diff --git a/.coveragerc b/.coveragerc index e16c2e461..d4925275f 100644 --- a/.coveragerc +++ b/.coveragerc @@ -4,3 +4,9 @@ omit = */llama_stack/providers/* */llama_stack/templates/* .venv/* + */llama_stack/cli/scripts/* + */llama_stack/ui/* + */llama_stack/distribution/ui/* + */llama_stack/strong_typing/* + */llama_stack/env.py + */__init__.py diff --git a/.github/CODEOWNERS b/.github/CODEOWNERS index b709c482f..85f781a4f 100644 --- a/.github/CODEOWNERS +++ b/.github/CODEOWNERS @@ -2,4 +2,4 @@ # These owners will be the default owners for everything in # the repo. Unless a later match takes precedence, -* @ashwinb @yanxi0830 @hardikjshah @raghotham @ehhuang @terrytangyuan @leseb @bbrowning @reluctantfuturist +* @ashwinb @yanxi0830 @hardikjshah @raghotham @ehhuang @terrytangyuan @leseb @bbrowning @reluctantfuturist @mattf @slekkala1 diff --git a/.github/ISSUE_TEMPLATE/tech-debt.yml b/.github/ISSUE_TEMPLATE/tech-debt.yml new file mode 100644 index 000000000..b281b3482 --- /dev/null +++ b/.github/ISSUE_TEMPLATE/tech-debt.yml @@ -0,0 +1,30 @@ +name: 🔧 Tech Debt +description: Something that is functional but should be improved or optimizied +labels: ["tech-debt"] +body: +- type: textarea + id: tech-debt-explanation + attributes: + label: 🤔 What is the technical debt you think should be addressed? + description: > + A clear and concise description of _what_ needs to be addressed - ensure you are describing + constitutes [technical debt](https://en.wikipedia.org/wiki/Technical_debt) and is not a bug + or feature request. + validations: + required: true + +- type: textarea + id: tech-debt-motivation + attributes: + label: 💡 What is the benefit of addressing this technical debt? + description: > + A clear and concise description of _why_ this work is needed. + validations: + required: true + +- type: textarea + id: other-thoughts + attributes: + label: Other thoughts + description: > + Any thoughts about how this may result in complexity in the codebase, or other trade-offs. diff --git a/.github/actions/setup-ollama/action.yml b/.github/actions/setup-ollama/action.yml index 1f6e9818b..bb08520f1 100644 --- a/.github/actions/setup-ollama/action.yml +++ b/.github/actions/setup-ollama/action.yml @@ -7,3 +7,5 @@ runs: shell: bash run: | docker run -d --name ollama -p 11434:11434 docker.io/leseb/ollama-with-models + echo "Verifying Ollama status..." + timeout 30 bash -c 'while ! curl -s -L http://127.0.0.1:11434; do sleep 1 && echo "."; done' diff --git a/.github/actions/setup-runner/action.yml b/.github/actions/setup-runner/action.yml index 25d7b6128..0be999fe2 100644 --- a/.github/actions/setup-runner/action.yml +++ b/.github/actions/setup-runner/action.yml @@ -5,6 +5,10 @@ inputs: description: The Python version to use required: false default: "3.12" + client-version: + description: The llama-stack-client-python version to test against (latest or published) + required: false + default: "latest" runs: using: "composite" steps: @@ -20,8 +24,17 @@ runs: run: | uv sync --all-groups uv pip install ollama faiss-cpu - # always test against the latest version of the client - # TODO: this is not necessarily a good idea. we need to test against both published and latest - # to find out backwards compatibility issues. - uv pip install git+https://github.com/meta-llama/llama-stack-client-python.git@main + + # Install llama-stack-client-python based on the client-version input + if [ "${{ inputs.client-version }}" = "latest" ]; then + echo "Installing latest llama-stack-client-python from main branch" + uv pip install git+https://github.com/meta-llama/llama-stack-client-python.git@main + elif [ "${{ inputs.client-version }}" = "published" ]; then + echo "Installing published llama-stack-client-python from PyPI" + uv pip install llama-stack-client + else + echo "Invalid client-version: ${{ inputs.client-version }}" + exit 1 + fi + uv pip install -e . diff --git a/.github/workflows/coverage-badge.yml b/.github/workflows/coverage-badge.yml new file mode 100644 index 000000000..6b2f133dd --- /dev/null +++ b/.github/workflows/coverage-badge.yml @@ -0,0 +1,57 @@ +name: Coverage Badge + +on: + push: + branches: [ main ] + paths: + - 'llama_stack/**' + - 'tests/unit/**' + - 'uv.lock' + - 'pyproject.toml' + - 'requirements.txt' + - '.github/workflows/unit-tests.yml' + - '.github/workflows/coverage-badge.yml' # This workflow + workflow_dispatch: + +jobs: + unit-tests: + runs-on: ubuntu-latest + steps: + - name: Checkout repository + uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2 + + - name: Install dependencies + uses: ./.github/actions/setup-runner + + - name: Run unit tests + run: | + ./scripts/unit-tests.sh + + - name: Coverage Badge + uses: tj-actions/coverage-badge-py@1788babcb24544eb5bbb6e0d374df5d1e54e670f # v2.0.4 + + - name: Verify Changed files + uses: tj-actions/verify-changed-files@a1c6acee9df209257a246f2cc6ae8cb6581c1edf # v20.0.4 + id: verify-changed-files + with: + files: coverage.svg + + - name: Commit files + if: steps.verify-changed-files.outputs.files_changed == 'true' + run: | + git config --local user.email "github-actions[bot]@users.noreply.github.com" + git config --local user.name "github-actions[bot]" + git add coverage.svg + git commit -m "Updated coverage.svg" + + - name: Create Pull Request + if: steps.verify-changed-files.outputs.files_changed == 'true' + uses: peter-evans/create-pull-request@271a8d0340265f705b14b6d32b9829c1cb33d45e # v7.0.8 + with: + token: ${{ secrets.GITHUB_TOKEN }} + title: "ci: [Automatic] Coverage Badge Update" + body: | + This PR updates the coverage badge based on the latest coverage report. + + Automatically generated by the [workflow coverage-badge.yaml](.github/workflows/coverage-badge.yaml) + delete-branch: true diff --git a/.github/workflows/gha_workflow_llama_stack_tests.yml b/.github/workflows/gha_workflow_llama_stack_tests.yml deleted file mode 100644 index 9eae291e9..000000000 --- a/.github/workflows/gha_workflow_llama_stack_tests.yml +++ /dev/null @@ -1,355 +0,0 @@ -name: "Run Llama-stack Tests" - -on: - #### Temporarily disable PR runs until tests run as intended within mainline. - #TODO Add this back. - #pull_request_target: - # types: ["opened"] - # branches: - # - 'main' - # paths: - # - 'llama_stack/**/*.py' - # - 'tests/**/*.py' - - workflow_dispatch: - inputs: - runner: - description: 'GHA Runner Scale Set label to run workflow on.' - required: true - default: "llama-stack-gha-runner-gpu" - - checkout_reference: - description: "The branch, tag, or SHA to checkout" - required: true - default: "main" - - debug: - description: 'Run debugging steps?' - required: false - default: "true" - - sleep_time: - description: '[DEBUG] sleep time for debugging' - required: true - default: "0" - - provider_id: - description: 'ID of your provider' - required: true - default: "meta_reference" - - model_id: - description: 'Shorthand name for target model ID (llama_3b or llama_8b)' - required: true - default: "llama_3b" - - model_override_3b: - description: 'Specify shorthand model for ' - required: false - default: "Llama3.2-3B-Instruct" - - model_override_8b: - description: 'Specify shorthand model for ' - required: false - default: "Llama3.1-8B-Instruct" - -env: - # ID used for each test's provider config - PROVIDER_ID: "${{ inputs.provider_id || 'meta_reference' }}" - - # Path to model checkpoints within EFS volume - MODEL_CHECKPOINT_DIR: "/data/llama" - - # Path to directory to run tests from - TESTS_PATH: "${{ github.workspace }}/llama_stack/providers/tests" - - # Keep track of a list of model IDs that are valid to use within pytest fixture marks - AVAILABLE_MODEL_IDs: "llama_3b llama_8b" - - # Shorthand name for model ID, used in pytest fixture marks - MODEL_ID: "${{ inputs.model_id || 'llama_3b' }}" - - # Override the `llama_3b` / `llama_8b' models, else use the default. - LLAMA_3B_OVERRIDE: "${{ inputs.model_override_3b || 'Llama3.2-3B-Instruct' }}" - LLAMA_8B_OVERRIDE: "${{ inputs.model_override_8b || 'Llama3.1-8B-Instruct' }}" - - # Defines which directories in TESTS_PATH to exclude from the test loop - EXCLUDED_DIRS: "__pycache__" - - # Defines the output xml reports generated after a test is run - REPORTS_GEN: "" - -jobs: - execute_workflow: - name: Execute workload on Self-Hosted GPU k8s runner - permissions: - pull-requests: write - defaults: - run: - shell: bash - runs-on: ${{ inputs.runner != '' && inputs.runner || 'llama-stack-gha-runner-gpu' }} - if: always() - steps: - - ############################## - #### INITIAL DEBUG CHECKS #### - ############################## - - name: "[DEBUG] Check content of the EFS mount" - id: debug_efs_volume - continue-on-error: true - if: inputs.debug == 'true' - run: | - echo "========= Content of the EFS mount =============" - ls -la ${{ env.MODEL_CHECKPOINT_DIR }} - - - name: "[DEBUG] Get runner container OS information" - id: debug_os_info - if: ${{ inputs.debug == 'true' }} - run: | - cat /etc/os-release - - - name: "[DEBUG] Print environment variables" - id: debug_env_vars - if: ${{ inputs.debug == 'true' }} - run: | - echo "PROVIDER_ID = ${PROVIDER_ID}" - echo "MODEL_CHECKPOINT_DIR = ${MODEL_CHECKPOINT_DIR}" - echo "AVAILABLE_MODEL_IDs = ${AVAILABLE_MODEL_IDs}" - echo "MODEL_ID = ${MODEL_ID}" - echo "LLAMA_3B_OVERRIDE = ${LLAMA_3B_OVERRIDE}" - echo "LLAMA_8B_OVERRIDE = ${LLAMA_8B_OVERRIDE}" - echo "EXCLUDED_DIRS = ${EXCLUDED_DIRS}" - echo "REPORTS_GEN = ${REPORTS_GEN}" - - ############################ - #### MODEL INPUT CHECKS #### - ############################ - - - name: "Check if env.model_id is valid" - id: check_model_id - run: | - if [[ " ${AVAILABLE_MODEL_IDs[@]} " =~ " ${MODEL_ID} " ]]; then - echo "Model ID '${MODEL_ID}' is valid." - else - echo "Model ID '${MODEL_ID}' is invalid. Terminating workflow." - exit 1 - fi - - ####################### - #### CODE CHECKOUT #### - ####################### - - name: "Checkout 'meta-llama/llama-stack' repository" - id: checkout_repo - uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2 - with: - ref: ${{ inputs.branch }} - - - name: "[DEBUG] Content of the repository after checkout" - id: debug_content_after_checkout - if: ${{ inputs.debug == 'true' }} - run: | - ls -la ${GITHUB_WORKSPACE} - - ########################################################## - #### OPTIONAL SLEEP DEBUG #### - # # - # Use to "exec" into the test k8s POD and run tests # - # manually to identify what dependencies are being used. # - # # - ########################################################## - - name: "[DEBUG] sleep" - id: debug_sleep - if: ${{ inputs.debug == 'true' && inputs.sleep_time != '' }} - run: | - sleep ${{ inputs.sleep_time }} - - ############################ - #### UPDATE SYSTEM PATH #### - ############################ - - name: "Update path: execute" - id: path_update_exec - run: | - # .local/bin is needed for certain libraries installed below to be recognized - # when calling their executable to install sub-dependencies - mkdir -p ${HOME}/.local/bin - echo "${HOME}/.local/bin" >> "$GITHUB_PATH" - - ##################################### - #### UPDATE CHECKPOINT DIRECTORY #### - ##################################### - - name: "Update checkpoint directory" - id: checkpoint_update - run: | - echo "Checkpoint directory: ${MODEL_CHECKPOINT_DIR}/$LLAMA_3B_OVERRIDE" - if [ "${MODEL_ID}" = "llama_3b" ] && [ -d "${MODEL_CHECKPOINT_DIR}/${LLAMA_3B_OVERRIDE}" ]; then - echo "MODEL_CHECKPOINT_DIR=${MODEL_CHECKPOINT_DIR}/${LLAMA_3B_OVERRIDE}" >> "$GITHUB_ENV" - elif [ "${MODEL_ID}" = "llama_8b" ] && [ -d "${MODEL_CHECKPOINT_DIR}/${LLAMA_8B_OVERRIDE}" ]; then - echo "MODEL_CHECKPOINT_DIR=${MODEL_CHECKPOINT_DIR}/${LLAMA_8B_OVERRIDE}" >> "$GITHUB_ENV" - else - echo "MODEL_ID & LLAMA_*B_OVERRIDE are not a valid pairing. Terminating workflow." - exit 1 - fi - - - name: "[DEBUG] Checkpoint update check" - id: debug_checkpoint_update - if: ${{ inputs.debug == 'true' }} - run: | - echo "MODEL_CHECKPOINT_DIR (after update) = ${MODEL_CHECKPOINT_DIR}" - - ################################## - #### DEPENDENCY INSTALLATIONS #### - ################################## - - name: "Installing 'apt' required packages" - id: install_apt - run: | - echo "[STEP] Installing 'apt' required packages" - sudo apt update -y - sudo apt install -y python3 python3-pip npm wget - - - name: "Installing packages with 'curl'" - id: install_curl - run: | - curl -fsSL https://ollama.com/install.sh | sh - - - name: "Installing packages with 'wget'" - id: install_wget - run: | - wget https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh - chmod +x Miniconda3-latest-Linux-x86_64.sh - ./Miniconda3-latest-Linux-x86_64.sh -b install -c pytorch -c nvidia faiss-gpu=1.9.0 - # Add miniconda3 bin to system path - echo "${HOME}/miniconda3/bin" >> "$GITHUB_PATH" - - - name: "Installing packages with 'npm'" - id: install_npm_generic - run: | - sudo npm install -g junit-merge - - - name: "Installing pip dependencies" - id: install_pip_generic - run: | - echo "[STEP] Installing 'llama-stack' models" - pip install -U pip setuptools - pip install -r requirements.txt - pip install -e . - pip install -U \ - torch torchvision \ - pytest pytest_asyncio \ - fairscale lm-format-enforcer \ - zmq chardet pypdf \ - pandas sentence_transformers together \ - aiosqlite - - name: "Installing packages with conda" - id: install_conda_generic - run: | - conda install -q -c pytorch -c nvidia faiss-gpu=1.9.0 - - ############################################################# - #### TESTING TO BE DONE FOR BOTH PRS AND MANUAL DISPATCH #### - ############################################################# - - name: "Run Tests: Loop" - id: run_tests_loop - working-directory: "${{ github.workspace }}" - run: | - pattern="" - for dir in llama_stack/providers/tests/*; do - if [ -d "$dir" ]; then - dir_name=$(basename "$dir") - if [[ ! " $EXCLUDED_DIRS " =~ " $dir_name " ]]; then - for file in "$dir"/test_*.py; do - test_name=$(basename "$file") - new_file="result-${dir_name}-${test_name}.xml" - if torchrun $(which pytest) -s -v ${TESTS_PATH}/${dir_name}/${test_name} -m "${PROVIDER_ID} and ${MODEL_ID}" \ - --junitxml="${{ github.workspace }}/${new_file}"; then - echo "Ran test: ${test_name}" - else - echo "Did NOT run test: ${test_name}" - fi - pattern+="${new_file} " - done - fi - fi - done - echo "REPORTS_GEN=$pattern" >> "$GITHUB_ENV" - - - name: "Test Summary: Merge" - id: test_summary_merge - working-directory: "${{ github.workspace }}" - run: | - echo "Merging the following test result files: ${REPORTS_GEN}" - # Defaults to merging them into 'merged-test-results.xml' - junit-merge ${{ env.REPORTS_GEN }} - - ############################################ - #### AUTOMATIC TESTING ON PULL REQUESTS #### - ############################################ - - #### Run tests #### - - - name: "PR - Run Tests" - id: pr_run_tests - working-directory: "${{ github.workspace }}" - if: github.event_name == 'pull_request_target' - run: | - echo "[STEP] Running PyTest tests at 'GITHUB_WORKSPACE' path: ${GITHUB_WORKSPACE} | path: ${{ github.workspace }}" - # (Optional) Add more tests here. - - # Merge test results with 'merged-test-results.xml' from above. - # junit-merge merged-test-results.xml - - #### Create test summary #### - - - name: "PR - Test Summary" - id: pr_test_summary_create - if: github.event_name == 'pull_request_target' - uses: test-summary/action@31493c76ec9e7aa675f1585d3ed6f1da69269a86 # v2.4 - with: - paths: "${{ github.workspace }}/merged-test-results.xml" - output: test-summary.md - - - name: "PR - Upload Test Summary" - id: pr_test_summary_upload - if: github.event_name == 'pull_request_target' - uses: actions/upload-artifact@ea165f8d65b6e75b540449e92b4886f43607fa02 # v4.6.2 - with: - name: test-summary - path: test-summary.md - - #### Update PR request #### - - - name: "PR - Update comment" - id: pr_update_comment - if: github.event_name == 'pull_request_target' - uses: thollander/actions-comment-pull-request@24bffb9b452ba05a4f3f77933840a6a841d1b32b # v3.0.1 - with: - filePath: test-summary.md - - ######################## - #### MANUAL TESTING #### - ######################## - - #### Run tests #### - - - name: "Manual - Run Tests: Prep" - id: manual_run_tests - working-directory: "${{ github.workspace }}" - if: github.event_name == 'workflow_dispatch' - run: | - echo "[STEP] Running PyTest tests at 'GITHUB_WORKSPACE' path: ${{ github.workspace }}" - - #TODO Use this when collection errors are resolved - # pytest -s -v -m "${PROVIDER_ID} and ${MODEL_ID}" --junitxml="${{ github.workspace }}/merged-test-results.xml" - - # (Optional) Add more tests here. - - # Merge test results with 'merged-test-results.xml' from above. - # junit-merge merged-test-results.xml - - #### Create test summary #### - - - name: "Manual - Test Summary" - id: manual_test_summary - if: always() && github.event_name == 'workflow_dispatch' - uses: test-summary/action@31493c76ec9e7aa675f1585d3ed6f1da69269a86 # v2.4 - with: - paths: "${{ github.workspace }}/merged-test-results.xml" diff --git a/.github/workflows/install-script-ci.yml b/.github/workflows/install-script-ci.yml index 2eb234c77..d711444e8 100644 --- a/.github/workflows/install-script-ci.yml +++ b/.github/workflows/install-script-ci.yml @@ -3,10 +3,10 @@ name: Installer CI on: pull_request: paths: - - 'install.sh' + - 'scripts/install.sh' push: paths: - - 'install.sh' + - 'scripts/install.sh' schedule: - cron: '0 2 * * *' # every day at 02:00 UTC @@ -16,11 +16,11 @@ jobs: steps: - uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # 4.2.2 - name: Run ShellCheck on install.sh - run: shellcheck install.sh + run: shellcheck scripts/install.sh smoke-test: needs: lint runs-on: ubuntu-latest steps: - uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # 4.2.2 - name: Run installer end-to-end - run: ./install.sh + run: ./scripts/install.sh diff --git a/.github/workflows/integration-auth-tests.yml b/.github/workflows/integration-auth-tests.yml index 4139d09ca..cf10e005c 100644 --- a/.github/workflows/integration-auth-tests.yml +++ b/.github/workflows/integration-auth-tests.yml @@ -35,7 +35,7 @@ jobs: - name: Install minikube if: ${{ matrix.auth-provider == 'kubernetes' }} - uses: medyagh/setup-minikube@cea33675329b799adccc9526aa5daccc26cd5052 # v0.0.19 + uses: medyagh/setup-minikube@e3c7f79eb1e997eabccc536a6cf318a2b0fe19d9 # v0.0.20 - name: Start minikube if: ${{ matrix.auth-provider == 'oauth2_token' }} @@ -73,9 +73,12 @@ jobs: server: port: 8321 EOF - yq eval '.server.auth = {"provider_type": "${{ matrix.auth-provider }}"}' -i $run_dir/run.yaml - yq eval '.server.auth.config = {"tls_cafile": "${{ env.KUBERNETES_CA_CERT_PATH }}", "issuer": "${{ env.KUBERNETES_ISSUER }}", "audience": "${{ env.KUBERNETES_AUDIENCE }}"}' -i $run_dir/run.yaml - yq eval '.server.auth.config.jwks = {"uri": "${{ env.KUBERNETES_API_SERVER_URL }}", "token": "${{ env.TOKEN }}"}' -i $run_dir/run.yaml + yq eval '.server.auth.provider_config.type = "${{ matrix.auth-provider }}"' -i $run_dir/run.yaml + yq eval '.server.auth.provider_config.tls_cafile = "${{ env.KUBERNETES_CA_CERT_PATH }}"' -i $run_dir/run.yaml + yq eval '.server.auth.provider_config.issuer = "${{ env.KUBERNETES_ISSUER }}"' -i $run_dir/run.yaml + yq eval '.server.auth.provider_config.audience = "${{ env.KUBERNETES_AUDIENCE }}"' -i $run_dir/run.yaml + yq eval '.server.auth.provider_config.jwks.uri = "${{ env.KUBERNETES_API_SERVER_URL }}"' -i $run_dir/run.yaml + yq eval '.server.auth.provider_config.jwks.token = "${{ env.TOKEN }}"' -i $run_dir/run.yaml cat $run_dir/run.yaml nohup uv run llama stack run $run_dir/run.yaml --image-type venv > server.log 2>&1 & diff --git a/.github/workflows/integration-sql-store-tests.yml b/.github/workflows/integration-sql-store-tests.yml new file mode 100644 index 000000000..aeeecf395 --- /dev/null +++ b/.github/workflows/integration-sql-store-tests.yml @@ -0,0 +1,70 @@ +name: SqlStore Integration Tests + +on: + push: + branches: [ main ] + pull_request: + branches: [ main ] + paths: + - 'llama_stack/providers/utils/sqlstore/**' + - 'tests/integration/sqlstore/**' + - 'uv.lock' + - 'pyproject.toml' + - 'requirements.txt' + - '.github/workflows/integration-sql-store-tests.yml' # This workflow + +concurrency: + group: ${{ github.workflow }}-${{ github.ref }} + cancel-in-progress: true + +jobs: + test-postgres: + runs-on: ubuntu-latest + strategy: + matrix: + python-version: ["3.12", "3.13"] + fail-fast: false + + services: + postgres: + image: postgres:15 + env: + POSTGRES_USER: llamastack + POSTGRES_PASSWORD: llamastack + POSTGRES_DB: llamastack + ports: + - 5432:5432 + options: >- + --health-cmd pg_isready + --health-interval 10s + --health-timeout 5s + --health-retries 5 + + steps: + - name: Checkout repository + uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2 + + - name: Install dependencies + uses: ./.github/actions/setup-runner + with: + python-version: ${{ matrix.python-version }} + + - name: Run SqlStore Integration Tests + env: + ENABLE_POSTGRES_TESTS: "true" + POSTGRES_HOST: localhost + POSTGRES_PORT: 5432 + POSTGRES_DB: llamastack + POSTGRES_USER: llamastack + POSTGRES_PASSWORD: llamastack + run: | + uv run pytest -sv tests/integration/providers/utils/sqlstore/ + + - name: Upload test logs + if: ${{ always() }} + uses: actions/upload-artifact@ea165f8d65b6e75b540449e92b4886f43607fa02 # v4.6.2 + with: + name: postgres-test-logs-${{ github.run_id }}-${{ github.run_attempt }}-${{ matrix.python-version }} + path: | + *.log + retention-days: 1 diff --git a/.github/workflows/integration-tests.yml b/.github/workflows/integration-tests.yml index 5c354331f..0b6c1be3b 100644 --- a/.github/workflows/integration-tests.yml +++ b/.github/workflows/integration-tests.yml @@ -7,27 +7,54 @@ on: branches: [ main ] paths: - 'llama_stack/**' - - 'tests/integration/**' + - 'tests/**' - 'uv.lock' - 'pyproject.toml' - 'requirements.txt' - '.github/workflows/integration-tests.yml' # This workflow + - '.github/actions/setup-ollama/action.yml' + schedule: + - cron: '0 0 * * *' # Daily at 12 AM UTC + workflow_dispatch: + inputs: + test-all-client-versions: + description: 'Test against both the latest and published versions' + type: boolean + default: false concurrency: group: ${{ github.workflow }}-${{ github.ref }} cancel-in-progress: true jobs: - test-matrix: + discover-tests: runs-on: ubuntu-latest + outputs: + test-type: ${{ steps.generate-matrix.outputs.test-type }} + steps: + - name: Checkout repository + uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2 + + - name: Generate test matrix + id: generate-matrix + run: | + # Get test directories dynamically, excluding non-test directories + TEST_TYPES=$(find tests/integration -maxdepth 1 -mindepth 1 -type d -printf "%f\n" | + grep -Ev "^(__pycache__|fixtures|test_cases)$" | + sort | jq -R -s -c 'split("\n")[:-1]') + echo "test-type=$TEST_TYPES" >> $GITHUB_OUTPUT + + test-matrix: + needs: discover-tests + runs-on: ubuntu-latest + strategy: + fail-fast: false matrix: - # Listing tests manually since some of them currently fail - # TODO: generate matrix list from tests/integration when fixed - test-type: [agents, inference, datasets, inspect, scoring, post_training, providers, tool_runtime, vector_io] + test-type: ${{ fromJson(needs.discover-tests.outputs.test-type) }} client-type: [library, server] python-version: ["3.12", "3.13"] - fail-fast: false # we want to run all tests regardless of failure + client-version: ${{ (github.event_name == 'schedule' || github.event.inputs.test-all-client-versions == 'true') && fromJSON('["published", "latest"]') || fromJSON('["latest"]') }} steps: - name: Checkout repository @@ -37,6 +64,7 @@ jobs: uses: ./.github/actions/setup-runner with: python-version: ${{ matrix.python-version }} + client-version: ${{ matrix.client-version }} - name: Setup ollama uses: ./.github/actions/setup-ollama @@ -53,10 +81,15 @@ jobs: - name: Run Integration Tests env: - INFERENCE_MODEL: "meta-llama/Llama-3.2-3B-Instruct" - OLLAMA_INFERENCE_MODEL: "meta-llama/Llama-3.2-3B-Instruct" # for library tests - ENABLE_OLLAMA: "ollama" # for library tests + OLLAMA_INFERENCE_MODEL: "llama3.2:3b-instruct-fp16" # for server tests + ENABLE_OLLAMA: "ollama" # for server tests OLLAMA_URL: "http://0.0.0.0:11434" + SAFETY_MODEL: "llama-guard3:1b" + LLAMA_STACK_CLIENT_TIMEOUT: "300" # Increased timeout for eval operations + # Use 'shell' to get pipefail behavior + # https://docs.github.com/en/actions/reference/workflow-syntax-for-github-actions#exit-codes-and-error-action-preference + # TODO: write a precommit hook to detect if a test contains a pipe but does not use 'shell: bash' + shell: bash run: | if [ "${{ matrix.client-type }}" == "library" ]; then stack_config="starter" @@ -65,8 +98,9 @@ jobs: fi uv run pytest -s -v tests/integration/${{ matrix.test-type }} --stack-config=${stack_config} \ -k "not(builtin_tool or safety_with_image or code_interpreter or test_rag)" \ - --text-model="ollama/meta-llama/Llama-3.2-3B-Instruct" \ + --text-model="ollama/llama3.2:3b-instruct-fp16" \ --embedding-model=all-MiniLM-L6-v2 \ + --safety-shield=$SAFETY_MODEL \ --color=yes \ --capture=tee-sys | tee pytest-${{ matrix.test-type }}.log @@ -85,7 +119,7 @@ jobs: if: ${{ always() }} uses: actions/upload-artifact@ea165f8d65b6e75b540449e92b4886f43607fa02 # v4.6.2 with: - name: logs-${{ github.run_id }}-${{ github.run_attempt }}-${{ matrix.client-type }}-${{ matrix.test-type }}-${{ matrix.python-version }} + name: logs-${{ github.run_id }}-${{ github.run_attempt }}-${{ matrix.client-type }}-${{ matrix.test-type }}-${{ matrix.python-version }}-${{ matrix.client-version }} path: | *.log retention-days: 1 diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml deleted file mode 100644 index 79c935005..000000000 --- a/.github/workflows/tests.yml +++ /dev/null @@ -1,69 +0,0 @@ -name: auto-tests - -on: - # pull_request: - workflow_dispatch: - inputs: - commit_sha: - description: 'Specific Commit SHA to trigger on' - required: false - default: $GITHUB_SHA # default to the last commit of $GITHUB_REF branch - -jobs: - test-llama-stack-as-library: - runs-on: ubuntu-latest - env: - TOGETHER_API_KEY: ${{ secrets.TOGETHER_API_KEY }} - FIREWORKS_API_KEY: ${{ secrets.FIREWORKS_API_KEY }} - TAVILY_SEARCH_API_KEY: ${{ secrets.TAVILY_SEARCH_API_KEY }} - strategy: - matrix: - provider: [fireworks, together] - steps: - - uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2 - with: - ref: ${{ github.event.inputs.commit_sha }} - - - name: Echo commit SHA - run: | - echo "Triggered on commit SHA: ${{ github.event.inputs.commit_sha }}" - git rev-parse HEAD - - - name: Install dependencies - run: | - python -m pip install --upgrade pip - pip install -r requirements.txt pytest - pip install -e . - - - name: Build providers - run: | - llama stack build --template ${{ matrix.provider }} --image-type venv - - - name: Install the latest llama-stack-client & llama-models packages - run: | - pip install -e git+https://github.com/meta-llama/llama-stack-client-python.git#egg=llama-stack-client - pip install -e git+https://github.com/meta-llama/llama-models.git#egg=llama-models - - - name: Run client-sdk test - working-directory: "${{ github.workspace }}" - env: - REPORT_OUTPUT: md_report.md - shell: bash - run: | - pip install --upgrade pytest-md-report - echo "REPORT_FILE=${REPORT_OUTPUT}" >> "$GITHUB_ENV" - - export INFERENCE_MODEL=meta-llama/Llama-3.1-8B-Instruct - LLAMA_STACK_CONFIG=./llama_stack/templates/${{ matrix.provider }}/run.yaml pytest --md-report --md-report-verbose=1 ./tests/client-sdk/inference/ --md-report-output "$REPORT_OUTPUT" - - - name: Output reports to the job summary - if: always() - shell: bash - run: | - if [ -f "$REPORT_FILE" ]; then - echo "
Test Report for ${{ matrix.provider }} " >> $GITHUB_STEP_SUMMARY - echo "" >> $GITHUB_STEP_SUMMARY - cat "$REPORT_FILE" >> $GITHUB_STEP_SUMMARY - echo "" >> $GITHUB_STEP_SUMMARY - echo "
" >> $GITHUB_STEP_SUMMARY - fi diff --git a/.github/workflows/unit-tests.yml b/.github/workflows/unit-tests.yml index e29045e52..41034b45f 100644 --- a/.github/workflows/unit-tests.yml +++ b/.github/workflows/unit-tests.yml @@ -36,7 +36,7 @@ jobs: - name: Run unit tests run: | - PYTHON_VERSION=${{ matrix.python }} ./scripts/unit-tests.sh --cov=llama_stack --junitxml=pytest-report-${{ matrix.python }}.xml --cov-report=html:htmlcov-${{ matrix.python }} + PYTHON_VERSION=${{ matrix.python }} ./scripts/unit-tests.sh --junitxml=pytest-report-${{ matrix.python }}.xml - name: Upload test results if: always() diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index ebbadefa6..cf72ecd0e 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -29,7 +29,7 @@ repos: - id: check-toml - repo: https://github.com/Lucas-C/pre-commit-hooks - rev: v1.5.4 + rev: v1.5.5 hooks: - id: insert-license files: \.py$|\.sh$ @@ -38,7 +38,7 @@ repos: - docs/license_header.txt - repo: https://github.com/astral-sh/ruff-pre-commit - rev: v0.9.4 + rev: v0.12.2 hooks: - id: ruff args: [ --fix ] @@ -46,14 +46,14 @@ repos: - id: ruff-format - repo: https://github.com/adamchainz/blacken-docs - rev: 1.19.0 + rev: 1.19.1 hooks: - id: blacken-docs additional_dependencies: - black==24.3.0 - repo: https://github.com/astral-sh/uv-pre-commit - rev: 0.7.8 + rev: 0.7.20 hooks: - id: uv-lock - id: uv-export @@ -66,7 +66,7 @@ repos: ] - repo: https://github.com/pre-commit/mirrors-mypy - rev: v1.15.0 + rev: v1.16.1 hooks: - id: mypy additional_dependencies: @@ -129,7 +129,28 @@ repos: require_serial: true always_run: true files: ^llama_stack/.*$ + - id: forbid-pytest-asyncio + name: Block @pytest.mark.asyncio and @pytest_asyncio.fixture + entry: bash + language: system + types: [python] + pass_filenames: true + args: + - -c + - | + grep -EnH '^[^#]*@pytest\.mark\.asyncio|@pytest_asyncio\.fixture' "$@" && { + echo; + echo "❌ Do not use @pytest.mark.asyncio or @pytest_asyncio.fixture." + echo " pytest is already configured with async-mode=auto." + echo; + exit 1; + } || true ci: autofix_commit_msg: 🎨 [pre-commit.ci] Auto format from pre-commit.com hooks autoupdate_commit_msg: ⬆ [pre-commit.ci] pre-commit autoupdate + autofix_prs: true + autoupdate_branch: '' + autoupdate_schedule: weekly + skip: [] + submodules: false diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index b9b25cedf..75b29213c 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -66,7 +66,7 @@ You can install the dependencies by running: ```bash cd llama-stack -uv sync --extra dev +uv sync --group dev uv pip install -e . source .venv/bin/activate ``` @@ -112,7 +112,7 @@ uv run pre-commit run --all-files ## Running tests -You can find the Llama Stack testing documentation here [here](tests/README.md). +You can find the Llama Stack testing documentation [here](https://github.com/meta-llama/llama-stack/blob/main/tests/README.md). ## Adding a new dependency to the project @@ -168,7 +168,7 @@ manually as they are auto-generated. ### Updating the provider documentation -If you have made changes to a provider's configuration, you should run `./scripts/distro_codegen.py` +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. diff --git a/README.md b/README.md index 1bebf6b19..7f0fed345 100644 --- a/README.md +++ b/README.md @@ -6,6 +6,7 @@ [![Discord](https://img.shields.io/discord/1257833999603335178?color=6A7EC2&logo=discord&logoColor=ffffff)](https://discord.gg/llama-stack) [![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) +![coverage badge](./coverage.svg) [**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) @@ -77,7 +78,7 @@ As more providers start supporting Llama 4, you can use them in Llama Stack as w To try Llama Stack locally, run: ```bash -curl -LsSf https://github.com/meta-llama/llama-stack/raw/main/install.sh | bash +curl -LsSf https://github.com/meta-llama/llama-stack/raw/main/scripts/install.sh | bash ``` ### Overview diff --git a/coverage.svg b/coverage.svg new file mode 100644 index 000000000..636889bb0 --- /dev/null +++ b/coverage.svg @@ -0,0 +1,21 @@ + + + + + + + + + + + + + + + + coverage + coverage + 44% + 44% + + diff --git a/docs/_static/llama-stack-spec.html b/docs/_static/llama-stack-spec.html index ae9ad5d4c..db5c57821 100644 --- a/docs/_static/llama-stack-spec.html +++ b/docs/_static/llama-stack-spec.html @@ -11132,8 +11132,38 @@ "title": "Trace" }, "Checkpoint": { - "description": "Checkpoint created during training runs", - "title": "Checkpoint" + "type": "object", + "properties": { + "identifier": { + "type": "string" + }, + "created_at": { + "type": "string", + "format": "date-time" + }, + "epoch": { + "type": "integer" + }, + "post_training_job_id": { + "type": "string" + }, + "path": { + "type": "string" + }, + "training_metrics": { + "$ref": "#/components/schemas/PostTrainingMetric" + } + }, + "additionalProperties": false, + "required": [ + "identifier", + "created_at", + "epoch", + "post_training_job_id", + "path" + ], + "title": "Checkpoint", + "description": "Checkpoint created during training runs" }, "PostTrainingJobArtifactsResponse": { "type": "object", @@ -11156,6 +11186,31 @@ "title": "PostTrainingJobArtifactsResponse", "description": "Artifacts of a finetuning job." }, + "PostTrainingMetric": { + "type": "object", + "properties": { + "epoch": { + "type": "integer" + }, + "train_loss": { + "type": "number" + }, + "validation_loss": { + "type": "number" + }, + "perplexity": { + "type": "number" + } + }, + "additionalProperties": false, + "required": [ + "epoch", + "train_loss", + "validation_loss", + "perplexity" + ], + "title": "PostTrainingMetric" + }, "PostTrainingJobStatusResponse": { "type": "object", "properties": { @@ -11285,6 +11340,9 @@ }, "embedding_dimension": { "type": "integer" + }, + "vector_db_name": { + "type": "string" } }, "additionalProperties": false, @@ -13535,10 +13593,6 @@ "provider_id": { "type": "string", "description": "The ID of the provider to use for this vector store." - }, - "provider_vector_db_id": { - "type": "string", - "description": "The provider-specific vector database ID." } }, "additionalProperties": false, @@ -14741,7 +14795,8 @@ "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": { - "type": "string", + "$ref": "#/components/schemas/RAGSearchMode", + "default": "vector", "description": "Search mode for retrieval—either \"vector\", \"keyword\", or \"hybrid\". Default \"vector\"." }, "ranker": { @@ -14776,6 +14831,16 @@ } } }, + "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": { @@ -15568,6 +15633,10 @@ "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." diff --git a/docs/_static/llama-stack-spec.yaml b/docs/_static/llama-stack-spec.yaml index 48cefe12b..29ba9dede 100644 --- a/docs/_static/llama-stack-spec.yaml +++ b/docs/_static/llama-stack-spec.yaml @@ -7838,8 +7838,30 @@ components: - start_time title: Trace Checkpoint: - description: Checkpoint created during training runs + type: object + properties: + identifier: + type: string + created_at: + type: string + format: date-time + epoch: + type: integer + post_training_job_id: + type: string + path: + type: string + training_metrics: + $ref: '#/components/schemas/PostTrainingMetric' + additionalProperties: false + required: + - identifier + - created_at + - epoch + - post_training_job_id + - path title: Checkpoint + description: Checkpoint created during training runs PostTrainingJobArtifactsResponse: type: object properties: @@ -7855,6 +7877,24 @@ components: - checkpoints title: PostTrainingJobArtifactsResponse description: Artifacts of a finetuning job. + PostTrainingMetric: + type: object + properties: + epoch: + type: integer + train_loss: + type: number + validation_loss: + type: number + perplexity: + type: number + additionalProperties: false + required: + - epoch + - train_loss + - validation_loss + - perplexity + title: PostTrainingMetric PostTrainingJobStatusResponse: type: object properties: @@ -7944,6 +7984,8 @@ components: type: string embedding_dimension: type: integer + vector_db_name: + type: string additionalProperties: false required: - identifier @@ -9454,10 +9496,6 @@ components: type: string description: >- The ID of the provider to use for this vector store. - provider_vector_db_id: - type: string - description: >- - The provider-specific vector database ID. additionalProperties: false required: - name @@ -10306,7 +10344,8 @@ components: content string), {metadata} (chunk metadata dict). Default: "Result {index}\nContent: {chunk.content}\nMetadata: {metadata}\n" mode: - type: string + $ref: '#/components/schemas/RAGSearchMode' + default: vector description: >- Search mode for retrieval—either "vector", "keyword", or "hybrid". Default "vector". @@ -10333,6 +10372,17 @@ components: 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: @@ -10893,6 +10943,9 @@ components: 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: >- diff --git a/docs/getting_started_llama4.ipynb b/docs/getting_started_llama4.ipynb index edefda28c..82aef6039 100644 --- a/docs/getting_started_llama4.ipynb +++ b/docs/getting_started_llama4.ipynb @@ -55,7 +55,7 @@ "\n", "MODEL=\"Llama-4-Scout-17B-16E-Instruct\"\n", "# get meta url from llama.com\n", - "!uv run --with llama-stackllama model download --source meta --model-id $MODEL --meta-url \n", + "!uv run --with llama-stack llama model download --source meta --model-id $MODEL --meta-url \n", "\n", "model_id = f\"meta-llama/{MODEL}\"" ] diff --git a/rfcs/RFC-0001-llama-stack.md b/docs/original_rfc.md similarity index 96% rename from rfcs/RFC-0001-llama-stack.md rename to docs/original_rfc.md index 222487bd6..dc95a04cb 100644 --- a/rfcs/RFC-0001-llama-stack.md +++ b/docs/original_rfc.md @@ -1,5 +1,7 @@ # The Llama Stack API +*Originally authored Jul 23, 2024* + **Authors:** * Meta: @raghotham, @ashwinb, @hjshah, @jspisak @@ -24,7 +26,7 @@ Meta releases weights of both the pretrained and instruction fine-tuned Llama mo ### Model Lifecycle -![Figure 1: Model Life Cycle](../docs/resources/model-lifecycle.png) +![Figure 1: Model Life Cycle](resources/model-lifecycle.png) For each of the operations that need to be performed (e.g. fine tuning, inference, evals etc) during the model life cycle, we identified the capabilities as toolchain APIs that are needed. Some of these capabilities are primitive operations like inference while other capabilities like synthetic data generation are composed of other capabilities. The list of APIs we have identified to support the lifecycle of Llama models is below: @@ -37,7 +39,7 @@ For each of the operations that need to be performed (e.g. fine tuning, inferenc ### Agentic System -![Figure 2: Agentic System](../docs/resources/agentic-system.png) +![Figure 2: Agentic System](resources/agentic-system.png) In addition to the model lifecycle, we considered the different components involved in an agentic system. Specifically around tool calling and shields. Since the model may decide to call tools, a single model inference call is not enough. What’s needed is an agentic loop consisting of tool calls and inference. The model provides separate tokens representing end-of-message and end-of-turn. A message represents a possible stopping point for execution where the model can inform the execution environment that a tool call needs to be made. The execution environment, upon execution, adds back the result to the context window and makes another inference call. This process can get repeated until an end-of-turn token is generated. Note that as of today, in the OSS world, such a “loop” is often coded explicitly via elaborate prompt engineering using a ReAct pattern (typically) or preconstructed execution graph. Llama 3.1 (and future Llamas) attempts to absorb this multi-step reasoning loop inside the main model itself. @@ -63,9 +65,9 @@ The sequence diagram that details the steps is [here](https://github.com/meta-ll We define the Llama Stack as a layer cake shown below. -![Figure 3: Llama Stack](../docs/resources/llama-stack.png) +![Figure 3: Llama Stack](resources/llama-stack.png) -The API is defined in the [YAML](../docs/_static/llama-stack-spec.yaml) and [HTML](../docs/_static/llama-stack-spec.html) files. +The API is defined in the [YAML](_static/llama-stack-spec.yaml) and [HTML](_static/llama-stack-spec.html) files. ## Sample implementations diff --git a/docs/quick_start.ipynb b/docs/quick_start.ipynb index 4ae1dbe8d..91cfb569c 100644 --- a/docs/quick_start.ipynb +++ b/docs/quick_start.ipynb @@ -145,12 +145,12 @@ " del os.environ[\"UV_SYSTEM_PYTHON\"]\n", "\n", "# this command installs all the dependencies needed for the llama stack server with the ollama inference provider\n", - "!uv run --with llama-stack llama stack build --template ollama --image-type venv --image-name myvenv\n", + "!uv run --with llama-stack llama stack build --template starter --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", - " f\"uv run --with llama-stack llama stack run ollama --image-type venv --image-name myvenv --env INFERENCE_MODEL=llama3.2:3b\",\n", + " f\"uv run --with llama-stack llama stack run starter --image-type venv --env INFERENCE_MODEL=llama3.2:3b\",\n", " shell=True,\n", " stdout=log_file,\n", " stderr=log_file,\n", @@ -249,18 +249,23 @@ ], "source": [ "from llama_stack_client import Agent, AgentEventLogger, RAGDocument, LlamaStackClient\n", + "import os\n", + "\n", + "os.environ[\"ENABLE_OLLAMA\"] = \"ollama\"\n", + "os.environ[\"OLLAMA_INFERENCE_MODEL\"] = \"llama3.2:3b\"\n", + "os.environ[\"OLLAMA_EMBEDDING_MODEL\"] = \"all-minilm:l6-v2\"\n", + "os.environ[\"OLLAMA_EMBEDDING_DIMENSION\"] = \"384\"\n", "\n", "vector_db_id = \"my_demo_vector_db\"\n", "client = LlamaStackClient(base_url=\"http://0.0.0.0:8321\")\n", "\n", "models = client.models.list()\n", "\n", - "# Select the first LLM and first embedding models\n", - "model_id = next(m for m in models if m.model_type == \"llm\").identifier\n", - "embedding_model_id = (\n", - " em := next(m for m in models if m.model_type == \"embedding\")\n", - ").identifier\n", - "embedding_dimension = em.metadata[\"embedding_dimension\"]\n", + "# Select the first ollama and first ollama's embedding model\n", + "model_id = next(m for m in models if m.model_type == \"llm\" and m.provider_id == \"ollama\").identifier\n", + "embedding_model = next(m for m in models if m.model_type == \"embedding\" and m.provider_id == \"ollama\")\n", + "embedding_model_id = embedding_model.identifier\n", + "embedding_dimension = embedding_model.metadata[\"embedding_dimension\"]\n", "\n", "_ = client.vector_dbs.register(\n", " vector_db_id=vector_db_id,\n", diff --git a/docs/source/advanced_apis/eval/index.md b/docs/source/advanced_apis/eval/index.md new file mode 100644 index 000000000..330380670 --- /dev/null +++ b/docs/source/advanced_apis/eval/index.md @@ -0,0 +1,6 @@ +# 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/eval/inline_meta-reference.md b/docs/source/advanced_apis/eval/inline_meta-reference.md new file mode 100644 index 000000000..606883c72 --- /dev/null +++ b/docs/source/advanced_apis/eval/inline_meta-reference.md @@ -0,0 +1,21 @@ +# 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/advanced_apis/eval/remote_nvidia.md b/docs/source/advanced_apis/eval/remote_nvidia.md new file mode 100644 index 000000000..cb764b511 --- /dev/null +++ b/docs/source/advanced_apis/eval/remote_nvidia.md @@ -0,0 +1,19 @@ +# 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/concepts/evaluation_concepts.md b/docs/source/advanced_apis/evaluation_concepts.md similarity index 100% rename from docs/source/concepts/evaluation_concepts.md rename to docs/source/advanced_apis/evaluation_concepts.md diff --git a/docs/source/advanced_apis/index.md b/docs/source/advanced_apis/index.md new file mode 100644 index 000000000..b10672c29 --- /dev/null +++ b/docs/source/advanced_apis/index.md @@ -0,0 +1,33 @@ +# 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/providers/post_training/huggingface.md b/docs/source/advanced_apis/post_training/huggingface.md similarity index 100% rename from docs/source/providers/post_training/huggingface.md rename to docs/source/advanced_apis/post_training/huggingface.md diff --git a/docs/source/advanced_apis/post_training/index.md b/docs/source/advanced_apis/post_training/index.md new file mode 100644 index 000000000..35d10d14b --- /dev/null +++ b/docs/source/advanced_apis/post_training/index.md @@ -0,0 +1,7 @@ +# 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 new file mode 100644 index 000000000..367258a1d --- /dev/null +++ b/docs/source/advanced_apis/post_training/inline_huggingface.md @@ -0,0 +1,33 @@ +# 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 new file mode 100644 index 000000000..82730e54b --- /dev/null +++ b/docs/source/advanced_apis/post_training/inline_torchtune.md @@ -0,0 +1,20 @@ +# 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/providers/post_training/nvidia_nemo.md b/docs/source/advanced_apis/post_training/nvidia_nemo.md similarity index 100% rename from docs/source/providers/post_training/nvidia_nemo.md rename to docs/source/advanced_apis/post_training/nvidia_nemo.md diff --git a/docs/source/advanced_apis/post_training/remote_nvidia.md b/docs/source/advanced_apis/post_training/remote_nvidia.md new file mode 100644 index 000000000..9a381d872 --- /dev/null +++ b/docs/source/advanced_apis/post_training/remote_nvidia.md @@ -0,0 +1,28 @@ +# 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/post_training/torchtune.md b/docs/source/advanced_apis/post_training/torchtune.md similarity index 100% rename from docs/source/providers/post_training/torchtune.md rename to docs/source/advanced_apis/post_training/torchtune.md diff --git a/docs/source/advanced_apis/scoring/index.md b/docs/source/advanced_apis/scoring/index.md new file mode 100644 index 000000000..3cf7af537 --- /dev/null +++ b/docs/source/advanced_apis/scoring/index.md @@ -0,0 +1,7 @@ +# 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/advanced_apis/scoring/inline_basic.md b/docs/source/advanced_apis/scoring/inline_basic.md new file mode 100644 index 000000000..e9e50cff4 --- /dev/null +++ b/docs/source/advanced_apis/scoring/inline_basic.md @@ -0,0 +1,13 @@ +# inline::basic + +## Description + +Basic scoring provider for simple evaluation metrics and scoring functions. + +## Sample Configuration + +```yaml +{} + +``` + diff --git a/docs/source/advanced_apis/scoring/inline_braintrust.md b/docs/source/advanced_apis/scoring/inline_braintrust.md new file mode 100644 index 000000000..70a6a1e26 --- /dev/null +++ b/docs/source/advanced_apis/scoring/inline_braintrust.md @@ -0,0 +1,19 @@ +# 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/advanced_apis/scoring/inline_llm-as-judge.md b/docs/source/advanced_apis/scoring/inline_llm-as-judge.md new file mode 100644 index 000000000..971e02897 --- /dev/null +++ b/docs/source/advanced_apis/scoring/inline_llm-as-judge.md @@ -0,0 +1,13 @@ +# 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/building_applications/index.md b/docs/source/building_applications/index.md index 51dff1702..67c79b783 100644 --- a/docs/source/building_applications/index.md +++ b/docs/source/building_applications/index.md @@ -1,4 +1,4 @@ -# Building AI Applications (Examples) +# AI Application Examples Llama Stack provides all the building blocks needed to create sophisticated AI applications. @@ -27,4 +27,5 @@ tools evals telemetry safety -``` +playground/index +``` \ No newline at end of file diff --git a/docs/source/playground/index.md b/docs/source/building_applications/playground/index.md similarity index 98% rename from docs/source/playground/index.md rename to docs/source/building_applications/playground/index.md index ded2b5772..85895f6a5 100644 --- a/docs/source/playground/index.md +++ b/docs/source/building_applications/playground/index.md @@ -1,4 +1,4 @@ -# Llama Stack Playground +## Llama Stack Playground ```{note} The Llama Stack Playground is currently experimental and subject to change. We welcome feedback and contributions to help improve it. @@ -9,7 +9,7 @@ The Llama Stack Playground is an simple interface which aims to: - 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 +### Key Features #### Playground Interactive pages for users to play with and explore Llama Stack API capabilities. @@ -90,7 +90,7 @@ Interactive pages for users to play with and explore Llama Stack API capabilitie - 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 +### Starting the Llama Stack Playground To start the Llama Stack Playground, run the following commands: diff --git a/docs/source/introduction/index.md b/docs/source/concepts/architecture.md similarity index 85% rename from docs/source/introduction/index.md rename to docs/source/concepts/architecture.md index 5ffa5e68d..50cc62c7c 100644 --- a/docs/source/introduction/index.md +++ b/docs/source/concepts/architecture.md @@ -1,31 +1,39 @@ -# Why Llama Stack? +## Llama Stack architecture -Building production AI applications today requires solving multiple challenges: - -**Infrastructure Complexity** -- Running large language models efficiently requires specialized infrastructure. -- Different deployment scenarios (local development, cloud, edge) need different solutions. -- Moving from development to production often requires significant rework. - -**Essential Capabilities** -- Safety guardrails and content filtering are necessary in an enterprise setting. -- Just model inference is not enough - Knowledge retrieval and RAG capabilities are required. -- Nearly any application needs composable multi-step workflows. -- Finally, without monitoring, observability and evaluation, you end up operating in the dark. - -**Lack of Flexibility and Choice** -- Directly integrating with multiple providers creates tight coupling. -- Different providers have different APIs and abstractions. -- Changing providers requires significant code changes. - - -### Our Solution: A Universal Stack +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 ``` +### Benefits of Llama stack + +#### Current challenges in custom AI applications + +Building production AI applications today requires solving multiple challenges: + +**Infrastructure Complexity** + +- Running large language models efficiently requires specialized infrastructure. +- Different deployment scenarios (local development, cloud, edge) need different solutions. +- Moving from development to production often requires significant rework. + +**Essential Capabilities** + +- Safety guardrails and content filtering are necessary in an enterprise setting. +- Just model inference is not enough - Knowledge retrieval and RAG capabilities are required. +- Nearly any application needs composable multi-step workflows. +- Without monitoring, observability and evaluation, you end up operating in the dark. + +**Lack of Flexibility and Choice** + +- Directly integrating with multiple providers creates tight coupling. +- Different providers have different APIs and abstractions. +- Changing providers requires significant code changes. + +#### Our Solution: A Universal Stack + Llama Stack addresses these challenges through a service-oriented, API-first approach: **Develop Anywhere, Deploy Everywhere** @@ -59,4 +67,4 @@ 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. +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 diff --git a/docs/source/concepts/index.md b/docs/source/concepts/index.md index 1c31dc232..a483132b8 100644 --- a/docs/source/concepts/index.md +++ b/docs/source/concepts/index.md @@ -2,6 +2,10 @@ 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 ``` @@ -10,14 +14,10 @@ Given Llama Stack's service-oriented philosophy, a few concepts and workflows ar :start-after: ## API Providers ``` -```{include} resources.md -:start-after: ## Resources -``` - ```{include} distributions.md :start-after: ## Distributions ``` -```{include} evaluation_concepts.md -:start-after: ## Evaluation Concepts +```{include} resources.md +:start-after: ## Resources ``` diff --git a/docs/source/conf.py b/docs/source/conf.py index 6e59dbdfb..20f1abf00 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -52,7 +52,18 @@ extensions = [ "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" diff --git a/docs/source/deploying/index.md b/docs/source/deploying/index.md new file mode 100644 index 000000000..73b5bf4f5 --- /dev/null +++ b/docs/source/deploying/index.md @@ -0,0 +1,4 @@ +# Deployment Examples + +```{include} kubernetes_deployment.md +``` \ No newline at end of file diff --git a/docs/source/distributions/kubernetes_deployment.md b/docs/source/deploying/kubernetes_deployment.md similarity index 82% rename from docs/source/distributions/kubernetes_deployment.md rename to docs/source/deploying/kubernetes_deployment.md index f43039824..7e9791d8d 100644 --- a/docs/source/distributions/kubernetes_deployment.md +++ b/docs/source/deploying/kubernetes_deployment.md @@ -1,10 +1,12 @@ -# Kubernetes Deployment Guide +## Kubernetes Deployment Guide Instead of starting the Llama Stack and vLLM servers locally. We can deploy them in a Kubernetes cluster. ### 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. +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. + First, create a local Kubernetes cluster via Kind: ``` @@ -217,3 +219,29 @@ Finally, we forward the Kubernetes service to a local port and test some inferen kubectl port-forward service/llama-stack-service 5000:5000 llama-stack-client --endpoint http://localhost:5000 inference chat-completion --message "hello, what model are you?" ``` + +## Deploying Llama Stack Server in AWS EKS + +We've also provided a script to deploy the Llama Stack server in an AWS EKS cluster. + +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) and get the client ID and client secret. + - Set the `Authorization callback URL` to `http:///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 +``` + +This script will: + +- 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 diff --git a/docs/source/distributions/building_distro.md b/docs/source/distributions/building_distro.md index d3fb28947..cd2c6b6a8 100644 --- a/docs/source/distributions/building_distro.md +++ b/docs/source/distributions/building_distro.md @@ -145,6 +145,10 @@ $ llama stack build --template starter ... You can now edit ~/.llama/distributions/llamastack-starter/starter-run.yaml and run `llama stack run ~/.llama/distributions/llamastack-starter/starter-run.yaml` ``` + +```{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 @@ -393,17 +397,17 @@ llama stack list ``` ``` -------------------------------+-----------------------------------------------------------------------------+--------------+------------+ -| Stack Name | Path | Build Config | Run Config | -+------------------------------+-----------------------------------------------------------------------------+--------------+------------+ -| together | /home/wenzhou/.llama/distributions/together | Yes | No | -+------------------------------+-----------------------------------------------------------------------------+--------------+------------+ -| bedrock | /home/wenzhou/.llama/distributions/bedrock | Yes | No | -+------------------------------+-----------------------------------------------------------------------------+--------------+------------+ -| starter | /home/wenzhou/.llama/distributions/starter | No | No | -+------------------------------+-----------------------------------------------------------------------------+--------------+------------+ -| remote-vllm | /home/wenzhou/.llama/distributions/remote-vllm | Yes | Yes | -+------------------------------+-----------------------------------------------------------------------------+--------------+------------+ +------------------------------+-----------------------------------------------------------------+--------------+------------+ +| Stack Name | Path | Build Config | Run Config | ++------------------------------+-----------------------------------------------------------------------------+--------------+ +| together | ~/.llama/distributions/together | Yes | No | ++------------------------------+-----------------------------------------------------------------------------+--------------+ +| bedrock | ~/.llama/distributions/bedrock | Yes | No | ++------------------------------+-----------------------------------------------------------------------------+--------------+ +| starter | ~/.llama/distributions/starter | Yes | Yes | ++------------------------------+-----------------------------------------------------------------------------+--------------+ +| remote-vllm | ~/.llama/distributions/remote-vllm | Yes | Yes | ++------------------------------+-----------------------------------------------------------------------------+--------------+ ``` ### Removing a Distribution diff --git a/docs/source/distributions/configuration.md b/docs/source/distributions/configuration.md index 1bba6677e..9548780c6 100644 --- a/docs/source/distributions/configuration.md +++ b/docs/source/distributions/configuration.md @@ -2,6 +2,10 @@ 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: +```{note} +The default `run.yaml` files generated by templates are starting points for your configuration. For guidance on customizing these files for your specific needs, see [Customizing Your run.yaml Configuration](customizing_run_yaml.md). +``` + ```{dropdown} 👋 Click here for a Sample Configuration File ```yaml @@ -56,8 +60,8 @@ shields: [] server: port: 8321 auth: - provider_type: "oauth2_token" - config: + provider_config: + type: "oauth2_token" jwks: uri: "https://my-token-issuing-svc.com/jwks" ``` @@ -226,6 +230,8 @@ server: ### Authentication Configuration +> **Breaking Change (v0.2.14)**: The authentication configuration structure has changed. The previous format with `provider_type` and `config` fields has been replaced with a unified `provider_config` field that includes the `type` field. Update your configuration files accordingly. + The `auth` section configures authentication for the server. When configured, all API requests must include a valid Bearer token in the Authorization header: ``` @@ -240,8 +246,8 @@ The server can be configured to use service account tokens for authorization, va ```yaml server: auth: - provider_type: "oauth2_token" - config: + provider_config: + type: "oauth2_token" jwks: uri: "https://kubernetes.default.svc:8443/openid/v1/jwks" token: "${env.TOKEN:+}" @@ -325,13 +331,25 @@ 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 ``` +#### GitHub Token Provider +Validates GitHub personal access tokens or OAuth tokens directly: +```yaml +server: + auth: + provider_config: + type: "github_token" + github_api_base_url: "https://api.github.com" # Or GitHub Enterprise URL +``` + +The provider fetches user information from GitHub and maps it to access attributes based on the `claims_mapping` configuration. + #### Custom Provider Validates tokens against a custom authentication endpoint: ```yaml server: auth: - provider_type: "custom" - config: + provider_config: + type: "custom" endpoint: "https://auth.example.com/validate" # URL of the auth endpoint ``` @@ -416,8 +434,8 @@ clients. server: port: 8321 auth: - provider_type: custom - config: + provider_config: + type: custom endpoint: https://auth.example.com/validate quota: kvstore: diff --git a/docs/source/distributions/customizing_run_yaml.md b/docs/source/distributions/customizing_run_yaml.md new file mode 100644 index 000000000..10067bab7 --- /dev/null +++ b/docs/source/distributions/customizing_run_yaml.md @@ -0,0 +1,40 @@ +# 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. + +## Key Points + +- **Templates are starting points**: Generated `run.yaml` files contain defaults for development/testing +- **Customization expected**: Update URLs, credentials, models, and settings for your environment +- **Version control separately**: Keep customized configs in your own repository +- **Environment-specific**: Create different configurations for dev, staging, production + +## What You Can Customize + +You can customize: +- **Provider endpoints**: Change `http://localhost:8000` to your actual servers +- **Swap providers**: Replace default providers (e.g., swap Tavily with Brave for search) +- **Storage paths**: Move from `/tmp/` to production directories +- **Authentication**: Add API keys, SSL, timeouts +- **Models**: Different model sizes for dev vs prod +- **Database settings**: Switch from SQLite to PostgreSQL +- **Tool configurations**: Add custom tools and integrations + +## Best Practices + +- Use environment variables for secrets and environment-specific values +- Create separate `run.yaml` files for different environments (dev, staging, prod) +- Document your changes with comments +- Test configurations before deployment +- Keep your customized configs in version control + +Example structure: +``` +your-project/ +├── configs/ +│ ├── dev-run.yaml +│ ├── prod-run.yaml +└── 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 diff --git a/docs/source/distributions/eks/apply.sh b/docs/source/distributions/eks/apply.sh new file mode 100755 index 000000000..3ad3dd263 --- /dev/null +++ b/docs/source/distributions/eks/apply.sh @@ -0,0 +1,19 @@ +#!/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 + +SCRIPT_DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" && pwd )" +K8S_DIR="${SCRIPT_DIR}/../k8s" + +echo "Setting up AWS EKS-specific storage class..." +kubectl apply -f gp3-topology-aware.yaml + +echo "Running main Kubernetes deployment..." +cd "${K8S_DIR}" +./apply.sh "$@" diff --git a/docs/source/distributions/eks/gp3-topology-aware.yaml b/docs/source/distributions/eks/gp3-topology-aware.yaml new file mode 100644 index 000000000..1192ba18c --- /dev/null +++ b/docs/source/distributions/eks/gp3-topology-aware.yaml @@ -0,0 +1,15 @@ +# Set up default storage class on AWS EKS +apiVersion: storage.k8s.io/v1 +kind: StorageClass +metadata: + name: gp3-topology-aware + annotations: + storageclass.kubernetes.io/is-default-class: "true" +parameters: + type: gp3 + iops: "3000" + throughput: "125" +provisioner: ebs.csi.aws.com +reclaimPolicy: Delete +volumeBindingMode: WaitForFirstConsumer +allowVolumeExpansion: true diff --git a/docs/source/distributions/index.md b/docs/source/distributions/index.md index 103a6131f..fce0347d3 100644 --- a/docs/source/distributions/index.md +++ b/docs/source/distributions/index.md @@ -6,13 +6,9 @@ This section provides an overview of the distributions available in Llama Stack. ```{toctree} :maxdepth: 3 - +list_of_distributions +building_distro +customizing_run_yaml importing_as_library configuration -list_of_distributions -kubernetes_deployment -building_distro -on_device_distro -remote_hosted_distro -self_hosted_distro ``` diff --git a/docs/source/distributions/k8s/apply.sh b/docs/source/distributions/k8s/apply.sh index 7ff7d28eb..3356da53e 100755 --- a/docs/source/distributions/k8s/apply.sh +++ b/docs/source/distributions/k8s/apply.sh @@ -6,16 +6,47 @@ # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. -export POSTGRES_USER=${POSTGRES_USER:-llamastack} -export POSTGRES_DB=${POSTGRES_DB:-llamastack} -export POSTGRES_PASSWORD=${POSTGRES_PASSWORD:-llamastack} +export POSTGRES_USER=llamastack +export POSTGRES_DB=llamastack +export POSTGRES_PASSWORD=llamastack + +export INFERENCE_MODEL=meta-llama/Llama-3.2-3B-Instruct +export SAFETY_MODEL=meta-llama/Llama-Guard-3-1B + +# HF_TOKEN should be set by the user; base64 encode it for the secret +if [ -n "${HF_TOKEN:-}" ]; then + export HF_TOKEN_BASE64=$(echo -n "$HF_TOKEN" | base64) +else + echo "ERROR: HF_TOKEN not set. You need it for vLLM to download models from Hugging Face." + exit 1 +fi + +if [ -z "${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" + exit 1 +fi + +if [ -z "${GITHUB_CLIENT_SECRET:-}" ]; then + echo "ERROR: GITHUB_CLIENT_SECRET not set. You need it for Github login to work. Refer to https://llama-stack.readthedocs.io/en/latest/deploying/index.html#kubernetes-deployment-guide" + exit 1 +fi + +if [ -z "${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" + exit 1 +fi + + -export INFERENCE_MODEL=${INFERENCE_MODEL:-meta-llama/Llama-3.2-3B-Instruct} -export SAFETY_MODEL=${SAFETY_MODEL:-meta-llama/Llama-Guard-3-1B} set -euo pipefail set -x +# Apply the HF token secret if HF_TOKEN is provided +if [ -n "${HF_TOKEN:-}" ]; then + envsubst < ./hf-token-secret.yaml.template | kubectl apply -f - +fi + envsubst < ./vllm-k8s.yaml.template | kubectl apply -f - envsubst < ./vllm-safety-k8s.yaml.template | kubectl apply -f - envsubst < ./postgres-k8s.yaml.template | kubectl apply -f - diff --git a/docs/source/distributions/k8s/hf-token-secret.yaml.template b/docs/source/distributions/k8s/hf-token-secret.yaml.template new file mode 100644 index 000000000..b6db8e7bc --- /dev/null +++ b/docs/source/distributions/k8s/hf-token-secret.yaml.template @@ -0,0 +1,7 @@ +apiVersion: v1 +kind: Secret +metadata: + name: hf-token-secret +type: Opaque +data: + token: ${HF_TOKEN_BASE64} diff --git a/docs/source/distributions/k8s/stack-configmap.yaml b/docs/source/distributions/k8s/stack-configmap.yaml index 0a08bca03..c505cba49 100644 --- a/docs/source/distributions/k8s/stack-configmap.yaml +++ b/docs/source/distributions/k8s/stack-configmap.yaml @@ -22,10 +22,10 @@ data: - 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} + 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: {} @@ -33,7 +33,7 @@ data: - provider_id: ${env.ENABLE_CHROMADB:+chromadb} provider_type: remote::chromadb config: - url: ${env.CHROMADB_URL:+} + url: ${env.CHROMADB_URL:=} safety: - provider_id: llama-guard provider_type: inline::llama-guard @@ -48,7 +48,7 @@ data: host: ${env.POSTGRES_HOST:=localhost} port: ${env.POSTGRES_PORT:=5432} db: ${env.POSTGRES_DB:=llamastack} - user: ${env.POSTGRES_USER:llamastack} + user: ${env.POSTGRES_USER:=llamastack} password: ${env.POSTGRES_PASSWORD:=llamastack} responses_store: type: postgres @@ -61,8 +61,8 @@ data: - provider_id: meta-reference provider_type: inline::meta-reference config: - service_name: ${env.OTEL_SERVICE_NAME:+} - sinks: ${env.TELEMETRY_SINKS:console} + service_name: "${env.OTEL_SERVICE_NAME:=\u200B}" + sinks: ${env.TELEMETRY_SINKS:=console} tool_runtime: - provider_id: brave-search provider_type: remote::brave-search @@ -122,6 +122,9 @@ data: provider_id: rag-runtime server: port: 8321 + auth: + provider_config: + type: github_token kind: ConfigMap metadata: creationTimestamp: null diff --git a/docs/source/distributions/k8s/stack-k8s.yaml.template b/docs/source/distributions/k8s/stack-k8s.yaml.template index 1cfc63ef5..912445f68 100644 --- a/docs/source/distributions/k8s/stack-k8s.yaml.template +++ b/docs/source/distributions/k8s/stack-k8s.yaml.template @@ -27,7 +27,7 @@ spec: spec: containers: - name: llama-stack - image: llamastack/distribution-remote-vllm:latest + image: llamastack/distribution-starter:latest imagePullPolicy: Always # since we have specified latest instead of a version env: - name: ENABLE_CHROMADB diff --git a/docs/source/distributions/k8s/stack_run_config.yaml b/docs/source/distributions/k8s/stack_run_config.yaml index 5ac08134c..4da1bd8b4 100644 --- a/docs/source/distributions/k8s/stack_run_config.yaml +++ b/docs/source/distributions/k8s/stack_run_config.yaml @@ -30,7 +30,7 @@ providers: - provider_id: ${env.ENABLE_CHROMADB:+chromadb} provider_type: remote::chromadb config: - url: ${env.CHROMADB_URL:+} + url: ${env.CHROMADB_URL:=} safety: - provider_id: llama-guard provider_type: inline::llama-guard @@ -58,8 +58,8 @@ providers: - provider_id: meta-reference provider_type: inline::meta-reference config: - service_name: ${env.OTEL_SERVICE_NAME:+console} - sinks: ${env.TELEMETRY_SINKS:+console} + service_name: "${env.OTEL_SERVICE_NAME:=\u200B}" + sinks: ${env.TELEMETRY_SINKS:=console} tool_runtime: - provider_id: brave-search provider_type: remote::brave-search @@ -119,3 +119,6 @@ tool_groups: provider_id: rag-runtime server: port: 8321 + auth: + provider_config: + type: github_token diff --git a/docs/source/distributions/k8s/ui-k8s.yaml.template b/docs/source/distributions/k8s/ui-k8s.yaml.template index ef1bf0c55..a6859cb86 100644 --- a/docs/source/distributions/k8s/ui-k8s.yaml.template +++ b/docs/source/distributions/k8s/ui-k8s.yaml.template @@ -26,6 +26,12 @@ spec: value: "http://llama-stack-service:8321" - name: LLAMA_STACK_UI_PORT value: "8322" + - name: GITHUB_CLIENT_ID + value: "${GITHUB_CLIENT_ID}" + - name: GITHUB_CLIENT_SECRET + value: "${GITHUB_CLIENT_SECRET}" + - name: NEXTAUTH_URL + value: "${LLAMA_STACK_UI_URL}:8322" args: - -c - | diff --git a/docs/source/distributions/k8s/vllm-k8s.yaml.template b/docs/source/distributions/k8s/vllm-k8s.yaml.template index 6256cc7e1..22bee4bbc 100644 --- a/docs/source/distributions/k8s/vllm-k8s.yaml.template +++ b/docs/source/distributions/k8s/vllm-k8s.yaml.template @@ -25,23 +25,17 @@ spec: app.kubernetes.io/name: vllm workload-type: inference spec: - affinity: - podAntiAffinity: - requiredDuringSchedulingIgnoredDuringExecution: - - labelSelector: - matchExpressions: - - key: workload-type - operator: In - values: - - inference - topologyKey: kubernetes.io/hostname # Ensures no two inference pods on same node + nodeSelector: + eks.amazonaws.com/nodegroup: gpu containers: - name: vllm image: vllm/vllm-openai:latest command: ["/bin/sh", "-c"] args: - - "vllm serve ${INFERENCE_MODEL} --dtype float16 --enforce-eager --max-model-len 4096 --gpu-memory-utilization 0.6" + - "vllm serve ${INFERENCE_MODEL} --dtype float16 --enforce-eager --max-model-len 4096 --gpu-memory-utilization 0.6 --enable-auto-tool-choice --tool-call-parser llama4_pythonic" env: + - name: INFERENCE_MODEL + value: "${INFERENCE_MODEL}" - name: HUGGING_FACE_HUB_TOKEN valueFrom: secretKeyRef: @@ -49,6 +43,11 @@ spec: key: token ports: - containerPort: 8000 + resources: + limits: + nvidia.com/gpu: 1 + requests: + nvidia.com/gpu: 1 volumeMounts: - name: llama-storage mountPath: /root/.cache/huggingface diff --git a/docs/source/distributions/k8s/vllm-safety-k8s.yaml.template b/docs/source/distributions/k8s/vllm-safety-k8s.yaml.template index 8857e83b6..37b2b9a6b 100644 --- a/docs/source/distributions/k8s/vllm-safety-k8s.yaml.template +++ b/docs/source/distributions/k8s/vllm-safety-k8s.yaml.template @@ -6,7 +6,6 @@ spec: accessModes: - ReadWriteOnce volumeMode: Filesystem - storageClassName: gp2 resources: requests: storage: 30Gi @@ -26,16 +25,8 @@ spec: app.kubernetes.io/name: vllm-safety workload-type: inference spec: - affinity: - podAntiAffinity: - requiredDuringSchedulingIgnoredDuringExecution: - - labelSelector: - matchExpressions: - - key: workload-type - operator: In - values: - - inference - topologyKey: kubernetes.io/hostname # Ensures no two inference pods on same node + nodeSelector: + eks.amazonaws.com/nodegroup: gpu containers: - name: vllm-safety image: vllm/vllm-openai:latest @@ -44,6 +35,8 @@ spec: "vllm serve ${SAFETY_MODEL} --dtype float16 --enforce-eager --max-model-len 4096 --port 8001 --gpu-memory-utilization 0.3" ] env: + - name: SAFETY_MODEL + value: "${SAFETY_MODEL}" - name: HUGGING_FACE_HUB_TOKEN valueFrom: secretKeyRef: @@ -51,6 +44,11 @@ spec: key: token ports: - containerPort: 8001 + resources: + limits: + nvidia.com/gpu: 1 + requests: + nvidia.com/gpu: 1 volumeMounts: - name: llama-storage mountPath: /root/.cache/huggingface diff --git a/docs/source/distributions/list_of_distributions.md b/docs/source/distributions/list_of_distributions.md index e468c3afa..ee01c92c4 100644 --- a/docs/source/distributions/list_of_distributions.md +++ b/docs/source/distributions/list_of_distributions.md @@ -39,6 +39,13 @@ docker pull llama-stack/distribution-meta-reference-gpu **Guides:** [Meta Reference GPU Guide](self_hosted_distro/meta-reference-gpu) +### 🖥️ Self-Hosted with NVIDA NeMo Microservices + +**Use `nvidia` if you:** +- Want to use Llama Stack with NVIDIA NeMo Microservices + +**Guides:** [NVIDIA Distribution Guide](self_hosted_distro/nvidia) + ### ☁️ Managed Hosting **Use remote-hosted endpoints if you:** diff --git a/docs/source/distributions/ondevice_distro/android_sdk.md b/docs/source/distributions/ondevice_distro/android_sdk.md index a097a2adf..1cddf1d1f 100644 --- a/docs/source/distributions/ondevice_distro/android_sdk.md +++ b/docs/source/distributions/ondevice_distro/android_sdk.md @@ -13,7 +13,7 @@ Latest Release Notes: [link](https://github.com/meta-llama/llama-stack-client-ko *Tagged releases are stable versions of the project. While we strive to maintain a stable main branch, it's not guaranteed to be free of bugs or issues.