuration - -In Llama Stack, Milvus can be configured in two ways: -- **Inline (Local) Configuration** - Uses Milvus-Lite for local storage -- **Remote Configuration** - Connects to a remote Milvus server - -### Inline (Local) Configuration - -The simplest method is local configuration, which requires setting `db_path`, a path for locally storing Milvus-Lite files: - -```yaml -vector_io: - - provider_id: milvus - provider_type: inline::milvus - config: - db_path: ~/.llama/distributions/together/milvus_store.db -``` - -### Remote Configuration - -Remote configuration is suitable for larger data storage requirements: - -#### Standard Remote Connection - -```yaml -vector_io: - - provider_id: milvus - provider_type: remote::milvus - config: - uri: "http://:" - token: ":" -``` - -#### TLS-Enabled Remote Connection (One-way TLS) - -For connections to Milvus instances with one-way TLS enabled: - -```yaml -vector_io: - - provider_id: milvus - provider_type: remote::milvus - config: - uri: "https://:" - token: ":" - secure: True - server_pem_path: "/path/to/server.pem" -``` - -#### Mutual TLS (mTLS) Remote Connection - -For connections to Milvus instances with mutual TLS (mTLS) enabled: - -```yaml -vector_io: - - provider_id: milvus - provider_type: remote::milvus - config: - uri: "https://:" - token: ":" - secure: True - ca_pem_path: "/path/to/ca.pem" - client_pem_path: "/path/to/client.pem" - client_key_path: "/path/to/client.key" -``` - -#### Key Parameters for TLS Configuration - -- **`secure`**: Enables TLS encryption when set to `true`. Defaults to `false`. -- **`server_pem_path`**: Path to the **server certificate** for verifying the server's identity (used in one-way TLS). -- **`ca_pem_path`**: Path to the **Certificate Authority (CA) certificate** for validating the server certificate (required in mTLS). -- **`client_pem_path`**: Path to the **client certificate** file (required for mTLS). -- **`client_key_path`**: Path to the **client private key** file (required for mTLS). - -## Search Modes - -Milvus supports three different search modes for both inline and remote configurations: - -### Vector Search -Vector search uses semantic similarity to find the most relevant chunks based on embedding vectors. This is the default search mode and works well for finding conceptually similar content. - -```python -# Vector search example -search_response = client.vector_stores.search( - vector_store_id=vector_store.id, - query="What is machine learning?", - search_mode="vector", - max_num_results=5, -) -``` - -### Keyword Search -Keyword search uses traditional text-based matching to find chunks containing specific terms or phrases. This is useful when you need exact term matches. - -```python -# Keyword search example -search_response = client.vector_stores.search( - vector_store_id=vector_store.id, - query="Python programming language", - search_mode="keyword", - max_num_results=5, -) -``` - -### Hybrid Search -Hybrid search combines both vector and keyword search methods to provide more comprehensive results. It leverages the strengths of both semantic similarity and exact term matching. - -#### Basic Hybrid Search -```python -# Basic hybrid search example (uses RRF ranker with default impact_factor=60.0) -search_response = client.vector_stores.search( - vector_store_id=vector_store.id, - query="neural networks in Python", - search_mode="hybrid", - max_num_results=5, -) -``` - -**Note**: The default `impact_factor` value of 60.0 was empirically determined to be optimal in the original RRF research paper: ["Reciprocal Rank Fusion outperforms Condorcet and individual Rank Learning Methods"](https://plg.uwaterloo.ca/~gvcormac/cormacksigir09-rrf.pdf) (Cormack et al., 2009). - -#### Hybrid Search with RRF (Reciprocal Rank Fusion) Ranker -RRF combines rankings from vector and keyword search by using reciprocal ranks. The impact factor controls how much weight is given to higher-ranked results. - -```python -# Hybrid search with custom RRF parameters -search_response = client.vector_stores.search( - vector_store_id=vector_store.id, - query="neural networks in Python", - search_mode="hybrid", - max_num_results=5, - ranking_options={ - "ranker": { - "type": "rrf", - "impact_factor": 100.0, # Higher values give more weight to top-ranked results - } - }, -) -``` - -#### Hybrid Search with Weighted Ranker -Weighted ranker linearly combines normalized scores from vector and keyword search. The alpha parameter controls the balance between the two search methods. - -```python -# Hybrid search with weighted ranker -search_response = client.vector_stores.search( - vector_store_id=vector_store.id, - query="neural networks in Python", - search_mode="hybrid", - max_num_results=5, - ranking_options={ - "ranker": { - "type": "weighted", - "alpha": 0.7, # 70% vector search, 30% keyword search - } - }, -) -``` - -For detailed documentation on RRF and Weighted rankers, please refer to the [Milvus Reranking Guide](https://milvus.io/docs/reranking.md). - -## Documentation -See the [Milvus documentation](https://milvus.io/docs/install-overview.md) for more details about Milvus in general. - -For more details on TLS configuration, refer to the [TLS setup guide](https://milvus.io/docs/tls.md). - - -## Configuration - -| Field | Type | Required | Default | Description | -|-------|------|----------|---------|-------------| -| `uri` | `` | No | | The URI of the Milvus server | -| `token` | `str \| None` | No | | The token of the Milvus server | -| `consistency_level` | `` | No | Strong | The consistency level of the Milvus server | -| `kvstore` | `utils.kvstore.config.RedisKVStoreConfig \| utils.kvstore.config.SqliteKVStoreConfig \| utils.kvstore.config.PostgresKVStoreConfig \| utils.kvstore.config.MongoDBKVStoreConfig` | No | sqlite | Config for KV store backend | -| `config` | `dict` | No | {} | This configuration allows additional fields to be passed through to the underlying Milvus client. See the [Milvus](https://milvus.io/docs/install-overview.md) documentation for more details about Milvus in general. | - -```{note} - This configuration class accepts additional fields beyond those listed above. You can pass any additional configuration options that will be forwarded to the underlying provider. - ``` - - -## Sample Configuration - -```yaml -uri: ${env.MILVUS_ENDPOINT} -token: ${env.MILVUS_TOKEN} -kvstore: - type: sqlite - db_path: ${env.SQLITE_STORE_DIR:=~/.llama/dummy}/milvus_remote_registry.db - -``` - diff --git a/docs/source/providers/vector_io/remote_pgvector.md b/docs/source/providers/vector_io/remote_pgvector.md deleted file mode 100644 index 74f588a13..000000000 --- a/docs/source/providers/vector_io/remote_pgvector.md +++ /dev/null @@ -1,58 +0,0 @@ -# remote::pgvector - -## Description - - -[PGVector](https://github.com/pgvector/pgvector) is a remote vector database provider for Llama Stack. It -allows you to store and query vectors directly in memory. -That means you'll get fast and efficient vector retrieval. - -## Features - -- Easy to use -- Fully integrated with Llama Stack - -## Usage - -To use PGVector in your Llama Stack project, follow these steps: - -1. Install the necessary dependencies. -2. Configure your Llama Stack project to use pgvector. (e.g. remote::pgvector). -3. Start storing and querying vectors. - -## Installation - -You can install PGVector using docker: - -```bash -docker pull pgvector/pgvector:pg17 -``` -## Documentation -See [PGVector's documentation](https://github.com/pgvector/pgvector) for more details about PGVector in general. - - -## Configuration - -| Field | Type | Required | Default | Description | -|-------|------|----------|---------|-------------| -| `host` | `str \| None` | No | localhost | | -| `port` | `int \| None` | No | 5432 | | -| `db` | `str \| None` | No | postgres | | -| `user` | `str \| None` | No | postgres | | -| `password` | `str \| None` | No | mysecretpassword | | -| `kvstore` | `utils.kvstore.config.RedisKVStoreConfig \| utils.kvstore.config.SqliteKVStoreConfig \| utils.kvstore.config.PostgresKVStoreConfig \| utils.kvstore.config.MongoDBKVStoreConfig, annotation=NoneType, required=False, default='sqlite', discriminator='type'` | No | | Config for KV store backend (SQLite only for now) | - -## Sample Configuration - -```yaml -host: ${env.PGVECTOR_HOST:=localhost} -port: ${env.PGVECTOR_PORT:=5432} -db: ${env.PGVECTOR_DB} -user: ${env.PGVECTOR_USER} -password: ${env.PGVECTOR_PASSWORD} -kvstore: - type: sqlite - db_path: ${env.SQLITE_STORE_DIR:=~/.llama/dummy}/pgvector_registry.db - -``` - diff --git a/docs/source/references/api_reference/index.md b/docs/source/references/api_reference/index.md deleted file mode 100644 index f93c73ea3..000000000 --- a/docs/source/references/api_reference/index.md +++ /dev/null @@ -1,6 +0,0 @@ -{.hide-title} -# API Reference - -```{raw} html - :file: ../../../_static/llama-stack-spec.html -``` diff --git a/docs/source/references/index.md b/docs/source/references/index.md deleted file mode 100644 index 51e3dd0ba..000000000 --- a/docs/source/references/index.md +++ /dev/null @@ -1,18 +0,0 @@ -# References - -- [API Reference](api_reference/index) for the Llama Stack API specification -- [Python SDK Reference](python_sdk_reference/index) -- [Llama CLI](llama_cli_reference/index) for building and running your Llama Stack server -- [Llama Stack Client CLI](llama_stack_client_cli_reference) for interacting with your Llama Stack server - -```{toctree} -:maxdepth: 1 -:hidden: - -api_reference/index -python_sdk_reference/index -llama_cli_reference/index -llama_stack_client_cli_reference -llama_cli_reference/download_models -evals_reference/index -``` diff --git a/docs/src/components/HomepageFeatures/index.js b/docs/src/components/HomepageFeatures/index.js new file mode 100644 index 000000000..78f410ba6 --- /dev/null +++ b/docs/src/components/HomepageFeatures/index.js @@ -0,0 +1,64 @@ +import React from 'react'; +import clsx from 'clsx'; +import styles from './styles.module.css'; + +const FeatureList = [ + { + title: 'Easy to Use', + Svg: require('@site/static/img/undraw_docusaurus_mountain.svg').default, + description: ( + <> + Docusaurus was designed from the ground up to be easily installed and + used to get your website up and running quickly. + + ), + }, + { + title: 'Focus on What Matters', + Svg: require('@site/static/img/undraw_docusaurus_tree.svg').default, + description: ( + <> + Docusaurus lets you focus on your docs, and we'll do the chores. Go + ahead and move your docs into the docs directory. + + ), + }, + { + title: 'Powered by React', + Svg: require('@site/static/img/undraw_docusaurus_react.svg').default, + description: ( + <> + Extend or customize your website layout by reusing React. Docusaurus can + be extended while reusing the same header and footer. + + ), + }, +]; + +function Feature({Svg, title, description}) { + return ( +
+
+ +
+
+

{title}

+

{description}

+
+
+ ); +} + +export default function HomepageFeatures() { + return ( +
+
+
+ {FeatureList.map((props, idx) => ( + + ))} +
+
+
+ ); +} diff --git a/docs/src/components/HomepageFeatures/styles.module.css b/docs/src/components/HomepageFeatures/styles.module.css new file mode 100644 index 000000000..b248eb2e5 --- /dev/null +++ b/docs/src/components/HomepageFeatures/styles.module.css @@ -0,0 +1,11 @@ +.features { + display: flex; + align-items: center; + padding: 2rem 0; + width: 100%; +} + +.featureSvg { + height: 200px; + width: 200px; +} diff --git a/docs/src/css/custom.css b/docs/src/css/custom.css new file mode 100644 index 000000000..0e4d95b9b --- /dev/null +++ b/docs/src/css/custom.css @@ -0,0 +1,191 @@ +/** + * Any CSS included here will be global. The classic template + * bundles Infima by default. Infima is a CSS framework designed to + * work well for content-centric websites. + */ + +/* You can override the default Infima variables here. */ +:root { + /* Llama Stack Original Theme - Based on llamastack.github.io */ + --ifm-color-primary: #4a4a68; + --ifm-color-primary-dark: #3a3a52; + --ifm-color-primary-darker: #332735; + --ifm-color-primary-darkest: #2b2129; + --ifm-color-primary-light: #5a5a7e; + --ifm-color-primary-lighter: #6a6a94; + --ifm-color-primary-lightest: #8080aa; + + /* Additional theme colors */ + --ifm-color-secondary: #1b263c; + --ifm-color-info: #2980b9; + --ifm-color-success: #16a085; + --ifm-color-warning: #f39c12; + --ifm-color-danger: #e74c3c; + + /* Background colors */ + --ifm-background-color: #ffffff; + --ifm-background-surface-color: #f8f9fa; + + /* Code and syntax highlighting */ + --ifm-code-font-size: 95%; + --ifm-pre-background: #1b263c; + --ifm-pre-color: #e1e5e9; + --docusaurus-highlighted-code-line-bg: rgba(51, 39, 53, 0.1); + + /* Link colors */ + --ifm-link-color: var(--ifm-color-primary); + --ifm-link-hover-color: var(--ifm-color-primary-darker); + + /* Navbar */ + --ifm-navbar-background-color: rgba(255, 255, 255, 0.95); + --ifm-navbar-shadow: 0 2px 4px rgba(0, 0, 0, 0.1); + + /* Hero section gradient - matching original theme */ + --hero-gradient: linear-gradient(90deg, #332735 0%, #1b263c 100%); + + /* OpenAPI method colors */ + --openapi-code-blue: #2980b9; + --openapi-code-green: #16a085; + --openapi-code-orange: #f39c12; + --openapi-code-red: #e74c3c; + --openapi-code-purple: #332735; +} + +/* For readability concerns, you should choose a lighter palette in dark mode. */ +[data-theme='dark'] { + /* Dark theme primary colors - lighter versions of original theme */ + --ifm-color-primary: #8080aa; + --ifm-color-primary-dark: #6a6a94; + --ifm-color-primary-darker: #5a5a7e; + --ifm-color-primary-darkest: #4a4a68; + --ifm-color-primary-light: #9090ba; + --ifm-color-primary-lighter: #a0a0ca; + --ifm-color-primary-lightest: #b0b0da; + + /* Dark theme background colors */ + --ifm-background-color: #1a1a1a; + --ifm-background-surface-color: #2a2a2a; + + /* Dark theme navbar */ + --ifm-navbar-background-color: rgba(26, 26, 26, 0.95); + + /* Dark theme code highlighting */ + --docusaurus-highlighted-code-line-bg: rgba(51, 39, 53, 0.3); + + /* Dark theme text colors */ + --ifm-font-color-base: #e1e5e9; + --ifm-font-color-secondary: #a0a6ac; +} + +/* Sidebar Method labels */ +.api-method>.menu__link { + align-items: center; + justify-content: start; +} + +.api-method>.menu__link::before { + width: 50px; + height: 20px; + font-size: 12px; + line-height: 20px; + text-transform: uppercase; + font-weight: 600; + border-radius: 0.25rem; + border: 1px solid; + margin-right: var(--ifm-spacing-horizontal); + text-align: center; + flex-shrink: 0; + border-color: transparent; + color: white; +} + +.get>.menu__link::before { + content: "get"; + background-color: var(--ifm-color-primary); +} + +.put>.menu__link::before { + content: "put"; + background-color: var(--openapi-code-blue); +} + +.post>.menu__link::before { + content: "post"; + background-color: var(--openapi-code-green); +} + +.delete>.menu__link::before { + content: "del"; + background-color: var(--openapi-code-red); +} + +.patch>.menu__link::before { + content: "patch"; + background-color: var(--openapi-code-orange); +} + +.footer--dark { + --ifm-footer-link-color: #ffffff; + --ifm-footer-title-color: #ffffff; +} + +.footer--dark .footer__link-item { + color: #ffffff; +} + +.footer--dark .footer__title { + color: #ffffff; +} + +/* OpenAPI theme fixes for light mode readability */ +/* Version badge fixes */ +.openapi__version-badge, +.theme-doc-version-badge, +[class*="version-badge"], +[class*="versionBadge"] { + background-color: #ffffff !important; + color: #333333 !important; + border: 1px solid #d1d5db !important; +} + +/* OpenAPI method badges in light mode */ +.openapi__method-badge, +[class*="method-badge"] { + color: #ffffff !important; +} + +/* Button fixes for light mode */ +.openapi__button, +.theme-api-docs-demo-panel button, +[class*="api-docs"] button, +button[class*="button"], +.openapi-explorer__response-schema button, +.openapi-tabs__operation button { + color: #ffffff !important; +} + +.openapi__button:hover, +.theme-api-docs-demo-panel button:hover, +[class*="api-docs"] button:hover, +button[class*="button"]:hover, +.openapi-explorer__response-schema button:hover, +.openapi-tabs__operation button:hover { + color: #ffffff !important; +} + +/* Navigation buttons (Next/Previous) */ +.pagination-nav__link, +.pagination-nav__label { + color: #333333 !important; +} + +.pagination-nav__link--next, +.pagination-nav__link--prev { + background-color: #ffffff !important; + border: 1px solid #d1d5db !important; +} + +.pagination-nav__link--next:hover, +.pagination-nav__link--prev:hover { + background-color: #f3f4f6 !important; +} diff --git a/docs/src/pages/index.js b/docs/src/pages/index.js new file mode 100644 index 000000000..c97959d77 --- /dev/null +++ b/docs/src/pages/index.js @@ -0,0 +1,163 @@ +import React from 'react'; +import clsx from 'clsx'; +import Layout from '@theme/Layout'; +import Link from '@docusaurus/Link'; +import useDocusaurusContext from '@docusaurus/useDocusaurusContext'; +import styles from './index.module.css'; + +function HomepageHeader() { + const {siteConfig} = useDocusaurusContext(); + return ( +
+
+
+

Build AI Applications with Llama Stack

+

+ Unified APIs for Inference, RAG, Agents, Tools, Safety, and Telemetry +

+
+ + 🚀 Get Started + + + 📚 API Reference + +
+
+
+
+ ); +} + +function QuickStart() { + return ( +
+
+
+
+

Quick Start

+

+ Get up and running with Llama Stack in just a few commands. Build your first RAG application locally. +

+
+
{`# Install uv and start Ollama
+ollama run llama3.2:3b --keepalive 60m
+
+# Run Llama Stack server
+OLLAMA_URL=http://localhost:11434 \\
+  uv run --with llama-stack \\
+  llama stack build --distro starter \\
+  --image-type venv --run
+
+# Try the Python SDK
+from llama_stack_client import LlamaStackClient
+
+client = LlamaStackClient(
+  base_url="http://localhost:8321"
+)
+
+response = client.inference.chat_completion(
+  model="Llama3.2-3B-Instruct",
+  messages=[{
+    "role": "user",
+    "content": "What is machine learning?"
+  }]
+)`}
+
+
+
+

Why Llama Stack?

+
+
+
🔗
+
+

Unified APIs

+

One consistent interface for all your AI needs - inference, safety, agents, and more.

+
+
+
+
🔄
+
+

Provider Flexibility

+

Swap between providers without code changes. Start local, deploy anywhere.

+
+
+
+
🛡️
+
+

Production Ready

+

Built-in safety, monitoring, and evaluation tools for enterprise applications.

+
+
+
+
📱
+
+

Multi-Platform

+

SDKs for Python, Node.js, iOS, Android, and REST APIs for any language.

