diff --git a/.github/PULL_REQUEST_TEMPLATE.md b/.github/PULL_REQUEST_TEMPLATE.md index af2058b9a..263828e1c 100644 --- a/.github/PULL_REQUEST_TEMPLATE.md +++ b/.github/PULL_REQUEST_TEMPLATE.md @@ -1,10 +1,8 @@ # What does this PR do? -[Provide a short summary of what this PR does and why. Link to relevant issues if applicable.] + -[//]: # (If resolving an issue, uncomment and update the line below) -[//]: # (Closes #[issue-number]) + + ## Test Plan -[Describe the tests you ran to verify your changes with result summaries. *Provide clear instructions so the plan can be easily re-executed.*] - -[//]: # (## Documentation) + diff --git a/.github/actions/setup-runner/action.yml b/.github/actions/setup-runner/action.yml new file mode 100644 index 000000000..6cba4fdc3 --- /dev/null +++ b/.github/actions/setup-runner/action.yml @@ -0,0 +1,22 @@ +name: Setup runner +description: Prepare a runner for the tests (install uv, python, project dependencies, etc.) +runs: + using: "composite" + steps: + - name: Install uv + uses: astral-sh/setup-uv@6b9c6063abd6010835644d4c2e1bef4cf5cd0fca # v6.0.1 + with: + python-version: "3.10" + activate-environment: true + version: 0.7.6 + + - name: Install dependencies + shell: bash + 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 + uv pip install -e . diff --git a/.github/workflows/Dockerfile b/.github/workflows/Dockerfile new file mode 100644 index 000000000..9261bd174 --- /dev/null +++ b/.github/workflows/Dockerfile @@ -0,0 +1 @@ +FROM localhost:5000/distribution-kvant:dev \ No newline at end of file diff --git a/.github/workflows/ci-playground.yaml b/.github/workflows/ci-playground.yaml new file mode 100644 index 000000000..251782855 --- /dev/null +++ b/.github/workflows/ci-playground.yaml @@ -0,0 +1,73 @@ +name: Build and Push playground container +run-name: Build and Push playground container +on: + workflow_dispatch: + #schedule: + # - cron: "0 10 * * *" + push: + branches: + - main + - kvant + tags: + - 'v*' + pull_request: + branches: + - main + - kvant +env: + IMAGE: git.kvant.cloud/${{github.repository}}-playground +jobs: + build-playground: + runs-on: ubuntu-latest + steps: + - name: Checkout + uses: actions/checkout@v4 + with: + fetch-depth: 0 + + - name: Set current time + uses: https://github.com/gerred/actions/current-time@master + id: current_time + + - name: Set up Docker Buildx + uses: docker/setup-buildx-action@v3 + + - name: Login to git.kvant.cloud registry + uses: docker/login-action@v3 + with: + registry: git.kvant.cloud + username: ${{ vars.ORG_PACKAGE_WRITER_USERNAME }} + password: ${{ secrets.ORG_PACKAGE_WRITER_TOKEN }} + + - name: Docker meta + id: meta + uses: docker/metadata-action@v5 + with: + # list of Docker images to use as base name for tags + images: | + ${{env.IMAGE}} + # generate Docker tags based on the following events/attributes + tags: | + type=schedule + type=ref,event=branch + type=ref,event=pr + type=ref,event=tag + type=semver,pattern={{version}} + + - name: Build and push to gitea registry + uses: docker/build-push-action@v6 + with: + push: ${{ github.event_name != 'pull_request' }} + tags: ${{ steps.meta.outputs.tags }} + labels: ${{ steps.meta.outputs.labels }} + context: . + file: llama_stack/distribution/ui/Containerfile + provenance: mode=max + sbom: true + build-args: | + BUILD_DATE=${{ steps.current_time.outputs.time }} + cache-from: | + type=registry,ref=${{ env.IMAGE }}:buildcache + type=registry,ref=${{ env.IMAGE }}:${{ github.ref_name }} + type=registry,ref=${{ env.IMAGE }}:main + cache-to: type=registry,ref=${{ env.IMAGE }}:buildcache,mode=max,image-manifest=true diff --git a/.github/workflows/ci.yaml b/.github/workflows/ci.yaml new file mode 100644 index 000000000..87f196cc2 --- /dev/null +++ b/.github/workflows/ci.yaml @@ -0,0 +1,98 @@ +name: Build and Push container +run-name: Build and Push container +on: + workflow_dispatch: + #schedule: + # - cron: "0 10 * * *" + push: + branches: + - main + - kvant + tags: + - 'v*' + pull_request: + branches: + - main + - kvant +env: + IMAGE: git.kvant.cloud/${{github.repository}} +jobs: + build: + runs-on: ubuntu-latest + services: + registry: + image: registry:2 + ports: + - 5000:5000 + steps: + - name: Checkout + uses: actions/checkout@v4 + with: + fetch-depth: 0 + + - name: Set current time + uses: https://github.com/gerred/actions/current-time@master + id: current_time + + - name: Set up Docker Buildx + uses: docker/setup-buildx-action@v3 + with: + driver-opts: network=host + + - name: Login to git.kvant.cloud registry + uses: docker/login-action@v3 + with: + registry: git.kvant.cloud + username: ${{ vars.ORG_PACKAGE_WRITER_USERNAME }} + password: ${{ secrets.ORG_PACKAGE_WRITER_TOKEN }} + + - name: Docker meta + id: meta + uses: docker/metadata-action@v5 + with: + # list of Docker images to use as base name for tags + images: | + ${{env.IMAGE}} + # generate Docker tags based on the following events/attributes + tags: | + type=schedule + type=ref,event=branch + type=ref,event=pr + type=ref,event=tag + type=semver,pattern={{version}} + + - name: Install uv + uses: https://github.com/astral-sh/setup-uv@v5 + with: + # Install a specific version of uv. + version: "0.7.8" + + - name: Build + env: + USE_COPY_NOT_MOUNT: true + LLAMA_STACK_DIR: . + run: | + uvx --from . llama stack build --template kvant --image-type container + + # docker tag distribution-kvant:dev ${{env.IMAGE}}:kvant + # docker push ${{env.IMAGE}}:kvant + + docker tag distribution-kvant:dev localhost:5000/distribution-kvant:dev + docker push localhost:5000/distribution-kvant:dev + + - name: Build and push to gitea registry + uses: docker/build-push-action@v6 + with: + push: ${{ github.event_name != 'pull_request' }} + tags: ${{ steps.meta.outputs.tags }} + labels: ${{ steps.meta.outputs.labels }} + context: .github/workflows + provenance: mode=max + sbom: true + build-args: | + BUILD_DATE=${{ steps.current_time.outputs.time }} + cache-from: | + type=registry,ref=${{ env.IMAGE }}:buildcache + type=registry,ref=${{ env.IMAGE }}:${{ github.ref_name }} + type=registry,ref=${{ env.IMAGE }}:main + cache-to: type=registry,ref=${{ env.IMAGE }}:buildcache,mode=max,image-manifest=true diff --git a/.github/workflows/changelog.yml b/.github/workflows_upstream/changelog.yml similarity index 100% rename from .github/workflows/changelog.yml rename to .github/workflows_upstream/changelog.yml diff --git a/.github/workflows/gha_workflow_llama_stack_tests.yml b/.github/workflows_upstream/gha_workflow_llama_stack_tests.yml similarity index 100% rename from .github/workflows/gha_workflow_llama_stack_tests.yml rename to .github/workflows_upstream/gha_workflow_llama_stack_tests.yml diff --git a/.github/workflows/install-script-ci.yml b/.github/workflows_upstream/install-script-ci.yml similarity index 100% rename from .github/workflows/install-script-ci.yml rename to .github/workflows_upstream/install-script-ci.yml diff --git a/.github/workflows/integration-auth-tests.yml b/.github/workflows_upstream/integration-auth-tests.yml similarity index 64% rename from .github/workflows/integration-auth-tests.yml rename to .github/workflows_upstream/integration-auth-tests.yml index 33fb4e802..a3a746246 100644 --- a/.github/workflows/integration-auth-tests.yml +++ b/.github/workflows_upstream/integration-auth-tests.yml @@ -23,23 +23,18 @@ jobs: runs-on: ubuntu-latest strategy: matrix: - auth-provider: [kubernetes] + auth-provider: [oauth2_token] fail-fast: false # we want to run all tests regardless of failure steps: - name: Checkout repository uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2 - - name: Install uv - uses: astral-sh/setup-uv@c7f87aa956e4c323abf06d5dec078e358f6b4d04 # v6.0.0 - with: - python-version: "3.10" - activate-environment: true + - name: Install dependencies + uses: ./.github/actions/setup-runner - - name: Set Up Environment and Install Dependencies + - name: Build Llama Stack run: | - uv sync --extra dev --extra test - uv pip install -e . llama stack build --template ollama --image-type venv - name: Install minikube @@ -47,29 +42,53 @@ jobs: uses: medyagh/setup-minikube@cea33675329b799adccc9526aa5daccc26cd5052 # v0.0.19 - name: Start minikube - if: ${{ matrix.auth-provider == 'kubernetes' }} + if: ${{ matrix.auth-provider == 'oauth2_token' }} run: | minikube start kubectl get pods -A - name: Configure Kube Auth - if: ${{ matrix.auth-provider == 'kubernetes' }} + if: ${{ matrix.auth-provider == 'oauth2_token' }} run: | kubectl create namespace llama-stack kubectl create serviceaccount llama-stack-auth -n llama-stack kubectl create rolebinding llama-stack-auth-rolebinding --clusterrole=admin --serviceaccount=llama-stack:llama-stack-auth -n llama-stack kubectl create token llama-stack-auth -n llama-stack > llama-stack-auth-token + cat <> $GITHUB_ENV + echo "KUBERNETES_API_SERVER_URL=$(kubectl get --raw /.well-known/openid-configuration| jq -r .jwks_uri)" >> $GITHUB_ENV echo "KUBERNETES_CA_CERT_PATH=$(kubectl config view --minify -o jsonpath='{.clusters[0].cluster.certificate-authority}')" >> $GITHUB_ENV + echo "KUBERNETES_ISSUER=$(kubectl get --raw /.well-known/openid-configuration| jq -r .issuer)" >> $GITHUB_ENV + echo "KUBERNETES_AUDIENCE=$(kubectl create token llama-stack-auth -n llama-stack --duration=1h | cut -d. -f2 | base64 -d | jq -r '.aud[0]')" >> $GITHUB_ENV - name: Set Kube Auth Config and run server env: INFERENCE_MODEL: "meta-llama/Llama-3.2-3B-Instruct" - if: ${{ matrix.auth-provider == 'kubernetes' }} + if: ${{ matrix.auth-provider == 'oauth2_token' }} run: | run_dir=$(mktemp -d) cat <<'EOF' > $run_dir/run.yaml @@ -81,10 +100,10 @@ jobs: port: 8321 EOF yq eval '.server.auth = {"provider_type": "${{ matrix.auth-provider }}"}' -i $run_dir/run.yaml - yq eval '.server.auth.config = {"api_server_url": "${{ env.KUBERNETES_API_SERVER_URL }}", "ca_cert_path": "${{ env.KUBERNETES_CA_CERT_PATH }}"}' -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 }}"}' -i $run_dir/run.yaml cat $run_dir/run.yaml - source .venv/bin/activate nohup uv run llama stack run $run_dir/run.yaml --image-type venv > server.log 2>&1 & - name: Wait for Llama Stack server to be ready diff --git a/.github/workflows/integration-tests.yml b/.github/workflows_upstream/integration-tests.yml similarity index 75% rename from .github/workflows/integration-tests.yml rename to .github/workflows_upstream/integration-tests.yml index d755ff0ae..d78e82c9d 100644 --- a/.github/workflows/integration-tests.yml +++ b/.github/workflows_upstream/integration-tests.yml @@ -24,7 +24,7 @@ jobs: 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] + test-type: [agents, inference, datasets, inspect, scoring, post_training, providers, tool_runtime] client-type: [library, http] fail-fast: false # we want to run all tests regardless of failure @@ -32,24 +32,14 @@ jobs: - name: Checkout repository uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2 - - name: Install uv - uses: astral-sh/setup-uv@c7f87aa956e4c323abf06d5dec078e358f6b4d04 # v6.0.0 - with: - python-version: "3.10" - activate-environment: true + - name: Install dependencies + uses: ./.github/actions/setup-runner - name: Setup ollama uses: ./.github/actions/setup-ollama - - name: Set Up Environment and Install Dependencies + - name: Build Llama Stack run: | - uv sync --extra dev --extra test - 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 - uv pip install -e . llama stack build --template ollama --image-type venv - name: Start Llama Stack server in background @@ -57,8 +47,7 @@ jobs: env: INFERENCE_MODEL: "meta-llama/Llama-3.2-3B-Instruct" run: | - source .venv/bin/activate - nohup uv run llama stack