* ## Android Demo App -Check out our demo app to see how to integrate Llama Stack into your Android app: [Android Demo App](https://github.com/meta-llama/llama-stack-client-kotlin/tree/examples/android_app) +Check out our demo app to see how to integrate Llama Stack into your Android app: [Android Demo App](https://github.com/meta-llama/llama-stack-client-kotlin/tree/latest-release/examples/android_app) The key files in the app are `ExampleLlamaStackLocalInference.kt`, `ExampleLlamaStackRemoteInference.kts`, and `MainActivity.java`. With encompassed business logic, the app shows how to use Llama Stack for both the environments. @@ -68,7 +68,7 @@ Ensure the Llama Stack server version is the same as the Kotlin SDK Library for Other inference providers: [Table](https://llama-stack.readthedocs.io/en/latest/index.html#supported-llama-stack-implementations) -How to set remote localhost in Demo App: [Settings](https://github.com/meta-llama/llama-stack-apps/tree/main/examples/android_app#settings) +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) ### Initialize the Client A client serves as the primary interface for interacting with a specific inference type and its associated parameters. Only after client is initialized then you can configure and start inferences. @@ -135,7 +135,7 @@ val result = client!!.inference().chatCompletionStreaming( ### Setup Custom Tool Calling -Android demo app for more details: [Custom Tool Calling](https://github.com/meta-llama/llama-stack-apps/tree/main/examples/android_app#tool-calling) +Android demo app for more details: [Custom Tool Calling](https://github.com/meta-llama/llama-stack-client-kotlin/tree/latest-release/examples/android_app#tool-calling) ## Advanced Users diff --git a/docs/source/distributions/self_hosted_distro/nvidia.md b/docs/source/distributions/self_hosted_distro/nvidia.md new file mode 100644 index 000000000..47e38f73d --- /dev/null +++ b/docs/source/distributions/self_hosted_distro/nvidia.md @@ -0,0 +1,177 @@ + +# NVIDIA Distribution + +The `llamastack/distribution-nvidia` distribution consists of the following provider configurations. + +| API | Provider(s) | +|-----|-------------| +| agents | `inline::meta-reference` | +| datasetio | `inline::localfs`, `remote::nvidia` | +| eval | `remote::nvidia` | +| inference | `remote::nvidia` | +| post_training | `remote::nvidia` | +| safety | `remote::nvidia` | +| scoring | `inline::basic` | +| telemetry | `inline::meta-reference` | +| tool_runtime | `inline::rag-runtime` | +| vector_io | `inline::faiss` | + + +### Environment Variables + +The following environment variables can be configured: + +- `NVIDIA_API_KEY`: NVIDIA API Key (default: ``) +- `NVIDIA_APPEND_API_VERSION`: Whether to append the API version to the base_url (default: `True`) +- `NVIDIA_DATASET_NAMESPACE`: NVIDIA Dataset Namespace (default: `default`) +- `NVIDIA_PROJECT_ID`: NVIDIA Project ID (default: `test-project`) +- `NVIDIA_CUSTOMIZER_URL`: NVIDIA Customizer URL (default: `https://customizer.api.nvidia.com`) +- `NVIDIA_OUTPUT_MODEL_DIR`: NVIDIA Output Model Directory (default: `test-example-model@v1`) +- `GUARDRAILS_SERVICE_URL`: URL for the NeMo Guardrails Service (default: `http://0.0.0.0:7331`) +- `NVIDIA_GUARDRAILS_CONFIG_ID`: NVIDIA Guardrail Configuration ID (default: `self-check`) +- `NVIDIA_EVALUATOR_URL`: URL for the NeMo Evaluator Service (default: `http://0.0.0.0:7331`) +- `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 (aliases: meta-llama/Llama-3-8B-Instruct)` +- `meta/llama3-70b-instruct (aliases: meta-llama/Llama-3-70B-Instruct)` +- `meta/llama-3.1-8b-instruct (aliases: meta-llama/Llama-3.1-8B-Instruct)` +- `meta/llama-3.1-70b-instruct (aliases: meta-llama/Llama-3.1-70B-Instruct)` +- `meta/llama-3.1-405b-instruct (aliases: meta-llama/Llama-3.1-405B-Instruct-FP8)` +- `meta/llama-3.2-1b-instruct (aliases: meta-llama/Llama-3.2-1B-Instruct)` +- `meta/llama-3.2-3b-instruct (aliases: meta-llama/Llama-3.2-3B-Instruct)` +- `meta/llama-3.2-11b-vision-instruct (aliases: meta-llama/Llama-3.2-11B-Vision-Instruct)` +- `meta/llama-3.2-90b-vision-instruct (aliases: meta-llama/Llama-3.2-90B-Vision-Instruct)` +- `meta/llama-3.3-70b-instruct (aliases: meta-llama/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 +### NVIDIA API Keys + +Make sure you have access to a NVIDIA API Key. You can get one by visiting [https://build.nvidia.com/](https://build.nvidia.com/). Use this key for the `NVIDIA_API_KEY` environment variable. + +### Deploy NeMo Microservices Platform +The NVIDIA NeMo microservices platform supports end-to-end microservice deployment of a complete AI flywheel on your Kubernetes cluster through the NeMo Microservices Helm Chart. Please reference the [NVIDIA NeMo Microservices documentation](https://docs.nvidia.com/nemo/microservices/latest/about/index.html) for platform prerequisites and instructions to install and deploy the platform. + +## Supported Services +Each Llama Stack API corresponds to a specific NeMo microservice. The core microservices (Customizer, Evaluator, Guardrails) are exposed by the same endpoint. The platform components (Data Store) are each exposed by separate endpoints. + +### Inference: NVIDIA NIM +NVIDIA NIM is used for running inference with registered models. There are two ways to access NVIDIA NIMs: + 1. Hosted (default): Preview APIs hosted at https://integrate.api.nvidia.com (Requires an API key) + 2. Self-hosted: NVIDIA NIMs that run on your own infrastructure. + +The deployed platform includes the NIM Proxy microservice, which is the service that provides to access your NIMs (for example, to run inference on a model). Set the `NVIDIA_BASE_URL` environment variable to use your NVIDIA NIM Proxy deployment. + +### 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. + +### 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. + +### 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. + +See the {repopath}`NVIDIA Post-Training docs::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. + +## 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`. + +Note: For improved inference speeds, we need to use NIM with `fast_outlines` guided decoding system (specified in the request body). This is the default if you deployed the platform with the NeMo Microservices Helm Chart. +```sh +# URL to NeMo NIM Proxy service +export NEMO_URL="http://nemo.test" + +curl --location "$NEMO_URL/v1/deployment/model-deployments" \ + -H 'accept: application/json' \ + -H 'Content-Type: application/json' \ + -d '{ + "name": "llama-3.2-1b-instruct", + "namespace": "meta", + "config": { + "model": "meta/llama-3.2-1b-instruct", + "nim_deployment": { + "image_name": "nvcr.io/nim/meta/llama-3.2-1b-instruct", + "image_tag": "1.8.3", + "pvc_size": "25Gi", + "gpu": 1, + "additional_envs": { + "NIM_GUIDED_DECODING_BACKEND": "fast_outlines" + } + } + } + }' +``` +This NIM deployment should take approximately 10 minutes to go live. [See the docs](https://docs.nvidia.com/nemo/microservices/latest/get-started/tutorials/deploy-nims.html) for more information on how to deploy a NIM and verify it's available for inference. + +You can also remove a deployed NIM to free up GPU resources, if needed. +```sh +export NEMO_URL="http://nemo.test" + +curl -X DELETE "$NEMO_URL/v1/deployment/model-deployments/meta/llama-3.1-8b-instruct" +``` + +## Running Llama Stack with NVIDIA + +You can do this via Conda or venv (build code), or Docker which has a pre-built image. + +### Via Docker + +This method allows you to get started quickly without having to build the distribution code. + +```bash +LLAMA_STACK_PORT=8321 +docker run \ + -it \ + --pull always \ + -p $LLAMA_STACK_PORT:$LLAMA_STACK_PORT \ + -v ./run.yaml:/root/my-run.yaml \ + llamastack/distribution-nvidia \ + --config /root/my-run.yaml \ + --port $LLAMA_STACK_PORT \ + --env NVIDIA_API_KEY=$NVIDIA_API_KEY +``` + +### Via Conda + +```bash +INFERENCE_MODEL=meta-llama/Llama-3.1-8b-Instruct +llama stack build --template nvidia --image-type conda +llama stack run ./run.yaml \ + --port 8321 \ + --env NVIDIA_API_KEY=$NVIDIA_API_KEY \ + --env INFERENCE_MODEL=$INFERENCE_MODEL +``` + +### Via venv + +If you've set up your local development environment, you can also build the image using your local virtual environment. + +```bash +INFERENCE_MODEL=meta-llama/Llama-3.1-8b-Instruct +llama stack build --template nvidia --image-type venv +llama stack run ./run.yaml \ + --port 8321 \ + --env NVIDIA_API_KEY=$NVIDIA_API_KEY \ + --env INFERENCE_MODEL=$INFERENCE_MODEL +``` + +## 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`. diff --git a/docs/source/distributions/self_hosted_distro/starter.md b/docs/source/distributions/self_hosted_distro/starter.md index 1138318b3..56cdd5e73 100644 --- a/docs/source/distributions/self_hosted_distro/starter.md +++ b/docs/source/distributions/self_hosted_distro/starter.md @@ -17,18 +17,18 @@ The `llamastack/distribution-starter` distribution is a comprehensive, multi-pro The starter distribution consists of the following provider configurations: -| API | Provider(s) | -|-----|-------------| -| agents | `inline::meta-reference` | -| datasetio | `remote::huggingface`, `inline::localfs` | -| eval | `inline::meta-reference` | -| files | `inline::localfs` | +| API | Provider(s) | +|-----|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| +| agents | `inline::meta-reference` | +| datasetio | `remote::huggingface`, `inline::localfs` | +| eval | `inline::meta-reference` | +| files | `inline::localfs` | | inference | `remote::openai`, `remote::fireworks`, `remote::together`, `remote::ollama`, `remote::anthropic`, `remote::gemini`, `remote::groq`, `remote::sambanova`, `remote::vllm`, `remote::tgi`, `remote::cerebras`, `remote::llama-openai-compat`, `remote::nvidia`, `remote::hf::serverless`, `remote::hf::endpoint`, `inline::sentence-transformers` | -| safety | `inline::llama-guard` | -| scoring | `inline::basic`, `inline::llm-as-judge`, `inline::braintrust` | -| telemetry | `inline::meta-reference` | -| tool_runtime | `remote::brave-search`, `remote::tavily-search`, `inline::rag-runtime`, `remote::model-context-protocol` | -| vector_io | `inline::faiss`, `inline::sqlite-vec`, `remote::chromadb`, `remote::pgvector` | +| safety | `inline::llama-guard` | +| scoring | `inline::basic`, `inline::llm-as-judge`, `inline::braintrust` | +| telemetry | `inline::meta-reference` | +| tool_runtime | `remote::brave-search`, `remote::tavily-search`, `inline::rag-runtime`, `remote::model-context-protocol` | +| vector_io | `inline::faiss`, `inline::sqlite-vec`, `inline::milvus`, `remote::chromadb`, `remote::pgvector` | ## Inference Providers @@ -167,7 +167,7 @@ When using the `:` pattern (like `${env.OLLAMA_INFERENCE_MODEL:__disabled__}`), ## Running the Distribution -You can run the starter distribution via Docker or Conda. +You can run the starter distribution via Docker, Conda, or venv. ### Via Docker @@ -186,17 +186,12 @@ docker run \ --port $LLAMA_STACK_PORT ``` -### Via Conda +### Via Conda or venv -Make sure you have done `uv pip install llama-stack` and have the Llama Stack CLI available. +Ensure you have configured the starter distribution using the environment variables explained above. ```bash -llama stack build --template starter --image-type conda -llama stack run distributions/starter/run.yaml \ - --port 8321 \ - --env OPENAI_API_KEY=your_openai_key \ - --env FIREWORKS_API_KEY=your_fireworks_key \ - --env TOGETHER_API_KEY=your_together_key +uv run --with llama-stack llama stack build --template starter --image-type --run ``` ## Example Usage diff --git a/docs/source/distributions/starting_llama_stack_server.md b/docs/source/distributions/starting_llama_stack_server.md index f74de6d48..91cb1fe88 100644 --- a/docs/source/distributions/starting_llama_stack_server.md +++ b/docs/source/distributions/starting_llama_stack_server.md @@ -28,5 +28,4 @@ If you have built a container image and want to deploy it in a Kubernetes cluste importing_as_library configuration -kubernetes_deployment ``` diff --git a/docs/source/getting_started/detailed_tutorial.md b/docs/source/getting_started/detailed_tutorial.md index d80ec3554..7ceae9072 100644 --- a/docs/source/getting_started/detailed_tutorial.md +++ b/docs/source/getting_started/detailed_tutorial.md @@ -1,4 +1,4 @@ -# Detailed Tutorial +## 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. A Llama Stack agent is a simple integrated system that can perform tasks by combining a Llama model for reasoning with @@ -10,7 +10,7 @@ Llama Stack is a stateful service with REST APIs to support seamless transition 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. -## Step 1: Installation and Setup +### Step 1: Installation and Setup Install Ollama by following the instructions on the [Ollama website](https://ollama.com/download), then download Llama 3.2 3B model, and then start the Ollama service. @@ -42,10 +42,10 @@ powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | ie Setup your virtual environment. ```bash -uv sync --python 3.10 +uv sync --python 3.12 source .venv/bin/activate ``` -## Step 2: Run Llama Stack +### 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} @@ -54,11 +54,12 @@ Llama Stack is a server that exposes multiple APIs, you connect with it using th 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. +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). 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 -INFERENCE_MODEL=llama3.2:3b llama stack build --template starter --image-type venv --run +ENABLE_OLLAMA=ollama OLLAMA_INFERENCE_MODEL="llama3.2:3b" llama stack build --template starter --image-type venv --run ``` ::: :::{tab-item} Using `conda` @@ -69,17 +70,18 @@ which defines the providers and their settings. Now let's build and run the Llama Stack config for Ollama. ```bash -INFERENCE_MODEL=llama3.2:3b llama stack build --template starter --image-type conda --image-name llama3-3b-conda --run +ENABLE_OLLAMA=ollama INFERENCE_MODEL="llama3.2:3b" llama stack build --template starter --image-type conda --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-ollama` as the container image. If you'd like to build your own image or customize the -configurations, please check out [this guide](../references/index.md). +`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). First lets setup some environment variables and create a local directory to mount into the container’s file system. ```bash export INFERENCE_MODEL="llama3.2:3b" +export ENABLE_OLLAMA=ollama export LLAMA_STACK_PORT=8321 mkdir -p ~/.llama ``` @@ -90,7 +92,7 @@ docker run -it \ --pull always \ -p $LLAMA_STACK_PORT:$LLAMA_STACK_PORT \ -v ~/.llama:/root/.llama \ - llamastack/distribution-ollama \ + llamastack/distribution-starter \ --port $LLAMA_STACK_PORT \ --env INFERENCE_MODEL=$INFERENCE_MODEL \ --env OLLAMA_URL=http://host.docker.internal:11434 @@ -112,7 +114,7 @@ docker run -it \ -p $LLAMA_STACK_PORT:$LLAMA_STACK_PORT \ -v ~/.llama:/root/.llama \ --network=host \ - llamastack/distribution-ollama \ + llamastack/distribution-starter \ --port $LLAMA_STACK_PORT \ --env INFERENCE_MODEL=$INFERENCE_MODEL \ --env OLLAMA_URL=http://localhost:11434 @@ -130,7 +132,7 @@ 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 +### 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. @@ -146,7 +148,7 @@ source .venv/bin/activate :::{tab-item} Install with `venv` ```bash -uv venv client --python 3.10 +uv venv client --python 3.12 source client/bin/activate pip install llama-stack-client ``` @@ -154,7 +156,7 @@ pip install llama-stack-client :::{tab-item} Install with `conda` ```bash -yes | conda create -n stack-client python=3.10 +yes | conda create -n stack-client python=3.12 conda activate stack-client pip install llama-stack-client ``` @@ -177,41 +179,60 @@ List the models llama-stack-client models list Available Models -┏━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓ -┃ model_type ┃ identifier ┃ provider_resource_id ┃ metadata ┃ provider_id ┃ -┡━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩ -│ embedding │ all-MiniLM-L6-v2 │ all-minilm:latest │ {'embedding_dimension': 384.0} │ ollama │ -├─────────────────┼─────────────────────────────────────┼─────────────────────────────────────┼───────────────────────────────────────────┼─────────────────┤ -│ llm │ llama3.2:3b │ llama3.2:3b │ │ ollama │ -└─────────────────┴─────────────────────────────────────┴─────────────────────────────────────┴───────────────────────────────────────────┴─────────────────┘ - -Total models: 2 +┏━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━┓ +┃ model_type ┃ identifier ┃ provider_resource_id ┃ metadata ┃ provider_id ┃ +┡━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━┩ +│ embedding │ ollama/all-minilm:l6-v2 │ all-minilm:l6-v2 │ {'embedding_dimension': 384.0} │ ollama │ +├─────────────────┼─────────────────────────────────────┼─────────────────────────────────────┼───────────────────────────────────────────┼───────────────────────┤ +│ ... │ ... │ ... │ │ ... │ +├─────────────────┼─────────────────────────────────────┼─────────────────────────────────────┼───────────────────────────────────────────┼───────────────────────┤ +│ llm │ ollama/Llama-3.2:3b │ llama3.2:3b │ │ ollama │ +└─────────────────┴─────────────────────────────────────┴─────────────────────────────────────┴───────────────────────────────────────────┴───────────────────────┘ ``` You can test basic Llama inference completion using the CLI. ```bash -llama-stack-client inference chat-completion --message "tell me a joke" +llama-stack-client inference chat-completion --model-id "ollama/llama3.2:3b" --message "tell me a joke" + ``` Sample output: ```python -ChatCompletionResponse( - completion_message=CompletionMessage( - content="Here's one:\n\nWhat do you call a fake noodle?\n\nAn impasta!", - role="assistant", - stop_reason="end_of_turn", - tool_calls=[], - ), - logprobs=None, - metrics=[ - Metric(metric="prompt_tokens", value=14.0, unit=None), - Metric(metric="completion_tokens", value=27.0, unit=None), - Metric(metric="total_tokens", value=41.0, unit=None), +OpenAIChatCompletion( + id="chatcmpl-08d7b2be-40f3-47ed-8f16-a6f29f2436af", + choices=[ + OpenAIChatCompletionChoice( + finish_reason="stop", + index=0, + message=OpenAIChatCompletionChoiceMessageOpenAIAssistantMessageParam( + role="assistant", + content="Why couldn't the bicycle stand up by itself?\n\nBecause it was two-tired.", + name=None, + tool_calls=None, + refusal=None, + annotations=None, + audio=None, + function_call=None, + ), + logprobs=None, + ) ], + created=1751725254, + model="llama3.2:3b", + object="chat.completion", + service_tier=None, + system_fingerprint="fp_ollama", + usage={ + "completion_tokens": 18, + "prompt_tokens": 29, + "total_tokens": 47, + "completion_tokens_details": None, + "prompt_tokens_details": None, + }, ) ``` -## Step 4: Run the Demos +### 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. @@ -221,7 +242,7 @@ Other SDKs are also available, please refer to the [Client SDK](../index.md#clie :::{tab-item} Basic Inference Now you can run inference using the Llama Stack client SDK. -### i. Create the Script +#### i. Create the Script Create a file `inference.py` and add the following code: ```python @@ -233,40 +254,36 @@ client = LlamaStackClient(base_url="http://localhost:8321") models = client.models.list() # Select the first LLM -llm = next(m for m in models if m.model_type == "llm") +llm = next(m for m in models if m.model_type == "llm" and m.provider_id == "ollama") model_id = llm.identifier print("Model:", model_id) -response = client.inference.chat_completion( - model_id=model_id, +response = client.chat.completions.create( + model=model_id, messages=[ {"role": "system", "content": "You are a helpful assistant."}, {"role": "user", "content": "Write a haiku about coding"}, ], ) -print(response.completion_message.content) +print(response) ``` -### ii. Run the Script +#### ii. Run the Script Let's run the script using `uv` ```bash uv run python inference.py ``` Which will output: ``` -Model: llama3.2:3b -Here is a haiku about coding: - -Lines of code unfold -Logic flows through digital night -Beauty in the bits +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 +#### i. Create the Script Create a file `agent.py` and add the following code: ```python @@ -278,7 +295,7 @@ import uuid client = LlamaStackClient(base_url=f"http://localhost:8321") models = client.models.list() -llm = next(m for m in models if m.model_type == "llm") +llm = next(m for m in models if m.model_type == "llm" and m.provider_id == "ollama") model_id = llm.identifier agent = Agent(client, model=model_id, instructions="You are a helpful assistant.") @@ -315,19 +332,20 @@ uv run python agent.py ```{dropdown} 👋 Click here to see the sample output Non-streaming ... - agent> I'm an artificial intelligence designed to assist and communicate with users like you. I don't have a personal identity, but I'm here to provide information, answer questions, and help with tasks to the best of my abilities. + agent> I'm an artificial intelligence designed to assist and communicate with users like you. I don't have a personal identity, but I can provide information, answer questions, and help with tasks to the best of my abilities. - I can be used for a wide range of purposes, such as: + I'm a large language model, which means I've been trained on a massive dataset of text from various sources, allowing me to understand and respond to a wide range of topics and questions. My purpose is to provide helpful and accurate information, and I'm constantly learning and improving my responses based on the interactions I have with users like you. + I can help with: + + * Answering questions on various subjects * Providing definitions and explanations * Offering suggestions and ideas - * Helping with language translation - * Assisting with writing and proofreading - * Generating text or responses to questions - * Playing simple games or chatting about topics of interest - - I'm constantly learning and improving my abilities, so feel free to ask me anything, and I'll do my best to help! + * Assisting with language-related tasks, such as proofreading and editing + * Generating text and content + * And more! + Feel free to ask me anything, and I'll do my best to help! Streaming ... AgentTurnResponseStreamChunk( │ event=TurnResponseEvent( @@ -421,15 +439,15 @@ uv run python agent.py Streaming with print helper... - inference> Déjà vu! + inference> Déjà vu! You're asking me again! - As I mentioned earlier, I'm an artificial intelligence language model. I don't have a personal identity or consciousness like humans do. I exist solely to process and respond to text-based inputs, providing information and assistance on a wide range of topics. + As I mentioned earlier, I'm a computer program designed to simulate conversation and answer questions. I don't have a personal identity or consciousness like a human would. I exist solely as a digital entity, running on computer servers and responding to inputs from users like you. - I'm a computer program designed to simulate human-like conversations, using natural language processing (NLP) and machine learning algorithms to understand and generate responses. My purpose is to help users like you with their questions, provide information, and engage in conversation. + I'm a type of artificial intelligence (AI) called a large language model, which means I've been trained on a massive dataset of text from various sources. This training allows me to understand and respond to a wide range of questions and topics. - Think of me as a virtual companion, a helpful tool designed to make your interactions more efficient and enjoyable. I don't have personal opinions, emotions, or biases, but I'm here to provide accurate and informative responses to the best of my abilities. + My purpose is to provide helpful and accurate information, answer questions, and assist users like you with tasks and conversations. I don't have personal preferences, emotions, or opinions like humans do. My goal is to be informative, neutral, and respectful in my responses. - So, who am I? I'm just a computer program designed to help you! + So, that's me in a nutshell! ``` ::: @@ -437,7 +455,7 @@ uv run python agent.py 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. -### i. Create the Script +#### i. Create the Script Create a file `rag_agent.py` and add the following code: ```python @@ -483,7 +501,11 @@ client.tool_runtime.rag_tool.insert( ) # Get the model being served -llm = next(m for m in client.models.list() if m.model_type == "llm") +llm = next( + m + for m in client.models.list() + if m.model_type == "llm" and m.provider_id == "ollama" +) model = llm.identifier # Create the RAG agent @@ -511,7 +533,7 @@ for t in turns: for event in AgentEventLogger().log(stream): event.print() ``` -### ii. Run the Script +#### ii. Run the Script Let's run the script using `uv` ```bash uv run python rag_agent.py diff --git a/docs/source/getting_started/index.md b/docs/source/getting_started/index.md index d9b06ee93..e941534c2 100644 --- a/docs/source/getting_started/index.md +++ b/docs/source/getting_started/index.md @@ -1,123 +1,13 @@ -# Quickstart +# Getting Started -Get started with Llama Stack in minutes! - -Llama Stack is a stateful service with REST APIs to support the seamless transition of AI applications across different -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. - -**💡 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) - -#### Step 1: Install and setup -1. Install [uv](https://docs.astral.sh/uv/) -2. Run inference on a Llama model with [Ollama](https://ollama.com/download) -```bash -ollama run llama3.2:3b --keepalive 60m +```{include} quickstart.md +:start-after: ## Quickstart ``` -#### Step 2: Run the Llama Stack server -We will use `uv` to run the Llama Stack server. -```bash -INFERENCE_MODEL=llama3.2:3b uv run --with llama-stack llama stack build --template starter --image-type venv --run + +```{include} libraries.md +:start-after: ## Libraries (SDKs) ``` -#### Step 3: Run the demo -Now open up a new terminal and copy the following script into a file named `demo_script.py`. -```python -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"] - -_ = client.vector_dbs.register( - vector_db_id=vector_db_id, - embedding_model=embedding_model_id, - embedding_dimension=embedding_dimension, - provider_id="faiss", -) -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=50, -) -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) - -response = agent.create_turn( - messages=[{"role": "user", "content": prompt}], - session_id=agent.create_session("rag_session"), - stream=True, -) - -for log in AgentEventLogger().log(response): - log.print() +```{include} detailed_tutorial.md +:start-after: ## Detailed Tutorial ``` -We will use `uv` to run the script -``` -uv run --with llama-stack-client,fire,requests demo_script.py -``` -And you should see output like below. -``` -rag_tool> Ingesting document: https://www.paulgraham.com/greatwork.html - -prompt> How do you do great work? - -inference> [knowledge_search(query="What is the key to doing great work")] - -tool_execution> Tool:knowledge_search Args:{'query': 'What is the key to doing great work'} - -tool_execution> Tool:knowledge_search Response:[TextContentItem(text='knowledge_search tool found 5 chunks:\nBEGIN of knowledge_search tool results.\n', type='text'), TextContentItem(text="Result 1:\nDocument_id:docum\nContent: work. Doing great work means doing something important\nso well that you expand people's ideas of what's possible. But\nthere's no threshold for importance. It's a matter of degree, and\noften hard to judge at the time anyway.\n", type='text'), TextContentItem(text="Result 2:\nDocument_id:docum\nContent: work. Doing great work means doing something important\nso well that you expand people's ideas of what's possible. But\nthere's no threshold for importance. It's a matter of degree, and\noften hard to judge at the time anyway.\n", type='text'), TextContentItem(text="Result 3:\nDocument_id:docum\nContent: work. Doing great work means doing something important\nso well that you expand people's ideas of what's possible. But\nthere's no threshold for importance. It's a matter of degree, and\noften hard to judge at the time anyway.\n", type='text'), TextContentItem(text="Result 4:\nDocument_id:docum\nContent: work. Doing great work means doing something important\nso well that you expand people's ideas of what's possible. But\nthere's no threshold for importance. It's a matter of degree, and\noften hard to judge at the time anyway.\n", type='text'), TextContentItem(text="Result 5:\nDocument_id:docum\nContent: work. Doing great work means doing something important\nso well that you expand people's ideas of what's possible. But\nthere's no threshold for importance. It's a matter of degree, and\noften hard to judge at the time anyway.\n", type='text'), TextContentItem(text='END of knowledge_search tool results.\n', type='text')] - -inference> Based on the search results, it seems that doing great work means doing something important so well that you expand people's ideas of what's possible. However, there is no clear threshold for importance, and it can be difficult to judge at the time. - -To further clarify, I would suggest that doing great work involves: - -* Completing tasks with high quality and attention to detail -* Expanding on existing knowledge or ideas -* Making a positive impact on others through your work -* Striving for excellence and continuous improvement - -Ultimately, great work is about making a meaningful contribution and leaving a lasting impression. -``` -Congratulations! You've successfully built your first RAG application using Llama Stack! 🎉🥳 - -## Next Steps - -Now you're ready to dive deeper into Llama Stack! -- Explore the [Detailed Tutorial](./detailed_tutorial.md). -- 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. -- 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/source/getting_started/libraries.md b/docs/source/getting_started/libraries.md new file mode 100644 index 000000000..a54a9b8d3 --- /dev/null +++ b/docs/source/getting_started/libraries.md @@ -0,0 +1,10 @@ +## Libraries (SDKs) + +We have a number of client-side SDKs available for different languages. + +| **Language** | **Client SDK** | **Package** | +| :----: | :----: | :----: | +| 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 diff --git a/docs/source/getting_started/quickstart.md b/docs/source/getting_started/quickstart.md new file mode 100644 index 000000000..59791643d --- /dev/null +++ b/docs/source/getting_started/quickstart.md @@ -0,0 +1,129 @@ +## Quickstart + +Get started with Llama Stack in minutes! + +Llama Stack is a stateful service with REST APIs to support the seamless transition of AI applications across different +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. + +**💡 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) + +#### Step 1: Install and setup +1. Install [uv](https://docs.astral.sh/uv/) +2. Run inference on a Llama model with [Ollama](https://ollama.com/download) +```bash +ollama run llama3.2:3b --keepalive 60m +``` +#### Step 2: Run the Llama Stack server +We will use `uv` to run the Llama Stack server. +```bash +ENABLE_OLLAMA=ollama OLLAMA_INFERENCE_MODEL=llama3.2:3b uv run --with llama-stack llama stack build --template starter --image-type venv --run +``` +#### Step 3: Run the demo +Now open up a new terminal and copy the following script into a file named `demo_script.py`. + +```python +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"] + +_ = client.vector_dbs.register( + vector_db_id=vector_db_id, + embedding_model=embedding_model_id, + embedding_dimension=embedding_dimension, + provider_id="faiss", +) +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=50, +) +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) + +response = agent.create_turn( + messages=[{"role": "user", "content": prompt}], + session_id=agent.create_session("rag_session"), + stream=True, +) + +for log in AgentEventLogger().log(response): + log.print() +``` +We will use `uv` to run the script +``` +uv run --with llama-stack-client,fire,requests demo_script.py +``` +And you should see output like below. +``` +rag_tool> Ingesting document: https://www.paulgraham.com/greatwork.html + +prompt> How do you do great work? + +inference> [knowledge_search(query="What is the key to doing great work")] + +tool_execution> Tool:knowledge_search Args:{'query': 'What is the key to doing great work'} + +tool_execution> Tool:knowledge_search Response:[TextContentItem(text='knowledge_search tool found 5 chunks:\nBEGIN of knowledge_search tool results.\n', type='text'), TextContentItem(text="Result 1:\nDocument_id:docum\nContent: work. Doing great work means doing something important\nso well that you expand people's ideas of what's possible. But\nthere's no threshold for importance. It's a matter of degree, and\noften hard to judge at the time anyway.\n", type='text'), TextContentItem(text="Result 2:\nDocument_id:docum\nContent: work. Doing great work means doing something important\nso well that you expand people's ideas of what's possible. But\nthere's no threshold for importance. It's a matter of degree, and\noften hard to judge at the time anyway.\n", type='text'), TextContentItem(text="Result 3:\nDocument_id:docum\nContent: work. Doing great work means doing something important\nso well that you expand people's ideas of what's possible. But\nthere's no threshold for importance. It's a matter of degree, and\noften hard to judge at the time anyway.\n", type='text'), TextContentItem(text="Result 4:\nDocument_id:docum\nContent: work. Doing great work means doing something important\nso well that you expand people's ideas of what's possible. But\nthere's no threshold for importance. It's a matter of degree, and\noften hard to judge at the time anyway.\n", type='text'), TextContentItem(text="Result 5:\nDocument_id:docum\nContent: work. Doing great work means doing something important\nso well that you expand people's ideas of what's possible. But\nthere's no threshold for importance. It's a matter of degree, and\noften hard to judge at the time anyway.\n", type='text'), TextContentItem(text='END of knowledge_search tool results.\n', type='text')] + +inference> Based on the search results, it seems that doing great work means doing something important so well that you expand people's ideas of what's possible. However, there is no clear threshold for importance, and it can be difficult to judge at the time. + +To further clarify, I would suggest that doing great work involves: + +* Completing tasks with high quality and attention to detail +* Expanding on existing knowledge or ideas +* Making a positive impact on others through your work +* Striving for excellence and continuous improvement + +Ultimately, great work is about making a meaningful contribution and leaving a lasting impression. +``` +Congratulations! You've successfully built your first RAG application using Llama Stack! 🎉🥳 + +```{admonition} HuggingFace access +:class: tip + +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). +- 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. +- 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/source/index.md b/docs/source/index.md index 755b228e3..c824ce94a 100644 --- a/docs/source/index.md +++ b/docs/source/index.md @@ -40,17 +40,6 @@ Kotlin. - Ready to build? Check out the [Quick Start](getting_started/index) to get started. - Want to contribute? See the [Contributing](contributing/index) guide. -## Client SDKs - -We have a number of client-side SDKs available for different languages. - -| **Language** | **Client SDK** | **Package** | -| :----: | :----: | :----: | -| 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) - ## 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. @@ -133,14 +122,12 @@ A number of "adapters" are available for some popular Inference and Vector Store self getting_started/index -getting_started/detailed_tutorial -introduction/index concepts/index -openai/index providers/index distributions/index +advanced_apis/index building_applications/index -playground/index +deploying/index contributing/index references/index ``` diff --git a/docs/source/providers/index.md b/docs/source/providers/index.md index f804582d7..596daa9ba 100644 --- a/docs/source/providers/index.md +++ b/docs/source/providers/index.md @@ -1,4 +1,4 @@ -# Providers Overview +# API Providers Overview 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., Meta Reference, Ollama, Fireworks, Together, AWS Bedrock, Groq, Cerebras, SambaNova, vLLM, OpenAI, Anthropic, Gemini, WatsonX, etc.), @@ -13,13 +13,25 @@ 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. ## 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. ```{toctree} :maxdepth: 1 -external +external.md +``` + +```{include} openai.md +:start-after: ## OpenAI API Compatibility +``` + +## Inference +Runs inference with an LLM. + +```{toctree} +:maxdepth: 1 + +inference/index ``` ## Agents @@ -40,33 +52,6 @@ Interfaces with datasets and data loaders. datasetio/index ``` -## Eval -Generates outputs (via Inference or Agents) and perform scoring. - -```{toctree} -:maxdepth: 1 - -eval/index -``` - -## Inference -Runs inference with an LLM. - -```{toctree} -:maxdepth: 1 - -inference/index -``` - -## Post Training -Fine-tunes a model. - -```{toctree} -:maxdepth: 1 - -post_training/index -``` - ## Safety Applies safety policies to the output at a Systems (not only model) level. @@ -76,15 +61,6 @@ Applies safety policies to the output at a Systems (not only model) level. safety/index ``` -## Scoring -Evaluates the outputs of the system. - -```{toctree} -:maxdepth: 1 - -scoring/index -``` - ## Telemetry Collects telemetry data from the system. @@ -94,15 +70,6 @@ Collects telemetry data from the system. telemetry/index ``` -## Tool Runtime -Is associated with the ToolGroup resouces. - -```{toctree} -:maxdepth: 1 - -tool_runtime/index -``` - ## Vector IO Vector IO refers to operations on vector databases, such as adding documents, searching, and deleting documents. @@ -114,3 +81,12 @@ io and database are used to store and retrieve documents for retrieval. vector_io/index ``` + +## Tool Runtime +Is associated with the ToolGroup resources. + +```{toctree} +:maxdepth: 1 + +tool_runtime/index +``` \ No newline at end of file diff --git a/docs/source/openai/index.md b/docs/source/providers/openai.md similarity index 93% rename from docs/source/openai/index.md rename to docs/source/providers/openai.md index 03a969cc5..44a615456 100644 --- a/docs/source/openai/index.md +++ b/docs/source/providers/openai.md @@ -1,14 +1,14 @@ -# OpenAI API Compatibility +## OpenAI API Compatibility -## Server path +### Server path Llama Stack exposes an OpenAI-compatible API endpoint at `/v1/openai/v1`. So, for a Llama Stack server running locally on port `8321`, the full url to the OpenAI-compatible API endpoint is `http://localhost:8321/v1/openai/v1`. -## Clients +### Clients You should be able to use any client that speaks OpenAI APIs with Llama Stack. We regularly test with the official Llama Stack clients as well as OpenAI's official Python client. -### Llama Stack Client +#### Llama Stack Client When using the Llama Stack client, set the `base_url` to the root of your Llama Stack server. It will automatically route OpenAI-compatible requests to the right server endpoint for you. @@ -18,7 +18,7 @@ from llama_stack_client import LlamaStackClient client = LlamaStackClient(base_url="http://localhost:8321") ``` -### OpenAI Client +#### OpenAI Client When using an OpenAI client, set the `base_url` to the `/v1/openai/v1` path on your Llama Stack server. @@ -30,9 +30,9 @@ client = OpenAI(base_url="http://localhost:8321/v1/openai/v1", api_key="none") Regardless of the client you choose, the following code examples should all work the same. -## APIs implemented +### APIs implemented -### Models +#### Models Many of the APIs require you to pass in a model parameter. To see the list of models available in your Llama Stack server: @@ -40,13 +40,13 @@ Many of the APIs require you to pass in a model parameter. To see the list of mo models = client.models.list() ``` -### Responses +#### 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. ::: -#### Simple inference +##### Simple inference Request: @@ -66,7 +66,7 @@ Syntax whispers secrets sweet Code's gentle silence ``` -#### Structured Output +##### Structured Output Request: @@ -106,9 +106,9 @@ Example output: { "participants": ["Alice", "Bob"] } ``` -### Chat Completions +#### Chat Completions -#### Simple inference +##### Simple inference Request: @@ -129,7 +129,7 @@ Logic flows like a river Code's gentle beauty ``` -#### Structured Output +##### Structured Output Request: @@ -170,9 +170,9 @@ Example output: { "participants": ["Alice", "Bob"] } ``` -### Completions +#### Completions -#### Simple inference +##### Simple inference Request: diff --git a/docs/source/providers/vector_io/inline_milvus.md b/docs/source/providers/vector_io/inline_milvus.md index 65c67f3ee..3b3aad3fc 100644 --- a/docs/source/providers/vector_io/inline_milvus.md +++ b/docs/source/providers/vector_io/inline_milvus.md @@ -11,7 +11,8 @@ Please refer to the remote provider documentation. | Field | Type | Required | Default | Description | |-------|------|----------|---------|-------------| | `db_path` | `` | No | PydanticUndefined | | -| `kvstore` | `utils.kvstore.config.RedisKVStoreConfig \| utils.kvstore.config.SqliteKVStoreConfig \| utils.kvstore.config.PostgresKVStoreConfig \| utils.kvstore.config.MongoDBKVStoreConfig` | No | sqlite | | +| `kvstore` | `utils.kvstore.config.RedisKVStoreConfig \| utils.kvstore.config.SqliteKVStoreConfig \| utils.kvstore.config.PostgresKVStoreConfig \| utils.kvstore.config.MongoDBKVStoreConfig` | No | sqlite | Config for KV store backend (SQLite only for now) | +| `consistency_level` | `` | No | Strong | The consistency level of the Milvus server | ## Sample Configuration diff --git a/docs/source/providers/vector_io/inline_sqlite-vec.md b/docs/source/providers/vector_io/inline_sqlite-vec.md index fd3ec1dc4..ae7c45b21 100644 --- a/docs/source/providers/vector_io/inline_sqlite-vec.md +++ b/docs/source/providers/vector_io/inline_sqlite-vec.md @@ -205,12 +205,16 @@ See [sqlite-vec's GitHub repo](https://github.com/asg017/sqlite-vec/tree/main) f | Field | Type | Required | Default | Description | |-------|------|----------|---------|-------------| -| `db_path` | `` | No | PydanticUndefined | | +| `db_path` | `` | No | PydanticUndefined | Path to the SQLite database file | +| `kvstore` | `utils.kvstore.config.RedisKVStoreConfig \| utils.kvstore.config.SqliteKVStoreConfig \| utils.kvstore.config.PostgresKVStoreConfig \| utils.kvstore.config.MongoDBKVStoreConfig` | No | sqlite | Config for KV store backend (SQLite only for now) | ## Sample Configuration ```yaml 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/source/providers/vector_io/inline_sqlite_vec.md index e4b69c9ab..7e14bb8bd 100644 --- a/docs/source/providers/vector_io/inline_sqlite_vec.md +++ b/docs/source/providers/vector_io/inline_sqlite_vec.md @@ -10,12 +10,16 @@ Please refer to the sqlite-vec provider documentation. | Field | Type | Required | Default | Description | |-------|------|----------|---------|-------------| -| `db_path` | `` | No | PydanticUndefined | | +| `db_path` | `` | No | PydanticUndefined | Path to the SQLite database file | +| `kvstore` | `utils.kvstore.config.RedisKVStoreConfig \| utils.kvstore.config.SqliteKVStoreConfig \| utils.kvstore.config.PostgresKVStoreConfig \| utils.kvstore.config.MongoDBKVStoreConfig` | No | sqlite | Config for KV store backend (SQLite only for now) | ## Sample Configuration ```yaml 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/remote_milvus.md b/docs/source/providers/vector_io/remote_milvus.md index 9017f0e22..6734d8315 100644 --- a/docs/source/providers/vector_io/remote_milvus.md +++ b/docs/source/providers/vector_io/remote_milvus.md @@ -114,6 +114,7 @@ For more details on TLS configuration, refer to the [TLS setup guide](https://mi | `uri` | `` | No | PydanticUndefined | The URI of the Milvus server | | `token` | `str \| None` | No | PydanticUndefined | 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. @@ -123,6 +124,9 @@ For more details on TLS configuration, refer to the [TLS setup guide](https://mi ```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 index 685b98f37..3e7d6e776 100644 --- a/docs/source/providers/vector_io/remote_pgvector.md +++ b/docs/source/providers/vector_io/remote_pgvector.md @@ -40,6 +40,7 @@ See [PGVector's documentation](https://github.com/pgvector/pgvector) for more de | `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 @@ -49,6 +50,9 @@ 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_weaviate.md b/docs/source/providers/vector_io/remote_weaviate.md index b7f811c35..d930515d5 100644 --- a/docs/source/providers/vector_io/remote_weaviate.md +++ b/docs/source/providers/vector_io/remote_weaviate.md @@ -36,7 +36,9 @@ See [Weaviate's documentation](https://weaviate.io/developers/weaviate) for more ## Sample Configuration ```yaml -{} +kvstore: + type: sqlite + db_path: ${env.SQLITE_STORE_DIR:=~/.llama/dummy}/weaviate_registry.db ``` diff --git a/docs/source/references/llama_stack_client_cli_reference.md b/docs/source/references/llama_stack_client_cli_reference.md index cd4dd4cd7..2d386dbfa 100644 --- a/docs/source/references/llama_stack_client_cli_reference.md +++ b/docs/source/references/llama_stack_client_cli_reference.md @@ -9,7 +9,8 @@ The `llama-stack-client` CLI allows you to query information about the distribut llama-stack-client Usage: llama-stack-client [OPTIONS] COMMAND [ARGS]... - Welcome to the LlamaStackClient CLI + Welcome to the llama-stack-client CLI - a command-line interface for + interacting with Llama Stack Options: --version Show the version and exit. @@ -35,6 +36,7 @@ Commands: ``` ### `llama-stack-client configure` +Configure Llama Stack Client CLI. ```bash llama-stack-client configure > Enter the host name of the Llama Stack distribution server: localhost @@ -42,7 +44,24 @@ llama-stack-client configure Done! You can now use the Llama Stack Client CLI with endpoint http://localhost:8321 ``` +Optional arguments: +- `--endpoint`: Llama Stack distribution endpoint +- `--api-key`: Llama Stack distribution API key + + + +## `llama-stack-client inspect version` +Inspect server configuration. +```bash +llama-stack-client inspect version +``` +```bash +VersionInfo(version='0.2.14') +``` + + ### `llama-stack-client providers list` +Show available providers on distribution endpoint ```bash llama-stack-client providers list ``` @@ -66,9 +85,74 @@ llama-stack-client providers list +-----------+----------------+-----------------+ ``` +### `llama-stack-client providers inspect` +Show specific provider configuration on distribution endpoint +```bash +llama-stack-client providers inspect +``` + + +## Inference +Inference (chat). + + +### `llama-stack-client inference chat-completion` +Show available inference chat completion endpoints on distribution endpoint +```bash +llama-stack-client inference chat-completion --message [--stream] [--session] [--model-id] +``` +```bash +OpenAIChatCompletion( + id='chatcmpl-aacd11f3-8899-4ec5-ac5b-e655132f6891', + choices=[ + OpenAIChatCompletionChoice( + finish_reason='stop', + index=0, + message=OpenAIChatCompletionChoiceMessageOpenAIAssistantMessageParam( + role='assistant', + content='The captain of the whaleship Pequod in Nathaniel Hawthorne\'s novel "Moby-Dick" is Captain +Ahab. He\'s a vengeful and obsessive old sailor who\'s determined to hunt down and kill the white sperm whale +Moby-Dick, whom he\'s lost his leg to in a previous encounter.', + name=None, + tool_calls=None, + refusal=None, + annotations=None, + audio=None, + function_call=None + ), + logprobs=None + ) + ], + created=1752578797, + model='llama3.2:3b-instruct-fp16', + object='chat.completion', + service_tier=None, + system_fingerprint='fp_ollama', + usage={ + 'completion_tokens': 67, + 'prompt_tokens': 33, + 'total_tokens': 100, + 'completion_tokens_details': None, + 'prompt_tokens_details': None + } +) +``` + +Required arguments: +**Note:** At least one of these parameters is required for chat completion +- `--message`: Message +- `--session`: Start a Chat Session + +Optional arguments: +- `--stream`: Stream +- `--model-id`: Model ID + ## Model Management +Manage GenAI models. + ### `llama-stack-client models list` +Show available llama models at distribution endpoint ```bash llama-stack-client models list ``` @@ -85,6 +169,7 @@ Total models: 1 ``` ### `llama-stack-client models get` +Show details of a specific model at the distribution endpoint ```bash llama-stack-client models get Llama3.1-8B-Instruct ``` @@ -105,69 +190,92 @@ Model RandomModel is not found at distribution endpoint host:port. Please ensure ``` ### `llama-stack-client models register` - +Register a new model at distribution endpoint ```bash -llama-stack-client models register [--provider-id ] [--provider-model-id ] [--metadata ] +llama-stack-client models register [--provider-id ] [--provider-model-id ] [--metadata ] [--model-type ] ``` -### `llama-stack-client models update` +Required arguments: +- `MODEL_ID`: Model ID +- `--provider-id`: Provider ID for the model +Optional arguments: +- `--provider-model-id`: Provider's model ID +- `--metadata`: JSON metadata for the model +- `--model-type`: Model type: `llm`, `embedding` + + +### `llama-stack-client models unregister` +Unregister a model from distribution endpoint ```bash -llama-stack-client models update [--provider-id ] [--provider-model-id ] [--metadata ] -``` - -### `llama-stack-client models delete` - -```bash -llama-stack-client models delete +llama-stack-client models unregister ``` ## Vector DB Management +Manage vector databases. + ### `llama-stack-client vector_dbs list` +Show available vector dbs on distribution endpoint ```bash llama-stack-client vector_dbs list ``` ``` -+--------------+----------------+---------------------+---------------+------------------------+ -| identifier | provider_id | provider_resource_id| vector_db_type| params | -+==============+================+=====================+===============+========================+ -| test_bank | meta-reference | test_bank | vector | embedding_model: all-MiniLM-L6-v2 - embedding_dimension: 384| -+--------------+----------------+---------------------+---------------+------------------------+ +┏━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓ +┃ identifier ┃ provider_id ┃ provider_resource_id ┃ vector_db_type ┃ params ┃ +┡━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩ +│ my_demo_vector_db │ faiss │ my_demo_vector_db │ │ embedding_dimension: 384 │ +│ │ │ │ │ embedding_model: all-MiniLM-L6-v2 │ +│ │ │ │ │ type: vector_db │ +│ │ │ │ │ │ +└──────────────────────────┴─────────────┴──────────────────────────┴────────────────┴───────────────────────────────────┘ ``` ### `llama-stack-client vector_dbs register` +Create a new vector db ```bash llama-stack-client vector_dbs register [--provider-id ] [--provider-vector-db-id ] [--embedding-model ] [--embedding-dimension ] ``` + +Required arguments: +- `VECTOR_DB_ID`: Vector DB ID + Optional arguments: - `--provider-id`: Provider ID for the vector db - `--provider-vector-db-id`: Provider's vector db ID -- `--embedding-model`: Embedding model to use. Default: "all-MiniLM-L6-v2" +- `--embedding-model`: Embedding model to use. Default: `all-MiniLM-L6-v2` - `--embedding-dimension`: Dimension of embeddings. Default: 384 ### `llama-stack-client vector_dbs unregister` +Delete a vector db ```bash llama-stack-client vector_dbs unregister ``` + +Required arguments: +- `VECTOR_DB_ID`: Vector DB ID + + ## Shield Management +Manage safety shield services. ### `llama-stack-client shields list` +Show available safety shields on distribution endpoint ```bash llama-stack-client shields list ``` ``` -+--------------+----------+----------------+-------------+ -| identifier | params | provider_id | type | -+==============+==========+================+=============+ -| llama_guard | {} | meta-reference | llama_guard | -+--------------+----------+----------------+-------------+ +┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓ +┃ identifier ┃ provider_alias ┃ params ┃ provider_id ┃ +┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩ +│ ollama │ ollama/llama-guard3:1b │ │ llama-guard │ +└──────────────────────────────────┴───────────────────────────────────────────────────────────────────────┴───────────────────────┴────────────────────────────────────┘ ``` ### `llama-stack-client shields register` +Register a new safety shield ```bash llama-stack-client shields register --shield-id [--provider-id ] [--provider-shield-id ] [--params ] ``` @@ -180,41 +288,29 @@ Optional arguments: - `--provider-shield-id`: Provider's shield ID - `--params`: JSON configuration parameters for the shield -## Eval Task Management - -### `llama-stack-client benchmarks list` -```bash -llama-stack-client benchmarks list -``` - -### `llama-stack-client benchmarks register` -```bash -llama-stack-client benchmarks register --eval-task-id --dataset-id --scoring-functions [ ...] [--provider-id ] [--provider-eval-task-id ] [--metadata ] -``` - -Required arguments: -- `--eval-task-id`: ID of the eval task -- `--dataset-id`: ID of the dataset to evaluate -- `--scoring-functions`: One or more scoring functions to use for evaluation - -Optional arguments: -- `--provider-id`: Provider ID for the eval task -- `--provider-eval-task-id`: Provider's eval task ID -- `--metadata`: Metadata for the eval task in JSON format ## Eval execution +Run evaluation tasks. + + ### `llama-stack-client eval run-benchmark` +Run a evaluation benchmark task ```bash -llama-stack-client eval run-benchmark [ ...] --eval-task-config --output-dir [--num-examples ] [--visualize] +llama-stack-client eval run-benchmark [ ...] --eval-task-config --output-dir --model-id [--num-examples ] [--visualize] [--repeat-penalty ] [--top-p ] [--max-tokens ] ``` Required arguments: - `--eval-task-config`: Path to the eval task config file in JSON format - `--output-dir`: Path to the directory where evaluation results will be saved +- `--model-id`: model id to run the benchmark eval on Optional arguments: - `--num-examples`: Number of examples to evaluate (useful for debugging) - `--visualize`: If set, visualizes evaluation results after completion +- `--repeat-penalty`: repeat-penalty in the sampling params to run generation +- `--top-p`: top-p in the sampling params to run generation +- `--max-tokens`: max-tokens in the sampling params to run generation +- `--temperature`: temperature in the sampling params to run generation Example benchmark_config.json: ```json @@ -231,21 +327,55 @@ Example benchmark_config.json: ``` ### `llama-stack-client eval run-scoring` +Run scoring from application datasets ```bash -llama-stack-client eval run-scoring --eval-task-config --output-dir [--num-examples ] [--visualize] +llama-stack-client eval run-scoring --output-dir [--num-examples ] [--visualize] ``` Required arguments: -- `--eval-task-config`: Path to the eval task config file in JSON format - `--output-dir`: Path to the directory where scoring results will be saved Optional arguments: - `--num-examples`: Number of examples to evaluate (useful for debugging) - `--visualize`: If set, visualizes scoring results after completion +- `--scoring-params-config`: Path to the scoring params config file in JSON format +- `--dataset-id`: Pre-registered dataset_id to score (from llama-stack-client datasets list) +- `--dataset-path`: Path to the dataset file to score + + +## Eval Tasks +Manage evaluation tasks. + +### `llama-stack-client eval_tasks list` +Show available eval tasks on distribution endpoint +```bash +llama-stack-client eval_tasks list +``` + + +### `llama-stack-client eval_tasks register` +Register a new eval task +```bash +llama-stack-client eval_tasks register --eval-task-id --dataset-id --scoring-functions [--provider-id ] [--provider-eval-task-id ] [--metadata ] +``` + + +Required arguments: +- `--eval-task-id`: ID of the eval task +- `--dataset-id`: ID of the dataset to evaluate +- `--scoring-functions`: Scoring functions to use for evaluation + +Optional arguments: +- `--provider-id`: Provider ID for the eval task +- `--provider-eval-task-id`: Provider's eval task ID + ## Tool Group Management +Manage available tool groups. + ### `llama-stack-client toolgroups list` +Show available llama toolgroups at distribution endpoint ```bash llama-stack-client toolgroups list ``` @@ -260,17 +390,28 @@ llama-stack-client toolgroups list ``` ### `llama-stack-client toolgroups get` +Get available llama toolgroups by id ```bash llama-stack-client toolgroups get ``` Shows detailed information about a specific toolgroup. If the toolgroup is not found, displays an error message. + +Required arguments: +- `TOOLGROUP_ID`: ID of the tool group + + ### `llama-stack-client toolgroups register` +Register a new toolgroup at distribution endpoint ```bash llama-stack-client toolgroups register [--provider-id ] [--provider-toolgroup-id ] [--mcp-config ] [--args ] ``` + +Required arguments: +- `TOOLGROUP_ID`: ID of the tool group + Optional arguments: - `--provider-id`: Provider ID for the toolgroup - `--provider-toolgroup-id`: Provider's toolgroup ID @@ -278,6 +419,172 @@ Optional arguments: - `--args`: JSON arguments for the toolgroup ### `llama-stack-client toolgroups unregister` +Unregister a toolgroup from distribution endpoint ```bash llama-stack-client toolgroups unregister ``` + + +Required arguments: +- `TOOLGROUP_ID`: ID of the tool group + + +## Datasets Management +Manage datasets. + + +### `llama-stack-client datasets list` +Show available datasets on distribution endpoint +```bash +llama-stack-client datasets list +``` + + +### `llama-stack-client datasets register` +```bash +llama-stack-client datasets register --dataset_id --purpose [--url ] [--dataset-id ] [--metadata ] +``` + +Required arguments: +- `--dataset_id`: Id of the dataset +- `--purpose`: Purpose of the dataset + +Optional arguments: +- `--metadata`: Metadata of the dataset +- `--url`: URL of the dataset +- `--dataset-path`: Local file path to the dataset. If specified, upload dataset via URL + + +### `llama-stack-client datasets unregister` +Remove a dataset +```bash +llama-stack-client datasets unregister +``` + + +Required arguments: +- `DATASET_ID`: Id of the dataset + + +## Scoring Functions Management +Manage scoring functions. + +### `llama-stack-client scoring_functions list` +Show available scoring functions on distribution endpoint +```bash +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. │ │ +└────────────────────────────────────────────┴──────────────┴───────────────────────────────────────────────────────────────┴──────────────────┘ +``` + + +### `llama-stack-client scoring_functions register` +Register a new scoring function +```bash +llama-stack-client scoring_functions register --scoring-fn-id --description --return-type [--provider-id ] [--provider-scoring-fn-id ] [--params ] +``` + + +Required arguments: +- `--scoring-fn-id`: Id of the scoring function +- `--description`: Description of the scoring function +- `--return-type`: Return type of the scoring function + +Optional arguments: +- `--provider-id`: Provider ID for the scoring function +- `--provider-scoring-fn-id`: Provider's scoring function ID +- `--params`: Parameters for the scoring function in JSON format + + +## Post Training Management +Post-training. + +### `llama-stack-client post_training list` +Show the list of available post training jobs +```bash +llama-stack-client post_training list +``` +```bash +["job-1", "job-2", "job-3"] +``` + + +### `llama-stack-client post_training artifacts` +Get the training artifacts of a specific post training job +```bash +llama-stack-client post_training artifacts --job-uuid +``` +```bash +JobArtifactsResponse(checkpoints=[], job_uuid='job-1') +``` + + +Required arguments: +- `--job-uuid`: Job UUID + + +### `llama-stack-client post_training supervised_fine_tune` +Kick off a supervised fine tune job +```bash +llama-stack-client post_training supervised_fine_tune --job-uuid --model --algorithm-config --training-config [--checkpoint-dir ] +``` + + +Required arguments: +- `--job-uuid`: Job UUID +- `--model`: Model ID +- `--algorithm-config`: Algorithm Config +- `--training-config`: Training Config + +Optional arguments: +- `--checkpoint-dir`: Checkpoint Config + + +### `llama-stack-client post_training status` +Show the status of a specific post training job +```bash +llama-stack-client post_training status --job-uuid +``` +```bash +JobStatusResponse( + checkpoints=[], + job_uuid='job-1', + status='completed', + completed_at="", + resources_allocated="", + scheduled_at="", + started_at="" +) +``` + + +Required arguments: +- `--job-uuid`: Job UUID + + +### `llama-stack-client post_training cancel` +Cancel the training job +```bash +llama-stack-client post_training cancel --job-uuid +``` +```bash +# This functionality is not yet implemented for llama-stack-client +╭────────────────────────────────────────────────────────────╮ +│ Failed to post_training cancel_training_job │ +│ │ +│ Error Type: InternalServerError │ +│ Details: Error code: 501 - {'detail': 'Not implemented: '} │ +╰────────────────────────────────────────────────────────────╯ +``` + + +Required arguments: +- `--job-uuid`: Job UUID diff --git a/llama_stack/apis/common/training_types.py b/llama_stack/apis/common/training_types.py index 46cd101af..a2c3b78f1 100644 --- a/llama_stack/apis/common/training_types.py +++ b/llama_stack/apis/common/training_types.py @@ -19,8 +19,10 @@ class PostTrainingMetric(BaseModel): perplexity: float -@json_schema_type(schema={"description": "Checkpoint created during training runs"}) +@json_schema_type class Checkpoint(BaseModel): + """Checkpoint created during training runs""" + identifier: str created_at: datetime epoch: int diff --git a/llama_stack/apis/models/models.py b/llama_stack/apis/models/models.py index 36da97e62..2143346d9 100644 --- a/llama_stack/apis/models/models.py +++ b/llama_stack/apis/models/models.py @@ -7,7 +7,7 @@ from enum import StrEnum from typing import Any, Literal, Protocol, runtime_checkable -from pydantic import BaseModel, ConfigDict, Field +from pydantic import BaseModel, ConfigDict, Field, field_validator from llama_stack.apis.resource import Resource, ResourceType from llama_stack.providers.utils.telemetry.trace_protocol import trace_protocol @@ -36,13 +36,21 @@ class Model(CommonModelFields, Resource): return self.identifier @property - def provider_model_id(self) -> str | None: + def provider_model_id(self) -> str: + assert self.provider_resource_id is not None, "Provider resource ID must be set" return self.provider_resource_id model_config = ConfigDict(protected_namespaces=()) model_type: ModelType = Field(default=ModelType.llm) + @field_validator("provider_resource_id") + @classmethod + def validate_provider_resource_id(cls, v): + if v is None: + raise ValueError("provider_resource_id cannot be None") + return v + class ModelInput(CommonModelFields): model_id: str diff --git a/llama_stack/apis/tools/rag_tool.py b/llama_stack/apis/tools/rag_tool.py index d497fe1a7..cfaa49488 100644 --- a/llama_stack/apis/tools/rag_tool.py +++ b/llama_stack/apis/tools/rag_tool.py @@ -87,6 +87,20 @@ class RAGQueryGenerator(Enum): custom = "custom" +@json_schema_type +class RAGSearchMode(Enum): + """ + 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 + """ + + VECTOR = "vector" + KEYWORD = "keyword" + HYBRID = "hybrid" + + @json_schema_type class DefaultRAGQueryGeneratorConfig(BaseModel): type: Literal["default"] = "default" @@ -128,7 +142,7 @@ class RAGQueryConfig(BaseModel): max_tokens_in_context: int = 4096 max_chunks: int = 5 chunk_template: str = "Result {index}\nContent: {chunk.content}\nMetadata: {metadata}\n" - mode: str | None = None + mode: RAGSearchMode | None = RAGSearchMode.VECTOR ranker: Ranker | None = Field(default=None) # Only used for hybrid mode @field_validator("chunk_template") diff --git a/llama_stack/apis/vector_dbs/vector_dbs.py b/llama_stack/apis/vector_dbs/vector_dbs.py index 405852476..0d160737a 100644 --- a/llama_stack/apis/vector_dbs/vector_dbs.py +++ b/llama_stack/apis/vector_dbs/vector_dbs.py @@ -19,6 +19,7 @@ class VectorDB(Resource): embedding_model: str embedding_dimension: int + vector_db_name: str | None = None @property def vector_db_id(self) -> str: @@ -70,6 +71,7 @@ class VectorDBs(Protocol): embedding_model: str, embedding_dimension: int | None = 384, provider_id: str | None = None, + vector_db_name: str | None = None, provider_vector_db_id: str | None = None, ) -> VectorDB: """Register a vector database. @@ -78,6 +80,7 @@ class VectorDBs(Protocol): :param embedding_model: The embedding model to use. :param embedding_dimension: The dimension of the embedding model. :param provider_id: The identifier of the provider. + :param vector_db_name: The name of the vector database. :param provider_vector_db_id: The identifier of the vector database in the provider. :returns: A VectorDB. """ diff --git a/llama_stack/apis/vector_io/vector_io.py b/llama_stack/apis/vector_io/vector_io.py index 2d4131315..618ac2a95 100644 --- a/llama_stack/apis/vector_io/vector_io.py +++ b/llama_stack/apis/vector_io/vector_io.py @@ -346,7 +346,6 @@ class VectorIO(Protocol): embedding_model: str | None = None, embedding_dimension: int | None = 384, provider_id: str | None = None, - provider_vector_db_id: str | None = None, ) -> VectorStoreObject: """Creates a vector store. @@ -358,7 +357,6 @@ class VectorIO(Protocol): :param embedding_model: The embedding model to use for this vector store. :param embedding_dimension: The dimension of the embedding vectors (default: 384). :param provider_id: The ID of the provider to use for this vector store. - :param provider_vector_db_id: The provider-specific vector database ID. :returns: A VectorStoreObject representing the created vector store. """ ... diff --git a/llama_stack/cli/stack/_build.py b/llama_stack/cli/stack/_build.py index 7ade6f17a..b573b2edc 100644 --- a/llama_stack/cli/stack/_build.py +++ b/llama_stack/cli/stack/_build.py @@ -93,7 +93,7 @@ def run_stack_build_command(args: argparse.Namespace) -> None: ) sys.exit(1) elif args.providers: - providers = dict() + providers_list: dict[str, str | list[str]] = dict() for api_provider in args.providers.split(","): if "=" not in api_provider: cprint( @@ -112,7 +112,15 @@ def run_stack_build_command(args: argparse.Namespace) -> None: ) sys.exit(1) if provider in providers_for_api: - providers.setdefault(api, []).append(provider) + if api not in providers_list: + providers_list[api] = [] + # Use type guarding to ensure we have a list + provider_value = providers_list[api] + if isinstance(provider_value, list): + provider_value.append(provider) + else: + # Convert string to list and append + providers_list[api] = [provider_value, provider] else: cprint( f"{provider} is not a valid provider for the {api} API.", @@ -121,7 +129,7 @@ def run_stack_build_command(args: argparse.Namespace) -> None: ) sys.exit(1) distribution_spec = DistributionSpec( - providers=providers, + providers=providers_list, description=",".join(args.providers), ) if not args.image_type: @@ -182,7 +190,7 @@ def run_stack_build_command(args: argparse.Namespace) -> None: cprint("Tip: use to see options for the providers.