+
+
+
+
+
+
+
+ ); +} + +function CommunityLinks() { + return ( +
+
+
+

Join the Community

+

+ Connect with developers building the future of AI applications +

+ +
+
+
+ ); +} + +export default function Home() { + const {siteConfig} = useDocusaurusContext(); + return ( + + +
+ + +
+
+ ); +} diff --git a/docs/src/pages/index.module.css b/docs/src/pages/index.module.css new file mode 100644 index 000000000..c3681653b --- /dev/null +++ b/docs/src/pages/index.module.css @@ -0,0 +1,283 @@ +/** + * CSS files with the .module.css suffix will be treated as CSS modules + * and scoped locally. + */ + +.heroBanner { + padding: 4rem 0; + text-align: center; + position: relative; + overflow: hidden; + background: var(--hero-gradient); + color: white; + display: flex; + align-items: center; +} + +.heroBanner::before { + content: ''; + position: absolute; + top: 0; + left: 0; + right: 0; + bottom: 0; + background: radial-gradient(circle at 30% 20%, rgba(255, 255, 255, 0.1) 0%, transparent 50%), + radial-gradient(circle at 70% 80%, rgba(255, 255, 255, 0.05) 0%, transparent 50%); + pointer-events: none; +} + +.heroContent { + max-width: 800px; + margin: 0 auto; +} + +.heroLogo { + height: 48px; + width: auto; + margin-bottom: 1.5rem; +} + +.heroTitle { + font-size: 2.8rem; + font-weight: 700; + margin-bottom: 1rem; + line-height: 1.2; +} + +.heroSubtitle { + font-size: 1.1rem; + font-weight: 400; + margin-bottom: 2rem; + opacity: 0.9; + line-height: 1.5; + max-width: 600px; + margin-left: auto; + margin-right: auto; +} + +.buttons { + display: flex; + align-items: center; + justify-content: center; + gap: 1rem; +} + +.heroBanner .getStartedButton { + background: white; + color: #332735; + border: 2px solid white; + font-weight: 600; + transition: all 0.3s ease; +} + +.heroBanner .getStartedButton:hover { + background: rgba(255, 255, 255, 0.9); + color: #2b2129; + border-color: rgba(255, 255, 255, 0.9); + transform: translateY(-2px); + box-shadow: 0 8px 25px rgba(0, 0, 0, 0.15); +} + +.heroBanner .apiButton { + background: transparent; + color: white; + border: 2px solid white; + font-weight: 600; + transition: all 0.3s ease; +} + +.heroBanner .apiButton:hover { + background: white; + border-color: white; + color: #332735; + transform: translateY(-2px); +} + +/* Quick Start Section */ +.quickStart { + padding: 4rem 0; + background: var(--ifm-background-color); +} + +.sectionTitle { + font-size: 2rem; + font-weight: 600; + margin-bottom: 0.75rem; + color: var(--ifm-color-emphasis-800); +} + +.sectionDescription { + font-size: 1rem; + color: var(--ifm-color-emphasis-600); + margin-bottom: 1.5rem; + line-height: 1.5; +} + +.codeBlock { + background: var(--ifm-color-gray-900); + border-radius: 8px; + padding: 1.5rem; + margin-top: 1.5rem; + box-shadow: 0 2px 10px rgba(0, 0, 0, 0.1); +} + +.codeBlock pre { + margin: 0; + padding: 0; + background: none; + border: none; +} + +.codeBlock code { + color: var(--ifm-color-gray-100); + font-family: 'Fira Code', 'Consolas', 'Monaco', monospace; + font-size: 0.9rem; + line-height: 1.6; +} + +/* Features */ +.features { + display: flex; + flex-direction: column; + gap: 1rem; + margin-top: 1.5rem; +} + +.feature { + display: flex; + align-items: flex-start; + gap: 1rem; + padding: 1rem; + border-radius: 8px; + background: var(--ifm-color-gray-50); + border: 1px solid var(--ifm-color-gray-200); + transition: all 0.2s ease; +} + +.feature:hover { + transform: translateY(-2px); + box-shadow: 0 8px 25px rgba(0, 0, 0, 0.1); + border-color: var(--ifm-color-primary-lighter); +} + +.featureIcon { + font-size: 2rem; + width: 3rem; + height: 3rem; + display: flex; + align-items: center; + justify-content: center; + background: var(--ifm-color-primary-lightest); + border-radius: 50%; + flex-shrink: 0; +} + +.feature h4 { + margin: 0 0 0.5rem 0; + font-size: 1.1rem; + font-weight: 600; + color: var(--ifm-color-emphasis-800); +} + +.feature p { + margin: 0; + color: var(--ifm-color-emphasis-600); + line-height: 1.5; +} + +/* Community Section */ +.community { + padding: 3rem 0; + background: var(--ifm-color-gray-50); + border-top: 1px solid var(--ifm-color-gray-200); +} + +.communityContent { + text-align: center; + max-width: 600px; + margin: 0 auto; +} + +.communityLinks { + display: flex; + justify-content: center; + gap: 1rem; + margin-top: 2rem; +} + +.communityButton { + display: flex; + align-items: center; + gap: 0.5rem; + font-weight: 600; + transition: all 0.3s ease; +} + +.communityButton:hover { + transform: translateY(-2px); + box-shadow: 0 8px 25px rgba(0, 0, 0, 0.1); +} + +.communityIcon { + font-size: 1.2rem; +} + +/* Responsive Design */ +@media screen and (max-width: 996px) { + .heroBanner { + padding: 3rem 2rem; + } + + .heroTitle { + font-size: 2.2rem; + } + + .heroSubtitle { + font-size: 1rem; + } + + .buttons { + flex-direction: column; + gap: 1rem; + } + + .quickStart { + padding: 3rem 0; + } + + .sectionTitle { + font-size: 1.75rem; + } + + .communityLinks { + flex-direction: column; + align-items: center; + } + + .communityButton { + width: 200px; + justify-content: center; + } +} + +@media screen and (max-width: 768px) { + .heroLogo { + height: 40px; + } + + .heroTitle { + font-size: 1.8rem; + } + + .codeBlock { + padding: 1rem; + } + + .codeBlock code { + font-size: 0.8rem; + } + + .feature { + padding: 0.75rem; + } +} diff --git a/docs/src/pages/markdown-page.md b/docs/src/pages/markdown-page.md new file mode 100644 index 000000000..9756c5b66 --- /dev/null +++ b/docs/src/pages/markdown-page.md @@ -0,0 +1,7 @@ +--- +title: Markdown page example +--- + +# Markdown page example + +You don't need React to write simple standalone pages. diff --git a/docs/source/references/evals_reference/resources/eval-concept.png b/docs/static/img/eval-concept.png similarity index 100% rename from docs/source/references/evals_reference/resources/eval-concept.png rename to docs/static/img/eval-concept.png diff --git a/docs/source/references/evals_reference/resources/eval-flow.png b/docs/static/img/eval-flow.png similarity index 100% rename from docs/source/references/evals_reference/resources/eval-flow.png rename to docs/static/img/eval-flow.png diff --git a/docs/static/img/llama-stack-logo.png b/docs/static/img/llama-stack-logo.png new file mode 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index 000000000..6f3b11cdb --- /dev/null +++ b/docs/tsconfig.json @@ -0,0 +1,7 @@ +{ + "extends": "@docusaurus/tsconfig", + "compilerOptions": { + "baseUrl": "." + }, + "exclude": [".docusaurus", "build"] +} diff --git a/docs/zero_to_hero_guide/00_Inference101.ipynb b/docs/zero_to_hero_guide/00_Inference101.ipynb index f8b0cc1a2..0da3b702c 100644 --- a/docs/zero_to_hero_guide/00_Inference101.ipynb +++ b/docs/zero_to_hero_guide/00_Inference101.ipynb @@ -9,7 +9,7 @@ "\n", "This document provides instructions on how to use Llama Stack's `chat_completion` function for generating text using the `Llama3.2-3B-Instruct` model. \n", "\n", - "Before you begin, please ensure Llama Stack is installed and set up by following the [Getting Started Guide](https://llama-stack.readthedocs.io/en/latest/getting_started/index.html).\n", + "Before you begin, please ensure Llama Stack is installed and set up by following the [Getting Started Guide](https://llamastack.github.io/latest/getting_started/index.html).\n", "\n", "\n", "### Table of Contents\n", diff --git a/docs/zero_to_hero_guide/01_Local_Cloud_Inference101.ipynb b/docs/zero_to_hero_guide/01_Local_Cloud_Inference101.ipynb index 4f6ca4080..dc56eee69 100644 --- a/docs/zero_to_hero_guide/01_Local_Cloud_Inference101.ipynb +++ b/docs/zero_to_hero_guide/01_Local_Cloud_Inference101.ipynb @@ -10,7 +10,7 @@ "This guide provides a streamlined setup to switch between local and cloud clients for text generation with Llama Stack’s `chat_completion` API. This setup enables automatic fallback to a cloud instance if the local client is unavailable.\n", "\n", "### Prerequisites\n", - "Before you begin, please ensure Llama Stack is installed and the distribution is set up by following the [Getting Started Guide](https://llama-stack.readthedocs.io/en/latest/). You will need to run two distributions, a local and a cloud distribution, for this demo to work.\n", + "Before you begin, please ensure Llama Stack is installed and the distribution is set up by following the [Getting Started Guide](https://llamastack.github.io/latest/getting_started/index.html). You will need to run two distributions, a local and a cloud distribution, for this demo to work.\n", "\n", "### Implementation" ] diff --git a/docs/zero_to_hero_guide/02_Prompt_Engineering101.ipynb b/docs/zero_to_hero_guide/02_Prompt_Engineering101.ipynb index f3566eeb3..bfc1d8067 100644 --- a/docs/zero_to_hero_guide/02_Prompt_Engineering101.ipynb +++ b/docs/zero_to_hero_guide/02_Prompt_Engineering101.ipynb @@ -11,7 +11,7 @@ "\n", "This interactive guide covers prompt engineering & best practices with Llama 3.2 and Llama Stack.\n", "\n", - "Before you begin, please ensure Llama Stack is installed and set up by following the [Getting Started Guide](https://llama-stack.readthedocs.io/en/latest/getting_started/index.html)." + "Before you begin, please ensure Llama Stack is installed and set up by following the [Getting Started Guide](https://llamastack.github.io/latest/getting_started/index.html)." ] }, { diff --git a/docs/zero_to_hero_guide/03_Image_Chat101.ipynb b/docs/zero_to_hero_guide/03_Image_Chat101.ipynb index 44a365b4a..dd866061f 100644 --- a/docs/zero_to_hero_guide/03_Image_Chat101.ipynb +++ b/docs/zero_to_hero_guide/03_Image_Chat101.ipynb @@ -7,7 +7,7 @@ "source": [ "## Getting Started with LlamaStack Vision API\n", "\n", - "Before you begin, please ensure Llama Stack is installed and set up by following the [Getting Started Guide](https://llama-stack.readthedocs.io/en/latest/getting_started/index.html).\n", + "Before you begin, please ensure Llama Stack is installed and set up by following the [Getting Started Guide](https://llamastack.github.io/latest/getting_started/index.html).\n", "\n", "Let's import the necessary packages" ] diff --git a/docs/zero_to_hero_guide/05_Memory101.ipynb b/docs/zero_to_hero_guide/05_Memory101.ipynb index 761c5210a..80507fc2b 100644 --- a/docs/zero_to_hero_guide/05_Memory101.ipynb +++ b/docs/zero_to_hero_guide/05_Memory101.ipynb @@ -26,7 +26,7 @@ "A running instance of the Llama Stack server (we'll use localhost in \n", "this tutorial)\n", "\n", - "Before you begin, please ensure Llama Stack is installed and set up by following the [Getting Started Guide](https://llama-stack.readthedocs.io/en/latest/getting_started/index.html).\n", + "Before you begin, please ensure Llama Stack is installed and set up by following the [Getting Started Guide](https://llamastack.github.io/latest/getting_started/index.html).\n", "\n", "Let's start by installing the required packages:" ] @@ -268,7 +268,7 @@ " # Split document content into chunks of 512 characters\n", " content = doc.content\n", " chunk_size = 512\n", - " \n", + "\n", " # Create chunks of the specified size\n", " for i in range(0, len(content), chunk_size):\n", " chunk_content = content[i:i+chunk_size]\n", diff --git a/docs/zero_to_hero_guide/06_Safety101.ipynb b/docs/zero_to_hero_guide/06_Safety101.ipynb index 91b809621..041604326 100644 --- a/docs/zero_to_hero_guide/06_Safety101.ipynb +++ b/docs/zero_to_hero_guide/06_Safety101.ipynb @@ -6,7 +6,7 @@ "source": [ "## Safety API 101\n", "\n", - "This document talks about the Safety APIs in Llama Stack. Before you begin, please ensure Llama Stack is installed and set up by following the [Getting Started Guide](https://llama-stack.readthedocs.io/en/latest/getting_started/index.html).\n", + "This document talks about the Safety APIs in Llama Stack. Before you begin, please ensure Llama Stack is installed and set up by following the [Getting Started Guide](https://llamastack.github.io/latest/getting_started/index.html).\n", "\n", "As outlined in our [Responsible Use Guide](https://www.llama.com/docs/how-to-guides/responsible-use-guide-resources/), LLM apps should deploy appropriate system level safeguards to mitigate safety and security risks of LLM system, similar to the following diagram:\n", "\n", diff --git a/docs/zero_to_hero_guide/07_Agents101.ipynb b/docs/zero_to_hero_guide/07_Agents101.ipynb index 905799946..e2e96df87 100644 --- a/docs/zero_to_hero_guide/07_Agents101.ipynb +++ b/docs/zero_to_hero_guide/07_Agents101.ipynb @@ -6,7 +6,7 @@ "source": [ "## Agentic API 101\n", "\n", - "This document talks about the Agentic APIs in Llama Stack. Before you begin, please ensure Llama Stack is installed and set up by following the [Getting Started Guide](https://llama-stack.readthedocs.io/en/latest/getting_started/index.html).\n", + "This document talks about the Agentic APIs in Llama Stack. Before you begin, please ensure Llama Stack is installed and set up by following the [Getting Started Guide](https://llamastack.github.io/latest/getting_started/index.html).\n", "\n", "Starting Llama 3.1 you can build agentic applications capable of:\n", "\n", diff --git a/docs/zero_to_hero_guide/README.md b/docs/zero_to_hero_guide/README.md index 9f1f42b30..4ca9dec72 100644 --- a/docs/zero_to_hero_guide/README.md +++ b/docs/zero_to_hero_guide/README.md @@ -9,13 +9,18 @@ If you're looking for more specific topics, we have a [Zero to Hero Guide](#next > If you'd prefer not to set up a local server, explore our notebook on [tool calling with the Together API](Tool_Calling101_Using_Together_Llama_Stack_Server.ipynb). This notebook will show you how to leverage together.ai's Llama Stack Server API, allowing you to get started with Llama Stack without the need for a locally built and running server. ## Table of Contents -1. [Setup and run ollama](#setup-ollama) -2. [Install Dependencies and Set Up Environment](#install-dependencies-and-set-up-environment) -3. [Build, Configure, and Run Llama Stack](#build-configure-and-run-llama-stack) -4. [Test with llama-stack-client CLI](#test-with-llama-stack-client-cli) -5. [Test with curl](#test-with-curl) -6. [Test with Python](#test-with-python) -7. [Next Steps](#next-steps) +- [Llama Stack: from Zero to Hero](#llama-stack-from-zero-to-hero) + - [Table of Contents](#table-of-contents) + - [Setup ollama](#setup-ollama) + - [Install Dependencies and Set Up Environment](#install-dependencies-and-set-up-environment) + - [Build, Configure, and Run Llama Stack](#build-configure-and-run-llama-stack) + - [Test with `llama-stack-client` CLI](#test-with-llama-stack-client-cli) + - [Test with `curl`](#test-with-curl) + - [Test with Python](#test-with-python) + - [1. Create Python Script (`test_llama_stack.py`)](#1-create-python-script-test_llama_stackpy) + - [2. Create a Chat Completion Request in Python](#2-create-a-chat-completion-request-in-python) + - [3. Run the Python Script](#3-run-the-python-script) + - [Next Steps](#next-steps) --- @@ -242,7 +247,7 @@ This command initializes the model to interact with your local Llama Stack insta ## Next Steps **Explore Other Guides**: Dive deeper into specific topics by following these guides: -- [Understanding Distribution](https://llama-stack.readthedocs.io/en/latest/concepts/index.html#distributions) +- [Understanding Distribution](https://llamastack.github.io/latest/concepts/index.html#distributions) - [Inference 101](00_Inference101.ipynb) - [Local and Cloud Model Toggling 101](01_Local_Cloud_Inference101.ipynb) - [Prompt Engineering](02_Prompt_Engineering101.ipynb) @@ -259,7 +264,7 @@ This command initializes the model to interact with your local Llama Stack insta - [Swift SDK](https://github.com/meta-llama/llama-stack-client-swift) - [Kotlin SDK](https://github.com/meta-llama/llama-stack-client-kotlin) -**Advanced Configuration**: Learn how to customize your Llama Stack distribution by referring to the [Building a Llama Stack Distribution](https://llama-stack.readthedocs.io/en/latest/distributions/building_distro.html) guide. +**Advanced Configuration**: Learn how to customize your Llama Stack distribution by referring to the [Building a Llama Stack Distribution](https://llamastack.github.io/latest/distributions/building_distro.html) guide. **Explore Example Apps**: Check out [llama-stack-apps](https://github.com/meta-llama/llama-stack-apps/tree/main/examples) for example applications built using Llama Stack. diff --git a/llama_stack/apis/agents/agents.py b/llama_stack/apis/agents/agents.py index 7dd3e9289..e53ca82e2 100644 --- a/llama_stack/apis/agents/agents.py +++ b/llama_stack/apis/agents/agents.py @@ -27,6 +27,7 @@ from llama_stack.apis.inference import ( ) from llama_stack.apis.safety import SafetyViolation from llama_stack.apis.tools import ToolDef +from llama_stack.apis.version import LLAMA_STACK_API_V1 from llama_stack.schema_utils import json_schema_type, register_schema, webmethod from .openai_responses import ( @@ -481,7 +482,7 @@ class Agents(Protocol): - Agents can also use Memory to retrieve information from knowledge bases. See the RAG Tool and Vector IO APIs for more details. """ - @webmethod(route="/agents", method="POST", descriptive_name="create_agent") + @webmethod(route="/agents", method="POST", descriptive_name="create_agent", level=LLAMA_STACK_API_V1) async def create_agent( self, agent_config: AgentConfig, @@ -494,7 +495,10 @@ class Agents(Protocol): ... @webmethod( - route="/agents/{agent_id}/session/{session_id}/turn", method="POST", descriptive_name="create_agent_turn" + route="/agents/{agent_id}/session/{session_id}/turn", + method="POST", + descriptive_name="create_agent_turn", + level=LLAMA_STACK_API_V1, ) async def create_agent_turn( self, @@ -524,6 +528,7 @@ class Agents(Protocol): route="/agents/{agent_id}/session/{session_id}/turn/{turn_id}/resume", method="POST", descriptive_name="resume_agent_turn", + level=LLAMA_STACK_API_V1, ) async def resume_agent_turn( self, @@ -549,6 +554,7 @@ class Agents(Protocol): @webmethod( route="/agents/{agent_id}/session/{session_id}/turn/{turn_id}", method="GET", + level=LLAMA_STACK_API_V1, ) async def get_agents_turn( self, @@ -568,6 +574,7 @@ class Agents(Protocol): @webmethod( route="/agents/{agent_id}/session/{session_id}/turn/{turn_id}/step/{step_id}", method="GET", + level=LLAMA_STACK_API_V1, ) async def get_agents_step( self, @@ -586,7 +593,12 @@ class Agents(Protocol): """ ... - @webmethod(route="/agents/{agent_id}/session", method="POST", descriptive_name="create_agent_session") + @webmethod( + route="/agents/{agent_id}/session", + method="POST", + descriptive_name="create_agent_session", + level=LLAMA_STACK_API_V1, + ) async def create_agent_session( self, agent_id: str, @@ -600,7 +612,7 @@ class Agents(Protocol): """ ... - @webmethod(route="/agents/{agent_id}/session/{session_id}", method="GET") + @webmethod(route="/agents/{agent_id}/session/{session_id}", method="GET", level=LLAMA_STACK_API_V1) async def get_agents_session( self, session_id: str, @@ -616,7 +628,7 @@ class Agents(Protocol): """ ... - @webmethod(route="/agents/{agent_id}/session/{session_id}", method="DELETE") + @webmethod(route="/agents/{agent_id}/session/{session_id}", method="DELETE", level=LLAMA_STACK_API_V1) async def delete_agents_session( self, session_id: str, @@ -629,7 +641,7 @@ class Agents(Protocol): """ ... - @webmethod(route="/agents/{agent_id}", method="DELETE") + @webmethod(route="/agents/{agent_id}", method="DELETE", level=LLAMA_STACK_API_V1) async def delete_agent( self, agent_id: str, @@ -640,7 +652,7 @@ class Agents(Protocol): """ ... - @webmethod(route="/agents", method="GET") + @webmethod(route="/agents", method="GET", level=LLAMA_STACK_API_V1) async def list_agents(self, start_index: int | None = None, limit: int | None = None) -> PaginatedResponse: """List all agents. @@ -650,7 +662,7 @@ class Agents(Protocol): """ ... - @webmethod(route="/agents/{agent_id}", method="GET") + @webmethod(route="/agents/{agent_id}", method="GET", level=LLAMA_STACK_API_V1) async def get_agent(self, agent_id: str) -> Agent: """Describe an agent by its ID. @@ -659,7 +671,7 @@ class Agents(Protocol): """ ... - @webmethod(route="/agents/{agent_id}/sessions", method="GET") + @webmethod(route="/agents/{agent_id}/sessions", method="GET", level=LLAMA_STACK_API_V1) async def list_agent_sessions( self, agent_id: str, @@ -682,7 +694,7 @@ class Agents(Protocol): # # Both of these APIs are inherently stateful. - @webmethod(route="/openai/v1/responses/{response_id}", method="GET") + @webmethod(route="/openai/v1/responses/{response_id}", method="GET", level=LLAMA_STACK_API_V1) async def get_openai_response( self, response_id: str, @@ -694,7 +706,7 @@ class Agents(Protocol): """ ... - @webmethod(route="/openai/v1/responses", method="POST") + @webmethod(route="/openai/v1/responses", method="POST", level=LLAMA_STACK_API_V1) async def create_openai_response( self, input: str | list[OpenAIResponseInput], @@ -719,7 +731,7 @@ class Agents(Protocol): """ ... - @webmethod(route="/openai/v1/responses", method="GET") + @webmethod(route="/openai/v1/responses", method="GET", level=LLAMA_STACK_API_V1) async def list_openai_responses( self, after: str | None = None, @@ -737,7 +749,7 @@ class Agents(Protocol): """ ... - @webmethod(route="/openai/v1/responses/{response_id}/input_items", method="GET") + @webmethod(route="/openai/v1/responses/{response_id}/input_items", method="GET", level=LLAMA_STACK_API_V1) async def list_openai_response_input_items( self, response_id: str, @@ -759,7 +771,7 @@ class Agents(Protocol): """ ... - @webmethod(route="/openai/v1/responses/{response_id}", method="DELETE") + @webmethod(route="/openai/v1/responses/{response_id}", method="DELETE", level=LLAMA_STACK_API_V1) async def delete_openai_response(self, response_id: str) -> OpenAIDeleteResponseObject: """Delete an OpenAI response by its ID. diff --git a/llama_stack/apis/batches/batches.py b/llama_stack/apis/batches/batches.py index c6bbd92eb..5890cbe04 100644 --- a/llama_stack/apis/batches/batches.py +++ b/llama_stack/apis/batches/batches.py @@ -8,6 +8,7 @@ from typing import Literal, Protocol, runtime_checkable from pydantic import BaseModel, Field +from llama_stack.apis.version import LLAMA_STACK_API_V1 from llama_stack.schema_utils import json_schema_type, webmethod try: @@ -42,7 +43,7 @@ class Batches(Protocol): Note: This API is currently under active development and may undergo changes. """ - @webmethod(route="/openai/v1/batches", method="POST") + @webmethod(route="/openai/v1/batches", method="POST", level=LLAMA_STACK_API_V1) async def create_batch( self, input_file_id: str, @@ -62,7 +63,7 @@ class Batches(Protocol): """ ... - @webmethod(route="/openai/v1/batches/{batch_id}", method="GET") + @webmethod(route="/openai/v1/batches/{batch_id}", method="GET", level=LLAMA_STACK_API_V1) async def retrieve_batch(self, batch_id: str) -> BatchObject: """Retrieve information about a specific batch. @@ -71,7 +72,7 @@ class Batches(Protocol): """ ... - @webmethod(route="/openai/v1/batches/{batch_id}/cancel", method="POST") + @webmethod(route="/openai/v1/batches/{batch_id}/cancel", method="POST", level=LLAMA_STACK_API_V1) async def cancel_batch(self, batch_id: str) -> BatchObject: """Cancel a batch that is in progress. @@ -80,7 +81,7 @@ class Batches(Protocol): """ ... - @webmethod(route="/openai/v1/batches", method="GET") + @webmethod(route="/openai/v1/batches", method="GET", level=LLAMA_STACK_API_V1) async def list_batches( self, after: str | None = None, diff --git a/llama_stack/apis/benchmarks/benchmarks.py b/llama_stack/apis/benchmarks/benchmarks.py index 706eaed6c..d87d45a60 100644 --- a/llama_stack/apis/benchmarks/benchmarks.py +++ b/llama_stack/apis/benchmarks/benchmarks.py @@ -8,6 +8,7 @@ from typing import Any, Literal, Protocol, runtime_checkable from pydantic import BaseModel, Field from llama_stack.apis.resource import Resource, ResourceType +from llama_stack.apis.version import LLAMA_STACK_API_V1, LLAMA_STACK_API_V1ALPHA from llama_stack.schema_utils import json_schema_type, webmethod @@ -53,7 +54,8 @@ class ListBenchmarksResponse(BaseModel): @runtime_checkable class Benchmarks(Protocol): - @webmethod(route="/eval/benchmarks", method="GET") + @webmethod(route="/eval/benchmarks", method="GET", level=LLAMA_STACK_API_V1, deprecated=True) + @webmethod(route="/eval/benchmarks", method="GET", level=LLAMA_STACK_API_V1ALPHA) async def list_benchmarks(self) -> ListBenchmarksResponse: """List all benchmarks. @@ -61,7 +63,8 @@ class Benchmarks(Protocol): """ ... - @webmethod(route="/eval/benchmarks/{benchmark_id}", method="GET") + @webmethod(route="/eval/benchmarks/{benchmark_id}", method="GET", level=LLAMA_STACK_API_V1, deprecated=True) + @webmethod(route="/eval/benchmarks/{benchmark_id}", method="GET", level=LLAMA_STACK_API_V1ALPHA) async def get_benchmark( self, benchmark_id: str, @@ -73,7 +76,8 @@ class Benchmarks(Protocol): """ ... - @webmethod(route="/eval/benchmarks", method="POST") + @webmethod(route="/eval/benchmarks", method="POST", level=LLAMA_STACK_API_V1, deprecated=True) + @webmethod(route="/eval/benchmarks", method="POST", level=LLAMA_STACK_API_V1ALPHA) async def register_benchmark( self, benchmark_id: str, @@ -93,3 +97,12 @@ class Benchmarks(Protocol): :param metadata: The metadata to use for the benchmark. """ ... + + @webmethod(route="/eval/benchmarks/{benchmark_id}", method="DELETE", level=LLAMA_STACK_API_V1, deprecated=True) + @webmethod(route="/eval/benchmarks/{benchmark_id}", method="DELETE", level=LLAMA_STACK_API_V1ALPHA) + async def unregister_benchmark(self, benchmark_id: str) -> None: + """Unregister a benchmark. + + :param benchmark_id: The ID of the benchmark to unregister. + """ + ... diff --git a/llama_stack/apis/common/errors.py b/llama_stack/apis/common/errors.py index ec3d2b1ce..4c9c0a818 100644 --- a/llama_stack/apis/common/errors.py +++ b/llama_stack/apis/common/errors.py @@ -79,3 +79,10 @@ class ConflictError(ValueError): def __init__(self, message: str) -> None: super().__init__(message) + + +class TokenValidationError(ValueError): + """raised when token validation fails during authentication""" + + def __init__(self, message: str) -> None: + super().__init__(message) diff --git a/llama_stack/apis/datasetio/datasetio.py b/llama_stack/apis/datasetio/datasetio.py index 1183983cc..27e5336bc 100644 --- a/llama_stack/apis/datasetio/datasetio.py +++ b/llama_stack/apis/datasetio/datasetio.py @@ -8,6 +8,7 @@ from typing import Any, Protocol, runtime_checkable from llama_stack.apis.common.responses import PaginatedResponse from llama_stack.apis.datasets import Dataset +from llama_stack.apis.version import LLAMA_STACK_API_V1 from llama_stack.schema_utils import webmethod @@ -20,7 +21,7 @@ class DatasetIO(Protocol): # keeping for aligning with inference/safety, but this is not used dataset_store: DatasetStore - @webmethod(route="/datasetio/iterrows/{dataset_id:path}", method="GET") + @webmethod(route="/datasetio/iterrows/{dataset_id:path}", method="GET", level=LLAMA_STACK_API_V1) async def iterrows( self, dataset_id: str, @@ -44,7 +45,7 @@ class DatasetIO(Protocol): """ ... - @webmethod(route="/datasetio/append-rows/{dataset_id:path}", method="POST") + @webmethod(route="/datasetio/append-rows/{dataset_id:path}", method="POST", level=LLAMA_STACK_API_V1) async def append_rows(self, dataset_id: str, rows: list[dict[str, Any]]) -> None: """Append rows to a dataset. diff --git a/llama_stack/apis/datasets/datasets.py b/llama_stack/apis/datasets/datasets.py index f347e0e29..be0cbf09a 100644 --- a/llama_stack/apis/datasets/datasets.py +++ b/llama_stack/apis/datasets/datasets.py @@ -10,6 +10,7 @@ from typing import Annotated, Any, Literal, Protocol from pydantic import BaseModel, Field from llama_stack.apis.resource import Resource, ResourceType +from llama_stack.apis.version import LLAMA_STACK_API_V1 from llama_stack.schema_utils import json_schema_type, register_schema, webmethod @@ -145,7 +146,7 @@ class ListDatasetsResponse(BaseModel): class Datasets(Protocol): - @webmethod(route="/datasets", method="POST") + @webmethod(route="/datasets", method="POST", level=LLAMA_STACK_API_V1) async def register_dataset( self, purpose: DatasetPurpose, @@ -214,7 +215,7 @@ class Datasets(Protocol): """ ... - @webmethod(route="/datasets/{dataset_id:path}", method="GET") + @webmethod(route="/datasets/{dataset_id:path}", method="GET", level=LLAMA_STACK_API_V1) async def get_dataset( self, dataset_id: str, @@ -226,7 +227,7 @@ class Datasets(Protocol): """ ... - @webmethod(route="/datasets", method="GET") + @webmethod(route="/datasets", method="GET", level=LLAMA_STACK_API_V1) async def list_datasets(self) -> ListDatasetsResponse: """List all datasets. @@ -234,7 +235,7 @@ class Datasets(Protocol): """ ... - @webmethod(route="/datasets/{dataset_id:path}", method="DELETE") + @webmethod(route="/datasets/{dataset_id:path}", method="DELETE", level=LLAMA_STACK_API_V1) async def unregister_dataset( self, dataset_id: str, diff --git a/llama_stack/apis/datatypes.py b/llama_stack/apis/datatypes.py index 87fc95917..8d0f2e26d 100644 --- a/llama_stack/apis/datatypes.py +++ b/llama_stack/apis/datatypes.py @@ -102,6 +102,7 @@ class Api(Enum, metaclass=DynamicApiMeta): :cvar benchmarks: Benchmark suite management :cvar tool_groups: Tool group organization :cvar files: File storage and management + :cvar prompts: Prompt versions and management :cvar inspect: Built-in system inspection and introspection """ @@ -127,6 +128,7 @@ class Api(Enum, metaclass=DynamicApiMeta): benchmarks = "benchmarks" tool_groups = "tool_groups" files = "files" + prompts = "prompts" # built-in API inspect = "inspect" diff --git a/llama_stack/apis/eval/eval.py b/llama_stack/apis/eval/eval.py index 83a0a8e56..bb81778f1 100644 --- a/llama_stack/apis/eval/eval.py +++ b/llama_stack/apis/eval/eval.py @@ -13,6 +13,7 @@ from llama_stack.apis.common.job_types import Job from llama_stack.apis.inference import SamplingParams, SystemMessage from llama_stack.apis.scoring import ScoringResult from llama_stack.apis.scoring_functions import ScoringFnParams +from llama_stack.apis.version import LLAMA_STACK_API_V1, LLAMA_STACK_API_V1ALPHA from llama_stack.schema_utils import json_schema_type, register_schema, webmethod @@ -83,7 +84,8 @@ class EvaluateResponse(BaseModel): class Eval(Protocol): """Llama Stack Evaluation API for running evaluations on model and agent candidates.""" - @webmethod(route="/eval/benchmarks/{benchmark_id}/jobs", method="POST") + @webmethod(route="/eval/benchmarks/{benchmark_id}/jobs", method="POST", level=LLAMA_STACK_API_V1, deprecated=True) + @webmethod(route="/eval/benchmarks/{benchmark_id}/jobs", method="POST", level=LLAMA_STACK_API_V1ALPHA) async def run_eval( self, benchmark_id: str, @@ -97,7 +99,10 @@ class Eval(Protocol): """ ... - @webmethod(route="/eval/benchmarks/{benchmark_id}/evaluations", method="POST") + @webmethod( + route="/eval/benchmarks/{benchmark_id}/evaluations", method="POST", level=LLAMA_STACK_API_V1, deprecated=True + ) + @webmethod(route="/eval/benchmarks/{benchmark_id}/evaluations", method="POST", level=LLAMA_STACK_API_V1ALPHA) async def evaluate_rows( self, benchmark_id: str, @@ -115,7 +120,10 @@ class Eval(Protocol): """ ... - @webmethod(route="/eval/benchmarks/{benchmark_id}/jobs/{job_id}", method="GET") + @webmethod( + route="/eval/benchmarks/{benchmark_id}/jobs/{job_id}", method="GET", level=LLAMA_STACK_API_V1, deprecated=True + ) + @webmethod(route="/eval/benchmarks/{benchmark_id}/jobs/{job_id}", method="GET", level=LLAMA_STACK_API_V1ALPHA) async def job_status(self, benchmark_id: str, job_id: str) -> Job: """Get the status of a job. @@ -125,7 +133,13 @@ class Eval(Protocol): """ ... - @webmethod(route="/eval/benchmarks/{benchmark_id}/jobs/{job_id}", method="DELETE") + @webmethod( + route="/eval/benchmarks/{benchmark_id}/jobs/{job_id}", + method="DELETE", + level=LLAMA_STACK_API_V1, + deprecated=True, + ) + @webmethod(route="/eval/benchmarks/{benchmark_id}/jobs/{job_id}", method="DELETE", level=LLAMA_STACK_API_V1ALPHA) async def job_cancel(self, benchmark_id: str, job_id: str) -> None: """Cancel a job. @@ -134,7 +148,15 @@ class Eval(Protocol): """ ... - @webmethod(route="/eval/benchmarks/{benchmark_id}/jobs/{job_id}/result", method="GET") + @webmethod( + route="/eval/benchmarks/{benchmark_id}/jobs/{job_id}/result", + method="GET", + level=LLAMA_STACK_API_V1, + deprecated=True, + ) + @webmethod( + route="/eval/benchmarks/{benchmark_id}/jobs/{job_id}/result", method="GET", level=LLAMA_STACK_API_V1ALPHA + ) async def job_result(self, benchmark_id: str, job_id: str) -> EvaluateResponse: """Get the result of a job. diff --git a/llama_stack/apis/files/files.py b/llama_stack/apis/files/files.py index a1b9dd4dc..7e45b55ee 100644 --- a/llama_stack/apis/files/files.py +++ b/llama_stack/apis/files/files.py @@ -5,12 +5,13 @@ # the root directory of this source tree. from enum import StrEnum -from typing import Annotated, Literal, Protocol, runtime_checkable +from typing import Annotated, ClassVar, Literal, Protocol, runtime_checkable from fastapi import File, Form, Response, UploadFile -from pydantic import BaseModel +from pydantic import BaseModel, Field from llama_stack.apis.common.responses import Order +from llama_stack.apis.version import LLAMA_STACK_API_V1 from llama_stack.providers.utils.telemetry.trace_protocol import trace_protocol from llama_stack.schema_utils import json_schema_type, webmethod @@ -49,6 +50,23 @@ class OpenAIFileObject(BaseModel): purpose: OpenAIFilePurpose +@json_schema_type +class ExpiresAfter(BaseModel): + """ + Control expiration of uploaded files. + + Params: + - anchor, must be "created_at" + - seconds, must be int between 3600 and 2592000 (1 hour to 30 days) + """ + + MIN: ClassVar[int] = 3600 # 1 hour + MAX: ClassVar[int] = 2592000 # 30 days + + anchor: Literal["created_at"] + seconds: int = Field(..., ge=3600, le=2592000) + + @json_schema_type class ListOpenAIFileResponse(BaseModel): """ @@ -87,11 +105,14 @@ class OpenAIFileDeleteResponse(BaseModel): @trace_protocol class Files(Protocol): # OpenAI Files API Endpoints - @webmethod(route="/openai/v1/files", method="POST") + @webmethod(route="/openai/v1/files", method="POST", level=LLAMA_STACK_API_V1) async def openai_upload_file( self, file: Annotated[UploadFile, File()], purpose: Annotated[OpenAIFilePurpose, Form()], + expires_after_anchor: Annotated[str | None, Form(alias="expires_after[anchor]")] = None, + expires_after_seconds: Annotated[int | None, Form(alias="expires_after[seconds]")] = None, + # TODO: expires_after is producing strange openapi spec, params are showing up as a required w/ oneOf being null ) -> OpenAIFileObject: """ Upload a file that can be used across various endpoints. @@ -99,6 +120,7 @@ class Files(Protocol): The file upload should be a multipart form request with: - file: The File object (not file name) to be uploaded. - purpose: The intended purpose of the uploaded file. + - expires_after: Optional form values describing expiration for the file. Expected expires_after[anchor] = "created_at", expires_after[seconds] = {integer}. Seconds must be between 3600 and 2592000 (1 hour to 30 days). :param file: The uploaded file object containing content and metadata (filename, content_type, etc.). :param purpose: The intended purpose of the uploaded file (e.g., "assistants", "fine-tune"). @@ -106,7 +128,7 @@ class Files(Protocol): """ ... - @webmethod(route="/openai/v1/files", method="GET") + @webmethod(route="/openai/v1/files", method="GET", level=LLAMA_STACK_API_V1) async def openai_list_files( self, after: str | None = None, @@ -125,7 +147,7 @@ class Files(Protocol): """ ... - @webmethod(route="/openai/v1/files/{file_id}", method="GET") + @webmethod(route="/openai/v1/files/{file_id}", method="GET", level=LLAMA_STACK_API_V1) async def openai_retrieve_file( self, file_id: str, @@ -138,7 +160,7 @@ class Files(Protocol): """ ... - @webmethod(route="/openai/v1/files/{file_id}", method="DELETE") + @webmethod(route="/openai/v1/files/{file_id}", method="DELETE", level=LLAMA_STACK_API_V1) async def openai_delete_file( self, file_id: str, @@ -151,7 +173,7 @@ class Files(Protocol): """ ... - @webmethod(route="/openai/v1/files/{file_id}/content", method="GET") + @webmethod(route="/openai/v1/files/{file_id}/content", method="GET", level=LLAMA_STACK_API_V1) async def openai_retrieve_file_content( self, file_id: str, diff --git a/llama_stack/apis/inference/inference.py b/llama_stack/apis/inference/inference.py index 9eb00549f..178f8fb08 100644 --- a/llama_stack/apis/inference/inference.py +++ b/llama_stack/apis/inference/inference.py @@ -21,6 +21,7 @@ from llama_stack.apis.common.content_types import ContentDelta, InterleavedConte from llama_stack.apis.common.responses import Order from llama_stack.apis.models import Model from llama_stack.apis.telemetry import MetricResponseMixin +from llama_stack.apis.version import LLAMA_STACK_API_V1 from llama_stack.models.llama.datatypes import ( BuiltinTool, StopReason, @@ -1006,7 +1007,7 @@ class InferenceProvider(Protocol): model_store: ModelStore | None = None - @webmethod(route="/inference/completion", method="POST") + @webmethod(route="/inference/completion", method="POST", level=LLAMA_STACK_API_V1) async def completion( self, model_id: str, @@ -1029,7 +1030,7 @@ class InferenceProvider(Protocol): """ ... - @webmethod(route="/inference/chat-completion", method="POST") + @webmethod(route="/inference/chat-completion", method="POST", level=LLAMA_STACK_API_V1) async def chat_completion( self, model_id: str, @@ -1069,7 +1070,7 @@ class InferenceProvider(Protocol): """ ... - @webmethod(route="/inference/embeddings", method="POST") + @webmethod(route="/inference/embeddings", method="POST", level=LLAMA_STACK_API_V1) async def embeddings( self, model_id: str, @@ -1089,7 +1090,7 @@ class InferenceProvider(Protocol): """ ... - @webmethod(route="/inference/rerank", method="POST", experimental=True) + @webmethod(route="/inference/rerank", method="POST", experimental=True, level=LLAMA_STACK_API_V1) async def rerank( self, model: str, @@ -1108,7 +1109,7 @@ class InferenceProvider(Protocol): raise NotImplementedError("Reranking is not implemented") return # this is so mypy's safe-super rule will consider the method concrete - @webmethod(route="/openai/v1/completions", method="POST") + @webmethod(route="/openai/v1/completions", method="POST", level=LLAMA_STACK_API_V1) async def openai_completion( self, # Standard OpenAI completion parameters @@ -1159,7 +1160,7 @@ class InferenceProvider(Protocol): """ ... - @webmethod(route="/openai/v1/chat/completions", method="POST") + @webmethod(route="/openai/v1/chat/completions", method="POST", level=LLAMA_STACK_API_V1) async def openai_chat_completion( self, model: str, @@ -1215,7 +1216,7 @@ class InferenceProvider(Protocol): """ ... - @webmethod(route="/openai/v1/embeddings", method="POST") + @webmethod(route="/openai/v1/embeddings", method="POST", level=LLAMA_STACK_API_V1) async def openai_embeddings( self, model: str, @@ -1244,7 +1245,7 @@ class Inference(InferenceProvider): - Embedding models: these models generate embeddings to be used for semantic search. """ - @webmethod(route="/openai/v1/chat/completions", method="GET") + @webmethod(route="/openai/v1/chat/completions", method="GET", level=LLAMA_STACK_API_V1) async def list_chat_completions( self, after: str | None = None, @@ -1262,7 +1263,7 @@ class Inference(InferenceProvider): """ raise NotImplementedError("List chat completions is not implemented") - @webmethod(route="/openai/v1/chat/completions/{completion_id}", method="GET") + @webmethod(route="/openai/v1/chat/completions/{completion_id}", method="GET", level=LLAMA_STACK_API_V1) async def get_chat_completion(self, completion_id: str) -> OpenAICompletionWithInputMessages: """Describe a chat completion by its ID. diff --git a/llama_stack/apis/inspect/inspect.py b/llama_stack/apis/inspect/inspect.py index 91d9c3da7..e859dbe59 100644 --- a/llama_stack/apis/inspect/inspect.py +++ b/llama_stack/apis/inspect/inspect.py @@ -8,6 +8,7 @@ from typing import Protocol, runtime_checkable from pydantic import BaseModel +from llama_stack.apis.version import LLAMA_STACK_API_V1 from llama_stack.providers.datatypes import HealthStatus from llama_stack.schema_utils import json_schema_type, webmethod @@ -57,7 +58,7 @@ class ListRoutesResponse(BaseModel): @runtime_checkable class Inspect(Protocol): - @webmethod(route="/inspect/routes", method="GET") + @webmethod(route="/inspect/routes", method="GET", level=LLAMA_STACK_API_V1) async def list_routes(self) -> ListRoutesResponse: """List all available API routes with their methods and implementing providers. @@ -65,7 +66,7 @@ class Inspect(Protocol): """ ... - @webmethod(route="/health", method="GET") + @webmethod(route="/health", method="GET", level=LLAMA_STACK_API_V1) async def health(self) -> HealthInfo: """Get the current health status of the service. @@ -73,7 +74,7 @@ class Inspect(Protocol): """ ... - @webmethod(route="/version", method="GET") + @webmethod(route="/version", method="GET", level=LLAMA_STACK_API_V1) async def version(self) -> VersionInfo: """Get the version of the service. diff --git a/llama_stack/apis/models/models.py b/llama_stack/apis/models/models.py index 1af6fc9df..a4f6a888b 100644 --- a/llama_stack/apis/models/models.py +++ b/llama_stack/apis/models/models.py @@ -10,6 +10,7 @@ from typing import Any, Literal, Protocol, runtime_checkable from pydantic import BaseModel, ConfigDict, Field, field_validator from llama_stack.apis.resource import Resource, ResourceType +from llama_stack.apis.version import LLAMA_STACK_API_V1 from llama_stack.providers.utils.telemetry.trace_protocol import trace_protocol from llama_stack.schema_utils import json_schema_type, webmethod @@ -102,7 +103,7 @@ class OpenAIListModelsResponse(BaseModel): @runtime_checkable @trace_protocol class Models(Protocol): - @webmethod(route="/models", method="GET") + @webmethod(route="/models", method="GET", level=LLAMA_STACK_API_V1) async def list_models(self) -> ListModelsResponse: """List all models. @@ -110,7 +111,7 @@ class Models(Protocol): """ ... - @webmethod(route="/openai/v1/models", method="GET") + @webmethod(route="/openai/v1/models", method="GET", level=LLAMA_STACK_API_V1) async def openai_list_models(self) -> OpenAIListModelsResponse: """List models using the OpenAI API. @@ -118,7 +119,7 @@ class Models(Protocol): """ ... - @webmethod(route="/models/{model_id:path}", method="GET") + @webmethod(route="/models/{model_id:path}", method="GET", level=LLAMA_STACK_API_V1) async def get_model( self, model_id: str, @@ -130,7 +131,7 @@ class Models(Protocol): """ ... - @webmethod(route="/models", method="POST") + @webmethod(route="/models", method="POST", level=LLAMA_STACK_API_V1) async def register_model( self, model_id: str, @@ -150,7 +151,7 @@ class Models(Protocol): """ ... - @webmethod(route="/models/{model_id:path}", method="DELETE") + @webmethod(route="/models/{model_id:path}", method="DELETE", level=LLAMA_STACK_API_V1) async def unregister_model( self, model_id: str, diff --git a/llama_stack/apis/post_training/post_training.py b/llama_stack/apis/post_training/post_training.py index c16221289..30a51f765 100644 --- a/llama_stack/apis/post_training/post_training.py +++ b/llama_stack/apis/post_training/post_training.py @@ -13,6 +13,7 @@ from pydantic import BaseModel, Field from llama_stack.apis.common.content_types import URL from llama_stack.apis.common.job_types import JobStatus from llama_stack.apis.common.training_types import Checkpoint +from llama_stack.apis.version import LLAMA_STACK_API_V1, LLAMA_STACK_API_V1ALPHA from llama_stack.schema_utils import json_schema_type, register_schema, webmethod @@ -283,7 +284,8 @@ class PostTrainingJobArtifactsResponse(BaseModel): class PostTraining(Protocol): - @webmethod(route="/post-training/supervised-fine-tune", method="POST") + @webmethod(route="/post-training/supervised-fine-tune", method="POST", level=LLAMA_STACK_API_V1, deprecated=True) + @webmethod(route="/post-training/supervised-fine-tune", method="POST", level=LLAMA_STACK_API_V1ALPHA) async def supervised_fine_tune( self, job_uuid: str, @@ -310,7 +312,8 @@ class PostTraining(Protocol): """ ... - @webmethod(route="/post-training/preference-optimize", method="POST") + @webmethod(route="/post-training/preference-optimize", method="POST", level=LLAMA_STACK_API_V1, deprecated=True) + @webmethod(route="/post-training/preference-optimize", method="POST", level=LLAMA_STACK_API_V1ALPHA) async def preference_optimize( self, job_uuid: str, @@ -332,7 +335,8 @@ class PostTraining(Protocol): """ ... - @webmethod(route="/post-training/jobs", method="GET") + @webmethod(route="/post-training/jobs", method="GET", level=LLAMA_STACK_API_V1, deprecated=True) + @webmethod(route="/post-training/jobs", method="GET", level=LLAMA_STACK_API_V1ALPHA) async def get_training_jobs(self) -> ListPostTrainingJobsResponse: """Get all training jobs. @@ -340,7 +344,8 @@ class PostTraining(Protocol): """ ... - @webmethod(route="/post-training/job/status", method="GET") + @webmethod(route="/post-training/job/status", method="GET", level=LLAMA_STACK_API_V1, deprecated=True) + @webmethod(route="/post-training/job/status", method="GET", level=LLAMA_STACK_API_V1ALPHA) async def get_training_job_status(self, job_uuid: str) -> PostTrainingJobStatusResponse: """Get the status of a training job. @@ -349,7 +354,8 @@ class PostTraining(Protocol): """ ... - @webmethod(route="/post-training/job/cancel", method="POST") + @webmethod(route="/post-training/job/cancel", method="POST", level=LLAMA_STACK_API_V1, deprecated=True) + @webmethod(route="/post-training/job/cancel", method="POST", level=LLAMA_STACK_API_V1ALPHA) async def cancel_training_job(self, job_uuid: str) -> None: """Cancel a training job. @@ -357,7 +363,8 @@ class PostTraining(Protocol): """ ... - @webmethod(route="/post-training/job/artifacts", method="GET") + @webmethod(route="/post-training/job/artifacts", method="GET", level=LLAMA_STACK_API_V1, deprecated=True) + @webmethod(route="/post-training/job/artifacts", method="GET", level=LLAMA_STACK_API_V1ALPHA) async def get_training_job_artifacts(self, job_uuid: str) -> PostTrainingJobArtifactsResponse: """Get the artifacts of a training job. diff --git a/llama_stack/apis/prompts/__init__.py b/llama_stack/apis/prompts/__init__.py new file mode 100644 index 000000000..6070f3450 --- /dev/null +++ b/llama_stack/apis/prompts/__init__.py @@ -0,0 +1,9 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the terms described in the LICENSE file in +# the root directory of this source tree. + +from .prompts import ListPromptsResponse, Prompt, Prompts + +__all__ = ["Prompt", "Prompts", "ListPromptsResponse"] diff --git a/llama_stack/apis/prompts/prompts.py b/llama_stack/apis/prompts/prompts.py new file mode 100644 index 000000000..c56185e25 --- /dev/null +++ b/llama_stack/apis/prompts/prompts.py @@ -0,0 +1,190 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the terms described in the LICENSE file in +# the root directory of this source tree. + +import re +import secrets +from typing import Protocol, runtime_checkable + +from pydantic import BaseModel, Field, field_validator, model_validator + +from llama_stack.apis.version import LLAMA_STACK_API_V1 +from llama_stack.providers.utils.telemetry.trace_protocol import trace_protocol +from llama_stack.schema_utils import json_schema_type, webmethod + + +@json_schema_type +class Prompt(BaseModel): + """A prompt resource representing a stored OpenAI Compatible prompt template in Llama Stack. + + :param prompt: The system prompt text with variable placeholders. Variables are only supported when using the Responses API. + :param version: Version (integer starting at 1, incremented on save) + :param prompt_id: Unique identifier formatted as 'pmpt_<48-digit-hash>' + :param variables: List of prompt variable names that can be used in the prompt template + :param is_default: Boolean indicating whether this version is the default version for this prompt + """ + + prompt: str | None = Field(default=None, description="The system prompt with variable placeholders") + version: int = Field(description="Version (integer starting at 1, incremented on save)", ge=1) + prompt_id: str = Field(description="Unique identifier in format 'pmpt_<48-digit-hash>'") + variables: list[str] = Field( + default_factory=list, description="List of variable names that can be used in the prompt template" + ) + is_default: bool = Field( + default=False, description="Boolean indicating whether this version is the default version" + ) + + @field_validator("prompt_id") + @classmethod + def validate_prompt_id(cls, prompt_id: str) -> str: + if not isinstance(prompt_id, str): + raise TypeError("prompt_id must be a string in format 'pmpt_<48-digit-hash>'") + + if not prompt_id.startswith("pmpt_"): + raise ValueError("prompt_id must start with 'pmpt_' prefix") + + hex_part = prompt_id[5:] + if len(hex_part) != 48: + raise ValueError("prompt_id must be in format 'pmpt_<48-digit-hash>' (48 lowercase hex chars)") + + for char in hex_part: + if char not in "0123456789abcdef": + raise ValueError("prompt_id hex part must contain only lowercase hex characters [0-9a-f]") + + return prompt_id + + @field_validator("version") + @classmethod + def validate_version(cls, prompt_version: int) -> int: + if prompt_version < 1: + raise ValueError("version must be >= 1") + return prompt_version + + @model_validator(mode="after") + def validate_prompt_variables(self): + """Validate that all variables used in the prompt are declared in the variables list.""" + if not self.prompt: + return self + + prompt_variables = set(re.findall(r"{{\s*(\w+)\s*}}", self.prompt)) + declared_variables = set(self.variables) + + undeclared = prompt_variables - declared_variables + if undeclared: + raise ValueError(f"Prompt contains undeclared variables: {sorted(undeclared)}") + + return self + + @classmethod + def generate_prompt_id(cls) -> str: + # Generate 48 hex characters (24 bytes) + random_bytes = secrets.token_bytes(24) + hex_string = random_bytes.hex() + return f"pmpt_{hex_string}" + + +class ListPromptsResponse(BaseModel): + """Response model to list prompts.""" + + data: list[Prompt] + + +@runtime_checkable +@trace_protocol +class Prompts(Protocol): + """Protocol for prompt management operations.""" + + @webmethod(route="/prompts", method="GET", level=LLAMA_STACK_API_V1) + async def list_prompts(self) -> ListPromptsResponse: + """List all prompts. + + :returns: A ListPromptsResponse containing all prompts. + """ + ... + + @webmethod(route="/prompts/{prompt_id}/versions", method="GET", level=LLAMA_STACK_API_V1) + async def list_prompt_versions( + self, + prompt_id: str, + ) -> ListPromptsResponse: + """List all versions of a specific prompt. + + :param prompt_id: The identifier of the prompt to list versions for. + :returns: A ListPromptsResponse containing all versions of the prompt. + """ + ... + + @webmethod(route="/prompts/{prompt_id}", method="GET", level=LLAMA_STACK_API_V1) + async def get_prompt( + self, + prompt_id: str, + version: int | None = None, + ) -> Prompt: + """Get a prompt by its identifier and optional version. + + :param prompt_id: The identifier of the prompt to get. + :param version: The version of the prompt to get (defaults to latest). + :returns: A Prompt resource. + """ + ... + + @webmethod(route="/prompts", method="POST", level=LLAMA_STACK_API_V1) + async def create_prompt( + self, + prompt: str, + variables: list[str] | None = None, + ) -> Prompt: + """Create a new prompt. + + :param prompt: The prompt text content with variable placeholders. + :param variables: List of variable names that can be used in the prompt template. + :returns: The created Prompt resource. + """ + ... + + @webmethod(route="/prompts/{prompt_id}", method="PUT", level=LLAMA_STACK_API_V1) + async def update_prompt( + self, + prompt_id: str, + prompt: str, + version: int, + variables: list[str] | None = None, + set_as_default: bool = True, + ) -> Prompt: + """Update an existing prompt (increments version). + + :param prompt_id: The identifier of the prompt to update. + :param prompt: The updated prompt text content. + :param version: The current version of the prompt being updated. + :param variables: Updated list of variable names that can be used in the prompt template. + :param set_as_default: Set the new version as the default (default=True). + :returns: The updated Prompt resource with incremented version. + """ + ... + + @webmethod(route="/prompts/{prompt_id}", method="DELETE", level=LLAMA_STACK_API_V1) + async def delete_prompt( + self, + prompt_id: str, + ) -> None: + """Delete a prompt. + + :param prompt_id: The identifier of the prompt to delete. + """ + ... + + @webmethod(route="/prompts/{prompt_id}/set-default-version", method="PUT", level=LLAMA_STACK_API_V1) + async def set_default_version( + self, + prompt_id: str, + version: int, + ) -> Prompt: + """Set which version of a prompt should be the default in get_prompt (latest). + + :param prompt_id: The identifier of the prompt. + :param version: The version to set as default. + :returns: The prompt with the specified version now set as default. + """ + ... diff --git a/llama_stack/apis/providers/providers.py b/llama_stack/apis/providers/providers.py index 8a1e93d8f..d1cff0f6c 100644 --- a/llama_stack/apis/providers/providers.py +++ b/llama_stack/apis/providers/providers.py @@ -8,6 +8,7 @@ from typing import Any, Protocol, runtime_checkable from pydantic import BaseModel +from llama_stack.apis.version import LLAMA_STACK_API_V1 from llama_stack.providers.datatypes import HealthResponse from llama_stack.schema_utils import json_schema_type, webmethod @@ -45,7 +46,7 @@ class Providers(Protocol): Providers API for inspecting, listing, and modifying providers and their configurations. """ - @webmethod(route="/providers", method="GET") + @webmethod(route="/providers", method="GET", level=LLAMA_STACK_API_V1) async def list_providers(self) -> ListProvidersResponse: """List all available providers. @@ -53,7 +54,7 @@ class Providers(Protocol): """ ... - @webmethod(route="/providers/{provider_id}", method="GET") + @webmethod(route="/providers/{provider_id}", method="GET", level=LLAMA_STACK_API_V1) async def inspect_provider(self, provider_id: str) -> ProviderInfo: """Get detailed information about a specific provider. diff --git a/llama_stack/apis/resource.py b/llama_stack/apis/resource.py index 3731fbf1d..7c4130f7d 100644 --- a/llama_stack/apis/resource.py +++ b/llama_stack/apis/resource.py @@ -19,6 +19,7 @@ class ResourceType(StrEnum): benchmark = "benchmark" tool = "tool" tool_group = "tool_group" + prompt = "prompt" class Resource(BaseModel): diff --git a/llama_stack/apis/safety/safety.py b/llama_stack/apis/safety/safety.py index 25ee03ec1..98367e9b0 100644 --- a/llama_stack/apis/safety/safety.py +++ b/llama_stack/apis/safety/safety.py @@ -11,6 +11,7 @@ from pydantic import BaseModel, Field from llama_stack.apis.inference import Message from llama_stack.apis.shields import Shield +from llama_stack.apis.version import LLAMA_STACK_API_V1 from llama_stack.providers.utils.telemetry.trace_protocol import trace_protocol from llama_stack.schema_utils import json_schema_type, webmethod @@ -97,7 +98,7 @@ class ShieldStore(Protocol): class Safety(Protocol): shield_store: ShieldStore - @webmethod(route="/safety/run-shield", method="POST") + @webmethod(route="/safety/run-shield", method="POST", level=LLAMA_STACK_API_V1) async def run_shield( self, shield_id: str, @@ -113,7 +114,7 @@ class Safety(Protocol): """ ... - @webmethod(route="/openai/v1/moderations", method="POST") + @webmethod(route="/openai/v1/moderations", method="POST", level=LLAMA_STACK_API_V1) async def run_moderation(self, input: str | list[str], model: str) -> ModerationObject: """Classifies if text and/or image inputs are potentially harmful. :param input: Input (or inputs) to classify. diff --git a/llama_stack/apis/scoring/scoring.py b/llama_stack/apis/scoring/scoring.py index 8ca599b44..03d943e94 100644 --- a/llama_stack/apis/scoring/scoring.py +++ b/llama_stack/apis/scoring/scoring.py @@ -9,6 +9,7 @@ from typing import Any, Protocol, runtime_checkable from pydantic import BaseModel from llama_stack.apis.scoring_functions import ScoringFn, ScoringFnParams +from llama_stack.apis.version import LLAMA_STACK_API_V1 from llama_stack.schema_utils import json_schema_type, webmethod # mapping of metric to value @@ -61,7 +62,7 @@ class ScoringFunctionStore(Protocol): class Scoring(Protocol): scoring_function_store: ScoringFunctionStore - @webmethod(route="/scoring/score-batch", method="POST") + @webmethod(route="/scoring/score-batch", method="POST", level=LLAMA_STACK_API_V1) async def score_batch( self, dataset_id: str, @@ -77,7 +78,7 @@ class Scoring(Protocol): """ ... - @webmethod(route="/scoring/score", method="POST") + @webmethod(route="/scoring/score", method="POST", level=LLAMA_STACK_API_V1) async def score( self, input_rows: list[dict[str, Any]], diff --git a/llama_stack/apis/scoring_functions/scoring_functions.py b/llama_stack/apis/scoring_functions/scoring_functions.py index 05b6325b7..fe49723ab 100644 --- a/llama_stack/apis/scoring_functions/scoring_functions.py +++ b/llama_stack/apis/scoring_functions/scoring_functions.py @@ -18,6 +18,7 @@ from pydantic import BaseModel, Field from llama_stack.apis.common.type_system import ParamType from llama_stack.apis.resource import Resource, ResourceType +from llama_stack.apis.version import LLAMA_STACK_API_V1 from llama_stack.schema_utils import json_schema_type, register_schema, webmethod @@ -160,7 +161,7 @@ class ListScoringFunctionsResponse(BaseModel): @runtime_checkable class ScoringFunctions(Protocol): - @webmethod(route="/scoring-functions", method="GET") + @webmethod(route="/scoring-functions", method="GET", level=LLAMA_STACK_API_V1) async def list_scoring_functions(self) -> ListScoringFunctionsResponse: """List all scoring functions. @@ -168,7 +169,7 @@ class ScoringFunctions(Protocol): """ ... - @webmethod(route="/scoring-functions/{scoring_fn_id:path}", method="GET") + @webmethod(route="/scoring-functions/{scoring_fn_id:path}", method="GET", level=LLAMA_STACK_API_V1) async def get_scoring_function(self, scoring_fn_id: str, /) -> ScoringFn: """Get a scoring function by its ID. @@ -177,7 +178,7 @@ class ScoringFunctions(Protocol): """ ... - @webmethod(route="/scoring-functions", method="POST") + @webmethod(route="/scoring-functions", method="POST", level=LLAMA_STACK_API_V1) async def register_scoring_function( self, scoring_fn_id: str, @@ -197,3 +198,11 @@ class ScoringFunctions(Protocol): :param params: The parameters for the scoring function for benchmark eval, these can be overridden for app eval. """ ... + + @webmethod(route="/scoring-functions/{scoring_fn_id:path}", method="DELETE", level=LLAMA_STACK_API_V1) + async def unregister_scoring_function(self, scoring_fn_id: str) -> None: + """Unregister a scoring function. + + :param scoring_fn_id: The ID of the scoring function to unregister. + """ + ... diff --git a/llama_stack/apis/shields/shields.py b/llama_stack/apis/shields/shields.py index ec1b85349..5d967cf02 100644 --- a/llama_stack/apis/shields/shields.py +++ b/llama_stack/apis/shields/shields.py @@ -9,6 +9,7 @@ from typing import Any, Literal, Protocol, runtime_checkable from pydantic import BaseModel from llama_stack.apis.resource import Resource, ResourceType +from llama_stack.apis.version import LLAMA_STACK_API_V1 from llama_stack.providers.utils.telemetry.trace_protocol import trace_protocol from llama_stack.schema_utils import json_schema_type, webmethod @@ -49,7 +50,7 @@ class ListShieldsResponse(BaseModel): @runtime_checkable @trace_protocol class Shields(Protocol): - @webmethod(route="/shields", method="GET") + @webmethod(route="/shields", method="GET", level=LLAMA_STACK_API_V1) async def list_shields(self) -> ListShieldsResponse: """List all shields. @@ -57,7 +58,7 @@ class Shields(Protocol): """ ... - @webmethod(route="/shields/{identifier:path}", method="GET") + @webmethod(route="/shields/{identifier:path}", method="GET", level=LLAMA_STACK_API_V1) async def get_shield(self, identifier: str) -> Shield: """Get a shield by its identifier. @@ -66,7 +67,7 @@ class Shields(Protocol): """ ... - @webmethod(route="/shields", method="POST") + @webmethod(route="/shields", method="POST", level=LLAMA_STACK_API_V1) async def register_shield( self, shield_id: str, @@ -84,7 +85,7 @@ class Shields(Protocol): """ ... - @webmethod(route="/shields/{identifier:path}", method="DELETE") + @webmethod(route="/shields/{identifier:path}", method="DELETE", level=LLAMA_STACK_API_V1) async def unregister_shield(self, identifier: str) -> None: """Unregister a shield. diff --git a/llama_stack/apis/synthetic_data_generation/synthetic_data_generation.py b/llama_stack/apis/synthetic_data_generation/synthetic_data_generation.py index a7af44b28..c13e2c17c 100644 --- a/llama_stack/apis/synthetic_data_generation/synthetic_data_generation.py +++ b/llama_stack/apis/synthetic_data_generation/synthetic_data_generation.py @@ -10,6 +10,7 @@ from typing import Any, Protocol from pydantic import BaseModel from llama_stack.apis.inference import Message +from llama_stack.apis.version import LLAMA_STACK_API_V1 from llama_stack.schema_utils import json_schema_type, webmethod @@ -59,7 +60,7 @@ class SyntheticDataGenerationResponse(BaseModel): class SyntheticDataGeneration(Protocol): - @webmethod(route="/synthetic-data-generation/generate") + @webmethod(route="/synthetic-data-generation/generate", level=LLAMA_STACK_API_V1) def synthetic_data_generate( self, dialogs: list[Message], diff --git a/llama_stack/apis/telemetry/telemetry.py b/llama_stack/apis/telemetry/telemetry.py index 8d1b5d697..29dd23989 100644 --- a/llama_stack/apis/telemetry/telemetry.py +++ b/llama_stack/apis/telemetry/telemetry.py @@ -16,6 +16,7 @@ from typing import ( from pydantic import BaseModel, Field +from llama_stack.apis.version import LLAMA_STACK_API_V1 from llama_stack.models.llama.datatypes import Primitive from llama_stack.schema_utils import json_schema_type, register_schema, webmethod @@ -412,7 +413,7 @@ class QueryMetricsResponse(BaseModel): @runtime_checkable class Telemetry(Protocol): - @webmethod(route="/telemetry/events", method="POST") + @webmethod(route="/telemetry/events", method="POST", level=LLAMA_STACK_API_V1) async def log_event( self, event: Event, @@ -425,7 +426,7 @@ class Telemetry(Protocol): """ ... - @webmethod(route="/telemetry/traces", method="POST", required_scope=REQUIRED_SCOPE) + @webmethod(route="/telemetry/traces", method="POST", required_scope=REQUIRED_SCOPE, level=LLAMA_STACK_API_V1) async def query_traces( self, attribute_filters: list[QueryCondition] | None = None, @@ -443,7 +444,9 @@ class Telemetry(Protocol): """ ... - @webmethod(route="/telemetry/traces/{trace_id:path}", method="GET", required_scope=REQUIRED_SCOPE) + @webmethod( + route="/telemetry/traces/{trace_id:path}", method="GET", required_scope=REQUIRED_SCOPE, level=LLAMA_STACK_API_V1 + ) async def get_trace(self, trace_id: str) -> Trace: """Get a trace by its ID. @@ -453,7 +456,10 @@ class Telemetry(Protocol): ... @webmethod( - route="/telemetry/traces/{trace_id:path}/spans/{span_id:path}", method="GET", required_scope=REQUIRED_SCOPE + route="/telemetry/traces/{trace_id:path}/spans/{span_id:path}", + method="GET", + required_scope=REQUIRED_SCOPE, + level=LLAMA_STACK_API_V1, ) async def get_span(self, trace_id: str, span_id: str) -> Span: """Get a span by its ID. @@ -464,7 +470,12 @@ class Telemetry(Protocol): """ ... - @webmethod(route="/telemetry/spans/{span_id:path}/tree", method="POST", required_scope=REQUIRED_SCOPE) + @webmethod( + route="/telemetry/spans/{span_id:path}/tree", + method="POST", + required_scope=REQUIRED_SCOPE, + level=LLAMA_STACK_API_V1, + ) async def get_span_tree( self, span_id: str, @@ -480,7 +491,7 @@ class Telemetry(Protocol): """ ... - @webmethod(route="/telemetry/spans", method="POST", required_scope=REQUIRED_SCOPE) + @webmethod(route="/telemetry/spans", method="POST", required_scope=REQUIRED_SCOPE, level=LLAMA_STACK_API_V1) async def query_spans( self, attribute_filters: list[QueryCondition], @@ -496,7 +507,7 @@ class Telemetry(Protocol): """ ... - @webmethod(route="/telemetry/spans/export", method="POST") + @webmethod(route="/telemetry/spans/export", method="POST", level=LLAMA_STACK_API_V1) async def save_spans_to_dataset( self, attribute_filters: list[QueryCondition], @@ -513,7 +524,9 @@ class Telemetry(Protocol): """ ... - @webmethod(route="/telemetry/metrics/{metric_name}", method="POST", required_scope=REQUIRED_SCOPE) + @webmethod( + route="/telemetry/metrics/{metric_name}", method="POST", required_scope=REQUIRED_SCOPE, level=LLAMA_STACK_API_V1 + ) async def query_metrics( self, metric_name: str, diff --git a/llama_stack/apis/tools/rag_tool.py b/llama_stack/apis/tools/rag_tool.py index 651016bd1..ed7847e23 100644 --- a/llama_stack/apis/tools/rag_tool.py +++ b/llama_stack/apis/tools/rag_tool.py @@ -11,6 +11,7 @@ from pydantic import BaseModel, Field, field_validator from typing_extensions import runtime_checkable from llama_stack.apis.common.content_types import URL, InterleavedContent +from llama_stack.apis.version import LLAMA_STACK_API_V1 from llama_stack.providers.utils.telemetry.trace_protocol import trace_protocol from llama_stack.schema_utils import json_schema_type, register_schema, webmethod @@ -185,7 +186,7 @@ class RAGQueryConfig(BaseModel): @runtime_checkable @trace_protocol class RAGToolRuntime(Protocol): - @webmethod(route="/tool-runtime/rag-tool/insert", method="POST") + @webmethod(route="/tool-runtime/rag-tool/insert", method="POST", level=LLAMA_STACK_API_V1) async def insert( self, documents: list[RAGDocument], @@ -200,7 +201,7 @@ class RAGToolRuntime(Protocol): """ ... - @webmethod(route="/tool-runtime/rag-tool/query", method="POST") + @webmethod(route="/tool-runtime/rag-tool/query", method="POST", level=LLAMA_STACK_API_V1) async def query( self, content: InterleavedContent, diff --git a/llama_stack/apis/tools/tools.py b/llama_stack/apis/tools/tools.py index 52b86375a..12bd9efa2 100644 --- a/llama_stack/apis/tools/tools.py +++ b/llama_stack/apis/tools/tools.py @@ -12,6 +12,7 @@ from typing_extensions import runtime_checkable from llama_stack.apis.common.content_types import URL, InterleavedContent from llama_stack.apis.resource import Resource, ResourceType +from llama_stack.apis.version import LLAMA_STACK_API_V1 from llama_stack.providers.utils.telemetry.trace_protocol import trace_protocol from llama_stack.schema_utils import json_schema_type, webmethod @@ -151,7 +152,7 @@ class ListToolDefsResponse(BaseModel): @runtime_checkable @trace_protocol class ToolGroups(Protocol): - @webmethod(route="/toolgroups", method="POST") + @webmethod(route="/toolgroups", method="POST", level=LLAMA_STACK_API_V1) async def register_tool_group( self, toolgroup_id: str, @@ -168,7 +169,7 @@ class ToolGroups(Protocol): """ ... - @webmethod(route="/toolgroups/{toolgroup_id:path}", method="GET") + @webmethod(route="/toolgroups/{toolgroup_id:path}", method="GET", level=LLAMA_STACK_API_V1) async def get_tool_group( self, toolgroup_id: str, @@ -180,7 +181,7 @@ class ToolGroups(Protocol): """ ... - @webmethod(route="/toolgroups", method="GET") + @webmethod(route="/toolgroups", method="GET", level=LLAMA_STACK_API_V1) async def list_tool_groups(self) -> ListToolGroupsResponse: """List tool groups with optional provider. @@ -188,7 +189,7 @@ class ToolGroups(Protocol): """ ... - @webmethod(route="/tools", method="GET") + @webmethod(route="/tools", method="GET", level=LLAMA_STACK_API_V1) async def list_tools(self, toolgroup_id: str | None = None) -> ListToolsResponse: """List tools with optional tool group. @@ -197,7 +198,7 @@ class ToolGroups(Protocol): """ ... - @webmethod(route="/tools/{tool_name:path}", method="GET") + @webmethod(route="/tools/{tool_name:path}", method="GET", level=LLAMA_STACK_API_V1) async def get_tool( self, tool_name: str, @@ -209,7 +210,7 @@ class ToolGroups(Protocol): """ ... - @webmethod(route="/toolgroups/{toolgroup_id:path}", method="DELETE") + @webmethod(route="/toolgroups/{toolgroup_id:path}", method="DELETE", level=LLAMA_STACK_API_V1) async def unregister_toolgroup( self, toolgroup_id: str, @@ -238,7 +239,7 @@ class ToolRuntime(Protocol): rag_tool: RAGToolRuntime | None = None # TODO: This needs to be renamed once OPEN API generator name conflict issue is fixed. - @webmethod(route="/tool-runtime/list-tools", method="GET") + @webmethod(route="/tool-runtime/list-tools", method="GET", level=LLAMA_STACK_API_V1) async def list_runtime_tools( self, tool_group_id: str | None = None, mcp_endpoint: URL | None = None ) -> ListToolDefsResponse: @@ -250,7 +251,7 @@ class ToolRuntime(Protocol): """ ... - @webmethod(route="/tool-runtime/invoke", method="POST") + @webmethod(route="/tool-runtime/invoke", method="POST", level=LLAMA_STACK_API_V1) async def invoke_tool(self, tool_name: str, kwargs: dict[str, Any]) -> ToolInvocationResult: """Run a tool with the given arguments. diff --git a/llama_stack/apis/vector_dbs/vector_dbs.py b/llama_stack/apis/vector_dbs/vector_dbs.py index 47820fa0f..521d129c6 100644 --- a/llama_stack/apis/vector_dbs/vector_dbs.py +++ b/llama_stack/apis/vector_dbs/vector_dbs.py @@ -9,6 +9,7 @@ from typing import Literal, Protocol, runtime_checkable from pydantic import BaseModel from llama_stack.apis.resource import Resource, ResourceType +from llama_stack.apis.version import LLAMA_STACK_API_V1 from llama_stack.providers.utils.telemetry.trace_protocol import trace_protocol from llama_stack.schema_utils import json_schema_type, webmethod @@ -65,7 +66,7 @@ class ListVectorDBsResponse(BaseModel): @runtime_checkable @trace_protocol class VectorDBs(Protocol): - @webmethod(route="/vector-dbs", method="GET") + @webmethod(route="/vector-dbs", method="GET", level=LLAMA_STACK_API_V1) async def list_vector_dbs(self) -> ListVectorDBsResponse: """List all vector databases. @@ -73,7 +74,7 @@ class VectorDBs(Protocol): """ ... - @webmethod(route="/vector-dbs/{vector_db_id:path}", method="GET") + @webmethod(route="/vector-dbs/{vector_db_id:path}", method="GET", level=LLAMA_STACK_API_V1) async def get_vector_db( self, vector_db_id: str, @@ -85,7 +86,7 @@ class VectorDBs(Protocol): """ ... - @webmethod(route="/vector-dbs", method="POST") + @webmethod(route="/vector-dbs", method="POST", level=LLAMA_STACK_API_V1) async def register_vector_db( self, vector_db_id: str, @@ -107,7 +108,7 @@ class VectorDBs(Protocol): """ ... - @webmethod(route="/vector-dbs/{vector_db_id:path}", method="DELETE") + @webmethod(route="/vector-dbs/{vector_db_id:path}", method="DELETE", level=LLAMA_STACK_API_V1) async def unregister_vector_db(self, vector_db_id: str) -> None: """Unregister a vector database. diff --git a/llama_stack/apis/vector_io/vector_io.py b/llama_stack/apis/vector_io/vector_io.py index 3e8065cfb..2850863c4 100644 --- a/llama_stack/apis/vector_io/vector_io.py +++ b/llama_stack/apis/vector_io/vector_io.py @@ -15,6 +15,7 @@ from pydantic import BaseModel, Field from llama_stack.apis.inference import InterleavedContent from llama_stack.apis.vector_dbs import VectorDB +from llama_stack.apis.version import LLAMA_STACK_API_V1 from llama_stack.providers.utils.telemetry.trace_protocol import trace_protocol from llama_stack.providers.utils.vector_io.vector_utils import generate_chunk_id from llama_stack.schema_utils import json_schema_type, webmethod @@ -437,7 +438,7 @@ class VectorIO(Protocol): # this will just block now until chunks are inserted, but it should # probably return a Job instance which can be polled for completion - @webmethod(route="/vector-io/insert", method="POST") + @webmethod(route="/vector-io/insert", method="POST", level=LLAMA_STACK_API_V1) async def insert_chunks( self, vector_db_id: str, @@ -455,7 +456,7 @@ class VectorIO(Protocol): """ ... - @webmethod(route="/vector-io/query", method="POST") + @webmethod(route="/vector-io/query", method="POST", level=LLAMA_STACK_API_V1) async def query_chunks( self, vector_db_id: str, @@ -472,7 +473,7 @@ class VectorIO(Protocol): ... # OpenAI Vector Stores API endpoints - @webmethod(route="/openai/v1/vector_stores", method="POST") + @webmethod(route="/openai/v1/vector_stores", method="POST", level=LLAMA_STACK_API_V1) async def openai_create_vector_store( self, name: str | None = None, @@ -498,7 +499,7 @@ class VectorIO(Protocol): """ ... - @webmethod(route="/openai/v1/vector_stores", method="GET") + @webmethod(route="/openai/v1/vector_stores", method="GET", level=LLAMA_STACK_API_V1) async def openai_list_vector_stores( self, limit: int | None = 20, @@ -516,7 +517,7 @@ class VectorIO(Protocol): """ ... - @webmethod(route="/openai/v1/vector_stores/{vector_store_id}", method="GET") + @webmethod(route="/openai/v1/vector_stores/{vector_store_id}", method="GET", level=LLAMA_STACK_API_V1) async def openai_retrieve_vector_store( self, vector_store_id: str, @@ -528,7 +529,7 @@ class VectorIO(Protocol): """ ... - @webmethod(route="/openai/v1/vector_stores/{vector_store_id}", method="POST") + @webmethod(route="/openai/v1/vector_stores/{vector_store_id}", method="POST", level=LLAMA_STACK_API_V1) async def openai_update_vector_store( self, vector_store_id: str, @@ -546,7 +547,7 @@ class VectorIO(Protocol): """ ... - @webmethod(route="/openai/v1/vector_stores/{vector_store_id}", method="DELETE") + @webmethod(route="/openai/v1/vector_stores/{vector_store_id}", method="DELETE", level=LLAMA_STACK_API_V1) async def openai_delete_vector_store( self, vector_store_id: str, @@ -558,7 +559,7 @@ class VectorIO(Protocol): """ ... - @webmethod(route="/openai/v1/vector_stores/{vector_store_id}/search", method="POST") + @webmethod(route="/openai/v1/vector_stores/{vector_store_id}/search", method="POST", level=LLAMA_STACK_API_V1) async def openai_search_vector_store( self, vector_store_id: str, @@ -584,7 +585,7 @@ class VectorIO(Protocol): """ ... - @webmethod(route="/openai/v1/vector_stores/{vector_store_id}/files", method="POST") + @webmethod(route="/openai/v1/vector_stores/{vector_store_id}/files", method="POST", level=LLAMA_STACK_API_V1) async def openai_attach_file_to_vector_store( self, vector_store_id: str, @@ -602,7 +603,7 @@ class VectorIO(Protocol): """ ... - @webmethod(route="/openai/v1/vector_stores/{vector_store_id}/files", method="GET") + @webmethod(route="/openai/v1/vector_stores/{vector_store_id}/files", method="GET", level=LLAMA_STACK_API_V1) async def openai_list_files_in_vector_store( self, vector_store_id: str, @@ -624,7 +625,9 @@ class VectorIO(Protocol): """ ... - @webmethod(route="/openai/v1/vector_stores/{vector_store_id}/files/{file_id}", method="GET") + @webmethod( + route="/openai/v1/vector_stores/{vector_store_id}/files/{file_id}", method="GET", level=LLAMA_STACK_API_V1 + ) async def openai_retrieve_vector_store_file( self, vector_store_id: str, @@ -638,7 +641,11 @@ class VectorIO(Protocol): """ ... - @webmethod(route="/openai/v1/vector_stores/{vector_store_id}/files/{file_id}/content", method="GET") + @webmethod( + route="/openai/v1/vector_stores/{vector_store_id}/files/{file_id}/content", + method="GET", + level=LLAMA_STACK_API_V1, + ) async def openai_retrieve_vector_store_file_contents( self, vector_store_id: str, @@ -652,7 +659,9 @@ class VectorIO(Protocol): """ ... - @webmethod(route="/openai/v1/vector_stores/{vector_store_id}/files/{file_id}", method="POST") + @webmethod( + route="/openai/v1/vector_stores/{vector_store_id}/files/{file_id}", method="POST", level=LLAMA_STACK_API_V1 + ) async def openai_update_vector_store_file( self, vector_store_id: str, @@ -668,7 +677,9 @@ class VectorIO(Protocol): """ ... - @webmethod(route="/openai/v1/vector_stores/{vector_store_id}/files/{file_id}", method="DELETE") + @webmethod( + route="/openai/v1/vector_stores/{vector_store_id}/files/{file_id}", method="DELETE", level=LLAMA_STACK_API_V1 + ) async def openai_delete_vector_store_file( self, vector_store_id: str, diff --git a/llama_stack/apis/version.py b/llama_stack/apis/version.py index 53ad6a854..6af039b1f 100644 --- a/llama_stack/apis/version.py +++ b/llama_stack/apis/version.py @@ -4,4 +4,6 @@ # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. -LLAMA_STACK_API_VERSION = "v1" +LLAMA_STACK_API_V1 = "v1" +LLAMA_STACK_API_V1BETA = "v1beta" +LLAMA_STACK_API_V1ALPHA = "v1alpha" diff --git a/llama_stack/cli/stack/_build.py b/llama_stack/cli/stack/_build.py index c6e204773..b14e6fe55 100644 --- a/llama_stack/cli/stack/_build.py +++ b/llama_stack/cli/stack/_build.py @@ -45,6 +45,7 @@ from llama_stack.core.utils.dynamic import instantiate_class_type from llama_stack.core.utils.exec import formulate_run_args, run_command from llama_stack.core.utils.image_types import LlamaStackImageType from llama_stack.providers.datatypes import Api +from llama_stack.providers.utils.sqlstore.sqlstore import SqliteSqlStoreConfig DISTRIBS_PATH = Path(__file__).parent.parent.parent / "distributions" @@ -294,6 +295,12 @@ def _generate_run_config( if build_config.external_providers_dir else EXTERNAL_PROVIDERS_DIR, ) + if not run_config.inference_store: + run_config.inference_store = SqliteSqlStoreConfig( + **SqliteSqlStoreConfig.sample_run_config( + __distro_dir__=(DISTRIBS_BASE_DIR / image_name).as_posix(), db_name="inference_store.db" + ) + ) # build providers dict provider_registry = get_provider_registry(build_config) for api in apis: diff --git a/llama_stack/cli/verify_download.py b/llama_stack/cli/verify_download.py index b7f4cfdb5..e738abb4f 100644 --- a/llama_stack/cli/verify_download.py +++ b/llama_stack/cli/verify_download.py @@ -48,15 +48,12 @@ def setup_verify_download_parser(parser: argparse.ArgumentParser) -> None: parser.set_defaults(func=partial(run_verify_cmd, parser=parser)) -def calculate_md5(filepath: Path, chunk_size: int = 8192) -> str: - # NOTE: MD5 is used here only for download integrity verification, - # not for security purposes - # TODO: switch to SHA256 - md5_hash = hashlib.md5(usedforsecurity=False) +def calculate_sha256(filepath: Path, chunk_size: int = 8192) -> str: + sha256_hash = hashlib.sha256() with open(filepath, "rb") as f: for chunk in iter(lambda: f.read(chunk_size), b""): - md5_hash.update(chunk) - return md5_hash.hexdigest() + sha256_hash.update(chunk) + return sha256_hash.hexdigest() def load_checksums(checklist_path: Path) -> dict[str, str]: @@ -64,10 +61,10 @@ def load_checksums(checklist_path: Path) -> dict[str, str]: with open(checklist_path) as f: for line in f: if line.strip(): - md5sum, filepath = line.strip().split(" ", 1) + sha256sum, filepath = line.strip().split(" ", 1) # Remove leading './' if present filepath = filepath.lstrip("./") - checksums[filepath] = md5sum + checksums[filepath] = sha256sum return checksums @@ -88,7 +85,7 @@ def verify_files(model_dir: Path, checksums: dict[str, str], console: Console) - matches = False if exists: - actual_hash = calculate_md5(full_path) + actual_hash = calculate_sha256(full_path) matches = actual_hash == expected_hash results.append( diff --git a/llama_stack/core/build_container.sh b/llama_stack/core/build_container.sh index 424b40a9d..8e47fc592 100755 --- a/llama_stack/core/build_container.sh +++ b/llama_stack/core/build_container.sh @@ -147,7 +147,7 @@ WORKDIR /app RUN dnf -y update && dnf install -y iputils git net-tools wget \ vim-minimal python3.12 python3.12-pip python3.12-wheel \ - python3.12-setuptools python3.12-devel gcc make && \ + python3.12-setuptools python3.12-devel gcc gcc-c++ make && \ ln -s /bin/pip3.12 /bin/pip && ln -s /bin/python3.12 /bin/python && dnf clean all ENV UV_SYSTEM_PYTHON=1 @@ -164,7 +164,7 @@ RUN apt-get update && apt-get install -y \ procps psmisc lsof \ traceroute \ bubblewrap \ - gcc \ + gcc g++ \ && rm -rf /var/lib/apt/lists/* ENV UV_SYSTEM_PYTHON=1 diff --git a/llama_stack/core/client.py b/llama_stack/core/client.py index 03e4fb051..49e01794e 100644 --- a/llama_stack/core/client.py +++ b/llama_stack/core/client.py @@ -15,7 +15,6 @@ import httpx from pydantic import BaseModel, parse_obj_as from termcolor import cprint -from llama_stack.apis.version import LLAMA_STACK_API_VERSION from llama_stack.providers.datatypes import RemoteProviderConfig _CLIENT_CLASSES = {} @@ -114,7 +113,24 @@ def create_api_client_class(protocol) -> type: break kwargs[param.name] = args[i] - url = f"{self.base_url}/{LLAMA_STACK_API_VERSION}/{webmethod.route.lstrip('/')}" + # Get all webmethods for this method (supports multiple decorators) + webmethods = getattr(method, "__webmethods__", []) + + if not webmethods: + raise RuntimeError(f"Method {method} has no webmethod decorators") + + # Choose the preferred webmethod (non-deprecated if available) + preferred_webmethod = None + for wm in webmethods: + if not getattr(wm, "deprecated", False): + preferred_webmethod = wm + break + + # If no non-deprecated found, use the first one + if preferred_webmethod is None: + preferred_webmethod = webmethods[0] + + url = f"{self.base_url}/{preferred_webmethod.level}/{preferred_webmethod.route.lstrip('/')}" def convert(value): if isinstance(value, list): diff --git a/llama_stack/core/datatypes.py b/llama_stack/core/datatypes.py index c3940fcbd..b5558c66f 100644 --- a/llama_stack/core/datatypes.py +++ b/llama_stack/core/datatypes.py @@ -7,6 +7,7 @@ from enum import StrEnum from pathlib import Path from typing import Annotated, Any, Literal, Self +from urllib.parse import urlparse from pydantic import BaseModel, Field, field_validator, model_validator @@ -120,10 +121,6 @@ class AutoRoutedProviderSpec(ProviderSpec): default=None, ) - @property - def pip_packages(self) -> list[str]: - raise AssertionError("Should not be called on AutoRoutedProviderSpec") - # Example: /models, /shields class RoutingTableProviderSpec(ProviderSpec): @@ -212,6 +209,7 @@ class AuthProviderType(StrEnum): OAUTH2_TOKEN = "oauth2_token" GITHUB_TOKEN = "github_token" CUSTOM = "custom" + KUBERNETES = "kubernetes" class OAuth2TokenAuthConfig(BaseModel): @@ -282,8 +280,45 @@ class GitHubTokenAuthConfig(BaseModel): ) +class KubernetesAuthProviderConfig(BaseModel): + """Configuration for Kubernetes authentication provider.""" + + type: Literal[AuthProviderType.KUBERNETES] = AuthProviderType.KUBERNETES + api_server_url: str = Field( + default="https://kubernetes.default.svc", + description="Kubernetes API server URL (e.g., https://api.cluster.domain:6443)", + ) + verify_tls: bool = Field(default=True, description="Whether to verify TLS certificates") + tls_cafile: Path | None = Field(default=None, description="Path to CA certificate file for TLS verification") + claims_mapping: dict[str, str] = Field( + default_factory=lambda: { + "username": "roles", + "groups": "roles", + }, + description="Mapping of Kubernetes user claims to access attributes", + ) + + @field_validator("api_server_url") + @classmethod + def validate_api_server_url(cls, v): + parsed = urlparse(v) + if not parsed.scheme or not parsed.netloc: + raise ValueError(f"api_server_url must be a valid URL with scheme and host: {v}") + if parsed.scheme not in ["http", "https"]: + raise ValueError(f"api_server_url scheme must be http or https: {v}") + return v + + @field_validator("claims_mapping") + @classmethod + def validate_claims_mapping(cls, v): + for key, value in v.items(): + if not value: + raise ValueError(f"claims_mapping value cannot be empty: {key}") + return v + + AuthProviderConfig = Annotated[ - OAuth2TokenAuthConfig | GitHubTokenAuthConfig | CustomAuthConfig, + OAuth2TokenAuthConfig | GitHubTokenAuthConfig | CustomAuthConfig | KubernetesAuthProviderConfig, Field(discriminator="type"), ] @@ -392,6 +427,12 @@ class ServerConfig(BaseModel): ) +class InferenceStoreConfig(BaseModel): + sql_store_config: SqlStoreConfig + max_write_queue_size: int = Field(default=10000, description="Max queued writes for inference store") + num_writers: int = Field(default=4, description="Number of concurrent background writers") + + class StackRunConfig(BaseModel): version: int = LLAMA_STACK_RUN_CONFIG_VERSION @@ -425,11 +466,12 @@ Configuration for the persistence store used by the distribution registry. If no a default SQLite store will be used.""", ) - inference_store: SqlStoreConfig | None = Field( + inference_store: InferenceStoreConfig | SqlStoreConfig | None = Field( default=None, description=""" -Configuration for the persistence store used by the inference API. If not specified, -a default SQLite store will be used.""", +Configuration for the persistence store used by the inference API. Can be either a +InferenceStoreConfig (with queue tuning parameters) or a SqlStoreConfig (deprecated). +If not specified, a default SQLite store will be used.""", ) # registry of "resources" in the distribution diff --git a/llama_stack/core/distribution.py b/llama_stack/core/distribution.py index 977eb5393..302ecb960 100644 --- a/llama_stack/core/distribution.py +++ b/llama_stack/core/distribution.py @@ -16,16 +16,18 @@ from llama_stack.core.datatypes import BuildConfig, DistributionSpec from llama_stack.core.external import load_external_apis from llama_stack.log import get_logger from llama_stack.providers.datatypes import ( - AdapterSpec, Api, InlineProviderSpec, ProviderSpec, - remote_provider_spec, + RemoteProviderSpec, ) logger = get_logger(name=__name__, category="core") +INTERNAL_APIS = {Api.inspect, Api.providers, Api.prompts} + + def stack_apis() -> list[Api]: return list(Api) @@ -70,31 +72,16 @@ def builtin_automatically_routed_apis() -> list[AutoRoutedApiInfo]: def providable_apis() -> list[Api]: routing_table_apis = {x.routing_table_api for x in builtin_automatically_routed_apis()} - return [api for api in Api if api not in routing_table_apis and api != Api.inspect and api != Api.providers] + return [api for api in Api if api not in routing_table_apis and api not in INTERNAL_APIS] def _load_remote_provider_spec(spec_data: dict[str, Any], api: Api) -> ProviderSpec: - adapter = AdapterSpec(**spec_data["adapter"]) - spec = remote_provider_spec( - api=api, - adapter=adapter, - api_dependencies=[Api(dep) for dep in spec_data.get("api_dependencies", [])], - ) + spec = RemoteProviderSpec(api=api, provider_type=f"remote::{spec_data['adapter_type']}", **spec_data) return spec def _load_inline_provider_spec(spec_data: dict[str, Any], api: Api, provider_name: str) -> ProviderSpec: - spec = InlineProviderSpec( - api=api, - provider_type=f"inline::{provider_name}", - pip_packages=spec_data.get("pip_packages", []), - module=spec_data["module"], - config_class=spec_data["config_class"], - api_dependencies=[Api(dep) for dep in spec_data.get("api_dependencies", [])], - optional_api_dependencies=[Api(dep) for dep in spec_data.get("optional_api_dependencies", [])], - provider_data_validator=spec_data.get("provider_data_validator"), - container_image=spec_data.get("container_image"), - ) + spec = InlineProviderSpec(api=api, provider_type=f"inline::{provider_name}", **spec_data) return spec diff --git a/llama_stack/core/library_client.py b/llama_stack/core/library_client.py index 9e7a8006c..e722e4de6 100644 --- a/llama_stack/core/library_client.py +++ b/llama_stack/core/library_client.py @@ -10,7 +10,6 @@ import json import logging # allow-direct-logging import os import sys -from concurrent.futures import ThreadPoolExecutor from enum import Enum from io import BytesIO from pathlib import Path @@ -41,7 +40,7 @@ from llama_stack.core.request_headers import ( from llama_stack.core.resolver import ProviderRegistry from llama_stack.core.server.routes import RouteImpls, find_matching_route, initialize_route_impls from llama_stack.core.stack import ( - construct_stack, + Stack, get_stack_run_config_from_distro, replace_env_vars, ) @@ -148,7 +147,6 @@ class LlamaStackAsLibraryClient(LlamaStackClient): self.async_client = AsyncLlamaStackAsLibraryClient( config_path_or_distro_name, custom_provider_registry, provider_data, skip_logger_removal ) - self.pool_executor = ThreadPoolExecutor(max_workers=4) self.provider_data = provider_data self.loop = asyncio.new_event_loop() @@ -254,7 +252,10 @@ class AsyncLlamaStackAsLibraryClient(AsyncLlamaStackClient): try: self.route_impls = None - self.impls = await construct_stack(self.config, self.custom_provider_registry) + + stack = Stack(self.config, self.custom_provider_registry) + await stack.initialize() + self.impls = stack.impls except ModuleNotFoundError as _e: cprint(_e.msg, color="red", file=sys.stderr) cprint( @@ -291,6 +292,7 @@ class AsyncLlamaStackAsLibraryClient(AsyncLlamaStackClient): ) raise _e + assert self.impls is not None if Api.telemetry in self.impls: setup_logger(self.impls[Api.telemetry]) diff --git a/llama_stack/providers/inline/scoring/basic/utils/bfcl/__init__.py b/llama_stack/core/prompts/__init__.py similarity index 100% rename from llama_stack/providers/inline/scoring/basic/utils/bfcl/__init__.py rename to llama_stack/core/prompts/__init__.py diff --git a/llama_stack/core/prompts/prompts.py b/llama_stack/core/prompts/prompts.py new file mode 100644 index 000000000..26e8f5cef --- /dev/null +++ b/llama_stack/core/prompts/prompts.py @@ -0,0 +1,233 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the terms described in the LICENSE file in +# the root directory of this source tree. + +import json +from typing import Any + +from pydantic import BaseModel + +from llama_stack.apis.prompts import ListPromptsResponse, Prompt, Prompts +from llama_stack.core.datatypes import StackRunConfig +from llama_stack.core.utils.config_dirs import DISTRIBS_BASE_DIR +from llama_stack.providers.utils.kvstore import KVStore, kvstore_impl +from llama_stack.providers.utils.kvstore.config import SqliteKVStoreConfig + + +class PromptServiceConfig(BaseModel): + """Configuration for the built-in prompt service. + + :param run_config: Stack run configuration containing distribution info + """ + + run_config: StackRunConfig + + +async def get_provider_impl(config: PromptServiceConfig, deps: dict[Any, Any]): + """Get the prompt service implementation.""" + impl = PromptServiceImpl(config, deps) + await impl.initialize() + return impl + + +class PromptServiceImpl(Prompts): + """Built-in prompt service implementation using KVStore.""" + + def __init__(self, config: PromptServiceConfig, deps: dict[Any, Any]): + self.config = config + self.deps = deps + self.kvstore: KVStore + + async def initialize(self) -> None: + kvstore_config = SqliteKVStoreConfig( + db_path=(DISTRIBS_BASE_DIR / self.config.run_config.image_name / "prompts.db").as_posix() + ) + self.kvstore = await kvstore_impl(kvstore_config) + + def _get_default_key(self, prompt_id: str) -> str: + """Get the KVStore key that stores the default version number.""" + return f"prompts:v1:{prompt_id}:default" + + async def _get_prompt_key(self, prompt_id: str, version: int | None = None) -> str: + """Get the KVStore key for prompt data, returning default version if applicable.""" + if version: + return self._get_version_key(prompt_id, str(version)) + + default_key = self._get_default_key(prompt_id) + resolved_version = await self.kvstore.get(default_key) + if resolved_version is None: + raise ValueError(f"Prompt {prompt_id}:default not found") + return self._get_version_key(prompt_id, resolved_version) + + def _get_version_key(self, prompt_id: str, version: str) -> str: + """Get the KVStore key for a specific prompt version.""" + return f"prompts:v1:{prompt_id}:{version}" + + def _get_list_key_prefix(self) -> str: + """Get the key prefix for listing prompts.""" + return "prompts:v1:" + + def _serialize_prompt(self, prompt: Prompt) -> str: + """Serialize a prompt to JSON string for storage.""" + return json.dumps( + { + "prompt_id": prompt.prompt_id, + "prompt": prompt.prompt, + "version": prompt.version, + "variables": prompt.variables or [], + "is_default": prompt.is_default, + } + ) + + def _deserialize_prompt(self, data: str) -> Prompt: + """Deserialize a prompt from JSON string.""" + obj = json.loads(data) + return Prompt( + prompt_id=obj["prompt_id"], + prompt=obj["prompt"], + version=obj["version"], + variables=obj.get("variables", []), + is_default=obj.get("is_default", False), + ) + + async def list_prompts(self) -> ListPromptsResponse: + """List all prompts (default versions only).""" + prefix = self._get_list_key_prefix() + keys = await self.kvstore.keys_in_range(prefix, prefix + "\xff") + + prompts = [] + for key in keys: + if key.endswith(":default"): + try: + default_version = await self.kvstore.get(key) + if default_version: + prompt_id = key.replace(prefix, "").replace(":default", "") + version_key = self._get_version_key(prompt_id, default_version) + data = await self.kvstore.get(version_key) + if data: + prompt = self._deserialize_prompt(data) + prompts.append(prompt) + except (json.JSONDecodeError, KeyError): + continue + + prompts.sort(key=lambda p: p.prompt_id or "", reverse=True) + return ListPromptsResponse(data=prompts) + + async def get_prompt(self, prompt_id: str, version: int | None = None) -> Prompt: + """Get a prompt by its identifier and optional version.""" + key = await self._get_prompt_key(prompt_id, version) + data = await self.kvstore.get(key) + if data is None: + raise ValueError(f"Prompt {prompt_id}:{version if version else 'default'} not found") + return self._deserialize_prompt(data) + + async def create_prompt( + self, + prompt: str, + variables: list[str] | None = None, + ) -> Prompt: + """Create a new prompt.""" + if variables is None: + variables = [] + + prompt_obj = Prompt( + prompt_id=Prompt.generate_prompt_id(), + prompt=prompt, + version=1, + variables=variables, + ) + + version_key = self._get_version_key(prompt_obj.prompt_id, str(prompt_obj.version)) + data = self._serialize_prompt(prompt_obj) + await self.kvstore.set(version_key, data) + + default_key = self._get_default_key(prompt_obj.prompt_id) + await self.kvstore.set(default_key, str(prompt_obj.version)) + + return prompt_obj + + async def update_prompt( + self, + prompt_id: str, + prompt: str, + version: int, + variables: list[str] | None = None, + set_as_default: bool = True, + ) -> Prompt: + """Update an existing prompt (increments version).""" + if version < 1: + raise ValueError("Version must be >= 1") + if variables is None: + variables = [] + + prompt_versions = await self.list_prompt_versions(prompt_id) + latest_prompt = max(prompt_versions.data, key=lambda x: int(x.version)) + + if version and latest_prompt.version != version: + raise ValueError( + f"'{version}' is not the latest prompt version for prompt_id='{prompt_id}'. Use the latest version '{latest_prompt.version}' in request." + ) + + current_version = latest_prompt.version if version is None else version + new_version = current_version + 1 + + updated_prompt = Prompt(prompt_id=prompt_id, prompt=prompt, version=new_version, variables=variables) + + version_key = self._get_version_key(prompt_id, str(new_version)) + data = self._serialize_prompt(updated_prompt) + await self.kvstore.set(version_key, data) + + if set_as_default: + await self.set_default_version(prompt_id, new_version) + + return updated_prompt + + async def delete_prompt(self, prompt_id: str) -> None: + """Delete a prompt and all its versions.""" + await self.get_prompt(prompt_id) + + prefix = f"prompts:v1:{prompt_id}:" + keys = await self.kvstore.keys_in_range(prefix, prefix + "\xff") + + for key in keys: + await self.kvstore.delete(key) + + async def list_prompt_versions(self, prompt_id: str) -> ListPromptsResponse: + """List all versions of a specific prompt.""" + prefix = f"prompts:v1:{prompt_id}:" + keys = await self.kvstore.keys_in_range(prefix, prefix + "\xff") + + default_version = None + prompts = [] + + for key in keys: + data = await self.kvstore.get(key) + if key.endswith(":default"): + default_version = data + else: + if data: + prompt_obj = self._deserialize_prompt(data) + prompts.append(prompt_obj) + + if not prompts: + raise ValueError(f"Prompt {prompt_id} not found") + + for prompt in prompts: + prompt.is_default = str(prompt.version) == default_version + + prompts.sort(key=lambda x: x.version) + return ListPromptsResponse(data=prompts) + + async def set_default_version(self, prompt_id: str, version: int) -> Prompt: + """Set which version of a prompt should be the default, If not set. the default is the latest.""" + version_key = self._get_version_key(prompt_id, str(version)) + data = await self.kvstore.get(version_key) + if data is None: + raise ValueError(f"Prompt {prompt_id} version {version} not found") + + default_key = self._get_default_key(prompt_id) + await self.kvstore.set(default_key, str(version)) + + return self._deserialize_prompt(data) diff --git a/llama_stack/core/resolver.py b/llama_stack/core/resolver.py index 7ac98dac8..373446de6 100644 --- a/llama_stack/core/resolver.py +++ b/llama_stack/core/resolver.py @@ -19,6 +19,7 @@ from llama_stack.apis.inference import Inference, InferenceProvider from llama_stack.apis.inspect import Inspect from llama_stack.apis.models import Models from llama_stack.apis.post_training import PostTraining +from llama_stack.apis.prompts import Prompts from llama_stack.apis.providers import Providers as ProvidersAPI from llama_stack.apis.safety import Safety from llama_stack.apis.scoring import Scoring @@ -93,6 +94,7 @@ def api_protocol_map(external_apis: dict[Api, ExternalApiSpec] | None = None) -> Api.tool_groups: ToolGroups, Api.tool_runtime: ToolRuntime, Api.files: Files, + Api.prompts: Prompts, } if external_apis: @@ -284,7 +286,15 @@ async def instantiate_providers( if provider.provider_id is None: continue - deps = {a: impls[a] for a in provider.spec.api_dependencies} + try: + deps = {a: impls[a] for a in provider.spec.api_dependencies} + except KeyError as e: + missing_api = e.args[0] + raise RuntimeError( + f"Failed to resolve '{provider.spec.api.value}' provider '{provider.provider_id}' of type '{provider.spec.provider_type}': " + f"required dependency '{missing_api.value}' is not available. " + f"Please add a '{missing_api.value}' provider to your configuration or check if the provider is properly configured." + ) from e for a in provider.spec.optional_api_dependencies: if a in impls: deps[a] = impls[a] diff --git a/llama_stack/core/routers/__init__.py b/llama_stack/core/routers/__init__.py index 1faace34a..f129f8ede 100644 --- a/llama_stack/core/routers/__init__.py +++ b/llama_stack/core/routers/__init__.py @@ -78,7 +78,10 @@ async def get_auto_router_impl( # TODO: move pass configs to routers instead if api == Api.inference and run_config.inference_store: - inference_store = InferenceStore(run_config.inference_store, policy) + inference_store = InferenceStore( + config=run_config.inference_store, + policy=policy, + ) await inference_store.initialize() api_to_dep_impl["store"] = inference_store diff --git a/llama_stack/core/routers/inference.py b/llama_stack/core/routers/inference.py index 2954f5080..fcf01a9c4 100644 --- a/llama_stack/core/routers/inference.py +++ b/llama_stack/core/routers/inference.py @@ -61,7 +61,7 @@ from llama_stack.models.llama.llama3.chat_format import ChatFormat from llama_stack.models.llama.llama3.tokenizer import Tokenizer from llama_stack.providers.datatypes import HealthResponse, HealthStatus, RoutingTable from llama_stack.providers.utils.inference.inference_store import InferenceStore -from llama_stack.providers.utils.telemetry.tracing import get_current_span +from llama_stack.providers.utils.telemetry.tracing import enqueue_event, get_current_span logger = get_logger(name=__name__, category="core::routers") @@ -88,6 +88,11 @@ class InferenceRouter(Inference): async def shutdown(self) -> None: logger.debug("InferenceRouter.shutdown") + if self.store: + try: + await self.store.shutdown() + except Exception as e: + logger.warning(f"Error during InferenceStore shutdown: {e}") async def register_model( self, @@ -158,7 +163,7 @@ class InferenceRouter(Inference): metrics = self._construct_metrics(prompt_tokens, completion_tokens, total_tokens, model) if self.telemetry: for metric in metrics: - await self.telemetry.log_event(metric) + enqueue_event(metric) return [MetricInResponse(metric=metric.metric, value=metric.value) for metric in metrics] async def _count_tokens( @@ -391,7 +396,7 @@ class InferenceRouter(Inference): model=model_obj, ) for metric in metrics: - await self.telemetry.log_event(metric) + enqueue_event(metric) # these metrics will show up in the client response. response.metrics = ( @@ -487,7 +492,7 @@ class InferenceRouter(Inference): # Store the response with the ID that will be returned to the client if self.store: - await self.store.store_chat_completion(response, messages) + asyncio.create_task(self.store.store_chat_completion(response, messages)) if self.telemetry: metrics = self._construct_metrics( @@ -497,7 +502,7 @@ class InferenceRouter(Inference): model=model_obj, ) for metric in metrics: - await self.telemetry.log_event(metric) + enqueue_event(metric) # these metrics will show up in the client response. response.metrics = ( metrics if not hasattr(response, "metrics") or response.metrics is None else response.metrics + metrics @@ -624,7 +629,7 @@ class InferenceRouter(Inference): "completion_tokens", "total_tokens", ]: # Only log completion and total tokens - await self.telemetry.log_event(metric) + enqueue_event(metric) # Return metrics in response async_metrics = [ @@ -670,7 +675,7 @@ class InferenceRouter(Inference): ) for metric in completion_metrics: if metric.metric in ["completion_tokens", "total_tokens"]: # Only log completion and total tokens - await self.telemetry.log_event(metric) + enqueue_event(metric) # Return metrics in response return [MetricInResponse(metric=metric.metric, value=metric.value) for metric in completion_metrics] @@ -715,7 +720,7 @@ class InferenceRouter(Inference): choices_data[idx] = { "content_parts": [], "tool_calls_builder": {}, - "finish_reason": None, + "finish_reason": "stop", "logprobs_content_parts": [], } current_choice_data = choices_data[idx] @@ -766,7 +771,7 @@ class InferenceRouter(Inference): model=model, ) for metric in metrics: - await self.telemetry.log_event(metric) + enqueue_event(metric) yield chunk finally: @@ -815,4 +820,4 @@ class InferenceRouter(Inference): object="chat.completion", ) logger.debug(f"InferenceRouter.completion_response: {final_response}") - await self.store.store_chat_completion(final_response, messages) + asyncio.create_task(self.store.store_chat_completion(final_response, messages)) diff --git a/llama_stack/core/routing_tables/benchmarks.py b/llama_stack/core/routing_tables/benchmarks.py index c875dee5b..8c87d395d 100644 --- a/llama_stack/core/routing_tables/benchmarks.py +++ b/llama_stack/core/routing_tables/benchmarks.py @@ -56,3 +56,7 @@ class BenchmarksRoutingTable(CommonRoutingTableImpl, Benchmarks): provider_resource_id=provider_benchmark_id, ) await self.register_object(benchmark) + + async def unregister_benchmark(self, benchmark_id: str) -> None: + existing_benchmark = await self.get_benchmark(benchmark_id) + await self.unregister_object(existing_benchmark) diff --git a/llama_stack/core/routing_tables/common.py b/llama_stack/core/routing_tables/common.py index e523746d8..ca2f3af42 100644 --- a/llama_stack/core/routing_tables/common.py +++ b/llama_stack/core/routing_tables/common.py @@ -64,6 +64,10 @@ async def unregister_object_from_provider(obj: RoutableObject, p: Any) -> None: return await p.unregister_shield(obj.identifier) elif api == Api.datasetio: return await p.unregister_dataset(obj.identifier) + elif api == Api.eval: + return await p.unregister_benchmark(obj.identifier) + elif api == Api.scoring: + return await p.unregister_scoring_function(obj.identifier) elif api == Api.tool_runtime: return await p.unregister_toolgroup(obj.identifier) else: diff --git a/llama_stack/core/routing_tables/models.py b/llama_stack/core/routing_tables/models.py index b6141efa9..641c73c16 100644 --- a/llama_stack/core/routing_tables/models.py +++ b/llama_stack/core/routing_tables/models.py @@ -33,7 +33,7 @@ class ModelsRoutingTable(CommonRoutingTableImpl, Models): try: models = await provider.list_models() except Exception as e: - logger.exception(f"Model refresh failed for provider {provider_id}: {e}") + logger.warning(f"Model refresh failed for provider {provider_id}: {e}") continue self.listed_providers.add(provider_id) diff --git a/llama_stack/core/routing_tables/scoring_functions.py b/llama_stack/core/routing_tables/scoring_functions.py index 71e5bed63..520f07014 100644 --- a/llama_stack/core/routing_tables/scoring_functions.py +++ b/llama_stack/core/routing_tables/scoring_functions.py @@ -60,3 +60,7 @@ class ScoringFunctionsRoutingTable(CommonRoutingTableImpl, ScoringFunctions): ) scoring_fn.provider_id = provider_id await self.register_object(scoring_fn) + + async def unregister_scoring_function(self, scoring_fn_id: str) -> None: + existing_scoring_fn = await self.get_scoring_function(scoring_fn_id) + await self.unregister_object(existing_scoring_fn) diff --git a/llama_stack/core/routing_tables/vector_dbs.py b/llama_stack/core/routing_tables/vector_dbs.py index 00f71b4fe..497894064 100644 --- a/llama_stack/core/routing_tables/vector_dbs.py +++ b/llama_stack/core/routing_tables/vector_dbs.py @@ -52,7 +52,6 @@ class VectorDBsRoutingTable(CommonRoutingTableImpl, VectorDBs): provider_vector_db_id: str | None = None, vector_db_name: str | None = None, ) -> VectorDB: - provider_vector_db_id = provider_vector_db_id or vector_db_id if provider_id is None: if len(self.impls_by_provider_id) > 0: provider_id = list(self.impls_by_provider_id.keys())[0] @@ -69,14 +68,33 @@ class VectorDBsRoutingTable(CommonRoutingTableImpl, VectorDBs): raise ModelTypeError(embedding_model, model.model_type, ModelType.embedding) if "embedding_dimension" not in model.metadata: raise ValueError(f"Model {embedding_model} does not have an embedding dimension") + + provider = self.impls_by_provider_id[provider_id] + logger.warning( + "VectorDB is being deprecated in future releases in favor of VectorStore. Please migrate your usage accordingly." + ) + vector_store = await provider.openai_create_vector_store( + name=vector_db_name or vector_db_id, + embedding_model=embedding_model, + embedding_dimension=model.metadata["embedding_dimension"], + provider_id=provider_id, + provider_vector_db_id=provider_vector_db_id, + ) + + vector_store_id = vector_store.id + actual_provider_vector_db_id = provider_vector_db_id or vector_store_id + logger.warning( + f"Ignoring vector_db_id {vector_db_id} and using vector_store_id {vector_store_id} instead. Setting VectorDB {vector_db_id} to VectorDB.vector_db_name" + ) + vector_db_data = { - "identifier": vector_db_id, + "identifier": vector_store_id, "type": ResourceType.vector_db.value, "provider_id": provider_id, - "provider_resource_id": provider_vector_db_id, + "provider_resource_id": actual_provider_vector_db_id, "embedding_model": embedding_model, "embedding_dimension": model.metadata["embedding_dimension"], - "vector_db_name": vector_db_name, + "vector_db_name": vector_store.name, } vector_db = TypeAdapter(VectorDBWithOwner).validate_python(vector_db_data) await self.register_object(vector_db) diff --git a/llama_stack/core/server/auth_providers.py b/llama_stack/core/server/auth_providers.py index a8af6f75a..38188c49a 100644 --- a/llama_stack/core/server/auth_providers.py +++ b/llama_stack/core/server/auth_providers.py @@ -8,16 +8,18 @@ import ssl import time from abc import ABC, abstractmethod from asyncio import Lock -from urllib.parse import parse_qs, urlparse +from urllib.parse import parse_qs, urljoin, urlparse import httpx from jose import jwt from pydantic import BaseModel, Field +from llama_stack.apis.common.errors import TokenValidationError from llama_stack.core.datatypes import ( AuthenticationConfig, CustomAuthConfig, GitHubTokenAuthConfig, + KubernetesAuthProviderConfig, OAuth2TokenAuthConfig, User, ) @@ -162,7 +164,7 @@ class OAuth2TokenAuthProvider(AuthProvider): auth=auth, timeout=10.0, # Add a reasonable timeout ) - if response.status_code != 200: + if response.status_code != httpx.codes.OK: logger.warning(f"Token introspection failed with status code: {response.status_code}") raise ValueError(f"Token introspection failed: {response.status_code}") @@ -272,7 +274,7 @@ class CustomAuthProvider(AuthProvider): json=auth_request.model_dump(), timeout=10.0, # Add a reasonable timeout ) - if response.status_code != 200: + if response.status_code != httpx.codes.OK: logger.warning(f"Authentication failed with status code: {response.status_code}") raise ValueError(f"Authentication failed: {response.status_code}") @@ -374,6 +376,89 @@ async def _get_github_user_info(access_token: str, github_api_base_url: str) -> } +class KubernetesAuthProvider(AuthProvider): + """ + Kubernetes authentication provider that validates tokens using the Kubernetes SelfSubjectReview API. + This provider integrates with Kubernetes API server by using the + /apis/authentication.k8s.io/v1/selfsubjectreviews endpoint to validate tokens and extract user information. + """ + + def __init__(self, config: KubernetesAuthProviderConfig): + self.config = config + + def _httpx_verify_value(self) -> bool | str: + """ + Build the value for httpx's `verify` parameter. + - False disables verification. + - Path string points to a CA bundle. + - True uses system defaults. + """ + if not self.config.verify_tls: + return False + if self.config.tls_cafile: + return self.config.tls_cafile.as_posix() + return True + + async def validate_token(self, token: str, scope: dict | None = None) -> User: + """Validate a token using Kubernetes SelfSubjectReview API endpoint.""" + # Build the Kubernetes SelfSubjectReview API endpoint URL + review_api_url = urljoin(self.config.api_server_url, "/apis/authentication.k8s.io/v1/selfsubjectreviews") + + # Create SelfSubjectReview request body + review_request = {"apiVersion": "authentication.k8s.io/v1", "kind": "SelfSubjectReview"} + verify = self._httpx_verify_value() + + try: + async with httpx.AsyncClient(verify=verify, timeout=10.0) as client: + response = await client.post( + review_api_url, + json=review_request, + headers={ + "Authorization": f"Bearer {token}", + "Content-Type": "application/json", + }, + ) + + if response.status_code == httpx.codes.UNAUTHORIZED: + raise TokenValidationError("Invalid token") + if response.status_code != httpx.codes.CREATED: + logger.warning(f"Kubernetes SelfSubjectReview API failed with status code: {response.status_code}") + raise TokenValidationError(f"Token validation failed: {response.status_code}") + + review_response = response.json() + # Extract user information from SelfSubjectReview response + status = review_response.get("status", {}) + if not status: + raise ValueError("No status found in SelfSubjectReview response") + + user_info = status.get("userInfo", {}) + if not user_info: + raise ValueError("No userInfo found in SelfSubjectReview response") + + username = user_info.get("username") + if not username: + raise ValueError("No username found in SelfSubjectReview response") + + # Build user attributes from Kubernetes user info + user_attributes = get_attributes_from_claims(user_info, self.config.claims_mapping) + + return User( + principal=username, + attributes=user_attributes, + ) + + except httpx.TimeoutException: + logger.warning("Kubernetes SelfSubjectReview API request timed out") + raise ValueError("Token validation timeout") from None + except Exception as e: + logger.warning(f"Error during token validation: {str(e)}") + raise ValueError(f"Token validation error: {str(e)}") from e + + async def close(self): + """Close any resources.""" + pass + + def create_auth_provider(config: AuthenticationConfig) -> AuthProvider: """Factory function to create the appropriate auth provider.""" provider_config = config.provider_config @@ -384,5 +469,7 @@ def create_auth_provider(config: AuthenticationConfig) -> AuthProvider: return OAuth2TokenAuthProvider(provider_config) elif isinstance(provider_config, GitHubTokenAuthConfig): return GitHubTokenAuthProvider(provider_config) + elif isinstance(provider_config, KubernetesAuthProviderConfig): + return KubernetesAuthProvider(provider_config) else: raise ValueError(f"Unknown authentication provider config type: {type(provider_config)}") diff --git a/llama_stack/core/server/routes.py b/llama_stack/core/server/routes.py index 7baf20da5..4970d0bf8 100644 --- a/llama_stack/core/server/routes.py +++ b/llama_stack/core/server/routes.py @@ -14,7 +14,6 @@ from starlette.routing import Route from llama_stack.apis.datatypes import Api, ExternalApiSpec from llama_stack.apis.tools import RAGToolRuntime, SpecialToolGroup -from llama_stack.apis.version import LLAMA_STACK_API_VERSION from llama_stack.core.resolver import api_protocol_map from llama_stack.schema_utils import WebMethod @@ -54,22 +53,23 @@ def get_all_api_routes( protocol_methods.append((f"{tool_group.value}.{name}", method)) for name, method in protocol_methods: - if not hasattr(method, "__webmethod__"): + # Get all webmethods for this method (supports multiple decorators) + webmethods = getattr(method, "__webmethods__", []) + if not webmethods: continue - # The __webmethod__ attribute is dynamically added by the @webmethod decorator - # mypy doesn't know about this dynamic attribute, so we ignore the attr-defined error - webmethod = method.__webmethod__ # type: ignore[attr-defined] - path = f"/{LLAMA_STACK_API_VERSION}/{webmethod.route.lstrip('/')}" - if webmethod.method == hdrs.METH_GET: - http_method = hdrs.METH_GET - elif webmethod.method == hdrs.METH_DELETE: - http_method = hdrs.METH_DELETE - else: - http_method = hdrs.METH_POST - routes.append( - (Route(path=path, methods=[http_method], name=name, endpoint=None), webmethod) - ) # setting endpoint to None since don't use a Router object + # Create routes for each webmethod decorator + for webmethod in webmethods: + path = f"/{webmethod.level}/{webmethod.route.lstrip('/')}" + if webmethod.method == hdrs.METH_GET: + http_method = hdrs.METH_GET + elif webmethod.method == hdrs.METH_DELETE: + http_method = hdrs.METH_DELETE + else: + http_method = hdrs.METH_POST + routes.append( + (Route(path=path, methods=[http_method], name=name, endpoint=None), webmethod) + ) # setting endpoint to None since don't use a Router object apis[api] = routes diff --git a/llama_stack/core/server/server.py b/llama_stack/core/server/server.py index d6dfc3435..7d119c139 100644 --- a/llama_stack/core/server/server.py +++ b/llama_stack/core/server/server.py @@ -6,6 +6,7 @@ import argparse import asyncio +import concurrent.futures import functools import inspect import json @@ -24,7 +25,6 @@ from typing import Annotated, Any, get_origin import httpx import rich.pretty import yaml -from aiohttp import hdrs from fastapi import Body, FastAPI, HTTPException, Request, Response from fastapi import Path as FastapiPath from fastapi.exceptions import RequestValidationError @@ -44,23 +44,17 @@ from llama_stack.core.datatypes import ( process_cors_config, ) from llama_stack.core.distribution import builtin_automatically_routed_apis -from llama_stack.core.external import ExternalApiSpec, load_external_apis +from llama_stack.core.external import load_external_apis from llama_stack.core.request_headers import ( PROVIDER_DATA_VAR, request_provider_data_context, user_from_scope, ) -from llama_stack.core.resolver import InvalidProviderError -from llama_stack.core.server.routes import ( - find_matching_route, - get_all_api_routes, - initialize_route_impls, -) +from llama_stack.core.server.routes import get_all_api_routes from llama_stack.core.stack import ( + Stack, cast_image_name_to_string, - construct_stack, replace_env_vars, - shutdown_stack, validate_env_pair, ) from llama_stack.core.utils.config import redact_sensitive_fields @@ -74,13 +68,12 @@ from llama_stack.providers.inline.telemetry.meta_reference.telemetry import ( ) from llama_stack.providers.utils.telemetry.tracing import ( CURRENT_TRACE_CONTEXT, - end_trace, setup_logger, - start_trace, ) from .auth import AuthenticationMiddleware from .quota import QuotaMiddleware +from .tracing import TracingMiddleware REPO_ROOT = Path(__file__).parent.parent.parent.parent @@ -132,15 +125,17 @@ def translate_exception(exc: Exception) -> HTTPException | RequestValidationErro }, ) elif isinstance(exc, ConflictError): - return HTTPException(status_code=409, detail=str(exc)) + return HTTPException(status_code=httpx.codes.CONFLICT, detail=str(exc)) elif isinstance(exc, ResourceNotFoundError): - return HTTPException(status_code=404, detail=str(exc)) + return HTTPException(status_code=httpx.codes.NOT_FOUND, detail=str(exc)) elif isinstance(exc, ValueError): return HTTPException(status_code=httpx.codes.BAD_REQUEST, detail=f"Invalid value: {str(exc)}") elif isinstance(exc, BadRequestError): return HTTPException(status_code=httpx.codes.BAD_REQUEST, detail=str(exc)) elif isinstance(exc, PermissionError | AccessDeniedError): return HTTPException(status_code=httpx.codes.FORBIDDEN, detail=f"Permission denied: {str(exc)}") + elif isinstance(exc, ConnectionError | httpx.ConnectError): + return HTTPException(status_code=httpx.codes.BAD_GATEWAY, detail=str(exc)) elif isinstance(exc, asyncio.TimeoutError | TimeoutError): return HTTPException(status_code=httpx.codes.GATEWAY_TIMEOUT, detail=f"Operation timed out: {str(exc)}") elif isinstance(exc, NotImplementedError): @@ -154,21 +149,34 @@ def translate_exception(exc: Exception) -> HTTPException | RequestValidationErro ) -async def shutdown(app): - """Initiate a graceful shutdown of the application. - - Handled by the lifespan context manager. The shutdown process involves - shutting down all implementations registered in the application. +class StackApp(FastAPI): """ - await shutdown_stack(app.__llama_stack_impls__) + A wrapper around the FastAPI application to hold a reference to the Stack instance so that we can + start background tasks (e.g. refresh model registry periodically) from the lifespan context manager. + """ + + def __init__(self, config: StackRunConfig, *args, **kwargs): + super().__init__(*args, **kwargs) + self.stack: Stack = Stack(config) + + # This code is called from a running event loop managed by uvicorn so we cannot simply call + # asyncio.run() to initialize the stack. We cannot await either since this is not an async + # function. + # As a workaround, we use a thread pool executor to run the initialize() method + # in a separate thread. + with concurrent.futures.ThreadPoolExecutor() as executor: + future = executor.submit(asyncio.run, self.stack.initialize()) + future.result() @asynccontextmanager -async def lifespan(app: FastAPI): +async def lifespan(app: StackApp): logger.info("Starting up") + assert app.stack is not None + app.stack.create_registry_refresh_task() yield logger.info("Shutting down") - await shutdown(app) + await app.stack.shutdown() def is_streaming_request(func_name: str, request: Request, **kwargs): @@ -285,65 +293,6 @@ def create_dynamic_typed_route(func: Any, method: str, route: str) -> Callable: return route_handler -class TracingMiddleware: - def __init__(self, app, impls, external_apis: dict[str, ExternalApiSpec]): - self.app = app - self.impls = impls - self.external_apis = external_apis - # FastAPI built-in paths that should bypass custom routing - self.fastapi_paths = ("/docs", "/redoc", "/openapi.json", "/favicon.ico", "/static") - - async def __call__(self, scope, receive, send): - if scope.get("type") == "lifespan": - return await self.app(scope, receive, send) - - path = scope.get("path", "") - - # Check if the path is a FastAPI built-in path - if path.startswith(self.fastapi_paths): - # Pass through to FastAPI's built-in handlers - logger.debug(f"Bypassing custom routing for FastAPI built-in path: {path}") - return await self.app(scope, receive, send) - - if not hasattr(self, "route_impls"): - self.route_impls = initialize_route_impls(self.impls, self.external_apis) - - try: - _, _, route_path, webmethod = find_matching_route( - scope.get("method", hdrs.METH_GET), path, self.route_impls - ) - except ValueError: - # If no matching endpoint is found, pass through to FastAPI - logger.debug(f"No matching route found for path: {path}, falling back to FastAPI") - return await self.app(scope, receive, send) - - trace_attributes = {"__location__": "server", "raw_path": path} - - # Extract W3C trace context headers and store as trace attributes - headers = dict(scope.get("headers", [])) - traceparent = headers.get(b"traceparent", b"").decode() - if traceparent: - trace_attributes["traceparent"] = traceparent - tracestate = headers.get(b"tracestate", b"").decode() - if tracestate: - trace_attributes["tracestate"] = tracestate - - trace_path = webmethod.descriptive_name or route_path - trace_context = await start_trace(trace_path, trace_attributes) - - async def send_with_trace_id(message): - if message["type"] == "http.response.start": - headers = message.get("headers", []) - headers.append([b"x-trace-id", str(trace_context.trace_id).encode()]) - message["headers"] = headers - await send(message) - - try: - return await self.app(scope, receive, send_with_trace_id) - finally: - await end_trace() - - class ClientVersionMiddleware: def __init__(self, app): self.app = app @@ -384,73 +333,61 @@ class ClientVersionMiddleware: return await self.app(scope, receive, send) -def main(args: argparse.Namespace | None = None): - """Start the LlamaStack server.""" - parser = argparse.ArgumentParser(description="Start the LlamaStack server.") +def create_app( + config_file: str | None = None, + env_vars: list[str] | None = None, +) -> StackApp: + """Create and configure the FastAPI application. - add_config_distro_args(parser) - parser.add_argument( - "--port", - type=int, - default=int(os.getenv("LLAMA_STACK_PORT", 8321)), - help="Port to listen on", - ) - parser.add_argument( - "--env", - action="append", - help="Environment variables in KEY=value format. Can be specified multiple times.", - ) + Args: + config_file: Path to config file. If None, uses LLAMA_STACK_CONFIG env var or default resolution. + env_vars: List of environment variables in KEY=value format. + disable_version_check: Whether to disable version checking. If None, uses LLAMA_STACK_DISABLE_VERSION_CHECK env var. - # Determine whether the server args are being passed by the "run" command, if this is the case - # the args will be passed as a Namespace object to the main function, otherwise they will be - # parsed from the command line - if args is None: - args = parser.parse_args() + Returns: + Configured StackApp instance. + """ + config_file = config_file or os.getenv("LLAMA_STACK_CONFIG") + if config_file is None: + raise ValueError("No config file provided and LLAMA_STACK_CONFIG env var is not set") - config_or_distro = get_config_from_args(args) - config_file = resolve_config_or_distro(config_or_distro, Mode.RUN) + config_file = resolve_config_or_distro(config_file, Mode.RUN) + # Load and process configuration logger_config = None with open(config_file) as fp: config_contents = yaml.safe_load(fp) if isinstance(config_contents, dict) and (cfg := config_contents.get("logging_config")): logger_config = LoggingConfig(**cfg) logger = get_logger(name=__name__, category="core::server", config=logger_config) - if args.env: - for env_pair in args.env: + + if env_vars: + for env_pair in env_vars: try: key, value = validate_env_pair(env_pair) - logger.info(f"Setting CLI environment variable {key} => {value}") + logger.info(f"Setting environment variable {key} => {value}") os.environ[key] = value except ValueError as e: logger.error(f"Error: {str(e)}") - sys.exit(1) + raise ValueError(f"Invalid environment variable format: {env_pair}") from e + config = replace_env_vars(config_contents) config = StackRunConfig(**cast_image_name_to_string(config)) _log_run_config(run_config=config) - app = FastAPI( + app = StackApp( lifespan=lifespan, docs_url="/docs", redoc_url="/redoc", openapi_url="/openapi.json", + config=config, ) if not os.environ.get("LLAMA_STACK_DISABLE_VERSION_CHECK"): app.add_middleware(ClientVersionMiddleware) - try: - # Create and set the event loop that will be used for both construction and server runtime - loop = asyncio.new_event_loop() - asyncio.set_event_loop(loop) - - # Construct the stack in the persistent event loop - impls = loop.run_until_complete(construct_stack(config)) - - except InvalidProviderError as e: - logger.error(f"Error: {str(e)}") - sys.exit(1) + impls = app.stack.impls if config.server.auth: logger.info(f"Enabling authentication with provider: {config.server.auth.provider_config.type.value}") @@ -513,6 +450,7 @@ def main(args: argparse.Namespace | None = None): apis_to_serve.add("inspect") apis_to_serve.add("providers") + apis_to_serve.add("prompts") for api_str in apis_to_serve: api = Api(api_str) @@ -550,9 +488,54 @@ def main(args: argparse.Namespace | None = None): app.exception_handler(RequestValidationError)(global_exception_handler) app.exception_handler(Exception)(global_exception_handler) - app.__llama_stack_impls__ = impls app.add_middleware(TracingMiddleware, impls=impls, external_apis=external_apis) + return app + + +def main(args: argparse.Namespace | None = None): + """Start the LlamaStack server.""" + parser = argparse.ArgumentParser(description="Start the LlamaStack server.") + + add_config_distro_args(parser) + parser.add_argument( + "--port", + type=int, + default=int(os.getenv("LLAMA_STACK_PORT", 8321)), + help="Port to listen on", + ) + parser.add_argument( + "--env", + action="append", + help="Environment variables in KEY=value format. Can be specified multiple times.", + ) + + # Determine whether the server args are being passed by the "run" command, if this is the case + # the args will be passed as a Namespace object to the main function, otherwise they will be + # parsed from the command line + if args is None: + args = parser.parse_args() + + config_or_distro = get_config_from_args(args) + + try: + app = create_app( + config_file=config_or_distro, + env_vars=args.env, + ) + except Exception as e: + logger.error(f"Error creating app: {str(e)}") + sys.exit(1) + + config_file = resolve_config_or_distro(config_or_distro, Mode.RUN) + with open(config_file) as fp: + config_contents = yaml.safe_load(fp) + if isinstance(config_contents, dict) and (cfg := config_contents.get("logging_config")): + logger_config = LoggingConfig(**cfg) + else: + logger_config = None + config = StackRunConfig(**cast_image_name_to_string(replace_env_vars(config_contents))) + import uvicorn # Configure SSL if certificates are provided @@ -590,7 +573,6 @@ def main(args: argparse.Namespace | None = None): if ssl_config: uvicorn_config.update(ssl_config) - # Run uvicorn in the existing event loop to preserve background tasks # We need to catch KeyboardInterrupt because uvicorn's signal handling # re-raises SIGINT signals using signal.raise_signal(), which Python # converts to KeyboardInterrupt. Without this catch, we'd get a confusing @@ -601,13 +583,9 @@ def main(args: argparse.Namespace | None = None): # Another approach would be to ignore SIGINT entirely - let uvicorn handle it through its own # signal handling but this is quite intrusive and not worth the effort. try: - loop.run_until_complete(uvicorn.Server(uvicorn.Config(**uvicorn_config)).serve()) + asyncio.run(uvicorn.Server(uvicorn.Config(**uvicorn_config)).serve()) except (KeyboardInterrupt, SystemExit): logger.info("Received interrupt signal, shutting down gracefully...") - finally: - if not loop.is_closed(): - logger.debug("Closing event loop") - loop.close() def _log_run_config(run_config: StackRunConfig): diff --git a/llama_stack/core/server/tracing.py b/llama_stack/core/server/tracing.py new file mode 100644 index 000000000..4c6df5b42 --- /dev/null +++ b/llama_stack/core/server/tracing.py @@ -0,0 +1,80 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the terms described in the LICENSE file in +# the root directory of this source tree. +from aiohttp import hdrs + +from llama_stack.core.external import ExternalApiSpec +from llama_stack.core.server.routes import find_matching_route, initialize_route_impls +from llama_stack.log import get_logger +from llama_stack.providers.utils.telemetry.tracing import end_trace, start_trace + +logger = get_logger(name=__name__, category="core::server") + + +class TracingMiddleware: + def __init__(self, app, impls, external_apis: dict[str, ExternalApiSpec]): + self.app = app + self.impls = impls + self.external_apis = external_apis + # FastAPI built-in paths that should bypass custom routing + self.fastapi_paths = ("/docs", "/redoc", "/openapi.json", "/favicon.ico", "/static") + + async def __call__(self, scope, receive, send): + if scope.get("type") == "lifespan": + return await self.app(scope, receive, send) + + path = scope.get("path", "") + + # Check if the path is a FastAPI built-in path + if path.startswith(self.fastapi_paths): + # Pass through to FastAPI's built-in handlers + logger.debug(f"Bypassing custom routing for FastAPI built-in path: {path}") + return await self.app(scope, receive, send) + + if not hasattr(self, "route_impls"): + self.route_impls = initialize_route_impls(self.impls, self.external_apis) + + try: + _, _, route_path, webmethod = find_matching_route( + scope.get("method", hdrs.METH_GET), path, self.route_impls + ) + except ValueError: + # If no matching endpoint is found, pass through to FastAPI + logger.debug(f"No matching route found for path: {path}, falling back to FastAPI") + return await self.app(scope, receive, send) + + # Log deprecation warning if route is deprecated + if getattr(webmethod, "deprecated", False): + logger.warning( + f"DEPRECATED ROUTE USED: {scope.get('method', 'GET')} {path} - " + f"This route is deprecated and may be removed in a future version. " + f"Please check the docs for the supported version." + ) + + trace_attributes = {"__location__": "server", "raw_path": path} + + # Extract W3C trace context headers and store as trace attributes + headers = dict(scope.get("headers", [])) + traceparent = headers.get(b"traceparent", b"").decode() + if traceparent: + trace_attributes["traceparent"] = traceparent + tracestate = headers.get(b"tracestate", b"").decode() + if tracestate: + trace_attributes["tracestate"] = tracestate + + trace_path = webmethod.descriptive_name or route_path + trace_context = await start_trace(trace_path, trace_attributes) + + async def send_with_trace_id(message): + if message["type"] == "http.response.start": + headers = message.get("headers", []) + headers.append([b"x-trace-id", str(trace_context.trace_id).encode()]) + message["headers"] = headers + await send(message) + + try: + return await self.app(scope, receive, send_with_trace_id) + finally: + await end_trace() diff --git a/llama_stack/core/stack.py b/llama_stack/core/stack.py index 1ed23a12a..3e14328a3 100644 --- a/llama_stack/core/stack.py +++ b/llama_stack/core/stack.py @@ -23,6 +23,7 @@ from llama_stack.apis.inference import Inference from llama_stack.apis.inspect import Inspect from llama_stack.apis.models import Models from llama_stack.apis.post_training import PostTraining +from llama_stack.apis.prompts import Prompts from llama_stack.apis.providers import Providers from llama_stack.apis.safety import Safety from llama_stack.apis.scoring import Scoring @@ -36,6 +37,7 @@ from llama_stack.apis.vector_io import VectorIO from llama_stack.core.datatypes import Provider, StackRunConfig from llama_stack.core.distribution import get_provider_registry from llama_stack.core.inspect import DistributionInspectConfig, DistributionInspectImpl +from llama_stack.core.prompts.prompts import PromptServiceConfig, PromptServiceImpl from llama_stack.core.providers import ProviderImpl, ProviderImplConfig from llama_stack.core.resolver import ProviderRegistry, resolve_impls from llama_stack.core.routing_tables.common import CommonRoutingTableImpl @@ -70,6 +72,7 @@ class LlamaStack( ToolRuntime, RAGToolRuntime, Files, + Prompts, ): pass @@ -103,12 +106,12 @@ async def register_resources(run_config: StackRunConfig, impls: dict[Api, Any]): method = getattr(impls[api], register_method) for obj in objects: - logger.debug(f"registering {rsrc.capitalize()} {obj} for provider {obj.provider_id}") - - # Do not register models on disabled providers - if hasattr(obj, "provider_id") and (not obj.provider_id or obj.provider_id == "__disabled__"): - logger.debug(f"Skipping {rsrc.capitalize()} registration for disabled provider.") - continue + if hasattr(obj, "provider_id"): + # Do not register models on disabled providers + if not obj.provider_id or obj.provider_id == "__disabled__": + logger.debug(f"Skipping {rsrc.capitalize()} registration for disabled provider.") + continue + logger.debug(f"registering {rsrc.capitalize()} {obj} for provider {obj.provider_id}") # we want to maintain the type information in arguments to method. # instead of method(**obj.model_dump()), which may convert a typed attr to a dict, @@ -223,7 +226,10 @@ def replace_env_vars(config: Any, path: str = "") -> Any: try: result = re.sub(pattern, get_env_var, config) - return _convert_string_to_proper_type(result) + # Only apply type conversion if substitution actually happened + if result != config: + return _convert_string_to_proper_type(result) + return result except EnvVarError as e: raise EnvVarError(e.var_name, e.path) from None @@ -300,76 +306,91 @@ def add_internal_implementations(impls: dict[Api, Any], run_config: StackRunConf ) impls[Api.providers] = providers_impl + prompts_impl = PromptServiceImpl( + PromptServiceConfig(run_config=run_config), + deps=impls, + ) + impls[Api.prompts] = prompts_impl -# Produces a stack of providers for the given run config. Not all APIs may be -# asked for in the run config. -async def construct_stack( - run_config: StackRunConfig, provider_registry: ProviderRegistry | None = None -) -> dict[Api, Any]: - if "LLAMA_STACK_TEST_INFERENCE_MODE" in os.environ: - from llama_stack.testing.inference_recorder import setup_inference_recording + +class Stack: + def __init__(self, run_config: StackRunConfig, provider_registry: ProviderRegistry | None = None): + self.run_config = run_config + self.provider_registry = provider_registry + self.impls = None + + # Produces a stack of providers for the given run config. Not all APIs may be + # asked for in the run config. + async def initialize(self): + if "LLAMA_STACK_TEST_INFERENCE_MODE" in os.environ: + from llama_stack.testing.inference_recorder import setup_inference_recording + + global TEST_RECORDING_CONTEXT + TEST_RECORDING_CONTEXT = setup_inference_recording() + if TEST_RECORDING_CONTEXT: + TEST_RECORDING_CONTEXT.__enter__() + logger.info(f"Inference recording enabled: mode={os.environ.get('LLAMA_STACK_TEST_INFERENCE_MODE')}") + + dist_registry, _ = await create_dist_registry(self.run_config.metadata_store, self.run_config.image_name) + policy = self.run_config.server.auth.access_policy if self.run_config.server.auth else [] + impls = await resolve_impls( + self.run_config, self.provider_registry or get_provider_registry(self.run_config), dist_registry, policy + ) + + # Add internal implementations after all other providers are resolved + add_internal_implementations(impls, self.run_config) + + if Api.prompts in impls: + await impls[Api.prompts].initialize() + + await register_resources(self.run_config, impls) + + await refresh_registry_once(impls) + self.impls = impls + + def create_registry_refresh_task(self): + assert self.impls is not None, "Must call initialize() before starting" + + global REGISTRY_REFRESH_TASK + REGISTRY_REFRESH_TASK = asyncio.create_task(refresh_registry_task(self.impls)) + + def cb(task): + import traceback + + if task.cancelled(): + logger.error("Model refresh task cancelled") + elif task.exception(): + logger.error(f"Model refresh task failed: {task.exception()}") + traceback.print_exception(task.exception()) + else: + logger.debug("Model refresh task completed") + + REGISTRY_REFRESH_TASK.add_done_callback(cb) + + async def shutdown(self): + for impl in self.impls.values(): + impl_name = impl.__class__.__name__ + logger.info(f"Shutting down {impl_name}") + try: + if hasattr(impl, "shutdown"): + await asyncio.wait_for(impl.shutdown(), timeout=5) + else: + logger.warning(f"No shutdown method for {impl_name}") + except TimeoutError: + logger.exception(f"Shutdown timeout for {impl_name}") + except (Exception, asyncio.CancelledError) as e: + logger.exception(f"Failed to shutdown {impl_name}: {e}") global TEST_RECORDING_CONTEXT - TEST_RECORDING_CONTEXT = setup_inference_recording() if TEST_RECORDING_CONTEXT: - TEST_RECORDING_CONTEXT.__enter__() - logger.info(f"Inference recording enabled: mode={os.environ.get('LLAMA_STACK_TEST_INFERENCE_MODE')}") + try: + TEST_RECORDING_CONTEXT.__exit__(None, None, None) + except Exception as e: + logger.error(f"Error during inference recording cleanup: {e}") - dist_registry, _ = await create_dist_registry(run_config.metadata_store, run_config.image_name) - policy = run_config.server.auth.access_policy if run_config.server.auth else [] - impls = await resolve_impls( - run_config, provider_registry or get_provider_registry(run_config), dist_registry, policy - ) - - # Add internal implementations after all other providers are resolved - add_internal_implementations(impls, run_config) - - await register_resources(run_config, impls) - - await refresh_registry_once(impls) - - global REGISTRY_REFRESH_TASK - REGISTRY_REFRESH_TASK = asyncio.create_task(refresh_registry_task(impls)) - - def cb(task): - import traceback - - if task.cancelled(): - logger.error("Model refresh task cancelled") - elif task.exception(): - logger.error(f"Model refresh task failed: {task.exception()}") - traceback.print_exception(task.exception()) - else: - logger.debug("Model refresh task completed") - - REGISTRY_REFRESH_TASK.add_done_callback(cb) - return impls - - -async def shutdown_stack(impls: dict[Api, Any]): - for impl in impls.values(): - impl_name = impl.__class__.__name__ - logger.info(f"Shutting down {impl_name}") - try: - if hasattr(impl, "shutdown"): - await asyncio.wait_for(impl.shutdown(), timeout=5) - else: - logger.warning(f"No shutdown method for {impl_name}") - except TimeoutError: - logger.exception(f"Shutdown timeout for {impl_name}") - except (Exception, asyncio.CancelledError) as e: - logger.exception(f"Failed to shutdown {impl_name}: {e}") - - global TEST_RECORDING_CONTEXT - if TEST_RECORDING_CONTEXT: - try: - TEST_RECORDING_CONTEXT.__exit__(None, None, None) - except Exception as e: - logger.error(f"Error during inference recording cleanup: {e}") - - global REGISTRY_REFRESH_TASK - if REGISTRY_REFRESH_TASK: - REGISTRY_REFRESH_TASK.cancel() + global REGISTRY_REFRESH_TASK + if REGISTRY_REFRESH_TASK: + REGISTRY_REFRESH_TASK.cancel() async def refresh_registry_once(impls: dict[Api, Any]): diff --git a/llama_stack/core/start_stack.sh b/llama_stack/core/start_stack.sh index a3fc83265..4c6824b56 100755 --- a/llama_stack/core/start_stack.sh +++ b/llama_stack/core/start_stack.sh @@ -123,6 +123,6 @@ if [[ "$env_type" == "venv" ]]; then $other_args elif [[ "$env_type" == "container" ]]; then echo -e "${RED}Warning: Llama Stack no longer supports running Containers via the 'llama stack run' command.${NC}" - echo -e "Please refer to the documentation for more information: https://llama-stack.readthedocs.io/en/latest/distributions/building_distro.html#llama-stack-build" + echo -e "Please refer to the documentation for more information: https://llamastack.github.io/latest/distributions/building_distro.html#llama-stack-build" exit 1 fi diff --git a/llama_stack/core/store/registry.py b/llama_stack/core/store/registry.py index 5f4abe9aa..a764d692a 100644 --- a/llama_stack/core/store/registry.py +++ b/llama_stack/core/store/registry.py @@ -96,9 +96,11 @@ class DiskDistributionRegistry(DistributionRegistry): async def register(self, obj: RoutableObjectWithProvider) -> bool: existing_obj = await self.get(obj.type, obj.identifier) - # dont register if the object's providerid already exists - if existing_obj and existing_obj.provider_id == obj.provider_id: - return False + # warn if the object's providerid is different but proceed with registration + if existing_obj and existing_obj.provider_id != obj.provider_id: + logger.warning( + f"Object {existing_obj.type}:{existing_obj.identifier}'s {existing_obj.provider_id} provider is being replaced with {obj.provider_id}" + ) await self.kvstore.set( KEY_FORMAT.format(type=obj.type, identifier=obj.identifier), diff --git a/llama_stack/core/ui/README.md b/llama_stack/core/ui/README.md index 05b4adc26..f1d85454b 100644 --- a/llama_stack/core/ui/README.md +++ b/llama_stack/core/ui/README.md @@ -6,7 +6,7 @@ ## Developer Setup -1. Start up Llama Stack API server. More details [here](https://llama-stack.readthedocs.io/en/latest/getting_started/index.html). +1. Start up Llama Stack API server. More details [here](https://llamastack.github.io/latest/getting_started/index.htmll). ``` llama stack build --distro together --image-type venv diff --git a/llama_stack/distributions/ci-tests/build.yaml b/llama_stack/distributions/ci-tests/build.yaml index 8e6c0bf67..a4d920cd6 100644 --- a/llama_stack/distributions/ci-tests/build.yaml +++ b/llama_stack/distributions/ci-tests/build.yaml @@ -17,6 +17,7 @@ distribution_spec: - provider_type: remote::vertexai - provider_type: remote::groq - provider_type: remote::sambanova + - provider_type: remote::azure - provider_type: inline::sentence-transformers vector_io: - provider_type: inline::faiss diff --git a/llama_stack/distributions/ci-tests/ci_tests.py b/llama_stack/distributions/ci-tests/ci_tests.py index 8fb61faca..ab102f5f3 100644 --- a/llama_stack/distributions/ci-tests/ci_tests.py +++ b/llama_stack/distributions/ci-tests/ci_tests.py @@ -11,9 +11,7 @@ from ..starter.starter import get_distribution_template as get_starter_distribut def get_distribution_template() -> DistributionTemplate: - template = get_starter_distribution_template() - name = "ci-tests" - template.name = name + template = get_starter_distribution_template(name="ci-tests") template.description = "CI tests for Llama Stack" return template diff --git a/llama_stack/distributions/ci-tests/run.yaml b/llama_stack/distributions/ci-tests/run.yaml index 7523df581..a478a3872 100644 --- a/llama_stack/distributions/ci-tests/run.yaml +++ b/llama_stack/distributions/ci-tests/run.yaml @@ -81,6 +81,13 @@ providers: config: url: https://api.sambanova.ai/v1 api_key: ${env.SAMBANOVA_API_KEY:=} + - provider_id: ${env.AZURE_API_KEY:+azure} + provider_type: remote::azure + config: + api_key: ${env.AZURE_API_KEY:=} + api_base: ${env.AZURE_API_BASE:=} + api_version: ${env.AZURE_API_VERSION:=} + api_type: ${env.AZURE_API_TYPE:=} - provider_id: sentence-transformers provider_type: inline::sentence-transformers vector_io: @@ -89,28 +96,28 @@ providers: config: kvstore: type: sqlite - db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/starter}/faiss_store.db + db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/ci-tests}/faiss_store.db - provider_id: sqlite-vec provider_type: inline::sqlite-vec config: - db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/starter}/sqlite_vec.db + db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/ci-tests}/sqlite_vec.db kvstore: type: sqlite - db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/starter}/sqlite_vec_registry.db + db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/ci-tests}/sqlite_vec_registry.db - provider_id: ${env.MILVUS_URL:+milvus} provider_type: inline::milvus config: - db_path: ${env.MILVUS_DB_PATH:=~/.llama/distributions/starter}/milvus.db + db_path: ${env.MILVUS_DB_PATH:=~/.llama/distributions/ci-tests}/milvus.db kvstore: type: sqlite - db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/starter}/milvus_registry.db + db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/ci-tests}/milvus_registry.db - provider_id: ${env.CHROMADB_URL:+chromadb} provider_type: remote::chromadb config: url: ${env.CHROMADB_URL:=} kvstore: type: sqlite - db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/starter/}/chroma_remote_registry.db + db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/ci-tests/}/chroma_remote_registry.db - provider_id: ${env.PGVECTOR_DB:+pgvector} provider_type: remote::pgvector config: @@ -121,15 +128,15 @@ providers: password: ${env.PGVECTOR_PASSWORD:=} kvstore: type: sqlite - db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/starter}/pgvector_registry.db + db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/ci-tests}/pgvector_registry.db files: - provider_id: meta-reference-files provider_type: inline::localfs config: - storage_dir: ${env.FILES_STORAGE_DIR:=~/.llama/distributions/starter/files} + storage_dir: ${env.FILES_STORAGE_DIR:=~/.llama/distributions/ci-tests/files} metadata_store: type: sqlite - db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/starter}/files_metadata.db + db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/ci-tests}/files_metadata.db safety: - provider_id: llama-guard provider_type: inline::llama-guard diff --git a/llama_stack/distributions/meta-reference-gpu/doc_template.md b/llama_stack/distributions/meta-reference-gpu/doc_template.md index ff45c3826..602d053c4 100644 --- a/llama_stack/distributions/meta-reference-gpu/doc_template.md +++ b/llama_stack/distributions/meta-reference-gpu/doc_template.md @@ -1,7 +1,7 @@ --- orphan: true --- -# Meta Reference Distribution +# Meta Reference GPU Distribution ```{toctree} :maxdepth: 2 @@ -29,7 +29,7 @@ The following environment variables can be configured: ## Prerequisite: Downloading Models -Please use `llama model list --downloaded` to check that you have llama model checkpoints downloaded in `~/.llama` before proceeding. See [installation guide](https://llama-stack.readthedocs.io/en/latest/references/llama_cli_reference/download_models.html) here to download the models. Run `llama model list` to see the available models to download, and `llama model download` to download the checkpoints. +Please use `llama model list --downloaded` to check that you have llama model checkpoints downloaded in `~/.llama` before proceeding. See [installation guide](../../references/llama_cli_reference/download_models.md) here to download the models. Run `llama model list` to see the available models to download, and `llama model download` to download the checkpoints. ``` $ llama model list --downloaded diff --git a/llama_stack/distributions/nvidia/build.yaml b/llama_stack/distributions/nvidia/build.yaml index f3e73a2c1..bc78756d2 100644 --- a/llama_stack/distributions/nvidia/build.yaml +++ b/llama_stack/distributions/nvidia/build.yaml @@ -23,6 +23,8 @@ distribution_spec: - provider_type: inline::basic tool_runtime: - provider_type: inline::rag-runtime + files: + - provider_type: inline::localfs image_type: venv additional_pip_packages: - aiosqlite diff --git a/llama_stack/distributions/nvidia/doc_template.md b/llama_stack/distributions/nvidia/doc_template.md index 56e99e523..fbee17ef8 100644 --- a/llama_stack/distributions/nvidia/doc_template.md +++ b/llama_stack/distributions/nvidia/doc_template.md @@ -49,22 +49,22 @@ The deployed platform includes the NIM Proxy microservice, which is the service ### Datasetio API: NeMo Data Store The NeMo Data Store microservice serves as the default file storage solution for the NeMo microservices platform. It exposts APIs compatible with the Hugging Face Hub client (`HfApi`), so you can use the client to interact with Data Store. The `NVIDIA_DATASETS_URL` environment variable should point to your NeMo Data Store endpoint. -See the {repopath}`NVIDIA Datasetio docs::llama_stack/providers/remote/datasetio/nvidia/README.md` for supported features and example usage. +See the [NVIDIA Datasetio docs](https://github.com/meta-llama/llama-stack/blob/main/llama_stack/providers/remote/datasetio/nvidia/README.md) for supported features and example usage. ### Eval API: NeMo Evaluator The NeMo Evaluator microservice supports evaluation of LLMs. Launching an Evaluation job with NeMo Evaluator requires an Evaluation Config (an object that contains metadata needed by the job). A Llama Stack Benchmark maps to an Evaluation Config, so registering a Benchmark creates an Evaluation Config in NeMo Evaluator. The `NVIDIA_EVALUATOR_URL` environment variable should point to your NeMo Microservices endpoint. -See the {repopath}`NVIDIA Eval docs::llama_stack/providers/remote/eval/nvidia/README.md` for supported features and example usage. +See the [NVIDIA Eval docs](https://github.com/meta-llama/llama-stack/blob/main/llama_stack/providers/remote/eval/nvidia/README.md) for supported features and example usage. ### Post-Training API: NeMo Customizer -The NeMo Customizer microservice supports fine-tuning models. You can reference {repopath}`this list of supported models::llama_stack/providers/remote/post_training/nvidia/models.py` that can be fine-tuned using Llama Stack. The `NVIDIA_CUSTOMIZER_URL` environment variable should point to your NeMo Microservices endpoint. +The NeMo Customizer microservice supports fine-tuning models. You can reference [this list of supported models](https://github.com/meta-llama/llama-stack/blob/main/llama_stack/providers/remote/post_training/nvidia/models.py) that can be fine-tuned using Llama Stack. The `NVIDIA_CUSTOMIZER_URL` environment variable should point to your NeMo Microservices endpoint. -See the {repopath}`NVIDIA Post-Training docs::llama_stack/providers/remote/post_training/nvidia/README.md` for supported features and example usage. +See the [NVIDIA Post-Training docs](https://github.com/meta-llama/llama-stack/blob/main/llama_stack/providers/remote/post_training/nvidia/README.md) for supported features and example usage. ### Safety API: NeMo Guardrails The NeMo Guardrails microservice sits between your application and the LLM, and adds checks and content moderation to a model. The `GUARDRAILS_SERVICE_URL` environment variable should point to your NeMo Microservices endpoint. -See the {repopath}`NVIDIA Safety docs::llama_stack/providers/remote/safety/nvidia/README.md` for supported features and example usage. +See the [NVIDIA Safety docs](https://github.com/meta-llama/llama-stack/blob/main/llama_stack/providers/remote/safety/nvidia/README.md) for supported features and example usage. ## Deploying models In order to use a registered model with the Llama Stack APIs, ensure the corresponding NIM is deployed to your environment. For example, you can use the NIM Proxy microservice to deploy `meta/llama-3.2-1b-instruct`. @@ -138,4 +138,4 @@ llama stack run ./run.yaml \ ``` ## Example Notebooks -For examples of how to use the NVIDIA Distribution to run inference, fine-tune, evaluate, and run safety checks on your LLMs, you can reference the example notebooks in {repopath}`docs/notebooks/nvidia`. +For examples of how to use the NVIDIA Distribution to run inference, fine-tune, evaluate, and run safety checks on your LLMs, you can reference the example notebooks in [docs/notebooks/nvidia](https://github.com/meta-llama/llama-stack/tree/main/docs/notebooks/nvidia). diff --git a/llama_stack/distributions/nvidia/nvidia.py b/llama_stack/distributions/nvidia/nvidia.py index aedda0ae9..b41eea130 100644 --- a/llama_stack/distributions/nvidia/nvidia.py +++ b/llama_stack/distributions/nvidia/nvidia.py @@ -7,15 +7,15 @@ from pathlib import Path from llama_stack.core.datatypes import BuildProvider, ModelInput, Provider, ShieldInput, ToolGroupInput -from llama_stack.distributions.template import DistributionTemplate, RunConfigSettings, get_model_registry +from llama_stack.distributions.template import DistributionTemplate, RunConfigSettings +from llama_stack.providers.inline.files.localfs.config import LocalfsFilesImplConfig from llama_stack.providers.remote.datasetio.nvidia import NvidiaDatasetIOConfig from llama_stack.providers.remote.eval.nvidia import NVIDIAEvalConfig from llama_stack.providers.remote.inference.nvidia import NVIDIAConfig -from llama_stack.providers.remote.inference.nvidia.models import MODEL_ENTRIES from llama_stack.providers.remote.safety.nvidia import NVIDIASafetyConfig -def get_distribution_template() -> DistributionTemplate: +def get_distribution_template(name: str = "nvidia") -> DistributionTemplate: providers = { "inference": [BuildProvider(provider_type="remote::nvidia")], "vector_io": [BuildProvider(provider_type="inline::faiss")], @@ -30,6 +30,7 @@ def get_distribution_template() -> DistributionTemplate: ], "scoring": [BuildProvider(provider_type="inline::basic")], "tool_runtime": [BuildProvider(provider_type="inline::rag-runtime")], + "files": [BuildProvider(provider_type="inline::localfs")], } inference_provider = Provider( @@ -52,6 +53,11 @@ def get_distribution_template() -> DistributionTemplate: provider_type="remote::nvidia", config=NVIDIAEvalConfig.sample_run_config(), ) + files_provider = Provider( + provider_id="meta-reference-files", + provider_type="inline::localfs", + config=LocalfsFilesImplConfig.sample_run_config(f"~/.llama/distributions/{name}"), + ) inference_model = ModelInput( model_id="${env.INFERENCE_MODEL}", provider_id="nvidia", @@ -61,9 +67,6 @@ def get_distribution_template() -> DistributionTemplate: provider_id="nvidia", ) - available_models = { - "nvidia": MODEL_ENTRIES, - } default_tool_groups = [ ToolGroupInput( toolgroup_id="builtin::rag", @@ -71,23 +74,21 @@ def get_distribution_template() -> DistributionTemplate: ), ] - default_models, _ = get_model_registry(available_models) return DistributionTemplate( - name="nvidia", + name=name, distro_type="self_hosted", description="Use NVIDIA NIM for running LLM inference, evaluation and safety", container_image=None, template_path=Path(__file__).parent / "doc_template.md", providers=providers, - available_models_by_provider=available_models, run_configs={ "run.yaml": RunConfigSettings( provider_overrides={ "inference": [inference_provider], "datasetio": [datasetio_provider], "eval": [eval_provider], + "files": [files_provider], }, - default_models=default_models, default_tool_groups=default_tool_groups, ), "run-with-safety.yaml": RunConfigSettings( @@ -97,6 +98,7 @@ def get_distribution_template() -> DistributionTemplate: safety_provider, ], "eval": [eval_provider], + "files": [files_provider], }, default_models=[inference_model, safety_model], default_shields=[ShieldInput(shield_id="${env.SAFETY_MODEL}", provider_id="nvidia")], diff --git a/llama_stack/distributions/nvidia/run-with-safety.yaml b/llama_stack/distributions/nvidia/run-with-safety.yaml index 015724050..5a958116e 100644 --- a/llama_stack/distributions/nvidia/run-with-safety.yaml +++ b/llama_stack/distributions/nvidia/run-with-safety.yaml @@ -4,6 +4,7 @@ apis: - agents - datasetio - eval +- files - inference - post_training - safety @@ -88,6 +89,14 @@ providers: tool_runtime: - provider_id: rag-runtime provider_type: inline::rag-runtime + files: + - provider_id: meta-reference-files + provider_type: inline::localfs + config: + storage_dir: ${env.FILES_STORAGE_DIR:=~/.llama/distributions/nvidia/files} + metadata_store: + type: sqlite + db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/nvidia}/files_metadata.db metadata_store: type: sqlite db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/nvidia}/registry.db diff --git a/llama_stack/distributions/nvidia/run.yaml b/llama_stack/distributions/nvidia/run.yaml index 8e915f586..3f3cfc514 100644 --- a/llama_stack/distributions/nvidia/run.yaml +++ b/llama_stack/distributions/nvidia/run.yaml @@ -4,6 +4,7 @@ apis: - agents - datasetio - eval +- files - inference - post_training - safety @@ -77,91 +78,21 @@ providers: tool_runtime: - provider_id: rag-runtime provider_type: inline::rag-runtime + files: + - provider_id: meta-reference-files + provider_type: inline::localfs + config: + storage_dir: ${env.FILES_STORAGE_DIR:=~/.llama/distributions/nvidia/files} + metadata_store: + type: sqlite + db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/nvidia}/files_metadata.db metadata_store: type: sqlite db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/nvidia}/registry.db inference_store: type: sqlite db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/nvidia}/inference_store.db -models: -- metadata: {} - model_id: meta/llama3-8b-instruct - provider_id: nvidia - provider_model_id: meta/llama3-8b-instruct - model_type: llm -- metadata: {} - model_id: meta/llama3-70b-instruct - provider_id: nvidia - provider_model_id: meta/llama3-70b-instruct - model_type: llm -- metadata: {} - model_id: meta/llama-3.1-8b-instruct - provider_id: nvidia - provider_model_id: meta/llama-3.1-8b-instruct - model_type: llm -- metadata: {} - model_id: meta/llama-3.1-70b-instruct - provider_id: nvidia - provider_model_id: meta/llama-3.1-70b-instruct - model_type: llm -- metadata: {} - model_id: meta/llama-3.1-405b-instruct - provider_id: nvidia - provider_model_id: meta/llama-3.1-405b-instruct - model_type: llm -- metadata: {} - model_id: meta/llama-3.2-1b-instruct - provider_id: nvidia - provider_model_id: meta/llama-3.2-1b-instruct - model_type: llm -- metadata: {} - model_id: meta/llama-3.2-3b-instruct - provider_id: nvidia - provider_model_id: meta/llama-3.2-3b-instruct - model_type: llm -- metadata: {} - model_id: meta/llama-3.2-11b-vision-instruct - provider_id: nvidia - provider_model_id: meta/llama-3.2-11b-vision-instruct - model_type: llm -- metadata: {} - model_id: meta/llama-3.2-90b-vision-instruct - provider_id: nvidia - provider_model_id: meta/llama-3.2-90b-vision-instruct - model_type: llm -- metadata: {} - model_id: meta/llama-3.3-70b-instruct - provider_id: nvidia - provider_model_id: meta/llama-3.3-70b-instruct - model_type: llm -- metadata: - embedding_dimension: 2048 - context_length: 8192 - model_id: nvidia/llama-3.2-nv-embedqa-1b-v2 - provider_id: nvidia - provider_model_id: nvidia/llama-3.2-nv-embedqa-1b-v2 - model_type: embedding -- metadata: - embedding_dimension: 1024 - context_length: 512 - model_id: nvidia/nv-embedqa-e5-v5 - provider_id: nvidia - provider_model_id: nvidia/nv-embedqa-e5-v5 - model_type: embedding -- metadata: - embedding_dimension: 4096 - context_length: 512 - model_id: nvidia/nv-embedqa-mistral-7b-v2 - provider_id: nvidia - provider_model_id: nvidia/nv-embedqa-mistral-7b-v2 - model_type: embedding -- metadata: - embedding_dimension: 1024 - context_length: 512 - model_id: snowflake/arctic-embed-l - provider_id: nvidia - provider_model_id: snowflake/arctic-embed-l - model_type: embedding +models: [] shields: [] vector_dbs: [] datasets: [] diff --git a/llama_stack/distributions/open-benchmark/open_benchmark.py b/llama_stack/distributions/open-benchmark/open_benchmark.py index af08ac7ba..1d84512cd 100644 --- a/llama_stack/distributions/open-benchmark/open_benchmark.py +++ b/llama_stack/distributions/open-benchmark/open_benchmark.py @@ -43,7 +43,7 @@ def get_inference_providers() -> tuple[list[Provider], dict[str, list[ProviderMo "openai", [ ProviderModelEntry( - provider_model_id="openai/gpt-4o", + provider_model_id="gpt-4o", model_type=ModelType.llm, ) ], @@ -53,7 +53,7 @@ def get_inference_providers() -> tuple[list[Provider], dict[str, list[ProviderMo "anthropic", [ ProviderModelEntry( - provider_model_id="anthropic/claude-3-5-sonnet-latest", + provider_model_id="claude-3-5-sonnet-latest", model_type=ModelType.llm, ) ], @@ -206,13 +206,6 @@ def get_distribution_template() -> DistributionTemplate: uri="huggingface://datasets/llamastack/math_500?split=test", ), ), - DatasetInput( - dataset_id="bfcl", - purpose=DatasetPurpose.eval_messages_answer, - source=URIDataSource( - uri="huggingface://datasets/llamastack/bfcl_v3?split=train", - ), - ), DatasetInput( dataset_id="ifeval", purpose=DatasetPurpose.eval_messages_answer, @@ -250,11 +243,6 @@ def get_distribution_template() -> DistributionTemplate: dataset_id="math_500", scoring_functions=["basic::regex_parser_math_response"], ), - BenchmarkInput( - benchmark_id="meta-reference-bfcl", - dataset_id="bfcl", - scoring_functions=["basic::bfcl"], - ), BenchmarkInput( benchmark_id="meta-reference-ifeval", dataset_id="ifeval", diff --git a/llama_stack/distributions/open-benchmark/run.yaml b/llama_stack/distributions/open-benchmark/run.yaml index 779bca47e..d068a0b5a 100644 --- a/llama_stack/distributions/open-benchmark/run.yaml +++ b/llama_stack/distributions/open-benchmark/run.yaml @@ -136,14 +136,14 @@ inference_store: db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/open-benchmark}/inference_store.db models: - metadata: {} - model_id: openai/gpt-4o + model_id: gpt-4o provider_id: openai - provider_model_id: openai/gpt-4o + provider_model_id: gpt-4o model_type: llm - metadata: {} - model_id: anthropic/claude-3-5-sonnet-latest + model_id: claude-3-5-sonnet-latest provider_id: anthropic - provider_model_id: anthropic/claude-3-5-sonnet-latest + provider_model_id: claude-3-5-sonnet-latest model_type: llm - metadata: {} model_id: gemini/gemini-1.5-flash @@ -188,12 +188,6 @@ datasets: uri: huggingface://datasets/llamastack/math_500?split=test metadata: {} dataset_id: math_500 -- purpose: eval/messages-answer - source: - type: uri - uri: huggingface://datasets/llamastack/bfcl_v3?split=train - metadata: {} - dataset_id: bfcl - purpose: eval/messages-answer source: type: uri @@ -228,11 +222,6 @@ benchmarks: - basic::regex_parser_math_response metadata: {} benchmark_id: meta-reference-math-500 -- dataset_id: bfcl - scoring_functions: - - basic::bfcl - metadata: {} - benchmark_id: meta-reference-bfcl - dataset_id: ifeval scoring_functions: - basic::ifeval diff --git a/llama_stack/distributions/starter-gpu/build.yaml b/llama_stack/distributions/starter-gpu/build.yaml index ff7c58e6f..05a2bf180 100644 --- a/llama_stack/distributions/starter-gpu/build.yaml +++ b/llama_stack/distributions/starter-gpu/build.yaml @@ -18,6 +18,7 @@ distribution_spec: - provider_type: remote::vertexai - provider_type: remote::groq - provider_type: remote::sambanova + - provider_type: remote::azure - provider_type: inline::sentence-transformers vector_io: - provider_type: inline::faiss diff --git a/llama_stack/distributions/starter-gpu/run.yaml b/llama_stack/distributions/starter-gpu/run.yaml index 8aed61519..786506706 100644 --- a/llama_stack/distributions/starter-gpu/run.yaml +++ b/llama_stack/distributions/starter-gpu/run.yaml @@ -81,6 +81,13 @@ providers: config: url: https://api.sambanova.ai/v1 api_key: ${env.SAMBANOVA_API_KEY:=} + - provider_id: ${env.AZURE_API_KEY:+azure} + provider_type: remote::azure + config: + api_key: ${env.AZURE_API_KEY:=} + api_base: ${env.AZURE_API_BASE:=} + api_version: ${env.AZURE_API_VERSION:=} + api_type: ${env.AZURE_API_TYPE:=} - provider_id: sentence-transformers provider_type: inline::sentence-transformers vector_io: @@ -89,28 +96,28 @@ providers: config: kvstore: type: sqlite - db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/starter}/faiss_store.db + db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/starter-gpu}/faiss_store.db - provider_id: sqlite-vec provider_type: inline::sqlite-vec config: - db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/starter}/sqlite_vec.db + db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/starter-gpu}/sqlite_vec.db kvstore: type: sqlite - db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/starter}/sqlite_vec_registry.db + db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/starter-gpu}/sqlite_vec_registry.db - provider_id: ${env.MILVUS_URL:+milvus} provider_type: inline::milvus config: - db_path: ${env.MILVUS_DB_PATH:=~/.llama/distributions/starter}/milvus.db + db_path: ${env.MILVUS_DB_PATH:=~/.llama/distributions/starter-gpu}/milvus.db kvstore: type: sqlite - db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/starter}/milvus_registry.db + db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/starter-gpu}/milvus_registry.db - provider_id: ${env.CHROMADB_URL:+chromadb} provider_type: remote::chromadb config: url: ${env.CHROMADB_URL:=} kvstore: type: sqlite - db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/starter/}/chroma_remote_registry.db + db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/starter-gpu/}/chroma_remote_registry.db - provider_id: ${env.PGVECTOR_DB:+pgvector} provider_type: remote::pgvector config: @@ -121,15 +128,15 @@ providers: password: ${env.PGVECTOR_PASSWORD:=} kvstore: type: sqlite - db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/starter}/pgvector_registry.db + db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/starter-gpu}/pgvector_registry.db files: - provider_id: meta-reference-files provider_type: inline::localfs config: - storage_dir: ${env.FILES_STORAGE_DIR:=~/.llama/distributions/starter/files} + storage_dir: ${env.FILES_STORAGE_DIR:=~/.llama/distributions/starter-gpu/files} metadata_store: type: sqlite - db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/starter}/files_metadata.db + db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/starter-gpu}/files_metadata.db safety: - provider_id: llama-guard provider_type: inline::llama-guard diff --git a/llama_stack/distributions/starter-gpu/starter_gpu.py b/llama_stack/distributions/starter-gpu/starter_gpu.py index 245334749..e7efcb283 100644 --- a/llama_stack/distributions/starter-gpu/starter_gpu.py +++ b/llama_stack/distributions/starter-gpu/starter_gpu.py @@ -11,9 +11,7 @@ from ..starter.starter import get_distribution_template as get_starter_distribut def get_distribution_template() -> DistributionTemplate: - template = get_starter_distribution_template() - name = "starter-gpu" - template.name = name + template = get_starter_distribution_template(name="starter-gpu") template.description = "Quick start template for running Llama Stack with several popular providers. This distribution is intended for GPU-enabled environments." template.providers["post_training"] = [ diff --git a/llama_stack/distributions/starter/build.yaml b/llama_stack/distributions/starter/build.yaml index e84e528da..2f0cd24fd 100644 --- a/llama_stack/distributions/starter/build.yaml +++ b/llama_stack/distributions/starter/build.yaml @@ -18,6 +18,7 @@ distribution_spec: - provider_type: remote::vertexai - provider_type: remote::groq - provider_type: remote::sambanova + - provider_type: remote::azure - provider_type: inline::sentence-transformers vector_io: - provider_type: inline::faiss diff --git a/llama_stack/distributions/starter/run.yaml b/llama_stack/distributions/starter/run.yaml index a3962b8aa..2814b2ced 100644 --- a/llama_stack/distributions/starter/run.yaml +++ b/llama_stack/distributions/starter/run.yaml @@ -81,6 +81,13 @@ providers: config: url: https://api.sambanova.ai/v1 api_key: ${env.SAMBANOVA_API_KEY:=} + - provider_id: ${env.AZURE_API_KEY:+azure} + provider_type: remote::azure + config: + api_key: ${env.AZURE_API_KEY:=} + api_base: ${env.AZURE_API_BASE:=} + api_version: ${env.AZURE_API_VERSION:=} + api_type: ${env.AZURE_API_TYPE:=} - provider_id: sentence-transformers provider_type: inline::sentence-transformers vector_io: diff --git a/llama_stack/distributions/starter/starter.py b/llama_stack/distributions/starter/starter.py index a4bbc6371..6bee51ff0 100644 --- a/llama_stack/distributions/starter/starter.py +++ b/llama_stack/distributions/starter/starter.py @@ -59,6 +59,7 @@ ENABLED_INFERENCE_PROVIDERS = [ "cerebras", "nvidia", "bedrock", + "azure", ] INFERENCE_PROVIDER_IDS = { @@ -68,6 +69,7 @@ INFERENCE_PROVIDER_IDS = { "cerebras": "${env.CEREBRAS_API_KEY:+cerebras}", "nvidia": "${env.NVIDIA_API_KEY:+nvidia}", "vertexai": "${env.VERTEX_AI_PROJECT:+vertexai}", + "azure": "${env.AZURE_API_KEY:+azure}", } @@ -76,12 +78,12 @@ def get_remote_inference_providers() -> list[Provider]: remote_providers = [ provider for provider in available_providers() - if isinstance(provider, RemoteProviderSpec) and provider.adapter.adapter_type in ENABLED_INFERENCE_PROVIDERS + if isinstance(provider, RemoteProviderSpec) and provider.adapter_type in ENABLED_INFERENCE_PROVIDERS ] inference_providers = [] for provider_spec in remote_providers: - provider_type = provider_spec.adapter.adapter_type + provider_type = provider_spec.adapter_type if provider_type in INFERENCE_PROVIDER_IDS: provider_id = INFERENCE_PROVIDER_IDS[provider_type] @@ -99,9 +101,8 @@ def get_remote_inference_providers() -> list[Provider]: return inference_providers -def get_distribution_template() -> DistributionTemplate: +def get_distribution_template(name: str = "starter") -> DistributionTemplate: remote_inference_providers = get_remote_inference_providers() - name = "starter" providers = { "inference": [BuildProvider(provider_type=p.provider_type, module=p.module) for p in remote_inference_providers] @@ -278,5 +279,21 @@ def get_distribution_template() -> DistributionTemplate: "http://localhost:11434", "Ollama URL", ), + "AZURE_API_KEY": ( + "", + "Azure API Key", + ), + "AZURE_API_BASE": ( + "", + "Azure API Base", + ), + "AZURE_API_VERSION": ( + "", + "Azure API Version", + ), + "AZURE_API_TYPE": ( + "azure", + "Azure API Type", + ), }, ) diff --git a/llama_stack/distributions/watsonx/run.yaml b/llama_stack/distributions/watsonx/run.yaml index f5fe31bef..92f367910 100644 --- a/llama_stack/distributions/watsonx/run.yaml +++ b/llama_stack/distributions/watsonx/run.yaml @@ -10,6 +10,7 @@ apis: - telemetry - tool_runtime - vector_io +- files providers: inference: - provider_id: watsonx @@ -94,6 +95,14 @@ providers: provider_type: inline::rag-runtime - provider_id: model-context-protocol provider_type: remote::model-context-protocol + files: + - provider_id: meta-reference-files + provider_type: inline::localfs + config: + storage_dir: ${env.FILES_STORAGE_DIR:=~/.llama/distributions/watsonx/files} + metadata_store: + type: sqlite + db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/watsonx}/files_metadata.db metadata_store: type: sqlite db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/watsonx}/registry.db diff --git a/llama_stack/distributions/watsonx/watsonx.py b/llama_stack/distributions/watsonx/watsonx.py index 1ef2ef339..c3cab5d1b 100644 --- a/llama_stack/distributions/watsonx/watsonx.py +++ b/llama_stack/distributions/watsonx/watsonx.py @@ -9,6 +9,7 @@ from pathlib import Path from llama_stack.apis.models import ModelType from llama_stack.core.datatypes import BuildProvider, ModelInput, Provider, ToolGroupInput from llama_stack.distributions.template import DistributionTemplate, RunConfigSettings, get_model_registry +from llama_stack.providers.inline.files.localfs.config import LocalfsFilesImplConfig from llama_stack.providers.inline.inference.sentence_transformers import ( SentenceTransformersInferenceConfig, ) @@ -16,7 +17,7 @@ from llama_stack.providers.remote.inference.watsonx import WatsonXConfig from llama_stack.providers.remote.inference.watsonx.models import MODEL_ENTRIES -def get_distribution_template() -> DistributionTemplate: +def get_distribution_template(name: str = "watsonx") -> DistributionTemplate: providers = { "inference": [ BuildProvider(provider_type="remote::watsonx"), @@ -42,6 +43,7 @@ def get_distribution_template() -> DistributionTemplate: BuildProvider(provider_type="inline::rag-runtime"), BuildProvider(provider_type="remote::model-context-protocol"), ], + "files": [BuildProvider(provider_type="inline::localfs")], } inference_provider = Provider( @@ -79,9 +81,14 @@ def get_distribution_template() -> DistributionTemplate: }, ) + files_provider = Provider( + provider_id="meta-reference-files", + provider_type="inline::localfs", + config=LocalfsFilesImplConfig.sample_run_config(f"~/.llama/distributions/{name}"), + ) default_models, _ = get_model_registry(available_models) return DistributionTemplate( - name="watsonx", + name=name, distro_type="remote_hosted", description="Use watsonx for running LLM inference", container_image=None, @@ -92,6 +99,7 @@ def get_distribution_template() -> DistributionTemplate: "run.yaml": RunConfigSettings( provider_overrides={ "inference": [inference_provider, embedding_provider], + "files": [files_provider], }, default_models=default_models + [embedding_model], default_tool_groups=default_tool_groups, diff --git a/llama_stack/providers/datatypes.py b/llama_stack/providers/datatypes.py index 5e15dd8e1..c8ff9cecb 100644 --- a/llama_stack/providers/datatypes.py +++ b/llama_stack/providers/datatypes.py @@ -131,6 +131,15 @@ class ProviderSpec(BaseModel): """, ) + pip_packages: list[str] = Field( + default_factory=list, + description="The pip dependencies needed for this implementation", + ) + + provider_data_validator: str | None = Field( + default=None, + ) + is_external: bool = Field(default=False, description="Notes whether this provider is an external provider.") # used internally by the resolver; this is a hack for now @@ -145,45 +154,8 @@ class RoutingTable(Protocol): async def get_provider_impl(self, routing_key: str) -> Any: ... -# TODO: this can now be inlined into RemoteProviderSpec -@json_schema_type -class AdapterSpec(BaseModel): - adapter_type: str = Field( - ..., - description="Unique identifier for this adapter", - ) - module: str = Field( - default_factory=str, - description=""" -Fully-qualified name of the module to import. The module is expected to have: - - - `get_adapter_impl(config, deps)`: returns the adapter implementation -""", - ) - pip_packages: list[str] = Field( - default_factory=list, - description="The pip dependencies needed for this implementation", - ) - config_class: str = Field( - description="Fully-qualified classname of the config for this provider", - ) - provider_data_validator: str | None = Field( - default=None, - ) - description: str | None = Field( - default=None, - description=""" -A description of the provider. This is used to display in the documentation. -""", - ) - - @json_schema_type class InlineProviderSpec(ProviderSpec): - pip_packages: list[str] = Field( - default_factory=list, - description="The pip dependencies needed for this implementation", - ) container_image: str | None = Field( default=None, description=""" @@ -191,10 +163,6 @@ The container image to use for this implementation. If one is provided, pip_pack If a provider depends on other providers, the dependencies MUST NOT specify a container image. """, ) - # module field is inherited from ProviderSpec - provider_data_validator: str | None = Field( - default=None, - ) description: str | None = Field( default=None, description=""" @@ -223,10 +191,15 @@ class RemoteProviderConfig(BaseModel): @json_schema_type class RemoteProviderSpec(ProviderSpec): - adapter: AdapterSpec = Field( + adapter_type: str = Field( + ..., + description="Unique identifier for this adapter", + ) + + description: str | None = Field( + default=None, description=""" -If some code is needed to convert the remote responses into Llama Stack compatible -API responses, specify the adapter here. +A description of the provider. This is used to display in the documentation. """, ) @@ -234,33 +207,6 @@ API responses, specify the adapter here. def container_image(self) -> str | None: return None - # module field is inherited from ProviderSpec - - @property - def pip_packages(self) -> list[str]: - return self.adapter.pip_packages - - @property - def provider_data_validator(self) -> str | None: - return self.adapter.provider_data_validator - - -def remote_provider_spec( - api: Api, - adapter: AdapterSpec, - api_dependencies: list[Api] | None = None, - optional_api_dependencies: list[Api] | None = None, -) -> RemoteProviderSpec: - return RemoteProviderSpec( - api=api, - provider_type=f"remote::{adapter.adapter_type}", - config_class=adapter.config_class, - module=adapter.module, - adapter=adapter, - api_dependencies=api_dependencies or [], - optional_api_dependencies=optional_api_dependencies or [], - ) - class HealthStatus(StrEnum): OK = "OK" diff --git a/llama_stack/providers/inline/batches/reference/batches.py b/llama_stack/providers/inline/batches/reference/batches.py index 26f0ad15a..e049518a4 100644 --- a/llama_stack/providers/inline/batches/reference/batches.py +++ b/llama_stack/providers/inline/batches/reference/batches.py @@ -178,9 +178,9 @@ class ReferenceBatchesImpl(Batches): # TODO: set expiration time for garbage collection - if endpoint not in ["/v1/chat/completions"]: + if endpoint not in ["/v1/chat/completions", "/v1/completions"]: raise ValueError( - f"Invalid endpoint: {endpoint}. Supported values: /v1/chat/completions. Code: invalid_value. Param: endpoint", + f"Invalid endpoint: {endpoint}. Supported values: /v1/chat/completions, /v1/completions. Code: invalid_value. Param: endpoint", ) if completion_window != "24h": @@ -424,13 +424,21 @@ class ReferenceBatchesImpl(Batches): ) valid = False - for param, expected_type, type_string in [ - ("model", str, "a string"), - # messages is specific to /v1/chat/completions - # we could skip validating messages here and let inference fail. however, - # that would be a very expensive way to find out messages is wrong. - ("messages", list, "an array"), # TODO: allow messages to be a string? - ]: + if batch.endpoint == "/v1/chat/completions": + required_params = [ + ("model", str, "a string"), + # messages is specific to /v1/chat/completions + # we could skip validating messages here and let inference fail. however, + # that would be a very expensive way to find out messages is wrong. + ("messages", list, "an array"), # TODO: allow messages to be a string? + ] + else: # /v1/completions + required_params = [ + ("model", str, "a string"), + ("prompt", str, "a string"), # TODO: allow prompt to be a list of strings?? + ] + + for param, expected_type, type_string in required_params: if param not in body: errors.append( BatchError( @@ -591,20 +599,37 @@ class ReferenceBatchesImpl(Batches): try: # TODO(SECURITY): review body for security issues - request.body["messages"] = [convert_to_openai_message_param(msg) for msg in request.body["messages"]] - chat_response = await self.inference_api.openai_chat_completion(**request.body) + if request.url == "/v1/chat/completions": + request.body["messages"] = [convert_to_openai_message_param(msg) for msg in request.body["messages"]] + chat_response = await self.inference_api.openai_chat_completion(**request.body) - # this is for mypy, we don't allow streaming so we'll get the right type - assert hasattr(chat_response, "model_dump_json"), "Chat response must have model_dump_json method" - return { - "id": request_id, - "custom_id": request.custom_id, - "response": { - "status_code": 200, - "request_id": request_id, # TODO: should this be different? - "body": chat_response.model_dump_json(), - }, - } + # this is for mypy, we don't allow streaming so we'll get the right type + assert hasattr(chat_response, "model_dump_json"), "Chat response must have model_dump_json method" + return { + "id": request_id, + "custom_id": request.custom_id, + "response": { + "status_code": 200, + "request_id": request_id, # TODO: should this be different? + "body": chat_response.model_dump_json(), + }, + } + else: # /v1/completions + completion_response = await self.inference_api.openai_completion(**request.body) + + # this is for mypy, we don't allow streaming so we'll get the right type + assert hasattr(completion_response, "model_dump_json"), ( + "Completion response must have model_dump_json method" + ) + return { + "id": request_id, + "custom_id": request.custom_id, + "response": { + "status_code": 200, + "request_id": request_id, + "body": completion_response.model_dump_json(), + }, + } except Exception as e: logger.info(f"Error processing request {request.custom_id} in batch {batch_id}: {e}") return { diff --git a/llama_stack/providers/inline/eval/meta_reference/eval.py b/llama_stack/providers/inline/eval/meta_reference/eval.py index 9ae2018c4..a03e8951c 100644 --- a/llama_stack/providers/inline/eval/meta_reference/eval.py +++ b/llama_stack/providers/inline/eval/meta_reference/eval.py @@ -75,6 +75,13 @@ class MetaReferenceEvalImpl( ) self.benchmarks[task_def.identifier] = task_def + async def unregister_benchmark(self, benchmark_id: str) -> None: + if benchmark_id in self.benchmarks: + del self.benchmarks[benchmark_id] + + key = f"{EVAL_TASKS_PREFIX}{benchmark_id}" + await self.kvstore.delete(key) + async def run_eval( self, benchmark_id: str, diff --git a/llama_stack/providers/inline/files/localfs/files.py b/llama_stack/providers/inline/files/localfs/files.py index 4f6d571a4..65cf8d815 100644 --- a/llama_stack/providers/inline/files/localfs/files.py +++ b/llama_stack/providers/inline/files/localfs/files.py @@ -44,7 +44,7 @@ class LocalfsFilesImpl(Files): storage_path.mkdir(parents=True, exist_ok=True) # Initialize SQL store for metadata - self.sql_store = AuthorizedSqlStore(sqlstore_impl(self.config.metadata_store)) + self.sql_store = AuthorizedSqlStore(sqlstore_impl(self.config.metadata_store), self.policy) await self.sql_store.create_table( "openai_files", { @@ -74,7 +74,7 @@ class LocalfsFilesImpl(Files): if not self.sql_store: raise RuntimeError("Files provider not initialized") - row = await self.sql_store.fetch_one("openai_files", policy=self.policy, where={"id": file_id}) + row = await self.sql_store.fetch_one("openai_files", where={"id": file_id}) if not row: raise ResourceNotFoundError(file_id, "File", "client.files.list()") @@ -86,11 +86,16 @@ class LocalfsFilesImpl(Files): self, file: Annotated[UploadFile, File()], purpose: Annotated[OpenAIFilePurpose, Form()], + expires_after_anchor: Annotated[str | None, Form(alias="expires_after[anchor]")] = None, + expires_after_seconds: Annotated[int | None, Form(alias="expires_after[seconds]")] = None, ) -> OpenAIFileObject: """Upload a file that can be used across various endpoints.""" if not self.sql_store: raise RuntimeError("Files provider not initialized") + if expires_after_anchor is not None or expires_after_seconds is not None: + raise NotImplementedError("File expiration is not supported by this provider") + file_id = self._generate_file_id() file_path = self._get_file_path(file_id) @@ -145,7 +150,6 @@ class LocalfsFilesImpl(Files): paginated_result = await self.sql_store.fetch_all( table="openai_files", - policy=self.policy, where=where_conditions if where_conditions else None, order_by=[("created_at", order.value)], cursor=("id", after) if after else None, diff --git a/llama_stack/providers/inline/scoring/basic/scoring.py b/llama_stack/providers/inline/scoring/basic/scoring.py index 91b10daae..b19b68039 100644 --- a/llama_stack/providers/inline/scoring/basic/scoring.py +++ b/llama_stack/providers/inline/scoring/basic/scoring.py @@ -22,7 +22,6 @@ from llama_stack.providers.utils.common.data_schema_validator import ( ) from .config import BasicScoringConfig -from .scoring_fn.bfcl_scoring_fn import BFCLScoringFn from .scoring_fn.docvqa_scoring_fn import DocVQAScoringFn from .scoring_fn.equality_scoring_fn import EqualityScoringFn from .scoring_fn.ifeval_scoring_fn import IfEvalScoringFn @@ -37,7 +36,6 @@ FIXED_FNS = [ SubsetOfScoringFn, RegexParserScoringFn, RegexParserMathResponseScoringFn, - BFCLScoringFn, IfEvalScoringFn, DocVQAScoringFn, ] diff --git a/llama_stack/providers/inline/scoring/basic/scoring_fn/bfcl_scoring_fn.py b/llama_stack/providers/inline/scoring/basic/scoring_fn/bfcl_scoring_fn.py deleted file mode 100644 index b29620be2..000000000 --- a/llama_stack/providers/inline/scoring/basic/scoring_fn/bfcl_scoring_fn.py +++ /dev/null @@ -1,93 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# All rights reserved. -# -# This source code is licensed under the terms described in the LICENSE file in -# the root directory of this source tree. - -import json -import re -from typing import Any - -from llama_stack.apis.scoring import ScoringResultRow -from llama_stack.apis.scoring_functions import ScoringFnParams -from llama_stack.providers.utils.scoring.base_scoring_fn import RegisteredBaseScoringFn - -from ..utils.bfcl.ast_parser import decode_ast -from ..utils.bfcl.checker import ast_checker, is_empty_output -from .fn_defs.bfcl import bfcl - - -def postprocess(x: dict[str, Any], test_category: str) -> dict[str, Any]: - contain_func_call = False - error = None - error_type = None - checker_result = {} - try: - prediction = decode_ast(x["generated_answer"], x["language"]) or "" - contain_func_call = True - # if not is_function_calling_format_output(prediction): - if is_empty_output(prediction): - contain_func_call = False - error = "Did not output in the specified format. Note: the model_result is wrapped in a string to ensure json serializability." - error_type = "ast_decoder:decoder_wrong_output_format" - else: - checker_result = ast_checker( - json.loads(x["function"]), - prediction, - json.loads(x["ground_truth"]), - x["language"], - test_category=test_category, - model_name="", - ) - except Exception as e: - prediction = "" - error = f"Invalid syntax. Failed to decode AST. {str(e)}" - error_type = "ast_decoder:decoder_failed" - return { - "prediction": prediction, - "contain_func_call": contain_func_call, - "valid": checker_result.get("valid", False), - "error": error or checker_result.get("error", ""), - "error_type": error_type or checker_result.get("error_type", ""), - } - - -def gen_valid(x: dict[str, Any]) -> dict[str, float]: - return {"valid": x["valid"]} - - -def gen_relevance_acc(x: dict[str, Any]) -> dict[str, float]: - # This function serves for both relevance and irrelevance tests, which share the exact opposite logic. - # If `test_category` is "irrelevance", the model is expected to output no function call. - # No function call means either the AST decoding fails (a error message is generated) or the decoded AST does not contain any function call (such as a empty list, `[]`). - # If `test_category` is "relevance", the model is expected to output to a function call, and empty list doesn't count as a function call. - acc = not x["contain_func_call"] if "irrelevance" in x["id"] else x["contain_func_call"] - return {"valid": float(acc)} - - -class BFCLScoringFn(RegisteredBaseScoringFn): - """ - A scoring_fn for BFCL - """ - - def __init__(self, *args, **kwargs) -> None: - super().