\n", color="green", file=sys.stderr) - providers = dict() + providers: dict[str, str | list[str]] = dict() for api, providers_for_api in get_provider_registry().items(): available_providers = [x for x in providers_for_api.keys() if x not in ("remote", "remote::sample")] if not available_providers: @@ -371,10 +379,16 @@ def _run_stack_build_command_from_build_config( if not image_name: raise ValueError("Please specify an image name when building a venv image") + # At this point, image_name should be guaranteed to be a string + if image_name is None: + raise ValueError("image_name should not be None after validation") + if template_name: build_dir = DISTRIBS_BASE_DIR / template_name build_file_path = build_dir / f"{template_name}-build.yaml" else: + if image_name is None: + raise ValueError("image_name cannot be None") build_dir = DISTRIBS_BASE_DIR / image_name build_file_path = build_dir / f"{image_name}-build.yaml" @@ -395,7 +409,7 @@ def _run_stack_build_command_from_build_config( build_file_path, image_name, template_or_config=template_name or config_path or str(build_file_path), - run_config=run_config_file, + run_config=run_config_file.as_posix() if run_config_file else None, ) if return_code != 0: raise RuntimeError(f"Failed to build image {image_name}") @@ -403,15 +417,16 @@ def _run_stack_build_command_from_build_config( if template_name: # copy run.yaml from template to build_dir instead of generating it again template_path = importlib.resources.files("llama_stack") / f"templates/{template_name}/run.yaml" + run_config_file = build_dir / f"{template_name}-run.yaml" + with importlib.resources.as_file(template_path) as path: - run_config_file = build_dir / f"{template_name}-run.yaml" shutil.copy(path, run_config_file) cprint("Build Successful!", color="green", file=sys.stderr) - cprint(f"You can find the newly-built template here: {template_path}", color="blue", file=sys.stderr) + cprint(f"You can find the newly-built template here: {run_config_file}", color="blue", file=sys.stderr) cprint( "You can run the new Llama Stack distro via: " - + colored(f"llama stack run {template_path} --image-type {build_config.image_type}", "blue"), + + colored(f"llama stack run {run_config_file} --image-type {build_config.image_type}", "blue"), color="green", file=sys.stderr, ) diff --git a/llama_stack/cli/stack/run.py b/llama_stack/cli/stack/run.py index 2f768957d..f4a119522 100644 --- a/llama_stack/cli/stack/run.py +++ b/llama_stack/cli/stack/run.py @@ -47,8 +47,7 @@ class StackRun(Subcommand): self.parser.add_argument( "--image-name", type=str, - default=os.environ.get("CONDA_DEFAULT_ENV"), - help="Name of the image to run. Defaults to the current environment", + help="Name of the image to run.", ) self.parser.add_argument( "--env", @@ -83,46 +82,57 @@ class StackRun(Subcommand): return ImageType.CONDA.value, args.image_name return args.image_type, args.image_name + def _resolve_config_and_template(self, args: argparse.Namespace) -> tuple[Path | None, str | None]: + """Resolve config file path and template name from args.config""" + from llama_stack.distribution.utils.config_dirs import DISTRIBS_BASE_DIR + + if not args.config: + return None, None + + config_file = Path(args.config) + has_yaml_suffix = args.config.endswith(".yaml") + template_name = None + + if not config_file.exists() and not has_yaml_suffix: + # check if this is a template + config_file = Path(REPO_ROOT) / "llama_stack" / "templates" / args.config / "run.yaml" + if config_file.exists(): + template_name = args.config + + if not config_file.exists() and not has_yaml_suffix: + # check if it's a build config saved to ~/.llama dir + config_file = Path(DISTRIBS_BASE_DIR / f"llamastack-{args.config}" / f"{args.config}-run.yaml") + + if not config_file.exists(): + self.parser.error( + f"File {str(config_file)} does not exist.\n\nPlease run `llama stack build` to generate (and optionally edit) a run.yaml file" + ) + + if not config_file.is_file(): + self.parser.error( + f"Config file must be a valid file path, '{config_file}' is not a file: type={type(config_file)}" + ) + + return config_file, template_name + def _run_stack_run_cmd(self, args: argparse.Namespace) -> None: import yaml from llama_stack.distribution.configure import parse_and_maybe_upgrade_config - from llama_stack.distribution.utils.config_dirs import DISTRIBS_BASE_DIR from llama_stack.distribution.utils.exec import formulate_run_args, run_command if args.enable_ui: self._start_ui_development_server(args.port) image_type, image_name = self._get_image_type_and_name(args) + # Resolve config file and template name first + config_file, template_name = self._resolve_config_and_template(args) + # Check if config is required based on image type - if (image_type in [ImageType.CONDA.value, ImageType.VENV.value]) and not args.config: + if (image_type in [ImageType.CONDA.value, ImageType.VENV.value]) and not config_file: self.parser.error("Config file is required for venv and conda environments") - if args.config: - config_file = Path(args.config) - has_yaml_suffix = args.config.endswith(".yaml") - template_name = None - - if not config_file.exists() and not has_yaml_suffix: - # check if this is a template - config_file = Path(REPO_ROOT) / "llama_stack" / "templates" / args.config / "run.yaml" - if config_file.exists(): - template_name = args.config - - if not config_file.exists() and not has_yaml_suffix: - # check if it's a build config saved to ~/.llama dir - config_file = Path(DISTRIBS_BASE_DIR / f"llamastack-{args.config}" / f"{args.config}-run.yaml") - - if not config_file.exists(): - self.parser.error( - f"File {str(config_file)} does not exist.\n\nPlease run `llama stack build` to generate (and optionally edit) a run.yaml file" - ) - - if not config_file.is_file(): - self.parser.error( - f"Config file must be a valid file path, '{config_file}' is not a file: type={type(config_file)}" - ) - + if config_file: logger.info(f"Using run configuration: {config_file}") try: @@ -138,8 +148,6 @@ class StackRun(Subcommand): self.parser.error(f"failed to parse config file '{config_file}':\n {e}") else: config = None - config_file = None - template_name = None # If neither image type nor image name is provided, assume the server should be run directly # using the current environment packages. @@ -155,8 +163,12 @@ class StackRun(Subcommand): # func=> if callable(getattr(args, arg)): continue - if arg == "config" and template_name: - server_args.config = str(config_file) + if arg == "config": + if template_name: + server_args.template = str(template_name) + else: + # Set the config file path + server_args.config = str(config_file) else: setattr(server_args, arg, getattr(args, arg)) diff --git a/llama_stack/distribution/access_control/access_control.py b/llama_stack/distribution/access_control/access_control.py index 075152ce4..64c0122c1 100644 --- a/llama_stack/distribution/access_control/access_control.py +++ b/llama_stack/distribution/access_control/access_control.py @@ -81,7 +81,7 @@ def is_action_allowed( if not len(policy): policy = default_policy() - qualified_resource_id = resource.type + "::" + resource.identifier + qualified_resource_id = f"{resource.type}::{resource.identifier}" for rule in policy: if rule.forbid and matches_scope(rule.forbid, action, qualified_resource_id, user.principal): if rule.when: diff --git a/llama_stack/distribution/build_container.sh b/llama_stack/distribution/build_container.sh index d9a918fb5..6e794b36f 100755 --- a/llama_stack/distribution/build_container.sh +++ b/llama_stack/distribution/build_container.sh @@ -96,7 +96,7 @@ FROM $container_base WORKDIR /app # We install the Python 3.12 dev headers and build tools so that any -# C‑extension wheels (e.g. polyleven, faiss‑cpu) can compile successfully. +# C-extension wheels (e.g. polyleven, faiss-cpu) can compile successfully. RUN dnf -y update && dnf install -y iputils git net-tools wget \ vim-minimal python3.12 python3.12-pip python3.12-wheel \ @@ -169,7 +169,7 @@ if [ -n "$run_config" ]; then echo "Copying external providers directory: $external_providers_dir" cp -r "$external_providers_dir" "$BUILD_CONTEXT_DIR/providers.d" add_to_container << EOF -COPY --chmod=g+w providers.d /.llama/providers.d +COPY providers.d /.llama/providers.d EOF fi diff --git a/llama_stack/distribution/configure.py b/llama_stack/distribution/configure.py index 35b216b30..2238eef93 100644 --- a/llama_stack/distribution/configure.py +++ b/llama_stack/distribution/configure.py @@ -17,7 +17,7 @@ from llama_stack.distribution.distribution import ( builtin_automatically_routed_apis, get_provider_registry, ) -from llama_stack.distribution.stack import replace_env_vars +from llama_stack.distribution.stack import cast_image_name_to_string, replace_env_vars from llama_stack.distribution.utils.config_dirs import EXTERNAL_PROVIDERS_DIR from llama_stack.distribution.utils.dynamic import instantiate_class_type from llama_stack.distribution.utils.prompt_for_config import prompt_for_config @@ -164,7 +164,8 @@ def upgrade_from_routing_table( def parse_and_maybe_upgrade_config(config_dict: dict[str, Any]) -> StackRunConfig: version = config_dict.get("version", None) if version == LLAMA_STACK_RUN_CONFIG_VERSION: - return StackRunConfig(**replace_env_vars(config_dict)) + processed_config_dict = replace_env_vars(config_dict) + return StackRunConfig(**cast_image_name_to_string(processed_config_dict)) if "routing_table" in config_dict: logger.info("Upgrading config...") @@ -175,4 +176,5 @@ def parse_and_maybe_upgrade_config(config_dict: dict[str, Any]) -> StackRunConfi if not config_dict.get("external_providers_dir", None): config_dict["external_providers_dir"] = EXTERNAL_PROVIDERS_DIR - return StackRunConfig(**replace_env_vars(config_dict)) + processed_config_dict = replace_env_vars(config_dict) + return StackRunConfig(**cast_image_name_to_string(processed_config_dict)) diff --git a/llama_stack/distribution/datatypes.py b/llama_stack/distribution/datatypes.py index 5e48ac0ad..ead1331f3 100644 --- a/llama_stack/distribution/datatypes.py +++ b/llama_stack/distribution/datatypes.py @@ -6,9 +6,9 @@ from enum import StrEnum from pathlib import Path -from typing import Annotated, Any +from typing import Annotated, Any, Literal, Self -from pydantic import BaseModel, Field, field_validator +from pydantic import BaseModel, Field, field_validator, model_validator from llama_stack.apis.benchmarks import Benchmark, BenchmarkInput from llama_stack.apis.datasetio import DatasetIO @@ -161,23 +161,113 @@ class LoggingConfig(BaseModel): ) +class OAuth2JWKSConfig(BaseModel): + # The JWKS URI for collecting public keys + uri: str + token: str | None = Field(default=None, description="token to authorise access to jwks") + key_recheck_period: int = Field(default=3600, description="The period to recheck the JWKS URI for key updates") + + +class OAuth2IntrospectionConfig(BaseModel): + url: str + client_id: str + client_secret: str + send_secret_in_body: bool = False + + class AuthProviderType(StrEnum): """Supported authentication provider types.""" OAUTH2_TOKEN = "oauth2_token" + GITHUB_TOKEN = "github_token" CUSTOM = "custom" +class OAuth2TokenAuthConfig(BaseModel): + """Configuration for OAuth2 token authentication.""" + + type: Literal[AuthProviderType.OAUTH2_TOKEN] = AuthProviderType.OAUTH2_TOKEN + audience: str = Field(default="llama-stack") + verify_tls: bool = Field(default=True) + tls_cafile: Path | None = Field(default=None) + issuer: str | None = Field(default=None, description="The OIDC issuer URL.") + claims_mapping: dict[str, str] = Field( + default_factory=lambda: { + "sub": "roles", + "username": "roles", + "groups": "teams", + "team": "teams", + "project": "projects", + "tenant": "namespaces", + "namespace": "namespaces", + }, + ) + jwks: OAuth2JWKSConfig | None = Field(default=None, description="JWKS configuration") + introspection: OAuth2IntrospectionConfig | None = Field( + default=None, description="OAuth2 introspection configuration" + ) + + @classmethod + @field_validator("claims_mapping") + 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 + + @model_validator(mode="after") + def validate_mode(self) -> Self: + if not self.jwks and not self.introspection: + raise ValueError("One of jwks or introspection must be configured") + if self.jwks and self.introspection: + raise ValueError("At present only one of jwks or introspection should be configured") + return self + + +class CustomAuthConfig(BaseModel): + """Configuration for custom authentication.""" + + type: Literal[AuthProviderType.CUSTOM] = AuthProviderType.CUSTOM + endpoint: str = Field( + ..., + description="Custom authentication endpoint URL", + ) + + +class GitHubTokenAuthConfig(BaseModel): + """Configuration for GitHub token authentication.""" + + type: Literal[AuthProviderType.GITHUB_TOKEN] = AuthProviderType.GITHUB_TOKEN + github_api_base_url: str = Field( + default="https://api.github.com", + description="Base URL for GitHub API (use https://api.github.com for public GitHub)", + ) + claims_mapping: dict[str, str] = Field( + default_factory=lambda: { + "login": "roles", + "organizations": "teams", + }, + description="Mapping from GitHub user fields to access attributes", + ) + + +AuthProviderConfig = Annotated[ + OAuth2TokenAuthConfig | GitHubTokenAuthConfig | CustomAuthConfig, + Field(discriminator="type"), +] + + class AuthenticationConfig(BaseModel): - provider_type: AuthProviderType = Field( + """Top-level authentication configuration.""" + + provider_config: AuthProviderConfig = Field( ..., - description="Type of authentication provider", + description="Authentication provider configuration", ) - config: dict[str, Any] = Field( - ..., - description="Provider-specific configuration", + access_policy: list[AccessRule] = Field( + default=[], + description="Rules for determining access to resources", ) - access_policy: list[AccessRule] = Field(default=[], description="Rules for determining access to resources") class AuthenticationRequiredError(Exception): diff --git a/llama_stack/distribution/resolver.py b/llama_stack/distribution/resolver.py index 46cd1161e..c83218276 100644 --- a/llama_stack/distribution/resolver.py +++ b/llama_stack/distribution/resolver.py @@ -200,7 +200,7 @@ def validate_and_prepare_providers( specs = {} for provider in providers: if not provider.provider_id or provider.provider_id == "__disabled__": - logger.warning(f"Provider `{provider.provider_type}` for API `{api}` is disabled") + logger.debug(f"Provider `{provider.provider_type}` for API `{api}` is disabled") continue validate_provider(provider, api, provider_registry) diff --git a/llama_stack/distribution/routers/vector_io.py b/llama_stack/distribution/routers/vector_io.py index 4bd5952dc..cd56ada7b 100644 --- a/llama_stack/distribution/routers/vector_io.py +++ b/llama_stack/distribution/routers/vector_io.py @@ -5,6 +5,7 @@ # the root directory of this source tree. import asyncio +import uuid from typing import Any from llama_stack.apis.common.content_types import ( @@ -81,6 +82,7 @@ class VectorIORouter(VectorIO): embedding_model: str, embedding_dimension: int | None = 384, provider_id: str | None = None, + vector_db_name: str | None = None, provider_vector_db_id: str | None = None, ) -> None: logger.debug(f"VectorIORouter.register_vector_db: {vector_db_id}, {embedding_model}") @@ -89,6 +91,7 @@ class VectorIORouter(VectorIO): embedding_model, embedding_dimension, provider_id, + vector_db_name, provider_vector_db_id, ) @@ -123,7 +126,6 @@ class VectorIORouter(VectorIO): embedding_model: str | None = None, embedding_dimension: int | None = None, provider_id: str | None = None, - provider_vector_db_id: str | None = None, ) -> VectorStoreObject: logger.debug(f"VectorIORouter.openai_create_vector_store: name={name}, provider_id={provider_id}") @@ -135,17 +137,17 @@ class VectorIORouter(VectorIO): embedding_model, embedding_dimension = embedding_model_info logger.info(f"No embedding model specified, using first available: {embedding_model}") - vector_db_id = name + vector_db_id = f"vs_{uuid.uuid4()}" registered_vector_db = await self.routing_table.register_vector_db( - vector_db_id, - embedding_model, - embedding_dimension, - provider_id, - provider_vector_db_id, + vector_db_id=vector_db_id, + embedding_model=embedding_model, + embedding_dimension=embedding_dimension, + provider_id=provider_id, + provider_vector_db_id=vector_db_id, + vector_db_name=name, ) - return await self.routing_table.get_provider_impl(registered_vector_db.identifier).openai_create_vector_store( - vector_db_id, + name=name, file_ids=file_ids, expires_after=expires_after, chunking_strategy=chunking_strategy, diff --git a/llama_stack/distribution/routing_tables/vector_dbs.py b/llama_stack/distribution/routing_tables/vector_dbs.py index 542e965f8..f861102c8 100644 --- a/llama_stack/distribution/routing_tables/vector_dbs.py +++ b/llama_stack/distribution/routing_tables/vector_dbs.py @@ -36,6 +36,7 @@ class VectorDBsRoutingTable(CommonRoutingTableImpl, VectorDBs): embedding_dimension: int | None = 384, provider_id: str | None = None, provider_vector_db_id: str | None = None, + vector_db_name: str | None = None, ) -> VectorDB: if provider_vector_db_id is None: provider_vector_db_id = vector_db_id @@ -62,6 +63,7 @@ class VectorDBsRoutingTable(CommonRoutingTableImpl, VectorDBs): "provider_resource_id": provider_vector_db_id, "embedding_model": embedding_model, "embedding_dimension": model.metadata["embedding_dimension"], + "vector_db_name": vector_db_name, } vector_db = TypeAdapter(VectorDBWithOwner).validate_python(vector_db_data) await self.register_object(vector_db) diff --git a/llama_stack/distribution/server/auth.py b/llama_stack/distribution/server/auth.py index 81b1ffd37..fadbf7b49 100644 --- a/llama_stack/distribution/server/auth.py +++ b/llama_stack/distribution/server/auth.py @@ -87,8 +87,12 @@ class AuthenticationMiddleware: headers = dict(scope.get("headers", [])) auth_header = headers.get(b"authorization", b"").decode() - if not auth_header or not auth_header.startswith("Bearer "): - return await self._send_auth_error(send, "Missing or invalid Authorization header") + if not auth_header: + error_msg = self.auth_provider.get_auth_error_message(scope) + return await self._send_auth_error(send, error_msg) + + if not auth_header.startswith("Bearer "): + return await self._send_auth_error(send, "Invalid Authorization header format") token = auth_header.split("Bearer ", 1)[1] diff --git a/llama_stack/distribution/server/auth_providers.py b/llama_stack/distribution/server/auth_providers.py index 173434652..9b0e182f5 100644 --- a/llama_stack/distribution/server/auth_providers.py +++ b/llama_stack/distribution/server/auth_providers.py @@ -8,15 +8,19 @@ import ssl import time from abc import ABC, abstractmethod from asyncio import Lock -from pathlib import Path -from typing import Self -from urllib.parse import parse_qs +from urllib.parse import parse_qs, urlparse import httpx from jose import jwt -from pydantic import BaseModel, Field, field_validator, model_validator +from pydantic import BaseModel, Field -from llama_stack.distribution.datatypes import AuthenticationConfig, AuthProviderType, User +from llama_stack.distribution.datatypes import ( + AuthenticationConfig, + CustomAuthConfig, + GitHubTokenAuthConfig, + OAuth2TokenAuthConfig, + User, +) from llama_stack.log import get_logger logger = get_logger(name=__name__, category="auth") @@ -38,9 +42,7 @@ class AuthRequestContext(BaseModel): headers: dict[str, str] = Field(description="HTTP headers from the original request (excluding Authorization)") - params: dict[str, list[str]] = Field( - description="Query parameters from the original request, parsed as dictionary of lists" - ) + params: dict[str, list[str]] = Field(default_factory=dict, description="Query parameters from the original request") class AuthRequest(BaseModel): @@ -62,6 +64,10 @@ class AuthProvider(ABC): """Clean up any resources.""" pass + def get_auth_error_message(self, scope: dict | None = None) -> str: + """Return provider-specific authentication error message.""" + return "Authentication required" + def get_attributes_from_claims(claims: dict[str, str], mapping: dict[str, str]) -> dict[str, list[str]]: attributes: dict[str, list[str]] = {} @@ -81,56 +87,6 @@ def get_attributes_from_claims(claims: dict[str, str], mapping: dict[str, str]) return attributes -class OAuth2JWKSConfig(BaseModel): - # The JWKS URI for collecting public keys - uri: str - token: str | None = Field(default=None, description="token to authorise access to jwks") - key_recheck_period: int = Field(default=3600, description="The period to recheck the JWKS URI for key updates") - - -class OAuth2IntrospectionConfig(BaseModel): - url: str - client_id: str - client_secret: str - send_secret_in_body: bool = False - - -class OAuth2TokenAuthProviderConfig(BaseModel): - audience: str = "llama-stack" - verify_tls: bool = True - tls_cafile: Path | None = None - issuer: str | None = Field(default=None, description="The OIDC issuer URL.") - claims_mapping: dict[str, str] = Field( - default_factory=lambda: { - "sub": "roles", - "username": "roles", - "groups": "teams", - "team": "teams", - "project": "projects", - "tenant": "namespaces", - "namespace": "namespaces", - }, - ) - jwks: OAuth2JWKSConfig | None - introspection: OAuth2IntrospectionConfig | None = None - - @classmethod - @field_validator("claims_mapping") - 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 - - @model_validator(mode="after") - def validate_mode(self) -> Self: - if not self.jwks and not self.introspection: - raise ValueError("One of jwks or introspection must be configured") - if self.jwks and self.introspection: - raise ValueError("At present only one of jwks or introspection should be configured") - return self - - class OAuth2TokenAuthProvider(AuthProvider): """ JWT token authentication provider that validates a JWT token and extracts access attributes. @@ -138,7 +94,7 @@ class OAuth2TokenAuthProvider(AuthProvider): This should be the standard authentication provider for most use cases. """ - def __init__(self, config: OAuth2TokenAuthProviderConfig): + def __init__(self, config: OAuth2TokenAuthConfig): self.config = config self._jwks_at: float = 0.0 self._jwks: dict[str, str] = {} @@ -170,7 +126,7 @@ class OAuth2TokenAuthProvider(AuthProvider): issuer=self.config.issuer, ) except Exception as exc: - raise ValueError(f"Invalid JWT token: {token}") from exc + raise ValueError("Invalid JWT token") from exc # There are other standard claims, the most relevant of which is `scope`. # We should incorporate these into the access attributes. @@ -232,6 +188,17 @@ class OAuth2TokenAuthProvider(AuthProvider): async def close(self): pass + def get_auth_error_message(self, scope: dict | None = None) -> str: + """Return OAuth2-specific authentication error message.""" + if self.config.issuer: + return f"Authentication required. Please provide a valid OAuth2 Bearer token from {self.config.issuer}" + elif self.config.introspection: + # Extract domain from introspection URL for a cleaner message + domain = urlparse(self.config.introspection.url).netloc + return f"Authentication required. Please provide a valid OAuth2 Bearer token validated by {domain}" + else: + return "Authentication required. Please provide a valid OAuth2 Bearer token in the Authorization header" + async def _refresh_jwks(self) -> None: """ Refresh the JWKS cache. @@ -264,14 +231,10 @@ class OAuth2TokenAuthProvider(AuthProvider): self._jwks_at = time.time() -class CustomAuthProviderConfig(BaseModel): - endpoint: str - - class CustomAuthProvider(AuthProvider): """Custom authentication provider that uses an external endpoint.""" - def __init__(self, config: CustomAuthProviderConfig): + def __init__(self, config: CustomAuthConfig): self.config = config self._client = None @@ -317,7 +280,7 @@ class CustomAuthProvider(AuthProvider): try: response_data = response.json() auth_response = AuthResponse(**response_data) - return User(auth_response.principal, auth_response.attributes) + return User(principal=auth_response.principal, attributes=auth_response.attributes) except Exception as e: logger.exception("Error parsing authentication response") raise ValueError("Invalid authentication response format") from e @@ -338,15 +301,88 @@ class CustomAuthProvider(AuthProvider): await self._client.aclose() self._client = None + def get_auth_error_message(self, scope: dict | None = None) -> str: + """Return custom auth provider-specific authentication error message.""" + domain = urlparse(self.config.endpoint).netloc + if domain: + return f"Authentication required. Please provide your API key as a Bearer token (validated by {domain})" + else: + return "Authentication required. Please provide your API key as a Bearer token in the Authorization header" + + +class GitHubTokenAuthProvider(AuthProvider): + """ + GitHub token authentication provider that validates GitHub access tokens directly. + + This provider accepts GitHub personal access tokens or OAuth tokens and verifies + them against the GitHub API to get user information. + """ + + def __init__(self, config: GitHubTokenAuthConfig): + self.config = config + + async def validate_token(self, token: str, scope: dict | None = None) -> User: + """Validate a GitHub token by calling the GitHub API. + + This validates tokens issued by GitHub (personal access tokens or OAuth tokens). + """ + try: + user_info = await _get_github_user_info(token, self.config.github_api_base_url) + except httpx.HTTPStatusError as e: + logger.warning(f"GitHub token validation failed: {e}") + raise ValueError("GitHub token validation failed. Please check your token and try again.") from e + + principal = user_info["user"]["login"] + + github_data = { + "login": user_info["user"]["login"], + "id": str(user_info["user"]["id"]), + "organizations": user_info.get("organizations", []), + } + + access_attributes = get_attributes_from_claims(github_data, self.config.claims_mapping) + + return User( + principal=principal, + attributes=access_attributes, + ) + + async def close(self): + """Clean up any resources.""" + pass + + def get_auth_error_message(self, scope: dict | None = None) -> str: + """Return GitHub-specific authentication error message.""" + return "Authentication required. Please provide a valid GitHub access token (https://docs.github.com/en/authentication/keeping-your-account-and-data-secure/managing-your-personal-access-tokens) in the Authorization header (Bearer )" + + +async def _get_github_user_info(access_token: str, github_api_base_url: str) -> dict: + """Fetch user info and organizations from GitHub API.""" + headers = { + "Authorization": f"Bearer {access_token}", + "Accept": "application/vnd.github.v3+json", + "User-Agent": "llama-stack", + } + + async with httpx.AsyncClient() as client: + user_response = await client.get(f"{github_api_base_url}/user", headers=headers, timeout=10.0) + user_response.raise_for_status() + user_data = user_response.json() + + return { + "user": user_data, + } + def create_auth_provider(config: AuthenticationConfig) -> AuthProvider: """Factory function to create the appropriate auth provider.""" - provider_type = config.provider_type.lower() + provider_config = config.provider_config - if provider_type == "custom": - return CustomAuthProvider(CustomAuthProviderConfig.model_validate(config.config)) - elif provider_type == "oauth2_token": - return OAuth2TokenAuthProvider(OAuth2TokenAuthProviderConfig.model_validate(config.config)) + if isinstance(provider_config, CustomAuthConfig): + return CustomAuthProvider(provider_config) + elif isinstance(provider_config, OAuth2TokenAuthConfig): + return OAuth2TokenAuthProvider(provider_config) + elif isinstance(provider_config, GitHubTokenAuthConfig): + return GitHubTokenAuthProvider(provider_config) else: - supported_providers = ", ".join([t.value for t in AuthProviderType]) - raise ValueError(f"Unsupported auth provider type: {provider_type}. Supported types are: {supported_providers}") + raise ValueError(f"Unknown authentication provider config type: {type(provider_config)}") diff --git a/llama_stack/distribution/server/server.py b/llama_stack/distribution/server/server.py index 681ab320d..e7e9e5e88 100644 --- a/llama_stack/distribution/server/server.py +++ b/llama_stack/distribution/server/server.py @@ -33,7 +33,11 @@ from pydantic import BaseModel, ValidationError from llama_stack.apis.common.responses import PaginatedResponse from llama_stack.distribution.access_control.access_control import AccessDeniedError -from llama_stack.distribution.datatypes import AuthenticationRequiredError, LoggingConfig, StackRunConfig +from llama_stack.distribution.datatypes import ( + AuthenticationRequiredError, + LoggingConfig, + StackRunConfig, +) from llama_stack.distribution.distribution import builtin_automatically_routed_apis from llama_stack.distribution.request_headers import PROVIDER_DATA_VAR, User, request_provider_data_context from llama_stack.distribution.resolver import InvalidProviderError @@ -43,6 +47,7 @@ from llama_stack.distribution.server.routes import ( initialize_route_impls, ) from llama_stack.distribution.stack import ( + cast_image_name_to_string, construct_stack, replace_env_vars, validate_env_pair, @@ -217,7 +222,7 @@ def create_dynamic_typed_route(func: Any, method: str, route: str) -> Callable: # Get auth attributes from the request scope user_attributes = request.scope.get("user_attributes", {}) principal = request.scope.get("principal", "") - user = User(principal, user_attributes) + user = User(principal=principal, attributes=user_attributes) await log_request_pre_validation(request) @@ -405,13 +410,13 @@ def main(args: argparse.Namespace | None = None): args = parser.parse_args() log_line = "" - if args.config: + if hasattr(args, "config") and args.config: # if the user provided a config file, use it, even if template was specified config_file = Path(args.config) if not config_file.exists(): raise ValueError(f"Config file {config_file} does not exist") log_line = f"Using config file: {config_file}" - elif args.template: + elif hasattr(args, "template") and args.template: config_file = Path(REPO_ROOT) / "llama_stack" / "templates" / args.template / "run.yaml" if not config_file.exists(): raise ValueError(f"Template {args.template} does not exist") @@ -435,14 +440,12 @@ def main(args: argparse.Namespace | None = None): logger.error(f"Error: {str(e)}") sys.exit(1) config = replace_env_vars(config_contents) - config = StackRunConfig(**config) + config = StackRunConfig(**cast_image_name_to_string(config)) # now that the logger is initialized, print the line about which type of config we are using. logger.info(log_line) - logger.info("Run configuration:") - safe_config = redact_sensitive_fields(config.model_dump()) - logger.info(yaml.dump(safe_config, indent=2)) + _log_run_config(run_config=config) app = FastAPI( lifespan=lifespan, @@ -450,12 +453,13 @@ def main(args: argparse.Namespace | None = None): redoc_url="/redoc", openapi_url="/openapi.json", ) + if not os.environ.get("LLAMA_STACK_DISABLE_VERSION_CHECK"): app.add_middleware(ClientVersionMiddleware) # Add authentication middleware if configured if config.server.auth: - logger.info(f"Enabling authentication with provider: {config.server.auth.provider_type.value}") + logger.info(f"Enabling authentication with provider: {config.server.auth.provider_config.type.value}") app.add_middleware(AuthenticationMiddleware, auth_config=config.server.auth) else: if config.server.quota: @@ -488,7 +492,13 @@ def main(args: argparse.Namespace | None = None): ) try: - impls = asyncio.run(construct_stack(config)) + # 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) @@ -586,7 +596,16 @@ def main(args: argparse.Namespace | None = None): if ssl_config: uvicorn_config.update(ssl_config) - uvicorn.run(**uvicorn_config) + # Run uvicorn in the existing event loop to preserve background tasks + loop.run_until_complete(uvicorn.Server(uvicorn.Config(**uvicorn_config)).serve()) + + +def _log_run_config(run_config: StackRunConfig): + """Logs the run config with redacted fields and disabled providers removed.""" + logger.info("Run configuration:") + safe_config = redact_sensitive_fields(run_config.model_dump(mode="json")) + clean_config = remove_disabled_providers(safe_config) + logger.info(yaml.dump(clean_config, indent=2)) def extract_path_params(route: str) -> list[str]: @@ -597,5 +616,20 @@ def extract_path_params(route: str) -> list[str]: return params +def remove_disabled_providers(obj): + if isinstance(obj, dict): + if ( + obj.get("provider_id") == "__disabled__" + or obj.get("shield_id") == "__disabled__" + or obj.get("provider_model_id") == "__disabled__" + ): + return None + return {k: v for k, v in ((k, remove_disabled_providers(v)) for k, v in obj.items()) if v is not None} + elif isinstance(obj, list): + return [item for item in (remove_disabled_providers(i) for i in obj) if item is not None] + else: + return obj + + if __name__ == "__main__": main() diff --git a/llama_stack/distribution/stack.py b/llama_stack/distribution/stack.py index 1a9237d6c..d7270156a 100644 --- a/llama_stack/distribution/stack.py +++ b/llama_stack/distribution/stack.py @@ -98,6 +98,7 @@ 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 obj.provider_id is not None and obj.provider_id == "__disabled__": logger.debug(f"Skipping {rsrc.capitalize()} registration for disabled provider.") @@ -112,6 +113,11 @@ async def register_resources(run_config: StackRunConfig, impls: dict[Api, Any]): ): logger.debug(f"Skipping {rsrc.capitalize()} registration for disabled model.") continue + + if hasattr(obj, "shield_id") and obj.shield_id is not None and obj.shield_id == "__disabled__": + logger.debug(f"Skipping {rsrc.capitalize()} registration for disabled shield.") + continue + # 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, # we use model_dump() to find all the attrs and then getattr to get the still typed value. @@ -166,7 +172,6 @@ def replace_env_vars(config: Any, path: str = "") -> Any: # Create a copy with resolved provider_id but original config disabled_provider = v.copy() disabled_provider["provider_id"] = resolved_provider_id - result.append(disabled_provider) continue except EnvVarError: # If we can't resolve the provider_id, continue with normal processing @@ -261,6 +266,13 @@ def _convert_string_to_proper_type(value: str) -> Any: return value +def cast_image_name_to_string(config_dict: dict[str, Any]) -> dict[str, Any]: + """Ensure that any value for a key 'image_name' in a config_dict is a string""" + if "image_name" in config_dict and config_dict["image_name"] is not None: + config_dict["image_name"] = str(config_dict["image_name"]) + return config_dict + + def validate_env_pair(env_pair: str) -> tuple[str, str]: """Validate and split an environment variable key-value pair.""" try: diff --git a/llama_stack/distribution/utils/context.py b/llama_stack/distribution/utils/context.py index 3fcd3315f..24b249890 100644 --- a/llama_stack/distribution/utils/context.py +++ b/llama_stack/distribution/utils/context.py @@ -6,12 +6,9 @@ from collections.abc import AsyncGenerator from contextvars import ContextVar -from typing import TypeVar - -T = TypeVar("T") -def preserve_contexts_async_generator( +def preserve_contexts_async_generator[T]( gen: AsyncGenerator[T, None], context_vars: list[ContextVar] ) -> AsyncGenerator[T, None]: """ diff --git a/llama_stack/models/llama/llama3/chat_format.py b/llama_stack/models/llama/llama3/chat_format.py index 7bb05d8db..0a973cf0c 100644 --- a/llama_stack/models/llama/llama3/chat_format.py +++ b/llama_stack/models/llama/llama3/chat_format.py @@ -8,6 +8,7 @@ import io import json import uuid from dataclasses import dataclass +from typing import Any from PIL import Image as PIL_Image @@ -184,16 +185,26 @@ class ChatFormat: content = content[: -len("<|eom_id|>")] stop_reason = StopReason.end_of_message - tool_name = None - tool_arguments = {} + tool_name: str | BuiltinTool | None = None + tool_arguments: dict[str, Any] = {} custom_tool_info = ToolUtils.maybe_extract_custom_tool_call(content) if custom_tool_info is not None: - tool_name, tool_arguments = custom_tool_info + # Type guard: ensure custom_tool_info is a tuple of correct types + if isinstance(custom_tool_info, tuple) and len(custom_tool_info) == 2: + extracted_tool_name, extracted_tool_arguments = custom_tool_info + # Handle both dict and str return types from the function + if isinstance(extracted_tool_arguments, dict): + tool_name, tool_arguments = extracted_tool_name, extracted_tool_arguments + else: + # If it's a string, treat it as a query parameter + tool_name, tool_arguments = extracted_tool_name, {"query": extracted_tool_arguments} + else: + tool_name, tool_arguments = None, {} # Sometimes when agent has custom tools alongside builin tools # Agent responds for builtin tool calls in the format of the custom tools # This code tries to handle that case - if tool_name in BuiltinTool.__members__: + if tool_name is not None and tool_name in BuiltinTool.__members__: tool_name = BuiltinTool[tool_name] if isinstance(tool_arguments, dict): tool_arguments = { diff --git a/llama_stack/models/llama/llama3_3/prompts.py b/llama_stack/models/llama/llama3_3/prompts.py index 60349e578..85796608a 100644 --- a/llama_stack/models/llama/llama3_3/prompts.py +++ b/llama_stack/models/llama/llama3_3/prompts.py @@ -178,6 +178,7 @@ def usecases() -> list[UseCase | str]: ), RawMessage(role="user", content="What is the 100th decimal of pi?"), RawMessage( + role="assistant", content="", stop_reason=StopReason.end_of_message, tool_calls=[ diff --git a/llama_stack/providers/inline/agents/meta_reference/safety.py b/llama_stack/providers/inline/agents/meta_reference/safety.py index 6b3573d8c..605f387b7 100644 --- a/llama_stack/providers/inline/agents/meta_reference/safety.py +++ b/llama_stack/providers/inline/agents/meta_reference/safety.py @@ -24,8 +24,8 @@ class ShieldRunnerMixin: def __init__( self, safety_api: Safety, - input_shields: list[str] = None, - output_shields: list[str] = None, + input_shields: list[str] | None = None, + output_shields: list[str] | None = None, ): self.safety_api = safety_api self.input_shields = input_shields @@ -37,6 +37,7 @@ class ShieldRunnerMixin: return await self.safety_api.run_shield( shield_id=identifier, messages=messages, + params={}, ) responses = await asyncio.gather(*[run_shield_with_span(identifier) for identifier in identifiers]) diff --git a/llama_stack/providers/inline/files/localfs/files.py b/llama_stack/providers/inline/files/localfs/files.py index 851ce2a6a..bdf8c42c7 100644 --- a/llama_stack/providers/inline/files/localfs/files.py +++ b/llama_stack/providers/inline/files/localfs/files.py @@ -51,6 +51,9 @@ class LocalfsFilesImpl(Files): }, ) + async def shutdown(self) -> None: + pass + def _generate_file_id(self) -> str: """Generate a unique file ID for OpenAI API.""" return f"file-{uuid.uuid4().hex}" diff --git a/llama_stack/providers/inline/inference/meta_reference/config.py b/llama_stack/providers/inline/inference/meta_reference/config.py index 9556b026a..961548f9c 100644 --- a/llama_stack/providers/inline/inference/meta_reference/config.py +++ b/llama_stack/providers/inline/inference/meta_reference/config.py @@ -39,7 +39,7 @@ class MetaReferenceInferenceConfig(BaseModel): def validate_model(cls, model: str) -> str: permitted_models = supported_inference_models() descriptors = [m.descriptor() for m in permitted_models] - repos = [m.huggingface_repo for m in permitted_models] + repos = [m.huggingface_repo for m in permitted_models if m.huggingface_repo is not None] if model not in (descriptors + repos): model_list = "\n\t".join(repos) raise ValueError(f"Unknown model: `{model}`. Choose from [\n\t{model_list}\n]") diff --git a/llama_stack/providers/inline/inference/meta_reference/parallel_utils.py b/llama_stack/providers/inline/inference/meta_reference/parallel_utils.py index 97e96b929..7ade75032 100644 --- a/llama_stack/providers/inline/inference/meta_reference/parallel_utils.py +++ b/llama_stack/providers/inline/inference/meta_reference/parallel_utils.py @@ -98,7 +98,7 @@ class ProcessingMessageWrapper(BaseModel): def mp_rank_0() -> bool: - return get_model_parallel_rank() == 0 + return bool(get_model_parallel_rank() == 0) def encode_msg(msg: ProcessingMessage) -> bytes: @@ -125,7 +125,7 @@ def retrieve_requests(reply_socket_url: str): reply_socket.send_multipart([client_id, encode_msg(obj)]) while True: - tasks = [None] + tasks: list[ProcessingMessage | None] = [None] if mp_rank_0(): client_id, maybe_task_json = maybe_get_work(reply_socket) if maybe_task_json is not None: @@ -152,7 +152,7 @@ def retrieve_requests(reply_socket_url: str): break for obj in out: - updates = [None] + updates: list[ProcessingMessage | None] = [None] if mp_rank_0(): _, update_json = maybe_get_work(reply_socket) update = maybe_parse_message(update_json) diff --git a/llama_stack/providers/inline/post_training/torchtune/post_training.py b/llama_stack/providers/inline/post_training/torchtune/post_training.py index c7d8d6758..d20e11b11 100644 --- a/llama_stack/providers/inline/post_training/torchtune/post_training.py +++ b/llama_stack/providers/inline/post_training/torchtune/post_training.py @@ -123,7 +123,8 @@ class TorchtunePostTrainingImpl: training_config: TrainingConfig, hyperparam_search_config: dict[str, Any], logger_config: dict[str, Any], - ) -> PostTrainingJob: ... + ) -> PostTrainingJob: + raise NotImplementedError() async def get_training_jobs(self) -> ListPostTrainingJobsResponse: return ListPostTrainingJobsResponse( diff --git a/llama_stack/providers/inline/safety/llama_guard/llama_guard.py b/llama_stack/providers/inline/safety/llama_guard/llama_guard.py index 30d7f93cd..9d359e053 100644 --- a/llama_stack/providers/inline/safety/llama_guard/llama_guard.py +++ b/llama_stack/providers/inline/safety/llama_guard/llama_guard.py @@ -146,10 +146,9 @@ class LlamaGuardSafetyImpl(Safety, ShieldsProtocolPrivate): pass async def register_shield(self, shield: Shield) -> None: - if shield.provider_resource_id not in LLAMA_GUARD_MODEL_IDS: - raise ValueError( - f"Unsupported Llama Guard type: {shield.provider_resource_id}. Allowed types: {LLAMA_GUARD_MODEL_IDS}" - ) + # Allow any model to be registered as a shield + # The model will be validated during runtime when making inference calls + pass async def run_shield( self, @@ -167,11 +166,25 @@ class LlamaGuardSafetyImpl(Safety, ShieldsProtocolPrivate): if len(messages) > 0 and messages[0].role != Role.user.value: messages[0] = UserMessage(content=messages[0].content) - model = LLAMA_GUARD_MODEL_IDS[shield.provider_resource_id] + # Use the inference API's model resolution instead of hardcoded mappings + # This allows the shield to work with any registered model + model_id = shield.provider_resource_id + + # Determine safety categories based on the model type + # For known Llama Guard models, use specific categories + if model_id in LLAMA_GUARD_MODEL_IDS: + # Use the mapped model for categories but the original model_id for inference + mapped_model = LLAMA_GUARD_MODEL_IDS[model_id] + safety_categories = MODEL_TO_SAFETY_CATEGORIES_MAP.get(mapped_model, DEFAULT_LG_V3_SAFETY_CATEGORIES) + else: + # For unknown models, use default Llama Guard 3 8B categories + safety_categories = DEFAULT_LG_V3_SAFETY_CATEGORIES + [CAT_CODE_INTERPRETER_ABUSE] + impl = LlamaGuardShield( - model=model, + model=model_id, inference_api=self.inference_api, excluded_categories=self.config.excluded_categories, + safety_categories=safety_categories, ) return await impl.run(messages) @@ -183,20 +196,21 @@ class LlamaGuardShield: model: str, inference_api: Inference, excluded_categories: list[str] | None = None, + safety_categories: list[str] | None = None, ): if excluded_categories is None: excluded_categories = [] + if safety_categories is None: + safety_categories = [] assert len(excluded_categories) == 0 or all( x in SAFETY_CATEGORIES_TO_CODE_MAP.values() for x in excluded_categories ), "Invalid categories in excluded categories. Expected format is ['S1', 'S2', ..]" - if model not in MODEL_TO_SAFETY_CATEGORIES_MAP: - raise ValueError(f"Unsupported model: {model}") - self.model = model self.inference_api = inference_api self.excluded_categories = excluded_categories + self.safety_categories = safety_categories def check_unsafe_response(self, response: str) -> str | None: match = re.match(r"^unsafe\n(.*)$", response) @@ -214,7 +228,7 @@ class LlamaGuardShield: final_categories = [] - all_categories = MODEL_TO_SAFETY_CATEGORIES_MAP[self.model] + all_categories = self.safety_categories for cat in all_categories: cat_code = SAFETY_CATEGORIES_TO_CODE_MAP[cat] if cat_code in excluded_categories: diff --git a/llama_stack/providers/inline/vector_io/faiss/faiss.py b/llama_stack/providers/inline/vector_io/faiss/faiss.py index 62a98413d..2a1370c56 100644 --- a/llama_stack/providers/inline/vector_io/faiss/faiss.py +++ b/llama_stack/providers/inline/vector_io/faiss/faiss.py @@ -181,8 +181,8 @@ class FaissVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorDBsProtocolPr ) self.cache[vector_db.identifier] = index - # Load existing OpenAI vector stores using the mixin method - self.openai_vector_stores = await self._load_openai_vector_stores() + # Load existing OpenAI vector stores into the in-memory cache + await self.initialize_openai_vector_stores() async def shutdown(self) -> None: # Cleanup if needed @@ -261,42 +261,10 @@ class FaissVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorDBsProtocolPr return await index.query_chunks(query, params) - # OpenAI Vector Store Mixin abstract method implementations - async def _save_openai_vector_store(self, store_id: str, store_info: dict[str, Any]) -> None: - """Save vector store metadata to kvstore.""" - assert self.kvstore is not None - key = f"{OPENAI_VECTOR_STORES_PREFIX}{store_id}" - await self.kvstore.set(key=key, value=json.dumps(store_info)) - - async def _load_openai_vector_stores(self) -> dict[str, dict[str, Any]]: - """Load all vector store metadata from kvstore.""" - assert self.kvstore is not None - start_key = OPENAI_VECTOR_STORES_PREFIX - end_key = f"{OPENAI_VECTOR_STORES_PREFIX}\xff" - stored_openai_stores = await self.kvstore.values_in_range(start_key, end_key) - - stores = {} - for store_data in stored_openai_stores: - store_info = json.loads(store_data) - stores[store_info["id"]] = store_info - return stores - - async def _update_openai_vector_store(self, store_id: str, store_info: dict[str, Any]) -> None: - """Update vector store metadata in kvstore.""" - assert self.kvstore is not None - key = f"{OPENAI_VECTOR_STORES_PREFIX}{store_id}" - await self.kvstore.set(key=key, value=json.dumps(store_info)) - - async def _delete_openai_vector_store_from_storage(self, store_id: str) -> None: - """Delete vector store metadata from kvstore.""" - assert self.kvstore is not None - key = f"{OPENAI_VECTOR_STORES_PREFIX}{store_id}" - await self.kvstore.delete(key) - async def _save_openai_vector_store_file( self, store_id: str, file_id: str, file_info: dict[str, Any], file_contents: list[dict[str, Any]] ) -> None: - """Save vector store file metadata to kvstore.""" + """Save vector store file data to kvstore.""" assert self.kvstore is not None key = f"{OPENAI_VECTOR_STORES_FILES_PREFIX}{store_id}:{file_id}" await self.kvstore.set(key=key, value=json.dumps(file_info)) @@ -324,7 +292,16 @@ class FaissVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorDBsProtocolPr await self.kvstore.set(key=key, value=json.dumps(file_info)) async def _delete_openai_vector_store_file_from_storage(self, store_id: str, file_id: str) -> None: - """Delete vector store file metadata from kvstore.""" + """Delete vector store data from kvstore.""" assert self.kvstore is not None - key = f"{OPENAI_VECTOR_STORES_FILES_PREFIX}{store_id}:{file_id}" - await self.kvstore.delete(key) + + keys_to_delete = [ + f"{OPENAI_VECTOR_STORES_FILES_PREFIX}{store_id}:{file_id}", + f"{OPENAI_VECTOR_STORES_FILES_CONTENTS_PREFIX}{store_id}:{file_id}", + ] + for key in keys_to_delete: + try: + await self.kvstore.delete(key) + except Exception as e: + logger.warning(f"Failed to delete key {key}: {e}") + continue diff --git a/llama_stack/providers/inline/vector_io/milvus/config.py b/llama_stack/providers/inline/vector_io/milvus/config.py index 61a8acd78..8cbd056be 100644 --- a/llama_stack/providers/inline/vector_io/milvus/config.py +++ b/llama_stack/providers/inline/vector_io/milvus/config.py @@ -6,7 +6,7 @@ from typing import Any -from pydantic import BaseModel +from pydantic import BaseModel, Field from llama_stack.providers.utils.kvstore.config import ( KVStoreConfig, @@ -18,7 +18,8 @@ from llama_stack.schema_utils import json_schema_type @json_schema_type class MilvusVectorIOConfig(BaseModel): db_path: str - kvstore: KVStoreConfig + kvstore: KVStoreConfig = Field(description="Config for KV store backend (SQLite only for now)") + consistency_level: str = Field(description="The consistency level of the Milvus server", default="Strong") @classmethod def sample_run_config(cls, __distro_dir__: str, **kwargs: Any) -> dict[str, Any]: diff --git a/llama_stack/providers/inline/vector_io/sqlite_vec/config.py b/llama_stack/providers/inline/vector_io/sqlite_vec/config.py index 4c57f4aba..525ed4b1f 100644 --- a/llama_stack/providers/inline/vector_io/sqlite_vec/config.py +++ b/llama_stack/providers/inline/vector_io/sqlite_vec/config.py @@ -6,14 +6,24 @@ from typing import Any -from pydantic import BaseModel +from pydantic import BaseModel, Field + +from llama_stack.providers.utils.kvstore.config import ( + KVStoreConfig, + SqliteKVStoreConfig, +) class SQLiteVectorIOConfig(BaseModel): - db_path: str + db_path: str = Field(description="Path to the SQLite database file") + kvstore: KVStoreConfig = Field(description="Config for KV store backend (SQLite only for now)") @classmethod def sample_run_config(cls, __distro_dir__: str) -> dict[str, Any]: return { "db_path": "${env.SQLITE_STORE_DIR:=" + __distro_dir__ + "}/" + "sqlite_vec.db", + "kvstore": SqliteKVStoreConfig.sample_run_config( + __distro_dir__=__distro_dir__, + db_name="sqlite_vec_registry.db", + ), } 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 7e977635a..060b5b15c 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 @@ -7,6 +7,7 @@ import asyncio import json import logging +import re import sqlite3 import struct from typing import Any @@ -24,6 +25,8 @@ from llama_stack.apis.vector_io import ( VectorIO, ) from llama_stack.providers.datatypes import VectorDBsProtocolPrivate +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 from llama_stack.providers.utils.memory.vector_store import ( RERANKER_TYPE_RRF, @@ -40,6 +43,13 @@ KEYWORD_SEARCH = "keyword" HYBRID_SEARCH = "hybrid" SEARCH_MODES = {VECTOR_SEARCH, KEYWORD_SEARCH, HYBRID_SEARCH} +VERSION = "v3" +VECTOR_DBS_PREFIX = f"vector_dbs:sqlite_vec:{VERSION}::" +VECTOR_INDEX_PREFIX = f"vector_index:sqlite_vec:{VERSION}::" +OPENAI_VECTOR_STORES_PREFIX = f"openai_vector_stores:sqlite_vec:{VERSION}::" +OPENAI_VECTOR_STORES_FILES_PREFIX = f"openai_vector_stores_files:sqlite_vec:{VERSION}::" +OPENAI_VECTOR_STORES_FILES_CONTENTS_PREFIX = f"openai_vector_stores_files_contents:sqlite_vec:{VERSION}::" + def serialize_vector(vector: list[float]) -> bytes: """Serialize a list of floats into a compact binary representation.""" @@ -108,6 +118,10 @@ def _rrf_rerank( return rrf_scores +def _make_sql_identifier(name: str) -> str: + return re.sub(r"[^a-zA-Z0-9_]", "_", name) + + class SQLiteVecIndex(EmbeddingIndex): """ An index implementation that stores embeddings in a SQLite virtual table using sqlite-vec. @@ -117,13 +131,14 @@ class SQLiteVecIndex(EmbeddingIndex): - An FTS5 table (fts_chunks_{bank_id}) for full-text keyword search. """ - def __init__(self, dimension: int, db_path: str, bank_id: str): + def __init__(self, dimension: int, db_path: str, bank_id: str, kvstore: KVStore | None = None): self.dimension = dimension self.db_path = db_path self.bank_id = bank_id - self.metadata_table = f"chunks_{bank_id}".replace("-", "_") - self.vector_table = f"vec_chunks_{bank_id}".replace("-", "_") - self.fts_table = f"fts_chunks_{bank_id}".replace("-", "_") + self.metadata_table = _make_sql_identifier(f"chunks_{bank_id}") + self.vector_table = _make_sql_identifier(f"vec_chunks_{bank_id}") + self.fts_table = _make_sql_identifier(f"fts_chunks_{bank_id}") + self.kvstore = kvstore @classmethod async def create(cls, dimension: int, db_path: str, bank_id: str): @@ -138,14 +153,14 @@ class SQLiteVecIndex(EmbeddingIndex): try: # Create the table to store chunk metadata. cur.execute(f""" - CREATE TABLE IF NOT EXISTS {self.metadata_table} ( + CREATE TABLE IF NOT EXISTS [{self.metadata_table}] ( id TEXT PRIMARY KEY, chunk TEXT ); """) # Create the virtual table for embeddings. cur.execute(f""" - CREATE VIRTUAL TABLE IF NOT EXISTS {self.vector_table} + CREATE VIRTUAL TABLE IF NOT EXISTS [{self.vector_table}] USING vec0(embedding FLOAT[{self.dimension}], id TEXT); """) connection.commit() @@ -153,7 +168,7 @@ class SQLiteVecIndex(EmbeddingIndex): # based on query. Implementation of the change on client side will allow passing the search_mode option # during initialization to make it easier to create the table that is required. cur.execute(f""" - CREATE VIRTUAL TABLE IF NOT EXISTS {self.fts_table} + CREATE VIRTUAL TABLE IF NOT EXISTS [{self.fts_table}] USING fts5(id, content); """) connection.commit() @@ -168,9 +183,9 @@ class SQLiteVecIndex(EmbeddingIndex): connection = _create_sqlite_connection(self.db_path) cur = connection.cursor() try: - cur.execute(f"DROP TABLE IF EXISTS {self.metadata_table};") - cur.execute(f"DROP TABLE IF EXISTS {self.vector_table};") - cur.execute(f"DROP TABLE IF EXISTS {self.fts_table};") + cur.execute(f"DROP TABLE IF EXISTS [{self.metadata_table}];") + cur.execute(f"DROP TABLE IF EXISTS [{self.vector_table}];") + cur.execute(f"DROP TABLE IF EXISTS [{self.fts_table}];") connection.commit() finally: cur.close() @@ -202,7 +217,7 @@ class SQLiteVecIndex(EmbeddingIndex): metadata_data = [(chunk.chunk_id, chunk.model_dump_json()) for chunk in batch_chunks] cur.executemany( f""" - INSERT INTO {self.metadata_table} (id, chunk) + INSERT INTO [{self.metadata_table}] (id, chunk) VALUES (?, ?) ON CONFLICT(id) DO UPDATE SET chunk = excluded.chunk; """, @@ -220,7 +235,7 @@ class SQLiteVecIndex(EmbeddingIndex): for chunk, emb in zip(batch_chunks, batch_embeddings, strict=True) ] cur.executemany( - f"INSERT INTO {self.vector_table} (id, embedding) VALUES (?, ?);", + f"INSERT INTO [{self.vector_table}] (id, embedding) VALUES (?, ?);", embedding_data, ) @@ -228,13 +243,13 @@ class SQLiteVecIndex(EmbeddingIndex): fts_data = [(chunk.chunk_id, chunk.content) for chunk in batch_chunks] # DELETE existing entries with same IDs (FTS5 doesn't support ON CONFLICT) cur.executemany( - f"DELETE FROM {self.fts_table} WHERE id = ?;", + f"DELETE FROM [{self.fts_table}] WHERE id = ?;", [(row[0],) for row in fts_data], ) # INSERT new entries cur.executemany( - f"INSERT INTO {self.fts_table} (id, content) VALUES (?, ?);", + f"INSERT INTO [{self.fts_table}] (id, content) VALUES (?, ?);", fts_data, ) @@ -270,8 +285,8 @@ class SQLiteVecIndex(EmbeddingIndex): emb_blob = serialize_vector(emb_list) query_sql = f""" SELECT m.id, m.chunk, v.distance - FROM {self.vector_table} AS v - JOIN {self.metadata_table} AS m ON m.id = v.id + FROM [{self.vector_table}] AS v + JOIN [{self.metadata_table}] AS m ON m.id = v.id WHERE v.embedding MATCH ? AND k = ? ORDER BY v.distance; """ @@ -312,9 +327,9 @@ class SQLiteVecIndex(EmbeddingIndex): cur = connection.cursor() try: query_sql = f""" - SELECT DISTINCT m.id, m.chunk, bm25({self.fts_table}) AS score - FROM {self.fts_table} AS f - JOIN {self.metadata_table} AS m ON m.id = f.id + SELECT DISTINCT m.id, m.chunk, bm25([{self.fts_table}]) AS score + FROM [{self.fts_table}] AS f + JOIN [{self.metadata_table}] AS m ON m.id = f.id WHERE f.content MATCH ? ORDER BY score ASC LIMIT ?; @@ -425,27 +440,81 @@ class SQLiteVecVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorDBsProtoc self.files_api = files_api self.cache: dict[str, VectorDBWithIndex] = {} self.openai_vector_stores: dict[str, dict[str, Any]] = {} + self.kvstore: KVStore | None = None async def initialize(self) -> None: - def _setup_connection(): - # Open a connection to the SQLite database (the file is specified in the config). + self.kvstore = await kvstore_impl(self.config.kvstore) + + start_key = VECTOR_DBS_PREFIX + end_key = f"{VECTOR_DBS_PREFIX}\xff" + stored_vector_dbs = await self.kvstore.values_in_range(start_key, end_key) + for db_json in stored_vector_dbs: + vector_db = VectorDB.model_validate_json(db_json) + index = await SQLiteVecIndex.create( + vector_db.embedding_dimension, + self.config.db_path, + vector_db.identifier, + ) + self.cache[vector_db.identifier] = VectorDBWithIndex(vector_db, index, self.inference_api) + + # Load existing OpenAI vector stores into the in-memory cache + await self.initialize_openai_vector_stores() + + async def shutdown(self) -> None: + # nothing to do since we don't maintain a persistent connection + pass + + async def list_vector_dbs(self) -> list[VectorDB]: + return [v.vector_db for v in self.cache.values()] + + async def register_vector_db(self, vector_db: VectorDB) -> None: + index = await SQLiteVecIndex.create( + vector_db.embedding_dimension, + self.config.db_path, + vector_db.identifier, + ) + self.cache[vector_db.identifier] = VectorDBWithIndex(vector_db, index, self.inference_api) + + async def _get_and_cache_vector_db_index(self, vector_db_id: str) -> VectorDBWithIndex | None: + if vector_db_id in self.cache: + return self.cache[vector_db_id] + + if self.vector_db_store is None: + raise ValueError(f"Vector DB {vector_db_id} not found") + + vector_db = self.vector_db_store.get_vector_db(vector_db_id) + if not vector_db: + raise ValueError(f"Vector DB {vector_db_id} not found") + + index = VectorDBWithIndex( + vector_db=vector_db, + index=SQLiteVecIndex( + dimension=vector_db.embedding_dimension, + db_path=self.config.db_path, + bank_id=vector_db.identifier, + kvstore=self.kvstore, + ), + inference_api=self.inference_api, + ) + self.cache[vector_db_id] = index + return index + + async def unregister_vector_db(self, vector_db_id: str) -> None: + if vector_db_id not in self.cache: + logger.warning(f"Vector DB {vector_db_id} not found") + return + await self.cache[vector_db_id].index.delete() + del self.cache[vector_db_id] + + async def _save_openai_vector_store_file( + self, store_id: str, file_id: str, file_info: dict[str, Any], file_contents: list[dict[str, Any]] + ) -> None: + """Save vector store file metadata to SQLite database.""" + + def _create_or_store(): connection = _create_sqlite_connection(self.config.db_path) cur = connection.cursor() try: - # Create a table to persist vector DB registrations. - cur.execute(""" - CREATE TABLE IF NOT EXISTS vector_dbs ( - id TEXT PRIMARY KEY, - metadata TEXT - ); - """) - # Create a table to persist OpenAI vector stores. - cur.execute(""" - CREATE TABLE IF NOT EXISTS openai_vector_stores ( - id TEXT PRIMARY KEY, - metadata TEXT - ); - """) # Create a table to persist OpenAI vector store files. cur.execute(""" CREATE TABLE IF NOT EXISTS openai_vector_store_files ( @@ -464,168 +533,6 @@ class SQLiteVecVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorDBsProtoc ); """) connection.commit() - # Load any existing vector DB registrations. - cur.execute("SELECT metadata FROM vector_dbs") - vector_db_rows = cur.fetchall() - return vector_db_rows - finally: - cur.close() - connection.close() - - vector_db_rows = await asyncio.to_thread(_setup_connection) - - # Load existing vector DBs - for row in vector_db_rows: - vector_db_data = row[0] - vector_db = VectorDB.model_validate_json(vector_db_data) - index = await SQLiteVecIndex.create( - vector_db.embedding_dimension, - self.config.db_path, - vector_db.identifier, - ) - self.cache[vector_db.identifier] = VectorDBWithIndex(vector_db, index, self.inference_api) - - # Load existing OpenAI vector stores using the mixin method - self.openai_vector_stores = await self._load_openai_vector_stores() - - async def shutdown(self) -> None: - # nothing to do since we don't maintain a persistent connection - pass - - async def register_vector_db(self, vector_db: VectorDB) -> None: - def _register_db(): - connection = _create_sqlite_connection(self.config.db_path) - cur = connection.cursor() - try: - cur.execute( - "INSERT OR REPLACE INTO vector_dbs (id, metadata) VALUES (?, ?)", - (vector_db.identifier, vector_db.model_dump_json()), - ) - connection.commit() - finally: - cur.close() - connection.close() - - await asyncio.to_thread(_register_db) - index = await SQLiteVecIndex.create( - vector_db.embedding_dimension, - self.config.db_path, - vector_db.identifier, - ) - self.cache[vector_db.identifier] = VectorDBWithIndex(vector_db, index, self.inference_api) - - async def list_vector_dbs(self) -> list[VectorDB]: - return [v.vector_db for v in self.cache.values()] - - async def unregister_vector_db(self, vector_db_id: str) -> None: - if vector_db_id not in self.cache: - logger.warning(f"Vector DB {vector_db_id} not found") - return - await self.cache[vector_db_id].index.delete() - del self.cache[vector_db_id] - - def _delete_vector_db_from_registry(): - connection = _create_sqlite_connection(self.config.db_path) - cur = connection.cursor() - try: - cur.execute("DELETE FROM vector_dbs WHERE id = ?", (vector_db_id,)) - connection.commit() - finally: - cur.close() - connection.close() - - await asyncio.to_thread(_delete_vector_db_from_registry) - - # OpenAI Vector Store Mixin abstract method implementations - async def _save_openai_vector_store(self, store_id: str, store_info: dict[str, Any]) -> None: - """Save vector store metadata to SQLite database.""" - - def _store(): - connection = _create_sqlite_connection(self.config.db_path) - cur = connection.cursor() - try: - cur.execute( - "INSERT OR REPLACE INTO openai_vector_stores (id, metadata) VALUES (?, ?)", - (store_id, json.dumps(store_info)), - ) - connection.commit() - except Exception as e: - logger.error(f"Error saving openai vector store {store_id}: {e}") - raise - finally: - cur.close() - connection.close() - - try: - await asyncio.to_thread(_store) - except Exception as e: - logger.error(f"Error saving openai vector store {store_id}: {e}") - raise - - async def _load_openai_vector_stores(self) -> dict[str, dict[str, Any]]: - """Load all vector store metadata from SQLite database.""" - - def _load(): - connection = _create_sqlite_connection(self.config.db_path) - cur = connection.cursor() - try: - cur.execute("SELECT metadata FROM openai_vector_stores") - rows = cur.fetchall() - return rows - finally: - cur.close() - connection.close() - - rows = await asyncio.to_thread(_load) - stores = {} - for row in rows: - store_data = row[0] - store_info = json.loads(store_data) - stores[store_info["id"]] = store_info - return stores - - async def _update_openai_vector_store(self, store_id: str, store_info: dict[str, Any]) -> None: - """Update vector store metadata in SQLite database.""" - - def _update(): - connection = _create_sqlite_connection(self.config.db_path) - cur = connection.cursor() - try: - cur.execute( - "UPDATE openai_vector_stores SET metadata = ? WHERE id = ?", - (json.dumps(store_info), store_id), - ) - connection.commit() - finally: - cur.close() - connection.close() - - await asyncio.to_thread(_update) - - async def _delete_openai_vector_store_from_storage(self, store_id: str) -> None: - """Delete vector store metadata from SQLite database.""" - - def _delete(): - connection = _create_sqlite_connection(self.config.db_path) - cur = connection.cursor() - try: - cur.execute("DELETE FROM openai_vector_stores WHERE id = ?", (store_id,)) - connection.commit() - finally: - cur.close() - connection.close() - - await asyncio.to_thread(_delete) - - async def _save_openai_vector_store_file( - self, store_id: str, file_id: str, file_info: dict[str, Any], file_contents: list[dict[str, Any]] - ) -> None: - """Save vector store file metadata to SQLite database.""" - - def _store(): - connection = _create_sqlite_connection(self.config.db_path) - cur = connection.cursor() - try: cur.execute( "INSERT OR REPLACE INTO openai_vector_store_files (store_id, file_id, metadata) VALUES (?, ?, ?)", (store_id, file_id, json.dumps(file_info)), @@ -643,7 +550,7 @@ class SQLiteVecVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorDBsProtoc connection.close() try: - await asyncio.to_thread(_store) + await asyncio.to_thread(_create_or_store) except Exception as e: logger.error(f"Error saving openai vector store file {store_id} {file_id}: {e}") raise @@ -722,6 +629,10 @@ class SQLiteVecVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorDBsProtoc cur.execute( "DELETE FROM openai_vector_store_files WHERE store_id = ? AND file_id = ?", (store_id, file_id) ) + cur.execute( + "DELETE FROM openai_vector_store_files_contents WHERE store_id = ? AND file_id = ?", + (store_id, file_id), + ) connection.commit() finally: cur.close() @@ -730,15 +641,17 @@ class SQLiteVecVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorDBsProtoc await asyncio.to_thread(_delete) async def insert_chunks(self, vector_db_id: str, chunks: list[Chunk], ttl_seconds: int | None = None) -> None: - if vector_db_id not in self.cache: - raise ValueError(f"Vector DB {vector_db_id} not found. Found: {list(self.cache.keys())}") + index = await self._get_and_cache_vector_db_index(vector_db_id) + if not index: + raise ValueError(f"Vector DB {vector_db_id} not found") # The VectorDBWithIndex helper is expected to compute embeddings via the inference_api # and then call our index's add_chunks. - await self.cache[vector_db_id].insert_chunks(chunks) + await index.insert_chunks(chunks) async def query_chunks( self, vector_db_id: str, query: Any, params: dict[str, Any] | None = None ) -> QueryChunksResponse: - if vector_db_id not in self.cache: + index = await self._get_and_cache_vector_db_index(vector_db_id) + if not index: raise ValueError(f"Vector DB {vector_db_id} not found") - return await self.cache[vector_db_id].query_chunks(query, params) + return await index.query_chunks(query, params) diff --git a/llama_stack/providers/remote/inference/anthropic/models.py b/llama_stack/providers/remote/inference/anthropic/models.py index afaf3c4e4..172e06c70 100644 --- a/llama_stack/providers/remote/inference/anthropic/models.py +++ b/llama_stack/providers/remote/inference/anthropic/models.py @@ -15,21 +15,26 @@ LLM_MODEL_IDS = [ "anthropic/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="anthropic/voyage-3", - model_type=ModelType.embedding, - metadata={"embedding_dimension": 1024, "context_length": 32000}, - ), - ProviderModelEntry( - provider_model_id="anthropic/voyage-3-lite", - model_type=ModelType.embedding, - metadata={"embedding_dimension": 512, "context_length": 32000}, - ), - ProviderModelEntry( - provider_model_id="anthropic/voyage-code-3", - model_type=ModelType.embedding, - metadata={"embedding_dimension": 1024, "context_length": 32000}, - ), -] +MODEL_ENTRIES = ( + [ProviderModelEntry(provider_model_id=m) for m in LLM_MODEL_IDS] + + [ + ProviderModelEntry( + provider_model_id="anthropic/voyage-3", + model_type=ModelType.embedding, + metadata={"embedding_dimension": 1024, "context_length": 32000}, + ), + ProviderModelEntry( + provider_model_id="anthropic/voyage-3-lite", + model_type=ModelType.embedding, + metadata={"embedding_dimension": 512, "context_length": 32000}, + ), + ProviderModelEntry( + provider_model_id="anthropic/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/bedrock/models.py b/llama_stack/providers/remote/inference/bedrock/models.py index ec8120049..17273c122 100644 --- a/llama_stack/providers/remote/inference/bedrock/models.py +++ b/llama_stack/providers/remote/inference/bedrock/models.py @@ -9,6 +9,10 @@ from llama_stack.providers.utils.inference.model_registry import ( build_hf_repo_model_entry, ) +SAFETY_MODELS_ENTRIES = [] + + +# https://docs.aws.amazon.com/bedrock/latest/userguide/models-supported.html MODEL_ENTRIES = [ build_hf_repo_model_entry( "meta.llama3-1-8b-instruct-v1:0", @@ -22,4 +26,4 @@ MODEL_ENTRIES = [ "meta.llama3-1-405b-instruct-v1:0", CoreModelId.llama3_1_405b_instruct.value, ), -] +] + SAFETY_MODELS_ENTRIES diff --git a/llama_stack/providers/remote/inference/cerebras/models.py b/llama_stack/providers/remote/inference/cerebras/models.py index 38301b32a..4de2e62c9 100644 --- a/llama_stack/providers/remote/inference/cerebras/models.py +++ b/llama_stack/providers/remote/inference/cerebras/models.py @@ -9,6 +9,9 @@ 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", @@ -18,4 +21,8 @@ MODEL_ENTRIES = [ "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/databricks.py b/llama_stack/providers/remote/inference/databricks/databricks.py index 1dc18b97f..34ee59212 100644 --- a/llama_stack/providers/remote/inference/databricks/databricks.py +++ b/llama_stack/providers/remote/inference/databricks/databricks.py @@ -47,7 +47,10 @@ from llama_stack.providers.utils.inference.prompt_adapter import ( from .config import DatabricksImplConfig -model_entries = [ +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, @@ -56,7 +59,7 @@ model_entries = [ "databricks-meta-llama-3-1-405b-instruct", CoreModelId.llama3_1_405b_instruct.value, ), -] +] + SAFETY_MODELS_ENTRIES class DatabricksInferenceAdapter( @@ -66,7 +69,7 @@ class DatabricksInferenceAdapter( OpenAICompletionToLlamaStackMixin, ): def __init__(self, config: DatabricksImplConfig) -> None: - ModelRegistryHelper.__init__(self, model_entries=model_entries) + ModelRegistryHelper.__init__(self, model_entries=MODEL_ENTRIES) self.config = config async def initialize(self) -> None: diff --git a/llama_stack/providers/remote/inference/fireworks/models.py b/llama_stack/providers/remote/inference/fireworks/models.py index 392aed72f..30807a0d4 100644 --- a/llama_stack/providers/remote/inference/fireworks/models.py +++ b/llama_stack/providers/remote/inference/fireworks/models.py @@ -11,6 +11,17 @@ from llama_stack.providers.utils.inference.model_registry import ( 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", @@ -40,14 +51,6 @@ MODEL_ENTRIES = [ "accounts/fireworks/models/llama-v3p3-70b-instruct", CoreModelId.llama3_3_70b_instruct.value, ), - 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, - ), build_hf_repo_model_entry( "accounts/fireworks/models/llama4-scout-instruct-basic", CoreModelId.llama4_scout_17b_16e_instruct.value, @@ -64,4 +67,4 @@ MODEL_ENTRIES = [ "context_length": 8192, }, ), -] +] + SAFETY_MODELS_ENTRIES diff --git a/llama_stack/providers/remote/inference/gemini/models.py b/llama_stack/providers/remote/inference/gemini/models.py index c4bb4f08b..a7f4732ec 100644 --- a/llama_stack/providers/remote/inference/gemini/models.py +++ b/llama_stack/providers/remote/inference/gemini/models.py @@ -17,11 +17,16 @@ LLM_MODEL_IDS = [ "gemini/gemini-2.5-pro", ] +SAFETY_MODELS_ENTRIES = [] -MODEL_ENTRIES = [ProviderModelEntry(provider_model_id=m) for m in LLM_MODEL_IDS] + [ - ProviderModelEntry( - provider_model_id="gemini/text-embedding-004", - model_type=ModelType.embedding, - metadata={"embedding_dimension": 768, "context_length": 2048}, - ), -] +MODEL_ENTRIES = ( + [ProviderModelEntry(provider_model_id=m) for m in LLM_MODEL_IDS] + + [ + ProviderModelEntry( + provider_model_id="gemini/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 4b295e788..91c6b6c17 100644 --- a/llama_stack/providers/remote/inference/groq/groq.py +++ b/llama_stack/providers/remote/inference/groq/groq.py @@ -38,24 +38,18 @@ class GroqInferenceAdapter(LiteLLMOpenAIMixin): provider_data_api_key_field="groq_api_key", ) self.config = config - self._openai_client = None async def initialize(self): await super().initialize() async def shutdown(self): await super().shutdown() - if self._openai_client: - await self._openai_client.close() - self._openai_client = None def _get_openai_client(self) -> AsyncOpenAI: - if not self._openai_client: - self._openai_client = AsyncOpenAI( - base_url=f"{self.config.url}/openai/v1", - api_key=self.config.api_key, - ) - return self._openai_client + return AsyncOpenAI( + base_url=f"{self.config.url}/openai/v1", + api_key=self.get_api_key(), + ) async def openai_chat_completion( self, diff --git a/llama_stack/providers/remote/inference/groq/models.py b/llama_stack/providers/remote/inference/groq/models.py index 0b4b81cfe..70c089c4a 100644 --- a/llama_stack/providers/remote/inference/groq/models.py +++ b/llama_stack/providers/remote/inference/groq/models.py @@ -10,6 +10,8 @@ from llama_stack.providers.utils.inference.model_registry import ( build_model_entry, ) +SAFETY_MODELS_ENTRIES = [] + MODEL_ENTRIES = [ build_hf_repo_model_entry( "groq/llama3-8b-8192", @@ -51,4 +53,4 @@ MODEL_ENTRIES = [ "groq/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 29b5e889a..5f9cb20b2 100644 --- a/llama_stack/providers/remote/inference/llama_openai_compat/llama.py +++ b/llama_stack/providers/remote/inference/llama_openai_compat/llama.py @@ -3,16 +3,17 @@ # # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. +import logging -from llama_stack.providers.remote.inference.llama_openai_compat.config import ( - LlamaCompatConfig, -) -from llama_stack.providers.utils.inference.litellm_openai_mixin import ( - LiteLLMOpenAIMixin, -) +from llama_api_client import AsyncLlamaAPIClient, NotFoundError + +from llama_stack.providers.remote.inference.llama_openai_compat.config import LlamaCompatConfig +from llama_stack.providers.utils.inference.litellm_openai_mixin import LiteLLMOpenAIMixin from .models import MODEL_ENTRIES +logger = logging.getLogger(__name__) + class LlamaCompatInferenceAdapter(LiteLLMOpenAIMixin): _config: LlamaCompatConfig @@ -27,8 +28,32 @@ class LlamaCompatInferenceAdapter(LiteLLMOpenAIMixin): ) self.config = config + async def check_model_availability(self, model: str) -> bool: + """ + Check if a specific model is available from Llama API. + + :param model: The model identifier to check. + :return: True if the model is available dynamically, False otherwise. + """ + try: + llama_api_client = self._get_llama_api_client() + retrieved_model = await llama_api_client.models.retrieve(model) + logger.info(f"Model {retrieved_model.id} is available from Llama API") + return True + + except NotFoundError: + logger.error(f"Model {model} is not available from Llama API") + return False + + except Exception as e: + logger.error(f"Failed to check model availability from Llama API: {e}") + return False + async def initialize(self): await super().initialize() async def shutdown(self): await super().shutdown() + + def _get_llama_api_client(self) -> AsyncLlamaAPIClient: + return AsyncLlamaAPIClient(api_key=self.get_api_key(), base_url=self.config.openai_compat_api_base) diff --git a/llama_stack/providers/remote/inference/nvidia/models.py b/llama_stack/providers/remote/inference/nvidia/models.py index 127a6ca59..76e579da8 100644 --- a/llama_stack/providers/remote/inference/nvidia/models.py +++ b/llama_stack/providers/remote/inference/nvidia/models.py @@ -11,6 +11,9 @@ from llama_stack.providers.utils.inference.model_registry import ( 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", @@ -99,4 +102,4 @@ MODEL_ENTRIES = [ ), # 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 1dd72da3f..cb7554523 100644 --- a/llama_stack/providers/remote/inference/nvidia/nvidia.py +++ b/llama_stack/providers/remote/inference/nvidia/nvidia.py @@ -7,10 +7,9 @@ import logging import warnings from collections.abc import AsyncIterator -from functools import lru_cache from typing import Any -from openai import APIConnectionError, AsyncOpenAI, BadRequestError +from openai import APIConnectionError, AsyncOpenAI, BadRequestError, NotFoundError from llama_stack.apis.common.content_types import ( InterleavedContent, @@ -41,11 +40,7 @@ from llama_stack.apis.inference import ( ToolChoice, ToolConfig, ) -from llama_stack.apis.models import Model, ModelType from llama_stack.models.llama.datatypes import ToolDefinition, ToolPromptFormat -from llama_stack.providers.utils.inference import ( - ALL_HUGGINGFACE_REPOS_TO_MODEL_DESCRIPTOR, -) from llama_stack.providers.utils.inference.model_registry import ( ModelRegistryHelper, ) @@ -93,41 +88,37 @@ class NVIDIAInferenceAdapter(Inference, ModelRegistryHelper): self._config = config - @lru_cache # noqa: B019 - def _get_client(self, provider_model_id: str) -> AsyncOpenAI: + async def check_model_availability(self, model: str) -> bool: """ - For hosted models, https://integrate.api.nvidia.com/v1 is the primary base_url. However, - some models are hosted on different URLs. This function returns the appropriate client - for the given provider_model_id. + Check if a specific model is available. - This relies on lru_cache and self._default_client to avoid creating a new client for each request - or for each model that is hosted on https://integrate.api.nvidia.com/v1. + :param model: The model identifier to check. + :return: True if the model is available dynamically, False otherwise. + """ + try: + await self._client.models.retrieve(model) + return True + except NotFoundError: + logger.error(f"Model {model} is not available") + except Exception as e: + logger.error(f"Failed to check model availability: {e}") + return False + + @property + def _client(self) -> AsyncOpenAI: + """ + Returns an OpenAI client for the configured NVIDIA API endpoint. - :param provider_model_id: The provider model ID :return: An OpenAI client """ - @lru_cache # noqa: B019 - def _get_client_for_base_url(base_url: str) -> AsyncOpenAI: - """ - Maintain a single OpenAI client per base_url. - """ - return AsyncOpenAI( - base_url=base_url, - api_key=(self._config.api_key.get_secret_value() if self._config.api_key else "NO KEY"), - timeout=self._config.timeout, - ) - - special_model_urls = { - "meta/llama-3.2-11b-vision-instruct": "https://ai.api.nvidia.com/v1/gr/meta/llama-3.2-11b-vision-instruct", - "meta/llama-3.2-90b-vision-instruct": "https://ai.api.nvidia.com/v1/gr/meta/llama-3.2-90b-vision-instruct", - } - base_url = f"{self._config.url}/v1" if self._config.append_api_version else self._config.url - if _is_nvidia_hosted(self._config) and provider_model_id in special_model_urls: - base_url = special_model_urls[provider_model_id] - return _get_client_for_base_url(base_url) + return AsyncOpenAI( + base_url=base_url, + api_key=(self._config.api_key.get_secret_value() if self._config.api_key else "NO KEY"), + timeout=self._config.timeout, + ) async def _get_provider_model_id(self, model_id: str) -> str: if not self.model_store: @@ -169,7 +160,7 @@ class NVIDIAInferenceAdapter(Inference, ModelRegistryHelper): ) try: - response = await self._get_client(provider_model_id).completions.create(**request) + response = await self._client.completions.create(**request) except APIConnectionError as e: raise ConnectionError(f"Failed to connect to NVIDIA NIM at {self._config.url}: {e}") from e @@ -222,7 +213,7 @@ class NVIDIAInferenceAdapter(Inference, ModelRegistryHelper): extra_body["input_type"] = task_type_options[task_type] try: - response = await self._get_client(provider_model_id).embeddings.create( + response = await self._client.embeddings.create( model=provider_model_id, input=input, extra_body=extra_body, @@ -283,7 +274,7 @@ class NVIDIAInferenceAdapter(Inference, ModelRegistryHelper): ) try: - response = await self._get_client(provider_model_id).chat.completions.create(**request) + response = await self._client.chat.completions.create(**request) except APIConnectionError as e: raise ConnectionError(f"Failed to connect to NVIDIA NIM at {self._config.url}: {e}") from e @@ -339,7 +330,7 @@ class NVIDIAInferenceAdapter(Inference, ModelRegistryHelper): ) try: - return await self._get_client(provider_model_id).completions.create(**params) + return await self._client.completions.create(**params) except APIConnectionError as e: raise ConnectionError(f"Failed to connect to NVIDIA NIM at {self._config.url}: {e}") from e @@ -398,47 +389,6 @@ class NVIDIAInferenceAdapter(Inference, ModelRegistryHelper): ) try: - return await self._get_client(provider_model_id).chat.completions.create(**params) + return await self._client.chat.completions.create(**params) except APIConnectionError as e: raise ConnectionError(f"Failed to connect to NVIDIA NIM at {self._config.url}: {e}") from e - - async def register_model(self, model: Model) -> Model: - """ - Allow non-llama model registration. - - Non-llama model registration: API Catalogue models, post-training models, etc. - client = LlamaStackAsLibraryClient("nvidia") - client.models.register( - model_id="mistralai/mixtral-8x7b-instruct-v0.1", - model_type=ModelType.llm, - provider_id="nvidia", - provider_model_id="mistralai/mixtral-8x7b-instruct-v0.1" - ) - - NOTE: Only supports models endpoints compatible with AsyncOpenAI base_url format. - """ - if model.model_type == ModelType.embedding: - # embedding models are always registered by their provider model id and does not need to be mapped to a llama model - provider_resource_id = model.provider_resource_id - else: - provider_resource_id = self.get_provider_model_id(model.provider_resource_id) - - if provider_resource_id: - model.provider_resource_id = provider_resource_id - else: - llama_model = model.metadata.get("llama_model") - existing_llama_model = self.get_llama_model(model.provider_resource_id) - if existing_llama_model: - if existing_llama_model != llama_model: - raise ValueError( - f"Provider model id '{model.provider_resource_id}' is already registered to a different llama model: '{existing_llama_model}'" - ) - else: - # not llama model - if llama_model in ALL_HUGGINGFACE_REPOS_TO_MODEL_DESCRIPTOR: - self.provider_id_to_llama_model_map[model.provider_resource_id] = ( - ALL_HUGGINGFACE_REPOS_TO_MODEL_DESCRIPTOR[llama_model] - ) - else: - self.alias_to_provider_id_map[model.provider_model_id] = model.provider_model_id - return model diff --git a/llama_stack/providers/remote/inference/ollama/models.py b/llama_stack/providers/remote/inference/ollama/models.py index cacf88861..7c0a19a1a 100644 --- a/llama_stack/providers/remote/inference/ollama/models.py +++ b/llama_stack/providers/remote/inference/ollama/models.py @@ -12,6 +12,19 @@ from llama_stack.providers.utils.inference.model_registry import ( 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", @@ -73,18 +86,8 @@ MODEL_ENTRIES = [ "llama3.3:70b", CoreModelId.llama3_3_70b_instruct.value, ), - # 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, - ), ProviderModelEntry( - provider_model_id="all-minilm:latest", + provider_model_id="all-minilm:l6-v2", aliases=["all-minilm"], model_type=ModelType.embedding, metadata={ @@ -100,4 +103,4 @@ MODEL_ENTRIES = [ "context_length": 8192, }, ), -] +] + SAFETY_MODELS_ENTRIES diff --git a/llama_stack/providers/remote/inference/openai/models.py b/llama_stack/providers/remote/inference/openai/models.py index 14a6955d5..37bee57de 100644 --- a/llama_stack/providers/remote/inference/openai/models.py +++ b/llama_stack/providers/remote/inference/openai/models.py @@ -48,16 +48,20 @@ 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() -] +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 72428422f..7e167f621 100644 --- a/llama_stack/providers/remote/inference/openai/openai.py +++ b/llama_stack/providers/remote/inference/openai/openai.py @@ -8,7 +8,7 @@ import logging from collections.abc import AsyncIterator from typing import Any -from openai import AsyncOpenAI +from openai import AsyncOpenAI, NotFoundError from llama_stack.apis.inference import ( OpenAIChatCompletion, @@ -59,9 +59,27 @@ class OpenAIInferenceAdapter(LiteLLMOpenAIMixin): # if we do not set this, users will be exposed to the # litellm specific model names, an abstraction leak. self.is_openai_compat = True - self._openai_client = AsyncOpenAI( - api_key=self.config.api_key, - ) + + async def check_model_availability(self, model: str) -> bool: + """ + Check if a specific model is available from OpenAI. + + :param model: The model identifier to check. + :return: True if the model is available dynamically, False otherwise. + """ + try: + openai_client = self._get_openai_client() + retrieved_model = await openai_client.models.retrieve(model) + logger.info(f"Model {retrieved_model.id} is available from OpenAI") + return True + + except NotFoundError: + logger.error(f"Model {model} is not available from OpenAI") + return False + + except Exception as e: + logger.error(f"Failed to check model availability from OpenAI: {e}") + return False async def initialize(self) -> None: await super().initialize() @@ -69,6 +87,11 @@ class OpenAIInferenceAdapter(LiteLLMOpenAIMixin): async def shutdown(self) -> None: await super().shutdown() + def _get_openai_client(self) -> AsyncOpenAI: + return AsyncOpenAI( + api_key=self.get_api_key(), + ) + async def openai_completion( self, model: str, @@ -120,7 +143,7 @@ class OpenAIInferenceAdapter(LiteLLMOpenAIMixin): user=user, suffix=suffix, ) - return await self._openai_client.completions.create(**params) + return await self._get_openai_client().completions.create(**params) async def openai_chat_completion( self, @@ -176,7 +199,7 @@ class OpenAIInferenceAdapter(LiteLLMOpenAIMixin): top_p=top_p, user=user, ) - return await self._openai_client.chat.completions.create(**params) + return await self._get_openai_client().chat.completions.create(**params) async def openai_embeddings( self, @@ -204,7 +227,7 @@ class OpenAIInferenceAdapter(LiteLLMOpenAIMixin): params["user"] = user # Call OpenAI embeddings API - response = await self._openai_client.embeddings.create(**params) + response = await self._get_openai_client().embeddings.create(**params) data = [] for i, embedding_data in enumerate(response.data): diff --git a/llama_stack/providers/remote/inference/runpod/runpod.py b/llama_stack/providers/remote/inference/runpod/runpod.py index 1863b8a50..ff2fe6401 100644 --- a/llama_stack/providers/remote/inference/runpod/runpod.py +++ b/llama_stack/providers/remote/inference/runpod/runpod.py @@ -11,7 +11,7 @@ from llama_stack.apis.inference import * # noqa: F403 from llama_stack.apis.inference import OpenAIEmbeddingsResponse # from llama_stack.providers.datatypes import ModelsProtocolPrivate -from llama_stack.providers.utils.inference.model_registry import ModelRegistryHelper +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, @@ -25,6 +25,8 @@ from llama_stack.providers.utils.inference.prompt_adapter import ( from .config import RunpodImplConfig +# https://docs.runpod.io/serverless/vllm/overview#compatible-models +# https://github.com/runpod-workers/worker-vllm/blob/main/README.md#compatible-model-architectures RUNPOD_SUPPORTED_MODELS = { "Llama3.1-8B": "meta-llama/Llama-3.1-8B", "Llama3.1-70B": "meta-llama/Llama-3.1-70B", @@ -40,6 +42,14 @@ RUNPOD_SUPPORTED_MODELS = { "Llama3.2-3B": "meta-llama/Llama-3.2-3B", } +SAFETY_MODELS_ENTRIES = [] + +# Create MODEL_ENTRIES from RUNPOD_SUPPORTED_MODELS for compatibility with starter template +MODEL_ENTRIES = [ + build_hf_repo_model_entry(provider_model_id, model_descriptor) + for provider_model_id, model_descriptor in RUNPOD_SUPPORTED_MODELS.items() +] + SAFETY_MODELS_ENTRIES + class RunpodInferenceAdapter( ModelRegistryHelper, @@ -61,25 +71,25 @@ class RunpodInferenceAdapter( self, model: str, content: InterleavedContent, - sampling_params: Optional[SamplingParams] = None, - response_format: Optional[ResponseFormat] = None, - stream: Optional[bool] = False, - logprobs: Optional[LogProbConfig] = None, + sampling_params: SamplingParams | None = None, + response_format: ResponseFormat | None = None, + stream: bool | None = False, + logprobs: LogProbConfig | None = None, ) -> AsyncGenerator: raise NotImplementedError() async def chat_completion( self, model: str, - messages: List[Message], - sampling_params: Optional[SamplingParams] = None, - response_format: Optional[ResponseFormat] = None, - tools: Optional[List[ToolDefinition]] = None, - tool_choice: Optional[ToolChoice] = ToolChoice.auto, - tool_prompt_format: Optional[ToolPromptFormat] = None, - stream: Optional[bool] = False, - logprobs: Optional[LogProbConfig] = None, - tool_config: Optional[ToolConfig] = None, + 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, + stream: bool | None = False, + logprobs: LogProbConfig | None = None, + tool_config: ToolConfig | None = None, ) -> AsyncGenerator: if sampling_params is None: sampling_params = SamplingParams() @@ -129,10 +139,10 @@ class RunpodInferenceAdapter( async def embeddings( self, model: str, - contents: List[str] | List[InterleavedContentItem], - text_truncation: Optional[TextTruncation] = TextTruncation.none, - output_dimension: Optional[int] = None, - task_type: Optional[EmbeddingTaskType] = None, + contents: list[str] | list[InterleavedContentItem], + text_truncation: TextTruncation | None = TextTruncation.none, + output_dimension: int | None = None, + task_type: EmbeddingTaskType | None = None, ) -> EmbeddingsResponse: raise NotImplementedError() diff --git a/llama_stack/providers/remote/inference/sambanova/models.py b/llama_stack/providers/remote/inference/sambanova/models.py index 9954fa7a0..0b8c2e042 100644 --- a/llama_stack/providers/remote/inference/sambanova/models.py +++ b/llama_stack/providers/remote/inference/sambanova/models.py @@ -9,6 +9,14 @@ from llama_stack.providers.utils.inference.model_registry import ( build_hf_repo_model_entry, ) +SAFETY_MODELS_ENTRIES = [ + build_hf_repo_model_entry( + "sambanova/Meta-Llama-Guard-3-8B", + CoreModelId.llama_guard_3_8b.value, + ), +] + + MODEL_ENTRIES = [ build_hf_repo_model_entry( "sambanova/Meta-Llama-3.1-8B-Instruct", @@ -46,8 +54,4 @@ MODEL_ENTRIES = [ "sambanova/Llama-4-Maverick-17B-128E-Instruct", CoreModelId.llama4_maverick_17b_128e_instruct.value, ), - build_hf_repo_model_entry( - "sambanova/Meta-Llama-Guard-3-8B", - CoreModelId.llama_guard_3_8b.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 20f863665..9c2dda889 100644 --- a/llama_stack/providers/remote/inference/sambanova/sambanova.py +++ b/llama_stack/providers/remote/inference/sambanova/sambanova.py @@ -7,6 +7,7 @@ import json from collections.abc import Iterable +import requests from openai.types.chat import ( ChatCompletionAssistantMessageParam as OpenAIChatCompletionAssistantMessage, ) @@ -56,6 +57,7 @@ from llama_stack.apis.inference import ( ToolResponseMessage, UserMessage, ) +from llama_stack.apis.models import Model from llama_stack.log import get_logger from llama_stack.models.llama.datatypes import BuiltinTool from llama_stack.providers.utils.inference.litellm_openai_mixin import LiteLLMOpenAIMixin @@ -176,10 +178,11 @@ class SambaNovaInferenceAdapter(LiteLLMOpenAIMixin): def __init__(self, config: SambaNovaImplConfig): self.config = config + self.environment_available_models = [] LiteLLMOpenAIMixin.__init__( self, model_entries=MODEL_ENTRIES, - api_key_from_config=self.config.api_key, + 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", ) @@ -246,6 +249,22 @@ class SambaNovaInferenceAdapter(LiteLLMOpenAIMixin): **get_sampling_options(request.sampling_params), } + async def register_model(self, model: Model) -> Model: + model_id = self.get_provider_model_id(model.provider_resource_id) + + list_models_url = self.config.url + "/models" + if len(self.environment_available_models) == 0: + try: + response = requests.get(list_models_url) + response.raise_for_status() + except requests.exceptions.RequestException as e: + raise RuntimeError(f"Request to {list_models_url} failed") from e + self.environment_available_models = [model.get("id") for model in response.json().get("data", {})] + + if model_id.split("sambanova/")[-1] not in self.environment_available_models: + logger.warning(f"Model {model_id} not available in {list_models_url}") + return model + async def initialize(self): await super().initialize() diff --git a/llama_stack/providers/remote/inference/together/models.py b/llama_stack/providers/remote/inference/together/models.py index 85e1b1848..3d19f8dec 100644 --- a/llama_stack/providers/remote/inference/together/models.py +++ b/llama_stack/providers/remote/inference/together/models.py @@ -11,6 +11,16 @@ from llama_stack.providers.utils.inference.model_registry import ( 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", @@ -40,14 +50,6 @@ MODEL_ENTRIES = [ "meta-llama/Llama-3.3-70B-Instruct-Turbo", CoreModelId.llama3_3_70b_instruct.value, ), - build_hf_repo_model_entry( - "meta-llama/Meta-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, - ), ProviderModelEntry( provider_model_id="togethercomputer/m2-bert-80M-8k-retrieval", model_type=ModelType.embedding, @@ -78,4 +80,4 @@ MODEL_ENTRIES = [ "together/meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8", ], ), -] +] + SAFETY_MODELS_ENTRIES diff --git a/llama_stack/providers/remote/inference/together/together.py b/llama_stack/providers/remote/inference/together/together.py index 9e6877b7c..e1eb934c5 100644 --- a/llama_stack/providers/remote/inference/together/together.py +++ b/llama_stack/providers/remote/inference/together/together.py @@ -68,19 +68,12 @@ class TogetherInferenceAdapter(ModelRegistryHelper, Inference, NeedsRequestProvi def __init__(self, config: TogetherImplConfig) -> None: ModelRegistryHelper.__init__(self, MODEL_ENTRIES) self.config = config - self._client = None - self._openai_client = None async def initialize(self) -> None: pass async def shutdown(self) -> None: - if self._client: - # Together client has no close method, so just set to None - self._client = None - if self._openai_client: - await self._openai_client.close() - self._openai_client = None + pass async def completion( self, @@ -108,29 +101,25 @@ class TogetherInferenceAdapter(ModelRegistryHelper, Inference, NeedsRequestProvi return await self._nonstream_completion(request) def _get_client(self) -> AsyncTogether: - if not self._client: - together_api_key = None - config_api_key = self.config.api_key.get_secret_value() if self.config.api_key else None - if config_api_key: - together_api_key = config_api_key - else: - provider_data = self.get_request_provider_data() - if provider_data is None or not provider_data.together_api_key: - raise ValueError( - 'Pass Together API Key in the header X-LlamaStack-Provider-Data as { "together_api_key": }' - ) - together_api_key = provider_data.together_api_key - self._client = AsyncTogether(api_key=together_api_key) - return self._client + together_api_key = None + config_api_key = self.config.api_key.get_secret_value() if self.config.api_key else None + if config_api_key: + together_api_key = config_api_key + else: + provider_data = self.get_request_provider_data() + if provider_data is None or not provider_data.together_api_key: + raise ValueError( + 'Pass Together API Key in the header X-LlamaStack-Provider-Data as { "together_api_key": }' + ) + together_api_key = provider_data.together_api_key + return AsyncTogether(api_key=together_api_key) def _get_openai_client(self) -> AsyncOpenAI: - if not self._openai_client: - together_client = self._get_client().client - self._openai_client = AsyncOpenAI( - base_url=together_client.base_url, - api_key=together_client.api_key, - ) - return self._openai_client + together_client = self._get_client().client + return AsyncOpenAI( + base_url=together_client.base_url, + api_key=together_client.api_key, + ) async def _nonstream_completion(self, request: CompletionRequest) -> ChatCompletionResponse: params = await self._get_params(request) diff --git a/llama_stack/providers/remote/safety/sambanova/sambanova.py b/llama_stack/providers/remote/safety/sambanova/sambanova.py index 84c8267ae..1a65f6aa1 100644 --- a/llama_stack/providers/remote/safety/sambanova/sambanova.py +++ b/llama_stack/providers/remote/safety/sambanova/sambanova.py @@ -33,6 +33,7 @@ CANNED_RESPONSE_TEXT = "I can't answer that. Can I help with something else?" class SambaNovaSafetyAdapter(Safety, ShieldsProtocolPrivate, NeedsRequestProviderData): def __init__(self, config: SambaNovaSafetyConfig) -> None: self.config = config + self.environment_available_models = [] async def initialize(self) -> None: pass @@ -54,18 +55,18 @@ class SambaNovaSafetyAdapter(Safety, ShieldsProtocolPrivate, NeedsRequestProvide async def register_shield(self, shield: Shield) -> None: list_models_url = self.config.url + "/models" - try: - response = requests.get(list_models_url) - response.raise_for_status() - except requests.exceptions.RequestException as e: - raise RuntimeError(f"Request to {list_models_url} failed") from e - available_models = [model.get("id") for model in response.json().get("data", {})] + if len(self.environment_available_models) == 0: + try: + response = requests.get(list_models_url) + response.raise_for_status() + except requests.exceptions.RequestException as e: + raise RuntimeError(f"Request to {list_models_url} failed") from e + self.environment_available_models = [model.get("id") for model in response.json().get("data", {})] if ( - len(available_models) == 0 - or "guard" not in shield.provider_resource_id.lower() - or shield.provider_resource_id.split("sambanova/")[-1] not in available_models + "guard" not in shield.provider_resource_id.lower() + or shield.provider_resource_id.split("sambanova/")[-1] not in self.environment_available_models ): - raise ValueError(f"Shield {shield.provider_resource_id} not found in SambaNova") + logger.warning(f"Shield {shield.provider_resource_id} not available in {list_models_url}") async def run_shield( self, shield_id: str, messages: list[Message], params: dict[str, Any] | None = None diff --git a/llama_stack/providers/remote/vector_io/chroma/chroma.py b/llama_stack/providers/remote/vector_io/chroma/chroma.py index 3bef39e9c..ffe2cba44 100644 --- a/llama_stack/providers/remote/vector_io/chroma/chroma.py +++ b/llama_stack/providers/remote/vector_io/chroma/chroma.py @@ -217,7 +217,6 @@ class ChromaVectorIOAdapter(VectorIO, VectorDBsProtocolPrivate): embedding_model: str | None = None, embedding_dimension: int | None = 384, provider_id: str | None = None, - provider_vector_db_id: str | None = None, ) -> VectorStoreObject: raise NotImplementedError("OpenAI Vector Stores API is not supported in Chroma") diff --git a/llama_stack/providers/remote/vector_io/milvus/__init__.py b/llama_stack/providers/remote/vector_io/milvus/__init__.py index 92dbfda2e..94761de0c 100644 --- a/llama_stack/providers/remote/vector_io/milvus/__init__.py +++ b/llama_stack/providers/remote/vector_io/milvus/__init__.py @@ -14,6 +14,6 @@ async def get_adapter_impl(config: MilvusVectorIOConfig, deps: dict[Api, Provide assert isinstance(config, MilvusVectorIOConfig), f"Unexpected config type: {type(config)}" - impl = MilvusVectorIOAdapter(config, deps[Api.inference]) + impl = MilvusVectorIOAdapter(config, deps[Api.inference], deps.get(Api.files, None)) await impl.initialize() return impl diff --git a/llama_stack/providers/remote/vector_io/milvus/config.py b/llama_stack/providers/remote/vector_io/milvus/config.py index b42233d6d..899d3678d 100644 --- a/llama_stack/providers/remote/vector_io/milvus/config.py +++ b/llama_stack/providers/remote/vector_io/milvus/config.py @@ -8,6 +8,7 @@ from typing import Any from pydantic import BaseModel, ConfigDict, Field +from llama_stack.providers.utils.kvstore.config import KVStoreConfig, SqliteKVStoreConfig from llama_stack.schema_utils import json_schema_type @@ -16,6 +17,7 @@ class MilvusVectorIOConfig(BaseModel): uri: str = Field(description="The URI of the Milvus server") token: str | None = Field(description="The token of the Milvus server") consistency_level: str = Field(description="The consistency level of the Milvus server", default="Strong") + kvstore: KVStoreConfig = Field(description="Config for KV store backend") # 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. @@ -23,4 +25,11 @@ class MilvusVectorIOConfig(BaseModel): @classmethod def sample_run_config(cls, __distro_dir__: str, **kwargs: Any) -> dict[str, Any]: - return {"uri": "${env.MILVUS_ENDPOINT}", "token": "${env.MILVUS_TOKEN}"} + return { + "uri": "${env.MILVUS_ENDPOINT}", + "token": "${env.MILVUS_TOKEN}", + "kvstore": SqliteKVStoreConfig.sample_run_config( + __distro_dir__=__distro_dir__, + db_name="milvus_remote_registry.db", + ), + } diff --git a/llama_stack/providers/remote/vector_io/milvus/milvus.py b/llama_stack/providers/remote/vector_io/milvus/milvus.py index 25fe237c0..f301942cb 100644 --- a/llama_stack/providers/remote/vector_io/milvus/milvus.py +++ b/llama_stack/providers/remote/vector_io/milvus/milvus.py @@ -12,7 +12,7 @@ import re from typing import Any from numpy.typing import NDArray -from pymilvus import DataType, MilvusClient +from pymilvus import DataType, Function, FunctionType, MilvusClient from llama_stack.apis.files.files import Files from llama_stack.apis.inference import Inference, InterleavedContent @@ -61,6 +61,11 @@ class MilvusIndex(EmbeddingIndex): self.consistency_level = consistency_level self.kvstore = kvstore + async def initialize(self): + # MilvusIndex does not require explicit initialization + # TODO: could move collection creation into initialization but it is not really necessary + pass + async def delete(self): if await asyncio.to_thread(self.client.has_collection, self.collection_name): await asyncio.to_thread(self.client.drop_collection, collection_name=self.collection_name) @@ -69,12 +74,66 @@ class MilvusIndex(EmbeddingIndex): assert len(chunks) == len(embeddings), ( f"Chunk length {len(chunks)} does not match embedding length {len(embeddings)}" ) + if not await asyncio.to_thread(self.client.has_collection, self.collection_name): + logger.info(f"Creating new collection {self.collection_name} with nullable sparse field") + # Create schema for vector search + schema = self.client.create_schema() + schema.add_field( + field_name="chunk_id", + datatype=DataType.VARCHAR, + is_primary=True, + max_length=100, + ) + schema.add_field( + field_name="content", + datatype=DataType.VARCHAR, + max_length=65535, + enable_analyzer=True, # Enable text analysis for BM25 + ) + schema.add_field( + field_name="vector", + datatype=DataType.FLOAT_VECTOR, + dim=len(embeddings[0]), + ) + schema.add_field( + field_name="chunk_content", + datatype=DataType.JSON, + ) + # Add sparse vector field for BM25 (required by the function) + schema.add_field( + field_name="sparse", + datatype=DataType.SPARSE_FLOAT_VECTOR, + ) + + # Create indexes + index_params = self.client.prepare_index_params() + index_params.add_index( + field_name="vector", + index_type="FLAT", + metric_type="COSINE", + ) + # Add index for sparse field (required by BM25 function) + index_params.add_index( + field_name="sparse", + index_type="SPARSE_INVERTED_INDEX", + metric_type="BM25", + ) + + # Add BM25 function for full-text search + bm25_function = Function( + name="text_bm25_emb", + input_field_names=["content"], + output_field_names=["sparse"], + function_type=FunctionType.BM25, + ) + schema.add_function(bm25_function) + await asyncio.to_thread( self.client.create_collection, self.collection_name, - dimension=len(embeddings[0]), - auto_id=True, + schema=schema, + index_params=index_params, consistency_level=self.consistency_level, ) @@ -83,8 +142,10 @@ class MilvusIndex(EmbeddingIndex): data.append( { "chunk_id": chunk.chunk_id, + "content": chunk.content, "vector": embedding, "chunk_content": chunk.model_dump(), + # sparse field will be handled by BM25 function automatically } ) try: @@ -102,6 +163,7 @@ class MilvusIndex(EmbeddingIndex): self.client.search, collection_name=self.collection_name, data=[embedding], + anns_field="vector", limit=k, output_fields=["*"], search_params={"params": {"radius": score_threshold}}, @@ -116,7 +178,64 @@ class MilvusIndex(EmbeddingIndex): k: int, score_threshold: float, ) -> QueryChunksResponse: - raise NotImplementedError("Keyword search is not supported in Milvus") + """ + Perform BM25-based keyword search using Milvus's built-in full-text search. + """ + try: + # Use Milvus's built-in BM25 search + search_res = await asyncio.to_thread( + self.client.search, + collection_name=self.collection_name, + data=[query_string], # Raw text query + anns_field="sparse", # Use sparse field for BM25 + output_fields=["chunk_content"], # Output the chunk content + limit=k, + search_params={ + "params": { + "drop_ratio_search": 0.2, # Ignore low-importance terms + } + }, + ) + + chunks = [] + scores = [] + for res in search_res[0]: + chunk = Chunk(**res["entity"]["chunk_content"]) + chunks.append(chunk) + scores.append(res["distance"]) # BM25 score from Milvus + + # Filter by score threshold + filtered_chunks = [chunk for chunk, score in zip(chunks, scores, strict=False) if score >= score_threshold] + filtered_scores = [score for score in scores if score >= score_threshold] + + return QueryChunksResponse(chunks=filtered_chunks, scores=filtered_scores) + + except Exception as e: + logger.error(f"Error performing BM25 search: {e}") + # Fallback to simple text search + return await self._fallback_keyword_search(query_string, k, score_threshold) + + async def _fallback_keyword_search( + self, + query_string: str, + k: int, + score_threshold: float, + ) -> QueryChunksResponse: + """ + Fallback to simple text search when BM25 search is not available. + """ + # Simple text search using content field + search_res = await asyncio.to_thread( + self.client.query, + collection_name=self.collection_name, + filter='content like "%{content}%"', + filter_params={"content": query_string}, + output_fields=["*"], + limit=k, + ) + chunks = [Chunk(**res["chunk_content"]) for res in search_res] + scores = [1.0] * len(chunks) # Simple binary score for text search + return QueryChunksResponse(chunks=chunks, scores=scores) async def query_hybrid( self, @@ -154,10 +273,10 @@ class MilvusVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorDBsProtocolP stored_vector_dbs = await self.kvstore.values_in_range(start_key, end_key) for vector_db_data in stored_vector_dbs: - vector_db = VectorDB.mdel_validate_json(vector_db_data) + vector_db = VectorDB.model_validate_json(vector_db_data) index = VectorDBWithIndex( vector_db, - index=await MilvusIndex( + index=MilvusIndex( client=self.client, collection_name=vector_db.identifier, consistency_level=self.config.consistency_level, @@ -174,7 +293,8 @@ class MilvusVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorDBsProtocolP uri = os.path.expanduser(self.config.db_path) self.client = MilvusClient(uri=uri) - self.openai_vector_stores = await self._load_openai_vector_stores() + # Load existing OpenAI vector stores into the in-memory cache + await self.initialize_openai_vector_stores() async def shutdown(self) -> None: self.client.close() @@ -199,6 +319,9 @@ class MilvusVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorDBsProtocolP if vector_db_id in self.cache: return self.cache[vector_db_id] + if self.vector_db_store is None: + raise ValueError(f"Vector DB {vector_db_id} not found") + vector_db = await self.vector_db_store.get_vector_db(vector_db_id) if not vector_db: raise ValueError(f"Vector DB {vector_db_id} not found") @@ -238,36 +361,16 @@ class MilvusVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorDBsProtocolP if not index: raise ValueError(f"Vector DB {vector_db_id} not found") + if params and params.get("mode") == "keyword": + # Check if this is inline Milvus (Milvus-Lite) + if hasattr(self.config, "db_path"): + raise NotImplementedError( + "Keyword search is not supported in Milvus-Lite. " + "Please use a remote Milvus server for keyword search functionality." + ) + return await index.query_chunks(query, params) - async def _save_openai_vector_store(self, store_id: str, store_info: dict[str, Any]) -> None: - """Save vector store metadata to persistent storage.""" - assert self.kvstore is not None - key = f"{OPENAI_VECTOR_STORES_PREFIX}{store_id}" - await self.kvstore.set(key=key, value=json.dumps(store_info)) - self.openai_vector_stores[store_id] = store_info - - async def _update_openai_vector_store(self, store_id: str, store_info: dict[str, Any]) -> None: - """Update vector store metadata in persistent storage.""" - assert self.kvstore is not None - key = f"{OPENAI_VECTOR_STORES_PREFIX}{store_id}" - await self.kvstore.set(key=key, value=json.dumps(store_info)) - self.openai_vector_stores[store_id] = store_info - - async def _delete_openai_vector_store_from_storage(self, store_id: str) -> None: - """Delete vector store metadata from persistent storage.""" - assert self.kvstore is not None - key = f"{OPENAI_VECTOR_STORES_PREFIX}{store_id}" - await self.kvstore.delete(key) - - async def _load_openai_vector_stores(self) -> dict[str, dict[str, Any]]: - """Load all vector store metadata from persistent storage.""" - assert self.kvstore is not None - start_key = OPENAI_VECTOR_STORES_PREFIX - end_key = f"{OPENAI_VECTOR_STORES_PREFIX}\xff" - stored = await self.kvstore.values_in_range(start_key, end_key) - return {json.loads(s)["id"]: json.loads(s) for s in stored} - async def _save_openai_vector_store_file( self, store_id: str, file_id: str, file_info: dict[str, Any], file_contents: list[dict[str, Any]] ) -> None: @@ -377,6 +480,29 @@ class MilvusVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorDBsProtocolP logger.error(f"Error loading openai vector store file {file_id} for store {store_id}: {e}") return {} + async def _update_openai_vector_store_file(self, store_id: str, file_id: str, file_info: dict[str, Any]) -> None: + """Update vector store file metadata in Milvus database.""" + try: + if not await asyncio.to_thread(self.client.has_collection, "openai_vector_store_files"): + return + + file_data = [ + { + "store_file_id": f"{store_id}_{file_id}", + "store_id": store_id, + "file_id": file_id, + "file_info": json.dumps(file_info), + } + ] + await asyncio.to_thread( + self.client.upsert, + collection_name="openai_vector_store_files", + data=file_data, + ) + except Exception as e: + logger.error(f"Error updating openai vector store file {file_id} for store {store_id}: {e}") + raise + async def _load_openai_vector_store_file_contents(self, store_id: str, file_id: str) -> list[dict[str, Any]]: """Load vector store file contents from Milvus database.""" try: @@ -405,29 +531,6 @@ class MilvusVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorDBsProtocolP logger.error(f"Error loading openai vector store file contents for {file_id} in store {store_id}: {e}") return [] - async def _update_openai_vector_store_file(self, store_id: str, file_id: str, file_info: dict[str, Any]) -> None: - """Update vector store file metadata in Milvus database.""" - try: - if not await asyncio.to_thread(self.client.has_collection, "openai_vector_store_files"): - return - - file_data = [ - { - "store_file_id": f"{store_id}_{file_id}", - "store_id": store_id, - "file_id": file_id, - "file_info": json.dumps(file_info), - } - ] - await asyncio.to_thread( - self.client.upsert, - collection_name="openai_vector_store_files", - data=file_data, - ) - except Exception as e: - logger.error(f"Error updating openai vector store file {file_id} for store {store_id}: {e}") - raise - async def _delete_openai_vector_store_file_from_storage(self, store_id: str, file_id: str) -> None: """Delete vector store file metadata from Milvus database.""" try: diff --git a/llama_stack/providers/remote/vector_io/pgvector/config.py b/llama_stack/providers/remote/vector_io/pgvector/config.py index 92908aa8a..334cbe5be 100644 --- a/llama_stack/providers/remote/vector_io/pgvector/config.py +++ b/llama_stack/providers/remote/vector_io/pgvector/config.py @@ -8,6 +8,10 @@ from typing import Any from pydantic import BaseModel, Field +from llama_stack.providers.utils.kvstore.config import ( + KVStoreConfig, + SqliteKVStoreConfig, +) from llama_stack.schema_utils import json_schema_type @@ -18,10 +22,12 @@ class PGVectorVectorIOConfig(BaseModel): db: str | None = Field(default="postgres") user: str | None = Field(default="postgres") password: str | None = Field(default="mysecretpassword") + kvstore: KVStoreConfig | None = Field(description="Config for KV store backend (SQLite only for now)", default=None) @classmethod def sample_run_config( cls, + __distro_dir__: str, host: str = "${env.PGVECTOR_HOST:=localhost}", port: int = "${env.PGVECTOR_PORT:=5432}", db: str = "${env.PGVECTOR_DB}", @@ -29,4 +35,14 @@ class PGVectorVectorIOConfig(BaseModel): password: str = "${env.PGVECTOR_PASSWORD}", **kwargs: Any, ) -> dict[str, Any]: - return {"host": host, "port": port, "db": db, "user": user, "password": password} + return { + "host": host, + "port": port, + "db": db, + "user": user, + "password": password, + "kvstore": SqliteKVStoreConfig.sample_run_config( + __distro_dir__=__distro_dir__, + db_name="pgvector_registry.db", + ), + } diff --git a/llama_stack/providers/remote/vector_io/pgvector/pgvector.py b/llama_stack/providers/remote/vector_io/pgvector/pgvector.py index c3cdef9b8..7fdd8af9b 100644 --- a/llama_stack/providers/remote/vector_io/pgvector/pgvector.py +++ b/llama_stack/providers/remote/vector_io/pgvector/pgvector.py @@ -13,24 +13,18 @@ from psycopg2 import sql from psycopg2.extras import Json, execute_values from pydantic import BaseModel, TypeAdapter +from llama_stack.apis.files.files import Files from llama_stack.apis.inference import InterleavedContent from llama_stack.apis.vector_dbs import VectorDB from llama_stack.apis.vector_io import ( Chunk, QueryChunksResponse, - SearchRankingOptions, VectorIO, - VectorStoreChunkingStrategy, - VectorStoreDeleteResponse, - VectorStoreFileContentsResponse, - VectorStoreFileObject, - VectorStoreFileStatus, - VectorStoreListFilesResponse, - VectorStoreListResponse, - VectorStoreObject, - VectorStoreSearchResponsePage, ) from llama_stack.providers.datatypes import Api, VectorDBsProtocolPrivate +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 from llama_stack.providers.utils.memory.vector_store import ( EmbeddingIndex, VectorDBWithIndex, @@ -40,6 +34,13 @@ from .config import PGVectorVectorIOConfig log = logging.getLogger(__name__) +VERSION = "v3" +VECTOR_DBS_PREFIX = f"vector_dbs:pgvector:{VERSION}::" +VECTOR_INDEX_PREFIX = f"vector_index:pgvector:{VERSION}::" +OPENAI_VECTOR_STORES_PREFIX = f"openai_vector_stores:pgvector:{VERSION}::" +OPENAI_VECTOR_STORES_FILES_PREFIX = f"openai_vector_stores_files:pgvector:{VERSION}::" +OPENAI_VECTOR_STORES_FILES_CONTENTS_PREFIX = f"openai_vector_stores_files_contents:pgvector:{VERSION}::" + def check_extension_version(cur): cur.execute("SELECT extversion FROM pg_extension WHERE extname = 'vector'") @@ -69,7 +70,7 @@ def load_models(cur, cls): class PGVectorIndex(EmbeddingIndex): - def __init__(self, vector_db: VectorDB, dimension: int, conn): + 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 @@ -77,6 +78,7 @@ class PGVectorIndex(EmbeddingIndex): # 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 cur.execute( f""" @@ -158,15 +160,28 @@ class PGVectorIndex(EmbeddingIndex): cur.execute(f"DROP TABLE IF EXISTS {self.table_name}") -class PGVectorVectorIOAdapter(VectorIO, VectorDBsProtocolPrivate): - def __init__(self, config: PGVectorVectorIOConfig, inference_api: Api.inference) -> None: +class PGVectorVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorDBsProtocolPrivate): + def __init__( + self, + config: PGVectorVectorIOConfig, + inference_api: Api.inference, + files_api: Files | None = None, + ) -> None: self.config = config self.inference_api = inference_api self.conn = None self.cache = {} + 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" async def initialize(self) -> None: log.info(f"Initializing PGVector memory adapter with config: {self.config}") + self.kvstore = await kvstore_impl(self.config.kvstore) + await self.initialize_openai_vector_stores() + try: self.conn = psycopg2.connect( host=self.config.host, @@ -201,14 +216,28 @@ class PGVectorVectorIOAdapter(VectorIO, VectorDBsProtocolPrivate): log.info("Connection to PGVector database server closed") async def register_vector_db(self, vector_db: VectorDB) -> None: + # Persist vector DB metadata in the KV store + assert self.kvstore is not None + # Upsert model metadata in Postgres upsert_models(self.conn, [(vector_db.identifier, vector_db)]) - index = PGVectorIndex(vector_db, vector_db.embedding_dimension, self.conn) - self.cache[vector_db.identifier] = VectorDBWithIndex(vector_db, index, self.inference_api) + # Create and cache the PGVector index table for the vector DB + index = VectorDBWithIndex( + vector_db, + index=PGVectorIndex(vector_db, vector_db.embedding_dimension, self.conn, kvstore=self.kvstore), + inference_api=self.inference_api, + ) + self.cache[vector_db.identifier] = index async def unregister_vector_db(self, vector_db_id: str) -> None: - await self.cache[vector_db_id].index.delete() - del self.cache[vector_db_id] + # Remove provider index and cache + if vector_db_id in self.cache: + await self.cache[vector_db_id].index.delete() + del self.cache[vector_db_id] + + # Delete vector DB metadata from KV store + assert self.kvstore is not None + await self.kvstore.delete(key=f"{VECTOR_DBS_PREFIX}{vector_db_id}") async def insert_chunks( self, @@ -237,107 +266,124 @@ class PGVectorVectorIOAdapter(VectorIO, VectorDBsProtocolPrivate): self.cache[vector_db_id] = VectorDBWithIndex(vector_db, index, self.inference_api) return self.cache[vector_db_id] - async def openai_create_vector_store( - self, - name: str, - file_ids: list[str] | None = None, - expires_after: dict[str, Any] | None = None, - chunking_strategy: dict[str, Any] | None = None, - metadata: dict[str, Any] | None = None, - embedding_model: str | None = None, - embedding_dimension: int | None = 384, - provider_id: str | None = None, - provider_vector_db_id: str | None = None, - ) -> VectorStoreObject: - raise NotImplementedError("OpenAI Vector Stores API is not supported in PGVector") + # OpenAI Vector Stores File operations are not supported in PGVector + async def _save_openai_vector_store_file( + self, store_id: str, file_id: str, file_info: dict[str, Any], file_contents: list[dict[str, Any]] + ) -> None: + """Save vector store file metadata to Postgres database.""" + if self.conn is None: + raise RuntimeError("PostgreSQL connection is not initialized") + try: + with self.conn.cursor(cursor_factory=psycopg2.extras.DictCursor) as cur: + cur.execute( + """ + CREATE TABLE IF NOT EXISTS openai_vector_store_files ( + store_id TEXT, + file_id TEXT, + metadata JSONB, + PRIMARY KEY (store_id, file_id) + ) + """ + ) + cur.execute( + """ + CREATE TABLE IF NOT EXISTS openai_vector_store_files_contents ( + store_id TEXT, + file_id TEXT, + contents JSONB, + PRIMARY KEY (store_id, file_id) + ) + """ + ) + # Insert file metadata + files_query = sql.SQL( + """ + INSERT INTO openai_vector_store_files (store_id, file_id, metadata) + VALUES %s + ON CONFLICT (store_id, file_id) DO UPDATE SET metadata = EXCLUDED.metadata + """ + ) + files_values = [(store_id, file_id, Json(file_info))] + execute_values(cur, files_query, files_values, template="(%s, %s, %s)") + # Insert file contents + contents_query = sql.SQL( + """ + INSERT INTO openai_vector_store_files_contents (store_id, file_id, contents) + VALUES %s + ON CONFLICT (store_id, file_id) DO UPDATE SET contents = EXCLUDED.contents + """ + ) + contents_values = [(store_id, file_id, Json(file_contents))] + execute_values(cur, contents_query, contents_values, template="(%s, %s, %s)") + except Exception as e: + log.error(f"Error saving openai vector store file {file_id} for store {store_id}: {e}") + raise - async def openai_list_vector_stores( - self, - limit: int | None = 20, - order: str | None = "desc", - after: str | None = None, - before: str | None = None, - ) -> VectorStoreListResponse: - raise NotImplementedError("OpenAI Vector Stores API is not supported in PGVector") + async def _load_openai_vector_store_file(self, store_id: str, file_id: str) -> dict[str, Any]: + """Load vector store file metadata from Postgres database.""" + if self.conn is None: + raise RuntimeError("PostgreSQL connection is not initialized") + try: + with self.conn.cursor(cursor_factory=psycopg2.extras.DictCursor) as cur: + cur.execute( + "SELECT metadata FROM openai_vector_store_files WHERE store_id = %s AND file_id = %s", + (store_id, file_id), + ) + row = cur.fetchone() + return row[0] if row and row[0] is not None else {} + except Exception as e: + log.error(f"Error loading openai vector store file {file_id} for store {store_id}: {e}") + return {} - async def openai_retrieve_vector_store( - self, - vector_store_id: str, - ) -> VectorStoreObject: - raise NotImplementedError("OpenAI Vector Stores API is not supported in PGVector") + async def _load_openai_vector_store_file_contents(self, store_id: str, file_id: str) -> list[dict[str, Any]]: + """Load vector store file contents from Postgres database.""" + if self.conn is None: + raise RuntimeError("PostgreSQL connection is not initialized") + try: + with self.conn.cursor(cursor_factory=psycopg2.extras.DictCursor) as cur: + cur.execute( + "SELECT contents FROM openai_vector_store_files_contents WHERE store_id = %s AND file_id = %s", + (store_id, file_id), + ) + row = cur.fetchone() + return row[0] if row and row[0] is not None else [] + except Exception as e: + log.error(f"Error loading openai vector store file contents for {file_id} in store {store_id}: {e}") + return [] - async def openai_update_vector_store( - self, - vector_store_id: str, - name: str | None = None, - expires_after: dict[str, Any] | None = None, - metadata: dict[str, Any] | None = None, - ) -> VectorStoreObject: - raise NotImplementedError("OpenAI Vector Stores API is not supported in PGVector") + async def _update_openai_vector_store_file(self, store_id: str, file_id: str, file_info: dict[str, Any]) -> None: + """Update vector store file metadata in Postgres database.""" + if self.conn is None: + raise RuntimeError("PostgreSQL connection is not initialized") + try: + with self.conn.cursor(cursor_factory=psycopg2.extras.DictCursor) as cur: + query = sql.SQL( + """ + INSERT INTO openai_vector_store_files (store_id, file_id, metadata) + VALUES %s + ON CONFLICT (store_id, file_id) DO UPDATE SET metadata = EXCLUDED.metadata + """ + ) + values = [(store_id, file_id, Json(file_info))] + execute_values(cur, query, values, template="(%s, %s, %s)") + except Exception as e: + log.error(f"Error updating openai vector store file {file_id} for store {store_id}: {e}") + raise - async def openai_delete_vector_store( - self, - vector_store_id: str, - ) -> VectorStoreDeleteResponse: - raise NotImplementedError("OpenAI Vector Stores API is not supported in PGVector") - - async def openai_search_vector_store( - self, - vector_store_id: str, - query: str | list[str], - filters: dict[str, Any] | None = None, - max_num_results: int | None = 10, - ranking_options: SearchRankingOptions | None = None, - rewrite_query: bool | None = False, - search_mode: str | None = "vector", - ) -> VectorStoreSearchResponsePage: - raise NotImplementedError("OpenAI Vector Stores API is not supported in PGVector") - - async def openai_attach_file_to_vector_store( - self, - vector_store_id: str, - file_id: str, - attributes: dict[str, Any] | None = None, - chunking_strategy: VectorStoreChunkingStrategy | None = None, - ) -> VectorStoreFileObject: - raise NotImplementedError("OpenAI Vector Stores API is not supported in PGVector") - - async def openai_list_files_in_vector_store( - self, - vector_store_id: str, - limit: int | None = 20, - order: str | None = "desc", - after: str | None = None, - before: str | None = None, - filter: VectorStoreFileStatus | None = None, - ) -> VectorStoreListFilesResponse: - raise NotImplementedError("OpenAI Vector Stores API is not supported in PGVector") - - async def openai_retrieve_vector_store_file( - self, - vector_store_id: str, - file_id: str, - ) -> VectorStoreFileObject: - raise NotImplementedError("OpenAI Vector Stores API is not supported in PGVector") - - async def openai_retrieve_vector_store_file_contents( - self, - vector_store_id: str, - file_id: str, - ) -> VectorStoreFileContentsResponse: - raise NotImplementedError("OpenAI Vector Stores API is not supported in PGVector") - - async def openai_update_vector_store_file( - self, - vector_store_id: str, - file_id: str, - attributes: dict[str, Any] | None = None, - ) -> VectorStoreFileObject: - raise NotImplementedError("OpenAI Vector Stores API is not supported in PGVector") - - async def openai_delete_vector_store_file( - self, - vector_store_id: str, - file_id: str, - ) -> VectorStoreFileObject: - raise NotImplementedError("OpenAI Vector Stores API is not supported in PGVector") + async def _delete_openai_vector_store_file_from_storage(self, store_id: str, file_id: str) -> None: + """Delete vector store file metadata from Postgres database.""" + if self.conn is None: + raise RuntimeError("PostgreSQL connection is not initialized") + try: + with self.conn.cursor(cursor_factory=psycopg2.extras.DictCursor) as cur: + cur.execute( + "DELETE FROM openai_vector_store_files WHERE store_id = %s AND file_id = %s", + (store_id, file_id), + ) + cur.execute( + "DELETE FROM openai_vector_store_files_contents WHERE store_id = %s AND file_id = %s", + (store_id, file_id), + ) + except Exception as e: + log.error(f"Error deleting openai vector store file {file_id} for store {store_id}: {e}") + raise diff --git a/llama_stack/providers/remote/vector_io/qdrant/qdrant.py b/llama_stack/providers/remote/vector_io/qdrant/qdrant.py index 09ea08fa0..5bdea0ce8 100644 --- a/llama_stack/providers/remote/vector_io/qdrant/qdrant.py +++ b/llama_stack/providers/remote/vector_io/qdrant/qdrant.py @@ -214,7 +214,6 @@ class QdrantVectorIOAdapter(VectorIO, VectorDBsProtocolPrivate): embedding_model: str | None = None, embedding_dimension: int | None = 384, provider_id: str | None = None, - provider_vector_db_id: str | None = None, ) -> VectorStoreObject: raise NotImplementedError("OpenAI Vector Stores API is not supported in Qdrant") diff --git a/llama_stack/providers/remote/vector_io/weaviate/config.py b/llama_stack/providers/remote/vector_io/weaviate/config.py index a8c6e3e2c..4283b8d3b 100644 --- a/llama_stack/providers/remote/vector_io/weaviate/config.py +++ b/llama_stack/providers/remote/vector_io/weaviate/config.py @@ -6,15 +6,26 @@ from typing import Any -from pydantic import BaseModel +from pydantic import BaseModel, Field + +from llama_stack.providers.utils.kvstore.config import ( + KVStoreConfig, + SqliteKVStoreConfig, +) class WeaviateRequestProviderData(BaseModel): weaviate_api_key: str weaviate_cluster_url: str + kvstore: KVStoreConfig | None = Field(description="Config for KV store backend (SQLite only for now)", default=None) class WeaviateVectorIOConfig(BaseModel): @classmethod - def sample_run_config(cls, **kwargs: Any) -> dict[str, Any]: - return {} + def sample_run_config(cls, __distro_dir__: str, **kwargs: Any) -> dict[str, Any]: + return { + "kvstore": SqliteKVStoreConfig.sample_run_config( + __distro_dir__=__distro_dir__, + db_name="weaviate_registry.db", + ), + } diff --git a/llama_stack/providers/remote/vector_io/weaviate/weaviate.py b/llama_stack/providers/remote/vector_io/weaviate/weaviate.py index c63dd70c6..35bb40454 100644 --- a/llama_stack/providers/remote/vector_io/weaviate/weaviate.py +++ b/llama_stack/providers/remote/vector_io/weaviate/weaviate.py @@ -14,10 +14,13 @@ from weaviate.classes.init import Auth from weaviate.classes.query import Filter from llama_stack.apis.common.content_types import InterleavedContent +from llama_stack.apis.files.files import Files from llama_stack.apis.vector_dbs import VectorDB from llama_stack.apis.vector_io import Chunk, QueryChunksResponse, VectorIO from llama_stack.distribution.request_headers import NeedsRequestProviderData from llama_stack.providers.datatypes import Api, VectorDBsProtocolPrivate +from llama_stack.providers.utils.kvstore import kvstore_impl +from llama_stack.providers.utils.kvstore.api import KVStore from llama_stack.providers.utils.memory.vector_store import ( EmbeddingIndex, VectorDBWithIndex, @@ -27,11 +30,19 @@ from .config import WeaviateRequestProviderData, WeaviateVectorIOConfig log = logging.getLogger(__name__) +VERSION = "v3" +VECTOR_DBS_PREFIX = f"vector_dbs:weaviate:{VERSION}::" +VECTOR_INDEX_PREFIX = f"vector_index:weaviate:{VERSION}::" +OPENAI_VECTOR_STORES_PREFIX = f"openai_vector_stores:weaviate:{VERSION}::" +OPENAI_VECTOR_STORES_FILES_PREFIX = f"openai_vector_stores_files:weaviate:{VERSION}::" +OPENAI_VECTOR_STORES_FILES_CONTENTS_PREFIX = f"openai_vector_stores_files_contents:weaviate:{VERSION}::" + class WeaviateIndex(EmbeddingIndex): - def __init__(self, client: weaviate.Client, collection_name: str): + def __init__(self, client: weaviate.Client, collection_name: str, kvstore: KVStore | None = None): self.client = client self.collection_name = collection_name + self.kvstore = kvstore async def add_chunks(self, chunks: list[Chunk], embeddings: NDArray): assert len(chunks) == len(embeddings), ( @@ -109,11 +120,21 @@ class WeaviateVectorIOAdapter( NeedsRequestProviderData, VectorDBsProtocolPrivate, ): - def __init__(self, config: WeaviateVectorIOConfig, inference_api: Api.inference) -> None: + def __init__( + self, + config: WeaviateVectorIOConfig, + inference_api: Api.inference, + files_api: Files | None, + ) -> None: self.config = config self.inference_api = inference_api self.client_cache = {} self.cache = {} + self.files_api = files_api + self.kvstore: KVStore | None = None + self.vector_db_store = None + self.openai_vector_stores: dict[str, dict[str, Any]] = {} + self.metadata_collection_name = "openai_vector_stores_metadata" def _get_client(self) -> weaviate.Client: provider_data = self.get_request_provider_data() @@ -132,7 +153,26 @@ class WeaviateVectorIOAdapter( return client async def initialize(self) -> None: - pass + """Set up KV store and load existing vector DBs and OpenAI vector stores.""" + # Initialize KV store for metadata + self.kvstore = await kvstore_impl(self.config.kvstore) + + # Load existing vector DB definitions + start_key = VECTOR_DBS_PREFIX + end_key = f"{VECTOR_DBS_PREFIX}\xff" + stored = await self.kvstore.values_in_range(start_key, end_key) + for raw in stored: + vector_db = VectorDB.model_validate_json(raw) + client = self._get_client() + idx = WeaviateIndex(client=client, collection_name=vector_db.identifier, kvstore=self.kvstore) + self.cache[vector_db.identifier] = VectorDBWithIndex( + vector_db=vector_db, + index=idx, + inference_api=self.inference_api, + ) + + # Load OpenAI vector stores metadata into cache + await self.initialize_openai_vector_stores() async def shutdown(self) -> None: for client in self.client_cache.values(): @@ -206,3 +246,21 @@ class WeaviateVectorIOAdapter( raise ValueError(f"Vector DB {vector_db_id} not found") return await index.query_chunks(query, params) + + # OpenAI Vector Stores File operations are not supported in Weaviate + async def _save_openai_vector_store_file( + self, store_id: str, file_id: str, file_info: dict[str, Any], file_contents: list[dict[str, Any]] + ) -> None: + raise NotImplementedError("OpenAI Vector Stores API is not supported in Weaviate") + + async def _load_openai_vector_store_file(self, store_id: str, file_id: str) -> dict[str, Any]: + raise NotImplementedError("OpenAI Vector Stores API is not supported in Weaviate") + + async def _load_openai_vector_store_file_contents(self, store_id: str, file_id: str) -> list[dict[str, Any]]: + raise NotImplementedError("OpenAI Vector Stores API is not supported in Weaviate") + + async def _update_openai_vector_store_file(self, store_id: str, file_id: str, file_info: dict[str, Any]) -> None: + raise NotImplementedError("OpenAI Vector Stores API is not supported in Weaviate") + + async def _delete_openai_vector_store_file_from_storage(self, store_id: str, file_id: str) -> None: + raise NotImplementedError("OpenAI Vector Stores API is not supported in Weaviate") diff --git a/llama_stack/providers/utils/inference/litellm_openai_mixin.py b/llama_stack/providers/utils/inference/litellm_openai_mixin.py index 188e82125..0de267f6c 100644 --- a/llama_stack/providers/utils/inference/litellm_openai_mixin.py +++ b/llama_stack/providers/utils/inference/litellm_openai_mixin.py @@ -13,7 +13,6 @@ from llama_stack.apis.common.content_types import ( InterleavedContent, InterleavedContentItem, ) -from llama_stack.apis.common.errors import UnsupportedModelError from llama_stack.apis.inference import ( ChatCompletionRequest, ChatCompletionResponse, @@ -39,7 +38,6 @@ from llama_stack.apis.inference import ( ToolDefinition, ToolPromptFormat, ) -from llama_stack.apis.models import Model from llama_stack.distribution.request_headers import NeedsRequestProviderData from llama_stack.log import get_logger from llama_stack.providers.utils.inference.model_registry import ModelRegistryHelper @@ -90,12 +88,6 @@ class LiteLLMOpenAIMixin( async def shutdown(self): pass - async def register_model(self, model: Model) -> Model: - model_id = self.get_provider_model_id(model.provider_resource_id) - if model_id is None: - raise UnsupportedModelError(model.provider_resource_id, self.alias_to_provider_id_map.keys()) - return model - def get_litellm_model_name(self, model_id: str) -> str: # users may be using openai/ prefix in their model names. the openai/models.py did this by default. # model_id.startswith("openai/") is for backwards compatibility. diff --git a/llama_stack/providers/utils/inference/model_registry.py b/llama_stack/providers/utils/inference/model_registry.py index 46c0ca7b5..801b8ea06 100644 --- a/llama_stack/providers/utils/inference/model_registry.py +++ b/llama_stack/providers/utils/inference/model_registry.py @@ -44,6 +44,7 @@ def build_hf_repo_model_entry( ] if additional_aliases: aliases.extend(additional_aliases) + aliases = [alias for alias in aliases if alias is not None] return ProviderModelEntry( provider_model_id=provider_model_id, aliases=aliases, @@ -82,15 +83,43 @@ class ModelRegistryHelper(ModelsProtocolPrivate): def get_llama_model(self, provider_model_id: str) -> str | None: return self.provider_id_to_llama_model_map.get(provider_model_id, None) + async def check_model_availability(self, model: str) -> bool: + """ + Check if a specific model is available from the provider (non-static check). + + This is for subclassing purposes, so providers can check if a specific + model is currently available for use through dynamic means (e.g., API calls). + + This method should NOT check statically configured model entries in + `self.alias_to_provider_id_map` - that is handled separately in register_model. + + Default implementation returns False (no dynamic models available). + + :param model: The model identifier to check. + :return: True if the model is available dynamically, False otherwise. + """ + return False + async def register_model(self, model: Model) -> Model: - if not (supported_model_id := self.get_provider_model_id(model.provider_resource_id)): - raise UnsupportedModelError(model.provider_resource_id, self.alias_to_provider_id_map.keys()) + # Check if model is supported in static configuration + supported_model_id = self.get_provider_model_id(model.provider_resource_id) + + # If not found in static config, check if it's available dynamically from provider + if not supported_model_id: + if await self.check_model_availability(model.provider_resource_id): + supported_model_id = model.provider_resource_id + else: + # note: we cannot provide a complete list of supported models without + # getting a complete list from the provider, so we return "..." + all_supported_models = [*self.alias_to_provider_id_map.keys(), "..."] + raise UnsupportedModelError(model.provider_resource_id, all_supported_models) + provider_resource_id = self.get_provider_model_id(model.model_id) if model.model_type == ModelType.embedding: # embedding models are always registered by their provider model id and does not need to be mapped to a llama model provider_resource_id = model.provider_resource_id if provider_resource_id: - if provider_resource_id != supported_model_id: # be idemopotent, only reject differences + if provider_resource_id != supported_model_id: # be idempotent, only reject differences raise ValueError( f"Model id '{model.model_id}' is already registered. Please use a different id or unregister it first." ) @@ -113,6 +142,7 @@ class ModelRegistryHelper(ModelsProtocolPrivate): ALL_HUGGINGFACE_REPOS_TO_MODEL_DESCRIPTOR[llama_model] ) + # Register the model alias, ensuring it maps to the correct provider model id self.alias_to_provider_id_map[model.model_id] = supported_model_id return model diff --git a/llama_stack/providers/utils/memory/openai_vector_store_mixin.py b/llama_stack/providers/utils/memory/openai_vector_store_mixin.py index 7c97ff7f6..e5328bc59 100644 --- a/llama_stack/providers/utils/memory/openai_vector_store_mixin.py +++ b/llama_stack/providers/utils/memory/openai_vector_store_mixin.py @@ -5,6 +5,7 @@ # the root directory of this source tree. import asyncio +import json import logging import mimetypes import time @@ -35,6 +36,7 @@ from llama_stack.apis.vector_io import ( VectorStoreSearchResponse, VectorStoreSearchResponsePage, ) +from llama_stack.providers.utils.kvstore.api import KVStore from llama_stack.providers.utils.memory.vector_store import content_from_data_and_mime_type, make_overlapped_chunks logger = logging.getLogger(__name__) @@ -59,26 +61,45 @@ class OpenAIVectorStoreMixin(ABC): # These should be provided by the implementing class openai_vector_stores: dict[str, dict[str, Any]] files_api: Files | None + # KV store for persisting OpenAI vector store metadata + kvstore: KVStore | None - @abstractmethod async def _save_openai_vector_store(self, store_id: str, store_info: dict[str, Any]) -> None: """Save vector store metadata to persistent storage.""" - pass + assert self.kvstore is not None + key = f"{OPENAI_VECTOR_STORES_PREFIX}{store_id}" + await self.kvstore.set(key=key, value=json.dumps(store_info)) + # update in-memory cache + self.openai_vector_stores[store_id] = store_info - @abstractmethod async def _load_openai_vector_stores(self) -> dict[str, dict[str, Any]]: """Load all vector store metadata from persistent storage.""" - pass + assert self.kvstore is not None + start_key = OPENAI_VECTOR_STORES_PREFIX + end_key = f"{OPENAI_VECTOR_STORES_PREFIX}\xff" + stored_data = await self.kvstore.values_in_range(start_key, end_key) + + stores: dict[str, dict[str, Any]] = {} + for item in stored_data: + info = json.loads(item) + stores[info["id"]] = info + return stores - @abstractmethod async def _update_openai_vector_store(self, store_id: str, store_info: dict[str, Any]) -> None: """Update vector store metadata in persistent storage.""" - pass + assert self.kvstore is not None + key = f"{OPENAI_VECTOR_STORES_PREFIX}{store_id}" + await self.kvstore.set(key=key, value=json.dumps(store_info)) + # update in-memory cache + self.openai_vector_stores[store_id] = store_info - @abstractmethod async def _delete_openai_vector_store_from_storage(self, store_id: str) -> None: """Delete vector store metadata from persistent storage.""" - pass + assert self.kvstore is not None + key = f"{OPENAI_VECTOR_STORES_PREFIX}{store_id}" + await self.kvstore.delete(key) + # remove from in-memory cache + self.openai_vector_stores.pop(store_id, None) @abstractmethod async def _save_openai_vector_store_file( @@ -117,6 +138,10 @@ class OpenAIVectorStoreMixin(ABC): """Unregister a vector database (provider-specific implementation).""" pass + async def initialize_openai_vector_stores(self) -> None: + """Load existing OpenAI vector stores into the in-memory cache.""" + self.openai_vector_stores = await self._load_openai_vector_stores() + @abstractmethod async def insert_chunks( self, @@ -147,8 +172,9 @@ class OpenAIVectorStoreMixin(ABC): provider_vector_db_id: str | None = None, ) -> VectorStoreObject: """Creates a vector store.""" - store_id = name or str(uuid.uuid4()) created_at = int(time.time()) + # Derive the canonical vector_db_id (allow override, else generate) + vector_db_id = provider_vector_db_id or f"vs_{uuid.uuid4()}" if provider_id is None: raise ValueError("Provider ID is required") @@ -156,19 +182,19 @@ class OpenAIVectorStoreMixin(ABC): if embedding_model is None: raise ValueError("Embedding model is required") - # Use provided embedding dimension or default to 384 + # Embedding dimension is required (defaulted to 384 if not provided) if embedding_dimension is None: raise ValueError("Embedding dimension is required") - provider_vector_db_id = provider_vector_db_id or store_id + # Register the VectorDB backing this vector store vector_db = VectorDB( - identifier=store_id, + identifier=vector_db_id, embedding_dimension=embedding_dimension, embedding_model=embedding_model, provider_id=provider_id, - provider_resource_id=provider_vector_db_id, + provider_resource_id=vector_db_id, + vector_db_name=name, ) - # Register the vector DB await self.register_vector_db(vector_db) # Create OpenAI vector store metadata @@ -182,11 +208,11 @@ class OpenAIVectorStoreMixin(ABC): in_progress=0, total=0, ) - store_info = { - "id": store_id, + store_info: dict[str, Any] = { + "id": vector_db_id, "object": "vector_store", "created_at": created_at, - "name": store_id, + "name": name, "usage_bytes": 0, "file_counts": file_counts.model_dump(), "status": status, @@ -206,18 +232,18 @@ class OpenAIVectorStoreMixin(ABC): store_info["metadata"] = metadata # Save to persistent storage (provider-specific) - await self._save_openai_vector_store(store_id, store_info) + await self._save_openai_vector_store(vector_db_id, store_info) # Store in memory cache - self.openai_vector_stores[store_id] = store_info + self.openai_vector_stores[vector_db_id] = store_info # Now that our vector store is created, attach any files that were provided file_ids = file_ids or [] - tasks = [self.openai_attach_file_to_vector_store(store_id, file_id) for file_id in file_ids] + tasks = [self.openai_attach_file_to_vector_store(vector_db_id, file_id) for file_id in file_ids] await asyncio.gather(*tasks) # Get the updated store info and return it - store_info = self.openai_vector_stores[store_id] + store_info = self.openai_vector_stores[vector_db_id] return VectorStoreObject.model_validate(store_info) async def openai_list_vector_stores( diff --git a/llama_stack/providers/utils/sqlstore/authorized_sqlstore.py b/llama_stack/providers/utils/sqlstore/authorized_sqlstore.py index 65401382f..864a7dbb6 100644 --- a/llama_stack/providers/utils/sqlstore/authorized_sqlstore.py +++ b/llama_stack/providers/utils/sqlstore/authorized_sqlstore.py @@ -15,6 +15,7 @@ from llama_stack.distribution.request_headers import get_authenticated_user from llama_stack.log import get_logger from .api import ColumnDefinition, ColumnType, PaginatedResponse, SqlStore +from .sqlstore import SqlStoreType logger = get_logger(name=__name__, category="authorized_sqlstore") @@ -38,22 +39,10 @@ SQL_OPTIMIZED_POLICY = [ class SqlRecord(ProtectedResource): - """Simple ProtectedResource implementation for SQL records.""" - - def __init__(self, record_id: str, table_name: str, access_attributes: dict[str, list[str]] | None = None): + def __init__(self, record_id: str, table_name: str, owner: User): self.type = f"sql_record::{table_name}" self.identifier = record_id - - if access_attributes: - self.owner = User( - principal="system", - attributes=access_attributes, - ) - else: - self.owner = User( - principal="system_public", - attributes=None, - ) + self.owner = owner class AuthorizedSqlStore: @@ -71,9 +60,18 @@ class AuthorizedSqlStore: :param sql_store: Base SqlStore implementation to wrap """ self.sql_store = sql_store - + self._detect_database_type() self._validate_sql_optimized_policy() + def _detect_database_type(self) -> None: + """Detect the database type from the underlying SQL store.""" + if not hasattr(self.sql_store, "config"): + raise ValueError("SqlStore must have a config attribute to be used with AuthorizedSqlStore") + + self.database_type = self.sql_store.config.type + if self.database_type not in [SqlStoreType.postgres, SqlStoreType.sqlite]: + raise ValueError(f"Unsupported database type: {self.database_type}") + def _validate_sql_optimized_policy(self) -> None: """Validate that SQL_OPTIMIZED_POLICY matches the actual default_policy(). @@ -91,22 +89,27 @@ class AuthorizedSqlStore: async def create_table(self, table: str, schema: Mapping[str, ColumnType | ColumnDefinition]) -> None: """Create a table with built-in access control support.""" - await self.sql_store.add_column_if_not_exists(table, "access_attributes", ColumnType.JSON) enhanced_schema = dict(schema) if "access_attributes" not in enhanced_schema: enhanced_schema["access_attributes"] = ColumnType.JSON + if "owner_principal" not in enhanced_schema: + enhanced_schema["owner_principal"] = ColumnType.STRING await self.sql_store.create_table(table, enhanced_schema) + await self.sql_store.add_column_if_not_exists(table, "access_attributes", ColumnType.JSON) + await self.sql_store.add_column_if_not_exists(table, "owner_principal", ColumnType.STRING) async def insert(self, table: str, data: Mapping[str, Any]) -> None: """Insert a row with automatic access control attribute capture.""" enhanced_data = dict(data) current_user = get_authenticated_user() - if current_user and current_user.attributes: + 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.insert(table, enhanced_data) @@ -136,9 +139,12 @@ class AuthorizedSqlStore: for row in rows.data: stored_access_attrs = row.get("access_attributes") + stored_owner_principal = row.get("owner_principal") or "" record_id = row.get("id", "unknown") - sql_record = SqlRecord(str(record_id), table, stored_access_attrs) + sql_record = SqlRecord( + str(record_id), table, User(principal=stored_owner_principal, attributes=stored_access_attrs) + ) if is_action_allowed(policy, Action.READ, sql_record, current_user): filtered_rows.append(row) @@ -176,43 +182,90 @@ class AuthorizedSqlStore: Only applies SQL filtering for the default policy to ensure correctness. For custom policies, uses conservative filtering to avoid blocking legitimate access. """ + current_user = get_authenticated_user() + if not policy or policy == SQL_OPTIMIZED_POLICY: - return self._build_default_policy_where_clause() + return self._build_default_policy_where_clause(current_user) else: return self._build_conservative_where_clause() - def _build_default_policy_where_clause(self) -> str: + def _json_extract(self, column: str, path: str) -> str: + """Extract JSON value (keeping JSON type). + + Args: + column: The JSON column name + path: The JSON path (e.g., 'roles', 'teams') + + Returns: + SQL expression to extract JSON value + """ + if self.database_type == SqlStoreType.postgres: + return f"{column}->'{path}'" + elif self.database_type == SqlStoreType.sqlite: + return f"JSON_EXTRACT({column}, '$.