__init__(*args, **kwargs) - self.supported_fn_defs_registry = { - bfcl.identifier: bfcl, - } - - async def score_row( - self, - input_row: dict[str, Any], - scoring_fn_identifier: str | None = "bfcl", - scoring_params: ScoringFnParams | None = None, - ) -> ScoringResultRow: - test_category = re.sub(r"_[0-9_-]+$", "", input_row["id"]) - score_result = postprocess(input_row, test_category) - if test_category in {"irrelevance", "live_relevance", "live_irrelevance"}: - score = gen_relevance_acc(score_result)["valid"] - else: - score = gen_valid(score_result)["valid"] - return { - "score": float(score), - } diff --git a/llama_stack/providers/inline/scoring/basic/scoring_fn/fn_defs/bfcl.py b/llama_stack/providers/inline/scoring/basic/scoring_fn/fn_defs/bfcl.py deleted file mode 100644 index 392d92c86..000000000 --- a/llama_stack/providers/inline/scoring/basic/scoring_fn/fn_defs/bfcl.py +++ /dev/null @@ -1,21 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# All rights reserved. -# -# This source code is licensed under the terms described in the LICENSE file in -# the root directory of this source tree. - -from llama_stack.apis.common.type_system import NumberType -from llama_stack.apis.scoring_functions import ( - AggregationFunctionType, - BasicScoringFnParams, - ScoringFn, -) - -bfcl = ScoringFn( - identifier="basic::bfcl", - description="BFCL complex scoring", - return_type=NumberType(), - provider_id="basic", - provider_resource_id="bfcl", - params=BasicScoringFnParams(aggregation_functions=[AggregationFunctionType.accuracy]), -) diff --git a/llama_stack/providers/inline/scoring/basic/utils/bfcl/ast_parser.py b/llama_stack/providers/inline/scoring/basic/utils/bfcl/ast_parser.py deleted file mode 100644 index 445cdfc77..000000000 --- a/llama_stack/providers/inline/scoring/basic/utils/bfcl/ast_parser.py +++ /dev/null @@ -1,296 +0,0 @@ -# ruff: noqa -# Copyright (c) Meta Platforms, Inc. and affiliates. -# All rights reserved. -# -# This source code is licensed under the terms described in the LICENSE file in -# the root directory of this source tree. -import ast - -from .tree_sitter import get_parser - - -def parse_java_function_call(source_code): - if not source_code.endswith(";"): - source_code += ";" # Necessary for the parser not to register an error - parser = get_parser("java") - tree = parser.parse(bytes(source_code, "utf8")) - root_node = tree.root_node - - if root_node.has_error: - raise Exception("Error parsing java the source code.") - - def get_text(node): - """Returns the text represented by the node.""" - return source_code[node.start_byte : node.end_byte] - - def traverse_node(node, nested=False): - if node.type == "string_literal": - if nested: - return get_text(node) - # Strip surrounding quotes from string literals - return get_text(node)[1:-1] - elif node.type == "character_literal": - if nested: - return get_text(node) - # Strip surrounding single quotes from character literals - return get_text(node)[1:-1] - """Traverse the node to collect texts for complex structures.""" - if node.type in [ - "identifier", - "class_literal", - "type_identifier", - "method_invocation", - ]: - return get_text(node) - elif node.type == "array_creation_expression": - # Handle array creation expression specifically - type_node = node.child_by_field_name("type") - value_node = node.child_by_field_name("value") - type_text = traverse_node(type_node, True) - value_text = traverse_node(value_node, True) - return f"new {type_text}[]{value_text}" - elif node.type == "object_creation_expression": - # Handle object creation expression specifically - type_node = node.child_by_field_name("type") - arguments_node = node.child_by_field_name("arguments") - type_text = traverse_node(type_node, True) - if arguments_node: - # Process each argument carefully, avoiding unnecessary punctuation - argument_texts = [] - for child in arguments_node.children: - if child.type not in [ - ",", - "(", - ")", - ]: # Exclude commas and parentheses - argument_text = traverse_node(child, True) - argument_texts.append(argument_text) - arguments_text = ", ".join(argument_texts) - return f"new {type_text}({arguments_text})" - else: - return f"new {type_text}()" - elif node.type == "set": - # Handling sets specifically - items = [traverse_node(n, True) for n in node.children if n.type not in [",", "set"]] - return "{" + ", ".join(items) + "}" - - elif node.child_count > 0: - return "".join(traverse_node(child, True) for child in node.children) - else: - return get_text(node) - - def extract_arguments(args_node): - arguments = {} - for child in args_node.children: - if child.type == "assignment_expression": - # For named parameters - name_node, value_node = child.children[0], child.children[2] - name = get_text(name_node) - value = traverse_node(value_node) - if name in arguments: - if not isinstance(arguments[name], list): - arguments[name] = [arguments[name]] - arguments[name].append(value) - else: - arguments[name] = value - # arguments.append({'name': name, 'value': value}) - elif child.type in ["identifier", "class_literal", "set"]: - # For unnamed parameters and handling sets - value = traverse_node(child) - if None in arguments: - if not isinstance(arguments[None], list): - arguments[None] = [arguments[None]] - arguments[None].append(value) - else: - arguments[None] = value - return arguments - - def traverse(node): - if node.type == "method_invocation": - # Extract the function name and its arguments - method_name = get_text(node.child_by_field_name("name")) - class_name_node = node.child_by_field_name("object") - if class_name_node: - class_name = get_text(class_name_node) - function_name = f"{class_name}.{method_name}" - else: - function_name = method_name - arguments_node = node.child_by_field_name("arguments") - if arguments_node: - arguments = extract_arguments(arguments_node) - for key, value in arguments.items(): - if isinstance(value, list): - raise Exception("Error: Multiple arguments with the same name are not supported.") - return [{function_name: arguments}] - - else: - for child in node.children: - result = traverse(child) - if result: - return result - - result = traverse(root_node) - return result if result else {} - - -def parse_javascript_function_call(source_code): - if not source_code.endswith(";"): - source_code += ";" # Necessary for the parser not to register an error - parser = get_parser("javascript") - # Parse the source code - tree = parser.parse(bytes(source_code, "utf8")) - root_node = tree.root_node - if root_node.has_error: - raise Exception("Error js parsing the source code.") - - # Function to recursively extract argument details - def extract_arguments(node): - args = {} - for child in node.children: - if child.type == "assignment_expression": - # Extract left (name) and right (value) parts of the assignment - name = child.children[0].text.decode("utf-8") - value = child.children[2].text.decode("utf-8") - if (value.startswith('"') and value.endswith('"')) or (value.startswith("'") and value.endswith("'")): - value = value[1:-1] # Trim the quotation marks - if name in args: - if not isinstance(args[name], list): - args[name] = [args[name]] - args[name].append(value) - else: - args[name] = value - - elif child.type == "identifier" or child.type == "true": - # Handle non-named arguments and boolean values - value = child.text.decode("utf-8") - if None in args: - if not isinstance(args[None], list): - args[None] = [args[None]] - args[None].append(value) - else: - args[None] = value - return args - - # Find the function call and extract its name and arguments - if root_node.type == "program": - for child in root_node.children: - if child.type == "expression_statement": - for sub_child in child.children: - if sub_child.type == "call_expression": - function_name = sub_child.children[0].text.decode("utf8") - arguments_node = sub_child.children[1] - parameters = extract_arguments(arguments_node) - for key, value in parameters.items(): - if isinstance(value, list): - raise Exception("Error: Multiple arguments with the same name are not supported.") - result = [{function_name: parameters}] - return result - - -def ast_parse(input_str, language="Python"): - if language == "Python": - cleaned_input = input_str.strip("[]'") - parsed = ast.parse(cleaned_input, mode="eval") - extracted = [] - if isinstance(parsed.body, ast.Call): - extracted.append(resolve_ast_call(parsed.body)) - else: - for elem in parsed.body.elts: - extracted.append(resolve_ast_call(elem)) - return extracted - elif language == "Java": - return parse_java_function_call(input_str[1:-1]) # Remove the [ and ] from the string - elif language == "JavaScript": - return parse_javascript_function_call(input_str[1:-1]) - else: - raise NotImplementedError(f"Unsupported language: {language}") - - -def resolve_ast_call(elem): - # Handle nested attributes for deeply nested module paths - func_parts = [] - func_part = elem.func - while isinstance(func_part, ast.Attribute): - func_parts.append(func_part.attr) - func_part = func_part.value - if isinstance(func_part, ast.Name): - func_parts.append(func_part.id) - func_name = ".".join(reversed(func_parts)) - args_dict = {} - # Parse when args are simply passed as an unnamed dictionary arg - for arg in elem.args: - if isinstance(arg, ast.Dict): - for key, value in zip(arg.keys, arg.values): - if isinstance(key, ast.Constant): - arg_name = key.value - output = resolve_ast_by_type(value) - args_dict[arg_name] = output - for arg in elem.keywords: - output = resolve_ast_by_type(arg.value) - args_dict[arg.arg] = output - return {func_name: args_dict} - - -def resolve_ast_by_type(value): - if isinstance(value, ast.Constant): - if value.value is Ellipsis: - output = "..." - else: - output = value.value - elif isinstance(value, ast.UnaryOp): - output = -value.operand.value - elif isinstance(value, ast.List): - output = [resolve_ast_by_type(v) for v in value.elts] - elif isinstance(value, ast.Dict): - output = {resolve_ast_by_type(k): resolve_ast_by_type(v) for k, v in zip(value.keys, value.values)} - elif isinstance(value, ast.NameConstant): # Added this condition to handle boolean values - output = value.value - elif isinstance(value, ast.BinOp): # Added this condition to handle function calls as arguments - output = eval(ast.unparse(value)) - elif isinstance(value, ast.Name): - output = value.id - elif isinstance(value, ast.Call): - if len(value.keywords) == 0: - output = ast.unparse(value) - else: - output = resolve_ast_call(value) - elif isinstance(value, ast.Tuple): - output = tuple(resolve_ast_by_type(v) for v in value.elts) - elif isinstance(value, ast.Lambda): - output = eval(ast.unparse(value.body[0].value)) - elif isinstance(value, ast.Ellipsis): - output = "..." - elif isinstance(value, ast.Subscript): - try: - output = ast.unparse(value.body[0].value) - except: - output = ast.unparse(value.value) + "[" + ast.unparse(value.slice) + "]" - else: - raise Exception(f"Unsupported AST type: {type(value)}") - return output - - -def decode_ast(result, language="Python"): - func = result - func = func.replace("\n", "") # remove new line characters - if not func.startswith("["): - func = "[" + func - if not func.endswith("]"): - func = func + "]" - decoded_output = ast_parse(func, language) - return decoded_output - - -def decode_execute(result): - func = result - func = func.replace("\n", "") # remove new line characters - if not func.startswith("["): - func = "[" + func - if not func.endswith("]"): - func = func + "]" - decode_output = ast_parse(func) - execution_list = [] - for function_call in decode_output: - for key, value in function_call.items(): - execution_list.append(f"{key}({','.join([f'{k}={repr(v)}' for k, v in value.items()])})") - return execution_list diff --git a/llama_stack/providers/inline/scoring/basic/utils/bfcl/checker.py b/llama_stack/providers/inline/scoring/basic/utils/bfcl/checker.py deleted file mode 100644 index f6aab123c..000000000 --- a/llama_stack/providers/inline/scoring/basic/utils/bfcl/checker.py +++ /dev/null @@ -1,989 +0,0 @@ -# ruff: noqa -# Copyright (c) Meta Platforms, Inc. and affiliates. -# All rights reserved. -# -# This source code is licensed under the terms described in the LICENSE file in -# the root directory of this source tree. -import json -import re -import time -from typing import Any - -# Comment out for now until we actually use the rest checker in evals -# import requests # Do not remove this import even though it seems to be unused. It's used in the executable_checker_rest function. - - -class NoAPIKeyError(Exception): - def __init__(self): - self.message = "❗️Please fill in the API keys in the function_credential_config.json file. If you do not provide the API keys, the executable test category results will be inaccurate." - super().__init__(self.message) - - -REAL_TIME_MATCH_ALLOWED_DIFFERENCE = 0.2 - - -JAVA_TYPE_CONVERSION = { - "byte": int, - "short": int, - "integer": int, - "float": float, - "double": float, - "long": int, - "boolean": bool, - "char": str, - "Array": list, - "ArrayList": list, - "Set": set, - "HashMap": dict, - "Hashtable": dict, - "Queue": list, # this can be `queue.Queue` as well, for simplicity we check with list - "Stack": list, - "String": str, - "any": str, -} - -JS_TYPE_CONVERSION = { - "String": str, - "integer": int, - "float": float, - "Bigint": int, - "Boolean": bool, - "dict": dict, - "array": list, - "any": str, -} - -# We switch to conditional import for the following two imports to avoid unnecessary installations. -# User doesn't need to setup the tree-sitter packages if they are not running the test for that language. -# from js_type_converter import js_type_converter -# from java_type_converter import java_type_converter - -PYTHON_TYPE_MAPPING = { - "string": str, - "integer": int, - "float": float, - "boolean": bool, - "array": list, - "tuple": list, - "dict": dict, - "any": str, -} - -# This is the list of types that we need to recursively check its values -PYTHON_NESTED_TYPE_CHECK_LIST = ["array", "tuple"] - - -NESTED_CONVERSION_TYPE_LIST = ["Array", "ArrayList", "array"] - - -#### Helper functions for AST #### -def find_description(func_descriptions, name): - if type(func_descriptions) == list: - for func_description in func_descriptions: - if func_description["name"] == name: - return func_description - return None - else: - # it is a dict, there is only one function - return func_descriptions - - -def get_possible_answer_type(possible_answer: list): - for answer in possible_answer: - if answer != "": # Optional parameter - return type(answer) - return None - - -def type_checker( - param: str, - value, - possible_answer: list, - expected_type_description: str, - expected_type_converted, - nested_type_converted, -): - # NOTE: This type checker only supports nested type checking for one level deep. - # We didn't implement recursive type checking for nested types, as it's not needed for the current use case and it's very complex. - - result: Any = { - "valid": True, - "error": [], - "is_variable": False, - "error_type": "type_error:simple", - } - - is_variable = False - # check for the case where a variable is used instead of a actual value. - # use the type in possible_answer as the expected type - possible_answer_type = get_possible_answer_type(possible_answer) - # if possible_answer only contains optional parameters, we can't determine the type - if possible_answer_type != None: - # we are being precise here. - # in fact, possible_answer_type should always be string, as that's how we treat varibale in possible_answer - if possible_answer_type != expected_type_converted: - is_variable = True - - # value is the same type as in function description - if type(value) == expected_type_converted: - # We don't need to do recursive check for simple types - if nested_type_converted == None: - result["is_variable"] = is_variable - return result - else: - for possible_answer_item in possible_answer: - flag = True # Each parameter should match to at least one possible answer type. - # Here, we assume that each item should be the same type. We could also relax it. - if type(possible_answer_item) == list: - for value_item in value: - checker_result = type_checker( - param, - value_item, - possible_answer_item, - str(nested_type_converted), - nested_type_converted, - None, - ) - if not checker_result["valid"]: - flag = False - break - - if flag: - return {"valid": True, "error": [], "is_variable": is_variable} - - result["valid"] = False - result["error"] = [ - f"Nested type checking failed for parameter {repr(param)}. Expected outer type {expected_type_description} with inner type {str(nested_type_converted)}. Parameter value: {repr(value)}." - ] - result["error_type"] = "type_error:nested" - - # value is not as expected, check for the case where a variable is used instead of a actual value - # use the type in possible_answer as the expected type - possible_answer_type = get_possible_answer_type(possible_answer) - # if possible_answer only contains optional parameters, we can't determine the type - if possible_answer_type != None: - # we are being precise here. - # in fact, possible_answer_type should always be string, as that's how we treat varibale in possible_answer - if type(value) == possible_answer_type: - result["is_variable"] = True - return result - - result["valid"] = False - result["error"].append( - f"Incorrect type for parameter {repr(param)}. Expected type {expected_type_description}, got {type(value).__name__}. Parameter value: {repr(value)}." - ) - result["error_type"] = "type_error:simple" - return result - - -def standardize_string(input_string: str): - # This function standardizes the string by removing all the spaces, ",./-_*^" punctuation, and converting it to lowercase - # It will also convert all the single quotes to double quotes - # This is used to compare the model output with the possible answers - # We don't want to punish model for answer like April 1, 2024 vs April 1,2024, vs April 1 2024 - regex_string = r"[ \,\.\/\-\_\*\^]" - return re.sub(regex_string, "", input_string).lower().replace("'", '"') - - -def string_checker(param: str, model_output: str, possible_answer: list): - standardize_possible_answer = [] - standardize_model_output = standardize_string(model_output) - for i in range(len(possible_answer)): - if type(possible_answer[i]) == str: - standardize_possible_answer.append(standardize_string(possible_answer[i])) - - if standardize_model_output not in standardize_possible_answer: - return { - "valid": False, - "error": [ - f"Invalid value for parameter {repr(param)}: {repr(model_output)}. Expected one of {possible_answer}. Case insensitive." - ], - "error_type": "value_error:string", - } - - return {"valid": True, "error": []} - - -def list_checker(param: str, model_output: list, possible_answer: list): - # Convert the tuple to a list - - standardize_model_output = list(model_output) - - # If the element in the list is a string, we need to standardize it - for i in range(len(standardize_model_output)): - if type(standardize_model_output[i]) == str: - standardize_model_output[i] = standardize_string(model_output[i]) - - standardize_possible_answer: Any = [] - # We also need to standardize the possible answers - for i in range(len(possible_answer)): - standardize_possible_answer.append([]) - for j in range(len(possible_answer[i])): - if type(possible_answer[i][j]) == str: - standardize_possible_answer[i].append(standardize_string(possible_answer[i][j])) - else: - standardize_possible_answer[i].append(possible_answer[i][j]) - - if standardize_model_output not in standardize_possible_answer: - return { - "valid": False, - "error": [ - f"Invalid value for parameter {repr(param)}: {repr(model_output)}. Expected one of {possible_answer}." - ], - "error_type": "value_error:list/tuple", - } - - return {"valid": True, "error": []} - - -def dict_checker(param: str, model_output: dict, possible_answers: list): - # This function works for simple dictionaries, but not dictionaries with nested dictionaries. - # The current dataset only contains simple dictionaries, so this is sufficient. - - result = {"valid": False, "error": [], "error_type": "dict_checker:unclear"} - for i in range(len(possible_answers)): - if possible_answers[i] == "": - continue - - result = {"valid": False, "error": [], "error_type": "dict_checker:unclear"} - - flag = True - - possible_answer = possible_answers[i] - # possible_anwer is a single dictionary - - for key, value in model_output.items(): - if key not in possible_answer: - result["valid"] = False - result["error"].append(f"Unexpected dict key parameter: '{key}'.") # type: ignore[attr-defined] - result["error_type"] = "value_error:dict_key" - flag = False - break - - standardize_value = value - # If the value is a string, we need to standardize it - if type(value) == str: - standardize_value = standardize_string(value) - - # We also need to standardize the possible answers if they are string - standardize_possible_answer = [] - for i in range(len(possible_answer[key])): - if type(possible_answer[key][i]) == str: - standardize_possible_answer.append(standardize_string(possible_answer[key][i])) - else: - standardize_possible_answer.append(possible_answer[key][i]) - - if standardize_value not in standardize_possible_answer: - result["valid"] = False - result["error"].append( # type: ignore[attr-defined] - f"Invalid value for parameter {repr(key)}: {repr(value)}. Expected one of {standardize_possible_answer}." - ) - result["error_type"] = "value_error:dict_value" - flag = False - break - - for key, value in possible_answer.items(): - if key not in model_output and "" not in value: - result["valid"] = False - result["error"].append(f"Missing dict key parameter: '{key}'.") # type: ignore[attr-defined] - result["error_type"] = "value_error:dict_key" - flag = False - break - - if flag: - return {"valid": True, "error": []} - - return result - - -def list_dict_checker(param: str, model_output: list, possible_answers: list): - # This function takes in a list of dictionaries and checks if each dictionary is valid - # The order of the dictionaries in the list must match the order of the possible answers - - result = {"valid": False, "error": [], "error_type": "list_dict_checker:unclear"} - - for answer_index in range(len(possible_answers)): - flag = True # True means so far, all dictionaries are valid - - # Only proceed if the number of dictionaries in the list matches the number of dictionaries in the possible answers - if len(model_output) != len(possible_answers[answer_index]): - result["valid"] = False - result["error"] = ["Wrong number of dictionaries in the list."] - result["error_type"] = "value_error:list_dict_count" - flag = False - continue - - for dict_index in range(len(model_output)): - result = dict_checker( - param, - model_output[dict_index], - [possible_answers[answer_index][dict_index]], - ) - if not result["valid"]: - flag = False - break - if flag: - return {"valid": True, "error": []} - - return result - - -def simple_function_checker( - func_description: dict, - model_output: dict, - possible_answer: dict, - language: str, - model_name: str, -): - possible_answer = list(possible_answer.values())[0] - # Extract function name and parameters details - func_name = func_description["name"] - param_details = func_description["parameters"]["properties"] - required_params = func_description["parameters"]["required"] - - # Initialize a result dictionary - result = { - "valid": True, - "error": [], - "error_type": "simple_function_checker:unclear", - } - - # Check if function name matches - if func_name not in model_output: - result["valid"] = False - result["error"].append( # type: ignore[attr-defined] - f"Function name {repr(func_name)} not found in model output." - ) - result["error_type"] = "simple_function_checker:wrong_func_name" - return result - - model_params = model_output[func_name] - - # Check for required parameters in model output - for param in required_params: - if param not in model_params: - result["valid"] = False - result["error"].append(f"Missing required parameter: {repr(param)}.") # type: ignore[attr-defined] - result["error_type"] = "simple_function_checker:missing_required" - return result - - # Validate types and values for each parameter in model output - for param, value in model_params.items(): - if param not in param_details or param not in possible_answer: - result["valid"] = False - result["error"].append(f"Unexpected parameter: {repr(param)}.") # type: ignore[attr-defined] - result["error_type"] = "simple_function_checker:unexpected_param" - return result - - full_param_details = param_details[param] - expected_type_description = full_param_details["type"] # This is a string - is_variable = False - nested_type_converted = None - - if language == "Java": - from evals.utils.bfcl.java_type_converter import java_type_converter - - expected_type_converted = JAVA_TYPE_CONVERSION[expected_type_description] - - if expected_type_description in JAVA_TYPE_CONVERSION: - if type(value) != str: - result["valid"] = False - result["error"].append( # type: ignore[attr-defined] - f"Incorrect type for parameter {repr(param)}. Expected type String, got {type(value).__name__}. Parameter value: {repr(value)}." - ) - result["error_type"] = "type_error:java" - return result - - if expected_type_description in NESTED_CONVERSION_TYPE_LIST: - nested_type = param_details[param]["items"]["type"] - nested_type_converted = JAVA_TYPE_CONVERSION[nested_type] - value = java_type_converter(value, expected_type_description, nested_type) - else: - value = java_type_converter(value, expected_type_description) - - elif language == "JavaScript": - from evals.utils.bfcl.js_type_converter import js_type_converter - - expected_type_converted = JS_TYPE_CONVERSION[expected_type_description] - - if expected_type_description in JS_TYPE_CONVERSION: - if type(value) != str: - result["valid"] = False - result["error"].append( # type: ignore[attr-defined] - f"Incorrect type for parameter {repr(param)}. Expected type String, got {type(value).__name__}. Parameter value: {repr(value)}." - ) - result["error_type"] = "type_error:js" - return result - - if expected_type_description in NESTED_CONVERSION_TYPE_LIST: - nested_type = param_details[param]["items"]["type"] - nested_type_converted = JS_TYPE_CONVERSION[nested_type] - value = js_type_converter(value, expected_type_description, nested_type) - else: - value = js_type_converter(value, expected_type_description) - - elif language == "Python": - expected_type_converted = PYTHON_TYPE_MAPPING[expected_type_description] - if expected_type_description in PYTHON_NESTED_TYPE_CHECK_LIST: - nested_type = param_details[param]["items"]["type"] - nested_type_converted = PYTHON_TYPE_MAPPING[nested_type] - - # We convert all tuple value to list when the expected type is tuple. - # The conversion is necessary because any tuple in the possible answer would become a list after being processed through json.dump() and json.load(). - # This does introduce some false positive (eg, when the model provides a list value instead of tuple). We hope to find a better solution in the future. - if expected_type_description == "tuple" and type(value) == tuple: - value = list(value) - - # Allow python auto conversion from int to float - if language == "Python" and expected_type_description == "float" and type(value) == int: - value = float(value) - - # Type checking - # In fact, we only check for Python here. - # Type check for other languages are handled by the type converter, and so their value (after conversion) is always correct. - type_check_result = type_checker( - param, - value, - possible_answer[param], - expected_type_description, - expected_type_converted, - nested_type_converted, - ) - is_variable = type_check_result["is_variable"] - if not type_check_result["valid"]: - return type_check_result - - # It doesn't make sense to special handle dictionaries and list of dictionaries if the value is a variable. - # We can just treat the variable as a string and use the normal flow. - if not is_variable: - # Special handle for dictionaries - if expected_type_converted == dict: - result = dict_checker(param, value, possible_answer[param]) - if not result["valid"]: - return result - continue - - # Special handle for list of dictionaries - elif expected_type_converted == list and nested_type_converted == dict: - result = list_dict_checker(param, value, possible_answer[param]) - if not result["valid"]: - return result - continue - - # Special handle for strings - elif expected_type_converted == str: - # We don't check for case sensitivity for string, as long as it's not a variable - result = string_checker(param, value, possible_answer[param]) - if not result["valid"]: - return result - continue - - elif expected_type_converted == list: - result = list_checker(param, value, possible_answer[param]) - if not result["valid"]: - return result - continue - - # Check if the value is within the possible answers - if value not in possible_answer[param]: - result["valid"] = False - result["error"].append( # type: ignore[attr-defined] - f"Invalid value for parameter {repr(param)}: {repr(value)}. Expected one of {possible_answer[param]}." - ) - result["error_type"] = "value_error:others" - return result - - # Check for optional parameters not provided but allowed - for param in possible_answer: - if param not in model_params and "" not in possible_answer[param]: - result["valid"] = False - result["error"].append( # type: ignore[attr-defined] - f"Optional parameter {repr(param)} not provided and not marked as optional." - ) - result["error_type"] = "simple_function_checker:missing_optional" - return result - - return result - - -def parallel_function_checker_enforce_order( - func_descriptions: list, - model_output: list, - possible_answers: dict, - language: str, - model_name: str, -): - if len(model_output) != len(possible_answers): - return { - "valid": False, - "error": ["Wrong number of functions."], - "error_type": "parallel_function_checker_enforce_order:wrong_count", - } - - func_name_list = list(possible_answers.keys()) - possible_answers_list = [] - - for key, value in possible_answers.items(): - possible_answers_list.append({key: value}) - - for i in range(len(possible_answers_list)): - func_description = find_description(func_descriptions, func_name_list[i]) - - result = simple_function_checker( - func_description, - model_output[i], - possible_answers_list[i], - language, - model_name, - ) - if not result["valid"]: - return result - - return {"valid": True, "error": []} - - -def parallel_function_checker_no_order( - func_descriptions: list, - model_output: list, - possible_answers: list, - language: str, - model_name: str, -): - if len(model_output) != len(possible_answers): - return { - "valid": False, - "error": ["Wrong number of functions."], - "error_type": "parallel_function_checker_no_order:wrong_count", - } - - matched_indices = [] - - # We go throught the possible answers one by one, and eliminate the model output that matches the possible answer - # It must be this way because we need ground truth to fetch the correct function description - for i in range(len(possible_answers)): - # possible_answers[i] is a dictionary with only one key - func_name_expected = list(possible_answers[i].keys())[0] - func_description = find_description(func_descriptions, func_name_expected) - - all_errors = [] - - for index in range(len(model_output)): - if index in matched_indices: - continue - - result = simple_function_checker( - func_description, - model_output[index], - possible_answers[i], - language, - model_name, - ) - - if result["valid"]: - matched_indices.append(index) - break - else: - all_errors.append( - { - f"Model Result Index {index}": { - "sub_error": result["error"], - "sub_error_type": result["error_type"], - "model_output_item": model_output[index], - "possible_answer_item": possible_answers[i], - } - } - ) - - if not result["valid"]: - considered_indices = [i for i in range(len(model_output)) if i not in matched_indices] - all_errors.insert( - 0, - f"Could not find a matching function among index {considered_indices} of model output for index {i} of possible answers.", # type: ignore[arg-type] - ) - return { - "valid": False, - "error": all_errors, - "error_type": "parallel_function_checker_no_order:cannot_find_match", - } - - return {"valid": True, "error": []} - - -def multiple_function_checker( - func_descriptions: list, - model_output: list, - possible_answers: list, - language: str, - model_name: str, -): - if len(model_output) != len(possible_answers): - return { - "valid": False, - "error": ["Wrong number of functions."], - "error_type": "multiple_function_checker:wrong_count", - } - - # possible_answers is a list of only one dictionary with only one key - func_name_expected = list(possible_answers[0].keys())[0] - func_description = find_description(func_descriptions, func_name_expected) - return simple_function_checker( - func_description, - model_output[0], - possible_answers[0], - language, - model_name, - ) - - -def patten_matcher(exec_output, expected_result, function_call, is_sanity_check): - result = {"valid": True, "error": [], "error_type": "executable_checker:unclear"} - - if type(exec_output) != type(expected_result): - return { - "valid": False, - "error": [ - f"Wrong execution result type for {repr(function_call)}. Expected type: {type(expected_result)}, but got: {type(exec_output)}." - ], - "error_type": "executable_checker:wrong_result_type", - "model_executed_output": exec_output, - } - if type(exec_output) == dict: - # We loose the requirement for the sanity check as the expected result used in the sanity check might not be the most up-to-date one. - # This happens when the key is a timestamp or a random number. - if is_sanity_check: - if len(exec_output) != len(expected_result): - return { - "valid": False, - "error": [ - f"Wrong execution result pattern for {repr(function_call)}. Expect type Dict, but wrong number of elements in the output. Expected length: {len(expected_result)}, but got: {len(exec_output)}." - ], - "error_type": "executable_checker:wrong_result_type:dict_length", - "model_executed_output": exec_output, - } - else: - return result - - for key, value in expected_result.items(): - if key not in exec_output: - return { - "valid": False, - "error": [ - f"Wrong execution result pattern for {repr(function_call)}. Expect type Dict, but key {repr(key)} not found in the model output." - ], - "error_type": "executable_checker:wrong_result_type:dict_key_not_found", - "model_executed_output": exec_output, - } - for key, value in exec_output.items(): - if key not in expected_result: - return { - "valid": False, - "error": [ - f"Wrong execution result pattern for {repr(function_call)}. Expect type Dict, but key {repr(key)} not expected in the model output." - ], - "error_type": "executable_checker:wrong_result_type:dict_extra_key", - "model_executed_output": exec_output, - } - if type(exec_output) == list: - if len(exec_output) != len(expected_result): - return { - "valid": False, - "error": [ - f"Wrong execution result pattern for {repr(function_call)}. Expect type list, but wrong number of elements in the output. Expected length: {len(expected_result)}, but got: {len(exec_output)}." - ], - "error_type": "executable_checker:wrong_result_type:list_length", - "model_executed_output": exec_output, - } - return result - - -#### Helper functions for Exec #### -def executable_checker_simple( - function_call: str, - expected_result, - expected_result_type: str, - is_sanity_check=False, -): - result = {"valid": True, "error": [], "error_type": "executable_checker:unclear"} - - exec_dict: Any = {} - - try: - exec( - "from executable_python_function import *" + "\nresult=" + function_call, - exec_dict, - ) - exec_output = exec_dict["result"] - except NoAPIKeyError as e: - raise e - except Exception as e: - result["valid"] = False - result["error"].append( # type: ignore[attr-defined] - f"Error in execution: {repr(function_call)}. Error: {str(e)}" - ) - result["error_type"] = "executable_checker:execution_error" - return result - - # We need to special handle the case where the execution result is a tuple and convert it to a list - # Because when json is stored, the tuple is converted to a list, and so the expected result is a list when loaded from json - if isinstance(exec_output, tuple): - exec_output = list(exec_output) - - if expected_result_type == "exact_match": - if exec_output != expected_result: - result["valid"] = False - result["error"].append( # type: ignore[attr-defined] - f"Wrong execution result for {repr(function_call)}. Expected: {expected_result}, but got: {exec_output}." - ) - result["error_type"] = "executable_checker:wrong_result" - result["model_executed_output"] = exec_output - return result - - elif expected_result_type == "real_time_match": - # Allow for 5% difference - if (type(expected_result) == float or type(expected_result) == int) and ( - type(exec_output) == float or type(exec_output) == int - ): - if not ( - expected_result * (1 - REAL_TIME_MATCH_ALLOWED_DIFFERENCE) - <= exec_output - <= expected_result * (1 + REAL_TIME_MATCH_ALLOWED_DIFFERENCE) - ): - result["valid"] = False - result["error"].append( # type: ignore[attr-defined] - f"Wrong execution result for {repr(function_call)}. Expected: {expected_result}, but got: {exec_output}. {REAL_TIME_MATCH_ALLOWED_DIFFERENCE * 100}% difference allowed." - ) - result["error_type"] = "executable_checker:wrong_result_real_time" - result["model_executed_output"] = exec_output - return result - else: - result["valid"] = False - result["error"].append( # type: ignore[attr-defined] - f"Wrong execution result for {repr(function_call)}. Expected: {expected_result}, but got: {exec_output}. Type needs to be float or int for real time match criteria." - ) - result["error_type"] = "executable_checker:wrong_result_real_time" - result["model_executed_output"] = exec_output - return result - - else: - # structural match - pattern_match_result = patten_matcher(exec_output, expected_result, function_call, is_sanity_check) - if not pattern_match_result["valid"]: - return pattern_match_result - - return result - - -def executable_checker_parallel_no_order( - decoded_result: list, expected_exec_result: list, expected_exec_result_type: list -): - if len(decoded_result) != len(expected_exec_result): - return { - "valid": False, - "error": [ - f"Wrong number of functions provided. Expected {len(expected_exec_result)}, but got {len(decoded_result)}." - ], - "error_type": "value_error:exec_result_count", - } - - matched_indices = [] - for i in range(len(expected_exec_result)): - all_errors = [] - for index in range(len(decoded_result)): - if index in matched_indices: - continue - - result = executable_checker_simple( - decoded_result[index], - expected_exec_result[i], - expected_exec_result_type[i], - False, - ) - - if result["valid"]: - matched_indices.append(index) - break - else: - all_errors.append( - { - f"Model Result Index {index}": { - "sub_error": result["error"], - "sub_error_type": result["error_type"], - "model_executed_output": ( - result["model_executed_output"] if "model_executed_output" in result else None - ), - } - } - ) - - if not result["valid"]: - considered_indices = [i for i in range(len(decoded_result)) if i not in matched_indices] - all_errors.insert( - 0, - f"Could not find a matching function among index {considered_indices} of model output for index {i} of possible answers.", # type: ignore[arg-type] - ) - return { - "valid": False, - "error": all_errors, - "error_type": "executable_checker:cannot_find_match", - } - - return {"valid": True, "error": [], "error_type": "executable_checker:unclear"} - - -#### Main function #### -def executable_checker_rest(func_call, idx): - # Move this here for now to avoid needing to read this file / fix paths to be relative to dataset_dir. Fix when it's actually needed / used. - EVAL_GROUND_TRUTH_PATH = "/mnt/wsfuse/fair_llm_v2/datasets/eval/bfcl/rest-eval-response_v5.jsonl" # Ground truth file for v5 for rest execution - with open(EVAL_GROUND_TRUTH_PATH, "r") as f: - EVAL_GROUND_TRUTH = f.readlines() - if "https://geocode.maps.co" in func_call: - time.sleep(2) - if "requests_get" in func_call: - func_call = func_call.replace("requests_get", "requests.get") - try: - response = eval(func_call) - except Exception as e: - return { - "valid": False, - "error": [f"Execution failed. {str(e)}"], - "error_type": "executable_checker_rest:execution_error", - } - - try: - if response.status_code == 200: - eval_GT_json = json.loads(EVAL_GROUND_TRUTH[idx]) - try: - if isinstance(eval_GT_json, dict): - if isinstance(response.json(), dict): - if set(eval_GT_json.keys()) == set(response.json().keys()): - return {"valid": True, "error": [], "error_type": ""} - return { - "valid": False, - "error": ["Key inconsistency"], - "error_type": "executable_checker_rest:wrong_key", - } - return { - "valid": False, - "error": [f"Expected dictionary, but got {type(response.json())}"], - "error_type": "executable_checker_rest:wrong_type", - } - - elif isinstance(eval_GT_json, list): - if isinstance(response.json(), list): - if len(eval_GT_json) != len(response.json()): - return { - "valid": False, - "error": [f"Response list length inconsistency."], - "error_type": "value_error:exec_result_rest_count", - } - - else: - for i in range(len(eval_GT_json)): - if set(eval_GT_json[i].keys()) != set(response.json()[i].keys()): - return { - "valid": False, - "error": [f"Key inconsistency"], - "error_type": "executable_checker_rest:wrong_key", - } - - return {"valid": True, "error": []} - else: - return { - "valid": False, - "error": [f"Expected list, but got {type(response.json())}"], - "error_type": "executable_checker_rest:wrong_type", - } - return { - "valid": False, - "error": [f"Expected dict or list, but got {type(response.json())}"], - "error_type": "executable_checker_rest:wrong_type", - } - except Exception as e: - return { - "valid": False, - "error": [ - f"Error in execution and type checking. Status code: {response.status_code}. Error: {str(e)}" - ], - "error_type": "executable_checker_rest:response_format_error", - } - else: - return { - "valid": False, - "error": [f"Execution result status code is not 200, got {response.status_code}"], - "error_type": "executable_checker_rest:wrong_status_code", - } - except Exception as e: - return { - "valid": False, - "error": [f"Cannot get status code of the response. Error: {str(e)}"], - "error_type": "executable_checker_rest:cannot_get_status_code", - } - - -def ast_checker(func_description, model_output, possible_answer, language, test_category, model_name): - if "parallel" in test_category: - return parallel_function_checker_no_order(func_description, model_output, possible_answer, language, model_name) - - elif "multiple" in test_category: - return multiple_function_checker(func_description, model_output, possible_answer, language, model_name) - - else: - if len(model_output) != 1: - return { - "valid": False, - "error": ["Wrong number of functions."], - "error_type": "simple_function_checker:wrong_count", - } - - return simple_function_checker( - func_description[0], - model_output[0], - possible_answer[0], - language, - model_name, - ) - - -def exec_checker(decoded_result: list, func_description: dict, test_category: str): - if "multiple" in test_category or "parallel" in test_category: - return executable_checker_parallel_no_order( - decoded_result, - func_description["execution_result"], - func_description["execution_result_type"], - ) - - else: - if len(decoded_result) != 1: - return { - "valid": False, - "error": ["Wrong number of functions."], - "error_type": "simple_exec_checker:wrong_count", - } - return executable_checker_simple( - decoded_result[0], - func_description["execution_result"][0], - func_description["execution_result_type"][0], - False, - ) - - -def is_empty_output(decoded_output): - # This function is a patch to the ast decoder for relevance detection - # Sometimes the ast decoder will parse successfully, but the input doens't really have a function call - # [], [{}], and anything that is not in function calling format is considered empty (and thus should be marked as correct) - if not is_function_calling_format_output(decoded_output): - return True - if len(decoded_output) == 0: - return True - if len(decoded_output) == 1 and len(decoded_output[0]) == 0: - return True - - -def is_function_calling_format_output(decoded_output): - # Ensure the output is a list of dictionaries - if type(decoded_output) == list: - for item in decoded_output: - if type(item) != dict: - return False - return True - return False diff --git a/llama_stack/providers/inline/scoring/basic/utils/bfcl/tree_sitter.py b/llama_stack/providers/inline/scoring/basic/utils/bfcl/tree_sitter.py deleted file mode 100644 index ed97ee360..000000000 --- a/llama_stack/providers/inline/scoring/basic/utils/bfcl/tree_sitter.py +++ /dev/null @@ -1,40 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# All rights reserved. -# -# This source code is licensed under the terms described in the LICENSE file in -# the root directory of this source tree. - -""" -Tree-sitter changes its API with unfortunate frequency. Modules that need it should -import it from here so that we can centrally manage things as necessary. -""" - -# These currently work with tree-sitter 0.23.0 -# NOTE: Don't import tree-sitter or any of the language modules in the main module -# because not all environments have them. Import lazily inside functions where needed. - -import importlib -import typing - -if typing.TYPE_CHECKING: - import tree_sitter - - -def get_language(language: str) -> "tree_sitter.Language": - import tree_sitter - - language_module_name = f"tree_sitter_{language}" - try: - language_module = importlib.import_module(language_module_name) - except ModuleNotFoundError as exc: - raise ValueError( - f"Language {language} is not found. Please install the tree-sitter-{language} package." - ) from exc - return tree_sitter.Language(language_module.language()) - - -def get_parser(language: str, **kwargs) -> "tree_sitter.Parser": - import tree_sitter - - lang = get_language(language) - return tree_sitter.Parser(lang, **kwargs) diff --git a/llama_stack/providers/inline/scoring/llm_as_judge/scoring.py b/llama_stack/providers/inline/scoring/llm_as_judge/scoring.py index fd651877c..9b7628524 100644 --- a/llama_stack/providers/inline/scoring/llm_as_judge/scoring.py +++ b/llama_stack/providers/inline/scoring/llm_as_judge/scoring.py @@ -63,6 +63,9 @@ class LlmAsJudgeScoringImpl( async def register_scoring_function(self, function_def: ScoringFn) -> None: self.llm_as_judge_fn.register_scoring_fn_def(function_def) + async def unregister_scoring_function(self, scoring_fn_id: str) -> None: + self.llm_as_judge_fn.unregister_scoring_fn_def(scoring_fn_id) + async def score_batch( self, dataset_id: str, diff --git a/llama_stack/providers/inline/tool_runtime/rag/__init__.py b/llama_stack/providers/inline/tool_runtime/rag/__init__.py index f9a6e5c55..f9a7e7b89 100644 --- a/llama_stack/providers/inline/tool_runtime/rag/__init__.py +++ b/llama_stack/providers/inline/tool_runtime/rag/__init__.py @@ -14,6 +14,6 @@ from .config import RagToolRuntimeConfig async def get_provider_impl(config: RagToolRuntimeConfig, deps: dict[Api, Any]): from .memory import MemoryToolRuntimeImpl - impl = MemoryToolRuntimeImpl(config, deps[Api.vector_io], deps[Api.inference]) + impl = MemoryToolRuntimeImpl(config, deps[Api.vector_io], deps[Api.inference], deps[Api.files]) await impl.initialize() return impl diff --git a/llama_stack/providers/inline/tool_runtime/rag/context_retriever.py b/llama_stack/providers/inline/tool_runtime/rag/context_retriever.py index be18430e4..9bc22f979 100644 --- a/llama_stack/providers/inline/tool_runtime/rag/context_retriever.py +++ b/llama_stack/providers/inline/tool_runtime/rag/context_retriever.py @@ -8,7 +8,7 @@ from jinja2 import Template from llama_stack.apis.common.content_types import InterleavedContent -from llama_stack.apis.inference import UserMessage +from llama_stack.apis.inference import OpenAIUserMessageParam from llama_stack.apis.tools.rag_tool import ( DefaultRAGQueryGeneratorConfig, LLMRAGQueryGeneratorConfig, @@ -61,16 +61,16 @@ async def llm_rag_query_generator( messages = [interleaved_content_as_str(content)] template = Template(config.template) - content = template.render({"messages": messages}) + rendered_content: str = template.render({"messages": messages}) model = config.model - message = UserMessage(content=content) - response = await inference_api.chat_completion( - model_id=model, + message = OpenAIUserMessageParam(content=rendered_content) + response = await inference_api.openai_chat_completion( + model=model, messages=[message], stream=False, ) - query = response.completion_message.content + query = response.choices[0].message.content return query diff --git a/llama_stack/providers/inline/tool_runtime/rag/memory.py b/llama_stack/providers/inline/tool_runtime/rag/memory.py index a1543457b..bc68f198d 100644 --- a/llama_stack/providers/inline/tool_runtime/rag/memory.py +++ b/llama_stack/providers/inline/tool_runtime/rag/memory.py @@ -5,10 +5,15 @@ # the root directory of this source tree. import asyncio +import base64 +import io +import mimetypes import secrets import string from typing import Any +import httpx +from fastapi import UploadFile from pydantic import TypeAdapter from llama_stack.apis.common.content_types import ( @@ -17,6 +22,7 @@ from llama_stack.apis.common.content_types import ( InterleavedContentItem, TextContentItem, ) +from llama_stack.apis.files import Files, OpenAIFilePurpose from llama_stack.apis.inference import Inference from llama_stack.apis.tools import ( ListToolDefsResponse, @@ -30,14 +36,16 @@ from llama_stack.apis.tools import ( ToolParameter, ToolRuntime, ) -from llama_stack.apis.vector_io import QueryChunksResponse, VectorIO +from llama_stack.apis.vector_io import ( + QueryChunksResponse, + VectorIO, + VectorStoreChunkingStrategyStatic, + VectorStoreChunkingStrategyStaticConfig, +) from llama_stack.log import get_logger from llama_stack.providers.datatypes import ToolGroupsProtocolPrivate from llama_stack.providers.utils.inference.prompt_adapter import interleaved_content_as_str -from llama_stack.providers.utils.memory.vector_store import ( - content_from_doc, - make_overlapped_chunks, -) +from llama_stack.providers.utils.memory.vector_store import parse_data_url from .config import RagToolRuntimeConfig from .context_retriever import generate_rag_query @@ -49,16 +57,59 @@ def make_random_string(length: int = 8): return "".join(secrets.choice(string.ascii_letters + string.digits) for _ in range(length)) +async def raw_data_from_doc(doc: RAGDocument) -> tuple[bytes, str]: + """Get raw binary data and mime type from a RAGDocument for file upload.""" + if isinstance(doc.content, URL): + if doc.content.uri.startswith("data:"): + parts = parse_data_url(doc.content.uri) + mime_type = parts["mimetype"] + data = parts["data"] + + if parts["is_base64"]: + file_data = base64.b64decode(data) + else: + file_data = data.encode("utf-8") + + return file_data, mime_type + else: + async with httpx.AsyncClient() as client: + r = await client.get(doc.content.uri) + r.raise_for_status() + mime_type = r.headers.get("content-type", "application/octet-stream") + return r.content, mime_type + else: + if isinstance(doc.content, str): + content_str = doc.content + else: + content_str = interleaved_content_as_str(doc.content) + + if content_str.startswith("data:"): + parts = parse_data_url(content_str) + mime_type = parts["mimetype"] + data = parts["data"] + + if parts["is_base64"]: + file_data = base64.b64decode(data) + else: + file_data = data.encode("utf-8") + + return file_data, mime_type + else: + return content_str.encode("utf-8"), "text/plain" + + class MemoryToolRuntimeImpl(ToolGroupsProtocolPrivate, ToolRuntime, RAGToolRuntime): def __init__( self, config: RagToolRuntimeConfig, vector_io_api: VectorIO, inference_api: Inference, + files_api: Files, ): self.config = config self.vector_io_api = vector_io_api self.inference_api = inference_api + self.files_api = files_api async def initialize(self): pass @@ -78,27 +129,56 @@ class MemoryToolRuntimeImpl(ToolGroupsProtocolPrivate, ToolRuntime, RAGToolRunti vector_db_id: str, chunk_size_in_tokens: int = 512, ) -> None: - chunks = [] - for doc in documents: - content = await content_from_doc(doc) - # TODO: we should add enrichment here as URLs won't be added to the metadata by default - chunks.extend( - make_overlapped_chunks( - doc.document_id, - content, - chunk_size_in_tokens, - chunk_size_in_tokens // 4, - doc.metadata, - ) - ) - - if not chunks: + if not documents: return - await self.vector_io_api.insert_chunks( - chunks=chunks, - vector_db_id=vector_db_id, - ) + for doc in documents: + try: + try: + file_data, mime_type = await raw_data_from_doc(doc) + except Exception as e: + log.error(f"Failed to extract content from document {doc.document_id}: {e}") + continue + + file_extension = mimetypes.guess_extension(mime_type) or ".txt" + filename = doc.metadata.get("filename", f"{doc.document_id}{file_extension}") + + file_obj = io.BytesIO(file_data) + file_obj.name = filename + + upload_file = UploadFile(file=file_obj, filename=filename) + + try: + created_file = await self.files_api.openai_upload_file( + file=upload_file, purpose=OpenAIFilePurpose.ASSISTANTS + ) + except Exception as e: + log.error(f"Failed to upload file for document {doc.document_id}: {e}") + continue + + chunking_strategy = VectorStoreChunkingStrategyStatic( + static=VectorStoreChunkingStrategyStaticConfig( + max_chunk_size_tokens=chunk_size_in_tokens, + chunk_overlap_tokens=chunk_size_in_tokens // 4, + ) + ) + + try: + await self.vector_io_api.openai_attach_file_to_vector_store( + vector_store_id=vector_db_id, + file_id=created_file.id, + attributes=doc.metadata, + chunking_strategy=chunking_strategy, + ) + except Exception as e: + log.error( + f"Failed to attach file {created_file.id} to vector store {vector_db_id} for document {doc.document_id}: {e}" + ) + continue + + except Exception as e: + log.error(f"Unexpected error processing document {doc.document_id}: {e}") + continue async def query( self, @@ -131,8 +211,18 @@ class MemoryToolRuntimeImpl(ToolGroupsProtocolPrivate, ToolRuntime, RAGToolRunti for vector_db_id in vector_db_ids ] results: list[QueryChunksResponse] = await asyncio.gather(*tasks) - chunks = [c for r in results for c in r.chunks] - scores = [s for r in results for s in r.scores] + + chunks = [] + scores = [] + + for vector_db_id, result in zip(vector_db_ids, results, strict=False): + for chunk, score in zip(result.chunks, result.scores, strict=False): + if not hasattr(chunk, "metadata") or chunk.metadata is None: + chunk.metadata = {} + chunk.metadata["vector_db_id"] = vector_db_id + + chunks.append(chunk) + scores.append(score) if not chunks: return RAGQueryResult(content=None) @@ -167,6 +257,7 @@ class MemoryToolRuntimeImpl(ToolGroupsProtocolPrivate, ToolRuntime, RAGToolRunti metadata_keys_to_exclude_from_context = [ "token_count", "metadata_token_count", + "vector_db_id", ] metadata_for_context = {} for k in chunk_metadata_keys_to_include_from_context: @@ -191,6 +282,7 @@ class MemoryToolRuntimeImpl(ToolGroupsProtocolPrivate, ToolRuntime, RAGToolRunti "document_ids": [c.metadata["document_id"] for c in chunks[: len(picked)]], "chunks": [c.content for c in chunks[: len(picked)]], "scores": scores[: len(picked)], + "vector_db_ids": [c.metadata["vector_db_id"] for c in chunks[: len(picked)]], }, ) @@ -226,7 +318,6 @@ class MemoryToolRuntimeImpl(ToolGroupsProtocolPrivate, ToolRuntime, RAGToolRunti if query_config: query_config = TypeAdapter(RAGQueryConfig).validate_python(query_config) else: - # handle someone passing an empty dict query_config = RAGQueryConfig() query = kwargs["query"] @@ -237,6 +328,6 @@ class MemoryToolRuntimeImpl(ToolGroupsProtocolPrivate, ToolRuntime, RAGToolRunti ) return ToolInvocationResult( - content=result.content, + content=result.content or [], metadata=result.metadata, ) diff --git a/llama_stack/providers/inline/vector_io/sqlite_vec/sqlite_vec.py b/llama_stack/providers/inline/vector_io/sqlite_vec/sqlite_vec.py index 7cf163960..f34f8f6fb 100644 --- a/llama_stack/providers/inline/vector_io/sqlite_vec/sqlite_vec.py +++ b/llama_stack/providers/inline/vector_io/sqlite_vec/sqlite_vec.py @@ -30,11 +30,11 @@ from llama_stack.providers.utils.kvstore.api import KVStore from llama_stack.providers.utils.memory.openai_vector_store_mixin import OpenAIVectorStoreMixin from llama_stack.providers.utils.memory.vector_store import ( RERANKER_TYPE_RRF, - RERANKER_TYPE_WEIGHTED, ChunkForDeletion, EmbeddingIndex, VectorDBWithIndex, ) +from llama_stack.providers.utils.vector_io.vector_utils import WeightedInMemoryAggregator logger = get_logger(name=__name__, category="vector_io") @@ -66,59 +66,6 @@ def _create_sqlite_connection(db_path): return connection -def _normalize_scores(scores: dict[str, float]) -> dict[str, float]: - """Normalize scores to [0,1] range using min-max normalization.""" - if not scores: - return {} - min_score = min(scores.values()) - max_score = max(scores.values()) - score_range = max_score - min_score - if score_range > 0: - return {doc_id: (score - min_score) / score_range for doc_id, score in scores.items()} - return dict.fromkeys(scores, 1.0) - - -def _weighted_rerank( - vector_scores: dict[str, float], - keyword_scores: dict[str, float], - alpha: float = 0.5, -) -> dict[str, float]: - """ReRanker that uses weighted average of scores.""" - all_ids = set(vector_scores.keys()) | set(keyword_scores.keys()) - normalized_vector_scores = _normalize_scores(vector_scores) - normalized_keyword_scores = _normalize_scores(keyword_scores) - - return { - doc_id: (alpha * normalized_keyword_scores.get(doc_id, 0.0)) - + ((1 - alpha) * normalized_vector_scores.get(doc_id, 0.0)) - for doc_id in all_ids - } - - -def _rrf_rerank( - vector_scores: dict[str, float], - keyword_scores: dict[str, float], - impact_factor: float = 60.0, -) -> dict[str, float]: - """ReRanker that uses Reciprocal Rank Fusion.""" - # Convert scores to ranks - vector_ranks = { - doc_id: i + 1 for i, (doc_id, _) in enumerate(sorted(vector_scores.items(), key=lambda x: x[1], reverse=True)) - } - keyword_ranks = { - doc_id: i + 1 for i, (doc_id, _) in enumerate(sorted(keyword_scores.items(), key=lambda x: x[1], reverse=True)) - } - - all_ids = set(vector_scores.keys()) | set(keyword_scores.keys()) - rrf_scores = {} - for doc_id in all_ids: - vector_rank = vector_ranks.get(doc_id, float("inf")) - keyword_rank = keyword_ranks.get(doc_id, float("inf")) - # RRF formula: score = 1/(k + r) where k is impact_factor and r is the rank - rrf_scores[doc_id] = (1.0 / (impact_factor + vector_rank)) + (1.0 / (impact_factor + keyword_rank)) - return rrf_scores - - def _make_sql_identifier(name: str) -> str: return re.sub(r"[^a-zA-Z0-9_]", "_", name) @@ -398,14 +345,10 @@ class SQLiteVecIndex(EmbeddingIndex): for chunk, score in zip(keyword_response.chunks, keyword_response.scores, strict=False) } - # Combine scores using the specified reranker - if reranker_type == RERANKER_TYPE_WEIGHTED: - alpha = reranker_params.get("alpha", 0.5) - combined_scores = _weighted_rerank(vector_scores, keyword_scores, alpha) - else: - # Default to RRF for None, RRF, or any unknown types - impact_factor = reranker_params.get("impact_factor", 60.0) - combined_scores = _rrf_rerank(vector_scores, keyword_scores, impact_factor) + # Combine scores using the reranking utility + combined_scores = WeightedInMemoryAggregator.combine_search_results( + vector_scores, keyword_scores, reranker_type, reranker_params + ) # Sort by combined score and get top k results sorted_items = sorted(combined_scores.items(), key=lambda x: x[1], reverse=True) diff --git a/llama_stack/providers/registry/batches.py b/llama_stack/providers/registry/batches.py index de7886efb..a07942486 100644 --- a/llama_stack/providers/registry/batches.py +++ b/llama_stack/providers/registry/batches.py @@ -13,7 +13,7 @@ def available_providers() -> list[ProviderSpec]: InlineProviderSpec( api=Api.batches, provider_type="inline::reference", - pip_packages=["openai"], + pip_packages=[], module="llama_stack.providers.inline.batches.reference", config_class="llama_stack.providers.inline.batches.reference.config.ReferenceBatchesImplConfig", api_dependencies=[ diff --git a/llama_stack/providers/registry/datasetio.py b/llama_stack/providers/registry/datasetio.py index 43cde83fb..a9feb0bac 100644 --- a/llama_stack/providers/registry/datasetio.py +++ b/llama_stack/providers/registry/datasetio.py @@ -6,11 +6,10 @@ from llama_stack.providers.datatypes import ( - AdapterSpec, Api, InlineProviderSpec, ProviderSpec, - remote_provider_spec, + RemoteProviderSpec, ) @@ -25,28 +24,26 @@ def available_providers() -> list[ProviderSpec]: api_dependencies=[], description="Local filesystem-based dataset I/O provider for reading and writing datasets to local storage.", ), - remote_provider_spec( + RemoteProviderSpec( api=Api.datasetio, - adapter=AdapterSpec( - adapter_type="huggingface", - pip_packages=[ - "datasets", - ], - module="llama_stack.providers.remote.datasetio.huggingface", - config_class="llama_stack.providers.remote.datasetio.huggingface.HuggingfaceDatasetIOConfig", - description="HuggingFace datasets provider for accessing and managing datasets from the HuggingFace Hub.", - ), + adapter_type="huggingface", + provider_type="remote::huggingface", + pip_packages=[ + "datasets>=4.0.0", + ], + module="llama_stack.providers.remote.datasetio.huggingface", + config_class="llama_stack.providers.remote.datasetio.huggingface.HuggingfaceDatasetIOConfig", + description="HuggingFace datasets provider for accessing and managing datasets from the HuggingFace Hub.", ), - remote_provider_spec( + RemoteProviderSpec( api=Api.datasetio, - adapter=AdapterSpec( - adapter_type="nvidia", - pip_packages=[ - "datasets", - ], - module="llama_stack.providers.remote.datasetio.nvidia", - config_class="llama_stack.providers.remote.datasetio.nvidia.NvidiaDatasetIOConfig", - description="NVIDIA's dataset I/O provider for accessing datasets from NVIDIA's data platform.", - ), + adapter_type="nvidia", + provider_type="remote::nvidia", + module="llama_stack.providers.remote.datasetio.nvidia", + config_class="llama_stack.providers.remote.datasetio.nvidia.NvidiaDatasetIOConfig", + pip_packages=[ + "datasets>=4.0.0", + ], + description="NVIDIA's dataset I/O provider for accessing datasets from NVIDIA's data platform.", ), ] diff --git a/llama_stack/providers/registry/eval.py b/llama_stack/providers/registry/eval.py index 9f0d17916..4ef0bb41f 100644 --- a/llama_stack/providers/registry/eval.py +++ b/llama_stack/providers/registry/eval.py @@ -5,7 +5,7 @@ # the root directory of this source tree. -from llama_stack.providers.datatypes import AdapterSpec, Api, InlineProviderSpec, ProviderSpec, remote_provider_spec +from llama_stack.providers.datatypes import Api, InlineProviderSpec, ProviderSpec, RemoteProviderSpec def available_providers() -> list[ProviderSpec]: @@ -25,17 +25,16 @@ def available_providers() -> list[ProviderSpec]: ], description="Meta's reference implementation of evaluation tasks with support for multiple languages and evaluation metrics.", ), - remote_provider_spec( + RemoteProviderSpec( api=Api.eval, - adapter=AdapterSpec( - adapter_type="nvidia", - pip_packages=[ - "requests", - ], - module="llama_stack.providers.remote.eval.nvidia", - config_class="llama_stack.providers.remote.eval.nvidia.NVIDIAEvalConfig", - description="NVIDIA's evaluation provider for running evaluation tasks on NVIDIA's platform.", - ), + adapter_type="nvidia", + pip_packages=[ + "requests", + ], + provider_type="remote::nvidia", + module="llama_stack.providers.remote.eval.nvidia", + config_class="llama_stack.providers.remote.eval.nvidia.NVIDIAEvalConfig", + description="NVIDIA's evaluation provider for running evaluation tasks on NVIDIA's platform.", api_dependencies=[ Api.datasetio, Api.datasets, diff --git a/llama_stack/providers/registry/files.py b/llama_stack/providers/registry/files.py index ebe90310c..9acabfacd 100644 --- a/llama_stack/providers/registry/files.py +++ b/llama_stack/providers/registry/files.py @@ -4,13 +4,7 @@ # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. -from llama_stack.providers.datatypes import ( - AdapterSpec, - Api, - InlineProviderSpec, - ProviderSpec, - remote_provider_spec, -) +from llama_stack.providers.datatypes import Api, InlineProviderSpec, ProviderSpec, RemoteProviderSpec from llama_stack.providers.utils.sqlstore.sqlstore import sql_store_pip_packages @@ -25,14 +19,13 @@ def available_providers() -> list[ProviderSpec]: config_class="llama_stack.providers.inline.files.localfs.config.LocalfsFilesImplConfig", description="Local filesystem-based file storage provider for managing files and documents locally.", ), - remote_provider_spec( + RemoteProviderSpec( api=Api.files, - adapter=AdapterSpec( - adapter_type="s3", - pip_packages=["boto3"] + sql_store_pip_packages, - module="llama_stack.providers.remote.files.s3", - config_class="llama_stack.providers.remote.files.s3.config.S3FilesImplConfig", - description="AWS S3-based file storage provider for scalable cloud file management with metadata persistence.", - ), + provider_type="remote::s3", + adapter_type="s3", + pip_packages=["boto3"] + sql_store_pip_packages, + module="llama_stack.providers.remote.files.s3", + config_class="llama_stack.providers.remote.files.s3.config.S3FilesImplConfig", + description="AWS S3-based file storage provider for scalable cloud file management with metadata persistence.", ), ] diff --git a/llama_stack/providers/registry/inference.py b/llama_stack/providers/registry/inference.py index 82b771a28..89d7f55e8 100644 --- a/llama_stack/providers/registry/inference.py +++ b/llama_stack/providers/registry/inference.py @@ -6,11 +6,10 @@ from llama_stack.providers.datatypes import ( - AdapterSpec, Api, InlineProviderSpec, ProviderSpec, - remote_provider_spec, + RemoteProviderSpec, ) META_REFERENCE_DEPS = [ @@ -49,180 +48,167 @@ def available_providers() -> list[ProviderSpec]: config_class="llama_stack.providers.inline.inference.sentence_transformers.config.SentenceTransformersInferenceConfig", description="Sentence Transformers inference provider for text embeddings and similarity search.", ), - remote_provider_spec( + RemoteProviderSpec( api=Api.inference, - adapter=AdapterSpec( - adapter_type="cerebras", - pip_packages=[ - "cerebras_cloud_sdk", - ], - module="llama_stack.providers.remote.inference.cerebras", - config_class="llama_stack.providers.remote.inference.cerebras.CerebrasImplConfig", - description="Cerebras inference provider for running models on Cerebras Cloud platform.", - ), + adapter_type="cerebras", + provider_type="remote::cerebras", + pip_packages=[ + "cerebras_cloud_sdk", + ], + module="llama_stack.providers.remote.inference.cerebras", + config_class="llama_stack.providers.remote.inference.cerebras.CerebrasImplConfig", + description="Cerebras inference provider for running models on Cerebras Cloud platform.", ), - remote_provider_spec( + RemoteProviderSpec( api=Api.inference, - adapter=AdapterSpec( - adapter_type="ollama", - pip_packages=["ollama", "aiohttp", "h11>=0.16.0"], - config_class="llama_stack.providers.remote.inference.ollama.OllamaImplConfig", - module="llama_stack.providers.remote.inference.ollama", - description="Ollama inference provider for running local models through the Ollama runtime.", - ), + adapter_type="ollama", + provider_type="remote::ollama", + pip_packages=["ollama", "aiohttp", "h11>=0.16.0"], + config_class="llama_stack.providers.remote.inference.ollama.OllamaImplConfig", + module="llama_stack.providers.remote.inference.ollama", + description="Ollama inference provider for running local models through the Ollama runtime.", ), - remote_provider_spec( + RemoteProviderSpec( api=Api.inference, - adapter=AdapterSpec( - adapter_type="vllm", - pip_packages=["openai"], - module="llama_stack.providers.remote.inference.vllm", - config_class="llama_stack.providers.remote.inference.vllm.VLLMInferenceAdapterConfig", - description="Remote vLLM inference provider for connecting to vLLM servers.", - ), + adapter_type="vllm", + provider_type="remote::vllm", + pip_packages=[], + module="llama_stack.providers.remote.inference.vllm", + config_class="llama_stack.providers.remote.inference.vllm.VLLMInferenceAdapterConfig", + provider_data_validator="llama_stack.providers.remote.inference.vllm.VLLMProviderDataValidator", + description="Remote vLLM inference provider for connecting to vLLM servers.", ), - remote_provider_spec( + RemoteProviderSpec( api=Api.inference, - adapter=AdapterSpec( - adapter_type="tgi", - pip_packages=["huggingface_hub", "aiohttp"], - module="llama_stack.providers.remote.inference.tgi", - config_class="llama_stack.providers.remote.inference.tgi.TGIImplConfig", - description="Text Generation Inference (TGI) provider for HuggingFace model serving.", - ), + adapter_type="tgi", + provider_type="remote::tgi", + pip_packages=["huggingface_hub", "aiohttp"], + module="llama_stack.providers.remote.inference.tgi", + config_class="llama_stack.providers.remote.inference.tgi.TGIImplConfig", + description="Text Generation Inference (TGI) provider for HuggingFace model serving.", ), - remote_provider_spec( + RemoteProviderSpec( api=Api.inference, - adapter=AdapterSpec( - adapter_type="hf::serverless", - pip_packages=["huggingface_hub", "aiohttp"], - module="llama_stack.providers.remote.inference.tgi", - config_class="llama_stack.providers.remote.inference.tgi.InferenceAPIImplConfig", - description="HuggingFace Inference API serverless provider for on-demand model inference.", - ), + adapter_type="hf::serverless", + provider_type="remote::hf::serverless", + pip_packages=["huggingface_hub", "aiohttp"], + module="llama_stack.providers.remote.inference.tgi", + config_class="llama_stack.providers.remote.inference.tgi.InferenceAPIImplConfig", + description="HuggingFace Inference API serverless provider for on-demand model inference.", ), - remote_provider_spec( + RemoteProviderSpec( api=Api.inference, - adapter=AdapterSpec( - adapter_type="hf::endpoint", - pip_packages=["huggingface_hub", "aiohttp"], - module="llama_stack.providers.remote.inference.tgi", - config_class="llama_stack.providers.remote.inference.tgi.InferenceEndpointImplConfig", - description="HuggingFace Inference Endpoints provider for dedicated model serving.", - ), + provider_type="remote::hf::endpoint", + adapter_type="hf::endpoint", + pip_packages=["huggingface_hub", "aiohttp"], + module="llama_stack.providers.remote.inference.tgi", + config_class="llama_stack.providers.remote.inference.tgi.InferenceEndpointImplConfig", + description="HuggingFace Inference Endpoints provider for dedicated model serving.", ), - remote_provider_spec( + RemoteProviderSpec( api=Api.inference, - adapter=AdapterSpec( - adapter_type="fireworks", - pip_packages=[ - "fireworks-ai", - ], - module="llama_stack.providers.remote.inference.fireworks", - config_class="llama_stack.providers.remote.inference.fireworks.FireworksImplConfig", - provider_data_validator="llama_stack.providers.remote.inference.fireworks.FireworksProviderDataValidator", - description="Fireworks AI inference provider for Llama models and other AI models on the Fireworks platform.", - ), + adapter_type="fireworks", + provider_type="remote::fireworks", + pip_packages=[ + "fireworks-ai<=0.17.16", + ], + module="llama_stack.providers.remote.inference.fireworks", + config_class="llama_stack.providers.remote.inference.fireworks.FireworksImplConfig", + provider_data_validator="llama_stack.providers.remote.inference.fireworks.FireworksProviderDataValidator", + description="Fireworks AI inference provider for Llama models and other AI models on the Fireworks platform.", ), - remote_provider_spec( + RemoteProviderSpec( api=Api.inference, - adapter=AdapterSpec( - adapter_type="together", - pip_packages=[ - "together", - ], - module="llama_stack.providers.remote.inference.together", - config_class="llama_stack.providers.remote.inference.together.TogetherImplConfig", - provider_data_validator="llama_stack.providers.remote.inference.together.TogetherProviderDataValidator", - description="Together AI inference provider for open-source models and collaborative AI development.", - ), + adapter_type="together", + provider_type="remote::together", + pip_packages=[ + "together", + ], + module="llama_stack.providers.remote.inference.together", + config_class="llama_stack.providers.remote.inference.together.TogetherImplConfig", + provider_data_validator="llama_stack.providers.remote.inference.together.TogetherProviderDataValidator", + description="Together AI inference provider for open-source models and collaborative AI development.", ), - remote_provider_spec( + RemoteProviderSpec( api=Api.inference, - adapter=AdapterSpec( - adapter_type="bedrock", - pip_packages=["boto3"], - module="llama_stack.providers.remote.inference.bedrock", - config_class="llama_stack.providers.remote.inference.bedrock.BedrockConfig", - description="AWS Bedrock inference provider for accessing various AI models through AWS's managed service.", - ), + adapter_type="bedrock", + provider_type="remote::bedrock", + pip_packages=["boto3"], + module="llama_stack.providers.remote.inference.bedrock", + config_class="llama_stack.providers.remote.inference.bedrock.BedrockConfig", + description="AWS Bedrock inference provider for accessing various AI models through AWS's managed service.", ), - remote_provider_spec( + RemoteProviderSpec( api=Api.inference, - adapter=AdapterSpec( - adapter_type="databricks", - pip_packages=[ - "openai", - ], - module="llama_stack.providers.remote.inference.databricks", - config_class="llama_stack.providers.remote.inference.databricks.DatabricksImplConfig", - description="Databricks inference provider for running models on Databricks' unified analytics platform.", - ), + adapter_type="databricks", + provider_type="remote::databricks", + pip_packages=["databricks-sdk"], + module="llama_stack.providers.remote.inference.databricks", + config_class="llama_stack.providers.remote.inference.databricks.DatabricksImplConfig", + description="Databricks inference provider for running models on Databricks' unified analytics platform.", ), - remote_provider_spec( + RemoteProviderSpec( api=Api.inference, - adapter=AdapterSpec( - adapter_type="nvidia", - pip_packages=[ - "openai", - ], - module="llama_stack.providers.remote.inference.nvidia", - config_class="llama_stack.providers.remote.inference.nvidia.NVIDIAConfig", - description="NVIDIA inference provider for accessing NVIDIA NIM models and AI services.", - ), + adapter_type="nvidia", + provider_type="remote::nvidia", + pip_packages=[], + module="llama_stack.providers.remote.inference.nvidia", + config_class="llama_stack.providers.remote.inference.nvidia.NVIDIAConfig", + description="NVIDIA inference provider for accessing NVIDIA NIM models and AI services.", ), - remote_provider_spec( + RemoteProviderSpec( api=Api.inference, - adapter=AdapterSpec( - adapter_type="runpod", - pip_packages=["openai"], - module="llama_stack.providers.remote.inference.runpod", - config_class="llama_stack.providers.remote.inference.runpod.RunpodImplConfig", - description="RunPod inference provider for running models on RunPod's cloud GPU platform.", - ), + adapter_type="runpod", + provider_type="remote::runpod", + pip_packages=[], + module="llama_stack.providers.remote.inference.runpod", + config_class="llama_stack.providers.remote.inference.runpod.RunpodImplConfig", + description="RunPod inference provider for running models on RunPod's cloud GPU platform.", ), - remote_provider_spec( + RemoteProviderSpec( api=Api.inference, - adapter=AdapterSpec( - adapter_type="openai", - pip_packages=["litellm"], - module="llama_stack.providers.remote.inference.openai", - config_class="llama_stack.providers.remote.inference.openai.OpenAIConfig", - provider_data_validator="llama_stack.providers.remote.inference.openai.config.OpenAIProviderDataValidator", - description="OpenAI inference provider for accessing GPT models and other OpenAI services.", - ), + adapter_type="openai", + provider_type="remote::openai", + pip_packages=["litellm"], + module="llama_stack.providers.remote.inference.openai", + config_class="llama_stack.providers.remote.inference.openai.OpenAIConfig", + provider_data_validator="llama_stack.providers.remote.inference.openai.config.OpenAIProviderDataValidator", + description="OpenAI inference provider for accessing GPT models and other OpenAI services.", ), - remote_provider_spec( + RemoteProviderSpec( api=Api.inference, - adapter=AdapterSpec( - adapter_type="anthropic", - pip_packages=["litellm"], - module="llama_stack.providers.remote.inference.anthropic", - config_class="llama_stack.providers.remote.inference.anthropic.AnthropicConfig", - provider_data_validator="llama_stack.providers.remote.inference.anthropic.config.AnthropicProviderDataValidator", - description="Anthropic inference provider for accessing Claude models and Anthropic's AI services.", - ), + adapter_type="anthropic", + provider_type="remote::anthropic", + pip_packages=["litellm"], + module="llama_stack.providers.remote.inference.anthropic", + config_class="llama_stack.providers.remote.inference.anthropic.AnthropicConfig", + provider_data_validator="llama_stack.providers.remote.inference.anthropic.config.AnthropicProviderDataValidator", + description="Anthropic inference provider for accessing Claude models and Anthropic's AI services.", ), - remote_provider_spec( + RemoteProviderSpec( api=Api.inference, - adapter=AdapterSpec( - adapter_type="gemini", - pip_packages=["litellm"], - module="llama_stack.providers.remote.inference.gemini", - config_class="llama_stack.providers.remote.inference.gemini.GeminiConfig", - provider_data_validator="llama_stack.providers.remote.inference.gemini.config.GeminiProviderDataValidator", - description="Google Gemini inference provider for accessing Gemini models and Google's AI services.", - ), + adapter_type="gemini", + provider_type="remote::gemini", + pip_packages=[ + "litellm", + ], + module="llama_stack.providers.remote.inference.gemini", + config_class="llama_stack.providers.remote.inference.gemini.GeminiConfig", + provider_data_validator="llama_stack.providers.remote.inference.gemini.config.GeminiProviderDataValidator", + description="Google Gemini inference provider for accessing Gemini models and Google's AI services.", ), - remote_provider_spec( + RemoteProviderSpec( api=Api.inference, - adapter=AdapterSpec( - adapter_type="vertexai", - pip_packages=["litellm", "google-cloud-aiplatform"], - module="llama_stack.providers.remote.inference.vertexai", - config_class="llama_stack.providers.remote.inference.vertexai.VertexAIConfig", - provider_data_validator="llama_stack.providers.remote.inference.vertexai.config.VertexAIProviderDataValidator", - description="""Google Vertex AI inference provider enables you to use Google's Gemini models through Google Cloud's Vertex AI platform, providing several advantages: + adapter_type="vertexai", + provider_type="remote::vertexai", + pip_packages=[ + "litellm", + "google-cloud-aiplatform", + ], + module="llama_stack.providers.remote.inference.vertexai", + config_class="llama_stack.providers.remote.inference.vertexai.VertexAIConfig", + provider_data_validator="llama_stack.providers.remote.inference.vertexai.config.VertexAIProviderDataValidator", + description="""Google Vertex AI inference provider enables you to use Google's Gemini models through Google Cloud's Vertex AI platform, providing several advantages: • Enterprise-grade security: Uses Google Cloud's security controls and IAM • Better integration: Seamless integration with other Google Cloud services @@ -242,61 +228,73 @@ Available Models: - vertex_ai/gemini-2.0-flash - vertex_ai/gemini-2.5-flash - vertex_ai/gemini-2.5-pro""", - ), ), - remote_provider_spec( + RemoteProviderSpec( api=Api.inference, - adapter=AdapterSpec( - adapter_type="groq", - pip_packages=["litellm"], - module="llama_stack.providers.remote.inference.groq", - config_class="llama_stack.providers.remote.inference.groq.GroqConfig", - provider_data_validator="llama_stack.providers.remote.inference.groq.config.GroqProviderDataValidator", - description="Groq inference provider for ultra-fast inference using Groq's LPU technology.", - ), + adapter_type="groq", + provider_type="remote::groq", + pip_packages=[ + "litellm", + ], + module="llama_stack.providers.remote.inference.groq", + config_class="llama_stack.providers.remote.inference.groq.GroqConfig", + provider_data_validator="llama_stack.providers.remote.inference.groq.config.GroqProviderDataValidator", + description="Groq inference provider for ultra-fast inference using Groq's LPU technology.", ), - remote_provider_spec( + RemoteProviderSpec( api=Api.inference, - adapter=AdapterSpec( - adapter_type="llama-openai-compat", - pip_packages=["litellm"], - module="llama_stack.providers.remote.inference.llama_openai_compat", - config_class="llama_stack.providers.remote.inference.llama_openai_compat.config.LlamaCompatConfig", - provider_data_validator="llama_stack.providers.remote.inference.llama_openai_compat.config.LlamaProviderDataValidator", - description="Llama OpenAI-compatible provider for using Llama models with OpenAI API format.", - ), + adapter_type="llama-openai-compat", + provider_type="remote::llama-openai-compat", + pip_packages=["litellm"], + module="llama_stack.providers.remote.inference.llama_openai_compat", + config_class="llama_stack.providers.remote.inference.llama_openai_compat.config.LlamaCompatConfig", + provider_data_validator="llama_stack.providers.remote.inference.llama_openai_compat.config.LlamaProviderDataValidator", + description="Llama OpenAI-compatible provider for using Llama models with OpenAI API format.", ), - remote_provider_spec( + RemoteProviderSpec( api=Api.inference, - adapter=AdapterSpec( - adapter_type="sambanova", - pip_packages=["litellm"], - module="llama_stack.providers.remote.inference.sambanova", - config_class="llama_stack.providers.remote.inference.sambanova.SambaNovaImplConfig", - provider_data_validator="llama_stack.providers.remote.inference.sambanova.config.SambaNovaProviderDataValidator", - description="SambaNova inference provider for running models on SambaNova's dataflow architecture.", - ), + adapter_type="sambanova", + provider_type="remote::sambanova", + pip_packages=[ + "litellm", + ], + module="llama_stack.providers.remote.inference.sambanova", + config_class="llama_stack.providers.remote.inference.sambanova.SambaNovaImplConfig", + provider_data_validator="llama_stack.providers.remote.inference.sambanova.config.SambaNovaProviderDataValidator", + description="SambaNova inference provider for running models on SambaNova's dataflow architecture.", ), - remote_provider_spec( + RemoteProviderSpec( api=Api.inference, - adapter=AdapterSpec( - adapter_type="passthrough", - pip_packages=[], - module="llama_stack.providers.remote.inference.passthrough", - config_class="llama_stack.providers.remote.inference.passthrough.PassthroughImplConfig", - provider_data_validator="llama_stack.providers.remote.inference.passthrough.PassthroughProviderDataValidator", - description="Passthrough inference provider for connecting to any external inference service not directly supported.", - ), + adapter_type="passthrough", + provider_type="remote::passthrough", + pip_packages=[], + module="llama_stack.providers.remote.inference.passthrough", + config_class="llama_stack.providers.remote.inference.passthrough.PassthroughImplConfig", + provider_data_validator="llama_stack.providers.remote.inference.passthrough.PassthroughProviderDataValidator", + description="Passthrough inference provider for connecting to any external inference service not directly supported.", ), - remote_provider_spec( + RemoteProviderSpec( api=Api.inference, - adapter=AdapterSpec( - adapter_type="watsonx", - pip_packages=["ibm_watson_machine_learning"], - module="llama_stack.providers.remote.inference.watsonx", - config_class="llama_stack.providers.remote.inference.watsonx.WatsonXConfig", - provider_data_validator="llama_stack.providers.remote.inference.watsonx.WatsonXProviderDataValidator", - description="IBM WatsonX inference provider for accessing AI models on IBM's WatsonX platform.", - ), + adapter_type="watsonx", + provider_type="remote::watsonx", + pip_packages=["ibm_watsonx_ai"], + module="llama_stack.providers.remote.inference.watsonx", + config_class="llama_stack.providers.remote.inference.watsonx.WatsonXConfig", + provider_data_validator="llama_stack.providers.remote.inference.watsonx.WatsonXProviderDataValidator", + description="IBM WatsonX inference provider for accessing AI models on IBM's WatsonX platform.", + ), + RemoteProviderSpec( + api=Api.inference, + provider_type="remote::azure", + adapter_type="azure", + pip_packages=["litellm"], + module="llama_stack.providers.remote.inference.azure", + config_class="llama_stack.providers.remote.inference.azure.AzureConfig", + provider_data_validator="llama_stack.providers.remote.inference.azure.config.AzureProviderDataValidator", + description=""" +Azure OpenAI inference provider for accessing GPT models and other Azure services. +Provider documentation +https://learn.microsoft.com/en-us/azure/ai-foundry/openai/overview +""", ), ] diff --git a/llama_stack/providers/registry/post_training.py b/llama_stack/providers/registry/post_training.py index 67238e3fc..2092e3b2d 100644 --- a/llama_stack/providers/registry/post_training.py +++ b/llama_stack/providers/registry/post_training.py @@ -7,7 +7,7 @@ from typing import cast -from llama_stack.providers.datatypes import AdapterSpec, Api, InlineProviderSpec, ProviderSpec, remote_provider_spec +from llama_stack.providers.datatypes import Api, InlineProviderSpec, ProviderSpec, RemoteProviderSpec # We provide two versions of these providers so that distributions can package the appropriate version of torch. # The CPU version is used for distributions that don't have GPU support -- they result in smaller container images. @@ -48,7 +48,7 @@ def available_providers() -> list[ProviderSpec]: InlineProviderSpec( api=Api.post_training, provider_type="inline::huggingface-gpu", - pip_packages=["trl", "transformers", "peft", "datasets", "torch"], + pip_packages=["trl", "transformers", "peft", "datasets>=4.0.0", "torch"], module="llama_stack.providers.inline.post_training.huggingface", config_class="llama_stack.providers.inline.post_training.huggingface.HuggingFacePostTrainingConfig", api_dependencies=[ @@ -57,14 +57,13 @@ def available_providers() -> list[ProviderSpec]: ], description="HuggingFace-based post-training provider for fine-tuning models using the HuggingFace ecosystem.", ), - remote_provider_spec( + RemoteProviderSpec( api=Api.post_training, - adapter=AdapterSpec( - adapter_type="nvidia", - pip_packages=["requests", "aiohttp"], - module="llama_stack.providers.remote.post_training.nvidia", - config_class="llama_stack.providers.remote.post_training.nvidia.NvidiaPostTrainingConfig", - description="NVIDIA's post-training provider for fine-tuning models on NVIDIA's platform.", - ), + adapter_type="nvidia", + provider_type="remote::nvidia", + pip_packages=["requests", "aiohttp"], + module="llama_stack.providers.remote.post_training.nvidia", + config_class="llama_stack.providers.remote.post_training.nvidia.NvidiaPostTrainingConfig", + description="NVIDIA's post-training provider for fine-tuning models on NVIDIA's platform.", ), ] diff --git a/llama_stack/providers/registry/safety.py b/llama_stack/providers/registry/safety.py index 9dd791bd8..b30074398 100644 --- a/llama_stack/providers/registry/safety.py +++ b/llama_stack/providers/registry/safety.py @@ -6,11 +6,10 @@ from llama_stack.providers.datatypes import ( - AdapterSpec, Api, InlineProviderSpec, ProviderSpec, - remote_provider_spec, + RemoteProviderSpec, ) @@ -48,35 +47,32 @@ def available_providers() -> list[ProviderSpec]: config_class="llama_stack.providers.inline.safety.code_scanner.CodeScannerConfig", description="Code Scanner safety provider for detecting security vulnerabilities and unsafe code patterns.", ), - remote_provider_spec( + RemoteProviderSpec( api=Api.safety, - adapter=AdapterSpec( - adapter_type="bedrock", - pip_packages=["boto3"], - module="llama_stack.providers.remote.safety.bedrock", - config_class="llama_stack.providers.remote.safety.bedrock.BedrockSafetyConfig", - description="AWS Bedrock safety provider for content moderation using AWS's safety services.", - ), + adapter_type="bedrock", + provider_type="remote::bedrock", + pip_packages=["boto3"], + module="llama_stack.providers.remote.safety.bedrock", + config_class="llama_stack.providers.remote.safety.bedrock.BedrockSafetyConfig", + description="AWS Bedrock safety provider for content moderation using AWS's safety services.", ), - remote_provider_spec( + RemoteProviderSpec( api=Api.safety, - adapter=AdapterSpec( - adapter_type="nvidia", - pip_packages=["requests"], - module="llama_stack.providers.remote.safety.nvidia", - config_class="llama_stack.providers.remote.safety.nvidia.NVIDIASafetyConfig", - description="NVIDIA's safety provider for content moderation and safety filtering.", - ), + adapter_type="nvidia", + provider_type="remote::nvidia", + pip_packages=["requests"], + module="llama_stack.providers.remote.safety.nvidia", + config_class="llama_stack.providers.remote.safety.nvidia.NVIDIASafetyConfig", + description="NVIDIA's safety provider for content moderation and safety filtering.", ), - remote_provider_spec( + RemoteProviderSpec( api=Api.safety, - adapter=AdapterSpec( - adapter_type="sambanova", - pip_packages=["litellm", "requests"], - module="llama_stack.providers.remote.safety.sambanova", - config_class="llama_stack.providers.remote.safety.sambanova.SambaNovaSafetyConfig", - provider_data_validator="llama_stack.providers.remote.safety.sambanova.config.SambaNovaProviderDataValidator", - description="SambaNova's safety provider for content moderation and safety filtering.", - ), + adapter_type="sambanova", + provider_type="remote::sambanova", + pip_packages=["litellm", "requests"], + module="llama_stack.providers.remote.safety.sambanova", + config_class="llama_stack.providers.remote.safety.sambanova.SambaNovaSafetyConfig", + provider_data_validator="llama_stack.providers.remote.safety.sambanova.config.SambaNovaProviderDataValidator", + description="SambaNova's safety provider for content moderation and safety filtering.", ), ] diff --git a/llama_stack/providers/registry/scoring.py b/llama_stack/providers/registry/scoring.py index 79293d888..a4ec54ed2 100644 --- a/llama_stack/providers/registry/scoring.py +++ b/llama_stack/providers/registry/scoring.py @@ -38,7 +38,7 @@ def available_providers() -> list[ProviderSpec]: InlineProviderSpec( api=Api.scoring, provider_type="inline::braintrust", - pip_packages=["autoevals", "openai"], + pip_packages=["autoevals"], module="llama_stack.providers.inline.scoring.braintrust", config_class="llama_stack.providers.inline.scoring.braintrust.BraintrustScoringConfig", api_dependencies=[ diff --git a/llama_stack/providers/registry/tool_runtime.py b/llama_stack/providers/registry/tool_runtime.py index 661851443..ad8c31dfd 100644 --- a/llama_stack/providers/registry/tool_runtime.py +++ b/llama_stack/providers/registry/tool_runtime.py @@ -6,11 +6,10 @@ from llama_stack.providers.datatypes import ( - AdapterSpec, Api, InlineProviderSpec, ProviderSpec, - remote_provider_spec, + RemoteProviderSpec, ) @@ -32,62 +31,57 @@ def available_providers() -> list[ProviderSpec]: ], module="llama_stack.providers.inline.tool_runtime.rag", config_class="llama_stack.providers.inline.tool_runtime.rag.config.RagToolRuntimeConfig", - api_dependencies=[Api.vector_io, Api.inference], + api_dependencies=[Api.vector_io, Api.inference, Api.files], description="RAG (Retrieval-Augmented Generation) tool runtime for document ingestion, chunking, and semantic search.", ), - remote_provider_spec( + RemoteProviderSpec( api=Api.tool_runtime, - adapter=AdapterSpec( - adapter_type="brave-search", - module="llama_stack.providers.remote.tool_runtime.brave_search", - config_class="llama_stack.providers.remote.tool_runtime.brave_search.config.BraveSearchToolConfig", - pip_packages=["requests"], - provider_data_validator="llama_stack.providers.remote.tool_runtime.brave_search.BraveSearchToolProviderDataValidator", - description="Brave Search tool for web search capabilities with privacy-focused results.", - ), + adapter_type="brave-search", + provider_type="remote::brave-search", + module="llama_stack.providers.remote.tool_runtime.brave_search", + config_class="llama_stack.providers.remote.tool_runtime.brave_search.config.BraveSearchToolConfig", + pip_packages=["requests"], + provider_data_validator="llama_stack.providers.remote.tool_runtime.brave_search.BraveSearchToolProviderDataValidator", + description="Brave Search tool for web search capabilities with privacy-focused results.", ), - remote_provider_spec( + RemoteProviderSpec( api=Api.tool_runtime, - adapter=AdapterSpec( - adapter_type="bing-search", - module="llama_stack.providers.remote.tool_runtime.bing_search", - config_class="llama_stack.providers.remote.tool_runtime.bing_search.config.BingSearchToolConfig", - pip_packages=["requests"], - provider_data_validator="llama_stack.providers.remote.tool_runtime.bing_search.BingSearchToolProviderDataValidator", - description="Bing Search tool for web search capabilities using Microsoft's search engine.", - ), + adapter_type="bing-search", + provider_type="remote::bing-search", + module="llama_stack.providers.remote.tool_runtime.bing_search", + config_class="llama_stack.providers.remote.tool_runtime.bing_search.config.BingSearchToolConfig", + pip_packages=["requests"], + provider_data_validator="llama_stack.providers.remote.tool_runtime.bing_search.BingSearchToolProviderDataValidator", + description="Bing Search tool for web search capabilities using Microsoft's search engine.", ), - remote_provider_spec( + RemoteProviderSpec( api=Api.tool_runtime, - adapter=AdapterSpec( - adapter_type="tavily-search", - module="llama_stack.providers.remote.tool_runtime.tavily_search", - config_class="llama_stack.providers.remote.tool_runtime.tavily_search.config.TavilySearchToolConfig", - pip_packages=["requests"], - provider_data_validator="llama_stack.providers.remote.tool_runtime.tavily_search.TavilySearchToolProviderDataValidator", - description="Tavily Search tool for AI-optimized web search with structured results.", - ), + adapter_type="tavily-search", + provider_type="remote::tavily-search", + module="llama_stack.providers.remote.tool_runtime.tavily_search", + config_class="llama_stack.providers.remote.tool_runtime.tavily_search.config.TavilySearchToolConfig", + pip_packages=["requests"], + provider_data_validator="llama_stack.providers.remote.tool_runtime.tavily_search.TavilySearchToolProviderDataValidator", + description="Tavily Search tool for AI-optimized web search with structured results.", ), - remote_provider_spec( + RemoteProviderSpec( api=Api.tool_runtime, - adapter=AdapterSpec( - adapter_type="wolfram-alpha", - module="llama_stack.providers.remote.tool_runtime.wolfram_alpha", - config_class="llama_stack.providers.remote.tool_runtime.wolfram_alpha.config.WolframAlphaToolConfig", - pip_packages=["requests"], - provider_data_validator="llama_stack.providers.remote.tool_runtime.wolfram_alpha.WolframAlphaToolProviderDataValidator", - description="Wolfram Alpha tool for computational knowledge and mathematical calculations.", - ), + adapter_type="wolfram-alpha", + provider_type="remote::wolfram-alpha", + module="llama_stack.providers.remote.tool_runtime.wolfram_alpha", + config_class="llama_stack.providers.remote.tool_runtime.wolfram_alpha.config.WolframAlphaToolConfig", + pip_packages=["requests"], + provider_data_validator="llama_stack.providers.remote.tool_runtime.wolfram_alpha.WolframAlphaToolProviderDataValidator", + description="Wolfram Alpha tool for computational knowledge and mathematical calculations.", ), - remote_provider_spec( + RemoteProviderSpec( api=Api.tool_runtime, - adapter=AdapterSpec( - adapter_type="model-context-protocol", - module="llama_stack.providers.remote.tool_runtime.model_context_protocol", - config_class="llama_stack.providers.remote.tool_runtime.model_context_protocol.config.MCPProviderConfig", - pip_packages=["mcp>=1.8.1"], - provider_data_validator="llama_stack.providers.remote.tool_runtime.model_context_protocol.config.MCPProviderDataValidator", - description="Model Context Protocol (MCP) tool for standardized tool calling and context management.", - ), + adapter_type="model-context-protocol", + provider_type="remote::model-context-protocol", + module="llama_stack.providers.remote.tool_runtime.model_context_protocol", + config_class="llama_stack.providers.remote.tool_runtime.model_context_protocol.config.MCPProviderConfig", + pip_packages=["mcp>=1.8.1"], + provider_data_validator="llama_stack.providers.remote.tool_runtime.model_context_protocol.config.MCPProviderDataValidator", + description="Model Context Protocol (MCP) tool for standardized tool calling and context management.", ), ] diff --git a/llama_stack/providers/registry/vector_io.py b/llama_stack/providers/registry/vector_io.py index 70148eb15..9816838e7 100644 --- a/llama_stack/providers/registry/vector_io.py +++ b/llama_stack/providers/registry/vector_io.py @@ -6,11 +6,10 @@ from llama_stack.providers.datatypes import ( - AdapterSpec, Api, InlineProviderSpec, ProviderSpec, - remote_provider_spec, + RemoteProviderSpec, ) @@ -300,14 +299,16 @@ See [sqlite-vec's GitHub repo](https://github.com/asg017/sqlite-vec/tree/main) f Please refer to the sqlite-vec provider documentation. """, ), - remote_provider_spec( - Api.vector_io, - AdapterSpec( - adapter_type="chromadb", - pip_packages=["chromadb-client"], - module="llama_stack.providers.remote.vector_io.chroma", - config_class="llama_stack.providers.remote.vector_io.chroma.ChromaVectorIOConfig", - description=""" + RemoteProviderSpec( + api=Api.vector_io, + adapter_type="chromadb", + provider_type="remote::chromadb", + pip_packages=["chromadb-client"], + module="llama_stack.providers.remote.vector_io.chroma", + config_class="llama_stack.providers.remote.vector_io.chroma.ChromaVectorIOConfig", + api_dependencies=[Api.inference], + optional_api_dependencies=[Api.files], + description=""" [Chroma](https://www.trychroma.com/) is an inline and remote vector database provider for Llama Stack. It allows you to store and query vectors directly within a Chroma database. That means you're not limited to storing vectors in memory or in a separate service. @@ -340,9 +341,6 @@ pip install chromadb ## Documentation See [Chroma's documentation](https://docs.trychroma.com/docs/overview/introduction) for more details about Chroma in general. """, - ), - api_dependencies=[Api.inference], - optional_api_dependencies=[Api.files], ), InlineProviderSpec( api=Api.vector_io, @@ -387,14 +385,16 @@ See [Chroma's documentation](https://docs.trychroma.com/docs/overview/introducti """, ), - remote_provider_spec( - Api.vector_io, - AdapterSpec( - adapter_type="pgvector", - pip_packages=["psycopg2-binary"], - module="llama_stack.providers.remote.vector_io.pgvector", - config_class="llama_stack.providers.remote.vector_io.pgvector.PGVectorVectorIOConfig", - description=""" + RemoteProviderSpec( + api=Api.vector_io, + adapter_type="pgvector", + provider_type="remote::pgvector", + pip_packages=["psycopg2-binary"], + module="llama_stack.providers.remote.vector_io.pgvector", + config_class="llama_stack.providers.remote.vector_io.pgvector.PGVectorVectorIOConfig", + api_dependencies=[Api.inference], + optional_api_dependencies=[Api.files], + description=""" [PGVector](https://github.com/pgvector/pgvector) is a remote vector database provider for Llama Stack. It allows you to store and query vectors directly in memory. That means you'll get fast and efficient vector retrieval. @@ -404,6 +404,60 @@ That means you'll get fast and efficient vector retrieval. - Easy to use - Fully integrated with Llama Stack +There are three implementations of search for PGVectoIndex available: + +1. Vector Search: +- How it works: + - Uses PostgreSQL's vector extension (pgvector) to perform similarity search + - Compares query embeddings against stored embeddings using Cosine distance or other distance metrics + - Eg. SQL query: SELECT document, embedding <=> %s::vector AS distance FROM table ORDER BY distance + +-Characteristics: + - Semantic understanding - finds documents similar in meaning even if they don't share keywords + - Works with high-dimensional vector embeddings (typically 768, 1024, or higher dimensions) + - Best for: Finding conceptually related content, handling synonyms, cross-language search + +2. Keyword Search +- How it works: + - Uses PostgreSQL's full-text search capabilities with tsvector and ts_rank + - Converts text to searchable tokens using to_tsvector('english', text). Default language is English. + - Eg. SQL query: SELECT document, ts_rank(tokenized_content, plainto_tsquery('english', %s)) AS score + +- Characteristics: + - Lexical matching - finds exact keyword matches and variations + - Uses GIN (Generalized Inverted Index) for fast text search performance + - Scoring: Uses PostgreSQL's ts_rank function for relevance scoring + - Best for: Exact term matching, proper names, technical terms, Boolean-style queries + +3. Hybrid Search +- How it works: + - Combines both vector and keyword search results + - Runs both searches independently, then merges results using configurable reranking + +- Two reranking strategies available: + - Reciprocal Rank Fusion (RRF) - (default: 60.0) + - Weighted Average - (default: 0.5) + +- Characteristics: + - Best of both worlds: semantic understanding + exact matching + - Documents appearing in both searches get boosted scores + - Configurable balance between semantic and lexical matching + - Best for: General-purpose search where you want both precision and recall + +4. Database Schema +The PGVector implementation stores data optimized for all three search types: +CREATE TABLE vector_store_xxx ( + id TEXT PRIMARY KEY, + document JSONB, -- Original document + embedding vector(dimension), -- For vector search + content_text TEXT, -- Raw text content + tokenized_content TSVECTOR -- For keyword search +); + +-- Indexes for performance +CREATE INDEX content_gin_idx ON table USING GIN(tokenized_content); -- Keyword search +-- Vector index created automatically by pgvector + ## Usage To use PGVector in your Llama Stack project, follow these steps: @@ -412,6 +466,25 @@ To use PGVector in your Llama Stack project, follow these steps: 2. Configure your Llama Stack project to use pgvector. (e.g. remote::pgvector). 3. Start storing and querying vectors. +## This is an example how you can set up your environment for using PGVector + +1. Export env vars: +```bash +export ENABLE_PGVECTOR=true +export PGVECTOR_HOST=localhost +export PGVECTOR_PORT=5432 +export PGVECTOR_DB=llamastack +export PGVECTOR_USER=llamastack +export PGVECTOR_PASSWORD=llamastack +``` + +2. Create DB: +```bash +psql -h localhost -U postgres -c "CREATE ROLE llamastack LOGIN PASSWORD 'llamastack';" +psql -h localhost -U postgres -c "CREATE DATABASE llamastack OWNER llamastack;" +psql -h localhost -U llamastack -d llamastack -c "CREATE EXTENSION IF NOT EXISTS vector;" +``` + ## Installation You can install PGVector using docker: @@ -422,19 +495,18 @@ docker pull pgvector/pgvector:pg17 ## Documentation See [PGVector's documentation](https://github.com/pgvector/pgvector) for more details about PGVector in general. """, - ), + ), + RemoteProviderSpec( + api=Api.vector_io, + adapter_type="weaviate", + provider_type="remote::weaviate", + pip_packages=["weaviate-client"], + module="llama_stack.providers.remote.vector_io.weaviate", + config_class="llama_stack.providers.remote.vector_io.weaviate.WeaviateVectorIOConfig", + provider_data_validator="llama_stack.providers.remote.vector_io.weaviate.WeaviateRequestProviderData", api_dependencies=[Api.inference], optional_api_dependencies=[Api.files], - ), - remote_provider_spec( - Api.vector_io, - AdapterSpec( - adapter_type="weaviate", - pip_packages=["weaviate-client"], - module="llama_stack.providers.remote.vector_io.weaviate", - config_class="llama_stack.providers.remote.vector_io.weaviate.WeaviateVectorIOConfig", - provider_data_validator="llama_stack.providers.remote.vector_io.weaviate.WeaviateRequestProviderData", - description=""" + description=""" [Weaviate](https://weaviate.io/) is a vector database provider for Llama Stack. It allows you to store and query vectors directly within a Weaviate database. That means you're not limited to storing vectors in memory or in a separate service. @@ -449,6 +521,7 @@ Weaviate supports: - Metadata filtering - Multi-modal retrieval + ## Usage To use Weaviate in your Llama Stack project, follow these steps: @@ -464,9 +537,6 @@ To install Weaviate see the [Weaviate quickstart documentation](https://weaviate ## Documentation See [Weaviate's documentation](https://weaviate.io/developers/weaviate) for more details about Weaviate in general. """, - ), - api_dependencies=[Api.inference], - optional_api_dependencies=[Api.files], ), InlineProviderSpec( api=Api.vector_io, @@ -520,28 +590,29 @@ docker pull qdrant/qdrant See the [Qdrant documentation](https://qdrant.tech/documentation/) for more details about Qdrant in general. """, ), - remote_provider_spec( - Api.vector_io, - AdapterSpec( - adapter_type="qdrant", - pip_packages=["qdrant-client"], - module="llama_stack.providers.remote.vector_io.qdrant", - config_class="llama_stack.providers.remote.vector_io.qdrant.QdrantVectorIOConfig", - description=""" -Please refer to the inline provider documentation. -""", - ), + RemoteProviderSpec( + api=Api.vector_io, + adapter_type="qdrant", + provider_type="remote::qdrant", + pip_packages=["qdrant-client"], + module="llama_stack.providers.remote.vector_io.qdrant", + config_class="llama_stack.providers.remote.vector_io.qdrant.QdrantVectorIOConfig", api_dependencies=[Api.inference], optional_api_dependencies=[Api.files], + description=""" +Please refer to the inline provider documentation. +""", ), - remote_provider_spec( - Api.vector_io, - AdapterSpec( - adapter_type="milvus", - pip_packages=["pymilvus>=2.4.10"], - module="llama_stack.providers.remote.vector_io.milvus", - config_class="llama_stack.providers.remote.vector_io.milvus.MilvusVectorIOConfig", - description=""" + RemoteProviderSpec( + api=Api.vector_io, + adapter_type="milvus", + provider_type="remote::milvus", + pip_packages=["pymilvus>=2.4.10"], + module="llama_stack.providers.remote.vector_io.milvus", + config_class="llama_stack.providers.remote.vector_io.milvus.MilvusVectorIOConfig", + api_dependencies=[Api.inference], + optional_api_dependencies=[Api.files], + description=""" [Milvus](https://milvus.io/) is an inline and remote vector database provider for Llama Stack. It allows you to store and query vectors directly within a Milvus database. That means you're not limited to storing vectors in memory or in a separate service. @@ -562,7 +633,13 @@ To use Milvus in your Llama Stack project, follow these steps: ## Installation -You can install Milvus using pymilvus: +If you want to use inline Milvus, you can install: + +```bash +pip install pymilvus[milvus-lite] +``` + +If you want to use remote Milvus, you can install: ```bash pip install pymilvus @@ -732,14 +809,11 @@ See the [Milvus documentation](https://milvus.io/docs/install-overview.md) for m For more details on TLS configuration, refer to the [TLS setup guide](https://milvus.io/docs/tls.md). """, - ), - api_dependencies=[Api.inference], - optional_api_dependencies=[Api.files], ), InlineProviderSpec( api=Api.vector_io, provider_type="inline::milvus", - pip_packages=["pymilvus>=2.4.10"], + pip_packages=["pymilvus[milvus-lite]>=2.4.10"], module="llama_stack.providers.inline.vector_io.milvus", config_class="llama_stack.providers.inline.vector_io.milvus.MilvusVectorIOConfig", api_dependencies=[Api.inference], diff --git a/llama_stack/providers/remote/eval/nvidia/eval.py b/llama_stack/providers/remote/eval/nvidia/eval.py index 3572de0ef..8fc7ffdd3 100644 --- a/llama_stack/providers/remote/eval/nvidia/eval.py +++ b/llama_stack/providers/remote/eval/nvidia/eval.py @@ -14,7 +14,6 @@ from llama_stack.apis.datasets import Datasets from llama_stack.apis.inference import Inference from llama_stack.apis.scoring import Scoring, ScoringResult from llama_stack.providers.datatypes import BenchmarksProtocolPrivate -from llama_stack.providers.remote.inference.nvidia.models import MODEL_ENTRIES from llama_stack.providers.utils.inference.model_registry import ModelRegistryHelper from .....apis.common.job_types import Job, JobStatus @@ -45,24 +44,29 @@ class NVIDIAEvalImpl( self.inference_api = inference_api self.agents_api = agents_api - ModelRegistryHelper.__init__(self, model_entries=MODEL_ENTRIES) + ModelRegistryHelper.__init__(self) async def initialize(self) -> None: ... async def shutdown(self) -> None: ... - async def _evaluator_get(self, path): + async def _evaluator_get(self, path: str): """Helper for making GET requests to the evaluator service.""" response = requests.get(url=f"{self.config.evaluator_url}{path}") response.raise_for_status() return response.json() - async def _evaluator_post(self, path, data): + async def _evaluator_post(self, path: str, data: dict[str, Any]): """Helper for making POST requests to the evaluator service.""" response = requests.post(url=f"{self.config.evaluator_url}{path}", json=data) response.raise_for_status() return response.json() + async def _evaluator_delete(self, path: str) -> None: + """Helper for making DELETE requests to the evaluator service.""" + response = requests.delete(url=f"{self.config.evaluator_url}{path}") + response.raise_for_status() + async def register_benchmark(self, task_def: Benchmark) -> None: """Register a benchmark as an evaluation configuration.""" await self._evaluator_post( @@ -75,6 +79,10 @@ class NVIDIAEvalImpl( }, ) + async def unregister_benchmark(self, benchmark_id: str) -> None: + """Unregister a benchmark evaluation configuration from NeMo Evaluator.""" + await self._evaluator_delete(f"/v1/evaluation/configs/{DEFAULT_NAMESPACE}/{benchmark_id}") + async def run_eval( self, benchmark_id: str, diff --git a/llama_stack/providers/remote/files/s3/__init__.py b/llama_stack/providers/remote/files/s3/__init__.py index 3f5dfc88a..7027f1db3 100644 --- a/llama_stack/providers/remote/files/s3/__init__.py +++ b/llama_stack/providers/remote/files/s3/__init__.py @@ -6,15 +6,14 @@ from typing import Any -from llama_stack.core.datatypes import Api +from llama_stack.core.datatypes import AccessRule, Api from .config import S3FilesImplConfig -async def get_adapter_impl(config: S3FilesImplConfig, deps: dict[Api, Any]): +async def get_adapter_impl(config: S3FilesImplConfig, deps: dict[Api, Any], policy: list[AccessRule] | None = None): from .files import S3FilesImpl - # TODO: authorization policies and user separation - impl = S3FilesImpl(config) + impl = S3FilesImpl(config, policy or []) await impl.initialize() return impl diff --git a/llama_stack/providers/remote/files/s3/files.py b/llama_stack/providers/remote/files/s3/files.py index 52e0cbbf4..8ea96af9e 100644 --- a/llama_stack/providers/remote/files/s3/files.py +++ b/llama_stack/providers/remote/files/s3/files.py @@ -4,9 +4,9 @@ # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. -import time import uuid -from typing import Annotated +from datetime import UTC, datetime +from typing import Annotated, Any import boto3 from botocore.exceptions import BotoCoreError, ClientError, NoCredentialsError @@ -15,14 +15,17 @@ from fastapi import File, Form, Response, UploadFile from llama_stack.apis.common.errors import ResourceNotFoundError from llama_stack.apis.common.responses import Order from llama_stack.apis.files import ( + ExpiresAfter, Files, ListOpenAIFileResponse, OpenAIFileDeleteResponse, OpenAIFileObject, OpenAIFilePurpose, ) +from llama_stack.core.datatypes import AccessRule from llama_stack.providers.utils.sqlstore.api import ColumnDefinition, ColumnType -from llama_stack.providers.utils.sqlstore.sqlstore import SqlStore, sqlstore_impl +from llama_stack.providers.utils.sqlstore.authorized_sqlstore import AuthorizedSqlStore +from llama_stack.providers.utils.sqlstore.sqlstore import sqlstore_impl from .config import S3FilesImplConfig @@ -83,22 +86,85 @@ async def _create_bucket_if_not_exists(client: boto3.client, config: S3FilesImpl raise RuntimeError(f"Failed to access S3 bucket '{config.bucket_name}': {e}") from e +def _make_file_object( + *, + id: str, + filename: str, + purpose: str, + bytes: int, + created_at: int, + expires_at: int, + **kwargs: Any, # here to ignore any additional fields, e.g. extra fields from AuthorizedSqlStore +) -> OpenAIFileObject: + """ + Construct an OpenAIFileObject and normalize expires_at. + + If expires_at is greater than the max we treat it as no-expiration and + return None for expires_at. + + The OpenAI spec says expires_at type is Integer, but the implementation + will return None for no expiration. + """ + obj = OpenAIFileObject( + id=id, + filename=filename, + purpose=OpenAIFilePurpose(purpose), + bytes=bytes, + created_at=created_at, + expires_at=expires_at, + ) + + if obj.expires_at is not None and obj.expires_at > (obj.created_at + ExpiresAfter.MAX): + obj.expires_at = None # type: ignore + + return obj + + class S3FilesImpl(Files): """S3-based implementation of the Files API.""" - # TODO: implement expiration, for now a silly offset - _SILLY_EXPIRATION_OFFSET = 100 * 365 * 24 * 60 * 60 - - def __init__(self, config: S3FilesImplConfig) -> None: + def __init__(self, config: S3FilesImplConfig, policy: list[AccessRule]) -> None: self._config = config + self.policy = policy self._client: boto3.client | None = None - self._sql_store: SqlStore | None = None + self._sql_store: AuthorizedSqlStore | None = None + + def _now(self) -> int: + """Return current UTC timestamp as int seconds.""" + return int(datetime.now(UTC).timestamp()) + + async def _get_file(self, file_id: str, return_expired: bool = False) -> dict[str, Any]: + where: dict[str, str | dict] = {"id": file_id} + if not return_expired: + where["expires_at"] = {">": self._now()} + if not (row := await self.sql_store.fetch_one("openai_files", where=where)): + raise ResourceNotFoundError(file_id, "File", "files.list()") + return row + + async def _delete_file(self, file_id: str) -> None: + """Delete a file from S3 and the database.""" + try: + self.client.delete_object( + Bucket=self._config.bucket_name, + Key=file_id, + ) + except ClientError as e: + if e.response["Error"]["Code"] != "NoSuchKey": + raise RuntimeError(f"Failed to delete file from S3: {e}") from e + + await self.sql_store.delete("openai_files", where={"id": file_id}) + + async def _delete_if_expired(self, file_id: str) -> None: + """If the file exists and is expired, delete it.""" + if row := await self._get_file(file_id, return_expired=True): + if (expires_at := row.get("expires_at")) and expires_at <= self._now(): + await self._delete_file(file_id) async def initialize(self) -> None: self._client = _create_s3_client(self._config) await _create_bucket_if_not_exists(self._client, self._config) - self._sql_store = sqlstore_impl(self._config.metadata_store) + self._sql_store = AuthorizedSqlStore(sqlstore_impl(self._config.metadata_store), self.policy) await self._sql_store.create_table( "openai_files", { @@ -121,7 +187,7 @@ class S3FilesImpl(Files): return self._client @property - def sql_store(self) -> SqlStore: + def sql_store(self) -> AuthorizedSqlStore: assert self._sql_store is not None, "Provider not initialized" return self._sql_store @@ -129,27 +195,47 @@ class S3FilesImpl(Files): self, file: Annotated[UploadFile, File()], purpose: Annotated[OpenAIFilePurpose, Form()], + expires_after_anchor: Annotated[str | None, Form(alias="expires_after[anchor]")] = None, + expires_after_seconds: Annotated[int | None, Form(alias="expires_after[seconds]")] = None, ) -> OpenAIFileObject: file_id = f"file-{uuid.uuid4().hex}" filename = getattr(file, "filename", None) or "uploaded_file" - created_at = int(time.time()) - expires_at = created_at + self._SILLY_EXPIRATION_OFFSET + created_at = self._now() + + expires_after = None + if expires_after_anchor is not None or expires_after_seconds is not None: + # we use ExpiresAfter to validate input + expires_after = ExpiresAfter( + anchor=expires_after_anchor, # type: ignore[arg-type] + seconds=expires_after_seconds, # type: ignore[arg-type] + ) + + # the default is no expiration. + # to implement no expiration we set an expiration beyond the max. + # we'll hide this fact from users when returning the file object. + expires_at = created_at + ExpiresAfter.MAX * 42 + # the default for BATCH files is 30 days, which happens to be the expiration max. + if purpose == OpenAIFilePurpose.BATCH: + expires_at = created_at + ExpiresAfter.MAX + + if expires_after is not None: + expires_at = created_at + expires_after.seconds + content = await file.read() file_size = len(content) - await self.sql_store.insert( - "openai_files", - { - "id": file_id, - "filename": filename, - "purpose": purpose.value, - "bytes": file_size, - "created_at": created_at, - "expires_at": expires_at, - }, - ) + entry: dict[str, Any] = { + "id": file_id, + "filename": filename, + "purpose": purpose.value, + "bytes": file_size, + "created_at": created_at, + "expires_at": expires_at, + } + + await self.sql_store.insert("openai_files", entry) try: self.client.put_object( @@ -163,14 +249,7 @@ class S3FilesImpl(Files): raise RuntimeError(f"Failed to upload file to S3: {e}") from e - return OpenAIFileObject( - id=file_id, - filename=filename, - purpose=purpose, - bytes=file_size, - created_at=created_at, - expires_at=expires_at, - ) + return _make_file_object(**entry) async def openai_list_files( self, @@ -183,29 +262,19 @@ class S3FilesImpl(Files): if not order: order = Order.desc - where_conditions = {} + where_conditions: dict[str, Any] = {"expires_at": {">": self._now()}} if purpose: where_conditions["purpose"] = purpose.value paginated_result = await self.sql_store.fetch_all( table="openai_files", - where=where_conditions if where_conditions else None, + where=where_conditions, order_by=[("created_at", order.value)], cursor=("id", after) if after else None, limit=limit, ) - files = [ - OpenAIFileObject( - id=row["id"], - filename=row["filename"], - purpose=OpenAIFilePurpose(row["purpose"]), - bytes=row["bytes"], - created_at=row["created_at"], - expires_at=row["expires_at"], - ) - for row in paginated_result.data - ] + files = [_make_file_object(**row) for row in paginated_result.data] return ListOpenAIFileResponse( data=files, @@ -216,41 +285,20 @@ class S3FilesImpl(Files): ) async def openai_retrieve_file(self, file_id: str) -> OpenAIFileObject: - row = await self.sql_store.fetch_one("openai_files", where={"id": file_id}) - if not row: - raise ResourceNotFoundError(file_id, "File", "files.list()") - - return OpenAIFileObject( - id=row["id"], - filename=row["filename"], - purpose=OpenAIFilePurpose(row["purpose"]), - bytes=row["bytes"], - created_at=row["created_at"], - expires_at=row["expires_at"], - ) + await self._delete_if_expired(file_id) + row = await self._get_file(file_id) + return _make_file_object(**row) async def openai_delete_file(self, file_id: str) -> OpenAIFileDeleteResponse: - row = await self.sql_store.fetch_one("openai_files", where={"id": file_id}) - if not row: - raise ResourceNotFoundError(file_id, "File", "files.list()") - - try: - self.client.delete_object( - Bucket=self._config.bucket_name, - Key=row["id"], - ) - except ClientError as e: - if e.response["Error"]["Code"] != "NoSuchKey": - raise RuntimeError(f"Failed to delete file from S3: {e}") from e - - await self.sql_store.delete("openai_files", where={"id": file_id}) - + await self._delete_if_expired(file_id) + _ = await self._get_file(file_id) # raises if not found + await self._delete_file(file_id) return OpenAIFileDeleteResponse(id=file_id, deleted=True) async def openai_retrieve_file_content(self, file_id: str) -> Response: - row = await self.sql_store.fetch_one("openai_files", where={"id": file_id}) - if not row: - raise ResourceNotFoundError(file_id, "File", "files.list()") + await self._delete_if_expired(file_id) + + row = await self._get_file(file_id) try: response = self.client.get_object( @@ -261,7 +309,7 @@ class S3FilesImpl(Files): content = response["Body"].read() except ClientError as e: if e.response["Error"]["Code"] == "NoSuchKey": - await self.sql_store.delete("openai_files", where={"id": file_id}) + await self._delete_file(file_id) raise ResourceNotFoundError(file_id, "File", "files.list()") from e raise RuntimeError(f"Failed to download file from S3: {e}") from e diff --git a/llama_stack/providers/remote/inference/anthropic/__init__.py b/llama_stack/providers/remote/inference/anthropic/__init__.py index 8b420a5a0..30d986808 100644 --- a/llama_stack/providers/remote/inference/anthropic/__init__.py +++ b/llama_stack/providers/remote/inference/anthropic/__init__.py @@ -4,15 +4,9 @@ # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. -from pydantic import BaseModel - from .config import AnthropicConfig -class AnthropicProviderDataValidator(BaseModel): - anthropic_api_key: str | None = None - - async def get_adapter_impl(config: AnthropicConfig, _deps): from .anthropic import AnthropicInferenceAdapter diff --git a/llama_stack/providers/remote/inference/anthropic/anthropic.py b/llama_stack/providers/remote/inference/anthropic/anthropic.py index 31626082b..cdde4a411 100644 --- a/llama_stack/providers/remote/inference/anthropic/anthropic.py +++ b/llama_stack/providers/remote/inference/anthropic/anthropic.py @@ -5,16 +5,27 @@ # the root directory of this source tree. from llama_stack.providers.utils.inference.litellm_openai_mixin import LiteLLMOpenAIMixin +from llama_stack.providers.utils.inference.openai_mixin import OpenAIMixin from .config import AnthropicConfig -from .models import MODEL_ENTRIES -class AnthropicInferenceAdapter(LiteLLMOpenAIMixin): +class AnthropicInferenceAdapter(OpenAIMixin, LiteLLMOpenAIMixin): + # source: https://docs.claude.com/en/docs/build-with-claude/embeddings + # TODO: add support for voyageai, which is where these models are hosted + # embedding_model_metadata = { + # "voyage-3-large": {"embedding_dimension": 1024, "context_length": 32000}, # supports dimensions 256, 512, 1024, 2048 + # "voyage-3.5": {"embedding_dimension": 1024, "context_length": 32000}, # supports dimensions 256, 512, 1024, 2048 + # "voyage-3.5-lite": {"embedding_dimension": 1024, "context_length": 32000}, # supports dimensions 256, 512, 1024, 2048 + # "voyage-code-3": {"embedding_dimension": 1024, "context_length": 32000}, # supports dimensions 256, 512, 1024, 2048 + # "voyage-finance-2": {"embedding_dimension": 1024, "context_length": 32000}, + # "voyage-law-2": {"embedding_dimension": 1024, "context_length": 16000}, + # "voyage-multimodal-3": {"embedding_dimension": 1024, "context_length": 32000}, + # } + def __init__(self, config: AnthropicConfig) -> None: LiteLLMOpenAIMixin.__init__( self, - MODEL_ENTRIES, litellm_provider_name="anthropic", api_key_from_config=config.api_key, provider_data_api_key_field="anthropic_api_key", @@ -26,3 +37,8 @@ class AnthropicInferenceAdapter(LiteLLMOpenAIMixin): async def shutdown(self) -> None: await super().shutdown() + + get_api_key = LiteLLMOpenAIMixin.get_api_key + + def get_base_url(self): + return "https://api.anthropic.com/v1" diff --git a/llama_stack/providers/remote/inference/anthropic/models.py b/llama_stack/providers/remote/inference/anthropic/models.py deleted file mode 100644 index 4cbe44b02..000000000 --- a/llama_stack/providers/remote/inference/anthropic/models.py +++ /dev/null @@ -1,40 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# All rights reserved. -# -# This source code is licensed under the terms described in the LICENSE file in -# the root directory of this source tree. - -from llama_stack.apis.models import ModelType -from llama_stack.providers.utils.inference.model_registry import ( - ProviderModelEntry, -) - -LLM_MODEL_IDS = [ - "claude-3-5-sonnet-latest", - "claude-3-7-sonnet-latest", - "claude-3-5-haiku-latest", -] - -SAFETY_MODELS_ENTRIES = [] - -MODEL_ENTRIES = ( - [ProviderModelEntry(provider_model_id=m) for m in LLM_MODEL_IDS] - + [ - ProviderModelEntry( - provider_model_id="voyage-3", - model_type=ModelType.embedding, - metadata={"embedding_dimension": 1024, "context_length": 32000}, - ), - ProviderModelEntry( - provider_model_id="voyage-3-lite", - model_type=ModelType.embedding, - metadata={"embedding_dimension": 512, "context_length": 32000}, - ), - ProviderModelEntry( - provider_model_id="voyage-code-3", - model_type=ModelType.embedding, - metadata={"embedding_dimension": 1024, "context_length": 32000}, - ), - ] - + SAFETY_MODELS_ENTRIES -) diff --git a/llama_stack/providers/remote/inference/azure/__init__.py b/llama_stack/providers/remote/inference/azure/__init__.py new file mode 100644 index 000000000..87bcaf309 --- /dev/null +++ b/llama_stack/providers/remote/inference/azure/__init__.py @@ -0,0 +1,15 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the terms described in the LICENSE file in +# the root directory of this source tree. + +from .config import AzureConfig + + +async def get_adapter_impl(config: AzureConfig, _deps): + from .azure import AzureInferenceAdapter + + impl = AzureInferenceAdapter(config) + await impl.initialize() + return impl diff --git a/llama_stack/providers/remote/inference/azure/azure.py b/llama_stack/providers/remote/inference/azure/azure.py new file mode 100644 index 000000000..a2c69b69c --- /dev/null +++ b/llama_stack/providers/remote/inference/azure/azure.py @@ -0,0 +1,62 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the terms described in the LICENSE file in +# the root directory of this source tree. + +from typing import Any +from urllib.parse import urljoin + +from llama_stack.apis.inference import ChatCompletionRequest +from llama_stack.providers.utils.inference.litellm_openai_mixin import ( + LiteLLMOpenAIMixin, +) +from llama_stack.providers.utils.inference.openai_mixin import OpenAIMixin + +from .config import AzureConfig + + +class AzureInferenceAdapter(OpenAIMixin, LiteLLMOpenAIMixin): + def __init__(self, config: AzureConfig) -> None: + LiteLLMOpenAIMixin.__init__( + self, + litellm_provider_name="azure", + api_key_from_config=config.api_key.get_secret_value(), + provider_data_api_key_field="azure_api_key", + openai_compat_api_base=str(config.api_base), + ) + self.config = config + + # Delegate the client data handling get_api_key method to LiteLLMOpenAIMixin + get_api_key = LiteLLMOpenAIMixin.get_api_key + + def get_base_url(self) -> str: + """ + Get the Azure API base URL. + + Returns the Azure API base URL from the configuration. + """ + return urljoin(str(self.config.api_base), "/openai/v1") + + async def _get_params(self, request: ChatCompletionRequest) -> dict[str, Any]: + # Get base parameters from parent + params = await super()._get_params(request) + + # Add Azure specific parameters + provider_data = self.get_request_provider_data() + if provider_data: + if getattr(provider_data, "azure_api_key", None): + params["api_key"] = provider_data.azure_api_key + if getattr(provider_data, "azure_api_base", None): + params["api_base"] = provider_data.azure_api_base + if getattr(provider_data, "azure_api_version", None): + params["api_version"] = provider_data.azure_api_version + if getattr(provider_data, "azure_api_type", None): + params["api_type"] = provider_data.azure_api_type + else: + params["api_key"] = self.config.api_key.get_secret_value() + params["api_base"] = str(self.config.api_base) + params["api_version"] = self.config.api_version + params["api_type"] = self.config.api_type + + return params diff --git a/llama_stack/providers/remote/inference/azure/config.py b/llama_stack/providers/remote/inference/azure/config.py new file mode 100644 index 000000000..fe9d61d53 --- /dev/null +++ b/llama_stack/providers/remote/inference/azure/config.py @@ -0,0 +1,63 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the terms described in the LICENSE file in +# the root directory of this source tree. + +import os +from typing import Any + +from pydantic import BaseModel, Field, HttpUrl, SecretStr + +from llama_stack.schema_utils import json_schema_type + + +class AzureProviderDataValidator(BaseModel): + azure_api_key: SecretStr = Field( + description="Azure API key for Azure", + ) + azure_api_base: HttpUrl = Field( + description="Azure API base for Azure (e.g., https://your-resource-name.openai.azure.com)", + ) + azure_api_version: str | None = Field( + default=None, + description="Azure API version for Azure (e.g., 2024-06-01)", + ) + azure_api_type: str | None = Field( + default="azure", + description="Azure API type for Azure (e.g., azure)", + ) + + +@json_schema_type +class AzureConfig(BaseModel): + api_key: SecretStr = Field( + description="Azure API key for Azure", + ) + api_base: HttpUrl = Field( + description="Azure API base for Azure (e.g., https://your-resource-name.openai.azure.com)", + ) + api_version: str | None = Field( + default_factory=lambda: os.getenv("AZURE_API_VERSION"), + description="Azure API version for Azure (e.g., 2024-12-01-preview)", + ) + api_type: str | None = Field( + default_factory=lambda: os.getenv("AZURE_API_TYPE", "azure"), + description="Azure API type for Azure (e.g., azure)", + ) + + @classmethod + def sample_run_config( + cls, + api_key: str = "${env.AZURE_API_KEY:=}", + api_base: str = "${env.AZURE_API_BASE:=}", + api_version: str = "${env.AZURE_API_VERSION:=}", + api_type: str = "${env.AZURE_API_TYPE:=}", + **kwargs, + ) -> dict[str, Any]: + return { + "api_key": api_key, + "api_base": api_base, + "api_version": api_version, + "api_type": api_type, + } diff --git a/llama_stack/providers/remote/inference/bedrock/bedrock.py b/llama_stack/providers/remote/inference/bedrock/bedrock.py index 63ea196f6..29b935bbd 100644 --- a/llama_stack/providers/remote/inference/bedrock/bedrock.py +++ b/llama_stack/providers/remote/inference/bedrock/bedrock.py @@ -53,6 +53,43 @@ from llama_stack.providers.utils.inference.prompt_adapter import ( from .models import MODEL_ENTRIES +REGION_PREFIX_MAP = { + "us": "us.", + "eu": "eu.", + "ap": "ap.", +} + + +def _get_region_prefix(region: str | None) -> str: + # AWS requires region prefixes for inference profiles + if region is None: + return "us." # default to US when we don't know + + # Handle case insensitive region matching + region_lower = region.lower() + for prefix in REGION_PREFIX_MAP: + if region_lower.startswith(f"{prefix}-"): + return REGION_PREFIX_MAP[prefix] + + # Fallback to US for anything we don't recognize + return "us." + + +def _to_inference_profile_id(model_id: str, region: str = None) -> str: + # Return ARNs unchanged + if model_id.startswith("arn:"): + return model_id + + # Return inference profile IDs that already have regional prefixes + if any(model_id.startswith(p) for p in REGION_PREFIX_MAP.values()): + return model_id + + # Default to US East when no region is provided + if region is None: + region = "us-east-1" + + return _get_region_prefix(region) + model_id + class BedrockInferenceAdapter( ModelRegistryHelper, @@ -61,7 +98,7 @@ class BedrockInferenceAdapter( OpenAICompletionToLlamaStackMixin, ): def __init__(self, config: BedrockConfig) -> None: - ModelRegistryHelper.__init__(self, MODEL_ENTRIES) + ModelRegistryHelper.__init__(self, model_entries=MODEL_ENTRIES) self._config = config self._client = None @@ -166,8 +203,13 @@ class BedrockInferenceAdapter( options["repetition_penalty"] = sampling_params.repetition_penalty prompt = await chat_completion_request_to_prompt(request, self.get_llama_model(request.model)) + + # Convert foundation model ID to inference profile ID + region_name = self.client.meta.region_name + inference_profile_id = _to_inference_profile_id(bedrock_model, region_name) + return { - "modelId": bedrock_model, + "modelId": inference_profile_id, "body": json.dumps( { "prompt": prompt, @@ -185,6 +227,11 @@ class BedrockInferenceAdapter( task_type: EmbeddingTaskType | None = None, ) -> EmbeddingsResponse: model = await self.model_store.get_model(model_id) + + # Convert foundation model ID to inference profile ID + region_name = self.client.meta.region_name + inference_profile_id = _to_inference_profile_id(model.provider_resource_id, region_name) + embeddings = [] for content in contents: assert not content_has_media(content), "Bedrock does not support media for embeddings" @@ -193,7 +240,7 @@ class BedrockInferenceAdapter( body = json.dumps(input_body) response = self.client.invoke_model( body=body, - modelId=model.provider_resource_id, + modelId=inference_profile_id, accept="application/json", contentType="application/json", ) diff --git a/llama_stack/providers/remote/inference/cerebras/cerebras.py b/llama_stack/providers/remote/inference/cerebras/cerebras.py index 5e07c49ee..6662f004d 100644 --- a/llama_stack/providers/remote/inference/cerebras/cerebras.py +++ b/llama_stack/providers/remote/inference/cerebras/cerebras.py @@ -5,6 +5,7 @@ # the root directory of this source tree. from collections.abc import AsyncGenerator +from urllib.parse import urljoin from cerebras.cloud.sdk import AsyncCerebras @@ -35,42 +36,41 @@ from llama_stack.providers.utils.inference.model_registry import ( ModelRegistryHelper, ) from llama_stack.providers.utils.inference.openai_compat import ( - OpenAIChatCompletionToLlamaStackMixin, - OpenAICompletionToLlamaStackMixin, get_sampling_options, process_chat_completion_response, process_chat_completion_stream_response, process_completion_response, process_completion_stream_response, ) +from llama_stack.providers.utils.inference.openai_mixin import OpenAIMixin from llama_stack.providers.utils.inference.prompt_adapter import ( chat_completion_request_to_prompt, completion_request_to_prompt, ) from .config import CerebrasImplConfig -from .models import MODEL_ENTRIES class CerebrasInferenceAdapter( + OpenAIMixin, ModelRegistryHelper, Inference, - OpenAIChatCompletionToLlamaStackMixin, - OpenAICompletionToLlamaStackMixin, ): def __init__(self, config: CerebrasImplConfig) -> None: - ModelRegistryHelper.__init__( - self, - model_entries=MODEL_ENTRIES, - ) self.config = config # TODO: make this use provider data, etc. like other providers - self.client = AsyncCerebras( + self._cerebras_client = AsyncCerebras( base_url=self.config.base_url, api_key=self.config.api_key.get_secret_value(), ) + def get_api_key(self) -> str: + return self.config.api_key.get_secret_value() + + def get_base_url(self) -> str: + return urljoin(self.config.base_url, "v1") + async def initialize(self) -> None: return @@ -107,14 +107,14 @@ class CerebrasInferenceAdapter( async def _nonstream_completion(self, request: CompletionRequest) -> CompletionResponse: params = await self._get_params(request) - r = await self.client.completions.create(**params) + r = await self._cerebras_client.completions.create(**params) return process_completion_response(r) async def _stream_completion(self, request: CompletionRequest) -> AsyncGenerator: params = await self._get_params(request) - stream = await self.client.completions.create(**params) + stream = await self._cerebras_client.completions.create(**params) async for chunk in process_completion_stream_response(stream): yield chunk @@ -156,14 +156,14 @@ class CerebrasInferenceAdapter( async def _nonstream_chat_completion(self, request: CompletionRequest) -> CompletionResponse: params = await self._get_params(request) - r = await self.client.completions.create(**params) + r = await self._cerebras_client.completions.create(**params) return process_chat_completion_response(r, request) async def _stream_chat_completion(self, request: CompletionRequest) -> AsyncGenerator: params = await self._get_params(request) - stream = await self.client.completions.create(**params) + stream = await self._cerebras_client.completions.create(**params) async for chunk in process_chat_completion_stream_response(stream, request): yield chunk diff --git a/llama_stack/providers/remote/inference/cerebras/config.py b/llama_stack/providers/remote/inference/cerebras/config.py index 699f6a1ef..519bd9119 100644 --- a/llama_stack/providers/remote/inference/cerebras/config.py +++ b/llama_stack/providers/remote/inference/cerebras/config.py @@ -20,8 +20,8 @@ class CerebrasImplConfig(BaseModel): default=os.environ.get("CEREBRAS_BASE_URL", DEFAULT_BASE_URL), description="Base URL for the Cerebras API", ) - api_key: SecretStr | None = Field( - default=os.environ.get("CEREBRAS_API_KEY"), + api_key: SecretStr = Field( + default=SecretStr(os.environ.get("CEREBRAS_API_KEY")), description="Cerebras API Key", ) diff --git a/llama_stack/providers/remote/inference/cerebras/models.py b/llama_stack/providers/remote/inference/cerebras/models.py deleted file mode 100644 index 4de2e62c9..000000000 --- a/llama_stack/providers/remote/inference/cerebras/models.py +++ /dev/null @@ -1,28 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# All rights reserved. -# -# This source code is licensed under the terms described in the LICENSE file in -# the root directory of this source tree. - -from llama_stack.models.llama.sku_types import CoreModelId -from llama_stack.providers.utils.inference.model_registry import ( - build_hf_repo_model_entry, -) - -SAFETY_MODELS_ENTRIES = [] - -# https://inference-docs.cerebras.ai/models -MODEL_ENTRIES = [ - build_hf_repo_model_entry( - "llama3.1-8b", - CoreModelId.llama3_1_8b_instruct.value, - ), - build_hf_repo_model_entry( - "llama-3.3-70b", - CoreModelId.llama3_3_70b_instruct.value, - ), - build_hf_repo_model_entry( - "llama-4-scout-17b-16e-instruct", - CoreModelId.llama4_scout_17b_16e_instruct.value, - ), -] + SAFETY_MODELS_ENTRIES diff --git a/llama_stack/providers/remote/inference/databricks/__init__.py b/llama_stack/providers/remote/inference/databricks/__init__.py index 89da31130..24f658a2b 100644 --- a/llama_stack/providers/remote/inference/databricks/__init__.py +++ b/llama_stack/providers/remote/inference/databricks/__init__.py @@ -5,10 +5,11 @@ # the root directory of this source tree. from .config import DatabricksImplConfig -from .databricks import DatabricksInferenceAdapter async def get_adapter_impl(config: DatabricksImplConfig, _deps): + from .databricks import DatabricksInferenceAdapter + assert isinstance(config, DatabricksImplConfig), f"Unexpected config type: {type(config)}" impl = DatabricksInferenceAdapter(config) await impl.initialize() diff --git a/llama_stack/providers/remote/inference/databricks/config.py b/llama_stack/providers/remote/inference/databricks/config.py index cc2a2c302..67cd0480c 100644 --- a/llama_stack/providers/remote/inference/databricks/config.py +++ b/llama_stack/providers/remote/inference/databricks/config.py @@ -6,7 +6,7 @@ from typing import Any -from pydantic import BaseModel, Field +from pydantic import BaseModel, Field, SecretStr from llama_stack.schema_utils import json_schema_type @@ -17,16 +17,16 @@ class DatabricksImplConfig(BaseModel): default=None, description="The URL for the Databricks model serving endpoint", ) - api_token: str = Field( - default=None, + api_token: SecretStr = Field( + default=SecretStr(None), description="The Databricks API token", ) @classmethod def sample_run_config( cls, - url: str = "${env.DATABRICKS_URL:=}", - api_token: str = "${env.DATABRICKS_API_TOKEN:=}", + url: str = "${env.DATABRICKS_HOST:=}", + api_token: str = "${env.DATABRICKS_TOKEN:=}", **kwargs: Any, ) -> dict[str, Any]: return { diff --git a/llama_stack/providers/remote/inference/databricks/databricks.py b/llama_stack/providers/remote/inference/databricks/databricks.py index 34ee59212..25fd9f3b7 100644 --- a/llama_stack/providers/remote/inference/databricks/databricks.py +++ b/llama_stack/providers/remote/inference/databricks/databricks.py @@ -4,23 +4,28 @@ # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. -from collections.abc import AsyncGenerator +from collections.abc import AsyncIterator +from typing import Any -from openai import OpenAI +from databricks.sdk import WorkspaceClient from llama_stack.apis.common.content_types import ( InterleavedContent, InterleavedContentItem, ) from llama_stack.apis.inference import ( - ChatCompletionRequest, ChatCompletionResponse, + ChatCompletionResponseStreamChunk, + CompletionResponse, + CompletionResponseStreamChunk, EmbeddingsResponse, EmbeddingTaskType, Inference, LogProbConfig, Message, - OpenAIEmbeddingsResponse, + Model, + ModelType, + OpenAICompletion, ResponseFormat, SamplingParams, TextTruncation, @@ -29,49 +34,33 @@ from llama_stack.apis.inference import ( ToolDefinition, ToolPromptFormat, ) -from llama_stack.models.llama.sku_types import CoreModelId -from llama_stack.providers.utils.inference.model_registry import ( - ModelRegistryHelper, - build_hf_repo_model_entry, -) -from llama_stack.providers.utils.inference.openai_compat import ( - OpenAIChatCompletionToLlamaStackMixin, - OpenAICompletionToLlamaStackMixin, - get_sampling_options, - process_chat_completion_response, - process_chat_completion_stream_response, -) -from llama_stack.providers.utils.inference.prompt_adapter import ( - chat_completion_request_to_prompt, -) +from llama_stack.log import get_logger +from llama_stack.providers.utils.inference.openai_mixin import OpenAIMixin from .config import DatabricksImplConfig -SAFETY_MODELS_ENTRIES = [] - -# https://docs.databricks.com/aws/en/machine-learning/model-serving/foundation-model-overview -MODEL_ENTRIES = [ - build_hf_repo_model_entry( - "databricks-meta-llama-3-1-70b-instruct", - CoreModelId.llama3_1_70b_instruct.value, - ), - build_hf_repo_model_entry( - "databricks-meta-llama-3-1-405b-instruct", - CoreModelId.llama3_1_405b_instruct.value, - ), -] + SAFETY_MODELS_ENTRIES +logger = get_logger(name=__name__, category="inference::databricks") class DatabricksInferenceAdapter( - ModelRegistryHelper, + OpenAIMixin, Inference, - OpenAIChatCompletionToLlamaStackMixin, - OpenAICompletionToLlamaStackMixin, ): + # source: https://docs.databricks.com/aws/en/machine-learning/foundation-model-apis/supported-models + embedding_model_metadata = { + "databricks-gte-large-en": {"embedding_dimension": 1024, "context_length": 8192}, + "databricks-bge-large-en": {"embedding_dimension": 1024, "context_length": 512}, + } + def __init__(self, config: DatabricksImplConfig) -> None: - ModelRegistryHelper.__init__(self, model_entries=MODEL_ENTRIES) self.config = config + def get_api_key(self) -> str: + return self.config.api_token.get_secret_value() + + def get_base_url(self) -> str: + return f"{self.config.url}/serving-endpoints" + async def initialize(self) -> None: return @@ -80,72 +69,54 @@ class DatabricksInferenceAdapter( async def completion( self, - model: str, + model_id: str, content: InterleavedContent, sampling_params: SamplingParams | None = None, response_format: ResponseFormat | None = None, stream: bool | None = False, logprobs: LogProbConfig | None = None, - ) -> AsyncGenerator: + ) -> CompletionResponse | AsyncIterator[CompletionResponseStreamChunk]: + raise NotImplementedError() + + async def openai_completion( + self, + model: str, + prompt: str | list[str] | list[int] | list[list[int]], + best_of: int | None = None, + echo: bool | None = None, + frequency_penalty: float | None = None, + logit_bias: dict[str, float] | None = None, + logprobs: bool | None = None, + max_tokens: int | None = None, + n: int | None = None, + presence_penalty: float | None = None, + seed: int | None = None, + stop: str | list[str] | None = None, + stream: bool | None = None, + stream_options: dict[str, Any] | None = None, + temperature: float | None = None, + top_p: float | None = None, + user: str | None = None, + guided_choice: list[str] | None = None, + prompt_logprobs: int | None = None, + suffix: str | None = None, + ) -> OpenAICompletion: raise NotImplementedError() async def chat_completion( self, - model: str, + model_id: str, messages: list[Message], sampling_params: SamplingParams | None = None, - response_format: ResponseFormat | None = None, tools: list[ToolDefinition] | None = None, tool_choice: ToolChoice | None = ToolChoice.auto, tool_prompt_format: ToolPromptFormat | None = None, + response_format: ResponseFormat | None = None, stream: bool | None = False, logprobs: LogProbConfig | None = None, tool_config: ToolConfig | None = None, - ) -> AsyncGenerator: - if sampling_params is None: - sampling_params = SamplingParams() - request = ChatCompletionRequest( - model=model, - messages=messages, - sampling_params=sampling_params, - tools=tools or [], - stream=stream, - logprobs=logprobs, - tool_config=tool_config, - ) - - client = OpenAI(base_url=self.config.url, api_key=self.config.api_token) - if stream: - return self._stream_chat_completion(request, client) - else: - return await self._nonstream_chat_completion(request, client) - - async def _nonstream_chat_completion( - self, request: ChatCompletionRequest, client: OpenAI - ) -> ChatCompletionResponse: - params = self._get_params(request) - r = client.completions.create(**params) - return process_chat_completion_response(r, request) - - async def _stream_chat_completion(self, request: ChatCompletionRequest, client: OpenAI) -> AsyncGenerator: - params = self._get_params(request) - - async def _to_async_generator(): - s = client.completions.create(**params) - for chunk in s: - yield chunk - - stream = _to_async_generator() - async for chunk in process_chat_completion_stream_response(stream, request): - yield chunk - - def _get_params(self, request: ChatCompletionRequest) -> dict: - return { - "model": request.model, - "prompt": chat_completion_request_to_prompt(request, self.get_llama_model(request.model)), - "stream": request.stream, - **get_sampling_options(request.sampling_params), - } + ) -> ChatCompletionResponse | AsyncIterator[ChatCompletionResponseStreamChunk]: + raise NotImplementedError() async def embeddings( self, @@ -157,12 +128,31 @@ class DatabricksInferenceAdapter( ) -> EmbeddingsResponse: raise NotImplementedError() - async def openai_embeddings( - self, - model: str, - input: str | list[str], - encoding_format: str | None = "float", - dimensions: int | None = None, - user: str | None = None, - ) -> OpenAIEmbeddingsResponse: - raise NotImplementedError() + async def list_models(self) -> list[Model] | None: + self._model_cache = {} # from OpenAIMixin + ws_client = WorkspaceClient(host=self.config.url, token=self.get_api_key()) # TODO: this is not async + endpoints = ws_client.serving_endpoints.list() + for endpoint in endpoints: + model = Model( + provider_id=self.__provider_id__, + provider_resource_id=endpoint.name, + identifier=endpoint.name, + ) + if endpoint.task == "llm/v1/chat": + model.model_type = ModelType.llm # this is redundant, but informative + elif endpoint.task == "llm/v1/embeddings": + if endpoint.name not in self.embedding_model_metadata: + logger.warning(f"No metadata information available for embedding model {endpoint.name}, skipping.") + continue + model.model_type = ModelType.embedding + model.metadata = self.embedding_model_metadata[endpoint.name] + else: + logger.warning(f"Unknown model type, skipping: {endpoint}") + continue + + self._model_cache[endpoint.name] = model + + return list(self._model_cache.values()) + + async def should_refresh_models(self) -> bool: + return False diff --git a/llama_stack/providers/remote/inference/fireworks/fireworks.py b/llama_stack/providers/remote/inference/fireworks/fireworks.py index e907e8ec6..cf7e93974 100644 --- a/llama_stack/providers/remote/inference/fireworks/fireworks.py +++ b/llama_stack/providers/remote/inference/fireworks/fireworks.py @@ -4,11 +4,9 @@ # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. -from collections.abc import AsyncGenerator, AsyncIterator -from typing import Any +from collections.abc import AsyncGenerator from fireworks.client import Fireworks -from openai import AsyncOpenAI from llama_stack.apis.common.content_types import ( InterleavedContent, @@ -24,12 +22,6 @@ from llama_stack.apis.inference import ( Inference, LogProbConfig, Message, - OpenAIChatCompletion, - OpenAIChatCompletionChunk, - OpenAICompletion, - OpenAIEmbeddingsResponse, - OpenAIMessageParam, - OpenAIResponseFormatParam, ResponseFormat, ResponseFormatType, SamplingParams, @@ -45,15 +37,14 @@ from llama_stack.providers.utils.inference.model_registry import ( ModelRegistryHelper, ) from llama_stack.providers.utils.inference.openai_compat import ( - OpenAIChatCompletionToLlamaStackMixin, convert_message_to_openai_dict, get_sampling_options, - prepare_openai_completion_params, process_chat_completion_response, process_chat_completion_stream_response, process_completion_response, process_completion_stream_response, ) +from llama_stack.providers.utils.inference.openai_mixin import OpenAIMixin from llama_stack.providers.utils.inference.prompt_adapter import ( chat_completion_request_to_prompt, completion_request_to_prompt, @@ -63,15 +54,18 @@ from llama_stack.providers.utils.inference.prompt_adapter import ( ) from .config import FireworksImplConfig -from .models import MODEL_ENTRIES logger = get_logger(name=__name__, category="inference::fireworks") -class FireworksInferenceAdapter(ModelRegistryHelper, Inference, NeedsRequestProviderData): +class FireworksInferenceAdapter(OpenAIMixin, ModelRegistryHelper, Inference, NeedsRequestProviderData): + embedding_model_metadata = { + "nomic-ai/nomic-embed-text-v1.5": {"embedding_dimension": 768, "context_length": 8192}, + } + def __init__(self, config: FireworksImplConfig) -> None: - ModelRegistryHelper.__init__(self, MODEL_ENTRIES, config.allowed_models) self.config = config + self.allowed_models = config.allowed_models async def initialize(self) -> None: pass @@ -79,7 +73,7 @@ class FireworksInferenceAdapter(ModelRegistryHelper, Inference, NeedsRequestProv async def shutdown(self) -> None: pass - def _get_api_key(self) -> str: + def get_api_key(self) -> str: config_api_key = self.config.api_key.get_secret_value() if self.config.api_key else None if config_api_key: return config_api_key @@ -91,15 +85,18 @@ class FireworksInferenceAdapter(ModelRegistryHelper, Inference, NeedsRequestProv ) return provider_data.fireworks_api_key - def _get_base_url(self) -> str: + def get_base_url(self) -> str: return "https://api.fireworks.ai/inference/v1" def _get_client(self) -> Fireworks: - fireworks_api_key = self._get_api_key() + fireworks_api_key = self.get_api_key() return Fireworks(api_key=fireworks_api_key) - def _get_openai_client(self) -> AsyncOpenAI: - return AsyncOpenAI(base_url=self._get_base_url(), api_key=self._get_api_key()) + def _preprocess_prompt_for_fireworks(self, prompt: str) -> str: + """Remove BOS token as Fireworks automatically prepends it""" + if prompt.startswith("<|begin_of_text|>"): + return prompt[len("<|begin_of_text|>") :] + return prompt async def completion( self, @@ -285,153 +282,3 @@ class FireworksInferenceAdapter(ModelRegistryHelper, Inference, NeedsRequestProv embeddings = [data.embedding for data in response.data] return EmbeddingsResponse(embeddings=embeddings) - - async def openai_embeddings( - self, - model: str, - input: str | list[str], - encoding_format: str | None = "float", - dimensions: int | None = None, - user: str | None = None, - ) -> OpenAIEmbeddingsResponse: - raise NotImplementedError() - - async def openai_completion( - self, - model: str, - prompt: str | list[str] | list[int] | list[list[int]], - best_of: int | None = None, - echo: bool | None = None, - frequency_penalty: float | None = None, - logit_bias: dict[str, float] | None = None, - logprobs: bool | None = None, - max_tokens: int | None = None, - n: int | None = None, - presence_penalty: float | None = None, - seed: int | None = None, - stop: str | list[str] | None = None, - stream: bool | None = None, - stream_options: dict[str, Any] | None = None, - temperature: float | None = None, - top_p: float | None = None, - user: str | None = None, - guided_choice: list[str] | None = None, - prompt_logprobs: int | None = None, - suffix: str | None = None, - ) -> OpenAICompletion: - model_obj = await self.model_store.get_model(model) - - # Fireworks always prepends with BOS - if isinstance(prompt, str) and prompt.startswith("<|begin_of_text|>"): - prompt = prompt[len("<|begin_of_text|>") :] - - params = await prepare_openai_completion_params( - model=model_obj.provider_resource_id, - prompt=prompt, - best_of=best_of, - echo=echo, - frequency_penalty=frequency_penalty, - logit_bias=logit_bias, - logprobs=logprobs, - max_tokens=max_tokens, - n=n, - presence_penalty=presence_penalty, - seed=seed, - stop=stop, - stream=stream, - stream_options=stream_options, - temperature=temperature, - top_p=top_p, - user=user, - ) - - return await self._get_openai_client().completions.create(**params) - - async def openai_chat_completion( - self, - model: str, - messages: list[OpenAIMessageParam], - frequency_penalty: float | None = None, - function_call: str | dict[str, Any] | None = None, - functions: list[dict[str, Any]] | None = None, - logit_bias: dict[str, float] | None = None, - logprobs: bool | None = None, - max_completion_tokens: int | None = None, - max_tokens: int | None = None, - n: int | None = None, - parallel_tool_calls: bool | None = None, - presence_penalty: float | None = None, - response_format: OpenAIResponseFormatParam | None = None, - seed: int | None = None, - stop: str | list[str] | None = None, - stream: bool | None = None, - stream_options: dict[str, Any] | None = None, - temperature: float | None = None, - tool_choice: str | dict[str, Any] | None = None, - tools: list[dict[str, Any]] | None = None, - top_logprobs: int | None = None, - top_p: float | None = None, - user: str | None = None, - ) -> OpenAIChatCompletion | AsyncIterator[OpenAIChatCompletionChunk]: - model_obj = await self.model_store.get_model(model) - - # Divert Llama Models through Llama Stack inference APIs because - # Fireworks chat completions OpenAI-compatible API does not support - # tool calls properly. - llama_model = self.get_llama_model(model_obj.provider_resource_id) - - if llama_model: - return await OpenAIChatCompletionToLlamaStackMixin.openai_chat_completion( - self, - model=model, - messages=messages, - frequency_penalty=frequency_penalty, - function_call=function_call, - functions=functions, - logit_bias=logit_bias, - logprobs=logprobs, - max_completion_tokens=max_completion_tokens, - max_tokens=max_tokens, - n=n, - parallel_tool_calls=parallel_tool_calls, - presence_penalty=presence_penalty, - response_format=response_format, - seed=seed, - stop=stop, - stream=stream, - stream_options=stream_options, - temperature=temperature, - tool_choice=tool_choice, - tools=tools, - top_logprobs=top_logprobs, - top_p=top_p, - user=user, - ) - - params = await prepare_openai_completion_params( - messages=messages, - frequency_penalty=frequency_penalty, - function_call=function_call, - functions=functions, - logit_bias=logit_bias, - logprobs=logprobs, - max_completion_tokens=max_completion_tokens, - max_tokens=max_tokens, - n=n, - parallel_tool_calls=parallel_tool_calls, - presence_penalty=presence_penalty, - response_format=response_format, - seed=seed, - stop=stop, - stream=stream, - stream_options=stream_options, - temperature=temperature, - tool_choice=tool_choice, - tools=tools, - top_logprobs=top_logprobs, - top_p=top_p, - user=user, - ) - - logger.debug(f"fireworks params: {params}") - return await self._get_openai_client().chat.completions.create(model=model_obj.provider_resource_id, **params) diff --git a/llama_stack/providers/remote/inference/fireworks/models.py b/llama_stack/providers/remote/inference/fireworks/models.py deleted file mode 100644 index 30807a0d4..000000000 --- a/llama_stack/providers/remote/inference/fireworks/models.py +++ /dev/null @@ -1,70 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# All rights reserved. -# -# This source code is licensed under the terms described in the LICENSE file in -# the root directory of this source tree. - -from llama_stack.apis.models import ModelType -from llama_stack.models.llama.sku_types import CoreModelId -from llama_stack.providers.utils.inference.model_registry import ( - ProviderModelEntry, - build_hf_repo_model_entry, -) - -SAFETY_MODELS_ENTRIES = [ - build_hf_repo_model_entry( - "accounts/fireworks/models/llama-guard-3-8b", - CoreModelId.llama_guard_3_8b.value, - ), - build_hf_repo_model_entry( - "accounts/fireworks/models/llama-guard-3-11b-vision", - CoreModelId.llama_guard_3_11b_vision.value, - ), -] - -MODEL_ENTRIES = [ - build_hf_repo_model_entry( - "accounts/fireworks/models/llama-v3p1-8b-instruct", - CoreModelId.llama3_1_8b_instruct.value, - ), - build_hf_repo_model_entry( - "accounts/fireworks/models/llama-v3p1-70b-instruct", - CoreModelId.llama3_1_70b_instruct.value, - ), - build_hf_repo_model_entry( - "accounts/fireworks/models/llama-v3p1-405b-instruct", - CoreModelId.llama3_1_405b_instruct.value, - ), - build_hf_repo_model_entry( - "accounts/fireworks/models/llama-v3p2-3b-instruct", - CoreModelId.llama3_2_3b_instruct.value, - ), - build_hf_repo_model_entry( - "accounts/fireworks/models/llama-v3p2-11b-vision-instruct", - CoreModelId.llama3_2_11b_vision_instruct.value, - ), - build_hf_repo_model_entry( - "accounts/fireworks/models/llama-v3p2-90b-vision-instruct", - CoreModelId.llama3_2_90b_vision_instruct.value, - ), - build_hf_repo_model_entry( - "accounts/fireworks/models/llama-v3p3-70b-instruct", - CoreModelId.llama3_3_70b_instruct.value, - ), - build_hf_repo_model_entry( - "accounts/fireworks/models/llama4-scout-instruct-basic", - CoreModelId.llama4_scout_17b_16e_instruct.value, - ), - build_hf_repo_model_entry( - "accounts/fireworks/models/llama4-maverick-instruct-basic", - CoreModelId.llama4_maverick_17b_128e_instruct.value, - ), - ProviderModelEntry( - provider_model_id="nomic-ai/nomic-embed-text-v1.5", - model_type=ModelType.embedding, - metadata={ - "embedding_dimension": 768, - "context_length": 8192, - }, - ), -] + SAFETY_MODELS_ENTRIES diff --git a/llama_stack/providers/remote/inference/gemini/__init__.py b/llama_stack/providers/remote/inference/gemini/__init__.py index 9d35da893..bda2f52d4 100644 --- a/llama_stack/providers/remote/inference/gemini/__init__.py +++ b/llama_stack/providers/remote/inference/gemini/__init__.py @@ -4,15 +4,9 @@ # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. -from pydantic import BaseModel - from .config import GeminiConfig -class GeminiProviderDataValidator(BaseModel): - gemini_api_key: str | None = None - - async def get_adapter_impl(config: GeminiConfig, _deps): from .gemini import GeminiInferenceAdapter diff --git a/llama_stack/providers/remote/inference/gemini/gemini.py b/llama_stack/providers/remote/inference/gemini/gemini.py index b6048eff7..30ceedff0 100644 --- a/llama_stack/providers/remote/inference/gemini/gemini.py +++ b/llama_stack/providers/remote/inference/gemini/gemini.py @@ -5,22 +5,30 @@ # the root directory of this source tree. from llama_stack.providers.utils.inference.litellm_openai_mixin import LiteLLMOpenAIMixin +from llama_stack.providers.utils.inference.openai_mixin import OpenAIMixin from .config import GeminiConfig -from .models import MODEL_ENTRIES -class GeminiInferenceAdapter(LiteLLMOpenAIMixin): +class GeminiInferenceAdapter(OpenAIMixin, LiteLLMOpenAIMixin): + embedding_model_metadata = { + "text-embedding-004": {"embedding_dimension": 768, "context_length": 2048}, + } + def __init__(self, config: GeminiConfig) -> None: LiteLLMOpenAIMixin.__init__( self, - MODEL_ENTRIES, litellm_provider_name="gemini", api_key_from_config=config.api_key, provider_data_api_key_field="gemini_api_key", ) self.config = config + get_api_key = LiteLLMOpenAIMixin.get_api_key + + def get_base_url(self): + return "https://generativelanguage.googleapis.com/v1beta/openai/" + async def initialize(self) -> None: await super().initialize() diff --git a/llama_stack/providers/remote/inference/gemini/models.py b/llama_stack/providers/remote/inference/gemini/models.py deleted file mode 100644 index bd696b0ac..000000000 --- a/llama_stack/providers/remote/inference/gemini/models.py +++ /dev/null @@ -1,34 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# All rights reserved. -# -# This source code is licensed under the terms described in the LICENSE file in -# the root directory of this source tree. - -from llama_stack.apis.models import ModelType -from llama_stack.providers.utils.inference.model_registry import ( - ProviderModelEntry, -) - -LLM_MODEL_IDS = [ - "gemini-1.5-flash", - "gemini-1.5-pro", - "gemini-2.0-flash", - "gemini-2.0-flash-lite", - "gemini-2.5-flash", - "gemini-2.5-flash-lite", - "gemini-2.5-pro", -] - -SAFETY_MODELS_ENTRIES = [] - -MODEL_ENTRIES = ( - [ProviderModelEntry(provider_model_id=m) for m in LLM_MODEL_IDS] - + [ - ProviderModelEntry( - provider_model_id="text-embedding-004", - model_type=ModelType.embedding, - metadata={"embedding_dimension": 768, "context_length": 2048}, - ), - ] - + SAFETY_MODELS_ENTRIES -) diff --git a/llama_stack/providers/remote/inference/groq/groq.py b/llama_stack/providers/remote/inference/groq/groq.py index fd7212de4..e449f2005 100644 --- a/llama_stack/providers/remote/inference/groq/groq.py +++ b/llama_stack/providers/remote/inference/groq/groq.py @@ -4,158 +4,32 @@ # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. -from collections.abc import AsyncIterator -from typing import Any -from openai import AsyncOpenAI - -from llama_stack.apis.inference import ( - OpenAIChatCompletion, - OpenAIChatCompletionChunk, - OpenAIChoiceDelta, - OpenAIChunkChoice, - OpenAIMessageParam, - OpenAIResponseFormatParam, - OpenAISystemMessageParam, -) from llama_stack.providers.remote.inference.groq.config import GroqConfig from llama_stack.providers.utils.inference.litellm_openai_mixin import LiteLLMOpenAIMixin -from llama_stack.providers.utils.inference.openai_compat import ( - prepare_openai_completion_params, -) - -from .models import MODEL_ENTRIES +from llama_stack.providers.utils.inference.openai_mixin import OpenAIMixin -class GroqInferenceAdapter(LiteLLMOpenAIMixin): +class GroqInferenceAdapter(OpenAIMixin, LiteLLMOpenAIMixin): _config: GroqConfig def __init__(self, config: GroqConfig): LiteLLMOpenAIMixin.__init__( self, - model_entries=MODEL_ENTRIES, litellm_provider_name="groq", api_key_from_config=config.api_key, provider_data_api_key_field="groq_api_key", ) self.config = config + # Delegate the client data handling get_api_key method to LiteLLMOpenAIMixin + get_api_key = LiteLLMOpenAIMixin.get_api_key + + def get_base_url(self) -> str: + return f"{self.config.url}/openai/v1" + async def initialize(self): await super().initialize() async def shutdown(self): await super().shutdown() - - def _get_openai_client(self) -> AsyncOpenAI: - return AsyncOpenAI( - base_url=f"{self.config.url}/openai/v1", - api_key=self.get_api_key(), - ) - - async def openai_chat_completion( - self, - model: str, - messages: list[OpenAIMessageParam], - frequency_penalty: float | None = None, - function_call: str | dict[str, Any] | None = None, - functions: list[dict[str, Any]] | None = None, - logit_bias: dict[str, float] | None = None, - logprobs: bool | None = None, - max_completion_tokens: int | None = None, - max_tokens: int | None = None, - n: int | None = None, - parallel_tool_calls: bool | None = None, - presence_penalty: float | None = None, - response_format: OpenAIResponseFormatParam | None = None, - seed: int | None = None, - stop: str | list[str] | None = None, - stream: bool | None = None, - stream_options: dict[str, Any] | None = None, - temperature: float | None = None, - tool_choice: str | dict[str, Any] | None = None, - tools: list[dict[str, Any]] | None = None, - top_logprobs: int | None = None, - top_p: float | None = None, - user: str | None = None, - ) -> OpenAIChatCompletion | AsyncIterator[OpenAIChatCompletionChunk]: - model_obj = await self.model_store.get_model(model) - - # Groq does not support json_schema response format, so we need to convert it to json_object - if response_format and response_format.type == "json_schema": - response_format.type = "json_object" - schema = response_format.json_schema.get("schema", {}) - response_format.json_schema = None - json_instructions = f"\nYour response should be a JSON object that matches the following schema: {schema}" - if messages and messages[0].role == "system": - messages[0].content = messages[0].content + json_instructions - else: - messages.insert(0, OpenAISystemMessageParam(content=json_instructions)) - - # Groq returns a 400 error if tools are provided but none are called - # So, set tool_choice to "required" to attempt to force a call - if tools and (not tool_choice or tool_choice == "auto"): - tool_choice = "required" - - params = await prepare_openai_completion_params( - model=model_obj.provider_resource_id, - messages=messages, - frequency_penalty=frequency_penalty, - function_call=function_call, - functions=functions, - logit_bias=logit_bias, - logprobs=logprobs, - max_completion_tokens=max_completion_tokens, - max_tokens=max_tokens, - n=n, - parallel_tool_calls=parallel_tool_calls, - presence_penalty=presence_penalty, - response_format=response_format, - seed=seed, - stop=stop, - stream=stream, - stream_options=stream_options, - temperature=temperature, - tool_choice=tool_choice, - tools=tools, - top_logprobs=top_logprobs, - top_p=top_p, - user=user, - ) - - # Groq does not support streaming requests that set response_format - fake_stream = False - if stream and response_format: - params["stream"] = False - fake_stream = True - - response = await self._get_openai_client().chat.completions.create(**params) - - if fake_stream: - chunk_choices = [] - for choice in response.choices: - delta = OpenAIChoiceDelta( - content=choice.message.content, - role=choice.message.role, - tool_calls=choice.message.tool_calls, - ) - chunk_choice = OpenAIChunkChoice( - delta=delta, - finish_reason=choice.finish_reason, - index=choice.index, - logprobs=None, - ) - chunk_choices.append(chunk_choice) - chunk = OpenAIChatCompletionChunk( - id=response.id, - choices=chunk_choices, - object="chat.completion.chunk", - created=response.created, - model=response.model, - ) - - async def _fake_stream_generator(): - yield chunk - - return _fake_stream_generator() - else: - return response diff --git a/llama_stack/providers/remote/inference/groq/models.py b/llama_stack/providers/remote/inference/groq/models.py deleted file mode 100644 index fac66db72..000000000 --- a/llama_stack/providers/remote/inference/groq/models.py +++ /dev/null @@ -1,48 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# All rights reserved. -# -# This source code is licensed under the terms described in the LICENSE file in -# the root directory of this source tree. - -from llama_stack.models.llama.sku_list import CoreModelId -from llama_stack.providers.utils.inference.model_registry import ( - build_hf_repo_model_entry, - build_model_entry, -) - -SAFETY_MODELS_ENTRIES = [] - -MODEL_ENTRIES = [ - build_hf_repo_model_entry( - "llama3-8b-8192", - CoreModelId.llama3_1_8b_instruct.value, - ), - build_model_entry( - "llama-3.1-8b-instant", - CoreModelId.llama3_1_8b_instruct.value, - ), - build_hf_repo_model_entry( - "llama3-70b-8192", - CoreModelId.llama3_70b_instruct.value, - ), - build_hf_repo_model_entry( - "llama-3.3-70b-versatile", - CoreModelId.llama3_3_70b_instruct.value, - ), - # Groq only contains a preview version for llama-3.2-3b - # Preview models aren't recommended for production use, but we include this one - # to pass the test fixture - # TODO(aidand): Replace this with a stable model once Groq supports it - build_hf_repo_model_entry( - "llama-3.2-3b-preview", - CoreModelId.llama3_2_3b_instruct.value, - ), - build_hf_repo_model_entry( - "meta-llama/llama-4-scout-17b-16e-instruct", - CoreModelId.llama4_scout_17b_16e_instruct.value, - ), - build_hf_repo_model_entry( - "meta-llama/llama-4-maverick-17b-128e-instruct", - CoreModelId.llama4_maverick_17b_128e_instruct.value, - ), -] + SAFETY_MODELS_ENTRIES diff --git a/llama_stack/providers/remote/inference/llama_openai_compat/llama.py b/llama_stack/providers/remote/inference/llama_openai_compat/llama.py index f2069b5e5..489b12a68 100644 --- a/llama_stack/providers/remote/inference/llama_openai_compat/llama.py +++ b/llama_stack/providers/remote/inference/llama_openai_compat/llama.py @@ -8,8 +8,6 @@ from llama_stack.providers.remote.inference.llama_openai_compat.config import Ll from llama_stack.providers.utils.inference.litellm_openai_mixin import LiteLLMOpenAIMixin from llama_stack.providers.utils.inference.openai_mixin import OpenAIMixin -from .models import MODEL_ENTRIES - logger = get_logger(name=__name__, category="inference::llama_openai_compat") @@ -30,7 +28,6 @@ class LlamaCompatInferenceAdapter(OpenAIMixin, LiteLLMOpenAIMixin): def __init__(self, config: LlamaCompatConfig): LiteLLMOpenAIMixin.__init__( self, - model_entries=MODEL_ENTRIES, litellm_provider_name="meta_llama", api_key_from_config=config.api_key, provider_data_api_key_field="llama_api_key", diff --git a/llama_stack/providers/remote/inference/llama_openai_compat/models.py b/llama_stack/providers/remote/inference/llama_openai_compat/models.py deleted file mode 100644 index 6285e98e1..000000000 --- a/llama_stack/providers/remote/inference/llama_openai_compat/models.py +++ /dev/null @@ -1,25 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# All rights reserved. -# -# This source code is licensed under the terms described in the LICENSE file in -# the root directory of this source tree. - -from llama_stack.models.llama.sku_types import CoreModelId -from llama_stack.providers.utils.inference.model_registry import ( - build_hf_repo_model_entry, -) - -MODEL_ENTRIES = [ - build_hf_repo_model_entry( - "Llama-3.3-70B-Instruct", - CoreModelId.llama3_3_70b_instruct.value, - ), - build_hf_repo_model_entry( - "Llama-4-Scout-17B-16E-Instruct-FP8", - CoreModelId.llama4_scout_17b_16e_instruct.value, - ), - build_hf_repo_model_entry( - "Llama-4-Maverick-17B-128E-Instruct-FP8", - CoreModelId.llama4_maverick_17b_128e_instruct.value, - ), -] diff --git a/llama_stack/providers/remote/inference/nvidia/NVIDIA.md b/llama_stack/providers/remote/inference/nvidia/NVIDIA.md index d96b29fef..d9c18533a 100644 --- a/llama_stack/providers/remote/inference/nvidia/NVIDIA.md +++ b/llama_stack/providers/remote/inference/nvidia/NVIDIA.md @@ -41,10 +41,10 @@ client.initialize() ### Create Completion -> Note on Completion API -> -> The hosted NVIDIA Llama NIMs (e.g., `meta-llama/Llama-3.1-8B-Instruct`) with ```NVIDIA_BASE_URL="https://integrate.api.nvidia.com"``` does not support the ```completion``` method, while the locally deployed NIM does. +The following example shows how to create a completion for an NVIDIA NIM. +> [!NOTE] +> The hosted NVIDIA Llama NIMs (for example ```meta-llama/Llama-3.1-8B-Instruct```) that have ```NVIDIA_BASE_URL="https://integrate.api.nvidia.com"``` do not support the ```completion``` method, while locally deployed NIMs do. ```python response = client.inference.completion( @@ -60,6 +60,8 @@ print(f"Response: {response.content}") ### Create Chat Completion +The following example shows how to create a chat completion for an NVIDIA NIM. + ```python response = client.inference.chat_completion( model_id="meta-llama/Llama-3.1-8B-Instruct", @@ -82,6 +84,9 @@ print(f"Response: {response.completion_message.content}") ``` ### Tool Calling Example ### + +The following example shows how to do tool calling for an NVIDIA NIM. + ```python from llama_stack.models.llama.datatypes import ToolDefinition, ToolParamDefinition @@ -117,6 +122,9 @@ if tool_response.completion_message.tool_calls: ``` ### Structured Output Example + +The following example shows how to do structured output for an NVIDIA NIM. + ```python from llama_stack.apis.inference import JsonSchemaResponseFormat, ResponseFormatType @@ -149,8 +157,10 @@ print(f"Structured Response: {structured_response.completion_message.content}") ``` ### Create Embeddings -> Note on OpenAI embeddings compatibility -> + +The following example shows how to create embeddings for an NVIDIA NIM. + +> [!NOTE] > NVIDIA asymmetric embedding models (e.g., `nvidia/llama-3.2-nv-embedqa-1b-v2`) require an `input_type` parameter not present in the standard OpenAI embeddings API. The NVIDIA Inference Adapter automatically sets `input_type="query"` when using the OpenAI-compatible embeddings endpoint for NVIDIA. For passage embeddings, use the `embeddings` API with `task_type="document"`. ```python @@ -160,4 +170,42 @@ response = client.inference.embeddings( task_type="query", ) print(f"Embeddings: {response.embeddings}") -``` \ No newline at end of file +``` + +### Vision Language Models Example + +The following example shows how to run vision inference by using an NVIDIA NIM. + +```python +def load_image_as_base64(image_path): + with open(image_path, "rb") as image_file: + img_bytes = image_file.read() + return base64.b64encode(img_bytes).decode("utf-8") + + +image_path = {path_to_the_image} +demo_image_b64 = load_image_as_base64(image_path) + +vlm_response = client.inference.chat_completion( + model_id="nvidia/vila", + messages=[ + { + "role": "user", + "content": [ + { + "type": "image", + "image": { + "data": demo_image_b64, + }, + }, + { + "type": "text", + "text": "Please describe what you see in this image in detail.", + }, + ], + } + ], +) + +print(f"VLM Response: {vlm_response.completion_message.content}") +``` diff --git a/llama_stack/providers/remote/inference/nvidia/models.py b/llama_stack/providers/remote/inference/nvidia/models.py deleted file mode 100644 index 76e579da8..000000000 --- a/llama_stack/providers/remote/inference/nvidia/models.py +++ /dev/null @@ -1,105 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# All rights reserved. -# -# This source code is licensed under the terms described in the LICENSE file in -# the root directory of this source tree. - -from llama_stack.apis.models import ModelType -from llama_stack.models.llama.sku_types import CoreModelId -from llama_stack.providers.utils.inference.model_registry import ( - ProviderModelEntry, - build_hf_repo_model_entry, -) - -SAFETY_MODELS_ENTRIES = [] - -# https://docs.nvidia.com/nim/large-language-models/latest/supported-llm-agnostic-architectures.html -MODEL_ENTRIES = [ - build_hf_repo_model_entry( - "meta/llama3-8b-instruct", - CoreModelId.llama3_8b_instruct.value, - ), - build_hf_repo_model_entry( - "meta/llama3-70b-instruct", - CoreModelId.llama3_70b_instruct.value, - ), - build_hf_repo_model_entry( - "meta/llama-3.1-8b-instruct", - CoreModelId.llama3_1_8b_instruct.value, - ), - build_hf_repo_model_entry( - "meta/llama-3.1-70b-instruct", - CoreModelId.llama3_1_70b_instruct.value, - ), - build_hf_repo_model_entry( - "meta/llama-3.1-405b-instruct", - CoreModelId.llama3_1_405b_instruct.value, - ), - build_hf_repo_model_entry( - "meta/llama-3.2-1b-instruct", - CoreModelId.llama3_2_1b_instruct.value, - ), - build_hf_repo_model_entry( - "meta/llama-3.2-3b-instruct", - CoreModelId.llama3_2_3b_instruct.value, - ), - build_hf_repo_model_entry( - "meta/llama-3.2-11b-vision-instruct", - CoreModelId.llama3_2_11b_vision_instruct.value, - ), - build_hf_repo_model_entry( - "meta/llama-3.2-90b-vision-instruct", - CoreModelId.llama3_2_90b_vision_instruct.value, - ), - build_hf_repo_model_entry( - "meta/llama-3.3-70b-instruct", - CoreModelId.llama3_3_70b_instruct.value, - ), - # NeMo Retriever Text Embedding models - - # - # https://docs.nvidia.com/nim/nemo-retriever/text-embedding/latest/support-matrix.html - # - # +-----------------------------------+--------+-----------+-----------+------------+ - # | Model ID | Max | Publisher | Embedding | Dynamic | - # | | Tokens | | Dimension | Embeddings | - # +-----------------------------------+--------+-----------+-----------+------------+ - # | nvidia/llama-3.2-nv-embedqa-1b-v2 | 8192 | NVIDIA | 2048 | Yes | - # | nvidia/nv-embedqa-e5-v5 | 512 | NVIDIA | 1024 | No | - # | nvidia/nv-embedqa-mistral-7b-v2 | 512 | NVIDIA | 4096 | No | - # | snowflake/arctic-embed-l | 512 | Snowflake | 1024 | No | - # +-----------------------------------+--------+-----------+-----------+------------+ - ProviderModelEntry( - provider_model_id="nvidia/llama-3.2-nv-embedqa-1b-v2", - model_type=ModelType.embedding, - metadata={ - "embedding_dimension": 2048, - "context_length": 8192, - }, - ), - ProviderModelEntry( - provider_model_id="nvidia/nv-embedqa-e5-v5", - model_type=ModelType.embedding, - metadata={ - "embedding_dimension": 1024, - "context_length": 512, - }, - ), - ProviderModelEntry( - provider_model_id="nvidia/nv-embedqa-mistral-7b-v2", - model_type=ModelType.embedding, - metadata={ - "embedding_dimension": 4096, - "context_length": 512, - }, - ), - ProviderModelEntry( - provider_model_id="snowflake/arctic-embed-l", - model_type=ModelType.embedding, - metadata={ - "embedding_dimension": 1024, - "context_length": 512, - }, - ), - # TODO(mf): how do we handle Nemotron models? - # "Llama3.1-Nemotron-51B-Instruct" -> "meta/llama-3.1-nemotron-51b-instruct", -] + SAFETY_MODELS_ENTRIES diff --git a/llama_stack/providers/remote/inference/nvidia/nvidia.py b/llama_stack/providers/remote/inference/nvidia/nvidia.py index a5475bc92..92094a0f3 100644 --- a/llama_stack/providers/remote/inference/nvidia/nvidia.py +++ b/llama_stack/providers/remote/inference/nvidia/nvidia.py @@ -37,9 +37,6 @@ from llama_stack.apis.inference import ( ) from llama_stack.log import get_logger from llama_stack.models.llama.datatypes import ToolDefinition, ToolPromptFormat -from llama_stack.providers.utils.inference.model_registry import ( - ModelRegistryHelper, -) from llama_stack.providers.utils.inference.openai_compat import ( convert_openai_chat_completion_choice, convert_openai_chat_completion_stream, @@ -48,7 +45,6 @@ from llama_stack.providers.utils.inference.openai_mixin import OpenAIMixin from llama_stack.providers.utils.inference.prompt_adapter import content_has_media from . import NVIDIAConfig -from .models import MODEL_ENTRIES from .openai_utils import ( convert_chat_completion_request, convert_completion_request, @@ -60,7 +56,7 @@ from .utils import _is_nvidia_hosted logger = get_logger(name=__name__, category="inference::nvidia") -class NVIDIAInferenceAdapter(OpenAIMixin, Inference, ModelRegistryHelper): +class NVIDIAInferenceAdapter(OpenAIMixin, Inference): """ NVIDIA Inference Adapter for Llama Stack. @@ -74,10 +70,15 @@ class NVIDIAInferenceAdapter(OpenAIMixin, Inference, ModelRegistryHelper): - ModelRegistryHelper.check_model_availability() just returns False and shows a warning """ - def __init__(self, config: NVIDIAConfig) -> None: - # TODO(mf): filter by available models - ModelRegistryHelper.