{path}')" + else: + raise ValueError(f"Unsupported database type: {self.database_type}") + + def _json_extract_text(self, column: str, path: str) -> str: + """Extract JSON value as text. + + Args: + column: The JSON column name + path: The JSON path (e.g., 'roles', 'teams') + + Returns: + SQL expression to extract JSON value as text + """ + if self.database_type == SqlStoreType.postgres: + return f"{column}->>'{path}'" + elif self.database_type == SqlStoreType.sqlite: + return f"JSON_EXTRACT({column}, '$.{path}')" + else: + raise ValueError(f"Unsupported database type: {self.database_type}") + + def _get_public_access_conditions(self) -> list[str]: + """Get the SQL conditions for public access.""" + # Public records are records that have no owner_principal or access_attributes + conditions = ["owner_principal = ''"] + if self.database_type == SqlStoreType.postgres: + # Postgres stores JSON null as 'null' + conditions.append("access_attributes::text = 'null'") + elif self.database_type == SqlStoreType.sqlite: + conditions.append("access_attributes = 'null'") + else: + raise ValueError(f"Unsupported database type: {self.database_type}") + return conditions + + def _build_default_policy_where_clause(self, current_user: User | None) -> str: """Build SQL WHERE clause for the default policy. Default policy: permit all actions when user in owners [roles, teams, projects, namespaces] This means user must match ALL attribute categories that exist in the resource. """ - current_user = get_authenticated_user() - - if not current_user or not current_user.attributes: - return "(access_attributes IS NULL OR access_attributes = 'null' OR access_attributes = '{}')" - else: - base_conditions = ["access_attributes IS NULL", "access_attributes = 'null'", "access_attributes = '{}'"] - - user_attr_conditions = [] + base_conditions = self._get_public_access_conditions() + user_attr_conditions = [] + if current_user and current_user.attributes: for attr_key, user_values in current_user.attributes.items(): if user_values: value_conditions = [] for value in user_values: - value_conditions.append(f"JSON_EXTRACT(access_attributes, '$.{attr_key}') LIKE '%\"{value}\"%'") + # Check if JSON array contains the value + escaped_value = value.replace("'", "''") + json_text = self._json_extract_text("access_attributes", attr_key) + value_conditions.append(f"({json_text} LIKE '%\"{escaped_value}\"%')") if value_conditions: - category_missing = f"JSON_EXTRACT(access_attributes, '$.{attr_key}') IS NULL" + # Check if the category is missing (NULL) + category_missing = f"{self._json_extract('access_attributes', attr_key)} IS NULL" user_matches_category = f"({' OR '.join(value_conditions)})" user_attr_conditions.append(f"({category_missing} OR {user_matches_category})") if user_attr_conditions: all_requirements_met = f"({' AND '.join(user_attr_conditions)})" base_conditions.append(all_requirements_met) - return f"({' OR '.join(base_conditions)})" - else: - return f"({' OR '.join(base_conditions)})" + + return f"({' OR '.join(base_conditions)})" def _build_conservative_where_clause(self) -> str: """Conservative SQL filtering for custom policies. @@ -222,5 +275,8 @@ class AuthorizedSqlStore: current_user = get_authenticated_user() if not current_user: - return "(access_attributes IS NULL OR access_attributes = 'null' OR access_attributes = '{}')" + # Only allow public records + base_conditions = self._get_public_access_conditions() + return f"({' OR '.join(base_conditions)})" + return "1=1" diff --git a/llama_stack/providers/utils/sqlstore/sqlalchemy_sqlstore.py b/llama_stack/providers/utils/sqlstore/sqlalchemy_sqlstore.py index 3aecb0d59..6414929db 100644 --- a/llama_stack/providers/utils/sqlstore/sqlalchemy_sqlstore.py +++ b/llama_stack/providers/utils/sqlstore/sqlalchemy_sqlstore.py @@ -244,35 +244,41 @@ class SqlAlchemySqlStoreImpl(SqlStore): engine = create_async_engine(self.config.engine_str) try: - inspector = inspect(engine) - - table_names = inspector.get_table_names() - if table not in table_names: - return - - existing_columns = inspector.get_columns(table) - column_names = [col["name"] for col in existing_columns] - - if column_name in column_names: - return - - sqlalchemy_type = TYPE_MAPPING.get(column_type) - if not sqlalchemy_type: - raise ValueError(f"Unsupported column type '{column_type}' for column '{column_name}'.") - - # Create the ALTER TABLE statement - # Note: We need to get the dialect-specific type name - dialect = engine.dialect - type_impl = sqlalchemy_type() - compiled_type = type_impl.compile(dialect=dialect) - - nullable_clause = "" if nullable else " NOT NULL" - add_column_sql = text(f"ALTER TABLE {table} ADD COLUMN {column_name} {compiled_type}{nullable_clause}") - async with engine.begin() as conn: + + def check_column_exists(sync_conn): + inspector = inspect(sync_conn) + + table_names = inspector.get_table_names() + if table not in table_names: + return False, False # table doesn't exist, column doesn't exist + + existing_columns = inspector.get_columns(table) + column_names = [col["name"] for col in existing_columns] + + return True, column_name in column_names # table exists, column exists or not + + table_exists, column_exists = await conn.run_sync(check_column_exists) + if not table_exists or column_exists: + return + + sqlalchemy_type = TYPE_MAPPING.get(column_type) + if not sqlalchemy_type: + raise ValueError(f"Unsupported column type '{column_type}' for column '{column_name}'.") + + # Create the ALTER TABLE statement + # Note: We need to get the dialect-specific type name + dialect = engine.dialect + type_impl = sqlalchemy_type() + compiled_type = type_impl.compile(dialect=dialect) + + nullable_clause = "" if nullable else " NOT NULL" + add_column_sql = text(f"ALTER TABLE {table} ADD COLUMN {column_name} {compiled_type}{nullable_clause}") + await conn.execute(add_column_sql) - except Exception: + except Exception as e: # If any error occurs during migration, log it but don't fail # The table creation will handle adding the column + logger.error(f"Error adding column {column_name} to table {table}: {e}") pass diff --git a/llama_stack/providers/utils/sqlstore/sqlstore.py b/llama_stack/providers/utils/sqlstore/sqlstore.py index 227c5abcd..9f7eefcf5 100644 --- a/llama_stack/providers/utils/sqlstore/sqlstore.py +++ b/llama_stack/providers/utils/sqlstore/sqlstore.py @@ -4,9 +4,8 @@ # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. - from abc import abstractmethod -from enum import Enum +from enum import StrEnum from pathlib import Path from typing import Annotated, Literal @@ -19,7 +18,7 @@ from .api import SqlStore sql_store_pip_packages = ["sqlalchemy[asyncio]", "aiosqlite", "asyncpg"] -class SqlStoreType(Enum): +class SqlStoreType(StrEnum): sqlite = "sqlite" postgres = "postgres" @@ -36,7 +35,7 @@ class SqlAlchemySqlStoreConfig(BaseModel): class SqliteSqlStoreConfig(SqlAlchemySqlStoreConfig): - type: Literal["sqlite"] = SqlStoreType.sqlite.value + type: Literal[SqlStoreType.sqlite] = SqlStoreType.sqlite db_path: str = Field( default=(RUNTIME_BASE_DIR / "sqlstore.db").as_posix(), description="Database path, e.g. ~/.llama/distributions/ollama/sqlstore.db", @@ -59,7 +58,7 @@ class SqliteSqlStoreConfig(SqlAlchemySqlStoreConfig): class PostgresSqlStoreConfig(SqlAlchemySqlStoreConfig): - type: Literal["postgres"] = SqlStoreType.postgres.value + type: Literal[SqlStoreType.postgres] = SqlStoreType.postgres host: str = "localhost" port: int = 5432 db: str = "llamastack" @@ -107,7 +106,7 @@ def get_pip_packages(store_config: dict | SqlStoreConfig) -> list[str]: def sqlstore_impl(config: SqlStoreConfig) -> SqlStore: - if config.type in [SqlStoreType.sqlite.value, SqlStoreType.postgres.value]: + if config.type in [SqlStoreType.sqlite, SqlStoreType.postgres]: from .sqlalchemy_sqlstore import SqlAlchemySqlStoreImpl impl = SqlAlchemySqlStoreImpl(config) diff --git a/llama_stack/providers/utils/telemetry/trace_protocol.py b/llama_stack/providers/utils/telemetry/trace_protocol.py index eb6d8b331..916f7622a 100644 --- a/llama_stack/providers/utils/telemetry/trace_protocol.py +++ b/llama_stack/providers/utils/telemetry/trace_protocol.py @@ -9,14 +9,12 @@ import inspect import json from collections.abc import AsyncGenerator, Callable from functools import wraps -from typing import Any, TypeVar +from typing import Any from pydantic import BaseModel from llama_stack.models.llama.datatypes import Primitive -T = TypeVar("T") - def serialize_value(value: Any) -> Primitive: return str(_prepare_for_json(value)) @@ -44,7 +42,7 @@ def _prepare_for_json(value: Any) -> str: return str(value) -def trace_protocol(cls: type[T]) -> type[T]: +def trace_protocol[T](cls: type[T]) -> type[T]: """ A class decorator that automatically traces all methods in a protocol/base class and its inheriting classes. diff --git a/llama_stack/templates/nvidia/__init__.py b/llama_stack/templates/nvidia/__init__.py new file mode 100644 index 000000000..24e2fbd21 --- /dev/null +++ b/llama_stack/templates/nvidia/__init__.py @@ -0,0 +1,7 @@ +# 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 .nvidia import get_distribution_template # noqa: F401 diff --git a/llama_stack/templates/nvidia/build.yaml b/llama_stack/templates/nvidia/build.yaml new file mode 100644 index 000000000..51685b2e3 --- /dev/null +++ b/llama_stack/templates/nvidia/build.yaml @@ -0,0 +1,29 @@ +version: 2 +distribution_spec: + description: Use NVIDIA NIM for running LLM inference, evaluation and safety + providers: + inference: + - remote::nvidia + vector_io: + - inline::faiss + safety: + - remote::nvidia + agents: + - inline::meta-reference + telemetry: + - inline::meta-reference + eval: + - remote::nvidia + post_training: + - remote::nvidia + datasetio: + - inline::localfs + - remote::nvidia + scoring: + - inline::basic + tool_runtime: + - inline::rag-runtime +image_type: conda +additional_pip_packages: +- aiosqlite +- sqlalchemy[asyncio] diff --git a/llama_stack/templates/nvidia/doc_template.md b/llama_stack/templates/nvidia/doc_template.md new file mode 100644 index 000000000..3cb8245df --- /dev/null +++ b/llama_stack/templates/nvidia/doc_template.md @@ -0,0 +1,149 @@ +# NVIDIA Distribution + +The `llamastack/distribution-{{ name }}` distribution consists of the following provider configurations. + +{{ providers_table }} + +{% if run_config_env_vars %} +### Environment Variables + +The following environment variables can be configured: + +{% for var, (default_value, description) in run_config_env_vars.items() %} +- `{{ var }}`: {{ description }} (default: `{{ default_value }}`) +{% endfor %} +{% endif %} + +{% if default_models %} +### Models + +The following models are available by default: + +{% for model in default_models %} +- `{{ model.model_id }} {{ model.doc_string }}` +{% endfor %} +{% endif %} + + +## Prerequisites +### NVIDIA API Keys + +Make sure you have access to a NVIDIA API Key. You can get one by visiting [https://build.nvidia.com/](https://build.nvidia.com/). Use this key for the `NVIDIA_API_KEY` environment variable. + +### Deploy NeMo Microservices Platform +The NVIDIA NeMo microservices platform supports end-to-end microservice deployment of a complete AI flywheel on your Kubernetes cluster through the NeMo Microservices Helm Chart. Please reference the [NVIDIA NeMo Microservices documentation](https://docs.nvidia.com/nemo/microservices/latest/about/index.html) for platform prerequisites and instructions to install and deploy the platform. + +## Supported Services +Each Llama Stack API corresponds to a specific NeMo microservice. The core microservices (Customizer, Evaluator, Guardrails) are exposed by the same endpoint. The platform components (Data Store) are each exposed by separate endpoints. + +### Inference: NVIDIA NIM +NVIDIA NIM is used for running inference with registered models. There are two ways to access NVIDIA NIMs: + 1. Hosted (default): Preview APIs hosted at https://integrate.api.nvidia.com (Requires an API key) + 2. Self-hosted: NVIDIA NIMs that run on your own infrastructure. + +The deployed platform includes the NIM Proxy microservice, which is the service that provides to access your NIMs (for example, to run inference on a model). Set the `NVIDIA_BASE_URL` environment variable to use your NVIDIA NIM Proxy deployment. + +### 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. + +### 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. + +### 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. + +See the {repopath}`NVIDIA Post-Training docs::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. + +## 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`. + +Note: For improved inference speeds, we need to use NIM with `fast_outlines` guided decoding system (specified in the request body). This is the default if you deployed the platform with the NeMo Microservices Helm Chart. +```sh +# URL to NeMo NIM Proxy service +export NEMO_URL="http://nemo.test" + +curl --location "$NEMO_URL/v1/deployment/model-deployments" \ + -H 'accept: application/json' \ + -H 'Content-Type: application/json' \ + -d '{ + "name": "llama-3.2-1b-instruct", + "namespace": "meta", + "config": { + "model": "meta/llama-3.2-1b-instruct", + "nim_deployment": { + "image_name": "nvcr.io/nim/meta/llama-3.2-1b-instruct", + "image_tag": "1.8.3", + "pvc_size": "25Gi", + "gpu": 1, + "additional_envs": { + "NIM_GUIDED_DECODING_BACKEND": "fast_outlines" + } + } + } + }' +``` +This NIM deployment should take approximately 10 minutes to go live. [See the docs](https://docs.nvidia.com/nemo/microservices/latest/get-started/tutorials/deploy-nims.html) for more information on how to deploy a NIM and verify it's available for inference. + +You can also remove a deployed NIM to free up GPU resources, if needed. +```sh +export NEMO_URL="http://nemo.test" + +curl -X DELETE "$NEMO_URL/v1/deployment/model-deployments/meta/llama-3.1-8b-instruct" +``` + +## Running Llama Stack with NVIDIA + +You can do this via Conda or venv (build code), or Docker which has a pre-built image. + +### Via Docker + +This method allows you to get started quickly without having to build the distribution code. + +```bash +LLAMA_STACK_PORT=8321 +docker run \ + -it \ + --pull always \ + -p $LLAMA_STACK_PORT:$LLAMA_STACK_PORT \ + -v ./run.yaml:/root/my-run.yaml \ + llamastack/distribution-{{ name }} \ + --config /root/my-run.yaml \ + --port $LLAMA_STACK_PORT \ + --env NVIDIA_API_KEY=$NVIDIA_API_KEY +``` + +### Via Conda + +```bash +INFERENCE_MODEL=meta-llama/Llama-3.1-8b-Instruct +llama stack build --template nvidia --image-type conda +llama stack run ./run.yaml \ + --port 8321 \ + --env NVIDIA_API_KEY=$NVIDIA_API_KEY \ + --env INFERENCE_MODEL=$INFERENCE_MODEL +``` + +### Via venv + +If you've set up your local development environment, you can also build the image using your local virtual environment. + +```bash +INFERENCE_MODEL=meta-llama/Llama-3.1-8b-Instruct +llama stack build --template nvidia --image-type venv +llama stack run ./run.yaml \ + --port 8321 \ + --env NVIDIA_API_KEY=$NVIDIA_API_KEY \ + --env INFERENCE_MODEL=$INFERENCE_MODEL +``` + +## 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`. diff --git a/llama_stack/templates/nvidia/nvidia.py b/llama_stack/templates/nvidia/nvidia.py new file mode 100644 index 000000000..e5c13aa74 --- /dev/null +++ b/llama_stack/templates/nvidia/nvidia.py @@ -0,0 +1,150 @@ +# 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 pathlib import Path + +from llama_stack.distribution.datatypes import ModelInput, Provider, ShieldInput, ToolGroupInput +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 +from llama_stack.templates.template import DistributionTemplate, RunConfigSettings, get_model_registry + + +def get_distribution_template() -> DistributionTemplate: + providers = { + "inference": ["remote::nvidia"], + "vector_io": ["inline::faiss"], + "safety": ["remote::nvidia"], + "agents": ["inline::meta-reference"], + "telemetry": ["inline::meta-reference"], + "eval": ["remote::nvidia"], + "post_training": ["remote::nvidia"], + "datasetio": ["inline::localfs", "remote::nvidia"], + "scoring": ["inline::basic"], + "tool_runtime": ["inline::rag-runtime"], + } + + inference_provider = Provider( + provider_id="nvidia", + provider_type="remote::nvidia", + config=NVIDIAConfig.sample_run_config(), + ) + safety_provider = Provider( + provider_id="nvidia", + provider_type="remote::nvidia", + config=NVIDIASafetyConfig.sample_run_config(), + ) + datasetio_provider = Provider( + provider_id="nvidia", + provider_type="remote::nvidia", + config=NvidiaDatasetIOConfig.sample_run_config(), + ) + eval_provider = Provider( + provider_id="nvidia", + provider_type="remote::nvidia", + config=NVIDIAEvalConfig.sample_run_config(), + ) + inference_model = ModelInput( + model_id="${env.INFERENCE_MODEL}", + provider_id="nvidia", + ) + safety_model = ModelInput( + model_id="${env.SAFETY_MODEL}", + provider_id="nvidia", + ) + + available_models = { + "nvidia": MODEL_ENTRIES, + } + default_tool_groups = [ + ToolGroupInput( + toolgroup_id="builtin::rag", + provider_id="rag-runtime", + ), + ] + + default_models, _ = get_model_registry(available_models) + return DistributionTemplate( + name="nvidia", + 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], + }, + default_models=default_models, + default_tool_groups=default_tool_groups, + ), + "run-with-safety.yaml": RunConfigSettings( + provider_overrides={ + "inference": [ + inference_provider, + safety_provider, + ], + "eval": [eval_provider], + }, + default_models=[inference_model, safety_model], + default_shields=[ShieldInput(shield_id="${env.SAFETY_MODEL}", provider_id="nvidia")], + default_tool_groups=default_tool_groups, + ), + }, + run_config_env_vars={ + "NVIDIA_API_KEY": ( + "", + "NVIDIA API Key", + ), + "NVIDIA_APPEND_API_VERSION": ( + "True", + "Whether to append the API version to the base_url", + ), + ## Nemo Customizer related variables + "NVIDIA_DATASET_NAMESPACE": ( + "default", + "NVIDIA Dataset Namespace", + ), + "NVIDIA_PROJECT_ID": ( + "test-project", + "NVIDIA Project ID", + ), + "NVIDIA_CUSTOMIZER_URL": ( + "https://customizer.api.nvidia.com", + "NVIDIA Customizer URL", + ), + "NVIDIA_OUTPUT_MODEL_DIR": ( + "test-example-model@v1", + "NVIDIA Output Model Directory", + ), + "GUARDRAILS_SERVICE_URL": ( + "http://0.0.0.0:7331", + "URL for the NeMo Guardrails Service", + ), + "NVIDIA_GUARDRAILS_CONFIG_ID": ( + "self-check", + "NVIDIA Guardrail Configuration ID", + ), + "NVIDIA_EVALUATOR_URL": ( + "http://0.0.0.0:7331", + "URL for the NeMo Evaluator Service", + ), + "INFERENCE_MODEL": ( + "Llama3.1-8B-Instruct", + "Inference model", + ), + "SAFETY_MODEL": ( + "meta/llama-3.1-8b-instruct", + "Name of the model to use for safety", + ), + }, + ) diff --git a/llama_stack/templates/nvidia/run-with-safety.yaml b/llama_stack/templates/nvidia/run-with-safety.yaml new file mode 100644 index 000000000..7017a5955 --- /dev/null +++ b/llama_stack/templates/nvidia/run-with-safety.yaml @@ -0,0 +1,119 @@ +version: 2 +image_name: nvidia +apis: +- agents +- datasetio +- eval +- inference +- post_training +- safety +- scoring +- telemetry +- tool_runtime +- vector_io +providers: + inference: + - provider_id: nvidia + provider_type: remote::nvidia + config: + 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} + - provider_id: nvidia + provider_type: remote::nvidia + config: + guardrails_service_url: ${env.GUARDRAILS_SERVICE_URL:=http://localhost:7331} + config_id: ${env.NVIDIA_GUARDRAILS_CONFIG_ID:=self-check} + vector_io: + - provider_id: faiss + provider_type: inline::faiss + config: + kvstore: + type: sqlite + db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/nvidia}/faiss_store.db + safety: + - provider_id: nvidia + provider_type: remote::nvidia + config: + guardrails_service_url: ${env.GUARDRAILS_SERVICE_URL:=http://localhost:7331} + config_id: ${env.NVIDIA_GUARDRAILS_CONFIG_ID:=self-check} + agents: + - provider_id: meta-reference + provider_type: inline::meta-reference + config: + persistence_store: + type: sqlite + db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/nvidia}/agents_store.db + responses_store: + type: sqlite + db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/nvidia}/responses_store.db + telemetry: + - provider_id: meta-reference + provider_type: inline::meta-reference + config: + service_name: "${env.OTEL_SERVICE_NAME:=\u200B}" + sinks: ${env.TELEMETRY_SINKS:=console,sqlite} + sqlite_db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/nvidia}/trace_store.db + otel_exporter_otlp_endpoint: ${env.OTEL_EXPORTER_OTLP_ENDPOINT:=} + eval: + - provider_id: nvidia + provider_type: remote::nvidia + config: + evaluator_url: ${env.NVIDIA_EVALUATOR_URL:=http://localhost:7331} + post_training: + - provider_id: nvidia + provider_type: remote::nvidia + config: + 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} + datasetio: + - provider_id: localfs + provider_type: inline::localfs + config: + kvstore: + type: sqlite + db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/nvidia}/localfs_datasetio.db + - provider_id: nvidia + provider_type: remote::nvidia + config: + 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} + scoring: + - provider_id: basic + provider_type: inline::basic + config: {} + tool_runtime: + - provider_id: rag-runtime + provider_type: inline::rag-runtime + config: {} +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: ${env.INFERENCE_MODEL} + provider_id: nvidia + model_type: llm +- metadata: {} + model_id: ${env.SAFETY_MODEL} + provider_id: nvidia + model_type: llm +shields: +- shield_id: ${env.SAFETY_MODEL} + provider_id: nvidia +vector_dbs: [] +datasets: [] +scoring_fns: [] +benchmarks: [] +tool_groups: +- toolgroup_id: builtin::rag + provider_id: rag-runtime +server: + port: 8321 diff --git a/llama_stack/templates/nvidia/run.yaml b/llama_stack/templates/nvidia/run.yaml new file mode 100644 index 000000000..ccddf11a2 --- /dev/null +++ b/llama_stack/templates/nvidia/run.yaml @@ -0,0 +1,226 @@ +version: 2 +image_name: nvidia +apis: +- agents +- datasetio +- eval +- inference +- post_training +- safety +- scoring +- telemetry +- tool_runtime +- vector_io +providers: + inference: + - provider_id: nvidia + provider_type: remote::nvidia + config: + 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} + vector_io: + - provider_id: faiss + provider_type: inline::faiss + config: + kvstore: + type: sqlite + db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/nvidia}/faiss_store.db + safety: + - provider_id: nvidia + provider_type: remote::nvidia + config: + guardrails_service_url: ${env.GUARDRAILS_SERVICE_URL:=http://localhost:7331} + config_id: ${env.NVIDIA_GUARDRAILS_CONFIG_ID:=self-check} + agents: + - provider_id: meta-reference + provider_type: inline::meta-reference + config: + persistence_store: + type: sqlite + db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/nvidia}/agents_store.db + responses_store: + type: sqlite + db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/nvidia}/responses_store.db + telemetry: + - provider_id: meta-reference + provider_type: inline::meta-reference + config: + service_name: "${env.OTEL_SERVICE_NAME:=\u200B}" + sinks: ${env.TELEMETRY_SINKS:=console,sqlite} + sqlite_db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/nvidia}/trace_store.db + otel_exporter_otlp_endpoint: ${env.OTEL_EXPORTER_OTLP_ENDPOINT:=} + eval: + - provider_id: nvidia + provider_type: remote::nvidia + config: + evaluator_url: ${env.NVIDIA_EVALUATOR_URL:=http://localhost:7331} + post_training: + - provider_id: nvidia + provider_type: remote::nvidia + config: + 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} + datasetio: + - provider_id: nvidia + provider_type: remote::nvidia + config: + 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} + scoring: + - provider_id: basic + provider_type: inline::basic + config: {} + tool_runtime: + - provider_id: rag-runtime + provider_type: inline::rag-runtime + config: {} +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-llama/Llama-3-8B-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/Llama-3-70B-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/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/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/Llama-3.1-405B-Instruct-FP8 + 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/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/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/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/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: {} + model_id: meta-llama/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 +shields: [] +vector_dbs: [] +datasets: [] +scoring_fns: [] +benchmarks: [] +tool_groups: +- toolgroup_id: builtin::rag + provider_id: rag-runtime +server: + port: 8321 diff --git a/llama_stack/templates/open-benchmark/open_benchmark.py b/llama_stack/templates/open-benchmark/open_benchmark.py index 942905dae..63a27e07f 100644 --- a/llama_stack/templates/open-benchmark/open_benchmark.py +++ b/llama_stack/templates/open-benchmark/open_benchmark.py @@ -128,6 +128,7 @@ def get_distribution_template() -> DistributionTemplate: provider_id="${env.ENABLE_PGVECTOR:+pgvector}", provider_type="remote::pgvector", config=PGVectorVectorIOConfig.sample_run_config( + f"~/.llama/distributions/{name}", db="${env.PGVECTOR_DB:=}", user="${env.PGVECTOR_USER:=}", password="${env.PGVECTOR_PASSWORD:=}", @@ -146,7 +147,8 @@ def get_distribution_template() -> DistributionTemplate: ), ] - default_models = get_model_registry(available_models) + [ + models, _ = get_model_registry(available_models) + default_models = models + [ ModelInput( model_id="meta-llama/Llama-3.3-70B-Instruct", provider_id="groq", diff --git a/llama_stack/templates/open-benchmark/run.yaml b/llama_stack/templates/open-benchmark/run.yaml index 76c029864..7d07cc4bf 100644 --- a/llama_stack/templates/open-benchmark/run.yaml +++ b/llama_stack/templates/open-benchmark/run.yaml @@ -39,6 +39,9 @@ providers: provider_type: inline::sqlite-vec config: db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/open-benchmark}/sqlite_vec.db + kvstore: + type: sqlite + db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/open-benchmark}/sqlite_vec_registry.db - provider_id: ${env.ENABLE_CHROMADB:+chromadb} provider_type: remote::chromadb config: @@ -51,6 +54,9 @@ providers: db: ${env.PGVECTOR_DB:=} user: ${env.PGVECTOR_USER:=} password: ${env.PGVECTOR_PASSWORD:=} + kvstore: + type: sqlite + db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/open-benchmark}/pgvector_registry.db safety: - provider_id: llama-guard provider_type: inline::llama-guard diff --git a/llama_stack/templates/starter/run.yaml b/llama_stack/templates/starter/run.yaml index 02288da44..27400348a 100644 --- a/llama_stack/templates/starter/run.yaml +++ b/llama_stack/templates/starter/run.yaml @@ -144,6 +144,9 @@ providers: provider_type: inline::sqlite-vec config: db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/starter}/sqlite_vec.db + kvstore: + type: sqlite + db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/starter}/sqlite_vec_registry.db - provider_id: ${env.ENABLE_MILVUS:=__disabled__} provider_type: inline::milvus config: @@ -163,6 +166,9 @@ providers: db: ${env.PGVECTOR_DB:=} user: ${env.PGVECTOR_USER:=} password: ${env.PGVECTOR_PASSWORD:=} + kvstore: + type: sqlite + db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/starter}/pgvector_registry.db files: - provider_id: meta-reference-files provider_type: inline::localfs @@ -256,11 +262,51 @@ inference_store: type: sqlite db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/starter}/inference_store.db models: +- metadata: + embedding_dimension: 384 + model_id: all-MiniLM-L6-v2 + provider_id: ${env.ENABLE_SENTENCE_TRANSFORMERS:=sentence-transformers} + model_type: embedding +- metadata: {} + model_id: ${env.ENABLE_CEREBRAS:=__disabled__}/llama3.1-8b + provider_id: ${env.ENABLE_CEREBRAS:=__disabled__} + provider_model_id: llama3.1-8b + model_type: llm +- metadata: {} + model_id: ${env.ENABLE_CEREBRAS:=__disabled__}/meta-llama/Llama-3.1-8B-Instruct + provider_id: ${env.ENABLE_CEREBRAS:=__disabled__} + provider_model_id: llama3.1-8b + model_type: llm +- metadata: {} + model_id: ${env.ENABLE_CEREBRAS:=__disabled__}/llama-3.3-70b + provider_id: ${env.ENABLE_CEREBRAS:=__disabled__} + provider_model_id: llama-3.3-70b + model_type: llm +- metadata: {} + model_id: ${env.ENABLE_CEREBRAS:=__disabled__}/meta-llama/Llama-3.3-70B-Instruct + provider_id: ${env.ENABLE_CEREBRAS:=__disabled__} + provider_model_id: llama-3.3-70b + model_type: llm +- metadata: {} + model_id: ${env.ENABLE_CEREBRAS:=__disabled__}/llama-4-scout-17b-16e-instruct + provider_id: ${env.ENABLE_CEREBRAS:=__disabled__} + provider_model_id: llama-4-scout-17b-16e-instruct + model_type: llm +- metadata: {} + model_id: ${env.ENABLE_CEREBRAS:=__disabled__}/meta-llama/Llama-4-Scout-17B-16E-Instruct + provider_id: ${env.ENABLE_CEREBRAS:=__disabled__} + provider_model_id: llama-4-scout-17b-16e-instruct + model_type: llm - metadata: {} model_id: ${env.ENABLE_OLLAMA:=__disabled__}/${env.OLLAMA_INFERENCE_MODEL:=__disabled__} provider_id: ${env.ENABLE_OLLAMA:=__disabled__} provider_model_id: ${env.OLLAMA_INFERENCE_MODEL:=__disabled__} model_type: llm +- metadata: {} + model_id: ${env.ENABLE_OLLAMA:=__disabled__}/${env.SAFETY_MODEL:=__disabled__} + provider_id: ${env.ENABLE_OLLAMA:=__disabled__} + provider_model_id: ${env.SAFETY_MODEL:=__disabled__} + model_type: llm - metadata: embedding_dimension: ${env.OLLAMA_EMBEDDING_DIMENSION:=384} model_id: ${env.ENABLE_OLLAMA:=__disabled__}/${env.OLLAMA_EMBEDDING_MODEL:=__disabled__} @@ -342,26 +388,6 @@ models: provider_id: ${env.ENABLE_FIREWORKS:=__disabled__} provider_model_id: accounts/fireworks/models/llama-v3p3-70b-instruct model_type: llm -- metadata: {} - model_id: ${env.ENABLE_FIREWORKS:=__disabled__}/accounts/fireworks/models/llama-guard-3-8b - provider_id: ${env.ENABLE_FIREWORKS:=__disabled__} - provider_model_id: accounts/fireworks/models/llama-guard-3-8b - model_type: llm -- metadata: {} - model_id: ${env.ENABLE_FIREWORKS:=__disabled__}/meta-llama/Llama-Guard-3-8B - provider_id: ${env.ENABLE_FIREWORKS:=__disabled__} - provider_model_id: accounts/fireworks/models/llama-guard-3-8b - model_type: llm -- metadata: {} - model_id: ${env.ENABLE_FIREWORKS:=__disabled__}/accounts/fireworks/models/llama-guard-3-11b-vision - provider_id: ${env.ENABLE_FIREWORKS:=__disabled__} - provider_model_id: accounts/fireworks/models/llama-guard-3-11b-vision - model_type: llm -- metadata: {} - model_id: ${env.ENABLE_FIREWORKS:=__disabled__}/meta-llama/Llama-Guard-3-11B-Vision - provider_id: ${env.ENABLE_FIREWORKS:=__disabled__} - provider_model_id: accounts/fireworks/models/llama-guard-3-11b-vision - model_type: llm - metadata: {} model_id: ${env.ENABLE_FIREWORKS:=__disabled__}/accounts/fireworks/models/llama4-scout-instruct-basic provider_id: ${env.ENABLE_FIREWORKS:=__disabled__} @@ -389,6 +415,26 @@ models: provider_id: ${env.ENABLE_FIREWORKS:=__disabled__} provider_model_id: nomic-ai/nomic-embed-text-v1.5 model_type: embedding +- metadata: {} + model_id: ${env.ENABLE_FIREWORKS:=__disabled__}/accounts/fireworks/models/llama-guard-3-8b + provider_id: ${env.ENABLE_FIREWORKS:=__disabled__} + provider_model_id: accounts/fireworks/models/llama-guard-3-8b + model_type: llm +- metadata: {} + model_id: ${env.ENABLE_FIREWORKS:=__disabled__}/meta-llama/Llama-Guard-3-8B + provider_id: ${env.ENABLE_FIREWORKS:=__disabled__} + provider_model_id: accounts/fireworks/models/llama-guard-3-8b + model_type: llm +- metadata: {} + model_id: ${env.ENABLE_FIREWORKS:=__disabled__}/accounts/fireworks/models/llama-guard-3-11b-vision + provider_id: ${env.ENABLE_FIREWORKS:=__disabled__} + provider_model_id: accounts/fireworks/models/llama-guard-3-11b-vision + model_type: llm +- metadata: {} + model_id: ${env.ENABLE_FIREWORKS:=__disabled__}/meta-llama/Llama-Guard-3-11B-Vision + provider_id: ${env.ENABLE_FIREWORKS:=__disabled__} + provider_model_id: accounts/fireworks/models/llama-guard-3-11b-vision + model_type: llm - metadata: {} model_id: ${env.ENABLE_TOGETHER:=__disabled__}/meta-llama/Meta-Llama-3.1-8B-Instruct-Turbo provider_id: ${env.ENABLE_TOGETHER:=__disabled__} @@ -459,26 +505,6 @@ models: provider_id: ${env.ENABLE_TOGETHER:=__disabled__} provider_model_id: meta-llama/Llama-3.3-70B-Instruct-Turbo model_type: llm -- metadata: {} - model_id: ${env.ENABLE_TOGETHER:=__disabled__}/meta-llama/Meta-Llama-Guard-3-8B - provider_id: ${env.ENABLE_TOGETHER:=__disabled__} - provider_model_id: meta-llama/Meta-Llama-Guard-3-8B - model_type: llm -- metadata: {} - model_id: ${env.ENABLE_TOGETHER:=__disabled__}/meta-llama/Llama-Guard-3-8B - provider_id: ${env.ENABLE_TOGETHER:=__disabled__} - provider_model_id: meta-llama/Meta-Llama-Guard-3-8B - model_type: llm -- metadata: {} - model_id: ${env.ENABLE_TOGETHER:=__disabled__}/meta-llama/Llama-Guard-3-11B-Vision-Turbo - provider_id: ${env.ENABLE_TOGETHER:=__disabled__} - provider_model_id: meta-llama/Llama-Guard-3-11B-Vision-Turbo - model_type: llm -- metadata: {} - model_id: ${env.ENABLE_TOGETHER:=__disabled__}/meta-llama/Llama-Guard-3-11B-Vision - provider_id: ${env.ENABLE_TOGETHER:=__disabled__} - provider_model_id: meta-llama/Llama-Guard-3-11B-Vision-Turbo - model_type: llm - metadata: embedding_dimension: 768 context_length: 8192 @@ -523,6 +549,264 @@ models: provider_id: ${env.ENABLE_TOGETHER:=__disabled__} provider_model_id: meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8 model_type: llm +- metadata: {} + model_id: ${env.ENABLE_TOGETHER:=__disabled__}/meta-llama/Llama-Guard-3-8B + provider_id: ${env.ENABLE_TOGETHER:=__disabled__} + provider_model_id: meta-llama/Llama-Guard-3-8B + model_type: llm +- metadata: {} + model_id: ${env.ENABLE_TOGETHER:=__disabled__}/meta-llama/Llama-Guard-3-8B + provider_id: ${env.ENABLE_TOGETHER:=__disabled__} + provider_model_id: meta-llama/Llama-Guard-3-8B + model_type: llm +- metadata: {} + model_id: ${env.ENABLE_TOGETHER:=__disabled__}/meta-llama/Llama-Guard-3-11B-Vision-Turbo + provider_id: ${env.ENABLE_TOGETHER:=__disabled__} + provider_model_id: meta-llama/Llama-Guard-3-11B-Vision-Turbo + model_type: llm +- metadata: {} + model_id: ${env.ENABLE_TOGETHER:=__disabled__}/meta-llama/Llama-Guard-3-11B-Vision + provider_id: ${env.ENABLE_TOGETHER:=__disabled__} + provider_model_id: meta-llama/Llama-Guard-3-11B-Vision-Turbo + model_type: llm +- metadata: {} + model_id: ${env.ENABLE_BEDROCK:=__disabled__}/meta.llama3-1-8b-instruct-v1:0 + provider_id: ${env.ENABLE_BEDROCK:=__disabled__} + provider_model_id: meta.llama3-1-8b-instruct-v1:0 + model_type: llm +- metadata: {} + model_id: ${env.ENABLE_BEDROCK:=__disabled__}/meta-llama/Llama-3.1-8B-Instruct + provider_id: ${env.ENABLE_BEDROCK:=__disabled__} + provider_model_id: meta.llama3-1-8b-instruct-v1:0 + model_type: llm +- metadata: {} + model_id: ${env.ENABLE_BEDROCK:=__disabled__}/meta.llama3-1-70b-instruct-v1:0 + provider_id: ${env.ENABLE_BEDROCK:=__disabled__} + provider_model_id: meta.llama3-1-70b-instruct-v1:0 + model_type: llm +- metadata: {} + model_id: ${env.ENABLE_BEDROCK:=__disabled__}/meta-llama/Llama-3.1-70B-Instruct + provider_id: ${env.ENABLE_BEDROCK:=__disabled__} + provider_model_id: meta.llama3-1-70b-instruct-v1:0 + model_type: llm +- metadata: {} + model_id: ${env.ENABLE_BEDROCK:=__disabled__}/meta.llama3-1-405b-instruct-v1:0 + provider_id: ${env.ENABLE_BEDROCK:=__disabled__} + provider_model_id: meta.llama3-1-405b-instruct-v1:0 + model_type: llm +- metadata: {} + model_id: ${env.ENABLE_BEDROCK:=__disabled__}/meta-llama/Llama-3.1-405B-Instruct-FP8 + provider_id: ${env.ENABLE_BEDROCK:=__disabled__} + provider_model_id: meta.llama3-1-405b-instruct-v1:0 + model_type: llm +- metadata: {} + model_id: ${env.ENABLE_DATABRICKS:=__disabled__}/databricks-meta-llama-3-1-70b-instruct + provider_id: ${env.ENABLE_DATABRICKS:=__disabled__} + provider_model_id: databricks-meta-llama-3-1-70b-instruct + model_type: llm +- metadata: {} + model_id: ${env.ENABLE_DATABRICKS:=__disabled__}/meta-llama/Llama-3.1-70B-Instruct + provider_id: ${env.ENABLE_DATABRICKS:=__disabled__} + provider_model_id: databricks-meta-llama-3-1-70b-instruct + model_type: llm +- metadata: {} + model_id: ${env.ENABLE_DATABRICKS:=__disabled__}/databricks-meta-llama-3-1-405b-instruct + provider_id: ${env.ENABLE_DATABRICKS:=__disabled__} + provider_model_id: databricks-meta-llama-3-1-405b-instruct + model_type: llm +- metadata: {} + model_id: ${env.ENABLE_DATABRICKS:=__disabled__}/meta-llama/Llama-3.1-405B-Instruct-FP8 + provider_id: ${env.ENABLE_DATABRICKS:=__disabled__} + provider_model_id: databricks-meta-llama-3-1-405b-instruct + model_type: llm +- metadata: {} + model_id: ${env.ENABLE_NVIDIA:=__disabled__}/meta/llama3-8b-instruct + provider_id: ${env.ENABLE_NVIDIA:=__disabled__} + provider_model_id: meta/llama3-8b-instruct + model_type: llm +- metadata: {} + model_id: ${env.ENABLE_NVIDIA:=__disabled__}/meta-llama/Llama-3-8B-Instruct + provider_id: ${env.ENABLE_NVIDIA:=__disabled__} + provider_model_id: meta/llama3-8b-instruct + model_type: llm +- metadata: {} + model_id: ${env.ENABLE_NVIDIA:=__disabled__}/meta/llama3-70b-instruct + provider_id: ${env.ENABLE_NVIDIA:=__disabled__} + provider_model_id: meta/llama3-70b-instruct + model_type: llm +- metadata: {} + model_id: ${env.ENABLE_NVIDIA:=__disabled__}/meta-llama/Llama-3-70B-Instruct + provider_id: ${env.ENABLE_NVIDIA:=__disabled__} + provider_model_id: meta/llama3-70b-instruct + model_type: llm +- metadata: {} + model_id: ${env.ENABLE_NVIDIA:=__disabled__}/meta/llama-3.1-8b-instruct + provider_id: ${env.ENABLE_NVIDIA:=__disabled__} + provider_model_id: meta/llama-3.1-8b-instruct + model_type: llm +- metadata: {} + model_id: ${env.ENABLE_NVIDIA:=__disabled__}/meta-llama/Llama-3.1-8B-Instruct + provider_id: ${env.ENABLE_NVIDIA:=__disabled__} + provider_model_id: meta/llama-3.1-8b-instruct + model_type: llm +- metadata: {} + model_id: ${env.ENABLE_NVIDIA:=__disabled__}/meta/llama-3.1-70b-instruct + provider_id: ${env.ENABLE_NVIDIA:=__disabled__} + provider_model_id: meta/llama-3.1-70b-instruct + model_type: llm +- metadata: {} + model_id: ${env.ENABLE_NVIDIA:=__disabled__}/meta-llama/Llama-3.1-70B-Instruct + provider_id: ${env.ENABLE_NVIDIA:=__disabled__} + provider_model_id: meta/llama-3.1-70b-instruct + model_type: llm +- metadata: {} + model_id: ${env.ENABLE_NVIDIA:=__disabled__}/meta/llama-3.1-405b-instruct + provider_id: ${env.ENABLE_NVIDIA:=__disabled__} + provider_model_id: meta/llama-3.1-405b-instruct + model_type: llm +- metadata: {} + model_id: ${env.ENABLE_NVIDIA:=__disabled__}/meta-llama/Llama-3.1-405B-Instruct-FP8 + provider_id: ${env.ENABLE_NVIDIA:=__disabled__} + provider_model_id: meta/llama-3.1-405b-instruct + model_type: llm +- metadata: {} + model_id: ${env.ENABLE_NVIDIA:=__disabled__}/meta/llama-3.2-1b-instruct + provider_id: ${env.ENABLE_NVIDIA:=__disabled__} + provider_model_id: meta/llama-3.2-1b-instruct + model_type: llm +- metadata: {} + model_id: ${env.ENABLE_NVIDIA:=__disabled__}/meta-llama/Llama-3.2-1B-Instruct + provider_id: ${env.ENABLE_NVIDIA:=__disabled__} + provider_model_id: meta/llama-3.2-1b-instruct + model_type: llm +- metadata: {} + model_id: ${env.ENABLE_NVIDIA:=__disabled__}/meta/llama-3.2-3b-instruct + provider_id: ${env.ENABLE_NVIDIA:=__disabled__} + provider_model_id: meta/llama-3.2-3b-instruct + model_type: llm +- metadata: {} + model_id: ${env.ENABLE_NVIDIA:=__disabled__}/meta-llama/Llama-3.2-3B-Instruct + provider_id: ${env.ENABLE_NVIDIA:=__disabled__} + provider_model_id: meta/llama-3.2-3b-instruct + model_type: llm +- metadata: {} + model_id: ${env.ENABLE_NVIDIA:=__disabled__}/meta/llama-3.2-11b-vision-instruct + provider_id: ${env.ENABLE_NVIDIA:=__disabled__} + provider_model_id: meta/llama-3.2-11b-vision-instruct + model_type: llm +- metadata: {} + model_id: ${env.ENABLE_NVIDIA:=__disabled__}/meta-llama/Llama-3.2-11B-Vision-Instruct + provider_id: ${env.ENABLE_NVIDIA:=__disabled__} + provider_model_id: meta/llama-3.2-11b-vision-instruct + model_type: llm +- metadata: {} + model_id: ${env.ENABLE_NVIDIA:=__disabled__}/meta/llama-3.2-90b-vision-instruct + provider_id: ${env.ENABLE_NVIDIA:=__disabled__} + provider_model_id: meta/llama-3.2-90b-vision-instruct + model_type: llm +- metadata: {} + model_id: ${env.ENABLE_NVIDIA:=__disabled__}/meta-llama/Llama-3.2-90B-Vision-Instruct + provider_id: ${env.ENABLE_NVIDIA:=__disabled__} + provider_model_id: meta/llama-3.2-90b-vision-instruct + model_type: llm +- metadata: {} + model_id: ${env.ENABLE_NVIDIA:=__disabled__}/meta/llama-3.3-70b-instruct + provider_id: ${env.ENABLE_NVIDIA:=__disabled__} + provider_model_id: meta/llama-3.3-70b-instruct + model_type: llm +- metadata: {} + model_id: ${env.ENABLE_NVIDIA:=__disabled__}/meta-llama/Llama-3.3-70B-Instruct + provider_id: ${env.ENABLE_NVIDIA:=__disabled__} + provider_model_id: meta/llama-3.3-70b-instruct + model_type: llm +- metadata: + embedding_dimension: 2048 + context_length: 8192 + model_id: ${env.ENABLE_NVIDIA:=__disabled__}/nvidia/llama-3.2-nv-embedqa-1b-v2 + provider_id: ${env.ENABLE_NVIDIA:=__disabled__} + provider_model_id: nvidia/llama-3.2-nv-embedqa-1b-v2 + model_type: embedding +- metadata: + embedding_dimension: 1024 + context_length: 512 + model_id: ${env.ENABLE_NVIDIA:=__disabled__}/nvidia/nv-embedqa-e5-v5 + provider_id: ${env.ENABLE_NVIDIA:=__disabled__} + provider_model_id: nvidia/nv-embedqa-e5-v5 + model_type: embedding +- metadata: + embedding_dimension: 4096 + context_length: 512 + model_id: ${env.ENABLE_NVIDIA:=__disabled__}/nvidia/nv-embedqa-mistral-7b-v2 + provider_id: ${env.ENABLE_NVIDIA:=__disabled__} + provider_model_id: nvidia/nv-embedqa-mistral-7b-v2 + model_type: embedding +- metadata: + embedding_dimension: 1024 + context_length: 512 + model_id: ${env.ENABLE_NVIDIA:=__disabled__}/snowflake/arctic-embed-l + provider_id: ${env.ENABLE_NVIDIA:=__disabled__} + provider_model_id: snowflake/arctic-embed-l + model_type: embedding +- metadata: {} + model_id: ${env.ENABLE_RUNPOD:=__disabled__}/Llama3.1-8B + provider_id: ${env.ENABLE_RUNPOD:=__disabled__} + provider_model_id: Llama3.1-8B + model_type: llm +- metadata: {} + model_id: ${env.ENABLE_RUNPOD:=__disabled__}/Llama3.1-70B + provider_id: ${env.ENABLE_RUNPOD:=__disabled__} + provider_model_id: Llama3.1-70B + model_type: llm +- metadata: {} + model_id: ${env.ENABLE_RUNPOD:=__disabled__}/Llama3.1-405B:bf16-mp8 + provider_id: ${env.ENABLE_RUNPOD:=__disabled__} + provider_model_id: Llama3.1-405B:bf16-mp8 + model_type: llm +- metadata: {} + model_id: ${env.ENABLE_RUNPOD:=__disabled__}/Llama3.1-405B + provider_id: ${env.ENABLE_RUNPOD:=__disabled__} + provider_model_id: Llama3.1-405B + model_type: llm +- metadata: {} + model_id: ${env.ENABLE_RUNPOD:=__disabled__}/Llama3.1-405B:bf16-mp16 + provider_id: ${env.ENABLE_RUNPOD:=__disabled__} + provider_model_id: Llama3.1-405B:bf16-mp16 + model_type: llm +- metadata: {} + model_id: ${env.ENABLE_RUNPOD:=__disabled__}/Llama3.1-8B-Instruct + provider_id: ${env.ENABLE_RUNPOD:=__disabled__} + provider_model_id: Llama3.1-8B-Instruct + model_type: llm +- metadata: {} + model_id: ${env.ENABLE_RUNPOD:=__disabled__}/Llama3.1-70B-Instruct + provider_id: ${env.ENABLE_RUNPOD:=__disabled__} + provider_model_id: Llama3.1-70B-Instruct + model_type: llm +- metadata: {} + model_id: ${env.ENABLE_RUNPOD:=__disabled__}/Llama3.1-405B-Instruct:bf16-mp8 + provider_id: ${env.ENABLE_RUNPOD:=__disabled__} + provider_model_id: Llama3.1-405B-Instruct:bf16-mp8 + model_type: llm +- metadata: {} + model_id: ${env.ENABLE_RUNPOD:=__disabled__}/Llama3.1-405B-Instruct + provider_id: ${env.ENABLE_RUNPOD:=__disabled__} + provider_model_id: Llama3.1-405B-Instruct + model_type: llm +- metadata: {} + model_id: ${env.ENABLE_RUNPOD:=__disabled__}/Llama3.1-405B-Instruct:bf16-mp16 + provider_id: ${env.ENABLE_RUNPOD:=__disabled__} + provider_model_id: Llama3.1-405B-Instruct:bf16-mp16 + model_type: llm +- metadata: {} + model_id: ${env.ENABLE_RUNPOD:=__disabled__}/Llama3.2-1B + provider_id: ${env.ENABLE_RUNPOD:=__disabled__} + provider_model_id: Llama3.2-1B + model_type: llm +- metadata: {} + model_id: ${env.ENABLE_RUNPOD:=__disabled__}/Llama3.2-3B + provider_id: ${env.ENABLE_RUNPOD:=__disabled__} + provider_model_id: Llama3.2-3B + model_type: llm - metadata: {} model_id: ${env.ENABLE_OPENAI:=__disabled__}/openai/gpt-4o provider_id: ${env.ENABLE_OPENAI:=__disabled__} @@ -889,12 +1173,9 @@ models: provider_id: ${env.ENABLE_SAMBANOVA:=__disabled__} provider_model_id: sambanova/Meta-Llama-Guard-3-8B model_type: llm -- metadata: - embedding_dimension: 384 - model_id: all-MiniLM-L6-v2 - provider_id: ${env.ENABLE_SENTENCE_TRANSFORMERS:=sentence-transformers} - model_type: embedding -shields: [] +shields: +- shield_id: ${env.SAFETY_MODEL:=__disabled__} + provider_shield_id: ${env.ENABLE_OLLAMA:=__disabled__}/${env.SAFETY_MODEL:=__disabled__} vector_dbs: [] datasets: [] scoring_fns: [] diff --git a/llama_stack/templates/starter/starter.py b/llama_stack/templates/starter/starter.py index 773693285..ec6e8fdce 100644 --- a/llama_stack/templates/starter/starter.py +++ b/llama_stack/templates/starter/starter.py @@ -31,6 +31,15 @@ from llama_stack.providers.registry.inference import available_providers from llama_stack.providers.remote.inference.anthropic.models import ( MODEL_ENTRIES as ANTHROPIC_MODEL_ENTRIES, ) +from llama_stack.providers.remote.inference.bedrock.models import ( + MODEL_ENTRIES as BEDROCK_MODEL_ENTRIES, +) +from llama_stack.providers.remote.inference.cerebras.models import ( + MODEL_ENTRIES as CEREBRAS_MODEL_ENTRIES, +) +from llama_stack.providers.remote.inference.databricks.databricks import ( + MODEL_ENTRIES as DATABRICKS_MODEL_ENTRIES, +) from llama_stack.providers.remote.inference.fireworks.models import ( MODEL_ENTRIES as FIREWORKS_MODEL_ENTRIES, ) @@ -40,9 +49,15 @@ from llama_stack.providers.remote.inference.gemini.models import ( from llama_stack.providers.remote.inference.groq.models import ( MODEL_ENTRIES as GROQ_MODEL_ENTRIES, ) +from llama_stack.providers.remote.inference.nvidia.models import ( + MODEL_ENTRIES as NVIDIA_MODEL_ENTRIES, +) from llama_stack.providers.remote.inference.openai.models import ( MODEL_ENTRIES as OPENAI_MODEL_ENTRIES, ) +from llama_stack.providers.remote.inference.runpod.runpod import ( + MODEL_ENTRIES as RUNPOD_MODEL_ENTRIES, +) from llama_stack.providers.remote.inference.sambanova.models import ( MODEL_ENTRIES as SAMBANOVA_MODEL_ENTRIES, ) @@ -59,6 +74,7 @@ from llama_stack.templates.template import ( DistributionTemplate, RunConfigSettings, get_model_registry, + get_shield_registry, ) @@ -72,6 +88,11 @@ def _get_model_entries_for_provider(provider_type: str) -> list[ProviderModelEnt "gemini": GEMINI_MODEL_ENTRIES, "groq": GROQ_MODEL_ENTRIES, "sambanova": SAMBANOVA_MODEL_ENTRIES, + "cerebras": CEREBRAS_MODEL_ENTRIES, + "bedrock": BEDROCK_MODEL_ENTRIES, + "databricks": DATABRICKS_MODEL_ENTRIES, + "nvidia": NVIDIA_MODEL_ENTRIES, + "runpod": RUNPOD_MODEL_ENTRIES, } # Special handling for providers with dynamic model entries @@ -81,6 +102,10 @@ def _get_model_entries_for_provider(provider_type: str) -> list[ProviderModelEnt provider_model_id="${env.OLLAMA_INFERENCE_MODEL:=__disabled__}", model_type=ModelType.llm, ), + ProviderModelEntry( + provider_model_id="${env.SAFETY_MODEL:=__disabled__}", + model_type=ModelType.llm, + ), ProviderModelEntry( provider_model_id="${env.OLLAMA_EMBEDDING_MODEL:=__disabled__}", model_type=ModelType.embedding, @@ -100,6 +125,20 @@ def _get_model_entries_for_provider(provider_type: str) -> list[ProviderModelEnt return model_entries_map.get(provider_type, []) +def _get_model_safety_entries_for_provider(provider_type: str) -> list[ProviderModelEntry]: + """Get model entries for a specific provider type.""" + safety_model_entries_map = { + "ollama": [ + ProviderModelEntry( + provider_model_id="${env.SAFETY_MODEL:=__disabled__}", + model_type=ModelType.llm, + ), + ], + } + + return safety_model_entries_map.get(provider_type, []) + + def _get_config_for_provider(provider_spec: ProviderSpec) -> dict[str, Any]: """Get configuration for a provider using its adapter's config class.""" config_class = instantiate_class_type(provider_spec.config_class) @@ -155,6 +194,23 @@ def get_remote_inference_providers() -> tuple[list[Provider], dict[str, list[Pro return inference_providers, available_models +# build a list of shields for all possible providers +def get_safety_models_for_providers(providers: list[Provider]) -> dict[str, list[ProviderModelEntry]]: + available_models = {} + for provider in providers: + provider_type = provider.provider_type.split("::")[1] + safety_model_entries = _get_model_safety_entries_for_provider(provider_type) + if len(safety_model_entries) == 0: + continue + + env_var = f"ENABLE_{provider_type.upper().replace('-', '_').replace('::', '_')}" + provider_id = f"${{env.{env_var}:=__disabled__}}" + + available_models[provider_id] = safety_model_entries + + return available_models + + def get_distribution_template() -> DistributionTemplate: remote_inference_providers, available_models = get_remote_inference_providers() @@ -185,6 +241,7 @@ def get_distribution_template() -> DistributionTemplate: provider_id="${env.ENABLE_PGVECTOR:=__disabled__}", provider_type="remote::pgvector", config=PGVectorVectorIOConfig.sample_run_config( + f"~/.llama/distributions/{name}", db="${env.PGVECTOR_DB:=}", user="${env.PGVECTOR_USER:=}", password="${env.PGVECTOR_PASSWORD:=}", @@ -244,7 +301,10 @@ def get_distribution_template() -> DistributionTemplate: }, ) - default_models = get_model_registry(available_models) + default_models, ids_conflict_in_models = get_model_registry(available_models) + + available_safety_models = get_safety_models_for_providers(remote_inference_providers) + shields = get_shield_registry(available_safety_models, ids_conflict_in_models) return DistributionTemplate( name=name, @@ -263,12 +323,10 @@ def get_distribution_template() -> DistributionTemplate: "files": [files_provider], "post_training": [post_training_provider], }, - default_models=default_models + [embedding_model], + default_models=[embedding_model] + default_models, default_tool_groups=default_tool_groups, # TODO: add a way to enable/disable shields on the fly - # default_shields=[ - # ShieldInput(provider_id="llama-guard", shield_id="${env.SAFETY_MODEL:=meta-llama/Llama-Guard-3-8B}") - # ], + default_shields=shields, ), }, run_config_env_vars={ diff --git a/llama_stack/templates/template.py b/llama_stack/templates/template.py index dceb13c8b..fb2528873 100644 --- a/llama_stack/templates/template.py +++ b/llama_stack/templates/template.py @@ -37,7 +37,7 @@ from llama_stack.providers.utils.sqlstore.sqlstore import get_pip_packages as ge def get_model_registry( available_models: dict[str, list[ProviderModelEntry]], -) -> list[ModelInput]: +) -> tuple[list[ModelInput], bool]: models = [] # check for conflicts in model ids @@ -74,7 +74,50 @@ def get_model_registry( metadata=entry.metadata, ) ) - return models + return models, ids_conflict + + +def get_shield_registry( + available_safety_models: dict[str, list[ProviderModelEntry]], + ids_conflict_in_models: bool, +) -> list[ShieldInput]: + shields = [] + + # check for conflicts in shield ids + all_ids = set() + ids_conflict = False + + for _, entries in available_safety_models.items(): + for entry in entries: + ids = [entry.provider_model_id] + entry.aliases + for model_id in ids: + if model_id in all_ids: + ids_conflict = True + rich.print( + f"[yellow]Shield id {model_id} conflicts; all shield ids will be prefixed with provider id[/yellow]" + ) + break + all_ids.update(ids) + if ids_conflict: + break + if ids_conflict: + break + + for provider_id, entries in available_safety_models.items(): + for entry in entries: + ids = [entry.provider_model_id] + entry.aliases + for model_id in ids: + identifier = f"{provider_id}/{model_id}" if ids_conflict and provider_id not in model_id else model_id + shields.append( + ShieldInput( + shield_id=identifier, + provider_shield_id=f"{provider_id}/{entry.provider_model_id}" + if ids_conflict_in_models + else entry.provider_model_id, + ) + ) + + return shields class DefaultModel(BaseModel): diff --git a/llama_stack/templates/watsonx/watsonx.py b/llama_stack/templates/watsonx/watsonx.py index 7fa3a55e5..ea185f05d 100644 --- a/llama_stack/templates/watsonx/watsonx.py +++ b/llama_stack/templates/watsonx/watsonx.py @@ -69,7 +69,7 @@ def get_distribution_template() -> DistributionTemplate: }, ) - default_models = get_model_registry(available_models) + default_models, _ = get_model_registry(available_models) return DistributionTemplate( name="watsonx", distro_type="remote_hosted", diff --git a/llama_stack/ui/app/api/auth/[...nextauth]/route.ts b/llama_stack/ui/app/api/auth/[...nextauth]/route.ts new file mode 100644 index 000000000..7b38c1bb4 --- /dev/null +++ b/llama_stack/ui/app/api/auth/[...nextauth]/route.ts @@ -0,0 +1,6 @@ +import NextAuth from "next-auth"; +import { authOptions } from "@/lib/auth"; + +const handler = NextAuth(authOptions); + +export { handler as GET, handler as POST }; diff --git a/llama_stack/ui/app/auth/signin/page.tsx b/llama_stack/ui/app/auth/signin/page.tsx new file mode 100644 index 000000000..c9510fd6b --- /dev/null +++ b/llama_stack/ui/app/auth/signin/page.tsx @@ -0,0 +1,118 @@ +"use client"; + +import { signIn, signOut, useSession } from "next-auth/react"; +import { Button } from "@/components/ui/button"; +import { + Card, + CardContent, + CardDescription, + CardHeader, + CardTitle, +} from "@/components/ui/card"; +import { Copy, Check, Home, Github } from "lucide-react"; +import { useState } from "react"; +import { useRouter } from "next/navigation"; + +export default function SignInPage() { + const { data: session, status } = useSession(); + const [copied, setCopied] = useState(false); + const router = useRouter(); + + const handleCopyToken = async () => { + if (session?.accessToken) { + await navigator.clipboard.writeText(session.accessToken); + setCopied(true); + setTimeout(() => setCopied(false), 2000); + } + }; + + if (status === "loading") { + return ( +
+
Loading...
+
+ ); + } + + return ( +
+ + + Authentication + + {session + ? "You are successfully authenticated!" + : "Sign in with GitHub to use your access token as an API key"} + + + + {!session ? ( + + ) : ( +
+
+ Signed in as {session.user?.email} +
+ + {session.accessToken && ( +
+
+ GitHub Access Token: +
+
+ + {session.accessToken} + + +
+
+ This GitHub token will be used as your API key for + authenticated Llama Stack requests. +
+
+ )} + +
+ + +
+
+ )} + + +
+ ); +} diff --git a/llama_stack/ui/app/layout.tsx b/llama_stack/ui/app/layout.tsx index ed8a6cd5d..19fb18c36 100644 --- a/llama_stack/ui/app/layout.tsx +++ b/llama_stack/ui/app/layout.tsx @@ -1,5 +1,6 @@ import type { Metadata } from "next"; import { ThemeProvider } from "@/components/ui/theme-provider"; +import { SessionProvider } from "@/components/providers/session-provider"; import { Geist, Geist_Mono } from "next/font/google"; import { ModeToggle } from "@/components/ui/mode-toggle"; import "./globals.css"; @@ -21,34 +22,38 @@ export const metadata: Metadata = { import { SidebarProvider, SidebarTrigger } from "@/components/ui/sidebar"; import { AppSidebar } from "@/components/layout/app-sidebar"; +import { SignInButton } from "@/components/ui/sign-in-button"; export default function Layout({ children }: { children: React.ReactNode }) { return ( - - - -
- {/* Header with aligned elements */} -
-
- + + + + +
+ {/* Header with aligned elements */} +
+
+ +
+
+
+ + +
-
-
- -
-
-
{children}
-
- - +
{children}
+ + + + ); diff --git a/llama_stack/ui/app/logs/chat-completions/[id]/page.tsx b/llama_stack/ui/app/logs/chat-completions/[id]/page.tsx index e6feef363..82aa3496e 100644 --- a/llama_stack/ui/app/logs/chat-completions/[id]/page.tsx +++ b/llama_stack/ui/app/logs/chat-completions/[id]/page.tsx @@ -4,11 +4,12 @@ import { useEffect, useState } from "react"; import { useParams } from "next/navigation"; import { ChatCompletion } from "@/lib/types"; import { ChatCompletionDetailView } from "@/components/chat-completions/chat-completion-detail"; -import { client } from "@/lib/client"; +import { useAuthClient } from "@/hooks/use-auth-client"; export default function ChatCompletionDetailPage() { const params = useParams(); const id = params.id as string; + const client = useAuthClient(); const [completionDetail, setCompletionDetail] = useState(null); @@ -45,7 +46,7 @@ export default function ChatCompletionDetailPage() { }; fetchCompletionDetail(); - }, [id]); + }, [id, client]); return ( ( null, @@ -109,7 +110,7 @@ export default function ResponseDetailPage() { }; fetchResponseDetail(); - }, [id]); + }, [id, client]); return ( (null); + const [files, setFiles] = useState([]); + const [isLoadingStore, setIsLoadingStore] = useState(true); + const [isLoadingFiles, setIsLoadingFiles] = useState(true); + const [errorStore, setErrorStore] = useState(null); + const [errorFiles, setErrorFiles] = useState(null); + + useEffect(() => { + if (!id) { + setErrorStore(new Error("Vector Store ID is missing.")); + setIsLoadingStore(false); + return; + } + const fetchStore = async () => { + setIsLoadingStore(true); + setErrorStore(null); + try { + const response = await client.vectorStores.retrieve(id); + setStore(response as VectorStore); + } catch (err) { + setErrorStore( + err instanceof Error + ? err + : new Error("Failed to load vector store."), + ); + } finally { + setIsLoadingStore(false); + } + }; + fetchStore(); + }, [id, client]); + + useEffect(() => { + if (!id) { + setErrorFiles(new Error("Vector Store ID is missing.")); + setIsLoadingFiles(false); + return; + } + const fetchFiles = async () => { + setIsLoadingFiles(true); + setErrorFiles(null); + try { + const result = await client.vectorStores.files.list(id as any); + setFiles((result as any).data); + } catch (err) { + setErrorFiles( + err instanceof Error ? err : new Error("Failed to load files."), + ); + } finally { + setIsLoadingFiles(false); + } + }; + fetchFiles(); + }, [id]); + + return ( + + ); +} diff --git a/llama_stack/ui/app/logs/vector-stores/layout.tsx b/llama_stack/ui/app/logs/vector-stores/layout.tsx new file mode 100644 index 000000000..9245f5486 --- /dev/null +++ b/llama_stack/ui/app/logs/vector-stores/layout.tsx @@ -0,0 +1,16 @@ +"use client"; + +import React from "react"; +import LogsLayout from "@/components/layout/logs-layout"; + +export default function VectorStoresLayout({ + children, +}: { + children: React.ReactNode; +}) { + return ( + + {children} + + ); +} diff --git a/llama_stack/ui/app/logs/vector-stores/page.tsx b/llama_stack/ui/app/logs/vector-stores/page.tsx new file mode 100644 index 000000000..29e1fabd6 --- /dev/null +++ b/llama_stack/ui/app/logs/vector-stores/page.tsx @@ -0,0 +1,121 @@ +"use client"; + +import React from "react"; +import { useAuthClient } from "@/hooks/use-auth-client"; +import type { + ListVectorStoresResponse, + VectorStore, +} from "llama-stack-client/resources/vector-stores/vector-stores"; +import { useRouter } from "next/navigation"; +import { usePagination } from "@/hooks/use-pagination"; +import { + Table, + TableBody, + TableCaption, + TableCell, + TableHead, + TableHeader, + TableRow, +} from "@/components/ui/table"; +import { Skeleton } from "@/components/ui/skeleton"; + +export default function VectorStoresPage() { + const client = useAuthClient(); + const router = useRouter(); + const { + data: stores, + status, + hasMore, + error, + loadMore, + } = usePagination({ + limit: 20, + order: "desc", + fetchFunction: async (client, params) => { + const response = await client.vectorStores.list({ + after: params.after, + limit: params.limit, + order: params.order, + } as any); + return response as ListVectorStoresResponse; + }, + errorMessagePrefix: "vector stores", + }); + + // Auto-load all pages for infinite scroll behavior (like Responses) + React.useEffect(() => { + if (status === "idle" && hasMore) { + loadMore(); + } + }, [status, hasMore, loadMore]); + + if (status === "loading") { + return ( +
+ + + +
+ ); + } + + if (status === "error") { + return
Error: {error?.message}
; + } + + if (!stores || stores.length === 0) { + return

No vector stores found.

; + } + + return ( +
+ + + + ID + Name + Created + Completed + Cancelled + Failed + In Progress + Total + Usage Bytes + Provider ID + Provider Vector DB ID + + + + {stores.map((store) => { + const fileCounts = store.file_counts; + const metadata = store.metadata || {}; + const providerId = metadata.provider_id ?? ""; + const providerDbId = metadata.provider_vector_db_id ?? ""; + + return ( + router.push(`/logs/vector-stores/${store.id}`)} + className="cursor-pointer hover:bg-muted/50" + > + {store.id} + {store.name} + + {new Date(store.created_at * 1000).toLocaleString()} + + {fileCounts.completed} + {fileCounts.cancelled} + {fileCounts.failed} + {fileCounts.in_progress} + {fileCounts.total} + {store.usage_bytes} + {providerId} + {providerDbId} + + ); + })} + +
+
+ ); +} diff --git a/llama_stack/ui/components/chat-completions/chat-completion-table.test.tsx b/llama_stack/ui/components/chat-completions/chat-completion-table.test.tsx index a32e795bc..9171e0106 100644 --- a/llama_stack/ui/components/chat-completions/chat-completion-table.test.tsx +++ b/llama_stack/ui/components/chat-completions/chat-completion-table.test.tsx @@ -12,24 +12,34 @@ jest.mock("next/navigation", () => ({ }), })); +// Mock next-auth +jest.mock("next-auth/react", () => ({ + useSession: () => ({ + status: "authenticated", + data: { accessToken: "mock-token" }, + }), +})); + // Mock helper functions jest.mock("@/lib/truncate-text"); jest.mock("@/lib/format-message-content"); -// Mock the client -jest.mock("@/lib/client", () => ({ - client: { - chat: { - completions: { - list: jest.fn(), - }, +// Mock the auth client hook +const mockClient = { + chat: { + completions: { + list: jest.fn(), }, }, +}; + +jest.mock("@/hooks/use-auth-client", () => ({ + useAuthClient: () => mockClient, })); // Mock the usePagination hook const mockLoadMore = jest.fn(); -jest.mock("@/hooks/usePagination", () => ({ +jest.mock("@/hooks/use-pagination", () => ({ usePagination: jest.fn(() => ({ data: [], status: "idle", @@ -47,7 +57,7 @@ import { } from "@/lib/format-message-content"; // Import the mocked hook -import { usePagination } from "@/hooks/usePagination"; +import { usePagination } from "@/hooks/use-pagination"; const mockedUsePagination = usePagination as jest.MockedFunction< typeof usePagination >; diff --git a/llama_stack/ui/components/chat-completions/chat-completions-table.tsx b/llama_stack/ui/components/chat-completions/chat-completions-table.tsx index d5838d877..65f6c71af 100644 --- a/llama_stack/ui/components/chat-completions/chat-completions-table.tsx +++ b/llama_stack/ui/components/chat-completions/chat-completions-table.tsx @@ -10,8 +10,7 @@ import { extractTextFromContentPart, extractDisplayableText, } from "@/lib/format-message-content"; -import { usePagination } from "@/hooks/usePagination"; -import { client } from "@/lib/client"; +import { usePagination } from "@/hooks/use-pagination"; interface ChatCompletionsTableProps { /** Optional pagination configuration */ @@ -32,12 +31,15 @@ function formatChatCompletionToRow(completion: ChatCompletion): LogTableRow { export function ChatCompletionsTable({ paginationOptions, }: ChatCompletionsTableProps) { - const fetchFunction = async (params: { - after?: string; - limit: number; - model?: string; - order?: string; - }) => { + const fetchFunction = async ( + client: ReturnType, + params: { + after?: string; + limit: number; + model?: string; + order?: string; + }, + ) => { const response = await client.chat.completions.list({ after: params.after, limit: params.limit, diff --git a/llama_stack/ui/components/layout/app-sidebar.tsx b/llama_stack/ui/components/layout/app-sidebar.tsx index 1c53d6cc5..532e43dbd 100644 --- a/llama_stack/ui/components/layout/app-sidebar.tsx +++ b/llama_stack/ui/components/layout/app-sidebar.tsx @@ -1,6 +1,11 @@ "use client"; -import { MessageSquareText, MessagesSquare, MoveUpRight } from "lucide-react"; +import { + MessageSquareText, + MessagesSquare, + MoveUpRight, + Database, +} from "lucide-react"; import Link from "next/link"; import { usePathname } from "next/navigation"; import { cn } from "@/lib/utils"; @@ -28,6 +33,11 @@ const logItems = [ url: "/logs/responses", icon: MessagesSquare, }, + { + title: "Vector Stores", + url: "/logs/vector-stores", + icon: Database, + }, { title: "Documentation", url: "https://llama-stack.readthedocs.io/en/latest/references/api_reference/index.html", @@ -57,13 +67,13 @@ export function AppSidebar() { className={cn( "justify-start", isActive && - "bg-gray-200 hover:bg-gray-200 text-primary hover:text-primary", + "bg-gray-200 dark:bg-gray-700 hover:bg-gray-200 dark:hover:bg-gray-700 text-gray-900 dark:text-gray-100", )} > diff --git a/llama_stack/ui/components/layout/detail-layout.tsx b/llama_stack/ui/components/layout/detail-layout.tsx index 58b912703..3013195a2 100644 --- a/llama_stack/ui/components/layout/detail-layout.tsx +++ b/llama_stack/ui/components/layout/detail-layout.tsx @@ -93,7 +93,9 @@ export function PropertyItem({ > {label}:{" "} {typeof value === "string" || typeof value === "number" ? ( - {value} + + {value} + ) : ( value )} @@ -112,7 +114,9 @@ export function PropertiesCard({ children }: PropertiesCardProps) { Properties -
    {children}
+
    + {children} +
); diff --git a/llama_stack/ui/components/logs/logs-table-scroll.test.tsx b/llama_stack/ui/components/logs/logs-table-scroll.test.tsx index 0a143cc16..a5c3fde46 100644 --- a/llama_stack/ui/components/logs/logs-table-scroll.test.tsx +++ b/llama_stack/ui/components/logs/logs-table-scroll.test.tsx @@ -12,7 +12,7 @@ jest.mock("next/navigation", () => ({ })); // Mock the useInfiniteScroll hook -jest.mock("@/hooks/useInfiniteScroll", () => ({ +jest.mock("@/hooks/use-infinite-scroll", () => ({ useInfiniteScroll: jest.fn((onLoadMore, options) => { const ref = React.useRef(null); diff --git a/llama_stack/ui/components/logs/logs-table.tsx b/llama_stack/ui/components/logs/logs-table.tsx index 72924cea8..3d4e609c7 100644 --- a/llama_stack/ui/components/logs/logs-table.tsx +++ b/llama_stack/ui/components/logs/logs-table.tsx @@ -4,7 +4,7 @@ import { useRouter } from "next/navigation"; import { useRef } from "react"; import { truncateText } from "@/lib/truncate-text"; import { PaginationStatus } from "@/lib/types"; -import { useInfiniteScroll } from "@/hooks/useInfiniteScroll"; +import { useInfiniteScroll } from "@/hooks/use-infinite-scroll"; import { Table, TableBody, diff --git a/llama_stack/ui/components/providers/session-provider.tsx b/llama_stack/ui/components/providers/session-provider.tsx new file mode 100644 index 000000000..e194e5f0a --- /dev/null +++ b/llama_stack/ui/components/providers/session-provider.tsx @@ -0,0 +1,7 @@ +"use client"; + +import { SessionProvider as NextAuthSessionProvider } from "next-auth/react"; + +export function SessionProvider({ children }: { children: React.ReactNode }) { + return {children}; +} diff --git a/llama_stack/ui/components/responses/responses-table.test.tsx b/llama_stack/ui/components/responses/responses-table.test.tsx index 2286f10d5..0338b9151 100644 --- a/llama_stack/ui/components/responses/responses-table.test.tsx +++ b/llama_stack/ui/components/responses/responses-table.test.tsx @@ -12,21 +12,31 @@ jest.mock("next/navigation", () => ({ }), })); +// Mock next-auth +jest.mock("next-auth/react", () => ({ + useSession: () => ({ + status: "authenticated", + data: { accessToken: "mock-token" }, + }), +})); + // Mock helper functions jest.mock("@/lib/truncate-text"); -// Mock the client -jest.mock("@/lib/client", () => ({ - client: { - responses: { - list: jest.fn(), - }, +// Mock the auth client hook +const mockClient = { + responses: { + list: jest.fn(), }, +}; + +jest.mock("@/hooks/use-auth-client", () => ({ + useAuthClient: () => mockClient, })); // Mock the usePagination hook const mockLoadMore = jest.fn(); -jest.mock("@/hooks/usePagination", () => ({ +jest.mock("@/hooks/use-pagination", () => ({ usePagination: jest.fn(() => ({ data: [], status: "idle", @@ -40,7 +50,7 @@ jest.mock("@/hooks/usePagination", () => ({ import { truncateText as originalTruncateText } from "@/lib/truncate-text"; // Import the mocked hook -import { usePagination } from "@/hooks/usePagination"; +import { usePagination } from "@/hooks/use-pagination"; const mockedUsePagination = usePagination as jest.MockedFunction< typeof usePagination >; diff --git a/llama_stack/ui/components/responses/responses-table.tsx b/llama_stack/ui/components/responses/responses-table.tsx index 116a2adbe..a3e8c0c15 100644 --- a/llama_stack/ui/components/responses/responses-table.tsx +++ b/llama_stack/ui/components/responses/responses-table.tsx @@ -6,8 +6,7 @@ import { UsePaginationOptions, } from "@/lib/types"; import { LogsTable, LogTableRow } from "@/components/logs/logs-table"; -import { usePagination } from "@/hooks/usePagination"; -import { client } from "@/lib/client"; +import { usePagination } from "@/hooks/use-pagination"; import type { ResponseListResponse } from "llama-stack-client/resources/responses/responses"; import { isMessageInput, @@ -125,12 +124,15 @@ function formatResponseToRow(response: OpenAIResponse): LogTableRow { } export function ResponsesTable({ paginationOptions }: ResponsesTableProps) { - const fetchFunction = async (params: { - after?: string; - limit: number; - model?: string; - order?: string; - }) => { + const fetchFunction = async ( + client: ReturnType, + params: { + after?: string; + limit: number; + model?: string; + order?: string; + }, + ) => { const response = await client.responses.list({ after: params.after, limit: params.limit, diff --git a/llama_stack/ui/components/ui/message-components.tsx b/llama_stack/ui/components/ui/message-components.tsx index 50ccd623e..39cb570b7 100644 --- a/llama_stack/ui/components/ui/message-components.tsx +++ b/llama_stack/ui/components/ui/message-components.tsx @@ -17,10 +17,10 @@ export const MessageBlock: React.FC = ({ }) => { return (
-

+

{label} {labelDetail && ( - + {labelDetail} )} diff --git a/llama_stack/ui/components/ui/sign-in-button.tsx b/llama_stack/ui/components/ui/sign-in-button.tsx new file mode 100644 index 000000000..caa15772f --- /dev/null +++ b/llama_stack/ui/components/ui/sign-in-button.tsx @@ -0,0 +1,25 @@ +"use client"; + +import { User } from "lucide-react"; +import Link from "next/link"; +import { useSession } from "next-auth/react"; +import { Button } from "./button"; + +export function SignInButton() { + const { data: session, status } = useSession(); + + return ( + + ); +} diff --git a/llama_stack/ui/components/vector-stores/vector-store-detail.tsx b/llama_stack/ui/components/vector-stores/vector-store-detail.tsx new file mode 100644 index 000000000..7c5c91dd3 --- /dev/null +++ b/llama_stack/ui/components/vector-stores/vector-store-detail.tsx @@ -0,0 +1,128 @@ +"use client"; + +import type { VectorStore } from "llama-stack-client/resources/vector-stores/vector-stores"; +import type { VectorStoreFile } from "llama-stack-client/resources/vector-stores/files"; +import { Card, CardContent, CardHeader, CardTitle } from "@/components/ui/card"; +import { Skeleton } from "@/components/ui/skeleton"; +import { + DetailLoadingView, + DetailErrorView, + DetailNotFoundView, + DetailLayout, + PropertiesCard, + PropertyItem, +} from "@/components/layout/detail-layout"; +import { + Table, + TableBody, + TableCaption, + TableCell, + TableHead, + TableHeader, + TableRow, +} from "@/components/ui/table"; + +interface VectorStoreDetailViewProps { + store: VectorStore | null; + files: VectorStoreFile[]; + isLoadingStore: boolean; + isLoadingFiles: boolean; + errorStore: Error | null; + errorFiles: Error | null; + id: string; +} + +export function VectorStoreDetailView({ + store, + files, + isLoadingStore, + isLoadingFiles, + errorStore, + errorFiles, + id, +}: VectorStoreDetailViewProps) { + const title = "Vector Store Details"; + + if (errorStore) { + return ; + } + if (isLoadingStore) { + return ; + } + if (!store) { + return ; + } + + const mainContent = ( + <> + + + Files + + + {isLoadingFiles ? ( + + ) : errorFiles ? ( +

+ Error loading files: {errorFiles.message} +
+ ) : files.length > 0 ? ( + + Files in this vector store + + + ID + Status + Created + Usage Bytes + + + + {files.map((file) => ( + + {file.id} + {file.status} + + {new Date(file.created_at * 1000).toLocaleString()} + + {file.usage_bytes} + + ))} + +
+ ) : ( +

+ No files in this vector store. +

+ )} + + + + ); + + const sidebar = ( + + + + + + + + + + + ); + + return ( + + ); +} diff --git a/llama_stack/ui/hooks/use-auth-client.ts b/llama_stack/ui/hooks/use-auth-client.ts new file mode 100644 index 000000000..94ae769f4 --- /dev/null +++ b/llama_stack/ui/hooks/use-auth-client.ts @@ -0,0 +1,24 @@ +import { useSession } from "next-auth/react"; +import { useMemo } from "react"; +import LlamaStackClient from "llama-stack-client"; + +export function useAuthClient() { + const { data: session } = useSession(); + + const client = useMemo(() => { + const clientHostname = + typeof window !== "undefined" ? window.location.origin : ""; + + const options: any = { + baseURL: `${clientHostname}/api`, + }; + + if (session?.accessToken) { + options.apiKey = session.accessToken; + } + + return new LlamaStackClient(options); + }, [session?.accessToken]); + + return client; +} diff --git a/llama_stack/ui/hooks/useInfiniteScroll.ts b/llama_stack/ui/hooks/use-infinite-scroll.ts similarity index 100% rename from llama_stack/ui/hooks/useInfiniteScroll.ts rename to llama_stack/ui/hooks/use-infinite-scroll.ts diff --git a/llama_stack/ui/hooks/usePagination.ts b/llama_stack/ui/hooks/use-pagination.ts similarity index 65% rename from llama_stack/ui/hooks/usePagination.ts rename to llama_stack/ui/hooks/use-pagination.ts index 55a8d4ac8..58847ece5 100644 --- a/llama_stack/ui/hooks/usePagination.ts +++ b/llama_stack/ui/hooks/use-pagination.ts @@ -2,6 +2,9 @@ import { useState, useCallback, useEffect, useRef } from "react"; import { PaginationStatus, UsePaginationOptions } from "@/lib/types"; +import { useSession } from "next-auth/react"; +import { useAuthClient } from "@/hooks/use-auth-client"; +import { useRouter } from "next/navigation"; interface PaginationState { data: T[]; @@ -28,13 +31,18 @@ export interface PaginationReturn { } interface UsePaginationParams extends UsePaginationOptions { - fetchFunction: (params: { - after?: string; - limit: number; - model?: string; - order?: string; - }) => Promise>; + fetchFunction: ( + client: ReturnType, + params: { + after?: string; + limit: number; + model?: string; + order?: string; + }, + ) => Promise>; errorMessagePrefix: string; + enabled?: boolean; + useAuth?: boolean; } export function usePagination({ @@ -43,7 +51,12 @@ export function usePagination({ order = "desc", fetchFunction, errorMessagePrefix, + enabled = true, + useAuth = true, }: UsePaginationParams): PaginationReturn { + const { status: sessionStatus } = useSession(); + const client = useAuthClient(); + const router = useRouter(); const [state, setState] = useState>({ data: [], status: "loading", @@ -74,7 +87,7 @@ export function usePagination({ error: null, })); - const response = await fetchFunction({ + const response = await fetchFunction(client, { after: after || undefined, limit: fetchLimit, ...(model && { model }), @@ -91,6 +104,17 @@ export function usePagination({ status: "idle", })); } catch (err) { + // Check if it's a 401 unauthorized error + if ( + err && + typeof err === "object" && + "status" in err && + err.status === 401 + ) { + router.push("/auth/signin"); + return; + } + const errorMessage = isInitialLoad ? `Failed to load ${errorMessagePrefix}. Please try refreshing the page.` : `Failed to load more ${errorMessagePrefix}. Please try again.`; @@ -107,7 +131,7 @@ export function usePagination({ })); } }, - [limit, model, order, fetchFunction, errorMessagePrefix], + [limit, model, order, fetchFunction, errorMessagePrefix, client, router], ); /** @@ -120,17 +144,28 @@ export function usePagination({ } }, [fetchData]); - // Auto-load initial data on mount + // Auto-load initial data on mount when enabled useEffect(() => { - if (!hasFetchedInitialData.current) { + // If using auth, wait for session to load + const isAuthReady = !useAuth || sessionStatus !== "loading"; + const shouldFetch = enabled && isAuthReady; + + if (shouldFetch && !hasFetchedInitialData.current) { hasFetchedInitialData.current = true; fetchData(); + } else if (!shouldFetch) { + // Reset the flag when disabled so it can fetch when re-enabled + hasFetchedInitialData.current = false; } - }, [fetchData]); + }, [fetchData, enabled, useAuth, sessionStatus]); + + // Override status if we're waiting for auth + const effectiveStatus = + useAuth && sessionStatus === "loading" ? "loading" : state.status; return { data: state.data, - status: state.status, + status: effectiveStatus, hasMore: state.hasMore, error: state.error, loadMore, diff --git a/llama_stack/ui/instrumentation.ts b/llama_stack/ui/instrumentation.ts new file mode 100644 index 000000000..903528443 --- /dev/null +++ b/llama_stack/ui/instrumentation.ts @@ -0,0 +1,11 @@ +/** + * Next.js Instrumentation + * This file is used for initializing monitoring, tracing, or other observability tools. + * It runs once when the server starts, before any application code. + * + * Learn more: https://nextjs.org/docs/app/building-your-application/optimizing/instrumentation + */ + +export async function register() { + await import("./lib/config-validator"); +} diff --git a/llama_stack/ui/lib/auth.ts b/llama_stack/ui/lib/auth.ts new file mode 100644 index 000000000..bc18b168b --- /dev/null +++ b/llama_stack/ui/lib/auth.ts @@ -0,0 +1,38 @@ +import { NextAuthOptions } from "next-auth"; +import GithubProvider from "next-auth/providers/github"; + +export const authOptions: NextAuthOptions = { + providers: [ + GithubProvider({ + clientId: process.env.GITHUB_CLIENT_ID!, + clientSecret: process.env.GITHUB_CLIENT_SECRET!, + authorization: { + params: { + scope: "read:user user:email", + }, + }, + }), + ], + debug: process.env.NODE_ENV === "development", + callbacks: { + async jwt({ token, account }) { + // Persist the OAuth access_token to the token right after signin + if (account) { + token.accessToken = account.access_token; + } + return token; + }, + async session({ session, token }) { + // Send properties to the client, like an access_token from a provider. + session.accessToken = token.accessToken as string; + return session; + }, + }, + pages: { + signIn: "/auth/signin", + error: "/auth/signin", // Redirect errors to our custom page + }, + session: { + strategy: "jwt", + }, +}; diff --git a/llama_stack/ui/lib/client.ts b/llama_stack/ui/lib/client.ts deleted file mode 100644 index 8492496e2..000000000 --- a/llama_stack/ui/lib/client.ts +++ /dev/null @@ -1,6 +0,0 @@ -import LlamaStackClient from "llama-stack-client"; - -export const client = new LlamaStackClient({ - baseURL: - typeof window !== "undefined" ? `${window.location.origin}/api` : "/api", -}); diff --git a/llama_stack/ui/lib/config-validator.ts b/llama_stack/ui/lib/config-validator.ts new file mode 100644 index 000000000..19f4397b8 --- /dev/null +++ b/llama_stack/ui/lib/config-validator.ts @@ -0,0 +1,56 @@ +/** + * Validates environment configuration for the application + * This is called during server initialization + */ +export function validateServerConfig() { + if (process.env.NODE_ENV === "development") { + console.log("🚀 Starting Llama Stack UI Server..."); + + // Check optional configurations + const optionalConfigs = { + NEXTAUTH_URL: process.env.NEXTAUTH_URL || "http://localhost:8322", + LLAMA_STACK_BACKEND_URL: + process.env.LLAMA_STACK_BACKEND_URL || "http://localhost:8321", + LLAMA_STACK_UI_PORT: process.env.LLAMA_STACK_UI_PORT || "8322", + GITHUB_CLIENT_ID: process.env.GITHUB_CLIENT_ID, + GITHUB_CLIENT_SECRET: process.env.GITHUB_CLIENT_SECRET, + }; + + console.log("\n📋 Configuration:"); + console.log(` - NextAuth URL: ${optionalConfigs.NEXTAUTH_URL}`); + console.log(` - Backend URL: ${optionalConfigs.LLAMA_STACK_BACKEND_URL}`); + console.log(` - UI Port: ${optionalConfigs.LLAMA_STACK_UI_PORT}`); + + // Check GitHub OAuth configuration + if ( + !optionalConfigs.GITHUB_CLIENT_ID || + !optionalConfigs.GITHUB_CLIENT_SECRET + ) { + console.log( + "\n📝 GitHub OAuth not configured (authentication features disabled)", + ); + console.log(" To enable GitHub OAuth:"); + console.log(" 1. Go to https://github.com/settings/applications/new"); + console.log( + " 2. Set Application name: Llama Stack UI (or your preferred name)", + ); + console.log(" 3. Set Homepage URL: http://localhost:8322"); + console.log( + " 4. Set Authorization callback URL: http://localhost:8322/api/auth/callback/github", + ); + console.log( + " 5. Create the app and copy the Client ID and Client Secret", + ); + console.log(" 6. Add them to your .env.local file:"); + console.log(" GITHUB_CLIENT_ID=your_client_id"); + console.log(" GITHUB_CLIENT_SECRET=your_client_secret"); + } else { + console.log(" - GitHub OAuth: ✅ Configured"); + } + + console.log(""); + } +} + +// Call this function when the module is imported +validateServerConfig(); diff --git a/llama_stack/ui/package-lock.json b/llama_stack/ui/package-lock.json index 3c60dbb39..158569241 100644 --- a/llama_stack/ui/package-lock.json +++ b/llama_stack/ui/package-lock.json @@ -15,9 +15,10 @@ "@radix-ui/react-tooltip": "^1.2.6", "class-variance-authority": "^0.7.1", "clsx": "^2.1.1", - "llama-stack-client": "0.2.9", + "llama-stack-client": "^0.2.14", "lucide-react": "^0.510.0", - "next": "15.3.2", + "next": "15.3.3", + "next-auth": "^4.24.11", "next-themes": "^0.4.6", "react": "^19.0.0", "react-dom": "^19.0.0", @@ -548,7 +549,6 @@ "version": "7.27.1", "resolved": "https://registry.npmjs.org/@babel/runtime/-/runtime-7.27.1.tgz", "integrity": 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+ "integrity": "sha512-a5ERWK1kh38ExDEfoO6qUHJb32rd7aYmPHuyCu3Fta/cnICvYmgd2uhuKXvPD+PXB+gCEYYEaQdIRAjCOwAKNA==", + "license": "MIT" + }, "node_modules/object-assign": { "version": "4.1.1", "resolved": "https://registry.npmjs.org/object-assign/-/object-assign-4.1.1.tgz", @@ -10092,6 +9698,15 @@ "node": ">=0.10.0" } }, + "node_modules/object-hash": { + "version": "2.2.0", + "resolved": "https://registry.npmjs.org/object-hash/-/object-hash-2.2.0.tgz", + "integrity": "sha512-gScRMn0bS5fH+IuwyIFgnh9zBdo4DV+6GhygmWM9HyNJSgS0hScp1f5vjtm7oIIOiT9trXrShAkLFSc2IqKNgw==", + "license": "MIT", + "engines": { + "node": ">= 6" + } + }, "node_modules/object-inspect": { "version": "1.13.4", "resolved": "https://registry.npmjs.org/object-inspect/-/object-inspect-1.13.4.tgz", @@ -10205,6 +9820,15 @@ "url": "https://github.com/sponsors/ljharb" } }, + "node_modules/oidc-token-hash": { + "version": "5.1.0", + "resolved": "https://registry.npmjs.org/oidc-token-hash/-/oidc-token-hash-5.1.0.tgz", + 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"license": "MIT", + "funding": { + "type": "opencollective", + "url": "https://opencollective.com/preact" + } + }, + "node_modules/preact-render-to-string": { + "version": "5.2.6", + "resolved": "https://registry.npmjs.org/preact-render-to-string/-/preact-render-to-string-5.2.6.tgz", + "integrity": "sha512-JyhErpYOvBV1hEPwIxc/fHWXPfnEGdRKxc8gFdAZ7XV4tlzyzG847XAyEZqoDnynP88akM4eaHcSOzNcLWFguw==", + "license": "MIT", + "dependencies": { + "pretty-format": "^3.8.0" + }, + "peerDependencies": { + "preact": ">=10" + } + }, + "node_modules/preact-render-to-string/node_modules/pretty-format": { + "version": "3.8.0", + "resolved": "https://registry.npmjs.org/pretty-format/-/pretty-format-3.8.0.tgz", + "integrity": "sha512-WuxUnVtlWL1OfZFQFuqvnvs6MiAGk9UNsBostyBOB0Is9wb5uRESevA6rnl/rkksXaGX3GzZhPup5d6Vp1nFew==", + "license": "MIT" + }, "node_modules/prelude-ls": { "version": "1.2.1", "resolved": "https://registry.npmjs.org/prelude-ls/-/prelude-ls-1.2.1.tgz", @@ -11025,6 +10710,7 @@ "version": 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"resolved": "https://registry.npmjs.org/tw-animate-css/-/tw-animate-css-1.2.9.tgz", @@ -12420,6 +12087,15 @@ } } }, + "node_modules/uuid": { + "version": "8.3.2", + "resolved": "https://registry.npmjs.org/uuid/-/uuid-8.3.2.tgz", + "integrity": "sha512-+NYs2QeMWy+GWFOEm9xnn6HCDp0l7QBD7ml8zLUmJ+93Q5NF0NocErnwkTkXVFNiX3/fpC6afS8Dhb/gz7R7eg==", + "license": "MIT", + "bin": { + "uuid": "dist/bin/uuid" + } + }, "node_modules/v8-compile-cache-lib": { "version": "3.0.1", "resolved": "https://registry.npmjs.org/v8-compile-cache-lib/-/v8-compile-cache-lib-3.0.1.tgz", diff --git a/llama_stack/ui/package.json b/llama_stack/ui/package.json index 040f803f4..9d448998c 100644 --- a/llama_stack/ui/package.json +++ b/llama_stack/ui/package.json @@ -20,9 +20,10 @@ "@radix-ui/react-tooltip": "^1.2.6", "class-variance-authority": "^0.7.1", "clsx": "^2.1.1", - "llama-stack-client": "0.2.13", + "llama-stack-client": "^0.2.15", "lucide-react": "^0.510.0", - "next": "15.3.2", + "next": "15.3.3", + "next-auth": "^4.24.11", "next-themes": "^0.4.6", "react": "^19.0.0", "react-dom": "^19.0.0", diff --git a/llama_stack/ui/types/next-auth.d.ts b/llama_stack/ui/types/next-auth.d.ts new file mode 100644 index 000000000..4ed718132 --- /dev/null +++ b/llama_stack/ui/types/next-auth.d.ts @@ -0,0 +1,7 @@ +import NextAuth from "next-auth"; + +declare module "next-auth" { + interface Session { + accessToken?: string; + } +} diff --git a/pyproject.toml b/pyproject.toml index 512db60da..15e2e10b4 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta" [project] name = "llama_stack" -version = "0.2.14" +version = "0.2.15" authors = [{ name = "Meta Llama", email = "llama-oss@meta.com" }] description = "Llama Stack" readme = "README.md" @@ -28,11 +28,12 @@ dependencies = [ "huggingface-hub>=0.30.0,<1.0", "jinja2>=3.1.6", "jsonschema", - "llama-stack-client>=0.2.14", + "llama-stack-client>=0.2.15", + "llama-api-client>=0.1.2", "openai>=1.66", "prompt-toolkit", "python-dotenv", - "python-jose", + "python-jose[cryptography]", "pydantic>=2", "rich", "starlette", @@ -42,8 +43,8 @@ dependencies = [ "h11>=0.16.0", "python-multipart>=0.0.20", # For fastapi Form "uvicorn>=0.34.0", # server - "opentelemetry-sdk", # server - "opentelemetry-exporter-otlp-proto-http", # server + "opentelemetry-sdk>=1.30.0", # server + "opentelemetry-exporter-otlp-proto-http>=1.30.0", # server "aiosqlite>=0.21.0", # server - for metadata store "asyncpg", # for metadata store ] @@ -52,18 +53,19 @@ dependencies = [ ui = [ "streamlit", "pandas", - "llama-stack-client>=0.2.14", + "llama-stack-client>=0.2.15", "streamlit-option-menu", ] [dependency-groups] dev = [ - "pytest", + "pytest>=8.4", "pytest-timeout", - "pytest-asyncio", + "pytest-asyncio>=1.0", "pytest-cov", "pytest-html", "pytest-json-report", + "pytest-socket", # For blocking network access in unit tests "nbval", # For notebook testing "black", "ruff", @@ -86,6 +88,10 @@ unit = [ "sqlalchemy[asyncio]>=2.0.41", "blobfile", "faiss-cpu", + "pymilvus>=2.5.12", + "litellm", + "together", + "coverage", ] # These are the core dependencies required for running integration tests. They are shared across all # providers. If a provider requires additional dependencies, please add them to your environment @@ -106,6 +112,7 @@ test = [ "sqlalchemy", "sqlalchemy[asyncio]>=2.0.41", "requests", + "pymilvus>=2.5.12", ] docs = [ "setuptools", @@ -120,6 +127,7 @@ docs = [ "sphinxcontrib.redoc", "sphinxcontrib.video", "sphinxcontrib.mermaid", + "sphinx-reredirects", "tomli", "linkify", "sphinxcontrib.openapi", @@ -223,9 +231,7 @@ follow_imports = "silent" # to exclude the entire directory. exclude = [ # As we fix more and more of these, we should remove them from the list - "^llama_stack/apis/common/training_types\\.py$", "^llama_stack/cli/download\\.py$", - "^llama_stack/cli/stack/_build\\.py$", "^llama_stack/distribution/build\\.py$", "^llama_stack/distribution/client\\.py$", "^llama_stack/distribution/request_headers\\.py$", @@ -237,29 +243,22 @@ exclude = [ "^llama_stack/distribution/store/registry\\.py$", "^llama_stack/distribution/utils/exec\\.py$", "^llama_stack/distribution/utils/prompt_for_config\\.py$", - "^llama_stack/models/llama/llama3/chat_format\\.py$", "^llama_stack/models/llama/llama3/interface\\.py$", "^llama_stack/models/llama/llama3/tokenizer\\.py$", "^llama_stack/models/llama/llama3/tool_utils\\.py$", - "^llama_stack/models/llama/llama3_3/prompts\\.py$", "^llama_stack/providers/inline/agents/meta_reference/", "^llama_stack/providers/inline/agents/meta_reference/agent_instance\\.py$", "^llama_stack/providers/inline/agents/meta_reference/agents\\.py$", - "^llama_stack/providers/inline/agents/meta_reference/safety\\.py$", "^llama_stack/providers/inline/datasetio/localfs/", "^llama_stack/providers/inline/eval/meta_reference/eval\\.py$", - "^llama_stack/providers/inline/inference/meta_reference/config\\.py$", "^llama_stack/providers/inline/inference/meta_reference/inference\\.py$", "^llama_stack/models/llama/llama3/generation\\.py$", "^llama_stack/models/llama/llama3/multimodal/model\\.py$", "^llama_stack/models/llama/llama4/", - "^llama_stack/providers/inline/inference/meta_reference/parallel_utils\\.py$", "^llama_stack/providers/inline/inference/meta_reference/quantization/fp8_impls\\.py$", - "^llama_stack/providers/inline/inference/meta_reference/quantization/loader\\.py$", "^llama_stack/providers/inline/inference/sentence_transformers/sentence_transformers\\.py$", "^llama_stack/providers/inline/inference/vllm/", "^llama_stack/providers/inline/post_training/common/validator\\.py$", - "^llama_stack/providers/inline/post_training/torchtune/post_training\\.py$", "^llama_stack/providers/inline/safety/code_scanner/", "^llama_stack/providers/inline/safety/llama_guard/", "^llama_stack/providers/inline/safety/prompt_guard/", @@ -344,3 +343,9 @@ warn_required_dynamic_aliases = true [tool.ruff.lint.pep8-naming] classmethod-decorators = ["classmethod", "pydantic.field_validator"] + +[tool.pytest.ini_options] +asyncio_mode = "auto" +markers = [ + "allow_network: Allow network access for specific unit tests", +] diff --git a/requirements.txt b/requirements.txt index 47f0d9660..1106efac5 100644 --- a/requirements.txt +++ b/requirements.txt @@ -13,6 +13,7 @@ annotated-types==0.7.0 anyio==4.8.0 # via # httpx + # llama-api-client # llama-stack-client # openai # starlette @@ -28,6 +29,8 @@ certifi==2025.1.31 # httpcore # httpx # requests +cffi==1.17.1 ; platform_python_implementation != 'PyPy' + # via cryptography charset-normalizer==3.4.1 # via requests click==8.1.8 @@ -38,6 +41,8 @@ colorama==0.4.6 ; sys_platform == 'win32' # via # click # tqdm +cryptography==45.0.5 + # via python-jose deprecated==1.2.18 # via # opentelemetry-api @@ -45,6 +50,7 @@ deprecated==1.2.18 # opentelemetry-semantic-conventions distro==1.9.0 # via + # llama-api-client # llama-stack-client # openai ecdsa==0.19.1 @@ -76,6 +82,7 @@ httpcore==1.0.9 # via httpx httpx==0.28.1 # via + # llama-api-client # llama-stack # llama-stack-client # openai @@ -97,7 +104,9 @@ jsonschema==4.23.0 # via llama-stack jsonschema-specifications==2024.10.1 # via jsonschema -llama-stack-client==0.2.14 +llama-api-client==0.1.2 + # via llama-stack +llama-stack-client==0.2.15 # via llama-stack markdown-it-py==3.0.0 # via rich @@ -156,9 +165,12 @@ pyasn1==0.4.8 # via # python-jose # rsa +pycparser==2.22 ; platform_python_implementation != 'PyPy' + # via cffi pydantic==2.10.6 # via # fastapi + # llama-api-client # llama-stack # llama-stack-client # openai @@ -209,6 +221,7 @@ six==1.17.0 sniffio==1.3.1 # via # anyio + # llama-api-client # llama-stack-client # openai starlette==0.45.3 @@ -233,6 +246,7 @@ typing-extensions==4.12.2 # anyio # fastapi # huggingface-hub + # llama-api-client # llama-stack-client # openai # opentelemetry-sdk diff --git a/install.sh b/scripts/install.sh similarity index 100% rename from install.sh rename to scripts/install.sh diff --git a/scripts/unit-tests.sh b/scripts/unit-tests.sh index 1fc3ff7fc..458cd383d 100755 --- a/scripts/unit-tests.sh +++ b/scripts/unit-tests.sh @@ -16,4 +16,9 @@ if [ $FOUND_PYTHON -ne 0 ]; then uv python install "$PYTHON_VERSION" fi -uv run --python "$PYTHON_VERSION" --with-editable . --group unit pytest --asyncio-mode=auto -s -v tests/unit/ $@ +# Run unit tests with coverage +uv run --python "$PYTHON_VERSION" --with-editable . --group unit \ + coverage run --source=llama_stack -m pytest -s -v tests/unit/ "$@" + +# Generate HTML coverage report +uv run --python "$PYTHON_VERSION" coverage html -d htmlcov-$PYTHON_VERSION diff --git a/tests/Containerfile b/tests/Containerfile index 3080d053a..d58216810 100644 --- a/tests/Containerfile +++ b/tests/Containerfile @@ -7,7 +7,8 @@ FROM --platform=linux/amd64 ollama/ollama:latest RUN ollama serve & \ sleep 5 && \ ollama pull llama3.2:3b-instruct-fp16 && \ - ollama pull all-minilm:latest + ollama pull all-minilm:l6-v2 && \ + ollama pull llama-guard3:1b # Set the entrypoint to start ollama serve ENTRYPOINT ["ollama", "serve"] diff --git a/tests/external-provider/llama-stack-provider-ollama/run.yaml b/tests/external-provider/llama-stack-provider-ollama/run.yaml index 60cff7503..65fd7571c 100644 --- a/tests/external-provider/llama-stack-provider-ollama/run.yaml +++ b/tests/external-provider/llama-stack-provider-ollama/run.yaml @@ -105,7 +105,7 @@ models: embedding_dimension: 384 model_id: all-MiniLM-L6-v2 provider_id: custom_ollama - provider_model_id: all-minilm:latest + provider_model_id: all-minilm:l6-v2 model_type: embedding shields: [] vector_dbs: [] diff --git a/tests/integration/agents/test_agents.py b/tests/integration/agents/test_agents.py index 66c9ab829..05549cf18 100644 --- a/tests/integration/agents/test_agents.py +++ b/tests/integration/agents/test_agents.py @@ -77,6 +77,24 @@ def agent_config(llama_stack_client, text_model_id): return agent_config +@pytest.fixture(scope="session") +def agent_config_without_safety(text_model_id): + agent_config = dict( + model=text_model_id, + instructions="You are a helpful assistant", + sampling_params={ + "strategy": { + "type": "top_p", + "temperature": 0.0001, + "top_p": 0.9, + }, + }, + tools=[], + enable_session_persistence=False, + ) + return agent_config + + def test_agent_simple(llama_stack_client, agent_config): agent = Agent(llama_stack_client, **agent_config) session_id = agent.create_session(f"test-session-{uuid4()}") @@ -491,7 +509,7 @@ def test_rag_agent(llama_stack_client, agent_config, rag_tool_name): assert expected_kw in response.output_message.content.lower() -def test_rag_agent_with_attachments(llama_stack_client, agent_config): +def test_rag_agent_with_attachments(llama_stack_client, agent_config_without_safety): urls = ["llama3.rst", "lora_finetune.rst"] documents = [ # passign as url @@ -514,14 +532,8 @@ def test_rag_agent_with_attachments(llama_stack_client, agent_config): metadata={}, ), ] - rag_agent = Agent(llama_stack_client, **agent_config) + rag_agent = Agent(llama_stack_client, **agent_config_without_safety) session_id = rag_agent.create_session(f"test-session-{uuid4()}") - user_prompts = [ - ( - "Instead of the standard multi-head attention, what attention type does Llama3-8B use?", - "grouped", - ), - ] user_prompts = [ ( "I am attaching some documentation for Torchtune. Help me answer questions I will ask next.", @@ -549,82 +561,6 @@ def test_rag_agent_with_attachments(llama_stack_client, agent_config): assert "lora" in response.output_message.content.lower() -@pytest.mark.skip(reason="Code interpreter is currently disabled in the Stack") -def test_rag_and_code_agent(llama_stack_client, agent_config): - if "llama-4" in agent_config["model"].lower(): - pytest.xfail("Not working for llama4") - - documents = [] - documents.append( - Document( - document_id="nba_wiki", - content="The NBA was created on August 3, 1949, with the merger of the Basketball Association of America (BAA) and the National Basketball League (NBL).", - metadata={}, - ) - ) - documents.append( - Document( - document_id="perplexity_wiki", - content="""Perplexity the company was founded in 2022 by Aravind Srinivas, Andy Konwinski, Denis Yarats and Johnny Ho, engineers with backgrounds in back-end systems, artificial intelligence (AI) and machine learning: - - Srinivas, the CEO, worked at OpenAI as an AI researcher. - Konwinski was among the founding team at Databricks. - Yarats, the CTO, was an AI research scientist at Meta. - Ho, the CSO, worked as an engineer at Quora, then as a quantitative trader on Wall Street.[5]""", - metadata={}, - ) - ) - vector_db_id = f"test-vector-db-{uuid4()}" - llama_stack_client.vector_dbs.register( - vector_db_id=vector_db_id, - embedding_model="all-MiniLM-L6-v2", - embedding_dimension=384, - ) - llama_stack_client.tool_runtime.rag_tool.insert( - documents=documents, - vector_db_id=vector_db_id, - chunk_size_in_tokens=128, - ) - agent_config = { - **agent_config, - "tools": [ - dict( - name="builtin::rag/knowledge_search", - args={"vector_db_ids": [vector_db_id]}, - ), - "builtin::code_interpreter", - ], - } - agent = Agent(llama_stack_client, **agent_config) - user_prompts = [ - ( - "when was Perplexity the company founded?", - [], - "knowledge_search", - "2022", - ), - ( - "when was the nba created?", - [], - "knowledge_search", - "1949", - ), - ] - - for prompt, docs, tool_name, expected_kw in user_prompts: - session_id = agent.create_session(f"test-session-{uuid4()}") - response = agent.create_turn( - messages=[{"role": "user", "content": prompt}], - session_id=session_id, - documents=docs, - stream=False, - ) - tool_execution_step = next(step for step in response.steps if step.step_type == "tool_execution") - assert tool_execution_step.tool_calls[0].tool_name == tool_name, f"Failed on {prompt}" - if expected_kw: - assert expected_kw in response.output_message.content.lower() - - @pytest.mark.parametrize( "client_tools", [(get_boiling_point, False), (get_boiling_point_with_metadata, True)], diff --git a/tests/integration/agents/test_persistence.py b/tests/integration/agents/test_persistence.py index ef35c97a5..49d9d42d0 100644 --- a/tests/integration/agents/test_persistence.py +++ b/tests/integration/agents/test_persistence.py @@ -44,7 +44,6 @@ def common_params(inference_model): ) -@pytest.mark.asyncio @pytest.mark.skip(reason="This test needs to be migrated to api / client-sdk world") async def test_delete_agents_and_sessions(self, agents_stack, common_params): agents_impl = agents_stack.impls[Api.agents] @@ -73,7 +72,6 @@ async def test_delete_agents_and_sessions(self, agents_stack, common_params): assert agent_response is None -@pytest.mark.asyncio @pytest.mark.skip(reason="This test needs to be migrated to api / client-sdk world") async def test_get_agent_turns_and_steps(self, agents_stack, sample_messages, common_params): agents_impl = agents_stack.impls[Api.agents] diff --git a/tests/integration/fixtures/common.py b/tests/integration/fixtures/common.py index 4e10fc954..749793b64 100644 --- a/tests/integration/fixtures/common.py +++ b/tests/integration/fixtures/common.py @@ -6,6 +6,7 @@ import inspect import os +import signal import socket import subprocess import tempfile @@ -42,9 +43,11 @@ def start_llama_stack_server(config_name: str) -> subprocess.Popen: process = subprocess.Popen( cmd, stdout=devnull, # redirect stdout to devnull to prevent deadlock - stderr=devnull, # redirect stderr to devnull to prevent deadlock + stderr=subprocess.PIPE, # keep stderr to see errors text=True, env={**os.environ, "LLAMA_STACK_LOG_FILE": "server.log"}, + # Create new process group so we can kill all child processes + preexec_fn=os.setsid, ) return process @@ -57,6 +60,7 @@ def wait_for_server_ready(base_url: str, timeout: int = 30, process: subprocess. while time.time() - start_time < timeout: if process and process.poll() is not None: print(f"Server process terminated with return code: {process.returncode}") + print(f"Server stderr: {process.stderr.read()}") return False try: @@ -196,7 +200,7 @@ def llama_stack_client(request, provider_data): server_process = start_llama_stack_server(config_name) # Wait for server to be ready - if not wait_for_server_ready(base_url, timeout=30, process=server_process): + if not wait_for_server_ready(base_url, timeout=120, process=server_process): print("Server failed to start within timeout") server_process.terminate() raise RuntimeError( @@ -214,6 +218,7 @@ def llama_stack_client(request, provider_data): return LlamaStackClient( base_url=base_url, provider_data=provider_data, + timeout=int(os.environ.get("LLAMA_STACK_CLIENT_TIMEOUT", "30")), ) # check if this looks like a URL using proper URL parsing @@ -266,14 +271,17 @@ def cleanup_server_process(request): print(f"Server process already terminated with return code: {server_process.returncode}") return try: - server_process.terminate() + print(f"Terminating process {server_process.pid} and its group...") + # Kill the entire process group + os.killpg(os.getpgid(server_process.pid), signal.SIGTERM) server_process.wait(timeout=10) - print("Server process terminated gracefully") + print("Server process and children terminated gracefully") except subprocess.TimeoutExpired: print("Server process did not terminate gracefully, killing it") - server_process.kill() + # Force kill the entire process group + os.killpg(os.getpgid(server_process.pid), signal.SIGKILL) server_process.wait() - print("Server process killed") + print("Server process and children killed") except Exception as e: print(f"Error during server cleanup: {e}") else: diff --git a/tests/integration/inference/test_openai_completion.py b/tests/integration/inference/test_openai_completion.py index 05aee5096..e82714ffd 100644 --- a/tests/integration/inference/test_openai_completion.py +++ b/tests/integration/inference/test_openai_completion.py @@ -71,7 +71,6 @@ def skip_if_model_doesnt_support_openai_chat_completion(client_with_models, mode "remote::cerebras", "remote::databricks", "remote::runpod", - "remote::sambanova", "remote::tgi", ): pytest.skip(f"Model {model_id} hosted by {provider.provider_type} doesn't support OpenAI chat completions.") diff --git a/tests/integration/inspect/test_inspect.py b/tests/integration/inspect/test_inspect.py index da704178d..1597a319b 100644 --- a/tests/integration/inspect/test_inspect.py +++ b/tests/integration/inspect/test_inspect.py @@ -4,20 +4,17 @@ # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. -import pytest from llama_stack_client import LlamaStackClient from llama_stack import LlamaStackAsLibraryClient class TestInspect: - @pytest.mark.asyncio def test_health(self, llama_stack_client: LlamaStackAsLibraryClient | LlamaStackClient): health = llama_stack_client.inspect.health() assert health is not None assert health.status == "OK" - @pytest.mark.asyncio def test_version(self, llama_stack_client: LlamaStackAsLibraryClient | LlamaStackClient): version = llama_stack_client.inspect.version() assert version is not None diff --git a/tests/integration/post_training/test_post_training.py b/tests/integration/post_training/test_post_training.py index bb4639d17..3d56b322f 100644 --- a/tests/integration/post_training/test_post_training.py +++ b/tests/integration/post_training/test_post_training.py @@ -123,14 +123,14 @@ class TestPostTraining: logger.info(f"Job artifacts: {artifacts}") # TODO: Fix these tests to properly represent the Jobs API in training - # @pytest.mark.asyncio + # # async def test_get_training_jobs(self, post_training_stack): # post_training_impl = post_training_stack # jobs_list = await post_training_impl.get_training_jobs() # assert isinstance(jobs_list, list) # assert jobs_list[0].job_uuid == "1234" - # @pytest.mark.asyncio + # # async def test_get_training_job_status(self, post_training_stack): # post_training_impl = post_training_stack # job_status = await post_training_impl.get_training_job_status("1234") @@ -139,7 +139,7 @@ class TestPostTraining: # assert job_status.status == JobStatus.completed # assert isinstance(job_status.checkpoints[0], Checkpoint) - # @pytest.mark.asyncio + # # async def test_get_training_job_artifacts(self, post_training_stack): # post_training_impl = post_training_stack # job_artifacts = await post_training_impl.get_training_job_artifacts("1234") diff --git a/tests/integration/providers/test_providers.py b/tests/integration/providers/test_providers.py index 8b153411c..fc65e2a10 100644 --- a/tests/integration/providers/test_providers.py +++ b/tests/integration/providers/test_providers.py @@ -4,14 +4,12 @@ # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. -import pytest from llama_stack_client import LlamaStackClient from llama_stack import LlamaStackAsLibraryClient class TestProviders: - @pytest.mark.asyncio def test_providers(self, llama_stack_client: LlamaStackAsLibraryClient | LlamaStackClient): provider_list = llama_stack_client.providers.list() assert provider_list is not None diff --git a/tests/integration/providers/utils/__init__.py b/tests/integration/providers/utils/__init__.py new file mode 100644 index 000000000..756f351d8 --- /dev/null +++ b/tests/integration/providers/utils/__init__.py @@ -0,0 +1,5 @@ +# 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. diff --git a/tests/integration/providers/utils/sqlstore/__init__.py b/tests/integration/providers/utils/sqlstore/__init__.py new file mode 100644 index 000000000..756f351d8 --- /dev/null +++ b/tests/integration/providers/utils/sqlstore/__init__.py @@ -0,0 +1,5 @@ +# 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. diff --git a/tests/integration/providers/utils/sqlstore/test_authorized_sqlstore.py b/tests/integration/providers/utils/sqlstore/test_authorized_sqlstore.py new file mode 100644 index 000000000..c32d6cd17 --- /dev/null +++ b/tests/integration/providers/utils/sqlstore/test_authorized_sqlstore.py @@ -0,0 +1,239 @@ +# 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 tempfile +from unittest.mock import patch + +import pytest + +from llama_stack.distribution.access_control.access_control import default_policy +from llama_stack.distribution.datatypes import User +from llama_stack.providers.utils.sqlstore.api import ColumnType +from llama_stack.providers.utils.sqlstore.authorized_sqlstore import AuthorizedSqlStore +from llama_stack.providers.utils.sqlstore.sqlstore import PostgresSqlStoreConfig, SqliteSqlStoreConfig, sqlstore_impl + + +def get_postgres_config(): + """Get PostgreSQL configuration if tests are enabled.""" + return PostgresSqlStoreConfig( + host=os.environ.get("POSTGRES_HOST", "localhost"), + port=int(os.environ.get("POSTGRES_PORT", "5432")), + db=os.environ.get("POSTGRES_DB", "llamastack"), + user=os.environ.get("POSTGRES_USER", "llamastack"), + password=os.environ.get("POSTGRES_PASSWORD", "llamastack"), + ) + + +def get_sqlite_config(): + """Get SQLite configuration with temporary file database.""" + temp_file = tempfile.NamedTemporaryFile(delete=False, suffix=".db") + temp_file.close() + return SqliteSqlStoreConfig(db_path=temp_file.name) + + +# Backend configurations for parametrized tests +BACKEND_CONFIGS = [ + pytest.param( + get_postgres_config, + marks=pytest.mark.skipif( + not os.environ.get("ENABLE_POSTGRES_TESTS"), + reason="PostgreSQL tests require ENABLE_POSTGRES_TESTS environment variable", + ), + id="postgres", + ), + pytest.param(get_sqlite_config, id="sqlite"), +] + + +@pytest.fixture +def authorized_store(backend_config): + """Set up authorized store with proper cleanup.""" + config_func = backend_config + + config = config_func() + + base_sqlstore = sqlstore_impl(config) + authorized_store = AuthorizedSqlStore(base_sqlstore) + + yield authorized_store + + if hasattr(config, "db_path"): + try: + os.unlink(config.db_path) + except (OSError, FileNotFoundError): + pass + + +async def create_test_table(authorized_store, table_name): + """Create a test table with standard schema.""" + await authorized_store.create_table( + table=table_name, + schema={ + "id": ColumnType.STRING, + "data": ColumnType.STRING, + }, + ) + + +async def cleanup_records(sql_store, table_name, record_ids): + """Clean up test records.""" + for record_id in record_ids: + try: + await sql_store.delete(table_name, {"id": record_id}) + except Exception: + pass + + +@pytest.mark.parametrize("backend_config", BACKEND_CONFIGS) +@patch("llama_stack.providers.utils.sqlstore.authorized_sqlstore.get_authenticated_user") +async def test_authorized_store_attributes(mock_get_authenticated_user, authorized_store, request): + """Test that JSON column comparisons work correctly for both PostgreSQL and SQLite""" + backend_name = request.node.callspec.id + + # Create test table + table_name = f"test_json_comparison_{backend_name}" + await create_test_table(authorized_store, table_name) + + try: + # Test with no authenticated user (should handle JSON null comparison) + mock_get_authenticated_user.return_value = None + + # Insert some test data + 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()) + assert len(result.data) == 1 + assert result.data[0]["id"] == "1" + assert result.data[0]["access_attributes"] is None + + # Test with authenticated user + test_user = User("test-user", {"roles": ["admin"]}) + mock_get_authenticated_user.return_value = test_user + + # Insert data with user attributes + 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()) + assert len(result.data) == 2 + + # Test with non-admin user + regular_user = User("regular-user", {"roles": ["user"]}) + mock_get_authenticated_user.return_value = regular_user + + # Should only see public record + result = await authorized_store.fetch_all(table_name, policy=default_policy()) + assert len(result.data) == 1 + assert result.data[0]["id"] == "1" + + # Test the category missing branch: user with multiple attributes + multi_user = User("multi-user", {"roles": ["admin"], "teams": ["dev"]}) + mock_get_authenticated_user.return_value = multi_user + + # Insert record with multi-user (has both roles and teams) + await authorized_store.insert(table_name, {"id": "3", "data": "multi_user_data"}) + + # Test different user types to create records with different attribute patterns + # Record with only roles (teams category will be missing) + roles_only_user = User("roles-user", {"roles": ["admin"]}) + mock_get_authenticated_user.return_value = roles_only_user + await authorized_store.insert(table_name, {"id": "4", "data": "roles_only_data"}) + + # Record with only teams (roles category will be missing) + teams_only_user = User("teams-user", {"teams": ["dev"]}) + mock_get_authenticated_user.return_value = teams_only_user + await authorized_store.insert(table_name, {"id": "5", "data": "teams_only_data"}) + + # Record with different roles/teams (shouldn't match our test user) + different_user = User("different-user", {"roles": ["user"], "teams": ["qa"]}) + mock_get_authenticated_user.return_value = different_user + await authorized_store.insert(table_name, {"id": "6", "data": "different_user_data"}) + + # 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()) + + # Should see: + # - public record (1) - no access_attributes + # - admin record (2) - user matches roles=admin, teams missing (allowed) + # - multi_user record (3) - user matches both roles=admin and teams=dev + # - roles_only record (4) - user matches roles=admin, teams missing (allowed) + # - teams_only record (5) - user matches teams=dev, roles missing (allowed) + # Should NOT see: + # - different_user record (6) - user doesn't match roles=user or teams=qa + expected_ids = {"1", "2", "3", "4", "5"} + actual_ids = {record["id"] for record in result.data} + assert actual_ids == expected_ids, f"Expected to see records {expected_ids} but got {actual_ids}" + + # Verify the category missing logic specifically + # Records 4 and 5 test the "category missing" branch where one attribute category is missing + category_test_ids = {record["id"] for record in result.data if record["id"] in ["4", "5"]} + assert category_test_ids == {"4", "5"}, ( + f"Category missing logic failed: expected 4,5 but got {category_test_ids}" + ) + + finally: + # Clean up records + await cleanup_records(authorized_store.sql_store, table_name, ["1", "2", "3", "4", "5", "6"]) + + +@pytest.mark.parametrize("backend_config", BACKEND_CONFIGS) +@patch("llama_stack.providers.utils.sqlstore.authorized_sqlstore.get_authenticated_user") +async def test_user_ownership_policy(mock_get_authenticated_user, authorized_store, request): + """Test that 'user is owner' policies work correctly with record ownership""" + from llama_stack.distribution.access_control.datatypes import AccessRule, Action, Scope + + backend_name = request.node.callspec.id + + # Create test table + table_name = f"test_ownership_{backend_name}" + await create_test_table(authorized_store, table_name) + + try: + # Test with first user who creates records + user1 = User("user1", {"roles": ["admin"]}) + mock_get_authenticated_user.return_value = user1 + + # Insert a record owned by user1 + await authorized_store.insert(table_name, {"id": "1", "data": "user1_data"}) + + # Test with second user + user2 = User("user2", {"roles": ["user"]}) + mock_get_authenticated_user.return_value = user2 + + # Insert a record owned by user2 + await authorized_store.insert(table_name, {"id": "2", "data": "user2_data"}) + + # Create a policy that only allows access when user is the owner + owner_only_policy = [ + AccessRule( + permit=Scope(actions=[Action.READ]), + when=["user is owner"], + ), + ] + + # 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) + 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) + 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) + assert len(result.data) == 0, f"Expected anonymous user to see 0 records, got {len(result.data)}" + + finally: + # Clean up records + await cleanup_records(authorized_store.sql_store, table_name, ["1", "2"]) diff --git a/tests/integration/telemetry/test_telemetry.py b/tests/integration/telemetry/test_telemetry.py index c65f87489..9df03da70 100644 --- a/tests/integration/telemetry/test_telemetry.py +++ b/tests/integration/telemetry/test_telemetry.py @@ -5,41 +5,183 @@ # the root directory of this source tree. import time +from datetime import UTC, datetime from uuid import uuid4 import pytest from llama_stack_client import Agent -@pytest.mark.skip(reason="telemetry is not stable") -def test_agent_query_spans(llama_stack_client, text_model_id): +@pytest.fixture(scope="module", autouse=True) +def setup_telemetry_data(llama_stack_client, text_model_id): + """Setup fixture that creates telemetry data before tests run.""" agent = Agent(llama_stack_client, model=text_model_id, instructions="You are a helpful assistant") - session_id = agent.create_session(f"test-session-{uuid4()}") - agent.create_turn( - messages=[ - { - "role": "user", - "content": "Give me a sentence that contains the word: hello", - } - ], - session_id=session_id, - stream=False, + + session_id = agent.create_session(f"test-setup-session-{uuid4()}") + + messages = [ + "What is 2 + 2?", + "Tell me a short joke", + ] + + for msg in messages: + agent.create_turn( + messages=[{"role": "user", "content": msg}], + session_id=session_id, + stream=False, + ) + + for i in range(2): + llama_stack_client.inference.chat_completion( + model_id=text_model_id, messages=[{"role": "user", "content": f"Test trace {i}"}] + ) + + start_time = time.time() + + while time.time() - start_time < 30: + traces = llama_stack_client.telemetry.query_traces(limit=10) + if len(traces) >= 4: + break + time.sleep(1) + + if len(traces) < 4: + pytest.fail(f"Failed to create sufficient telemetry data after 30s. Got {len(traces)} traces.") + + yield + + +def test_query_traces_basic(llama_stack_client): + """Test basic trace querying functionality with proper data validation.""" + all_traces = llama_stack_client.telemetry.query_traces(limit=5) + + assert isinstance(all_traces, list), "Should return a list of traces" + assert len(all_traces) >= 4, "Should have at least 4 traces from setup" + + # Verify trace structure and data quality + first_trace = all_traces[0] + assert hasattr(first_trace, "trace_id"), "Trace should have trace_id" + assert hasattr(first_trace, "start_time"), "Trace should have start_time" + assert hasattr(first_trace, "root_span_id"), "Trace should have root_span_id" + + # Validate trace_id is a valid UUID format + assert isinstance(first_trace.trace_id, str) and len(first_trace.trace_id) > 0, ( + "trace_id should be non-empty string" ) - # Wait for the span to be logged - time.sleep(2) + # Validate start_time format and not in the future + now = datetime.now(UTC) + if isinstance(first_trace.start_time, str): + trace_time = datetime.fromisoformat(first_trace.start_time.replace("Z", "+00:00")) + else: + # start_time is already a datetime object + trace_time = first_trace.start_time + if trace_time.tzinfo is None: + trace_time = trace_time.replace(tzinfo=UTC) - agent_logs = [] + # Ensure trace time is not in the future (but allow any age in the past for persistent test data) + time_diff = (now - trace_time).total_seconds() + assert time_diff >= 0, f"Trace start_time should not be in the future, got {time_diff}s" - for span in llama_stack_client.telemetry.query_spans( - attribute_filters=[ - {"key": "session_id", "op": "eq", "value": session_id}, - ], - attributes_to_return=["input", "output"], - ): - if span.attributes["output"] != "no shields": - agent_logs.append(span.attributes) + # Validate root_span_id exists and is non-empty + assert isinstance(first_trace.root_span_id, str) and len(first_trace.root_span_id) > 0, ( + "root_span_id should be non-empty string" + ) - assert len(agent_logs) == 1 - assert "Give me a sentence that contains the word: hello" in agent_logs[0]["input"] - assert "hello" in agent_logs[0]["output"].lower() + # Test querying specific trace by ID + specific_trace = llama_stack_client.telemetry.get_trace(trace_id=first_trace.trace_id) + assert specific_trace.trace_id == first_trace.trace_id, "Retrieved trace should match requested ID" + assert specific_trace.start_time == first_trace.start_time, "Retrieved trace should have same start_time" + assert specific_trace.root_span_id == first_trace.root_span_id, "Retrieved trace should have same root_span_id" + + # Test pagination with proper validation + recent_traces = llama_stack_client.telemetry.query_traces(limit=3, offset=0) + assert len(recent_traces) <= 3, "Should return at most 3 traces when limit=3" + assert len(recent_traces) >= 1, "Should return at least 1 trace" + + # Verify all traces have required fields + for trace in recent_traces: + assert hasattr(trace, "trace_id") and trace.trace_id, "Each trace should have non-empty trace_id" + assert hasattr(trace, "start_time") and trace.start_time, "Each trace should have non-empty start_time" + assert hasattr(trace, "root_span_id") and trace.root_span_id, "Each trace should have non-empty root_span_id" + + +def test_query_spans_basic(llama_stack_client): + """Test basic span querying functionality with proper validation.""" + spans = llama_stack_client.telemetry.query_spans(attribute_filters=[], attributes_to_return=[]) + + assert isinstance(spans, list), "Should return a list of spans" + assert len(spans) >= 1, "Should have at least one span from setup" + + # Verify span structure and data quality + first_span = spans[0] + required_attrs = ["span_id", "name", "trace_id"] + for attr in required_attrs: + assert hasattr(first_span, attr), f"Span should have {attr} attribute" + assert getattr(first_span, attr), f"Span {attr} should not be empty" + + # Validate span data types and content + assert isinstance(first_span.span_id, str) and len(first_span.span_id) > 0, "span_id should be non-empty string" + assert isinstance(first_span.name, str) and len(first_span.name) > 0, "span name should be non-empty string" + assert isinstance(first_span.trace_id, str) and len(first_span.trace_id) > 0, "trace_id should be non-empty string" + + # Verify span belongs to a valid trace (test with traces we know exist) + all_traces = llama_stack_client.telemetry.query_traces(limit=10) + trace_ids = {t.trace_id for t in all_traces} + if first_span.trace_id in trace_ids: + trace = llama_stack_client.telemetry.get_trace(trace_id=first_span.trace_id) + assert trace is not None, "Should be able to retrieve trace for valid trace_id" + assert trace.trace_id == first_span.trace_id, "Trace ID should match span's trace_id" + + # Test with span filtering and validate results + filtered_spans = llama_stack_client.telemetry.query_spans( + attribute_filters=[{"key": "name", "op": "eq", "value": first_span.name}], + attributes_to_return=["name", "span_id"], + ) + assert isinstance(filtered_spans, list), "Should return a list with span name filter" + + # Validate filtered spans if filtering works + if len(filtered_spans) > 0: + for span in filtered_spans: + assert hasattr(span, "name"), "Filtered spans should have name attribute" + assert hasattr(span, "span_id"), "Filtered spans should have span_id attribute" + assert span.name == first_span.name, "Filtered spans should match the filter criteria" + assert isinstance(span.span_id, str) and len(span.span_id) > 0, "Filtered span_id should be valid" + + # Test that all spans have consistent structure + for span in spans: + for attr in required_attrs: + assert hasattr(span, attr) and getattr(span, attr), f"All spans should have non-empty {attr}" + + +def test_telemetry_pagination(llama_stack_client): + """Test pagination in telemetry queries.""" + # Get total count of traces + all_traces = llama_stack_client.telemetry.query_traces(limit=20) + total_count = len(all_traces) + assert total_count >= 4, "Should have at least 4 traces from setup" + + # Test trace pagination + page1 = llama_stack_client.telemetry.query_traces(limit=2, offset=0) + page2 = llama_stack_client.telemetry.query_traces(limit=2, offset=2) + + assert len(page1) == 2, "First page should have exactly 2 traces" + assert len(page2) >= 1, "Second page should have at least 1 trace" + + # Verify no overlap between pages + page1_ids = {t.trace_id for t in page1} + page2_ids = {t.trace_id for t in page2} + assert len(page1_ids.intersection(page2_ids)) == 0, "Pages should contain different traces" + + # Test ordering + ordered_traces = llama_stack_client.telemetry.query_traces(limit=5, order_by=["start_time"]) + assert len(ordered_traces) >= 4, "Should have at least 4 traces for ordering test" + + # Verify ordering by start_time + for i in range(len(ordered_traces) - 1): + current_time = ordered_traces[i].start_time + next_time = ordered_traces[i + 1].start_time + assert current_time <= next_time, f"Traces should be ordered by start_time: {current_time} > {next_time}" + + # Test limit behavior + limited = llama_stack_client.telemetry.query_traces(limit=3) + assert len(limited) == 3, "Should return exactly 3 traces when limit=3" diff --git a/tests/integration/vector_io/test_openai_vector_stores.py b/tests/integration/vector_io/test_openai_vector_stores.py index cc2860e26..d7300348b 100644 --- a/tests/integration/vector_io/test_openai_vector_stores.py +++ b/tests/integration/vector_io/test_openai_vector_stores.py @@ -31,7 +31,7 @@ def skip_if_provider_doesnt_support_openai_vector_stores(client_with_models): def skip_if_provider_doesnt_support_openai_vector_store_files_api(client_with_models): vector_io_providers = [p for p in client_with_models.providers.list() if p.api == "vector_io"] for p in vector_io_providers: - if p.provider_type in ["inline::faiss", "inline::sqlite-vec", "inline::milvus"]: + if p.provider_type in ["inline::faiss", "inline::sqlite-vec", "inline::milvus", "remote::pgvector"]: return pytest.skip("OpenAI vector stores are not supported by any provider") @@ -821,6 +821,59 @@ def test_openai_vector_store_update_file(compat_client_with_empty_stores, client assert retrieved_file.attributes["foo"] == "baz" +def test_create_vector_store_files_duplicate_vector_store_name(compat_client_with_empty_stores, client_with_models): + """ + This test confirms that client.vector_stores.create() creates a unique ID + """ + skip_if_provider_doesnt_support_openai_vector_stores(client_with_models) + skip_if_provider_doesnt_support_openai_vector_store_files_api(client_with_models) + + if isinstance(compat_client_with_empty_stores, LlamaStackClient): + pytest.skip("Vector Store Files create is not yet supported with LlamaStackClient") + + compat_client = compat_client_with_empty_stores + + # Create a vector store with files + file_ids = [] + for i in range(3): + with BytesIO(f"This is a test file {i}".encode()) as file_buffer: + file_buffer.name = f"openai_test_{i}.txt" + file = compat_client.files.create(file=file_buffer, purpose="assistants") + file_ids.append(file.id) + + vector_store = compat_client.vector_stores.create( + name="test_store_with_files", + ) + assert vector_store.file_counts.completed == 0 + assert vector_store.file_counts.total == 0 + assert vector_store.file_counts.cancelled == 0 + assert vector_store.file_counts.failed == 0 + assert vector_store.file_counts.in_progress == 0 + + vector_store2 = compat_client.vector_stores.create( + name="test_store_with_files", + ) + + vector_stores_list = compat_client.vector_stores.list() + assert len(vector_stores_list.data) == 2 + + created_file = compat_client.vector_stores.files.create( + vector_store_id=vector_store.id, + file_id=file_ids[0], + ) + assert created_file.status == "completed" + + _ = compat_client.vector_stores.delete(vector_store2.id) + created_file_from_non_deleted_vector_store = compat_client.vector_stores.files.create( + vector_store_id=vector_store.id, + file_id=file_ids[1], + ) + assert created_file_from_non_deleted_vector_store.status == "completed" + + vector_stores_list_post_delete = compat_client.vector_stores.list() + assert len(vector_stores_list_post_delete.data) == 1 + + @pytest.mark.skip(reason="Client library needs to be scaffolded to support search_mode parameter") def test_openai_vector_store_search_modes(): """Test OpenAI vector store search with different search modes. diff --git a/tests/unit/README.md b/tests/unit/README.md index c95c3a0e7..06e22fb8c 100644 --- a/tests/unit/README.md +++ b/tests/unit/README.md @@ -1,9 +1,17 @@ # Llama Stack Unit Tests +## Unit Tests + +Unit tests verify individual components and functions in isolation. They are fast, reliable, and don't require external services. + +### Prerequisites + +1. **Python Environment**: Ensure you have Python 3.12+ installed +2. **uv Package Manager**: Install `uv` if not already installed + You can run the unit tests by running: ```bash -source .venv/bin/activate ./scripts/unit-tests.sh [PYTEST_ARGS] ``` @@ -19,3 +27,21 @@ If you'd like to run for a non-default version of Python (currently 3.12), pass source .venv/bin/activate PYTHON_VERSION=3.13 ./scripts/unit-tests.sh ``` + +### Test Configuration + +- **Test Discovery**: Tests are automatically discovered in the `tests/unit/` directory +- **Async Support**: Tests use `--asyncio-mode=auto` for automatic async test handling +- **Coverage**: Tests generate coverage reports in `htmlcov/` directory +- **Python Version**: Defaults to Python 3.12, but can be overridden with `PYTHON_VERSION` environment variable + +### Coverage Reports + +After running tests, you can view coverage reports: + +```bash +# Open HTML coverage report in browser +open htmlcov/index.html # macOS +xdg-open htmlcov/index.html # Linux +start htmlcov/index.html # Windows +``` diff --git a/tests/unit/cli/test_stack_config.py b/tests/unit/cli/test_stack_config.py index d2b6f4b08..a41049006 100644 --- a/tests/unit/cli/test_stack_config.py +++ b/tests/unit/cli/test_stack_config.py @@ -15,6 +15,37 @@ from llama_stack.distribution.configure import ( ) +@pytest.fixture +def config_with_image_name_int(): + return yaml.safe_load( + f""" + version: {LLAMA_STACK_RUN_CONFIG_VERSION} + image_name: 1234 + apis_to_serve: [] + built_at: {datetime.now().isoformat()} + providers: + inference: + - provider_id: provider1 + provider_type: inline::meta-reference + config: {{}} + safety: + - provider_id: provider1 + provider_type: inline::meta-reference + config: + llama_guard_shield: + model: Llama-Guard-3-1B + excluded_categories: [] + disable_input_check: false + disable_output_check: false + enable_prompt_guard: false + memory: + - provider_id: provider1 + provider_type: inline::meta-reference + config: {{}} + """ + ) + + @pytest.fixture def up_to_date_config(): return yaml.safe_load( @@ -125,3 +156,8 @@ def test_parse_and_maybe_upgrade_config_old_format(old_config): def test_parse_and_maybe_upgrade_config_invalid(invalid_config): with pytest.raises(KeyError): parse_and_maybe_upgrade_config(invalid_config) + + +def test_parse_and_maybe_upgrade_config_image_name_int(config_with_image_name_int): + result = parse_and_maybe_upgrade_config(config_with_image_name_int) + assert isinstance(result.image_name, str) diff --git a/tests/unit/conftest.py b/tests/unit/conftest.py index aedac0386..b5eb1217d 100644 --- a/tests/unit/conftest.py +++ b/tests/unit/conftest.py @@ -4,6 +4,17 @@ # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. +import pytest_socket + # We need to import the fixtures here so that pytest can find them # but ruff doesn't think they are used and removes the import. "noqa: F401" prevents them from being removed from .fixtures import cached_disk_dist_registry, disk_dist_registry, sqlite_kvstore # noqa: F401 + + +def pytest_runtest_setup(item): + """Setup for each test - check if network access should be allowed.""" + if "allow_network" in item.keywords: + pytest_socket.enable_socket() + else: + # Allowing Unix sockets is necessary for some tests that use local servers and mocks + pytest_socket.disable_socket(allow_unix_socket=True) diff --git a/tests/unit/distribution/routers/test_routing_tables.py b/tests/unit/distribution/routers/test_routing_tables.py index 0eeb68167..3ba042bd9 100644 --- a/tests/unit/distribution/routers/test_routing_tables.py +++ b/tests/unit/distribution/routers/test_routing_tables.py @@ -8,8 +8,6 @@ from unittest.mock import AsyncMock -import pytest - from llama_stack.apis.common.type_system import NumberType from llama_stack.apis.datasets.datasets import Dataset, DatasetPurpose, URIDataSource from llama_stack.apis.datatypes import Api @@ -119,7 +117,6 @@ class ToolGroupsImpl(Impl): ) -@pytest.mark.asyncio async def test_models_routing_table(cached_disk_dist_registry): table = ModelsRoutingTable({"test_provider": InferenceImpl()}, cached_disk_dist_registry, {}) await table.initialize() @@ -161,7 +158,6 @@ async def test_models_routing_table(cached_disk_dist_registry): assert len(openai_models.data) == 0 -@pytest.mark.asyncio async def test_shields_routing_table(cached_disk_dist_registry): table = ShieldsRoutingTable({"test_provider": SafetyImpl()}, cached_disk_dist_registry, {}) await table.initialize() @@ -177,7 +173,6 @@ async def test_shields_routing_table(cached_disk_dist_registry): assert "test-shield-2" in shield_ids -@pytest.mark.asyncio async def test_vectordbs_routing_table(cached_disk_dist_registry): table = VectorDBsRoutingTable({"test_provider": VectorDBImpl()}, cached_disk_dist_registry, {}) await table.initialize() @@ -233,7 +228,6 @@ async def test_datasets_routing_table(cached_disk_dist_registry): assert len(datasets.data) == 0 -@pytest.mark.asyncio async def test_scoring_functions_routing_table(cached_disk_dist_registry): table = ScoringFunctionsRoutingTable({"test_provider": ScoringFunctionsImpl()}, cached_disk_dist_registry, {}) await table.initialize() @@ -259,7 +253,6 @@ async def test_scoring_functions_routing_table(cached_disk_dist_registry): assert "test-scoring-fn-2" in scoring_fn_ids -@pytest.mark.asyncio async def test_benchmarks_routing_table(cached_disk_dist_registry): table = BenchmarksRoutingTable({"test_provider": BenchmarksImpl()}, cached_disk_dist_registry, {}) await table.initialize() @@ -277,7 +270,6 @@ async def test_benchmarks_routing_table(cached_disk_dist_registry): assert "test-benchmark" in benchmark_ids -@pytest.mark.asyncio async def test_tool_groups_routing_table(cached_disk_dist_registry): table = ToolGroupsRoutingTable({"test_provider": ToolGroupsImpl()}, cached_disk_dist_registry, {}) await table.initialize() diff --git a/tests/unit/distribution/test_context.py b/tests/unit/distribution/test_context.py index 84944bfe8..7914be51d 100644 --- a/tests/unit/distribution/test_context.py +++ b/tests/unit/distribution/test_context.py @@ -13,7 +13,6 @@ import pytest from llama_stack.distribution.utils.context import preserve_contexts_async_generator -@pytest.mark.asyncio async def test_preserve_contexts_with_exception(): # Create context variable context_var = ContextVar("exception_var", default="initial") @@ -41,7 +40,6 @@ async def test_preserve_contexts_with_exception(): context_var.reset(token) -@pytest.mark.asyncio async def test_preserve_contexts_empty_generator(): # Create context variable context_var = ContextVar("empty_var", default="initial") @@ -66,7 +64,6 @@ async def test_preserve_contexts_empty_generator(): context_var.reset(token) -@pytest.mark.asyncio async def test_preserve_contexts_across_event_loops(): """ Test that context variables are preserved across event loop boundaries with nested generators. diff --git a/tests/unit/files/test_files.py b/tests/unit/files/test_files.py index ef1dc9743..785077e91 100644 --- a/tests/unit/files/test_files.py +++ b/tests/unit/files/test_files.py @@ -6,7 +6,6 @@ import pytest -import pytest_asyncio from llama_stack.apis.common.responses import Order from llama_stack.apis.files import OpenAIFilePurpose @@ -29,7 +28,7 @@ class MockUploadFile: return self.content -@pytest_asyncio.fixture +@pytest.fixture async def files_provider(tmp_path): """Create a files provider with temporary storage for testing.""" storage_dir = tmp_path / "files" @@ -68,7 +67,6 @@ def large_file(): class TestOpenAIFilesAPI: """Test suite for OpenAI Files API endpoints.""" - @pytest.mark.asyncio async def test_upload_file_success(self, files_provider, sample_text_file): """Test successful file upload.""" # Upload file @@ -82,7 +80,6 @@ class TestOpenAIFilesAPI: assert result.created_at > 0 assert result.expires_at > result.created_at - @pytest.mark.asyncio async def test_upload_different_purposes(self, files_provider, sample_text_file): """Test uploading files with different purposes.""" purposes = list(OpenAIFilePurpose) @@ -93,7 +90,6 @@ class TestOpenAIFilesAPI: uploaded_files.append(result) assert result.purpose == purpose - @pytest.mark.asyncio async def test_upload_different_file_types(self, files_provider, sample_text_file, sample_json_file, large_file): """Test uploading different types and sizes of files.""" files_to_test = [ @@ -107,7 +103,6 @@ class TestOpenAIFilesAPI: assert result.filename == expected_filename assert result.bytes == len(file_obj.content) - @pytest.mark.asyncio async def test_list_files_empty(self, files_provider): """Test listing files when no files exist.""" result = await files_provider.openai_list_files() @@ -117,7 +112,6 @@ class TestOpenAIFilesAPI: assert result.first_id == "" assert result.last_id == "" - @pytest.mark.asyncio async def test_list_files_with_content(self, files_provider, sample_text_file, sample_json_file): """Test listing files when files exist.""" # Upload multiple files @@ -132,7 +126,6 @@ class TestOpenAIFilesAPI: assert file1.id in file_ids assert file2.id in file_ids - @pytest.mark.asyncio async def test_list_files_with_purpose_filter(self, files_provider, sample_text_file): """Test listing files with purpose filtering.""" # Upload file with specific purpose @@ -146,7 +139,6 @@ class TestOpenAIFilesAPI: assert result.data[0].id == uploaded_file.id assert result.data[0].purpose == OpenAIFilePurpose.ASSISTANTS - @pytest.mark.asyncio async def test_list_files_with_limit(self, files_provider, sample_text_file): """Test listing files with limit parameter.""" # Upload multiple files @@ -157,7 +149,6 @@ class TestOpenAIFilesAPI: result = await files_provider.openai_list_files(limit=3) assert len(result.data) == 3 - @pytest.mark.asyncio async def test_list_files_with_order(self, files_provider, sample_text_file): """Test listing files with different order.""" # Upload multiple files @@ -178,7 +169,6 @@ class TestOpenAIFilesAPI: # Oldest should be first assert result_asc.data[0].created_at <= result_asc.data[1].created_at <= result_asc.data[2].created_at - @pytest.mark.asyncio async def test_retrieve_file_success(self, files_provider, sample_text_file): """Test successful file retrieval.""" # Upload file @@ -197,13 +187,11 @@ class TestOpenAIFilesAPI: assert retrieved_file.created_at == uploaded_file.created_at assert retrieved_file.expires_at == uploaded_file.expires_at - @pytest.mark.asyncio async def test_retrieve_file_not_found(self, files_provider): """Test retrieving a non-existent file.""" with pytest.raises(ValueError, match="File with id file-nonexistent not found"): await files_provider.openai_retrieve_file("file-nonexistent") - @pytest.mark.asyncio async def test_retrieve_file_content_success(self, files_provider, sample_text_file): """Test successful file content retrieval.""" # Upload file @@ -217,13 +205,11 @@ class TestOpenAIFilesAPI: # Verify content assert content.body == sample_text_file.content - @pytest.mark.asyncio async def test_retrieve_file_content_not_found(self, files_provider): """Test retrieving content of a non-existent file.""" with pytest.raises(ValueError, match="File with id file-nonexistent not found"): await files_provider.openai_retrieve_file_content("file-nonexistent") - @pytest.mark.asyncio async def test_delete_file_success(self, files_provider, sample_text_file): """Test successful file deletion.""" # Upload file @@ -245,13 +231,11 @@ class TestOpenAIFilesAPI: with pytest.raises(ValueError, match=f"File with id {uploaded_file.id} not found"): await files_provider.openai_retrieve_file(uploaded_file.id) - @pytest.mark.asyncio async def test_delete_file_not_found(self, files_provider): """Test deleting a non-existent file.""" with pytest.raises(ValueError, match="File with id file-nonexistent not found"): await files_provider.openai_delete_file("file-nonexistent") - @pytest.mark.asyncio async def test_file_persistence_across_operations(self, files_provider, sample_text_file): """Test that files persist correctly across multiple operations.""" # Upload file @@ -279,7 +263,6 @@ class TestOpenAIFilesAPI: files_list = await files_provider.openai_list_files() assert len(files_list.data) == 0 - @pytest.mark.asyncio async def test_multiple_files_operations(self, files_provider, sample_text_file, sample_json_file): """Test operations with multiple files.""" # Upload multiple files @@ -302,7 +285,6 @@ class TestOpenAIFilesAPI: content = await files_provider.openai_retrieve_file_content(file2.id) assert content.body == sample_json_file.content - @pytest.mark.asyncio async def test_file_id_uniqueness(self, files_provider, sample_text_file): """Test that each uploaded file gets a unique ID.""" file_ids = set() @@ -316,7 +298,6 @@ class TestOpenAIFilesAPI: file_ids.add(uploaded_file.id) assert uploaded_file.id.startswith("file-") - @pytest.mark.asyncio async def test_file_no_filename_handling(self, files_provider): """Test handling files with no filename.""" file_without_name = MockUploadFile(b"content", None) # No filename @@ -327,7 +308,6 @@ class TestOpenAIFilesAPI: assert uploaded_file.filename == "uploaded_file" # Default filename - @pytest.mark.asyncio async def test_after_pagination_works(self, files_provider, sample_text_file): """Test that 'after' pagination works correctly.""" # Upload multiple files to test pagination diff --git a/tests/unit/fixtures.py b/tests/unit/fixtures.py index 4e50c5e08..7174d2e78 100644 --- a/tests/unit/fixtures.py +++ b/tests/unit/fixtures.py @@ -4,14 +4,14 @@ # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. -import