__init__(self, model_entries=MODEL_ENTRIES) + # source: https://docs.nvidia.com/nim/nemo-retriever/text-embedding/latest/support-matrix.html + embedding_model_metadata = { + "nvidia/llama-3.2-nv-embedqa-1b-v2": {"embedding_dimension": 2048, "context_length": 8192}, + "nvidia/nv-embedqa-e5-v5": {"embedding_dimension": 512, "context_length": 1024}, + "nvidia/nv-embedqa-mistral-7b-v2": {"embedding_dimension": 512, "context_length": 4096}, + "snowflake/arctic-embed-l": {"embedding_dimension": 512, "context_length": 1024}, + } + def __init__(self, config: NVIDIAConfig) -> None: logger.info(f"Initializing NVIDIAInferenceAdapter({config.url})...") if _is_nvidia_hosted(config): diff --git a/llama_stack/providers/remote/inference/ollama/models.py b/llama_stack/providers/remote/inference/ollama/models.py deleted file mode 100644 index 7c0a19a1a..000000000 --- a/llama_stack/providers/remote/inference/ollama/models.py +++ /dev/null @@ -1,106 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# All rights reserved. -# -# This source code is licensed under the terms described in the LICENSE file in -# the root directory of this source tree. - -from llama_stack.apis.models import ModelType -from llama_stack.models.llama.sku_types import CoreModelId -from llama_stack.providers.utils.inference.model_registry import ( - ProviderModelEntry, - build_hf_repo_model_entry, - build_model_entry, -) - -SAFETY_MODELS_ENTRIES = [ - # The Llama Guard models don't have their full fp16 versions - # so we are going to alias their default version to the canonical SKU - build_hf_repo_model_entry( - "llama-guard3:8b", - CoreModelId.llama_guard_3_8b.value, - ), - build_hf_repo_model_entry( - "llama-guard3:1b", - CoreModelId.llama_guard_3_1b.value, - ), -] - -MODEL_ENTRIES = [ - build_hf_repo_model_entry( - "llama3.1:8b-instruct-fp16", - CoreModelId.llama3_1_8b_instruct.value, - ), - build_model_entry( - "llama3.1:8b", - CoreModelId.llama3_1_8b_instruct.value, - ), - build_hf_repo_model_entry( - "llama3.1:70b-instruct-fp16", - CoreModelId.llama3_1_70b_instruct.value, - ), - build_model_entry( - "llama3.1:70b", - CoreModelId.llama3_1_70b_instruct.value, - ), - build_hf_repo_model_entry( - "llama3.1:405b-instruct-fp16", - CoreModelId.llama3_1_405b_instruct.value, - ), - build_model_entry( - "llama3.1:405b", - CoreModelId.llama3_1_405b_instruct.value, - ), - build_hf_repo_model_entry( - "llama3.2:1b-instruct-fp16", - CoreModelId.llama3_2_1b_instruct.value, - ), - build_model_entry( - "llama3.2:1b", - CoreModelId.llama3_2_1b_instruct.value, - ), - build_hf_repo_model_entry( - "llama3.2:3b-instruct-fp16", - CoreModelId.llama3_2_3b_instruct.value, - ), - build_model_entry( - "llama3.2:3b", - CoreModelId.llama3_2_3b_instruct.value, - ), - build_hf_repo_model_entry( - "llama3.2-vision:11b-instruct-fp16", - CoreModelId.llama3_2_11b_vision_instruct.value, - ), - build_model_entry( - "llama3.2-vision:latest", - CoreModelId.llama3_2_11b_vision_instruct.value, - ), - build_hf_repo_model_entry( - "llama3.2-vision:90b-instruct-fp16", - CoreModelId.llama3_2_90b_vision_instruct.value, - ), - build_model_entry( - "llama3.2-vision:90b", - CoreModelId.llama3_2_90b_vision_instruct.value, - ), - build_hf_repo_model_entry( - "llama3.3:70b", - CoreModelId.llama3_3_70b_instruct.value, - ), - ProviderModelEntry( - provider_model_id="all-minilm:l6-v2", - aliases=["all-minilm"], - model_type=ModelType.embedding, - metadata={ - "embedding_dimension": 384, - "context_length": 512, - }, - ), - ProviderModelEntry( - provider_model_id="nomic-embed-text", - model_type=ModelType.embedding, - metadata={ - "embedding_dimension": 768, - "context_length": 8192, - }, - ), -] + SAFETY_MODELS_ENTRIES diff --git a/llama_stack/providers/remote/inference/ollama/ollama.py b/llama_stack/providers/remote/inference/ollama/ollama.py index fcaf5ee92..81a5fb9ad 100644 --- a/llama_stack/providers/remote/inference/ollama/ollama.py +++ b/llama_stack/providers/remote/inference/ollama/ollama.py @@ -7,12 +7,10 @@ import asyncio import base64 -import uuid from collections.abc import AsyncGenerator, AsyncIterator from typing import Any -from ollama import AsyncClient # type: ignore[attr-defined] -from openai import AsyncOpenAI +from ollama import AsyncClient as AsyncOllamaClient from llama_stack.apis.common.content_types import ( ImageContentItem, @@ -37,9 +35,6 @@ from llama_stack.apis.inference import ( Message, OpenAIChatCompletion, OpenAIChatCompletionChunk, - OpenAICompletion, - OpenAIEmbeddingsResponse, - OpenAIEmbeddingUsage, OpenAIMessageParam, OpenAIResponseFormatParam, ResponseFormat, @@ -50,8 +45,9 @@ from llama_stack.apis.inference import ( ToolDefinition, ToolPromptFormat, ) -from llama_stack.apis.models import Model, ModelType +from llama_stack.apis.models import Model from llama_stack.log import get_logger +from llama_stack.models.llama.sku_types import CoreModelId from llama_stack.providers.datatypes import ( HealthResponse, HealthStatus, @@ -60,19 +56,19 @@ from llama_stack.providers.datatypes import ( from llama_stack.providers.remote.inference.ollama.config import OllamaImplConfig from llama_stack.providers.utils.inference.model_registry import ( ModelRegistryHelper, + build_hf_repo_model_entry, ) from llama_stack.providers.utils.inference.openai_compat import ( OpenAICompatCompletionChoice, OpenAICompatCompletionResponse, - b64_encode_openai_embeddings_response, get_sampling_options, prepare_openai_completion_params, - prepare_openai_embeddings_params, process_chat_completion_response, process_chat_completion_stream_response, process_completion_response, process_completion_stream_response, ) +from llama_stack.providers.utils.inference.openai_mixin import OpenAIMixin from llama_stack.providers.utils.inference.prompt_adapter import ( chat_completion_request_to_prompt, completion_request_to_prompt, @@ -83,103 +79,83 @@ from llama_stack.providers.utils.inference.prompt_adapter import ( request_has_media, ) -from .models import MODEL_ENTRIES - logger = get_logger(name=__name__, category="inference::ollama") class OllamaInferenceAdapter( + OpenAIMixin, InferenceProvider, ModelsProtocolPrivate, ): # automatically set by the resolver when instantiating the provider __provider_id__: str + embedding_model_metadata = { + "all-minilm:l6-v2": { + "embedding_dimension": 384, + "context_length": 512, + }, + "nomic-embed-text:latest": { + "embedding_dimension": 768, + "context_length": 8192, + }, + "nomic-embed-text:v1.5": { + "embedding_dimension": 768, + "context_length": 8192, + }, + "nomic-embed-text:137m-v1.5-fp16": { + "embedding_dimension": 768, + "context_length": 8192, + }, + } + def __init__(self, config: OllamaImplConfig) -> None: - self.register_helper = ModelRegistryHelper(MODEL_ENTRIES) + # TODO: remove ModelRegistryHelper.__init__ when completion and + # chat_completion are. this exists to satisfy the input / + # output processing for llama models. specifically, + # tool_calling is handled by raw template processing, + # instead of using the /api/chat endpoint w/ tools=... + ModelRegistryHelper.__init__( + self, + model_entries=[ + build_hf_repo_model_entry( + "llama3.2:3b-instruct-fp16", + CoreModelId.llama3_2_3b_instruct.value, + ), + build_hf_repo_model_entry( + "llama-guard3:1b", + CoreModelId.llama_guard_3_1b.value, + ), + ], + ) self.config = config - self._clients: dict[asyncio.AbstractEventLoop, AsyncClient] = {} - self._openai_client = None + self._clients: dict[asyncio.AbstractEventLoop, AsyncOllamaClient] = {} @property - def client(self) -> AsyncClient: + def ollama_client(self) -> AsyncOllamaClient: # ollama client attaches itself to the current event loop (sadly?) loop = asyncio.get_running_loop() if loop not in self._clients: - self._clients[loop] = AsyncClient(host=self.config.url) + self._clients[loop] = AsyncOllamaClient(host=self.config.url) return self._clients[loop] - @property - def openai_client(self) -> AsyncOpenAI: - if self._openai_client is None: - url = self.config.url.rstrip("/") - self._openai_client = AsyncOpenAI(base_url=f"{url}/v1", api_key="ollama") - return self._openai_client + def get_api_key(self): + return "NO_KEY" + + def get_base_url(self): + return self.config.url.rstrip("/") + "/v1" async def initialize(self) -> None: logger.info(f"checking connectivity to Ollama at `{self.config.url}`...") - health_response = await self.health() - if health_response["status"] == HealthStatus.ERROR: + r = await self.health() + if r["status"] == HealthStatus.ERROR: logger.warning( - "Ollama Server is not running, make sure to start it using `ollama serve` in a separate terminal" + f"Ollama Server is not running (message: {r['message']}). Make sure to start it using `ollama serve` in a separate terminal" ) async def should_refresh_models(self) -> bool: return self.config.refresh_models - async def list_models(self) -> list[Model] | None: - provider_id = self.__provider_id__ - response = await self.client.list() - - # always add the two embedding models which can be pulled on demand - models = [ - Model( - identifier="all-minilm:l6-v2", - provider_resource_id="all-minilm:l6-v2", - provider_id=provider_id, - metadata={ - "embedding_dimension": 384, - "context_length": 512, - }, - model_type=ModelType.embedding, - ), - # add all-minilm alias - Model( - identifier="all-minilm", - provider_resource_id="all-minilm:l6-v2", - provider_id=provider_id, - metadata={ - "embedding_dimension": 384, - "context_length": 512, - }, - model_type=ModelType.embedding, - ), - Model( - identifier="nomic-embed-text", - provider_resource_id="nomic-embed-text", - provider_id=provider_id, - metadata={ - "embedding_dimension": 768, - "context_length": 8192, - }, - model_type=ModelType.embedding, - ), - ] - for m in response.models: - # kill embedding models since we don't know dimensions for them - if "bert" in m.details.family: - continue - models.append( - Model( - identifier=m.model, - provider_resource_id=m.model, - provider_id=provider_id, - metadata={}, - model_type=ModelType.llm, - ) - ) - return models - async def health(self) -> HealthResponse: """ Performs a health check by verifying connectivity to the Ollama server. @@ -189,7 +165,7 @@ class OllamaInferenceAdapter( HealthResponse: A dictionary containing the health status. """ try: - await self.client.ps() + await self.ollama_client.ps() return HealthResponse(status=HealthStatus.OK) except Exception as e: return HealthResponse(status=HealthStatus.ERROR, message=f"Health check failed: {str(e)}") @@ -238,7 +214,7 @@ class OllamaInferenceAdapter( params = await self._get_params(request) async def _generate_and_convert_to_openai_compat(): - s = await self.client.generate(**params) + s = await self.ollama_client.generate(**params) async for chunk in s: choice = OpenAICompatCompletionChoice( finish_reason=chunk["done_reason"] if chunk["done"] else None, @@ -254,7 +230,7 @@ class OllamaInferenceAdapter( async def _nonstream_completion(self, request: CompletionRequest) -> CompletionResponse: params = await self._get_params(request) - r = await self.client.generate(**params) + r = await self.ollama_client.generate(**params) choice = OpenAICompatCompletionChoice( finish_reason=r["done_reason"] if r["done"] else None, @@ -308,7 +284,7 @@ class OllamaInferenceAdapter( input_dict: dict[str, Any] = {} media_present = request_has_media(request) - llama_model = self.register_helper.get_llama_model(request.model) + llama_model = self.get_llama_model(request.model) if isinstance(request, ChatCompletionRequest): if media_present or not llama_model: contents = [await convert_message_to_openai_dict_for_ollama(m) for m in request.messages] @@ -346,9 +322,9 @@ class OllamaInferenceAdapter( async def _nonstream_chat_completion(self, request: ChatCompletionRequest) -> ChatCompletionResponse: params = await self._get_params(request) if "messages" in params: - r = await self.client.chat(**params) + r = await self.ollama_client.chat(**params) else: - r = await self.client.generate(**params) + r = await self.ollama_client.generate(**params) if "message" in r: choice = OpenAICompatCompletionChoice( @@ -372,9 +348,9 @@ class OllamaInferenceAdapter( async def _generate_and_convert_to_openai_compat(): if "messages" in params: - s = await self.client.chat(**params) + s = await self.ollama_client.chat(**params) else: - s = await self.client.generate(**params) + s = await self.ollama_client.generate(**params) async for chunk in s: if "message" in chunk: choice = OpenAICompatCompletionChoice( @@ -407,7 +383,7 @@ class OllamaInferenceAdapter( assert all(not content_has_media(content) for content in contents), ( "Ollama does not support media for embeddings" ) - response = await self.client.embed( + response = await self.ollama_client.embed( model=model.provider_resource_id, input=[interleaved_content_as_str(content) for content in contents], ) @@ -416,121 +392,16 @@ class OllamaInferenceAdapter( return EmbeddingsResponse(embeddings=embeddings) async def register_model(self, model: Model) -> Model: - try: - model = await self.register_helper.register_model(model) - except ValueError: - pass # Ignore statically unknown model, will check live listing + if await self.check_model_availability(model.provider_model_id): + return model + elif await self.check_model_availability(f"{model.provider_model_id}:latest"): + model.provider_resource_id = f"{model.provider_model_id}:latest" + logger.warning( + f"Imprecise provider resource id was used but 'latest' is available in Ollama - using '{model.provider_model_id}'" + ) + return model - if model.model_type == ModelType.embedding: - response = await self.client.list() - if model.provider_resource_id not in [m.model for m in response.models]: - await self.client.pull(model.provider_resource_id) - - # we use list() here instead of ps() - - # - ps() only lists running models, not available models - # - models not currently running are run by the ollama server as needed - response = await self.client.list() - available_models = [m.model for m in response.models] - - provider_resource_id = model.provider_resource_id - assert provider_resource_id is not None # mypy - if provider_resource_id not in available_models: - available_models_latest = [m.model.split(":latest")[0] for m in response.models] - if provider_resource_id in available_models_latest: - logger.warning( - f"Imprecise provider resource id was used but 'latest' is available in Ollama - using '{model.provider_resource_id}:latest'" - ) - return model - raise UnsupportedModelError(provider_resource_id, available_models) - - # mutating this should be considered an anti-pattern - model.provider_resource_id = provider_resource_id - - return model - - async def openai_embeddings( - self, - model: str, - input: str | list[str], - encoding_format: str | None = "float", - dimensions: int | None = None, - user: str | None = None, - ) -> OpenAIEmbeddingsResponse: - model_obj = await self._get_model(model) - if model_obj.provider_resource_id is None: - raise ValueError(f"Model {model} has no provider_resource_id set") - - # Note, at the moment Ollama does not support encoding_format, dimensions, and user parameters - params = prepare_openai_embeddings_params( - model=model_obj.provider_resource_id, - input=input, - encoding_format=encoding_format, - dimensions=dimensions, - user=user, - ) - - response = await self.openai_client.embeddings.create(**params) - data = b64_encode_openai_embeddings_response(response.data, encoding_format) - - usage = OpenAIEmbeddingUsage( - prompt_tokens=response.usage.prompt_tokens, - total_tokens=response.usage.total_tokens, - ) - # TODO: Investigate why model_obj.identifier is used instead of response.model - return OpenAIEmbeddingsResponse( - data=data, - model=model_obj.identifier, - usage=usage, - ) - - async def openai_completion( - self, - model: str, - prompt: str | list[str] | list[int] | list[list[int]], - best_of: int | None = None, - echo: bool | None = None, - frequency_penalty: float | None = None, - logit_bias: dict[str, float] | None = None, - logprobs: bool | None = None, - max_tokens: int | None = None, - n: int | None = None, - presence_penalty: float | None = None, - seed: int | None = None, - stop: str | list[str] | None = None, - stream: bool | None = None, - stream_options: dict[str, Any] | None = None, - temperature: float | None = None, - top_p: float | None = None, - user: str | None = None, - guided_choice: list[str] | None = None, - prompt_logprobs: int | None = None, - suffix: str | None = None, - ) -> OpenAICompletion: - if not isinstance(prompt, str): - raise ValueError("Ollama does not support non-string prompts for completion") - - model_obj = await self._get_model(model) - params = await prepare_openai_completion_params( - model=model_obj.provider_resource_id, - prompt=prompt, - best_of=best_of, - echo=echo, - frequency_penalty=frequency_penalty, - logit_bias=logit_bias, - logprobs=logprobs, - max_tokens=max_tokens, - n=n, - presence_penalty=presence_penalty, - seed=seed, - stop=stop, - stream=stream, - stream_options=stream_options, - temperature=temperature, - top_p=top_p, - user=user, - suffix=suffix, - ) - return await self.openai_client.completions.create(**params) # type: ignore + raise UnsupportedModelError(model.provider_model_id, list(self._model_cache.keys())) async def openai_chat_completion( self, @@ -599,25 +470,7 @@ class OllamaInferenceAdapter( top_p=top_p, user=user, ) - response = await self.openai_client.chat.completions.create(**params) - return await self._adjust_ollama_chat_completion_response_ids(response) - - async def _adjust_ollama_chat_completion_response_ids( - self, - response: OpenAIChatCompletion | AsyncIterator[OpenAIChatCompletionChunk], - ) -> OpenAIChatCompletion | AsyncIterator[OpenAIChatCompletionChunk]: - id = f"chatcmpl-{uuid.uuid4()}" - if isinstance(response, AsyncIterator): - - async def stream_with_chunk_ids() -> AsyncIterator[OpenAIChatCompletionChunk]: - async for chunk in response: - chunk.id = id - yield chunk - - return stream_with_chunk_ids() - else: - response.id = id - return response + return await OpenAIMixin.openai_chat_completion(self, **params) async def convert_message_to_openai_dict_for_ollama(message: Message) -> list[dict]: diff --git a/llama_stack/providers/remote/inference/openai/__init__.py b/llama_stack/providers/remote/inference/openai/__init__.py index c245dbe10..bd3daeb9a 100644 --- a/llama_stack/providers/remote/inference/openai/__init__.py +++ b/llama_stack/providers/remote/inference/openai/__init__.py @@ -4,15 +4,9 @@ # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. -from pydantic import BaseModel - from .config import OpenAIConfig -class OpenAIProviderDataValidator(BaseModel): - openai_api_key: str | None = None - - async def get_adapter_impl(config: OpenAIConfig, _deps): from .openai import OpenAIInferenceAdapter diff --git a/llama_stack/providers/remote/inference/openai/models.py b/llama_stack/providers/remote/inference/openai/models.py deleted file mode 100644 index 28d0c4b41..000000000 --- a/llama_stack/providers/remote/inference/openai/models.py +++ /dev/null @@ -1,60 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# All rights reserved. -# -# This source code is licensed under the terms described in the LICENSE file in -# the root directory of this source tree. - -from dataclasses import dataclass - -from llama_stack.apis.models import ModelType -from llama_stack.providers.utils.inference.model_registry import ( - ProviderModelEntry, -) - -LLM_MODEL_IDS = [ - "gpt-3.5-turbo-0125", - "gpt-3.5-turbo", - "gpt-3.5-turbo-instruct", - "gpt-4", - "gpt-4-turbo", - "gpt-4o", - "gpt-4o-2024-08-06", - "gpt-4o-mini", - "gpt-4o-audio-preview", - "chatgpt-4o-latest", - "o1", - "o1-mini", - "o3-mini", - "o4-mini", -] - - -@dataclass -class EmbeddingModelInfo: - """Structured representation of embedding model information.""" - - embedding_dimension: int - context_length: int - - -EMBEDDING_MODEL_IDS: dict[str, EmbeddingModelInfo] = { - "text-embedding-3-small": EmbeddingModelInfo(1536, 8192), - "text-embedding-3-large": EmbeddingModelInfo(3072, 8192), -} -SAFETY_MODELS_ENTRIES = [] - -MODEL_ENTRIES = ( - [ProviderModelEntry(provider_model_id=m) for m in LLM_MODEL_IDS] - + [ - ProviderModelEntry( - provider_model_id=model_id, - model_type=ModelType.embedding, - metadata={ - "embedding_dimension": model_info.embedding_dimension, - "context_length": model_info.context_length, - }, - ) - for model_id, model_info in EMBEDDING_MODEL_IDS.items() - ] - + SAFETY_MODELS_ENTRIES -) diff --git a/llama_stack/providers/remote/inference/openai/openai.py b/llama_stack/providers/remote/inference/openai/openai.py index 57934a9c8..9b341ede2 100644 --- a/llama_stack/providers/remote/inference/openai/openai.py +++ b/llama_stack/providers/remote/inference/openai/openai.py @@ -9,7 +9,6 @@ from llama_stack.providers.utils.inference.litellm_openai_mixin import LiteLLMOp from llama_stack.providers.utils.inference.openai_mixin import OpenAIMixin from .config import OpenAIConfig -from .models import MODEL_ENTRIES logger = get_logger(name=__name__, category="inference::openai") @@ -38,10 +37,14 @@ class OpenAIInferenceAdapter(OpenAIMixin, LiteLLMOpenAIMixin): - ModelRegistryHelper.check_model_availability() (inherited by LiteLLMOpenAIMixin) just returns False and shows a warning """ + embedding_model_metadata = { + "text-embedding-3-small": {"embedding_dimension": 1536, "context_length": 8192}, + "text-embedding-3-large": {"embedding_dimension": 3072, "context_length": 8192}, + } + def __init__(self, config: OpenAIConfig) -> None: LiteLLMOpenAIMixin.__init__( self, - MODEL_ENTRIES, litellm_provider_name="openai", api_key_from_config=config.api_key, provider_data_api_key_field="openai_api_key", diff --git a/llama_stack/providers/remote/inference/passthrough/passthrough.py b/llama_stack/providers/remote/inference/passthrough/passthrough.py index 2f1cd40f2..a2bdf0369 100644 --- a/llama_stack/providers/remote/inference/passthrough/passthrough.py +++ b/llama_stack/providers/remote/inference/passthrough/passthrough.py @@ -43,7 +43,7 @@ from .config import PassthroughImplConfig class PassthroughInferenceAdapter(Inference): def __init__(self, config: PassthroughImplConfig) -> None: - ModelRegistryHelper.__init__(self, []) + ModelRegistryHelper.__init__(self) self.config = config async def initialize(self) -> None: diff --git a/llama_stack/providers/remote/inference/sambanova/models.py b/llama_stack/providers/remote/inference/sambanova/models.py deleted file mode 100644 index db781eb86..000000000 --- a/llama_stack/providers/remote/inference/sambanova/models.py +++ /dev/null @@ -1,28 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# All rights reserved. -# -# This source code is licensed under the terms described in the LICENSE file in -# the root directory of this source tree. - -from llama_stack.models.llama.sku_types import CoreModelId -from llama_stack.providers.utils.inference.model_registry import ( - build_hf_repo_model_entry, -) - -SAFETY_MODELS_ENTRIES = [] - - -MODEL_ENTRIES = [ - build_hf_repo_model_entry( - "Meta-Llama-3.1-8B-Instruct", - CoreModelId.llama3_1_8b_instruct.value, - ), - build_hf_repo_model_entry( - "Meta-Llama-3.3-70B-Instruct", - CoreModelId.llama3_3_70b_instruct.value, - ), - build_hf_repo_model_entry( - "Llama-4-Maverick-17B-128E-Instruct", - CoreModelId.llama4_maverick_17b_128e_instruct.value, - ), -] + SAFETY_MODELS_ENTRIES diff --git a/llama_stack/providers/remote/inference/sambanova/sambanova.py b/llama_stack/providers/remote/inference/sambanova/sambanova.py index 96469acac..6121e81f7 100644 --- a/llama_stack/providers/remote/inference/sambanova/sambanova.py +++ b/llama_stack/providers/remote/inference/sambanova/sambanova.py @@ -4,19 +4,30 @@ # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. + from llama_stack.providers.utils.inference.litellm_openai_mixin import LiteLLMOpenAIMixin +from llama_stack.providers.utils.inference.openai_mixin import OpenAIMixin from .config import SambaNovaImplConfig -from .models import MODEL_ENTRIES -class SambaNovaInferenceAdapter(LiteLLMOpenAIMixin): +class SambaNovaInferenceAdapter(OpenAIMixin, LiteLLMOpenAIMixin): + """ + SambaNova Inference Adapter for Llama Stack. + + Note: The inheritance order is important here. OpenAIMixin must come before + LiteLLMOpenAIMixin to ensure that OpenAIMixin.check_model_availability() + is used instead of LiteLLMOpenAIMixin.check_model_availability(). + + - OpenAIMixin.check_model_availability() queries the /v1/models to check if a model exists + - LiteLLMOpenAIMixin.check_model_availability() checks the static registry within LiteLLM + """ + def __init__(self, config: SambaNovaImplConfig): self.config = config self.environment_available_models = [] LiteLLMOpenAIMixin.__init__( self, - model_entries=MODEL_ENTRIES, litellm_provider_name="sambanova", api_key_from_config=self.config.api_key.get_secret_value() if self.config.api_key else None, provider_data_api_key_field="sambanova_api_key", @@ -24,3 +35,14 @@ class SambaNovaInferenceAdapter(LiteLLMOpenAIMixin): download_images=True, # SambaNova requires base64 image encoding json_schema_strict=False, # SambaNova doesn't support strict=True yet ) + + # Delegate the client data handling get_api_key method to LiteLLMOpenAIMixin + get_api_key = LiteLLMOpenAIMixin.get_api_key + + def get_base_url(self) -> str: + """ + Get the base URL for OpenAI mixin. + + :return: The SambaNova base URL + """ + return self.config.url diff --git a/llama_stack/providers/remote/inference/tgi/tgi.py b/llama_stack/providers/remote/inference/tgi/tgi.py index 97c72d14c..27597900f 100644 --- a/llama_stack/providers/remote/inference/tgi/tgi.py +++ b/llama_stack/providers/remote/inference/tgi/tgi.py @@ -8,6 +8,7 @@ from collections.abc import AsyncGenerator from huggingface_hub import AsyncInferenceClient, HfApi +from pydantic import SecretStr from llama_stack.apis.common.content_types import ( InterleavedContent, @@ -33,6 +34,7 @@ from llama_stack.apis.inference import ( ToolPromptFormat, ) from llama_stack.apis.models import Model +from llama_stack.apis.models.models import ModelType from llama_stack.log import get_logger from llama_stack.models.llama.sku_list import all_registered_models from llama_stack.providers.datatypes import ModelsProtocolPrivate @@ -41,16 +43,15 @@ from llama_stack.providers.utils.inference.model_registry import ( build_hf_repo_model_entry, ) from llama_stack.providers.utils.inference.openai_compat import ( - OpenAIChatCompletionToLlamaStackMixin, OpenAICompatCompletionChoice, OpenAICompatCompletionResponse, - OpenAICompletionToLlamaStackMixin, get_sampling_options, process_chat_completion_response, process_chat_completion_stream_response, process_completion_response, process_completion_stream_response, ) +from llama_stack.providers.utils.inference.openai_mixin import OpenAIMixin from llama_stack.providers.utils.inference.prompt_adapter import ( chat_completion_request_to_model_input_info, completion_request_to_prompt_model_input_info, @@ -73,26 +74,49 @@ def build_hf_repo_model_entries(): class _HfAdapter( + OpenAIMixin, Inference, - OpenAIChatCompletionToLlamaStackMixin, - OpenAICompletionToLlamaStackMixin, ModelsProtocolPrivate, ): - client: AsyncInferenceClient + url: str + api_key: SecretStr + + hf_client: AsyncInferenceClient max_tokens: int model_id: str + overwrite_completion_id = True # TGI always returns id="" + def __init__(self) -> None: self.register_helper = ModelRegistryHelper(build_hf_repo_model_entries()) self.huggingface_repo_to_llama_model_id = { model.huggingface_repo: model.descriptor() for model in all_registered_models() if model.huggingface_repo } + def get_api_key(self): + return self.api_key.get_secret_value() + + def get_base_url(self): + return self.url + async def shutdown(self) -> None: pass + async def list_models(self) -> list[Model] | None: + models = [] + async for model in self.client.models.list(): + models.append( + Model( + identifier=model.id, + provider_resource_id=model.id, + provider_id=self.__provider_id__, + metadata={}, + model_type=ModelType.llm, + ) + ) + return models + async def register_model(self, model: Model) -> Model: - model = await self.register_helper.register_model(model) if model.provider_resource_id != self.model_id: raise ValueError( f"Model {model.provider_resource_id} does not match the model {self.model_id} served by TGI." @@ -176,7 +200,7 @@ class _HfAdapter( params = await self._get_params_for_completion(request) async def _generate_and_convert_to_openai_compat(): - s = await self.client.text_generation(**params) + s = await self.hf_client.text_generation(**params) async for chunk in s: token_result = chunk.token finish_reason = None @@ -194,7 +218,7 @@ class _HfAdapter( async def _nonstream_completion(self, request: CompletionRequest) -> AsyncGenerator: params = await self._get_params_for_completion(request) - r = await self.client.text_generation(**params) + r = await self.hf_client.text_generation(**params) choice = OpenAICompatCompletionChoice( finish_reason=r.details.finish_reason, @@ -241,7 +265,7 @@ class _HfAdapter( async def _nonstream_chat_completion(self, request: ChatCompletionRequest) -> ChatCompletionResponse: params = await self._get_params(request) - r = await self.client.text_generation(**params) + r = await self.hf_client.text_generation(**params) choice = OpenAICompatCompletionChoice( finish_reason=r.details.finish_reason, @@ -256,7 +280,7 @@ class _HfAdapter( params = await self._get_params(request) async def _generate_and_convert_to_openai_compat(): - s = await self.client.text_generation(**params) + s = await self.hf_client.text_generation(**params) async for chunk in s: token_result = chunk.token @@ -308,18 +332,21 @@ class TGIAdapter(_HfAdapter): if not config.url: raise ValueError("You must provide a URL in run.yaml (or via the TGI_URL environment variable) to use TGI.") log.info(f"Initializing TGI client with url={config.url}") - self.client = AsyncInferenceClient(model=config.url, provider="hf-inference") - endpoint_info = await self.client.get_endpoint_info() + self.hf_client = AsyncInferenceClient(model=config.url, provider="hf-inference") + endpoint_info = await self.hf_client.get_endpoint_info() self.max_tokens = endpoint_info["max_total_tokens"] self.model_id = endpoint_info["model_id"] + self.url = f"{config.url.rstrip('/')}/v1" + self.api_key = SecretStr("NO_KEY") class InferenceAPIAdapter(_HfAdapter): async def initialize(self, config: InferenceAPIImplConfig) -> None: - self.client = AsyncInferenceClient(model=config.huggingface_repo, token=config.api_token.get_secret_value()) - endpoint_info = await self.client.get_endpoint_info() + self.hf_client = AsyncInferenceClient(model=config.huggingface_repo, token=config.api_token.get_secret_value()) + endpoint_info = await self.hf_client.get_endpoint_info() self.max_tokens = endpoint_info["max_total_tokens"] self.model_id = endpoint_info["model_id"] + # TODO: how do we set url for this? class InferenceEndpointAdapter(_HfAdapter): @@ -331,6 +358,7 @@ class InferenceEndpointAdapter(_HfAdapter): endpoint.wait(timeout=60) # Initialize the adapter - self.client = endpoint.async_client + self.hf_client = endpoint.async_client self.model_id = endpoint.repository self.max_tokens = int(endpoint.raw["model"]["image"]["custom"]["env"]["MAX_TOTAL_TOKENS"]) + # TODO: how do we set url for this? diff --git a/llama_stack/providers/remote/inference/together/models.py b/llama_stack/providers/remote/inference/together/models.py deleted file mode 100644 index 575ec1f3d..000000000 --- a/llama_stack/providers/remote/inference/together/models.py +++ /dev/null @@ -1,77 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# All rights reserved. -# -# This source code is licensed under the terms described in the LICENSE file in -# the root directory of this source tree. - -from llama_stack.apis.models import ModelType -from llama_stack.models.llama.sku_types import CoreModelId -from llama_stack.providers.utils.inference.model_registry import ( - ProviderModelEntry, - build_hf_repo_model_entry, -) - -SAFETY_MODELS_ENTRIES = [ - build_hf_repo_model_entry( - "meta-llama/Llama-Guard-3-8B", - CoreModelId.llama_guard_3_8b.value, - ), - build_hf_repo_model_entry( - "meta-llama/Llama-Guard-3-11B-Vision-Turbo", - CoreModelId.llama_guard_3_11b_vision.value, - ), -] -MODEL_ENTRIES = [ - build_hf_repo_model_entry( - "meta-llama/Meta-Llama-3.1-8B-Instruct-Turbo", - CoreModelId.llama3_1_8b_instruct.value, - ), - build_hf_repo_model_entry( - "meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo", - CoreModelId.llama3_1_70b_instruct.value, - ), - build_hf_repo_model_entry( - "meta-llama/Meta-Llama-3.1-405B-Instruct-Turbo", - CoreModelId.llama3_1_405b_instruct.value, - ), - build_hf_repo_model_entry( - "meta-llama/Llama-3.2-3B-Instruct-Turbo", - CoreModelId.llama3_2_3b_instruct.value, - ), - build_hf_repo_model_entry( - "meta-llama/Llama-3.2-11B-Vision-Instruct-Turbo", - CoreModelId.llama3_2_11b_vision_instruct.value, - ), - build_hf_repo_model_entry( - "meta-llama/Llama-3.2-90B-Vision-Instruct-Turbo", - CoreModelId.llama3_2_90b_vision_instruct.value, - ), - build_hf_repo_model_entry( - "meta-llama/Llama-3.3-70B-Instruct-Turbo", - CoreModelId.llama3_3_70b_instruct.value, - ), - ProviderModelEntry( - provider_model_id="togethercomputer/m2-bert-80M-8k-retrieval", - model_type=ModelType.embedding, - metadata={ - "embedding_dimension": 768, - "context_length": 8192, - }, - ), - ProviderModelEntry( - provider_model_id="togethercomputer/m2-bert-80M-32k-retrieval", - model_type=ModelType.embedding, - metadata={ - "embedding_dimension": 768, - "context_length": 32768, - }, - ), - build_hf_repo_model_entry( - "meta-llama/Llama-4-Scout-17B-16E-Instruct", - CoreModelId.llama4_scout_17b_16e_instruct.value, - ), - build_hf_repo_model_entry( - "meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8", - CoreModelId.llama4_maverick_17b_128e_instruct.value, - ), -] + SAFETY_MODELS_ENTRIES diff --git a/llama_stack/providers/remote/inference/together/together.py b/llama_stack/providers/remote/inference/together/together.py index 54c76607f..37973d635 100644 --- a/llama_stack/providers/remote/inference/together/together.py +++ b/llama_stack/providers/remote/inference/together/together.py @@ -4,11 +4,11 @@ # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. -from collections.abc import AsyncGenerator, AsyncIterator -from typing import Any +from collections.abc import AsyncGenerator from openai import AsyncOpenAI from together import AsyncTogether +from together.constants import BASE_URL from llama_stack.apis.common.content_types import ( InterleavedContent, @@ -23,12 +23,7 @@ from llama_stack.apis.inference import ( Inference, LogProbConfig, Message, - OpenAIChatCompletion, - OpenAIChatCompletionChunk, - OpenAICompletion, OpenAIEmbeddingsResponse, - OpenAIMessageParam, - OpenAIResponseFormatParam, ResponseFormat, ResponseFormatType, SamplingParams, @@ -38,18 +33,20 @@ from llama_stack.apis.inference import ( ToolDefinition, ToolPromptFormat, ) +from llama_stack.apis.inference.inference import OpenAIEmbeddingUsage +from llama_stack.apis.models import Model, ModelType from llama_stack.core.request_headers import NeedsRequestProviderData from llama_stack.log import get_logger from llama_stack.providers.utils.inference.model_registry import ModelRegistryHelper from llama_stack.providers.utils.inference.openai_compat import ( convert_message_to_openai_dict, get_sampling_options, - prepare_openai_completion_params, process_chat_completion_response, process_chat_completion_stream_response, process_completion_response, process_completion_stream_response, ) +from llama_stack.providers.utils.inference.openai_mixin import OpenAIMixin from llama_stack.providers.utils.inference.prompt_adapter import ( chat_completion_request_to_prompt, completion_request_to_prompt, @@ -59,15 +56,29 @@ from llama_stack.providers.utils.inference.prompt_adapter import ( ) from .config import TogetherImplConfig -from .models import MODEL_ENTRIES logger = get_logger(name=__name__, category="inference::together") -class TogetherInferenceAdapter(ModelRegistryHelper, Inference, NeedsRequestProviderData): +class TogetherInferenceAdapter(OpenAIMixin, ModelRegistryHelper, Inference, NeedsRequestProviderData): + embedding_model_metadata = { + "togethercomputer/m2-bert-80M-32k-retrieval": {"embedding_dimension": 768, "context_length": 32768}, + "BAAI/bge-large-en-v1.5": {"embedding_dimension": 1024, "context_length": 512}, + "BAAI/bge-base-en-v1.5": {"embedding_dimension": 768, "context_length": 512}, + "Alibaba-NLP/gte-modernbert-base": {"embedding_dimension": 768, "context_length": 8192}, + "intfloat/multilingual-e5-large-instruct": {"embedding_dimension": 1024, "context_length": 512}, + } + def __init__(self, config: TogetherImplConfig) -> None: - ModelRegistryHelper.__init__(self, MODEL_ENTRIES, config.allowed_models) self.config = config + self.allowed_models = config.allowed_models + self._model_cache: dict[str, Model] = {} + + def get_api_key(self): + return self.config.api_key.get_secret_value() + + def get_base_url(self): + return BASE_URL async def initialize(self) -> None: pass @@ -255,6 +266,38 @@ class TogetherInferenceAdapter(ModelRegistryHelper, Inference, NeedsRequestProvi embeddings = [item.embedding for item in r.data] return EmbeddingsResponse(embeddings=embeddings) + async def list_models(self) -> list[Model] | None: + self._model_cache = {} + # Together's /v1/models is not compatible with OpenAI's /v1/models. Together support ticket #13355 -> will not fix, use Together's own client + for m in await self._get_client().models.list(): + if m.type == "embedding": + if m.id not in self.embedding_model_metadata: + logger.warning(f"Unknown embedding dimension for model {m.id}, skipping.") + continue + metadata = self.embedding_model_metadata[m.id] + self._model_cache[m.id] = Model( + provider_id=self.__provider_id__, + provider_resource_id=m.id, + identifier=m.id, + model_type=ModelType.embedding, + metadata=metadata, + ) + else: + self._model_cache[m.id] = Model( + provider_id=self.__provider_id__, + provider_resource_id=m.id, + identifier=m.id, + model_type=ModelType.llm, + ) + + return self._model_cache.values() + + async def should_refresh_models(self) -> bool: + return True + + async def check_model_availability(self, model): + return model in self._model_cache + async def openai_embeddings( self, model: str, @@ -263,125 +306,36 @@ class TogetherInferenceAdapter(ModelRegistryHelper, Inference, NeedsRequestProvi dimensions: int | None = None, user: str | None = None, ) -> OpenAIEmbeddingsResponse: - raise NotImplementedError() + """ + Together's OpenAI-compatible embeddings endpoint is not compatible with + the standard OpenAI embeddings endpoint. - async def openai_completion( - self, - model: str, - prompt: str | list[str] | list[int] | list[list[int]], - best_of: int | None = None, - echo: bool | None = None, - frequency_penalty: float | None = None, - logit_bias: dict[str, float] | None = None, - logprobs: bool | None = None, - max_tokens: int | None = None, - n: int | None = None, - presence_penalty: float | None = None, - seed: int | None = None, - stop: str | list[str] | None = None, - stream: bool | None = None, - stream_options: dict[str, Any] | None = None, - temperature: float | None = None, - top_p: float | None = None, - user: str | None = None, - guided_choice: list[str] | None = None, - prompt_logprobs: int | None = None, - suffix: str | None = None, - ) -> OpenAICompletion: - model_obj = await self.model_store.get_model(model) - params = await prepare_openai_completion_params( - model=model_obj.provider_resource_id, - prompt=prompt, - best_of=best_of, - echo=echo, - frequency_penalty=frequency_penalty, - logit_bias=logit_bias, - logprobs=logprobs, - max_tokens=max_tokens, - n=n, - presence_penalty=presence_penalty, - seed=seed, - stop=stop, - stream=stream, - stream_options=stream_options, - temperature=temperature, - top_p=top_p, - user=user, + The endpoint - + - not all models return usage information + - does not support user param, returns 400 Unrecognized request arguments supplied: user + - does not support dimensions param, returns 400 Unrecognized request arguments supplied: dimensions + """ + # Together support ticket #13332 -> will not fix + if user is not None: + raise ValueError("Together's embeddings endpoint does not support user param.") + # Together support ticket #13333 -> escalated + if dimensions is not None: + raise ValueError("Together's embeddings endpoint does not support dimensions param.") + + response = await self.client.embeddings.create( + model=await self._get_provider_model_id(model), + input=input, + encoding_format=encoding_format, ) - return await self._get_openai_client().completions.create(**params) # type: ignore - async def openai_chat_completion( - self, - model: str, - messages: list[OpenAIMessageParam], - frequency_penalty: float | None = None, - function_call: str | dict[str, Any] | None = None, - functions: list[dict[str, Any]] | None = None, - logit_bias: dict[str, float] | None = None, - logprobs: bool | None = None, - max_completion_tokens: int | None = None, - max_tokens: int | None = None, - n: int | None = None, - parallel_tool_calls: bool | None = None, - presence_penalty: float | None = None, - response_format: OpenAIResponseFormatParam | None = None, - seed: int | None = None, - stop: str | list[str] | None = None, - stream: bool | None = None, - stream_options: dict[str, Any] | None = None, - temperature: float | None = None, - tool_choice: str | dict[str, Any] | None = None, - tools: list[dict[str, Any]] | None = None, - top_logprobs: int | None = None, - top_p: float | None = None, - user: str | None = None, - ) -> OpenAIChatCompletion | AsyncIterator[OpenAIChatCompletionChunk]: - model_obj = await self.model_store.get_model(model) - params = await prepare_openai_completion_params( - model=model_obj.provider_resource_id, - messages=messages, - frequency_penalty=frequency_penalty, - function_call=function_call, - functions=functions, - logit_bias=logit_bias, - logprobs=logprobs, - max_completion_tokens=max_completion_tokens, - max_tokens=max_tokens, - n=n, - parallel_tool_calls=parallel_tool_calls, - presence_penalty=presence_penalty, - response_format=response_format, - seed=seed, - stop=stop, - stream=stream, - stream_options=stream_options, - temperature=temperature, - tool_choice=tool_choice, - tools=tools, - top_logprobs=top_logprobs, - top_p=top_p, - user=user, - ) - if params.get("stream", False): - return self._stream_openai_chat_completion(params) - return await self._get_openai_client().chat.completions.create(**params) # type: ignore + response.model = model # return the user the same model id they provided, avoid exposing the provider model id - async def _stream_openai_chat_completion(self, params: dict) -> AsyncGenerator: - # together.ai sometimes adds usage data to the stream, even if include_usage is False - # This causes an unexpected final chunk with empty choices array to be sent - # to clients that may not handle it gracefully. - include_usage = False - if params.get("stream_options", None): - include_usage = params["stream_options"].get("include_usage", False) - stream = await self._get_openai_client().chat.completions.create(**params) + # Together support ticket #13330 -> escalated + # - togethercomputer/m2-bert-80M-32k-retrieval *does not* return usage information + if not hasattr(response, "usage") or response.usage is None: + logger.warning( + f"Together's embedding endpoint for {model} did not return usage information, substituting -1s." + ) + response.usage = OpenAIEmbeddingUsage(prompt_tokens=-1, total_tokens=-1) - seen_finish_reason = False - async for chunk in stream: - # Final usage chunk with no choices that the user didn't request, so discard - if not include_usage and seen_finish_reason and len(chunk.choices) == 0: - break - yield chunk - for choice in chunk.choices: - if choice.finish_reason: - seen_finish_reason = True - break + return response diff --git a/llama_stack/providers/remote/inference/vertexai/models.py b/llama_stack/providers/remote/inference/vertexai/models.py deleted file mode 100644 index e72db533d..000000000 --- a/llama_stack/providers/remote/inference/vertexai/models.py +++ /dev/null @@ -1,20 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# All rights reserved. -# -# This source code is licensed under the terms described in the LICENSE file in -# the root directory of this source tree. - -from llama_stack.providers.utils.inference.model_registry import ( - ProviderModelEntry, -) - -# Vertex AI model IDs with vertex_ai/ prefix as required by litellm -LLM_MODEL_IDS = [ - "vertex_ai/gemini-2.0-flash", - "vertex_ai/gemini-2.5-flash", - "vertex_ai/gemini-2.5-pro", -] - -SAFETY_MODELS_ENTRIES = list[ProviderModelEntry]() - -MODEL_ENTRIES = [ProviderModelEntry(provider_model_id=m) for m in LLM_MODEL_IDS] + SAFETY_MODELS_ENTRIES diff --git a/llama_stack/providers/remote/inference/vertexai/vertexai.py b/llama_stack/providers/remote/inference/vertexai/vertexai.py index 8807fd0e6..770d21a2a 100644 --- a/llama_stack/providers/remote/inference/vertexai/vertexai.py +++ b/llama_stack/providers/remote/inference/vertexai/vertexai.py @@ -6,20 +6,22 @@ from typing import Any +import google.auth.transport.requests +from google.auth import default + from llama_stack.apis.inference import ChatCompletionRequest from llama_stack.providers.utils.inference.litellm_openai_mixin import ( LiteLLMOpenAIMixin, ) +from llama_stack.providers.utils.inference.openai_mixin import OpenAIMixin from .config import VertexAIConfig -from .models import MODEL_ENTRIES -class VertexAIInferenceAdapter(LiteLLMOpenAIMixin): +class VertexAIInferenceAdapter(OpenAIMixin, LiteLLMOpenAIMixin): def __init__(self, config: VertexAIConfig) -> None: LiteLLMOpenAIMixin.__init__( self, - MODEL_ENTRIES, litellm_provider_name="vertex_ai", api_key_from_config=None, # Vertex AI uses ADC, not API keys provider_data_api_key_field="vertex_project", # Use project for validation @@ -27,9 +29,30 @@ class VertexAIInferenceAdapter(LiteLLMOpenAIMixin): self.config = config def get_api_key(self) -> str: - # Vertex AI doesn't use API keys, it uses Application Default Credentials - # Return empty string to let litellm handle authentication via ADC - return "" + """ + Get an access token for Vertex AI using Application Default Credentials. + + Vertex AI uses ADC instead of API keys. This method obtains an access token + from the default credentials and returns it for use with the OpenAI-compatible client. + """ + try: + # Get default credentials - will read from GOOGLE_APPLICATION_CREDENTIALS + credentials, _ = default(scopes=["https://www.googleapis.com/auth/cloud-platform"]) + credentials.refresh(google.auth.transport.requests.Request()) + return str(credentials.token) + except Exception: + # If we can't get credentials, return empty string to let LiteLLM handle it + # This allows the LiteLLM mixin to work with ADC directly + return "" + + def get_base_url(self) -> str: + """ + Get the Vertex AI OpenAI-compatible API base URL. + + Returns the Vertex AI OpenAI-compatible endpoint URL. + Source: https://cloud.google.com/vertex-ai/generative-ai/docs/start/openai + """ + return f"https://{self.config.location}-aiplatform.googleapis.com/v1/projects/{self.config.project}/locations/{self.config.location}/endpoints/openapi" async def _get_params(self, request: ChatCompletionRequest) -> dict[str, Any]: # Get base parameters from parent diff --git a/llama_stack/providers/remote/inference/vllm/__init__.py b/llama_stack/providers/remote/inference/vllm/__init__.py index e4322a6aa..1f196e507 100644 --- a/llama_stack/providers/remote/inference/vllm/__init__.py +++ b/llama_stack/providers/remote/inference/vllm/__init__.py @@ -4,9 +4,15 @@ # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. +from pydantic import BaseModel + from .config import VLLMInferenceAdapterConfig +class VLLMProviderDataValidator(BaseModel): + vllm_api_token: str | None = None + + async def get_adapter_impl(config: VLLMInferenceAdapterConfig, _deps): from .vllm import VLLMInferenceAdapter diff --git a/llama_stack/providers/remote/inference/vllm/vllm.py b/llama_stack/providers/remote/inference/vllm/vllm.py index 9e9a80ca5..8fbb4b815 100644 --- a/llama_stack/providers/remote/inference/vllm/vllm.py +++ b/llama_stack/providers/remote/inference/vllm/vllm.py @@ -6,6 +6,7 @@ import json from collections.abc import AsyncGenerator, AsyncIterator from typing import Any +from urllib.parse import urljoin import httpx from openai import APIConnectionError, AsyncOpenAI @@ -38,13 +39,6 @@ from llama_stack.apis.inference import ( LogProbConfig, Message, ModelStore, - OpenAIChatCompletion, - OpenAICompletion, - OpenAIEmbeddingData, - OpenAIEmbeddingsResponse, - OpenAIEmbeddingUsage, - OpenAIMessageParam, - OpenAIResponseFormatParam, ResponseFormat, SamplingParams, TextTruncation, @@ -62,6 +56,7 @@ from llama_stack.providers.datatypes import ( HealthStatus, ModelsProtocolPrivate, ) +from llama_stack.providers.utils.inference.litellm_openai_mixin import LiteLLMOpenAIMixin from llama_stack.providers.utils.inference.model_registry import ( ModelRegistryHelper, build_hf_repo_model_entry, @@ -69,13 +64,14 @@ from llama_stack.providers.utils.inference.model_registry import ( from llama_stack.providers.utils.inference.openai_compat import ( UnparseableToolCall, convert_message_to_openai_dict, + convert_openai_chat_completion_stream, convert_tool_call, get_sampling_options, - prepare_openai_completion_params, process_chat_completion_stream_response, process_completion_response, process_completion_stream_response, ) +from llama_stack.providers.utils.inference.openai_mixin import OpenAIMixin from llama_stack.providers.utils.inference.prompt_adapter import ( completion_request_to_prompt, content_has_media, @@ -288,15 +284,30 @@ async def _process_vllm_chat_completion_stream_response( yield c -class VLLMInferenceAdapter(Inference, ModelsProtocolPrivate): +class VLLMInferenceAdapter(OpenAIMixin, LiteLLMOpenAIMixin, Inference, ModelsProtocolPrivate): # automatically set by the resolver when instantiating the provider __provider_id__: str model_store: ModelStore | None = None def __init__(self, config: VLLMInferenceAdapterConfig) -> None: + LiteLLMOpenAIMixin.__init__( + self, + model_entries=build_hf_repo_model_entries(), + litellm_provider_name="vllm", + api_key_from_config=config.api_token, + provider_data_api_key_field="vllm_api_token", + openai_compat_api_base=config.url, + ) self.register_helper = ModelRegistryHelper(build_hf_repo_model_entries()) self.config = config - self.client = None + + get_api_key = LiteLLMOpenAIMixin.get_api_key + + def get_base_url(self) -> str: + """Get the base URL from config.""" + if not self.config.url: + raise ValueError("No base URL configured") + return self.config.url async def initialize(self) -> None: if not self.config.url: @@ -305,11 +316,10 @@ class VLLMInferenceAdapter(Inference, ModelsProtocolPrivate): ) async def should_refresh_models(self) -> bool: + # Strictly respecting the refresh_models directive return self.config.refresh_models async def list_models(self) -> list[Model] | None: - self._lazy_initialize_client() - assert self.client is not None # mypy models = [] async for m in self.client.models.list(): model_type = ModelType.llm # unclear how to determine embedding vs. llm models @@ -335,14 +345,19 @@ class VLLMInferenceAdapter(Inference, ModelsProtocolPrivate): Performs a health check by verifying connectivity to the remote vLLM server. This method is used by the Provider API to verify that the service is running correctly. + Uses the unauthenticated /health endpoint. Returns: HealthResponse: A dictionary containing the health status. """ try: - client = self._create_client() if self.client is None else self.client - _ = [m async for m in client.models.list()] # Ensure the client is initialized - return HealthResponse(status=HealthStatus.OK) + base_url = self.get_base_url() + health_url = urljoin(base_url, "health") + + async with httpx.AsyncClient() as client: + response = await client.get(health_url) + response.raise_for_status() + return HealthResponse(status=HealthStatus.OK) except Exception as e: return HealthResponse(status=HealthStatus.ERROR, message=f"Health check failed: {str(e)}") @@ -351,21 +366,10 @@ class VLLMInferenceAdapter(Inference, ModelsProtocolPrivate): raise ValueError("Model store not set") return await self.model_store.get_model(model_id) - def _lazy_initialize_client(self): - if self.client is not None: - return + def get_extra_client_params(self): + return {"http_client": httpx.AsyncClient(verify=self.config.tls_verify)} - log.info(f"Initializing vLLM client with base_url={self.config.url}") - self.client = self._create_client() - - def _create_client(self): - return AsyncOpenAI( - base_url=self.config.url, - api_key=self.config.api_token, - http_client=httpx.AsyncClient(verify=self.config.tls_verify), - ) - - async def completion( + async def completion( # type: ignore[override] # Return type more specific than base class which is allows for both streaming and non-streaming responses. self, model_id: str, content: InterleavedContent, @@ -374,7 +378,6 @@ class VLLMInferenceAdapter(Inference, ModelsProtocolPrivate): stream: bool | None = False, logprobs: LogProbConfig | None = None, ) -> CompletionResponse | AsyncGenerator[CompletionResponseStreamChunk, None]: - self._lazy_initialize_client() if sampling_params is None: sampling_params = SamplingParams() model = await self._get_model(model_id) @@ -406,7 +409,6 @@ class VLLMInferenceAdapter(Inference, ModelsProtocolPrivate): logprobs: LogProbConfig | None = None, tool_config: ToolConfig | None = None, ) -> ChatCompletionResponse | AsyncGenerator[ChatCompletionResponseStreamChunk, None]: - self._lazy_initialize_client() if sampling_params is None: sampling_params = SamplingParams() model = await self._get_model(model_id) @@ -429,13 +431,14 @@ class VLLMInferenceAdapter(Inference, ModelsProtocolPrivate): tool_config=tool_config, ) if stream: - return self._stream_chat_completion(request, self.client) + return self._stream_chat_completion_with_client(request, self.client) else: return await self._nonstream_chat_completion(request, self.client) async def _nonstream_chat_completion( self, request: ChatCompletionRequest, client: AsyncOpenAI ) -> ChatCompletionResponse: + assert self.client is not None params = await self._get_params(request) r = await client.chat.completions.create(**params) choice = r.choices[0] @@ -449,9 +452,24 @@ class VLLMInferenceAdapter(Inference, ModelsProtocolPrivate): ) return result - async def _stream_chat_completion( + async def _stream_chat_completion(self, response: Any) -> AsyncIterator[ChatCompletionResponseStreamChunk]: + # This method is called from LiteLLMOpenAIMixin.chat_completion + # The response parameter contains the litellm response + # We need to convert it to our format + async def _stream_generator(): + async for chunk in response: + yield chunk + + async for chunk in convert_openai_chat_completion_stream( + _stream_generator(), enable_incremental_tool_calls=True + ): + yield chunk + + async def _stream_chat_completion_with_client( self, request: ChatCompletionRequest, client: AsyncOpenAI ) -> AsyncGenerator[ChatCompletionResponseStreamChunk, None]: + """Helper method for streaming with explicit client parameter.""" + assert self.client is not None params = await self._get_params(request) stream = await client.chat.completions.create(**params) @@ -463,7 +481,8 @@ class VLLMInferenceAdapter(Inference, ModelsProtocolPrivate): yield chunk async def _nonstream_completion(self, request: CompletionRequest) -> CompletionResponse: - assert self.client is not None + if self.client is None: + raise RuntimeError("Client is not initialized") params = await self._get_params(request) r = await self.client.completions.create(**params) return process_completion_response(r) @@ -471,7 +490,8 @@ class VLLMInferenceAdapter(Inference, ModelsProtocolPrivate): async def _stream_completion( self, request: CompletionRequest ) -> AsyncGenerator[CompletionResponseStreamChunk, None]: - assert self.client is not None + if self.client is None: + raise RuntimeError("Client is not initialized") params = await self._get_params(request) stream = await self.client.completions.create(**params) @@ -479,16 +499,12 @@ class VLLMInferenceAdapter(Inference, ModelsProtocolPrivate): yield chunk async def register_model(self, model: Model) -> Model: - # register_model is called during Llama Stack initialization, hence we cannot init self.client if not initialized yet. - # self.client should only be created after the initialization is complete to avoid asyncio cross-context errors. - # Changing this may lead to unpredictable behavior. - client = self._create_client() if self.client is None else self.client try: model = await self.register_helper.register_model(model) except ValueError: pass # Ignore statically unknown model, will check live listing try: - res = await client.models.list() + res = self.client.models.list() except APIConnectionError as e: raise ValueError( f"Failed to connect to vLLM at {self.config.url}. Please check if vLLM is running and accessible at that URL." @@ -543,8 +559,6 @@ class VLLMInferenceAdapter(Inference, ModelsProtocolPrivate): output_dimension: int | None = None, task_type: EmbeddingTaskType | None = None, ) -> EmbeddingsResponse: - self._lazy_initialize_client() - assert self.client is not None model = await self._get_model(model_id) kwargs = {} @@ -560,154 +574,3 @@ class VLLMInferenceAdapter(Inference, ModelsProtocolPrivate): embeddings = [data.embedding for data in response.data] return EmbeddingsResponse(embeddings=embeddings) - - async def openai_embeddings( - self, - model: str, - input: str | list[str], - encoding_format: str | None = "float", - dimensions: int | None = None, - user: str | None = None, - ) -> OpenAIEmbeddingsResponse: - self._lazy_initialize_client() - assert self.client is not None - model_obj = await self._get_model(model) - assert model_obj.model_type == ModelType.embedding - - # Convert input to list if it's a string - input_list = [input] if isinstance(input, str) else input - - # Call vLLM embeddings endpoint with encoding_format - response = await self.client.embeddings.create( - model=model_obj.provider_resource_id, - input=input_list, - dimensions=dimensions, - encoding_format=encoding_format, - ) - - # Convert response to OpenAI format - data = [ - OpenAIEmbeddingData( - embedding=embedding_data.embedding, - index=i, - ) - for i, embedding_data in enumerate(response.data) - ] - - # Not returning actual token usage since vLLM doesn't provide it - usage = OpenAIEmbeddingUsage(prompt_tokens=-1, total_tokens=-1) - - return OpenAIEmbeddingsResponse( - data=data, - model=model_obj.provider_resource_id, - usage=usage, - ) - - async def openai_completion( - self, - model: str, - prompt: str | list[str] | list[int] | list[list[int]], - best_of: int | None = None, - echo: bool | None = None, - frequency_penalty: float | None = None, - logit_bias: dict[str, float] | None = None, - logprobs: bool | None = None, - max_tokens: int | None = None, - n: int | None = None, - presence_penalty: float | None = None, - seed: int | None = None, - stop: str | list[str] | None = None, - stream: bool | None = None, - stream_options: dict[str, Any] | None = None, - temperature: float | None = None, - top_p: float | None = None, - user: str | None = None, - guided_choice: list[str] | None = None, - prompt_logprobs: int | None = None, - suffix: str | None = None, - ) -> OpenAICompletion: - self._lazy_initialize_client() - model_obj = await self._get_model(model) - - extra_body: dict[str, Any] = {} - if prompt_logprobs is not None and prompt_logprobs >= 0: - extra_body["prompt_logprobs"] = prompt_logprobs - if guided_choice: - extra_body["guided_choice"] = guided_choice - - params = await prepare_openai_completion_params( - model=model_obj.provider_resource_id, - prompt=prompt, - best_of=best_of, - echo=echo, - frequency_penalty=frequency_penalty, - logit_bias=logit_bias, - logprobs=logprobs, - max_tokens=max_tokens, - n=n, - presence_penalty=presence_penalty, - seed=seed, - stop=stop, - stream=stream, - stream_options=stream_options, - temperature=temperature, - top_p=top_p, - user=user, - extra_body=extra_body, - ) - return await self.client.completions.create(**params) # type: ignore - - async def openai_chat_completion( - self, - model: str, - messages: list[OpenAIMessageParam], - frequency_penalty: float | None = None, - function_call: str | dict[str, Any] | None = None, - functions: list[dict[str, Any]] | None = None, - logit_bias: dict[str, float] | None = None, - logprobs: bool | None = None, - max_completion_tokens: int | None = None, - max_tokens: int | None = None, - n: int | None = None, - parallel_tool_calls: bool | None = None, - presence_penalty: float | None = None, - response_format: OpenAIResponseFormatParam | None = None, - seed: int | None = None, - stop: str | list[str] | None = None, - stream: bool | None = None, - stream_options: dict[str, Any] | None = None, - temperature: float | None = None, - tool_choice: str | dict[str, Any] | None = None, - tools: list[dict[str, Any]] | None = None, - top_logprobs: int | None = None, - top_p: float | None = None, - user: str | None = None, - ) -> OpenAIChatCompletion | AsyncIterator[OpenAIChatCompletionChunk]: - self._lazy_initialize_client() - model_obj = await self._get_model(model) - params = await prepare_openai_completion_params( - model=model_obj.provider_resource_id, - messages=messages, - frequency_penalty=frequency_penalty, - function_call=function_call, - functions=functions, - logit_bias=logit_bias, - logprobs=logprobs, - max_completion_tokens=max_completion_tokens, - max_tokens=max_tokens, - n=n, - parallel_tool_calls=parallel_tool_calls, - presence_penalty=presence_penalty, - response_format=response_format, - seed=seed, - stop=stop, - stream=stream, - stream_options=stream_options, - temperature=temperature, - tool_choice=tool_choice, - tools=tools, - top_logprobs=top_logprobs, - top_p=top_p, - user=user, - ) - return await self.client.chat.completions.create(**params) # type: ignore diff --git a/llama_stack/providers/remote/inference/watsonx/config.py b/llama_stack/providers/remote/inference/watsonx/config.py index ae4bd55c1..42c25d93e 100644 --- a/llama_stack/providers/remote/inference/watsonx/config.py +++ b/llama_stack/providers/remote/inference/watsonx/config.py @@ -26,11 +26,11 @@ class WatsonXConfig(BaseModel): ) api_key: SecretStr | None = Field( default_factory=lambda: os.getenv("WATSONX_API_KEY"), - description="The watsonx API key, only needed of using the hosted service", + description="The watsonx API key", ) project_id: str | None = Field( default_factory=lambda: os.getenv("WATSONX_PROJECT_ID"), - description="The Project ID key, only needed of using the hosted service", + description="The Project ID key", ) timeout: int = Field( default=60, diff --git a/llama_stack/providers/remote/inference/watsonx/watsonx.py b/llama_stack/providers/remote/inference/watsonx/watsonx.py index 78161d1cb..cb8b45565 100644 --- a/llama_stack/providers/remote/inference/watsonx/watsonx.py +++ b/llama_stack/providers/remote/inference/watsonx/watsonx.py @@ -7,8 +7,8 @@ from collections.abc import AsyncGenerator, AsyncIterator from typing import Any -from ibm_watson_machine_learning.foundation_models import Model -from ibm_watson_machine_learning.metanames import GenTextParamsMetaNames as GenParams +from ibm_watsonx_ai.foundation_models import Model +from ibm_watsonx_ai.metanames import GenTextParamsMetaNames as GenParams from openai import AsyncOpenAI from llama_stack.apis.common.content_types import InterleavedContent, InterleavedContentItem @@ -38,6 +38,7 @@ from llama_stack.apis.inference import ( TopKSamplingStrategy, TopPSamplingStrategy, ) +from llama_stack.log import get_logger from llama_stack.providers.utils.inference.model_registry import ModelRegistryHelper from llama_stack.providers.utils.inference.openai_compat import ( OpenAICompatCompletionChoice, @@ -57,14 +58,29 @@ from llama_stack.providers.utils.inference.prompt_adapter import ( from . import WatsonXConfig from .models import MODEL_ENTRIES +logger = get_logger(name=__name__, category="inference::watsonx") + + +# Note on structured output +# WatsonX returns responses with a json embedded into a string. +# Examples: + +# ChatCompletionResponse(completion_message=CompletionMessage(content='```json\n{\n +# "first_name": "Michael",\n "last_name": "Jordan",\n'...) +# Not even a valid JSON, but we can still extract the JSON from the content + +# CompletionResponse(content=' \nThe best answer is $\\boxed{\\{"name": "Michael Jordan", +# "year_born": "1963", "year_retired": "2003"\\}}$') +# Find the start of the boxed content + class WatsonXInferenceAdapter(Inference, ModelRegistryHelper): def __init__(self, config: WatsonXConfig) -> None: - ModelRegistryHelper.__init__(self, MODEL_ENTRIES) - - print(f"Initializing watsonx InferenceAdapter({config.url})...") + ModelRegistryHelper.__init__(self, model_entries=MODEL_ENTRIES) + logger.info(f"Initializing watsonx InferenceAdapter({config.url})...") self._config = config + self._openai_client: AsyncOpenAI | None = None self._project_id = self._config.project_id diff --git a/llama_stack/providers/remote/vector_io/pgvector/pgvector.py b/llama_stack/providers/remote/vector_io/pgvector/pgvector.py index 1c8d361c2..1c140e782 100644 --- a/llama_stack/providers/remote/vector_io/pgvector/pgvector.py +++ b/llama_stack/providers/remote/vector_io/pgvector/pgvector.py @@ -4,6 +4,7 @@ # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. +import heapq from typing import Any import psycopg2 @@ -23,6 +24,9 @@ from llama_stack.apis.vector_io import ( ) from llama_stack.log import get_logger from llama_stack.providers.datatypes import Api, VectorDBsProtocolPrivate +from llama_stack.providers.utils.inference.prompt_adapter import ( + interleaved_content_as_str, +) from llama_stack.providers.utils.kvstore import kvstore_impl from llama_stack.providers.utils.kvstore.api import KVStore from llama_stack.providers.utils.memory.openai_vector_store_mixin import OpenAIVectorStoreMixin @@ -31,6 +35,7 @@ from llama_stack.providers.utils.memory.vector_store import ( EmbeddingIndex, VectorDBWithIndex, ) +from llama_stack.providers.utils.vector_io.vector_utils import WeightedInMemoryAggregator, sanitize_collection_name from .config import PGVectorVectorIOConfig @@ -72,25 +77,63 @@ def load_models(cur, cls): class PGVectorIndex(EmbeddingIndex): - def __init__(self, vector_db: VectorDB, dimension: int, conn, kvstore: KVStore | None = None): - self.conn = conn - with conn.cursor(cursor_factory=psycopg2.extras.DictCursor) as cur: - # Sanitize the table name by replacing hyphens with underscores - # SQL doesn't allow hyphens in table names, and vector_db.identifier may contain hyphens - # when created with patterns like "test-vector-db-{uuid4()}" - sanitized_identifier = vector_db.identifier.replace("-", "_") - self.table_name = f"vector_store_{sanitized_identifier}" - self.kvstore = kvstore + # reference: https://github.com/pgvector/pgvector?tab=readme-ov-file#querying + PGVECTOR_DISTANCE_METRIC_TO_SEARCH_FUNCTION: dict[str, str] = { + "L2": "<->", + "L1": "<+>", + "COSINE": "<=>", + "INNER_PRODUCT": "<#>", + "HAMMING": "<~>", + "JACCARD": "<%>", + } - cur.execute( - f""" - CREATE TABLE IF NOT EXISTS {self.table_name} ( - id TEXT PRIMARY KEY, - document JSONB, - embedding vector({dimension}) + def __init__( + self, + vector_db: VectorDB, + dimension: int, + conn: psycopg2.extensions.connection, + kvstore: KVStore | None = None, + distance_metric: str = "COSINE", + ): + self.vector_db = vector_db + self.dimension = dimension + self.conn = conn + self.kvstore = kvstore + self.check_distance_metric_availability(distance_metric) + self.distance_metric = distance_metric + self.table_name = None + + async def initialize(self) -> None: + try: + with self.conn.cursor(cursor_factory=psycopg2.extras.DictCursor) as cur: + # Sanitize the table name by replacing hyphens with underscores + # SQL doesn't allow hyphens in table names, and vector_db.identifier may contain hyphens + # when created with patterns like "test-vector-db-{uuid4()}" + sanitized_identifier = sanitize_collection_name(self.vector_db.identifier) + self.table_name = f"vs_{sanitized_identifier}" + + cur.execute( + f""" + CREATE TABLE IF NOT EXISTS {self.table_name} ( + id TEXT PRIMARY KEY, + document JSONB, + embedding vector({self.dimension}), + content_text TEXT, + tokenized_content TSVECTOR + ) + """ ) - """ - ) + + # Create GIN index for full-text search performance + cur.execute( + f""" + CREATE INDEX IF NOT EXISTS {self.table_name}_content_gin_idx + ON {self.table_name} USING GIN(tokenized_content) + """ + ) + except Exception as e: + log.exception(f"Error creating PGVectorIndex for vector_db: {self.vector_db.identifier}") + raise RuntimeError(f"Error creating PGVectorIndex for vector_db: {self.vector_db.identifier}") from e async def add_chunks(self, chunks: list[Chunk], embeddings: NDArray): assert len(chunks) == len(embeddings), ( @@ -99,29 +142,49 @@ class PGVectorIndex(EmbeddingIndex): values = [] for i, chunk in enumerate(chunks): + content_text = interleaved_content_as_str(chunk.content) values.append( ( f"{chunk.chunk_id}", Json(chunk.model_dump()), embeddings[i].tolist(), + content_text, + content_text, # Pass content_text twice - once for content_text column, once for to_tsvector function. Eg. to_tsvector(content_text) = tokenized_content ) ) query = sql.SQL( f""" - INSERT INTO {self.table_name} (id, document, embedding) + INSERT INTO {self.table_name} (id, document, embedding, content_text, tokenized_content) VALUES %s - ON CONFLICT (id) DO UPDATE SET embedding = EXCLUDED.embedding, document = EXCLUDED.document + ON CONFLICT (id) DO UPDATE SET + embedding = EXCLUDED.embedding, + document = EXCLUDED.document, + content_text = EXCLUDED.content_text, + tokenized_content = EXCLUDED.tokenized_content """ ) with self.conn.cursor(cursor_factory=psycopg2.extras.DictCursor) as cur: - execute_values(cur, query, values, template="(%s, %s, %s::vector)") + execute_values(cur, query, values, template="(%s, %s, %s::vector, %s, to_tsvector('english', %s))") async def query_vector(self, embedding: NDArray, k: int, score_threshold: float) -> QueryChunksResponse: + """ + Performs vector similarity search using PostgreSQL's search function. Default distance metric is COSINE. + + Args: + embedding: The query embedding vector + k: Number of results to return + score_threshold: Minimum similarity score threshold + + Returns: + QueryChunksResponse with combined results + """ + pgvector_search_function = self.get_pgvector_search_function() + with self.conn.cursor(cursor_factory=psycopg2.extras.DictCursor) as cur: cur.execute( f""" - SELECT document, embedding <-> %s::vector AS distance + SELECT document, embedding {pgvector_search_function} %s::vector AS distance FROM {self.table_name} ORDER BY distance LIMIT %s @@ -147,7 +210,40 @@ class PGVectorIndex(EmbeddingIndex): k: int, score_threshold: float, ) -> QueryChunksResponse: - raise NotImplementedError("Keyword search is not supported in PGVector") + """ + Performs keyword-based search using PostgreSQL's full-text search with ts_rank scoring. + + Args: + query_string: The text query for keyword search + k: Number of results to return + score_threshold: Minimum similarity score threshold + + Returns: + QueryChunksResponse with combined results + """ + with self.conn.cursor(cursor_factory=psycopg2.extras.DictCursor) as cur: + # Use plainto_tsquery to handle user input safely and ts_rank for relevance scoring + cur.execute( + f""" + SELECT document, ts_rank(tokenized_content, plainto_tsquery('english', %s)) AS score + FROM {self.table_name} + WHERE tokenized_content @@ plainto_tsquery('english', %s) + ORDER BY score DESC + LIMIT %s + """, + (query_string, query_string, k), + ) + results = cur.fetchall() + + chunks = [] + scores = [] + for doc, score in results: + if score < score_threshold: + continue + chunks.append(Chunk(**doc)) + scores.append(float(score)) + + return QueryChunksResponse(chunks=chunks, scores=scores) async def query_hybrid( self, @@ -158,7 +254,59 @@ class PGVectorIndex(EmbeddingIndex): reranker_type: str, reranker_params: dict[str, Any] | None = None, ) -> QueryChunksResponse: - raise NotImplementedError("Hybrid search is not supported in PGVector") + """ + Hybrid search combining vector similarity and keyword search using configurable reranking. + + Args: + embedding: The query embedding vector + query_string: The text query for keyword search + k: Number of results to return + score_threshold: Minimum similarity score threshold + reranker_type: Type of reranker to use ("rrf" or "weighted") + reranker_params: Parameters for the reranker + + Returns: + QueryChunksResponse with combined results + """ + if reranker_params is None: + reranker_params = {} + + # Get results from both search methods + vector_response = await self.query_vector(embedding, k, score_threshold) + keyword_response = await self.query_keyword(query_string, k, score_threshold) + + # Convert responses to score dictionaries using chunk_id + vector_scores = { + chunk.chunk_id: score for chunk, score in zip(vector_response.chunks, vector_response.scores, strict=False) + } + keyword_scores = { + chunk.chunk_id: score + for chunk, score in zip(keyword_response.chunks, keyword_response.scores, strict=False) + } + + # Combine scores using the reranking utility + combined_scores = WeightedInMemoryAggregator.combine_search_results( + vector_scores, keyword_scores, reranker_type, reranker_params + ) + + # Efficient top-k selection because it only tracks the k best candidates it's seen so far + top_k_items = heapq.nlargest(k, combined_scores.items(), key=lambda x: x[1]) + + # Filter by score threshold + filtered_items = [(doc_id, score) for doc_id, score in top_k_items if score >= score_threshold] + + # Create a map of chunk_id to chunk for both responses + chunk_map = {c.chunk_id: c for c in vector_response.chunks + keyword_response.chunks} + + # Use the map to look up chunks by their IDs + chunks = [] + scores = [] + for doc_id, score in filtered_items: + if doc_id in chunk_map: + chunks.append(chunk_map[doc_id]) + scores.append(score) + + return QueryChunksResponse(chunks=chunks, scores=scores) async def delete(self): with self.conn.cursor(cursor_factory=psycopg2.extras.DictCursor) as cur: @@ -170,6 +318,25 @@ class PGVectorIndex(EmbeddingIndex): with self.conn.cursor(cursor_factory=psycopg2.extras.DictCursor) as cur: cur.execute(f"DELETE FROM {self.table_name} WHERE id = ANY(%s)", (chunk_ids,)) + def get_pgvector_search_function(self) -> str: + return self.PGVECTOR_DISTANCE_METRIC_TO_SEARCH_FUNCTION[self.distance_metric] + + def check_distance_metric_availability(self, distance_metric: str) -> None: + """Check if the distance metric is supported by PGVector. + + Args: + distance_metric: The distance metric to check + + Raises: + ValueError: If the distance metric is not supported + """ + if distance_metric not in self.PGVECTOR_DISTANCE_METRIC_TO_SEARCH_FUNCTION: + supported_metrics = list(self.PGVECTOR_DISTANCE_METRIC_TO_SEARCH_FUNCTION.keys()) + raise ValueError( + f"Distance metric '{distance_metric}' is not supported by PGVector. " + f"Supported metrics are: {', '.join(supported_metrics)}" + ) + class PGVectorVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorDBsProtocolPrivate): def __init__( @@ -185,8 +352,8 @@ class PGVectorVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorDBsProtoco self.files_api = files_api self.kvstore: KVStore | None = None self.vector_db_store = None - self.openai_vector_store: dict[str, dict[str, Any]] = {} - self.metadatadata_collection_name = "openai_vector_stores_metadata" + self.openai_vector_stores: dict[str, dict[str, Any]] = {} + self.metadata_collection_name = "openai_vector_stores_metadata" async def initialize(self) -> None: log.info(f"Initializing PGVector memory adapter with config: {self.config}") @@ -233,9 +400,13 @@ class PGVectorVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorDBsProtoco upsert_models(self.conn, [(vector_db.identifier, vector_db)]) # Create and cache the PGVector index table for the vector DB + pgvector_index = PGVectorIndex( + vector_db=vector_db, dimension=vector_db.embedding_dimension, conn=self.conn, kvstore=self.kvstore + ) + await pgvector_index.initialize() index = VectorDBWithIndex( vector_db, - index=PGVectorIndex(vector_db, vector_db.embedding_dimension, self.conn, kvstore=self.kvstore), + index=pgvector_index, inference_api=self.inference_api, ) self.cache[vector_db.identifier] = index @@ -272,8 +443,15 @@ class PGVectorVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorDBsProtoco if vector_db_id in self.cache: return self.cache[vector_db_id] + if self.vector_db_store is None: + raise VectorStoreNotFoundError(vector_db_id) + vector_db = await self.vector_db_store.get_vector_db(vector_db_id) + if not vector_db: + raise VectorStoreNotFoundError(vector_db_id) + index = PGVectorIndex(vector_db, vector_db.embedding_dimension, self.conn) + await index.initialize() self.cache[vector_db_id] = VectorDBWithIndex(vector_db, index, self.inference_api) return self.cache[vector_db_id] diff --git a/llama_stack/providers/remote/vector_io/qdrant/qdrant.py b/llama_stack/providers/remote/vector_io/qdrant/qdrant.py index 0a0faa23a..ec3869495 100644 --- a/llama_stack/providers/remote/vector_io/qdrant/qdrant.py +++ b/llama_stack/providers/remote/vector_io/qdrant/qdrant.py @@ -5,6 +5,7 @@ # the root directory of this source tree. import asyncio +import hashlib import uuid from typing import Any @@ -49,10 +50,13 @@ def convert_id(_id: str) -> str: Converts any string into a UUID string based on a seed. Qdrant accepts UUID strings and unsigned integers as point ID. - We use a seed to convert each string into a UUID string deterministically. + We use a SHA-256 hash to convert each string into a UUID string deterministically. This allows us to overwrite the same point with the original ID. """ - return str(uuid.uuid5(uuid.NAMESPACE_DNS, _id)) + hash_input = f"qdrant_id:{_id}".encode() + sha256_hash = hashlib.sha256(hash_input).hexdigest() + # Use the first 32 characters to create a valid UUID + return str(uuid.UUID(sha256_hash[:32])) class QdrantIndex(EmbeddingIndex): diff --git a/llama_stack/providers/utils/bedrock/config.py b/llama_stack/providers/utils/bedrock/config.py index b25617d76..2745c88cb 100644 --- a/llama_stack/providers/utils/bedrock/config.py +++ b/llama_stack/providers/utils/bedrock/config.py @@ -4,53 +4,55 @@ # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. +import os + from pydantic import BaseModel, Field class BedrockBaseConfig(BaseModel): aws_access_key_id: str | None = Field( - default=None, + default_factory=lambda: os.getenv("AWS_ACCESS_KEY_ID"), description="The AWS access key to use. Default use environment variable: AWS_ACCESS_KEY_ID", ) aws_secret_access_key: str | None = Field( - default=None, + default_factory=lambda: os.getenv("AWS_SECRET_ACCESS_KEY"), description="The AWS secret access key to use. Default use environment variable: AWS_SECRET_ACCESS_KEY", ) aws_session_token: str | None = Field( - default=None, + default_factory=lambda: os.getenv("AWS_SESSION_TOKEN"), description="The AWS session token to use. Default use environment variable: AWS_SESSION_TOKEN", ) region_name: str | None = Field( - default=None, + default_factory=lambda: os.getenv("AWS_DEFAULT_REGION"), description="The default AWS Region to use, for example, us-west-1 or us-west-2." "Default use environment variable: AWS_DEFAULT_REGION", ) profile_name: str | None = Field( - default=None, + default_factory=lambda: os.getenv("AWS_PROFILE"), description="The profile name that contains credentials to use.Default use environment variable: AWS_PROFILE", ) total_max_attempts: int | None = Field( - default=None, + default_factory=lambda: int(val) if (val := os.getenv("AWS_MAX_ATTEMPTS")) else None, description="An integer representing the maximum number of attempts that will be made for a single request, " "including the initial attempt. Default use environment variable: AWS_MAX_ATTEMPTS", ) retry_mode: str | None = Field( - default=None, + default_factory=lambda: os.getenv("AWS_RETRY_MODE"), description="A string representing the type of retries Boto3 will perform." "Default use environment variable: AWS_RETRY_MODE", ) connect_timeout: float | None = Field( - default=60, + default_factory=lambda: float(os.getenv("AWS_CONNECT_TIMEOUT", "60")), description="The time in seconds till a timeout exception is thrown when attempting to make a connection. " "The default is 60 seconds.", ) read_timeout: float | None = Field( - default=60, + default_factory=lambda: float(os.getenv("AWS_READ_TIMEOUT", "60")), description="The time in seconds till a timeout exception is thrown when attempting to read from a connection." "The default is 60 seconds.", ) session_ttl: int | None = Field( - default=3600, + default_factory=lambda: int(os.getenv("AWS_SESSION_TTL", "3600")), description="The time in seconds till a session expires. The default is 3600 seconds (1 hour).", ) diff --git a/llama_stack/providers/utils/inference/embedding_mixin.py b/llama_stack/providers/utils/inference/embedding_mixin.py index 65ba2854b..9bd0aa8ce 100644 --- a/llama_stack/providers/utils/inference/embedding_mixin.py +++ b/llama_stack/providers/utils/inference/embedding_mixin.py @@ -4,6 +4,7 @@ # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. +import asyncio import base64 import struct from typing import TYPE_CHECKING @@ -43,9 +44,11 @@ class SentenceTransformerEmbeddingMixin: task_type: EmbeddingTaskType | None = None, ) -> EmbeddingsResponse: model = await self.model_store.get_model(model_id) - embedding_model = self._load_sentence_transformer_model(model.provider_resource_id) - embeddings = embedding_model.encode( - [interleaved_content_as_str(content) for content in contents], show_progress_bar=False + embedding_model = await self._load_sentence_transformer_model(model.provider_resource_id) + embeddings = await asyncio.to_thread( + embedding_model.encode, + [interleaved_content_as_str(content) for content in contents], + show_progress_bar=False, ) return EmbeddingsResponse(embeddings=embeddings) @@ -64,8 +67,8 @@ class SentenceTransformerEmbeddingMixin: # Get the model and generate embeddings model_obj = await self.model_store.get_model(model) - embedding_model = self._load_sentence_transformer_model(model_obj.provider_resource_id) - embeddings = embedding_model.encode(input_list, show_progress_bar=False) + embedding_model = await self._load_sentence_transformer_model(model_obj.provider_resource_id) + embeddings = await asyncio.to_thread(embedding_model.encode, input_list, show_progress_bar=False) # Convert embeddings to the requested format data = [] @@ -93,7 +96,7 @@ class SentenceTransformerEmbeddingMixin: usage=usage, ) - def _load_sentence_transformer_model(self, model: str) -> "SentenceTransformer": + async def _load_sentence_transformer_model(self, model: str) -> "SentenceTransformer": global EMBEDDING_MODELS loaded_model = EMBEDDING_MODELS.get(model) @@ -101,8 +104,12 @@ class SentenceTransformerEmbeddingMixin: return loaded_model log.info(f"Loading sentence transformer for {model}...") - from sentence_transformers import SentenceTransformer - loaded_model = SentenceTransformer(model) + def _load_model(): + from sentence_transformers import SentenceTransformer + + return SentenceTransformer(model) + + loaded_model = await asyncio.to_thread(_load_model) EMBEDDING_MODELS[model] = loaded_model return loaded_model diff --git a/llama_stack/providers/utils/inference/inference_store.py b/llama_stack/providers/utils/inference/inference_store.py index 43006cfd5..ffc9f3e11 100644 --- a/llama_stack/providers/utils/inference/inference_store.py +++ b/llama_stack/providers/utils/inference/inference_store.py @@ -3,6 +3,11 @@ # # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. +import asyncio +from typing import Any + +from sqlalchemy.exc import IntegrityError + from llama_stack.apis.inference import ( ListOpenAIChatCompletionResponse, OpenAIChatCompletion, @@ -10,27 +15,46 @@ from llama_stack.apis.inference import ( OpenAIMessageParam, Order, ) -from llama_stack.core.datatypes import AccessRule -from llama_stack.core.utils.config_dirs import RUNTIME_BASE_DIR +from llama_stack.core.datatypes import AccessRule, InferenceStoreConfig +from llama_stack.log import get_logger from ..sqlstore.api import ColumnDefinition, ColumnType from ..sqlstore.authorized_sqlstore import AuthorizedSqlStore -from ..sqlstore.sqlstore import SqliteSqlStoreConfig, SqlStoreConfig, sqlstore_impl +from ..sqlstore.sqlstore import SqlStoreConfig, SqlStoreType, sqlstore_impl + +logger = get_logger(name=__name__, category="inference_store") class InferenceStore: - def __init__(self, sql_store_config: SqlStoreConfig, policy: list[AccessRule]): - if not sql_store_config: - sql_store_config = SqliteSqlStoreConfig( - db_path=(RUNTIME_BASE_DIR / "sqlstore.db").as_posix(), + def __init__( + self, + config: InferenceStoreConfig | SqlStoreConfig, + policy: list[AccessRule], + ): + # Handle backward compatibility + if not isinstance(config, InferenceStoreConfig): + # Legacy: SqlStoreConfig passed directly as config + config = InferenceStoreConfig( + sql_store_config=config, ) - self.sql_store_config = sql_store_config + + self.config = config + self.sql_store_config = config.sql_store_config self.sql_store = None self.policy = policy + # Disable write queue for SQLite to avoid concurrency issues + self.enable_write_queue = self.sql_store_config.type != SqlStoreType.sqlite + + # Async write queue and worker control + self._queue: asyncio.Queue[tuple[OpenAIChatCompletion, list[OpenAIMessageParam]]] | None = None + self._worker_tasks: list[asyncio.Task[Any]] = [] + self._max_write_queue_size: int = config.max_write_queue_size + self._num_writers: int = max(1, config.num_writers) + async def initialize(self): """Create the necessary tables if they don't exist.""" - self.sql_store = AuthorizedSqlStore(sqlstore_impl(self.sql_store_config)) + self.sql_store = AuthorizedSqlStore(sqlstore_impl(self.sql_store_config), self.policy) await self.sql_store.create_table( "chat_completions", { @@ -42,23 +66,109 @@ class InferenceStore: }, ) + if self.enable_write_queue: + self._queue = asyncio.Queue(maxsize=self._max_write_queue_size) + for _ in range(self._num_writers): + self._worker_tasks.append(asyncio.create_task(self._worker_loop())) + else: + logger.info("Write queue disabled for SQLite to avoid concurrency issues") + + async def shutdown(self) -> None: + if not self._worker_tasks: + return + if self._queue is not None: + await self._queue.join() + for t in self._worker_tasks: + if not t.done(): + t.cancel() + for t in self._worker_tasks: + try: + await t + except asyncio.CancelledError: + pass + self._worker_tasks.clear() + + async def flush(self) -> None: + """Wait for all queued writes to complete. Useful for testing.""" + if self.enable_write_queue and self._queue is not None: + await self._queue.join() + async def store_chat_completion( self, chat_completion: OpenAIChatCompletion, input_messages: list[OpenAIMessageParam] ) -> None: - if not self.sql_store: + if self.enable_write_queue: + if self._queue is None: + raise ValueError("Inference store is not initialized") + try: + self._queue.put_nowait((chat_completion, input_messages)) + except asyncio.QueueFull: + logger.warning( + f"Write queue full; adding chat completion id={getattr(chat_completion, 'id', '')}" + ) + await self._queue.put((chat_completion, input_messages)) + else: + await self._write_chat_completion(chat_completion, input_messages) + + async def _worker_loop(self) -> None: + assert self._queue is not None + while True: + try: + item = await self._queue.get() + except asyncio.CancelledError: + break + chat_completion, input_messages = item + try: + await self._write_chat_completion(chat_completion, input_messages) + except Exception as e: # noqa: BLE001 + logger.error(f"Error writing chat completion: {e}") + finally: + self._queue.task_done() + + async def _write_chat_completion( + self, chat_completion: OpenAIChatCompletion, input_messages: list[OpenAIMessageParam] + ) -> None: + if self.sql_store is None: raise ValueError("Inference store is not initialized") data = chat_completion.model_dump() + record_data = { + "id": data["id"], + "created": data["created"], + "model": data["model"], + "choices": data["choices"], + "input_messages": [message.model_dump() for message in input_messages], + } - await self.sql_store.insert( - table="chat_completions", - data={ - "id": data["id"], - "created": data["created"], - "model": data["model"], - "choices": data["choices"], - "input_messages": [message.model_dump() for message in input_messages], - }, + try: + await self.sql_store.insert( + table="chat_completions", + data=record_data, + ) + except IntegrityError as e: + # Duplicate chat completion IDs can be generated during tests especially if they are replaying + # recorded responses across different tests. No need to warn or error under those circumstances. + # In the wild, this is not likely to happen at all (no evidence) so we aren't really hiding any problem. + + # Check if it's a unique constraint violation + error_message = str(e.orig) if e.orig else str(e) + if self._is_unique_constraint_error(error_message): + # Update the existing record instead + await self.sql_store.update(table="chat_completions", data=record_data, where={"id": data["id"]}) + else: + # Re-raise if it's not a unique constraint error + raise + + def _is_unique_constraint_error(self, error_message: str) -> bool: + """Check if the error is specifically a unique constraint violation.""" + error_lower = error_message.lower() + return any( + indicator in error_lower + for indicator in [ + "unique constraint failed", # SQLite + "duplicate key", # PostgreSQL + "unique violation", # PostgreSQL alternative + "duplicate entry", # MySQL + ] ) async def list_chat_completions( @@ -92,7 +202,6 @@ class InferenceStore: order_by=[("created", order.value)], cursor=("id", after) if after else None, limit=limit, - policy=self.policy, ) data = [ @@ -119,7 +228,6 @@ class InferenceStore: row = await self.sql_store.fetch_one( table="chat_completions", where={"id": completion_id}, - policy=self.policy, ) if not row: diff --git a/llama_stack/providers/utils/inference/litellm_openai_mixin.py b/llama_stack/providers/utils/inference/litellm_openai_mixin.py index 9bd43e4c9..b1e38f323 100644 --- a/llama_stack/providers/utils/inference/litellm_openai_mixin.py +++ b/llama_stack/providers/utils/inference/litellm_openai_mixin.py @@ -40,7 +40,7 @@ from llama_stack.apis.inference import ( ) from llama_stack.core.request_headers import NeedsRequestProviderData from llama_stack.log import get_logger -from llama_stack.providers.utils.inference.model_registry import ModelRegistryHelper +from llama_stack.providers.utils.inference.model_registry import ModelRegistryHelper, ProviderModelEntry from llama_stack.providers.utils.inference.openai_compat import ( b64_encode_openai_embeddings_response, convert_message_to_openai_dict_new, @@ -67,10 +67,10 @@ class LiteLLMOpenAIMixin( # when calling litellm. def __init__( self, - model_entries, litellm_provider_name: str, api_key_from_config: str | None, provider_data_api_key_field: str, + model_entries: list[ProviderModelEntry] | None = None, openai_compat_api_base: str | None = None, download_images: bool = False, json_schema_strict: bool = True, @@ -86,7 +86,7 @@ class LiteLLMOpenAIMixin( :param download_images: Whether to download images and convert to base64 for message conversion. :param json_schema_strict: Whether to use strict mode for JSON schema validation. """ - ModelRegistryHelper.__init__(self, model_entries) + ModelRegistryHelper.__init__(self, model_entries=model_entries) self.litellm_provider_name = litellm_provider_name self.api_key_from_config = api_key_from_config diff --git a/llama_stack/providers/utils/inference/model_registry.py b/llama_stack/providers/utils/inference/model_registry.py index 44add8f9e..ff15b2d43 100644 --- a/llama_stack/providers/utils/inference/model_registry.py +++ b/llama_stack/providers/utils/inference/model_registry.py @@ -11,7 +11,6 @@ from pydantic import BaseModel, Field from llama_stack.apis.common.errors import UnsupportedModelError from llama_stack.apis.models import ModelType from llama_stack.log import get_logger -from llama_stack.models.llama.sku_list import all_registered_models from llama_stack.providers.datatypes import Model, ModelsProtocolPrivate from llama_stack.providers.utils.inference import ( ALL_HUGGINGFACE_REPOS_TO_MODEL_DESCRIPTOR, @@ -37,13 +36,6 @@ class ProviderModelEntry(BaseModel): metadata: dict[str, Any] = Field(default_factory=dict) -def get_huggingface_repo(model_descriptor: str) -> str | None: - for model in all_registered_models(): - if model.descriptor() == model_descriptor: - return model.huggingface_repo - return None - - def build_hf_repo_model_entry( provider_model_id: str, model_descriptor: str, @@ -63,25 +55,20 @@ def build_hf_repo_model_entry( ) -def build_model_entry(provider_model_id: str, model_descriptor: str) -> ProviderModelEntry: - return ProviderModelEntry( - provider_model_id=provider_model_id, - aliases=[], - llama_model=model_descriptor, - model_type=ModelType.llm, - ) - - class ModelRegistryHelper(ModelsProtocolPrivate): __provider_id__: str - def __init__(self, model_entries: list[ProviderModelEntry], allowed_models: list[str] | None = None): - self.model_entries = model_entries + def __init__( + self, + model_entries: list[ProviderModelEntry] | None = None, + allowed_models: list[str] | None = None, + ): self.allowed_models = allowed_models self.alias_to_provider_id_map = {} self.provider_id_to_llama_model_map = {} - for entry in model_entries: + self.model_entries = model_entries or [] + for entry in self.model_entries: for alias in entry.aliases: self.alias_to_provider_id_map[alias] = entry.provider_model_id @@ -103,7 +90,7 @@ class ModelRegistryHelper(ModelsProtocolPrivate): Model( identifier=id, provider_resource_id=entry.provider_model_id, - model_type=ModelType.llm, + model_type=entry.model_type, metadata=entry.metadata, provider_id=self.__provider_id__, ) diff --git a/llama_stack/providers/utils/inference/openai_mixin.py b/llama_stack/providers/utils/inference/openai_mixin.py index f60deee6e..84211dc96 100644 --- a/llama_stack/providers/utils/inference/openai_mixin.py +++ b/llama_stack/providers/utils/inference/openai_mixin.py @@ -4,11 +4,11 @@ # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. +import uuid from abc import ABC, abstractmethod from collections.abc import AsyncIterator from typing import Any -import openai from openai import NOT_GIVEN, AsyncOpenAI from llama_stack.apis.inference import ( @@ -22,13 +22,15 @@ from llama_stack.apis.inference import ( OpenAIMessageParam, OpenAIResponseFormatParam, ) +from llama_stack.apis.models import ModelType from llama_stack.log import get_logger +from llama_stack.providers.utils.inference.model_registry import ModelRegistryHelper from llama_stack.providers.utils.inference.openai_compat import prepare_openai_completion_params logger = get_logger(name=__name__, category="providers::utils") -class OpenAIMixin(ABC): +class OpenAIMixin(ModelRegistryHelper, ABC): """ Mixin class that provides OpenAI-specific functionality for inference providers. This class handles direct OpenAI API calls using the AsyncOpenAI client. @@ -43,6 +45,24 @@ class OpenAIMixin(ABC): The model_store is set in routing_tables/common.py during provider initialization. """ + # Allow subclasses to control whether to overwrite the 'id' field in OpenAI responses + # is overwritten with a client-side generated id. + # + # This is useful for providers that do not return a unique id in the response. + overwrite_completion_id: bool = False + + # Embedding model metadata for this provider + # Can be set by subclasses or instances to provide embedding models + # Format: {"model_id": {"embedding_dimension": 1536, "context_length": 8192}} + embedding_model_metadata: dict[str, dict[str, int]] = {} + + # Cache of available models keyed by model ID + # This is set in list_models() and used in check_model_availability() + _model_cache: dict[str, Model] = {} + + # List of allowed models for this provider, if empty all models allowed + allowed_models: list[str] = [] + @abstractmethod def get_api_key(self) -> str: """ @@ -67,6 +87,17 @@ class OpenAIMixin(ABC): """ pass + def get_extra_client_params(self) -> dict[str, Any]: + """ + Get any extra parameters to pass to the AsyncOpenAI client. + + Child classes can override this method to provide additional parameters + such as timeout settings, proxies, etc. + + :return: A dictionary of extra parameters + """ + return {} + @property def client(self) -> AsyncOpenAI: """ @@ -78,6 +109,7 @@ class OpenAIMixin(ABC): return AsyncOpenAI( api_key=self.get_api_key(), base_url=self.get_base_url(), + **self.get_extra_client_params(), ) async def _get_provider_model_id(self, model: str) -> str: @@ -98,6 +130,23 @@ class OpenAIMixin(ABC): raise ValueError(f"Model {model} has no provider_resource_id") return model_obj.provider_resource_id + async def _maybe_overwrite_id(self, resp: Any, stream: bool | None) -> Any: + if not self.overwrite_completion_id: + return resp + + new_id = f"cltsd-{uuid.uuid4()}" + if stream: + + async def _gen(): + async for chunk in resp: + chunk.id = new_id + yield chunk + + return _gen() + else: + resp.id = new_id + return resp + async def openai_completion( self, model: str, @@ -124,13 +173,18 @@ class OpenAIMixin(ABC): """ Direct OpenAI completion API call. """ - if guided_choice is not None: - logger.warning("guided_choice is not supported by the OpenAI API. Ignoring.") - if prompt_logprobs is not None: - logger.warning("prompt_logprobs is not supported by the OpenAI API. Ignoring.") + # Handle parameters that are not supported by OpenAI API, but may be by the provider + # prompt_logprobs is supported by vLLM + # guided_choice is supported by vLLM + # TODO: test coverage + extra_body: dict[str, Any] = {} + if prompt_logprobs is not None and prompt_logprobs >= 0: + extra_body["prompt_logprobs"] = prompt_logprobs + if guided_choice: + extra_body["guided_choice"] = guided_choice # TODO: fix openai_completion to return type compatible with OpenAI's API response - return await self.client.completions.create( # type: ignore[no-any-return] + resp = await self.client.completions.create( **await prepare_openai_completion_params( model=await self._get_provider_model_id(model), prompt=prompt, @@ -150,9 +204,12 @@ class OpenAIMixin(ABC): top_p=top_p, user=user, suffix=suffix, - ) + ), + extra_body=extra_body, ) + return await self._maybe_overwrite_id(resp, stream) # type: ignore[no-any-return] + async def openai_chat_completion( self, model: str, @@ -182,8 +239,7 @@ class OpenAIMixin(ABC): """ Direct OpenAI chat completion API call. """ - # Type ignore because return types are compatible - return await self.client.chat.completions.create( # type: ignore[no-any-return] + resp = await self.client.chat.completions.create( **await prepare_openai_completion_params( model=await self._get_provider_model_id(model), messages=messages, @@ -211,6 +267,8 @@ class OpenAIMixin(ABC): ) ) + return await self._maybe_overwrite_id(resp, stream) # type: ignore[no-any-return] + async def openai_embeddings( self, model: str, @@ -247,26 +305,53 @@ class OpenAIMixin(ABC): return OpenAIEmbeddingsResponse( data=data, - model=response.model, + model=model, usage=usage, ) + async def list_models(self) -> list[Model] | None: + """ + List available models from the provider's /v1/models endpoint augmented with static embedding model metadata. + + Also, caches the models in self._model_cache for use in check_model_availability(). + + :return: A list of Model instances representing available models. + """ + self._model_cache = {} + + async for m in self.client.models.list(): + if self.allowed_models and m.id not in self.allowed_models: + logger.info(f"Skipping model {m.id} as it is not in the allowed models list") + continue + if metadata := self.embedding_model_metadata.get(m.id): + # This is an embedding model - augment with metadata + model = Model( + provider_id=self.__provider_id__, # type: ignore[attr-defined] + provider_resource_id=m.id, + identifier=m.id, + model_type=ModelType.embedding, + metadata=metadata, + ) + else: + # This is an LLM + model = Model( + provider_id=self.__provider_id__, # type: ignore[attr-defined] + provider_resource_id=m.id, + identifier=m.id, + model_type=ModelType.llm, + ) + self._model_cache[m.id] = model + + return list(self._model_cache.values()) + async def check_model_availability(self, model: str) -> bool: """ - Check if a specific model is available from OpenAI. + Check if a specific model is available from the provider's /v1/models. :param model: The model identifier to check. :return: True if the model is available dynamically, False otherwise. """ - try: - # Direct model lookup - returns model or raises NotFoundError - await self.client.models.retrieve(model) - return True - except openai.NotFoundError: - # Model doesn't exist - this is expected for unavailable models - pass - except Exception as e: - # All other errors (auth, rate limit, network, etc.) - logger.warning(f"Failed to check model availability for {model}: {e}") + if not self._model_cache: + await self.list_models() - return False + return model in self._model_cache diff --git a/llama_stack/providers/utils/memory/vector_store.py b/llama_stack/providers/utils/memory/vector_store.py index b74080384..aaa470970 100644 --- a/llama_stack/providers/utils/memory/vector_store.py +++ b/llama_stack/providers/utils/memory/vector_store.py @@ -294,12 +294,12 @@ class VectorDBWithIndex: _validate_embedding(c.embedding, i, self.vector_db.embedding_dimension) if chunks_to_embed: - resp = await self.inference_api.embeddings( + resp = await self.inference_api.openai_embeddings( self.vector_db.embedding_model, [c.content for c in chunks_to_embed], ) - for c, embedding in zip(chunks_to_embed, resp.embeddings, strict=False): - c.embedding = embedding + for c, data in zip(chunks_to_embed, resp.data, strict=False): + c.embedding = data.embedding embeddings = np.array([c.embedding for c in chunks], dtype=np.float32) await self.index.add_chunks(chunks, embeddings) @@ -334,8 +334,8 @@ class VectorDBWithIndex: if mode == "keyword": return await self.index.query_keyword(query_string, k, score_threshold) - embeddings_response = await self.inference_api.embeddings(self.vector_db.embedding_model, [query_string]) - query_vector = np.array(embeddings_response.embeddings[0], dtype=np.float32) + embeddings_response = await self.inference_api.openai_embeddings(self.vector_db.embedding_model, [query_string]) + query_vector = np.array(embeddings_response.data[0].embedding, dtype=np.float32) if mode == "hybrid": return await self.index.query_hybrid( query_vector, query_string, k, score_threshold, reranker_type, reranker_params diff --git a/llama_stack/providers/utils/responses/responses_store.py b/llama_stack/providers/utils/responses/responses_store.py index 04778ed1c..829cd8a62 100644 --- a/llama_stack/providers/utils/responses/responses_store.py +++ b/llama_stack/providers/utils/responses/responses_store.py @@ -28,8 +28,7 @@ class ResponsesStore: sql_store_config = SqliteSqlStoreConfig( db_path=(RUNTIME_BASE_DIR / "sqlstore.db").as_posix(), ) - self.sql_store = AuthorizedSqlStore(sqlstore_impl(sql_store_config)) - self.policy = policy + self.sql_store = AuthorizedSqlStore(sqlstore_impl(sql_store_config), policy) async def initialize(self): """Create the necessary tables if they don't exist.""" @@ -87,7 +86,6 @@ class ResponsesStore: order_by=[("created_at", order.value)], cursor=("id", after) if after else None, limit=limit, - policy=self.policy, ) data = [OpenAIResponseObjectWithInput(**row["response_object"]) for row in paginated_result.data] @@ -105,7 +103,6 @@ class ResponsesStore: row = await self.sql_store.fetch_one( "openai_responses", where={"id": response_id}, - policy=self.policy, ) if not row: @@ -116,7 +113,7 @@ class ResponsesStore: return OpenAIResponseObjectWithInput(**row["response_object"]) async def delete_response_object(self, response_id: str) -> OpenAIDeleteResponseObject: - row = await self.sql_store.fetch_one("openai_responses", where={"id": response_id}, policy=self.policy) + row = await self.sql_store.fetch_one("openai_responses", where={"id": response_id}) if not row: raise ValueError(f"Response with id {response_id} not found") await self.sql_store.delete("openai_responses", where={"id": response_id}) diff --git a/llama_stack/providers/utils/sqlstore/authorized_sqlstore.py b/llama_stack/providers/utils/sqlstore/authorized_sqlstore.py index 867ba2f55..ab67f7052 100644 --- a/llama_stack/providers/utils/sqlstore/authorized_sqlstore.py +++ b/llama_stack/providers/utils/sqlstore/authorized_sqlstore.py @@ -53,13 +53,15 @@ class AuthorizedSqlStore: access control policies, user attribute capture, and SQL filtering optimization. """ - def __init__(self, sql_store: SqlStore): + def __init__(self, sql_store: SqlStore, policy: list[AccessRule]): """ Initialize the authorization layer. :param sql_store: Base SqlStore implementation to wrap + :param policy: Access control policy to use for authorization """ self.sql_store = sql_store + self.policy = policy self._detect_database_type() self._validate_sql_optimized_policy() @@ -117,14 +119,13 @@ class AuthorizedSqlStore: async def fetch_all( self, table: str, - policy: list[AccessRule], where: Mapping[str, Any] | None = None, limit: int | None = None, order_by: list[tuple[str, Literal["asc", "desc"]]] | None = None, cursor: tuple[str, str] | None = None, ) -> PaginatedResponse: """Fetch all rows with automatic access control filtering.""" - access_where = self._build_access_control_where_clause(policy) + access_where = self._build_access_control_where_clause(self.policy) rows = await self.sql_store.fetch_all( table=table, where=where, @@ -146,7 +147,7 @@ class AuthorizedSqlStore: str(record_id), table, User(principal=stored_owner_principal, attributes=stored_access_attrs) ) - if is_action_allowed(policy, Action.READ, sql_record, current_user): + if is_action_allowed(self.policy, Action.READ, sql_record, current_user): filtered_rows.append(row) return PaginatedResponse( @@ -157,14 +158,12 @@ class AuthorizedSqlStore: async def fetch_one( self, table: str, - policy: list[AccessRule], where: Mapping[str, Any] | None = None, order_by: list[tuple[str, Literal["asc", "desc"]]] | None = None, ) -> dict[str, Any] | None: """Fetch one row with automatic access control checking.""" results = await self.fetch_all( table=table, - policy=policy, where=where, limit=1, order_by=order_by, @@ -172,6 +171,20 @@ class AuthorizedSqlStore: return results.data[0] if results.data else None + async def update(self, table: str, data: Mapping[str, Any], where: Mapping[str, Any]) -> None: + """Update rows with automatic access control attribute capture.""" + enhanced_data = dict(data) + + current_user = get_authenticated_user() + if current_user: + enhanced_data["owner_principal"] = current_user.principal + enhanced_data["access_attributes"] = current_user.attributes + else: + enhanced_data["owner_principal"] = None + enhanced_data["access_attributes"] = None + + await self.sql_store.update(table, enhanced_data, where) + async def delete(self, table: str, where: Mapping[str, Any]) -> None: """Delete rows with automatic access control filtering.""" await self.sql_store.delete(table, where) diff --git a/llama_stack/providers/utils/sqlstore/sqlalchemy_sqlstore.py b/llama_stack/providers/utils/sqlstore/sqlalchemy_sqlstore.py index f75c35314..46ed8c1d1 100644 --- a/llama_stack/providers/utils/sqlstore/sqlalchemy_sqlstore.py +++ b/llama_stack/providers/utils/sqlstore/sqlalchemy_sqlstore.py @@ -23,6 +23,7 @@ from sqlalchemy import ( ) from sqlalchemy.ext.asyncio import async_sessionmaker, create_async_engine from sqlalchemy.ext.asyncio.engine import AsyncEngine +from sqlalchemy.sql.elements import ColumnElement from llama_stack.apis.common.responses import PaginatedResponse from llama_stack.log import get_logger @@ -43,6 +44,30 @@ TYPE_MAPPING: dict[ColumnType, Any] = { } +def _build_where_expr(column: ColumnElement, value: Any) -> ColumnElement: + """Return a SQLAlchemy expression for a where condition. + + `value` may be a simple scalar (equality) or a mapping like {">": 123}. + The returned expression is a SQLAlchemy ColumnElement usable in query.where(...). + """ + if isinstance(value, Mapping): + if len(value) != 1: + raise ValueError(f"Operator mapping must have a single operator, got: {value}") + op, operand = next(iter(value.items())) + if op == "==" or op == "=": + return column == operand + if op == ">": + return column > operand + if op == "<": + return column < operand + if op == ">=": + return column >= operand + if op == "<=": + return column <= operand + raise ValueError(f"Unsupported operator '{op}' in where mapping") + return column == value + + class SqlAlchemySqlStoreImpl(SqlStore): def __init__(self, config: SqlAlchemySqlStoreConfig): self.config = config @@ -111,7 +136,7 @@ class SqlAlchemySqlStoreImpl(SqlStore): if where: for key, value in where.items(): - query = query.where(table_obj.c[key] == value) + query = query.where(_build_where_expr(table_obj.c[key], value)) if where_sql: query = query.where(text(where_sql)) @@ -222,7 +247,7 @@ class SqlAlchemySqlStoreImpl(SqlStore): async with self.async_session() as session: stmt = self.metadata.tables[table].update() for key, value in where.items(): - stmt = stmt.where(self.metadata.tables[table].c[key] == value) + stmt = stmt.where(_build_where_expr(self.metadata.tables[table].c[key], value)) await session.execute(stmt, data) await session.commit() @@ -233,7 +258,7 @@ class SqlAlchemySqlStoreImpl(SqlStore): async with self.async_session() as session: stmt = self.metadata.tables[table].delete() for key, value in where.items(): - stmt = stmt.where(self.metadata.tables[table].c[key] == value) + stmt = stmt.where(_build_where_expr(self.metadata.tables[table].c[key], value)) await session.execute(stmt) await session.commit() diff --git a/llama_stack/providers/utils/telemetry/tracing.py b/llama_stack/providers/utils/telemetry/tracing.py index 7694003b5..62cceb13e 100644 --- a/llama_stack/providers/utils/telemetry/tracing.py +++ b/llama_stack/providers/utils/telemetry/tracing.py @@ -8,7 +8,7 @@ import asyncio import contextvars import logging # allow-direct-logging import queue -import random +import secrets import sys import threading import time @@ -18,6 +18,7 @@ from functools import wraps from typing import Any from llama_stack.apis.telemetry import ( + Event, LogSeverity, Span, SpanEndPayload, @@ -75,16 +76,16 @@ def span_id_to_str(span_id: int) -> str: def generate_span_id() -> str: - span_id = random.getrandbits(64) + span_id = secrets.randbits(64) while span_id == INVALID_SPAN_ID: - span_id = random.getrandbits(64) + span_id = secrets.randbits(64) return span_id_to_str(span_id) def generate_trace_id() -> str: - trace_id = random.getrandbits(128) + trace_id = secrets.randbits(128) while trace_id == INVALID_TRACE_ID: - trace_id = random.getrandbits(128) + trace_id = secrets.randbits(128) return trace_id_to_str(trace_id) @@ -98,7 +99,7 @@ class BackgroundLogger: def __init__(self, api: Telemetry, capacity: int = 100000): self.api = api self.log_queue: queue.Queue[Any] = queue.Queue(maxsize=capacity) - self.worker_thread = threading.Thread(target=self._process_logs, daemon=True) + self.worker_thread = threading.Thread(target=self._worker, daemon=True) self.worker_thread.start() self._last_queue_full_log_time: float = 0.0 self._dropped_since_last_notice: int = 0 @@ -118,12 +119,16 @@ class BackgroundLogger: self._last_queue_full_log_time = current_time self._dropped_since_last_notice = 0 - def _process_logs(self): + def _worker(self): + loop = asyncio.new_event_loop() + asyncio.set_event_loop(loop) + loop.run_until_complete(self._process_logs()) + + async def _process_logs(self): while True: try: event = self.log_queue.get() - # figure out how to use a thread's native loop - asyncio.run(self.api.log_event(event)) + await self.api.log_event(event) except Exception: import traceback @@ -136,6 +141,19 @@ class BackgroundLogger: self.log_queue.join() +def enqueue_event(event: Event) -> None: + """Enqueue a telemetry event to the background logger if available. + + This provides a non-blocking path for routers and other hot paths to + submit telemetry without awaiting the Telemetry API, reducing contention + with the main event loop. + """ + global BACKGROUND_LOGGER + if BACKGROUND_LOGGER is None: + raise RuntimeError("Telemetry API not initialized") + BACKGROUND_LOGGER.log_event(event) + + class TraceContext: spans: list[Span] = [] @@ -256,11 +274,7 @@ class TelemetryHandler(logging.Handler): if record.module in ("asyncio", "selector_events"): return - global CURRENT_TRACE_CONTEXT, BACKGROUND_LOGGER - - if BACKGROUND_LOGGER is None: - raise RuntimeError("Telemetry API not initialized") - + global CURRENT_TRACE_CONTEXT context = CURRENT_TRACE_CONTEXT.get() if context is None: return @@ -269,7 +283,7 @@ class TelemetryHandler(logging.Handler): if span is None: return - BACKGROUND_LOGGER.log_event( + enqueue_event( UnstructuredLogEvent( trace_id=span.trace_id, span_id=span.span_id, diff --git a/llama_stack/providers/utils/tools/mcp.py b/llama_stack/providers/utils/tools/mcp.py index 02f7aaf8a..fc8e2f377 100644 --- a/llama_stack/providers/utils/tools/mcp.py +++ b/llama_stack/providers/utils/tools/mcp.py @@ -67,6 +67,38 @@ async def client_wrapper(endpoint: str, headers: dict[str, str]) -> AsyncGenerat raise AuthenticationRequiredError(exc) from exc if i == len(connection_strategies) - 1: raise + except* httpx.ConnectError as eg: + # Connection refused, server down, network unreachable + if i == len(connection_strategies) - 1: + error_msg = f"Failed to connect to MCP server at {endpoint}: Connection refused" + logger.error(f"MCP connection error: {error_msg}") + raise ConnectionError(error_msg) from eg + else: + logger.warning( + f"failed to connect to MCP server at {endpoint} via {strategy.name}, falling back to {connection_strategies[i + 1].name}" + ) + except* httpx.TimeoutException as eg: + # Request timeout, server too slow + if i == len(connection_strategies) - 1: + error_msg = f"MCP server at {endpoint} timed out" + logger.error(f"MCP timeout error: {error_msg}") + raise TimeoutError(error_msg) from eg + else: + logger.warning( + f"MCP server at {endpoint} timed out via {strategy.name}, falling back to {connection_strategies[i + 1].name}" + ) + except* httpx.RequestError as eg: + # DNS resolution failures, network errors, invalid URLs + if i == len(connection_strategies) - 1: + # Get the first exception's message for the error string + exc_msg = str(eg.exceptions[0]) if eg.exceptions else "Unknown error" + error_msg = f"Network error connecting to MCP server at {endpoint}: {exc_msg}" + logger.error(f"MCP network error: {error_msg}") + raise ConnectionError(error_msg) from eg + else: + logger.warning( + f"network error connecting to MCP server at {endpoint} via {strategy.name}, falling back to {connection_strategies[i + 1].name}" + ) except* McpError: if i < len(connection_strategies) - 1: logger.warning( diff --git a/llama_stack/providers/utils/vector_io/vector_utils.py b/llama_stack/providers/utils/vector_io/vector_utils.py index f2888043e..324f35405 100644 --- a/llama_stack/providers/utils/vector_io/vector_utils.py +++ b/llama_stack/providers/utils/vector_io/vector_utils.py @@ -12,14 +12,12 @@ import uuid def generate_chunk_id(document_id: str, chunk_text: str, chunk_window: str | None = None) -> str: """ Generate a unique chunk ID using a hash of the document ID and chunk text. - - Note: MD5 is used only to calculate an identifier, not for security purposes. - Adding usedforsecurity=False for compatibility with FIPS environments. + Then use the first 32 characters of the hash to create a UUID. """ hash_input = f"{document_id}:{chunk_text}".encode() if chunk_window: hash_input += f":{chunk_window}".encode() - return str(uuid.UUID(hashlib.md5(hash_input, usedforsecurity=False).hexdigest())) + return str(uuid.UUID(hashlib.sha256(hash_input).hexdigest()[:32])) def proper_case(s: str) -> str: @@ -37,3 +35,122 @@ def sanitize_collection_name(name: str, weaviate_format=False) -> str: else: s = proper_case(re.sub(r"[^a-zA-Z0-9]", "", name)) return s + + +class WeightedInMemoryAggregator: + @staticmethod + def _normalize_scores(scores: dict[str, float]) -> dict[str, float]: + """ + Normalize scores to 0-1 range using min-max normalization. + + Args: + scores: dictionary of scores with document IDs as keys and scores as values + + Returns: + Normalized scores with document IDs as keys and normalized scores as values + """ + if not scores: + return {} + min_score, max_score = min(scores.values()), max(scores.values()) + score_range = max_score - min_score + if score_range > 0: + return {doc_id: (score - min_score) / score_range for doc_id, score in scores.items()} + return dict.fromkeys(scores, 1.0) + + @staticmethod + def weighted_rerank( + vector_scores: dict[str, float], + keyword_scores: dict[str, float], + alpha: float = 0.5, + ) -> dict[str, float]: + """ + Rerank via weighted average of scores. + + Args: + vector_scores: scores from vector search + keyword_scores: scores from keyword search + alpha: weight factor between 0 and 1 (default: 0.5) + 0 = keyword only, 1 = vector only, 0.5 = equal weight + + Returns: + All unique document IDs with weighted combined scores + """ + all_ids = set(vector_scores.keys()) | set(keyword_scores.keys()) + normalized_vector_scores = WeightedInMemoryAggregator._normalize_scores(vector_scores) + normalized_keyword_scores = WeightedInMemoryAggregator._normalize_scores(keyword_scores) + + # Weighted formula: score = (1-alpha) * keyword_score + alpha * vector_score + # alpha=0 means keyword only, alpha=1 means vector only + return { + doc_id: ((1 - alpha) * normalized_keyword_scores.get(doc_id, 0.0)) + + (alpha * normalized_vector_scores.get(doc_id, 0.0)) + for doc_id in all_ids + } + + @staticmethod + def rrf_rerank( + vector_scores: dict[str, float], + keyword_scores: dict[str, float], + impact_factor: float = 60.0, + ) -> dict[str, float]: + """ + Rerank via Reciprocal Rank Fusion. + + Args: + vector_scores: scores from vector search + keyword_scores: scores from keyword search + impact_factor: impact factor for RRF (default: 60.0) + + Returns: + All unique document IDs with RRF combined scores + """ + + # Convert scores to ranks + vector_ranks = { + doc_id: i + 1 + for i, (doc_id, _) in enumerate(sorted(vector_scores.items(), key=lambda x: x[1], reverse=True)) + } + keyword_ranks = { + doc_id: i + 1 + for i, (doc_id, _) in enumerate(sorted(keyword_scores.items(), key=lambda x: x[1], reverse=True)) + } + + all_ids = set(vector_scores.keys()) | set(keyword_scores.keys()) + rrf_scores = {} + for doc_id in all_ids: + vector_rank = vector_ranks.get(doc_id, float("inf")) + keyword_rank = keyword_ranks.get(doc_id, float("inf")) + + # RRF formula: score = 1/(k + r) where k is impact_factor (default: 60.0) and r is the rank + rrf_scores[doc_id] = (1.0 / (impact_factor + vector_rank)) + (1.0 / (impact_factor + keyword_rank)) + return rrf_scores + + @staticmethod + def combine_search_results( + vector_scores: dict[str, float], + keyword_scores: dict[str, float], + reranker_type: str = "rrf", + reranker_params: dict[str, float] | None = None, + ) -> dict[str, float]: + """ + Combine vector and keyword search results using specified reranking strategy. + + Args: + vector_scores: scores from vector search + keyword_scores: scores from keyword search + reranker_type: type of reranker to use (default: RERANKER_TYPE_RRF) + reranker_params: parameters for the reranker + + Returns: + All unique document IDs with combined scores + """ + if reranker_params is None: + reranker_params = {} + + if reranker_type == "weighted": + alpha = reranker_params.get("alpha", 0.5) + return WeightedInMemoryAggregator.weighted_rerank(vector_scores, keyword_scores, alpha) + else: + # Default to RRF for None, RRF, or any unknown types + impact_factor = reranker_params.get("impact_factor", 60.0) + return WeightedInMemoryAggregator.rrf_rerank(vector_scores, keyword_scores, impact_factor) diff --git a/llama_stack/schema_utils.py b/llama_stack/schema_utils.py index 93382a881..4f8b4edff 100644 --- a/llama_stack/schema_utils.py +++ b/llama_stack/schema_utils.py @@ -13,6 +13,7 @@ from .strong_typing.schema import json_schema_type, register_schema # noqa: F40 @dataclass class WebMethod: + level: str | None = None route: str | None = None public: bool = False request_examples: list[Any] | None = None @@ -23,6 +24,7 @@ class WebMethod: descriptive_name: str | None = None experimental: bool | None = False required_scope: str | None = None + deprecated: bool | None = False T = TypeVar("T", bound=Callable[..., Any]) @@ -31,6 +33,7 @@ T = TypeVar("T", bound=Callable[..., Any]) def webmethod( route: str | None = None, method: str | None = None, + level: str | None = None, public: bool | None = False, request_examples: list[Any] | None = None, response_examples: list[Any] | None = None, @@ -38,6 +41,7 @@ def webmethod( descriptive_name: str | None = None, experimental: bool | None = False, required_scope: str | None = None, + deprecated: bool | None = False, ) -> Callable[[T], T]: """ Decorator that supplies additional metadata to an endpoint operation function. @@ -51,9 +55,10 @@ def webmethod( """ def wrap(func: T) -> T: - func.__webmethod__ = WebMethod( # type: ignore + webmethod_obj = WebMethod( route=route, method=method, + level=level, public=public or False, request_examples=request_examples, response_examples=response_examples, @@ -61,7 +66,16 @@ def webmethod( descriptive_name=descriptive_name, experimental=experimental, required_scope=required_scope, + deprecated=deprecated, ) + + # Store all webmethods in a list to support multiple decorators + if not hasattr(func, "__webmethods__"): + func.__webmethods__ = [] # type: ignore + func.__webmethods__.append(webmethod_obj) # type: ignore + + # Keep the last one as __webmethod__ for backwards compatibility + func.__webmethod__ = webmethod_obj # type: ignore return func return wrap diff --git a/llama_stack/testing/inference_recorder.py b/llama_stack/testing/inference_recorder.py index 8fa5f5f2e..dacf69606 100644 --- a/llama_stack/testing/inference_recorder.py +++ b/llama_stack/testing/inference_recorder.py @@ -15,6 +15,8 @@ from enum import StrEnum from pathlib import Path from typing import Any, Literal, cast +from openai import NOT_GIVEN + from llama_stack.log import get_logger logger = get_logger(__name__, category="testing") @@ -30,6 +32,9 @@ from openai.types.completion_choice import CompletionChoice CompletionChoice.model_fields["finish_reason"].annotation = Literal["stop", "length", "content_filter"] | None CompletionChoice.model_rebuild() +REPO_ROOT = Path(__file__).parent.parent.parent +DEFAULT_STORAGE_DIR = REPO_ROOT / "tests/integration/recordings" + class InferenceMode(StrEnum): LIVE = "live" @@ -51,7 +56,7 @@ def normalize_request(method: str, url: str, headers: dict[str, Any], body: dict def get_inference_mode() -> InferenceMode: - return InferenceMode(os.environ.get("LLAMA_STACK_TEST_INFERENCE_MODE", "live").lower()) + return InferenceMode(os.environ.get("LLAMA_STACK_TEST_INFERENCE_MODE", "replay").lower()) def setup_inference_recording(): @@ -60,28 +65,18 @@ def setup_inference_recording(): to increase their reliability and reduce reliance on expensive, external services. Currently, this is only supported for OpenAI and Ollama clients. These should cover the vast majority of use cases. - Calls to the /models endpoint are not currently trapped. We probably need to add support for this. - Two environment variables are required: - - LLAMA_STACK_TEST_INFERENCE_MODE: The mode to run in. Must be 'live', 'record', or 'replay'. - - LLAMA_STACK_TEST_RECORDING_DIR: The directory to store the recordings in. + Two environment variables are supported: + - LLAMA_STACK_TEST_INFERENCE_MODE: The mode to run in. Must be 'live', 'record', or 'replay'. Default is 'replay'. + - LLAMA_STACK_TEST_RECORDING_DIR: The directory to store the recordings in. Default is 'tests/integration/recordings'. - The recordings are stored in a SQLite database and a JSON file for each request. The SQLite database is used to - quickly find the correct recording for a given request. The JSON files are used to store the request and response - bodies. + The recordings are stored as JSON files. """ mode = get_inference_mode() - - if mode not in InferenceMode: - raise ValueError(f"Invalid LLAMA_STACK_TEST_INFERENCE_MODE: {mode}. Must be 'live', 'record', or 'replay'") - if mode == InferenceMode.LIVE: return None - if "LLAMA_STACK_TEST_RECORDING_DIR" not in os.environ: - raise ValueError("LLAMA_STACK_TEST_RECORDING_DIR must be set for recording or replaying") - storage_dir = os.environ["LLAMA_STACK_TEST_RECORDING_DIR"] - + storage_dir = os.environ.get("LLAMA_STACK_TEST_RECORDING_DIR", DEFAULT_STORAGE_DIR) return inference_recording(mode=mode, storage_dir=storage_dir) @@ -112,8 +107,12 @@ def _deserialize_response(data: dict[str, Any]) -> Any: return cls.model_validate(data["__data__"]) except (ImportError, AttributeError, TypeError, ValueError) as e: - logger.warning(f"Failed to deserialize object of type {data['__type__']}: {e}") - return data["__data__"] + logger.warning(f"Failed to deserialize object of type {data['__type__']} with model_validate: {e}") + try: + return cls.model_construct(**data["__data__"]) + except Exception as e: + logger.warning(f"Failed to deserialize object of type {data['__type__']} with model_construct: {e}") + return data["__data__"] return data @@ -134,8 +133,8 @@ class ResponseStorage: def store_recording(self, request_hash: str, request: dict[str, Any], response: dict[str, Any]): """Store a request/response pair.""" # Generate unique response filename - response_file = f"{request_hash[:12]}.json" - response_path = self.responses_dir / response_file + short_hash = request_hash[:12] + response_file = f"{short_hash}.json" # Serialize response body if needed serialized_response = dict(response) @@ -147,6 +146,14 @@ class ResponseStorage: # Handle single response serialized_response["body"] = _serialize_response(serialized_response["body"]) + # If this is an Ollama /api/tags recording, include models digest in filename to distinguish variants + endpoint = request.get("endpoint") + if endpoint in ("/api/tags", "/v1/models"): + digest = _model_identifiers_digest(endpoint, response) + response_file = f"models-{short_hash}-{digest}.json" + + response_path = self.responses_dir / response_file + # Save response to JSON file with open(response_path, "w") as f: json.dump({"request": request, "response": serialized_response}, f, indent=2) @@ -161,31 +168,96 @@ class ResponseStorage: if not response_path.exists(): return None - with open(response_path) as f: - data = json.load(f) + return _recording_from_file(response_path) - # Deserialize response body if needed - if "response" in data and "body" in data["response"]: - if isinstance(data["response"]["body"], list): - # Handle streaming responses - data["response"]["body"] = [_deserialize_response(chunk) for chunk in data["response"]["body"]] - else: - # Handle single response - data["response"]["body"] = _deserialize_response(data["response"]["body"]) + def _model_list_responses(self, short_hash: str) -> list[dict[str, Any]]: + results: list[dict[str, Any]] = [] + for path in self.responses_dir.glob(f"models-{short_hash}-*.json"): + data = _recording_from_file(path) + results.append(data) + return results - return cast(dict[str, Any], data) + +def _recording_from_file(response_path) -> dict[str, Any]: + with open(response_path) as f: + data = json.load(f) + + # Deserialize response body if needed + if "response" in data and "body" in data["response"]: + if isinstance(data["response"]["body"], list): + # Handle streaming responses + data["response"]["body"] = [_deserialize_response(chunk) for chunk in data["response"]["body"]] + else: + # Handle single response + data["response"]["body"] = _deserialize_response(data["response"]["body"]) + + return cast(dict[str, Any], data) + + +def _model_identifiers_digest(endpoint: str, response: dict[str, Any]) -> str: + def _extract_model_identifiers(): + """Extract a stable set of identifiers for model-list endpoints. + + Supported endpoints: + - '/api/tags' (Ollama): response body has 'models': [ { name/model/digest/id/... }, ... ] + - '/v1/models' (OpenAI): response body is: [ { id: ... }, ... ] + Returns a list of unique identifiers or None if structure doesn't match. + """ + if "models" in response["body"]: + # ollama + items = response["body"]["models"] + else: + # openai + items = response["body"] + idents = [m.model if endpoint == "/api/tags" else m.id for m in items] + return sorted(set(idents)) + + identifiers = _extract_model_identifiers() + return hashlib.sha256(("|".join(identifiers)).encode("utf-8")).hexdigest()[:8] + + +def _combine_model_list_responses(endpoint: str, records: list[dict[str, Any]]) -> dict[str, Any] | None: + """Return a single, unioned recording for supported model-list endpoints.""" + seen: dict[str, dict[str, Any]] = {} + for rec in records: + body = rec["response"]["body"] + if endpoint == "/v1/models": + for m in body: + key = m.id + seen[key] = m + elif endpoint == "/api/tags": + for m in body.models: + key = m.model + seen[key] = m + + ordered = [seen[k] for k in sorted(seen.keys())] + canonical = records[0] + canonical_req = canonical.get("request", {}) + if isinstance(canonical_req, dict): + canonical_req["endpoint"] = endpoint + body = ordered + if endpoint == "/api/tags": + from ollama import ListResponse + + body = ListResponse(models=ordered) + return {"request": canonical_req, "response": {"body": body, "is_streaming": False}} async def _patched_inference_method(original_method, self, client_type, endpoint, *args, **kwargs): global _current_mode, _current_storage if _current_mode == InferenceMode.LIVE or _current_storage is None: - # Normal operation - return await original_method(self, *args, **kwargs) + if endpoint == "/v1/models": + return original_method(self, *args, **kwargs) + else: + return await original_method(self, *args, **kwargs) # Get base URL based on client type if client_type == "openai": base_url = str(self._client.base_url) + + # the OpenAI client methods may pass NOT_GIVEN for unset parameters; filter these out + kwargs = {k: v for k, v in kwargs.items() if v is not NOT_GIVEN} elif client_type == "ollama": # Get base URL from the client (Ollama client uses host attribute) base_url = getattr(self, "host", "http://localhost:11434") @@ -195,8 +267,10 @@ async def _patched_inference_method(original_method, self, client_type, endpoint raise ValueError(f"Unknown client type: {client_type}") url = base_url.rstrip("/") + endpoint - - # Normalize request for matching + # Special handling for Databricks URLs to avoid leaking workspace info + # e.g. https://adb-1234567890123456.7.cloud.databricks.com -> https://...cloud.databricks.com + if "cloud.databricks.com" in url: + url = "__databricks__" + url.split("cloud.databricks.com")[-1] method = "POST" headers = {} body = kwargs @@ -204,7 +278,12 @@ async def _patched_inference_method(original_method, self, client_type, endpoint request_hash = normalize_request(method, url, headers, body) if _current_mode == InferenceMode.REPLAY: - recording = _current_storage.find_recording(request_hash) + # Special handling for model-list endpoints: return union of all responses + if endpoint in ("/api/tags", "/v1/models"): + records = _current_storage._model_list_responses(request_hash[:12]) + recording = _combine_model_list_responses(endpoint, records) + else: + recording = _current_storage.find_recording(request_hash) if recording: response_body = recording["response"]["body"] @@ -222,11 +301,18 @@ async def _patched_inference_method(original_method, self, client_type, endpoint f"No recorded response found for request hash: {request_hash}\n" f"Request: {method} {url} {body}\n" f"Model: {body.get('model', 'unknown')}\n" - f"To record this response, run with LLAMA_STACK_INFERENCE_MODE=record" + f"To record this response, run with LLAMA_STACK_TEST_INFERENCE_MODE=record" ) elif _current_mode == InferenceMode.RECORD: - response = await original_method(self, *args, **kwargs) + if endpoint == "/v1/models": + response = original_method(self, *args, **kwargs) + else: + response = await original_method(self, *args, **kwargs) + + # we want to store the result of the iterator, not the iterator itself + if endpoint == "/v1/models": + response = [m async for m in response] request_data = { "method": method, @@ -274,12 +360,14 @@ def patch_inference_clients(): from openai.resources.chat.completions import AsyncCompletions as AsyncChatCompletions from openai.resources.completions import AsyncCompletions from openai.resources.embeddings import AsyncEmbeddings + from openai.resources.models import AsyncModels # Store original methods for both OpenAI and Ollama clients _original_methods = { "chat_completions_create": AsyncChatCompletions.create, "completions_create": AsyncCompletions.create, "embeddings_create": AsyncEmbeddings.create, + "models_list": AsyncModels.list, "ollama_generate": OllamaAsyncClient.generate, "ollama_chat": OllamaAsyncClient.chat, "ollama_embed": OllamaAsyncClient.embed, @@ -304,10 +392,20 @@ def patch_inference_clients(): _original_methods["embeddings_create"], self, "openai", "/v1/embeddings", *args, **kwargs ) + def patched_models_list(self, *args, **kwargs): + async def _iter(): + for item in await _patched_inference_method( + _original_methods["models_list"], self, "openai", "/v1/models", *args, **kwargs + ): + yield item + + return _iter() + # Apply OpenAI patches AsyncChatCompletions.create = patched_chat_completions_create AsyncCompletions.create = patched_completions_create AsyncEmbeddings.create = patched_embeddings_create + AsyncModels.list = patched_models_list # Create patched methods for Ollama client async def patched_ollama_generate(self, *args, **kwargs): @@ -361,11 +459,13 @@ def unpatch_inference_clients(): from openai.resources.chat.completions import AsyncCompletions as AsyncChatCompletions from openai.resources.completions import AsyncCompletions from openai.resources.embeddings import AsyncEmbeddings + from openai.resources.models import AsyncModels # Restore OpenAI client methods AsyncChatCompletions.create = _original_methods["chat_completions_create"] AsyncCompletions.create = _original_methods["completions_create"] AsyncEmbeddings.create = _original_methods["embeddings_create"] + AsyncModels.list = _original_methods["models_list"] # Restore Ollama client methods if they were patched OllamaAsyncClient.generate = _original_methods["ollama_generate"] @@ -379,16 +479,10 @@ def unpatch_inference_clients(): @contextmanager -def inference_recording(mode: str = "live", storage_dir: str | Path | None = None) -> Generator[None, None, None]: +def inference_recording(mode: str, storage_dir: str | Path | None = None) -> Generator[None, None, None]: """Context manager for inference recording/replaying.""" global _current_mode, _current_storage - # Set defaults - if storage_dir is None: - storage_dir_path = Path.home() / ".llama" / "recordings" - else: - storage_dir_path = Path(storage_dir) - # Store previous state prev_mode = _current_mode prev_storage = _current_storage @@ -397,7 +491,9 @@ def inference_recording(mode: str = "live", storage_dir: str | Path | None = Non _current_mode = mode if mode in ["record", "replay"]: - _current_storage = ResponseStorage(storage_dir_path) + if storage_dir is None: + raise ValueError("storage_dir is required for record and replay modes") + _current_storage = ResponseStorage(Path(storage_dir)) patch_inference_clients() yield diff --git a/llama_stack/ui/app/chat-playground/chunk-processor.test.tsx b/llama_stack/ui/app/chat-playground/chunk-processor.test.tsx new file mode 100644 index 000000000..70e8b3afa --- /dev/null +++ b/llama_stack/ui/app/chat-playground/chunk-processor.test.tsx @@ -0,0 +1,610 @@ +import { describe, test, expect } from "@jest/globals"; + +// Extract the exact processChunk function implementation for testing +function createProcessChunk() { + return (chunk: unknown): { text: string | null; isToolCall: boolean } => { + const chunkObj = chunk as Record; + + // Helper function to check if content contains function call JSON + const containsToolCall = (content: string): boolean => { + return ( + content.includes('"type": "function"') || + content.includes('"name": "knowledge_search"') || + content.includes('"parameters":') || + !!content.match(/\{"type":\s*"function".*?\}/) + ); + }; + + // Check if this chunk contains a tool call (function call) + let isToolCall = false; + + // Check direct chunk content if it's a string + if (typeof chunk === "string") { + isToolCall = containsToolCall(chunk); + } + + // Check delta structures + if ( + chunkObj?.delta && + typeof chunkObj.delta === "object" && + chunkObj.delta !== null + ) { + const delta = chunkObj.delta as Record; + if ("tool_calls" in delta) { + isToolCall = true; + } + if (typeof delta.text === "string") { + if (containsToolCall(delta.text)) { + isToolCall = true; + } + } + } + + // Check event structures + if ( + chunkObj?.event && + typeof chunkObj.event === "object" && + chunkObj.event !== null + ) { + const event = chunkObj.event as Record; + + // Check event payload + if ( + event?.payload && + typeof event.payload === "object" && + event.payload !== null + ) { + const payload = event.payload as Record; + if (typeof payload.content === "string") { + if (containsToolCall(payload.content)) { + isToolCall = true; + } + } + + // Check payload delta + if ( + payload?.delta && + typeof payload.delta === "object" && + payload.delta !== null + ) { + const delta = payload.delta as Record; + if (typeof delta.text === "string") { + if (containsToolCall(delta.text)) { + isToolCall = true; + } + } + } + } + + // Check event delta + if ( + event?.delta && + typeof event.delta === "object" && + event.delta !== null + ) { + const delta = event.delta as Record; + if (typeof delta.text === "string") { + if (containsToolCall(delta.text)) { + isToolCall = true; + } + } + if (typeof delta.content === "string") { + if (containsToolCall(delta.content)) { + isToolCall = true; + } + } + } + } + + // if it's a tool call, skip it (don't display in chat) + if (isToolCall) { + return { text: null, isToolCall: true }; + } + + // Extract text content from various chunk formats + let text: string | null = null; + + // Helper function to extract clean text content, filtering out function calls + const extractCleanText = (content: string): string | null => { + if (containsToolCall(content)) { + try { + // Try to parse and extract non-function call parts + const jsonMatch = content.match( + /\{"type":\s*"function"[^}]*\}[^}]*\}/ + ); + if (jsonMatch) { + const jsonPart = jsonMatch[0]; + const parsedJson = JSON.parse(jsonPart); + + // If it's a function call, extract text after JSON + if (parsedJson.type === "function") { + const textAfterJson = content + .substring(content.indexOf(jsonPart) + jsonPart.length) + .trim(); + return textAfterJson || null; + } + } + // If we can't parse it properly, skip the whole thing + return null; + } catch { + return null; + } + } + return content; + }; + + // Try direct delta text + if ( + chunkObj?.delta && + typeof chunkObj.delta === "object" && + chunkObj.delta !== null + ) { + const delta = chunkObj.delta as Record; + if (typeof delta.text === "string") { + text = extractCleanText(delta.text); + } + } + + // Try event structures + if ( + !text && + chunkObj?.event && + typeof chunkObj.event === "object" && + chunkObj.event !== null + ) { + const event = chunkObj.event as Record; + + // Try event payload content + if ( + event?.payload && + typeof event.payload === "object" && + event.payload !== null + ) { + const payload = event.payload as Record; + + // Try direct payload content + if (typeof payload.content === "string") { + text = extractCleanText(payload.content); + } + + // Try turn_complete event structure: payload.turn.output_message.content + if ( + !text && + payload?.turn && + typeof payload.turn === "object" && + payload.turn !== null + ) { + const turn = payload.turn as Record; + if ( + turn?.output_message && + typeof turn.output_message === "object" && + turn.output_message !== null + ) { + const outputMessage = turn.output_message as Record< + string, + unknown + >; + if (typeof outputMessage.content === "string") { + text = extractCleanText(outputMessage.content); + } + } + + // Fallback to model_response in steps if no output_message + if ( + !text && + turn?.steps && + Array.isArray(turn.steps) && + turn.steps.length > 0 + ) { + for (const step of turn.steps) { + if (step && typeof step === "object" && step !== null) { + const stepObj = step as Record; + if ( + stepObj?.model_response && + typeof stepObj.model_response === "object" && + stepObj.model_response !== null + ) { + const modelResponse = stepObj.model_response as Record< + string, + unknown + >; + if (typeof modelResponse.content === "string") { + text = extractCleanText(modelResponse.content); + break; + } + } + } + } + } + } + + // Try payload delta + if ( + !text && + payload?.delta && + typeof payload.delta === "object" && + payload.delta !== null + ) { + const delta = payload.delta as Record; + if (typeof delta.text === "string") { + text = extractCleanText(delta.text); + } + } + } + + // Try event delta + if ( + !text && + event?.delta && + typeof event.delta === "object" && + event.delta !== null + ) { + const delta = event.delta as Record; + if (typeof delta.text === "string") { + text = extractCleanText(delta.text); + } + if (!text && typeof delta.content === "string") { + text = extractCleanText(delta.content); + } + } + } + + // Try choices structure (ChatML format) + if ( + !text && + chunkObj?.choices && + Array.isArray(chunkObj.choices) && + chunkObj.choices.length > 0 + ) { + const choice = chunkObj.choices[0] as Record; + if ( + choice?.delta && + typeof choice.delta === "object" && + choice.delta !== null + ) { + const delta = choice.delta as Record; + if (typeof delta.content === "string") { + text = extractCleanText(delta.content); + } + } + } + + // Try direct string content + if (!text && typeof chunk === "string") { + text = extractCleanText(chunk); + } + + return { text, isToolCall: false }; + }; +} + +describe("Chunk Processor", () => { + const processChunk = createProcessChunk(); + + describe("Real Event Structures", () => { + test("handles turn_complete event with cancellation policy response", () => { + const chunk = { + event: { + payload: { + event_type: "turn_complete", + turn: { + turn_id: "50a2d6b7-49ed-4d1e-b1c2-6d68b3f726db", + session_id: "e7f62b8e-518c-4450-82df-e65fe49f27a3", + input_messages: [ + { + role: "user", + content: "nice, what's the cancellation policy?", + context: null, + }, + ], + steps: [ + { + turn_id: "50a2d6b7-49ed-4d1e-b1c2-6d68b3f726db", + step_id: "54074310-af42-414c-9ffe-fba5b2ead0ad", + started_at: "2025-08-27T18:15:25.870703Z", + completed_at: "2025-08-27T18:15:51.288993Z", + step_type: "inference", + model_response: { + role: "assistant", + content: + "According to the search results, the cancellation policy for Red Hat Summit is as follows:\n\n* Cancellations must be received by 5 PM EDT on April 18, 2025 for a 50% refund of the registration fee.\n* No refunds will be given for cancellations received after 5 PM EDT on April 18, 2025.\n* Cancellation of travel reservations and hotel reservations are the responsibility of the registrant.", + stop_reason: "end_of_turn", + tool_calls: [], + }, + }, + ], + output_message: { + role: "assistant", + content: + "According to the search results, the cancellation policy for Red Hat Summit is as follows:\n\n* Cancellations must be received by 5 PM EDT on April 18, 2025 for a 50% refund of the registration fee.\n* No refunds will be given for cancellations received after 5 PM EDT on April 18, 2025.\n* Cancellation of travel reservations and hotel reservations are the responsibility of the registrant.", + stop_reason: "end_of_turn", + tool_calls: [], + }, + output_attachments: [], + started_at: "2025-08-27T18:15:25.868548Z", + completed_at: "2025-08-27T18:15:51.289262Z", + }, + }, + }, + }; + + const result = processChunk(chunk); + expect(result.isToolCall).toBe(false); + expect(result.text).toContain( + "According to the search results, the cancellation policy for Red Hat Summit is as follows:" + ); + expect(result.text).toContain("5 PM EDT on April 18, 2025"); + }); + + test("handles turn_complete event with address response", () => { + const chunk = { + event: { + payload: { + event_type: "turn_complete", + turn: { + turn_id: "2f4a1520-8ecc-4cb7-bb7b-886939e042b0", + session_id: "e7f62b8e-518c-4450-82df-e65fe49f27a3", + input_messages: [ + { + role: "user", + content: "what's francisco's address", + context: null, + }, + ], + steps: [ + { + turn_id: "2f4a1520-8ecc-4cb7-bb7b-886939e042b0", + step_id: "c13dd277-1acb-4419-8fbf-d5e2f45392ea", + started_at: "2025-08-27T18:14:52.558761Z", + completed_at: "2025-08-27T18:15:11.306032Z", + step_type: "inference", + model_response: { + role: "assistant", + content: + "Francisco Arceo's address is:\n\nRed Hat\nUnited States\n17 Primrose Ln \nBasking Ridge New Jersey 07920", + stop_reason: "end_of_turn", + tool_calls: [], + }, + }, + ], + output_message: { + role: "assistant", + content: + "Francisco Arceo's address is:\n\nRed Hat\nUnited States\n17 Primrose Ln \nBasking Ridge New Jersey 07920", + stop_reason: "end_of_turn", + tool_calls: [], + }, + output_attachments: [], + started_at: "2025-08-27T18:14:52.553707Z", + completed_at: "2025-08-27T18:15:11.306729Z", + }, + }, + }, + }; + + const result = processChunk(chunk); + expect(result.isToolCall).toBe(false); + expect(result.text).toContain("Francisco Arceo's address is:"); + expect(result.text).toContain("17 Primrose Ln"); + expect(result.text).toContain("Basking Ridge New Jersey 07920"); + }); + + test("handles turn_complete event with ticket cost response", () => { + const chunk = { + event: { + payload: { + event_type: "turn_complete", + turn: { + turn_id: "7ef244a3-efee-42ca-a9c8-942865251002", + session_id: "e7f62b8e-518c-4450-82df-e65fe49f27a3", + input_messages: [ + { + role: "user", + content: "what was the ticket cost for summit?", + context: null, + }, + ], + steps: [ + { + turn_id: "7ef244a3-efee-42ca-a9c8-942865251002", + step_id: "7651dda0-315a-472d-b1c1-3c2725f55bc5", + started_at: "2025-08-27T18:14:21.710611Z", + completed_at: "2025-08-27T18:14:39.706452Z", + step_type: "inference", + model_response: { + role: "assistant", + content: + "The ticket cost for the Red Hat Summit was $999.00 for a conference pass.", + stop_reason: "end_of_turn", + tool_calls: [], + }, + }, + ], + output_message: { + role: "assistant", + content: + "The ticket cost for the Red Hat Summit was $999.00 for a conference pass.", + stop_reason: "end_of_turn", + tool_calls: [], + }, + output_attachments: [], + started_at: "2025-08-27T18:14:21.705289Z", + completed_at: "2025-08-27T18:14:39.706752Z", + }, + }, + }, + }; + + const result = processChunk(chunk); + expect(result.isToolCall).toBe(false); + expect(result.text).toBe( + "The ticket cost for the Red Hat Summit was $999.00 for a conference pass." + ); + }); + }); + + describe("Function Call Detection", () => { + test("detects function calls in direct string chunks", () => { + const chunk = + '{"type": "function", "name": "knowledge_search", "parameters": {"query": "test"}}'; + const result = processChunk(chunk); + expect(result.isToolCall).toBe(true); + expect(result.text).toBe(null); + }); + + test("detects function calls in event payload content", () => { + const chunk = { + event: { + payload: { + content: + '{"type": "function", "name": "knowledge_search", "parameters": {"query": "test"}}', + }, + }, + }; + const result = processChunk(chunk); + expect(result.isToolCall).toBe(true); + expect(result.text).toBe(null); + }); + + test("detects tool_calls in delta structure", () => { + const chunk = { + delta: { + tool_calls: [{ function: { name: "knowledge_search" } }], + }, + }; + const result = processChunk(chunk); + expect(result.isToolCall).toBe(true); + expect(result.text).toBe(null); + }); + + test("detects function call in mixed content but skips it", () => { + const chunk = + '{"type": "function", "name": "knowledge_search", "parameters": {"query": "test"}} Based on the search results, here is your answer.'; + const result = processChunk(chunk); + // This is detected as a tool call and skipped entirely - the implementation prioritizes safety + expect(result.isToolCall).toBe(true); + expect(result.text).toBe(null); + }); + }); + + describe("Text Extraction", () => { + test("extracts text from direct string chunks", () => { + const chunk = "Hello, this is a normal response."; + const result = processChunk(chunk); + expect(result.isToolCall).toBe(false); + expect(result.text).toBe("Hello, this is a normal response."); + }); + + test("extracts text from delta structure", () => { + const chunk = { + delta: { + text: "Hello, this is a normal response.", + }, + }; + const result = processChunk(chunk); + expect(result.isToolCall).toBe(false); + expect(result.text).toBe("Hello, this is a normal response."); + }); + + test("extracts text from choices structure", () => { + const chunk = { + choices: [ + { + delta: { + content: "Hello, this is a normal response.", + }, + }, + ], + }; + const result = processChunk(chunk); + expect(result.isToolCall).toBe(false); + expect(result.text).toBe("Hello, this is a normal response."); + }); + + test("prioritizes output_message over model_response in turn structure", () => { + const chunk = { + event: { + payload: { + turn: { + steps: [ + { + model_response: { + content: "Model response content.", + }, + }, + ], + output_message: { + content: "Final output message content.", + }, + }, + }, + }, + }; + const result = processChunk(chunk); + expect(result.isToolCall).toBe(false); + expect(result.text).toBe("Final output message content."); + }); + + test("falls back to model_response when no output_message", () => { + const chunk = { + event: { + payload: { + turn: { + steps: [ + { + model_response: { + content: "This is from the model response.", + }, + }, + ], + }, + }, + }, + }; + const result = processChunk(chunk); + expect(result.isToolCall).toBe(false); + expect(result.text).toBe("This is from the model response."); + }); + }); + + describe("Edge Cases", () => { + test("handles empty chunks", () => { + const result = processChunk(""); + expect(result.isToolCall).toBe(false); + expect(result.text).toBe(""); + }); + + test("handles null chunks", () => { + const result = processChunk(null); + expect(result.isToolCall).toBe(false); + expect(result.text).toBe(null); + }); + + test("handles undefined chunks", () => { + const result = processChunk(undefined); + expect(result.isToolCall).toBe(false); + expect(result.text).toBe(null); + }); + + test("handles chunks with no text content", () => { + const chunk = { + event: { + metadata: { + timestamp: "2024-01-01", + }, + }, + }; + const result = processChunk(chunk); + expect(result.isToolCall).toBe(false); + expect(result.text).toBe(null); + }); + + test("handles malformed JSON in function calls gracefully", () => { + const chunk = + '{"type": "function", "name": "knowledge_search"} incomplete json'; + const result = processChunk(chunk); + expect(result.isToolCall).toBe(true); + expect(result.text).toBe(null); + }); + }); +}); diff --git a/llama_stack/ui/app/chat-playground/page.test.tsx b/llama_stack/ui/app/chat-playground/page.test.tsx index 54c15f95a..d9025e523 100644 --- a/llama_stack/ui/app/chat-playground/page.test.tsx +++ b/llama_stack/ui/app/chat-playground/page.test.tsx @@ -31,6 +31,9 @@ const mockClient = { toolgroups: { list: jest.fn(), }, + vectorDBs: { + list: jest.fn(), + }, }; jest.mock("@/hooks/use-auth-client", () => ({ @@ -164,7 +167,7 @@ describe("ChatPlaygroundPage", () => { session_name: "Test Session", started_at: new Date().toISOString(), turns: [], - }); // No turns by default + }); mockClient.agents.retrieve.mockResolvedValue({ agent_id: "test-agent", agent_config: { @@ -417,7 +420,6 @@ describe("ChatPlaygroundPage", () => { }); await waitFor(() => { - // first agent should be auto-selected expect(mockClient.agents.session.create).toHaveBeenCalledWith( "agent_123", { session_name: "Default Session" } @@ -464,7 +466,7 @@ describe("ChatPlaygroundPage", () => { }); }); - test("hides delete button when only one agent exists", async () => { + test("shows delete button even when only one agent exists", async () => { mockClient.agents.list.mockResolvedValue({ data: [mockAgents[0]], }); @@ -474,9 +476,7 @@ describe("ChatPlaygroundPage", () => { }); await waitFor(() => { - expect( - screen.queryByTitle("Delete current agent") - ).not.toBeInTheDocument(); + expect(screen.getByTitle("Delete current agent")).toBeInTheDocument(); }); }); @@ -505,7 +505,7 @@ describe("ChatPlaygroundPage", () => { await waitFor(() => { expect(mockClient.agents.delete).toHaveBeenCalledWith("agent_123"); expect(global.confirm).toHaveBeenCalledWith( - "Are you sure you want to delete this agent? This action cannot be undone and will delete all associated sessions." + "Are you sure you want to delete this agent? This action cannot be undone and will delete the agent and all its sessions." ); }); @@ -584,4 +584,207 @@ describe("ChatPlaygroundPage", () => { consoleSpy.mockRestore(); }); }); + + describe("RAG File Upload", () => { + let mockFileReader: { + readAsDataURL: jest.Mock; + readAsText: jest.Mock; + result: string | null; + onload: (() => void) | null; + onerror: (() => void) | null; + }; + let mockRAGTool: { + insert: jest.Mock; + }; + + beforeEach(() => { + mockFileReader = { + readAsDataURL: jest.fn(), + readAsText: jest.fn(), + result: null, + onload: null, + onerror: null, + }; + global.FileReader = jest.fn(() => mockFileReader); + + mockRAGTool = { + insert: jest.fn().mockResolvedValue({}), + }; + mockClient.toolRuntime = { + ragTool: mockRAGTool, + }; + }); + + afterEach(() => { + jest.clearAllMocks(); + }); + + test("handles text file upload", async () => { + new File(["Hello, world!"], "test.txt", { + type: "text/plain", + }); + + mockClient.agents.retrieve.mockResolvedValue({ + agent_id: "test-agent", + agent_config: { + toolgroups: [ + { + name: "builtin::rag/knowledge_search", + args: { vector_db_ids: ["test-vector-db"] }, + }, + ], + }, + }); + + await act(async () => { + render(); + }); + + await waitFor(() => { + expect(screen.getByTestId("chat-component")).toBeInTheDocument(); + }); + + const chatComponent = screen.getByTestId("chat-component"); + chatComponent.getAttribute("data-onragfileupload"); + + // this is a simplified test + expect(mockRAGTool.insert).not.toHaveBeenCalled(); + }); + + test("handles PDF file upload with FileReader", async () => { + new File([new ArrayBuffer(1000)], "test.pdf", { + type: "application/pdf", + }); + + const mockDataURL = "data:application/pdf;base64,JVBERi0xLjQK"; + mockFileReader.result = mockDataURL; + + mockClient.agents.retrieve.mockResolvedValue({ + agent_id: "test-agent", + agent_config: { + toolgroups: [ + { + name: "builtin::rag/knowledge_search", + args: { vector_db_ids: ["test-vector-db"] }, + }, + ], + }, + }); + + await act(async () => { + render(); + }); + + await waitFor(() => { + expect(screen.getByTestId("chat-component")).toBeInTheDocument(); + }); + + expect(global.FileReader).toBeDefined(); + }); + + test("handles different file types correctly", () => { + const getContentType = (filename: string): string => { + const ext = filename.toLowerCase().split(".").pop(); + switch (ext) { + case "pdf": + return "application/pdf"; + case "txt": + return "text/plain"; + case "md": + return "text/markdown"; + case "html": + return "text/html"; + case "csv": + return "text/csv"; + case "json": + return "application/json"; + case "docx": + return "application/vnd.openxmlformats-officedocument.wordprocessingml.document"; + case "doc": + return "application/msword"; + default: + return "application/octet-stream"; + } + }; + + expect(getContentType("test.pdf")).toBe("application/pdf"); + expect(getContentType("test.txt")).toBe("text/plain"); + expect(getContentType("test.md")).toBe("text/markdown"); + expect(getContentType("test.html")).toBe("text/html"); + expect(getContentType("test.csv")).toBe("text/csv"); + expect(getContentType("test.json")).toBe("application/json"); + expect(getContentType("test.docx")).toBe( + "application/vnd.openxmlformats-officedocument.wordprocessingml.document" + ); + expect(getContentType("test.doc")).toBe("application/msword"); + expect(getContentType("test.unknown")).toBe("application/octet-stream"); + }); + + test("determines text vs binary file types correctly", () => { + const isTextFile = (mimeType: string): boolean => { + return ( + mimeType.startsWith("text/") || + mimeType === "application/json" || + mimeType === "text/markdown" || + mimeType === "text/html" || + mimeType === "text/csv" + ); + }; + + expect(isTextFile("text/plain")).toBe(true); + expect(isTextFile("text/markdown")).toBe(true); + expect(isTextFile("text/html")).toBe(true); + expect(isTextFile("text/csv")).toBe(true); + expect(isTextFile("application/json")).toBe(true); + + expect(isTextFile("application/pdf")).toBe(false); + expect(isTextFile("application/msword")).toBe(false); + expect( + isTextFile( + "application/vnd.openxmlformats-officedocument.wordprocessingml.document" + ) + ).toBe(false); + expect(isTextFile("application/octet-stream")).toBe(false); + }); + + test("handles FileReader error gracefully", async () => { + const pdfFile = new File([new ArrayBuffer(1000)], "test.pdf", { + type: "application/pdf", + }); + + mockFileReader.onerror = jest.fn(); + const mockError = new Error("FileReader failed"); + + const fileReaderPromise = new Promise((resolve, reject) => { + const reader = new FileReader(); + reader.onload = () => resolve(reader.result as string); + reader.onerror = () => reject(reader.error || mockError); + reader.readAsDataURL(pdfFile); + + setTimeout(() => { + reader.onerror?.(new ProgressEvent("error")); + }, 0); + }); + + await expect(fileReaderPromise).rejects.toBeDefined(); + }); + + test("handles large file upload with FileReader approach", () => { + // create a large file + const largeFile = new File( + [new ArrayBuffer(10 * 1024 * 1024)], + "large.pdf", + { + type: "application/pdf", + } + ); + + expect(largeFile.size).toBe(10 * 1024 * 1024); // 10MB + + expect(global.FileReader).toBeDefined(); + + const reader = new FileReader(); + expect(reader.readAsDataURL).toBeDefined(); + }); + }); }); diff --git a/llama_stack/ui/app/chat-playground/page.tsx b/llama_stack/ui/app/chat-playground/page.tsx index f26791a41..0417f7083 100644 --- a/llama_stack/ui/app/chat-playground/page.tsx +++ b/llama_stack/ui/app/chat-playground/page.tsx @@ -15,6 +15,7 @@ import { Input } from "@/components/ui/input"; import { Trash2 } from "lucide-react"; import { Chat } from "@/components/chat-playground/chat"; import { type Message } from "@/components/chat-playground/chat-message"; +import { VectorDBCreator } from "@/components/chat-playground/vector-db-creator"; import { useAuthClient } from "@/hooks/use-auth-client"; import type { Model } from "llama-stack-client/resources/models"; import type { TurnCreateParams } from "llama-stack-client/resources/agents/turn"; @@ -22,6 +23,10 @@ import { SessionUtils, type ChatSession, } from "@/components/chat-playground/conversations"; +import { + cleanMessageContent, + extractCleanText, +} from "@/lib/message-content-utils"; export default function ChatPlaygroundPage() { const [currentSession, setCurrentSession] = useState( null @@ -65,6 +70,20 @@ export default function ChatPlaygroundPage() { provider_resource_id?: string; }> >([]); + const [showCreateVectorDB, setShowCreateVectorDB] = useState(false); + const [availableVectorDBs, setAvailableVectorDBs] = useState< + Array<{ + identifier: string; + vector_db_name?: string; + embedding_model: string; + }> + >([]); + const [uploadNotification, setUploadNotification] = useState<{ + show: boolean; + message: string; + type: "success" | "error" | "loading"; + }>({ show: false, message: "", type: "success" }); + const [selectedVectorDBs, setSelectedVectorDBs] = useState([]); const client = useAuthClient(); const abortControllerRef = useRef(null); @@ -73,26 +92,22 @@ export default function ChatPlaygroundPage() { const loadAgentConfig = useCallback( async (agentId: string) => { try { - console.log("Loading agent config for:", agentId); - // try to load from cache first const cachedConfig = SessionUtils.loadAgentConfig(agentId); if (cachedConfig) { - console.log("✅ Loaded agent config from cache:", cachedConfig); setSelectedAgentConfig({ toolgroups: cachedConfig.toolgroups, }); return; } - console.log("📡 Fetching agent config from API..."); const agentDetails = await client.agents.retrieve(agentId); - console.log("Agent details retrieved:", agentDetails); - console.log("Agent config:", agentDetails.agent_config); - console.log("Agent toolgroups:", agentDetails.agent_config?.toolgroups); - // cache the config - SessionUtils.saveAgentConfig(agentId, agentDetails.agent_config); + // cache config + SessionUtils.saveAgentConfig(agentId, { + ...agentDetails.agent_config, + toolgroups: agentDetails.agent_config?.toolgroups, + }); setSelectedAgentConfig({ toolgroups: agentDetails.agent_config?.toolgroups, @@ -116,7 +131,7 @@ export default function ChatPlaygroundPage() { id: response.session_id, name: "Default Session", messages: [], - selectedModel: selectedModel, // Use current selected model + selectedModel: selectedModel, // use current selected model systemMessage: "You are a helpful assistant.", agentId, createdAt: Date.now(), @@ -124,10 +139,6 @@ export default function ChatPlaygroundPage() { }; setCurrentSession(defaultSession); - console.log( - `💾 Saving default session ID for agent ${agentId}:`, - defaultSession.id - ); SessionUtils.saveCurrentSessionId(defaultSession.id, agentId); // cache entire session data SessionUtils.saveSessionData(agentId, defaultSession); @@ -152,7 +163,6 @@ export default function ChatPlaygroundPage() { const messages: Message[] = []; for (const turn of session.turns) { - // add user messages if (turn.input_messages && Array.isArray(turn.input_messages)) { for (const input of turn.input_messages) { if (input.role === "user" && input.content) { @@ -169,15 +179,18 @@ export default function ChatPlaygroundPage() { } } - // add assistant message from output_message if (turn.output_message && turn.output_message.content) { + console.log("Raw message content:", turn.output_message.content); + console.log("Content type:", typeof turn.output_message.content); + + const cleanContent = cleanMessageContent( + turn.output_message.content + ); + messages.push({ id: `${turn.turn_id}-assistant-${messages.length}`, role: "assistant", - content: - typeof turn.output_message.content === "string" - ? turn.output_message.content - : JSON.stringify(turn.output_message.content), + content: cleanContent, createdAt: new Date( turn.completed_at || turn.started_at || Date.now() ), @@ -197,27 +210,22 @@ export default function ChatPlaygroundPage() { const loadAgentSessions = useCallback( async (agentId: string) => { try { - console.log("Loading sessions for agent:", agentId); const response = await client.agents.session.list(agentId); - console.log("Available sessions:", response.data); if ( response.data && Array.isArray(response.data) && response.data.length > 0 ) { - // check for a previously saved session ID for this specific agent + // check for saved session ID for this agent const savedSessionId = SessionUtils.loadCurrentSessionId(agentId); - console.log(`Saved session ID for agent ${agentId}:`, savedSessionId); - - // try to load cached session data first + // try to load cached agent session data first if (savedSessionId) { const cachedSession = SessionUtils.loadSessionData( agentId, savedSessionId ); if (cachedSession) { - console.log("✅ Loaded session from cache:", cachedSession.id); setCurrentSession(cachedSession); SessionUtils.saveCurrentSessionId(cachedSession.id, agentId); return; @@ -238,7 +246,8 @@ export default function ChatPlaygroundPage() { // try to find saved session id in available sessions if (savedSessionId) { const foundSession = response.data.find( - (s: { session_id: string }) => s.session_id === savedSessionId + (s: { [key: string]: unknown }) => + (s as { session_id: string }).session_id === savedSessionId ); console.log("Found saved session in list:", foundSession); if (foundSession) { @@ -269,7 +278,7 @@ export default function ChatPlaygroundPage() { id: sessionToLoad.session_id, name: sessionToLoad.session_name || "Session", messages, - selectedModel: selectedModel || "", // Preserve current model or use empty + selectedModel: selectedModel || "", systemMessage: "You are a helpful assistant.", agentId, createdAt: sessionToLoad.started_at @@ -330,7 +339,8 @@ export default function ChatPlaygroundPage() { // if we have a saved agent ID, find it in the available agents if (savedAgentId) { const foundAgent = agentList.data.find( - (a: { agent_id: string }) => a.agent_id === savedAgentId + (a: { [key: string]: unknown }) => + (a as { agent_id: string }).agent_id === savedAgentId ); if (foundAgent) { agentToSelect = foundAgent as typeof agentToSelect; @@ -353,14 +363,10 @@ export default function ChatPlaygroundPage() { fetchAgents(); - // fetch available toolgroups const fetchToolgroups = async () => { try { - console.log("Fetching toolgroups..."); const toolgroups = await client.toolgroups.list(); - console.log("Toolgroups response:", toolgroups); - // The client returns data directly, not wrapped in .data const toolGroupsArray = Array.isArray(toolgroups) ? toolgroups : toolgroups && @@ -381,7 +387,6 @@ export default function ChatPlaygroundPage() { if (toolGroupsArray && Array.isArray(toolGroupsArray)) { setAvailableToolgroups(toolGroupsArray); - console.log("Set toolgroups:", toolGroupsArray); } else { console.error("Invalid toolgroups data format:", toolgroups); } @@ -398,6 +403,24 @@ export default function ChatPlaygroundPage() { }; fetchToolgroups(); + + const fetchVectorDBs = async () => { + try { + const vectorDBs = await client.vectorDBs.list(); + + const vectorDBsArray = Array.isArray(vectorDBs) ? vectorDBs : []; + + if (vectorDBsArray && Array.isArray(vectorDBsArray)) { + setAvailableVectorDBs(vectorDBsArray); + } else { + console.error("Invalid vector DBs data format:", vectorDBs); + } + } catch (error) { + console.error("Error fetching vector DBs:", error); + } + }; + + fetchVectorDBs(); }, [client, loadAgentSessions, loadAgentConfig]); const createNewAgent = useCallback( @@ -405,24 +428,35 @@ export default function ChatPlaygroundPage() { name: string, instructions: string, model: string, - toolgroups: string[] = [] + toolgroups: string[] = [], + vectorDBs: string[] = [] ) => { try { - console.log("Creating agent with toolgroups:", toolgroups); + const processedToolgroups = toolgroups.map(toolgroup => { + if (toolgroup === "builtin::rag" && vectorDBs.length > 0) { + return { + name: "builtin::rag/knowledge_search", + args: { + vector_db_ids: vectorDBs, + }, + }; + } + return toolgroup; + }); + const agentConfig = { model, instructions, name: name || undefined, enable_session_persistence: true, - toolgroups: toolgroups.length > 0 ? toolgroups : undefined, + toolgroups: + processedToolgroups.length > 0 ? processedToolgroups : undefined, }; - console.log("Agent config being sent:", agentConfig); const response = await client.agents.create({ agent_config: agentConfig, }); - // refresh agents list const agentList = await client.agents.list(); setAgents( (agentList.data as Array<{ @@ -436,7 +470,6 @@ export default function ChatPlaygroundPage() { }>) || [] ); - // set the new agent as selected setSelectedAgentId(response.agent_id); await loadAgentConfig(response.agent_id); await loadAgentSessions(response.agent_id); @@ -450,24 +483,47 @@ export default function ChatPlaygroundPage() { [client, loadAgentSessions, loadAgentConfig] ); + const handleVectorDBCreated = useCallback( + // eslint-disable-next-line @typescript-eslint/no-unused-vars + async (_vectorDbId: string) => { + setShowCreateVectorDB(false); + + try { + const vectorDBs = await client.vectorDBs.list(); + const vectorDBsArray = Array.isArray(vectorDBs) ? vectorDBs : []; + + if (vectorDBsArray && Array.isArray(vectorDBsArray)) { + setAvailableVectorDBs(vectorDBsArray); + } + } catch (error) { + console.error("Error refreshing vector DBs:", error); + } + }, + [client] + ); + const deleteAgent = useCallback( async (agentId: string) => { - if (agents.length <= 1) { - return; - } - if ( confirm( - "Are you sure you want to delete this agent? This action cannot be undone and will delete all associated sessions." + "Are you sure you want to delete this agent? This action cannot be undone and will delete the agent and all its sessions." ) ) { try { - await client.agents.delete(agentId); + // there's a known error where the delete API returns 500 even on success + try { + await client.agents.delete(agentId); + console.log("Agent deleted successfully"); + } catch (deleteError) { + // log the error but don't re-throw - we know deletion succeeded + console.log( + "Agent delete API returned error (but deletion likely succeeded):", + deleteError + ); + } - // clear cached data for agent SessionUtils.clearAgentCache(agentId); - // Refresh agents list const agentList = await client.agents.list(); setAgents( (agentList.data as Array<{ @@ -481,10 +537,11 @@ export default function ChatPlaygroundPage() { }>) || [] ); - // if we deleted the current agent, switch to another one + // if we delete current agent, switch to another if (selectedAgentId === agentId) { const remainingAgents = agentList.data?.filter( - (a: { agent_id: string }) => a.agent_id !== agentId + (a: { [key: string]: unknown }) => + (a as { agent_id: string }).agent_id !== agentId ); if (remainingAgents && remainingAgents.length > 0) { const newAgent = remainingAgents[0] as { @@ -501,7 +558,7 @@ export default function ChatPlaygroundPage() { await loadAgentConfig(newAgent.agent_id); await loadAgentSessions(newAgent.agent_id); } else { - // No agents left + // no agents left setSelectedAgentId(""); setCurrentSession(null); setSelectedAgentConfig(null); @@ -509,10 +566,76 @@ export default function ChatPlaygroundPage() { } } catch (error) { console.error("Error deleting agent:", error); + + // check if this is known server bug where deletion succeeds but returns 500 + // The error message will typically contain status codes or "Could not find agent" + const errorMessage = + error instanceof Error ? error.message : String(error); + const isKnownServerBug = + errorMessage.includes("500") || + errorMessage.includes("Internal Server Error") || + errorMessage.includes("Could not find agent") || + errorMessage.includes("400"); + + if (isKnownServerBug) { + console.log( + "Agent deletion succeeded despite error, cleaning up UI" + ); + SessionUtils.clearAgentCache(agentId); + try { + const agentList = await client.agents.list(); + setAgents( + (agentList.data as Array<{ + agent_id: string; + agent_config?: { + agent_name?: string; + name?: string; + instructions?: string; + }; + [key: string]: unknown; + }>) || [] + ); + + if (selectedAgentId === agentId) { + const remainingAgents = agentList.data?.filter( + (a: { [key: string]: unknown }) => + (a as { agent_id: string }).agent_id !== agentId + ); + if (remainingAgents && remainingAgents.length > 0) { + const newAgent = remainingAgents[0] as { + agent_id: string; + agent_config?: { + agent_name?: string; + name?: string; + instructions?: string; + }; + [key: string]: unknown; + }; + setSelectedAgentId(newAgent.agent_id); + SessionUtils.saveCurrentAgentId(newAgent.agent_id); + await loadAgentConfig(newAgent.agent_id); + await loadAgentSessions(newAgent.agent_id); + } else { + // no agents left + setSelectedAgentId(""); + setCurrentSession(null); + setSelectedAgentConfig(null); + } + } + } catch (refreshError) { + console.error("Error refreshing agents list:", refreshError); + } + } else { + // show error that we don't know about to user + console.error("Unexpected error during agent deletion:", error); + if (error instanceof Error) { + alert(`Failed to delete agent: ${error.message}`); + } + } } } }, - [agents.length, client, selectedAgentId, loadAgentConfig, loadAgentSessions] + [client, selectedAgentId, loadAgentConfig, loadAgentSessions] ); const handleModelChange = useCallback((newModel: string) => { @@ -530,10 +653,6 @@ export default function ChatPlaygroundPage() { useEffect(() => { if (currentSession) { - console.log( - `💾 Auto-saving session ID for agent ${currentSession.agentId}:`, - currentSession.id - ); SessionUtils.saveCurrentSessionId( currentSession.id, currentSession.agentId @@ -556,8 +675,12 @@ export default function ChatPlaygroundPage() { setModelsLoading(true); setModelsError(null); const modelList = await client.models.list(); + + // store all models (including embedding models for vector DB creation) + setModels(modelList); + + // set default LLM model for chat const llmModels = modelList.filter(model => model.model_type === "llm"); - setModels(llmModels); if (llmModels.length > 0) { handleModelChange(llmModels[0].identifier); } @@ -614,7 +737,7 @@ export default function ChatPlaygroundPage() { messages: [...prev.messages, userMessage], updatedAt: Date.now(), }; - // Update cache with new message + // update cache with new message SessionUtils.saveSessionData(prev.agentId, updatedSession); return updatedSession; }); @@ -653,7 +776,8 @@ export default function ChatPlaygroundPage() { turnParams, { signal: abortController.signal, - } as { signal: AbortSignal } + timeout: 300000, // 5 minutes timeout for RAG queries + } as { signal: AbortSignal; timeout: number } ); const assistantMessage: Message = { @@ -663,42 +787,242 @@ export default function ChatPlaygroundPage() { createdAt: new Date(), }; - const extractDeltaText = (chunk: unknown): string | null => { - // this is an awful way to handle different chunk formats, but i'm not sure if there's much of a better way - if (chunk?.delta?.text && typeof chunk.delta.text === "string") { - return chunk.delta.text; - } + const processChunk = ( + chunk: unknown + ): { text: string | null; isToolCall: boolean } => { + const chunkObj = chunk as Record; - if ( - chunk?.event?.delta?.text && - typeof chunk.event.delta.text === "string" - ) { - return chunk.event.delta.text; - } + // helper to check if content contains function call JSON + const containsToolCall = (content: string): boolean => { + return ( + content.includes('"type": "function"') || + content.includes('"name": "knowledge_search"') || + content.includes('"parameters":') || + !!content.match(/\{"type":\s*"function".*?\}/) + ); + }; - if ( - chunk?.choices?.[0]?.delta?.content && - typeof chunk.choices[0].delta.content === "string" - ) { - return chunk.choices[0].delta.content; - } + let isToolCall = false; + let potentialContent = ""; if (typeof chunk === "string") { - return chunk; + potentialContent = chunk; + isToolCall = containsToolCall(chunk); } if ( - chunk?.event?.payload?.delta?.text && - typeof chunk.event.payload.delta.text === "string" + chunkObj?.delta && + typeof chunkObj.delta === "object" && + chunkObj.delta !== null ) { - return chunk.event.payload.delta.text; + const delta = chunkObj.delta as Record; + if ("tool_calls" in delta) { + isToolCall = true; + } + if (typeof delta.text === "string") { + potentialContent = delta.text; + if (containsToolCall(delta.text)) { + isToolCall = true; + } + } } - if (process.env.NODE_ENV !== "production") { - console.debug("Unrecognized chunk format:", chunk); + if ( + chunkObj?.event && + typeof chunkObj.event === "object" && + chunkObj.event !== null + ) { + const event = chunkObj.event as Record; + + if ( + event?.payload && + typeof event.payload === "object" && + event.payload !== null + ) { + const payload = event.payload as Record; + if (typeof payload.content === "string") { + potentialContent = payload.content; + if (containsToolCall(payload.content)) { + isToolCall = true; + } + } + + if ( + payload?.delta && + typeof payload.delta === "object" && + payload.delta !== null + ) { + const delta = payload.delta as Record; + if (typeof delta.text === "string") { + potentialContent = delta.text; + if (containsToolCall(delta.text)) { + isToolCall = true; + } + } + } + } + + if ( + event?.delta && + typeof event.delta === "object" && + event.delta !== null + ) { + const delta = event.delta as Record; + if (typeof delta.text === "string") { + potentialContent = delta.text; + if (containsToolCall(delta.text)) { + isToolCall = true; + } + } + if (typeof delta.content === "string") { + // eslint-disable-next-line @typescript-eslint/no-unused-vars + potentialContent = delta.content; + if (containsToolCall(delta.content)) { + isToolCall = true; + } + } + } } - return null; + // if it's a tool call, skip it (don't display in chat) + if (isToolCall) { + return { text: null, isToolCall: true }; + } + + let text: string | null = null; + + if ( + chunkObj?.delta && + typeof chunkObj.delta === "object" && + chunkObj.delta !== null + ) { + const delta = chunkObj.delta as Record; + if (typeof delta.text === "string") { + text = extractCleanText(delta.text); + } + } + + if ( + !text && + chunkObj?.event && + typeof chunkObj.event === "object" && + chunkObj.event !== null + ) { + const event = chunkObj.event as Record; + + if ( + event?.payload && + typeof event.payload === "object" && + event.payload !== null + ) { + const payload = event.payload as Record; + + if (typeof payload.content === "string") { + text = extractCleanText(payload.content); + } + + if ( + !text && + payload?.turn && + typeof payload.turn === "object" && + payload.turn !== null + ) { + const turn = payload.turn as Record; + if ( + turn?.output_message && + typeof turn.output_message === "object" && + turn.output_message !== null + ) { + const outputMessage = turn.output_message as Record< + string, + unknown + >; + if (typeof outputMessage.content === "string") { + text = extractCleanText(outputMessage.content); + } + } + + if ( + !text && + turn?.steps && + Array.isArray(turn.steps) && + turn.steps.length > 0 + ) { + for (const step of turn.steps) { + if (step && typeof step === "object" && step !== null) { + const stepObj = step as Record; + if ( + stepObj?.model_response && + typeof stepObj.model_response === "object" && + stepObj.model_response !== null + ) { + const modelResponse = stepObj.model_response as Record< + string, + unknown + >; + if (typeof modelResponse.content === "string") { + text = extractCleanText(modelResponse.content); + break; + } + } + } + } + } + } + + if ( + !text && + payload?.delta && + typeof payload.delta === "object" && + payload.delta !== null + ) { + const delta = payload.delta as Record; + if (typeof delta.text === "string") { + text = extractCleanText(delta.text); + } + } + } + + if ( + !text && + event?.delta && + typeof event.delta === "object" && + event.delta !== null + ) { + const delta = event.delta as Record; + if (typeof delta.text === "string") { + text = extractCleanText(delta.text); + } + if (!text && typeof delta.content === "string") { + text = extractCleanText(delta.content); + } + } + } + + if ( + !text && + chunkObj?.choices && + Array.isArray(chunkObj.choices) && + chunkObj.choices.length > 0 + ) { + const choice = chunkObj.choices[0] as Record; + if ( + choice?.delta && + typeof choice.delta === "object" && + choice.delta !== null + ) { + const delta = choice.delta as Record; + if (typeof delta.content === "string") { + text = extractCleanText(delta.content); + } + } + } + + if (!text && typeof chunk === "string") { + text = extractCleanText(chunk); + } + + return { text, isToolCall: false }; }; setCurrentSession(prev => { if (!prev) return null; @@ -713,8 +1037,34 @@ export default function ChatPlaygroundPage() { }); let fullContent = ""; + for await (const chunk of response) { - const deltaText = extractDeltaText(chunk); + const { text: deltaText } = processChunk(chunk); + + // logging for debugging function calls + // if (deltaText && deltaText.includes("knowledge_search")) { + // console.log("🔍 Function call detected in text output:", deltaText); + // console.log("🔍 Original chunk:", JSON.stringify(chunk, null, 2)); + // } + + if (chunk && typeof chunk === "object" && "event" in chunk) { + const event = ( + chunk as { + event: { + payload?: { + event_type?: string; + turn?: { output_message?: { content?: string } }; + }; + }; + } + ).event; + if (event?.payload?.event_type === "turn_complete") { + const content = event?.payload?.turn?.output_message?.content; + if (content && content.includes("knowledge_search")) { + console.log("🔍 Function call found in turn_complete:", content); + } + } + } if (deltaText) { fullContent += deltaText; @@ -732,9 +1082,9 @@ export default function ChatPlaygroundPage() { messages: newMessages, updatedAt: Date.now(), }; - // update cache with streaming content (throttled) + // update cache with streaming content if (fullContent.length % 100 === 0) { - // Only cache every 100 characters to avoid spam + // Only cache every 100 characters SessionUtils.saveSessionData(prev.agentId, updatedSession); } return updatedSession; @@ -809,8 +1159,180 @@ export default function ChatPlaygroundPage() { setError(null); }; + const handleRAGFileUpload = async (file: File) => { + if (!selectedAgentConfig?.toolgroups || !selectedAgentId) { + setError("No agent selected or agent has no RAG tools configured"); + return; + } + + // find RAG toolgroups that have vector_db_ids configured + const ragToolgroups = selectedAgentConfig.toolgroups.filter(toolgroup => { + if (typeof toolgroup === "object" && toolgroup.name?.includes("rag")) { + return toolgroup.args && "vector_db_ids" in toolgroup.args; + } + return false; + }); + + if (ragToolgroups.length === 0) { + setError("Current agent has no vector databases configured for RAG"); + return; + } + + try { + setError(null); + console.log("Uploading file using RAG tool..."); + + setUploadNotification({ + show: true, + message: `📄 Uploading and indexing "${file.name}"...`, + type: "loading", + }); + + const vectorDbIds = ragToolgroups.flatMap(toolgroup => { + if ( + typeof toolgroup === "object" && + toolgroup.args && + "vector_db_ids" in toolgroup.args + ) { + return toolgroup.args.vector_db_ids as string[]; + } + return []; + }); + + // determine mime type from file extension - this should be in the Llama Stack Client IMO + const getContentType = (filename: string): string => { + const ext = filename.toLowerCase().split(".").pop(); + switch (ext) { + case "pdf": + return "application/pdf"; + case "txt": + return "text/plain"; + case "md": + return "text/markdown"; + case "html": + return "text/html"; + case "csv": + return "text/csv"; + case "json": + return "application/json"; + case "docx": + return "application/vnd.openxmlformats-officedocument.wordprocessingml.document"; + case "doc": + return "application/msword"; + default: + return "application/octet-stream"; + } + }; + + const mimeType = getContentType(file.name); + let fileContent: string; + + // handle text files vs binary files differently + const isTextFile = + mimeType.startsWith("text/") || + mimeType === "application/json" || + mimeType === "text/markdown" || + mimeType === "text/html" || + mimeType === "text/csv"; + + if (isTextFile) { + fileContent = await file.text(); + } else { + // for PDFs and other binary files, create a data URL + // use FileReader for efficient base64 conversion + fileContent = await new Promise((resolve, reject) => { + const reader = new FileReader(); + reader.onload = () => resolve(reader.result as string); + reader.onerror = () => reject(reader.error); + reader.readAsDataURL(file); + }); + } + + for (const vectorDbId of vectorDbIds) { + await client.toolRuntime.ragTool.insert({ + documents: [ + { + content: fileContent, + document_id: `${file.name}-${Date.now()}`, + metadata: { + filename: file.name, + file_size: file.size, + uploaded_at: new Date().toISOString(), + agent_id: selectedAgentId, + }, + mime_type: mimeType, + }, + ], + vector_db_id: vectorDbId, + // TODO: parameterize this somewhere, probably in settings + chunk_size_in_tokens: 512, + }); + } + + console.log("✅ File successfully uploaded using RAG tool"); + + setUploadNotification({ + show: true, + message: `📄 File "${file.name}" uploaded and indexed successfully!`, + type: "success", + }); + + setTimeout(() => { + setUploadNotification(prev => ({ ...prev, show: false })); + }, 4000); + } catch (err) { + console.error("Error uploading file using RAG tool:", err); + const errorMessage = + err instanceof Error + ? `Failed to upload file: ${err.message}` + : "Failed to upload file using RAG tool"; + + setUploadNotification({ + show: true, + message: errorMessage, + type: "error", + }); + + setTimeout(() => { + setUploadNotification(prev => ({ ...prev, show: false })); + }, 6000); + } + }; + return (
+ {/* Upload Notification */} + {uploadNotification.show && ( +
+
+ {uploadNotification.type === "loading" && ( +
+ )} + + {uploadNotification.message} + + {uploadNotification.type !== "loading" && ( + + )} +
+
+ )} + {/* Header */}
@@ -822,7 +1344,6 @@ export default function ChatPlaygroundPage() { - {selectedAgentId && agents.length > 1 && ( + {selectedAgentId && (
)} - - setCurrentSession(prev => - prev ? { ...prev, messages, updatedAt: Date.now() } : prev - ) - } - /> + {!agentsLoading && agents.length === 0 ? ( +
+
+
🦙
+

+ Create an Agent with Llama Stack +

+

+ To get started, create your first agent. Each agent is + configured with specific instructions, models, and tools to + help you with different tasks. +

+ +
+
+ ) : ( + + setCurrentSession(prev => + prev ? { ...prev, messages, updatedAt: Date.now() } : prev + ) + } + onRAGFileUpload={handleRAGFileUpload} + /> + )}
@@ -1086,14 +1662,16 @@ export default function ChatPlaygroundPage() { - {models.map(model => ( - - {model.identifier} - - ))} + {models + .filter(model => model.model_type === "llm") + .map(model => ( + + {model.identifier} + + ))} @@ -1137,21 +1715,12 @@ export default function ChatPlaygroundPage() { toolgroup.identifier )} onChange={e => { - console.log( - "Tool selection changed:", - toolgroup.identifier, - e.target.checked - ); if (e.target.checked) { setSelectedToolgroups(prev => { const newSelection = [ ...prev, toolgroup.identifier, ]; - console.log( - "New selected toolgroups:", - newSelection - ); return newSelection; }); } else { @@ -1159,10 +1728,6 @@ export default function ChatPlaygroundPage() { const newSelection = prev.filter( id => id !== toolgroup.identifier ); - console.log( - "New selected toolgroups:", - newSelection - ); return newSelection; }); } @@ -1194,6 +1759,80 @@ export default function ChatPlaygroundPage() { text generation agents work without tools.

+ + {/* Vector DB Configuration for RAG */} + {selectedToolgroups.includes("builtin::rag") && ( +
+ +
+ + + {availableVectorDBs.length} available + +
+
+ {availableVectorDBs.length === 0 ? ( +

+ No vector databases available. Create one to use RAG + tools. +

+ ) : ( + availableVectorDBs.map(vectorDB => ( + + )) + )} +
+ {selectedVectorDBs.length === 0 && + selectedToolgroups.includes("builtin::rag") && ( +

+ ⚠️ RAG tool selected but no vector databases chosen. + Create or select a vector database. +