pytest_asyncio +import pytest from llama_stack.distribution.store.registry import CachedDiskDistributionRegistry, DiskDistributionRegistry from llama_stack.providers.utils.kvstore.config import SqliteKVStoreConfig from llama_stack.providers.utils.kvstore.sqlite import SqliteKVStoreImpl -@pytest_asyncio.fixture(scope="function") +@pytest.fixture(scope="function") async def sqlite_kvstore(tmp_path): db_path = tmp_path / "test_kv.db" kvstore_config = SqliteKVStoreConfig(db_path=db_path.as_posix()) @@ -20,14 +20,14 @@ async def sqlite_kvstore(tmp_path): yield kvstore -@pytest_asyncio.fixture(scope="function") +@pytest.fixture(scope="function") async def disk_dist_registry(sqlite_kvstore): registry = DiskDistributionRegistry(sqlite_kvstore) await registry.initialize() yield registry -@pytest_asyncio.fixture(scope="function") +@pytest.fixture(scope="function") async def cached_disk_dist_registry(sqlite_kvstore): registry = CachedDiskDistributionRegistry(sqlite_kvstore) await registry.initialize() diff --git a/tests/unit/models/test_prompt_adapter.py b/tests/unit/models/test_prompt_adapter.py index 0e2780e50..0362eb5dd 100644 --- a/tests/unit/models/test_prompt_adapter.py +++ b/tests/unit/models/test_prompt_adapter.py @@ -4,14 +4,13 @@ # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. -import asyncio -import unittest from llama_stack.apis.inference import ( ChatCompletionRequest, CompletionMessage, StopReason, SystemMessage, + SystemMessageBehavior, ToolCall, ToolConfig, UserMessage, @@ -25,264 +24,266 @@ from llama_stack.models.llama.datatypes import ( from llama_stack.providers.utils.inference.prompt_adapter import ( chat_completion_request_to_messages, chat_completion_request_to_prompt, + interleaved_content_as_str, ) MODEL = "Llama3.1-8B-Instruct" MODEL3_2 = "Llama3.2-3B-Instruct" -class PrepareMessagesTests(unittest.IsolatedAsyncioTestCase): - async def asyncSetUp(self): - asyncio.get_running_loop().set_debug(False) +async def test_system_default(): + content = "Hello !" + request = ChatCompletionRequest( + model=MODEL, + messages=[ + UserMessage(content=content), + ], + ) + messages = chat_completion_request_to_messages(request, MODEL) + assert len(messages) == 2 + assert messages[-1].content == content + assert "Cutting Knowledge Date: December 2023" in interleaved_content_as_str(messages[0].content) - async def test_system_default(self): - content = "Hello !" - request = ChatCompletionRequest( - model=MODEL, - messages=[ - UserMessage(content=content), - ], - ) - messages = chat_completion_request_to_messages(request, MODEL) - self.assertEqual(len(messages), 2) - self.assertEqual(messages[-1].content, content) - self.assertTrue("Cutting Knowledge Date: December 2023" in messages[0].content) - async def test_system_builtin_only(self): - content = "Hello !" - request = ChatCompletionRequest( - model=MODEL, - messages=[ - UserMessage(content=content), - ], - tools=[ - ToolDefinition(tool_name=BuiltinTool.code_interpreter), - ToolDefinition(tool_name=BuiltinTool.brave_search), - ], - ) - messages = chat_completion_request_to_messages(request, MODEL) - self.assertEqual(len(messages), 2) - self.assertEqual(messages[-1].content, content) - self.assertTrue("Cutting Knowledge Date: December 2023" in messages[0].content) - self.assertTrue("Tools: brave_search" in messages[0].content) +async def test_system_builtin_only(): + content = "Hello !" + request = ChatCompletionRequest( + model=MODEL, + messages=[ + UserMessage(content=content), + ], + tools=[ + ToolDefinition(tool_name=BuiltinTool.code_interpreter), + ToolDefinition(tool_name=BuiltinTool.brave_search), + ], + ) + messages = chat_completion_request_to_messages(request, MODEL) + assert len(messages) == 2 + assert messages[-1].content == content + assert "Cutting Knowledge Date: December 2023" in interleaved_content_as_str(messages[0].content) + assert "Tools: brave_search" in interleaved_content_as_str(messages[0].content) - async def test_system_custom_only(self): - content = "Hello !" - request = ChatCompletionRequest( - model=MODEL, - messages=[ - UserMessage(content=content), - ], - tools=[ - ToolDefinition( - tool_name="custom1", - description="custom1 tool", - parameters={ - "param1": ToolParamDefinition( - param_type="str", - description="param1 description", - required=True, - ), - }, - ) - ], - tool_config=ToolConfig(tool_prompt_format=ToolPromptFormat.json), - ) - messages = chat_completion_request_to_messages(request, MODEL) - self.assertEqual(len(messages), 3) - self.assertTrue("Environment: ipython" in messages[0].content) - self.assertTrue("Return function calls in JSON format" in messages[1].content) - self.assertEqual(messages[-1].content, content) +async def test_system_custom_only(): + content = "Hello !" + request = ChatCompletionRequest( + model=MODEL, + messages=[ + UserMessage(content=content), + ], + tools=[ + ToolDefinition( + tool_name="custom1", + description="custom1 tool", + parameters={ + "param1": ToolParamDefinition( + param_type="str", + description="param1 description", + required=True, + ), + }, + ) + ], + tool_config=ToolConfig(tool_prompt_format=ToolPromptFormat.json), + ) + messages = chat_completion_request_to_messages(request, MODEL) + assert len(messages) == 3 + assert "Environment: ipython" in interleaved_content_as_str(messages[0].content) - async def test_system_custom_and_builtin(self): - content = "Hello !" - request = ChatCompletionRequest( - model=MODEL, - messages=[ - UserMessage(content=content), - ], - tools=[ - ToolDefinition(tool_name=BuiltinTool.code_interpreter), - ToolDefinition(tool_name=BuiltinTool.brave_search), - ToolDefinition( - tool_name="custom1", - description="custom1 tool", - parameters={ - "param1": ToolParamDefinition( - param_type="str", - description="param1 description", - required=True, - ), - }, - ), - ], - ) - messages = chat_completion_request_to_messages(request, MODEL) - self.assertEqual(len(messages), 3) + assert "Return function calls in JSON format" in interleaved_content_as_str(messages[1].content) + assert messages[-1].content == content - self.assertTrue("Environment: ipython" in messages[0].content) - self.assertTrue("Tools: brave_search" in messages[0].content) - self.assertTrue("Return function calls in JSON format" in messages[1].content) - self.assertEqual(messages[-1].content, content) - - async def test_completion_message_encoding(self): - request = ChatCompletionRequest( - model=MODEL3_2, - messages=[ - UserMessage(content="hello"), - CompletionMessage( - content="", - stop_reason=StopReason.end_of_turn, - tool_calls=[ - ToolCall( - tool_name="custom1", - arguments={"param1": "value1"}, - call_id="123", - ) - ], - ), - ], - tools=[ - ToolDefinition( - tool_name="custom1", - description="custom1 tool", - parameters={ - "param1": ToolParamDefinition( - param_type="str", - description="param1 description", - required=True, - ), - }, - ), - ], - tool_config=ToolConfig(tool_prompt_format=ToolPromptFormat.python_list), - ) - prompt = await chat_completion_request_to_prompt(request, request.model) - self.assertIn('[custom1(param1="value1")]', prompt) - - request.model = MODEL - request.tool_config.tool_prompt_format = ToolPromptFormat.json - prompt = await chat_completion_request_to_prompt(request, request.model) - self.assertIn( - '{"type": "function", "name": "custom1", "parameters": {"param1": "value1"}}', - prompt, - ) - - async def test_user_provided_system_message(self): - content = "Hello !" - system_prompt = "You are a pirate" - request = ChatCompletionRequest( - model=MODEL, - messages=[ - SystemMessage(content=system_prompt), - UserMessage(content=content), - ], - tools=[ - ToolDefinition(tool_name=BuiltinTool.code_interpreter), - ], - ) - messages = chat_completion_request_to_messages(request, MODEL) - self.assertEqual(len(messages), 2, messages) - self.assertTrue(messages[0].content.endswith(system_prompt)) - - self.assertEqual(messages[-1].content, content) - - async def test_repalce_system_message_behavior_builtin_tools(self): - content = "Hello !" - system_prompt = "You are a pirate" - request = ChatCompletionRequest( - model=MODEL, - messages=[ - SystemMessage(content=system_prompt), - UserMessage(content=content), - ], - tools=[ - ToolDefinition(tool_name=BuiltinTool.code_interpreter), - ], - tool_config=ToolConfig( - tool_choice="auto", - tool_prompt_format="python_list", - system_message_behavior="replace", +async def test_system_custom_and_builtin(): + content = "Hello !" + request = ChatCompletionRequest( + model=MODEL, + messages=[ + UserMessage(content=content), + ], + tools=[ + ToolDefinition(tool_name=BuiltinTool.code_interpreter), + ToolDefinition(tool_name=BuiltinTool.brave_search), + ToolDefinition( + tool_name="custom1", + description="custom1 tool", + parameters={ + "param1": ToolParamDefinition( + param_type="str", + description="param1 description", + required=True, + ), + }, ), - ) - messages = chat_completion_request_to_messages(request, MODEL3_2) - self.assertEqual(len(messages), 2, messages) - self.assertTrue(messages[0].content.endswith(system_prompt)) - self.assertIn("Environment: ipython", messages[0].content) - self.assertEqual(messages[-1].content, content) + ], + ) + messages = chat_completion_request_to_messages(request, MODEL) + assert len(messages) == 3 - async def test_repalce_system_message_behavior_custom_tools(self): - content = "Hello !" - system_prompt = "You are a pirate" - request = ChatCompletionRequest( - model=MODEL, - messages=[ - SystemMessage(content=system_prompt), - UserMessage(content=content), - ], - tools=[ - ToolDefinition(tool_name=BuiltinTool.code_interpreter), - ToolDefinition( - tool_name="custom1", - description="custom1 tool", - parameters={ - "param1": ToolParamDefinition( - param_type="str", - description="param1 description", - required=True, - ), - }, - ), - ], - tool_config=ToolConfig( - tool_choice="auto", - tool_prompt_format="python_list", - system_message_behavior="replace", + assert "Environment: ipython" in interleaved_content_as_str(messages[0].content) + assert "Tools: brave_search" in interleaved_content_as_str(messages[0].content) + + assert "Return function calls in JSON format" in interleaved_content_as_str(messages[1].content) + assert messages[-1].content == content + + +async def test_completion_message_encoding(): + request = ChatCompletionRequest( + model=MODEL3_2, + messages=[ + UserMessage(content="hello"), + CompletionMessage( + content="", + stop_reason=StopReason.end_of_turn, + tool_calls=[ + ToolCall( + tool_name="custom1", + arguments={"param1": "value1"}, + call_id="123", + ) + ], ), - ) - messages = chat_completion_request_to_messages(request, MODEL3_2) - - self.assertEqual(len(messages), 2, messages) - self.assertTrue(messages[0].content.endswith(system_prompt)) - self.assertIn("Environment: ipython", messages[0].content) - self.assertEqual(messages[-1].content, content) - - async def test_replace_system_message_behavior_custom_tools_with_template(self): - content = "Hello !" - system_prompt = "You are a pirate {{ function_description }}" - request = ChatCompletionRequest( - model=MODEL, - messages=[ - SystemMessage(content=system_prompt), - UserMessage(content=content), - ], - tools=[ - ToolDefinition(tool_name=BuiltinTool.code_interpreter), - ToolDefinition( - tool_name="custom1", - description="custom1 tool", - parameters={ - "param1": ToolParamDefinition( - param_type="str", - description="param1 description", - required=True, - ), - }, - ), - ], - tool_config=ToolConfig( - tool_choice="auto", - tool_prompt_format="python_list", - system_message_behavior="replace", + ], + tools=[ + ToolDefinition( + tool_name="custom1", + description="custom1 tool", + parameters={ + "param1": ToolParamDefinition( + param_type="str", + description="param1 description", + required=True, + ), + }, ), - ) - messages = chat_completion_request_to_messages(request, MODEL3_2) + ], + tool_config=ToolConfig(tool_prompt_format=ToolPromptFormat.python_list), + ) + prompt = await chat_completion_request_to_prompt(request, request.model) + assert '[custom1(param1="value1")]' in prompt - self.assertEqual(len(messages), 2, messages) - self.assertIn("Environment: ipython", messages[0].content) - self.assertIn("You are a pirate", messages[0].content) - # function description is present in the system prompt - self.assertIn('"name": "custom1"', messages[0].content) - self.assertEqual(messages[-1].content, content) + request.model = MODEL + request.tool_config = ToolConfig(tool_prompt_format=ToolPromptFormat.json) + prompt = await chat_completion_request_to_prompt(request, request.model) + assert '{"type": "function", "name": "custom1", "parameters": {"param1": "value1"}}' in prompt + + +async def test_user_provided_system_message(): + content = "Hello !" + system_prompt = "You are a pirate" + request = ChatCompletionRequest( + model=MODEL, + messages=[ + SystemMessage(content=system_prompt), + UserMessage(content=content), + ], + tools=[ + ToolDefinition(tool_name=BuiltinTool.code_interpreter), + ], + ) + messages = chat_completion_request_to_messages(request, MODEL) + assert len(messages) == 2 + assert interleaved_content_as_str(messages[0].content).endswith(system_prompt) + + assert messages[-1].content == content + + +async def test_replace_system_message_behavior_builtin_tools(): + content = "Hello !" + system_prompt = "You are a pirate" + request = ChatCompletionRequest( + model=MODEL, + messages=[ + SystemMessage(content=system_prompt), + UserMessage(content=content), + ], + tools=[ + ToolDefinition(tool_name=BuiltinTool.code_interpreter), + ], + tool_config=ToolConfig( + tool_choice="auto", + tool_prompt_format=ToolPromptFormat.python_list, + system_message_behavior=SystemMessageBehavior.replace, + ), + ) + messages = chat_completion_request_to_messages(request, MODEL3_2) + assert len(messages) == 2 + assert interleaved_content_as_str(messages[0].content).endswith(system_prompt) + assert "Environment: ipython" in interleaved_content_as_str(messages[0].content) + assert messages[-1].content == content + + +async def test_replace_system_message_behavior_custom_tools(): + content = "Hello !" + system_prompt = "You are a pirate" + request = ChatCompletionRequest( + model=MODEL, + messages=[ + SystemMessage(content=system_prompt), + UserMessage(content=content), + ], + tools=[ + ToolDefinition(tool_name=BuiltinTool.code_interpreter), + ToolDefinition( + tool_name="custom1", + description="custom1 tool", + parameters={ + "param1": ToolParamDefinition( + param_type="str", + description="param1 description", + required=True, + ), + }, + ), + ], + tool_config=ToolConfig( + tool_choice="auto", + tool_prompt_format=ToolPromptFormat.python_list, + system_message_behavior=SystemMessageBehavior.replace, + ), + ) + messages = chat_completion_request_to_messages(request, MODEL3_2) + + assert len(messages) == 2 + assert interleaved_content_as_str(messages[0].content).endswith(system_prompt) + assert "Environment: ipython" in interleaved_content_as_str(messages[0].content) + assert messages[-1].content == content + + +async def test_replace_system_message_behavior_custom_tools_with_template(): + content = "Hello !" + system_prompt = "You are a pirate {{ function_description }}" + request = ChatCompletionRequest( + model=MODEL, + messages=[ + SystemMessage(content=system_prompt), + UserMessage(content=content), + ], + tools=[ + ToolDefinition(tool_name=BuiltinTool.code_interpreter), + ToolDefinition( + tool_name="custom1", + description="custom1 tool", + parameters={ + "param1": ToolParamDefinition( + param_type="str", + description="param1 description", + required=True, + ), + }, + ), + ], + tool_config=ToolConfig( + tool_choice="auto", + tool_prompt_format=ToolPromptFormat.python_list, + system_message_behavior=SystemMessageBehavior.replace, + ), + ) + messages = chat_completion_request_to_messages(request, MODEL3_2) + + assert len(messages) == 2 + assert "Environment: ipython" in interleaved_content_as_str(messages[0].content) + assert "You are a pirate" in interleaved_content_as_str(messages[0].content) + # function description is present in the system prompt + assert '"name": "custom1"' in interleaved_content_as_str(messages[0].content) + assert messages[-1].content == content diff --git a/tests/unit/models/test_system_prompts.py b/tests/unit/models/test_system_prompts.py index 1f4ccc7e3..f5580f4c5 100644 --- a/tests/unit/models/test_system_prompts.py +++ b/tests/unit/models/test_system_prompts.py @@ -12,7 +12,6 @@ # the top-level of this source tree. import textwrap -import unittest from datetime import datetime from llama_stack.models.llama.llama3.prompt_templates import ( @@ -24,59 +23,61 @@ from llama_stack.models.llama.llama3.prompt_templates import ( ) -class PromptTemplateTests(unittest.TestCase): - def check_generator_output(self, generator): - for example in generator.data_examples(): - pt = generator.gen(example) - text = pt.render() - # print(text) # debugging - if not example: - continue - for tool in example: - assert tool.tool_name in text - - def test_system_default(self): - generator = SystemDefaultGenerator() - today = datetime.now().strftime("%d %B %Y") - expected_text = f"Cutting Knowledge Date: December 2023\nToday Date: {today}" - assert expected_text.strip("\n") == generator.gen(generator.data_examples()[0]).render() - - def test_system_builtin_only(self): - generator = BuiltinToolGenerator() - expected_text = textwrap.dedent( - """ - Environment: ipython - Tools: brave_search, wolfram_alpha - """ - ) - assert expected_text.strip("\n") == generator.gen(generator.data_examples()[0]).render() - - def test_system_custom_only(self): - self.maxDiff = None - generator = JsonCustomToolGenerator() - self.check_generator_output(generator) - - def test_system_custom_function_tag(self): - self.maxDiff = None - generator = FunctionTagCustomToolGenerator() - self.check_generator_output(generator) - - def test_llama_3_2_system_zero_shot(self): - generator = PythonListCustomToolGenerator() - self.check_generator_output(generator) - - def test_llama_3_2_provided_system_prompt(self): - generator = PythonListCustomToolGenerator() - user_system_prompt = textwrap.dedent( - """ - Overriding message. - - {{ function_description }} - """ - ) - example = generator.data_examples()[0] - - pt = generator.gen(example, user_system_prompt) +def check_generator_output(generator): + for example in generator.data_examples(): + pt = generator.gen(example) text = pt.render() - assert "Overriding message." in text - assert '"name": "get_weather"' in text + if not example: + continue + for tool in example: + assert tool.tool_name in text + + +def test_system_default(): + generator = SystemDefaultGenerator() + today = datetime.now().strftime("%d %B %Y") + expected_text = f"Cutting Knowledge Date: December 2023\nToday Date: {today}" + assert expected_text.strip("\n") == generator.gen(generator.data_examples()[0]).render() + + +def test_system_builtin_only(): + generator = BuiltinToolGenerator() + expected_text = textwrap.dedent( + """ + Environment: ipython + Tools: brave_search, wolfram_alpha + """ + ) + assert expected_text.strip("\n") == generator.gen(generator.data_examples()[0]).render() + + +def test_system_custom_only(): + generator = JsonCustomToolGenerator() + check_generator_output(generator) + + +def test_system_custom_function_tag(): + generator = FunctionTagCustomToolGenerator() + check_generator_output(generator) + + +def test_llama_3_2_system_zero_shot(): + generator = PythonListCustomToolGenerator() + check_generator_output(generator) + + +def test_llama_3_2_provided_system_prompt(): + generator = PythonListCustomToolGenerator() + user_system_prompt = textwrap.dedent( + """ + Overriding message. + + {{ function_description }} + """ + ) + example = generator.data_examples()[0] + + pt = generator.gen(example, user_system_prompt) + text = pt.render() + assert "Overriding message." in text + assert '"name": "get_weather"' in text diff --git a/tests/unit/providers/agent/test_meta_reference_agent.py b/tests/unit/providers/agent/test_meta_reference_agent.py index 7a7d52892..c06d9ab0e 100644 --- a/tests/unit/providers/agent/test_meta_reference_agent.py +++ b/tests/unit/providers/agent/test_meta_reference_agent.py @@ -8,7 +8,6 @@ from datetime import datetime from unittest.mock import AsyncMock import pytest -import pytest_asyncio from llama_stack.apis.agents import ( Agent, @@ -50,7 +49,7 @@ def config(tmp_path): ) -@pytest_asyncio.fixture +@pytest.fixture async def agents_impl(config, mock_apis): impl = MetaReferenceAgentsImpl( config, @@ -117,7 +116,6 @@ def sample_agent_config(): ) -@pytest.mark.asyncio async def test_create_agent(agents_impl, sample_agent_config): response = await agents_impl.create_agent(sample_agent_config) @@ -132,7 +130,6 @@ async def test_create_agent(agents_impl, sample_agent_config): assert isinstance(agent_info.created_at, datetime) -@pytest.mark.asyncio async def test_get_agent(agents_impl, sample_agent_config): create_response = await agents_impl.create_agent(sample_agent_config) agent_id = create_response.agent_id @@ -146,7 +143,6 @@ async def test_get_agent(agents_impl, sample_agent_config): assert isinstance(agent.created_at, datetime) -@pytest.mark.asyncio async def test_list_agents(agents_impl, sample_agent_config): agent1_response = await agents_impl.create_agent(sample_agent_config) agent2_response = await agents_impl.create_agent(sample_agent_config) @@ -160,7 +156,6 @@ async def test_list_agents(agents_impl, sample_agent_config): assert agent2_response.agent_id in agent_ids -@pytest.mark.asyncio @pytest.mark.parametrize("enable_session_persistence", [True, False]) async def test_create_agent_session_persistence(agents_impl, sample_agent_config, enable_session_persistence): # Create an agent with specified persistence setting @@ -188,7 +183,6 @@ async def test_create_agent_session_persistence(agents_impl, sample_agent_config await agents_impl.get_agents_session(agent_id, session_response.session_id) -@pytest.mark.asyncio @pytest.mark.parametrize("enable_session_persistence", [True, False]) async def test_list_agent_sessions_persistence(agents_impl, sample_agent_config, enable_session_persistence): # Create an agent with specified persistence setting @@ -221,7 +215,6 @@ async def test_list_agent_sessions_persistence(agents_impl, sample_agent_config, assert session2.session_id in {s["session_id"] for s in sessions.data} -@pytest.mark.asyncio async def test_delete_agent(agents_impl, sample_agent_config): # Create an agent response = await agents_impl.create_agent(sample_agent_config) diff --git a/tests/unit/providers/agents/meta_reference/test_openai_responses.py b/tests/unit/providers/agents/meta_reference/test_openai_responses.py index 0d1ef8eca..6485e3512 100644 --- a/tests/unit/providers/agents/meta_reference/test_openai_responses.py +++ b/tests/unit/providers/agents/meta_reference/test_openai_responses.py @@ -122,7 +122,6 @@ async def fake_stream(fixture: str = "simple_chat_completion.yaml"): ) -@pytest.mark.asyncio async def test_create_openai_response_with_string_input(openai_responses_impl, mock_inference_api): """Test creating an OpenAI response with a simple string input.""" # Setup @@ -155,7 +154,6 @@ async def test_create_openai_response_with_string_input(openai_responses_impl, m assert result.output[0].content[0].text == "Dublin" -@pytest.mark.asyncio async def test_create_openai_response_with_string_input_with_tools(openai_responses_impl, mock_inference_api): """Test creating an OpenAI response with a simple string input and tools.""" # Setup @@ -224,7 +222,6 @@ async def test_create_openai_response_with_string_input_with_tools(openai_respon assert result.output[1].content[0].annotations == [] -@pytest.mark.asyncio async def test_create_openai_response_with_tool_call_type_none(openai_responses_impl, mock_inference_api): """Test creating an OpenAI response with a tool call response that has a type of None.""" # Setup @@ -294,7 +291,6 @@ async def test_create_openai_response_with_tool_call_type_none(openai_responses_ assert chunks[1].response.output[0].name == "get_weather" -@pytest.mark.asyncio async def test_create_openai_response_with_multiple_messages(openai_responses_impl, mock_inference_api): """Test creating an OpenAI response with multiple messages.""" # Setup @@ -340,7 +336,6 @@ async def test_create_openai_response_with_multiple_messages(openai_responses_im assert isinstance(inference_messages[i], OpenAIDeveloperMessageParam) -@pytest.mark.asyncio async def test_prepend_previous_response_none(openai_responses_impl): """Test prepending no previous response to a new response.""" @@ -348,7 +343,6 @@ async def test_prepend_previous_response_none(openai_responses_impl): assert input == "fake_input" -@pytest.mark.asyncio async def test_prepend_previous_response_basic(openai_responses_impl, mock_responses_store): """Test prepending a basic previous response to a new response.""" @@ -388,7 +382,6 @@ async def test_prepend_previous_response_basic(openai_responses_impl, mock_respo assert input[2].content == "fake_input" -@pytest.mark.asyncio async def test_prepend_previous_response_web_search(openai_responses_impl, mock_responses_store): """Test prepending a web search previous response to a new response.""" input_item_message = OpenAIResponseMessage( @@ -434,7 +427,6 @@ async def test_prepend_previous_response_web_search(openai_responses_impl, mock_ assert input[3].content == "fake_input" -@pytest.mark.asyncio async def test_create_openai_response_with_instructions(openai_responses_impl, mock_inference_api): # Setup input_text = "What is the capital of Ireland?" @@ -463,7 +455,6 @@ async def test_create_openai_response_with_instructions(openai_responses_impl, m assert sent_messages[1].content == input_text -@pytest.mark.asyncio async def test_create_openai_response_with_instructions_and_multiple_messages( openai_responses_impl, mock_inference_api ): @@ -508,7 +499,6 @@ async def test_create_openai_response_with_instructions_and_multiple_messages( assert sent_messages[3].content == "Which is the largest?" -@pytest.mark.asyncio async def test_create_openai_response_with_instructions_and_previous_response( openai_responses_impl, mock_responses_store, mock_inference_api ): @@ -565,7 +555,6 @@ async def test_create_openai_response_with_instructions_and_previous_response( assert sent_messages[3].content == "Which is the largest?" -@pytest.mark.asyncio async def test_list_openai_response_input_items_delegation(openai_responses_impl, mock_responses_store): """Test that list_openai_response_input_items properly delegates to responses_store with correct parameters.""" # Setup @@ -601,7 +590,6 @@ async def test_list_openai_response_input_items_delegation(openai_responses_impl assert result.data[0].id == "msg_123" -@pytest.mark.asyncio async def test_responses_store_list_input_items_logic(): """Test ResponsesStore list_response_input_items logic - mocks get_response_object to test actual ordering/limiting.""" @@ -680,7 +668,6 @@ async def test_responses_store_list_input_items_logic(): assert len(result.data) == 0 # Should return no items -@pytest.mark.asyncio async def test_store_response_uses_rehydrated_input_with_previous_response( openai_responses_impl, mock_responses_store, mock_inference_api ): @@ -747,7 +734,6 @@ async def test_store_response_uses_rehydrated_input_with_previous_response( assert result.status == "completed" -@pytest.mark.asyncio @pytest.mark.parametrize( "text_format, response_format", [ @@ -787,7 +773,6 @@ async def test_create_openai_response_with_text_format( assert first_call.kwargs["response_format"] == response_format -@pytest.mark.asyncio async def test_create_openai_response_with_invalid_text_format(openai_responses_impl, mock_inference_api): """Test creating an OpenAI response with an invalid text format.""" # Setup diff --git a/tests/unit/providers/agents/test_persistence_access_control.py b/tests/unit/providers/agents/test_persistence_access_control.py index 656d1e53c..26001fcf1 100644 --- a/tests/unit/providers/agents/test_persistence_access_control.py +++ b/tests/unit/providers/agents/test_persistence_access_control.py @@ -9,7 +9,6 @@ from datetime import datetime from unittest.mock import patch import pytest -import pytest_asyncio from llama_stack.apis.agents import Turn from llama_stack.apis.inference import CompletionMessage, StopReason @@ -17,13 +16,12 @@ from llama_stack.distribution.datatypes import User from llama_stack.providers.inline.agents.meta_reference.persistence import AgentPersistence, AgentSessionInfo -@pytest_asyncio.fixture +@pytest.fixture async def test_setup(sqlite_kvstore): agent_persistence = AgentPersistence(agent_id="test_agent", kvstore=sqlite_kvstore, policy={}) yield agent_persistence -@pytest.mark.asyncio @patch("llama_stack.providers.inline.agents.meta_reference.persistence.get_authenticated_user") async def test_session_creation_with_access_attributes(mock_get_authenticated_user, test_setup): agent_persistence = test_setup @@ -44,7 +42,6 @@ async def test_session_creation_with_access_attributes(mock_get_authenticated_us assert session_info.owner.attributes["teams"] == ["ai-team"] -@pytest.mark.asyncio @patch("llama_stack.providers.inline.agents.meta_reference.persistence.get_authenticated_user") async def test_session_access_control(mock_get_authenticated_user, test_setup): agent_persistence = test_setup @@ -79,7 +76,6 @@ async def test_session_access_control(mock_get_authenticated_user, test_setup): assert retrieved_session is None -@pytest.mark.asyncio @patch("llama_stack.providers.inline.agents.meta_reference.persistence.get_authenticated_user") async def test_turn_access_control(mock_get_authenticated_user, test_setup): agent_persistence = test_setup @@ -133,7 +129,6 @@ async def test_turn_access_control(mock_get_authenticated_user, test_setup): await agent_persistence.get_session_turns(session_id) -@pytest.mark.asyncio @patch("llama_stack.providers.inline.agents.meta_reference.persistence.get_authenticated_user") async def test_tool_call_and_infer_iters_access_control(mock_get_authenticated_user, test_setup): agent_persistence = test_setup diff --git a/tests/unit/providers/inference/test_inference_client_caching.py b/tests/unit/providers/inference/test_inference_client_caching.py new file mode 100644 index 000000000..c9a931d47 --- /dev/null +++ b/tests/unit/providers/inference/test_inference_client_caching.py @@ -0,0 +1,73 @@ +# 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 unittest.mock import MagicMock + +from llama_stack.distribution.request_headers import request_provider_data_context +from llama_stack.providers.remote.inference.groq.config import GroqConfig +from llama_stack.providers.remote.inference.groq.groq import GroqInferenceAdapter +from llama_stack.providers.remote.inference.openai.config import OpenAIConfig +from llama_stack.providers.remote.inference.openai.openai import OpenAIInferenceAdapter +from llama_stack.providers.remote.inference.together.config import TogetherImplConfig +from llama_stack.providers.remote.inference.together.together import TogetherInferenceAdapter + + +def test_groq_provider_openai_client_caching(): + """Ensure the Groq provider does not cache api keys across client requests""" + + config = GroqConfig() + inference_adapter = GroqInferenceAdapter(config) + + inference_adapter.__provider_spec__ = MagicMock() + inference_adapter.__provider_spec__.provider_data_validator = ( + "llama_stack.providers.remote.inference.groq.config.GroqProviderDataValidator" + ) + + for api_key in ["test1", "test2"]: + 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 + + +def test_openai_provider_openai_client_caching(): + """Ensure the OpenAI provider does not cache api keys across client requests""" + + config = OpenAIConfig() + inference_adapter = OpenAIInferenceAdapter(config) + + inference_adapter.__provider_spec__ = MagicMock() + inference_adapter.__provider_spec__.provider_data_validator = ( + "llama_stack.providers.remote.inference.openai.config.OpenAIProviderDataValidator" + ) + + for api_key in ["test1", "test2"]: + 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 + + +def test_together_provider_openai_client_caching(): + """Ensure the Together provider does not cache api keys across client requests""" + + config = TogetherImplConfig() + inference_adapter = TogetherInferenceAdapter(config) + + inference_adapter.__provider_spec__ = MagicMock() + inference_adapter.__provider_spec__.provider_data_validator = ( + "llama_stack.providers.remote.inference.together.TogetherProviderDataValidator" + ) + + for api_key in ["test1", "test2"]: + with request_provider_data_context({"x-llamastack-provider-data": json.dumps({"together_api_key": api_key})}): + together_client = inference_adapter._get_client() + assert together_client.client.api_key == api_key + openai_client = inference_adapter._get_openai_client() + assert openai_client.api_key == api_key diff --git a/tests/unit/providers/inference/test_remote_vllm.py b/tests/unit/providers/inference/test_remote_vllm.py index eaa9b40da..5c2ad03ab 100644 --- a/tests/unit/providers/inference/test_remote_vllm.py +++ b/tests/unit/providers/inference/test_remote_vllm.py @@ -14,7 +14,6 @@ from typing import Any from unittest.mock import AsyncMock, MagicMock, patch import pytest -import pytest_asyncio from openai.types.chat.chat_completion_chunk import ( ChatCompletionChunk as OpenAIChatCompletionChunk, ) @@ -103,7 +102,7 @@ def mock_openai_models_list(): yield mock_list -@pytest_asyncio.fixture(scope="module") +@pytest.fixture(scope="module") async def vllm_inference_adapter(): config = VLLMInferenceAdapterConfig(url="http://mocked.localhost:12345") inference_adapter = VLLMInferenceAdapter(config) @@ -112,7 +111,6 @@ async def vllm_inference_adapter(): return inference_adapter -@pytest.mark.asyncio async def test_register_model_checks_vllm(mock_openai_models_list, vllm_inference_adapter): async def mock_openai_models(): yield OpenAIModel(id="foo", created=1, object="model", owned_by="test") @@ -125,7 +123,6 @@ async def test_register_model_checks_vllm(mock_openai_models_list, vllm_inferenc mock_openai_models_list.assert_called() -@pytest.mark.asyncio async def test_old_vllm_tool_choice(vllm_inference_adapter): """ Test that we set tool_choice to none when no tools are in use @@ -149,7 +146,6 @@ async def test_old_vllm_tool_choice(vllm_inference_adapter): assert request.tool_config.tool_choice == ToolChoice.none -@pytest.mark.asyncio 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.""" @@ -192,7 +188,6 @@ async def test_tool_call_response(vllm_inference_adapter): ] -@pytest.mark.asyncio async def test_tool_call_delta_empty_tool_call_buf(): """ Test that we don't generate extra chunks when processing a @@ -222,7 +217,6 @@ async def test_tool_call_delta_empty_tool_call_buf(): assert chunks[1].event.stop_reason == StopReason.end_of_turn -@pytest.mark.asyncio async def test_tool_call_delta_streaming_arguments_dict(): async def mock_stream(): mock_chunk_1 = OpenAIChatCompletionChunk( @@ -297,7 +291,6 @@ async def test_tool_call_delta_streaming_arguments_dict(): assert chunks[2].event.event_type.value == "complete" -@pytest.mark.asyncio async def test_multiple_tool_calls(): async def mock_stream(): mock_chunk_1 = OpenAIChatCompletionChunk( @@ -376,7 +369,6 @@ async def test_multiple_tool_calls(): assert chunks[3].event.event_type.value == "complete" -@pytest.mark.asyncio async def test_process_vllm_chat_completion_stream_response_no_choices(): """ Test that we don't error out when vLLM returns no choices for a @@ -401,6 +393,7 @@ 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) @@ -453,7 +446,6 @@ def test_chat_completion_doesnt_block_event_loop(caplog): assert not asyncio_warnings -@pytest.mark.asyncio async def test_get_params_empty_tools(vllm_inference_adapter): request = ChatCompletionRequest( tools=[], @@ -464,7 +456,6 @@ async def test_get_params_empty_tools(vllm_inference_adapter): assert "tools" not in params -@pytest.mark.asyncio async def test_process_vllm_chat_completion_stream_response_tool_call_args_last_chunk(): """ Tests the edge case where the model returns the arguments for the tool call in the same chunk that @@ -543,7 +534,6 @@ async def test_process_vllm_chat_completion_stream_response_tool_call_args_last_ assert chunks[-2].event.delta.tool_call.arguments == mock_tool_arguments -@pytest.mark.asyncio async def test_process_vllm_chat_completion_stream_response_no_finish_reason(): """ Tests the edge case where the model requests a tool call and stays idle without explicitly providing the @@ -596,7 +586,6 @@ async def test_process_vllm_chat_completion_stream_response_no_finish_reason(): assert chunks[-2].event.delta.tool_call.arguments == mock_tool_arguments -@pytest.mark.asyncio async def test_process_vllm_chat_completion_stream_response_tool_without_args(): """ Tests the edge case where no arguments are provided for the tool call. @@ -645,7 +634,6 @@ async def test_process_vllm_chat_completion_stream_response_tool_without_args(): assert chunks[-2].event.delta.tool_call.arguments == {} -@pytest.mark.asyncio async def test_health_status_success(vllm_inference_adapter): """ Test the health method of VLLM InferenceAdapter when the connection is successful. @@ -679,7 +667,6 @@ async def test_health_status_success(vllm_inference_adapter): mock_models.list.assert_called_once() -@pytest.mark.asyncio async def test_health_status_failure(vllm_inference_adapter): """ Test the health method of VLLM InferenceAdapter when the connection fails. diff --git a/tests/unit/providers/nvidia/test_datastore.py b/tests/unit/providers/nvidia/test_datastore.py index a17e51a9c..b59636f7b 100644 --- a/tests/unit/providers/nvidia/test_datastore.py +++ b/tests/unit/providers/nvidia/test_datastore.py @@ -5,103 +5,110 @@ # the root directory of this source tree. import os -import unittest from unittest.mock import patch import pytest from llama_stack.apis.datasets import Dataset, DatasetPurpose, URIDataSource +from llama_stack.apis.resource import ResourceType from llama_stack.providers.remote.datasetio.nvidia.config import NvidiaDatasetIOConfig from llama_stack.providers.remote.datasetio.nvidia.datasetio import NvidiaDatasetIOAdapter -class TestNvidiaDatastore(unittest.TestCase): - def setUp(self): - os.environ["NVIDIA_DATASETS_URL"] = "http://nemo.test/datasets" +@pytest.fixture +def nvidia_adapter(): + """Fixture to set up NvidiaDatasetIOAdapter with mocked requests.""" + os.environ["NVIDIA_DATASETS_URL"] = "http://nemo.test/datasets" - config = NvidiaDatasetIOConfig( - datasets_url=os.environ["NVIDIA_DATASETS_URL"], dataset_namespace="default", project_id="default" - ) - self.adapter = NvidiaDatasetIOAdapter(config) - self.make_request_patcher = patch( - "llama_stack.providers.remote.datasetio.nvidia.datasetio.NvidiaDatasetIOAdapter._make_request" - ) - self.mock_make_request = self.make_request_patcher.start() + config = NvidiaDatasetIOConfig( + datasets_url=os.environ["NVIDIA_DATASETS_URL"], dataset_namespace="default", project_id="default" + ) + adapter = NvidiaDatasetIOAdapter(config) - def tearDown(self): - self.make_request_patcher.stop() + with patch( + "llama_stack.providers.remote.datasetio.nvidia.datasetio.NvidiaDatasetIOAdapter._make_request" + ) as mock_make_request: + yield adapter, mock_make_request - @pytest.fixture(autouse=True) - def inject_fixtures(self, run_async): - self.run_async = run_async - def _assert_request(self, mock_call, expected_method, expected_path, expected_json=None): - """Helper method to verify request details in mock calls.""" - call_args = mock_call.call_args +def _assert_request(mock_call, expected_method, expected_path, expected_json=None): + """Helper function to verify request details in mock calls.""" + call_args = mock_call.call_args - assert call_args[0][0] == expected_method - assert call_args[0][1] == expected_path + assert call_args[0][0] == expected_method + assert call_args[0][1] == expected_path - if expected_json: - for key, value in expected_json.items(): - assert call_args[1]["json"][key] == value + if expected_json: + for key, value in expected_json.items(): + assert call_args[1]["json"][key] == value - def test_register_dataset(self): - self.mock_make_request.return_value = { - "id": "dataset-123456", + +def test_register_dataset(nvidia_adapter, run_async): + adapter, mock_make_request = nvidia_adapter + mock_make_request.return_value = { + "id": "dataset-123456", + "name": "test-dataset", + "namespace": "default", + } + + dataset_def = Dataset( + identifier="test-dataset", + type=ResourceType.dataset, + provider_resource_id="", + provider_id="", + purpose=DatasetPurpose.post_training_messages, + source=URIDataSource(uri="https://example.com/data.jsonl"), + metadata={"provider_id": "nvidia", "format": "jsonl", "description": "Test dataset description"}, + ) + + run_async(adapter.register_dataset(dataset_def)) + + mock_make_request.assert_called_once() + _assert_request( + mock_make_request, + "POST", + "/v1/datasets", + expected_json={ "name": "test-dataset", "namespace": "default", - } + "files_url": "https://example.com/data.jsonl", + "project": "default", + "format": "jsonl", + "description": "Test dataset description", + }, + ) - dataset_def = Dataset( - identifier="test-dataset", - type="dataset", - provider_resource_id="", - provider_id="", - purpose=DatasetPurpose.post_training_messages, - source=URIDataSource(uri="https://example.com/data.jsonl"), - metadata={"provider_id": "nvidia", "format": "jsonl", "description": "Test dataset description"}, - ) - self.run_async(self.adapter.register_dataset(dataset_def)) +def test_unregister_dataset(nvidia_adapter, run_async): + adapter, mock_make_request = nvidia_adapter + mock_make_request.return_value = { + "message": "Resource deleted successfully.", + "id": "dataset-81RSQp7FKX3rdBtKvF9Skn", + "deleted_at": None, + } + dataset_id = "test-dataset" - self.mock_make_request.assert_called_once() - self._assert_request( - self.mock_make_request, - "POST", - "/v1/datasets", - expected_json={ - "name": "test-dataset", - "namespace": "default", - "files_url": "https://example.com/data.jsonl", - "project": "default", - "format": "jsonl", - "description": "Test dataset description", - }, - ) + run_async(adapter.unregister_dataset(dataset_id)) - def test_unregister_dataset(self): - self.mock_make_request.return_value = { - "message": "Resource deleted successfully.", - "id": "dataset-81RSQp7FKX3rdBtKvF9Skn", - "deleted_at": None, - } - dataset_id = "test-dataset" + mock_make_request.assert_called_once() + _assert_request(mock_make_request, "DELETE", "/v1/datasets/default/test-dataset") - self.run_async(self.adapter.unregister_dataset(dataset_id)) - self.mock_make_request.assert_called_once() - self._assert_request(self.mock_make_request, "DELETE", "/v1/datasets/default/test-dataset") +def test_register_dataset_with_custom_namespace_project(run_async): + """Test with custom namespace and project configuration.""" + os.environ["NVIDIA_DATASETS_URL"] = "http://nemo.test/datasets" - def test_register_dataset_with_custom_namespace_project(self): - custom_config = NvidiaDatasetIOConfig( - datasets_url=os.environ["NVIDIA_DATASETS_URL"], - dataset_namespace="custom-namespace", - project_id="custom-project", - ) - custom_adapter = NvidiaDatasetIOAdapter(custom_config) + custom_config = NvidiaDatasetIOConfig( + datasets_url=os.environ["NVIDIA_DATASETS_URL"], + dataset_namespace="custom-namespace", + project_id="custom-project", + ) + custom_adapter = NvidiaDatasetIOAdapter(custom_config) - self.mock_make_request.return_value = { + with patch( + "llama_stack.providers.remote.datasetio.nvidia.datasetio.NvidiaDatasetIOAdapter._make_request" + ) as mock_make_request: + mock_make_request.return_value = { "id": "dataset-123456", "name": "test-dataset", "namespace": "custom-namespace", @@ -109,7 +116,7 @@ class TestNvidiaDatastore(unittest.TestCase): dataset_def = Dataset( identifier="test-dataset", - type="dataset", + type=ResourceType.dataset, provider_resource_id="", provider_id="", purpose=DatasetPurpose.post_training_messages, @@ -117,11 +124,11 @@ class TestNvidiaDatastore(unittest.TestCase): metadata={"format": "jsonl"}, ) - self.run_async(custom_adapter.register_dataset(dataset_def)) + run_async(custom_adapter.register_dataset(dataset_def)) - self.mock_make_request.assert_called_once() - self._assert_request( - self.mock_make_request, + mock_make_request.assert_called_once() + _assert_request( + mock_make_request, "POST", "/v1/datasets", expected_json={ @@ -132,7 +139,3 @@ class TestNvidiaDatastore(unittest.TestCase): "format": "jsonl", }, ) - - -if __name__ == "__main__": - unittest.main() diff --git a/tests/unit/providers/nvidia/test_parameters.py b/tests/unit/providers/nvidia/test_parameters.py index cc33f7609..7e4323bd7 100644 --- a/tests/unit/providers/nvidia/test_parameters.py +++ b/tests/unit/providers/nvidia/test_parameters.py @@ -5,7 +5,6 @@ # the root directory of this source tree. import os -import unittest import warnings from unittest.mock import patch @@ -27,14 +26,13 @@ from llama_stack.providers.remote.post_training.nvidia.post_training import ( ) -class TestNvidiaParameters(unittest.TestCase): - def setUp(self): - os.environ["NVIDIA_BASE_URL"] = "http://nemo.test" +class TestNvidiaParameters: + @pytest.fixture(autouse=True) + def setup_and_teardown(self): + """Setup and teardown for each test method.""" os.environ["NVIDIA_CUSTOMIZER_URL"] = "http://nemo.test" - config = NvidiaPostTrainingConfig( - base_url=os.environ["NVIDIA_BASE_URL"], customizer_url=os.environ["NVIDIA_CUSTOMIZER_URL"], api_key=None - ) + config = NvidiaPostTrainingConfig(customizer_url=os.environ["NVIDIA_CUSTOMIZER_URL"], api_key=None) self.adapter = NvidiaPostTrainingAdapter(config) self.make_request_patcher = patch( @@ -48,7 +46,8 @@ class TestNvidiaParameters(unittest.TestCase): "updated_at": "2025-03-04T13:07:47.543605", } - def tearDown(self): + yield + self.make_request_patcher.stop() def _assert_request_params(self, expected_json): @@ -166,8 +165,8 @@ class TestNvidiaParameters(unittest.TestCase): self.run_async( self.adapter.supervised_fine_tune( - job_uuid=required_job_uuid, # Required parameter - model=required_model, # Required parameter + job_uuid=required_job_uuid, + model=required_model, checkpoint_dir="", algorithm_config=algorithm_config, training_config=convert_pydantic_to_json_value(training_config), @@ -198,7 +197,6 @@ class TestNvidiaParameters(unittest.TestCase): data_config = DataConfig( dataset_id="test-dataset", batch_size=8, - # Unsupported parameters shuffle=True, data_format=DatasetFormat.instruct, validation_dataset_id="val-dataset", @@ -207,20 +205,16 @@ class TestNvidiaParameters(unittest.TestCase): optimizer_config = OptimizerConfig( lr=0.0001, weight_decay=0.01, - # Unsupported parameters optimizer_type=OptimizerType.adam, num_warmup_steps=100, ) - efficiency_config = EfficiencyConfig( - enable_activation_checkpointing=True # Unsupported parameter - ) + efficiency_config = EfficiencyConfig(enable_activation_checkpointing=True) training_config = TrainingConfig( n_epochs=1, data_config=data_config, optimizer_config=optimizer_config, - # Unsupported parameters efficiency_config=efficiency_config, max_steps_per_epoch=1000, gradient_accumulation_steps=4, @@ -228,7 +222,6 @@ class TestNvidiaParameters(unittest.TestCase): dtype="bf16", ) - # Capture warnings with warnings.catch_warnings(record=True) as w: warnings.simplefilter("always") @@ -236,7 +229,7 @@ class TestNvidiaParameters(unittest.TestCase): self.adapter.supervised_fine_tune( job_uuid="test-job", model="meta-llama/Llama-3.1-8B-Instruct", - checkpoint_dir="test-dir", # Unsupported parameter + checkpoint_dir="test-dir", algorithm_config=LoraFinetuningConfig( type="LoRA", apply_lora_to_mlp=True, @@ -246,8 +239,8 @@ class TestNvidiaParameters(unittest.TestCase): lora_attn_modules=["q_proj", "k_proj", "v_proj", "o_proj"], ), training_config=convert_pydantic_to_json_value(training_config), - logger_config={"test": "value"}, # Unsupported parameter - hyperparam_search_config={"test": "value"}, # Unsupported parameter + logger_config={"test": "value"}, + hyperparam_search_config={"test": "value"}, ) ) @@ -265,7 +258,6 @@ class TestNvidiaParameters(unittest.TestCase): "gradient_accumulation_steps", "max_validation_steps", "dtype", - # required unsupported parameters "rank", "apply_lora_to_output", "lora_attn_modules", @@ -273,7 +265,3 @@ class TestNvidiaParameters(unittest.TestCase): ] for field in fields: assert any(field in text for text in warning_texts) - - -if __name__ == "__main__": - unittest.main() diff --git a/tests/unit/providers/nvidia/test_supervised_fine_tuning.py b/tests/unit/providers/nvidia/test_supervised_fine_tuning.py index 97ca02fba..bc474f3bc 100644 --- a/tests/unit/providers/nvidia/test_supervised_fine_tuning.py +++ b/tests/unit/providers/nvidia/test_supervised_fine_tuning.py @@ -5,13 +5,11 @@ # the root directory of this source tree. import os -import unittest import warnings from unittest.mock import patch import pytest -from llama_stack.apis.models import Model, ModelType from llama_stack.apis.post_training.post_training import ( DataConfig, DatasetFormat, @@ -22,7 +20,6 @@ from llama_stack.apis.post_training.post_training import ( TrainingConfig, ) from llama_stack.distribution.library_client import convert_pydantic_to_json_value -from llama_stack.providers.remote.inference.nvidia.nvidia import NVIDIAConfig, NVIDIAInferenceAdapter from llama_stack.providers.remote.post_training.nvidia.post_training import ( ListNvidiaPostTrainingJobs, NvidiaPostTrainingAdapter, @@ -32,331 +29,297 @@ from llama_stack.providers.remote.post_training.nvidia.post_training import ( ) -class TestNvidiaPostTraining(unittest.TestCase): - def setUp(self): - os.environ["NVIDIA_BASE_URL"] = "http://nemo.test" # needed for llm inference - os.environ["NVIDIA_CUSTOMIZER_URL"] = "http://nemo.test" # needed for nemo customizer +@pytest.fixture +def nvidia_post_training_adapter(): + """Fixture to create and configure the NVIDIA post training adapter.""" + os.environ["NVIDIA_CUSTOMIZER_URL"] = "http://nemo.test" # needed for nemo customizer - config = NvidiaPostTrainingConfig( - base_url=os.environ["NVIDIA_BASE_URL"], customizer_url=os.environ["NVIDIA_CUSTOMIZER_URL"], api_key=None + config = NvidiaPostTrainingConfig(customizer_url=os.environ["NVIDIA_CUSTOMIZER_URL"], api_key=None) + adapter = NvidiaPostTrainingAdapter(config) + + with patch.object(adapter, "_make_request") as mock_make_request: + yield adapter, mock_make_request + + +def _assert_request(mock_call, expected_method, expected_path, expected_params=None, expected_json=None): + """Helper method to verify request details in mock calls.""" + call_args = mock_call.call_args + + if expected_method and expected_path: + if isinstance(call_args[0], tuple) and len(call_args[0]) == 2: + assert call_args[0] == (expected_method, expected_path) + else: + assert call_args[1]["method"] == expected_method + assert call_args[1]["path"] == expected_path + + if expected_params: + assert call_args[1]["params"] == expected_params + + if expected_json: + for key, value in expected_json.items(): + assert call_args[1]["json"][key] == value + + +async def test_supervised_fine_tune(nvidia_post_training_adapter): + """Test the supervised fine-tuning API call.""" + adapter, mock_make_request = nvidia_post_training_adapter + mock_make_request.return_value = { + "id": "cust-JGTaMbJMdqjJU8WbQdN9Q2", + "created_at": "2024-12-09T04:06:28.542884", + "updated_at": "2024-12-09T04:06:28.542884", + "config": { + "schema_version": "1.0", + "id": "af783f5b-d985-4e5b-bbb7-f9eec39cc0b1", + "created_at": "2024-12-09T04:06:28.542657", + "updated_at": "2024-12-09T04:06:28.569837", + "custom_fields": {}, + "name": "meta-llama/Llama-3.1-8B-Instruct", + "base_model": "meta-llama/Llama-3.1-8B-Instruct", + "model_path": "llama-3_1-8b-instruct", + "training_types": [], + "finetuning_types": ["lora"], + "precision": "bf16", + "num_gpus": 4, + "num_nodes": 1, + "micro_batch_size": 1, + "tensor_parallel_size": 1, + "max_seq_length": 4096, + }, + "dataset": { + "schema_version": "1.0", + "id": "dataset-XU4pvGzr5tvawnbVxeJMTb", + "created_at": "2024-12-09T04:06:28.542657", + "updated_at": "2024-12-09T04:06:28.542660", + "custom_fields": {}, + "name": "sample-basic-test", + "version_id": "main", + "version_tags": [], + }, + "hyperparameters": { + "finetuning_type": "lora", + "training_type": "sft", + "batch_size": 16, + "epochs": 2, + "learning_rate": 0.0001, + "lora": {"alpha": 16}, + }, + "output_model": "default/job-1234", + "status": "created", + "project": "default", + "custom_fields": {}, + "ownership": {"created_by": "me", "access_policies": {}}, + } + + algorithm_config = LoraFinetuningConfig( + type="LoRA", + apply_lora_to_mlp=True, + apply_lora_to_output=True, + alpha=16, + rank=16, + lora_attn_modules=["q_proj", "k_proj", "v_proj", "o_proj"], + ) + + data_config = DataConfig( + dataset_id="sample-basic-test", batch_size=16, shuffle=False, data_format=DatasetFormat.instruct + ) + + optimizer_config = OptimizerConfig( + optimizer_type=OptimizerType.adam, + lr=0.0001, + weight_decay=0.01, + num_warmup_steps=100, + ) + + training_config = TrainingConfig( + n_epochs=2, + data_config=data_config, + optimizer_config=optimizer_config, + ) + + with warnings.catch_warnings(record=True): + warnings.simplefilter("always") + training_job = await adapter.supervised_fine_tune( + job_uuid="1234", + model="meta/llama-3.2-1b-instruct@v1.0.0+L40", + checkpoint_dir="", + algorithm_config=algorithm_config, + training_config=convert_pydantic_to_json_value(training_config), + logger_config={}, + hyperparam_search_config={}, ) - self.adapter = NvidiaPostTrainingAdapter(config) - self.make_request_patcher = patch( - "llama_stack.providers.remote.post_training.nvidia.post_training.NvidiaPostTrainingAdapter._make_request" - ) - self.mock_make_request = self.make_request_patcher.start() - # Mock the inference client - inference_config = NVIDIAConfig(base_url=os.environ["NVIDIA_BASE_URL"], api_key=None) - self.inference_adapter = NVIDIAInferenceAdapter(inference_config) + # check the output is a PostTrainingJob + assert isinstance(training_job, NvidiaPostTrainingJob) + assert training_job.job_uuid == "cust-JGTaMbJMdqjJU8WbQdN9Q2" - self.mock_client = unittest.mock.MagicMock() - self.mock_client.chat.completions.create = unittest.mock.AsyncMock() - self.inference_mock_make_request = self.mock_client.chat.completions.create - self.inference_make_request_patcher = patch( - "llama_stack.providers.remote.inference.nvidia.nvidia.NVIDIAInferenceAdapter._get_client", - return_value=self.mock_client, - ) - self.inference_make_request_patcher.start() - - def tearDown(self): - self.make_request_patcher.stop() - self.inference_make_request_patcher.stop() - - @pytest.fixture(autouse=True) - def inject_fixtures(self, run_async): - self.run_async = run_async - - def _assert_request(self, mock_call, expected_method, expected_path, expected_params=None, expected_json=None): - """Helper method to verify request details in mock calls.""" - call_args = mock_call.call_args - - if expected_method and expected_path: - if isinstance(call_args[0], tuple) and len(call_args[0]) == 2: - assert call_args[0] == (expected_method, expected_path) - else: - assert call_args[1]["method"] == expected_method - assert call_args[1]["path"] == expected_path - - if expected_params: - assert call_args[1]["params"] == expected_params - - if expected_json: - for key, value in expected_json.items(): - assert call_args[1]["json"][key] == value - - def test_supervised_fine_tune(self): - """Test the supervised fine-tuning API call.""" - self.mock_make_request.return_value = { - "id": "cust-JGTaMbJMdqjJU8WbQdN9Q2", - "created_at": "2024-12-09T04:06:28.542884", - "updated_at": "2024-12-09T04:06:28.542884", - "config": { - "schema_version": "1.0", - "id": "af783f5b-d985-4e5b-bbb7-f9eec39cc0b1", - "created_at": "2024-12-09T04:06:28.542657", - "updated_at": "2024-12-09T04:06:28.569837", - "custom_fields": {}, - "name": "meta-llama/Llama-3.1-8B-Instruct", - "base_model": "meta-llama/Llama-3.1-8B-Instruct", - "model_path": "llama-3_1-8b-instruct", - "training_types": [], - "finetuning_types": ["lora"], - "precision": "bf16", - "num_gpus": 4, - "num_nodes": 1, - "micro_batch_size": 1, - "tensor_parallel_size": 1, - "max_seq_length": 4096, - }, - "dataset": { - "schema_version": "1.0", - "id": "dataset-XU4pvGzr5tvawnbVxeJMTb", - "created_at": "2024-12-09T04:06:28.542657", - "updated_at": "2024-12-09T04:06:28.542660", - "custom_fields": {}, - "name": "sample-basic-test", - "version_id": "main", - "version_tags": [], - }, + mock_make_request.assert_called_once() + _assert_request( + mock_make_request, + "POST", + "/v1/customization/jobs", + expected_json={ + "config": "meta/llama-3.2-1b-instruct@v1.0.0+L40", + "dataset": {"name": "sample-basic-test", "namespace": "default"}, "hyperparameters": { - "finetuning_type": "lora", "training_type": "sft", - "batch_size": 16, + "finetuning_type": "lora", "epochs": 2, + "batch_size": 16, "learning_rate": 0.0001, + "weight_decay": 0.01, "lora": {"alpha": 16}, }, - "output_model": "default/job-1234", - "status": "created", - "project": "default", - "custom_fields": {}, - "ownership": {"created_by": "me", "access_policies": {}}, + }, + ) + + +async def test_supervised_fine_tune_with_qat(nvidia_post_training_adapter): + """Test that QAT configuration raises NotImplementedError.""" + adapter, mock_make_request = nvidia_post_training_adapter + + algorithm_config = QATFinetuningConfig(type="QAT", quantizer_name="quantizer_name", group_size=1) + data_config = DataConfig( + dataset_id="sample-basic-test", batch_size=16, shuffle=False, data_format=DatasetFormat.instruct + ) + optimizer_config = OptimizerConfig( + optimizer_type=OptimizerType.adam, + lr=0.0001, + weight_decay=0.01, + num_warmup_steps=100, + ) + training_config = TrainingConfig( + n_epochs=2, + data_config=data_config, + optimizer_config=optimizer_config, + ) + + # This will raise NotImplementedError since QAT is not supported + with pytest.raises(NotImplementedError): + await adapter.supervised_fine_tune( + job_uuid="1234", + model="meta/llama-3.2-1b-instruct@v1.0.0+L40", + checkpoint_dir="", + algorithm_config=algorithm_config, + training_config=convert_pydantic_to_json_value(training_config), + logger_config={}, + hyperparam_search_config={}, + ) + + +async def test_get_training_job_status(nvidia_post_training_adapter): + """Test getting training job status with different statuses.""" + adapter, mock_make_request = nvidia_post_training_adapter + + customizer_status_to_job_status = [ + ("running", "in_progress"), + ("completed", "completed"), + ("failed", "failed"), + ("cancelled", "cancelled"), + ("pending", "scheduled"), + ("unknown", "scheduled"), + ] + + for customizer_status, expected_status in customizer_status_to_job_status: + mock_make_request.return_value = { + "created_at": "2024-12-09T04:06:28.580220", + "updated_at": "2024-12-09T04:21:19.852832", + "status": customizer_status, + "steps_completed": 1210, + "epochs_completed": 2, + "percentage_done": 100.0, + "best_epoch": 2, + "train_loss": 1.718016266822815, + "val_loss": 1.8661999702453613, } - algorithm_config = LoraFinetuningConfig( - type="LoRA", - apply_lora_to_mlp=True, - apply_lora_to_output=True, - alpha=16, - rank=16, - lora_attn_modules=["q_proj", "k_proj", "v_proj", "o_proj"], - ) - - data_config = DataConfig( - dataset_id="sample-basic-test", batch_size=16, shuffle=False, data_format=DatasetFormat.instruct - ) - - optimizer_config = OptimizerConfig( - optimizer_type=OptimizerType.adam, - lr=0.0001, - weight_decay=0.01, - num_warmup_steps=100, - ) - - training_config = TrainingConfig( - n_epochs=2, - data_config=data_config, - optimizer_config=optimizer_config, - ) - - with warnings.catch_warnings(record=True): - warnings.simplefilter("always") - training_job = self.run_async( - self.adapter.supervised_fine_tune( - job_uuid="1234", - model="meta/llama-3.2-1b-instruct@v1.0.0+L40", - checkpoint_dir="", - algorithm_config=algorithm_config, - training_config=convert_pydantic_to_json_value(training_config), - logger_config={}, - hyperparam_search_config={}, - ) - ) - - # check the output is a PostTrainingJob - assert isinstance(training_job, NvidiaPostTrainingJob) - assert training_job.job_uuid == "cust-JGTaMbJMdqjJU8WbQdN9Q2" - - self.mock_make_request.assert_called_once() - self._assert_request( - self.mock_make_request, - "POST", - "/v1/customization/jobs", - expected_json={ - "config": "meta/llama-3.2-1b-instruct@v1.0.0+L40", - "dataset": {"name": "sample-basic-test", "namespace": "default"}, - "hyperparameters": { - "training_type": "sft", - "finetuning_type": "lora", - "epochs": 2, - "batch_size": 16, - "learning_rate": 0.0001, - "weight_decay": 0.01, - "lora": {"alpha": 16}, - }, - }, - ) - - def test_supervised_fine_tune_with_qat(self): - algorithm_config = QATFinetuningConfig(type="QAT", quantizer_name="quantizer_name", group_size=1) - data_config = DataConfig( - dataset_id="sample-basic-test", batch_size=16, shuffle=False, data_format=DatasetFormat.instruct - ) - optimizer_config = OptimizerConfig( - optimizer_type=OptimizerType.adam, - lr=0.0001, - weight_decay=0.01, - num_warmup_steps=100, - ) - training_config = TrainingConfig( - n_epochs=2, - data_config=data_config, - optimizer_config=optimizer_config, - ) - # This will raise NotImplementedError since QAT is not supported - with self.assertRaises(NotImplementedError): - self.run_async( - self.adapter.supervised_fine_tune( - job_uuid="1234", - model="meta/llama-3.2-1b-instruct@v1.0.0+L40", - checkpoint_dir="", - algorithm_config=algorithm_config, - training_config=convert_pydantic_to_json_value(training_config), - logger_config={}, - hyperparam_search_config={}, - ) - ) - - def test_get_training_job_status(self): - customizer_status_to_job_status = [ - ("running", "in_progress"), - ("completed", "completed"), - ("failed", "failed"), - ("cancelled", "cancelled"), - ("pending", "scheduled"), - ("unknown", "scheduled"), - ] - - for customizer_status, expected_status in customizer_status_to_job_status: - with self.subTest(customizer_status=customizer_status, expected_status=expected_status): - self.mock_make_request.return_value = { - "created_at": "2024-12-09T04:06:28.580220", - "updated_at": "2024-12-09T04:21:19.852832", - "status": customizer_status, - "steps_completed": 1210, - "epochs_completed": 2, - "percentage_done": 100.0, - "best_epoch": 2, - "train_loss": 1.718016266822815, - "val_loss": 1.8661999702453613, - } - - job_id = "cust-JGTaMbJMdqjJU8WbQdN9Q2" - - status = self.run_async(self.adapter.get_training_job_status(job_uuid=job_id)) - - assert isinstance(status, NvidiaPostTrainingJobStatusResponse) - assert status.status.value == expected_status - assert status.steps_completed == 1210 - assert status.epochs_completed == 2 - assert status.percentage_done == 100.0 - assert status.best_epoch == 2 - assert status.train_loss == 1.718016266822815 - assert status.val_loss == 1.8661999702453613 - - self._assert_request( - self.mock_make_request, - "GET", - f"/v1/customization/jobs/{job_id}/status", - expected_params={"job_id": job_id}, - ) - - def test_get_training_jobs(self): job_id = "cust-JGTaMbJMdqjJU8WbQdN9Q2" - self.mock_make_request.return_value = { - "data": [ - { - "id": job_id, - "created_at": "2024-12-09T04:06:28.542884", - "updated_at": "2024-12-09T04:21:19.852832", - "config": { - "name": "meta-llama/Llama-3.1-8B-Instruct", - "base_model": "meta-llama/Llama-3.1-8B-Instruct", - }, - "dataset": {"name": "default/sample-basic-test"}, - "hyperparameters": { - "finetuning_type": "lora", - "training_type": "sft", - "batch_size": 16, - "epochs": 2, - "learning_rate": 0.0001, - "lora": {"adapter_dim": 16, "adapter_dropout": 0.1}, - }, - "output_model": "default/job-1234", - "status": "completed", - "project": "default", - } - ] - } - jobs = self.run_async(self.adapter.get_training_jobs()) + status = await adapter.get_training_job_status(job_uuid=job_id) - assert isinstance(jobs, ListNvidiaPostTrainingJobs) - assert len(jobs.data) == 1 - job = jobs.data[0] - assert job.job_uuid == job_id - assert job.status.value == "completed" + assert isinstance(status, NvidiaPostTrainingJobStatusResponse) + assert status.status.value == expected_status + # Note: The response object inherits extra fields via ConfigDict(extra="allow") + # So these attributes should be accessible using getattr with defaults + assert getattr(status, "steps_completed", None) == 1210 + assert getattr(status, "epochs_completed", None) == 2 + assert getattr(status, "percentage_done", None) == 100.0 + assert getattr(status, "best_epoch", None) == 2 + assert getattr(status, "train_loss", None) == 1.718016266822815 + assert getattr(status, "val_loss", None) == 1.8661999702453613 - self.mock_make_request.assert_called_once() - self._assert_request( - self.mock_make_request, + _assert_request( + mock_make_request, "GET", - "/v1/customization/jobs", - expected_params={"page": 1, "page_size": 10, "sort": "created_at"}, - ) - - def test_cancel_training_job(self): - self.mock_make_request.return_value = {} # Empty response for successful cancellation - job_id = "cust-JGTaMbJMdqjJU8WbQdN9Q2" - - result = self.run_async(self.adapter.cancel_training_job(job_uuid=job_id)) - - assert result is None - - self.mock_make_request.assert_called_once() - self._assert_request( - self.mock_make_request, - "POST", - f"/v1/customization/jobs/{job_id}/cancel", + f"/v1/customization/jobs/{job_id}/status", expected_params={"job_id": job_id}, ) - def test_inference_register_model(self): - model_id = "default/job-1234" - model_type = ModelType.llm - model = Model( - identifier=model_id, - provider_id="nvidia", - provider_model_id=model_id, - provider_resource_id=model_id, - model_type=model_type, - ) - result = self.run_async(self.inference_adapter.register_model(model)) - assert result == model - assert len(self.inference_adapter.alias_to_provider_id_map) > 1 - assert self.inference_adapter.get_provider_model_id(model.provider_model_id) == model_id - - with patch.object(self.inference_adapter, "chat_completion") as mock_chat_completion: - self.run_async( - self.inference_adapter.chat_completion( - model_id=model_id, - messages=[{"role": "user", "content": "Hello, model"}], - ) - ) - - mock_chat_completion.assert_called() + mock_make_request.reset_mock() -if __name__ == "__main__": - unittest.main() +async def test_get_training_jobs(nvidia_post_training_adapter): + """Test getting list of training jobs.""" + adapter, mock_make_request = nvidia_post_training_adapter + + job_id = "cust-JGTaMbJMdqjJU8WbQdN9Q2" + mock_make_request.return_value = { + "data": [ + { + "id": job_id, + "created_at": "2024-12-09T04:06:28.542884", + "updated_at": "2024-12-09T04:21:19.852832", + "config": { + "name": "meta-llama/Llama-3.1-8B-Instruct", + "base_model": "meta-llama/Llama-3.1-8B-Instruct", + }, + "dataset": {"name": "default/sample-basic-test"}, + "hyperparameters": { + "finetuning_type": "lora", + "training_type": "sft", + "batch_size": 16, + "epochs": 2, + "learning_rate": 0.0001, + "lora": {"adapter_dim": 16, "adapter_dropout": 0.1}, + }, + "output_model": "default/job-1234", + "status": "completed", + "project": "default", + } + ] + } + + jobs = await adapter.get_training_jobs() + + assert isinstance(jobs, ListNvidiaPostTrainingJobs) + assert len(jobs.data) == 1 + job = jobs.data[0] + assert job.job_uuid == job_id + assert job.status.value == "completed" + + mock_make_request.assert_called_once() + _assert_request( + mock_make_request, + "GET", + "/v1/customization/jobs", + expected_params={"page": 1, "page_size": 10, "sort": "created_at"}, + ) + + +async def test_cancel_training_job(nvidia_post_training_adapter): + """Test canceling a training job.""" + adapter, mock_make_request = nvidia_post_training_adapter + + mock_make_request.return_value = {} # Empty response for successful cancellation + job_id = "cust-JGTaMbJMdqjJU8WbQdN9Q2" + + result = await adapter.cancel_training_job(job_uuid=job_id) + + assert result is None + + mock_make_request.assert_called_once() + _assert_request( + mock_make_request, + "POST", + f"/v1/customization/jobs/{job_id}/cancel", + expected_params={"job_id": job_id}, + ) diff --git a/tests/unit/providers/utils/inference/test_openai_compat.py b/tests/unit/providers/utils/inference/test_openai_compat.py index 3598e4810..f57f6c9b3 100644 --- a/tests/unit/providers/utils/inference/test_openai_compat.py +++ b/tests/unit/providers/utils/inference/test_openai_compat.py @@ -4,7 +4,6 @@ # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. -import pytest from llama_stack.apis.common.content_types import TextContentItem from llama_stack.apis.inference import ( @@ -23,7 +22,6 @@ from llama_stack.providers.utils.inference.openai_compat import ( ) -@pytest.mark.asyncio async def test_convert_message_to_openai_dict(): message = UserMessage(content=[TextContentItem(text="Hello, world!")], role="user") assert await convert_message_to_openai_dict(message) == { @@ -33,7 +31,6 @@ async def test_convert_message_to_openai_dict(): # Test convert_message_to_openai_dict with a tool call -@pytest.mark.asyncio async def test_convert_message_to_openai_dict_with_tool_call(): message = CompletionMessage( content="", @@ -54,7 +51,6 @@ async def test_convert_message_to_openai_dict_with_tool_call(): } -@pytest.mark.asyncio async def test_convert_message_to_openai_dict_with_builtin_tool_call(): message = CompletionMessage( content="", @@ -80,7 +76,6 @@ async def test_convert_message_to_openai_dict_with_builtin_tool_call(): } -@pytest.mark.asyncio async def test_openai_messages_to_messages_with_content_str(): openai_messages = [ OpenAISystemMessageParam(content="system message"), @@ -98,7 +93,6 @@ async def test_openai_messages_to_messages_with_content_str(): assert llama_messages[2].content == "assistant message" -@pytest.mark.asyncio async def test_openai_messages_to_messages_with_content_list(): openai_messages = [ OpenAISystemMessageParam(content=[OpenAIChatCompletionContentPartTextParam(text="system message")]), diff --git a/tests/unit/providers/utils/memory/test_vector_store.py b/tests/unit/providers/utils/memory/test_vector_store.py index 220c21994..90b229262 100644 --- a/tests/unit/providers/utils/memory/test_vector_store.py +++ b/tests/unit/providers/utils/memory/test_vector_store.py @@ -13,7 +13,6 @@ from llama_stack.apis.tools import RAGDocument from llama_stack.providers.utils.memory.vector_store import content_from_data_and_mime_type, content_from_doc -@pytest.mark.asyncio async def test_content_from_doc_with_url(): """Test extracting content from RAGDocument with URL content.""" mock_url = URL(uri="https://example.com") @@ -33,7 +32,6 @@ async def test_content_from_doc_with_url(): mock_instance.get.assert_called_once_with(mock_url.uri) -@pytest.mark.asyncio async def test_content_from_doc_with_pdf_url(): """Test extracting content from RAGDocument with URL pointing to a PDF.""" mock_url = URL(uri="https://example.com/document.pdf") @@ -58,7 +56,6 @@ async def test_content_from_doc_with_pdf_url(): mock_parse_pdf.assert_called_once_with(b"PDF binary data") -@pytest.mark.asyncio async def test_content_from_doc_with_data_url(): """Test extracting content from RAGDocument with data URL content.""" data_url = "data:text/plain;base64,SGVsbG8gV29ybGQ=" # "Hello World" base64 encoded @@ -74,7 +71,6 @@ async def test_content_from_doc_with_data_url(): mock_content_from_data.assert_called_once_with(data_url) -@pytest.mark.asyncio async def test_content_from_doc_with_string(): """Test extracting content from RAGDocument with string content.""" content_string = "This is plain text content" @@ -85,7 +81,6 @@ async def test_content_from_doc_with_string(): assert result == content_string -@pytest.mark.asyncio async def test_content_from_doc_with_string_url(): """Test extracting content from RAGDocument with string URL content.""" url_string = "https://example.com" @@ -105,7 +100,6 @@ async def test_content_from_doc_with_string_url(): mock_instance.get.assert_called_once_with(url_string) -@pytest.mark.asyncio async def test_content_from_doc_with_string_pdf_url(): """Test extracting content from RAGDocument with string URL pointing to a PDF.""" url_string = "https://example.com/document.pdf" @@ -130,7 +124,6 @@ async def test_content_from_doc_with_string_pdf_url(): mock_parse_pdf.assert_called_once_with(b"PDF binary data") -@pytest.mark.asyncio async def test_content_from_doc_with_interleaved_content(): """Test extracting content from RAGDocument with InterleavedContent (the new case added in the commit).""" interleaved_content = [TextContentItem(text="First item"), TextContentItem(text="Second item")] diff --git a/tests/unit/providers/utils/test_model_registry.py b/tests/unit/providers/utils/test_model_registry.py index 10fa1e075..1a1705961 100644 --- a/tests/unit/providers/utils/test_model_registry.py +++ b/tests/unit/providers/utils/test_model_registry.py @@ -87,18 +87,46 @@ def helper(known_provider_model: ProviderModelEntry, known_provider_model2: Prov return ModelRegistryHelper([known_provider_model, known_provider_model2]) -@pytest.mark.asyncio +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) + self._available_models = available_models + + async def check_model_availability(self, model: str) -> bool: + return model in self._available_models + + +@pytest.fixture +def dynamic_model() -> Model: + """A model that's not in static config but available dynamically.""" + return Model( + provider_id="provider", + identifier="dynamic-model", + provider_resource_id="dynamic-provider-id", + ) + + +@pytest.fixture +def helper_with_dynamic_models( + known_provider_model: ProviderModelEntry, known_provider_model2: ProviderModelEntry, dynamic_model: Model +) -> MockModelRegistryHelperWithDynamicModels: + """Helper that includes dynamically available models.""" + return MockModelRegistryHelperWithDynamicModels( + [known_provider_model, known_provider_model2], [dynamic_model.provider_resource_id] + ) + + async def test_lookup_unknown_model(helper: ModelRegistryHelper, unknown_model: Model) -> None: assert helper.get_provider_model_id(unknown_model.model_id) is None -@pytest.mark.asyncio async def test_register_unknown_provider_model(helper: ModelRegistryHelper, unknown_model: Model) -> None: with pytest.raises(ValueError): await helper.register_model(unknown_model) -@pytest.mark.asyncio async def test_register_model(helper: ModelRegistryHelper, known_model: Model) -> None: model = Model( provider_id=known_model.provider_id, @@ -110,7 +138,6 @@ async def test_register_model(helper: ModelRegistryHelper, known_model: Model) - assert helper.get_provider_model_id(model.model_id) == model.provider_resource_id -@pytest.mark.asyncio async def test_register_model_from_alias(helper: ModelRegistryHelper, known_model: Model) -> None: model = Model( provider_id=known_model.provider_id, @@ -122,13 +149,11 @@ async def test_register_model_from_alias(helper: ModelRegistryHelper, known_mode assert helper.get_provider_model_id(model.model_id) == known_model.provider_resource_id -@pytest.mark.asyncio async def test_register_model_existing(helper: ModelRegistryHelper, known_model: Model) -> None: await helper.register_model(known_model) assert helper.get_provider_model_id(known_model.model_id) == known_model.provider_resource_id -@pytest.mark.asyncio async def test_register_model_existing_different( helper: ModelRegistryHelper, known_model: Model, known_model2: Model ) -> None: @@ -137,7 +162,6 @@ async def test_register_model_existing_different( await helper.register_model(known_model) -@pytest.mark.asyncio async def test_unregister_model(helper: ModelRegistryHelper, known_model: Model) -> None: await helper.register_model(known_model) # duplicate entry assert helper.get_provider_model_id(known_model.model_id) == known_model.provider_model_id @@ -145,19 +169,76 @@ async def test_unregister_model(helper: ModelRegistryHelper, known_model: Model) assert helper.get_provider_model_id(known_model.model_id) is None -@pytest.mark.asyncio async def test_unregister_unknown_model(helper: ModelRegistryHelper, unknown_model: Model) -> None: with pytest.raises(ValueError): await helper.unregister_model(unknown_model.model_id) -@pytest.mark.asyncio async def test_register_model_during_init(helper: ModelRegistryHelper, known_model: Model) -> None: assert helper.get_provider_model_id(known_model.provider_resource_id) == known_model.provider_model_id -@pytest.mark.asyncio async def test_unregister_model_during_init(helper: ModelRegistryHelper, known_model: Model) -> None: assert helper.get_provider_model_id(known_model.provider_resource_id) == known_model.provider_model_id await helper.unregister_model(known_model.provider_resource_id) assert helper.get_provider_model_id(known_model.provider_resource_id) is None + + +async def test_register_model_from_check_model_availability( + helper_with_dynamic_models: MockModelRegistryHelperWithDynamicModels, dynamic_model: Model +) -> None: + """Test that models returned by check_model_availability can be registered.""" + # Verify the model is not in static config + assert helper_with_dynamic_models.get_provider_model_id(dynamic_model.provider_resource_id) is None + + # But it should be available via check_model_availability + is_available = await helper_with_dynamic_models.check_model_availability(dynamic_model.provider_resource_id) + assert is_available + + # Registration should succeed + registered_model = await helper_with_dynamic_models.register_model(dynamic_model) + assert registered_model == dynamic_model + + # Model should now be registered and accessible + assert ( + helper_with_dynamic_models.get_provider_model_id(dynamic_model.model_id) == dynamic_model.provider_resource_id + ) + + +async def test_register_model_not_in_static_or_dynamic( + helper_with_dynamic_models: MockModelRegistryHelperWithDynamicModels, unknown_model: Model +) -> None: + """Test that models not in static config or dynamic models are rejected.""" + # Verify the model is not in static config + assert helper_with_dynamic_models.get_provider_model_id(unknown_model.provider_resource_id) is None + + # And not available via check_model_availability + is_available = await helper_with_dynamic_models.check_model_availability(unknown_model.provider_resource_id) + assert not is_available + + # Registration should fail with comprehensive error message + with pytest.raises(Exception) as exc_info: # UnsupportedModelError + await helper_with_dynamic_models.register_model(unknown_model) + + # Error should include static models and "..." for dynamic models + error_str = str(exc_info.value) + assert "..." in error_str # "..." should be in error message + + +async def test_register_alias_for_dynamic_model( + helper_with_dynamic_models: MockModelRegistryHelperWithDynamicModels, dynamic_model: Model +) -> None: + """Test that we can register an alias that maps to a dynamically available model.""" + # Create a model with a different identifier but same provider_resource_id + alias_model = Model( + provider_id=dynamic_model.provider_id, + identifier="dynamic-model-alias", + provider_resource_id=dynamic_model.provider_resource_id, + ) + + # Registration should succeed since the provider_resource_id is available dynamically + registered_model = await helper_with_dynamic_models.register_model(alias_model) + assert registered_model == alias_model + + # Both the original provider_resource_id and the new alias should work + assert helper_with_dynamic_models.get_provider_model_id(alias_model.model_id) == dynamic_model.provider_resource_id