+ )} +
+ )}
@@ -1204,12 +1843,14 @@ export default function ChatPlaygroundPage() { newAgentName, newAgentInstructions, selectedModel, - selectedToolgroups + selectedToolgroups, + selectedVectorDBs ); setShowCreateAgent(false); setNewAgentName(""); setNewAgentInstructions("You are a helpful assistant."); setSelectedToolgroups([]); + setSelectedVectorDBs([]); } catch (error) { console.error("Failed to create agent:", error); } @@ -1226,6 +1867,7 @@ export default function ChatPlaygroundPage() { setNewAgentName(""); setNewAgentInstructions("You are a helpful assistant."); setSelectedToolgroups([]); + setSelectedVectorDBs([]); }} className="flex-1" > @@ -1235,6 +1877,17 @@ export default function ChatPlaygroundPage() {
)} + + {/* Create Vector DB Modal */} + {showCreateVectorDB && ( +
+ setShowCreateVectorDB(false)} + /> +
+ )} ); } diff --git a/llama_stack/ui/components/chat-playground/chat.tsx b/llama_stack/ui/components/chat-playground/chat.tsx index 023bf0728..3b37c4dfe 100644 --- a/llama_stack/ui/components/chat-playground/chat.tsx +++ b/llama_stack/ui/components/chat-playground/chat.tsx @@ -35,6 +35,7 @@ interface ChatPropsBase { ) => void; setMessages?: (messages: Message[]) => void; transcribeAudio?: (blob: Blob) => Promise; + onRAGFileUpload?: (file: File) => Promise; } interface ChatPropsWithoutSuggestions extends ChatPropsBase { @@ -62,6 +63,7 @@ export function Chat({ onRateResponse, setMessages, transcribeAudio, + onRAGFileUpload, }: ChatProps) { const lastMessage = messages.at(-1); const isEmpty = messages.length === 0; @@ -226,16 +228,17 @@ export function Chat({ isPending={isGenerating || isTyping} handleSubmit={handleSubmit} > - {({ files, setFiles }) => ( + {() => ( {}} stop={handleStop} isGenerating={isGenerating} transcribeAudio={transcribeAudio} + onRAGFileUpload={onRAGFileUpload} /> )} diff --git a/llama_stack/ui/components/chat-playground/conversations.tsx b/llama_stack/ui/components/chat-playground/conversations.tsx index 1a9c960fe..40045b9fe 100644 --- a/llama_stack/ui/components/chat-playground/conversations.tsx +++ b/llama_stack/ui/components/chat-playground/conversations.tsx @@ -14,6 +14,7 @@ import { Card } from "@/components/ui/card"; import { Trash2 } from "lucide-react"; import type { Message } from "@/components/chat-playground/chat-message"; import { useAuthClient } from "@/hooks/use-auth-client"; +import { cleanMessageContent } from "@/lib/message-content-utils"; import type { Session, SessionCreateParams, @@ -219,10 +220,7 @@ export function Conversations({ messages.push({ id: `${turn.turn_id}-assistant-${messages.length}`, role: "assistant", - content: - typeof turn.output_message.content === "string" - ? turn.output_message.content - : JSON.stringify(turn.output_message.content), + content: cleanMessageContent(turn.output_message.content), createdAt: new Date( turn.completed_at || turn.started_at || Date.now() ), @@ -271,7 +269,7 @@ export function Conversations({ ); const deleteSession = async (sessionId: string) => { - if (sessions.length <= 1 || !selectedAgentId) { + if (!selectedAgentId) { return; } @@ -324,7 +322,6 @@ export function Conversations({ } }, [currentSession]); - // Don't render if no agent is selected if (!selectedAgentId) { return null; } @@ -357,7 +354,7 @@ export function Conversations({ + New - {currentSession && sessions.length > 1 && ( + {currentSession && ( + {onCancel && ( + + )} + + + +
+ Note: This will create a new vector database that can + be used with RAG tools. After creation, you'll be able to upload + documents and use it for knowledge search in your agent conversations. +
+ + ); +} diff --git a/llama_stack/ui/lib/message-content-utils.ts b/llama_stack/ui/lib/message-content-utils.ts new file mode 100644 index 000000000..378f8d669 --- /dev/null +++ b/llama_stack/ui/lib/message-content-utils.ts @@ -0,0 +1,51 @@ +// check if content contains function call JSON +export const containsToolCall = (content: string): boolean => { + return ( + content.includes('"type": "function"') || + content.includes('"name": "knowledge_search"') || + content.includes('"parameters":') || + !!content.match(/\{"type":\s*"function".*?\}/) + ); +}; + +export const extractCleanText = (content: string): string | null => { + if (containsToolCall(content)) { + try { + // parse and extract non-function call parts + const jsonMatch = content.match(/\{"type":\s*"function"[^}]*\}[^}]*\}/); + if (jsonMatch) { + const jsonPart = jsonMatch[0]; + const parsedJson = JSON.parse(jsonPart); + + // if function call, extract text after JSON + if (parsedJson.type === "function") { + const textAfterJson = content + .substring(content.indexOf(jsonPart) + jsonPart.length) + .trim(); + return textAfterJson || null; + } + } + return null; + } catch { + return null; + } + } + return content; +}; + +// removes function call JSON handling different content types +export const cleanMessageContent = ( + content: string | unknown[] | unknown +): string => { + if (typeof content === "string") { + const cleaned = extractCleanText(content); + return cleaned || ""; + } else if (Array.isArray(content)) { + return content + .filter((item: { type: string }) => item.type === "text") + .map((item: { text: string }) => item.text) + .join(""); + } else { + return JSON.stringify(content); + } +}; diff --git a/llama_stack/ui/package-lock.json b/llama_stack/ui/package-lock.json index 2da25615c..638655639 100644 --- a/llama_stack/ui/package-lock.json +++ b/llama_stack/ui/package-lock.json @@ -9,27 +9,27 @@ "version": "0.1.0", "dependencies": { "@radix-ui/react-collapsible": "^1.1.12", - "@radix-ui/react-dialog": "^1.1.13", - "@radix-ui/react-dropdown-menu": "^2.1.14", - "@radix-ui/react-select": "^2.2.5", + "@radix-ui/react-dialog": "^1.1.15", + "@radix-ui/react-dropdown-menu": "^2.1.16", + "@radix-ui/react-select": "^2.2.6", "@radix-ui/react-separator": "^1.1.7", "@radix-ui/react-slot": "^1.2.3", - "@radix-ui/react-tooltip": "^1.2.6", + "@radix-ui/react-tooltip": "^1.2.8", "class-variance-authority": "^0.7.1", "clsx": "^2.1.1", - "framer-motion": "^11.18.2", - "llama-stack-client": "^0.2.19", - "lucide-react": "^0.510.0", - "next": "15.3.3", + "framer-motion": "^12.23.12", + "llama-stack-client": "^0.2.22", + "lucide-react": "^0.542.0", + "next": "15.5.3", "next-auth": "^4.24.11", "next-themes": "^0.4.6", "react": "^19.0.0", - "react-dom": "^19.0.0", + "react-dom": "^19.1.1", "react-markdown": "^10.1.0", "remark-gfm": "^4.0.1", - "remeda": "^2.30.0", + "remeda": "^2.32.0", "shiki": "^1.29.2", - "sonner": "^2.0.6", + "sonner": "^2.0.7", "tailwind-merge": "^3.3.1" }, "devDependencies": { @@ -39,16 +39,16 @@ "@testing-library/jest-dom": "^6.8.0", "@testing-library/react": "^16.3.0", "@types/jest": "^29.5.14", - "@types/node": "^20", + "@types/node": "^24", "@types/react": "^19", "@types/react-dom": "^19", "eslint": "^9", - "eslint-config-next": "15.3.2", + "eslint-config-next": "15.5.2", "eslint-config-prettier": "^10.1.8", "eslint-plugin-prettier": "^5.5.4", "jest": "^29.7.0", - "jest-environment-jsdom": "^29.7.0", - "prettier": "3.5.3", + "jest-environment-jsdom": "^30.1.2", + "prettier": "3.6.2", "tailwindcss": "^4", "ts-node": "^10.9.2", "tw-animate-css": "^1.2.9", @@ -89,6 +89,27 @@ "node": ">=6.0.0" } }, + "node_modules/@asamuzakjp/css-color": { + "version": "3.2.0", + "resolved": "https://registry.npmjs.org/@asamuzakjp/css-color/-/css-color-3.2.0.tgz", + "integrity": "sha512-K1A6z8tS3XsmCMM86xoWdn7Fkdn9m6RSVtocUrJYIwZnFVkng/PvkEoWtOWmP+Scc6saYWHWZYbndEEXxl24jw==", + "dev": true, + "license": "MIT", + "dependencies": { + "@csstools/css-calc": "^2.1.3", + "@csstools/css-color-parser": "^3.0.9", + "@csstools/css-parser-algorithms": "^3.0.4", + "@csstools/css-tokenizer": "^3.0.3", + "lru-cache": "^10.4.3" + } + }, + "node_modules/@asamuzakjp/css-color/node_modules/lru-cache": { + "version": "10.4.3", + "resolved": "https://registry.npmjs.org/lru-cache/-/lru-cache-10.4.3.tgz", + "integrity": "sha512-JNAzZcXrCt42VGLuYz0zfAzDfAvJWW6AfYlDBQyDV5DClI2m5sAmK+OIO7s59XfsRsWHp02jAJrRadPRGTt6SQ==", + "dev": true, + "license": "ISC" + }, "node_modules/@babel/code-frame": { "version": "7.27.1", "resolved": "https://registry.npmjs.org/@babel/code-frame/-/code-frame-7.27.1.tgz", @@ -651,6 +672,121 @@ "@jridgewell/sourcemap-codec": "^1.4.10" } }, + "node_modules/@csstools/color-helpers": { + "version": "5.1.0", + "resolved": "https://registry.npmjs.org/@csstools/color-helpers/-/color-helpers-5.1.0.tgz", + "integrity": "sha512-S11EXWJyy0Mz5SYvRmY8nJYTFFd1LCNV+7cXyAgQtOOuzb4EsgfqDufL+9esx72/eLhsRdGZwaldu/h+E4t4BA==", + "dev": true, + "funding": 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"dependencies": { - "xml-name-validator": "^4.0.0" + "xml-name-validator": "^5.0.0" }, "engines": { - "node": ">=14" + "node": ">=18" } }, "node_modules/walker": { @@ -14334,26 +14469,26 @@ "license": "BSD-2-Clause" }, "node_modules/whatwg-encoding": { - "version": "2.0.0", - "resolved": "https://registry.npmjs.org/whatwg-encoding/-/whatwg-encoding-2.0.0.tgz", - "integrity": "sha512-p41ogyeMUrw3jWclHWTQg1k05DSVXPLcVxRTYsXUk+ZooOCZLcoYgPZ/HL/D/N+uQPOtcp1me1WhBEaX02mhWg==", + "version": "3.1.1", + "resolved": "https://registry.npmjs.org/whatwg-encoding/-/whatwg-encoding-3.1.1.tgz", + "integrity": "sha512-6qN4hJdMwfYBtE3YBTTHhoeuUrDBPZmbQaxWAqSALV/MeEnR5z1xd8UKud2RAkFoPkmB+hli1TZSnyi84xz1vQ==", "dev": true, "license": "MIT", "dependencies": { "iconv-lite": "0.6.3" }, "engines": { - "node": ">=12" + "node": ">=18" } }, "node_modules/whatwg-mimetype": { - "version": "3.0.0", - "resolved": "https://registry.npmjs.org/whatwg-mimetype/-/whatwg-mimetype-3.0.0.tgz", - "integrity": 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"version": "4.0.0", - "resolved": "https://registry.npmjs.org/xml-name-validator/-/xml-name-validator-4.0.0.tgz", - "integrity": "sha512-ICP2e+jsHvAj2E2lIHxa5tjXRlKDJo4IdvPvCXbXQGdzSfmSpNVyIKMvoZHjDY9DP0zV17iI85o90vRFXNccRw==", + "version": "5.0.0", + "resolved": "https://registry.npmjs.org/xml-name-validator/-/xml-name-validator-5.0.0.tgz", + "integrity": "sha512-EvGK8EJ3DhaHfbRlETOWAS5pO9MZITeauHKJyb8wyajUfQUenkIg2MvLDTZ4T/TgIcm3HU0TFBgWWboAZ30UHg==", "dev": true, "license": "Apache-2.0", "engines": { - "node": ">=12" + "node": ">=18" } }, "node_modules/xmlchars": { diff --git a/llama_stack/ui/package.json b/llama_stack/ui/package.json index 31c836057..2ba81ea84 100644 --- a/llama_stack/ui/package.json +++ b/llama_stack/ui/package.json @@ -14,27 +14,27 @@ }, "dependencies": { "@radix-ui/react-collapsible": "^1.1.12", - "@radix-ui/react-dialog": "^1.1.13", - "@radix-ui/react-dropdown-menu": "^2.1.14", - "@radix-ui/react-select": "^2.2.5", + "@radix-ui/react-dialog": "^1.1.15", + "@radix-ui/react-dropdown-menu": "^2.1.16", + "@radix-ui/react-select": "^2.2.6", "@radix-ui/react-separator": "^1.1.7", "@radix-ui/react-slot": "^1.2.3", - "@radix-ui/react-tooltip": "^1.2.6", + "@radix-ui/react-tooltip": "^1.2.8", "class-variance-authority": "^0.7.1", "clsx": "^2.1.1", - "framer-motion": "^11.18.2", - "llama-stack-client": "^0.2.19", - "lucide-react": "^0.510.0", - "next": "15.3.3", + "framer-motion": "^12.23.12", + "llama-stack-client": "^0.2.22", + "lucide-react": "^0.542.0", + "next": "15.5.3", "next-auth": "^4.24.11", "next-themes": "^0.4.6", "react": "^19.0.0", - "react-dom": "^19.0.0", + "react-dom": "^19.1.1", "react-markdown": "^10.1.0", "remark-gfm": "^4.0.1", - "remeda": "^2.30.0", + "remeda": "^2.32.0", "shiki": "^1.29.2", - "sonner": "^2.0.6", + "sonner": "^2.0.7", "tailwind-merge": "^3.3.1" }, "devDependencies": { @@ -44,16 +44,16 @@ "@testing-library/jest-dom": "^6.8.0", "@testing-library/react": "^16.3.0", "@types/jest": "^29.5.14", - "@types/node": "^20", + "@types/node": "^24", "@types/react": "^19", "@types/react-dom": "^19", "eslint": "^9", - "eslint-config-next": "15.3.2", + "eslint-config-next": "15.5.2", "eslint-config-prettier": "^10.1.8", "eslint-plugin-prettier": "^5.5.4", "jest": "^29.7.0", - "jest-environment-jsdom": "^29.7.0", - "prettier": "3.5.3", + "jest-environment-jsdom": "^30.1.2", + "prettier": "3.6.2", "tailwindcss": "^4", "ts-node": "^10.9.2", "tw-animate-css": "^1.2.9", diff --git a/pyproject.toml b/pyproject.toml index dd8529546..86a32f978 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -7,7 +7,7 @@ required-version = ">=0.7.0" [project] name = "llama_stack" -version = "0.2.19" +version = "0.2.22" authors = [{ name = "Meta Llama", email = "llama-oss@meta.com" }] description = "Llama Stack" readme = "README.md" @@ -31,13 +31,12 @@ dependencies = [ "huggingface-hub>=0.34.0,<1.0", "jinja2>=3.1.6", "jsonschema", - "llama-stack-client>=0.2.19", - "llama-api-client>=0.1.2", - "openai>=1.99.6,<1.100.0", + "llama-stack-client>=0.2.22", + "openai>=1.100.0", # for expires_after support "prompt-toolkit", "python-dotenv", "python-jose[cryptography]", - "pydantic>=2", + "pydantic>=2.11.9", "rich", "starlette", "termcolor", @@ -56,7 +55,7 @@ dependencies = [ ui = [ "streamlit", "pandas", - "llama-stack-client>=0.2.19", + "llama-stack-client>=0.2.22", "streamlit-option-menu", ] @@ -81,9 +80,9 @@ dev = [ unit = [ "sqlite-vec", "ollama", - "openai", "aiosqlite", "aiohttp", + "psycopg2-binary>=2.9.0", "pypdf", "mcp", "chardet", @@ -92,7 +91,7 @@ unit = [ "sqlalchemy[asyncio]>=2.0.41", "blobfile", "faiss-cpu", - "pymilvus>=2.5.12", + "pymilvus>=2.6.1", "milvus-lite>=2.5.0", "litellm", "together", @@ -105,21 +104,21 @@ unit = [ # separately. If you are using "uv" to execute your tests, you can use the "--group" flag to specify extra # dependencies. test = [ - "openai", "aiosqlite", "aiohttp", "torch>=2.6.0", "torchvision>=0.21.0", "chardet", + "psycopg2-binary>=2.9.0", "pypdf", "mcp", - "datasets", + "datasets>=4.0.0", "autoevals", "transformers", "sqlalchemy", "sqlalchemy[asyncio]>=2.0.41", "requests", - "pymilvus>=2.5.12", + "pymilvus>=2.6.1", "milvus-lite>=2.5.0", "weaviate-client>=4.16.4", ] @@ -142,9 +141,9 @@ docs = [ "sphinxcontrib.openapi", "requests", ] -codegen = ["rich", "pydantic", "jinja2>=3.1.6"] +codegen = ["rich", "pydantic>=2.11.9", "jinja2>=3.1.6"] benchmark = [ - "locust>=2.37.14", + "locust>=2.39.1", ] [project.urls] @@ -178,6 +177,7 @@ exclude = [ ".pre-commit-config.yaml", "*.md", ".flake8", + "benchmarking/k8s-benchmark/results", ] [tool.ruff.lint] @@ -355,6 +355,7 @@ warn_required_dynamic_aliases = true classmethod-decorators = ["classmethod", "pydantic.field_validator"] [tool.pytest.ini_options] +addopts = ["--durations=10"] asyncio_mode = "auto" markers = [ "allow_network: Allow network access for specific unit tests", diff --git a/scripts/distro_codegen.py b/scripts/distro_codegen.py index b6698ef9a..ff5025b78 100755 --- a/scripts/distro_codegen.py +++ b/scripts/distro_codegen.py @@ -56,7 +56,7 @@ def process_distro(distro_dir: Path, progress, change_tracker: ChangedPathTracke distro = template_func() yaml_output_dir = REPO_ROOT / "llama_stack" / "distributions" / distro.name - doc_output_dir = REPO_ROOT / "docs/source/distributions" / f"{distro.distro_type}_distro" + doc_output_dir = REPO_ROOT / "docs/docs/distributions" / f"{distro.distro_type}_distro" change_tracker.add_paths(yaml_output_dir, doc_output_dir) distro.save_distribution( yaml_output_dir=yaml_output_dir, diff --git a/scripts/get_setup_env.py b/scripts/get_setup_env.py new file mode 100755 index 000000000..fad601e76 --- /dev/null +++ b/scripts/get_setup_env.py @@ -0,0 +1,71 @@ +#!/usr/bin/env python3 +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the terms described in the LICENSE file in +# the root directory of this source tree. + +""" +Small helper script to extract environment variables from a test setup. +Used by integration-tests.sh to set environment variables before starting the server. +""" + +import argparse +import sys + +from tests.integration.suites import SETUP_DEFINITIONS, SUITE_DEFINITIONS + + +def get_setup_env_vars(setup_name, suite_name=None): + """ + Get environment variables for a setup, with optional suite default fallback. + + Args: + setup_name: Name of the setup (e.g., 'ollama', 'gpt') + suite_name: Optional suite name to get default setup if setup_name is None + + Returns: + Dictionary of environment variables + """ + # If no setup specified, try to get default from suite + if not setup_name and suite_name: + suite = SUITE_DEFINITIONS.get(suite_name) + if suite and suite.default_setup: + setup_name = suite.default_setup + + if not setup_name: + return {} + + setup = SETUP_DEFINITIONS.get(setup_name) + if not setup: + print( + f"Error: Unknown setup '{setup_name}'. Available: {', '.join(sorted(SETUP_DEFINITIONS.keys()))}", + file=sys.stderr, + ) + sys.exit(1) + + return setup.env + + +def main(): + parser = argparse.ArgumentParser(description="Extract environment variables from a test setup") + parser.add_argument("--setup", help="Setup name (e.g., ollama, gpt)") + parser.add_argument("--suite", help="Suite name to get default setup from if --setup not provided") + parser.add_argument("--format", choices=["bash", "json"], default="bash", help="Output format (default: bash)") + + args = parser.parse_args() + + env_vars = get_setup_env_vars(args.setup, args.suite) + + if args.format == "bash": + # Output as bash export statements + for key, value in env_vars.items(): + print(f"export {key}='{value}'") + elif args.format == "json": + import json + + print(json.dumps(env_vars)) + + +if __name__ == "__main__": + main() diff --git a/scripts/github/schedule-record-workflow.sh b/scripts/github/schedule-record-workflow.sh index e381b60b6..afe664f80 100755 --- a/scripts/github/schedule-record-workflow.sh +++ b/scripts/github/schedule-record-workflow.sh @@ -13,9 +13,10 @@ set -euo pipefail # Default values BRANCH="" +PR_HEAD_REPO="" TEST_SUBDIRS="" -TEST_PROVIDER="ollama" -RUN_VISION_TESTS=false +TEST_SETUP="ollama" +TEST_SUITE="base" TEST_PATTERN="" # Help function @@ -27,24 +28,24 @@ Trigger the integration test recording workflow remotely. This way you do not ne OPTIONS: -b, --branch BRANCH Branch to run the workflow on (defaults to current branch) - -s, --test-subdirs DIRS Comma-separated list of test subdirectories to run (REQUIRED) - -p, --test-provider PROVIDER Test provider to use: vllm or ollama (default: ollama) - -v, --run-vision-tests Include vision tests in the recording - -k, --test-pattern PATTERN Regex pattern to pass to pytest -k + -t, --suite SUITE Test suite to use: base, responses, vision, etc. (default: base) + -p, --setup SETUP Test setup to use: vllm, ollama, gpt, etc. (default: ollama) + -s, --subdirs DIRS Comma-separated list of test subdirectories to run (overrides suite) + -k, --pattern PATTERN Regex pattern to pass to pytest -k -h, --help Show this help message EXAMPLES: # Record tests for current branch with agents subdirectory - $0 --test-subdirs "agents" + $0 --subdirs "agents" # Record tests for specific branch with vision tests - $0 -b my-feature-branch --test-subdirs "inference" --run-vision-tests + $0 -b my-feature-branch --suite vision - # Record multiple test subdirectories with specific provider - $0 --test-subdirs "agents,inference" --test-provider vllm + # Record multiple test subdirectories with specific setup + $0 --subdirs "agents,inference" --setup vllm # Record tests matching a specific pattern - $0 --test-subdirs "inference" --test-pattern "test_streaming" + $0 --subdirs "inference" --pattern "test_streaming" EOF } @@ -63,19 +64,19 @@ while [[ $# -gt 0 ]]; do BRANCH="$2" shift 2 ;; - -s|--test-subdirs) + -s|--subdirs) TEST_SUBDIRS="$2" shift 2 ;; - -p|--test-provider) - TEST_PROVIDER="$2" + -p|--setup) + TEST_SETUP="$2" shift 2 ;; - -v|--run-vision-tests) - RUN_VISION_TESTS=true - shift + -t|--suite) + TEST_SUITE="$2" + shift 2 ;; - -k|--test-pattern) + -k|--pattern) TEST_PATTERN="$2" shift 2 ;; @@ -92,22 +93,17 @@ while [[ $# -gt 0 ]]; do done # Validate required parameters -if [[ -z "$TEST_SUBDIRS" ]]; then - echo "Error: --test-subdirs is required" - echo "Please specify which test subdirectories to run, e.g.:" - echo " $0 --test-subdirs \"agents,inference\"" - echo " $0 --test-subdirs \"inference\" --run-vision-tests" +if [[ -z "$TEST_SUBDIRS" && -z "$TEST_SUITE" ]]; then + echo "Error: --subdirs or --suite is required" + echo "Please specify which test subdirectories to run or test suite to use, e.g.:" + echo " $0 --subdirs \"agents,inference\"" + echo " $0 --suite vision" echo "" exit 1 fi -# Validate test provider -if [[ "$TEST_PROVIDER" != "vllm" && "$TEST_PROVIDER" != "ollama" ]]; then - echo "❌ Error: Invalid test provider '$TEST_PROVIDER'" - echo " Supported providers: vllm, ollama" - echo " Example: $0 --test-subdirs \"agents\" --test-provider vllm" - exit 1 -fi +# Validate test setup (optional - setups are validated by the workflow itself) +# Common setups: ollama, vllm, gpt, etc. # Check if required tools are installed if ! command -v gh &> /dev/null; then @@ -237,22 +233,25 @@ fi # Build the workflow dispatch command echo "Triggering integration test recording workflow..." echo "Branch: $BRANCH" -echo "Test provider: $TEST_PROVIDER" +echo "Test setup: $TEST_SETUP" echo "Test subdirs: $TEST_SUBDIRS" -echo "Run vision tests: $RUN_VISION_TESTS" +echo "Test suite: $TEST_SUITE" echo "Test pattern: ${TEST_PATTERN:-"(none)"}" echo "" # Prepare inputs for gh workflow run -INPUTS="-f test-subdirs='$TEST_SUBDIRS'" -if [[ -n "$TEST_PROVIDER" ]]; then - INPUTS="$INPUTS -f test-provider='$TEST_PROVIDER'" +INPUTS= +if [[ -n "$TEST_SUBDIRS" ]]; then + INPUTS="$INPUTS -f subdirs='$TEST_SUBDIRS'" fi -if [[ "$RUN_VISION_TESTS" == "true" ]]; then - INPUTS="$INPUTS -f run-vision-tests=true" +if [[ -n "$TEST_SETUP" ]]; then + INPUTS="$INPUTS -f test-setup='$TEST_SETUP'" +fi +if [[ -n "$TEST_SUITE" ]]; then + INPUTS="$INPUTS -f suite='$TEST_SUITE'" fi if [[ -n "$TEST_PATTERN" ]]; then - INPUTS="$INPUTS -f test-pattern='$TEST_PATTERN'" + INPUTS="$INPUTS -f pattern='$TEST_PATTERN'" fi # Run the workflow diff --git a/scripts/install.sh b/scripts/install.sh index e49924512..f6fbc259c 100755 --- a/scripts/install.sh +++ b/scripts/install.sh @@ -92,11 +92,11 @@ Options: -h, --help Show this help message For more information: - Documentation: https://llama-stack.readthedocs.io/ - GitHub: https://github.com/meta-llama/llama-stack + Documentation: https://llamastack.github.io/latest/ + GitHub: https://github.com/llamastack/llama-stack Report issues: - https://github.com/meta-llama/llama-stack/issues + https://github.com/llamastack/llama-stack/issues EOF } @@ -241,8 +241,8 @@ fi log "" log "🎉 Llama Stack is ready!" log "👉 API endpoint: http://localhost:${PORT}" -log "📖 Documentation: https://llama-stack.readthedocs.io/en/latest/references/index.html" +log "📖 Documentation: https://llamastack.github.io/latest/references/api_reference/index.html" log "💻 To access the llama stack CLI, exec into the container:" log " $ENGINE exec -ti llama-stack bash" -log "🐛 Report an issue @ https://github.com/meta-llama/llama-stack/issues if you think it's a bug" +log "🐛 Report an issue @ https://github.com/llamastack/llama-stack/issues if you think it's a bug" log "" diff --git a/scripts/integration-tests.sh b/scripts/integration-tests.sh index e152444e1..eee60951d 100755 --- a/scripts/integration-tests.sh +++ b/scripts/integration-tests.sh @@ -13,10 +13,10 @@ set -euo pipefail # Default values STACK_CONFIG="" -PROVIDER="" +TEST_SUITE="base" +TEST_SETUP="" TEST_SUBDIRS="" TEST_PATTERN="" -RUN_VISION_TESTS="false" INFERENCE_MODE="replay" EXTRA_PARAMS="" @@ -27,25 +27,30 @@ Usage: $0 [OPTIONS] Options: --stack-config STRING Stack configuration to use (required) - --provider STRING Provider to use (ollama, vllm, etc.) (required) - --test-subdirs STRING Comma-separated list of test subdirectories to run (default: 'inference') - --run-vision-tests Run vision tests instead of regular tests + --suite STRING Test suite to run (default: 'base') + --setup STRING Test setup (models, env) to use (e.g., 'ollama', 'ollama-vision', 'gpt', 'vllm') --inference-mode STRING Inference mode: record or replay (default: replay) - --test-pattern STRING Regex pattern to pass to pytest -k + --subdirs STRING Comma-separated list of test subdirectories to run (overrides suite) + --pattern STRING Regex pattern to pass to pytest -k --help Show this help message +Suites are defined in tests/integration/suites.py and define which tests to run. +Setups are defined in tests/integration/setups.py and provide global configuration (models, env). + +You can also specify subdirectories (of tests/integration) to select tests from, which will override the suite. + Examples: # Basic inference tests with ollama - $0 --stack-config server:ci-tests --provider ollama + $0 --stack-config server:ci-tests --suite base --setup ollama # Multiple test directories with vllm - $0 --stack-config server:ci-tests --provider vllm --test-subdirs 'inference,agents' + $0 --stack-config server:ci-tests --subdirs 'inference,agents' --setup vllm # Vision tests with ollama - $0 --stack-config server:ci-tests --provider ollama --run-vision-tests + $0 --stack-config server:ci-tests --suite vision # default setup for this suite is ollama-vision # Record mode for updating test recordings - $0 --stack-config server:ci-tests --provider ollama --inference-mode record + $0 --stack-config server:ci-tests --suite base --inference-mode record EOF } @@ -56,23 +61,23 @@ while [[ $# -gt 0 ]]; do STACK_CONFIG="$2" shift 2 ;; - --provider) - PROVIDER="$2" + --setup) + TEST_SETUP="$2" shift 2 ;; - --test-subdirs) + --subdirs) TEST_SUBDIRS="$2" shift 2 ;; - --run-vision-tests) - RUN_VISION_TESTS="true" - shift + --suite) + TEST_SUITE="$2" + shift 2 ;; --inference-mode) INFERENCE_MODE="$2" shift 2 ;; - --test-pattern) + --pattern) TEST_PATTERN="$2" shift 2 ;; @@ -96,18 +101,23 @@ if [[ -z "$STACK_CONFIG" ]]; then exit 1 fi -if [[ -z "$PROVIDER" ]]; then - echo "Error: --provider is required" +if [[ -z "$TEST_SETUP" && -n "$TEST_SUBDIRS" ]]; then + echo "Error: --test-setup is required when --test-subdirs is provided" usage exit 1 fi +if [[ -z "$TEST_SUITE" && -z "$TEST_SUBDIRS" ]]; then + echo "Error: --test-suite or --test-subdirs is required" + exit 1 +fi + echo "=== Llama Stack Integration Test Runner ===" echo "Stack Config: $STACK_CONFIG" -echo "Provider: $PROVIDER" -echo "Test Subdirs: $TEST_SUBDIRS" -echo "Vision Tests: $RUN_VISION_TESTS" +echo "Setup: $TEST_SETUP" echo "Inference Mode: $INFERENCE_MODE" +echo "Test Suite: $TEST_SUITE" +echo "Test Subdirs: $TEST_SUBDIRS" echo "Test Pattern: $TEST_PATTERN" echo "" @@ -122,31 +132,28 @@ echo "" # Set environment variables export LLAMA_STACK_CLIENT_TIMEOUT=300 -export LLAMA_STACK_TEST_INFERENCE_MODE="$INFERENCE_MODE" - -# Configure provider-specific settings -if [[ "$PROVIDER" == "ollama" ]]; then - export OLLAMA_URL="http://0.0.0.0:11434" - export TEXT_MODEL="ollama/llama3.2:3b-instruct-fp16" - export SAFETY_MODEL="ollama/llama-guard3:1b" - EXTRA_PARAMS="--safety-shield=llama-guard" -else - export VLLM_URL="http://localhost:8000/v1" - export TEXT_MODEL="vllm/meta-llama/Llama-3.2-1B-Instruct" - EXTRA_PARAMS="" -fi THIS_DIR=$(dirname "$0") + +if [[ -n "$TEST_SETUP" ]]; then + EXTRA_PARAMS="--setup=$TEST_SETUP" +fi + +# Apply setup-specific environment variables (needed for server startup and tests) +echo "=== Applying Setup Environment Variables ===" + +# the server needs this +export LLAMA_STACK_TEST_INFERENCE_MODE="$INFERENCE_MODE" + +SETUP_ENV=$(PYTHONPATH=$THIS_DIR/.. python "$THIS_DIR/get_setup_env.py" --suite "$TEST_SUITE" --setup "$TEST_SETUP" --format bash) +echo "Setting up environment variables:" +echo "$SETUP_ENV" +eval "$SETUP_ENV" +echo "" + ROOT_DIR="$THIS_DIR/.." cd $ROOT_DIR -# Set recording directory -if [[ "$RUN_VISION_TESTS" == "true" ]]; then - export LLAMA_STACK_TEST_RECORDING_DIR="tests/integration/recordings/vision" -else - export LLAMA_STACK_TEST_RECORDING_DIR="tests/integration/recordings" -fi - # check if "llama" and "pytest" are available. this script does not use `uv run` given # it can be used in a pre-release environment where we have not been able to tell # uv about pre-release dependencies properly (yet). @@ -162,6 +169,18 @@ fi # Start Llama Stack Server if needed if [[ "$STACK_CONFIG" == *"server:"* ]]; then + stop_server() { + echo "Stopping Llama Stack Server..." + pids=$(lsof -i :8321 | awk 'NR>1 {print $2}') + if [[ -n "$pids" ]]; then + echo "Killing Llama Stack Server processes: $pids" + kill -9 $pids + else + echo "No Llama Stack Server processes found ?!" + fi + echo "Llama Stack Server stopped" + } + # check if server is already running if curl -s http://localhost:8321/v1/health 2>/dev/null | grep -q "OK"; then echo "Llama Stack Server is already running, skipping start" @@ -185,14 +204,16 @@ if [[ "$STACK_CONFIG" == *"server:"* ]]; then done echo "" fi + + trap stop_server EXIT ERR INT TERM fi # Run tests echo "=== Running Integration Tests ===" EXCLUDE_TESTS="builtin_tool or safety_with_image or code_interpreter or test_rag" -# Additional exclusions for vllm provider -if [[ "$PROVIDER" == "vllm" ]]; then +# Additional exclusions for vllm setup +if [[ "$TEST_SETUP" == "vllm" ]]; then EXCLUDE_TESTS="${EXCLUDE_TESTS} or test_inference_store_tool_calls" fi @@ -201,86 +222,50 @@ if [[ -n "$TEST_PATTERN" ]]; then PYTEST_PATTERN="${PYTEST_PATTERN} and $TEST_PATTERN" fi -# Run vision tests if specified -if [[ "$RUN_VISION_TESTS" == "true" ]]; then - echo "Running vision tests..." - set +e - pytest -s -v tests/integration/inference/test_vision_inference.py \ - --stack-config="$STACK_CONFIG" \ - -k "$PYTEST_PATTERN" \ - --vision-model=ollama/llama3.2-vision:11b \ - --embedding-model=sentence-transformers/all-MiniLM-L6-v2 \ - --color=yes $EXTRA_PARAMS \ - --capture=tee-sys - exit_code=$? - set -e - - if [ $exit_code -eq 0 ]; then - echo "✅ Vision tests completed successfully" - elif [ $exit_code -eq 5 ]; then - echo "⚠️ No vision tests collected (pattern matched no tests)" - else - echo "❌ Vision tests failed" - exit 1 - fi - exit 0 -fi - -# Run regular tests -if [[ -z "$TEST_SUBDIRS" ]]; then - TEST_SUBDIRS=$(find tests/integration -maxdepth 1 -mindepth 1 -type d | - sed 's|tests/integration/||' | - grep -Ev "^(__pycache__|fixtures|test_cases|recordings|non_ci|post_training)$" | - sort) -fi echo "Test subdirs to run: $TEST_SUBDIRS" -# Collect all test files for the specified test types -TEST_FILES="" -for test_subdir in $(echo "$TEST_SUBDIRS" | tr ',' '\n'); do - # Skip certain test types for vllm provider - if [[ "$PROVIDER" == "vllm" ]]; then - if [[ "$test_subdir" == "safety" ]] || [[ "$test_subdir" == "post_training" ]] || [[ "$test_subdir" == "tool_runtime" ]]; then - echo "Skipping $test_subdir for vllm provider" - continue +if [[ -n "$TEST_SUBDIRS" ]]; then + # Collect all test files for the specified test types + TEST_FILES="" + for test_subdir in $(echo "$TEST_SUBDIRS" | tr ',' '\n'); do + if [[ -d "tests/integration/$test_subdir" ]]; then + # Find all Python test files in this directory + test_files=$(find tests/integration/$test_subdir -name "test_*.py" -o -name "*_test.py") + if [[ -n "$test_files" ]]; then + TEST_FILES="$TEST_FILES $test_files" + echo "Added test files from $test_subdir: $(echo $test_files | wc -w) files" + fi + else + echo "Warning: Directory tests/integration/$test_subdir does not exist" fi + done + + if [[ -z "$TEST_FILES" ]]; then + echo "No test files found for the specified test types" + exit 1 fi - if [[ "$STACK_CONFIG" != *"server:"* ]] && [[ "$test_subdir" == "batches" ]]; then - echo "Skipping $test_subdir for library client until types are supported" - continue - fi + echo "" + echo "=== Running all collected tests in a single pytest command ===" + echo "Total test files: $(echo $TEST_FILES | wc -w)" - if [[ -d "tests/integration/$test_subdir" ]]; then - # Find all Python test files in this directory - test_files=$(find tests/integration/$test_subdir -name "test_*.py" -o -name "*_test.py") - if [[ -n "$test_files" ]]; then - TEST_FILES="$TEST_FILES $test_files" - echo "Added test files from $test_subdir: $(echo $test_files | wc -w) files" - fi - else - echo "Warning: Directory tests/integration/$test_subdir does not exist" - fi -done - -if [[ -z "$TEST_FILES" ]]; then - echo "No test files found for the specified test types" - exit 1 + PYTEST_TARGET="$TEST_FILES" +else + PYTEST_TARGET="tests/integration/" + EXTRA_PARAMS="$EXTRA_PARAMS --suite=$TEST_SUITE" fi -echo "" -echo "=== Running all collected tests in a single pytest command ===" -echo "Total test files: $(echo $TEST_FILES | wc -w)" - set +e -pytest -s -v $TEST_FILES \ +set -x +pytest -s -v $PYTEST_TARGET \ --stack-config="$STACK_CONFIG" \ + --inference-mode="$INFERENCE_MODE" \ -k "$PYTEST_PATTERN" \ - --text-model="$TEXT_MODEL" \ - --embedding-model=sentence-transformers/all-MiniLM-L6-v2 \ - --color=yes $EXTRA_PARAMS \ + $EXTRA_PARAMS \ + --color=yes \ --capture=tee-sys exit_code=$? +set +x set -e if [ $exit_code -eq 0 ]; then diff --git a/scripts/provider_codegen.py b/scripts/provider_codegen.py index 17efa2138..34e4c0687 100755 --- a/scripts/provider_codegen.py +++ b/scripts/provider_codegen.py @@ -158,7 +158,7 @@ def get_config_class_info(config_class_path: str) -> dict[str, Any]: def generate_provider_docs(progress, provider_spec: Any, api_name: str) -> str: - """Generate markdown documentation for a provider.""" + """Generate MDX documentation for a provider.""" provider_type = provider_spec.provider_type config_class = provider_spec.config_class @@ -166,10 +166,7 @@ def generate_provider_docs(progress, provider_spec: Any, api_name: str) -> str: if "error" in config_info: progress.print(config_info["error"]) - md_lines = [] - md_lines.append(f"# {provider_type}") - md_lines.append("") - + # Extract description for frontmatter description = "" if hasattr(provider_spec, "description") and provider_spec.description: description = provider_spec.description @@ -182,6 +179,37 @@ def generate_provider_docs(progress, provider_spec: Any, api_name: str) -> str: elif config_info.get("docstring"): description = config_info["docstring"] + # Create sidebar label (clean up provider_type for display) + sidebar_label = provider_type.replace("::", " - ").replace("_", " ") + if sidebar_label.startswith("inline - "): + sidebar_label = sidebar_label[9:].title() # Remove "inline - " prefix and title case + else: + sidebar_label = sidebar_label.title() + + md_lines = [] + + # Add YAML frontmatter + md_lines.append("---") + if description: + # Handle multi-line descriptions in YAML - keep it simple for single line + if "\n" in description.strip(): + md_lines.append("description: |") + for line in description.strip().split("\n"): + # Avoid trailing whitespace by only adding spaces to non-empty lines + md_lines.append(f" {line}" if line.strip() else "") + else: + # For single line descriptions, format properly for YAML + clean_desc = description.strip().replace('"', '\\"') + md_lines.append(f'description: "{clean_desc}"') + md_lines.append(f"sidebar_label: {sidebar_label}") + md_lines.append(f"title: {provider_type}") + md_lines.append("---") + md_lines.append("") + + # Add main title + md_lines.append(f"# {provider_type}") + md_lines.append("") + if description: md_lines.append("## Description") md_lines.append("") @@ -198,16 +226,51 @@ def generate_provider_docs(progress, provider_spec: Any, api_name: str) -> str: field_type = field_info["type"].replace("|", "\\|") required = "Yes" if field_info["required"] else "No" default = str(field_info["default"]) if field_info["default"] is not None else "" - description = field_info["description"] or "" - md_lines.append(f"| `{field_name}` | `{field_type}` | {required} | {default} | {description} |") + # Handle multiline default values and escape problematic characters for MDX + if "\n" in default: + # For multiline defaults, escape angle brackets and use
for line breaks + lines = default.split("\n") + escaped_lines = [] + for line in lines: + if line.strip(): + # Escape angle brackets and wrap template tokens in backticks + escaped_line = line.strip().replace("<", "<").replace(">", ">") + if ("{" in escaped_line and "}" in escaped_line) or ( + "<|" in escaped_line and "|>" in escaped_line + ): + escaped_lines.append(f"`{escaped_line}`") + else: + escaped_lines.append(escaped_line) + else: + escaped_lines.append("") + default = "
".join(escaped_lines) + else: + # For single line defaults, escape angle brackets first + escaped_default = default.replace("<", "<").replace(">", ">") + # Then wrap template tokens in backticks + if ("{" in escaped_default and "}" in escaped_default) or ( + "<|" in escaped_default and "|>" in escaped_default + ): + default = f"`{escaped_default}`" + else: + # Apply additional escaping for curly braces + default = escaped_default.replace("{", "{").replace("}", "}") + + description_text = field_info["description"] or "" + # Escape curly braces in description text for MDX compatibility + description_text = description_text.replace("{", "{").replace("}", "}") + + md_lines.append(f"| `{field_name}` | `{field_type}` | {required} | {default} | {description_text} |") md_lines.append("") if config_info.get("accepts_extra_config"): + md_lines.append(":::note") md_lines.append( - "```{note}\n This configuration class accepts additional fields beyond those listed above. You can pass any additional configuration options that will be forwarded to the underlying provider.\n ```\n" + "This configuration class accepts additional fields beyond those listed above. You can pass any additional configuration options that will be forwarded to the underlying provider." ) + md_lines.append(":::") md_lines.append("") if config_info.get("sample_config"): @@ -240,24 +303,61 @@ def generate_provider_docs(progress, provider_spec: Any, api_name: str) -> str: return obj sample_config_dict = convert_pydantic_to_dict(sample_config) - md_lines.append(yaml.dump(sample_config_dict, default_flow_style=False, sort_keys=False)) + # Strip trailing newlines from yaml.dump to prevent extra blank lines + yaml_output = yaml.dump(sample_config_dict, default_flow_style=False, sort_keys=False).rstrip() + md_lines.append(yaml_output) else: md_lines.append("# No sample configuration available.") except Exception as e: md_lines.append(f"# Error generating sample config: {str(e)}") md_lines.append("```") - md_lines.append("") if hasattr(provider_spec, "deprecation_warning") and provider_spec.deprecation_warning: md_lines.append("## Deprecation Notice") md_lines.append("") - md_lines.append(f"```{{warning}}\n{provider_spec.deprecation_warning}\n```") - md_lines.append("") + md_lines.append(":::warning") + md_lines.append(provider_spec.deprecation_warning) + md_lines.append(":::") if hasattr(provider_spec, "deprecation_error") and provider_spec.deprecation_error: md_lines.append("## Deprecation Error") md_lines.append("") - md_lines.append(f"❌ **Error**: {provider_spec.deprecation_error}") + md_lines.append(":::danger") + md_lines.append(f"**Error**: {provider_spec.deprecation_error}") + md_lines.append(":::") + + return "\n".join(md_lines) + "\n" + + +def generate_index_docs(api_name: str, api_docstring: str | None, provider_entries: list) -> str: + """Generate MDX documentation for the index file.""" + # Create sidebar label for the API + sidebar_label = api_name.replace("_", " ").title() + + md_lines = [] + + # Add YAML frontmatter for index + md_lines.append("---") + if api_docstring: + clean_desc = api_docstring.strip().replace('"', '\\"') + md_lines.append(f'description: "{clean_desc}"') + md_lines.append(f"sidebar_label: {sidebar_label}") + md_lines.append(f"title: {api_name.title()}") + md_lines.append("---") + md_lines.append("") + + # Add main content + md_lines.append(f"# {api_name.title()}") + md_lines.append("") + md_lines.append("## Overview") + md_lines.append("") + + if api_docstring: + cleaned_docstring = api_docstring.strip() + md_lines.append(f"{cleaned_docstring}") + md_lines.append("") + + md_lines.append(f"This section contains documentation for all available providers for the **{api_name}** API.") return "\n".join(md_lines) + "\n" @@ -272,41 +372,35 @@ def process_provider_registry(progress, change_tracker: ChangedPathTracker) -> N for api, providers in provider_registry.items(): api_name = api.value - doc_output_dir = REPO_ROOT / "docs" / "source" / "providers" / api_name + doc_output_dir = REPO_ROOT / "docs" / "docs" / "providers" / api_name doc_output_dir.mkdir(parents=True, exist_ok=True) change_tracker.add_paths(doc_output_dir) - index_content = [] - index_content.append(f"# {api_name.title()}\n") - index_content.append("## Overview\n") - api_docstring = get_api_docstring(api_name) - if api_docstring: - cleaned_docstring = api_docstring.strip() - index_content.append(f"{cleaned_docstring}\n") - - index_content.append( - f"This section contains documentation for all available providers for the **{api_name}** API.\n" - ) - - index_content.append("## Providers\n") - - toctree_entries = [] + provider_entries = [] for provider_type, provider in sorted(providers.items()): filename = provider_type.replace("::", "_").replace(":", "_") - provider_doc_file = doc_output_dir / f"{filename}.md" + provider_doc_file = doc_output_dir / f"{filename}.mdx" provider_docs = generate_provider_docs(progress, provider, api_name) provider_doc_file.write_text(provider_docs) change_tracker.add_paths(provider_doc_file) - toctree_entries.append(f"{filename}") - index_content.append(f"```{{toctree}}\n:maxdepth: 1\n\n{'\n'.join(toctree_entries)}\n```\n") + # Create display name for the index + display_name = provider_type.replace("::", " - ").replace("_", " ") + if display_name.startswith("inline - "): + display_name = display_name[9:].title() + else: + display_name = display_name.title() - index_file = doc_output_dir / "index.md" - index_file.write_text("\n".join(index_content)) + provider_entries.append({"filename": filename, "display_name": display_name}) + + # Generate index file with frontmatter + index_content = generate_index_docs(api_name, api_docstring, provider_entries) + index_file = doc_output_dir / "index.mdx" + index_file.write_text(index_content) change_tracker.add_paths(index_file) except Exception as e: diff --git a/tests/README.md b/tests/README.md index 3b129fbd9..c00829d3e 100644 --- a/tests/README.md +++ b/tests/README.md @@ -38,26 +38,15 @@ For running integration tests, you must provide a few things: - a distribution name (e.g., `starter`) or a path to a `run.yaml` file - a comma-separated list of api=provider pairs, e.g. `inference=fireworks,safety=llama-guard,agents=meta-reference`. This is most useful for testing a single API surface. -- Whether you are using replay or live mode for inference. This is specified with the LLAMA_STACK_TEST_INFERENCE_MODE environment variable. The default mode currently is "live" -- that is certainly surprising, but we will fix this soon. - - Any API keys you need to use should be set in the environment, or can be passed in with the --env option. You can run the integration tests in replay mode with: ```bash # Run all tests with existing recordings -LLAMA_STACK_TEST_INFERENCE_MODE=replay \ - LLAMA_STACK_TEST_RECORDING_DIR=tests/integration/recordings \ uv run --group test \ pytest -sv tests/integration/ --stack-config=starter ``` -If you don't specify LLAMA_STACK_TEST_INFERENCE_MODE, by default it will be in "live" mode -- that is, it will make real API calls. - -```bash -# Test against live APIs -FIREWORKS_API_KEY=your_key pytest -sv tests/integration/inference --stack-config=starter -``` - ### Re-recording tests #### Local Re-recording (Manual Setup Required) @@ -66,7 +55,6 @@ If you want to re-record tests locally, you can do so with: ```bash LLAMA_STACK_TEST_INFERENCE_MODE=record \ - LLAMA_STACK_TEST_RECORDING_DIR=tests/integration/recordings \ uv run --group test \ pytest -sv tests/integration/ --stack-config=starter -k "" ``` @@ -89,7 +77,7 @@ You must be careful when re-recording. CI workflows assume a specific setup for ./scripts/github/schedule-record-workflow.sh --test-subdirs "agents,inference" # Record with vision tests enabled -./scripts/github/schedule-record-workflow.sh --test-subdirs "inference" --run-vision-tests +./scripts/github/schedule-record-workflow.sh --test-suite vision # Record with specific provider ./scripts/github/schedule-record-workflow.sh --test-subdirs "agents" --test-provider vllm diff --git a/tests/external/kaze.yaml b/tests/external/kaze.yaml index c61ac0e31..1b42f2e14 100644 --- a/tests/external/kaze.yaml +++ b/tests/external/kaze.yaml @@ -1,6 +1,5 @@ -adapter: - adapter_type: kaze - pip_packages: ["tests/external/llama-stack-provider-kaze"] - config_class: llama_stack_provider_kaze.config.KazeProviderConfig - module: llama_stack_provider_kaze +adapter_type: kaze +pip_packages: ["tests/external/llama-stack-provider-kaze"] +config_class: llama_stack_provider_kaze.config.KazeProviderConfig +module: llama_stack_provider_kaze optional_api_dependencies: [] diff --git a/tests/external/llama-stack-api-weather/src/llama_stack_api_weather/weather.py b/tests/external/llama-stack-api-weather/src/llama_stack_api_weather/weather.py index 4b3bfb641..e97a9d8fb 100644 --- a/tests/external/llama-stack-api-weather/src/llama_stack_api_weather/weather.py +++ b/tests/external/llama-stack-api-weather/src/llama_stack_api_weather/weather.py @@ -6,7 +6,8 @@ from typing import Protocol -from llama_stack.providers.datatypes import AdapterSpec, Api, ProviderSpec, RemoteProviderSpec +from llama_stack.apis.version import LLAMA_STACK_API_V1 +from llama_stack.providers.datatypes import Api, ProviderSpec, RemoteProviderSpec from llama_stack.schema_utils import webmethod @@ -16,12 +17,9 @@ def available_providers() -> list[ProviderSpec]: api=Api.weather, provider_type="remote::kaze", config_class="llama_stack_provider_kaze.KazeProviderConfig", - adapter=AdapterSpec( - adapter_type="kaze", - module="llama_stack_provider_kaze", - pip_packages=["llama_stack_provider_kaze"], - config_class="llama_stack_provider_kaze.KazeProviderConfig", - ), + adapter_type="kaze", + module="llama_stack_provider_kaze", + pip_packages=["llama_stack_provider_kaze"], ), ] @@ -31,7 +29,7 @@ class WeatherProvider(Protocol): A protocol for the Weather API. """ - @webmethod(route="/weather/locations", method="GET") + @webmethod(route="/weather/locations", method="GET", level=LLAMA_STACK_API_V1) async def get_available_locations() -> dict[str, list[str]]: """ Get the available locations. diff --git a/tests/integration/README.md b/tests/integration/README.md index 46d66fd79..467f97e02 100644 --- a/tests/integration/README.md +++ b/tests/integration/README.md @@ -6,9 +6,7 @@ Integration tests verify complete workflows across different providers using Lla ```bash # Run all integration tests with existing recordings -LLAMA_STACK_TEST_INFERENCE_MODE=replay \ - LLAMA_STACK_TEST_RECORDING_DIR=tests/integration/recordings \ - uv run --group test \ +uv run --group test \ pytest -sv tests/integration/ --stack-config=starter ``` @@ -42,6 +40,37 @@ Model parameters can be influenced by the following options: Each of these are comma-separated lists and can be used to generate multiple parameter combinations. Note that tests will be skipped if no model is specified. +### Suites and Setups + +- `--suite`: single named suite that narrows which tests are collected. +- Available suites: + - `base`: collects most tests (excludes responses and post_training) + - `responses`: collects tests under `tests/integration/responses` (needs strong tool-calling models) + - `vision`: collects only `tests/integration/inference/test_vision_inference.py` +- `--setup`: global configuration that can be used with any suite. Setups prefill model/env defaults; explicit CLI flags always win. + - Available setups: + - `ollama`: Local Ollama provider with lightweight models (sets OLLAMA_URL, uses llama3.2:3b-instruct-fp16) + - `vllm`: VLLM provider for efficient local inference (sets VLLM_URL, uses Llama-3.2-1B-Instruct) + - `gpt`: OpenAI GPT models for high-quality responses (uses gpt-4o) + - `claude`: Anthropic Claude models for high-quality responses (uses claude-3-5-sonnet) + +Examples + +```bash +# Fast responses run with a strong tool-calling model +pytest -s -v tests/integration --stack-config=server:starter --suite=responses --setup=gpt + +# Fast single-file vision run with Ollama defaults +pytest -s -v tests/integration --stack-config=server:starter --suite=vision --setup=ollama + +# Base suite with VLLM for performance +pytest -s -v tests/integration --stack-config=server:starter --suite=base --setup=vllm + +# Override a default from setup +pytest -s -v tests/integration --stack-config=server:starter \ + --suite=responses --setup=gpt --embedding-model=text-embedding-3-small +``` + ## Examples ### Testing against a Server @@ -98,29 +127,24 @@ pytest -s -v tests/integration/vector_io/ \ The testing system supports three modes controlled by environment variables: -### LIVE Mode (Default) -Tests make real API calls: +### REPLAY Mode (Default) +Uses cached responses instead of making API calls: ```bash -LLAMA_STACK_TEST_INFERENCE_MODE=live pytest tests/integration/ +pytest tests/integration/ ``` - ### RECORD Mode Captures API interactions for later replay: ```bash -LLAMA_STACK_TEST_INFERENCE_MODE=record \ -LLAMA_STACK_TEST_RECORDING_DIR=tests/integration/recordings \ -pytest tests/integration/inference/test_new_feature.py +pytest tests/integration/inference/test_new_feature.py --inference-mode=record ``` -### REPLAY Mode -Uses cached responses instead of making API calls: +### LIVE Mode +Tests make real API calls (but not recorded): ```bash -LLAMA_STACK_TEST_INFERENCE_MODE=replay \ -LLAMA_STACK_TEST_RECORDING_DIR=tests/integration/recordings \ -pytest tests/integration/ +pytest tests/integration/ --inference-mode=live ``` -Note that right now you must specify the recording directory. This is because different tests use different recording directories and we don't (yet) have a fool-proof way to map a test to a recording directory. We are working on this. +By default, the recording directory is `tests/integration/recordings`. You can override this by setting the `LLAMA_STACK_TEST_RECORDING_DIR` environment variable. ## Managing Recordings @@ -138,16 +162,14 @@ cat recordings/responses/abc123.json | jq '.' #### Remote Re-recording (Recommended) Use the automated workflow script for easier re-recording: ```bash -./scripts/github/schedule-record-workflow.sh --test-subdirs "inference,agents" +./scripts/github/schedule-record-workflow.sh --subdirs "inference,agents" ``` See the [main testing guide](../README.md#remote-re-recording-recommended) for full details. #### Local Re-recording ```bash # Re-record specific tests -LLAMA_STACK_TEST_INFERENCE_MODE=record \ -LLAMA_STACK_TEST_RECORDING_DIR=tests/integration/recordings \ -pytest -s -v --stack-config=server:starter tests/integration/inference/test_modified.py +pytest -s -v --stack-config=server:starter tests/integration/inference/test_modified.py --inference-mode=record ``` Note that when re-recording tests, you must use a Stack pointing to a server (i.e., `server:starter`). This subtlety exists because the set of tests run in server are a superset of the set of tests run in the library client. diff --git a/tests/integration/batches/test_batches.py b/tests/integration/batches/test_batches.py index 59811b7a4..d55a68bd3 100644 --- a/tests/integration/batches/test_batches.py +++ b/tests/integration/batches/test_batches.py @@ -268,3 +268,58 @@ class TestBatchesIntegration: deleted_error_file = openai_client.files.delete(final_batch.error_file_id) assert deleted_error_file.deleted, f"Error file {final_batch.error_file_id} was not deleted successfully" + + def test_batch_e2e_completions(self, openai_client, batch_helper, text_model_id): + """Run an end-to-end batch with a single successful text completion request.""" + request_body = {"model": text_model_id, "prompt": "Say completions", "max_tokens": 20} + + batch_requests = [ + { + "custom_id": "success-1", + "method": "POST", + "url": "/v1/completions", + "body": request_body, + } + ] + + with batch_helper.create_file(batch_requests) as uploaded_file: + batch = openai_client.batches.create( + input_file_id=uploaded_file.id, + endpoint="/v1/completions", + completion_window="24h", + metadata={"test": "e2e_completions_success"}, + ) + + final_batch = batch_helper.wait_for( + batch.id, + max_wait_time=3 * 60, + expected_statuses={"completed"}, + timeout_action="skip", + ) + + assert final_batch.status == "completed" + assert final_batch.request_counts is not None + assert final_batch.request_counts.total == 1 + assert final_batch.request_counts.completed == 1 + assert final_batch.output_file_id is not None + + output_content = openai_client.files.content(final_batch.output_file_id) + if isinstance(output_content, str): + output_text = output_content + else: + output_text = output_content.content.decode("utf-8") + + output_lines = output_text.strip().split("\n") + assert len(output_lines) == 1 + + result = json.loads(output_lines[0]) + assert result["custom_id"] == "success-1" + assert "response" in result + assert result["response"]["status_code"] == 200 + + deleted_output_file = openai_client.files.delete(final_batch.output_file_id) + assert deleted_output_file.deleted + + if final_batch.error_file_id is not None: + deleted_error_file = openai_client.files.delete(final_batch.error_file_id) + assert deleted_error_file.deleted diff --git a/tests/integration/conftest.py b/tests/integration/conftest.py index 234d762ce..4735264c3 100644 --- a/tests/integration/conftest.py +++ b/tests/integration/conftest.py @@ -6,15 +6,17 @@ import inspect import itertools import os -import platform import textwrap import time +from pathlib import Path import pytest from dotenv import load_dotenv from llama_stack.log import get_logger +from .suites import SETUP_DEFINITIONS, SUITE_DEFINITIONS + logger = get_logger(__name__, category="tests") @@ -30,6 +32,8 @@ def pytest_runtest_makereport(item, call): def pytest_sessionstart(session): # stop macOS from complaining about duplicate OpenMP libraries os.environ["KMP_DUPLICATE_LIB_OK"] = "TRUE" + if "LLAMA_STACK_TEST_INFERENCE_MODE" not in os.environ: + os.environ["LLAMA_STACK_TEST_INFERENCE_MODE"] = "replay" def pytest_runtest_teardown(item): @@ -59,9 +63,36 @@ def pytest_configure(config): key, value = env_var.split("=", 1) os.environ[key] = value - if platform.system() == "Darwin": # Darwin is the system name for macOS - os.environ["DISABLE_CODE_SANDBOX"] = "1" - logger.info("Setting DISABLE_CODE_SANDBOX=1 for macOS") + inference_mode = config.getoption("--inference-mode") + os.environ["LLAMA_STACK_TEST_INFERENCE_MODE"] = inference_mode + + suite = config.getoption("--suite") + if suite: + if suite not in SUITE_DEFINITIONS: + raise pytest.UsageError(f"Unknown suite: {suite}. Available: {', '.join(sorted(SUITE_DEFINITIONS.keys()))}") + + # Apply setups (global parameterizations): env + defaults + setup = config.getoption("--setup") + if suite and not setup: + setup = SUITE_DEFINITIONS[suite].default_setup + + if setup: + if setup not in SETUP_DEFINITIONS: + raise pytest.UsageError( + f"Unknown setup '{setup}'. Available: {', '.join(sorted(SETUP_DEFINITIONS.keys()))}" + ) + + setup_obj = SETUP_DEFINITIONS[setup] + logger.info(f"Applying setup '{setup}'{' for suite ' + suite if suite else ''}") + # Apply env first + for k, v in setup_obj.env.items(): + if k not in os.environ: + os.environ[k] = str(v) + # Apply defaults if not provided explicitly + for dest, value in setup_obj.defaults.items(): + current = getattr(config.option, dest, None) + if not current: + setattr(config.option, dest, value) def pytest_addoption(parser): @@ -103,16 +134,32 @@ def pytest_addoption(parser): default=384, help="Output dimensionality of the embedding model to use for testing. Default: 384", ) + parser.addoption( - "--record-responses", - action="store_true", - help="Record new API responses instead of using cached ones.", + "--inference-mode", + help="Inference mode: { record, replay, live } (default: replay)", + choices=["record", "replay", "live"], + default="replay", ) parser.addoption( "--report", help="Path where the test report should be written, e.g. --report=/path/to/report.md", ) + available_suites = ", ".join(sorted(SUITE_DEFINITIONS.keys())) + suite_help = ( + f"Single test suite to run (narrows collection). Available: {available_suites}. Example: --suite=responses" + ) + parser.addoption("--suite", help=suite_help) + + # Global setups for any suite + available_setups = ", ".join(sorted(SETUP_DEFINITIONS.keys())) + setup_help = ( + f"Global test setup configuration. Available: {available_setups}. " + "Can be used with any suite. Example: --setup=ollama" + ) + parser.addoption("--setup", help=setup_help) + MODEL_SHORT_IDS = { "meta-llama/Llama-3.2-3B-Instruct": "3B", @@ -195,3 +242,36 @@ def pytest_generate_tests(metafunc): pytest_plugins = ["tests.integration.fixtures.common"] + + +def pytest_ignore_collect(path: str, config: pytest.Config) -> bool: + """Skip collecting paths outside the selected suite roots for speed.""" + suite = config.getoption("--suite") + if not suite: + return False + + sobj = SUITE_DEFINITIONS.get(suite) + roots: list[str] = sobj.get("roots", []) if isinstance(sobj, dict) else getattr(sobj, "roots", []) + if not roots: + return False + + p = Path(str(path)).resolve() + + # Only constrain within tests/integration to avoid ignoring unrelated tests + integration_root = (Path(str(config.rootpath)) / "tests" / "integration").resolve() + if not p.is_relative_to(integration_root): + return False + + for r in roots: + rp = (Path(str(config.rootpath)) / r).resolve() + if rp.is_file(): + # Allow the exact file and any ancestor directories so pytest can walk into it. + if p == rp: + return False + if p.is_dir() and rp.is_relative_to(p): + return False + else: + # Allow anything inside an allowed directory + if p.is_relative_to(rp): + return False + return True diff --git a/tests/integration/files/test_files.py b/tests/integration/files/test_files.py index 67351d4f7..516b0bd98 100644 --- a/tests/integration/files/test_files.py +++ b/tests/integration/files/test_files.py @@ -8,6 +8,7 @@ from io import BytesIO from unittest.mock import patch import pytest +import requests from llama_stack.core.datatypes import User @@ -79,6 +80,88 @@ def test_openai_client_basic_operations(openai_client): pass # ignore 404 +@pytest.mark.xfail(message="expires_after not available on all providers") +def test_expires_after(openai_client): + """Test uploading a file with expires_after parameter.""" + client = openai_client + + uploaded_file = None + try: + with BytesIO(b"expires_after test") as file_buffer: + file_buffer.name = "expires_after.txt" + uploaded_file = client.files.create( + file=file_buffer, + purpose="assistants", + expires_after={"anchor": "created_at", "seconds": 4545}, + ) + + assert uploaded_file.expires_at is not None + assert uploaded_file.expires_at == uploaded_file.created_at + 4545 + + listed = client.files.list() + ids = [f.id for f in listed.data] + assert uploaded_file.id in ids + + retrieved = client.files.retrieve(uploaded_file.id) + assert retrieved.id == uploaded_file.id + + finally: + if uploaded_file is not None: + try: + client.files.delete(uploaded_file.id) + except Exception: + pass + + +@pytest.mark.xfail(message="expires_after not available on all providers") +def test_expires_after_requests(openai_client): + """Upload a file using requests multipart/form-data and bracketed expires_after fields. + + This ensures clients that send form fields like `expires_after[anchor]` and + `expires_after[seconds]` are handled by the server. + """ + base_url = f"{openai_client.base_url}files" + + uploaded_id = None + try: + files = {"file": ("expires_after_with_requests.txt", BytesIO(b"expires_after via requests"))} + data = { + "purpose": "assistants", + "expires_after[anchor]": "created_at", + "expires_after[seconds]": "4545", + } + + session = requests.Session() + request = requests.Request("POST", base_url, files=files, data=data) + prepared = session.prepare_request(request) + resp = session.send(prepared, timeout=30) + resp.raise_for_status() + result = resp.json() + + assert result.get("id", "").startswith("file-") + uploaded_id = result["id"] + assert result.get("created_at") is not None + assert result.get("expires_at") == result["created_at"] + 4545 + + list_resp = requests.get(base_url, timeout=30) + list_resp.raise_for_status() + listed = list_resp.json() + ids = [f["id"] for f in listed.get("data", [])] + assert uploaded_id in ids + + retrieve_resp = requests.get(f"{base_url}/{uploaded_id}", timeout=30) + retrieve_resp.raise_for_status() + retrieved = retrieve_resp.json() + assert retrieved["id"] == uploaded_id + + finally: + if uploaded_id: + try: + requests.delete(f"{base_url}/{uploaded_id}", timeout=30) + except Exception: + pass + + @pytest.mark.xfail(message="User isolation broken for current providers, must be fixed.") @patch("llama_stack.providers.utils.sqlstore.authorized_sqlstore.get_authenticated_user") def test_files_authentication_isolation(mock_get_authenticated_user, llama_stack_client): diff --git a/tests/integration/inference/test_openai_completion.py b/tests/integration/inference/test_openai_completion.py index 72137662d..931c144af 100644 --- a/tests/integration/inference/test_openai_completion.py +++ b/tests/integration/inference/test_openai_completion.py @@ -5,11 +5,26 @@ # the root directory of this source tree. +import time +import unicodedata + import pytest from ..test_cases.test_case import TestCase +def _normalize_text(text: str) -> str: + """ + Normalize Unicode text by removing diacritical marks for comparison. + + The test case streaming_01 expects the answer "Sol" for the question "What's the name of the Sun + in latin?", but the model is returning "sōl" (with a macron over the 'o'), which is the correct + Latin spelling. The test is failing because it's doing a simple case-insensitive string search + for "sol" but the actual response contains the diacritical mark. + """ + return unicodedata.normalize("NFD", text).encode("ascii", "ignore").decode("ascii").lower() + + def provider_from_model(client_with_models, model_id): models = {m.identifier: m for m in client_with_models.models.list()} models.update({m.provider_resource_id: m for m in client_with_models.models.list()}) @@ -25,7 +40,6 @@ def skip_if_model_doesnt_support_openai_completion(client_with_models, model_id) "inline::sentence-transformers", "inline::vllm", "remote::bedrock", - "remote::cerebras", "remote::databricks", # Technically Nvidia does support OpenAI completions, but none of their hosted models # support both completions and chat completions endpoint and all the Llama models are @@ -33,8 +47,17 @@ def skip_if_model_doesnt_support_openai_completion(client_with_models, model_id) "remote::nvidia", "remote::runpod", "remote::sambanova", - "remote::tgi", "remote::vertexai", + # {"error":{"message":"Unknown request URL: GET /openai/v1/completions. Please check the URL for typos, + # or see the docs at https://console.groq.com/docs/","type":"invalid_request_error","code":"unknown_url"}} + "remote::groq", + "remote::gemini", # https://generativelanguage.googleapis.com/v1beta/openai/completions -> 404 + "remote::anthropic", # at least claude-3-{5,7}-{haiku,sonnet}-* / claude-{sonnet,opus}-4-* are not supported + "remote::azure", # {'error': {'code': 'OperationNotSupported', 'message': 'The completion operation + # does not work with the specified model, gpt-5-mini. Please choose different model and try + # again. You can learn more about which models can be used with each operation here: + # https://go.microsoft.com/fwlink/?linkid=2197993.'}}"} + "remote::watsonx", # return 404 when hitting the /openai/v1 endpoint ): pytest.skip(f"Model {model_id} hosted by {provider.provider_type} doesn't support OpenAI completions.") @@ -56,6 +79,31 @@ def skip_if_model_doesnt_support_suffix(client_with_models, model_id): pytest.skip(f"Provider {provider.provider_type} doesn't support suffix.") +def skip_if_doesnt_support_n(client_with_models, model_id): + provider = provider_from_model(client_with_models, model_id) + if provider.provider_type in ( + "remote::sambanova", + "remote::ollama", + # https://console.groq.com/docs/openai#currently-unsupported-openai-features + # -> Error code: 400 - {'error': {'message': "'n' : number must be at most 1", 'type': 'invalid_request_error'}} + "remote::groq", + # Error code: 400 - [{'error': {'code': 400, 'message': 'Only one candidate can be specified in the + # current model', 'status': 'INVALID_ARGUMENT'}}] + "remote::gemini", + # https://docs.anthropic.com/en/api/openai-sdk#simple-fields + "remote::anthropic", + "remote::vertexai", + # Error code: 400 - [{'error': {'code': 400, 'message': 'Unable to submit request because candidateCount must be 1 but + # the entered value was 2. Update the candidateCount value and try again.', 'status': 'INVALID_ARGUMENT'} + "remote::tgi", # TGI ignores n param silently + "remote::together", # `n` > 1 is not supported when streaming tokens. Please disable `stream` + # Error code 400 - {'message': '"n" > 1 is not currently supported', 'type': 'invalid_request_error', 'param': 'n', 'code': 'wrong_api_format'} + "remote::cerebras", + "remote::databricks", # Bad request: parameter "n" must be equal to 1 for streaming mode + ): + pytest.skip(f"Model {model_id} hosted by {provider.provider_type} doesn't support n param.") + + def skip_if_model_doesnt_support_openai_chat_completion(client_with_models, model_id): provider = provider_from_model(client_with_models, model_id) if provider.provider_type in ( @@ -63,10 +111,10 @@ def skip_if_model_doesnt_support_openai_chat_completion(client_with_models, mode "inline::sentence-transformers", "inline::vllm", "remote::bedrock", - "remote::cerebras", "remote::databricks", + "remote::cerebras", "remote::runpod", - "remote::tgi", + "remote::watsonx", # watsonx returns 404 when hitting the /openai/v1 endpoint ): pytest.skip(f"Model {model_id} hosted by {provider.provider_type} doesn't support OpenAI chat completions.") @@ -130,7 +178,8 @@ def test_openai_completion_non_streaming_suffix(llama_stack_client, client_with_ assert len(response.choices) > 0 choice = response.choices[0] assert len(choice.text) > 5 - assert "france" in choice.text.lower() + normalized_text = _normalize_text(choice.text) + assert "france" in normalized_text @pytest.mark.parametrize( @@ -221,7 +270,9 @@ def test_openai_chat_completion_non_streaming(compat_client, client_with_models, ) message_content = response.choices[0].message.content.lower().strip() assert len(message_content) > 0 - assert expected.lower() in message_content + normalized_expected = _normalize_text(expected) + normalized_content = _normalize_text(message_content) + assert normalized_expected in normalized_content @pytest.mark.parametrize( @@ -245,10 +296,13 @@ def test_openai_chat_completion_streaming(compat_client, client_with_models, tex ) streamed_content = [] for chunk in response: - if chunk.choices[0].delta.content: + # On some providers like Azure, the choices are empty on the first chunk, so we need to check for that + if chunk.choices and len(chunk.choices) > 0 and chunk.choices[0].delta.content: streamed_content.append(chunk.choices[0].delta.content.lower().strip()) assert len(streamed_content) > 0 - assert expected.lower() in "".join(streamed_content) + normalized_expected = _normalize_text(expected) + normalized_content = _normalize_text("".join(streamed_content)) + assert normalized_expected in normalized_content @pytest.mark.parametrize( @@ -260,10 +314,7 @@ def test_openai_chat_completion_streaming(compat_client, client_with_models, tex ) def test_openai_chat_completion_streaming_with_n(compat_client, client_with_models, text_model_id, test_case): skip_if_model_doesnt_support_openai_chat_completion(client_with_models, text_model_id) - - provider = provider_from_model(client_with_models, text_model_id) - if provider.provider_type == "remote::ollama": - pytest.skip(f"Model {text_model_id} hosted by {provider.provider_type} doesn't support n > 1.") + skip_if_doesnt_support_n(client_with_models, text_model_id) tc = TestCase(test_case) question = tc["question"] @@ -284,8 +335,12 @@ def test_openai_chat_completion_streaming_with_n(compat_client, client_with_mode streamed_content.get(choice.index, "") + choice.delta.content.lower().strip() ) assert len(streamed_content) == 2 + normalized_expected = _normalize_text(expected) for i, content in streamed_content.items(): - assert expected.lower() in content, f"Choice {i}: Expected {expected.lower()} in {content}" + normalized_content = _normalize_text(content) + assert normalized_expected in normalized_content, ( + f"Choice {i}: Expected {normalized_expected} in {normalized_content}" + ) @pytest.mark.parametrize( @@ -315,16 +370,23 @@ def test_inference_store(compat_client, client_with_models, text_model_id, strea content = "" response_id = None for chunk in response: - if response_id is None: + if response_id is None and chunk.id: response_id = chunk.id - if chunk.choices[0].delta.content: + if chunk.choices and len(chunk.choices) > 0 and chunk.choices[0].delta.content: content += chunk.choices[0].delta.content else: response_id = response.id content = response.choices[0].message.content - responses = client.chat.completions.list(limit=1000) - assert response_id in [r.id for r in responses.data] + tries = 0 + while tries < 10: + responses = client.chat.completions.list(limit=1000) + if response_id in [r.id for r in responses.data]: + break + else: + tries += 1 + time.sleep(0.1) + assert tries < 10, f"Response {response_id} not found after 1 second" retrieved_response = client.chat.completions.retrieve(response_id) assert retrieved_response.id == response_id @@ -379,15 +441,28 @@ def test_inference_store_tool_calls(compat_client, client_with_models, text_mode content = "" response_id = None for chunk in response: - if response_id is None: + if response_id is None and chunk.id: response_id = chunk.id - if delta := chunk.choices[0].delta: - if delta.content: - content += delta.content + if chunk.choices and len(chunk.choices) > 0: + if delta := chunk.choices[0].delta: + if delta.content: + content += delta.content else: response_id = response.id content = response.choices[0].message.content + # wait for the response to be stored + tries = 0 + while tries < 10: + responses = client.chat.completions.list(limit=1000) + if response_id in [r.id for r in responses.data]: + break + else: + tries += 1 + time.sleep(0.1) + + assert tries < 10, f"Response {response_id} not found after 1 second" + responses = client.chat.completions.list(limit=1000) assert response_id in [r.id for r in responses.data] @@ -441,4 +516,5 @@ def test_openai_chat_completion_non_streaming_with_file(openai_client, client_wi stream=False, ) message_content = response.choices[0].message.content.lower().strip() - assert "hello world" in message_content + normalized_content = _normalize_text(message_content) + assert "hello world" in normalized_content diff --git a/tests/integration/inference/test_openai_embeddings.py b/tests/integration/inference/test_openai_embeddings.py index 2c545cc43..92064b651 100644 --- a/tests/integration/inference/test_openai_embeddings.py +++ b/tests/integration/inference/test_openai_embeddings.py @@ -29,9 +29,40 @@ def provider_from_model(client_with_models, model_id): return providers[provider_id] -def skip_if_model_doesnt_support_variable_dimensions(model_id): - if "text-embedding-3" not in model_id: - pytest.skip("{model_id} does not support variable output embedding dimensions") +def skip_if_model_doesnt_support_user_param(client, model_id): + provider = provider_from_model(client, model_id) + if provider.provider_type in ( + "remote::together", # service returns 400 + "remote::fireworks", # service returns 400 malformed input + ): + pytest.skip(f"Model {model_id} hosted by {provider.provider_type} does not support user param.") + + +def skip_if_model_doesnt_support_encoding_format_base64(client, model_id): + provider = provider_from_model(client, model_id) + if provider.provider_type in ( + "remote::databricks", # param silently ignored, always returns floats + "remote::fireworks", # param silently ignored, always returns list of floats + "remote::ollama", # param silently ignored, always returns list of floats + ): + pytest.skip(f"Model {model_id} hosted by {provider.provider_type} does not support encoding_format='base64'.") + + +def skip_if_model_doesnt_support_variable_dimensions(client_with_models, model_id): + provider = provider_from_model(client_with_models, model_id) + if provider.provider_type in ( + "remote::together", # returns 400 + "inline::sentence-transformers", + # Error code: 400 - {'error_code': 'BAD_REQUEST', 'message': 'Bad request: json: unknown field "dimensions"\n'} + "remote::databricks", + ): + pytest.skip( + f"Model {model_id} hosted by {provider.provider_type} does not support variable output embedding dimensions." + ) + if provider.provider_type == "remote::openai" and "text-embedding-3" not in model_id: + pytest.skip( + f"Model {model_id} hosted by {provider.provider_type} does not support variable output embedding dimensions." + ) @pytest.fixture(params=["openai_client", "llama_stack_client"]) @@ -47,7 +78,6 @@ def skip_if_model_doesnt_support_openai_embeddings(client, model_id): "inline::meta-reference", "remote::bedrock", "remote::cerebras", - "remote::databricks", "remote::runpod", "remote::sambanova", "remote::tgi", @@ -92,6 +122,7 @@ def test_openai_embeddings_multiple_strings(compat_client, client_with_models, e response = compat_client.embeddings.create( model=embedding_model_id, input=input_texts, + encoding_format="float", ) assert response.object == "list" @@ -127,7 +158,7 @@ def test_openai_embeddings_with_encoding_format_float(compat_client, client_with def test_openai_embeddings_with_dimensions(compat_client, client_with_models, embedding_model_id): """Test OpenAI embeddings endpoint with custom dimensions parameter.""" skip_if_model_doesnt_support_openai_embeddings(client_with_models, embedding_model_id) - skip_if_model_doesnt_support_variable_dimensions(embedding_model_id) + skip_if_model_doesnt_support_variable_dimensions(client_with_models, embedding_model_id) input_text = "Test dimensions parameter" dimensions = 16 @@ -148,6 +179,7 @@ def test_openai_embeddings_with_dimensions(compat_client, client_with_models, em def test_openai_embeddings_with_user_parameter(compat_client, client_with_models, embedding_model_id): """Test OpenAI embeddings endpoint with user parameter.""" skip_if_model_doesnt_support_openai_embeddings(client_with_models, embedding_model_id) + skip_if_model_doesnt_support_user_param(client_with_models, embedding_model_id) input_text = "Test user parameter" user_id = "test-user-123" @@ -196,11 +228,13 @@ def test_openai_embeddings_different_inputs_different_outputs(compat_client, cli response1 = compat_client.embeddings.create( model=embedding_model_id, input=input_text1, + encoding_format="float", ) response2 = compat_client.embeddings.create( model=embedding_model_id, input=input_text2, + encoding_format="float", ) embedding1 = response1.data[0].embedding @@ -214,7 +248,8 @@ def test_openai_embeddings_different_inputs_different_outputs(compat_client, cli def test_openai_embeddings_with_encoding_format_base64(compat_client, client_with_models, embedding_model_id): """Test OpenAI embeddings endpoint with base64 encoding format.""" skip_if_model_doesnt_support_openai_embeddings(client_with_models, embedding_model_id) - skip_if_model_doesnt_support_variable_dimensions(embedding_model_id) + skip_if_model_doesnt_support_encoding_format_base64(client_with_models, embedding_model_id) + skip_if_model_doesnt_support_variable_dimensions(client_with_models, embedding_model_id) input_text = "Test base64 encoding format" dimensions = 12 @@ -247,6 +282,7 @@ def test_openai_embeddings_with_encoding_format_base64(compat_client, client_wit def test_openai_embeddings_base64_batch_processing(compat_client, client_with_models, embedding_model_id): """Test OpenAI embeddings endpoint with base64 encoding for batch processing.""" skip_if_model_doesnt_support_openai_embeddings(client_with_models, embedding_model_id) + skip_if_model_doesnt_support_encoding_format_base64(client_with_models, embedding_model_id) input_texts = ["First text for base64", "Second text for base64", "Third text for base64"] @@ -255,7 +291,6 @@ def test_openai_embeddings_base64_batch_processing(compat_client, client_with_mo input=input_texts, encoding_format="base64", ) - # Validate response structure assert response.object == "list" assert response.model == embedding_model_id diff --git a/tests/integration/inference/test_text_inference.py b/tests/integration/inference/test_text_inference.py index d7ffe5929..a5f95a963 100644 --- a/tests/integration/inference/test_text_inference.py +++ b/tests/integration/inference/test_text_inference.py @@ -32,6 +32,7 @@ def skip_if_model_doesnt_support_completion(client_with_models, model_id): "remote::vertexai", "remote::groq", "remote::sambanova", + "remote::azure", ) or "openai-compat" in provider.provider_type ): @@ -44,7 +45,7 @@ def skip_if_model_doesnt_support_json_schema_structured_output(client_with_model provider_id = models[model_id].provider_id providers = {p.provider_id: p for p in client_with_models.providers.list()} provider = providers[provider_id] - if provider.provider_type in ("remote::sambanova",): + if provider.provider_type in ("remote::sambanova", "remote::azure", "remote::watsonx"): pytest.skip( f"Model {model_id} hosted by {provider.provider_type} doesn't support json_schema structured output" ) @@ -210,6 +211,7 @@ def test_text_completion_log_probs_streaming(client_with_models, text_model_id, ) def test_text_completion_structured_output(client_with_models, text_model_id, test_case): skip_if_model_doesnt_support_completion(client_with_models, text_model_id) + skip_if_model_doesnt_support_json_schema_structured_output(client_with_models, text_model_id) class AnswerFormat(BaseModel): name: str diff --git a/tests/integration/providers/utils/sqlstore/test_authorized_sqlstore.py b/tests/integration/providers/utils/sqlstore/test_authorized_sqlstore.py index 4002f2e1f..98bef0f2c 100644 --- a/tests/integration/providers/utils/sqlstore/test_authorized_sqlstore.py +++ b/tests/integration/providers/utils/sqlstore/test_authorized_sqlstore.py @@ -57,7 +57,7 @@ def authorized_store(backend_config): config = config_func() base_sqlstore = sqlstore_impl(config) - authorized_store = AuthorizedSqlStore(base_sqlstore) + authorized_store = AuthorizedSqlStore(base_sqlstore, default_policy()) yield authorized_store @@ -106,7 +106,7 @@ async def test_authorized_store_attributes(mock_get_authenticated_user, authoriz await authorized_store.insert(table_name, {"id": "1", "data": "public_data"}) # Test fetching with no user - should not error on JSON comparison - result = await authorized_store.fetch_all(table_name, policy=default_policy()) + result = await authorized_store.fetch_all(table_name) assert len(result.data) == 1 assert result.data[0]["id"] == "1" assert result.data[0]["access_attributes"] is None @@ -119,7 +119,7 @@ async def test_authorized_store_attributes(mock_get_authenticated_user, authoriz await authorized_store.insert(table_name, {"id": "2", "data": "admin_data"}) # Fetch all - admin should see both - result = await authorized_store.fetch_all(table_name, policy=default_policy()) + result = await authorized_store.fetch_all(table_name) assert len(result.data) == 2 # Test with non-admin user @@ -127,7 +127,7 @@ async def test_authorized_store_attributes(mock_get_authenticated_user, authoriz mock_get_authenticated_user.return_value = regular_user # Should only see public record - result = await authorized_store.fetch_all(table_name, policy=default_policy()) + result = await authorized_store.fetch_all(table_name) assert len(result.data) == 1 assert result.data[0]["id"] == "1" @@ -156,7 +156,7 @@ async def test_authorized_store_attributes(mock_get_authenticated_user, authoriz # Now test with the multi-user who has both roles=admin and teams=dev mock_get_authenticated_user.return_value = multi_user - result = await authorized_store.fetch_all(table_name, policy=default_policy()) + result = await authorized_store.fetch_all(table_name) # Should see: # - public record (1) - no access_attributes @@ -217,21 +217,24 @@ async def test_user_ownership_policy(mock_get_authenticated_user, authorized_sto ), ] + # Create a new authorized store with the owner-only policy + owner_only_store = AuthorizedSqlStore(authorized_store.sql_store, owner_only_policy) + # Test user1 access - should only see their own record mock_get_authenticated_user.return_value = user1 - result = await authorized_store.fetch_all(table_name, policy=owner_only_policy) + result = await owner_only_store.fetch_all(table_name) assert len(result.data) == 1, f"Expected user1 to see 1 record, got {len(result.data)}" assert result.data[0]["id"] == "1", f"Expected user1's record, got {result.data[0]['id']}" # Test user2 access - should only see their own record mock_get_authenticated_user.return_value = user2 - result = await authorized_store.fetch_all(table_name, policy=owner_only_policy) + result = await owner_only_store.fetch_all(table_name) assert len(result.data) == 1, f"Expected user2 to see 1 record, got {len(result.data)}" assert result.data[0]["id"] == "2", f"Expected user2's record, got {result.data[0]['id']}" # Test with anonymous user - should see no records mock_get_authenticated_user.return_value = None - result = await authorized_store.fetch_all(table_name, policy=owner_only_policy) + result = await owner_only_store.fetch_all(table_name) assert len(result.data) == 0, f"Expected anonymous user to see 0 records, got {len(result.data)}" finally: diff --git a/tests/integration/recordings/responses/00ba04f74a96.json b/tests/integration/recordings/responses/00ba04f74a96.json index d2e482d76..642c58414 100644 --- a/tests/integration/recordings/responses/00ba04f74a96.json +++ b/tests/integration/recordings/responses/00ba04f74a96.json @@ -20,15 +20,15 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama-guard3:1b", - "created_at": "2025-08-01T23:12:53.860911Z", + "created_at": "2025-09-03T17:37:35.23084Z", "done": true, "done_reason": "stop", - "total_duration": 249137667, - "load_duration": 152509542, + "total_duration": 195981375, + "load_duration": 110522917, "prompt_eval_count": 216, - "prompt_eval_duration": 71000000, + "prompt_eval_duration": 72393958, "eval_count": 2, - "eval_duration": 24000000, + "eval_duration": 11843000, "response": "safe", "thinking": null, "context": null diff --git a/tests/integration/recordings/responses/04172112ffbb.json b/tests/integration/recordings/responses/04172112ffbb.json index bf94b0697..da5f58a50 100644 --- a/tests/integration/recordings/responses/04172112ffbb.json +++ b/tests/integration/recordings/responses/04172112ffbb.json @@ -21,7 +21,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-07-31T17:59:18.033900164Z", + "created_at": "2025-09-03T17:41:43.950283Z", "done": false, "done_reason": null, "total_duration": null, @@ -39,7 +39,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-07-31T17:59:18.213371151Z", + "created_at": "2025-09-03T17:41:43.991122Z", "done": false, "done_reason": null, "total_duration": null, @@ -57,7 +57,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-07-31T17:59:18.387513976Z", + "created_at": "2025-09-03T17:41:44.031378Z", "done": false, "done_reason": null, "total_duration": null, @@ -75,7 +75,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-07-31T17:59:18.564344287Z", + "created_at": "2025-09-03T17:41:44.073098Z", "done": false, "done_reason": null, "total_duration": null, @@ -93,7 +93,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-07-31T17:59:18.746579415Z", + "created_at": "2025-09-03T17:41:44.115961Z", "done": false, "done_reason": null, "total_duration": null, @@ -111,7 +111,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-07-31T17:59:18.923276047Z", + "created_at": "2025-09-03T17:41:44.156517Z", "done": false, "done_reason": null, "total_duration": null, @@ -129,7 +129,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-07-31T17:59:19.099961963Z", + "created_at": "2025-09-03T17:41:44.197079Z", "done": false, "done_reason": null, "total_duration": null, @@ -147,7 +147,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-07-31T17:59:19.275621884Z", + "created_at": "2025-09-03T17:41:44.237565Z", "done": false, "done_reason": null, "total_duration": null, @@ -165,7 +165,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-07-31T17:59:19.452204196Z", + "created_at": "2025-09-03T17:41:44.277755Z", "done": false, "done_reason": null, "total_duration": null, @@ -183,7 +183,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-07-31T17:59:19.626937514Z", + "created_at": "2025-09-03T17:41:44.318476Z", "done": false, "done_reason": null, "total_duration": null, @@ -201,7 +201,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-07-31T17:59:19.805566767Z", + "created_at": "2025-09-03T17:41:44.358628Z", "done": false, "done_reason": null, "total_duration": null, @@ -219,7 +219,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-07-31T17:59:19.985987477Z", + "created_at": "2025-09-03T17:41:44.398984Z", "done": false, "done_reason": null, "total_duration": null, @@ -237,7 +237,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-07-31T17:59:20.166458601Z", + "created_at": "2025-09-03T17:41:44.439232Z", "done": false, "done_reason": null, "total_duration": null, @@ -255,7 +255,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - 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"is_streaming": true + } +} diff --git a/tests/integration/recordings/responses/140187e305dc.json b/tests/integration/recordings/responses/140187e305dc.json index 44d00c96f..69b9712eb 100644 --- a/tests/integration/recordings/responses/140187e305dc.json +++ b/tests/integration/recordings/responses/140187e305dc.json @@ -1,7 +1,7 @@ { "request": { "method": "POST", - "url": "http://localhost:11434/v1/v1/chat/completions", + "url": "http://0.0.0.0:11434/v1/v1/chat/completions", "headers": {}, "body": { "model": "llama3.2:3b-instruct-fp16", @@ -20,14 +20,14 @@ "body": { "__type__": "openai.types.chat.chat_completion.ChatCompletion", "__data__": { - "id": "chatcmpl-876", + "id": "chatcmpl-974", "choices": [ { "finish_reason": "stop", "index": 0, "logprobs": null, "message": { - "content": "I'm afraid I don't have a built-in ability to directly interface with or \"test\" OpenAI models, including the original GPT-1 model. However, I can explain how you might approach this task:\n\nThe OpenAI GPT-1 is a large transformer-based language model that was trained on a massive dataset of text and achieved state-of-the-art results in various natural language processing tasks.\n\nTo test or evaluate the performance of a model like GPT-1, you would typically follow these steps:\n\n1. **Get access to the OpenAI API**: The OpenAI API provides a way for developers to interact with the GPT-1 model programmatically. You can sign up for an API key on the OpenAI website.\n2. **Choose a testing platform or environment**: You'll need a compute platform that supports the necessary algorithms and data structures to run inference on the GPT-1 model. Some popular options include AWS, Google Cloud, or Azure Compute Virtual Machines.\n3. **Prepare your test input data**: This will involve creating text inputs in the format expected by the OpenAI API (i.e., a JSON object containing the text to be processed).\n4. **Use the OpenAI Python library or SDK**: The OpenAI Python library provides an easy-to-use interface for interacting with the GPT-1 model through the API.\n\nHere's some example code that demonstrates how you might use the OpenAI Flask API to test a single input:\n\n```python\nfrom flask import Flask, request, jsonify\nimport json\n\napp = Flask(__name__)\n\n@ app . route ( '/ /gpt-en ', ' Text ', methods = ['POST'])\ndef gpt_en () -> Json :\n data = request . get_json ()\n if not data or \"message\" in ( data ):\n return None , 400 , { ' error' : \"Input must be a text string.\" }\n response = []\n while True:\n message = \"\"\n for token in data [\"input\"]:\n response_text = f\"{data['prompt']} {token}\"\n data[\"input\"] = [response_text]\n new_response = gpt_en()(data)\n if all([not item or not isinstance(item, dict) for item in new_response]):\n break\n\n message = json . dumps ({}\"text\": response_text})\n response.append(message)\n\n return jsonify ({\"output\": response}), 200 , {}\n\nif __name__ == \"__main__\":\n app.run(debug=True)\n```\n\n5. **Evaluate the output**: Once you have processed your test input data using the GPT-1 model, you can evaluate the accuracy of the generated responses.\n\nKeep in mind that this is just a basic example to illustrate how you might approach testing the OpenAI GPT-1 model.", + "content": "I'm happy to help you test the OpenAI API, however I can not access the API.\n\nInstead why don't we follow these steps:\n\n* Check documentation\n* Contact support\n* Reach out to their community forum. \n\nLet me know if I can be of any additional assistance", "refusal": null, "role": "assistant", "annotations": null, @@ -37,15 +37,15 @@ } } ], - "created": 1754510050, + 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diff --git a/tests/integration/recordings/responses/17253d7cc667.json b/tests/integration/recordings/responses/17253d7cc667.json index 1013a8b08..290c0395b 100644 --- a/tests/integration/recordings/responses/17253d7cc667.json +++ b/tests/integration/recordings/responses/17253d7cc667.json @@ -20,15 +20,15 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama-guard3:1b", - "created_at": "2025-08-01T23:12:52.919624Z", + "created_at": "2025-09-03T17:37:34.308033Z", "done": true, "done_reason": "stop", - "total_duration": 201956834, - "load_duration": 105132584, + "total_duration": 200296000, + "load_duration": 115974708, "prompt_eval_count": 212, - "prompt_eval_duration": 75000000, + "prompt_eval_duration": 72173459, "eval_count": 2, - "eval_duration": 20000000, + "eval_duration": 11536750, "response": "safe", "thinking": null, "context": null diff --git a/tests/integration/recordings/responses/173ecb3aab28.json b/tests/integration/recordings/responses/173ecb3aab28.json index bc550edd5..0c29b278b 100644 --- a/tests/integration/recordings/responses/173ecb3aab28.json +++ b/tests/integration/recordings/responses/173ecb3aab28.json @@ -40,7 +40,7 @@ { "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk", "__data__": { - "id": "chatcmpl-457", + "id": "chatcmpl-921", "choices": [ { "delta": { @@ -55,7 +55,7 @@ "logprobs": null } ], - "created": 1754090032, + "created": 1756920971, "model": "llama3.2:3b-instruct-fp16", "object": "chat.completion.chunk", "service_tier": null, @@ -66,7 +66,7 @@ { "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk", "__data__": { - "id": "chatcmpl-457", + "id": "chatcmpl-921", "choices": [ { "delta": { @@ -81,7 +81,7 @@ "logprobs": null } ], - "created": 1754090032, + "created": 1756920971, "model": "llama3.2:3b-instruct-fp16", "object": "chat.completion.chunk", "service_tier": null, @@ -92,7 +92,7 @@ { "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk", "__data__": { - "id": "chatcmpl-457", + "id": "chatcmpl-921", "choices": [ { "delta": { @@ -107,7 +107,7 @@ "logprobs": null } ], - "created": 1754090032, + "created": 1756920971, "model": "llama3.2:3b-instruct-fp16", "object": "chat.completion.chunk", "service_tier": null, @@ -118,7 +118,7 @@ { "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk", "__data__": { - "id": "chatcmpl-457", + "id": "chatcmpl-921", "choices": [ { "delta": { @@ -133,7 +133,7 @@ "logprobs": null } ], - "created": 1754090032, + "created": 1756920971, "model": "llama3.2:3b-instruct-fp16", "object": "chat.completion.chunk", "service_tier": null, @@ -144,7 +144,7 @@ { "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk", "__data__": { - "id": "chatcmpl-457", + "id": "chatcmpl-921", "choices": [ { "delta": { @@ -159,7 +159,7 @@ "logprobs": null } ], - "created": 1754090032, + "created": 1756920971, "model": "llama3.2:3b-instruct-fp16", "object": "chat.completion.chunk", "service_tier": null, @@ -170,7 +170,7 @@ { "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk", "__data__": { - "id": "chatcmpl-457", + "id": "chatcmpl-921", "choices": [ { "delta": { @@ -185,7 +185,7 @@ "logprobs": null } ], - "created": 1754090032, + "created": 1756920971, "model": "llama3.2:3b-instruct-fp16", "object": "chat.completion.chunk", "service_tier": null, @@ -196,7 +196,7 @@ { "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk", "__data__": { - "id": "chatcmpl-457", + "id": "chatcmpl-921", "choices": [ { "delta": { @@ -211,7 +211,7 @@ "logprobs": null } ], - "created": 1754090032, + "created": 1756920971, "model": "llama3.2:3b-instruct-fp16", "object": "chat.completion.chunk", "service_tier": null, @@ -222,7 +222,7 @@ { "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk", "__data__": { - "id": "chatcmpl-457", + "id": "chatcmpl-921", "choices": [ { "delta": { @@ -237,7 +237,7 @@ "logprobs": null } ], - "created": 1754090032, + "created": 1756920971, "model": "llama3.2:3b-instruct-fp16", "object": "chat.completion.chunk", "service_tier": null, diff --git a/tests/integration/recordings/responses/174458ad71b2.json b/tests/integration/recordings/responses/174458ad71b2.json index 2dcb85262..ba99d54e6 100644 --- a/tests/integration/recordings/responses/174458ad71b2.json +++ b/tests/integration/recordings/responses/174458ad71b2.json @@ -20,15 +20,15 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama-guard3:1b", - "created_at": "2025-08-01T23:12:53.580806Z", + "created_at": "2025-09-03T17:37:34.994704Z", "done": true, "done_reason": "stop", - "total_duration": 205732750, - "load_duration": 98967000, + "total_duration": 339570875, + "load_duration": 262794125, "prompt_eval_count": 213, - "prompt_eval_duration": 86000000, + "prompt_eval_duration": 64061000, "eval_count": 2, - "eval_duration": 18000000, + "eval_duration": 11839042, "response": "safe", "thinking": null, "context": null diff --git a/tests/integration/recordings/responses/178016edef0e.json b/tests/integration/recordings/responses/178016edef0e.json index be545c221..83746aa33 100644 --- a/tests/integration/recordings/responses/178016edef0e.json +++ b/tests/integration/recordings/responses/178016edef0e.json @@ -20,15 +20,15 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama-guard3:1b", - "created_at": "2025-08-01T23:12:52.354566Z", + "created_at": "2025-09-03T17:37:33.769233Z", "done": true, "done_reason": "stop", - "total_duration": 605192500, - "load_duration": 457087166, + "total_duration": 253836584, + "load_duration": 138624959, "prompt_eval_count": 210, - "prompt_eval_duration": 63000000, + "prompt_eval_duration": 69496125, "eval_count": 5, - "eval_duration": 84000000, + "eval_duration": 45062833, "response": "unsafe\nS12", "thinking": null, "context": null diff --git a/tests/integration/recordings/responses/197228e26971.json b/tests/integration/recordings/responses/197228e26971.json index 6c1730df2..4fa9e2126 100644 --- a/tests/integration/recordings/responses/197228e26971.json +++ b/tests/integration/recordings/responses/197228e26971.json @@ -20,15 +20,15 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama-guard3:1b", - "created_at": "2025-08-01T23:12:52.686478Z", + "created_at": "2025-09-03T17:37:34.074233Z", "done": true, "done_reason": "stop", - "total_duration": 304136208, - "load_duration": 155977000, + "total_duration": 270746375, + "load_duration": 156423042, "prompt_eval_count": 213, - "prompt_eval_duration": 71000000, + "prompt_eval_duration": 70338083, "eval_count": 5, - "eval_duration": 76000000, + "eval_duration": 43379167, "response": "unsafe\nS2", "thinking": null, "context": null diff --git a/tests/integration/recordings/responses/198ef7208389.json b/tests/integration/recordings/responses/198ef7208389.json index b196d3be2..f0f9d6a7d 100644 --- a/tests/integration/recordings/responses/198ef7208389.json +++ b/tests/integration/recordings/responses/198ef7208389.json @@ -20,15 +20,15 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama-guard3:1b", - "created_at": "2025-08-01T23:12:51.186501Z", + "created_at": "2025-09-03T17:37:32.84197Z", "done": true, "done_reason": "stop", - "total_duration": 3146184459, - "load_duration": 2533467917, + "total_duration": 21572898667, + "load_duration": 21155275042, "prompt_eval_count": 212, - "prompt_eval_duration": 526000000, + "prompt_eval_duration": 371898125, "eval_count": 5, - "eval_duration": 83000000, + "eval_duration": 43290458, "response": "unsafe\nS1", "thinking": null, "context": null diff --git a/tests/integration/recordings/responses/1adfaa0e062e.json b/tests/integration/recordings/responses/1adfaa0e062e.json index 5a3d44394..253c230d9 100644 --- a/tests/integration/recordings/responses/1adfaa0e062e.json +++ b/tests/integration/recordings/responses/1adfaa0e062e.json @@ -20,15 +20,15 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama-guard3:1b", - "created_at": "2025-08-01T23:12:53.332041Z", + "created_at": "2025-09-03T17:37:34.607413Z", "done": true, "done_reason": "stop", - "total_duration": 365895333, - "load_duration": 257825208, + "total_duration": 267812042, + "load_duration": 181570000, "prompt_eval_count": 213, - "prompt_eval_duration": 78000000, + "prompt_eval_duration": 73947375, "eval_count": 2, - "eval_duration": 28000000, + "eval_duration": 11708000, "response": "safe", "thinking": null, "context": null diff --git a/tests/integration/recordings/responses/1b8394f90636.json b/tests/integration/recordings/responses/1b8394f90636.json index f5885805b..6857c6840 100644 --- a/tests/integration/recordings/responses/1b8394f90636.json +++ b/tests/integration/recordings/responses/1b8394f90636.json @@ -22,15 +22,15 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:05.685988Z", + "created_at": "2025-09-03T17:36:13.821929Z", "done": true, "done_reason": "stop", - "total_duration": 14128980625, - "load_duration": 7220159208, + "total_duration": 1907912167, + "load_duration": 90979292, "prompt_eval_count": 18, - "prompt_eval_duration": 4658000000, + "prompt_eval_duration": 77350291, "eval_count": 43, - "eval_duration": 2224000000, + "eval_duration": 1738568334, "response": " _______.\n\nThe best answer is blue. The traditional nursery rhyme goes like this:\n\nRoses are red,\nViolets are blue,\nSugar is sweet,\nAnd so are you! (Or something similar.)", "thinking": null, "context": null diff --git a/tests/integration/recordings/responses/1b92be674e2a.json b/tests/integration/recordings/responses/1b92be674e2a.json index 2ed061949..e5f05bf54 100644 --- a/tests/integration/recordings/responses/1b92be674e2a.json +++ b/tests/integration/recordings/responses/1b92be674e2a.json @@ -20,15 +20,15 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-07-31T17:50:06.140190726Z", + "created_at": "2025-09-03T17:39:38.236797Z", "done": true, "done_reason": "stop", - "total_duration": 5213341378, - "load_duration": 43943569, + "total_duration": 1296281500, + "load_duration": 283393917, "prompt_eval_count": 23, - "prompt_eval_duration": 1049424427, + "prompt_eval_duration": 75453042, "eval_count": 24, - "eval_duration": 4119422888, + "eval_duration": 936860125, "response": "Mark Zuckerberg is the founder, chairman and CEO of Meta, which he originally founded as Facebook in 2004.", "thinking": null, "context": null diff --git a/tests/integration/recordings/responses/1d54570bbe4b.json b/tests/integration/recordings/responses/1d54570bbe4b.json new file mode 100644 index 000000000..8fc686895 --- /dev/null +++ b/tests/integration/recordings/responses/1d54570bbe4b.json @@ -0,0 +1,2353 @@ +{ + "request": { + "method": "POST", + "url": "https://api.together.xyz/v1/v1/embeddings", + "headers": {}, + "body": { + "model": "togethercomputer/m2-bert-80M-32k-retrieval", + "input": [ + "Hello, world!", + "How are you today?", + "This is a test." + ], + "encoding_format": "float" + }, + "endpoint": "/v1/embeddings", + "model": "togethercomputer/m2-bert-80M-32k-retrieval" + }, + "response": { + "body": { + "__type__": "openai.types.create_embedding_response.CreateEmbeddingResponse", + "__data__": { + "data": [ + { + "embedding": [ + -0.017041557, + -0.07436493, + 0.02897635, + -0.032216743, + 0.0056444216, + -0.029015187, + 0.06512343, + -0.040310342, + 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b/tests/integration/recordings/responses/2d187a11704c.json index fbfcb91f8..c0f746ffe 100644 --- a/tests/integration/recordings/responses/2d187a11704c.json +++ b/tests/integration/recordings/responses/2d187a11704c.json @@ -22,7 +22,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:11.938867Z", + "created_at": "2025-09-03T17:37:56.566151Z", "done": false, "done_reason": null, "total_duration": null, @@ -40,7 +40,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:11.991247Z", + "created_at": "2025-09-03T17:37:56.609308Z", "done": false, "done_reason": null, "total_duration": null, @@ -58,7 +58,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:12.043953Z", + "created_at": "2025-09-03T17:37:56.651314Z", "done": false, "done_reason": null, "total_duration": null, @@ -76,7 +76,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:12.096001Z", + "created_at": "2025-09-03T17:37:56.693185Z", "done": false, "done_reason": null, "total_duration": null, @@ -94,7 +94,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:12.150454Z", + "created_at": "2025-09-03T17:37:56.734643Z", "done": false, "done_reason": null, "total_duration": null, @@ -112,7 +112,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:12.201249Z", + "created_at": "2025-09-03T17:37:56.776343Z", "done": false, "done_reason": null, "total_duration": null, @@ -130,7 +130,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:12.252534Z", + "created_at": "2025-09-03T17:37:56.81705Z", "done": false, "done_reason": null, "total_duration": null, @@ -148,7 +148,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:12.30063Z", + "created_at": "2025-09-03T17:37:56.857959Z", "done": false, "done_reason": null, "total_duration": null, @@ -166,7 +166,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:12.351034Z", + "created_at": "2025-09-03T17:37:56.899424Z", "done": false, "done_reason": null, "total_duration": null, @@ -184,7 +184,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:12.405032Z", + "created_at": "2025-09-03T17:37:56.939218Z", "done": false, "done_reason": null, "total_duration": null, @@ -202,7 +202,7 @@ "__type__": 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"__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:15.747168Z", + "created_at": "2025-09-03T17:37:59.529Z", "done": false, "done_reason": null, "total_duration": null, @@ -1336,7 +1336,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:15.797196Z", + "created_at": "2025-09-03T17:37:59.569417Z", "done": false, "done_reason": null, "total_duration": null, @@ -1354,7 +1354,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:15.845587Z", + "created_at": "2025-09-03T17:37:59.610542Z", "done": false, "done_reason": null, "total_duration": null, @@ -1372,7 +1372,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:15.897171Z", + "created_at": "2025-09-03T17:37:59.651411Z", "done": false, "done_reason": null, 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"2025-08-01T23:14:16.093853Z", + "created_at": "2025-09-03T17:37:59.812507Z", "done": false, "done_reason": null, "total_duration": null, @@ -1462,7 +1462,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:16.144847Z", + "created_at": "2025-09-03T17:37:59.852762Z", "done": false, "done_reason": null, "total_duration": null, @@ -1480,7 +1480,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:16.197888Z", + "created_at": "2025-09-03T17:37:59.892984Z", "done": false, "done_reason": null, "total_duration": null, @@ -1498,7 +1498,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:16.250854Z", + "created_at": "2025-09-03T17:37:59.933555Z", "done": false, "done_reason": null, "total_duration": null, @@ -1516,7 +1516,7 @@ "__type__": 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a/tests/integration/recordings/responses/325a72db5755.json b/tests/integration/recordings/responses/325a72db5755.json index a41db435b..ca3eea2f3 100644 --- a/tests/integration/recordings/responses/325a72db5755.json +++ b/tests/integration/recordings/responses/325a72db5755.json @@ -1,7 +1,7 @@ { "request": { "method": "POST", - "url": "http://localhost:11434/v1/v1/chat/completions", + "url": "http://0.0.0.0:11434/v1/v1/chat/completions", "headers": {}, "body": { "model": "llama3.2:3b-instruct-fp16", @@ -21,7 +21,7 @@ { "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk", "__data__": { - "id": "chatcmpl-312", + "id": "chatcmpl-923", "choices": [ { "delta": { @@ -36,7 +36,7 @@ "logprobs": null } ], - "created": 1754081853, + "created": 1756921364, "model": "llama3.2:3b-instruct-fp16", "object": "chat.completion.chunk", "service_tier": null, @@ -47,7 +47,7 @@ { "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk", "__data__": { - "id": 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"index": 0, "logprobs": null, "message": { - "content": "I'm ready to help", + "content": "It looks like we've", "refusal": null, "role": "assistant", "annotations": null, @@ -37,7 +37,7 @@ } } ], - "created": 1755294941, + "created": 1756921086, "model": "llama3.2:3b-instruct-fp16", "object": "chat.completion", "service_tier": null, diff --git a/tests/integration/recordings/responses/3c3f13cb7794.json b/tests/integration/recordings/responses/3c3f13cb7794.json index a1f240a9c..117fbcceb 100644 --- a/tests/integration/recordings/responses/3c3f13cb7794.json +++ b/tests/integration/recordings/responses/3c3f13cb7794.json @@ -21,7 +21,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:11.338232Z", + "created_at": "2025-09-03T17:36:18.136699Z", "done": false, "done_reason": null, "total_duration": null, @@ -39,7 +39,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:11.39419Z", + "created_at": "2025-09-03T17:36:18.177622Z", "done": false, "done_reason": null, "total_duration": null, @@ -57,7 +57,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:11.445346Z", + "created_at": "2025-09-03T17:36:18.218104Z", "done": false, "done_reason": null, "total_duration": null, @@ -75,7 +75,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:11.496701Z", + "created_at": "2025-09-03T17:36:18.258837Z", "done": false, "done_reason": null, "total_duration": null, @@ -93,7 +93,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:11.546804Z", + "created_at": "2025-09-03T17:36:18.299715Z", "done": false, "done_reason": null, "total_duration": null, @@ -111,7 +111,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:11.601009Z", + "created_at": "2025-09-03T17:36:18.341602Z", "done": false, "done_reason": null, "total_duration": null, @@ -129,7 +129,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:11.652788Z", + "created_at": "2025-09-03T17:36:18.385504Z", "done": false, "done_reason": null, "total_duration": null, @@ -147,7 +147,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:11.703325Z", + "created_at": "2025-09-03T17:36:18.429427Z", "done": false, "done_reason": null, "total_duration": null, @@ -165,7 +165,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:11.754033Z", + "created_at": "2025-09-03T17:36:18.473547Z", "done": false, "done_reason": null, "total_duration": null, @@ -183,7 +183,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:11.804654Z", + "created_at": "2025-09-03T17:36:18.516327Z", "done": false, "done_reason": null, "total_duration": null, @@ -201,15 +201,15 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:11.854841Z", + "created_at": "2025-09-03T17:36:18.559332Z", "done": true, "done_reason": "stop", - "total_duration": 652371000, - "load_duration": 42086042, + "total_duration": 628034000, + "load_duration": 116384417, "prompt_eval_count": 26, - "prompt_eval_duration": 78000000, + "prompt_eval_duration": 87798792, "eval_count": 11, - "eval_duration": 531000000, + "eval_duration": 423189583, "response": "", "thinking": null, "context": null diff --git a/tests/integration/recordings/responses/3ca695048bee.json b/tests/integration/recordings/responses/3ca695048bee.json index bed6762e7..b307b2f98 100644 --- a/tests/integration/recordings/responses/3ca695048bee.json +++ b/tests/integration/recordings/responses/3ca695048bee.json @@ -1,7 +1,7 @@ { "request": { "method": "POST", - "url": "http://localhost:11434/v1/v1/chat/completions", + "url": "http://0.0.0.0:11434/v1/v1/chat/completions", "headers": {}, "body": { "model": "llama3.2:3b-instruct-fp16", @@ -39,7 +39,7 @@ { "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk", "__data__": { - "id": "chatcmpl-490", + "id": "chatcmpl-3", "choices": [ { "delta": { @@ -50,7 +50,7 @@ "tool_calls": [ { "index": 0, - "id": "call_rolv1ozt", + "id": "call_3kigugt3", "function": { "arguments": "{\"city\":\"Tokyo\"}", "name": "get_weather" @@ -64,7 +64,7 @@ "logprobs": null } ], - "created": 1754081852, + "created": 1756921361, "model": "llama3.2:3b-instruct-fp16", "object": "chat.completion.chunk", "service_tier": null, @@ -75,7 +75,7 @@ { "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk", "__data__": { - "id": "chatcmpl-490", + "id": "chatcmpl-3", "choices": [ { "delta": { @@ -85,12 +85,12 @@ "role": "assistant", "tool_calls": null }, - "finish_reason": "stop", + "finish_reason": "tool_calls", "index": 0, "logprobs": null } ], - "created": 1754081852, + "created": 1756921361, "model": "llama3.2:3b-instruct-fp16", "object": "chat.completion.chunk", "service_tier": null, diff --git a/tests/integration/recordings/responses/3cdb5cab6ce6.json b/tests/integration/recordings/responses/3cdb5cab6ce6.json new file mode 100644 index 000000000..1640b256c --- /dev/null +++ b/tests/integration/recordings/responses/3cdb5cab6ce6.json @@ -0,0 +1,66 @@ +{ + "request": { + "method": "POST", + "url": "https://api.cerebras.ai/v1/v1/chat/completions", + "headers": {}, + "body": { + "model": "llama-3.3-70b", + "messages": [ + { + "role": "user", + "content": "Which planet do humans live on?" + } + ], + "stream": false + }, + "endpoint": "/v1/chat/completions", + "model": "llama-3.3-70b" + }, + "response": { + "body": { + "__type__": "openai.types.chat.chat_completion.ChatCompletion", + "__data__": { + "id": "chatcmpl-839aab91-21a7-4ed9-b224-d22e524eda37", + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "logprobs": null, + "message": { + "content": "Humans live on Earth.", + "refusal": null, + "role": "assistant", + "annotations": null, + "audio": null, + "function_call": null, + "tool_calls": null + } + } + ], + "created": 1758191360, + "model": "llama-3.3-70b", + "object": "chat.completion", + "service_tier": null, + "system_fingerprint": "fp_c5ec625e72d41732d8fd", + "usage": { + "completion_tokens": 6, + "prompt_tokens": 42, + "total_tokens": 48, + "completion_tokens_details": null, + "prompt_tokens_details": { + "audio_tokens": null, + "cached_tokens": 0 + } + }, + "time_info": { + "queue_time": 0.00028033, + "prompt_time": 0.001467015, + "completion_time": 0.007069593, + "total_time": 0.010509490966796875, + "created": 1758191360 + } + } + }, + "is_streaming": false + } +} diff --git a/tests/integration/recordings/responses/3d89a56a76b1.json b/tests/integration/recordings/responses/3d89a56a76b1.json new file mode 100644 index 000000000..ba6a107af --- /dev/null +++ b/tests/integration/recordings/responses/3d89a56a76b1.json @@ -0,0 +1,46 @@ +{ + "request": { + "method": "POST", + "url": "https://api.together.xyz/v1/v1/embeddings", + "headers": {}, + "body": { + "model": "togethercomputer/m2-bert-80M-32k-retrieval", + "input": [ + "First text for base64", + "Second text for base64", + "Third text for base64" + ], + "encoding_format": "base64" + }, + "endpoint": "/v1/embeddings", + "model": "togethercomputer/m2-bert-80M-32k-retrieval" + }, + "response": { + "body": { + "__type__": "openai.types.create_embedding_response.CreateEmbeddingResponse", + "__data__": { + "data": [ + { + "embedding": 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--- a/tests/integration/recordings/responses/44fb9cf5875f.json +++ b/tests/integration/recordings/responses/44fb9cf5875f.json @@ -20,15 +20,15 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-07-31T17:59:42.166585642Z", + "created_at": "2025-09-03T17:41:49.581065Z", "done": true, "done_reason": "stop", - "total_duration": 9490295253, - "load_duration": 42349084, + "total_duration": 2391571708, + "load_duration": 182022958, "prompt_eval_count": 20, - "prompt_eval_duration": 545470166, + "prompt_eval_duration": 74456583, "eval_count": 51, - "eval_duration": 8901928284, + "eval_duration": 2134471458, "response": "It seems like you're trying to test the system, but I'm not sure what specific functionality or feature you'd like to test. Could you please provide more context or clarify what you're looking for? I'll do my best to assist you!", "thinking": null, "context": null diff --git a/tests/integration/recordings/responses/48d2fb183a2a.json b/tests/integration/recordings/responses/48d2fb183a2a.json index c8fbcb07d..1b5ee286c 100644 --- a/tests/integration/recordings/responses/48d2fb183a2a.json +++ b/tests/integration/recordings/responses/48d2fb183a2a.json @@ -67,15 +67,15 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:40.583477Z", + "created_at": "2025-09-03T17:36:40.283084Z", "done": true, "done_reason": "stop", - "total_duration": 3928481500, - "load_duration": 151903250, + "total_duration": 2900042958, + "load_duration": 83372125, "prompt_eval_count": 259, - "prompt_eval_duration": 468000000, + "prompt_eval_duration": 352890750, "eval_count": 60, - "eval_duration": 3306000000, + "eval_duration": 2462885208, "response": "{\n \"first_name\": \"Michael\",\n \"last_name\": \"Jordan\",\n \"year_of_birth\": 1963,\n \"nba_stats\": {\n \"year_for_draft\": 1984,\n \"num_seasons_in_nba\": 15\n }\n}", "thinking": null, "context": null diff --git a/tests/integration/recordings/responses/4ca6152a0eb8.json b/tests/integration/recordings/responses/4ca6152a0eb8.json new file mode 100644 index 000000000..cb222cdf8 --- /dev/null +++ b/tests/integration/recordings/responses/4ca6152a0eb8.json @@ -0,0 +1,59 @@ +{ + "request": { + "method": "POST", + "url": "https://api.together.xyz/v1/v1/chat/completions", + "headers": {}, + "body": { + "model": "meta-llama/Llama-3.3-70B-Instruct-Turbo-Free", + "messages": [ + { + "role": "user", + "content": "Which planet has rings around it with a name starting with letter S?" + } + ], + "stream": false + }, + "endpoint": "/v1/chat/completions", + "model": "meta-llama/Llama-3.3-70B-Instruct-Turbo-Free" + }, + "response": { + "body": { + "__type__": "openai.types.chat.chat_completion.ChatCompletion", + "__data__": { + "id": "oBUtaEp-62bZhn-9801a2718d0ed123", + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "logprobs": null, + "message": { + "content": "The planet with rings around it that starts with the letter S is Saturn. 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Telemetry in the context of AI, such as OpenAI, typically refers to the automated process of collecting, transmitting, and analyzing data from remote sources, in this case, user interactions with AI models. Here\u2019s how you might approach testing telemetry creation:\n\n### 1. **Define Telemetry Requirements**\n- **Identify Data Points:** Determine what data points are crucial for your analysis. This could include user input, model responses, interaction timestamps, user IDs, etc.\n- **Purpose of Telemetry:** Understand why you are collecting this data. Is it for model improvement, user experience enhancement, or security monitoring?\n\n### 2. **Implement Data Collection**\n- **API Integration:** If you're using OpenAI's API, ensure you have the necessary permissions and access to collect the required data. OpenAI provides APIs for interacting with their models, and you can collect telemetry data through these interactions.\n- **Logging Mechanisms:** Implement logging mechanisms in your application to capture relevant data. This could involve server-side logging for API calls or client-side logging for user interactions.\n\n### 3. **Data Transmission**\n- **Secure Data Transfer:** Ensure that the data collected is transmitted securely to your analytics or storage system. HTTPS is a standard for secure data transmission over the internet.\n- **Data Format:** Decide on a data format for transmission. JSON is commonly used due to its readability and ease of parsing.\n\n### 4. **Data Analysis**\n- **Analytics Tools:** Utilize appropriate analytics tools or platforms to process and analyze the collected data. This could range from simple statistical analysis to complex machine learning models.\n- **Visualization:** Use data visualization techniques to represent the insights gained from the telemetry data. This helps in understanding trends, patterns, and areas for improvement.\n\n### 5. **Testing the Telemetry System**\n- **Mock Data:** Test your telemetry system with mock data to ensure it can collect, transmit, and analyze data correctly without affecting real users.\n- **Real-World Testing:** Once the system seems to work with mock data, gradually introduce it to real-world scenarios, starting with a small user base to identify and fix any issues that arise.\n- **Feedback Loop:** Implement a feedback loop to continuously monitor the telemetry system's performance and the insights it provides, making adjustments as necessary.\n\n### Example of Testing with Mock Data\nIf you're testing an OpenAI model's interaction telemetry, you might simulate user queries and model responses, then verify that this data is correctly logged, transmitted, and analyzed. For instance, in Python, you might use a mock library to simulate API calls and then assert that the expected data is collected and processed.\n\n```python\nimport unittest\nfrom unittest.mock import Mock\nimport json\n\nclass TestTelemetry(unittest.TestCase):\n\n def test_telemetry_data_collection(self):\n # Mock user interaction\n user_input = \"Hello, how are you?\"\n model_response = \"I'm doing well, thanks.\"\n\n # Mock the API call\n api_call_mock = Mock(return_value=model_response)\n\n # Simulate the interaction\n response = api_call_mock(user_input)\n\n # Verify telemetry data is collected correctly\n expected_data = {\n 'user_input': user_input,\n 'model_response': model_response,\n # Other relevant data points\n }\n\n # Assuming `collect_telemetry_data` is your function to collect telemetry\n collected_data = collect_telemetry_data(user_input, response)\n\n self.assertEqual(collected_data, expected_data)\n\nif __name__ == '__main__':\n unittest.main()\n```\n\nThis example simplifies the process and focuses on the concept. Actual implementation details may vary based on your specific requirements, the technologies you're using, and the OpenAI APIs or models you're interacting with.", + "refusal": null, + "role": "assistant", + "annotations": null, + "audio": null, + "function_call": null, + "tool_calls": [] + }, + "seed": 3434953141173799400 + } + ], + "created": 1758820620, + "model": "meta-llama/Llama-3.3-70B-Instruct-Turbo-Free", + "object": "chat.completion", + "service_tier": null, + "system_fingerprint": null, + "usage": { + "completion_tokens": 791, + "prompt_tokens": 40, + "total_tokens": 831, + "completion_tokens_details": null, + "prompt_tokens_details": null, + "cached_tokens": 0 + }, + "prompt": [] + } + }, + "is_streaming": false + } +} diff --git a/tests/integration/recordings/responses/5370751803dc.json b/tests/integration/recordings/responses/5370751803dc.json new file mode 100644 index 000000000..af1d8efab --- /dev/null +++ b/tests/integration/recordings/responses/5370751803dc.json @@ 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"__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:04.455647Z", + "created_at": "2025-09-03T17:37:51.850067Z", "done": false, "done_reason": null, "total_duration": null, @@ -58,7 +58,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:04.509581Z", + "created_at": "2025-09-03T17:37:51.892443Z", "done": false, "done_reason": null, "total_duration": null, @@ -76,7 +76,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:04.56592Z", + "created_at": "2025-09-03T17:37:51.934364Z", "done": false, "done_reason": null, "total_duration": null, @@ -94,7 +94,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:04.616979Z", + "created_at": "2025-09-03T17:37:51.978382Z", "done": false, "done_reason": null, "total_duration": null, @@ -112,7 +112,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:04.671413Z", + "created_at": "2025-09-03T17:37:52.019332Z", "done": false, "done_reason": null, "total_duration": null, @@ -130,7 +130,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:04.725494Z", + "created_at": "2025-09-03T17:37:52.060708Z", "done": false, "done_reason": null, "total_duration": null, @@ -148,7 +148,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:04.779905Z", + "created_at": "2025-09-03T17:37:52.102717Z", "done": false, "done_reason": null, "total_duration": null, @@ -166,7 +166,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:04.829791Z", + "created_at": "2025-09-03T17:37:52.143996Z", "done": false, "done_reason": null, "total_duration": null, @@ -184,7 +184,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:04.880729Z", + "created_at": "2025-09-03T17:37:52.185479Z", "done": false, "done_reason": null, "total_duration": null, @@ -202,7 +202,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:04.93338Z", + "created_at": "2025-09-03T17:37:52.227562Z", "done": false, "done_reason": null, "total_duration": null, @@ -220,7 +220,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:04.981714Z", + "created_at": "2025-09-03T17:37:52.270178Z", "done": false, "done_reason": null, "total_duration": null, @@ -238,7 +238,7 @@ "__type__": 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b/tests/integration/recordings/responses/565b1072cb9d.json @@ -0,0 +1,46 @@ +{ + "request": { + "method": "POST", + "url": "https://api.together.xyz/v1/v1/completions", + "headers": {}, + "body": { + "model": "meta-llama/Llama-3.3-70B-Instruct-Turbo-Free", + "prompt": "Respond to this question and explain your answer. 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a/tests/integration/recordings/responses/6f96090aa955.json b/tests/integration/recordings/responses/6f96090aa955.json index d5131d389..d0ac20442 100644 --- a/tests/integration/recordings/responses/6f96090aa955.json +++ b/tests/integration/recordings/responses/6f96090aa955.json @@ -1,7 +1,7 @@ { "request": { "method": "POST", - "url": "http://localhost:11434/v1/v1/chat/completions", + "url": "http://0.0.0.0:11434/v1/v1/chat/completions", "headers": {}, "body": { "model": "llama3.2:3b-instruct-fp16", @@ -21,7 +21,7 @@ { "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk", "__data__": { - "id": "chatcmpl-333", + "id": "chatcmpl-698", "choices": [ { "delta": { @@ -36,7 +36,7 @@ "logprobs": null } ], - "created": 1754081849, + "created": 1756921359, "model": "llama3.2:3b-instruct-fp16", "object": "chat.completion.chunk", "service_tier": null, @@ -47,7 +47,7 @@ { "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk", "__data__": { - "id": "chatcmpl-333", + "id": "chatcmpl-698", "choices": [ { "delta": { @@ -62,7 +62,7 @@ "logprobs": null } ], - "created": 1754081849, + "created": 1756921359, "model": "llama3.2:3b-instruct-fp16", "object": "chat.completion.chunk", "service_tier": null, @@ -73,11 +73,11 @@ { "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk", "__data__": { - "id": "chatcmpl-333", + "id": "chatcmpl-698", "choices": [ { "delta": { - "content": " Welcome", + "content": " It", "function_call": null, "refusal": null, "role": "assistant", @@ -88,7 +88,7 @@ "logprobs": null } ], - "created": 1754081849, + "created": 1756921359, "model": "llama3.2:3b-instruct-fp16", "object": "chat.completion.chunk", "service_tier": null, @@ -99,7 +99,59 @@ { "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk", "__data__": { - "id": "chatcmpl-333", + "id": "chatcmpl-698", + "choices": [ + { + "delta": { + "content": "'s", + "function_call": null, + "refusal": null, + "role": "assistant", + "tool_calls": null + }, + "finish_reason": null, + "index": 0, + "logprobs": null + } + ], + "created": 1756921359, + "model": "llama3.2:3b-instruct-fp16", + "object": "chat.completion.chunk", + "service_tier": null, + "system_fingerprint": "fp_ollama", + "usage": null + } + }, + { + "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk", + "__data__": { + "id": "chatcmpl-698", + "choices": [ + { + "delta": { + "content": " nice", + "function_call": null, + "refusal": null, + "role": "assistant", + "tool_calls": null + }, + "finish_reason": null, + "index": 0, + "logprobs": null + } + ], + "created": 1756921359, + "model": "llama3.2:3b-instruct-fp16", + "object": "chat.completion.chunk", + "service_tier": null, + "system_fingerprint": "fp_ollama", + "usage": null + } + }, + { + "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk", + "__data__": { + "id": "chatcmpl-698", "choices": [ { "delta": { @@ -114,7 +166,7 @@ "logprobs": null } ], - "created": 1754081849, + "created": 1756921359, "model": "llama3.2:3b-instruct-fp16", "object": "chat.completion.chunk", "service_tier": null, @@ -125,11 +177,11 @@ { "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk", "__data__": { - "id": "chatcmpl-333", + "id": "chatcmpl-698", "choices": [ { "delta": { - "content": " our", + "content": " meet", "function_call": null, "refusal": null, "role": "assistant", @@ -140,7 +192,7 @@ "logprobs": null } ], - "created": 1754081849, + "created": 1756921359, "model": "llama3.2:3b-instruct-fp16", "object": "chat.completion.chunk", "service_tier": null, @@ -151,11 +203,11 @@ { "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk", "__data__": { - "id": "chatcmpl-333", + "id": "chatcmpl-698", "choices": [ { "delta": { - "content": " conversation", + "content": " you", "function_call": null, "refusal": null, "role": "assistant", @@ -166,7 +218,7 @@ "logprobs": null } ], - "created": 1754081849, + "created": 1756921359, "model": "llama3.2:3b-instruct-fp16", "object": "chat.completion.chunk", "service_tier": null, @@ -177,7 +229,7 @@ { "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk", "__data__": { - "id": "chatcmpl-333", + "id": "chatcmpl-698", "choices": [ { "delta": { @@ -192,7 +244,7 @@ "logprobs": null } ], - "created": 1754081849, + "created": 1756921359, "model": "llama3.2:3b-instruct-fp16", "object": "chat.completion.chunk", "service_tier": null, @@ -203,7 +255,7 @@ { "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk", "__data__": { - "id": "chatcmpl-333", + "id": "chatcmpl-698", "choices": [ { "delta": { @@ -218,7 +270,7 @@ "logprobs": null } ], - "created": 1754081850, + "created": 1756921359, "model": "llama3.2:3b-instruct-fp16", "object": "chat.completion.chunk", "service_tier": null, @@ -229,7 +281,7 @@ { "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk", "__data__": { - "id": "chatcmpl-333", + "id": "chatcmpl-698", "choices": [ { "delta": { @@ -244,7 +296,7 @@ "logprobs": null } ], - "created": 1754081850, + "created": 1756921359, "model": "llama3.2:3b-instruct-fp16", "object": "chat.completion.chunk", "service_tier": null, @@ -255,7 +307,7 @@ { "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk", "__data__": { - "id": "chatcmpl-333", + "id": "chatcmpl-698", "choices": [ { "delta": { @@ -270,7 +322,7 @@ "logprobs": null } ], - "created": 1754081850, + "created": 1756921359, "model": "llama3.2:3b-instruct-fp16", "object": "chat.completion.chunk", "service_tier": null, @@ -281,7 +333,7 @@ { "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk", "__data__": { - "id": "chatcmpl-333", + "id": "chatcmpl-698", "choices": [ { "delta": { @@ -296,7 +348,7 @@ "logprobs": null } ], - "created": 1754081850, + "created": 1756921359, "model": "llama3.2:3b-instruct-fp16", "object": "chat.completion.chunk", "service_tier": null, @@ -307,7 +359,7 @@ { "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk", "__data__": { - "id": "chatcmpl-333", + "id": "chatcmpl-698", "choices": [ { "delta": { @@ -322,7 +374,7 @@ "logprobs": null } ], - "created": 1754081850, + "created": 1756921359, "model": "llama3.2:3b-instruct-fp16", "object": "chat.completion.chunk", "service_tier": null, @@ -333,7 +385,7 @@ { "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk", "__data__": { - "id": "chatcmpl-333", + "id": "chatcmpl-698", "choices": [ { "delta": { @@ -348,7 +400,7 @@ "logprobs": null } ], - "created": 1754081850, + "created": 1756921359, "model": "llama3.2:3b-instruct-fp16", "object": "chat.completion.chunk", "service_tier": null, @@ -359,7 +411,7 @@ { "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk", "__data__": { - "id": "chatcmpl-333", + "id": "chatcmpl-698", "choices": [ { "delta": { @@ -374,7 +426,7 @@ "logprobs": null } ], - "created": 1754081850, + "created": 1756921359, "model": "llama3.2:3b-instruct-fp16", "object": "chat.completion.chunk", "service_tier": null, @@ -385,7 +437,7 @@ { "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk", "__data__": { - "id": "chatcmpl-333", + "id": "chatcmpl-698", "choices": [ { "delta": { @@ -400,7 +452,7 @@ "logprobs": null } ], - "created": 1754081850, + "created": 1756921359, "model": "llama3.2:3b-instruct-fp16", "object": "chat.completion.chunk", "service_tier": null, @@ -411,33 +463,7 @@ { "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk", "__data__": { - "id": "chatcmpl-333", - "choices": [ - { - "delta": { - "content": ",", - "function_call": null, - "refusal": null, - "role": "assistant", - "tool_calls": null - }, - "finish_reason": null, - "index": 0, - "logprobs": null - } - ], - "created": 1754081850, - "model": "llama3.2:3b-instruct-fp16", - "object": "chat.completion.chunk", - "service_tier": null, - "system_fingerprint": "fp_ollama", - "usage": null - } - }, - { - "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk", - "__data__": { - "id": "chatcmpl-333", + "id": "chatcmpl-698", "choices": [ { "delta": { @@ -452,7 +478,7 @@ "logprobs": null } ], - "created": 1754081850, + "created": 1756921359, "model": "llama3.2:3b-instruct-fp16", "object": "chat.completion.chunk", "service_tier": null, @@ -463,7 +489,7 @@ { "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk", "__data__": { - "id": "chatcmpl-333", + "id": "chatcmpl-698", "choices": [ { "delta": { @@ -478,7 +504,7 @@ "logprobs": null } ], - "created": 1754081850, + "created": 1756921359, "model": "llama3.2:3b-instruct-fp16", "object": "chat.completion.chunk", "service_tier": null, @@ -489,7 +515,7 @@ { "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk", "__data__": { - "id": "chatcmpl-333", + "id": "chatcmpl-698", "choices": [ { "delta": { @@ -504,7 +530,7 @@ "logprobs": null } ], - "created": 1754081850, + "created": 1756921360, "model": "llama3.2:3b-instruct-fp16", "object": "chat.completion.chunk", "service_tier": null, @@ -515,7 +541,7 @@ { "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk", "__data__": { - "id": "chatcmpl-333", + "id": "chatcmpl-698", "choices": [ { "delta": { @@ -530,7 +556,7 @@ "logprobs": null } ], - "created": 1754081850, + "created": 1756921360, "model": "llama3.2:3b-instruct-fp16", "object": "chat.completion.chunk", "service_tier": null, @@ -541,7 +567,7 @@ { "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk", "__data__": { - "id": "chatcmpl-333", + "id": "chatcmpl-698", "choices": [ { "delta": { @@ -556,7 +582,7 @@ "logprobs": null } ], - "created": 1754081850, + "created": 1756921360, "model": "llama3.2:3b-instruct-fp16", "object": "chat.completion.chunk", "service_tier": null, @@ -567,7 +593,7 @@ { "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk", "__data__": { - "id": "chatcmpl-333", + "id": "chatcmpl-698", "choices": [ { "delta": { @@ -582,7 +608,7 @@ "logprobs": null } ], - "created": 1754081850, + "created": 1756921360, "model": "llama3.2:3b-instruct-fp16", "object": "chat.completion.chunk", "service_tier": null, @@ -593,7 +619,7 @@ { "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk", "__data__": { - "id": "chatcmpl-333", + "id": "chatcmpl-698", "choices": [ { "delta": { @@ -608,7 +634,7 @@ "logprobs": null } ], - "created": 1754081850, + "created": 1756921360, "model": "llama3.2:3b-instruct-fp16", "object": "chat.completion.chunk", "service_tier": null, @@ -619,7 +645,7 @@ { "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk", "__data__": { - "id": "chatcmpl-333", + "id": "chatcmpl-698", "choices": [ { "delta": { @@ -634,7 +660,7 @@ "logprobs": null } ], - "created": 1754081850, + "created": 1756921360, "model": "llama3.2:3b-instruct-fp16", "object": "chat.completion.chunk", "service_tier": null, diff --git a/tests/integration/recordings/responses/6fbea1abca7c.json b/tests/integration/recordings/responses/6fbea1abca7c.json index 576fc7de1..c16fe1268 100644 --- a/tests/integration/recordings/responses/6fbea1abca7c.json +++ b/tests/integration/recordings/responses/6fbea1abca7c.json @@ -22,7 +22,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:20.337763Z", + "created_at": "2025-09-03T17:38:01.89965Z", "done": false, "done_reason": null, "total_duration": null, @@ -40,7 +40,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:20.394358Z", + "created_at": "2025-09-03T17:38:01.941253Z", "done": false, "done_reason": null, "total_duration": null, @@ -58,7 +58,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:20.451349Z", + "created_at": "2025-09-03T17:38:01.982621Z", "done": false, "done_reason": null, "total_duration": null, @@ -76,7 +76,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:20.504443Z", + "created_at": "2025-09-03T17:38:02.024144Z", "done": false, "done_reason": null, "total_duration": null, @@ -94,7 +94,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:20.555779Z", + "created_at": "2025-09-03T17:38:02.065495Z", "done": false, "done_reason": null, "total_duration": null, @@ -112,7 +112,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:20.607807Z", + "created_at": "2025-09-03T17:38:02.107529Z", "done": false, "done_reason": null, "total_duration": null, @@ -130,7 +130,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:20.660627Z", + "created_at": "2025-09-03T17:38:02.149217Z", "done": false, "done_reason": null, "total_duration": null, @@ -148,7 +148,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:20.711562Z", + "created_at": "2025-09-03T17:38:02.190357Z", "done": false, "done_reason": null, "total_duration": null, @@ -166,7 +166,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:20.761822Z", + "created_at": "2025-09-03T17:38:02.231501Z", "done": false, "done_reason": null, "total_duration": null, @@ -184,7 +184,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:20.81712Z", + "created_at": "2025-09-03T17:38:02.272546Z", "done": false, "done_reason": null, "total_duration": null, @@ -202,7 +202,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:20.868755Z", + "created_at": "2025-09-03T17:38:02.313561Z", "done": false, "done_reason": null, "total_duration": null, @@ -220,7 +220,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:20.921049Z", + "created_at": "2025-09-03T17:38:02.354563Z", "done": false, "done_reason": null, "total_duration": null, @@ -238,7 +238,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:20.973584Z", + "created_at": "2025-09-03T17:38:02.395585Z", "done": false, "done_reason": null, "total_duration": null, @@ -256,7 +256,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:21.030707Z", + "created_at": "2025-09-03T17:38:02.436854Z", "done": false, "done_reason": null, "total_duration": null, @@ -274,7 +274,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:21.082015Z", + "created_at": "2025-09-03T17:38:02.47814Z", "done": false, "done_reason": null, "total_duration": null, @@ -292,7 +292,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:21.132945Z", + "created_at": "2025-09-03T17:38:02.519661Z", "done": false, "done_reason": null, "total_duration": null, @@ -310,7 +310,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:21.187452Z", + "created_at": "2025-09-03T17:38:02.561119Z", "done": false, "done_reason": null, "total_duration": null, @@ -328,7 +328,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:21.239827Z", + "created_at": "2025-09-03T17:38:02.602821Z", "done": false, "done_reason": null, "total_duration": null, @@ -346,15 +346,15 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:21.294154Z", + "created_at": "2025-09-03T17:38:02.644633Z", "done": true, "done_reason": "stop", - "total_duration": 1929211666, - "load_duration": 61298666, + "total_duration": 1375629459, + "load_duration": 94090250, "prompt_eval_count": 386, - "prompt_eval_duration": 908000000, + "prompt_eval_duration": 535119167, "eval_count": 19, - "eval_duration": 959000000, + "eval_duration": 745684041, "response": "", "thinking": null, "context": null diff --git a/tests/integration/recordings/responses/6fe1d4fedf12.json b/tests/integration/recordings/responses/6fe1d4fedf12.json index 733c7bd55..8fd079a85 100644 --- a/tests/integration/recordings/responses/6fe1d4fedf12.json +++ b/tests/integration/recordings/responses/6fe1d4fedf12.json @@ -1,7 +1,7 @@ { "request": { "method": "POST", - "url": "http://localhost:11434/v1/v1/chat/completions", + "url": "http://0.0.0.0:11434/v1/v1/chat/completions", "headers": {}, "body": { "model": "llama3.2:3b-instruct-fp16", @@ -24,7 +24,7 @@ { "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk", "__data__": { - "id": "chatcmpl-381", + "id": "chatcmpl-358", "choices": [ { "delta": { @@ -39,7 +39,7 @@ "logprobs": null } ], - "created": 1755228961, + "created": 1756921324, "model": "llama3.2:3b-instruct-fp16", "object": "chat.completion.chunk", "service_tier": null, @@ -50,11 +50,11 @@ { "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk", "__data__": { - "id": "chatcmpl-381", + "id": "chatcmpl-358", "choices": [ { "delta": { - "content": " don", + "content": "'m", "function_call": null, "refusal": null, "role": "assistant", @@ -65,7 +65,7 @@ "logprobs": null } ], - "created": 1755228961, + "created": 1756921324, "model": "llama3.2:3b-instruct-fp16", "object": "chat.completion.chunk", "service_tier": null, @@ -76,11 +76,11 @@ { "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk", "__data__": { - "id": "chatcmpl-381", + "id": "chatcmpl-358", "choices": [ { "delta": { - "content": "'t", + "content": " not", "function_call": null, "refusal": null, "role": "assistant", @@ -91,7 +91,7 @@ "logprobs": null } ], - "created": 1755228961, + "created": 1756921324, "model": "llama3.2:3b-instruct-fp16", "object": "chat.completion.chunk", "service_tier": null, @@ -102,11 +102,11 @@ { "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk", "__data__": { - "id": "chatcmpl-381", + "id": "chatcmpl-358", "choices": [ { "delta": { - "content": " have", + "content": " able", "function_call": null, "refusal": null, "role": "assistant", @@ -117,7 +117,7 @@ "logprobs": null } 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+20,15 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-07-31T17:51:16.201313167Z", + "created_at": "2025-09-03T17:39:54.374714Z", "done": true, "done_reason": "stop", - "total_duration": 27475921912, - "load_duration": 40564716, + "total_duration": 6321793333, + "load_duration": 182255958, "prompt_eval_count": 25, - "prompt_eval_duration": 964907432, + "prompt_eval_duration": 67964459, "eval_count": 150, - "eval_duration": 26469935419, + "eval_duration": 6070867875, "response": "The smallest country in the world is the Vatican City, which has a total area of approximately 0.44 km\u00b2 (0.17 sq mi). 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It's also home to numerous iconic landmarks like St. Peter's Basilica and the Sistine Chapel.\n\nInterestingly, the Vatican City is not only the smallest country in the world but also the most densely populated, with a population of just over 800 people!", "thinking": null, "context": null diff --git a/tests/integration/recordings/responses/7550dd0d24bc.json b/tests/integration/recordings/responses/7550dd0d24bc.json new file mode 100644 index 000000000..c9ef94783 --- /dev/null +++ b/tests/integration/recordings/responses/7550dd0d24bc.json @@ -0,0 +1,166 @@ +{ + "request": { + "method": "POST", + "url": "https://api.together.xyz/v1/v1/chat/completions", + "headers": {}, + "body": { + "model": "meta-llama/Llama-3.3-70B-Instruct-Turbo-Free", + "messages": [ + { + "role": "user", + "content": "Message A: What is the capital of France?" + }, + { + "role": "assistant", + "content": "The capital of France is Paris." + }, + { + "role": "user", + "content": "Message B: What about Spain?" + }, + { + "role": "assistant", + "content": "The capital of Spain is Madrid." + }, + { + "role": "user", + "content": "Message C: And Italy?" + } + ], + "response_format": { + "type": "text" + }, + "stream": true + }, + "endpoint": "/v1/chat/completions", + "model": "meta-llama/Llama-3.3-70B-Instruct-Turbo-Free" + }, + "response": { + "body": [ + { + "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk", + "__data__": { + "id": "oCfv2aN-4Yz4kd-984c20cb7a8c8fa8", + "choices": [ + { + "delta": { + "content": "[", + "function_call": null, + "refusal": null, + "role": "assistant", + "tool_calls": null, + "token_id": 58 + }, + "finish_reason": null, + "index": 0, + "logprobs": null, + "text": "[", + "seed": null + } + ], + "created": 1758820154, + "model": "meta-llama/Llama-3.3-70B-Instruct-Turbo-Free", + "object": "chat.completion.chunk", + "service_tier": null, + "system_fingerprint": null, + "usage": null + } + }, + { + "__type__": 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b/tests/integration/recordings/responses/7b4815aba6c5.json @@ -22,7 +22,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:13:59.222059Z", + "created_at": "2025-09-03T17:37:48.840898Z", "done": false, "done_reason": null, "total_duration": null, @@ -40,7 +40,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:13:59.273466Z", + "created_at": "2025-09-03T17:37:48.883619Z", "done": false, "done_reason": null, "total_duration": null, @@ -58,7 +58,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:13:59.325562Z", + "created_at": "2025-09-03T17:37:48.92504Z", "done": false, "done_reason": null, "total_duration": null, @@ -76,7 +76,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:13:59.379223Z", + "created_at": "2025-09-03T17:37:48.966274Z", "done": false, "done_reason": null, "total_duration": null, @@ -94,7 +94,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:13:59.436435Z", + "created_at": "2025-09-03T17:37:49.007525Z", "done": false, "done_reason": null, "total_duration": null, @@ -112,7 +112,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:13:59.48928Z", + "created_at": "2025-09-03T17:37:49.049125Z", "done": false, "done_reason": null, "total_duration": null, @@ -130,7 +130,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:13:59.547102Z", + "created_at": "2025-09-03T17:37:49.090893Z", "done": false, "done_reason": null, "total_duration": null, @@ -148,7 +148,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:13:59.60579Z", + "created_at": "2025-09-03T17:37:49.132101Z", "done": false, "done_reason": null, "total_duration": null, @@ -166,7 +166,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:13:59.660149Z", + "created_at": "2025-09-03T17:37:49.17401Z", "done": false, "done_reason": null, "total_duration": null, @@ -184,7 +184,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:13:59.719166Z", + "created_at": "2025-09-03T17:37:49.216115Z", "done": false, "done_reason": null, "total_duration": null, @@ -202,7 +202,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:13:59.773893Z", + "created_at": "2025-09-03T17:37:49.257109Z", "done": false, "done_reason": null, "total_duration": null, @@ -220,7 +220,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:13:59.827636Z", + "created_at": "2025-09-03T17:37:49.298731Z", "done": false, "done_reason": null, "total_duration": null, @@ -238,7 +238,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:13:59.905205Z", + "created_at": "2025-09-03T17:37:49.338833Z", "done": false, "done_reason": null, "total_duration": null, @@ -256,7 +256,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:13:59.959347Z", + "created_at": "2025-09-03T17:37:49.38053Z", "done": false, "done_reason": null, "total_duration": null, @@ -274,7 +274,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:00.037904Z", + "created_at": "2025-09-03T17:37:49.421378Z", "done": false, "done_reason": null, "total_duration": null, @@ -292,7 +292,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:00.093527Z", + "created_at": "2025-09-03T17:37:49.462646Z", "done": false, "done_reason": null, "total_duration": null, @@ -310,7 +310,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:00.151329Z", + "created_at": "2025-09-03T17:37:49.503814Z", "done": false, "done_reason": null, "total_duration": null, @@ -328,7 +328,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:00.209463Z", + "created_at": "2025-09-03T17:37:49.545397Z", "done": false, "done_reason": null, "total_duration": null, @@ -346,15 +346,15 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:00.268012Z", + "created_at": "2025-09-03T17:37:49.586834Z", "done": true, "done_reason": "stop", - "total_duration": 1981034959, - "load_duration": 53445084, + "total_duration": 1409239209, + "load_duration": 118889250, "prompt_eval_count": 368, - "prompt_eval_duration": 880000000, + "prompt_eval_duration": 543077166, "eval_count": 19, - "eval_duration": 1046000000, + "eval_duration": 746733584, "response": "", "thinking": null, "context": null diff --git a/tests/integration/recordings/responses/7e6806cba34a.json b/tests/integration/recordings/responses/7e6806cba34a.json index 7b1d5261e..e2e32da73 100644 --- a/tests/integration/recordings/responses/7e6806cba34a.json +++ b/tests/integration/recordings/responses/7e6806cba34a.json @@ -21,7 +21,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-07-31T17:59:14.382398152Z", + "created_at": "2025-09-03T17:41:43.22891Z", "done": false, "done_reason": null, "total_duration": null, @@ -39,7 +39,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-07-31T17:59:14.561084788Z", + "created_at": "2025-09-03T17:41:43.268911Z", "done": false, "done_reason": null, "total_duration": null, @@ -57,7 +57,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-07-31T17:59:14.743154167Z", + "created_at": "2025-09-03T17:41:43.310121Z", "done": false, "done_reason": null, "total_duration": null, @@ -75,7 +75,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-07-31T17:59:14.920818124Z", + "created_at": "2025-09-03T17:41:43.35053Z", "done": false, "done_reason": null, "total_duration": null, @@ -93,7 +93,7 @@ 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"2025-09-03T17:41:43.512029Z", "done": false, "done_reason": null, "total_duration": null, @@ -165,7 +165,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-07-31T17:59:15.802614623Z", + "created_at": "2025-09-03T17:41:43.55268Z", "done": false, "done_reason": null, "total_duration": null, @@ -183,7 +183,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-07-31T17:59:15.978698104Z", + "created_at": "2025-09-03T17:41:43.594309Z", "done": false, "done_reason": null, "total_duration": null, @@ -201,7 +201,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-07-31T17:59:16.160654179Z", + "created_at": "2025-09-03T17:41:43.635445Z", "done": false, "done_reason": null, "total_duration": null, @@ -219,7 +219,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { 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b/tests/integration/recordings/responses/7ef63231b9f8.json @@ -0,0 +1,56 @@ +{ + "request": { + "method": "POST", + "url": "http://localhost:8080/v1/v1/chat/completions", + "headers": {}, + "body": { + "model": "Qwen/Qwen3-0.6B", + "messages": [ + { + "role": "user", + "content": "Which planet has rings around it with a name starting with letter S?" + } + ], + "stream": false + }, + "endpoint": "/v1/chat/completions", + "model": "Qwen/Qwen3-0.6B" + }, + "response": { + "body": { + "__type__": "openai.types.chat.chat_completion.ChatCompletion", + "__data__": { + "id": "", + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "logprobs": null, + "message": { + "content": "\nOkay, so the user is asking which planet has rings around it and its name starts with the letter S. Let me think... I know that the Sun is a star, not a planet. So the Moon is a natural satellite, which has the Moon's name and rings. But the Moon's name starts with M, not S. The Earth has the name Earth, but the rings aren't really around the Earth in any real sense. Mars has a thin ring of dust. Venus and Mercury don't have rings in the sense of planetary rings as we know. Wait, maybe the answer is the Moon, even though it's not the same as the name starting with S. But the question says a planet, so if there's a planet named S, that would be it. But actually, the only planet with rings is Jupiter. Wait, Jupiter has a famous system of rings. But why does the question mention a planet with a name starting with S? Maybe there's a trick. Let me double-check. Jupiter's name starts with J, so maybe the answer is Venus? But Venus doesn't have rings. Mercury, too, doesn't. The Moon, as a planet, a dwarf planet, and has rings. Despite the name, the rings are around it. So the answer would be the Moon. Therefore, the planet with rings and name starting with S is the Moon.\n\n\nThe planet with rings around it and a name starting with the letter **S** is the **Moon**. Though its name doesn't start with an **S**, it is technically a dwarf planet and has the rings in its orbit. Oops Saturn!", + "refusal": null, + "role": "assistant", + "annotations": null, + "audio": null, + "function_call": null, + "tool_calls": null + } + } + ], + "created": 1757550394, + "model": "Qwen/Qwen3-0.6B", + "object": "chat.completion", + "service_tier": null, + "system_fingerprint": "3.3.5-dev0-sha-1b90c50", + "usage": { + "completion_tokens": 336, + "prompt_tokens": 22, + "total_tokens": 358, + "completion_tokens_details": null, + "prompt_tokens_details": null + } + } + }, + "is_streaming": false + } +} diff --git a/tests/integration/recordings/responses/7f53b458dad9.json b/tests/integration/recordings/responses/7f53b458dad9.json new file mode 100644 index 000000000..a7a98c739 --- /dev/null +++ b/tests/integration/recordings/responses/7f53b458dad9.json @@ -0,0 +1,83 @@ +{ + "request": { + "method": "POST", + "url": "__databricks__/serving-endpoints/v1/chat/completions", + "headers": {}, + "body": { + "model": "databricks-meta-llama-3-3-70b-instruct", + "messages": [ + { + "role": "user", + "content": "What's the weather in Tokyo? 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a/tests/integration/recordings/responses/80e4404d8987.json b/tests/integration/recordings/responses/80e4404d8987.json index 8cfe1836d..7eabfc363 100644 --- a/tests/integration/recordings/responses/80e4404d8987.json +++ b/tests/integration/recordings/responses/80e4404d8987.json @@ -22,7 +22,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:13:56.463658Z", + "created_at": "2025-09-03T17:37:46.708948Z", "done": false, "done_reason": null, "total_duration": null, @@ -40,7 +40,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:13:56.51846Z", + "created_at": "2025-09-03T17:37:46.749031Z", "done": false, "done_reason": null, "total_duration": null, @@ -58,7 +58,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:13:56.569676Z", + "created_at": "2025-09-03T17:37:46.790192Z", "done": false, "done_reason": null, "total_duration": null, @@ -76,7 +76,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:13:56.621666Z", + "created_at": "2025-09-03T17:37:46.831093Z", "done": false, "done_reason": null, "total_duration": null, @@ -94,7 +94,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:13:56.675114Z", + "created_at": "2025-09-03T17:37:46.873135Z", "done": false, "done_reason": null, "total_duration": null, @@ -112,7 +112,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:13:56.727649Z", + "created_at": "2025-09-03T17:37:46.91375Z", "done": false, "done_reason": null, "total_duration": null, @@ -130,7 +130,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:13:56.780249Z", + "created_at": "2025-09-03T17:37:46.95439Z", "done": false, "done_reason": null, "total_duration": null, @@ -148,7 +148,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:13:56.834148Z", + "created_at": "2025-09-03T17:37:46.995224Z", "done": false, "done_reason": null, "total_duration": null, @@ -166,7 +166,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:13:56.885509Z", + "created_at": "2025-09-03T17:37:47.035887Z", "done": false, "done_reason": null, "total_duration": null, @@ -184,15 +184,15 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:13:56.936635Z", + "created_at": "2025-09-03T17:37:47.076806Z", "done": true, "done_reason": "stop", - "total_duration": 1572591291, - "load_duration": 77121041, + "total_duration": 2069654958, + "load_duration": 177579833, "prompt_eval_count": 31, - "prompt_eval_duration": 1019000000, + "prompt_eval_duration": 1521851250, "eval_count": 10, - "eval_duration": 474000000, + "eval_duration": 369478042, "response": "", "thinking": null, "context": null diff --git a/tests/integration/recordings/responses/8295382a8e7c.json b/tests/integration/recordings/responses/8295382a8e7c.json index 6e1dc793d..6a38dde20 100644 --- a/tests/integration/recordings/responses/8295382a8e7c.json +++ b/tests/integration/recordings/responses/8295382a8e7c.json @@ -1,7 +1,7 @@ { "request": { "method": "POST", - "url": "http://localhost:11434/v1/v1/chat/completions", + "url": "http://0.0.0.0:11434/v1/v1/chat/completions", "headers": {}, "body": { "model": "llama3.2:3b-instruct-fp16", @@ -20,14 +20,14 @@ "body": { "__type__": "openai.types.chat.chat_completion.ChatCompletion", "__data__": { - "id": "chatcmpl-99", + "id": "chatcmpl-713", "choices": [ { "finish_reason": "stop", "index": 0, "logprobs": null, "message": { - "content": "I'd be happy to help you test the OpenAI 2 architecture!\n\nOpenAI 2 is a neural network model developed by OpenAI, and it's not exactly possible for me to directly \"test\" it. However, I can guide you through a simplified example of how to verify if OpenAI 2 has been implemented correctly in a specific codebase.\n\nHere's an outline of the steps:\n\n1. **Understand the basics**: Before we dive into testing, make sure you understand the architectural and functional details of OpenAI 2.\n2. **Get access to the model**: You'll need to obtain a trained OpenAI 2 model or implement it from scratch using a language like PyTorch or TensorFlow.\n3. **Implement a validation framework**: Create a simple validation framework that uses common tasks, such as classification on the GLUE benchmark, to evaluate the performance of your OpenAI 2 model.\n\nHere's a simplified code example in PyTorch:\n```python\nimport torch\nfrom transformers import AutoModelForSequenceClassification, AutoTokenizer\n\n# Load pre-trained OpenAI 2 Model(s)\nmodel_name = \"github/openai/OpenAIAccelerated-Text-To-Speech\"\nmodel_class = AutoModelForSequenceClassification\ntokenizer = AutoTokenizer.from_pretrained(model_name)\n\n# Initialize the model and tokenizer\nmodel = model_class(pretrained=True, num_labels=8) # adjust label number according to your task\ntokenizer = tokenizer\n\ndef evaluate_model():\n batch_size = 100\n device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n\n # Create a validation dataset from the GLUE benchmark\n glue_datasets = [ datasets[\"glue\"][\"sst2\"], datasets[\"glue\"][\"mnli\"] ]\n val_dataset = []\n for i, gds in enumerate(glue_datasets):\n data = gds[:10000] # take only first few examples to speed up evaluation\n input_ids = tokenizer encodings(data[\"sentence1\"], \n attention_mask=data[\"attention_mask\"],\n max_length=512,\n padding=\"max_length\",\n truncation=True,\n return_tensors=\"pt\")\n\n for example in data:\n for sentence in [example['sentence1'], example['sentence2']]:\n input_ids = input_ids.to(device)\n outputs = model(input_ids, labels=None) # adjust label to empty\n \n\n # Compute evaluation metrics\n predictions = []\n labels = []\n accuracy = torch.zeros(8).to(device)\n\n for sentence in data.values():\n sentenceids = [input_ids[\"input_ids\"].flatten()]\n _, pred_labels = model(sentenceids)\n if len(predictions) == 0:\n labels.extend([1, 2])\n else:\n assert len(labels)==len(sentences), 'error'\n labels.append(preds[-1]) \n\n # Append the prediction to the list\n predictions.append(pred)\n\n return accuracy\n\naccuracy = evaluate_model()\nprint(\"Accuracy:\", accuracy)\n```\n\nAfter running this code, you should get an estimate of the model's performance on the GLUE benchmark. Keep in mind that this is a simplified example and real-world openai 2 testing may require more involved validation processes.\n\nI hope this helps! Let me know if you have any further questions or if there are any specific areas where you'd like more information.", + "content": "I'd be happy to help you test the Transformer-XL (denoted as \"Test Trace OpenAI 2\") model, but first I need to clarify a few things:\n\n1. **Transformer-XL** is not an official name from OpenAI. It's actually a variant of the Transformer model proposed in the paper \"Long-Short Term Memory Are General: A Study on The Curvature of Time\" (2017) by Jinyu Chen, et al.\n2. **Trace OpenAI 2** sounds like a specific version or configuration of the Transformer-XL model, possibly developed by OpenAI.\n\nGiven these clarifications, I'll provide you with a general idea of how to test the Transformer-XL (or \"Test Trace OpenAI 2\") model using popular frameworks and libraries. Please note that this is not an exhaustive or definitive guide.\n\nTo test the Transformer-XL model, you can follow these steps:\n\n1. **Install the necessary dependencies**: You'll need a deep learning framework like TensorFlow or PyTorch, as well as a library for natural language processing (NLP) like Hugging Face's transformers.\n2. **Load the pre-trained weights**: You can use a pre-trained model checkpoint from Hugging Face's Transformers library or load your own weights trained on a specific task or dataset.\n3. **Prepare your data**: Load your text data into tokens, such as words or characters, and preprocess it according to the specific requirements of the Transformer-XL architecture (e.g., tokenization, padding, etc.).\n4. **Configure the model**: Adjust the hyperparameters to suit your specific test case, including the model's configuration, batch size, learning rate, etc.\n5. **Run the inference**: Use the loaded pre-trained weights to perform inference on your test data.\n\nHere's some sample Python code using PyTorch and Hugging Face's Transformers library to get you started:\n```python\nimport torch\nfrom transformers import LongformerForSequenceClassification, LongformerTokenizer\n\n# Load pre-trained weights\nmodel = LongformerForSequenceClassification.from_pretrained('test-trace-openai-2')\n\n# Prepare data\ntokenizer = model.tokenizer\ntext = \"This is a test sentence\"\ninputs = tokenizer(text, return_tensors='pt')\noutput = model(inputs['input_ids'], attention_mask=inputs['attention_mask'])\n\n# Print the results\nprint(output.logits)\n```\nPlease note that this code snippet is just an example and may not work as-is. You'll need to adapt it to your specific requirements and test data.\n\nKeep in mind that testing a model's performance on a specific task or dataset requires careful consideration of factors like:\n\n* **Test data quality**: Your test data should accurately represent the underlying distribution of your target dataset.\n* **Model evaluation metrics**: Choose relevant evaluation metrics that measure the model's performance on your specific task, such as accuracy, precision, recall, F1-score, etc.\n\nFeel free to ask if you have any further questions or need more guidance!", "refusal": null, "role": "assistant", "annotations": null, @@ -37,15 +37,15 @@ } } ], - "created": 1754510064, + "created": 1756921250, "model": "llama3.2:3b-instruct-fp16", "object": "chat.completion", "service_tier": null, "system_fingerprint": "fp_ollama", "usage": { - "completion_tokens": 694, + "completion_tokens": 614, "prompt_tokens": 31, - "total_tokens": 725, + "total_tokens": 645, "completion_tokens_details": null, "prompt_tokens_details": null } diff --git a/tests/integration/recordings/responses/836f51dfb3c5.json b/tests/integration/recordings/responses/836f51dfb3c5.json index 833545737..85f3aff00 100644 --- a/tests/integration/recordings/responses/836f51dfb3c5.json +++ b/tests/integration/recordings/responses/836f51dfb3c5.json @@ -20,15 +20,15 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama-guard3:1b", - "created_at": "2025-08-01T23:14:03.770002Z", + "created_at": "2025-09-03T17:37:51.562847Z", "done": true, "done_reason": "stop", - "total_duration": 395965875, - "load_duration": 178888708, + "total_duration": 272296250, + "load_duration": 131747125, "prompt_eval_count": 214, - "prompt_eval_duration": 170000000, + "prompt_eval_duration": 124006709, "eval_count": 2, - "eval_duration": 44000000, + "eval_duration": 15572291, "response": "safe", "thinking": null, "context": null diff --git a/tests/integration/recordings/responses/840fbb380b73.json b/tests/integration/recordings/responses/840fbb380b73.json index a3fb7ccd8..4367d8788 100644 --- a/tests/integration/recordings/responses/840fbb380b73.json +++ b/tests/integration/recordings/responses/840fbb380b73.json @@ -20,15 +20,15 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama-guard3:1b", - "created_at": "2025-08-01T23:13:57.935921Z", + "created_at": "2025-09-03T17:37:47.871962Z", "done": true, "done_reason": "stop", - "total_duration": 313787333, - "load_duration": 89797542, + "total_duration": 301629042, + "load_duration": 102832917, "prompt_eval_count": 233, - "prompt_eval_duration": 167000000, + "prompt_eval_duration": 154806625, "eval_count": 5, - "eval_duration": 55000000, + "eval_duration": 43361542, "response": "unsafe\nS1", "thinking": null, "context": null diff --git a/tests/integration/recordings/responses/84cab42e1f5c.json b/tests/integration/recordings/responses/84cab42e1f5c.json index 423dd16da..611e67218 100644 --- a/tests/integration/recordings/responses/84cab42e1f5c.json +++ b/tests/integration/recordings/responses/84cab42e1f5c.json @@ -17,7 +17,7 @@ { "__type__": "openai.types.completion.Completion", "__data__": { - "id": "cmpl-905", + "id": "cmpl-374", "choices": [ { "finish_reason": null, @@ -26,7 +26,7 @@ "text": "Blue" } ], - "created": 1754348148, + "created": 1756921025, "model": "llama3.2:3b-instruct-fp16", "object": "text_completion", "system_fingerprint": "fp_ollama", @@ -36,7 +36,7 @@ { "__type__": "openai.types.completion.Completion", "__data__": { - "id": "cmpl-905", + "id": "cmpl-374", "choices": [ { "finish_reason": null, @@ -45,7 +45,7 @@ "text": ".\n\n" } ], - "created": 1754348148, + "created": 1756921025, "model": "llama3.2:3b-instruct-fp16", "object": "text_completion", "system_fingerprint": "fp_ollama", @@ -55,7 +55,7 @@ { "__type__": "openai.types.completion.Completion", "__data__": { - "id": "cmpl-905", + "id": "cmpl-374", "choices": [ { "finish_reason": null, @@ -64,7 +64,7 @@ "text": "My" } ], - "created": 1754348148, + "created": 1756921025, "model": "llama3.2:3b-instruct-fp16", "object": "text_completion", "system_fingerprint": "fp_ollama", @@ -74,16 +74,16 @@ { "__type__": "openai.types.completion.Completion", "__data__": { - "id": "cmpl-905", + "id": "cmpl-374", "choices": [ { "finish_reason": null, "index": 0, "logprobs": null, - "text": " response" + "text": " answer" } ], - "created": 1754348148, + "created": 1756921025, "model": "llama3.2:3b-instruct-fp16", "object": "text_completion", "system_fingerprint": "fp_ollama", @@ -93,7 +93,7 @@ { "__type__": "openai.types.completion.Completion", "__data__": { - "id": "cmpl-905", + "id": "cmpl-374", "choices": [ { "finish_reason": null, @@ -102,7 +102,7 @@ "text": " is" } ], - "created": 1754348148, + "created": 1756921025, "model": "llama3.2:3b-instruct-fp16", "object": "text_completion", "system_fingerprint": "fp_ollama", @@ -112,634 +112,7 @@ { "__type__": "openai.types.completion.Completion", "__data__": { - "id": "cmpl-905", - "choices": [ - { - "finish_reason": null, - "index": 0, - "logprobs": null, - "text": " based" - } - ], - "created": 1754348149, - "model": "llama3.2:3b-instruct-fp16", - "object": "text_completion", - "system_fingerprint": "fp_ollama", - "usage": null - } - }, - { - "__type__": "openai.types.completion.Completion", - "__data__": { - "id": "cmpl-905", - "choices": [ - { - "finish_reason": null, - "index": 0, - "logprobs": null, - "text": " on" - } - ], - "created": 1754348149, - "model": "llama3.2:3b-instruct-fp16", - "object": "text_completion", - "system_fingerprint": "fp_ollama", - "usage": null - } - }, - { - "__type__": "openai.types.completion.Completion", - "__data__": { - "id": "cmpl-905", - "choices": [ - { - "finish_reason": null, - "index": 0, - "logprobs": null, - "text": " a" - } - ], - "created": 1754348149, - "model": "llama3.2:3b-instruct-fp16", - "object": "text_completion", - "system_fingerprint": "fp_ollama", - "usage": null - } - }, - { - "__type__": "openai.types.completion.Completion", - "__data__": { - "id": "cmpl-905", - "choices": [ - { - "finish_reason": null, - "index": 0, - "logprobs": null, - "text": " common" - } - ], - "created": 1754348149, - "model": "llama3.2:3b-instruct-fp16", - "object": "text_completion", - "system_fingerprint": "fp_ollama", - "usage": null - } - }, - { - "__type__": "openai.types.completion.Completion", - "__data__": { - "id": "cmpl-905", - "choices": [ - { - "finish_reason": null, - "index": 0, - "logprobs": null, - "text": " English" - } - ], - "created": 1754348149, - "model": "llama3.2:3b-instruct-fp16", - "object": "text_completion", - "system_fingerprint": "fp_ollama", - "usage": null - } - }, - { - "__type__": "openai.types.completion.Completion", - "__data__": { - "id": "cmpl-905", - "choices": [ - { - "finish_reason": null, - "index": 0, - "logprobs": null, - "text": " rhyme" - } - ], - "created": 1754348149, - "model": "llama3.2:3b-instruct-fp16", - "object": "text_completion", - "system_fingerprint": "fp_ollama", - "usage": null - } - }, - { - "__type__": "openai.types.completion.Completion", - "__data__": { - "id": "cmpl-905", - "choices": [ - { - "finish_reason": null, - "index": 0, - "logprobs": null, - "text": " or" - } - ], - "created": 1754348149, - "model": "llama3.2:3b-instruct-fp16", - "object": "text_completion", - "system_fingerprint": "fp_ollama", - "usage": null - } - }, - { - "__type__": "openai.types.completion.Completion", - "__data__": { - "id": "cmpl-905", - "choices": [ - { - "finish_reason": null, - "index": 0, - "logprobs": null, - "text": " poem" - } - ], - "created": 1754348149, - "model": "llama3.2:3b-instruct-fp16", - "object": "text_completion", - "system_fingerprint": "fp_ollama", - "usage": null - } - }, - { - "__type__": "openai.types.completion.Completion", - "__data__": { - "id": "cmpl-905", - "choices": [ - { - "finish_reason": null, - "index": 0, - "logprobs": null, - "text": " that" - } - ], - "created": 1754348149, - "model": "llama3.2:3b-instruct-fp16", - "object": "text_completion", - "system_fingerprint": "fp_ollama", - "usage": null - } - }, - { - "__type__": "openai.types.completion.Completion", - "__data__": { - "id": "cmpl-905", - "choices": [ - { - "finish_reason": null, - "index": 0, - "logprobs": null, - "text": " completes" - } - ], - "created": 1754348149, - "model": "llama3.2:3b-instruct-fp16", - "object": "text_completion", - "system_fingerprint": "fp_ollama", - "usage": null - } - }, - { - "__type__": "openai.types.completion.Completion", - "__data__": { - "id": "cmpl-905", - "choices": [ - { - "finish_reason": null, - "index": 0, - "logprobs": null, - "text": " the" - } - ], - "created": 1754348149, - "model": "llama3.2:3b-instruct-fp16", - "object": "text_completion", - "system_fingerprint": "fp_ollama", - "usage": null - } - }, - { - "__type__": 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"system_fingerprint": "fp_ollama", - "usage": null - } - }, - { - "__type__": "openai.types.completion.Completion", - "__data__": { - "id": "cmpl-905", - "choices": [ - { - "finish_reason": null, - "index": 0, - "logprobs": null, - "text": "," - } - ], - "created": 1754348150, - "model": "llama3.2:3b-instruct-fp16", - "object": "text_completion", - "system_fingerprint": "fp_ollama", - "usage": null - } - }, - { - "__type__": "openai.types.completion.Completion", - "__data__": { - "id": "cmpl-905", - "choices": [ - { - "finish_reason": null, - "index": 0, - "logprobs": null, - "text": " v" - } - ], - "created": 1754348150, - "model": "llama3.2:3b-instruct-fp16", - "object": "text_completion", - "system_fingerprint": "fp_ollama", - "usage": null - } - }, - { - "__type__": "openai.types.completion.Completion", - "__data__": { - "id": "cmpl-905", - "choices": [ - { - "finish_reason": null, - "index": 0, - "logprobs": null, - "text": "io" - } - ], - "created": 1754348150, - "model": "llama3.2:3b-instruct-fp16", - "object": "text_completion", - "system_fingerprint": "fp_ollama", - "usage": null - } - }, - { - "__type__": "openai.types.completion.Completion", - "__data__": { - "id": "cmpl-905", - "choices": [ - { - "finish_reason": null, - "index": 0, - "logprobs": null, - "text": "lets" - } - ], - "created": 1754348150, - "model": "llama3.2:3b-instruct-fp16", - "object": "text_completion", - "system_fingerprint": "fp_ollama", - "usage": null - } - }, - { - "__type__": "openai.types.completion.Completion", - "__data__": { - "id": "cmpl-905", - "choices": [ - { - "finish_reason": null, - "index": 0, - "logprobs": null, - "text": " are" - } - ], - "created": 1754348150, - "model": "llama3.2:3b-instruct-fp16", - "object": "text_completion", - "system_fingerprint": "fp_ollama", - "usage": null - } - }, - { - "__type__": "openai.types.completion.Completion", - "__data__": { - "id": "cmpl-905", + "id": "cmpl-374", "choices": [ { "finish_reason": null, @@ -748,7 +121,7 @@ "text": " blue" } ], - "created": 1754348150, + "created": 1756921025, "model": "llama3.2:3b-instruct-fp16", "object": "text_completion", "system_fingerprint": "fp_ollama", @@ -758,16 +131,16 @@ { "__type__": "openai.types.completion.Completion", "__data__": { - "id": "cmpl-905", + "id": "cmpl-374", "choices": [ { "finish_reason": null, "index": 0, "logprobs": null, - "text": "\".\n\n" + "text": " because" } ], - "created": 1754348150, + "created": 1756921025, "model": "llama3.2:3b-instruct-fp16", "object": "text_completion", "system_fingerprint": "fp_ollama", @@ -777,16 +150,16 @@ { "__type__": "openai.types.completion.Completion", "__data__": { - "id": "cmpl-905", + "id": "cmpl-374", "choices": [ { "finish_reason": null, "index": 0, "logprobs": null, - "text": "The" + "text": " it" } ], - "created": 1754348150, + "created": 1756921025, "model": "llama3.2:3b-instruct-fp16", "object": "text_completion", "system_fingerprint": "fp_ollama", @@ -796,16 +169,16 @@ { "__type__": "openai.types.completion.Completion", "__data__": { - "id": "cmpl-905", + "id": "cmpl-374", "choices": [ { "finish_reason": null, "index": 0, "logprobs": null, - "text": " use" + "text": "'s" } ], - "created": 1754348150, + "created": 1756921025, "model": "llama3.2:3b-instruct-fp16", "object": "text_completion", "system_fingerprint": "fp_ollama", @@ -815,7 +188,121 @@ { "__type__": "openai.types.completion.Completion", "__data__": { - "id": "cmpl-905", + "id": "cmpl-374", + "choices": [ + { + "finish_reason": null, + "index": 0, + "logprobs": null, + "text": " a" + } + ], + "created": 1756921025, + "model": "llama3.2:3b-instruct-fp16", + "object": "text_completion", + "system_fingerprint": "fp_ollama", + "usage": null + } + }, + { + "__type__": "openai.types.completion.Completion", + "__data__": { + "id": "cmpl-374", + "choices": [ + { + "finish_reason": null, + "index": 0, + "logprobs": null, + "text": " common" + } + ], + "created": 1756921025, + "model": "llama3.2:3b-instruct-fp16", + "object": "text_completion", + "system_fingerprint": "fp_ollama", + "usage": null + } + }, + { + "__type__": "openai.types.completion.Completion", + "__data__": { + "id": "cmpl-374", + "choices": [ + { + "finish_reason": null, + "index": 0, + "logprobs": null, + "text": " and" + } + ], + "created": 1756921025, + "model": "llama3.2:3b-instruct-fp16", + "object": "text_completion", + "system_fingerprint": "fp_ollama", + "usage": null + } + }, + { + "__type__": "openai.types.completion.Completion", + "__data__": { + "id": "cmpl-374", + "choices": [ + { + "finish_reason": null, + "index": 0, + "logprobs": null, + "text": " well" + } + ], + "created": 1756921025, + "model": "llama3.2:3b-instruct-fp16", + "object": "text_completion", + "system_fingerprint": "fp_ollama", + "usage": null + } + }, + { + "__type__": "openai.types.completion.Completion", + "__data__": { + "id": "cmpl-374", + "choices": [ + { + "finish_reason": null, + "index": 0, + "logprobs": null, + "text": "-known" + } + ], + "created": 1756921025, + "model": "llama3.2:3b-instruct-fp16", + "object": "text_completion", + "system_fingerprint": "fp_ollama", + "usage": null + } + }, + { + "__type__": "openai.types.completion.Completion", + "__data__": { + "id": "cmpl-374", + "choices": [ + { + "finish_reason": null, + "index": 0, + "logprobs": null, + "text": " completion" + } + ], + "created": 1756921025, + "model": "llama3.2:3b-instruct-fp16", + "object": "text_completion", + "system_fingerprint": "fp_ollama", + "usage": null + } + }, + { + "__type__": "openai.types.completion.Completion", + "__data__": { + "id": "cmpl-374", "choices": [ { "finish_reason": null, @@ -824,7 +311,7 @@ "text": " of" } ], - "created": 1754348151, + "created": 1756921026, "model": "llama3.2:3b-instruct-fp16", "object": "text_completion", "system_fingerprint": "fp_ollama", @@ -834,7 +321,7 @@ { "__type__": "openai.types.completion.Completion", "__data__": { - "id": "cmpl-905", + "id": "cmpl-374", "choices": [ { "finish_reason": null, @@ -843,7 +330,7 @@ "text": " the" } ], - "created": 1754348151, + "created": 1756921026, "model": "llama3.2:3b-instruct-fp16", "object": "text_completion", "system_fingerprint": "fp_ollama", @@ -853,16 +340,16 @@ { "__type__": "openai.types.completion.Completion", "__data__": { - "id": "cmpl-905", + "id": "cmpl-374", "choices": [ { "finish_reason": null, "index": 0, "logprobs": null, - "text": " word" + "text": " classic" } ], - "created": 1754348151, + "created": 1756921026, "model": "llama3.2:3b-instruct-fp16", "object": "text_completion", "system_fingerprint": "fp_ollama", @@ -872,7 +359,64 @@ { "__type__": "openai.types.completion.Completion", "__data__": { - "id": "cmpl-905", + "id": "cmpl-374", + "choices": [ + { + "finish_reason": null, + "index": 0, + "logprobs": null, + "text": " tongue" + } + ], + "created": 1756921026, + "model": "llama3.2:3b-instruct-fp16", + "object": "text_completion", + "system_fingerprint": "fp_ollama", + "usage": null + } + }, + { + "__type__": "openai.types.completion.Completion", + "__data__": { + "id": "cmpl-374", + "choices": [ + { + "finish_reason": null, + "index": 0, + "logprobs": null, + "text": "-tw" + } + ], + "created": 1756921026, + "model": "llama3.2:3b-instruct-fp16", + "object": "text_completion", + "system_fingerprint": "fp_ollama", + "usage": null + } + }, + { + "__type__": "openai.types.completion.Completion", + "__data__": { + "id": "cmpl-374", + "choices": [ + { + "finish_reason": null, + "index": 0, + "logprobs": null, + "text": "ister" + } + ], + "created": 1756921026, + "model": "llama3.2:3b-instruct-fp16", + "object": "text_completion", + "system_fingerprint": "fp_ollama", + "usage": null + } + }, + { + "__type__": "openai.types.completion.Completion", + "__data__": { + "id": "cmpl-374", "choices": [ { "finish_reason": null, @@ -881,7 +425,7 @@ "text": " \"" } ], - "created": 1754348151, + "created": 1756921026, "model": "llama3.2:3b-instruct-fp16", "object": "text_completion", "system_fingerprint": "fp_ollama", @@ -891,16 +435,16 @@ { "__type__": "openai.types.completion.Completion", "__data__": { - "id": "cmpl-905", + "id": "cmpl-374", "choices": [ { "finish_reason": null, "index": 0, "logprobs": null, - "text": "blue" + "text": "R" } ], - "created": 1754348151, + "created": 1756921026, "model": "llama3.2:3b-instruct-fp16", "object": "text_completion", "system_fingerprint": "fp_ollama", @@ -910,7 +454,159 @@ { "__type__": "openai.types.completion.Completion", "__data__": { - "id": "cmpl-905", + "id": "cmpl-374", + "choices": [ + { + "finish_reason": null, + "index": 0, + "logprobs": null, + "text": "oses" + } + ], + "created": 1756921026, + "model": "llama3.2:3b-instruct-fp16", + "object": "text_completion", + "system_fingerprint": "fp_ollama", + "usage": null + } + }, + { + "__type__": "openai.types.completion.Completion", + "__data__": { + "id": "cmpl-374", + "choices": [ + { + "finish_reason": null, + "index": 0, + "logprobs": null, + "text": " are" + } + ], + "created": 1756921026, + "model": "llama3.2:3b-instruct-fp16", + "object": "text_completion", + "system_fingerprint": "fp_ollama", + "usage": null + } + }, + { + "__type__": "openai.types.completion.Completion", + "__data__": { + "id": "cmpl-374", + "choices": [ + { + "finish_reason": null, + "index": 0, + "logprobs": null, + "text": " red" + } + ], + "created": 1756921026, + "model": "llama3.2:3b-instruct-fp16", + "object": "text_completion", + "system_fingerprint": "fp_ollama", + "usage": null + } + }, + { + "__type__": "openai.types.completion.Completion", + "__data__": { + "id": "cmpl-374", + "choices": [ + { + "finish_reason": null, + "index": 0, + "logprobs": null, + "text": "," + } + ], + "created": 1756921026, + "model": "llama3.2:3b-instruct-fp16", + "object": "text_completion", + "system_fingerprint": "fp_ollama", + "usage": null + } + }, + { + "__type__": "openai.types.completion.Completion", + "__data__": { + "id": "cmpl-374", + "choices": [ + { + "finish_reason": null, + "index": 0, + "logprobs": null, + "text": " v" + } + ], + "created": 1756921026, + "model": "llama3.2:3b-instruct-fp16", + "object": "text_completion", + "system_fingerprint": "fp_ollama", + "usage": null + } + }, + { + "__type__": "openai.types.completion.Completion", + "__data__": { + "id": "cmpl-374", + "choices": [ + { + "finish_reason": null, + "index": 0, + "logprobs": null, + "text": "io" + } + ], + "created": 1756921026, + "model": "llama3.2:3b-instruct-fp16", + "object": "text_completion", + "system_fingerprint": "fp_ollama", + "usage": null + } + }, + { + "__type__": "openai.types.completion.Completion", + "__data__": { + "id": "cmpl-374", + "choices": [ + { + "finish_reason": null, + "index": 0, + "logprobs": null, + "text": "lets" + } + ], + "created": 1756921026, + "model": "llama3.2:3b-instruct-fp16", + "object": "text_completion", + "system_fingerprint": "fp_ollama", + "usage": null + } + }, + { + "__type__": "openai.types.completion.Completion", + "__data__": { + "id": "cmpl-374", 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index 8a9b076fd..11e0fb402 100644 --- a/tests/integration/recordings/responses/97d3812bfccb.json +++ b/tests/integration/recordings/responses/97d3812bfccb.json @@ -20,15 +20,15 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama-guard3:1b", - "created_at": "2025-08-01T23:14:06.082832Z", + "created_at": "2025-09-03T17:37:52.965106Z", "done": true, "done_reason": "stop", - "total_duration": 421905083, - "load_duration": 88557750, + "total_duration": 376594792, + "load_duration": 158273792, "prompt_eval_count": 217, - "prompt_eval_duration": 278000000, + "prompt_eval_duration": 177001375, "eval_count": 5, - "eval_duration": 54000000, + "eval_duration": 40927500, "response": "unsafe\nS1", "thinking": null, "context": null diff --git a/tests/integration/recordings/responses/97e259c0d3e5.json b/tests/integration/recordings/responses/97e259c0d3e5.json index cd083c9a8..2e47bca80 100644 --- a/tests/integration/recordings/responses/97e259c0d3e5.json +++ b/tests/integration/recordings/responses/97e259c0d3e5.json @@ -22,7 +22,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:07.138696Z", + "created_at": "2025-09-03T17:37:53.505006Z", "done": false, "done_reason": null, "total_duration": null, @@ -40,7 +40,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:07.195013Z", + "created_at": "2025-09-03T17:37:53.547032Z", "done": false, "done_reason": null, "total_duration": null, @@ -58,7 +58,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-01T23:14:07.246591Z", + "created_at": "2025-09-03T17:37:53.588985Z", "done": false, "done_reason": null, "total_duration": null, @@ -76,7 +76,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": 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a/tests/integration/recordings/responses/9fadf5a3d68f.json +++ b/tests/integration/recordings/responses/9fadf5a3d68f.json @@ -20,15 +20,15 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama-guard3:1b", - "created_at": "2025-08-01T23:14:22.168612Z", + "created_at": "2025-09-03T17:38:03.270261Z", "done": true, "done_reason": "stop", - "total_duration": 198446125, - "load_duration": 31859666, + "total_duration": 244051875, + "load_duration": 111239500, "prompt_eval_count": 224, - "prompt_eval_duration": 151000000, + "prompt_eval_duration": 120962791, "eval_count": 2, - "eval_duration": 13000000, + "eval_duration": 11306292, "response": "safe", "thinking": null, "context": null diff --git a/tests/integration/recordings/responses/a0c4df33879f.json b/tests/integration/recordings/responses/a0c4df33879f.json index f134e0bed..7898e5b02 100644 --- a/tests/integration/recordings/responses/a0c4df33879f.json +++ b/tests/integration/recordings/responses/a0c4df33879f.json @@ -1,7 +1,7 @@ { "request": { "method": "POST", - "url": "http://localhost:11434/v1/v1/chat/completions", + "url": "http://0.0.0.0:11434/v1/v1/chat/completions", "headers": {}, "body": { "model": "llama3.2:3b-instruct-fp16", @@ -21,7 +21,7 @@ { "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk", "__data__": { - "id": "chatcmpl-458", + "id": "chatcmpl-792", "choices": [ { "delta": { @@ -36,7 +36,7 @@ "logprobs": null } ], - "created": 1754081845, + "created": 1756921356, "model": "llama3.2:3b-instruct-fp16", "object": "chat.completion.chunk", "service_tier": null, @@ -47,7 +47,7 @@ { "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk", "__data__": { - "id": "chatcmpl-458", + "id": "chatcmpl-792", "choices": [ { "delta": { @@ -62,7 +62,7 @@ "logprobs": null } ], - "created": 1754081845, + "created": 1756921356, "model": "llama3.2:3b-instruct-fp16", "object": "chat.completion.chunk", "service_tier": null, @@ -73,319 +73,7 @@ { "__type__": 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"http://localhost:11434/v1/v1/chat/completions", + "url": "http://0.0.0.0:11434/v1/v1/chat/completions", "headers": {}, "body": { "model": "llama3.2:3b-instruct-fp16", @@ -20,14 +20,14 @@ "body": { "__type__": "openai.types.chat.chat_completion.ChatCompletion", "__data__": { - "id": "chatcmpl-560", + "id": "chatcmpl-715", "choices": [ { "finish_reason": "stop", "index": 0, "logprobs": null, "message": { - "content": "Hello! It's nice to meet you. How can I assist you today?", + "content": "Hello! It's nice to meet you. Is there something I can help you with or would you like to chat?", "refusal": null, "role": "assistant", "annotations": null, @@ -37,15 +37,15 @@ } } ], - "created": 1754081856, + "created": 1756921367, "model": "llama3.2:3b-instruct-fp16", "object": "chat.completion", "service_tier": null, "system_fingerprint": "fp_ollama", "usage": { - "completion_tokens": 17, + "completion_tokens": 25, "prompt_tokens": 29, - "total_tokens": 46, + "total_tokens": 54, "completion_tokens_details": null, "prompt_tokens_details": null } diff --git a/tests/integration/recordings/responses/a5187d9d5057.json b/tests/integration/recordings/responses/a5187d9d5057.json index 0dedba066..edacd5fa6 100644 --- a/tests/integration/recordings/responses/a5187d9d5057.json +++ b/tests/integration/recordings/responses/a5187d9d5057.json @@ -1,7 +1,7 @@ { "request": { "method": "POST", - "url": "http://localhost:11434/v1/v1/chat/completions", + "url": "http://0.0.0.0:11434/v1/v1/chat/completions", "headers": {}, "body": { "model": "llama3.2:3b-instruct-fp16", @@ -20,14 +20,14 @@ "body": { "__type__": "openai.types.chat.chat_completion.ChatCompletion", "__data__": { - "id": "chatcmpl-64", + "id": "chatcmpl-376", "choices": [ { "finish_reason": "stop", "index": 0, "logprobs": null, "message": { - "content": "You want to test the capabilities of the OpenAI Text-to-Text model (also known as T0).\n\nPlease note that I'll be using a pre-trained model, so my responses might not be entirely customized to your specific prompt or context. That being said, I'll do my best to mimic the behavior of the original model.\n\nWhat would you like to test or ask? Please provide a prompt or question, and I'll respond accordingly.\n\n(Note: if you'd like to run a longer experiment or try out specific models like text completion or code generation, feel free to let me know and we can figure out a way to collaborate.)", + "content": "I'll simulate a test for the LA-1030/5B linear accelerator, specifically for the H8 (High Voltage) model found in early models of the test rail. Note that this is not meant to be taken as actual test results but rather a demonstration.\n\n### Introduction:\nThe LA-1030/5B was used primarily for high-energy physics and nuclear research during the 1970s and 1980s. This linear accelerator was capable of producing proton beams with energies up to several GeV. The H8 model, also known as the 'High Voltage' component, is a series of power supplies that drive the high voltage DC (HV) accelerators.\n\n### Test Setup:\n\n- **Test Goal:** Measure the output of the LA-1030/5B H8 model linear accelerator and assess its ability to generate stable, high-voltage direct current (HVDC) to power it properly.\n \n - The setup consists of a single test rail containing one of these H8 modules. A precise DC voltage is supplied to the linear accelerator via an external DC source.\n\n### Operating Parameters:\n\n- **Input Voltage:** To ensure the linear accelerator operates within its safe operating parameters, input voltages will be varied from 20KV to 140KV.\n- **Current Delivery:** Monitoring current at these different output levels requires a precise multimeter or oscilloscope. \n- **Voltage Level and Current Stability:** The voltage should stabilize as close as possible to the desired output level.\n\n### Potential Issues\n\n1.) Over-Pressure in H8 Modules\n - During high voltage levels, there's a risk of over-pressurization in the component casing due to the vacuum properties within the modules.\n - Check for any external signs of stress or leakage.\n2.) Current Limitation and Arcing\n - High current requirements demand close monitoring of the accelerator components and associated connections.\n - An excessive arc can be detrimental to electronics connected to the system.\n3.) Interlocks and Safe Guards\n\n- **Ensure alignment:** Prevent accidental triggering.\n\n4.) Insulation integrity \n - Potential risks from faulty or non-insulated components\n\n### Results Analysis:\n\nBased on this hypothetical test, some potential results could include:\n1. Output voltage stability for the chosen input voltages\n2. Ability to exceed the accelerator's highest voltage ratings.\n3. Consistency between different current levels at various output voltage tests.\n\nThis exercise is a general simulation and might not reflect real-world conditions or performance specifications of an actual LA-1030/5B linear accelerator. 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/dev/null +++ b/tests/integration/recordings/responses/c791119e6359.json @@ -0,0 +1,98 @@ +{ + "request": { + "method": "POST", + "url": "https://shan-mfbb618r-eastus2.cognitiveservices.azure.com/openai/v1/v1/chat/completions", + "headers": {}, + "body": { + "model": "gpt-5-mini", + "messages": [ + { + "role": "user", + "content": "What's the weather in Tokyo? Use the get_weather function to get the weather." + } + ], + "stream": false, + "tools": [ + { + "type": "function", + "function": { + "name": "get_weather", + "description": "Get the weather in a given city", + "parameters": { + "type": "object", + "properties": { + "city": { + "type": "string", + "description": "The city to get the weather for" + } + } + } + } + } + ] + }, + "endpoint": "/v1/chat/completions", + "model": "gpt-5-mini" + }, + "response": { + "body": { + "__type__": "openai.types.chat.chat_completion.ChatCompletion", + "__data__": { + "id": "chatcmpl-CECIwq9Odd0mOJMmw7ytv8iEazH4H", + "choices": [ + { + "finish_reason": "tool_calls", + "index": 0, + "logprobs": null, + "message": { + "content": null, + "refusal": null, + "role": "assistant", + "annotations": [], + "audio": null, + "function_call": null, + "tool_calls": [ + { + "id": "call_yw18spRc1jjUlEyabbXBhB33", + "function": { + "arguments": "{\"city\":\"Tokyo\"}", + "name": "get_weather" + }, + "type": "function" + } + ] + }, + "content_filter_results": {} + } + ], + "created": 1757499926, + "model": "gpt-5-mini-2025-08-07", + "object": "chat.completion", + "service_tier": null, + "system_fingerprint": null, + "usage": { + "completion_tokens": 88, + "prompt_tokens": 151, + "total_tokens": 239, + "completion_tokens_details": { + "accepted_prediction_tokens": 0, + "audio_tokens": 0, + "reasoning_tokens": 64, + "rejected_prediction_tokens": 0 + }, + "prompt_tokens_details": { + "audio_tokens": 0, + "cached_tokens": 0 + } + }, + "prompt_filter_results": [ + { + "prompt_index": 0, + "content_filter_results": {} + } + ] + } + }, + "is_streaming": false + } +} diff --git a/tests/integration/recordings/responses/c8a59b661fd5.json b/tests/integration/recordings/responses/c8a59b661fd5.json new file mode 100644 index 000000000..e6ec001a9 --- /dev/null +++ b/tests/integration/recordings/responses/c8a59b661fd5.json @@ -0,0 +1,46 @@ +{ + "request": { + "method": "POST", + "url": "https://api.together.xyz/v1/v1/completions", + "headers": {}, + "body": { + "model": "meta-llama/Llama-3.3-70B-Instruct-Turbo-Free", + "prompt": "Say completions", + "max_tokens": 20, + "extra_body": {} + }, + "endpoint": "/v1/completions", + "model": "meta-llama/Llama-3.3-70B-Instruct-Turbo-Free" + }, + "response": { + "body": { + "__type__": "openai.types.completion.Completion", + "__data__": { + "id": "oCfwct8-4Yz4kd-984c2861287d4cc3", + "choices": [ + { + "finish_reason": "length", + "index": 0, + "logprobs": null, + "text": " of the following sentences:\n* The _most_ important thing in life is...\n* The _least", + "seed": 12050749903881546000 + } + ], + "created": 1758820465, + "model": "meta-llama/Llama-3.3-70B-Instruct-Turbo-Free", + "object": "text.completion", + "system_fingerprint": null, + "usage": { + "completion_tokens": 20, + "prompt_tokens": 3, + "total_tokens": 23, + "completion_tokens_details": null, + "prompt_tokens_details": null, + "cached_tokens": 0 + }, + "prompt": [] + } + }, + "is_streaming": false + } +} diff --git a/tests/integration/recordings/responses/c9cba6f3ee38.json b/tests/integration/recordings/responses/c9cba6f3ee38.json index 488ac6563..02363c70e 100644 --- a/tests/integration/recordings/responses/c9cba6f3ee38.json +++ b/tests/integration/recordings/responses/c9cba6f3ee38.json @@ -20,15 +20,15 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama-guard3:1b", - "created_at": "2025-08-01T23:14:21.909783Z", + "created_at": "2025-09-03T17:38:03.002753Z", "done": true, "done_reason": "stop", - "total_duration": 311036333, - "load_duration": 37569542, + "total_duration": 334941166, + "load_duration": 149512166, "prompt_eval_count": 219, - "prompt_eval_duration": 259000000, + "prompt_eval_duration": 173843500, "eval_count": 2, - "eval_duration": 12000000, + "eval_duration": 11119166, "response": "safe", "thinking": null, "context": null diff --git a/tests/integration/recordings/responses/cb3df2a1dc22.json b/tests/integration/recordings/responses/cb3df2a1dc22.json index d65945ac1..41db65a5e 100644 --- a/tests/integration/recordings/responses/cb3df2a1dc22.json +++ b/tests/integration/recordings/responses/cb3df2a1dc22.json @@ -1,7 +1,7 @@ { "request": { "method": "POST", - "url": "http://localhost:11434/v1/v1/chat/completions", + "url": "http://0.0.0.0:11434/v1/v1/chat/completions", "headers": {}, "body": { "model": "llama3.2:3b-instruct-fp16", @@ -20,14 +20,14 @@ "body": { "__type__": "openai.types.chat.chat_completion.ChatCompletion", "__data__": { - "id": "chatcmpl-877", + "id": "chatcmpl-271", "choices": [ { "finish_reason": "stop", "index": 0, "logprobs": null, "message": { - "content": "I'm not capable of directly testing the functionality of external systems like Telemetry. However, I can provide you with some general information about creating telemetry data and offer suggestions on how to set up a basic telemetry system.\r\n\r\nTelemetry is the automatic measurement, reporting, and transmission of data from sensors or other devices. In the context of OpenAI, telemetry refers to the collection and analysis of data related to the company's products and services.\r\n\r\nTo create telemetry creation using the OpenAI APIs you would need to follow these steps:\r\n\r\n1. Register for an OpenAI account and get an access token.\r\n2. Choose the OpenAI API that you want to use (e.g., GPT-3).\r\n3. Create a new file or project in your preferred programming language or framework.\r\n4. Import the necessary libraries and modules to interact with the OpenAI API.\r\n5. Use the OpenAI API to create and send telemetry data.\r\n\r\nHere is an example of how you might create a basic telemetry system using Python and the OpenAI GPT-3 API:\r\n\r\n```python\r\nimport os\r\nimport json\r\n\r\n# Set your OpenAI access token\r\naccess_token = \"YOUR_OPENAI_ACCESS_TOKEN\"\r\n\r\n# Define the telemetry data\r\ntelemetry_data = {\r\n \"name\": \"example-telemetry\",\r\n \"description\": \"Example telemetry data.\r\n\r\n # Define the telemetry metrics\r\n \"metrics\": [\r\n {\"key\": \"users\", \"value\": 100},\r\n {\"key\": \" engagement\", \"value\": 20}\r\n ]\r\n}\r\n\r\n# Convert the telemetry data to JSON\r\ntelemetry_json = json.dumps(telemetry_data)\r\n\r\n# Set the OpenAI API endpoint and headers\r\napi_endpoint = \"https://api.openai.com/v1/telemetry\"\r\nheaders = {\r\n \"Authorization\": f\"Bearer {access_token}\",\r\n \"Content-Type\": \"application/json\"\r\n}\r\n\r\n# Send the telemetry data to the OpenAI API\r\nimport requests\r\n\r\nresponse = requests.post(api_endpoint, headers=headers, data=telemetry_json)\r\n\r\n# Check if the request was successful\r\nif response.status_code == 200:\r\n print(\"Telemetry data sent successfully\")\r\nelse:\r\n print(\"Error sending telemetry data\")\r\n```\n\nPlease note that this is a basic example and you should adjust it according to your needs. Also, the specific implementation details may vary depending on the OpenAI API you're using and the programming language or framework you're working with.\r\n\r\nI hope this helps! Let me know if you have any further questions.", + "content": "# OpenAI Telemetry Creation Testing\n\nThis guide provides a test environment for creating and analyzing telemetries using the OpenAI API.\n\n## Prerequisites\n\n- Python 3.8 or higher\n- `requests` library\n- An OpenAI API key (create an account on the [OpenAI website](https://openai.com/))\n\n## Step 1: Install Required Libraries\n\nFirst, install the required libraries by running the following command in your terminal:\n\n```bash\npip install requests\n```\n\n## Step 2: Create a Telemetry Object\n\nCreate a new Python file (e.g., `telemetry.py`) and import the required library. Define a telemetry object with the relevant data:\n\n```python\n# Import the requests library\nimport requests\n\nclass Telemetry:\n def __init__(self, api_key):\n \"\"\"\n Initialize the telemetry object.\n\n Args:\n api_key (str): The OpenAI API key.\n \"\"\"\n\n self.api_key = api_key\n\n def create_telemetry(self, id, data):\n \"\"\"\n Create a new telemetry entry using the OpenAI API.\n\n Args:\n id (str): The ID of the model or dataset.\n data (dict): The telemetry data to be created.\n\n Returns:\n dict: The response from the OpenAI API.\n\n Raises:\n ValueError: If the request fails.\n \"\"\"\n\n url = f\"https://api.openai.com/v1/models/{id}/telemetry\"\n\n headers = {\n \"Authorization\": self.api_key,\n \"Content-Type\": \"application/json\",\n }\n\n telemetry_data = {\"events\": data}\n\n response = requests.post(url, json=telemetry_data, headers=headers)\n\n if not response.ok:\n raise ValueError(\"Failed to create telemetry\")\n\n return response.json()\n```\n\n## Step 3: Usage Example\n\nHere's an example usage of the `Telemetry` class:\n\n```python\n# Create a new Telemetry object with your OpenAI API key\ntelemetry = Telemetry(\n \"YOUR_OPENAI_API_KEY_HERE\"\n)\n\n# Define the telemetry data\ndata = {\"event\": \"example_event\"}\n\n# Create a new telemetry entry\nid = \"my_model_id\" # Replace with your model or dataset ID\n\ntry:\n result = telemetry.create_telemetry(id, data)\n print(result)\nexcept ValueError as e:\n print(e)\n```\n\nThis code creates a new `Telemetry` object, defines some sample telemetry data, and uses the `create_telemetry` method to create a new telemetry entry. The response from the OpenAI API is printed out.\n\nNote: Replace `\"YOUR_OPENAI_API_KEY_HERE\"` with your actual OpenAI API key.\n\n## Conclusion\n\nThis guide provides a basic example of how to create telemetries using the OpenAI API. You can modify the code and implement additional features as needed for your project.\n\nStay updated on our latest tutorials and guides:\n\n* [Check out our Discord channel](link): https://discord.gg/openai-exists\n\nHappy coding!", "refusal": null, "role": "assistant", "annotations": null, @@ -37,15 +37,15 @@ } } ], - "created": 1754510083, + "created": 1756921299, "model": "llama3.2:3b-instruct-fp16", "object": "chat.completion", "service_tier": null, "system_fingerprint": "fp_ollama", "usage": { - "completion_tokens": 505, + "completion_tokens": 633, "prompt_tokens": 30, - "total_tokens": 535, + "total_tokens": 663, "completion_tokens_details": null, "prompt_tokens_details": null } diff --git a/tests/integration/recordings/responses/cd094caaf1c0.json b/tests/integration/recordings/responses/cd094caaf1c0.json index c0b3873d3..70a3d334d 100644 --- a/tests/integration/recordings/responses/cd094caaf1c0.json +++ b/tests/integration/recordings/responses/cd094caaf1c0.json @@ -21,7 +21,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:14.822116Z", + "created_at": "2025-09-03T17:36:21.138019Z", "done": false, "done_reason": null, "total_duration": null, @@ -39,7 +39,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:14.874482Z", + "created_at": "2025-09-03T17:36:21.179853Z", "done": false, "done_reason": null, "total_duration": null, @@ -57,7 +57,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:14.926533Z", + "created_at": "2025-09-03T17:36:21.220635Z", "done": false, "done_reason": null, "total_duration": null, @@ -75,7 +75,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:14.980659Z", + "created_at": "2025-09-03T17:36:21.261418Z", "done": false, "done_reason": null, "total_duration": null, @@ -93,7 +93,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:15.036126Z", + "created_at": "2025-09-03T17:36:21.301991Z", "done": false, "done_reason": null, "total_duration": null, @@ -111,7 +111,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:15.087015Z", + "created_at": "2025-09-03T17:36:21.3425Z", "done": false, "done_reason": null, "total_duration": null, @@ -129,7 +129,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:15.137306Z", + "created_at": "2025-09-03T17:36:21.38302Z", "done": false, "done_reason": null, "total_duration": null, @@ -147,7 +147,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:15.189129Z", + "created_at": "2025-09-03T17:36:21.423862Z", "done": false, "done_reason": null, "total_duration": null, @@ -165,7 +165,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:15.240264Z", + "created_at": "2025-09-03T17:36:21.464611Z", "done": false, "done_reason": null, "total_duration": null, @@ -183,7 +183,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:15.291201Z", + "created_at": "2025-09-03T17:36:21.505714Z", "done": false, "done_reason": null, "total_duration": null, @@ -201,7 +201,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:15.341476Z", + "created_at": "2025-09-03T17:36:21.547075Z", "done": false, "done_reason": null, "total_duration": null, @@ -219,7 +219,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:15.39284Z", + "created_at": "2025-09-03T17:36:21.588896Z", "done": false, "done_reason": null, "total_duration": null, @@ -237,7 +237,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:15.44438Z", + "created_at": "2025-09-03T17:36:21.629146Z", "done": false, "done_reason": null, "total_duration": null, @@ -255,7 +255,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:15.497561Z", + "created_at": "2025-09-03T17:36:21.669722Z", "done": false, "done_reason": null, "total_duration": null, @@ -273,7 +273,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:15.550461Z", + "created_at": "2025-09-03T17:36:21.710707Z", "done": false, "done_reason": null, "total_duration": null, @@ -291,7 +291,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:15.599866Z", + "created_at": "2025-09-03T17:36:21.751267Z", "done": false, "done_reason": null, "total_duration": null, @@ -309,7 +309,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:15.651899Z", + "created_at": "2025-09-03T17:36:21.791565Z", "done": false, "done_reason": null, "total_duration": null, @@ -327,7 +327,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:15.702896Z", + "created_at": "2025-09-03T17:36:21.83176Z", "done": false, "done_reason": null, "total_duration": null, @@ -345,7 +345,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:15.75492Z", + "created_at": "2025-09-03T17:36:21.872029Z", "done": false, "done_reason": null, "total_duration": null, @@ -363,7 +363,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:15.805824Z", + "created_at": "2025-09-03T17:36:21.914066Z", "done": false, "done_reason": null, "total_duration": null, @@ -381,7 +381,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:15.8564Z", + "created_at": "2025-09-03T17:36:21.955317Z", "done": false, "done_reason": null, "total_duration": null, @@ -399,7 +399,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:15.907374Z", + "created_at": "2025-09-03T17:36:21.995588Z", "done": false, "done_reason": null, "total_duration": null, @@ -417,7 +417,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:15.959599Z", + "created_at": "2025-09-03T17:36:22.03605Z", "done": false, "done_reason": null, "total_duration": null, @@ -435,7 +435,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:16.012545Z", + "created_at": "2025-09-03T17:36:22.076924Z", "done": false, "done_reason": null, "total_duration": null, @@ -453,7 +453,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:16.065508Z", + "created_at": "2025-09-03T17:36:22.117922Z", "done": false, "done_reason": null, "total_duration": null, @@ -471,7 +471,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:16.122471Z", + "created_at": "2025-09-03T17:36:22.158925Z", "done": false, "done_reason": null, "total_duration": null, @@ -489,7 +489,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:16.175606Z", + "created_at": "2025-09-03T17:36:22.199113Z", "done": false, "done_reason": null, "total_duration": null, @@ -507,7 +507,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:16.227171Z", + "created_at": "2025-09-03T17:36:22.239797Z", "done": false, "done_reason": null, "total_duration": null, @@ -525,7 +525,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:16.278522Z", + "created_at": "2025-09-03T17:36:22.280592Z", "done": false, "done_reason": null, "total_duration": null, @@ -543,7 +543,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:16.329492Z", + "created_at": "2025-09-03T17:36:22.321607Z", "done": false, "done_reason": null, "total_duration": null, @@ -561,7 +561,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:16.381232Z", + "created_at": "2025-09-03T17:36:22.36237Z", "done": false, "done_reason": null, "total_duration": null, @@ -579,7 +579,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:16.43463Z", + "created_at": "2025-09-03T17:36:22.402735Z", "done": false, "done_reason": null, "total_duration": null, @@ -597,7 +597,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:16.483135Z", + "created_at": "2025-09-03T17:36:22.44328Z", "done": false, "done_reason": null, "total_duration": null, @@ -615,7 +615,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:16.544729Z", + "created_at": "2025-09-03T17:36:22.48369Z", "done": false, "done_reason": null, "total_duration": null, @@ -633,7 +633,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:16.605218Z", + "created_at": "2025-09-03T17:36:22.524383Z", "done": false, "done_reason": null, "total_duration": null, @@ -651,7 +651,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:16.660652Z", + "created_at": "2025-09-03T17:36:22.564975Z", "done": false, "done_reason": null, "total_duration": null, @@ -669,7 +669,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:16.718606Z", + "created_at": "2025-09-03T17:36:22.605886Z", "done": false, "done_reason": null, "total_duration": null, @@ -687,7 +687,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:16.772786Z", + "created_at": "2025-09-03T17:36:22.646199Z", "done": false, "done_reason": null, "total_duration": null, @@ -705,7 +705,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:16.826904Z", + "created_at": "2025-09-03T17:36:22.686594Z", "done": false, "done_reason": null, "total_duration": null, @@ -723,7 +723,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:16.878735Z", + "created_at": "2025-09-03T17:36:22.726941Z", "done": false, "done_reason": null, "total_duration": null, @@ -741,7 +741,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:16.931262Z", + "created_at": "2025-09-03T17:36:22.767696Z", "done": false, "done_reason": null, "total_duration": null, @@ -759,7 +759,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:16.984266Z", + "created_at": "2025-09-03T17:36:22.810962Z", "done": false, "done_reason": null, "total_duration": null, @@ -777,7 +777,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:17.035518Z", + "created_at": "2025-09-03T17:36:22.851903Z", "done": false, "done_reason": null, "total_duration": null, @@ -795,7 +795,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:17.084669Z", + "created_at": "2025-09-03T17:36:22.892412Z", "done": false, "done_reason": null, "total_duration": null, @@ -813,7 +813,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:17.138856Z", + "created_at": "2025-09-03T17:36:22.932877Z", "done": false, "done_reason": null, "total_duration": null, @@ -831,7 +831,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:17.19578Z", + "created_at": "2025-09-03T17:36:22.973247Z", "done": false, "done_reason": null, "total_duration": null, @@ -849,7 +849,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:17.254009Z", + "created_at": "2025-09-03T17:36:23.013989Z", "done": false, "done_reason": null, "total_duration": null, @@ -867,7 +867,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:17.307391Z", + "created_at": "2025-09-03T17:36:23.054251Z", "done": false, "done_reason": null, "total_duration": null, @@ -885,7 +885,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:17.363223Z", + "created_at": "2025-09-03T17:36:23.094676Z", "done": false, "done_reason": null, "total_duration": null, @@ -903,7 +903,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:17.420075Z", + "created_at": "2025-09-03T17:36:23.135452Z", "done": false, "done_reason": null, "total_duration": null, @@ -921,7 +921,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:17.475276Z", + "created_at": "2025-09-03T17:36:23.176336Z", "done": false, "done_reason": null, "total_duration": null, @@ -939,7 +939,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:17.529886Z", + "created_at": "2025-09-03T17:36:23.216888Z", "done": false, "done_reason": null, "total_duration": null, @@ -957,7 +957,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:17.587218Z", + "created_at": "2025-09-03T17:36:23.257355Z", "done": false, "done_reason": null, "total_duration": null, @@ -975,7 +975,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:17.640408Z", + "created_at": "2025-09-03T17:36:23.297487Z", "done": false, "done_reason": null, "total_duration": null, @@ -993,7 +993,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:17.692792Z", + "created_at": "2025-09-03T17:36:23.337777Z", "done": false, "done_reason": null, "total_duration": null, @@ -1011,7 +1011,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:17.746336Z", + "created_at": "2025-09-03T17:36:23.37817Z", "done": false, "done_reason": null, "total_duration": null, @@ -1029,7 +1029,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:17.801383Z", + "created_at": "2025-09-03T17:36:23.418119Z", "done": false, "done_reason": null, "total_duration": null, @@ -1047,7 +1047,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:17.854621Z", + "created_at": "2025-09-03T17:36:23.458074Z", "done": false, "done_reason": null, "total_duration": null, @@ -1065,7 +1065,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:17.911212Z", + "created_at": "2025-09-03T17:36:23.498828Z", "done": false, "done_reason": null, "total_duration": null, @@ -1083,7 +1083,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:17.970851Z", + "created_at": "2025-09-03T17:36:23.539337Z", "done": false, "done_reason": null, "total_duration": null, @@ -1101,7 +1101,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - 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"created_at": "2025-08-04T22:55:18.247029Z", + "created_at": "2025-09-03T17:36:23.743994Z", "done": false, "done_reason": null, "total_duration": null, @@ -1191,7 +1191,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:18.298673Z", + "created_at": "2025-09-03T17:36:23.784238Z", "done": false, "done_reason": null, "total_duration": null, @@ -1209,7 +1209,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:18.346985Z", + "created_at": "2025-09-03T17:36:23.824425Z", "done": false, "done_reason": null, "total_duration": null, @@ -1227,7 +1227,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:18.396338Z", + "created_at": "2025-09-03T17:36:23.864711Z", "done": false, "done_reason": null, "total_duration": null, @@ -1245,7 +1245,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:18.44707Z", + "created_at": "2025-09-03T17:36:23.904729Z", "done": false, "done_reason": null, "total_duration": null, @@ -1263,7 +1263,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:18.500596Z", + "created_at": "2025-09-03T17:36:23.944762Z", "done": false, "done_reason": null, "total_duration": null, @@ -1281,7 +1281,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:18.552919Z", + "created_at": "2025-09-03T17:36:23.985199Z", "done": false, "done_reason": null, "total_duration": null, @@ -1299,7 +1299,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:18.605569Z", + "created_at": "2025-09-03T17:36:24.025821Z", "done": false, "done_reason": null, "total_duration": null, @@ -1317,7 +1317,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:18.657753Z", + "created_at": "2025-09-03T17:36:24.066639Z", "done": false, "done_reason": null, "total_duration": null, @@ -1335,7 +1335,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:18.712933Z", + "created_at": "2025-09-03T17:36:24.109215Z", "done": false, "done_reason": null, "total_duration": null, @@ -1353,7 +1353,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:18.765708Z", + "created_at": "2025-09-03T17:36:24.15123Z", "done": false, "done_reason": null, "total_duration": null, @@ -1371,7 +1371,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:18.81852Z", + "created_at": "2025-09-03T17:36:24.192856Z", "done": false, "done_reason": null, "total_duration": null, @@ -1389,7 +1389,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:18.870752Z", + "created_at": "2025-09-03T17:36:24.23433Z", "done": false, "done_reason": null, "total_duration": null, @@ -1407,7 +1407,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:18.922652Z", + "created_at": "2025-09-03T17:36:24.275212Z", "done": false, "done_reason": null, "total_duration": null, @@ -1425,7 +1425,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:18.974032Z", + "created_at": "2025-09-03T17:36:24.315722Z", "done": false, "done_reason": null, "total_duration": null, @@ -1443,7 +1443,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:19.025272Z", + "created_at": "2025-09-03T17:36:24.355996Z", "done": false, "done_reason": null, "total_duration": null, @@ -1461,7 +1461,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:19.076061Z", + "created_at": "2025-09-03T17:36:24.396181Z", "done": false, "done_reason": null, "total_duration": null, @@ -1479,7 +1479,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:19.126893Z", + "created_at": "2025-09-03T17:36:24.43716Z", "done": false, "done_reason": null, "total_duration": null, @@ -1497,7 +1497,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:19.179123Z", + "created_at": "2025-09-03T17:36:24.478009Z", "done": false, "done_reason": null, "total_duration": null, @@ -1515,7 +1515,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:19.230189Z", + "created_at": "2025-09-03T17:36:24.519697Z", "done": false, "done_reason": null, "total_duration": null, @@ -1533,7 +1533,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:19.280582Z", + "created_at": "2025-09-03T17:36:24.562228Z", "done": false, "done_reason": null, "total_duration": null, @@ -1551,7 +1551,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:19.330127Z", + "created_at": "2025-09-03T17:36:24.604366Z", "done": false, "done_reason": null, "total_duration": null, @@ -1569,7 +1569,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:19.379656Z", + "created_at": "2025-09-03T17:36:24.645258Z", "done": false, "done_reason": null, "total_duration": null, @@ -1587,7 +1587,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:19.430197Z", + "created_at": "2025-09-03T17:36:24.686966Z", "done": false, "done_reason": null, "total_duration": null, @@ -1605,7 +1605,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:19.48034Z", + "created_at": "2025-09-03T17:36:24.726702Z", "done": false, "done_reason": null, "total_duration": null, @@ -1623,7 +1623,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:19.530546Z", + "created_at": "2025-09-03T17:36:24.766742Z", "done": false, "done_reason": null, "total_duration": null, @@ -1641,7 +1641,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:19.583294Z", + "created_at": "2025-09-03T17:36:24.806841Z", "done": false, "done_reason": null, "total_duration": null, @@ -1659,7 +1659,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:19.630956Z", + "created_at": "2025-09-03T17:36:24.846655Z", "done": false, "done_reason": null, "total_duration": null, @@ -1677,7 +1677,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:19.682434Z", + "created_at": "2025-09-03T17:36:24.886602Z", "done": false, "done_reason": null, "total_duration": null, @@ -1695,7 +1695,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:19.731714Z", + "created_at": "2025-09-03T17:36:24.926582Z", "done": false, "done_reason": null, "total_duration": null, @@ -1713,7 +1713,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:19.780871Z", + "created_at": "2025-09-03T17:36:24.966301Z", "done": false, "done_reason": null, "total_duration": null, @@ -1731,7 +1731,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:19.829955Z", + "created_at": "2025-09-03T17:36:25.006614Z", "done": false, "done_reason": null, "total_duration": null, @@ -1749,7 +1749,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:19.880971Z", + "created_at": "2025-09-03T17:36:25.046631Z", "done": false, "done_reason": null, "total_duration": null, @@ -1767,7 +1767,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:19.931241Z", + "created_at": "2025-09-03T17:36:25.086885Z", "done": false, "done_reason": null, "total_duration": null, @@ -1785,7 +1785,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:19.980096Z", + "created_at": "2025-09-03T17:36:25.127555Z", "done": false, "done_reason": null, "total_duration": null, @@ -1803,7 +1803,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:20.03407Z", + "created_at": "2025-09-03T17:36:25.168437Z", "done": false, "done_reason": null, "total_duration": null, @@ -1821,7 +1821,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:20.090735Z", + "created_at": "2025-09-03T17:36:25.20913Z", "done": false, "done_reason": null, "total_duration": null, @@ -1839,7 +1839,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:20.153924Z", + "created_at": "2025-09-03T17:36:25.249991Z", "done": false, "done_reason": null, "total_duration": null, @@ -1857,7 +1857,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:20.220305Z", + "created_at": "2025-09-03T17:36:25.29007Z", "done": false, "done_reason": null, "total_duration": null, @@ -1875,7 +1875,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:20.304523Z", + "created_at": "2025-09-03T17:36:25.331038Z", "done": false, "done_reason": null, "total_duration": null, @@ -1893,7 +1893,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - 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"created_at": "2025-08-04T22:55:21.0516Z", + "created_at": "2025-09-03T17:36:25.702827Z", "done": false, "done_reason": null, "total_duration": null, @@ -2055,7 +2055,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:21.118611Z", + "created_at": "2025-09-03T17:36:25.743383Z", "done": false, "done_reason": null, "total_duration": null, @@ -2073,7 +2073,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:21.182092Z", + "created_at": "2025-09-03T17:36:25.785523Z", "done": false, "done_reason": null, "total_duration": null, @@ -2091,7 +2091,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:21.241399Z", + "created_at": "2025-09-03T17:36:25.828276Z", "done": false, "done_reason": null, "total_duration": null, @@ -2109,7 +2109,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:21.296699Z", + "created_at": "2025-09-03T17:36:25.871231Z", "done": false, "done_reason": null, "total_duration": null, @@ -2127,7 +2127,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:21.355772Z", + "created_at": "2025-09-03T17:36:25.913246Z", "done": false, "done_reason": null, "total_duration": null, @@ -2145,7 +2145,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:21.410821Z", + "created_at": "2025-09-03T17:36:25.955162Z", "done": false, "done_reason": null, "total_duration": null, @@ -2163,7 +2163,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:21.46582Z", + "created_at": "2025-09-03T17:36:25.997821Z", "done": false, "done_reason": null, "total_duration": null, @@ -2181,7 +2181,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - 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"created_at": "2025-08-04T22:55:21.839261Z", + "created_at": "2025-09-03T17:36:26.210934Z", "done": false, "done_reason": null, "total_duration": null, @@ -2271,7 +2271,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:21.90185Z", + "created_at": "2025-09-03T17:36:26.25385Z", "done": false, "done_reason": null, "total_duration": null, @@ -2289,7 +2289,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:21.96248Z", + "created_at": "2025-09-03T17:36:26.295017Z", "done": false, "done_reason": null, "total_duration": null, @@ -2307,7 +2307,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:22.024705Z", + "created_at": "2025-09-03T17:36:26.335776Z", "done": false, "done_reason": null, "total_duration": null, @@ -2325,7 +2325,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:22.079411Z", + "created_at": "2025-09-03T17:36:26.377421Z", "done": false, "done_reason": null, "total_duration": null, @@ -2343,7 +2343,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:22.132835Z", + "created_at": "2025-09-03T17:36:26.419324Z", "done": false, "done_reason": null, "total_duration": null, @@ -2361,7 +2361,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:22.189848Z", + "created_at": "2025-09-03T17:36:26.460598Z", "done": false, "done_reason": null, "total_duration": null, @@ -2379,7 +2379,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:22.252016Z", + "created_at": "2025-09-03T17:36:26.502926Z", "done": false, "done_reason": null, "total_duration": null, @@ -2397,7 +2397,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:22.316246Z", + "created_at": "2025-09-03T17:36:26.545467Z", "done": false, "done_reason": null, "total_duration": null, @@ -2415,7 +2415,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:22.384612Z", + "created_at": "2025-09-03T17:36:26.587384Z", "done": false, "done_reason": null, "total_duration": null, @@ -2433,7 +2433,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:22.444066Z", + "created_at": "2025-09-03T17:36:26.628641Z", "done": false, "done_reason": null, "total_duration": null, @@ -2451,7 +2451,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:22.50686Z", + "created_at": "2025-09-03T17:36:26.669783Z", "done": false, "done_reason": null, "total_duration": null, @@ -2469,7 +2469,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:22.562225Z", + "created_at": "2025-09-03T17:36:26.710862Z", "done": false, "done_reason": null, "total_duration": null, @@ -2487,7 +2487,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:22.621013Z", + "created_at": "2025-09-03T17:36:26.751949Z", "done": false, "done_reason": null, "total_duration": null, @@ -2505,7 +2505,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:22.682489Z", + "created_at": "2025-09-03T17:36:26.793375Z", "done": false, "done_reason": null, "total_duration": null, @@ -2523,7 +2523,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:22.754211Z", + "created_at": "2025-09-03T17:36:26.835697Z", "done": false, "done_reason": null, "total_duration": null, @@ -2541,7 +2541,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:22.813395Z", + "created_at": "2025-09-03T17:36:26.876139Z", "done": false, "done_reason": null, "total_duration": null, @@ -2559,7 +2559,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:22.872143Z", + "created_at": "2025-09-03T17:36:26.917322Z", "done": false, "done_reason": null, "total_duration": null, @@ -2577,7 +2577,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:22.930176Z", + "created_at": "2025-09-03T17:36:26.958405Z", "done": false, "done_reason": null, "total_duration": null, @@ -2595,7 +2595,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:22.989936Z", + "created_at": "2025-09-03T17:36:26.999602Z", "done": false, "done_reason": null, "total_duration": null, @@ -2613,7 +2613,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:23.052675Z", + "created_at": "2025-09-03T17:36:27.041369Z", "done": false, "done_reason": null, "total_duration": null, @@ -2631,7 +2631,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:23.116141Z", + "created_at": "2025-09-03T17:36:27.082117Z", "done": false, "done_reason": null, "total_duration": null, @@ -2649,7 +2649,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:23.171904Z", + "created_at": "2025-09-03T17:36:27.124286Z", "done": false, "done_reason": null, "total_duration": null, @@ -2667,7 +2667,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:23.226341Z", + "created_at": "2025-09-03T17:36:27.165354Z", "done": false, "done_reason": null, "total_duration": null, @@ -2685,7 +2685,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:23.279164Z", + "created_at": "2025-09-03T17:36:27.206517Z", "done": false, "done_reason": null, "total_duration": null, @@ -2703,7 +2703,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:23.331167Z", + "created_at": "2025-09-03T17:36:27.247418Z", "done": false, "done_reason": null, "total_duration": null, @@ -2721,7 +2721,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:23.3852Z", + "created_at": "2025-09-03T17:36:27.288727Z", "done": false, "done_reason": null, "total_duration": null, @@ -2739,7 +2739,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:23.441499Z", + "created_at": "2025-09-03T17:36:27.32952Z", "done": false, "done_reason": null, "total_duration": null, @@ -2757,7 +2757,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:23.495317Z", + "created_at": "2025-09-03T17:36:27.37057Z", "done": false, "done_reason": null, "total_duration": null, @@ -2775,7 +2775,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:23.55017Z", + "created_at": "2025-09-03T17:36:27.413166Z", "done": false, "done_reason": null, "total_duration": null, @@ -2793,7 +2793,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:23.600579Z", + "created_at": "2025-09-03T17:36:27.453878Z", "done": false, "done_reason": null, "total_duration": null, @@ -2811,7 +2811,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:23.654506Z", + "created_at": "2025-09-03T17:36:27.495693Z", "done": false, "done_reason": null, "total_duration": null, @@ -2829,7 +2829,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:23.709135Z", + "created_at": "2025-09-03T17:36:27.536879Z", "done": false, "done_reason": null, "total_duration": null, @@ -2847,7 +2847,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:23.760466Z", + "created_at": "2025-09-03T17:36:27.578071Z", "done": false, "done_reason": null, "total_duration": null, @@ -2865,7 +2865,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:23.813218Z", + "created_at": "2025-09-03T17:36:27.619459Z", "done": false, "done_reason": null, "total_duration": null, @@ -2883,7 +2883,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:23.865353Z", + "created_at": "2025-09-03T17:36:27.660329Z", "done": false, "done_reason": null, "total_duration": null, @@ -2901,7 +2901,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:23.922629Z", + "created_at": "2025-09-03T17:36:27.701195Z", "done": false, "done_reason": null, "total_duration": null, @@ -2919,7 +2919,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:23.975942Z", + "created_at": "2025-09-03T17:36:27.74184Z", "done": false, "done_reason": null, "total_duration": null, @@ -2937,7 +2937,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:24.028952Z", + "created_at": "2025-09-03T17:36:27.782435Z", "done": false, "done_reason": null, "total_duration": null, @@ -2955,7 +2955,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:24.086171Z", + "created_at": "2025-09-03T17:36:27.822698Z", "done": false, "done_reason": null, "total_duration": null, @@ -2973,7 +2973,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:24.145184Z", + "created_at": "2025-09-03T17:36:27.863482Z", "done": false, "done_reason": null, "total_duration": null, @@ -2991,7 +2991,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:24.201279Z", + "created_at": "2025-09-03T17:36:27.904189Z", "done": false, "done_reason": null, "total_duration": null, @@ -3009,7 +3009,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:24.255619Z", + "created_at": "2025-09-03T17:36:27.944927Z", "done": false, "done_reason": null, "total_duration": null, @@ -3027,7 +3027,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:24.311758Z", + "created_at": "2025-09-03T17:36:27.985583Z", "done": false, "done_reason": null, "total_duration": null, @@ -3045,7 +3045,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:24.369104Z", + "created_at": "2025-09-03T17:36:28.026811Z", "done": false, "done_reason": null, "total_duration": null, @@ -3063,7 +3063,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:24.423674Z", + "created_at": "2025-09-03T17:36:28.067929Z", "done": false, "done_reason": null, "total_duration": null, @@ -3081,7 +3081,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:24.47792Z", + "created_at": "2025-09-03T17:36:28.108844Z", "done": false, "done_reason": null, "total_duration": null, @@ -3099,7 +3099,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:24.531093Z", + "created_at": "2025-09-03T17:36:28.149655Z", "done": false, "done_reason": null, "total_duration": null, @@ -3117,7 +3117,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:24.582555Z", + "created_at": "2025-09-03T17:36:28.190377Z", "done": false, "done_reason": null, "total_duration": null, @@ -3135,7 +3135,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:24.63568Z", + "created_at": "2025-09-03T17:36:28.230919Z", "done": false, "done_reason": null, "total_duration": null, @@ -3153,7 +3153,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:24.689009Z", + "created_at": "2025-09-03T17:36:28.271506Z", "done": false, "done_reason": null, "total_duration": null, @@ -3171,7 +3171,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:24.742834Z", + "created_at": "2025-09-03T17:36:28.313533Z", "done": false, "done_reason": null, "total_duration": null, @@ -3189,7 +3189,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:24.79443Z", + "created_at": "2025-09-03T17:36:28.356508Z", "done": false, "done_reason": null, "total_duration": null, @@ -3207,7 +3207,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:24.845937Z", + "created_at": "2025-09-03T17:36:28.397379Z", "done": false, "done_reason": null, "total_duration": null, @@ -3225,7 +3225,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:24.896501Z", + "created_at": "2025-09-03T17:36:28.438016Z", "done": false, "done_reason": null, "total_duration": null, @@ -3243,7 +3243,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:24.952958Z", + "created_at": "2025-09-03T17:36:28.47858Z", "done": false, "done_reason": null, "total_duration": null, @@ -3261,7 +3261,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:25.009085Z", + "created_at": "2025-09-03T17:36:28.519407Z", "done": false, "done_reason": null, "total_duration": null, @@ -3279,7 +3279,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:25.067495Z", + "created_at": "2025-09-03T17:36:28.560412Z", "done": false, "done_reason": null, "total_duration": null, @@ -3297,7 +3297,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:25.121739Z", + "created_at": "2025-09-03T17:36:28.601727Z", "done": false, "done_reason": null, "total_duration": null, @@ -3315,7 +3315,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:25.172013Z", + "created_at": "2025-09-03T17:36:28.64332Z", "done": false, "done_reason": null, "total_duration": null, @@ -3333,7 +3333,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:25.222982Z", + "created_at": "2025-09-03T17:36:28.683692Z", "done": false, "done_reason": null, "total_duration": null, @@ -3351,7 +3351,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:25.274019Z", + "created_at": "2025-09-03T17:36:28.724325Z", "done": false, "done_reason": null, "total_duration": null, @@ -3369,7 +3369,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:25.324668Z", + "created_at": "2025-09-03T17:36:28.764731Z", "done": false, "done_reason": null, "total_duration": null, @@ -3387,7 +3387,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:25.377987Z", + "created_at": "2025-09-03T17:36:28.805214Z", "done": false, "done_reason": null, "total_duration": null, @@ -3405,7 +3405,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:25.429358Z", + "created_at": "2025-09-03T17:36:28.845962Z", "done": false, "done_reason": null, "total_duration": null, @@ -3423,7 +3423,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:25.481004Z", + "created_at": "2025-09-03T17:36:28.886874Z", "done": false, "done_reason": null, "total_duration": null, @@ -3441,7 +3441,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:25.534764Z", + "created_at": "2025-09-03T17:36:28.927442Z", "done": false, "done_reason": null, "total_duration": null, @@ -3459,7 +3459,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:25.587324Z", + "created_at": "2025-09-03T17:36:28.967837Z", "done": false, "done_reason": null, "total_duration": null, @@ -3477,7 +3477,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:25.639379Z", + "created_at": "2025-09-03T17:36:29.008786Z", "done": false, "done_reason": null, "total_duration": null, @@ -3495,7 +3495,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:25.692618Z", + "created_at": "2025-09-03T17:36:29.049817Z", "done": false, "done_reason": null, "total_duration": null, @@ -3513,7 +3513,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:25.74473Z", + "created_at": "2025-09-03T17:36:29.090455Z", "done": false, "done_reason": null, "total_duration": null, @@ -3531,7 +3531,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:25.803002Z", + "created_at": "2025-09-03T17:36:29.131723Z", "done": false, "done_reason": null, "total_duration": null, @@ -3549,7 +3549,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:25.858781Z", + "created_at": "2025-09-03T17:36:29.172582Z", "done": false, "done_reason": null, "total_duration": null, @@ -3567,7 +3567,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:25.916114Z", + "created_at": "2025-09-03T17:36:29.214861Z", "done": false, "done_reason": null, "total_duration": null, @@ -3585,7 +3585,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:25.968791Z", + "created_at": "2025-09-03T17:36:29.256056Z", "done": false, "done_reason": null, "total_duration": null, @@ -3603,7 +3603,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:26.023195Z", + "created_at": "2025-09-03T17:36:29.296825Z", "done": false, "done_reason": null, "total_duration": null, @@ -3621,7 +3621,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:26.076958Z", + "created_at": "2025-09-03T17:36:29.337822Z", "done": false, "done_reason": null, "total_duration": null, @@ -3639,7 +3639,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:26.128711Z", + "created_at": "2025-09-03T17:36:29.378894Z", "done": false, "done_reason": null, "total_duration": null, @@ -3657,7 +3657,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:26.187987Z", + "created_at": "2025-09-03T17:36:29.419586Z", "done": false, "done_reason": null, "total_duration": null, @@ -3675,7 +3675,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:26.241555Z", + "created_at": "2025-09-03T17:36:29.459743Z", "done": false, "done_reason": null, "total_duration": null, @@ -3693,7 +3693,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:26.292588Z", + "created_at": "2025-09-03T17:36:29.500928Z", "done": false, "done_reason": null, "total_duration": null, @@ -3711,7 +3711,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:26.345649Z", + "created_at": "2025-09-03T17:36:29.541823Z", "done": false, "done_reason": null, "total_duration": null, @@ -3729,7 +3729,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:26.39865Z", + "created_at": "2025-09-03T17:36:29.583225Z", "done": false, "done_reason": null, "total_duration": null, @@ -3747,7 +3747,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:26.44719Z", + "created_at": "2025-09-03T17:36:29.62471Z", "done": false, "done_reason": null, "total_duration": null, @@ -3765,7 +3765,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:26.499784Z", + "created_at": "2025-09-03T17:36:29.665624Z", "done": false, "done_reason": null, "total_duration": null, @@ -3783,7 +3783,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:26.552673Z", + "created_at": "2025-09-03T17:36:29.706601Z", "done": false, "done_reason": null, "total_duration": null, @@ -3801,7 +3801,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:26.60472Z", + "created_at": "2025-09-03T17:36:29.747221Z", "done": false, "done_reason": null, "total_duration": null, @@ -3819,7 +3819,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:26.656364Z", + "created_at": "2025-09-03T17:36:29.787753Z", "done": false, "done_reason": null, "total_duration": null, @@ -3837,7 +3837,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:26.710318Z", + "created_at": "2025-09-03T17:36:29.828297Z", "done": false, "done_reason": null, "total_duration": null, @@ -3855,7 +3855,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:26.763384Z", + "created_at": "2025-09-03T17:36:29.86906Z", "done": false, "done_reason": null, "total_duration": null, @@ -3873,7 +3873,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:26.813607Z", + "created_at": "2025-09-03T17:36:29.909608Z", "done": false, "done_reason": null, "total_duration": null, @@ -3891,7 +3891,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:26.866943Z", + "created_at": "2025-09-03T17:36:29.950119Z", "done": false, "done_reason": null, "total_duration": null, @@ -3909,7 +3909,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:26.918563Z", + "created_at": "2025-09-03T17:36:29.990856Z", "done": false, "done_reason": null, "total_duration": null, @@ -3927,7 +3927,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:26.969428Z", + "created_at": "2025-09-03T17:36:30.031737Z", "done": false, "done_reason": null, "total_duration": null, @@ -3945,7 +3945,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:27.023314Z", + "created_at": "2025-09-03T17:36:30.072804Z", "done": false, "done_reason": null, "total_duration": null, @@ -3963,7 +3963,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:27.075325Z", + "created_at": "2025-09-03T17:36:30.115879Z", "done": false, "done_reason": null, "total_duration": null, @@ -3981,7 +3981,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:27.128289Z", + "created_at": "2025-09-03T17:36:30.157268Z", "done": false, "done_reason": null, "total_duration": null, @@ -3999,7 +3999,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:27.190218Z", + "created_at": "2025-09-03T17:36:30.198026Z", "done": false, "done_reason": null, "total_duration": null, @@ -4017,7 +4017,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:27.246086Z", + "created_at": "2025-09-03T17:36:30.238729Z", "done": false, "done_reason": null, "total_duration": null, @@ -4035,7 +4035,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:27.306117Z", + "created_at": "2025-09-03T17:36:30.279348Z", "done": false, "done_reason": null, "total_duration": null, @@ -4053,7 +4053,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:27.359915Z", + "created_at": "2025-09-03T17:36:30.31988Z", "done": false, "done_reason": null, "total_duration": null, @@ -4071,7 +4071,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:27.419018Z", + "created_at": "2025-09-03T17:36:30.360471Z", "done": false, "done_reason": null, "total_duration": null, @@ -4089,7 +4089,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:27.476634Z", + "created_at": "2025-09-03T17:36:30.401158Z", "done": false, "done_reason": null, "total_duration": null, @@ -4107,7 +4107,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:27.535904Z", + "created_at": "2025-09-03T17:36:30.441986Z", "done": false, "done_reason": null, "total_duration": null, @@ -4125,7 +4125,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:27.588323Z", + "created_at": "2025-09-03T17:36:30.482303Z", "done": false, "done_reason": null, "total_duration": null, @@ -4143,7 +4143,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:27.641718Z", + "created_at": "2025-09-03T17:36:30.523844Z", "done": false, "done_reason": null, "total_duration": null, @@ -4161,7 +4161,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:27.699892Z", + "created_at": "2025-09-03T17:36:30.564853Z", "done": false, "done_reason": null, "total_duration": null, @@ -4179,7 +4179,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:27.754283Z", + "created_at": "2025-09-03T17:36:30.605812Z", "done": false, "done_reason": null, "total_duration": null, @@ -4197,7 +4197,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:27.806748Z", + "created_at": "2025-09-03T17:36:30.646752Z", "done": false, "done_reason": null, "total_duration": null, @@ -4215,7 +4215,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:27.859134Z", + "created_at": "2025-09-03T17:36:30.68766Z", "done": false, "done_reason": null, "total_duration": null, @@ -4233,7 +4233,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:27.911671Z", + "created_at": "2025-09-03T17:36:30.728603Z", "done": false, "done_reason": null, "total_duration": null, @@ -4251,7 +4251,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:27.964185Z", + "created_at": "2025-09-03T17:36:30.769336Z", "done": false, "done_reason": null, "total_duration": null, @@ -4269,7 +4269,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:28.021644Z", + "created_at": "2025-09-03T17:36:30.80994Z", "done": false, "done_reason": null, "total_duration": null, @@ -4287,7 +4287,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:28.082519Z", + "created_at": "2025-09-03T17:36:30.850918Z", "done": false, "done_reason": null, "total_duration": null, @@ -4305,7 +4305,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:28.14397Z", + "created_at": "2025-09-03T17:36:30.89149Z", "done": false, "done_reason": null, "total_duration": null, @@ -4323,7 +4323,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:28.205905Z", + "created_at": "2025-09-03T17:36:30.932133Z", "done": false, "done_reason": null, "total_duration": null, @@ -4341,7 +4341,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - 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"created_at": "2025-08-04T22:55:28.479545Z", + "created_at": "2025-09-03T17:36:31.13892Z", "done": false, "done_reason": null, "total_duration": null, @@ -4431,7 +4431,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:28.531416Z", + "created_at": "2025-09-03T17:36:31.179559Z", "done": false, "done_reason": null, "total_duration": null, @@ -4449,7 +4449,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:28.58181Z", + "created_at": "2025-09-03T17:36:31.220282Z", "done": false, "done_reason": null, "total_duration": null, @@ -4467,7 +4467,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:28.632489Z", + "created_at": "2025-09-03T17:36:31.260847Z", "done": false, "done_reason": null, "total_duration": null, @@ -4485,7 +4485,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:28.684096Z", + "created_at": "2025-09-03T17:36:31.301689Z", "done": false, "done_reason": null, "total_duration": null, @@ -4503,7 +4503,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:28.737131Z", + "created_at": "2025-09-03T17:36:31.342413Z", "done": false, "done_reason": null, "total_duration": null, @@ -4521,7 +4521,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:28.789945Z", + "created_at": "2025-09-03T17:36:31.383094Z", "done": false, "done_reason": null, "total_duration": null, @@ -4539,7 +4539,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:28.842126Z", + "created_at": "2025-09-03T17:36:31.424087Z", "done": false, "done_reason": null, "total_duration": null, @@ -4557,7 +4557,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:28.895142Z", + "created_at": "2025-09-03T17:36:31.465298Z", "done": false, "done_reason": null, "total_duration": null, @@ -4575,7 +4575,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:28.947434Z", + "created_at": "2025-09-03T17:36:31.506962Z", "done": false, "done_reason": null, "total_duration": null, @@ -4593,7 +4593,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:29.003682Z", + "created_at": "2025-09-03T17:36:31.548213Z", "done": false, "done_reason": null, "total_duration": null, @@ -4611,7 +4611,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:29.056399Z", + "created_at": "2025-09-03T17:36:31.589913Z", "done": false, "done_reason": null, "total_duration": null, @@ -4629,7 +4629,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:29.109724Z", + "created_at": "2025-09-03T17:36:31.630948Z", "done": false, "done_reason": null, "total_duration": null, @@ -4647,7 +4647,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:29.163194Z", + "created_at": "2025-09-03T17:36:31.672087Z", "done": false, "done_reason": null, "total_duration": null, @@ -4665,7 +4665,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:29.217213Z", + "created_at": "2025-09-03T17:36:31.713337Z", "done": false, "done_reason": null, "total_duration": null, @@ -4683,7 +4683,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:29.269168Z", + "created_at": "2025-09-03T17:36:31.754423Z", "done": false, "done_reason": null, "total_duration": null, @@ -4701,7 +4701,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:29.321308Z", + "created_at": "2025-09-03T17:36:31.795742Z", "done": false, "done_reason": null, "total_duration": null, @@ -4719,7 +4719,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:29.374321Z", + "created_at": "2025-09-03T17:36:31.836637Z", "done": false, "done_reason": null, "total_duration": null, @@ -4737,7 +4737,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:29.427106Z", + "created_at": "2025-09-03T17:36:31.878115Z", "done": false, "done_reason": null, "total_duration": null, @@ -4755,7 +4755,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:29.479022Z", + "created_at": "2025-09-03T17:36:31.919569Z", "done": false, "done_reason": null, "total_duration": null, @@ -4773,7 +4773,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:29.536933Z", + "created_at": "2025-09-03T17:36:31.960615Z", "done": false, "done_reason": null, "total_duration": null, @@ -4791,7 +4791,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:29.589411Z", + "created_at": "2025-09-03T17:36:32.001695Z", "done": false, "done_reason": null, "total_duration": null, @@ -4809,7 +4809,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:29.641976Z", + "created_at": "2025-09-03T17:36:32.042291Z", "done": false, "done_reason": null, "total_duration": null, @@ -4827,7 +4827,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:29.693984Z", + "created_at": "2025-09-03T17:36:32.082564Z", "done": false, "done_reason": null, "total_duration": null, @@ -4845,7 +4845,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:29.746091Z", + "created_at": "2025-09-03T17:36:32.123962Z", "done": false, "done_reason": null, "total_duration": null, @@ -4863,7 +4863,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:29.79699Z", + "created_at": "2025-09-03T17:36:32.164847Z", "done": false, "done_reason": null, "total_duration": null, @@ -4881,7 +4881,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:29.849326Z", + "created_at": "2025-09-03T17:36:32.205607Z", "done": false, "done_reason": null, "total_duration": null, @@ -4899,7 +4899,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:29.90127Z", + "created_at": "2025-09-03T17:36:32.246372Z", "done": false, "done_reason": null, "total_duration": null, @@ -4917,7 +4917,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:29.953331Z", + "created_at": "2025-09-03T17:36:32.287091Z", "done": false, "done_reason": null, "total_duration": null, @@ -4935,7 +4935,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:30.006229Z", + "created_at": "2025-09-03T17:36:32.32769Z", "done": false, "done_reason": null, "total_duration": null, @@ -4953,7 +4953,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:30.057576Z", + "created_at": "2025-09-03T17:36:32.368571Z", "done": false, "done_reason": null, "total_duration": null, @@ -4971,7 +4971,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:30.108201Z", + "created_at": "2025-09-03T17:36:32.409389Z", "done": false, "done_reason": null, "total_duration": null, @@ -4989,7 +4989,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:30.159044Z", + "created_at": "2025-09-03T17:36:32.450109Z", "done": false, "done_reason": null, "total_duration": null, @@ -5007,7 +5007,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:30.211179Z", + "created_at": "2025-09-03T17:36:32.491077Z", "done": false, "done_reason": null, "total_duration": null, @@ -5025,7 +5025,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:30.26223Z", + "created_at": "2025-09-03T17:36:32.532737Z", "done": false, "done_reason": null, "total_duration": null, @@ -5043,7 +5043,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:30.314187Z", + "created_at": "2025-09-03T17:36:32.572701Z", "done": false, "done_reason": null, "total_duration": null, @@ -5061,7 +5061,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:30.368683Z", + "created_at": "2025-09-03T17:36:32.614093Z", "done": false, "done_reason": null, "total_duration": null, @@ -5079,7 +5079,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:30.423991Z", + "created_at": "2025-09-03T17:36:32.655113Z", "done": false, "done_reason": null, "total_duration": null, @@ -5097,7 +5097,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:30.475926Z", + "created_at": "2025-09-03T17:36:32.696438Z", "done": false, "done_reason": null, "total_duration": null, @@ -5115,7 +5115,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:30.535785Z", + "created_at": "2025-09-03T17:36:32.73788Z", "done": false, "done_reason": null, "total_duration": null, @@ -5133,7 +5133,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:30.591719Z", + "created_at": "2025-09-03T17:36:32.780775Z", "done": false, "done_reason": null, "total_duration": null, @@ -5151,7 +5151,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:30.645659Z", + "created_at": "2025-09-03T17:36:32.823196Z", "done": false, "done_reason": null, "total_duration": null, @@ -5169,7 +5169,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:30.698314Z", + "created_at": "2025-09-03T17:36:32.86428Z", "done": false, "done_reason": null, "total_duration": null, @@ -5187,7 +5187,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:30.747479Z", + "created_at": "2025-09-03T17:36:32.905305Z", "done": false, "done_reason": null, "total_duration": null, @@ -5205,7 +5205,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:30.799751Z", + "created_at": "2025-09-03T17:36:32.946086Z", "done": false, "done_reason": null, "total_duration": null, @@ -5223,7 +5223,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:30.854603Z", + "created_at": "2025-09-03T17:36:32.986849Z", "done": false, "done_reason": null, "total_duration": null, @@ -5241,7 +5241,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:30.907564Z", + "created_at": "2025-09-03T17:36:33.028251Z", "done": false, "done_reason": null, "total_duration": null, @@ -5259,7 +5259,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:30.961713Z", + "created_at": "2025-09-03T17:36:33.069225Z", "done": false, "done_reason": null, "total_duration": null, @@ -5277,7 +5277,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:31.016244Z", + "created_at": "2025-09-03T17:36:33.110717Z", "done": false, "done_reason": null, "total_duration": null, @@ -5295,7 +5295,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:31.069635Z", + "created_at": "2025-09-03T17:36:33.151703Z", "done": false, "done_reason": null, "total_duration": null, @@ -5313,7 +5313,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:31.1225Z", + "created_at": "2025-09-03T17:36:33.192643Z", "done": false, "done_reason": null, "total_duration": null, @@ -5331,7 +5331,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:31.17487Z", + "created_at": "2025-09-03T17:36:33.233604Z", "done": false, "done_reason": null, "total_duration": null, @@ -5349,7 +5349,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:31.226231Z", + "created_at": "2025-09-03T17:36:33.274665Z", "done": false, "done_reason": null, "total_duration": null, @@ -5367,7 +5367,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:31.28044Z", + "created_at": "2025-09-03T17:36:33.315311Z", "done": false, "done_reason": null, "total_duration": null, @@ -5385,7 +5385,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:31.338834Z", + "created_at": "2025-09-03T17:36:33.356272Z", "done": false, "done_reason": null, "total_duration": null, @@ -5403,7 +5403,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:31.39313Z", + "created_at": "2025-09-03T17:36:33.397164Z", "done": false, "done_reason": null, "total_duration": null, @@ -5421,7 +5421,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - 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"created_at": "2025-08-04T22:55:31.657366Z", + "created_at": "2025-09-03T17:36:33.602614Z", "done": false, "done_reason": null, "total_duration": null, @@ -5511,7 +5511,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:31.710345Z", + "created_at": "2025-09-03T17:36:33.643517Z", "done": false, "done_reason": null, "total_duration": null, @@ -5529,7 +5529,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:31.761482Z", + "created_at": "2025-09-03T17:36:33.69501Z", "done": false, "done_reason": null, "total_duration": null, @@ -5547,7 +5547,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:31.812505Z", + "created_at": "2025-09-03T17:36:33.744642Z", "done": false, "done_reason": null, "total_duration": null, @@ -5565,7 +5565,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:31.864427Z", + "created_at": "2025-09-03T17:36:33.788023Z", "done": false, "done_reason": null, "total_duration": null, @@ -5583,7 +5583,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:31.915242Z", + "created_at": "2025-09-03T17:36:33.830123Z", "done": false, "done_reason": null, "total_duration": null, @@ -5601,7 +5601,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:31.967322Z", + "created_at": "2025-09-03T17:36:33.873234Z", "done": false, "done_reason": null, "total_duration": null, @@ -5619,7 +5619,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:32.018589Z", + "created_at": "2025-09-03T17:36:33.91574Z", "done": false, "done_reason": null, "total_duration": null, @@ -5637,7 +5637,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:32.070624Z", + "created_at": "2025-09-03T17:36:33.958165Z", "done": false, "done_reason": null, "total_duration": null, @@ -5655,7 +5655,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:32.121703Z", + "created_at": "2025-09-03T17:36:34.000544Z", "done": false, "done_reason": null, "total_duration": null, @@ -5673,7 +5673,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:32.174718Z", + "created_at": "2025-09-03T17:36:34.043824Z", "done": false, "done_reason": null, "total_duration": null, @@ -5691,7 +5691,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:32.23641Z", + "created_at": "2025-09-03T17:36:34.086339Z", "done": false, "done_reason": null, "total_duration": null, @@ -5709,7 +5709,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:32.294487Z", + "created_at": "2025-09-03T17:36:34.128863Z", "done": false, "done_reason": null, "total_duration": null, @@ -5727,7 +5727,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:32.354809Z", + "created_at": "2025-09-03T17:36:34.171675Z", "done": false, "done_reason": null, "total_duration": null, @@ -5745,7 +5745,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:32.409827Z", + "created_at": "2025-09-03T17:36:34.214025Z", "done": false, "done_reason": null, "total_duration": null, @@ -5763,7 +5763,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:32.467898Z", + "created_at": "2025-09-03T17:36:34.256135Z", "done": false, "done_reason": null, "total_duration": null, @@ -5781,7 +5781,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:32.525406Z", + "created_at": "2025-09-03T17:36:34.298571Z", "done": false, "done_reason": null, "total_duration": null, @@ -5799,7 +5799,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:32.580356Z", + "created_at": "2025-09-03T17:36:34.340742Z", "done": false, "done_reason": null, "total_duration": null, @@ -5817,7 +5817,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:32.637738Z", + "created_at": "2025-09-03T17:36:34.38192Z", "done": false, "done_reason": null, "total_duration": null, @@ -5835,7 +5835,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:32.691339Z", + "created_at": "2025-09-03T17:36:34.423807Z", "done": false, "done_reason": null, "total_duration": null, @@ -5853,7 +5853,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:32.75193Z", + "created_at": "2025-09-03T17:36:34.465059Z", "done": false, "done_reason": null, "total_duration": null, @@ -5871,7 +5871,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:32.809022Z", + "created_at": "2025-09-03T17:36:34.506527Z", "done": false, "done_reason": null, "total_duration": null, @@ -5889,7 +5889,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:32.868509Z", + "created_at": "2025-09-03T17:36:34.547797Z", "done": false, "done_reason": null, "total_duration": null, @@ -5907,7 +5907,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:32.927239Z", + "created_at": "2025-09-03T17:36:34.589189Z", "done": false, "done_reason": null, "total_duration": null, @@ -5925,7 +5925,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:32.985536Z", + "created_at": "2025-09-03T17:36:34.632479Z", "done": false, "done_reason": null, "total_duration": null, @@ -5943,7 +5943,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:33.040875Z", + "created_at": "2025-09-03T17:36:34.673914Z", "done": false, "done_reason": null, "total_duration": null, @@ -5961,7 +5961,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:33.099492Z", + "created_at": "2025-09-03T17:36:34.714561Z", "done": false, "done_reason": null, "total_duration": null, @@ -5979,7 +5979,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:33.151102Z", + "created_at": "2025-09-03T17:36:34.755794Z", "done": false, "done_reason": null, "total_duration": null, @@ -5997,7 +5997,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:33.2036Z", + "created_at": "2025-09-03T17:36:34.797365Z", "done": false, "done_reason": null, "total_duration": null, @@ -6015,7 +6015,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:33.255217Z", + "created_at": "2025-09-03T17:36:34.839305Z", "done": false, "done_reason": null, "total_duration": null, @@ -6033,7 +6033,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:33.306726Z", + "created_at": "2025-09-03T17:36:34.881479Z", "done": false, "done_reason": null, "total_duration": null, @@ -6051,7 +6051,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:33.357871Z", + "created_at": "2025-09-03T17:36:34.923518Z", "done": false, "done_reason": null, "total_duration": null, @@ -6069,7 +6069,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:33.410678Z", + "created_at": "2025-09-03T17:36:34.964593Z", "done": false, "done_reason": null, "total_duration": null, @@ -6087,7 +6087,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:33.473848Z", + "created_at": "2025-09-03T17:36:35.005594Z", "done": false, "done_reason": null, "total_duration": null, @@ -6105,7 +6105,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:33.530364Z", + "created_at": "2025-09-03T17:36:35.047897Z", "done": false, "done_reason": null, "total_duration": null, @@ -6123,7 +6123,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:33.588387Z", + "created_at": "2025-09-03T17:36:35.088945Z", "done": false, "done_reason": null, "total_duration": null, @@ -6141,7 +6141,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:33.644848Z", + "created_at": "2025-09-03T17:36:35.130496Z", "done": false, "done_reason": null, "total_duration": null, @@ -6159,7 +6159,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:33.702142Z", + "created_at": "2025-09-03T17:36:35.171697Z", "done": false, "done_reason": null, "total_duration": null, @@ -6177,7 +6177,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:33.757078Z", + "created_at": "2025-09-03T17:36:35.212785Z", "done": false, "done_reason": null, "total_duration": null, @@ -6195,7 +6195,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:33.809287Z", + "created_at": "2025-09-03T17:36:35.254Z", "done": false, "done_reason": null, "total_duration": null, @@ -6213,7 +6213,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:33.863545Z", + "created_at": "2025-09-03T17:36:35.294945Z", "done": false, "done_reason": null, "total_duration": null, @@ -6231,7 +6231,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:33.921183Z", + "created_at": "2025-09-03T17:36:35.335904Z", "done": false, "done_reason": null, "total_duration": null, @@ -6249,7 +6249,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:33.972308Z", + "created_at": "2025-09-03T17:36:35.376911Z", "done": false, "done_reason": null, "total_duration": null, @@ -6267,7 +6267,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:34.024699Z", + "created_at": "2025-09-03T17:36:35.417931Z", "done": false, "done_reason": null, "total_duration": null, @@ -6285,7 +6285,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:34.078626Z", + "created_at": "2025-09-03T17:36:35.45891Z", "done": false, "done_reason": null, "total_duration": null, @@ -6303,7 +6303,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:34.132072Z", + "created_at": "2025-09-03T17:36:35.501211Z", "done": false, "done_reason": null, "total_duration": null, @@ -6321,7 +6321,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:34.185534Z", + "created_at": "2025-09-03T17:36:35.543696Z", "done": false, "done_reason": null, "total_duration": null, @@ -6339,7 +6339,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:34.237811Z", + "created_at": "2025-09-03T17:36:35.584233Z", "done": false, "done_reason": null, "total_duration": null, @@ -6357,7 +6357,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:34.289202Z", + "created_at": "2025-09-03T17:36:35.626596Z", "done": false, "done_reason": null, "total_duration": null, @@ -6375,7 +6375,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:34.341588Z", + "created_at": "2025-09-03T17:36:35.667752Z", "done": false, "done_reason": null, "total_duration": null, @@ -6393,7 +6393,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:34.393213Z", + "created_at": "2025-09-03T17:36:35.70907Z", "done": false, "done_reason": null, "total_duration": null, @@ -6411,7 +6411,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:34.444819Z", + "created_at": "2025-09-03T17:36:35.749741Z", "done": false, "done_reason": null, "total_duration": null, @@ -6429,7 +6429,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:34.497564Z", + "created_at": "2025-09-03T17:36:35.79089Z", "done": false, "done_reason": null, "total_duration": null, @@ -6447,7 +6447,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:34.552231Z", + "created_at": "2025-09-03T17:36:35.832516Z", "done": false, "done_reason": null, "total_duration": null, @@ -6465,7 +6465,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:34.608902Z", + "created_at": "2025-09-03T17:36:35.874088Z", "done": false, "done_reason": null, "total_duration": null, @@ -6483,7 +6483,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:34.66848Z", + "created_at": "2025-09-03T17:36:35.915661Z", "done": false, "done_reason": null, "total_duration": null, @@ -6501,7 +6501,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - 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"created_at": "2025-08-04T22:55:35.151407Z", + "created_at": "2025-09-03T17:36:36.288872Z", "done": false, "done_reason": null, "total_duration": null, @@ -6663,7 +6663,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:35.20241Z", + "created_at": "2025-09-03T17:36:36.330688Z", "done": false, "done_reason": null, "total_duration": null, @@ -6681,7 +6681,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:35.254715Z", + "created_at": "2025-09-03T17:36:36.372212Z", "done": false, "done_reason": null, "total_duration": null, @@ -6699,7 +6699,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:35.305634Z", + "created_at": "2025-09-03T17:36:36.415315Z", "done": false, "done_reason": null, "total_duration": null, @@ -6717,7 +6717,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:35.357517Z", + "created_at": "2025-09-03T17:36:36.458461Z", "done": false, "done_reason": null, "total_duration": null, @@ -6735,7 +6735,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:35.410715Z", + "created_at": "2025-09-03T17:36:36.501868Z", "done": false, "done_reason": null, "total_duration": null, @@ -6753,7 +6753,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:35.464886Z", + "created_at": "2025-09-03T17:36:36.544291Z", "done": false, "done_reason": null, "total_duration": null, @@ -6771,7 +6771,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:35.515495Z", + "created_at": "2025-09-03T17:36:36.58593Z", "done": false, "done_reason": null, "total_duration": null, @@ -6789,7 +6789,7 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - 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"done_reason": null, "total_duration": null, @@ -39,15 +39,15 @@ "__type__": "ollama._types.GenerateResponse", "__data__": { "model": "llama3.2:3b-instruct-fp16", - "created_at": "2025-08-04T22:55:41.619829Z", + "created_at": "2025-09-03T17:36:41.014682Z", "done": true, "done_reason": "stop", - "total_duration": 915493834, - "load_duration": 167838417, + "total_duration": 693115125, + "load_duration": 114019375, "prompt_eval_count": 386, - "prompt_eval_duration": 683000000, + "prompt_eval_duration": 535931209, "eval_count": 2, - "eval_duration": 63000000, + "eval_duration": 42505166, "response": "", "thinking": null, "context": null diff --git a/tests/integration/recordings/responses/decfd950646c.json b/tests/integration/recordings/responses/decfd950646c.json index f62340c27..c46fa8686 100644 --- a/tests/integration/recordings/responses/decfd950646c.json +++ b/tests/integration/recordings/responses/decfd950646c.json @@ -1,7 +1,7 @@ { "request": { "method": "POST", - "url": 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+ "finish_reason": "stop", + "index": 0, + "logprobs": null, + "message": { + "content": "\nOkay, the user is asking which planet humans live on. I need to make sure I answer this accurately. First, I should recall what I know about our solar system. The Earth is our home, and it's in our solar system. There are eight planets in total, right? Let me check that. Mercury, Venus, Earth, Mars, Jupiter, Saturn, Uranus, and Neptune. Yep, that's the list.\n\nBut wait, the user might be confusing Earth with Mars. I should clarify that Earth is the only planet known to support life. The other planets are mostly gas giants and have no liquid water, so they don't support life as Earth does. So the answer should be Earth. I should also mention that although there are other planets, none have liquid water, which makes the answer more complete.\n\nI need to make sure there are no alternatives. Maybe some people might think Mars, but I know that's not the case. Also, it's good to mention that life on Earth is closely linked to the presence of water, which is why Earth is our only planet with that characteristic. That way, the answer is not only accurate but also informative.\n\n\nHumans live on **Earth**, the planet that supports life as we know it. The Earth is the only known planet in our solar system where liquid water exists and where life can occur. Other planets are considered \"gas giants\" or \"ice giants\" due to their extreme conditions and lack of liquid water, making them inhospitable for life.", + "refusal": null, + "role": "assistant", + "annotations": null, + "audio": null, + "function_call": null, + "tool_calls": null + } + } + ], + "created": 1757550390, + "model": "Qwen/Qwen3-0.6B", + "object": "chat.completion", + "service_tier": null, + "system_fingerprint": "3.3.5-dev0-sha-1b90c50", + "usage": { + "completion_tokens": 312, + "prompt_tokens": 15, + "total_tokens": 327, + "completion_tokens_details": null, + "prompt_tokens_details": null + } + } + }, + "is_streaming": false + } +} diff --git a/tests/integration/recordings/responses/e0a6dce1d94b.json b/tests/integration/recordings/responses/e0a6dce1d94b.json new file mode 100644 index 000000000..4a285b30b --- /dev/null +++ b/tests/integration/recordings/responses/e0a6dce1d94b.json @@ -0,0 +1,422 @@ +{ + "request": { + "method": "POST", + "url": 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"llama3.2:3b-instruct-fp16", @@ -22,14 +22,14 @@ "body": { "__type__": "openai.types.chat.chat_completion.ChatCompletion", "__data__": { - "id": "chatcmpl-494", + "id": "chatcmpl-662", "choices": [ { "finish_reason": "length", "index": 0, "logprobs": null, "message": { - "content": "To test the OpenAI API with a temperature of 1, you can use the following Python code:\n\n```python\nimport requests\n\ndef generate_text(model_name, prompt, temperature=1):\n # Set the API endpoint and parameters\n url = \"https://api.openai.com/v1/models/\" + model_name + \"/generate\"\n params = {\n \"prompt\": prompt,\n \"temperature\": temperature\n }\n\n # Send a GET request to the API\n response =", + "content": "To test the prompt understanding of OpenAI's text generation capabilities, I'll simulate a conversation. \n\nYou mentioned testing the model with a temperature setting of 1. The temperature parameter in OpenAI's text models controls the diversity and coherence of generated text.\n\nA temperature of 1 is considered \"colder\" than usual, meaning the model will generate more coherent but potentially less diverse text compared to higher temperatures (e.g., 0.5 or 0.7).\n\nPlease provide a prompt for", "refusal": null, "role": "assistant", "annotations": null, @@ -39,7 +39,7 @@ } } ], - "created": 1754510067, + "created": 1756921259, "model": "llama3.2:3b-instruct-fp16", "object": "chat.completion", "service_tier": null, diff --git a/tests/integration/recordings/responses/e509387fc329.json b/tests/integration/recordings/responses/e509387fc329.json new file mode 100644 index 000000000..5f1600dab --- /dev/null +++ b/tests/integration/recordings/responses/e509387fc329.json @@ -0,0 +1,168 @@ +{ + "request": { + "method": "POST", + "url": "__databricks__/serving-endpoints/v1/chat/completions", + "headers": {}, + "body": { + "model": "databricks-meta-llama-3-3-70b-instruct", + "messages": [ + { + "role": "user", + "content": "What's the weather in Tokyo? 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It is the third planet from the Sun and is the only known planet in the universe that currently supports human life.", + "content": "Humans live on Earth. It's a terrestrial planet in the Solar System, located in the outer reaches of the Sun's gravitational pull.", "refusal": null, "role": "assistant", "annotations": null, @@ -37,15 +37,15 @@ } } ], - "created": 1754081845, + "created": 1756921355, "model": "llama3.2:3b-instruct-fp16", "object": "chat.completion", "service_tier": null, "system_fingerprint": "fp_ollama", "usage": { - "completion_tokens": 29, + "completion_tokens": 28, "prompt_tokens": 32, - "total_tokens": 61, + "total_tokens": 60, "completion_tokens_details": null, "prompt_tokens_details": null } diff --git a/tests/integration/recordings/responses/ecae140151d1.json b/tests/integration/recordings/responses/ecae140151d1.json new file mode 100644 index 000000000..433597080 --- /dev/null +++ b/tests/integration/recordings/responses/ecae140151d1.json @@ -0,0 +1,43 @@ +{ + "request": { + "method": "POST", + "url": "http://0.0.0.0:11434/v1/v1/completions", + "headers": {}, + "body": { + "model": "llama3.2:3b-instruct-fp16", + 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"2025-08-01T00:04:49.747466769Z", + "created_at": "2025-09-03T17:54:22.416981Z", "done": false, "done_reason": null, "total_duration": null, @@ -75,7 +75,7 @@ "__type__": "ollama._types.ChatResponse", "__data__": { "model": "llama3.2-vision:11b", - "created_at": "2025-08-01T00:04:50.156146804Z", + "created_at": "2025-09-03T17:54:22.477481Z", "done": false, "done_reason": null, "total_duration": null, @@ -97,7 +97,7 @@ "__type__": "ollama._types.ChatResponse", "__data__": { "model": "llama3.2-vision:11b", - "created_at": "2025-08-01T00:04:50.566195243Z", + "created_at": "2025-09-03T17:54:22.53807Z", "done": false, "done_reason": null, "total_duration": null, @@ -119,7 +119,7 @@ "__type__": "ollama._types.ChatResponse", "__data__": { "model": "llama3.2-vision:11b", - "created_at": "2025-08-01T00:04:50.975121211Z", + "created_at": "2025-09-03T17:54:22.59701Z", "done": false, "done_reason": null, "total_duration": null, @@ -141,7 +141,7 @@ "__type__": "ollama._types.ChatResponse", "__data__": { "model": "llama3.2-vision:11b", - "created_at": "2025-08-01T00:04:51.388779549Z", + "created_at": "2025-09-03T17:54:22.655848Z", "done": false, "done_reason": null, "total_duration": null, @@ -163,7 +163,7 @@ "__type__": "ollama._types.ChatResponse", "__data__": { "model": "llama3.2-vision:11b", - "created_at": "2025-08-01T00:04:51.79897453Z", + "created_at": "2025-09-03T17:54:22.715363Z", "done": false, "done_reason": null, "total_duration": null, @@ -185,7 +185,7 @@ "__type__": "ollama._types.ChatResponse", "__data__": { "model": "llama3.2-vision:11b", - "created_at": "2025-08-01T00:04:52.209608504Z", + "created_at": "2025-09-03T17:54:22.773865Z", "done": false, "done_reason": null, "total_duration": null, @@ -207,7 +207,7 @@ "__type__": "ollama._types.ChatResponse", "__data__": { "model": "llama3.2-vision:11b", - "created_at": "2025-08-01T00:04:52.619045995Z", + "created_at": "2025-09-03T17:54:22.832338Z", "done": false, "done_reason": null, "total_duration": null, @@ 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"object": "model", + "owned_by": "Cerebras" + } + }, + { + "__type__": "openai.types.model.Model", + "__data__": { + "id": "llama3.1-8b", + "created": 0, + "object": "model", + "owned_by": "Cerebras" + } + }, + { + "__type__": "openai.types.model.Model", + "__data__": { + "id": "qwen-3-32b", + "created": 0, + "object": "model", + "owned_by": "Cerebras" + } + }, + { + "__type__": "openai.types.model.Model", + "__data__": { + "id": "gpt-oss-120b", + "created": 0, + "object": "model", + "owned_by": "Cerebras" + } + }, + { + "__type__": "openai.types.model.Model", + "__data__": { + "id": "qwen-3-235b-a22b-thinking-2507", + "created": 0, + "object": "model", + "owned_by": "Cerebras" + } + }, + { + "__type__": "openai.types.model.Model", + "__data__": { + "id": "llama-3.3-70b", + "created": 0, + "object": "model", + "owned_by": "Cerebras" + } + }, + { + "__type__": "openai.types.model.Model", + "__data__": { + "id": "qwen-3-coder-480b", + "created": 0, + "object": "model", + "owned_by": "Cerebras" + } + } + ], + "is_streaming": false + } +} diff --git a/tests/integration/recordings/vision/index.sqlite b/tests/integration/recordings/vision/index.sqlite deleted file mode 100644 index 6ff587c43..000000000 Binary files a/tests/integration/recordings/vision/index.sqlite and /dev/null differ diff --git a/tests/integration/recordings/vision/responses/3877ecf1bc62.json b/tests/integration/recordings/vision/responses/3877ecf1bc62.json deleted file mode 100644 index 819ec31c0..000000000 --- a/tests/integration/recordings/vision/responses/3877ecf1bc62.json +++ /dev/null @@ -1,22 +0,0 @@ -{ - "request": { - "method": "POST", - "url": "http://localhost:11434/api/pull", - "headers": {}, - "body": {}, - "endpoint": "/api/pull", - "model": "" - }, - "response": { - "body": { - "__type__": "ollama._types.ProgressResponse", - "__data__": { - "status": "success", - "completed": null, - "total": null, - "digest": null - } - }, - "is_streaming": false - } -} diff --git a/tests/integration/recordings/vision/responses/4096743baf8e.json b/tests/integration/recordings/vision/responses/4096743baf8e.json deleted file mode 100644 index 880f1b597..000000000 --- a/tests/integration/recordings/vision/responses/4096743baf8e.json +++ /dev/null @@ -1,56 +0,0 @@ -{ - "request": { - "method": "POST", - "url": "http://localhost:11434/v1/v1/completions", - "headers": {}, - "body": { - "model": "llama3.2:3b-instruct-fp16", - "messages": [ - { - "role": "user", - "content": "Test trace openai 0" - } - ], - "stream": false - }, - "endpoint": "/v1/completions", - "model": "llama3.2:3b-instruct-fp16" - }, - "response": { - "body": { - "__type__": "openai.types.chat.chat_completion.ChatCompletion", - "__data__": { - "id": "chatcmpl-971", - "choices": [ - { - "finish_reason": "stop", - "index": 0, - "logprobs": null, - "message": { - "content": "I'm happy to help you with testing the test API for OpenAI's Model 0, but I need to clarify a few things.\n\nOpenAI's Model 0 is an early version of their AI model, and it's not publicly available. However, I can simulate some interactions with a hypothetical API that might be similar to what they provide.\n\nHere's an example test:\n```\nPOST /test HTTP/1.1\nHost: 0 api.openai.com\n\nContent-Type: application/json\n\n{\n \"text\": \"This is a prompt for testing the Model 0 API\"\n}\n```\n\nPlease note that this is not an official API, and you should not try to interact with it directly. However, I can simulate a response for you:\n\n```\nHTTP/1.1 200 OK\nContent-Type: application/json\n\n{\n \"complete\": false,\n \"error\": null\n}\n```\n\nIn a real-world scenario, the Model 0 API would likely respond with much more complex and accurate results. For example:\n\n```\nHTTP/1.1 200 OK\nContent-Type: application/json\n\n{\n \"id\": \"\",\n \"text\": {\n \"parent_id\": \"\",\n \"text\": \"I can generate text similar to human writing.\"\n }\n}\n```", - "refusal": null, - "role": "assistant", - "annotations": null, - "audio": null, - "function_call": null, - "tool_calls": null - } - } - ], - "created": 1754003706, - "model": "llama3.2:3b-instruct-fp16", - "object": "chat.completion", - "service_tier": null, - "system_fingerprint": "fp_ollama", - "usage": { - "completion_tokens": 272, - "prompt_tokens": 31, - "total_tokens": 303, - "completion_tokens_details": null, - "prompt_tokens_details": null - } - } - }, - "is_streaming": false - } -} diff --git a/tests/integration/recordings/vision/responses/4a3a4447b16b.json b/tests/integration/recordings/vision/responses/4a3a4447b16b.json deleted file mode 100644 index a99e1fcc3..000000000 --- a/tests/integration/recordings/vision/responses/4a3a4447b16b.json +++ /dev/null @@ -1,68 +0,0 @@ -{ - "request": { - "method": "POST", - "url": "http://localhost:11434/api/tags", - "headers": {}, - "body": {}, - "endpoint": "/api/tags", - "model": "" - }, - "response": { - "body": { - "__type__": "ollama._types.ListResponse", - "__data__": { - "models": [ - { - "model": "nomic-embed-text:latest", - "modified_at": "2025-07-31T23:55:40.635067Z", - "digest": "0a109f422b47e3a30ba2b10eca18548e944e8a23073ee3f3e947efcf3c45e59f", - "size": 274302450, - "details": { - "parent_model": "", - "format": "gguf", - "family": "nomic-bert", - "families": [ - "nomic-bert" - ], - "parameter_size": "137M", - "quantization_level": "F16" - } - }, - { - "model": "all-minilm:l6-v2", - "modified_at": "2025-07-30T17:18:31Z", - "digest": "1b226e2802dbb772b5fc32a58f103ca1804ef7501331012de126ab22f67475ef", - "size": 45960996, - "details": { - "parent_model": "", - "format": "gguf", - "family": "bert", - "families": [ - "bert" - ], - "parameter_size": "23M", - "quantization_level": "F16" - } - }, - { - "model": "llama3.2-vision:11b", - "modified_at": "2025-07-30T17:18:21Z", - "digest": "6f2f9757ae97e8a3f8ea33d6adb2b11d93d9a35bef277cd2c0b1b5af8e8d0b1e", - "size": 7816589186, - "details": { - "parent_model": "", - "format": "gguf", - "family": "mllama", - "families": [ - "mllama" - ], - "parameter_size": "10.7B", - "quantization_level": "Q4_K_M" - } - } - ] - } - }, - "is_streaming": false - } -} diff --git a/tests/integration/recordings/vision/responses/67198cbad48f.json b/tests/integration/recordings/vision/responses/67198cbad48f.json deleted file mode 100644 index 8326d5329..000000000 --- a/tests/integration/recordings/vision/responses/67198cbad48f.json +++ /dev/null @@ -1,56 +0,0 @@ -{ - "request": { - "method": "POST", - "url": "http://localhost:11434/v1/v1/completions", - "headers": {}, - "body": { - "model": "llama3.2:3b-instruct-fp16", - "messages": [ - { - "role": "user", - "content": "Test OpenAI telemetry creation" - } - ], - "stream": false - }, - "endpoint": "/v1/completions", - "model": "llama3.2:3b-instruct-fp16" - }, - "response": { - "body": { - "__type__": "openai.types.chat.chat_completion.ChatCompletion", - "__data__": { - "id": "chatcmpl-517", - "choices": [ - { - "finish_reason": "stop", - "index": 0, - "logprobs": null, - "message": { - "content": "I'm happy to help you test OpenAI's telemetry creation feature. However, I need to inform you that OpenAI's models are not designed for direct testing and may not support the kind of feedback you're looking for.\n\nThat being said, we can try a simulated testing process using this chat interface. Here's how we can go about it:\n\n1. **Test the chat model:** Before we dive into telemetry creation, let's test the conversation system itself.\n2. **Try out general queries and statements**: See if I can respond to various questions and prompt topics with accuracy. This will help you gauge the effectiveness of my language processing abilities within this interface.\n3. **Create a simulated telemetry request:** Based on your feedback about our chat, describe what kind of information would be needed as a telemetry point for monitoring conversations like ours.\n\nGo ahead and give me some test data or prompt topics so we can proceed with creating a simulated \"telemetry\" creation process.", - "refusal": null, - "role": "assistant", - "annotations": null, - "audio": null, - "function_call": null, - "tool_calls": null - } - } - ], - "created": 1754003724, - "model": "llama3.2:3b-instruct-fp16", - "object": "chat.completion", - "service_tier": null, - "system_fingerprint": "fp_ollama", - "usage": { - "completion_tokens": 195, - "prompt_tokens": 30, - "total_tokens": 225, - "completion_tokens_details": null, - "prompt_tokens_details": null - } - } - }, - "is_streaming": false - } -} diff --git a/tests/integration/recordings/vision/responses/c9667519ad7c.json b/tests/integration/recordings/vision/responses/c9667519ad7c.json deleted file mode 100644 index ce0322da9..000000000 --- a/tests/integration/recordings/vision/responses/c9667519ad7c.json +++ /dev/null @@ -1,58 +0,0 @@ -{ - "request": { - "method": "POST", - "url": "http://localhost:11434/v1/v1/completions", - "headers": {}, - "body": { - "model": "llama3.2:3b-instruct-fp16", - "messages": [ - { - "role": "user", - "content": "Test trace openai with temperature 1" - } - ], - "max_tokens": 100, - "stream": false, - "temperature": 0.7 - }, - "endpoint": "/v1/completions", - "model": "llama3.2:3b-instruct-fp16" - }, - "response": { - "body": { - "__type__": "openai.types.chat.chat_completion.ChatCompletion", - "__data__": { - "id": "chatcmpl-82", - "choices": [ - { - "finish_reason": "length", - "index": 0, - "logprobs": null, - "message": { - "content": "To test the trace functionality of OpenAI's API with a temperature of 1, you can use the following Python code:\n```\nimport torch\nfrom transformers import AutoModelForCausalLM, AutoTokenizer\n\n# Load pre-trained model and tokenizer\nmodel_name = \"CompVis/transformers-base-tiny\"\nmodel = AutoModelForCausalLM.from_pretrained(model_name)\ntokenizer = AutoTokenizer.from_pretrained(model_name)\n\n# Set temperature to 1\ntemperature = 1.", - "refusal": null, - "role": "assistant", - "annotations": null, - "audio": null, - "function_call": null, - "tool_calls": null - } - } - ], - "created": 1754003715, - "model": "llama3.2:3b-instruct-fp16", - "object": "chat.completion", - "service_tier": null, - "system_fingerprint": "fp_ollama", - "usage": { - "completion_tokens": 100, - "prompt_tokens": 33, - "total_tokens": 133, - "completion_tokens_details": null, - "prompt_tokens_details": null - } - } - }, - "is_streaming": false - } -} diff --git a/tests/integration/recordings/vision/responses/d0ac68cbde69.json b/tests/integration/recordings/vision/responses/d0ac68cbde69.json deleted file mode 100644 index b37962fb6..000000000 --- a/tests/integration/recordings/vision/responses/d0ac68cbde69.json +++ /dev/null @@ -1,19 +0,0 @@ -{ - "request": { - "method": "POST", - "url": "http://localhost:11434/api/ps", - "headers": {}, - "body": {}, - "endpoint": "/api/ps", - "model": "" - }, - "response": { - "body": { - "__type__": "ollama._types.ProcessResponse", - "__data__": { - "models": [] - } - }, - "is_streaming": false - } -} diff --git a/tests/integration/recordings/vision/responses/d4f56d7d1996.json b/tests/integration/recordings/vision/responses/d4f56d7d1996.json deleted file mode 100644 index 47468b71e..000000000 --- a/tests/integration/recordings/vision/responses/d4f56d7d1996.json +++ /dev/null @@ -1,56 +0,0 @@ -{ - "request": { - "method": "POST", - "url": "http://localhost:11434/v1/v1/completions", - "headers": {}, - "body": { - "model": "llama3.2:3b-instruct-fp16", - "messages": [ - { - "role": "user", - "content": "Test trace openai 2" - } - ], - "stream": false - }, - "endpoint": "/v1/completions", - "model": "llama3.2:3b-instruct-fp16" - }, - "response": { - "body": { - "__type__": "openai.types.chat.chat_completion.ChatCompletion", - "__data__": { - "id": "chatcmpl-661", - "choices": [ - { - "finish_reason": "stop", - "index": 0, - "logprobs": null, - "message": { - "content": "You want to test the text-to-image capabilities of the OpenAI 2 model. To do this, we can use a simple interface in Python to prompt the model and see if it generates an image.\n\nHere's an example code snippet that shows how you can test the model:\n```\nimport numpy as np\nfrom PIL import Image\nfrom io import BytesIO\n\n# Load the OpenAI 2 model weights\nmodel_weights = \"path/to/openai2/model_weights.json\"\n\n# Load the model\nmodel = torch.hub.load(\"openai\", \"image-model\", pretrain_model_path=model_weights)\n\n# Set up a prompt for the model\nprompt = \"A picture of a futuristic cityscape at sunset\"\n\n# Use the model to generate an image\nwith torch.no_grad():\n image = model(prompt, return_tensor=True).numpy()\n\n# Save the generated image to a file\nimg = Image.fromarray(np.uint8(image))\nimg.save(\"generated_image.png\")\n\nprint(\"Generated image saved to 'generated_image.png'\")\n```\nPlease note that:\n\n1. You need to have PyTorch installed (`pip install torch torchvision`) and downloaded the OpenAI 2 model weights from their repository.\n2. The `image-model` library is used for text-to-image synthesis, which can be installed with `pip install image-model`.\n3. You may need to adjust the prompt and the output settings according to your specific use case.\n\nAlso note that, the openai2 model requires pre-trained on CelebA and FFHQ datasets and its text-to-image capabilities might not work as well as trained specifically for this type of task.\n\nYou can find more information about how to use the `image-model` library at their official documentation: https://github.com/karpathy/vis-dlg\n\nAlso, you can try other text-to-image models like DALL-E or Stable Diffusion using Python libraries like Hugging Face Transformers and PyTorch.", - "refusal": null, - "role": "assistant", - "annotations": null, - "audio": null, - "function_call": null, - "tool_calls": null - } - } - ], - "created": 1754003713, - "model": "llama3.2:3b-instruct-fp16", - "object": "chat.completion", - "service_tier": null, - "system_fingerprint": "fp_ollama", - "usage": { - "completion_tokens": 395, - "prompt_tokens": 31, - "total_tokens": 426, - "completion_tokens_details": null, - "prompt_tokens_details": null - } - } - }, - "is_streaming": false - } -} diff --git a/tests/integration/non_ci/responses/__init__.py b/tests/integration/responses/__init__.py similarity index 100% rename from tests/integration/non_ci/responses/__init__.py rename to tests/integration/responses/__init__.py diff --git a/tests/integration/non_ci/responses/fixtures/__init__.py b/tests/integration/responses/fixtures/__init__.py similarity index 100% rename from tests/integration/non_ci/responses/fixtures/__init__.py rename to tests/integration/responses/fixtures/__init__.py diff --git a/tests/integration/non_ci/responses/fixtures/fixtures.py b/tests/integration/responses/fixtures/fixtures.py similarity index 100% rename from tests/integration/non_ci/responses/fixtures/fixtures.py rename to tests/integration/responses/fixtures/fixtures.py diff --git a/tests/integration/non_ci/responses/fixtures/images/vision_test_1.jpg b/tests/integration/responses/fixtures/images/vision_test_1.jpg similarity index 100% rename from tests/integration/non_ci/responses/fixtures/images/vision_test_1.jpg rename to tests/integration/responses/fixtures/images/vision_test_1.jpg diff --git a/tests/integration/non_ci/responses/fixtures/images/vision_test_2.jpg b/tests/integration/responses/fixtures/images/vision_test_2.jpg similarity index 100% rename from tests/integration/non_ci/responses/fixtures/images/vision_test_2.jpg rename to tests/integration/responses/fixtures/images/vision_test_2.jpg diff --git a/tests/integration/non_ci/responses/fixtures/images/vision_test_3.jpg b/tests/integration/responses/fixtures/images/vision_test_3.jpg similarity index 100% rename from tests/integration/non_ci/responses/fixtures/images/vision_test_3.jpg rename to tests/integration/responses/fixtures/images/vision_test_3.jpg diff --git a/tests/integration/non_ci/responses/fixtures/pdfs/llama_stack_and_models.pdf b/tests/integration/responses/fixtures/pdfs/llama_stack_and_models.pdf similarity index 100% rename from tests/integration/non_ci/responses/fixtures/pdfs/llama_stack_and_models.pdf rename to tests/integration/responses/fixtures/pdfs/llama_stack_and_models.pdf diff --git a/tests/integration/non_ci/responses/fixtures/test_cases.py b/tests/integration/responses/fixtures/test_cases.py similarity index 100% rename from tests/integration/non_ci/responses/fixtures/test_cases.py rename to tests/integration/responses/fixtures/test_cases.py diff --git a/tests/integration/non_ci/responses/helpers.py b/tests/integration/responses/helpers.py similarity index 100% rename from tests/integration/non_ci/responses/helpers.py rename to tests/integration/responses/helpers.py diff --git a/tests/integration/non_ci/responses/streaming_assertions.py b/tests/integration/responses/streaming_assertions.py similarity index 100% rename from tests/integration/non_ci/responses/streaming_assertions.py rename to tests/integration/responses/streaming_assertions.py diff --git a/tests/integration/non_ci/responses/test_basic_responses.py b/tests/integration/responses/test_basic_responses.py similarity index 100% rename from tests/integration/non_ci/responses/test_basic_responses.py rename to tests/integration/responses/test_basic_responses.py diff --git a/tests/integration/non_ci/responses/test_file_search.py b/tests/integration/responses/test_file_search.py similarity index 100% rename from tests/integration/non_ci/responses/test_file_search.py rename to tests/integration/responses/test_file_search.py diff --git a/tests/integration/non_ci/responses/test_tool_responses.py b/tests/integration/responses/test_tool_responses.py similarity index 100% rename from tests/integration/non_ci/responses/test_tool_responses.py rename to tests/integration/responses/test_tool_responses.py diff --git a/tests/integration/scoring/test_scoring.py b/tests/integration/scoring/test_scoring.py index 315ff050c..1112f9164 100644 --- a/tests/integration/scoring/test_scoring.py +++ b/tests/integration/scoring/test_scoring.py @@ -9,6 +9,7 @@ from pathlib import Path import pandas as pd import pytest +import requests @pytest.fixture @@ -77,7 +78,46 @@ def test_scoring_functions_register( assert len(list_response) > 0 assert any(x.identifier == sample_scoring_fn_id for x in list_response) - # TODO: add unregister api for scoring functions + +def test_scoring_functions_unregister( + llama_stack_client, + sample_scoring_fn_id, + judge_model_id, + sample_judge_prompt_template, +): + llm_as_judge_provider = [ + x + for x in llama_stack_client.providers.list() + if x.api == "scoring" and x.provider_type == "inline::llm-as-judge" + ] + if len(llm_as_judge_provider) == 0: + pytest.skip("No llm-as-judge provider found, cannot test unregister") + + llm_as_judge_provider_id = llm_as_judge_provider[0].provider_id + + # Register first + register_scoring_function( + llama_stack_client, + llm_as_judge_provider_id, + sample_scoring_fn_id, + judge_model_id, + sample_judge_prompt_template, + ) + + # Ensure it is present + list_response = llama_stack_client.scoring_functions.list() + assert any(x.identifier == sample_scoring_fn_id for x in list_response) + + # Unregister scoring fn + try: + base_url = llama_stack_client.base_url + except AttributeError: + pytest.skip("No server base_url available; cannot test HTTP unregister in library mode") + + resp = requests.delete(f"{base_url}/v1/scoring-functions/{sample_scoring_fn_id}", timeout=30) + assert resp.status_code in (200, 204) + list_after = llama_stack_client.scoring_functions.list() + assert all(x.identifier != sample_scoring_fn_id for x in list_after) @pytest.mark.parametrize("scoring_fn_id", ["basic::equality"]) diff --git a/tests/integration/suites.py b/tests/integration/suites.py new file mode 100644 index 000000000..860b8c6ba --- /dev/null +++ b/tests/integration/suites.py @@ -0,0 +1,161 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the terms described in the LICENSE file in +# the root directory of this source tree. + +# Central definition of integration test suites. You can use these suites by passing --suite=name to pytest. +# For example: +# +# ```bash +# pytest tests/integration/ --suite=vision --setup=ollama +# ``` +# +""" +Each suite defines what to run (roots). Suites can be run with different global setups defined in setups.py. +Setups provide environment variables and model defaults that can be reused across multiple suites. + +CLI examples: + pytest tests/integration --suite=responses --setup=gpt + pytest tests/integration --suite=vision --setup=ollama + pytest tests/integration --suite=base --setup=vllm +""" + +from pathlib import Path + +from pydantic import BaseModel, Field + +this_dir = Path(__file__).parent + + +class Suite(BaseModel): + name: str + roots: list[str] + default_setup: str | None = None + + +class Setup(BaseModel): + """A reusable test configuration with environment and CLI defaults.""" + + name: str + description: str + defaults: dict[str, str] = Field(default_factory=dict) + env: dict[str, str] = Field(default_factory=dict) + + +# Global setups - can be used with any suite "technically" but in reality, some setups might work +# only for specific test suites. +SETUP_DEFINITIONS: dict[str, Setup] = { + "ollama": Setup( + name="ollama", + description="Local Ollama provider with text + safety models", + env={ + "OLLAMA_URL": "http://0.0.0.0:11434", + "SAFETY_MODEL": "ollama/llama-guard3:1b", + }, + defaults={ + "text_model": "ollama/llama3.2:3b-instruct-fp16", + "embedding_model": "ollama/all-minilm:l6-v2", + "safety_model": "ollama/llama-guard3:1b", + "safety_shield": "llama-guard", + }, + ), + "ollama-vision": Setup( + name="ollama", + description="Local Ollama provider with a vision model", + env={ + "OLLAMA_URL": "http://0.0.0.0:11434", + }, + defaults={ + "vision_model": "ollama/llama3.2-vision:11b", + "embedding_model": "ollama/all-minilm:l6-v2", + }, + ), + "vllm": Setup( + name="vllm", + description="vLLM provider with a text model", + env={ + "VLLM_URL": "http://localhost:8000/v1", + }, + defaults={ + "text_model": "vllm/meta-llama/Llama-3.2-1B-Instruct", + "embedding_model": "sentence-transformers/all-MiniLM-L6-v2", + }, + ), + "gpt": Setup( + name="gpt", + description="OpenAI GPT models for high-quality responses and tool calling", + defaults={ + "text_model": "openai/gpt-4o", + "embedding_model": "openai/text-embedding-3-small", + }, + ), + "tgi": Setup( + name="tgi", + description="Text Generation Inference (TGI) provider with a text model", + env={ + "TGI_URL": "http://localhost:8080", + }, + defaults={ + "text_model": "tgi/Qwen/Qwen3-0.6B", + }, + ), + "together": Setup( + name="together", + description="Together computer models", + defaults={ + "text_model": "together/meta-llama/Llama-3.3-70B-Instruct-Turbo-Free", + "embedding_model": "together/togethercomputer/m2-bert-80M-32k-retrieval", + }, + ), + "cerebras": Setup( + name="cerebras", + description="Cerebras models", + defaults={ + "text_model": "cerebras/llama-3.3-70b", + }, + ), + "databricks": Setup( + name="databricks", + description="Databricks models", + defaults={ + "text_model": "databricks/databricks-meta-llama-3-3-70b-instruct", + "embedding_model": "databricks/databricks-bge-large-en", + }, + ), + "fireworks": Setup( + name="fireworks", + description="Fireworks provider with a text model", + defaults={ + "text_model": "accounts/fireworks/models/llama-v3p1-8b-instruct", + "vision_model": "accounts/fireworks/models/llama-v3p2-90b-vision-instruct", + "embedding_model": "nomic-ai/nomic-embed-text-v1.5", + }, + ), +} + + +base_roots = [ + str(p) + for p in this_dir.glob("*") + if p.is_dir() + and p.name not in ("__pycache__", "fixtures", "test_cases", "recordings", "responses", "post_training") +] + +SUITE_DEFINITIONS: dict[str, Suite] = { + "base": Suite( + name="base", + roots=base_roots, + default_setup="ollama", + ), + "responses": Suite( + name="responses", + roots=["tests/integration/responses"], + default_setup="gpt", + ), + "vision": Suite( + name="vision", + roots=["tests/integration/inference/test_vision_inference.py"], + default_setup="ollama-vision", + ), +} diff --git a/tests/integration/telemetry/test_openai_telemetry.py b/tests/integration/telemetry/test_openai_telemetry.py index cdd9b6702..b3ffb6b09 100644 --- a/tests/integration/telemetry/test_openai_telemetry.py +++ b/tests/integration/telemetry/test_openai_telemetry.py @@ -49,16 +49,13 @@ def setup_openai_telemetry_data(llama_stack_client, text_model_id): traces = llama_stack_client.telemetry.query_traces(limit=10) if len(traces) >= 5: # 5 OpenAI completion traces break - time.sleep(1) + time.sleep(0.1) if len(traces) < 5: pytest.fail( f"Failed to create sufficient OpenAI completion telemetry data after 30s. Got {len(traces)} traces." ) - # Wait for 5 seconds to ensure traces has completed logging - time.sleep(5) - yield @@ -185,11 +182,13 @@ def test_openai_completion_creates_telemetry(llama_stack_client, text_model_id): assert len(response.choices) > 0, "Response should have at least one choice" # Wait for telemetry to be recorded - time.sleep(3) - - # Check that we have more traces now - final_traces = llama_stack_client.telemetry.query_traces(limit=20) - final_count = len(final_traces) + start_time = time.time() + while time.time() - start_time < 30: + final_traces = llama_stack_client.telemetry.query_traces(limit=20) + final_count = len(final_traces) + if final_count > initial_count: + break + time.sleep(0.1) # Should have at least as many traces as before (might have more due to other activity) assert final_count >= initial_count, "Should have at least as many traces after OpenAI call" diff --git a/tests/integration/telemetry/test_telemetry.py b/tests/integration/telemetry/test_telemetry.py index d363edbc0..e86da954e 100644 --- a/tests/integration/telemetry/test_telemetry.py +++ b/tests/integration/telemetry/test_telemetry.py @@ -42,14 +42,11 @@ def setup_telemetry_data(llama_stack_client, text_model_id): traces = llama_stack_client.telemetry.query_traces(limit=10) if len(traces) >= 4: break - time.sleep(1) + time.sleep(0.1) if len(traces) < 4: pytest.fail(f"Failed to create sufficient telemetry data after 30s. Got {len(traces)} traces.") - # Wait for 5 seconds to ensure traces has completed logging - time.sleep(5) - yield diff --git a/tests/integration/telemetry/test_telemetry_metrics.py b/tests/integration/telemetry/test_telemetry_metrics.py index 4ba2bd2d9..1d8312ae2 100644 --- a/tests/integration/telemetry/test_telemetry_metrics.py +++ b/tests/integration/telemetry/test_telemetry_metrics.py @@ -46,10 +46,7 @@ def setup_telemetry_metrics_data(openai_client, client_with_models, text_model_i break except Exception: pass - time.sleep(1) - - # Wait additional time to ensure all metrics are processed - time.sleep(5) + time.sleep(0.1) # Return the token lists for use in tests return {"prompt_tokens": prompt_tokens, "completion_tokens": completion_tokens, "total_tokens": total_tokens} diff --git a/tests/integration/tool_runtime/test_rag_tool.py b/tests/integration/tool_runtime/test_rag_tool.py index 2affe2a2d..b78c39af8 100644 --- a/tests/integration/tool_runtime/test_rag_tool.py +++ b/tests/integration/tool_runtime/test_rag_tool.py @@ -17,10 +17,14 @@ def client_with_empty_registry(client_with_models): client_with_models.vector_dbs.unregister(vector_db_id=vector_db_id) clear_registry() + + try: + client_with_models.toolgroups.register(toolgroup_id="builtin::rag", provider_id="rag-runtime") + except Exception: + pass + yield client_with_models - # you must clean after the last test if you were running tests against - # a stateful server instance clear_registry() @@ -66,12 +70,13 @@ def assert_valid_text_response(response): def test_vector_db_insert_inline_and_query( client_with_empty_registry, sample_documents, embedding_model_id, embedding_dimension ): - vector_db_id = "test_vector_db" - client_with_empty_registry.vector_dbs.register( - vector_db_id=vector_db_id, + vector_db_name = "test_vector_db" + vector_db = client_with_empty_registry.vector_dbs.register( + vector_db_id=vector_db_name, embedding_model=embedding_model_id, embedding_dimension=embedding_dimension, ) + vector_db_id = vector_db.identifier client_with_empty_registry.tool_runtime.rag_tool.insert( documents=sample_documents, @@ -134,7 +139,11 @@ def test_vector_db_insert_from_url_and_query( # list to check memory bank is successfully registered available_vector_dbs = [vector_db.identifier for vector_db in client_with_empty_registry.vector_dbs.list()] - assert vector_db_id in available_vector_dbs + # VectorDB is being migrated to VectorStore, so the ID will be different + # Just check that at least one vector DB was registered + assert len(available_vector_dbs) > 0 + # Use the actual registered vector_db_id for subsequent operations + actual_vector_db_id = available_vector_dbs[0] urls = [ "memory_optimizations.rst", @@ -153,13 +162,13 @@ def test_vector_db_insert_from_url_and_query( client_with_empty_registry.tool_runtime.rag_tool.insert( documents=documents, - vector_db_id=vector_db_id, + vector_db_id=actual_vector_db_id, chunk_size_in_tokens=512, ) # Query for the name of method response1 = client_with_empty_registry.vector_io.query( - vector_db_id=vector_db_id, + vector_db_id=actual_vector_db_id, query="What's the name of the fine-tunning method used?", ) assert_valid_chunk_response(response1) @@ -167,13 +176,117 @@ def test_vector_db_insert_from_url_and_query( # Query for the name of model response2 = client_with_empty_registry.vector_io.query( - vector_db_id=vector_db_id, + vector_db_id=actual_vector_db_id, query="Which Llama model is mentioned?", ) assert_valid_chunk_response(response2) assert any("llama2" in chunk.content.lower() for chunk in response2.chunks) +def test_rag_tool_openai_apis(client_with_empty_registry, embedding_model_id, embedding_dimension): + vector_db_id = "test_openai_vector_db" + + client_with_empty_registry.vector_dbs.register( + vector_db_id=vector_db_id, + embedding_model=embedding_model_id, + embedding_dimension=embedding_dimension, + ) + + available_vector_dbs = [vector_db.identifier for vector_db in client_with_empty_registry.vector_dbs.list()] + actual_vector_db_id = available_vector_dbs[0] + + # different document formats that should work with OpenAI APIs + documents = [ + Document( + document_id="text-doc", + content="This is a plain text document about machine learning algorithms.", + metadata={"type": "text", "category": "AI"}, + ), + Document( + document_id="url-doc", + content="https://raw.githubusercontent.com/pytorch/torchtune/main/docs/source/tutorials/chat.rst", + mime_type="text/plain", + metadata={"type": "url", "source": "pytorch"}, + ), + Document( + document_id="data-url-doc", + content="data:text/plain;base64,VGhpcyBpcyBhIGRhdGEgVVJMIGRvY3VtZW50IGFib3V0IGRlZXAgbGVhcm5pbmcu", # "This is a data URL document about deep learning." + metadata={"type": "data_url", "encoding": "base64"}, + ), + ] + + client_with_empty_registry.tool_runtime.rag_tool.insert( + documents=documents, + vector_db_id=actual_vector_db_id, + chunk_size_in_tokens=256, + ) + + files_list = client_with_empty_registry.files.list() + assert len(files_list.data) >= len(documents), ( + f"Expected at least {len(documents)} files, got {len(files_list.data)}" + ) + + vector_store_files = client_with_empty_registry.vector_io.openai_list_files_in_vector_store( + vector_store_id=actual_vector_db_id + ) + assert len(vector_store_files.data) >= len(documents), f"Expected at least {len(documents)} files in vector store" + + response = client_with_empty_registry.tool_runtime.rag_tool.query( + vector_db_ids=[actual_vector_db_id], + content="Tell me about machine learning and deep learning", + ) + + assert_valid_text_response(response) + content_text = " ".join([chunk.text for chunk in response.content]).lower() + assert "machine learning" in content_text or "deep learning" in content_text + + +def test_rag_tool_exception_handling(client_with_empty_registry, embedding_model_id, embedding_dimension): + vector_db_id = "test_exception_handling" + + client_with_empty_registry.vector_dbs.register( + vector_db_id=vector_db_id, + embedding_model=embedding_model_id, + embedding_dimension=embedding_dimension, + ) + + available_vector_dbs = [vector_db.identifier for vector_db in client_with_empty_registry.vector_dbs.list()] + actual_vector_db_id = available_vector_dbs[0] + + documents = [ + Document( + document_id="valid-doc", + content="This is a valid document that should be processed successfully.", + metadata={"status": "valid"}, + ), + Document( + document_id="invalid-url-doc", + content="https://nonexistent-domain-12345.com/invalid.txt", + metadata={"status": "invalid_url"}, + ), + Document( + document_id="another-valid-doc", + content="This is another valid document for testing resilience.", + metadata={"status": "valid"}, + ), + ] + + client_with_empty_registry.tool_runtime.rag_tool.insert( + documents=documents, + vector_db_id=actual_vector_db_id, + chunk_size_in_tokens=256, + ) + + response = client_with_empty_registry.tool_runtime.rag_tool.query( + vector_db_ids=[actual_vector_db_id], + content="valid document", + ) + + assert_valid_text_response(response) + content_text = " ".join([chunk.text for chunk in response.content]).lower() + assert "valid document" in content_text + + def test_rag_tool_insert_and_query(client_with_empty_registry, embedding_model_id, embedding_dimension): providers = [p for p in client_with_empty_registry.providers.list() if p.api == "vector_io"] assert len(providers) > 0 @@ -187,7 +300,11 @@ def test_rag_tool_insert_and_query(client_with_empty_registry, embedding_model_i ) available_vector_dbs = [vector_db.identifier for vector_db in client_with_empty_registry.vector_dbs.list()] - assert vector_db_id in available_vector_dbs + # VectorDB is being migrated to VectorStore, so the ID will be different + # Just check that at least one vector DB was registered + assert len(available_vector_dbs) > 0 + # Use the actual registered vector_db_id for subsequent operations + actual_vector_db_id = available_vector_dbs[0] urls = [ "memory_optimizations.rst", @@ -206,19 +323,19 @@ def test_rag_tool_insert_and_query(client_with_empty_registry, embedding_model_i client_with_empty_registry.tool_runtime.rag_tool.insert( documents=documents, - vector_db_id=vector_db_id, + vector_db_id=actual_vector_db_id, chunk_size_in_tokens=512, ) response_with_metadata = client_with_empty_registry.tool_runtime.rag_tool.query( - vector_db_ids=[vector_db_id], + vector_db_ids=[actual_vector_db_id], content="What is the name of the method used for fine-tuning?", ) assert_valid_text_response(response_with_metadata) assert any("metadata:" in chunk.text.lower() for chunk in response_with_metadata.content) response_without_metadata = client_with_empty_registry.tool_runtime.rag_tool.query( - vector_db_ids=[vector_db_id], + vector_db_ids=[actual_vector_db_id], content="What is the name of the method used for fine-tuning?", query_config={ "include_metadata_in_content": True, @@ -230,9 +347,113 @@ def test_rag_tool_insert_and_query(client_with_empty_registry, embedding_model_i with pytest.raises((ValueError, BadRequestError)): client_with_empty_registry.tool_runtime.rag_tool.query( - vector_db_ids=[vector_db_id], + vector_db_ids=[actual_vector_db_id], content="What is the name of the method used for fine-tuning?", query_config={ "chunk_template": "This should raise a ValueError because it is missing the proper template variables", }, ) + + +def test_rag_tool_query_generation(client_with_empty_registry, embedding_model_id, embedding_dimension): + vector_db_id = "test_query_generation_db" + + client_with_empty_registry.vector_dbs.register( + vector_db_id=vector_db_id, + embedding_model=embedding_model_id, + embedding_dimension=embedding_dimension, + ) + + available_vector_dbs = [vector_db.identifier for vector_db in client_with_empty_registry.vector_dbs.list()] + actual_vector_db_id = available_vector_dbs[0] + + documents = [ + Document( + document_id="ai-doc", + content="Artificial intelligence and machine learning are transforming technology.", + metadata={"category": "AI"}, + ), + Document( + document_id="banana-doc", + content="Don't bring a banana to a knife fight.", + metadata={"category": "wisdom"}, + ), + ] + + client_with_empty_registry.tool_runtime.rag_tool.insert( + documents=documents, + vector_db_id=actual_vector_db_id, + chunk_size_in_tokens=256, + ) + + response = client_with_empty_registry.tool_runtime.rag_tool.query( + vector_db_ids=[actual_vector_db_id], + content="Tell me about AI", + ) + + assert_valid_text_response(response) + content_text = " ".join([chunk.text for chunk in response.content]).lower() + assert "artificial intelligence" in content_text or "machine learning" in content_text + + +def test_rag_tool_pdf_data_url_handling(client_with_empty_registry, embedding_model_id, embedding_dimension): + vector_db_id = "test_pdf_data_url_db" + + client_with_empty_registry.vector_dbs.register( + vector_db_id=vector_db_id, + embedding_model=embedding_model_id, + embedding_dimension=embedding_dimension, + ) + + available_vector_dbs = [vector_db.identifier for vector_db in client_with_empty_registry.vector_dbs.list()] + actual_vector_db_id = available_vector_dbs[0] + + sample_pdf = b"%PDF-1.3\n3 0 obj\n<
>\nendobj\n4 0 obj\n<>\nstream\nx\x9c\x15\xcc1\x0e\x820\x18@\xe1\x9dS\xbcM]jk$\xd5\xd5(\x83!\x86\xa1\x17\xf8\xa3\xa5`LIh+\xd7W\xc6\xf7\r\xef\xc0\xbd\xd2\xaa\xb6,\xd5\xc5\xb1o\x0c\xa6VZ\xe3znn%\xf3o\xab\xb1\xe7\xa3:Y\xdc\x8bm\xeb\xf3&1\xc8\xd7\xd3\x97\xc82\xe6\x81\x87\xe42\xcb\x87Vb(\x12<\xdd<=}Jc\x0cL\x91\xee\xda$\xb5\xc3\xbd\xd7\xe9\x0f\x8d\x97 $\nendstream\nendobj\n1 0 obj\n<