diff --git a/tests/unit/providers/utils/test_scheduler.py b/tests/unit/providers/utils/test_scheduler.py index 25b4935de..e5ee74bfa 100644 --- a/tests/unit/providers/utils/test_scheduler.py +++ b/tests/unit/providers/utils/test_scheduler.py @@ -11,7 +11,6 @@ import pytest from llama_stack.providers.utils.scheduler import JobStatus, Scheduler -@pytest.mark.asyncio async def test_scheduler_unknown_backend(): with pytest.raises(ValueError): Scheduler(backend="unknown") @@ -26,7 +25,6 @@ async def wait_for_job_completed(sched: Scheduler, job_id: str) -> None: raise TimeoutError(f"Job {job_id} did not complete in time.") -@pytest.mark.asyncio async def test_scheduler_naive(): sched = Scheduler() @@ -87,7 +85,6 @@ async def test_scheduler_naive(): assert job.logs[0][0] < job.logs[1][0] -@pytest.mark.asyncio async def test_scheduler_naive_handler_raises(): sched = Scheduler() diff --git a/tests/unit/providers/vector_io/conftest.py b/tests/unit/providers/vector_io/conftest.py index 5eaca8a25..9f86f877d 100644 --- a/tests/unit/providers/vector_io/conftest.py +++ b/tests/unit/providers/vector_io/conftest.py @@ -8,10 +8,20 @@ import random import numpy as np import pytest +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.providers.inline.vector_io.faiss.config import FaissVectorIOConfig +from llama_stack.providers.inline.vector_io.faiss.faiss import FaissIndex, FaissVectorIOAdapter +from llama_stack.providers.inline.vector_io.milvus.config import MilvusVectorIOConfig, SqliteKVStoreConfig +from llama_stack.providers.inline.vector_io.sqlite_vec import SQLiteVectorIOConfig +from llama_stack.providers.inline.vector_io.sqlite_vec.sqlite_vec import SQLiteVecIndex, SQLiteVecVectorIOAdapter +from llama_stack.providers.remote.vector_io.milvus.milvus import MilvusIndex, MilvusVectorIOAdapter EMBEDDING_DIMENSION = 384 +COLLECTION_PREFIX = "test_collection" +MILVUS_ALIAS = "test_milvus" @pytest.fixture @@ -50,7 +60,194 @@ def sample_chunks(): return sample +@pytest.fixture(scope="session") +def sample_chunks_with_metadata(): + """Generates chunks that force multiple batches for a single document to expose ID conflicts.""" + n, k = 10, 3 + sample = [ + Chunk( + content=f"Sentence {i} from document {j}", + metadata={"document_id": f"document-{j}"}, + chunk_metadata=ChunkMetadata( + document_id=f"document-{j}", + chunk_id=f"document-{j}-chunk-{i}", + source=f"example source-{j}-{i}", + ), + ) + for j in range(k) + for i in range(n) + ] + return sample + + @pytest.fixture(scope="session") def sample_embeddings(sample_chunks): np.random.seed(42) return np.array([np.random.rand(EMBEDDING_DIMENSION).astype(np.float32) for _ in sample_chunks]) + + +@pytest.fixture(scope="session") +def sample_embeddings_with_metadata(sample_chunks_with_metadata): + np.random.seed(42) + return np.array([np.random.rand(EMBEDDING_DIMENSION).astype(np.float32) for _ in sample_chunks_with_metadata]) + + +@pytest.fixture(params=["milvus", "sqlite_vec", "faiss"]) +def vector_provider(request): + return request.param + + +@pytest.fixture(scope="session") +def mock_inference_api(embedding_dimension): + class MockInferenceAPI: + async def embed_batch(self, texts: list[str]) -> list[list[float]]: + return [np.random.rand(embedding_dimension).astype(np.float32).tolist() for _ in texts] + + return MockInferenceAPI() + + +@pytest.fixture +async def unique_kvstore_config(tmp_path_factory): + # Generate a unique filename for this test + unique_id = f"test_kv_{np.random.randint(1e6)}" + temp_dir = tmp_path_factory.getbasetemp() + db_path = str(temp_dir / f"{unique_id}.db") + + return SqliteKVStoreConfig(db_path=db_path) + + +@pytest.fixture(scope="session") +def sqlite_vec_db_path(tmp_path_factory): + db_path = str(tmp_path_factory.getbasetemp() / "test_sqlite_vec.db") + return db_path + + +@pytest.fixture +async def sqlite_vec_vec_index(embedding_dimension, tmp_path_factory): + temp_dir = tmp_path_factory.getbasetemp() + db_path = str(temp_dir / f"test_sqlite_vec_{np.random.randint(1e6)}.db") + bank_id = f"sqlite_vec_bank_{np.random.randint(1e6)}" + index = SQLiteVecIndex(embedding_dimension, db_path, bank_id) + await index.initialize() + index.db_path = db_path + yield index + index.delete() + + +@pytest.fixture +async def sqlite_vec_adapter(sqlite_vec_db_path, mock_inference_api, embedding_dimension): + config = SQLiteVectorIOConfig( + db_path=sqlite_vec_db_path, + kvstore=SqliteKVStoreConfig(), + ) + adapter = SQLiteVecVectorIOAdapter( + config=config, + inference_api=mock_inference_api, + files_api=None, + ) + collection_id = f"sqlite_test_collection_{np.random.randint(1e6)}" + await adapter.initialize() + await adapter.register_vector_db( + VectorDB( + identifier=collection_id, + provider_id="test_provider", + embedding_model="test_model", + embedding_dimension=embedding_dimension, + ) + ) + adapter.test_collection_id = collection_id + yield adapter + await adapter.shutdown() + + +@pytest.fixture(scope="session") +def milvus_vec_db_path(tmp_path_factory): + db_path = str(tmp_path_factory.getbasetemp() / "test_milvus.db") + return db_path + + +@pytest.fixture +async def milvus_vec_index(milvus_vec_db_path, embedding_dimension): + client = MilvusClient(milvus_vec_db_path) + name = f"{COLLECTION_PREFIX}_{np.random.randint(1e6)}" + connections.connect(alias=MILVUS_ALIAS, uri=milvus_vec_db_path) + index = MilvusIndex(client, name, consistency_level="Strong") + index.db_path = milvus_vec_db_path + yield index + + +@pytest.fixture +async def milvus_vec_adapter(milvus_vec_db_path, mock_inference_api): + config = MilvusVectorIOConfig( + db_path=milvus_vec_db_path, + kvstore=SqliteKVStoreConfig(), + ) + adapter = MilvusVectorIOAdapter( + config=config, + inference_api=mock_inference_api, + files_api=None, + ) + await adapter.initialize() + await adapter.register_vector_db( + VectorDB( + identifier=adapter.metadata_collection_name, + provider_id="test_provider", + embedding_model="test_model", + embedding_dimension=128, + ) + ) + yield adapter + await adapter.shutdown() + + +@pytest.fixture +def faiss_vec_db_path(tmp_path_factory): + db_path = str(tmp_path_factory.getbasetemp() / "test_faiss.db") + return db_path + + +@pytest.fixture +async def faiss_vec_index(embedding_dimension): + index = FaissIndex(embedding_dimension) + yield index + await index.delete() + + +@pytest.fixture +async def faiss_vec_adapter(unique_kvstore_config, mock_inference_api, embedding_dimension): + config = FaissVectorIOConfig( + kvstore=unique_kvstore_config, + ) + adapter = FaissVectorIOAdapter( + config=config, + inference_api=mock_inference_api, + files_api=None, + ) + await adapter.initialize() + await adapter.register_vector_db( + VectorDB( + identifier=f"faiss_test_collection_{np.random.randint(1e6)}", + provider_id="test_provider", + embedding_model="test_model", + embedding_dimension=embedding_dimension, + ) + ) + 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.""" + if vector_provider == "milvus": + return request.getfixturevalue("milvus_vec_adapter") + elif vector_provider == "faiss": + return request.getfixturevalue("faiss_vec_adapter") + else: + return request.getfixturevalue("sqlite_vec_adapter") + + +@pytest.fixture +def vector_index(vector_provider, request): + """Returns appropriate vector index based on provider parameter""" + return request.getfixturevalue(f"{vector_provider}_vec_index") diff --git a/tests/unit/providers/vector_io/remote/test_milvus.py b/tests/unit/providers/vector_io/remote/test_milvus.py new file mode 100644 index 000000000..145edf7fb --- /dev/null +++ b/tests/unit/providers/vector_io/remote/test_milvus.py @@ -0,0 +1,185 @@ +# 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, patch + +import numpy as np +import pytest + +from llama_stack.apis.vector_io import QueryChunksResponse + +# Mock the entire pymilvus module +pymilvus_mock = MagicMock() +pymilvus_mock.DataType = MagicMock() +pymilvus_mock.MilvusClient = MagicMock + +# Apply the mock before importing MilvusIndex +with patch.dict("sys.modules", {"pymilvus": pymilvus_mock}): + from llama_stack.providers.remote.vector_io.milvus.milvus import MilvusIndex + +# This test is a unit test for the MilvusVectorIOAdapter class. This should only contain +# tests which are specific to this class. More general (API-level) tests should be placed in +# tests/integration/vector_io/ +# +# How to run this test: +# +# pytest tests/unit/providers/vector_io/test_milvus.py \ +# -v -s --tb=short --disable-warnings --asyncio-mode=auto + +MILVUS_PROVIDER = "milvus" + + +@pytest.fixture +async def mock_milvus_client() -> MagicMock: + """Create a mock Milvus client with common method behaviors.""" + client = MagicMock() + + # Mock collection operations + client.has_collection.return_value = False # Initially no collection + client.create_collection.return_value = None + client.drop_collection.return_value = None + + # Mock insert operation + client.insert.return_value = {"insert_count": 10} + + # Mock search operation - return mock results (data should be dict, not JSON string) + client.search.return_value = [ + [ + { + "id": 0, + "distance": 0.1, + "entity": {"chunk_content": {"content": "mock chunk 1", "metadata": {"document_id": "doc1"}}}, + }, + { + "id": 1, + "distance": 0.2, + "entity": {"chunk_content": {"content": "mock chunk 2", "metadata": {"document_id": "doc2"}}}, + }, + ] + ] + + # Mock query operation for keyword search (data should be dict, not JSON string) + client.query.return_value = [ + { + "chunk_id": "chunk1", + "chunk_content": {"content": "mock chunk 1", "metadata": {"document_id": "doc1"}}, + "score": 0.9, + }, + { + "chunk_id": "chunk2", + "chunk_content": {"content": "mock chunk 2", "metadata": {"document_id": "doc2"}}, + "score": 0.8, + }, + { + "chunk_id": "chunk3", + "chunk_content": {"content": "mock chunk 3", "metadata": {"document_id": "doc3"}}, + "score": 0.7, + }, + ] + + return client + + +@pytest.fixture +async def milvus_index(mock_milvus_client): + """Create a MilvusIndex with mocked client.""" + index = MilvusIndex(client=mock_milvus_client, collection_name="test_collection") + yield index + # No real cleanup needed since we're using mocks + + +async def test_add_chunks(milvus_index, sample_chunks, sample_embeddings, mock_milvus_client): + # Setup: collection doesn't exist initially, then exists after creation + mock_milvus_client.has_collection.side_effect = [False, True] + + await milvus_index.add_chunks(sample_chunks, sample_embeddings) + + # Verify collection was created and data was inserted + mock_milvus_client.create_collection.assert_called_once() + mock_milvus_client.insert.assert_called_once() + + # Verify the insert call had the right number of chunks + insert_call = mock_milvus_client.insert.call_args + assert len(insert_call[1]["data"]) == len(sample_chunks) + + +async def test_query_chunks_vector( + milvus_index, sample_chunks, sample_embeddings, embedding_dimension, mock_milvus_client +): + # Setup: Add chunks first + mock_milvus_client.has_collection.return_value = True + await milvus_index.add_chunks(sample_chunks, sample_embeddings) + + # Test vector search + query_embedding = np.random.rand(embedding_dimension).astype(np.float32) + response = await milvus_index.query_vector(query_embedding, k=2, score_threshold=0.0) + + assert isinstance(response, QueryChunksResponse) + assert len(response.chunks) == 2 + mock_milvus_client.search.assert_called_once() + + +async def test_query_chunks_keyword_search(milvus_index, sample_chunks, sample_embeddings, mock_milvus_client): + mock_milvus_client.has_collection.return_value = True + await milvus_index.add_chunks(sample_chunks, sample_embeddings) + + # Test keyword search + query_string = "Sentence 5" + response = await milvus_index.query_keyword(query_string=query_string, k=2, score_threshold=0.0) + + assert isinstance(response, QueryChunksResponse) + assert len(response.chunks) == 2 + + +async def test_bm25_fallback_to_simple_search(milvus_index, sample_chunks, sample_embeddings, mock_milvus_client): + """Test that when BM25 search fails, the system falls back to simple text search.""" + mock_milvus_client.has_collection.return_value = True + await milvus_index.add_chunks(sample_chunks, sample_embeddings) + + # Force BM25 search to fail + mock_milvus_client.search.side_effect = Exception("BM25 search not available") + + # Mock simple text search results + mock_milvus_client.query.return_value = [ + { + "chunk_id": "chunk1", + "chunk_content": {"content": "Python programming language", "metadata": {"document_id": "doc1"}}, + }, + { + "chunk_id": "chunk2", + "chunk_content": {"content": "Machine learning algorithms", "metadata": {"document_id": "doc2"}}, + }, + ] + + # Test keyword search that should fall back to simple text search + query_string = "Python" + response = await milvus_index.query_keyword(query_string=query_string, k=3, score_threshold=0.0) + + # Verify response structure + assert isinstance(response, QueryChunksResponse) + assert len(response.chunks) > 0, "Fallback search should return results" + + # Verify that simple text search was used (query method called instead of search) + mock_milvus_client.query.assert_called_once() + mock_milvus_client.search.assert_called_once() # Called once but failed + + # Verify the query uses parameterized filter with filter_params + query_call_args = mock_milvus_client.query.call_args + assert "filter" in query_call_args[1], "Query should include filter for text search" + assert "filter_params" in query_call_args[1], "Query should use parameterized filter" + assert query_call_args[1]["filter_params"]["content"] == "Python", "Filter params should contain the search term" + + # Verify all returned chunks have score 1.0 (simple binary scoring) + assert all(score == 1.0 for score in response.scores), "Simple text search should use binary scoring" + + +async def test_delete_collection(milvus_index, mock_milvus_client): + # Test collection deletion + mock_milvus_client.has_collection.return_value = True + + await milvus_index.delete() + + mock_milvus_client.drop_collection.assert_called_once_with(collection_name=milvus_index.collection_name) diff --git a/tests/unit/providers/vector_io/test_faiss.py b/tests/unit/providers/vector_io/test_faiss.py index 8348b84e3..90108d7a0 100644 --- a/tests/unit/providers/vector_io/test_faiss.py +++ b/tests/unit/providers/vector_io/test_faiss.py @@ -9,7 +9,6 @@ from unittest.mock import AsyncMock, MagicMock, patch import numpy as np import pytest -import pytest_asyncio from llama_stack.apis.files import Files from llama_stack.apis.inference import EmbeddingsResponse, Inference @@ -91,13 +90,13 @@ def faiss_config(): return config -@pytest_asyncio.fixture +@pytest.fixture async def faiss_index(embedding_dimension): index = await FaissIndex.create(dimension=embedding_dimension) yield index -@pytest_asyncio.fixture +@pytest.fixture async def faiss_adapter(faiss_config, mock_inference_api, mock_files_api) -> FaissVectorIOAdapter: # Create the adapter adapter = FaissVectorIOAdapter(config=faiss_config, inference_api=mock_inference_api, files_api=mock_files_api) @@ -113,7 +112,6 @@ async def faiss_adapter(faiss_config, mock_inference_api, mock_files_api) -> Fai yield adapter -@pytest.mark.asyncio async def test_faiss_query_vector_returns_infinity_when_query_and_embedding_are_identical( faiss_index, sample_chunks, sample_embeddings, embedding_dimension ): @@ -136,7 +134,6 @@ async def test_faiss_query_vector_returns_infinity_when_query_and_embedding_are_ assert response.chunks[1] == sample_chunks[1] -@pytest.mark.asyncio async def test_health_success(): """Test that the health check returns OK status when faiss is working correctly.""" # Create a fresh instance of FaissVectorIOAdapter for testing @@ -160,7 +157,6 @@ async def test_health_success(): mock_index_flat.assert_called_once_with(128) # VECTOR_DIMENSION is 128 -@pytest.mark.asyncio async def test_health_failure(): """Test that the health check returns ERROR status when faiss encounters an error.""" # Create a fresh instance of FaissVectorIOAdapter for testing diff --git a/tests/unit/providers/vector_io/test_qdrant.py b/tests/unit/providers/vector_io/test_qdrant.py index 6902c8850..d3ffe711c 100644 --- a/tests/unit/providers/vector_io/test_qdrant.py +++ b/tests/unit/providers/vector_io/test_qdrant.py @@ -10,7 +10,6 @@ from typing import Any from unittest.mock import AsyncMock, MagicMock, patch import pytest -import pytest_asyncio from llama_stack.apis.inference import EmbeddingsResponse, Inference from llama_stack.apis.vector_io import ( @@ -68,7 +67,7 @@ def mock_api_service(sample_embeddings): return mock_api_service -@pytest_asyncio.fixture +@pytest.fixture async def qdrant_adapter(qdrant_config, mock_vector_db_store, mock_api_service, loop) -> QdrantVectorIOAdapter: adapter = QdrantVectorIOAdapter(config=qdrant_config, inference_api=mock_api_service) adapter.vector_db_store = mock_vector_db_store @@ -80,7 +79,6 @@ async def qdrant_adapter(qdrant_config, mock_vector_db_store, mock_api_service, __QUERY = "Sample query" -@pytest.mark.asyncio @pytest.mark.parametrize("max_query_chunks, expected_chunks", [(2, 2), (100, 60)]) async def test_qdrant_adapter_returns_expected_chunks( qdrant_adapter: QdrantVectorIOAdapter, @@ -111,7 +109,6 @@ def _prepare_for_json(value: Any) -> str: @patch("llama_stack.providers.utils.telemetry.trace_protocol._prepare_for_json", new=_prepare_for_json) -@pytest.mark.asyncio async def test_qdrant_register_and_unregister_vector_db( qdrant_adapter: QdrantVectorIOAdapter, mock_vector_db, diff --git a/tests/unit/providers/vector_io/test_sqlite_vec.py b/tests/unit/providers/vector_io/test_sqlite_vec.py index 5d9d92cf3..23c4d6ff6 100644 --- a/tests/unit/providers/vector_io/test_sqlite_vec.py +++ b/tests/unit/providers/vector_io/test_sqlite_vec.py @@ -8,7 +8,6 @@ import asyncio import numpy as np import pytest -import pytest_asyncio from llama_stack.apis.vector_io import Chunk, QueryChunksResponse from llama_stack.providers.inline.vector_io.sqlite_vec.sqlite_vec import ( @@ -34,48 +33,23 @@ def loop(): return asyncio.new_event_loop() -@pytest_asyncio.fixture(scope="session", autouse=True) +@pytest.fixture async def sqlite_vec_index(embedding_dimension, tmp_path_factory): temp_dir = tmp_path_factory.getbasetemp() db_path = str(temp_dir / "test_sqlite.db") - index = await SQLiteVecIndex.create(dimension=embedding_dimension, db_path=db_path, bank_id="test_bank") + index = await SQLiteVecIndex.create(dimension=embedding_dimension, db_path=db_path, bank_id="test_bank.123") yield index await index.delete() -@pytest.mark.asyncio -async def test_add_chunks(sqlite_vec_index, sample_chunks, sample_embeddings): - await sqlite_vec_index.add_chunks(sample_chunks, sample_embeddings, batch_size=2) - connection = _create_sqlite_connection(sqlite_vec_index.db_path) - cur = connection.cursor() - cur.execute(f"SELECT COUNT(*) FROM {sqlite_vec_index.metadata_table}") - count = cur.fetchone()[0] - assert count == len(sample_chunks) - cur.close() - connection.close() +async def test_query_chunk_metadata(sqlite_vec_index, sample_chunks_with_metadata, sample_embeddings_with_metadata): + await sqlite_vec_index.add_chunks(sample_chunks_with_metadata, sample_embeddings_with_metadata) + response = await sqlite_vec_index.query_vector(sample_embeddings_with_metadata[-1], k=2, score_threshold=0.0) + assert response.chunks[0].chunk_metadata == sample_chunks_with_metadata[-1].chunk_metadata -@pytest.mark.asyncio -async def test_query_chunks_vector(sqlite_vec_index, sample_chunks, sample_embeddings, embedding_dimension): - await sqlite_vec_index.add_chunks(sample_chunks, sample_embeddings) - query_embedding = np.random.rand(embedding_dimension).astype(np.float32) - response = await sqlite_vec_index.query_vector(query_embedding, k=2, score_threshold=0.0) - assert isinstance(response, QueryChunksResponse) - assert len(response.chunks) == 2 - - -@pytest.mark.xfail(reason="Chunk Metadata not yet supported for SQLite-vec", strict=True) -async def test_query_chunk_metadata(sqlite_vec_index, sample_chunks, sample_embeddings): - await sqlite_vec_index.add_chunks(sample_chunks, sample_embeddings) - query_embedding = sample_embeddings[0] - response = await sqlite_vec_index.query_vector(query_embedding, k=2, score_threshold=0.0) - assert response.chunks[-1].chunk_metadata == sample_chunks[-1].chunk_metadata - - -@pytest.mark.asyncio async def test_query_chunks_full_text_search(sqlite_vec_index, sample_chunks, sample_embeddings): await sqlite_vec_index.add_chunks(sample_chunks, sample_embeddings) - query_string = "Sentence 5" response = await sqlite_vec_index.query_keyword(k=3, score_threshold=0.0, query_string=query_string) @@ -91,7 +65,6 @@ async def test_query_chunks_full_text_search(sqlite_vec_index, sample_chunks, sa assert len(response_no_results.chunks) == 0, f"Expected 0 results, but got {len(response_no_results.chunks)}" -@pytest.mark.asyncio async def test_query_chunks_hybrid(sqlite_vec_index, sample_chunks, sample_embeddings): await sqlite_vec_index.add_chunks(sample_chunks, sample_embeddings) @@ -113,7 +86,6 @@ async def test_query_chunks_hybrid(sqlite_vec_index, sample_chunks, sample_embed assert all(response.scores[i] >= response.scores[i + 1] for i in range(len(response.scores) - 1)) -@pytest.mark.asyncio async def test_query_chunks_full_text_search_k_greater_than_results(sqlite_vec_index, sample_chunks, sample_embeddings): # Re-initialize with a clean index await sqlite_vec_index.add_chunks(sample_chunks, sample_embeddings) @@ -126,7 +98,6 @@ async def test_query_chunks_full_text_search_k_greater_than_results(sqlite_vec_i assert any("Sentence 1 from document 0" in chunk.content for chunk in response.chunks), "Expected chunk not found" -@pytest.mark.asyncio async def test_chunk_id_conflict(sqlite_vec_index, sample_chunks, embedding_dimension): """Test that chunk IDs do not conflict across batches when inserting chunks.""" # Reduce batch size to force multiple batches for same document @@ -139,7 +110,7 @@ async def test_chunk_id_conflict(sqlite_vec_index, sample_chunks, embedding_dime cur = connection.cursor() # Retrieve all chunk IDs to check for duplicates - cur.execute(f"SELECT id FROM {sqlite_vec_index.metadata_table}") + cur.execute(f"SELECT id FROM [{sqlite_vec_index.metadata_table}]") chunk_ids = [row[0] for row in cur.fetchall()] cur.close() connection.close() @@ -148,7 +119,7 @@ async def test_chunk_id_conflict(sqlite_vec_index, sample_chunks, embedding_dime assert len(chunk_ids) == len(set(chunk_ids)), "Duplicate chunk IDs detected across batches!" -@pytest_asyncio.fixture(scope="session") +@pytest.fixture(scope="session") async def sqlite_vec_adapter(sqlite_connection): config = type("Config", (object,), {"db_path": ":memory:"}) # Mock config with in-memory database adapter = SQLiteVecVectorIOAdapter(config=config, inference_api=None) @@ -157,7 +128,6 @@ async def sqlite_vec_adapter(sqlite_connection): await adapter.shutdown() -@pytest.mark.asyncio async def test_query_chunks_hybrid_no_keyword_matches(sqlite_vec_index, sample_chunks, sample_embeddings): """Test hybrid search when keyword search returns no matches - should still return vector results.""" await sqlite_vec_index.add_chunks(sample_chunks, sample_embeddings) @@ -186,7 +156,6 @@ async def test_query_chunks_hybrid_no_keyword_matches(sqlite_vec_index, sample_c assert all(response.scores[i] >= response.scores[i + 1] for i in range(len(response.scores) - 1)) -@pytest.mark.asyncio async def test_query_chunks_hybrid_score_threshold(sqlite_vec_index, sample_chunks, sample_embeddings): """Test hybrid search with a high score threshold.""" await sqlite_vec_index.add_chunks(sample_chunks, sample_embeddings) @@ -208,7 +177,6 @@ async def test_query_chunks_hybrid_score_threshold(sqlite_vec_index, sample_chun assert len(response.chunks) == 0 -@pytest.mark.asyncio async def test_query_chunks_hybrid_different_embedding( sqlite_vec_index, sample_chunks, sample_embeddings, embedding_dimension ): @@ -234,7 +202,6 @@ async def test_query_chunks_hybrid_different_embedding( assert all(response.scores[i] >= response.scores[i + 1] for i in range(len(response.scores) - 1)) -@pytest.mark.asyncio async def test_query_chunks_hybrid_rrf_ranking(sqlite_vec_index, sample_chunks, sample_embeddings): """Test that RRF properly combines rankings when documents appear in both search methods.""" await sqlite_vec_index.add_chunks(sample_chunks, sample_embeddings) @@ -259,7 +226,6 @@ async def test_query_chunks_hybrid_rrf_ranking(sqlite_vec_index, sample_chunks, assert all(response.scores[i] >= response.scores[i + 1] for i in range(len(response.scores) - 1)) -@pytest.mark.asyncio async def test_query_chunks_hybrid_score_selection(sqlite_vec_index, sample_chunks, sample_embeddings): await sqlite_vec_index.add_chunks(sample_chunks, sample_embeddings) @@ -307,7 +273,6 @@ async def test_query_chunks_hybrid_score_selection(sqlite_vec_index, sample_chun assert response.scores[0] == pytest.approx(2.0 / 61.0, rel=1e-6) # Should behave like RRF -@pytest.mark.asyncio async def test_query_chunks_hybrid_mixed_results(sqlite_vec_index, sample_chunks, sample_embeddings): """Test hybrid search with documents that appear in only one search method.""" await sqlite_vec_index.add_chunks(sample_chunks, sample_embeddings) @@ -336,7 +301,6 @@ async def test_query_chunks_hybrid_mixed_results(sqlite_vec_index, sample_chunks assert "document-2" in doc_ids # From keyword search -@pytest.mark.asyncio async def test_query_chunks_hybrid_weighted_reranker_parametrization( sqlite_vec_index, sample_chunks, sample_embeddings ): @@ -392,7 +356,6 @@ async def test_query_chunks_hybrid_weighted_reranker_parametrization( ) -@pytest.mark.asyncio async def test_query_chunks_hybrid_rrf_impact_factor(sqlite_vec_index, sample_chunks, sample_embeddings): """Test RRFReRanker with different impact factors.""" await sqlite_vec_index.add_chunks(sample_chunks, sample_embeddings) @@ -424,7 +387,6 @@ async def test_query_chunks_hybrid_rrf_impact_factor(sqlite_vec_index, sample_ch assert response.scores[0] == pytest.approx(2.0 / 101.0, rel=1e-6) -@pytest.mark.asyncio async def test_query_chunks_hybrid_edge_cases(sqlite_vec_index, sample_chunks, sample_embeddings): await sqlite_vec_index.add_chunks(sample_chunks, sample_embeddings) @@ -468,7 +430,6 @@ async def test_query_chunks_hybrid_edge_cases(sqlite_vec_index, sample_chunks, s assert len(response.chunks) <= 100 -@pytest.mark.asyncio async def test_query_chunks_hybrid_tie_breaking( sqlite_vec_index, sample_embeddings, embedding_dimension, tmp_path_factory ): diff --git a/tests/unit/providers/vector_io/test_vector_io_openai_vector_stores.py b/tests/unit/providers/vector_io/test_vector_io_openai_vector_stores.py new file mode 100644 index 000000000..bf7663d2e --- /dev/null +++ b/tests/unit/providers/vector_io/test_vector_io_openai_vector_stores.py @@ -0,0 +1,292 @@ +# 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 time +from unittest.mock import AsyncMock + +import numpy as np +import pytest + +from llama_stack.apis.vector_dbs import VectorDB +from llama_stack.apis.vector_io import Chunk, QueryChunksResponse +from llama_stack.providers.remote.vector_io.milvus.milvus import VECTOR_DBS_PREFIX + +# This test is a unit test for the inline VectoerIO providers. This should only contain +# tests which are specific to this class. More general (API-level) tests should be placed in +# tests/integration/vector_io/ +# +# How to run this test: +# +# pytest tests/unit/providers/vector_io/test_vector_io_openai_vector_stores.py \ +# -v -s --tb=short --disable-warnings --asyncio-mode=auto + + +async def test_initialize_index(vector_index): + await vector_index.initialize() + + +async def test_add_chunks_query_vector(vector_index, sample_chunks, sample_embeddings): + vector_index.delete() + vector_index.initialize() + await vector_index.add_chunks(sample_chunks, sample_embeddings) + resp = await vector_index.query_vector(sample_embeddings[0], k=1, score_threshold=-1) + assert resp.chunks[0].content == sample_chunks[0].content + vector_index.delete() + + +async def test_chunk_id_conflict(vector_index, sample_chunks, embedding_dimension): + embeddings = np.random.rand(len(sample_chunks), embedding_dimension).astype(np.float32) + await vector_index.add_chunks(sample_chunks, embeddings) + resp = await vector_index.query_vector( + np.random.rand(embedding_dimension).astype(np.float32), + k=len(sample_chunks), + score_threshold=-1, + ) + + contents = [chunk.content for chunk in resp.chunks] + assert len(contents) == len(set(contents)) + + +async def test_initialize_adapter_with_existing_kvstore(vector_io_adapter): + key = f"{VECTOR_DBS_PREFIX}db1" + dummy = VectorDB( + identifier="foo_db", provider_id="test_provider", embedding_model="test_model", embedding_dimension=128 + ) + await vector_io_adapter.kvstore.set(key=key, value=json.dumps(dummy.model_dump())) + + await vector_io_adapter.initialize() + + +async def test_persistence_across_adapter_restarts(vector_io_adapter): + await vector_io_adapter.initialize() + dummy = VectorDB( + identifier="foo_db", provider_id="test_provider", embedding_model="test_model", embedding_dimension=128 + ) + await vector_io_adapter.register_vector_db(dummy) + await vector_io_adapter.shutdown() + + await vector_io_adapter.initialize() + assert "foo_db" in vector_io_adapter.cache + await vector_io_adapter.shutdown() + + +async def test_register_and_unregister_vector_db(vector_io_adapter): + unique_id = f"foo_db_{np.random.randint(1e6)}" + dummy = VectorDB( + identifier=unique_id, provider_id="test_provider", embedding_model="test_model", embedding_dimension=128 + ) + + await vector_io_adapter.register_vector_db(dummy) + assert dummy.identifier in vector_io_adapter.cache + await vector_io_adapter.unregister_vector_db(dummy.identifier) + assert dummy.identifier not in vector_io_adapter.cache + + +async def test_query_unregistered_raises(vector_io_adapter): + fake_emb = np.zeros(8, dtype=np.float32) + with pytest.raises(ValueError): + await vector_io_adapter.query_chunks("no_such_db", fake_emb) + + +async def test_insert_chunks_calls_underlying_index(vector_io_adapter): + fake_index = AsyncMock() + vector_io_adapter.cache["db1"] = fake_index + + chunks = ["chunk1", "chunk2"] + await vector_io_adapter.insert_chunks("db1", chunks) + + fake_index.insert_chunks.assert_awaited_once_with(chunks) + + +async def test_insert_chunks_missing_db_raises(vector_io_adapter): + vector_io_adapter._get_and_cache_vector_db_index = AsyncMock(return_value=None) + + with pytest.raises(ValueError): + await vector_io_adapter.insert_chunks("db_not_exist", []) + + +async def test_query_chunks_calls_underlying_index_and_returns(vector_io_adapter): + expected = QueryChunksResponse(chunks=[Chunk(content="c1")], scores=[0.1]) + fake_index = AsyncMock(query_chunks=AsyncMock(return_value=expected)) + vector_io_adapter.cache["db1"] = fake_index + + response = await vector_io_adapter.query_chunks("db1", "my_query", {"param": 1}) + + fake_index.query_chunks.assert_awaited_once_with("my_query", {"param": 1}) + assert response is expected + + +async def test_query_chunks_missing_db_raises(vector_io_adapter): + vector_io_adapter._get_and_cache_vector_db_index = AsyncMock(return_value=None) + + with pytest.raises(ValueError): + await vector_io_adapter.query_chunks("db_missing", "q", None) + + +async def test_save_openai_vector_store(vector_io_adapter): + store_id = "vs_1234" + openai_vector_store = { + "id": store_id, + "name": "Test Store", + "description": "A test OpenAI vector store", + "vector_db_id": "test_db", + "embedding_model": "test_model", + } + + await vector_io_adapter._save_openai_vector_store(store_id, openai_vector_store) + + assert openai_vector_store["id"] in vector_io_adapter.openai_vector_stores + assert vector_io_adapter.openai_vector_stores[openai_vector_store["id"]] == openai_vector_store + + +async def test_update_openai_vector_store(vector_io_adapter): + store_id = "vs_1234" + openai_vector_store = { + "id": store_id, + "name": "Test Store", + "description": "A test OpenAI vector store", + "vector_db_id": "test_db", + "embedding_model": "test_model", + } + + await vector_io_adapter._save_openai_vector_store(store_id, openai_vector_store) + openai_vector_store["description"] = "Updated description" + await vector_io_adapter._update_openai_vector_store(store_id, openai_vector_store) + assert vector_io_adapter.openai_vector_stores[openai_vector_store["id"]] == openai_vector_store + + +async def test_delete_openai_vector_store(vector_io_adapter): + store_id = "vs_1234" + openai_vector_store = { + "id": store_id, + "name": "Test Store", + "description": "A test OpenAI vector store", + "vector_db_id": "test_db", + "embedding_model": "test_model", + } + + await vector_io_adapter._save_openai_vector_store(store_id, openai_vector_store) + await vector_io_adapter._delete_openai_vector_store_from_storage(store_id) + assert openai_vector_store["id"] not in vector_io_adapter.openai_vector_stores + + +async def test_load_openai_vector_stores(vector_io_adapter): + store_id = "vs_1234" + openai_vector_store = { + "id": store_id, + "name": "Test Store", + "description": "A test OpenAI vector store", + "vector_db_id": "test_db", + "embedding_model": "test_model", + } + + await vector_io_adapter._save_openai_vector_store(store_id, openai_vector_store) + loaded_stores = await vector_io_adapter._load_openai_vector_stores() + assert loaded_stores[store_id] == openai_vector_store + + +async def test_save_openai_vector_store_file(vector_io_adapter, tmp_path_factory): + store_id = "vs_1234" + file_id = "file_1234" + + file_info = { + "id": file_id, + "status": "completed", + "vector_store_id": store_id, + "attributes": {}, + "filename": "test_file.txt", + "created_at": int(time.time()), + } + + file_contents = [ + {"content": "Test content", "chunk_metadata": {"chunk_id": "chunk_001"}, "metadata": {"file_id": file_id}} + ] + + # validating we don't raise an exception + await vector_io_adapter._save_openai_vector_store_file(store_id, file_id, file_info, file_contents) + + +async def test_update_openai_vector_store_file(vector_io_adapter, tmp_path_factory): + store_id = "vs_1234" + file_id = "file_1234" + + file_info = { + "id": file_id, + "status": "completed", + "vector_store_id": store_id, + "attributes": {}, + "filename": "test_file.txt", + "created_at": int(time.time()), + } + + file_contents = [ + {"content": "Test content", "chunk_metadata": {"chunk_id": "chunk_001"}, "metadata": {"file_id": file_id}} + ] + + await vector_io_adapter._save_openai_vector_store_file(store_id, file_id, file_info, file_contents) + + updated_file_info = file_info.copy() + updated_file_info["filename"] = "updated_test_file.txt" + + await vector_io_adapter._update_openai_vector_store_file( + store_id, + file_id, + updated_file_info, + ) + + loaded_contents = await vector_io_adapter._load_openai_vector_store_file(store_id, file_id) + assert loaded_contents == updated_file_info + assert loaded_contents != file_info + + +async def test_load_openai_vector_store_file_contents(vector_io_adapter, tmp_path_factory): + store_id = "vs_1234" + file_id = "file_1234" + + file_info = { + "id": file_id, + "status": "completed", + "vector_store_id": store_id, + "attributes": {}, + "filename": "test_file.txt", + "created_at": int(time.time()), + } + + file_contents = [ + {"content": "Test content", "chunk_metadata": {"chunk_id": "chunk_001"}, "metadata": {"file_id": file_id}} + ] + + await vector_io_adapter._save_openai_vector_store_file(store_id, file_id, file_info, file_contents) + + loaded_contents = await vector_io_adapter._load_openai_vector_store_file_contents(store_id, file_id) + assert loaded_contents == file_contents + + +async def test_delete_openai_vector_store_file_from_storage(vector_io_adapter, tmp_path_factory): + store_id = "vs_1234" + file_id = "file_1234" + + file_info = { + "id": file_id, + "status": "completed", + "vector_store_id": store_id, + "attributes": {}, + "filename": "test_file.txt", + "created_at": int(time.time()), + } + + file_contents = [ + {"content": "Test content", "chunk_metadata": {"chunk_id": "chunk_001"}, "metadata": {"file_id": file_id}} + ] + + await vector_io_adapter._save_openai_vector_store_file(store_id, file_id, file_info, file_contents) + await vector_io_adapter._delete_openai_vector_store_file_from_storage(store_id, file_id) + + loaded_file_info = await vector_io_adapter._load_openai_vector_store_file(store_id, file_id) + assert loaded_file_info == {} + loaded_contents = await vector_io_adapter._load_openai_vector_store_file_contents(store_id, file_id) + assert loaded_contents == [] diff --git a/tests/unit/rag/test_rag_query.py b/tests/unit/rag/test_rag_query.py index d2dd1783b..a9149541a 100644 --- a/tests/unit/rag/test_rag_query.py +++ b/tests/unit/rag/test_rag_query.py @@ -8,6 +8,7 @@ from unittest.mock import AsyncMock, MagicMock import pytest +from llama_stack.apis.tools.rag_tool import RAGQueryConfig from llama_stack.apis.vector_io import ( Chunk, ChunkMetadata, @@ -17,13 +18,11 @@ from llama_stack.providers.inline.tool_runtime.rag.memory import MemoryToolRunti class TestRagQuery: - @pytest.mark.asyncio async def test_query_raises_on_empty_vector_db_ids(self): rag_tool = MemoryToolRuntimeImpl(config=MagicMock(), vector_io_api=MagicMock(), inference_api=MagicMock()) with pytest.raises(ValueError): await rag_tool.query(content=MagicMock(), vector_db_ids=[]) - @pytest.mark.asyncio async def test_query_chunk_metadata_handling(self): rag_tool = MemoryToolRuntimeImpl(config=MagicMock(), vector_io_api=MagicMock(), inference_api=MagicMock()) content = "test query content" @@ -60,3 +59,13 @@ class TestRagQuery: ) assert expected_metadata_string in result.content[1].text assert result.content is not None + + async def test_query_raises_incorrect_mode(self): + with pytest.raises(ValueError): + RAGQueryConfig(mode="invalid_mode") + + async def test_query_accepts_valid_modes(self): + RAGQueryConfig() # Test default (vector) + RAGQueryConfig(mode="vector") # Test vector + RAGQueryConfig(mode="keyword") # Test keyword + RAGQueryConfig(mode="hybrid") # Test hybrid diff --git a/tests/unit/rag/test_vector_store.py b/tests/unit/rag/test_vector_store.py index 9d6b9ee67..919f97ba7 100644 --- a/tests/unit/rag/test_vector_store.py +++ b/tests/unit/rag/test_vector_store.py @@ -112,7 +112,6 @@ class TestValidateEmbedding: class TestVectorStore: - @pytest.mark.asyncio async def test_returns_content_from_pdf_data_uri(self): data_uri = data_url_from_file(DUMMY_PDF_PATH) doc = RAGDocument( @@ -124,7 +123,7 @@ class TestVectorStore: content = await content_from_doc(doc) assert content in DUMMY_PDF_TEXT_CHOICES - @pytest.mark.asyncio + @pytest.mark.allow_network async def test_downloads_pdf_and_returns_content(self): # Using GitHub to host the PDF file url = "https://raw.githubusercontent.com/meta-llama/llama-stack/da035d69cfca915318eaf485770a467ca3c2a238/llama_stack/providers/tests/memory/fixtures/dummy.pdf" @@ -137,7 +136,7 @@ class TestVectorStore: content = await content_from_doc(doc) assert content in DUMMY_PDF_TEXT_CHOICES - @pytest.mark.asyncio + @pytest.mark.allow_network async def test_downloads_pdf_and_returns_content_with_url_object(self): # Using GitHub to host the PDF file url = "https://raw.githubusercontent.com/meta-llama/llama-stack/da035d69cfca915318eaf485770a467ca3c2a238/llama_stack/providers/tests/memory/fixtures/dummy.pdf" @@ -204,7 +203,6 @@ class TestVectorStore: class TestVectorDBWithIndex: - @pytest.mark.asyncio async def test_insert_chunks_without_embeddings(self): mock_vector_db = MagicMock() mock_vector_db.embedding_model = "test-model without embeddings" @@ -230,7 +228,6 @@ class TestVectorDBWithIndex: assert args[0] == chunks assert np.array_equal(args[1], np.array([[0.1, 0.2, 0.3], [0.4, 0.5, 0.6]], dtype=np.float32)) - @pytest.mark.asyncio async def test_insert_chunks_with_valid_embeddings(self): mock_vector_db = MagicMock() mock_vector_db.embedding_model = "test-model with embeddings" @@ -255,7 +252,6 @@ class TestVectorDBWithIndex: assert args[0] == chunks assert np.array_equal(args[1], np.array([[0.1, 0.2, 0.3], [0.4, 0.5, 0.6]], dtype=np.float32)) - @pytest.mark.asyncio async def test_insert_chunks_with_invalid_embeddings(self): mock_vector_db = MagicMock() mock_vector_db.embedding_dimension = 3 @@ -295,7 +291,6 @@ class TestVectorDBWithIndex: mock_inference_api.embeddings.assert_not_called() mock_index.add_chunks.assert_not_called() - @pytest.mark.asyncio async def test_insert_chunks_with_partially_precomputed_embeddings(self): mock_vector_db = MagicMock() mock_vector_db.embedding_model = "test-model with partial embeddings" diff --git a/tests/unit/registry/test_registry.py b/tests/unit/registry/test_registry.py index 909581bb7..87fe18d54 100644 --- a/tests/unit/registry/test_registry.py +++ b/tests/unit/registry/test_registry.py @@ -38,14 +38,12 @@ def sample_model(): ) -@pytest.mark.asyncio async def test_registry_initialization(disk_dist_registry): # Test empty registry result = await disk_dist_registry.get("nonexistent", "nonexistent") assert result is None -@pytest.mark.asyncio async def test_basic_registration(disk_dist_registry, sample_vector_db, sample_model): print(f"Registering {sample_vector_db}") await disk_dist_registry.register(sample_vector_db) @@ -64,7 +62,6 @@ async def test_basic_registration(disk_dist_registry, sample_vector_db, sample_m assert result_model.provider_id == sample_model.provider_id -@pytest.mark.asyncio async def test_cached_registry_initialization(sqlite_kvstore, sample_vector_db, sample_model): # First populate the disk registry disk_registry = DiskDistributionRegistry(sqlite_kvstore) @@ -85,7 +82,6 @@ async def test_cached_registry_initialization(sqlite_kvstore, sample_vector_db, assert result_vector_db.provider_id == sample_vector_db.provider_id -@pytest.mark.asyncio async def test_cached_registry_updates(cached_disk_dist_registry): new_vector_db = VectorDB( identifier="test_vector_db_2", @@ -112,7 +108,6 @@ async def test_cached_registry_updates(cached_disk_dist_registry): assert result_vector_db.provider_id == new_vector_db.provider_id -@pytest.mark.asyncio async def test_duplicate_provider_registration(cached_disk_dist_registry): original_vector_db = VectorDB( identifier="test_vector_db_2", @@ -137,7 +132,6 @@ async def test_duplicate_provider_registration(cached_disk_dist_registry): assert result.embedding_model == original_vector_db.embedding_model # Original values preserved -@pytest.mark.asyncio async def test_get_all_objects(cached_disk_dist_registry): # Create multiple test banks # Create multiple test banks @@ -170,7 +164,6 @@ async def test_get_all_objects(cached_disk_dist_registry): assert stored_vector_db.embedding_dimension == original_vector_db.embedding_dimension -@pytest.mark.asyncio async def test_parse_registry_values_error_handling(sqlite_kvstore): valid_db = VectorDB( identifier="valid_vector_db", @@ -209,7 +202,6 @@ async def test_parse_registry_values_error_handling(sqlite_kvstore): assert invalid_obj is None -@pytest.mark.asyncio async def test_cached_registry_error_handling(sqlite_kvstore): valid_db = VectorDB( identifier="valid_cached_db", diff --git a/tests/unit/registry/test_registry_acl.py b/tests/unit/registry/test_registry_acl.py index 48b3ac51b..6cfb20944 100644 --- a/tests/unit/registry/test_registry_acl.py +++ b/tests/unit/registry/test_registry_acl.py @@ -5,14 +5,11 @@ # the root directory of this source tree. -import pytest - from llama_stack.apis.models import ModelType from llama_stack.distribution.datatypes import ModelWithOwner, User from llama_stack.distribution.store.registry import CachedDiskDistributionRegistry -@pytest.mark.asyncio async def test_registry_cache_with_acl(cached_disk_dist_registry): model = ModelWithOwner( identifier="model-acl", @@ -48,7 +45,6 @@ async def test_registry_cache_with_acl(cached_disk_dist_registry): assert new_model.owner.attributes["teams"] == ["ai-team"] -@pytest.mark.asyncio async def test_registry_empty_acl(cached_disk_dist_registry): model = ModelWithOwner( identifier="model-empty-acl", @@ -85,7 +81,6 @@ async def test_registry_empty_acl(cached_disk_dist_registry): assert len(all_models) == 2 -@pytest.mark.asyncio async def test_registry_serialization(cached_disk_dist_registry): attributes = { "roles": ["admin", "researcher"], diff --git a/tests/unit/server/test_access_control.py b/tests/unit/server/test_access_control.py index af03ddacb..fb9c6f95e 100644 --- a/tests/unit/server/test_access_control.py +++ b/tests/unit/server/test_access_control.py @@ -7,7 +7,6 @@ from unittest.mock import MagicMock, Mock, patch import pytest -import pytest_asyncio import yaml from pydantic import TypeAdapter, ValidationError @@ -27,7 +26,7 @@ def _return_model(model): return model -@pytest_asyncio.fixture +@pytest.fixture async def test_setup(cached_disk_dist_registry): mock_inference = Mock() mock_inference.__provider_spec__ = MagicMock() @@ -41,7 +40,6 @@ async def test_setup(cached_disk_dist_registry): yield cached_disk_dist_registry, routing_table -@pytest.mark.asyncio @patch("llama_stack.distribution.routing_tables.common.get_authenticated_user") async def test_access_control_with_cache(mock_get_authenticated_user, test_setup): registry, routing_table = test_setup @@ -106,7 +104,6 @@ async def test_access_control_with_cache(mock_get_authenticated_user, test_setup await routing_table.get_model("model-admin") -@pytest.mark.asyncio @patch("llama_stack.distribution.routing_tables.common.get_authenticated_user") async def test_access_control_and_updates(mock_get_authenticated_user, test_setup): registry, routing_table = test_setup @@ -145,7 +142,6 @@ async def test_access_control_and_updates(mock_get_authenticated_user, test_setu assert model.identifier == "model-updates" -@pytest.mark.asyncio @patch("llama_stack.distribution.routing_tables.common.get_authenticated_user") async def test_access_control_empty_attributes(mock_get_authenticated_user, test_setup): registry, routing_table = test_setup @@ -170,7 +166,6 @@ async def test_access_control_empty_attributes(mock_get_authenticated_user, test assert "model-empty-attrs" in model_ids -@pytest.mark.asyncio @patch("llama_stack.distribution.routing_tables.common.get_authenticated_user") async def test_no_user_attributes(mock_get_authenticated_user, test_setup): registry, routing_table = test_setup @@ -201,7 +196,6 @@ async def test_no_user_attributes(mock_get_authenticated_user, test_setup): assert all_models.data[0].identifier == "model-public-2" -@pytest.mark.asyncio @patch("llama_stack.distribution.routing_tables.common.get_authenticated_user") async def test_automatic_access_attributes(mock_get_authenticated_user, test_setup): """Test that newly created resources inherit access attributes from their creator.""" @@ -246,7 +240,7 @@ async def test_automatic_access_attributes(mock_get_authenticated_user, test_set assert model.identifier == "auto-access-model" -@pytest_asyncio.fixture +@pytest.fixture async def test_setup_with_access_policy(cached_disk_dist_registry): mock_inference = Mock() mock_inference.__provider_spec__ = MagicMock() @@ -281,7 +275,6 @@ async def test_setup_with_access_policy(cached_disk_dist_registry): yield routing_table -@pytest.mark.asyncio @patch("llama_stack.distribution.routing_tables.common.get_authenticated_user") async def test_access_policy(mock_get_authenticated_user, test_setup_with_access_policy): routing_table = test_setup_with_access_policy diff --git a/tests/unit/server/test_auth.py b/tests/unit/server/test_auth.py index 4410048c5..7012a7f17 100644 --- a/tests/unit/server/test_auth.py +++ b/tests/unit/server/test_auth.py @@ -11,10 +11,16 @@ import pytest from fastapi import FastAPI from fastapi.testclient import TestClient -from llama_stack.distribution.datatypes import AuthenticationConfig +from llama_stack.distribution.datatypes import ( + AuthenticationConfig, + AuthProviderType, + CustomAuthConfig, + OAuth2IntrospectionConfig, + OAuth2JWKSConfig, + OAuth2TokenAuthConfig, +) from llama_stack.distribution.server.auth import AuthenticationMiddleware from llama_stack.distribution.server.auth_providers import ( - AuthProviderType, get_attributes_from_claims, ) @@ -61,24 +67,11 @@ def invalid_token(): def http_app(mock_auth_endpoint): app = FastAPI() auth_config = AuthenticationConfig( - provider_type=AuthProviderType.CUSTOM, - config={"endpoint": mock_auth_endpoint}, - ) - app.add_middleware(AuthenticationMiddleware, auth_config=auth_config) - - @app.get("/test") - def test_endpoint(): - return {"message": "Authentication successful"} - - return app - - -@pytest.fixture -def k8s_app(): - app = FastAPI() - auth_config = AuthenticationConfig( - provider_type=AuthProviderType.KUBERNETES, - config={"api_server_url": "https://kubernetes.default.svc"}, + provider_config=CustomAuthConfig( + type=AuthProviderType.CUSTOM, + endpoint=mock_auth_endpoint, + ), + access_policy=[], ) app.add_middleware(AuthenticationMiddleware, auth_config=auth_config) @@ -94,11 +87,6 @@ def http_client(http_app): return TestClient(http_app) -@pytest.fixture -def k8s_client(k8s_app): - return TestClient(k8s_app) - - @pytest.fixture def mock_scope(): return { @@ -117,18 +105,11 @@ def mock_scope(): def mock_http_middleware(mock_auth_endpoint): mock_app = AsyncMock() auth_config = AuthenticationConfig( - provider_type=AuthProviderType.CUSTOM, - config={"endpoint": mock_auth_endpoint}, - ) - return AuthenticationMiddleware(mock_app, auth_config), mock_app - - -@pytest.fixture -def mock_k8s_middleware(): - mock_app = AsyncMock() - auth_config = AuthenticationConfig( - provider_type=AuthProviderType.KUBERNETES, - config={"api_server_url": "https://kubernetes.default.svc"}, + provider_config=CustomAuthConfig( + type=AuthProviderType.CUSTOM, + endpoint=mock_auth_endpoint, + ), + access_policy=[], ) return AuthenticationMiddleware(mock_app, auth_config), mock_app @@ -161,13 +142,14 @@ async def mock_post_exception(*args, **kwargs): def test_missing_auth_header(http_client): response = http_client.get("/test") assert response.status_code == 401 - assert "Missing or invalid Authorization header" in response.json()["error"]["message"] + assert "Authentication required" in response.json()["error"]["message"] + assert "validated by mock-auth-service" in response.json()["error"]["message"] def test_invalid_auth_header_format(http_client): response = http_client.get("/test", headers={"Authorization": "InvalidFormat token123"}) assert response.status_code == 401 - assert "Missing or invalid Authorization header" in response.json()["error"]["message"] + assert "Invalid Authorization header format" in response.json()["error"]["message"] @patch("httpx.AsyncClient.post", new=mock_post_success) @@ -220,7 +202,6 @@ def test_http_auth_request_payload(http_client, valid_api_key, mock_auth_endpoin assert "param2" in payload["request"]["params"] -@pytest.mark.asyncio async def test_http_middleware_with_access_attributes(mock_http_middleware, mock_scope): """Test HTTP middleware behavior with access attributes""" middleware, mock_app = mock_http_middleware @@ -262,14 +243,14 @@ async def test_http_middleware_with_access_attributes(mock_http_middleware, mock def oauth2_app(): app = FastAPI() auth_config = AuthenticationConfig( - provider_type=AuthProviderType.OAUTH2_TOKEN, - config={ - "jwks": { - "uri": "http://mock-authz-service/token/introspect", - "key_recheck_period": "3600", - }, - "audience": "llama-stack", - }, + provider_config=OAuth2TokenAuthConfig( + type=AuthProviderType.OAUTH2_TOKEN, + jwks=OAuth2JWKSConfig( + uri="http://mock-authz-service/token/introspect", + ), + audience="llama-stack", + ), + access_policy=[], ) app.add_middleware(AuthenticationMiddleware, auth_config=auth_config) @@ -288,13 +269,14 @@ def oauth2_client(oauth2_app): def test_missing_auth_header_oauth2(oauth2_client): response = oauth2_client.get("/test") assert response.status_code == 401 - assert "Missing or invalid Authorization header" in response.json()["error"]["message"] + assert "Authentication required" in response.json()["error"]["message"] + assert "OAuth2 Bearer token" in response.json()["error"]["message"] def test_invalid_auth_header_format_oauth2(oauth2_client): response = oauth2_client.get("/test", headers={"Authorization": "InvalidFormat token123"}) assert response.status_code == 401 - assert "Missing or invalid Authorization header" in response.json()["error"]["message"] + assert "Invalid Authorization header format" in response.json()["error"]["message"] async def mock_jwks_response(*args, **kwargs): @@ -358,15 +340,16 @@ async def mock_auth_jwks_response(*args, **kwargs): def oauth2_app_with_jwks_token(): app = FastAPI() auth_config = AuthenticationConfig( - provider_type=AuthProviderType.OAUTH2_TOKEN, - config={ - "jwks": { - "uri": "http://mock-authz-service/token/introspect", - "key_recheck_period": "3600", - "token": "my-jwks-token", - }, - "audience": "llama-stack", - }, + provider_config=OAuth2TokenAuthConfig( + type=AuthProviderType.OAUTH2_TOKEN, + jwks=OAuth2JWKSConfig( + uri="http://mock-authz-service/token/introspect", + key_recheck_period=3600, + token="my-jwks-token", + ), + audience="llama-stack", + ), + access_policy=[], ) app.add_middleware(AuthenticationMiddleware, auth_config=auth_config) @@ -449,11 +432,15 @@ def mock_introspection_endpoint(): def introspection_app(mock_introspection_endpoint): app = FastAPI() auth_config = AuthenticationConfig( - provider_type=AuthProviderType.OAUTH2_TOKEN, - config={ - "jwks": None, - "introspection": {"url": mock_introspection_endpoint, "client_id": "myclient", "client_secret": "abcdefg"}, - }, + provider_config=OAuth2TokenAuthConfig( + type=AuthProviderType.OAUTH2_TOKEN, + introspection=OAuth2IntrospectionConfig( + url=mock_introspection_endpoint, + client_id="myclient", + client_secret="abcdefg", + ), + ), + access_policy=[], ) app.add_middleware(AuthenticationMiddleware, auth_config=auth_config) @@ -468,22 +455,22 @@ def introspection_app(mock_introspection_endpoint): def introspection_app_with_custom_mapping(mock_introspection_endpoint): app = FastAPI() auth_config = AuthenticationConfig( - provider_type=AuthProviderType.OAUTH2_TOKEN, - config={ - "jwks": None, - "introspection": { - "url": mock_introspection_endpoint, - "client_id": "myclient", - "client_secret": "abcdefg", - "send_secret_in_body": "true", - }, - "claims_mapping": { + provider_config=OAuth2TokenAuthConfig( + type=AuthProviderType.OAUTH2_TOKEN, + introspection=OAuth2IntrospectionConfig( + url=mock_introspection_endpoint, + client_id="myclient", + client_secret="abcdefg", + send_secret_in_body=True, + ), + claims_mapping={ "sub": "roles", "scope": "roles", "groups": "teams", "aud": "namespaces", }, - }, + ), + access_policy=[], ) app.add_middleware(AuthenticationMiddleware, auth_config=auth_config) @@ -507,13 +494,14 @@ def introspection_client_with_custom_mapping(introspection_app_with_custom_mappi def test_missing_auth_header_introspection(introspection_client): response = introspection_client.get("/test") assert response.status_code == 401 - assert "Missing or invalid Authorization header" in response.json()["error"]["message"] + assert "Authentication required" in response.json()["error"]["message"] + assert "OAuth2 Bearer token" in response.json()["error"]["message"] def test_invalid_auth_header_format_introspection(introspection_client): response = introspection_client.get("/test", headers={"Authorization": "InvalidFormat token123"}) assert response.status_code == 401 - assert "Missing or invalid Authorization header" in response.json()["error"]["message"] + assert "Invalid Authorization header format" in response.json()["error"]["message"] async def mock_introspection_active(*args, **kwargs): diff --git a/tests/unit/server/test_auth_github.py b/tests/unit/server/test_auth_github.py new file mode 100644 index 000000000..24e60f60f --- /dev/null +++ b/tests/unit/server/test_auth_github.py @@ -0,0 +1,200 @@ +# 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 AsyncMock, patch + +import httpx +import pytest +from fastapi import FastAPI +from fastapi.testclient import TestClient + +from llama_stack.distribution.datatypes import AuthenticationConfig, AuthProviderType, GitHubTokenAuthConfig +from llama_stack.distribution.server.auth import AuthenticationMiddleware + + +class MockResponse: + def __init__(self, status_code, json_data): + self.status_code = status_code + self._json_data = json_data + + def json(self): + return self._json_data + + def raise_for_status(self): + if self.status_code != 200: + # Create a mock request for the HTTPStatusError + mock_request = httpx.Request("GET", "https://api.github.com/user") + raise httpx.HTTPStatusError(f"HTTP error: {self.status_code}", request=mock_request, response=self) + + +@pytest.fixture +def github_token_app(): + app = FastAPI() + + # Configure GitHub token auth + auth_config = AuthenticationConfig( + provider_config=GitHubTokenAuthConfig( + type=AuthProviderType.GITHUB_TOKEN, + github_api_base_url="https://api.github.com", + claims_mapping={ + "login": "username", + "id": "user_id", + "organizations": "teams", + }, + ), + access_policy=[], + ) + + # Add auth middleware + app.add_middleware(AuthenticationMiddleware, auth_config=auth_config) + + @app.get("/test") + def test_endpoint(): + return {"message": "Authentication successful"} + + return app + + +@pytest.fixture +def github_token_client(github_token_app): + return TestClient(github_token_app) + + +def test_authenticated_endpoint_without_token(github_token_client): + """Test accessing protected endpoint without token""" + response = github_token_client.get("/test") + assert response.status_code == 401 + assert "Authentication required" in response.json()["error"]["message"] + assert "GitHub access token" in response.json()["error"]["message"] + + +def test_authenticated_endpoint_with_invalid_bearer_format(github_token_client): + """Test accessing protected endpoint with invalid bearer format""" + response = github_token_client.get("/test", headers={"Authorization": "InvalidFormat token123"}) + assert response.status_code == 401 + assert "Invalid Authorization header format" in response.json()["error"]["message"] + + +@patch("llama_stack.distribution.server.auth_providers.httpx.AsyncClient") +def test_authenticated_endpoint_with_valid_github_token(mock_client_class, github_token_client): + """Test accessing protected endpoint with valid GitHub token""" + # Mock the GitHub API responses + mock_client = AsyncMock() + mock_client_class.return_value.__aenter__.return_value = mock_client + + # Mock successful user API response + mock_client.get.side_effect = [ + MockResponse( + 200, + { + "login": "testuser", + "id": 12345, + "email": "test@example.com", + "name": "Test User", + }, + ), + MockResponse( + 200, + [ + {"login": "test-org-1"}, + {"login": "test-org-2"}, + ], + ), + ] + + response = github_token_client.get("/test", headers={"Authorization": "Bearer github_token_123"}) + assert response.status_code == 200 + assert response.json()["message"] == "Authentication successful" + + # Verify the GitHub API was called correctly + assert mock_client.get.call_count == 1 + calls = mock_client.get.call_args_list + assert calls[0][0][0] == "https://api.github.com/user" + + # Check authorization header was passed + assert calls[0][1]["headers"]["Authorization"] == "Bearer github_token_123" + + +@patch("llama_stack.distribution.server.auth_providers.httpx.AsyncClient") +def test_authenticated_endpoint_with_invalid_github_token(mock_client_class, github_token_client): + """Test accessing protected endpoint with invalid GitHub token""" + # Mock the GitHub API to return 401 Unauthorized + mock_client = AsyncMock() + mock_client_class.return_value.__aenter__.return_value = mock_client + + # Mock failed user API response + mock_client.get.return_value = MockResponse(401, {"message": "Bad credentials"}) + + response = github_token_client.get("/test", headers={"Authorization": "Bearer invalid_token"}) + assert response.status_code == 401 + assert ( + "GitHub token validation failed. Please check your token and try again." in response.json()["error"]["message"] + ) + + +@patch("llama_stack.distribution.server.auth_providers.httpx.AsyncClient") +def test_github_enterprise_support(mock_client_class): + """Test GitHub Enterprise support with custom API base URL""" + app = FastAPI() + + # Configure GitHub token auth with enterprise URL + auth_config = AuthenticationConfig( + provider_config=GitHubTokenAuthConfig( + type=AuthProviderType.GITHUB_TOKEN, + github_api_base_url="https://github.enterprise.com/api/v3", + ), + access_policy=[], + ) + + app.add_middleware(AuthenticationMiddleware, auth_config=auth_config) + + @app.get("/test") + def test_endpoint(): + return {"message": "Authentication successful"} + + client = TestClient(app) + + # Mock the GitHub Enterprise API responses + mock_client = AsyncMock() + mock_client_class.return_value.__aenter__.return_value = mock_client + + # Mock successful user API response + mock_client.get.side_effect = [ + MockResponse( + 200, + { + "login": "enterprise_user", + "id": 99999, + "email": "user@enterprise.com", + }, + ), + MockResponse( + 200, + [ + {"login": "enterprise-org"}, + ], + ), + ] + + response = client.get("/test", headers={"Authorization": "Bearer enterprise_token"}) + assert response.status_code == 200 + + # Verify the correct GitHub Enterprise URLs were called + assert mock_client.get.call_count == 1 + calls = mock_client.get.call_args_list + assert calls[0][0][0] == "https://github.enterprise.com/api/v3/user" + + +def test_github_token_auth_error_message_format(github_token_client): + """Test that the error message for missing auth is properly formatted""" + response = github_token_client.get("/test") + assert response.status_code == 401 + + error_data = response.json() + assert "error" in error_data + assert "message" in error_data["error"] + assert "Authentication required" in error_data["error"]["message"] + assert "https://docs.github.com" in error_data["error"]["message"] # Contains link to GitHub docs diff --git a/tests/unit/server/test_replace_env_vars.py b/tests/unit/server/test_replace_env_vars.py index 432d6aee5..55817044d 100644 --- a/tests/unit/server/test_replace_env_vars.py +++ b/tests/unit/server/test_replace_env_vars.py @@ -5,73 +5,86 @@ # the root directory of this source tree. import os -import unittest + +import pytest from llama_stack.distribution.stack import replace_env_vars -class TestReplaceEnvVars(unittest.TestCase): - def setUp(self): - # Clear any existing environment variables we'll use in tests - for var in ["TEST_VAR", "EMPTY_VAR", "ZERO_VAR"]: - if var in os.environ: - del os.environ[var] +@pytest.fixture +def setup_env_vars(): + # Clear any existing environment variables we'll use in tests + for var in ["TEST_VAR", "EMPTY_VAR", "ZERO_VAR"]: + if var in os.environ: + del os.environ[var] - # Set up test environment variables - os.environ["TEST_VAR"] = "test_value" - os.environ["EMPTY_VAR"] = "" - os.environ["ZERO_VAR"] = "0" + # Set up test environment variables + os.environ["TEST_VAR"] = "test_value" + os.environ["EMPTY_VAR"] = "" + os.environ["ZERO_VAR"] = "0" - def test_simple_replacement(self): - self.assertEqual(replace_env_vars("${env.TEST_VAR}"), "test_value") + yield - def test_default_value_when_not_set(self): - self.assertEqual(replace_env_vars("${env.NOT_SET:=default}"), "default") - - def test_default_value_when_set(self): - self.assertEqual(replace_env_vars("${env.TEST_VAR:=default}"), "test_value") - - def test_default_value_when_empty(self): - self.assertEqual(replace_env_vars("${env.EMPTY_VAR:=default}"), "default") - - def test_none_value_when_empty(self): - self.assertEqual(replace_env_vars("${env.EMPTY_VAR:=}"), None) - - def test_value_when_set(self): - self.assertEqual(replace_env_vars("${env.TEST_VAR:=}"), "test_value") - - def test_empty_var_no_default(self): - self.assertEqual(replace_env_vars("${env.EMPTY_VAR_NO_DEFAULT:+}"), None) - - def test_conditional_value_when_set(self): - self.assertEqual(replace_env_vars("${env.TEST_VAR:+conditional}"), "conditional") - - def test_conditional_value_when_not_set(self): - self.assertEqual(replace_env_vars("${env.NOT_SET:+conditional}"), None) - - def test_conditional_value_when_empty(self): - self.assertEqual(replace_env_vars("${env.EMPTY_VAR:+conditional}"), None) - - def test_conditional_value_with_zero(self): - self.assertEqual(replace_env_vars("${env.ZERO_VAR:+conditional}"), "conditional") - - def test_mixed_syntax(self): - self.assertEqual( - replace_env_vars("${env.TEST_VAR:=default} and ${env.NOT_SET:+conditional}"), "test_value and " - ) - self.assertEqual( - replace_env_vars("${env.NOT_SET:=default} and ${env.TEST_VAR:+conditional}"), "default and conditional" - ) - - def test_nested_structures(self): - data = { - "key1": "${env.TEST_VAR:=default}", - "key2": ["${env.NOT_SET:=default}", "${env.TEST_VAR:+conditional}"], - "key3": {"nested": "${env.NOT_SET:+conditional}"}, - } - expected = {"key1": "test_value", "key2": ["default", "conditional"], "key3": {"nested": None}} - self.assertEqual(replace_env_vars(data), expected) + # Cleanup after test + for var in ["TEST_VAR", "EMPTY_VAR", "ZERO_VAR"]: + if var in os.environ: + del os.environ[var] -if __name__ == "__main__": - unittest.main() +def test_simple_replacement(setup_env_vars): + assert replace_env_vars("${env.TEST_VAR}") == "test_value" + + +def test_default_value_when_not_set(setup_env_vars): + assert replace_env_vars("${env.NOT_SET:=default}") == "default" + + +def test_default_value_when_set(setup_env_vars): + assert replace_env_vars("${env.TEST_VAR:=default}") == "test_value" + + +def test_default_value_when_empty(setup_env_vars): + assert replace_env_vars("${env.EMPTY_VAR:=default}") == "default" + + +def test_none_value_when_empty(setup_env_vars): + assert replace_env_vars("${env.EMPTY_VAR:=}") is None + + +def test_value_when_set(setup_env_vars): + assert replace_env_vars("${env.TEST_VAR:=}") == "test_value" + + +def test_empty_var_no_default(setup_env_vars): + assert replace_env_vars("${env.EMPTY_VAR_NO_DEFAULT:+}") is None + + +def test_conditional_value_when_set(setup_env_vars): + assert replace_env_vars("${env.TEST_VAR:+conditional}") == "conditional" + + +def test_conditional_value_when_not_set(setup_env_vars): + assert replace_env_vars("${env.NOT_SET:+conditional}") is None + + +def test_conditional_value_when_empty(setup_env_vars): + assert replace_env_vars("${env.EMPTY_VAR:+conditional}") is None + + +def test_conditional_value_with_zero(setup_env_vars): + assert replace_env_vars("${env.ZERO_VAR:+conditional}") == "conditional" + + +def test_mixed_syntax(setup_env_vars): + assert replace_env_vars("${env.TEST_VAR:=default} and ${env.NOT_SET:+conditional}") == "test_value and " + assert replace_env_vars("${env.NOT_SET:=default} and ${env.TEST_VAR:+conditional}") == "default and conditional" + + +def test_nested_structures(setup_env_vars): + data = { + "key1": "${env.TEST_VAR:=default}", + "key2": ["${env.NOT_SET:=default}", "${env.TEST_VAR:+conditional}"], + "key3": {"nested": "${env.NOT_SET:+conditional}"}, + } + expected = {"key1": "test_value", "key2": ["default", "conditional"], "key3": {"nested": None}} + assert replace_env_vars(data) == expected diff --git a/tests/unit/server/test_resolver.py b/tests/unit/server/test_resolver.py index acf4da0a3..a348590b1 100644 --- a/tests/unit/server/test_resolver.py +++ b/tests/unit/server/test_resolver.py @@ -9,7 +9,6 @@ import sys from typing import Any, Protocol from unittest.mock import AsyncMock, MagicMock -import pytest from pydantic import BaseModel, Field from llama_stack.apis.inference import Inference @@ -66,7 +65,6 @@ class SampleImpl: pass -@pytest.mark.asyncio async def test_resolve_impls_basic(): # Create a real provider spec provider_spec = InlineProviderSpec( diff --git a/tests/unit/server/test_sse.py b/tests/unit/server/test_sse.py index 60e9f4609..d42857186 100644 --- a/tests/unit/server/test_sse.py +++ b/tests/unit/server/test_sse.py @@ -7,13 +7,10 @@ import asyncio from unittest.mock import AsyncMock, MagicMock -import pytest - from llama_stack.apis.common.responses import PaginatedResponse from llama_stack.distribution.server.server import create_dynamic_typed_route, create_sse_event, sse_generator -@pytest.mark.asyncio async def test_sse_generator_basic(): # An AsyncIterator wrapped in an Awaitable, just like our web methods async def async_event_gen(): @@ -35,7 +32,6 @@ async def test_sse_generator_basic(): assert seen_events[1] == create_sse_event("Test event 2") -@pytest.mark.asyncio async def test_sse_generator_client_disconnected(): # An AsyncIterator wrapped in an Awaitable, just like our web methods async def async_event_gen(): @@ -58,7 +54,6 @@ async def test_sse_generator_client_disconnected(): assert seen_events[0] == create_sse_event("Test event 1") -@pytest.mark.asyncio async def test_sse_generator_client_disconnected_before_response_starts(): # Disconnect before the response starts async def async_event_gen(): @@ -75,7 +70,6 @@ async def test_sse_generator_client_disconnected_before_response_starts(): assert len(seen_events) == 0 -@pytest.mark.asyncio async def test_sse_generator_error_before_response_starts(): # Raise an error before the response starts async def async_event_gen(): @@ -93,7 +87,6 @@ async def test_sse_generator_error_before_response_starts(): assert 'data: {"error":' in seen_events[0] -@pytest.mark.asyncio async def test_paginated_response_url_setting(): """Test that PaginatedResponse gets url set to route path.""" diff --git a/tests/unit/utils/inference/test_inference_store.py b/tests/unit/utils/inference/test_inference_store.py index de619c760..730f54a05 100644 --- a/tests/unit/utils/inference/test_inference_store.py +++ b/tests/unit/utils/inference/test_inference_store.py @@ -42,7 +42,6 @@ def create_test_chat_completion( ) -@pytest.mark.asyncio async def test_inference_store_pagination_basic(): """Test basic pagination functionality.""" with TemporaryDirectory() as tmp_dir: @@ -88,7 +87,6 @@ async def test_inference_store_pagination_basic(): assert result3.has_more is False -@pytest.mark.asyncio async def test_inference_store_pagination_ascending(): """Test pagination with ascending order.""" with TemporaryDirectory() as tmp_dir: @@ -123,7 +121,6 @@ async def test_inference_store_pagination_ascending(): assert result2.has_more is True -@pytest.mark.asyncio async def test_inference_store_pagination_with_model_filter(): """Test pagination combined with model filtering.""" with TemporaryDirectory() as tmp_dir: @@ -161,7 +158,6 @@ async def test_inference_store_pagination_with_model_filter(): assert result2.has_more is False -@pytest.mark.asyncio async def test_inference_store_pagination_invalid_after(): """Test error handling for invalid 'after' parameter.""" with TemporaryDirectory() as tmp_dir: @@ -174,7 +170,6 @@ async def test_inference_store_pagination_invalid_after(): await store.list_chat_completions(after="non-existent", limit=2) -@pytest.mark.asyncio async def test_inference_store_pagination_no_limit(): """Test pagination behavior when no limit is specified.""" with TemporaryDirectory() as tmp_dir: diff --git a/tests/unit/utils/responses/test_responses_store.py b/tests/unit/utils/responses/test_responses_store.py index 3f25e2524..44d4b30da 100644 --- a/tests/unit/utils/responses/test_responses_store.py +++ b/tests/unit/utils/responses/test_responses_store.py @@ -44,7 +44,6 @@ def create_test_response_input(content: str, input_id: str) -> OpenAIResponseInp ) -@pytest.mark.asyncio async def test_responses_store_pagination_basic(): """Test basic pagination functionality for responses store.""" with TemporaryDirectory() as tmp_dir: @@ -90,7 +89,6 @@ async def test_responses_store_pagination_basic(): assert result3.has_more is False -@pytest.mark.asyncio async def test_responses_store_pagination_ascending(): """Test pagination with ascending order.""" with TemporaryDirectory() as tmp_dir: @@ -125,7 +123,6 @@ async def test_responses_store_pagination_ascending(): assert result2.has_more is True -@pytest.mark.asyncio async def test_responses_store_pagination_with_model_filter(): """Test pagination combined with model filtering.""" with TemporaryDirectory() as tmp_dir: @@ -163,7 +160,6 @@ async def test_responses_store_pagination_with_model_filter(): assert result2.has_more is False -@pytest.mark.asyncio async def test_responses_store_pagination_invalid_after(): """Test error handling for invalid 'after' parameter.""" with TemporaryDirectory() as tmp_dir: @@ -176,7 +172,6 @@ async def test_responses_store_pagination_invalid_after(): await store.list_responses(after="non-existent", limit=2) -@pytest.mark.asyncio async def test_responses_store_pagination_no_limit(): """Test pagination behavior when no limit is specified.""" with TemporaryDirectory() as tmp_dir: @@ -205,7 +200,6 @@ async def test_responses_store_pagination_no_limit(): assert result.has_more is False -@pytest.mark.asyncio async def test_responses_store_get_response_object(): """Test retrieving a single response object.""" with TemporaryDirectory() as tmp_dir: @@ -230,7 +224,6 @@ async def test_responses_store_get_response_object(): await store.get_response_object("non-existent") -@pytest.mark.asyncio async def test_responses_store_input_items_pagination(): """Test pagination functionality for input items.""" with TemporaryDirectory() as tmp_dir: @@ -308,7 +301,6 @@ async def test_responses_store_input_items_pagination(): await store.list_response_input_items("test-resp", before="some-id", after="other-id") -@pytest.mark.asyncio async def test_responses_store_input_items_before_pagination(): """Test before pagination functionality for input items.""" with TemporaryDirectory() as tmp_dir: diff --git a/tests/unit/utils/sqlstore/test_sqlstore.py b/tests/unit/utils/sqlstore/test_sqlstore.py index c4230a396..778f0b658 100644 --- a/tests/unit/utils/sqlstore/test_sqlstore.py +++ b/tests/unit/utils/sqlstore/test_sqlstore.py @@ -14,7 +14,6 @@ from llama_stack.providers.utils.sqlstore.sqlalchemy_sqlstore import SqlAlchemyS from llama_stack.providers.utils.sqlstore.sqlstore import SqliteSqlStoreConfig -@pytest.mark.asyncio async def test_sqlite_sqlstore(): with TemporaryDirectory() as tmp_dir: db_name = "test.db" @@ -66,7 +65,6 @@ async def test_sqlite_sqlstore(): assert result.has_more is False -@pytest.mark.asyncio async def test_sqlstore_pagination_basic(): """Test basic pagination functionality at the SQL store level.""" with TemporaryDirectory() as tmp_dir: @@ -131,7 +129,6 @@ async def test_sqlstore_pagination_basic(): assert result3.has_more is False -@pytest.mark.asyncio async def test_sqlstore_pagination_with_filter(): """Test pagination with WHERE conditions.""" with TemporaryDirectory() as tmp_dir: @@ -184,7 +181,6 @@ async def test_sqlstore_pagination_with_filter(): assert result2.has_more is False -@pytest.mark.asyncio async def test_sqlstore_pagination_ascending_order(): """Test pagination with ascending order.""" with TemporaryDirectory() as tmp_dir: @@ -233,7 +229,6 @@ async def test_sqlstore_pagination_ascending_order(): assert result2.has_more is True -@pytest.mark.asyncio async def test_sqlstore_pagination_multi_column_ordering_error(): """Test that multi-column ordering raises an error when using cursor pagination.""" with TemporaryDirectory() as tmp_dir: @@ -271,7 +266,6 @@ async def test_sqlstore_pagination_multi_column_ordering_error(): assert result.data[0]["id"] == "task1" -@pytest.mark.asyncio async def test_sqlstore_pagination_cursor_requires_order_by(): """Test that cursor pagination requires order_by parameter.""" with TemporaryDirectory() as tmp_dir: @@ -289,7 +283,6 @@ async def test_sqlstore_pagination_cursor_requires_order_by(): ) -@pytest.mark.asyncio async def test_sqlstore_pagination_error_handling(): """Test error handling for invalid columns and cursor IDs.""" with TemporaryDirectory() as tmp_dir: @@ -339,7 +332,6 @@ async def test_sqlstore_pagination_error_handling(): ) -@pytest.mark.asyncio 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 b457176a7..066f67a98 100644 --- a/tests/unit/utils/test_authorized_sqlstore.py +++ b/tests/unit/utils/test_authorized_sqlstore.py @@ -7,8 +7,6 @@ from tempfile import TemporaryDirectory from unittest.mock import patch -import pytest - from llama_stack.distribution.access_control.access_control import default_policy, is_action_allowed from llama_stack.distribution.access_control.datatypes import Action from llama_stack.distribution.datatypes import User @@ -18,7 +16,6 @@ from llama_stack.providers.utils.sqlstore.sqlalchemy_sqlstore import SqlAlchemyS from llama_stack.providers.utils.sqlstore.sqlstore import SqliteSqlStoreConfig -@pytest.mark.asyncio @patch("llama_stack.providers.utils.sqlstore.authorized_sqlstore.get_authenticated_user") async def test_authorized_fetch_with_where_sql_access_control(mock_get_authenticated_user): """Test that fetch_all works correctly with where_sql for access control""" @@ -81,7 +78,6 @@ async def test_authorized_fetch_with_where_sql_access_control(mock_get_authentic assert row["title"] == "User Document" -@pytest.mark.asyncio @patch("llama_stack.providers.utils.sqlstore.authorized_sqlstore.get_authenticated_user") async def test_sql_policy_consistency(mock_get_authenticated_user): """Test that SQL WHERE clause logic exactly matches is_action_allowed policy logic""" @@ -104,19 +100,17 @@ async def test_sql_policy_consistency(mock_get_authenticated_user): # Test scenarios with different access control patterns test_scenarios = [ - # Scenario 1: Public record (no access control) + # Scenario 1: Public record (no access control - represents None user insert) {"id": "1", "name": "public", "access_attributes": None}, - # Scenario 2: Empty access control (should be treated as public) - {"id": "2", "name": "empty", "access_attributes": {}}, - # Scenario 3: Record with roles requirement - {"id": "3", "name": "admin-only", "access_attributes": {"roles": ["admin"]}}, - # Scenario 4: Record with multiple attribute categories - {"id": "4", "name": "admin-ml-team", "access_attributes": {"roles": ["admin"], "teams": ["ml-team"]}}, - # Scenario 5: Record with teams only (missing roles category) - {"id": "5", "name": "ml-team-only", "access_attributes": {"teams": ["ml-team"]}}, - # Scenario 6: Record with roles and projects + # Scenario 2: Record with roles requirement + {"id": "2", "name": "admin-only", "access_attributes": {"roles": ["admin"]}}, + # Scenario 3: Record with multiple attribute categories + {"id": "3", "name": "admin-ml-team", "access_attributes": {"roles": ["admin"], "teams": ["ml-team"]}}, + # Scenario 4: Record with teams only (missing roles category) + {"id": "4", "name": "ml-team-only", "access_attributes": {"teams": ["ml-team"]}}, + # Scenario 5: Record with roles and projects { - "id": "6", + "id": "5", "name": "admin-project-x", "access_attributes": {"roles": ["admin"], "projects": ["project-x"]}, }, @@ -155,7 +149,9 @@ async def test_sql_policy_consistency(mock_get_authenticated_user): policy_ids = set() for scenario in test_scenarios: sql_record = SqlRecord( - record_id=scenario["id"], table_name="resources", access_attributes=scenario["access_attributes"] + record_id=scenario["id"], + table_name="resources", + owner=User(principal="test-user", attributes=scenario["access_attributes"]), ) if is_action_allowed(policy, Action.READ, sql_record, user): @@ -168,7 +164,6 @@ async def test_sql_policy_consistency(mock_get_authenticated_user): ) -@pytest.mark.asyncio @patch("llama_stack.providers.utils.sqlstore.authorized_sqlstore.get_authenticated_user") 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