>\nendobj\n5 0 obj\n<>\nendobj\n2 0 obj\n<<\n/ProcSet [/PDF /Text /ImageB /ImageC /ImageI]\n/Font <<\n/F1 5 0 R\n>>\n/XObject <<\n>>\n>>\nendobj\n6 0 obj\n<<\n/Producer (PyFPDF 1.7.2 http://pyfpdf.googlecode.com/)\n/Title (This is a sample title.)\n/Author (Llama Stack Developers)\n/CreationDate (D:20250312165548)\n>>\nendobj\n7 0 obj\n<<\n/Type /Catalog\n/Pages 1 0 R\n/OpenAction [3 0 R /FitH null]\n/PageLayout /OneColumn\n>>\nendobj\nxref\n0 8\n0000000000 65535 f \n0000000272 00000 n \n0000000455 00000 n \n0000000009 00000 n \n0000000087 00000 n \n0000000359 00000 n \n0000000559 00000 n \n0000000734 00000 n \ntrailer\n<<\n/Size 8\n/Root 7 0 R\n/Info 6 0 R\n>>\nstartxref\n837\n%%EOF\n" + + import base64 + + pdf_base64 = base64.b64encode(sample_pdf).decode("utf-8") + pdf_data_url = f"data:application/pdf;base64,{pdf_base64}" + + documents = [ + Document( + document_id="test-pdf-data-url", + content=pdf_data_url, + metadata={"type": "pdf", "source": "data_url"}, + ), + ] + + client_with_empty_registry.tool_runtime.rag_tool.insert( + documents=documents, + vector_db_id=actual_vector_db_id, + chunk_size_in_tokens=256, + ) + + files_list = client_with_empty_registry.files.list() + assert len(files_list.data) >= 1, "PDF should have been uploaded to Files API" + + pdf_file = None + for file in files_list.data: + if file.filename and "test-pdf-data-url" in file.filename: + pdf_file = file + break + + assert pdf_file is not None, "PDF file should be found in Files API" + assert pdf_file.bytes == len(sample_pdf), f"File size should match original PDF ({len(sample_pdf)} bytes)" + + file_content = client_with_empty_registry.files.retrieve_content(pdf_file.id) + assert file_content.startswith(b"%PDF-"), "Retrieved file should be a valid PDF" + + vector_store_files = client_with_empty_registry.vector_io.openai_list_files_in_vector_store( + vector_store_id=actual_vector_db_id + ) + assert len(vector_store_files.data) >= 1, "PDF should be attached to vector store" + + response = client_with_empty_registry.tool_runtime.rag_tool.query( + vector_db_ids=[actual_vector_db_id], + content="sample title", + ) + + assert_valid_text_response(response) + content_text = " ".join([chunk.text for chunk in response.content]).lower() + assert "sample title" in content_text or "title" in content_text diff --git a/tests/integration/vector_io/test_openai_vector_stores.py b/tests/integration/vector_io/test_openai_vector_stores.py index 82868164f..c67036eab 100644 --- a/tests/integration/vector_io/test_openai_vector_stores.py +++ b/tests/integration/vector_io/test_openai_vector_stores.py @@ -57,11 +57,13 @@ def skip_if_provider_doesnt_support_openai_vector_stores_search(client_with_mode "inline::sqlite-vec", "remote::milvus", "inline::milvus", + "remote::pgvector", ], "hybrid": [ "inline::sqlite-vec", "inline::milvus", "remote::milvus", + "remote::pgvector", ], } supported_providers = search_mode_support.get(search_mode, []) diff --git a/tests/integration/vector_io/test_vector_io.py b/tests/integration/vector_io/test_vector_io.py index 07faa0db1..979eff6bb 100644 --- a/tests/integration/vector_io/test_vector_io.py +++ b/tests/integration/vector_io/test_vector_io.py @@ -47,34 +47,45 @@ def client_with_empty_registry(client_with_models): def test_vector_db_retrieve(client_with_empty_registry, embedding_model_id, embedding_dimension): - # Register a memory bank first - vector_db_id = "test_vector_db" - client_with_empty_registry.vector_dbs.register( - vector_db_id=vector_db_id, + vector_db_name = "test_vector_db" + register_response = client_with_empty_registry.vector_dbs.register( + vector_db_id=vector_db_name, embedding_model=embedding_model_id, embedding_dimension=embedding_dimension, ) + actual_vector_db_id = register_response.identifier + # Retrieve the memory bank and validate its properties - response = client_with_empty_registry.vector_dbs.retrieve(vector_db_id=vector_db_id) + response = client_with_empty_registry.vector_dbs.retrieve(vector_db_id=actual_vector_db_id) assert response is not None - assert response.identifier == vector_db_id + assert response.identifier == actual_vector_db_id assert response.embedding_model == embedding_model_id - assert response.provider_resource_id == vector_db_id + assert response.identifier.startswith("vs_") def test_vector_db_register(client_with_empty_registry, embedding_model_id, embedding_dimension): - vector_db_id = "test_vector_db" - client_with_empty_registry.vector_dbs.register( - vector_db_id=vector_db_id, + vector_db_name = "test_vector_db" + response = client_with_empty_registry.vector_dbs.register( + vector_db_id=vector_db_name, embedding_model=embedding_model_id, embedding_dimension=embedding_dimension, ) - vector_dbs_after_register = [vector_db.identifier for vector_db in client_with_empty_registry.vector_dbs.list()] - assert vector_dbs_after_register == [vector_db_id] + actual_vector_db_id = response.identifier + assert actual_vector_db_id.startswith("vs_") + assert actual_vector_db_id != vector_db_name - client_with_empty_registry.vector_dbs.unregister(vector_db_id=vector_db_id) + vector_dbs_after_register = [vector_db.identifier for vector_db in client_with_empty_registry.vector_dbs.list()] + assert vector_dbs_after_register == [actual_vector_db_id] + + vector_stores = client_with_empty_registry.vector_stores.list() + assert len(vector_stores.data) == 1 + vector_store = vector_stores.data[0] + assert vector_store.id == actual_vector_db_id + assert vector_store.name == vector_db_name + + client_with_empty_registry.vector_dbs.unregister(vector_db_id=actual_vector_db_id) vector_dbs = [vector_db.identifier for vector_db in client_with_empty_registry.vector_dbs.list()] assert len(vector_dbs) == 0 @@ -91,20 +102,22 @@ def test_vector_db_register(client_with_empty_registry, embedding_model_id, embe ], ) def test_insert_chunks(client_with_empty_registry, embedding_model_id, embedding_dimension, sample_chunks, test_case): - vector_db_id = "test_vector_db" - client_with_empty_registry.vector_dbs.register( - vector_db_id=vector_db_id, + vector_db_name = "test_vector_db" + register_response = client_with_empty_registry.vector_dbs.register( + vector_db_id=vector_db_name, embedding_model=embedding_model_id, embedding_dimension=embedding_dimension, ) + actual_vector_db_id = register_response.identifier + client_with_empty_registry.vector_io.insert( - vector_db_id=vector_db_id, + vector_db_id=actual_vector_db_id, chunks=sample_chunks, ) response = client_with_empty_registry.vector_io.query( - vector_db_id=vector_db_id, + vector_db_id=actual_vector_db_id, query="What is the capital of France?", ) assert response is not None @@ -113,7 +126,7 @@ def test_insert_chunks(client_with_empty_registry, embedding_model_id, embedding query, expected_doc_id = test_case response = client_with_empty_registry.vector_io.query( - vector_db_id=vector_db_id, + vector_db_id=actual_vector_db_id, query=query, ) assert response is not None @@ -128,13 +141,15 @@ def test_insert_chunks_with_precomputed_embeddings(client_with_empty_registry, e "remote::qdrant": {"score_threshold": -1.0}, "inline::qdrant": {"score_threshold": -1.0}, } - vector_db_id = "test_precomputed_embeddings_db" - client_with_empty_registry.vector_dbs.register( - vector_db_id=vector_db_id, + vector_db_name = "test_precomputed_embeddings_db" + register_response = client_with_empty_registry.vector_dbs.register( + vector_db_id=vector_db_name, embedding_model=embedding_model_id, embedding_dimension=embedding_dimension, ) + actual_vector_db_id = register_response.identifier + chunks_with_embeddings = [ Chunk( content="This is a test chunk with precomputed embedding.", @@ -144,13 +159,13 @@ def test_insert_chunks_with_precomputed_embeddings(client_with_empty_registry, e ] client_with_empty_registry.vector_io.insert( - vector_db_id=vector_db_id, + vector_db_id=actual_vector_db_id, chunks=chunks_with_embeddings, ) provider = [p.provider_id for p in client_with_empty_registry.providers.list() if p.api == "vector_io"][0] response = client_with_empty_registry.vector_io.query( - vector_db_id=vector_db_id, + vector_db_id=actual_vector_db_id, query="precomputed embedding test", params=vector_io_provider_params_dict.get(provider, None), ) @@ -173,13 +188,15 @@ def test_query_returns_valid_object_when_identical_to_embedding_in_vdb( "remote::qdrant": {"score_threshold": 0.0}, "inline::qdrant": {"score_threshold": 0.0}, } - vector_db_id = "test_precomputed_embeddings_db" - client_with_empty_registry.vector_dbs.register( - vector_db_id=vector_db_id, + vector_db_name = "test_precomputed_embeddings_db" + register_response = client_with_empty_registry.vector_dbs.register( + vector_db_id=vector_db_name, embedding_model=embedding_model_id, embedding_dimension=embedding_dimension, ) + actual_vector_db_id = register_response.identifier + chunks_with_embeddings = [ Chunk( content="duplicate", @@ -189,13 +206,13 @@ def test_query_returns_valid_object_when_identical_to_embedding_in_vdb( ] client_with_empty_registry.vector_io.insert( - vector_db_id=vector_db_id, + vector_db_id=actual_vector_db_id, chunks=chunks_with_embeddings, ) provider = [p.provider_id for p in client_with_empty_registry.providers.list() if p.api == "vector_io"][0] response = client_with_empty_registry.vector_io.query( - vector_db_id=vector_db_id, + vector_db_id=actual_vector_db_id, query="duplicate", params=vector_io_provider_params_dict.get(provider, None), ) diff --git a/tests/unit/distribution/routers/test_routing_tables.py b/tests/unit/distribution/routers/test_routing_tables.py index 2652f5c8d..bbfea3f46 100644 --- a/tests/unit/distribution/routers/test_routing_tables.py +++ b/tests/unit/distribution/routers/test_routing_tables.py @@ -105,6 +105,9 @@ class ScoringFunctionsImpl(Impl): async def register_scoring_function(self, scoring_fn): return scoring_fn + async def unregister_scoring_function(self, scoring_fn_id: str): + return scoring_fn_id + class BenchmarksImpl(Impl): def __init__(self): @@ -113,6 +116,9 @@ class BenchmarksImpl(Impl): async def register_benchmark(self, benchmark): return benchmark + async def unregister_benchmark(self, benchmark_id: str): + return benchmark_id + class ToolGroupsImpl(Impl): def __init__(self): @@ -146,6 +152,20 @@ class VectorDBImpl(Impl): async def unregister_vector_db(self, vector_db_id: str): return vector_db_id + async def openai_create_vector_store(self, **kwargs): + import time + import uuid + + from llama_stack.apis.vector_io.vector_io import VectorStoreFileCounts, VectorStoreObject + + vector_store_id = kwargs.get("provider_vector_db_id") or f"vs_{uuid.uuid4()}" + return VectorStoreObject( + id=vector_store_id, + name=kwargs.get("name", vector_store_id), + created_at=int(time.time()), + file_counts=VectorStoreFileCounts(completed=0, cancelled=0, failed=0, in_progress=0, total=0), + ) + async def test_models_routing_table(cached_disk_dist_registry): table = ModelsRoutingTable({"test_provider": InferenceImpl()}, cached_disk_dist_registry, {}) @@ -247,17 +267,21 @@ async def test_vectordbs_routing_table(cached_disk_dist_registry): ) # Register multiple vector databases and verify listing - await table.register_vector_db(vector_db_id="test-vectordb", embedding_model="test_provider/test-model") - await table.register_vector_db(vector_db_id="test-vectordb-2", embedding_model="test_provider/test-model") + vdb1 = await table.register_vector_db(vector_db_id="test-vectordb", embedding_model="test_provider/test-model") + vdb2 = await table.register_vector_db(vector_db_id="test-vectordb-2", embedding_model="test_provider/test-model") vector_dbs = await table.list_vector_dbs() assert len(vector_dbs.data) == 2 vector_db_ids = {v.identifier for v in vector_dbs.data} - assert "test-vectordb" in vector_db_ids - assert "test-vectordb-2" in vector_db_ids + assert vdb1.identifier in vector_db_ids + assert vdb2.identifier in vector_db_ids - await table.unregister_vector_db(vector_db_id="test-vectordb") - await table.unregister_vector_db(vector_db_id="test-vectordb-2") + # Verify they have UUID-based identifiers + assert vdb1.identifier.startswith("vs_") + assert vdb2.identifier.startswith("vs_") + + await table.unregister_vector_db(vector_db_id=vdb1.identifier) + await table.unregister_vector_db(vector_db_id=vdb2.identifier) vector_dbs = await table.list_vector_dbs() assert len(vector_dbs.data) == 0 @@ -312,6 +336,13 @@ async def test_scoring_functions_routing_table(cached_disk_dist_registry): assert "test-scoring-fn" in scoring_fn_ids assert "test-scoring-fn-2" in scoring_fn_ids + # Unregister scoring functions and verify listing + for i in range(len(scoring_functions.data)): + await table.unregister_scoring_function(scoring_functions.data[i].scoring_fn_id) + + scoring_functions_list_after_deletion = await table.list_scoring_functions() + assert len(scoring_functions_list_after_deletion.data) == 0 + async def test_benchmarks_routing_table(cached_disk_dist_registry): table = BenchmarksRoutingTable({"test_provider": BenchmarksImpl()}, cached_disk_dist_registry, {}) @@ -329,6 +360,15 @@ async def test_benchmarks_routing_table(cached_disk_dist_registry): benchmark_ids = {b.identifier for b in benchmarks.data} assert "test-benchmark" in benchmark_ids + # Unregister the benchmark and verify removal + await table.unregister_benchmark(benchmark_id="test-benchmark") + benchmarks_after = await table.list_benchmarks() + assert len(benchmarks_after.data) == 0 + + # Unregistering a non-existent benchmark should raise a clear error + with pytest.raises(ValueError, match="Benchmark 'dummy_benchmark' not found"): + await table.unregister_benchmark(benchmark_id="dummy_benchmark") + async def test_tool_groups_routing_table(cached_disk_dist_registry): table = ToolGroupsRoutingTable({"test_provider": ToolGroupsImpl()}, cached_disk_dist_registry, {}) diff --git a/tests/unit/distribution/routing_tables/test_vector_dbs.py b/tests/unit/distribution/routing_tables/test_vector_dbs.py index 789eda433..3444f64c2 100644 --- a/tests/unit/distribution/routing_tables/test_vector_dbs.py +++ b/tests/unit/distribution/routing_tables/test_vector_dbs.py @@ -7,6 +7,7 @@ # Unit tests for the routing tables vector_dbs import time +import uuid from unittest.mock import AsyncMock import pytest @@ -34,6 +35,7 @@ from tests.unit.distribution.routers.test_routing_tables import Impl, InferenceI class VectorDBImpl(Impl): def __init__(self): super().__init__(Api.vector_io) + self.vector_stores = {} async def register_vector_db(self, vector_db: VectorDB): return vector_db @@ -114,8 +116,35 @@ class VectorDBImpl(Impl): async def openai_delete_vector_store_file(self, vector_store_id, file_id): return VectorStoreFileDeleteResponse(id=file_id, deleted=True) + async def openai_create_vector_store( + self, + name=None, + embedding_model=None, + embedding_dimension=None, + provider_id=None, + provider_vector_db_id=None, + **kwargs, + ): + vector_store_id = provider_vector_db_id or f"vs_{uuid.uuid4()}" + vector_store = VectorStoreObject( + id=vector_store_id, + name=name or vector_store_id, + created_at=int(time.time()), + file_counts=VectorStoreFileCounts(completed=0, cancelled=0, failed=0, in_progress=0, total=0), + ) + self.vector_stores[vector_store_id] = vector_store + return vector_store + + async def openai_list_vector_stores(self, **kwargs): + from llama_stack.apis.vector_io.vector_io import VectorStoreListResponse + + return VectorStoreListResponse( + data=list(self.vector_stores.values()), has_more=False, first_id=None, last_id=None + ) + async def test_vectordbs_routing_table(cached_disk_dist_registry): + n = 10 table = VectorDBsRoutingTable({"test_provider": VectorDBImpl()}, cached_disk_dist_registry, {}) await table.initialize() @@ -129,22 +158,98 @@ async def test_vectordbs_routing_table(cached_disk_dist_registry): ) # Register multiple vector databases and verify listing - await table.register_vector_db(vector_db_id="test-vectordb", embedding_model="test-model") - await table.register_vector_db(vector_db_id="test-vectordb-2", embedding_model="test-model") + vdb_dict = {} + for i in range(n): + vdb_dict[i] = await table.register_vector_db(vector_db_id=f"test-vectordb-{i}", embedding_model="test-model") + vector_dbs = await table.list_vector_dbs() - assert len(vector_dbs.data) == 2 + assert len(vector_dbs.data) == len(vdb_dict) vector_db_ids = {v.identifier for v in vector_dbs.data} - assert "test-vectordb" in vector_db_ids - assert "test-vectordb-2" in vector_db_ids - - await table.unregister_vector_db(vector_db_id="test-vectordb") - await table.unregister_vector_db(vector_db_id="test-vectordb-2") + for k in vdb_dict: + assert vdb_dict[k].identifier in vector_db_ids + for k in vdb_dict: + await table.unregister_vector_db(vector_db_id=vdb_dict[k].identifier) vector_dbs = await table.list_vector_dbs() assert len(vector_dbs.data) == 0 +async def test_vector_db_and_vector_store_id_mapping(cached_disk_dist_registry): + n = 10 + impl = VectorDBImpl() + table = VectorDBsRoutingTable({"test_provider": impl}, cached_disk_dist_registry, {}) + await table.initialize() + + m_table = ModelsRoutingTable({"test_provider": InferenceImpl()}, cached_disk_dist_registry, {}) + await m_table.initialize() + await m_table.register_model( + model_id="test-model", + provider_id="test_provider", + metadata={"embedding_dimension": 128}, + model_type=ModelType.embedding, + ) + + vdb_dict = {} + for i in range(n): + vdb_dict[i] = await table.register_vector_db(vector_db_id=f"test-vectordb-{i}", embedding_model="test-model") + + vector_dbs = await table.list_vector_dbs() + vector_db_ids = {v.identifier for v in vector_dbs.data} + + vector_stores = await impl.openai_list_vector_stores() + vector_store_ids = {v.id for v in vector_stores.data} + + assert vector_db_ids == vector_store_ids, ( + f"Vector DB IDs {vector_db_ids} don't match vector store IDs {vector_store_ids}" + ) + + for vector_store in vector_stores.data: + vector_db = await table.get_vector_db(vector_store.id) + assert vector_store.name == vector_db.vector_db_name, ( + f"Vector store name {vector_store.name} doesn't match vector store ID {vector_store.id}" + ) + + for vector_db_id in vector_db_ids: + await table.unregister_vector_db(vector_db_id) + + assert len((await table.list_vector_dbs()).data) == 0 + + +async def test_vector_db_id_becomes_vector_store_name(cached_disk_dist_registry): + impl = VectorDBImpl() + table = VectorDBsRoutingTable({"test_provider": impl}, cached_disk_dist_registry, {}) + await table.initialize() + + m_table = ModelsRoutingTable({"test_provider": InferenceImpl()}, cached_disk_dist_registry, {}) + await m_table.initialize() + await m_table.register_model( + model_id="test-model", + provider_id="test_provider", + metadata={"embedding_dimension": 128}, + model_type=ModelType.embedding, + ) + + user_provided_id = "my-custom-vector-db" + await table.register_vector_db(vector_db_id=user_provided_id, embedding_model="test-model") + + vector_stores = await impl.openai_list_vector_stores() + assert len(vector_stores.data) == 1 + + vector_store = vector_stores.data[0] + + assert vector_store.name == user_provided_id + + assert vector_store.id.startswith("vs_") + assert vector_store.id != user_provided_id + + vector_dbs = await table.list_vector_dbs() + assert len(vector_dbs.data) == 1 + assert vector_dbs.data[0].identifier == vector_store.id + + await table.unregister_vector_db(vector_store.id) + + async def test_openai_vector_stores_routing_table_roles(cached_disk_dist_registry): impl = VectorDBImpl() impl.openai_retrieve_vector_store = AsyncMock(return_value="OK") @@ -164,7 +269,8 @@ async def test_openai_vector_stores_routing_table_roles(cached_disk_dist_registr authorized_user = User(principal="alice", attributes={"roles": [authorized_team]}) with request_provider_data_context({}, authorized_user): - _ = await table.register_vector_db(vector_db_id="vs1", embedding_model="test-model") + registered_vdb = await table.register_vector_db(vector_db_id="vs1", embedding_model="test-model") + authorized_table = registered_vdb.identifier # Use the actual generated ID # Authorized reader with request_provider_data_context({}, authorized_user): @@ -227,7 +333,8 @@ async def test_openai_vector_stores_routing_table_actions(cached_disk_dist_regis ) with request_provider_data_context({}, admin_user): - await table.register_vector_db(vector_db_id=vector_db_id, embedding_model="test-model") + registered_vdb = await table.register_vector_db(vector_db_id=vector_db_id, embedding_model="test-model") + vector_db_id = registered_vdb.identifier # Use the actual generated ID read_methods = [ (table.openai_retrieve_vector_store, (vector_db_id,), {}), diff --git a/tests/unit/distribution/test_distribution.py b/tests/unit/distribution/test_distribution.py index c72106e46..f24de0644 100644 --- a/tests/unit/distribution/test_distribution.py +++ b/tests/unit/distribution/test_distribution.py @@ -12,7 +12,7 @@ import yaml from pydantic import BaseModel, Field, ValidationError from llama_stack.core.datatypes import Api, Provider, StackRunConfig -from llama_stack.core.distribution import get_provider_registry +from llama_stack.core.distribution import INTERNAL_APIS, get_provider_registry, providable_apis from llama_stack.providers.datatypes import ProviderSpec @@ -66,10 +66,9 @@ def base_config(tmp_path): def provider_spec_yaml(): """Common provider spec YAML for testing.""" return """ -adapter: - adapter_type: test_provider - config_class: test_provider.config.TestProviderConfig - module: test_provider +adapter_type: test_provider +config_class: test_provider.config.TestProviderConfig +module: test_provider api_dependencies: - safety """ @@ -152,6 +151,24 @@ class TestProviderRegistry: assert registry[Api.inference]["test_provider"].provider_type == "test_provider" assert registry[Api.inference]["test_provider"].api == Api.inference + def test_internal_apis_excluded(self): + """Test that internal APIs are excluded and APIs without provider registries are marked as internal.""" + import importlib + + apis = providable_apis() + + for internal_api in INTERNAL_APIS: + assert internal_api not in apis, f"Internal API {internal_api} should not be in providable_apis" + + for api in apis: + module_name = f"llama_stack.providers.registry.{api.name.lower()}" + try: + importlib.import_module(module_name) + except ImportError as err: + raise AssertionError( + f"API {api} is in providable_apis but has no provider registry module ({module_name})" + ) from err + def test_external_remote_providers(self, api_directories, mock_providers, base_config, provider_spec_yaml): """Test loading external remote providers from YAML files.""" remote_dir, _ = api_directories @@ -164,9 +181,9 @@ class TestProviderRegistry: assert Api.inference in registry assert "remote::test_provider" in registry[Api.inference] provider = registry[Api.inference]["remote::test_provider"] - assert provider.adapter.adapter_type == "test_provider" - assert provider.adapter.module == "test_provider" - assert provider.adapter.config_class == "test_provider.config.TestProviderConfig" + assert provider.adapter_type == "test_provider" + assert provider.module == "test_provider" + assert provider.config_class == "test_provider.config.TestProviderConfig" assert Api.safety in provider.api_dependencies def test_external_inline_providers(self, api_directories, mock_providers, base_config, inline_provider_spec_yaml): @@ -228,8 +245,7 @@ class TestProviderRegistry: """Test handling of malformed remote provider spec (missing required fields).""" remote_dir, _ = api_directories malformed_spec = """ -adapter: - adapter_type: test_provider +adapter_type: test_provider # Missing required fields api_dependencies: - safety @@ -252,7 +268,7 @@ pip_packages: with open(inline_dir / "malformed.yaml", "w") as f: f.write(malformed_spec) - with pytest.raises(KeyError) as exc_info: + with pytest.raises(ValidationError) as exc_info: get_provider_registry(base_config) assert "config_class" in str(exc_info.value) diff --git a/tests/unit/distribution/test_inference_recordings.py b/tests/unit/distribution/test_inference_recordings.py index dd80b0caf..5740357c1 100644 --- a/tests/unit/distribution/test_inference_recordings.py +++ b/tests/unit/distribution/test_inference_recordings.py @@ -6,16 +6,18 @@ import tempfile from pathlib import Path -from unittest.mock import patch +from unittest.mock import AsyncMock, Mock, patch import pytest -from openai import AsyncOpenAI +from openai import NOT_GIVEN, AsyncOpenAI +from openai.types.model import Model as OpenAIModel # Import the real Pydantic response types instead of using Mocks from llama_stack.apis.inference import ( OpenAIAssistantMessageParam, OpenAIChatCompletion, OpenAIChoice, + OpenAICompletion, OpenAIEmbeddingData, OpenAIEmbeddingsResponse, OpenAIEmbeddingUsage, @@ -153,24 +155,22 @@ class TestInferenceRecording: async def test_recording_mode(self, temp_storage_dir, real_openai_chat_response): """Test that recording mode captures and stores responses.""" - - async def mock_create(*args, **kwargs): - return real_openai_chat_response - temp_storage_dir = temp_storage_dir / "test_recording_mode" - with patch("openai.resources.chat.completions.AsyncCompletions.create", side_effect=mock_create): - with inference_recording(mode=InferenceMode.RECORD, storage_dir=str(temp_storage_dir)): - client = AsyncOpenAI(base_url="http://localhost:11434/v1", api_key="test") + with inference_recording(mode=InferenceMode.RECORD, storage_dir=str(temp_storage_dir)): + client = AsyncOpenAI(base_url="http://localhost:11434/v1", api_key="test") + client.chat.completions._post = AsyncMock(return_value=real_openai_chat_response) - response = await client.chat.completions.create( - model="llama3.2:3b", - messages=[{"role": "user", "content": "Hello, how are you?"}], - temperature=0.7, - max_tokens=50, - ) + response = await client.chat.completions.create( + model="llama3.2:3b", + messages=[{"role": "user", "content": "Hello, how are you?"}], + temperature=0.7, + max_tokens=50, + user=NOT_GIVEN, + ) - # Verify the response was returned correctly - assert response.choices[0].message.content == "Hello! I'm doing well, thank you for asking." + # Verify the response was returned correctly + assert response.choices[0].message.content == "Hello! I'm doing well, thank you for asking." + client.chat.completions._post.assert_called_once() # Verify recording was stored storage = ResponseStorage(temp_storage_dir) @@ -178,40 +178,74 @@ class TestInferenceRecording: async def test_replay_mode(self, temp_storage_dir, real_openai_chat_response): """Test that replay mode returns stored responses without making real calls.""" - - async def mock_create(*args, **kwargs): - return real_openai_chat_response - temp_storage_dir = temp_storage_dir / "test_replay_mode" # First, record a response - with patch("openai.resources.chat.completions.AsyncCompletions.create", side_effect=mock_create): - with inference_recording(mode=InferenceMode.RECORD, storage_dir=str(temp_storage_dir)): - client = AsyncOpenAI(base_url="http://localhost:11434/v1", api_key="test") + with inference_recording(mode=InferenceMode.RECORD, storage_dir=str(temp_storage_dir)): + client = AsyncOpenAI(base_url="http://localhost:11434/v1", api_key="test") + client.chat.completions._post = AsyncMock(return_value=real_openai_chat_response) - response = await client.chat.completions.create( - model="llama3.2:3b", - messages=[{"role": "user", "content": "Hello, how are you?"}], - temperature=0.7, - max_tokens=50, - ) + response = await client.chat.completions.create( + model="llama3.2:3b", + messages=[{"role": "user", "content": "Hello, how are you?"}], + temperature=0.7, + max_tokens=50, + user=NOT_GIVEN, + ) + client.chat.completions._post.assert_called_once() # Now test replay mode - should not call the original method - with patch("openai.resources.chat.completions.AsyncCompletions.create") as mock_create_patch: - with inference_recording(mode=InferenceMode.REPLAY, storage_dir=str(temp_storage_dir)): - client = AsyncOpenAI(base_url="http://localhost:11434/v1", api_key="test") + with inference_recording(mode=InferenceMode.REPLAY, storage_dir=str(temp_storage_dir)): + client = AsyncOpenAI(base_url="http://localhost:11434/v1", api_key="test") + client.chat.completions._post = AsyncMock(return_value=real_openai_chat_response) - response = await client.chat.completions.create( - model="llama3.2:3b", - messages=[{"role": "user", "content": "Hello, how are you?"}], - temperature=0.7, - max_tokens=50, - ) + response = await client.chat.completions.create( + model="llama3.2:3b", + messages=[{"role": "user", "content": "Hello, how are you?"}], + temperature=0.7, + max_tokens=50, + ) - # Verify we got the recorded response - assert response.choices[0].message.content == "Hello! I'm doing well, thank you for asking." + # Verify we got the recorded response + assert response.choices[0].message.content == "Hello! I'm doing well, thank you for asking." - # Verify the original method was NOT called - mock_create_patch.assert_not_called() + # Verify the original method was NOT called + client.chat.completions._post.assert_not_called() + + async def test_replay_mode_models(self, temp_storage_dir): + """Test that replay mode returns stored responses without making real model listing calls.""" + + async def _async_iterator(models): + for model in models: + yield model + + models = [ + OpenAIModel(id="foo", created=1, object="model", owned_by="test"), + OpenAIModel(id="bar", created=2, object="model", owned_by="test"), + ] + + expected_ids = {m.id for m in models} + + temp_storage_dir = temp_storage_dir / "test_replay_mode_models" + + # baseline - mock works without recording + client = AsyncOpenAI(base_url="http://localhost:11434/v1", api_key="test") + client.models._get_api_list = Mock(return_value=_async_iterator(models)) + assert {m.id async for m in client.models.list()} == expected_ids + client.models._get_api_list.assert_called_once() + + # record the call + with inference_recording(mode=InferenceMode.RECORD, storage_dir=temp_storage_dir): + client = AsyncOpenAI(base_url="http://localhost:11434/v1", api_key="test") + client.models._get_api_list = Mock(return_value=_async_iterator(models)) + assert {m.id async for m in client.models.list()} == expected_ids + client.models._get_api_list.assert_called_once() + + # replay the call + with inference_recording(mode=InferenceMode.REPLAY, storage_dir=temp_storage_dir): + client = AsyncOpenAI(base_url="http://localhost:11434/v1", api_key="test") + client.models._get_api_list = Mock(return_value=_async_iterator(models)) + assert {m.id async for m in client.models.list()} == expected_ids + client.models._get_api_list.assert_not_called() async def test_replay_missing_recording(self, temp_storage_dir): """Test that replay mode fails when no recording is found.""" @@ -228,36 +262,110 @@ class TestInferenceRecording: async def test_embeddings_recording(self, temp_storage_dir, real_embeddings_response): """Test recording and replay of embeddings calls.""" - async def mock_create(*args, **kwargs): - return real_embeddings_response + # baseline - mock works without recording + client = AsyncOpenAI(base_url="http://localhost:11434/v1", api_key="test") + client.embeddings._post = AsyncMock(return_value=real_embeddings_response) + response = await client.embeddings.create( + model=real_embeddings_response.model, + input=["Hello world", "Test embedding"], + encoding_format=NOT_GIVEN, + ) + assert len(response.data) == 2 + assert response.data[0].embedding == [0.1, 0.2, 0.3] + client.embeddings._post.assert_called_once() temp_storage_dir = temp_storage_dir / "test_embeddings_recording" # Record - with patch("openai.resources.embeddings.AsyncEmbeddings.create", side_effect=mock_create): - with inference_recording(mode=InferenceMode.RECORD, storage_dir=str(temp_storage_dir)): - client = AsyncOpenAI(base_url="http://localhost:11434/v1", api_key="test") + with inference_recording(mode=InferenceMode.RECORD, storage_dir=str(temp_storage_dir)): + client = AsyncOpenAI(base_url="http://localhost:11434/v1", api_key="test") + client.embeddings._post = AsyncMock(return_value=real_embeddings_response) - response = await client.embeddings.create( - model="nomic-embed-text", input=["Hello world", "Test embedding"] - ) + response = await client.embeddings.create( + model=real_embeddings_response.model, + input=["Hello world", "Test embedding"], + encoding_format=NOT_GIVEN, + dimensions=NOT_GIVEN, + user=NOT_GIVEN, + ) - assert len(response.data) == 2 + assert len(response.data) == 2 # Replay - with patch("openai.resources.embeddings.AsyncEmbeddings.create") as mock_create_patch: - with inference_recording(mode=InferenceMode.REPLAY, storage_dir=str(temp_storage_dir)): - client = AsyncOpenAI(base_url="http://localhost:11434/v1", api_key="test") + with inference_recording(mode=InferenceMode.REPLAY, storage_dir=str(temp_storage_dir)): + client = AsyncOpenAI(base_url="http://localhost:11434/v1", api_key="test") + client.embeddings._post = AsyncMock(return_value=real_embeddings_response) - response = await client.embeddings.create( - model="nomic-embed-text", input=["Hello world", "Test embedding"] - ) + response = await client.embeddings.create( + model=real_embeddings_response.model, + input=["Hello world", "Test embedding"], + ) - # Verify we got the recorded response - assert len(response.data) == 2 - assert response.data[0].embedding == [0.1, 0.2, 0.3] + # Verify we got the recorded response + assert len(response.data) == 2 + assert response.data[0].embedding == [0.1, 0.2, 0.3] - # Verify original method was not called - mock_create_patch.assert_not_called() + # Verify original method was not called + client.embeddings._post.assert_not_called() + + async def test_completions_recording(self, temp_storage_dir): + real_completions_response = OpenAICompletion( + id="test_completion", + object="text_completion", + created=1234567890, + model="llama3.2:3b", + choices=[ + { + "text": "Hello! I'm doing well, thank you for asking.", + "index": 0, + "logprobs": None, + "finish_reason": "stop", + } + ], + ) + + temp_storage_dir = temp_storage_dir / "test_completions_recording" + + # baseline - mock works without recording + client = AsyncOpenAI(base_url="http://localhost:11434/v1", api_key="test") + client.completions._post = AsyncMock(return_value=real_completions_response) + response = await client.completions.create( + model=real_completions_response.model, + prompt="Hello, how are you?", + temperature=0.7, + max_tokens=50, + user=NOT_GIVEN, + ) + assert response.choices[0].text == real_completions_response.choices[0].text + client.completions._post.assert_called_once() + + # Record + with inference_recording(mode=InferenceMode.RECORD, storage_dir=str(temp_storage_dir)): + client = AsyncOpenAI(base_url="http://localhost:11434/v1", api_key="test") + client.completions._post = AsyncMock(return_value=real_completions_response) + + response = await client.completions.create( + model=real_completions_response.model, + prompt="Hello, how are you?", + temperature=0.7, + max_tokens=50, + user=NOT_GIVEN, + ) + + assert response.choices[0].text == real_completions_response.choices[0].text + client.completions._post.assert_called_once() + + # Replay + with inference_recording(mode=InferenceMode.REPLAY, storage_dir=str(temp_storage_dir)): + client = AsyncOpenAI(base_url="http://localhost:11434/v1", api_key="test") + client.completions._post = AsyncMock(return_value=real_completions_response) + response = await client.completions.create( + model=real_completions_response.model, + prompt="Hello, how are you?", + temperature=0.7, + max_tokens=50, + ) + assert response.choices[0].text == real_completions_response.choices[0].text + client.completions._post.assert_not_called() async def test_live_mode(self, real_openai_chat_response): """Test that live mode passes through to original methods.""" @@ -266,7 +374,7 @@ class TestInferenceRecording: return real_openai_chat_response with patch("openai.resources.chat.completions.AsyncCompletions.create", side_effect=mock_create): - with inference_recording(mode=InferenceMode.LIVE): + with inference_recording(mode=InferenceMode.LIVE, storage_dir="foo"): client = AsyncOpenAI(base_url="http://localhost:11434/v1", api_key="test") response = await client.chat.completions.create( diff --git a/tests/unit/distribution/test_library_client_initialization.py b/tests/unit/distribution/test_library_client_initialization.py index b7e7a1857..b01a5c3e2 100644 --- a/tests/unit/distribution/test_library_client_initialization.py +++ b/tests/unit/distribution/test_library_client_initialization.py @@ -27,13 +27,17 @@ class TestLlamaStackAsLibraryClientAutoInitialization: mock_impls = {} mock_route_impls = RouteImpls({}) - async def mock_construct_stack(config, custom_provider_registry): - return mock_impls + class MockStack: + def __init__(self, config, custom_provider_registry=None): + self.impls = mock_impls + + async def initialize(self): + pass def mock_initialize_route_impls(impls): return mock_route_impls - monkeypatch.setattr("llama_stack.core.library_client.construct_stack", mock_construct_stack) + monkeypatch.setattr("llama_stack.core.library_client.Stack", MockStack) monkeypatch.setattr("llama_stack.core.library_client.initialize_route_impls", mock_initialize_route_impls) client = LlamaStackAsLibraryClient("ci-tests") @@ -46,13 +50,17 @@ class TestLlamaStackAsLibraryClientAutoInitialization: mock_impls = {} mock_route_impls = RouteImpls({}) - async def mock_construct_stack(config, custom_provider_registry): - return mock_impls + class MockStack: + def __init__(self, config, custom_provider_registry=None): + self.impls = mock_impls + + async def initialize(self): + pass def mock_initialize_route_impls(impls): return mock_route_impls - monkeypatch.setattr("llama_stack.core.library_client.construct_stack", mock_construct_stack) + monkeypatch.setattr("llama_stack.core.library_client.Stack", MockStack) monkeypatch.setattr("llama_stack.core.library_client.initialize_route_impls", mock_initialize_route_impls) client = AsyncLlamaStackAsLibraryClient("ci-tests") @@ -68,13 +76,17 @@ class TestLlamaStackAsLibraryClientAutoInitialization: mock_impls = {} mock_route_impls = RouteImpls({}) - async def mock_construct_stack(config, custom_provider_registry): - return mock_impls + class MockStack: + def __init__(self, config, custom_provider_registry=None): + self.impls = mock_impls + + async def initialize(self): + pass def mock_initialize_route_impls(impls): return mock_route_impls - monkeypatch.setattr("llama_stack.core.library_client.construct_stack", mock_construct_stack) + monkeypatch.setattr("llama_stack.core.library_client.Stack", MockStack) monkeypatch.setattr("llama_stack.core.library_client.initialize_route_impls", mock_initialize_route_impls) client = LlamaStackAsLibraryClient("ci-tests") @@ -90,13 +102,17 @@ class TestLlamaStackAsLibraryClientAutoInitialization: mock_impls = {} mock_route_impls = RouteImpls({}) - async def mock_construct_stack(config, custom_provider_registry): - return mock_impls + class MockStack: + def __init__(self, config, custom_provider_registry=None): + self.impls = mock_impls + + async def initialize(self): + pass def mock_initialize_route_impls(impls): return mock_route_impls - monkeypatch.setattr("llama_stack.core.library_client.construct_stack", mock_construct_stack) + monkeypatch.setattr("llama_stack.core.library_client.Stack", MockStack) monkeypatch.setattr("llama_stack.core.library_client.initialize_route_impls", mock_initialize_route_impls) client = AsyncLlamaStackAsLibraryClient("ci-tests") @@ -112,13 +128,17 @@ class TestLlamaStackAsLibraryClientAutoInitialization: mock_impls = {} mock_route_impls = RouteImpls({}) - async def mock_construct_stack(config, custom_provider_registry): - return mock_impls + class MockStack: + def __init__(self, config, custom_provider_registry=None): + self.impls = mock_impls + + async def initialize(self): + pass def mock_initialize_route_impls(impls): return mock_route_impls - monkeypatch.setattr("llama_stack.core.library_client.construct_stack", mock_construct_stack) + monkeypatch.setattr("llama_stack.core.library_client.Stack", MockStack) monkeypatch.setattr("llama_stack.core.library_client.initialize_route_impls", mock_initialize_route_impls) sync_client = LlamaStackAsLibraryClient("ci-tests") diff --git a/docs/contbuild.sh b/tests/unit/prompts/prompts/__init__.py similarity index 75% rename from docs/contbuild.sh rename to tests/unit/prompts/prompts/__init__.py index c3687a3c8..756f351d8 100644 --- a/docs/contbuild.sh +++ b/tests/unit/prompts/prompts/__init__.py @@ -3,5 +3,3 @@ # # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. - -sphinx-autobuild --write-all source build/html --watch source/ diff --git a/tests/unit/prompts/prompts/conftest.py b/tests/unit/prompts/prompts/conftest.py new file mode 100644 index 000000000..b2c619e49 --- /dev/null +++ b/tests/unit/prompts/prompts/conftest.py @@ -0,0 +1,30 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the terms described in the LICENSE file in +# the root directory of this source tree. + +import random + +import pytest + +from llama_stack.core.prompts.prompts import PromptServiceConfig, PromptServiceImpl +from llama_stack.providers.utils.kvstore.config import SqliteKVStoreConfig + + +@pytest.fixture +async def temp_prompt_store(tmp_path_factory): + unique_id = f"prompt_store_{random.randint(1, 1000000)}" + temp_dir = tmp_path_factory.getbasetemp() + db_path = str(temp_dir / f"{unique_id}.db") + + from llama_stack.core.datatypes import StackRunConfig + from llama_stack.providers.utils.kvstore import kvstore_impl + + mock_run_config = StackRunConfig(image_name="test-distribution", apis=[], providers={}) + config = PromptServiceConfig(run_config=mock_run_config) + store = PromptServiceImpl(config, deps={}) + + store.kvstore = await kvstore_impl(SqliteKVStoreConfig(db_path=db_path)) + + yield store diff --git a/tests/unit/prompts/prompts/test_prompts.py b/tests/unit/prompts/prompts/test_prompts.py new file mode 100644 index 000000000..792e55530 --- /dev/null +++ b/tests/unit/prompts/prompts/test_prompts.py @@ -0,0 +1,144 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the terms described in the LICENSE file in +# the root directory of this source tree. + + +import pytest + + +class TestPrompts: + async def test_create_and_get_prompt(self, temp_prompt_store): + prompt = await temp_prompt_store.create_prompt("Hello world!", ["name"]) + assert prompt.prompt == "Hello world!" + assert prompt.version == 1 + assert prompt.prompt_id.startswith("pmpt_") + assert prompt.variables == ["name"] + + retrieved = await temp_prompt_store.get_prompt(prompt.prompt_id) + assert retrieved.prompt_id == prompt.prompt_id + assert retrieved.prompt == prompt.prompt + + async def test_update_prompt(self, temp_prompt_store): + prompt = await temp_prompt_store.create_prompt("Original") + updated = await temp_prompt_store.update_prompt(prompt.prompt_id, "Updated", 1, ["v"]) + assert updated.version == 2 + assert updated.prompt == "Updated" + + async def test_update_prompt_with_version(self, temp_prompt_store): + version_for_update = 1 + + prompt = await temp_prompt_store.create_prompt("Original") + assert prompt.version == 1 + prompt = await temp_prompt_store.update_prompt(prompt.prompt_id, "Updated", version_for_update, ["v"]) + assert prompt.version == 2 + + with pytest.raises(ValueError): + # now this is a stale version + await temp_prompt_store.update_prompt(prompt.prompt_id, "Another Update", version_for_update, ["v"]) + + with pytest.raises(ValueError): + # this version does not exist + await temp_prompt_store.update_prompt(prompt.prompt_id, "Another Update", 99, ["v"]) + + async def test_delete_prompt(self, temp_prompt_store): + prompt = await temp_prompt_store.create_prompt("to be deleted") + await temp_prompt_store.delete_prompt(prompt.prompt_id) + with pytest.raises(ValueError): + await temp_prompt_store.get_prompt(prompt.prompt_id) + + async def test_list_prompts(self, temp_prompt_store): + response = await temp_prompt_store.list_prompts() + assert response.data == [] + + await temp_prompt_store.create_prompt("first") + await temp_prompt_store.create_prompt("second") + + response = await temp_prompt_store.list_prompts() + assert len(response.data) == 2 + + async def test_version(self, temp_prompt_store): + prompt = await temp_prompt_store.create_prompt("V1") + await temp_prompt_store.update_prompt(prompt.prompt_id, "V2", 1) + + v1 = await temp_prompt_store.get_prompt(prompt.prompt_id, version=1) + assert v1.version == 1 and v1.prompt == "V1" + + latest = await temp_prompt_store.get_prompt(prompt.prompt_id) + assert latest.version == 2 and latest.prompt == "V2" + + async def test_set_default_version(self, temp_prompt_store): + prompt0 = await temp_prompt_store.create_prompt("V1") + prompt1 = await temp_prompt_store.update_prompt(prompt0.prompt_id, "V2", 1) + + assert (await temp_prompt_store.get_prompt(prompt0.prompt_id)).version == 2 + prompt_default = await temp_prompt_store.set_default_version(prompt0.prompt_id, 1) + assert (await temp_prompt_store.get_prompt(prompt0.prompt_id)).version == 1 + assert prompt_default.version == 1 + + prompt2 = await temp_prompt_store.update_prompt(prompt0.prompt_id, "V3", prompt1.version) + assert prompt2.version == 3 + + async def test_prompt_id_generation_and_validation(self, temp_prompt_store): + prompt = await temp_prompt_store.create_prompt("Test") + assert prompt.prompt_id.startswith("pmpt_") + assert len(prompt.prompt_id) == 53 + + with pytest.raises(ValueError): + await temp_prompt_store.get_prompt("invalid_id") + + async def test_list_shows_default_versions(self, temp_prompt_store): + prompt = await temp_prompt_store.create_prompt("V1") + await temp_prompt_store.update_prompt(prompt.prompt_id, "V2", 1) + await temp_prompt_store.update_prompt(prompt.prompt_id, "V3", 2) + + response = await temp_prompt_store.list_prompts() + listed_prompt = response.data[0] + assert listed_prompt.version == 3 and listed_prompt.prompt == "V3" + + await temp_prompt_store.set_default_version(prompt.prompt_id, 1) + + response = await temp_prompt_store.list_prompts() + listed_prompt = response.data[0] + assert listed_prompt.version == 1 and listed_prompt.prompt == "V1" + assert not (await temp_prompt_store.get_prompt(prompt.prompt_id, 3)).is_default + + async def test_get_all_prompt_versions(self, temp_prompt_store): + prompt = await temp_prompt_store.create_prompt("V1") + await temp_prompt_store.update_prompt(prompt.prompt_id, "V2", 1) + await temp_prompt_store.update_prompt(prompt.prompt_id, "V3", 2) + + versions = (await temp_prompt_store.list_prompt_versions(prompt.prompt_id)).data + assert len(versions) == 3 + assert [v.version for v in versions] == [1, 2, 3] + assert [v.is_default for v in versions] == [False, False, True] + + await temp_prompt_store.set_default_version(prompt.prompt_id, 2) + versions = (await temp_prompt_store.list_prompt_versions(prompt.prompt_id)).data + assert [v.is_default for v in versions] == [False, True, False] + + with pytest.raises(ValueError): + await temp_prompt_store.list_prompt_versions("nonexistent") + + async def test_prompt_variable_validation(self, temp_prompt_store): + prompt = await temp_prompt_store.create_prompt("Hello {{ name }}, you live in {{ city }}!", ["name", "city"]) + assert prompt.variables == ["name", "city"] + + prompt_no_vars = await temp_prompt_store.create_prompt("Hello world!", []) + assert prompt_no_vars.variables == [] + + with pytest.raises(ValueError, match="undeclared variables"): + await temp_prompt_store.create_prompt("Hello {{ name }}, invalid {{ unknown }}!", ["name"]) + + async def test_update_prompt_set_as_default_behavior(self, temp_prompt_store): + prompt = await temp_prompt_store.create_prompt("V1") + assert (await temp_prompt_store.get_prompt(prompt.prompt_id)).version == 1 + + prompt_v2 = await temp_prompt_store.update_prompt(prompt.prompt_id, "V2", 1, [], set_as_default=True) + assert prompt_v2.version == 2 + assert (await temp_prompt_store.get_prompt(prompt.prompt_id)).version == 2 + + prompt_v3 = await temp_prompt_store.update_prompt(prompt.prompt_id, "V3", 2, [], set_as_default=False) + assert prompt_v3.version == 3 + assert (await temp_prompt_store.get_prompt(prompt.prompt_id)).version == 2 diff --git a/tests/unit/providers/batches/test_reference.py b/tests/unit/providers/batches/test_reference.py index 0ca866f7b..dfef5e040 100644 --- a/tests/unit/providers/batches/test_reference.py +++ b/tests/unit/providers/batches/test_reference.py @@ -46,7 +46,8 @@ The tests are categorized and outlined below, keep this updated: * test_validate_input_url_mismatch (negative) * test_validate_input_multiple_errors_per_request (negative) * test_validate_input_invalid_request_format (negative) - * test_validate_input_missing_parameters (parametrized negative - custom_id, method, url, body, model, messages missing validation) + * test_validate_input_missing_parameters_chat_completions (parametrized negative - custom_id, method, url, body, model, messages missing validation for chat/completions) + * test_validate_input_missing_parameters_completions (parametrized negative - custom_id, method, url, body, model, prompt missing validation for completions) * test_validate_input_invalid_parameter_types (parametrized negative - custom_id, url, method, body, model, messages type validation) The tests use temporary SQLite databases for isolation and mock external @@ -213,7 +214,6 @@ class TestReferenceBatchesImpl: "endpoint", [ "/v1/embeddings", - "/v1/completions", "/v1/invalid/endpoint", "", ], @@ -499,8 +499,10 @@ class TestReferenceBatchesImpl: ("messages", "body.messages", "invalid_request", "Messages parameter is required"), ], ) - async def test_validate_input_missing_parameters(self, provider, param_name, param_path, error_code, error_message): - """Test _validate_input when file contains request with missing required parameters.""" + async def test_validate_input_missing_parameters_chat_completions( + self, provider, param_name, param_path, error_code, error_message + ): + """Test _validate_input when file contains request with missing required parameters for chat completions.""" provider.files_api.openai_retrieve_file = AsyncMock() mock_response = MagicMock() @@ -541,6 +543,61 @@ class TestReferenceBatchesImpl: assert errors[0].message == error_message assert errors[0].param == param_path + @pytest.mark.parametrize( + "param_name,param_path,error_code,error_message", + [ + ("custom_id", "custom_id", "missing_required_parameter", "Missing required parameter: custom_id"), + ("method", "method", "missing_required_parameter", "Missing required parameter: method"), + ("url", "url", "missing_required_parameter", "Missing required parameter: url"), + ("body", "body", "missing_required_parameter", "Missing required parameter: body"), + ("model", "body.model", "invalid_request", "Model parameter is required"), + ("prompt", "body.prompt", "invalid_request", "Prompt parameter is required"), + ], + ) + async def test_validate_input_missing_parameters_completions( + self, provider, param_name, param_path, error_code, error_message + ): + """Test _validate_input when file contains request with missing required parameters for text completions.""" + provider.files_api.openai_retrieve_file = AsyncMock() + mock_response = MagicMock() + + base_request = { + "custom_id": "req-1", + "method": "POST", + "url": "/v1/completions", + "body": {"model": "test-model", "prompt": "Hello"}, + } + + # Remove the specific parameter being tested + if "." in param_path: + top_level, nested_param = param_path.split(".", 1) + del base_request[top_level][nested_param] + else: + del base_request[param_name] + + mock_response.body = json.dumps(base_request).encode() + provider.files_api.openai_retrieve_file_content = AsyncMock(return_value=mock_response) + + batch = BatchObject( + id="batch_test", + object="batch", + endpoint="/v1/completions", + input_file_id=f"missing_{param_name}_file", + completion_window="24h", + status="validating", + created_at=1234567890, + ) + + errors, requests = await provider._validate_input(batch) + + assert len(errors) == 1 + assert len(requests) == 0 + + assert errors[0].code == error_code + assert errors[0].line == 1 + assert errors[0].message == error_message + assert errors[0].param == param_path + async def test_validate_input_url_mismatch(self, provider): """Test _validate_input when file contains request with URL that doesn't match batch endpoint.""" provider.files_api.openai_retrieve_file = AsyncMock() diff --git a/tests/unit/providers/files/conftest.py b/tests/unit/providers/files/conftest.py new file mode 100644 index 000000000..46282e3dc --- /dev/null +++ b/tests/unit/providers/files/conftest.py @@ -0,0 +1,62 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the terms described in the LICENSE file in +# the root directory of this source tree. + +import boto3 +import pytest +from moto import mock_aws + +from llama_stack.providers.remote.files.s3 import S3FilesImplConfig, get_adapter_impl +from llama_stack.providers.utils.sqlstore.sqlstore import SqliteSqlStoreConfig + + +class MockUploadFile: + def __init__(self, content: bytes, filename: str, content_type: str = "text/plain"): + self.content = content + self.filename = filename + self.content_type = content_type + + async def read(self): + return self.content + + +@pytest.fixture +def sample_text_file(): + content = b"Hello, this is a test file for the S3 Files API!" + return MockUploadFile(content, "sample_text_file-0.txt") + + +@pytest.fixture +def sample_text_file2(): + content = b"Hello, this is a second test file for the S3 Files API!" + return MockUploadFile(content, "sample_text_file-1.txt") + + +@pytest.fixture +def s3_config(tmp_path): + db_path = tmp_path / "s3_files_metadata.db" + + return S3FilesImplConfig( + bucket_name=f"test-bucket-{tmp_path.name}", + region="not-a-region", + auto_create_bucket=True, + metadata_store=SqliteSqlStoreConfig(db_path=db_path.as_posix()), + ) + + +@pytest.fixture +def s3_client(): + # we use `with mock_aws()` because @mock_aws decorator does not support + # being a generator + with mock_aws(): + # must yield or the mock will be reset before it is used + yield boto3.client("s3") + + +@pytest.fixture +async def s3_provider(s3_config, s3_client): # s3_client provides the moto mock, don't remove it + provider = await get_adapter_impl(s3_config, {}) + yield provider + await provider.shutdown() diff --git a/tests/unit/providers/files/test_s3_files.py b/tests/unit/providers/files/test_s3_files.py index daa250f10..c665bf124 100644 --- a/tests/unit/providers/files/test_s3_files.py +++ b/tests/unit/providers/files/test_s3_files.py @@ -6,63 +6,11 @@ from unittest.mock import patch -import boto3 import pytest from botocore.exceptions import ClientError -from moto import mock_aws from llama_stack.apis.common.errors import ResourceNotFoundError from llama_stack.apis.files import OpenAIFilePurpose -from llama_stack.providers.remote.files.s3 import ( - S3FilesImplConfig, - get_adapter_impl, -) -from llama_stack.providers.utils.sqlstore.sqlstore import SqliteSqlStoreConfig - - -class MockUploadFile: - def __init__(self, content: bytes, filename: str, content_type: str = "text/plain"): - self.content = content - self.filename = filename - self.content_type = content_type - - async def read(self): - return self.content - - -@pytest.fixture -def s3_config(tmp_path): - db_path = tmp_path / "s3_files_metadata.db" - - return S3FilesImplConfig( - bucket_name="test-bucket", - region="not-a-region", - auto_create_bucket=True, - metadata_store=SqliteSqlStoreConfig(db_path=db_path.as_posix()), - ) - - -@pytest.fixture -def s3_client(): - """Create a mocked S3 client for testing.""" - # we use `with mock_aws()` because @mock_aws decorator does not support being a generator - with mock_aws(): - # must yield or the mock will be reset before it is used - yield boto3.client("s3") - - -@pytest.fixture -async def s3_provider(s3_config, s3_client): - """Create an S3 files provider with mocked S3 for testing.""" - provider = await get_adapter_impl(s3_config, {}) - yield provider - await provider.shutdown() - - -@pytest.fixture -def sample_text_file(): - content = b"Hello, this is a test file for the S3 Files API!" - return MockUploadFile(content, "sample_text_file.txt") class TestS3FilesImpl: @@ -143,7 +91,7 @@ class TestS3FilesImpl: s3_client.head_object(Bucket=s3_config.bucket_name, Key=uploaded.id) assert exc_info.value.response["Error"]["Code"] == "404" - async def test_list_files(self, s3_provider, sample_text_file): + async def test_list_files(self, s3_provider, sample_text_file, sample_text_file2): """Test listing files after uploading some.""" sample_text_file.filename = "test_list_files_with_content_file1" file1 = await s3_provider.openai_upload_file( @@ -151,9 +99,9 @@ class TestS3FilesImpl: purpose=OpenAIFilePurpose.ASSISTANTS, ) - file2_content = MockUploadFile(b"Second file content", "test_list_files_with_content_file2") + sample_text_file2.filename = "test_list_files_with_content_file2" file2 = await s3_provider.openai_upload_file( - file=file2_content, + file=sample_text_file2, purpose=OpenAIFilePurpose.BATCH, ) @@ -164,7 +112,7 @@ class TestS3FilesImpl: assert file1.id in file_ids assert file2.id in file_ids - async def test_list_files_with_purpose_filter(self, s3_provider, sample_text_file): + async def test_list_files_with_purpose_filter(self, s3_provider, sample_text_file, sample_text_file2): """Test listing files with purpose filter.""" sample_text_file.filename = "test_list_files_with_purpose_filter_file1" file1 = await s3_provider.openai_upload_file( @@ -172,9 +120,9 @@ class TestS3FilesImpl: purpose=OpenAIFilePurpose.ASSISTANTS, ) - file2_content = MockUploadFile(b"Batch file content", "test_list_files_with_purpose_filter_file2") + sample_text_file2.filename = "test_list_files_with_purpose_filter_file2" await s3_provider.openai_upload_file( - file=file2_content, + file=sample_text_file2, purpose=OpenAIFilePurpose.BATCH, ) @@ -249,3 +197,104 @@ class TestS3FilesImpl: files_list = await s3_provider.openai_list_files() assert len(files_list.data) == 0, "No file metadata should remain after failed upload" + + @pytest.mark.parametrize("purpose", [p for p in OpenAIFilePurpose if p != OpenAIFilePurpose.BATCH]) + async def test_default_no_expiration(self, s3_provider, sample_text_file, purpose): + """Test that by default files have no expiration.""" + sample_text_file.filename = "test_default_no_expiration" + uploaded = await s3_provider.openai_upload_file( + file=sample_text_file, + purpose=purpose, + ) + assert uploaded.expires_at is None, "By default files should have no expiration" + + async def test_default_batch_expiration(self, s3_provider, sample_text_file): + """Test that by default batch files have an expiration.""" + sample_text_file.filename = "test_default_batch_an_expiration" + uploaded = await s3_provider.openai_upload_file( + file=sample_text_file, + purpose=OpenAIFilePurpose.BATCH, + ) + assert uploaded.expires_at is not None, "By default batch files should have an expiration" + thirty_days_seconds = 30 * 24 * 3600 + assert uploaded.expires_at == uploaded.created_at + thirty_days_seconds, ( + "Batch default expiration should be 30 days" + ) + + async def test_expired_file_is_unavailable(self, s3_provider, sample_text_file, s3_config, s3_client): + """Uploaded file that has expired should not be listed or retrievable/deletable.""" + with patch.object(s3_provider, "_now") as mock_now: # control time + two_hours = 2 * 60 * 60 + + mock_now.return_value = 0 + + sample_text_file.filename = "test_expired_file" + uploaded = await s3_provider.openai_upload_file( + file=sample_text_file, + purpose=OpenAIFilePurpose.ASSISTANTS, + expires_after_anchor="created_at", + expires_after_seconds=two_hours, + ) + + mock_now.return_value = two_hours * 2 # fast forward 4 hours + + listed = await s3_provider.openai_list_files() + assert uploaded.id not in [f.id for f in listed.data] + + with pytest.raises(ResourceNotFoundError, match="not found"): + await s3_provider.openai_retrieve_file(uploaded.id) + + with pytest.raises(ResourceNotFoundError, match="not found"): + await s3_provider.openai_retrieve_file_content(uploaded.id) + + with pytest.raises(ResourceNotFoundError, match="not found"): + await s3_provider.openai_delete_file(uploaded.id) + + with pytest.raises(ClientError) as exc_info: + s3_client.head_object(Bucket=s3_config.bucket_name, Key=uploaded.id) + assert exc_info.value.response["Error"]["Code"] == "404" + + with pytest.raises(ResourceNotFoundError, match="not found"): + await s3_provider._get_file(uploaded.id, return_expired=True) + + async def test_unsupported_expires_after_anchor(self, s3_provider, sample_text_file): + """Unsupported anchor value should raise ValueError.""" + sample_text_file.filename = "test_unsupported_expires_after_anchor" + + with pytest.raises(ValueError, match="Input should be 'created_at'"): + await s3_provider.openai_upload_file( + file=sample_text_file, + purpose=OpenAIFilePurpose.ASSISTANTS, + expires_after_anchor="now", + expires_after_seconds=3600, + ) + + async def test_nonint_expires_after_seconds(self, s3_provider, sample_text_file): + """Non-integer seconds in expires_after should raise ValueError.""" + sample_text_file.filename = "test_nonint_expires_after_seconds" + + with pytest.raises(ValueError, match="should be a valid integer"): + await s3_provider.openai_upload_file( + file=sample_text_file, + purpose=OpenAIFilePurpose.ASSISTANTS, + expires_after_anchor="created_at", + expires_after_seconds="many", + ) + + async def test_expires_after_seconds_out_of_bounds(self, s3_provider, sample_text_file): + """Seconds outside allowed range should raise ValueError.""" + with pytest.raises(ValueError, match="greater than or equal to 3600"): + await s3_provider.openai_upload_file( + file=sample_text_file, + purpose=OpenAIFilePurpose.ASSISTANTS, + expires_after_anchor="created_at", + expires_after_seconds=3599, + ) + + with pytest.raises(ValueError, match="less than or equal to 2592000"): + await s3_provider.openai_upload_file( + file=sample_text_file, + purpose=OpenAIFilePurpose.ASSISTANTS, + expires_after_anchor="created_at", + expires_after_seconds=2592001, + ) diff --git a/tests/unit/providers/files/test_s3_files_auth.py b/tests/unit/providers/files/test_s3_files_auth.py new file mode 100644 index 000000000..6097f2808 --- /dev/null +++ b/tests/unit/providers/files/test_s3_files_auth.py @@ -0,0 +1,89 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the terms described in the LICENSE file in +# the root directory of this source tree. + +from unittest.mock import patch + +import pytest + +from llama_stack.apis.common.errors import ResourceNotFoundError +from llama_stack.apis.files import OpenAIFilePurpose +from llama_stack.core.datatypes import User +from llama_stack.providers.remote.files.s3.files import S3FilesImpl + + +async def test_listing_hides_other_users_file(s3_provider, sample_text_file): + """Listing should not show files uploaded by other users.""" + user_a = User("user-a", {"roles": ["team-a"]}) + user_b = User("user-b", {"roles": ["team-b"]}) + + with patch("llama_stack.providers.utils.sqlstore.authorized_sqlstore.get_authenticated_user") as mock_get_user: + mock_get_user.return_value = user_a + uploaded = await s3_provider.openai_upload_file(file=sample_text_file, purpose=OpenAIFilePurpose.ASSISTANTS) + + with patch("llama_stack.providers.utils.sqlstore.authorized_sqlstore.get_authenticated_user") as mock_get_user: + mock_get_user.return_value = user_b + listed = await s3_provider.openai_list_files() + assert all(f.id != uploaded.id for f in listed.data) + + +@pytest.mark.parametrize( + "op", + [S3FilesImpl.openai_retrieve_file, S3FilesImpl.openai_retrieve_file_content, S3FilesImpl.openai_delete_file], + ids=["retrieve", "content", "delete"], +) +async def test_cannot_access_other_user_file(s3_provider, sample_text_file, op): + """Operations (metadata/content/delete) on another user's file should raise ResourceNotFoundError. + + `op` is an async callable (provider, file_id) -> awaits the requested operation. + """ + user_a = User("user-a", {"roles": ["team-a"]}) + user_b = User("user-b", {"roles": ["team-b"]}) + + with patch("llama_stack.providers.utils.sqlstore.authorized_sqlstore.get_authenticated_user") as mock_get_user: + mock_get_user.return_value = user_a + uploaded = await s3_provider.openai_upload_file(file=sample_text_file, purpose=OpenAIFilePurpose.ASSISTANTS) + + with patch("llama_stack.providers.utils.sqlstore.authorized_sqlstore.get_authenticated_user") as mock_get_user: + mock_get_user.return_value = user_b + with pytest.raises(ResourceNotFoundError): + await op(s3_provider, uploaded.id) + + +async def test_shared_role_allows_listing(s3_provider, sample_text_file): + """Listing should show files uploaded by other users when roles are shared.""" + user_a = User("user-a", {"roles": ["shared-role"]}) + user_b = User("user-b", {"roles": ["shared-role"]}) + + with patch("llama_stack.providers.utils.sqlstore.authorized_sqlstore.get_authenticated_user") as mock_get_user: + mock_get_user.return_value = user_a + uploaded = await s3_provider.openai_upload_file(file=sample_text_file, purpose=OpenAIFilePurpose.ASSISTANTS) + + with patch("llama_stack.providers.utils.sqlstore.authorized_sqlstore.get_authenticated_user") as mock_get_user: + mock_get_user.return_value = user_b + listed = await s3_provider.openai_list_files() + assert any(f.id == uploaded.id for f in listed.data) + + +@pytest.mark.parametrize( + "op", + [S3FilesImpl.openai_retrieve_file, S3FilesImpl.openai_retrieve_file_content, S3FilesImpl.openai_delete_file], + ids=["retrieve", "content", "delete"], +) +async def test_shared_role_allows_access(s3_provider, sample_text_file, op): + """Operations (metadata/content/delete) on another user's file should succeed when users share a role. + + `op` is an async callable (provider, file_id) -> awaits the requested operation. + """ + user_x = User("user-x", {"roles": ["shared-role"]}) + user_y = User("user-y", {"roles": ["shared-role"]}) + + with patch("llama_stack.providers.utils.sqlstore.authorized_sqlstore.get_authenticated_user") as mock_get_user: + mock_get_user.return_value = user_x + uploaded = await s3_provider.openai_upload_file(file=sample_text_file, purpose=OpenAIFilePurpose.ASSISTANTS) + + with patch("llama_stack.providers.utils.sqlstore.authorized_sqlstore.get_authenticated_user") as mock_get_user: + mock_get_user.return_value = user_y + await op(s3_provider, uploaded.id) diff --git a/tests/unit/providers/inference/bedrock/test_config.py b/tests/unit/providers/inference/bedrock/test_config.py new file mode 100644 index 000000000..1b8639f2e --- /dev/null +++ b/tests/unit/providers/inference/bedrock/test_config.py @@ -0,0 +1,63 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the terms described in the LICENSE file in +# the root directory of this source tree. + +import os +from unittest.mock import patch + +from llama_stack.providers.utils.bedrock.config import BedrockBaseConfig + + +class TestBedrockBaseConfig: + def test_defaults_work_without_env_vars(self): + with patch.dict(os.environ, {}, clear=True): + config = BedrockBaseConfig() + + # Basic creds should be None + assert config.aws_access_key_id is None + assert config.aws_secret_access_key is None + assert config.region_name is None + + # Timeouts get defaults + assert config.connect_timeout == 60.0 + assert config.read_timeout == 60.0 + assert config.session_ttl == 3600 + + def test_env_vars_get_picked_up(self): + env_vars = { + "AWS_ACCESS_KEY_ID": "AKIATEST123", + "AWS_SECRET_ACCESS_KEY": "secret123", + "AWS_DEFAULT_REGION": "us-west-2", + "AWS_MAX_ATTEMPTS": "5", + "AWS_RETRY_MODE": "adaptive", + "AWS_CONNECT_TIMEOUT": "30", + } + + with patch.dict(os.environ, env_vars, clear=True): + config = BedrockBaseConfig() + + assert config.aws_access_key_id == "AKIATEST123" + assert config.aws_secret_access_key == "secret123" + assert config.region_name == "us-west-2" + assert config.total_max_attempts == 5 + assert config.retry_mode == "adaptive" + assert config.connect_timeout == 30.0 + + def test_partial_env_setup(self): + # Just setting one timeout var + with patch.dict(os.environ, {"AWS_CONNECT_TIMEOUT": "120"}, clear=True): + config = BedrockBaseConfig() + + assert config.connect_timeout == 120.0 + assert config.read_timeout == 60.0 # still default + assert config.aws_access_key_id is None + + def test_bad_max_attempts_breaks(self): + with patch.dict(os.environ, {"AWS_MAX_ATTEMPTS": "not_a_number"}, clear=True): + try: + BedrockBaseConfig() + raise AssertionError("Should have failed on bad int conversion") + except ValueError: + pass # expected diff --git a/tests/unit/providers/inference/test_inference_client_caching.py b/tests/unit/providers/inference/test_inference_client_caching.py index b371cf907..f4b3201e9 100644 --- a/tests/unit/providers/inference/test_inference_client_caching.py +++ b/tests/unit/providers/inference/test_inference_client_caching.py @@ -33,8 +33,7 @@ def test_groq_provider_openai_client_caching(): with request_provider_data_context( {"x-llamastack-provider-data": json.dumps({inference_adapter.provider_data_api_key_field: api_key})} ): - openai_client = inference_adapter._get_openai_client() - assert openai_client.api_key == api_key + assert inference_adapter.client.api_key == api_key def test_openai_provider_openai_client_caching(): diff --git a/tests/unit/providers/inference/test_litellm_openai_mixin.py b/tests/unit/providers/inference/test_litellm_openai_mixin.py index bbc437edf..dc17e6abf 100644 --- a/tests/unit/providers/inference/test_litellm_openai_mixin.py +++ b/tests/unit/providers/inference/test_litellm_openai_mixin.py @@ -26,7 +26,6 @@ class TestProviderDataValidator(BaseModel): class TestLiteLLMAdapter(LiteLLMOpenAIMixin): def __init__(self, config: TestConfig): super().__init__( - model_entries=[], litellm_provider_name="test", api_key_from_config=config.api_key, provider_data_api_key_field="test_api_key", diff --git a/tests/unit/providers/inference/test_openai_base_url_config.py b/tests/unit/providers/inference/test_openai_base_url_config.py index 150f6210b..903772f0c 100644 --- a/tests/unit/providers/inference/test_openai_base_url_config.py +++ b/tests/unit/providers/inference/test_openai_base_url_config.py @@ -5,7 +5,7 @@ # the root directory of this source tree. import os -from unittest.mock import AsyncMock, MagicMock, patch +from unittest.mock import MagicMock, patch from llama_stack.core.stack import replace_env_vars from llama_stack.providers.remote.inference.openai.config import OpenAIConfig @@ -80,11 +80,22 @@ class TestOpenAIBaseURLConfig: # Mock the get_api_key method adapter.get_api_key = MagicMock(return_value="test-key") - # Mock the AsyncOpenAI client and its models.retrieve method + # Mock a model object that will be returned by models.list() + mock_model = MagicMock() + mock_model.id = "gpt-4" + + # Create an async iterator that yields our mock model + async def mock_async_iterator(): + yield mock_model + + # Mock the AsyncOpenAI client and its models.list method mock_client = MagicMock() - mock_client.models.retrieve = AsyncMock(return_value=MagicMock()) + mock_client.models.list = MagicMock(return_value=mock_async_iterator()) mock_openai_class.return_value = mock_client + # Set the __provider_id__ attribute that's expected by list_models + adapter.__provider_id__ = "openai" + # Call check_model_availability and verify it returns True assert await adapter.check_model_availability("gpt-4") @@ -94,8 +105,8 @@ class TestOpenAIBaseURLConfig: base_url=custom_url, ) - # Verify the method was called and returned True - mock_client.models.retrieve.assert_called_once_with("gpt-4") + # Verify the models.list method was called + mock_client.models.list.assert_called_once() @patch.dict(os.environ, {"OPENAI_BASE_URL": "https://proxy.openai.com/v1"}) @patch("llama_stack.providers.utils.inference.openai_mixin.AsyncOpenAI") @@ -110,11 +121,22 @@ class TestOpenAIBaseURLConfig: # Mock the get_api_key method adapter.get_api_key = MagicMock(return_value="test-key") - # Mock the AsyncOpenAI client + # Mock a model object that will be returned by models.list() + mock_model = MagicMock() + mock_model.id = "gpt-4" + + # Create an async iterator that yields our mock model + async def mock_async_iterator(): + yield mock_model + + # Mock the AsyncOpenAI client and its models.list method mock_client = MagicMock() - mock_client.models.retrieve = AsyncMock(return_value=MagicMock()) + mock_client.models.list = MagicMock(return_value=mock_async_iterator()) mock_openai_class.return_value = mock_client + # Set the __provider_id__ attribute that's expected by list_models + adapter.__provider_id__ = "openai" + # Call check_model_availability and verify it returns True assert await adapter.check_model_availability("gpt-4") diff --git a/tests/unit/providers/inference/test_remote_vllm.py b/tests/unit/providers/inference/test_remote_vllm.py index ce0e930b1..4dc2e0c16 100644 --- a/tests/unit/providers/inference/test_remote_vllm.py +++ b/tests/unit/providers/inference/test_remote_vllm.py @@ -6,19 +6,15 @@ import asyncio import json -import logging # allow-direct-logging -import threading import time -from http.server import BaseHTTPRequestHandler, HTTPServer -from typing import Any -from unittest.mock import AsyncMock, MagicMock, patch +from unittest.mock import AsyncMock, MagicMock, PropertyMock, patch import pytest from openai.types.chat.chat_completion_chunk import ( ChatCompletionChunk as OpenAIChatCompletionChunk, ) from openai.types.chat.chat_completion_chunk import ( - Choice as OpenAIChoice, + Choice as OpenAIChoiceChunk, ) from openai.types.chat.chat_completion_chunk import ( ChoiceDelta as OpenAIChoiceDelta, @@ -35,6 +31,9 @@ from llama_stack.apis.inference import ( ChatCompletionRequest, ChatCompletionResponseEventType, CompletionMessage, + OpenAIAssistantMessageParam, + OpenAIChatCompletion, + OpenAIChoice, SystemMessage, ToolChoice, ToolConfig, @@ -61,52 +60,21 @@ from llama_stack.providers.remote.inference.vllm.vllm import ( # -v -s --tb=short --disable-warnings -class MockInferenceAdapterWithSleep: - def __init__(self, sleep_time: int, response: dict[str, Any]): - self.httpd = None - - class DelayedRequestHandler(BaseHTTPRequestHandler): - # ruff: noqa: N802 - def do_POST(self): - time.sleep(sleep_time) - response_body = json.dumps(response).encode("utf-8") - self.send_response(code=200) - self.send_header("Content-Type", "application/json") - self.send_header("Content-Length", len(response_body)) - self.end_headers() - self.wfile.write(response_body) - - self.request_handler = DelayedRequestHandler - - def __enter__(self): - httpd = HTTPServer(("", 0), self.request_handler) - self.httpd = httpd - host, port = httpd.server_address - httpd_thread = threading.Thread(target=httpd.serve_forever) - httpd_thread.daemon = True # stop server if this thread terminates - httpd_thread.start() - - config = VLLMInferenceAdapterConfig(url=f"http://{host}:{port}") - inference_adapter = VLLMInferenceAdapter(config) - return inference_adapter - - def __exit__(self, _exc_type, _exc_value, _traceback): - if self.httpd: - self.httpd.shutdown() - self.httpd.server_close() - - @pytest.fixture(scope="module") def mock_openai_models_list(): - with patch("openai.resources.models.AsyncModels.list", new_callable=AsyncMock) as mock_list: + with patch("openai.resources.models.AsyncModels.list") as mock_list: yield mock_list -@pytest.fixture(scope="module") +@pytest.fixture(scope="function") async def vllm_inference_adapter(): config = VLLMInferenceAdapterConfig(url="http://mocked.localhost:12345") inference_adapter = VLLMInferenceAdapter(config) inference_adapter.model_store = AsyncMock() + # Mock the __provider_spec__ attribute that would normally be set by the resolver + inference_adapter.__provider_spec__ = MagicMock() + inference_adapter.__provider_spec__.provider_type = "vllm-inference" + inference_adapter.__provider_spec__.provider_data_validator = MagicMock() await inference_adapter.initialize() return inference_adapter @@ -150,10 +118,16 @@ async def test_tool_call_response(vllm_inference_adapter): """Verify that tool call arguments from a CompletionMessage are correctly converted into the expected JSON format.""" - # Patch the call to vllm so we can inspect the arguments sent were correct - with patch.object( - vllm_inference_adapter.client.chat.completions, "create", new_callable=AsyncMock - ) as mock_nonstream_completion: + # Patch the client property to avoid instantiating a real AsyncOpenAI client + with patch.object(VLLMInferenceAdapter, "client", new_callable=PropertyMock) as mock_create_client: + mock_client = MagicMock() + mock_client.chat.completions.create = AsyncMock() + mock_create_client.return_value = mock_client + + # Mock the model to return a proper provider_resource_id + mock_model = Model(identifier="mock-model", provider_resource_id="mock-model", provider_id="vllm-inference") + vllm_inference_adapter.model_store.get_model.return_value = mock_model + messages = [ SystemMessage(content="You are a helpful assistant"), UserMessage(content="How many?"), @@ -179,7 +153,7 @@ async def test_tool_call_response(vllm_inference_adapter): tool_config=ToolConfig(tool_choice=ToolChoice.auto), ) - assert mock_nonstream_completion.call_args.kwargs["messages"][2]["tool_calls"] == [ + assert mock_client.chat.completions.create.call_args.kwargs["messages"][2]["tool_calls"] == [ { "id": "foo", "type": "function", @@ -199,7 +173,7 @@ async def test_tool_call_delta_empty_tool_call_buf(): async def mock_stream(): delta = OpenAIChoiceDelta(content="", tool_calls=None) - choices = [OpenAIChoice(delta=delta, finish_reason="stop", index=0)] + choices = [OpenAIChoiceChunk(delta=delta, finish_reason="stop", index=0)] mock_chunk = OpenAIChatCompletionChunk( id="chunk-1", created=1, @@ -225,7 +199,7 @@ async def test_tool_call_delta_streaming_arguments_dict(): model="foo", object="chat.completion.chunk", choices=[ - OpenAIChoice( + OpenAIChoiceChunk( delta=OpenAIChoiceDelta( content="", tool_calls=[ @@ -250,7 +224,7 @@ async def test_tool_call_delta_streaming_arguments_dict(): model="foo", object="chat.completion.chunk", choices=[ - OpenAIChoice( + OpenAIChoiceChunk( delta=OpenAIChoiceDelta( content="", tool_calls=[ @@ -275,7 +249,9 @@ async def test_tool_call_delta_streaming_arguments_dict(): model="foo", object="chat.completion.chunk", choices=[ - OpenAIChoice(delta=OpenAIChoiceDelta(content="", tool_calls=None), finish_reason="tool_calls", index=0) + OpenAIChoiceChunk( + delta=OpenAIChoiceDelta(content="", tool_calls=None), finish_reason="tool_calls", index=0 + ) ], ) for chunk in [mock_chunk_1, mock_chunk_2, mock_chunk_3]: @@ -299,7 +275,7 @@ async def test_multiple_tool_calls(): model="foo", object="chat.completion.chunk", choices=[ - OpenAIChoice( + OpenAIChoiceChunk( delta=OpenAIChoiceDelta( content="", tool_calls=[ @@ -324,7 +300,7 @@ async def test_multiple_tool_calls(): model="foo", object="chat.completion.chunk", choices=[ - OpenAIChoice( + OpenAIChoiceChunk( delta=OpenAIChoiceDelta( content="", tool_calls=[ @@ -349,7 +325,9 @@ async def test_multiple_tool_calls(): model="foo", object="chat.completion.chunk", choices=[ - OpenAIChoice(delta=OpenAIChoiceDelta(content="", tool_calls=None), finish_reason="tool_calls", index=0) + OpenAIChoiceChunk( + delta=OpenAIChoiceDelta(content="", tool_calls=None), finish_reason="tool_calls", index=0 + ) ], ) for chunk in [mock_chunk_1, mock_chunk_2, mock_chunk_3]: @@ -393,59 +371,6 @@ async def test_process_vllm_chat_completion_stream_response_no_choices(): assert chunks[0].event.event_type.value == "start" -@pytest.mark.allow_network -def test_chat_completion_doesnt_block_event_loop(caplog): - loop = asyncio.new_event_loop() - loop.set_debug(True) - caplog.set_level(logging.WARNING) - - # Log when event loop is blocked for more than 200ms - loop.slow_callback_duration = 0.5 - # Sleep for 500ms in our delayed http response - sleep_time = 0.5 - - mock_model = Model(identifier="mock-model", provider_resource_id="mock-model", provider_id="vllm-inference") - mock_response = { - "id": "chatcmpl-abc123", - "object": "chat.completion", - "created": 1, - "modle": "mock-model", - "choices": [ - { - "message": {"content": ""}, - "logprobs": None, - "finish_reason": "stop", - "index": 0, - } - ], - } - - async def do_chat_completion(): - await inference_adapter.chat_completion( - "mock-model", - [], - stream=False, - tools=None, - tool_config=ToolConfig(tool_choice=ToolChoice.auto), - ) - - with MockInferenceAdapterWithSleep(sleep_time, mock_response) as inference_adapter: - inference_adapter.model_store = AsyncMock() - inference_adapter.model_store.get_model.return_value = mock_model - loop.run_until_complete(inference_adapter.initialize()) - - # Clear the logs so far and run the actual chat completion we care about - caplog.clear() - loop.run_until_complete(do_chat_completion()) - - # Ensure we don't have any asyncio warnings in the captured log - # records from our chat completion call. A message gets logged - # here any time we exceed the slow_callback_duration configured - # above. - asyncio_warnings = [record.message for record in caplog.records if record.name == "asyncio"] - assert not asyncio_warnings - - async def test_get_params_empty_tools(vllm_inference_adapter): request = ChatCompletionRequest( tools=[], @@ -638,33 +563,29 @@ async def test_health_status_success(vllm_inference_adapter): """ Test the health method of VLLM InferenceAdapter when the connection is successful. - This test verifies that the health method returns a HealthResponse with status OK, only - when the connection to the vLLM server is successful. + This test verifies that the health method returns a HealthResponse with status OK + when the /health endpoint responds successfully. """ - # Set vllm_inference_adapter.client to None to ensure _create_client is called - vllm_inference_adapter.client = None - with patch.object(vllm_inference_adapter, "_create_client") as mock_create_client: - # Create mock client and models - mock_client = MagicMock() - mock_models = MagicMock() + with patch("httpx.AsyncClient") as mock_client_class: + # Create mock response + mock_response = MagicMock() + mock_response.raise_for_status.return_value = None - # Create a mock async iterator that yields a model when iterated - async def mock_list(): - for model in [MagicMock()]: - yield model - - # Set up the models.list to return our mock async iterator - mock_models.list.return_value = mock_list() - mock_client.models = mock_models - mock_create_client.return_value = mock_client + # Create mock client instance + mock_client_instance = MagicMock() + mock_client_instance.get = AsyncMock(return_value=mock_response) + mock_client_class.return_value.__aenter__.return_value = mock_client_instance # Call the health method health_response = await vllm_inference_adapter.health() + # Verify the response assert health_response["status"] == HealthStatus.OK - # Verify that models.list was called - mock_models.list.assert_called_once() + # Verify that the health endpoint was called + mock_client_instance.get.assert_called_once() + call_args = mock_client_instance.get.call_args[0] + assert call_args[0].endswith("/health") async def test_health_status_failure(vllm_inference_adapter): @@ -674,26 +595,190 @@ async def test_health_status_failure(vllm_inference_adapter): This test verifies that the health method returns a HealthResponse with status ERROR and an appropriate error message when the connection to the vLLM server fails. """ - vllm_inference_adapter.client = None - with patch.object(vllm_inference_adapter, "_create_client") as mock_create_client: - # Create mock client and models - mock_client = MagicMock() - mock_models = MagicMock() - - # Create a mock async iterator that raises an exception when iterated - async def mock_list(): - raise Exception("Connection failed") - yield # Unreachable code - - # Set up the models.list to return our mock async iterator - mock_models.list.return_value = mock_list() - mock_client.models = mock_models - mock_create_client.return_value = mock_client + with patch("httpx.AsyncClient") as mock_client_class: + # Create mock client instance that raises an exception + mock_client_instance = MagicMock() + mock_client_instance.get.side_effect = Exception("Connection failed") + mock_client_class.return_value.__aenter__.return_value = mock_client_instance # Call the health method health_response = await vllm_inference_adapter.health() + # Verify the response assert health_response["status"] == HealthStatus.ERROR assert "Health check failed: Connection failed" in health_response["message"] - mock_models.list.assert_called_once() + +async def test_health_status_no_static_api_key(vllm_inference_adapter): + """ + Test the health method of VLLM InferenceAdapter when no static API key is provided. + + This test verifies that the health method returns a HealthResponse with status OK + when the /health endpoint responds successfully, regardless of API token configuration. + """ + with patch("httpx.AsyncClient") as mock_client_class: + # Create mock response + mock_response = MagicMock() + mock_response.raise_for_status.return_value = None + + # Create mock client instance + mock_client_instance = MagicMock() + mock_client_instance.get = AsyncMock(return_value=mock_response) + mock_client_class.return_value.__aenter__.return_value = mock_client_instance + + # Call the health method + health_response = await vllm_inference_adapter.health() + + # Verify the response + assert health_response["status"] == HealthStatus.OK + + +async def test_openai_chat_completion_is_async(vllm_inference_adapter): + """ + Verify that openai_chat_completion is async and doesn't block the event loop. + + To do this we mock the underlying inference with a sleep, start multiple + inference calls in parallel, and ensure the total time taken is less + than the sum of the individual sleep times. + """ + sleep_time = 0.5 + + async def mock_create(*args, **kwargs): + await asyncio.sleep(sleep_time) + return OpenAIChatCompletion( + id="chatcmpl-abc123", + created=1, + model="mock-model", + choices=[ + OpenAIChoice( + message=OpenAIAssistantMessageParam( + content="nothing interesting", + ), + finish_reason="stop", + index=0, + ) + ], + ) + + async def do_inference(): + await vllm_inference_adapter.openai_chat_completion( + "mock-model", messages=["one fish", "two fish"], stream=False + ) + + with patch.object(VLLMInferenceAdapter, "client", new_callable=PropertyMock) as mock_create_client: + mock_client = MagicMock() + mock_client.chat.completions.create = AsyncMock(side_effect=mock_create) + mock_create_client.return_value = mock_client + + start_time = time.time() + await asyncio.gather(do_inference(), do_inference(), do_inference(), do_inference()) + total_time = time.time() - start_time + + assert mock_create_client.call_count == 4 # no cheating + assert total_time < (sleep_time * 2), f"Total time taken: {total_time}s exceeded expected max" + + +async def test_should_refresh_models(): + """ + Test the should_refresh_models method with different refresh_models configurations. + + This test verifies that: + 1. When refresh_models is True, should_refresh_models returns True regardless of api_token + 2. When refresh_models is False, should_refresh_models returns False regardless of api_token + """ + + # Test case 1: refresh_models is True, api_token is None + config1 = VLLMInferenceAdapterConfig(url="http://test.localhost", api_token=None, refresh_models=True) + adapter1 = VLLMInferenceAdapter(config1) + result1 = await adapter1.should_refresh_models() + assert result1 is True, "should_refresh_models should return True when refresh_models is True" + + # Test case 2: refresh_models is True, api_token is empty string + config2 = VLLMInferenceAdapterConfig(url="http://test.localhost", api_token="", refresh_models=True) + adapter2 = VLLMInferenceAdapter(config2) + result2 = await adapter2.should_refresh_models() + assert result2 is True, "should_refresh_models should return True when refresh_models is True" + + # Test case 3: refresh_models is True, api_token is "fake" (default) + config3 = VLLMInferenceAdapterConfig(url="http://test.localhost", api_token="fake", refresh_models=True) + adapter3 = VLLMInferenceAdapter(config3) + result3 = await adapter3.should_refresh_models() + assert result3 is True, "should_refresh_models should return True when refresh_models is True" + + # Test case 4: refresh_models is True, api_token is real token + config4 = VLLMInferenceAdapterConfig(url="http://test.localhost", api_token="real-token-123", refresh_models=True) + adapter4 = VLLMInferenceAdapter(config4) + result4 = await adapter4.should_refresh_models() + assert result4 is True, "should_refresh_models should return True when refresh_models is True" + + # Test case 5: refresh_models is False, api_token is real token + config5 = VLLMInferenceAdapterConfig(url="http://test.localhost", api_token="real-token-456", refresh_models=False) + adapter5 = VLLMInferenceAdapter(config5) + result5 = await adapter5.should_refresh_models() + assert result5 is False, "should_refresh_models should return False when refresh_models is False" + + +async def test_provider_data_var_context_propagation(vllm_inference_adapter): + """ + Test that PROVIDER_DATA_VAR context is properly propagated through the vLLM inference adapter. + This ensures that dynamic provider data (like API tokens) can be passed through context. + Note: The base URL is always taken from config.url, not from provider data. + """ + # Mock the AsyncOpenAI class to capture provider data + with ( + patch("llama_stack.providers.utils.inference.openai_mixin.AsyncOpenAI") as mock_openai_class, + patch.object(vllm_inference_adapter, "get_request_provider_data") as mock_get_provider_data, + ): + mock_client = AsyncMock() + mock_client.chat.completions.create = AsyncMock() + mock_openai_class.return_value = mock_client + + # Mock provider data to return test data + mock_provider_data = MagicMock() + mock_provider_data.vllm_api_token = "test-token-123" + mock_provider_data.vllm_url = "http://test-server:8000/v1" + mock_get_provider_data.return_value = mock_provider_data + + # Mock the model + mock_model = Model(identifier="test-model", provider_resource_id="test-model", provider_id="vllm-inference") + vllm_inference_adapter.model_store.get_model.return_value = mock_model + + try: + # Execute chat completion + await vllm_inference_adapter.chat_completion( + "test-model", + [UserMessage(content="Hello")], + stream=False, + tools=None, + tool_config=ToolConfig(tool_choice=ToolChoice.auto), + ) + + # Verify that ALL client calls were made with the correct parameters + calls = mock_openai_class.call_args_list + incorrect_calls = [] + + for i, call in enumerate(calls): + api_key = call[1]["api_key"] + base_url = call[1]["base_url"] + + if api_key != "test-token-123" or base_url != "http://mocked.localhost:12345": + incorrect_calls.append({"call_index": i, "api_key": api_key, "base_url": base_url}) + + if incorrect_calls: + error_msg = ( + f"Found {len(incorrect_calls)} calls with incorrect parameters out of {len(calls)} total calls:\n" + ) + for incorrect_call in incorrect_calls: + error_msg += f" Call {incorrect_call['call_index']}: api_key='{incorrect_call['api_key']}', base_url='{incorrect_call['base_url']}'\n" + error_msg += "Expected: api_key='test-token-123', base_url='http://mocked.localhost:12345'" + raise AssertionError(error_msg) + + # Ensure at least one call was made + assert len(calls) >= 1, "No AsyncOpenAI client calls were made" + + # Verify that chat completion was called + mock_client.chat.completions.create.assert_called_once() + + finally: + # Clean up context + pass diff --git a/tests/unit/providers/nvidia/test_eval.py b/tests/unit/providers/nvidia/test_eval.py index 584ca2101..55dfd7bee 100644 --- a/tests/unit/providers/nvidia/test_eval.py +++ b/tests/unit/providers/nvidia/test_eval.py @@ -52,14 +52,19 @@ class TestNVIDIAEvalImpl(unittest.TestCase): self.evaluator_post_patcher = patch( "llama_stack.providers.remote.eval.nvidia.eval.NVIDIAEvalImpl._evaluator_post" ) + self.evaluator_delete_patcher = patch( + "llama_stack.providers.remote.eval.nvidia.eval.NVIDIAEvalImpl._evaluator_delete" + ) self.mock_evaluator_get = self.evaluator_get_patcher.start() self.mock_evaluator_post = self.evaluator_post_patcher.start() + self.mock_evaluator_delete = self.evaluator_delete_patcher.start() def tearDown(self): """Clean up after each test.""" self.evaluator_get_patcher.stop() self.evaluator_post_patcher.stop() + self.evaluator_delete_patcher.stop() def _assert_request_body(self, expected_json): """Helper method to verify request body in Evaluator POST request is correct""" @@ -115,6 +120,13 @@ class TestNVIDIAEvalImpl(unittest.TestCase): self.mock_evaluator_post.assert_called_once() self._assert_request_body({"namespace": benchmark.provider_id, "name": benchmark.identifier, **eval_config}) + def test_unregister_benchmark(self): + # Unregister the benchmark + self.run_async(self.eval_impl.unregister_benchmark(benchmark_id=MOCK_BENCHMARK_ID)) + + # Verify the Evaluator API was called correctly + self.mock_evaluator_delete.assert_called_once_with(f"/v1/evaluation/configs/nvidia/{MOCK_BENCHMARK_ID}") + def test_run_eval(self): benchmark_config = BenchmarkConfig( eval_candidate=ModelCandidate( @@ -138,7 +150,7 @@ class TestNVIDIAEvalImpl(unittest.TestCase): self._assert_request_body( { "config": f"nvidia/{MOCK_BENCHMARK_ID}", - "target": {"type": "model", "model": "meta/llama-3.1-8b-instruct"}, + "target": {"type": "model", "model": "Llama3.1-8B-Instruct"}, } ) diff --git a/tests/unit/providers/test_bedrock.py b/tests/unit/providers/test_bedrock.py new file mode 100644 index 000000000..1ff07bbbe --- /dev/null +++ b/tests/unit/providers/test_bedrock.py @@ -0,0 +1,53 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the terms described in the LICENSE file in +# the root directory of this source tree. + +from llama_stack.providers.remote.inference.bedrock.bedrock import ( + _get_region_prefix, + _to_inference_profile_id, +) + + +def test_region_prefixes(): + assert _get_region_prefix("us-east-1") == "us." + assert _get_region_prefix("eu-west-1") == "eu." + assert _get_region_prefix("ap-south-1") == "ap." + assert _get_region_prefix("ca-central-1") == "us." + + # Test case insensitive + assert _get_region_prefix("US-EAST-1") == "us." + assert _get_region_prefix("EU-WEST-1") == "eu." + assert _get_region_prefix("Ap-South-1") == "ap." + + # Test None region + assert _get_region_prefix(None) == "us." + + +def test_model_id_conversion(): + # Basic conversion + assert ( + _to_inference_profile_id("meta.llama3-1-70b-instruct-v1:0", "us-east-1") == "us.meta.llama3-1-70b-instruct-v1:0" + ) + + # Already has prefix + assert ( + _to_inference_profile_id("us.meta.llama3-1-70b-instruct-v1:0", "us-east-1") + == "us.meta.llama3-1-70b-instruct-v1:0" + ) + + # ARN should be returned unchanged + arn = "arn:aws:bedrock:us-east-1:123456789012:inference-profile/us.meta.llama3-1-70b-instruct-v1:0" + assert _to_inference_profile_id(arn, "us-east-1") == arn + + # ARN should be returned unchanged even without region + assert _to_inference_profile_id(arn) == arn + + # Optional region parameter defaults to us-east-1 + assert _to_inference_profile_id("meta.llama3-1-70b-instruct-v1:0") == "us.meta.llama3-1-70b-instruct-v1:0" + + # Different regions work with optional parameter + assert ( + _to_inference_profile_id("meta.llama3-1-70b-instruct-v1:0", "eu-west-1") == "eu.meta.llama3-1-70b-instruct-v1:0" + ) diff --git a/tests/unit/providers/utils/inference/test_openai_mixin.py b/tests/unit/providers/utils/inference/test_openai_mixin.py new file mode 100644 index 000000000..d62292542 --- /dev/null +++ b/tests/unit/providers/utils/inference/test_openai_mixin.py @@ -0,0 +1,291 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the terms described in the LICENSE file in +# the root directory of this source tree. + +from unittest.mock import MagicMock, PropertyMock, patch + +import pytest + +from llama_stack.apis.inference import Model +from llama_stack.apis.models import ModelType +from llama_stack.providers.utils.inference.openai_mixin import OpenAIMixin + + +class OpenAIMixinImpl(OpenAIMixin): + def __init__(self): + self.__provider_id__ = "test-provider" + + def get_api_key(self) -> str: + raise NotImplementedError("This method should be mocked in tests") + + def get_base_url(self) -> str: + raise NotImplementedError("This method should be mocked in tests") + + +class OpenAIMixinWithEmbeddingsImpl(OpenAIMixin): + """Test implementation with embedding model metadata""" + + embedding_model_metadata = { + "text-embedding-3-small": {"embedding_dimension": 1536, "context_length": 8192}, + "text-embedding-ada-002": {"embedding_dimension": 1536, "context_length": 8192}, + } + + __provider_id__ = "test-provider" + + def get_api_key(self) -> str: + raise NotImplementedError("This method should be mocked in tests") + + def get_base_url(self) -> str: + raise NotImplementedError("This method should be mocked in tests") + + +@pytest.fixture +def mixin(): + """Create a test instance of OpenAIMixin""" + return OpenAIMixinImpl() + + +@pytest.fixture +def mixin_with_embeddings(): + """Create a test instance of OpenAIMixin with embedding model metadata""" + return OpenAIMixinWithEmbeddingsImpl() + + +@pytest.fixture +def mock_models(): + """Create multiple mock OpenAI model objects""" + models = [MagicMock(id=id) for id in ["some-mock-model-id", "another-mock-model-id", "final-mock-model-id"]] + return models + + +@pytest.fixture +def mock_client_with_models(mock_models): + """Create a mock client with models.list() set up to return mock_models""" + mock_client = MagicMock() + + async def mock_models_list(): + for model in mock_models: + yield model + + mock_client.models.list.return_value = mock_models_list() + return mock_client + + +@pytest.fixture +def mock_client_with_empty_models(): + """Create a mock client with models.list() set up to return empty list""" + mock_client = MagicMock() + + async def mock_empty_models_list(): + return + yield # Make it an async generator but don't yield anything + + mock_client.models.list.return_value = mock_empty_models_list() + return mock_client + + +@pytest.fixture +def mock_client_with_exception(): + """Create a mock client with models.list() set up to raise an exception""" + mock_client = MagicMock() + mock_client.models.list.side_effect = Exception("API Error") + return mock_client + + +@pytest.fixture +def mock_client_context(): + """Fixture that provides a context manager for mocking the OpenAI client""" + + def _mock_client_context(mixin, mock_client): + return patch.object(type(mixin), "client", new_callable=PropertyMock, return_value=mock_client) + + return _mock_client_context + + +class TestOpenAIMixinListModels: + """Test cases for the list_models method""" + + async def test_list_models_success(self, mixin, mock_client_with_models, mock_client_context): + """Test successful model listing""" + assert len(mixin._model_cache) == 0 + + with mock_client_context(mixin, mock_client_with_models): + result = await mixin.list_models() + + assert result is not None + assert len(result) == 3 + + model_ids = [model.identifier for model in result] + assert "some-mock-model-id" in model_ids + assert "another-mock-model-id" in model_ids + assert "final-mock-model-id" in model_ids + + for model in result: + assert model.provider_id == "test-provider" + assert model.model_type == ModelType.llm + assert model.provider_resource_id == model.identifier + + assert len(mixin._model_cache) == 3 + for model_id in ["some-mock-model-id", "another-mock-model-id", "final-mock-model-id"]: + assert model_id in mixin._model_cache + cached_model = mixin._model_cache[model_id] + assert cached_model.identifier == model_id + assert cached_model.provider_resource_id == model_id + + async def test_list_models_empty_response(self, mixin, mock_client_with_empty_models, mock_client_context): + """Test handling of empty model list""" + with mock_client_context(mixin, mock_client_with_empty_models): + result = await mixin.list_models() + + assert result is not None + assert len(result) == 0 + assert len(mixin._model_cache) == 0 + + +class TestOpenAIMixinCheckModelAvailability: + """Test cases for the check_model_availability method""" + + async def test_check_model_availability_with_cache(self, mixin, mock_client_with_models, mock_client_context): + """Test model availability check when cache is populated""" + with mock_client_context(mixin, mock_client_with_models): + mock_client_with_models.models.list.assert_not_called() + await mixin.list_models() + mock_client_with_models.models.list.assert_called_once() + + assert await mixin.check_model_availability("some-mock-model-id") + assert await mixin.check_model_availability("another-mock-model-id") + assert await mixin.check_model_availability("final-mock-model-id") + assert not await mixin.check_model_availability("non-existent-model") + mock_client_with_models.models.list.assert_called_once() + + async def test_check_model_availability_without_cache(self, mixin, mock_client_with_models, mock_client_context): + """Test model availability check when cache is empty (calls list_models)""" + assert len(mixin._model_cache) == 0 + + with mock_client_context(mixin, mock_client_with_models): + mock_client_with_models.models.list.assert_not_called() + assert await mixin.check_model_availability("some-mock-model-id") + mock_client_with_models.models.list.assert_called_once() + + assert len(mixin._model_cache) == 3 + assert "some-mock-model-id" in mixin._model_cache + + async def test_check_model_availability_model_not_found(self, mixin, mock_client_with_models, mock_client_context): + """Test model availability check for non-existent model""" + with mock_client_context(mixin, mock_client_with_models): + mock_client_with_models.models.list.assert_not_called() + assert not await mixin.check_model_availability("non-existent-model") + mock_client_with_models.models.list.assert_called_once() + + assert len(mixin._model_cache) == 3 + + +class TestOpenAIMixinCacheBehavior: + """Test cases for cache behavior and edge cases""" + + async def test_cache_overwrites_on_list_models_call(self, mixin, mock_client_with_models, mock_client_context): + """Test that calling list_models overwrites existing cache""" + initial_model = Model( + provider_id="test-provider", + provider_resource_id="old-model", + identifier="old-model", + model_type=ModelType.llm, + ) + mixin._model_cache = {"old-model": initial_model} + + with mock_client_context(mixin, mock_client_with_models): + await mixin.list_models() + + assert len(mixin._model_cache) == 3 + assert "old-model" not in mixin._model_cache + assert "some-mock-model-id" in mixin._model_cache + assert "another-mock-model-id" in mixin._model_cache + assert "final-mock-model-id" in mixin._model_cache + + +class TestOpenAIMixinEmbeddingModelMetadata: + """Test cases for embedding_model_metadata attribute functionality""" + + async def test_embedding_model_identified_and_augmented(self, mixin_with_embeddings, mock_client_context): + """Test that models in embedding_model_metadata are correctly identified as embeddings with metadata""" + # Create mock models: 1 embedding model and 1 LLM, while there are 2 known embedding models + mock_embedding_model = MagicMock(id="text-embedding-3-small") + mock_llm_model = MagicMock(id="gpt-4") + mock_models = [mock_embedding_model, mock_llm_model] + + mock_client = MagicMock() + + async def mock_models_list(): + for model in mock_models: + yield model + + mock_client.models.list.return_value = mock_models_list() + + with mock_client_context(mixin_with_embeddings, mock_client): + result = await mixin_with_embeddings.list_models() + + assert result is not None + assert len(result) == 2 + + # Find the models in the result + embedding_model = next(m for m in result if m.identifier == "text-embedding-3-small") + llm_model = next(m for m in result if m.identifier == "gpt-4") + + # Check embedding model + assert embedding_model.model_type == ModelType.embedding + assert embedding_model.metadata == {"embedding_dimension": 1536, "context_length": 8192} + assert embedding_model.provider_id == "test-provider" + assert embedding_model.provider_resource_id == "text-embedding-3-small" + + # Check LLM model + assert llm_model.model_type == ModelType.llm + assert llm_model.metadata == {} # No metadata for LLMs + assert llm_model.provider_id == "test-provider" + assert llm_model.provider_resource_id == "gpt-4" + + +class TestOpenAIMixinAllowedModels: + """Test cases for allowed_models filtering functionality""" + + async def test_list_models_with_allowed_models_filter(self, mixin, mock_client_with_models, mock_client_context): + """Test that list_models filters models based on allowed_models set""" + mixin.allowed_models = {"some-mock-model-id", "another-mock-model-id"} + + with mock_client_context(mixin, mock_client_with_models): + result = await mixin.list_models() + + assert result is not None + assert len(result) == 2 + + model_ids = [model.identifier for model in result] + assert "some-mock-model-id" in model_ids + assert "another-mock-model-id" in model_ids + assert "final-mock-model-id" not in model_ids + + async def test_list_models_with_empty_allowed_models(self, mixin, mock_client_with_models, mock_client_context): + """Test that empty allowed_models set allows all models""" + assert len(mixin.allowed_models) == 0 + + with mock_client_context(mixin, mock_client_with_models): + result = await mixin.list_models() + + assert result is not None + assert len(result) == 3 # All models should be included + + model_ids = [model.identifier for model in result] + assert "some-mock-model-id" in model_ids + assert "another-mock-model-id" in model_ids + assert "final-mock-model-id" in model_ids + + async def test_check_model_availability_with_allowed_models( + self, mixin, mock_client_with_models, mock_client_context + ): + """Test that check_model_availability respects allowed_models""" + mixin.allowed_models = {"final-mock-model-id"} + + with mock_client_context(mixin, mock_client_with_models): + assert await mixin.check_model_availability("final-mock-model-id") + assert not await mixin.check_model_availability("some-mock-model-id") + assert not await mixin.check_model_availability("another-mock-model-id") diff --git a/tests/unit/providers/utils/memory/test_reranking.py b/tests/unit/providers/utils/memory/test_reranking.py new file mode 100644 index 000000000..02d7a1b6a --- /dev/null +++ b/tests/unit/providers/utils/memory/test_reranking.py @@ -0,0 +1,248 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the terms described in the LICENSE file in +# the root directory of this source tree. + + +from llama_stack.providers.utils.memory.vector_store import RERANKER_TYPE_RRF, RERANKER_TYPE_WEIGHTED +from llama_stack.providers.utils.vector_io.vector_utils import WeightedInMemoryAggregator + + +class TestNormalizeScores: + """Test cases for score normalization.""" + + def test_normalize_scores_basic(self): + """Test basic score normalization.""" + scores = {"doc1": 10.0, "doc2": 5.0, "doc3": 0.0} + normalized = WeightedInMemoryAggregator._normalize_scores(scores) + + assert normalized["doc1"] == 1.0 # Max score + assert normalized["doc3"] == 0.0 # Min score + assert normalized["doc2"] == 0.5 # Middle score + assert all(0 <= score <= 1 for score in normalized.values()) + + def test_normalize_scores_identical(self): + """Test normalization when all scores are identical.""" + scores = {"doc1": 5.0, "doc2": 5.0, "doc3": 5.0} + normalized = WeightedInMemoryAggregator._normalize_scores(scores) + + # All scores should be 1.0 when identical + assert all(score == 1.0 for score in normalized.values()) + + def test_normalize_scores_empty(self): + """Test normalization with empty scores.""" + scores = {} + normalized = WeightedInMemoryAggregator._normalize_scores(scores) + + assert normalized == {} + + def test_normalize_scores_single(self): + """Test normalization with single score.""" + scores = {"doc1": 7.5} + normalized = WeightedInMemoryAggregator._normalize_scores(scores) + + assert normalized["doc1"] == 1.0 + + +class TestWeightedRerank: + """Test cases for weighted reranking.""" + + def test_weighted_rerank_basic(self): + """Test basic weighted reranking.""" + vector_scores = {"doc1": 0.9, "doc2": 0.7, "doc3": 0.5} + keyword_scores = {"doc1": 0.6, "doc2": 0.8, "doc4": 0.9} + + combined = WeightedInMemoryAggregator.weighted_rerank(vector_scores, keyword_scores, alpha=0.5) + + # Should include all documents + expected_docs = {"doc1", "doc2", "doc3", "doc4"} + assert set(combined.keys()) == expected_docs + + # All scores should be between 0 and 1 + assert all(0 <= score <= 1 for score in combined.values()) + + # doc1 appears in both searches, should have higher combined score + assert combined["doc1"] > 0 + + def test_weighted_rerank_alpha_zero(self): + """Test weighted reranking with alpha=0 (keyword only).""" + vector_scores = {"doc1": 0.9, "doc2": 0.7, "doc3": 0.5} # All docs present in vector + keyword_scores = {"doc1": 0.1, "doc2": 0.3, "doc3": 0.9} # All docs present in keyword + + combined = WeightedInMemoryAggregator.weighted_rerank(vector_scores, keyword_scores, alpha=0.0) + + # Alpha=0 means vector scores are ignored, keyword scores dominate + # doc3 should score highest since it has highest keyword score + assert combined["doc3"] > combined["doc2"] > combined["doc1"] + + def test_weighted_rerank_alpha_one(self): + """Test weighted reranking with alpha=1 (vector only).""" + vector_scores = {"doc1": 0.9, "doc2": 0.7, "doc3": 0.5} # All docs present in vector + keyword_scores = {"doc1": 0.1, "doc2": 0.3, "doc3": 0.9} # All docs present in keyword + + combined = WeightedInMemoryAggregator.weighted_rerank(vector_scores, keyword_scores, alpha=1.0) + + # Alpha=1 means keyword scores are ignored, vector scores dominate + # doc1 should score highest since it has highest vector score + assert combined["doc1"] > combined["doc2"] > combined["doc3"] + + def test_weighted_rerank_no_overlap(self): + """Test weighted reranking with no overlapping documents.""" + vector_scores = {"doc1": 0.9, "doc2": 0.7} + keyword_scores = {"doc3": 0.8, "doc4": 0.6} + + combined = WeightedInMemoryAggregator.weighted_rerank(vector_scores, keyword_scores, alpha=0.5) + + assert len(combined) == 4 + # With min-max normalization, lowest scoring docs in each group get 0.0 + # but highest scoring docs should get positive scores + assert all(score >= 0 for score in combined.values()) + assert combined["doc1"] > 0 # highest vector score + assert combined["doc3"] > 0 # highest keyword score + + +class TestRRFRerank: + """Test cases for RRF (Reciprocal Rank Fusion) reranking.""" + + def test_rrf_rerank_basic(self): + """Test basic RRF reranking.""" + vector_scores = {"doc1": 0.9, "doc2": 0.7, "doc3": 0.5} + keyword_scores = {"doc1": 0.6, "doc2": 0.8, "doc4": 0.9} + + combined = WeightedInMemoryAggregator.rrf_rerank(vector_scores, keyword_scores, impact_factor=60.0) + + # Should include all documents + expected_docs = {"doc1", "doc2", "doc3", "doc4"} + assert set(combined.keys()) == expected_docs + + # All scores should be positive + assert all(score > 0 for score in combined.values()) + + # Documents appearing in both searches should have higher scores + # doc1 and doc2 appear in both, doc3 and doc4 appear in only one + assert combined["doc1"] > combined["doc3"] + assert combined["doc2"] > combined["doc4"] + + def test_rrf_rerank_rank_calculation(self): + """Test that RRF correctly calculates ranks.""" + # Create clear ranking order + vector_scores = {"doc1": 1.0, "doc2": 0.8, "doc3": 0.6} # Ranks: 1, 2, 3 + keyword_scores = {"doc1": 0.5, "doc2": 1.0, "doc3": 0.7} # Ranks: 3, 1, 2 + + combined = WeightedInMemoryAggregator.rrf_rerank(vector_scores, keyword_scores, impact_factor=60.0) + + # doc1: rank 1 in vector, rank 3 in keyword + # doc2: rank 2 in vector, rank 1 in keyword + # doc3: rank 3 in vector, rank 2 in keyword + + # doc2 should have the highest combined score (ranks 2+1=3) + # followed by doc1 (ranks 1+3=4) and doc3 (ranks 3+2=5) + # Remember: lower rank sum = higher RRF score + assert combined["doc2"] > combined["doc1"] > combined["doc3"] + + def test_rrf_rerank_impact_factor(self): + """Test that impact factor affects RRF scores.""" + vector_scores = {"doc1": 0.9, "doc2": 0.7} + keyword_scores = {"doc1": 0.8, "doc2": 0.6} + + combined_low = WeightedInMemoryAggregator.rrf_rerank(vector_scores, keyword_scores, impact_factor=10.0) + combined_high = WeightedInMemoryAggregator.rrf_rerank(vector_scores, keyword_scores, impact_factor=100.0) + + # Higher impact factor should generally result in lower scores + # (because 1/(k+r) decreases as k increases) + assert combined_low["doc1"] > combined_high["doc1"] + assert combined_low["doc2"] > combined_high["doc2"] + + def test_rrf_rerank_missing_documents(self): + """Test RRF handling of documents missing from one search.""" + vector_scores = {"doc1": 0.9, "doc2": 0.7} + keyword_scores = {"doc1": 0.8, "doc3": 0.6} + + combined = WeightedInMemoryAggregator.rrf_rerank(vector_scores, keyword_scores, impact_factor=60.0) + + # Should include all documents + assert len(combined) == 3 + + # doc1 appears in both searches, should have highest score + assert combined["doc1"] > combined["doc2"] + assert combined["doc1"] > combined["doc3"] + + +class TestCombineSearchResults: + """Test cases for the main combine_search_results function.""" + + def test_combine_search_results_rrf_default(self): + """Test combining with RRF as default.""" + vector_scores = {"doc1": 0.9, "doc2": 0.7} + keyword_scores = {"doc1": 0.6, "doc3": 0.8} + + combined = WeightedInMemoryAggregator.combine_search_results(vector_scores, keyword_scores) + + # Should default to RRF + assert len(combined) == 3 + assert all(score > 0 for score in combined.values()) + + def test_combine_search_results_rrf_explicit(self): + """Test combining with explicit RRF.""" + vector_scores = {"doc1": 0.9, "doc2": 0.7} + keyword_scores = {"doc1": 0.6, "doc3": 0.8} + + combined = WeightedInMemoryAggregator.combine_search_results( + vector_scores, keyword_scores, reranker_type=RERANKER_TYPE_RRF, reranker_params={"impact_factor": 50.0} + ) + + assert len(combined) == 3 + assert all(score > 0 for score in combined.values()) + + def test_combine_search_results_weighted(self): + """Test combining with weighted reranking.""" + vector_scores = {"doc1": 0.9, "doc2": 0.7} + keyword_scores = {"doc1": 0.6, "doc3": 0.8} + + combined = WeightedInMemoryAggregator.combine_search_results( + vector_scores, keyword_scores, reranker_type=RERANKER_TYPE_WEIGHTED, reranker_params={"alpha": 0.3} + ) + + assert len(combined) == 3 + assert all(0 <= score <= 1 for score in combined.values()) + + def test_combine_search_results_unknown_type(self): + """Test combining with unknown reranker type defaults to RRF.""" + vector_scores = {"doc1": 0.9} + keyword_scores = {"doc2": 0.8} + + combined = WeightedInMemoryAggregator.combine_search_results( + vector_scores, keyword_scores, reranker_type="unknown_type" + ) + + # Should fall back to RRF + assert len(combined) == 2 + assert all(score > 0 for score in combined.values()) + + def test_combine_search_results_empty_params(self): + """Test combining with empty parameters.""" + vector_scores = {"doc1": 0.9} + keyword_scores = {"doc2": 0.8} + + combined = WeightedInMemoryAggregator.combine_search_results(vector_scores, keyword_scores, reranker_params={}) + + # Should use default parameters + assert len(combined) == 2 + assert all(score > 0 for score in combined.values()) + + def test_combine_search_results_empty_scores(self): + """Test combining with empty score dictionaries.""" + # Test with empty vector scores + combined = WeightedInMemoryAggregator.combine_search_results({}, {"doc1": 0.8}) + assert len(combined) == 1 + assert combined["doc1"] > 0 + + # Test with empty keyword scores + combined = WeightedInMemoryAggregator.combine_search_results({"doc1": 0.9}, {}) + assert len(combined) == 1 + assert combined["doc1"] > 0 + + # Test with both empty + combined = WeightedInMemoryAggregator.combine_search_results({}, {}) + assert len(combined) == 0 diff --git a/tests/unit/providers/utils/memory/test_vector_store.py b/tests/unit/providers/utils/memory/test_vector_store.py index 90b229262..590bdd1d2 100644 --- a/tests/unit/providers/utils/memory/test_vector_store.py +++ b/tests/unit/providers/utils/memory/test_vector_store.py @@ -178,3 +178,41 @@ def test_content_from_data_and_mime_type_both_encodings_fail(): # Should raise an exception instead of returning empty string with pytest.raises(UnicodeDecodeError): content_from_data_and_mime_type(data, mime_type) + + +async def test_memory_tool_error_handling(): + """Test that memory tool handles various failures gracefully without crashing.""" + from llama_stack.providers.inline.tool_runtime.rag.config import RagToolRuntimeConfig + from llama_stack.providers.inline.tool_runtime.rag.memory import MemoryToolRuntimeImpl + + config = RagToolRuntimeConfig() + memory_tool = MemoryToolRuntimeImpl( + config=config, + vector_io_api=AsyncMock(), + inference_api=AsyncMock(), + files_api=AsyncMock(), + ) + + docs = [ + RAGDocument(document_id="good_doc", content="Good content", metadata={}), + RAGDocument(document_id="bad_url_doc", content=URL(uri="https://bad.url"), metadata={}), + RAGDocument(document_id="another_good_doc", content="Another good content", metadata={}), + ] + + mock_file1 = MagicMock() + mock_file1.id = "file_good1" + mock_file2 = MagicMock() + mock_file2.id = "file_good2" + memory_tool.files_api.openai_upload_file.side_effect = [mock_file1, mock_file2] + + with patch("httpx.AsyncClient") as mock_client: + mock_instance = AsyncMock() + mock_instance.get.side_effect = Exception("Bad URL") + mock_client.return_value.__aenter__.return_value = mock_instance + + # won't raise exception despite one document failing + await memory_tool.insert(docs, "vector_store_123") + + # processed 2 documents successfully, skipped 1 + assert memory_tool.files_api.openai_upload_file.call_count == 2 + assert memory_tool.vector_io_api.openai_attach_file_to_vector_store.call_count == 2 diff --git a/tests/unit/providers/utils/test_model_registry.py b/tests/unit/providers/utils/test_model_registry.py index db1630000..04e75aa82 100644 --- a/tests/unit/providers/utils/test_model_registry.py +++ b/tests/unit/providers/utils/test_model_registry.py @@ -84,14 +84,14 @@ def unknown_model() -> Model: @pytest.fixture def helper(known_provider_model: ProviderModelEntry, known_provider_model2: ProviderModelEntry) -> ModelRegistryHelper: - return ModelRegistryHelper([known_provider_model, known_provider_model2]) + return ModelRegistryHelper(model_entries=[known_provider_model, known_provider_model2]) class MockModelRegistryHelperWithDynamicModels(ModelRegistryHelper): """Test helper that simulates a provider with dynamically available models.""" def __init__(self, model_entries: list[ProviderModelEntry], available_models: list[str]): - super().__init__(model_entries) + super().__init__(model_entries=model_entries) self._available_models = available_models async def check_model_availability(self, model: str) -> bool: diff --git a/tests/unit/providers/vector_io/conftest.py b/tests/unit/providers/vector_io/conftest.py index f71073651..91bddd037 100644 --- a/tests/unit/providers/vector_io/conftest.py +++ b/tests/unit/providers/vector_io/conftest.py @@ -5,6 +5,7 @@ # the root directory of this source tree. import random +from unittest.mock import AsyncMock, MagicMock, patch import numpy as np import pytest @@ -12,7 +13,7 @@ from chromadb import PersistentClient from pymilvus import MilvusClient, connections from llama_stack.apis.vector_dbs import VectorDB -from llama_stack.apis.vector_io import Chunk, ChunkMetadata +from llama_stack.apis.vector_io import Chunk, ChunkMetadata, QueryChunksResponse from llama_stack.providers.inline.vector_io.chroma.config import ChromaVectorIOConfig from llama_stack.providers.inline.vector_io.faiss.config import FaissVectorIOConfig from llama_stack.providers.inline.vector_io.faiss.faiss import FaissIndex, FaissVectorIOAdapter @@ -22,6 +23,8 @@ from llama_stack.providers.inline.vector_io.sqlite_vec import SQLiteVectorIOConf from llama_stack.providers.inline.vector_io.sqlite_vec.sqlite_vec import SQLiteVecIndex, SQLiteVecVectorIOAdapter from llama_stack.providers.remote.vector_io.chroma.chroma import ChromaIndex, ChromaVectorIOAdapter, maybe_await from llama_stack.providers.remote.vector_io.milvus.milvus import MilvusIndex, MilvusVectorIOAdapter +from llama_stack.providers.remote.vector_io.pgvector.config import PGVectorVectorIOConfig +from llama_stack.providers.remote.vector_io.pgvector.pgvector import PGVectorIndex, PGVectorVectorIOAdapter from llama_stack.providers.remote.vector_io.qdrant.qdrant import QdrantVectorIOAdapter EMBEDDING_DIMENSION = 384 @@ -29,7 +32,7 @@ COLLECTION_PREFIX = "test_collection" MILVUS_ALIAS = "test_milvus" -@pytest.fixture(params=["milvus", "sqlite_vec", "faiss", "chroma"]) +@pytest.fixture(params=["milvus", "sqlite_vec", "faiss", "chroma", "pgvector"]) def vector_provider(request): return request.param @@ -333,15 +336,127 @@ async def qdrant_vec_index(qdrant_vec_db_path, embedding_dimension): await index.delete() +@pytest.fixture +def mock_psycopg2_connection(): + connection = MagicMock() + cursor = MagicMock() + + cursor.__enter__ = MagicMock(return_value=cursor) + cursor.__exit__ = MagicMock() + + connection.cursor.return_value = cursor + + return connection, cursor + + +@pytest.fixture +async def pgvector_vec_index(embedding_dimension, mock_psycopg2_connection): + connection, cursor = mock_psycopg2_connection + + vector_db = VectorDB( + identifier="test-vector-db", + embedding_model="test-model", + embedding_dimension=embedding_dimension, + provider_id="pgvector", + provider_resource_id="pgvector:test-vector-db", + ) + + with patch("llama_stack.providers.remote.vector_io.pgvector.pgvector.psycopg2"): + with patch("llama_stack.providers.remote.vector_io.pgvector.pgvector.execute_values"): + index = PGVectorIndex(vector_db, embedding_dimension, connection, distance_metric="COSINE") + index._test_chunks = [] + original_add_chunks = index.add_chunks + + async def mock_add_chunks(chunks, embeddings): + index._test_chunks = list(chunks) + await original_add_chunks(chunks, embeddings) + + index.add_chunks = mock_add_chunks + + async def mock_query_vector(embedding, k, score_threshold): + chunks = index._test_chunks[:k] if hasattr(index, "_test_chunks") else [] + scores = [1.0] * len(chunks) + return QueryChunksResponse(chunks=chunks, scores=scores) + + index.query_vector = mock_query_vector + + yield index + + +@pytest.fixture +async def pgvector_vec_adapter(mock_inference_api, embedding_dimension): + config = PGVectorVectorIOConfig( + host="localhost", + port=5432, + db="test_db", + user="test_user", + password="test_password", + kvstore=SqliteKVStoreConfig(), + ) + + adapter = PGVectorVectorIOAdapter(config, mock_inference_api, None) + + with patch("llama_stack.providers.remote.vector_io.pgvector.pgvector.psycopg2.connect") as mock_connect: + mock_conn = MagicMock() + mock_cursor = MagicMock() + mock_cursor.__enter__ = MagicMock(return_value=mock_cursor) + mock_cursor.__exit__ = MagicMock() + mock_conn.cursor.return_value = mock_cursor + mock_conn.autocommit = True + mock_connect.return_value = mock_conn + + with patch( + "llama_stack.providers.remote.vector_io.pgvector.pgvector.check_extension_version" + ) as mock_check_version: + mock_check_version.return_value = "0.5.1" + + with patch("llama_stack.providers.utils.kvstore.kvstore_impl") as mock_kvstore_impl: + mock_kvstore = AsyncMock() + mock_kvstore_impl.return_value = mock_kvstore + + with patch.object(adapter, "initialize_openai_vector_stores", new_callable=AsyncMock): + with patch("llama_stack.providers.remote.vector_io.pgvector.pgvector.upsert_models"): + await adapter.initialize() + adapter.conn = mock_conn + + async def mock_insert_chunks(vector_db_id, chunks, ttl_seconds=None): + index = await adapter._get_and_cache_vector_db_index(vector_db_id) + if not index: + raise ValueError(f"Vector DB {vector_db_id} not found") + await index.insert_chunks(chunks) + + adapter.insert_chunks = mock_insert_chunks + + async def mock_query_chunks(vector_db_id, query, params=None): + index = await adapter._get_and_cache_vector_db_index(vector_db_id) + if not index: + raise ValueError(f"Vector DB {vector_db_id} not found") + return await index.query_chunks(query, params) + + adapter.query_chunks = mock_query_chunks + + test_vector_db = VectorDB( + identifier=f"pgvector_test_collection_{random.randint(1, 1_000_000)}", + provider_id="test_provider", + embedding_model="test_model", + embedding_dimension=embedding_dimension, + ) + await adapter.register_vector_db(test_vector_db) + adapter.test_collection_id = test_vector_db.identifier + + yield adapter + await adapter.shutdown() + + @pytest.fixture def vector_io_adapter(vector_provider, request): - """Returns the appropriate vector IO adapter based on the provider parameter.""" vector_provider_dict = { "milvus": "milvus_vec_adapter", "faiss": "faiss_vec_adapter", "sqlite_vec": "sqlite_vec_adapter", "chroma": "chroma_vec_adapter", "qdrant": "qdrant_vec_adapter", + "pgvector": "pgvector_vec_adapter", } return request.getfixturevalue(vector_provider_dict[vector_provider]) diff --git a/tests/unit/providers/vector_io/remote/test_pgvector.py b/tests/unit/providers/vector_io/remote/test_pgvector.py new file mode 100644 index 000000000..6f498bf46 --- /dev/null +++ b/tests/unit/providers/vector_io/remote/test_pgvector.py @@ -0,0 +1,138 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the terms described in the LICENSE file in +# the root directory of this source tree. + +import asyncio +from unittest.mock import patch + +import pytest + +from llama_stack.apis.vector_dbs import VectorDB +from llama_stack.providers.remote.vector_io.pgvector.pgvector import PGVectorIndex + +PGVECTOR_PROVIDER = "pgvector" + + +@pytest.fixture(scope="session") +def loop(): + return asyncio.new_event_loop() + + +@pytest.fixture +def embedding_dimension(): + """Default embedding dimension for tests.""" + return 384 + + +@pytest.fixture +async def pgvector_index(embedding_dimension, mock_psycopg2_connection): + """Create a PGVectorIndex instance with mocked database connection.""" + connection, cursor = mock_psycopg2_connection + + vector_db = VectorDB( + identifier="test-vector-db", + embedding_model="test-model", + embedding_dimension=embedding_dimension, + provider_id=PGVECTOR_PROVIDER, + provider_resource_id=f"{PGVECTOR_PROVIDER}:test-vector-db", + ) + + with patch("llama_stack.providers.remote.vector_io.pgvector.pgvector.psycopg2"): + # Use explicit COSINE distance metric for consistent testing + index = PGVectorIndex(vector_db, embedding_dimension, connection, distance_metric="COSINE") + + return index, cursor + + +class TestPGVectorIndex: + def test_distance_metric_validation(self, embedding_dimension, mock_psycopg2_connection): + connection, cursor = mock_psycopg2_connection + + vector_db = VectorDB( + identifier="test-vector-db", + embedding_model="test-model", + embedding_dimension=embedding_dimension, + provider_id=PGVECTOR_PROVIDER, + provider_resource_id=f"{PGVECTOR_PROVIDER}:test-vector-db", + ) + + with patch("llama_stack.providers.remote.vector_io.pgvector.pgvector.psycopg2"): + index = PGVectorIndex(vector_db, embedding_dimension, connection, distance_metric="L2") + assert index.distance_metric == "L2" + with pytest.raises(ValueError, match="Distance metric 'INVALID' is not supported"): + PGVectorIndex(vector_db, embedding_dimension, connection, distance_metric="INVALID") + + def test_get_pgvector_search_function(self, pgvector_index): + index, cursor = pgvector_index + supported_metrics = index.PGVECTOR_DISTANCE_METRIC_TO_SEARCH_FUNCTION + + for metric, function in supported_metrics.items(): + index.distance_metric = metric + assert index.get_pgvector_search_function() == function + + def test_check_distance_metric_availability(self, pgvector_index): + index, cursor = pgvector_index + supported_metrics = index.PGVECTOR_DISTANCE_METRIC_TO_SEARCH_FUNCTION + + for metric in supported_metrics: + index.check_distance_metric_availability(metric) + + with pytest.raises(ValueError, match="Distance metric 'INVALID' is not supported"): + index.check_distance_metric_availability("INVALID") + + def test_constructor_invalid_distance_metric(self, embedding_dimension, mock_psycopg2_connection): + connection, cursor = mock_psycopg2_connection + + vector_db = VectorDB( + identifier="test-vector-db", + embedding_model="test-model", + embedding_dimension=embedding_dimension, + provider_id=PGVECTOR_PROVIDER, + provider_resource_id=f"{PGVECTOR_PROVIDER}:test-vector-db", + ) + + with patch("llama_stack.providers.remote.vector_io.pgvector.pgvector.psycopg2"): + with pytest.raises(ValueError, match="Distance metric 'INVALID_METRIC' is not supported by PGVector"): + PGVectorIndex(vector_db, embedding_dimension, connection, distance_metric="INVALID_METRIC") + + with pytest.raises(ValueError, match="Supported metrics are:"): + PGVectorIndex(vector_db, embedding_dimension, connection, distance_metric="UNKNOWN") + + try: + index = PGVectorIndex(vector_db, embedding_dimension, connection, distance_metric="COSINE") + assert index.distance_metric == "COSINE" + except ValueError: + pytest.fail("Valid distance metric 'COSINE' should not raise ValueError") + + def test_constructor_all_supported_distance_metrics(self, embedding_dimension, mock_psycopg2_connection): + connection, cursor = mock_psycopg2_connection + + vector_db = VectorDB( + identifier="test-vector-db", + embedding_model="test-model", + embedding_dimension=embedding_dimension, + provider_id=PGVECTOR_PROVIDER, + provider_resource_id=f"{PGVECTOR_PROVIDER}:test-vector-db", + ) + + supported_metrics = ["L2", "L1", "COSINE", "INNER_PRODUCT", "HAMMING", "JACCARD"] + + with patch("llama_stack.providers.remote.vector_io.pgvector.pgvector.psycopg2"): + for metric in supported_metrics: + try: + index = PGVectorIndex(vector_db, embedding_dimension, connection, distance_metric=metric) + assert index.distance_metric == metric + + expected_operators = { + "L2": "<->", + "L1": "<+>", + "COSINE": "<=>", + "INNER_PRODUCT": "<#>", + "HAMMING": "<~>", + "JACCARD": "<%>", + } + assert index.get_pgvector_search_function() == expected_operators[metric] + except Exception as e: + pytest.fail(f"Valid distance metric '{metric}' should not raise exception: {e}") diff --git a/tests/unit/providers/vector_io/test_qdrant.py b/tests/unit/providers/vector_io/test_qdrant.py index 4207cbee3..aab5b6f45 100644 --- a/tests/unit/providers/vector_io/test_qdrant.py +++ b/tests/unit/providers/vector_io/test_qdrant.py @@ -11,7 +11,8 @@ from unittest.mock import AsyncMock, MagicMock, patch import pytest -from llama_stack.apis.inference import EmbeddingsResponse, Inference +from llama_stack.apis.inference import Inference +from llama_stack.apis.inference.inference import OpenAIEmbeddingData, OpenAIEmbeddingsResponse, OpenAIEmbeddingUsage from llama_stack.apis.vector_io import ( QueryChunksResponse, VectorDB, @@ -53,7 +54,9 @@ def mock_vector_db(vector_db_id) -> MagicMock: mock_vector_db.identifier = vector_db_id mock_vector_db.embedding_dimension = 384 mock_vector_db.model_dump_json.return_value = ( - '{"identifier": "' + vector_db_id + '", "embedding_model": "embedding_model", "embedding_dimension": 384}' + '{"identifier": "' + + vector_db_id + + '", "provider_id": "qdrant", "embedding_model": "embedding_model", "embedding_dimension": 384}' ) return mock_vector_db @@ -68,7 +71,13 @@ def mock_vector_db_store(mock_vector_db) -> MagicMock: @pytest.fixture def mock_api_service(sample_embeddings): mock_api_service = MagicMock(spec=Inference) - mock_api_service.embeddings = AsyncMock(return_value=EmbeddingsResponse(embeddings=sample_embeddings)) + mock_api_service.openai_embeddings = AsyncMock( + return_value=OpenAIEmbeddingsResponse( + model="mock-embedding-model", + data=[OpenAIEmbeddingData(embedding=sample, index=i) for i, sample in enumerate(sample_embeddings)], + usage=OpenAIEmbeddingUsage(prompt_tokens=10, total_tokens=10), + ) + ) return mock_api_service diff --git a/tests/unit/providers/vector_io/test_vector_utils.py b/tests/unit/providers/vector_io/test_vector_utils.py index a5d803a82..10ebe5bfb 100644 --- a/tests/unit/providers/vector_io/test_vector_utils.py +++ b/tests/unit/providers/vector_io/test_vector_utils.py @@ -26,9 +26,9 @@ def test_generate_chunk_id(): chunk_ids = sorted([chunk.chunk_id for chunk in chunks]) assert chunk_ids == [ - "177a1368-f6a8-0c50-6e92-18677f2c3de3", - "bc744db3-1b25-0a9c-cdff-b6ba3df73c36", - "f68df25d-d9aa-ab4d-5684-64a233add20d", + "31d1f9a3-c8d2-66e7-3c37-af2acd329778", + "d07dade7-29c0-cda7-df29-0249a1dcbc3e", + "d14f75a1-5855-7f72-2c78-d9fc4275a346", ] @@ -36,14 +36,14 @@ def test_generate_chunk_id_with_window(): chunk = Chunk(content="test", metadata={"document_id": "doc-1"}) chunk_id1 = generate_chunk_id("doc-1", chunk, chunk_window="0-1") chunk_id2 = generate_chunk_id("doc-1", chunk, chunk_window="1-2") - assert chunk_id1 == "149018fe-d0eb-0f8d-5f7f-726bdd2aeedb" - assert chunk_id2 == "4562c1ee-9971-1f3b-51a6-7d05e5211154" + assert chunk_id1 == "8630321a-d9cb-2bb6-cd28-ebf68dafd866" + assert chunk_id2 == "13a1c09a-cbda-b61a-2d1a-7baa90888685" def test_chunk_id(): # Test with existing chunk ID chunk_with_id = Chunk(content="test", metadata={"document_id": "existing-id"}) - assert chunk_with_id.chunk_id == "84ededcc-b80b-a83e-1a20-ca6515a11350" + assert chunk_with_id.chunk_id == "11704f92-42b6-61df-bf85-6473e7708fbd" # Test with document ID in metadata chunk_with_doc_id = Chunk(content="test", metadata={"document_id": "doc-1"}) diff --git a/tests/unit/rag/test_rag_query.py b/tests/unit/rag/test_rag_query.py index 05ccecb99..a45b66f02 100644 --- a/tests/unit/rag/test_rag_query.py +++ b/tests/unit/rag/test_rag_query.py @@ -19,12 +19,16 @@ from llama_stack.providers.inline.tool_runtime.rag.memory import MemoryToolRunti class TestRagQuery: async def test_query_raises_on_empty_vector_db_ids(self): - rag_tool = MemoryToolRuntimeImpl(config=MagicMock(), vector_io_api=MagicMock(), inference_api=MagicMock()) + rag_tool = MemoryToolRuntimeImpl( + config=MagicMock(), vector_io_api=MagicMock(), inference_api=MagicMock(), files_api=MagicMock() + ) with pytest.raises(ValueError): await rag_tool.query(content=MagicMock(), vector_db_ids=[]) async def test_query_chunk_metadata_handling(self): - rag_tool = MemoryToolRuntimeImpl(config=MagicMock(), vector_io_api=MagicMock(), inference_api=MagicMock()) + rag_tool = MemoryToolRuntimeImpl( + config=MagicMock(), vector_io_api=MagicMock(), inference_api=MagicMock(), files_api=MagicMock() + ) content = "test query content" vector_db_ids = ["db1"] @@ -77,3 +81,58 @@ class TestRagQuery: # Test that invalid mode raises an error with pytest.raises(ValueError): RAGQueryConfig(mode="wrong_mode") + + async def test_query_adds_vector_db_id_to_chunk_metadata(self): + rag_tool = MemoryToolRuntimeImpl( + config=MagicMock(), + vector_io_api=MagicMock(), + inference_api=MagicMock(), + files_api=MagicMock(), + ) + + vector_db_ids = ["db1", "db2"] + + # Fake chunks from each DB + chunk_metadata1 = ChunkMetadata( + document_id="doc1", + chunk_id="chunk1", + source="test_source1", + metadata_token_count=5, + ) + chunk1 = Chunk( + content="chunk from db1", + metadata={"vector_db_id": "db1", "document_id": "doc1"}, + stored_chunk_id="c1", + chunk_metadata=chunk_metadata1, + ) + + chunk_metadata2 = ChunkMetadata( + document_id="doc2", + chunk_id="chunk2", + source="test_source2", + metadata_token_count=5, + ) + chunk2 = Chunk( + content="chunk from db2", + metadata={"vector_db_id": "db2", "document_id": "doc2"}, + stored_chunk_id="c2", + chunk_metadata=chunk_metadata2, + ) + + rag_tool.vector_io_api.query_chunks = AsyncMock( + side_effect=[ + QueryChunksResponse(chunks=[chunk1], scores=[0.9]), + QueryChunksResponse(chunks=[chunk2], scores=[0.8]), + ] + ) + + result = await rag_tool.query(content="test", vector_db_ids=vector_db_ids) + returned_chunks = result.metadata["chunks"] + returned_scores = result.metadata["scores"] + returned_doc_ids = result.metadata["document_ids"] + returned_vector_db_ids = result.metadata["vector_db_ids"] + + assert returned_chunks == ["chunk from db1", "chunk from db2"] + assert returned_scores == (0.9, 0.8) + assert returned_doc_ids == ["doc1", "doc2"] + assert returned_vector_db_ids == ["db1", "db2"] diff --git a/tests/unit/rag/test_vector_store.py b/tests/unit/rag/test_vector_store.py index 919f97ba7..8c017a551 100644 --- a/tests/unit/rag/test_vector_store.py +++ b/tests/unit/rag/test_vector_store.py @@ -13,6 +13,7 @@ from unittest.mock import AsyncMock, MagicMock import numpy as np import pytest +from llama_stack.apis.inference.inference import OpenAIEmbeddingData from llama_stack.apis.tools import RAGDocument from llama_stack.apis.vector_io import Chunk from llama_stack.providers.utils.memory.vector_store import ( @@ -218,11 +219,16 @@ class TestVectorDBWithIndex: Chunk(content="Test 2", embedding=None, metadata={}), ] - mock_inference_api.embeddings.return_value.embeddings = [[0.1, 0.2, 0.3], [0.4, 0.5, 0.6]] + mock_inference_api.openai_embeddings.return_value.data = [ + OpenAIEmbeddingData(embedding=[0.1, 0.2, 0.3], index=0), + OpenAIEmbeddingData(embedding=[0.4, 0.5, 0.6], index=1), + ] await vector_db_with_index.insert_chunks(chunks) - mock_inference_api.embeddings.assert_called_once_with("test-model without embeddings", ["Test 1", "Test 2"]) + mock_inference_api.openai_embeddings.assert_called_once_with( + "test-model without embeddings", ["Test 1", "Test 2"] + ) mock_index.add_chunks.assert_called_once() args = mock_index.add_chunks.call_args[0] assert args[0] == chunks @@ -246,7 +252,7 @@ class TestVectorDBWithIndex: await vector_db_with_index.insert_chunks(chunks) - mock_inference_api.embeddings.assert_not_called() + mock_inference_api.openai_embeddings.assert_not_called() mock_index.add_chunks.assert_called_once() args = mock_index.add_chunks.call_args[0] assert args[0] == chunks @@ -288,7 +294,7 @@ class TestVectorDBWithIndex: with pytest.raises(ValueError, match="has dimension 4, expected 3"): await vector_db_with_index.insert_chunks(chunks_wrong_dim) - mock_inference_api.embeddings.assert_not_called() + mock_inference_api.openai_embeddings.assert_not_called() mock_index.add_chunks.assert_not_called() async def test_insert_chunks_with_partially_precomputed_embeddings(self): @@ -308,11 +314,14 @@ class TestVectorDBWithIndex: Chunk(content="Test 3", embedding=None, metadata={}), ] - mock_inference_api.embeddings.return_value.embeddings = [[0.1, 0.1, 0.1], [0.3, 0.3, 0.3]] + mock_inference_api.openai_embeddings.return_value.data = [ + OpenAIEmbeddingData(embedding=[0.1, 0.1, 0.1], index=0), + OpenAIEmbeddingData(embedding=[0.3, 0.3, 0.3], index=1), + ] await vector_db_with_index.insert_chunks(chunks) - mock_inference_api.embeddings.assert_called_once_with( + mock_inference_api.openai_embeddings.assert_called_once_with( "test-model with partial embeddings", ["Test 1", "Test 3"] ) mock_index.add_chunks.assert_called_once() diff --git a/tests/unit/registry/test_registry.py b/tests/unit/registry/test_registry.py index 4ea4a20b9..9873bec5b 100644 --- a/tests/unit/registry/test_registry.py +++ b/tests/unit/registry/test_registry.py @@ -129,7 +129,7 @@ async def test_duplicate_provider_registration(cached_disk_dist_registry): result = await cached_disk_dist_registry.get("vector_db", "test_vector_db_2") assert result is not None - assert result.embedding_model == original_vector_db.embedding_model # Original values preserved + assert result.embedding_model == duplicate_vector_db.embedding_model # Original values preserved async def test_get_all_objects(cached_disk_dist_registry): @@ -174,10 +174,14 @@ async def test_parse_registry_values_error_handling(sqlite_kvstore): ) await sqlite_kvstore.set( - KEY_FORMAT.format(type="vector_db", identifier="valid_vector_db"), valid_db.model_dump_json() + KEY_FORMAT.format(type="vector_db", identifier="valid_vector_db"), + valid_db.model_dump_json(), ) - await sqlite_kvstore.set(KEY_FORMAT.format(type="vector_db", identifier="corrupted_json"), "{not valid json") + await sqlite_kvstore.set( + KEY_FORMAT.format(type="vector_db", identifier="corrupted_json"), + "{not valid json", + ) await sqlite_kvstore.set( KEY_FORMAT.format(type="vector_db", identifier="missing_fields"), @@ -212,7 +216,8 @@ async def test_cached_registry_error_handling(sqlite_kvstore): ) await sqlite_kvstore.set( - KEY_FORMAT.format(type="vector_db", identifier="valid_cached_db"), valid_db.model_dump_json() + KEY_FORMAT.format(type="vector_db", identifier="valid_cached_db"), + valid_db.model_dump_json(), ) await sqlite_kvstore.set( diff --git a/tests/unit/server/test_auth.py b/tests/unit/server/test_auth.py index 37b543976..205e0ce65 100644 --- a/tests/unit/server/test_auth.py +++ b/tests/unit/server/test_auth.py @@ -774,3 +774,136 @@ def test_has_required_scope_function(): # Test no user (auth disabled) assert _has_required_scope("test.read", None) + + +@pytest.fixture +def mock_kubernetes_api_server(): + return "https://api.cluster.example.com:6443" + + +@pytest.fixture +def kubernetes_auth_app(mock_kubernetes_api_server): + app = FastAPI() + auth_config = AuthenticationConfig( + provider_config={ + "type": "kubernetes", + "api_server_url": mock_kubernetes_api_server, + "verify_tls": False, + "claims_mapping": { + "username": "roles", + "groups": "roles", + "uid": "uid_attr", + }, + }, + ) + app.add_middleware(AuthenticationMiddleware, auth_config=auth_config, impls={}) + + @app.get("/test") + def test_endpoint(): + return {"message": "Authentication successful"} + + return app + + +@pytest.fixture +def kubernetes_auth_client(kubernetes_auth_app): + return TestClient(kubernetes_auth_app) + + +def test_missing_auth_header_kubernetes_auth(kubernetes_auth_client): + response = kubernetes_auth_client.get("/test") + assert response.status_code == 401 + assert "Authentication required" in response.json()["error"]["message"] + + +def test_invalid_auth_header_format_kubernetes_auth(kubernetes_auth_client): + response = kubernetes_auth_client.get("/test", headers={"Authorization": "InvalidFormat token123"}) + assert response.status_code == 401 + assert "Invalid Authorization header format" in response.json()["error"]["message"] + + +async def mock_kubernetes_selfsubjectreview_success(*args, **kwargs): + return MockResponse( + 201, + { + "apiVersion": "authentication.k8s.io/v1", + "kind": "SelfSubjectReview", + "metadata": {"creationTimestamp": "2025-07-15T13:53:56Z"}, + "status": { + "userInfo": { + "username": "alice", + "uid": "alice-uid-123", + "groups": ["system:authenticated", "developers", "admins"], + "extra": {"scopes.authorization.openshift.io": ["user:full"]}, + } + }, + }, + ) + + +async def mock_kubernetes_selfsubjectreview_failure(*args, **kwargs): + return MockResponse(401, {"message": "Unauthorized"}) + + +async def mock_kubernetes_selfsubjectreview_http_error(*args, **kwargs): + return MockResponse(500, {"message": "Internal Server Error"}) + + +@patch("httpx.AsyncClient.post", new=mock_kubernetes_selfsubjectreview_success) +def test_valid_kubernetes_auth_authentication(kubernetes_auth_client, valid_token): + response = kubernetes_auth_client.get("/test", headers={"Authorization": f"Bearer {valid_token}"}) + assert response.status_code == 200 + assert response.json() == {"message": "Authentication successful"} + + +@patch("httpx.AsyncClient.post", new=mock_kubernetes_selfsubjectreview_failure) +def test_invalid_kubernetes_auth_authentication(kubernetes_auth_client, invalid_token): + response = kubernetes_auth_client.get("/test", headers={"Authorization": f"Bearer {invalid_token}"}) + assert response.status_code == 401 + assert "Invalid token" in response.json()["error"]["message"] + + +@patch("httpx.AsyncClient.post", new=mock_kubernetes_selfsubjectreview_http_error) +def test_kubernetes_auth_http_error(kubernetes_auth_client, valid_token): + response = kubernetes_auth_client.get("/test", headers={"Authorization": f"Bearer {valid_token}"}) + assert response.status_code == 401 + assert "Token validation failed" in response.json()["error"]["message"] + + +def test_kubernetes_auth_request_payload(kubernetes_auth_client, valid_token, mock_kubernetes_api_server): + with patch("httpx.AsyncClient.post") as mock_post: + mock_response = MockResponse( + 200, + { + "apiVersion": "authentication.k8s.io/v1", + "kind": "SelfSubjectReview", + "metadata": {"creationTimestamp": "2025-07-15T13:53:56Z"}, + "status": { + "userInfo": { + "username": "test-user", + "uid": "test-uid", + "groups": ["test-group"], + } + }, + }, + ) + mock_post.return_value = mock_response + + kubernetes_auth_client.get("/test", headers={"Authorization": f"Bearer {valid_token}"}) + + # Verify the request was made with correct parameters + mock_post.assert_called_once() + call_args = mock_post.call_args + + # Check URL (passed as positional argument) + assert call_args[0][0] == f"{mock_kubernetes_api_server}/apis/authentication.k8s.io/v1/selfsubjectreviews" + + # Check headers (passed as keyword argument) + headers = call_args[1]["headers"] + assert headers["Authorization"] == f"Bearer {valid_token}" + assert headers["Content-Type"] == "application/json" + + # Check request body (passed as keyword argument) + request_body = call_args[1]["json"] + assert request_body["apiVersion"] == "authentication.k8s.io/v1" + assert request_body["kind"] == "SelfSubjectReview" diff --git a/tests/unit/server/test_replace_env_vars.py b/tests/unit/server/test_replace_env_vars.py index 0dda682c0..14b3b7231 100644 --- a/tests/unit/server/test_replace_env_vars.py +++ b/tests/unit/server/test_replace_env_vars.py @@ -88,3 +88,10 @@ def test_nested_structures(setup_env_vars): } expected = {"key1": "test_value", "key2": ["default", "conditional"], "key3": {"nested": None}} assert replace_env_vars(data) == expected + + +def test_explicit_strings_preserved(setup_env_vars): + # Explicit strings that look like numbers/booleans should remain strings + data = {"port": "8080", "enabled": "true", "count": "123", "ratio": "3.14"} + expected = {"port": "8080", "enabled": "true", "count": "123", "ratio": "3.14"} + assert replace_env_vars(data) == expected diff --git a/tests/unit/server/test_server.py b/tests/unit/server/test_server.py index 803111fc7..f21bbdd67 100644 --- a/tests/unit/server/test_server.py +++ b/tests/unit/server/test_server.py @@ -113,6 +113,15 @@ class TestTranslateException: assert result.status_code == 504 assert result.detail == "Operation timed out: " + def test_translate_connection_error(self): + """Test that ConnectionError is translated to 502 HTTP status.""" + exc = ConnectionError("Failed to connect to MCP server at http://localhost:9999/sse: Connection refused") + result = translate_exception(exc) + + assert isinstance(result, HTTPException) + assert result.status_code == 502 + assert result.detail == "Failed to connect to MCP server at http://localhost:9999/sse: Connection refused" + def test_translate_not_implemented_error(self): """Test that NotImplementedError is translated to 501 HTTP status.""" exc = NotImplementedError("Not implemented") diff --git a/tests/unit/utils/inference/test_inference_store.py b/tests/unit/utils/inference/test_inference_store.py index 730f54a05..f6d63490a 100644 --- a/tests/unit/utils/inference/test_inference_store.py +++ b/tests/unit/utils/inference/test_inference_store.py @@ -65,6 +65,9 @@ async def test_inference_store_pagination_basic(): input_messages = [OpenAIUserMessageParam(role="user", content=f"Test message for {completion_id}")] await store.store_chat_completion(completion, input_messages) + # Wait for all queued writes to complete + await store.flush() + # Test 1: First page with limit=2, descending order (default) result = await store.list_chat_completions(limit=2, order=Order.desc) assert len(result.data) == 2 @@ -108,6 +111,9 @@ async def test_inference_store_pagination_ascending(): input_messages = [OpenAIUserMessageParam(role="user", content=f"Test message for {completion_id}")] await store.store_chat_completion(completion, input_messages) + # Wait for all queued writes to complete + await store.flush() + # Test ascending order pagination result = await store.list_chat_completions(limit=1, order=Order.asc) assert len(result.data) == 1 @@ -143,6 +149,9 @@ async def test_inference_store_pagination_with_model_filter(): input_messages = [OpenAIUserMessageParam(role="user", content=f"Test message for {completion_id}")] await store.store_chat_completion(completion, input_messages) + # Wait for all queued writes to complete + await store.flush() + # Test pagination with model filter result = await store.list_chat_completions(limit=1, model="model-a", order=Order.desc) assert len(result.data) == 1 @@ -190,6 +199,9 @@ async def test_inference_store_pagination_no_limit(): input_messages = [OpenAIUserMessageParam(role="user", content=f"Test message for {completion_id}")] await store.store_chat_completion(completion, input_messages) + # Wait for all queued writes to complete + await store.flush() + # Test without limit result = await store.list_chat_completions(order=Order.desc) assert len(result.data) == 2 diff --git a/tests/unit/utils/sqlstore/test_sqlstore.py b/tests/unit/utils/sqlstore/test_sqlstore.py index 778f0b658..ba59ec7ec 100644 --- a/tests/unit/utils/sqlstore/test_sqlstore.py +++ b/tests/unit/utils/sqlstore/test_sqlstore.py @@ -332,6 +332,63 @@ async def test_sqlstore_pagination_error_handling(): ) +async def test_where_operator_gt_and_update_delete(): + with TemporaryDirectory() as tmp_dir: + db_path = tmp_dir + "/test.db" + store = SqlAlchemySqlStoreImpl(SqliteSqlStoreConfig(db_path=db_path)) + + await store.create_table( + "items", + { + "id": ColumnType.INTEGER, + "value": ColumnType.INTEGER, + "name": ColumnType.STRING, + }, + ) + + await store.insert("items", {"id": 1, "value": 10, "name": "one"}) + await store.insert("items", {"id": 2, "value": 20, "name": "two"}) + await store.insert("items", {"id": 3, "value": 30, "name": "three"}) + + result = await store.fetch_all("items", where={"value": {">": 15}}) + assert {r["id"] for r in result.data} == {2, 3} + + row = await store.fetch_one("items", where={"value": {">=": 30}}) + assert row["id"] == 3 + + await store.update("items", {"name": "small"}, {"value": {"<": 25}}) + rows = (await store.fetch_all("items")).data + names = {r["id"]: r["name"] for r in rows} + assert names[1] == "small" + assert names[2] == "small" + assert names[3] == "three" + + await store.delete("items", {"id": {"==": 2}}) + rows_after = (await store.fetch_all("items")).data + assert {r["id"] for r in rows_after} == {1, 3} + + +async def test_where_operator_edge_cases(): + with TemporaryDirectory() as tmp_dir: + db_path = tmp_dir + "/test.db" + store = SqlAlchemySqlStoreImpl(SqliteSqlStoreConfig(db_path=db_path)) + + await store.create_table( + "events", + {"id": ColumnType.STRING, "ts": ColumnType.INTEGER}, + ) + + base = 1024 + await store.insert("events", {"id": "a", "ts": base - 10}) + await store.insert("events", {"id": "b", "ts": base + 10}) + + row = await store.fetch_one("events", where={"id": "a"}) + assert row["id"] == "a" + + with pytest.raises(ValueError, match="Unsupported operator"): + await store.fetch_all("events", where={"ts": {"!=": base}}) + + async def test_sqlstore_pagination_custom_key_column(): """Test pagination with custom primary key column (not 'id').""" with TemporaryDirectory() as tmp_dir: diff --git a/tests/unit/utils/test_authorized_sqlstore.py b/tests/unit/utils/test_authorized_sqlstore.py index 90eb706e4..d85e784a9 100644 --- a/tests/unit/utils/test_authorized_sqlstore.py +++ b/tests/unit/utils/test_authorized_sqlstore.py @@ -26,7 +26,7 @@ async def test_authorized_fetch_with_where_sql_access_control(mock_get_authentic db_path=tmp_dir + "/" + db_name, ) ) - sqlstore = AuthorizedSqlStore(base_sqlstore) + sqlstore = AuthorizedSqlStore(base_sqlstore, default_policy()) # Create table with access control await sqlstore.create_table( @@ -56,24 +56,24 @@ async def test_authorized_fetch_with_where_sql_access_control(mock_get_authentic mock_get_authenticated_user.return_value = admin_user # Admin should see both documents - result = await sqlstore.fetch_all("documents", policy=default_policy(), where={"id": 1}) + result = await sqlstore.fetch_all("documents", where={"id": 1}) assert len(result.data) == 1 assert result.data[0]["title"] == "Admin Document" # User should only see their document mock_get_authenticated_user.return_value = regular_user - result = await sqlstore.fetch_all("documents", policy=default_policy(), where={"id": 1}) + result = await sqlstore.fetch_all("documents", where={"id": 1}) assert len(result.data) == 0 - result = await sqlstore.fetch_all("documents", policy=default_policy(), where={"id": 2}) + result = await sqlstore.fetch_all("documents", where={"id": 2}) assert len(result.data) == 1 assert result.data[0]["title"] == "User Document" - row = await sqlstore.fetch_one("documents", policy=default_policy(), where={"id": 1}) + row = await sqlstore.fetch_one("documents", where={"id": 1}) assert row is None - row = await sqlstore.fetch_one("documents", policy=default_policy(), where={"id": 2}) + row = await sqlstore.fetch_one("documents", where={"id": 2}) assert row is not None assert row["title"] == "User Document" @@ -88,7 +88,7 @@ async def test_sql_policy_consistency(mock_get_authenticated_user): db_path=tmp_dir + "/" + db_name, ) ) - sqlstore = AuthorizedSqlStore(base_sqlstore) + sqlstore = AuthorizedSqlStore(base_sqlstore, default_policy()) await sqlstore.create_table( table="resources", @@ -144,7 +144,7 @@ async def test_sql_policy_consistency(mock_get_authenticated_user): user = User(principal=user_data["principal"], attributes=user_data["attributes"]) mock_get_authenticated_user.return_value = user - sql_results = await sqlstore.fetch_all("resources", policy=policy) + sql_results = await sqlstore.fetch_all("resources") sql_ids = {row["id"] for row in sql_results.data} policy_ids = set() for scenario in test_scenarios: @@ -174,7 +174,7 @@ async def test_authorized_store_user_attribute_capture(mock_get_authenticated_us db_path=tmp_dir + "/" + db_name, ) ) - authorized_store = AuthorizedSqlStore(base_sqlstore) + authorized_store = AuthorizedSqlStore(base_sqlstore, default_policy()) await authorized_store.create_table( table="user_data", diff --git a/uv.lock b/uv.lock index 0626caba6..0833a9d77 100644 --- a/uv.lock +++ b/uv.lock @@ -895,7 +895,6 @@ dependencies = [ { name = "numpy" }, { name = "packaging" }, ] -sdist = { url = "https://files.pythonhosted.org/packages/5c/f4/7c2136f4660ca504266cc08b38df2aa1db14fea93393b82e099ff34d7290/faiss_cpu-1.11.0.post1.tar.gz", hash = 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"2025-08-12T17:07:06.293Z" }, -] - [[package]] name = "llama-stack" -version = "0.2.19" +version = "0.2.22" source = { editable = "." } dependencies = [ { name = "aiohttp" }, @@ -1780,7 +1762,6 @@ dependencies = [ { name = "huggingface-hub" }, { name = "jinja2" }, { name = "jsonschema" }, - { name = "llama-api-client" }, { name = "llama-stack-client" }, { name = "openai" }, { name = "opentelemetry-exporter-otlp-proto-http" }, @@ -1858,7 +1839,7 @@ test = [ { name = "datasets" }, { name = "mcp" }, { name = "milvus-lite" }, - { name = "openai" }, + { name = "psycopg2-binary" }, { name = "pymilvus" }, { name = "pypdf" }, { name = "requests" }, @@ -1883,7 +1864,7 @@ unit = [ { name = "milvus-lite" }, { name = "moto", extra = ["s3"] }, { name = "ollama" }, - { name = "openai" }, + { name = "psycopg2-binary" }, { name = "pymilvus" }, { name = "pypdf" }, { name = "qdrant-client" }, @@ -1904,16 +1885,15 @@ requires-dist = [ { name = "huggingface-hub", specifier = ">=0.34.0,<1.0" }, { name = "jinja2", specifier = ">=3.1.6" }, { name = "jsonschema" }, - { name = "llama-api-client", specifier = ">=0.1.2" }, - { name = "llama-stack-client", specifier = ">=0.2.19" }, - { name = "llama-stack-client", marker = "extra == 'ui'", specifier = ">=0.2.19" }, - { name = "openai", specifier = ">=1.99.6,<1.100.0" }, + { name = "llama-stack-client", specifier = ">=0.2.22" }, + { name = "llama-stack-client", marker = "extra == 'ui'", specifier = ">=0.2.22" }, + { name = "openai", specifier = ">=1.100.0" }, { name = "opentelemetry-exporter-otlp-proto-http", specifier = ">=1.30.0" }, { name = "opentelemetry-sdk", specifier = ">=1.30.0" }, { name = "pandas", marker = "extra == 'ui'" }, { name = "pillow" }, { name = "prompt-toolkit" }, - { name = "pydantic", specifier = ">=2" }, + { name = "pydantic", specifier = ">=2.11.9" }, { name = "python-dotenv" }, { name = "python-jose", extras = ["cryptography"] }, { name = "python-multipart", specifier = ">=0.0.20" }, @@ -1928,10 +1908,10 @@ 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