run ./llama_stack/templates/ollama/run.yaml --image-type venv > server.log 2>&1 & + LLAMA_STACK_LOG_FILE=server.log nohup uv run llama stack run ./llama_stack/templates/ollama/run.yaml --image-type venv & - name: Wait for Llama Stack server to be ready if: matrix.client-type == 'http' @@ -86,6 +75,12 @@ jobs: exit 1 fi + - name: Check Storage and Memory Available Before Tests + if: ${{ always() }} + run: | + free -h + df -h + - name: Run Integration Tests env: INFERENCE_MODEL: "meta-llama/Llama-3.2-3B-Instruct" @@ -95,17 +90,24 @@ jobs: else stack_config="http://localhost:8321" fi - uv run pytest -v tests/integration/${{ matrix.test-type }} --stack-config=${stack_config} \ + uv run pytest -s -v tests/integration/${{ matrix.test-type }} --stack-config=${stack_config} \ -k "not(builtin_tool or safety_with_image or code_interpreter or test_rag)" \ --text-model="meta-llama/Llama-3.2-3B-Instruct" \ --embedding-model=all-MiniLM-L6-v2 + - name: Check Storage and Memory Available After Tests + if: ${{ always() }} + run: | + free -h + df -h + - name: Write ollama logs to file + if: ${{ always() }} run: | sudo journalctl -u ollama.service > ollama.log - name: Upload all logs to artifacts - if: always() + 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 }} diff --git a/.github/workflows/pre-commit.yml b/.github/workflows_upstream/pre-commit.yml similarity index 97% rename from .github/workflows/pre-commit.yml rename to .github/workflows_upstream/pre-commit.yml index 4df04fbb0..2bbd52c53 100644 --- a/.github/workflows/pre-commit.yml +++ b/.github/workflows_upstream/pre-commit.yml @@ -29,6 +29,7 @@ jobs: - uses: pre-commit/action@2c7b3805fd2a0fd8c1884dcaebf91fc102a13ecd # v3.0.1 env: SKIP: no-commit-to-branch + RUFF_OUTPUT_FORMAT: github - name: Verify if there are any diff files after pre-commit run: | diff --git a/.github/workflows/providers-build.yml b/.github/workflows_upstream/providers-build.yml similarity index 73% rename from .github/workflows/providers-build.yml rename to .github/workflows_upstream/providers-build.yml index 0fd7904d4..cf53459b9 100644 --- a/.github/workflows/providers-build.yml +++ b/.github/workflows_upstream/providers-build.yml @@ -50,21 +50,8 @@ jobs: - name: Checkout repository uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2 - - name: Set up Python - uses: actions/setup-python@a26af69be951a213d495a4c3e4e4022e16d87065 # v5.6.0 - with: - python-version: '3.10' - - - name: Install uv - uses: astral-sh/setup-uv@c7f87aa956e4c323abf06d5dec078e358f6b4d04 # v6.0.0 - with: - python-version: "3.10" - - - name: Install LlamaStack - run: | - uv venv - source .venv/bin/activate - uv pip install -e . + - name: Install dependencies + uses: ./.github/actions/setup-runner - name: Print build dependencies run: | @@ -79,7 +66,6 @@ jobs: - name: Print dependencies in the image if: matrix.image-type == 'venv' run: | - source test/bin/activate uv pip list build-single-provider: @@ -88,21 +74,8 @@ jobs: - name: Checkout repository uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2 - - name: Set up Python - uses: actions/setup-python@a26af69be951a213d495a4c3e4e4022e16d87065 # v5.6.0 - with: - python-version: '3.10' - - - name: Install uv - uses: astral-sh/setup-uv@c7f87aa956e4c323abf06d5dec078e358f6b4d04 # v6.0.0 - with: - python-version: "3.10" - - - name: Install LlamaStack - run: | - uv venv - source .venv/bin/activate - uv pip install -e . + - name: Install dependencies + uses: ./.github/actions/setup-runner - name: Build a single provider run: | @@ -114,21 +87,8 @@ jobs: - name: Checkout repository uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2 - - name: Set up Python - uses: actions/setup-python@a26af69be951a213d495a4c3e4e4022e16d87065 # v5.6.0 - with: - python-version: '3.10' - - - name: Install uv - uses: astral-sh/setup-uv@c7f87aa956e4c323abf06d5dec078e358f6b4d04 # v6.0.0 - with: - python-version: "3.10" - - - name: Install LlamaStack - run: | - uv venv - source .venv/bin/activate - uv pip install -e . + - name: Install dependencies + uses: ./.github/actions/setup-runner - name: Build a single provider run: | @@ -152,21 +112,8 @@ jobs: - name: Checkout repository uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2 - - name: Set up Python - uses: actions/setup-python@a26af69be951a213d495a4c3e4e4022e16d87065 # v5.6.0 - with: - python-version: '3.10' - - - name: Install uv - uses: astral-sh/setup-uv@0c5e2b8115b80b4c7c5ddf6ffdd634974642d182 # v5.4.1 - with: - python-version: "3.10" - - - name: Install LlamaStack - run: | - uv venv - source .venv/bin/activate - uv pip install -e . + - name: Install dependencies + uses: ./.github/actions/setup-runner - name: Pin template to UBI9 base run: | diff --git a/.github/workflows/semantic-pr.yml b/.github/workflows_upstream/semantic-pr.yml similarity index 100% rename from .github/workflows/semantic-pr.yml rename to .github/workflows_upstream/semantic-pr.yml diff --git a/.github/workflows/stale_bot.yml b/.github/workflows_upstream/stale_bot.yml similarity index 100% rename from .github/workflows/stale_bot.yml rename to .github/workflows_upstream/stale_bot.yml diff --git a/.github/workflows/test-external-providers.yml b/.github/workflows_upstream/test-external-providers.yml similarity index 88% rename from .github/workflows/test-external-providers.yml rename to .github/workflows_upstream/test-external-providers.yml index 8c75dde25..06ab7cf3c 100644 --- a/.github/workflows/test-external-providers.yml +++ b/.github/workflows_upstream/test-external-providers.yml @@ -25,15 +25,8 @@ jobs: - name: Checkout repository uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2 - - name: Install uv - uses: astral-sh/setup-uv@c7f87aa956e4c323abf06d5dec078e358f6b4d04 # v6.0.0 - with: - python-version: "3.10" - - - name: Set Up Environment and Install Dependencies - run: | - uv sync --extra dev --extra test - uv pip install -e . + - name: Install dependencies + uses: ./.github/actions/setup-runner - name: Apply image type to config file run: | @@ -59,7 +52,6 @@ jobs: env: INFERENCE_MODEL: "meta-llama/Llama-3.2-3B-Instruct" run: | - source ci-test/bin/activate uv run pip list nohup uv run --active llama stack run tests/external-provider/llama-stack-provider-ollama/run.yaml --image-type ${{ matrix.image-type }} > server.log 2>&1 & diff --git a/.github/workflows/tests.yml b/.github/workflows_upstream/tests.yml similarity index 100% rename from .github/workflows/tests.yml rename to .github/workflows_upstream/tests.yml diff --git a/.github/workflows/unit-tests.yml b/.github/workflows_upstream/unit-tests.yml similarity index 73% rename from .github/workflows/unit-tests.yml rename to .github/workflows_upstream/unit-tests.yml index 64a5bba37..fc0459f0f 100644 --- a/.github/workflows/unit-tests.yml +++ b/.github/workflows_upstream/unit-tests.yml @@ -30,17 +30,11 @@ jobs: - "3.12" - "3.13" steps: - - uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2 + - name: Checkout repository + uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2 - - name: Set up Python ${{ matrix.python }} - uses: actions/setup-python@a26af69be951a213d495a4c3e4e4022e16d87065 # v5.6.0 - with: - python-version: ${{ matrix.python }} - - - uses: astral-sh/setup-uv@c7f87aa956e4c323abf06d5dec078e358f6b4d04 # v6.0.0 - with: - python-version: ${{ matrix.python }} - enable-cache: false + - name: Install dependencies + uses: ./.github/actions/setup-runner - name: Run unit tests run: | diff --git a/.github/workflows/update-readthedocs.yml b/.github/workflows_upstream/update-readthedocs.yml similarity index 81% rename from .github/workflows/update-readthedocs.yml rename to .github/workflows_upstream/update-readthedocs.yml index 094942368..981332a77 100644 --- a/.github/workflows/update-readthedocs.yml +++ b/.github/workflows_upstream/update-readthedocs.yml @@ -37,16 +37,8 @@ jobs: - name: Checkout repository uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2 - - name: Set up Python - uses: actions/setup-python@a26af69be951a213d495a4c3e4e4022e16d87065 # v5.6.0 - with: - python-version: '3.11' - - - name: Install the latest version of uv - uses: astral-sh/setup-uv@c7f87aa956e4c323abf06d5dec078e358f6b4d04 # v6.0.0 - - - name: Sync with uv - run: uv sync --extra docs + - name: Install dependencies + uses: ./.github/actions/setup-runner - name: Build HTML run: | diff --git a/.gitignore b/.gitignore index 0ef25cdf1..747acdc7b 100644 --- a/.gitignore +++ b/.gitignore @@ -6,6 +6,7 @@ dev_requirements.txt build .DS_Store llama_stack/configs/* +.cursor/ xcuserdata/ *.hmap .DS_Store @@ -23,3 +24,4 @@ venv/ pytest-report.xml .coverage .python-version +data diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index e78fcd158..aaec469e4 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -53,7 +53,7 @@ repos: - black==24.3.0 - repo: https://github.com/astral-sh/uv-pre-commit - rev: 0.6.3 + rev: 0.7.8 hooks: - id: uv-lock - id: uv-export @@ -61,6 +61,7 @@ repos: "--frozen", "--no-hashes", "--no-emit-project", + "--no-default-groups", "--output-file=requirements.txt" ] @@ -88,20 +89,17 @@ repos: - id: distro-codegen name: Distribution Template Codegen additional_dependencies: - - uv==0.6.0 - entry: uv run --extra codegen ./scripts/distro_codegen.py + - uv==0.7.8 + entry: uv run --group codegen ./scripts/distro_codegen.py language: python pass_filenames: false require_serial: true files: ^llama_stack/templates/.*$|^llama_stack/providers/.*/inference/.*/models\.py$ - -- repo: local - hooks: - id: openapi-codegen name: API Spec Codegen additional_dependencies: - - uv==0.6.2 - entry: sh -c 'uv run --with ".[dev]" ./docs/openapi_generator/run_openapi_generator.sh > /dev/null' + - uv==0.7.8 + entry: sh -c 'uv run ./docs/openapi_generator/run_openapi_generator.sh > /dev/null' language: python pass_filenames: false require_serial: true diff --git a/.readthedocs.yaml b/.readthedocs.yaml index f114dbf9b..461977a6c 100644 --- a/.readthedocs.yaml +++ b/.readthedocs.yaml @@ -5,28 +5,21 @@ # Required version: 2 +# Build documentation in the "docs/" directory with Sphinx +sphinx: + configuration: docs/source/conf.py + # Set the OS, Python version and other tools you might need build: os: ubuntu-22.04 tools: python: "3.12" - # You can also specify other tool versions: - # nodejs: "19" - # rust: "1.64" - # golang: "1.19" - -# Build documentation in the "docs/" directory with Sphinx -sphinx: - configuration: docs/source/conf.py - -# Optionally build your docs in additional formats such as PDF and ePub -# formats: -# - pdf -# - epub - -# Optional but recommended, declare the Python requirements required -# to build your documentation -# See https://docs.readthedocs.io/en/stable/guides/reproducible-builds.html -python: - install: - - requirements: docs/requirements.txt + jobs: + pre_create_environment: + - asdf plugin add uv + - asdf install uv latest + - asdf global uv latest + create_environment: + - uv venv "${READTHEDOCS_VIRTUALENV_PATH}" + install: + - UV_PROJECT_ENVIRONMENT="${READTHEDOCS_VIRTUALENV_PATH}" uv sync --frozen --group docs diff --git a/CHANGELOG.md b/CHANGELOG.md index a0b008982..f7644a5af 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -1,5 +1,26 @@ # Changelog +# v0.2.7 +Published on: 2025-05-16T20:38:10Z + +## Highlights + +This is a small update. But a couple highlights: + +* feat: function tools in OpenAI Responses by @bbrowning in https://github.com/meta-llama/llama-stack/pull/2094, getting closer to ready. Streaming is the next missing piece. +* feat: Adding support for customizing chunk context in RAG insertion and querying by @franciscojavierarceo in https://github.com/meta-llama/llama-stack/pull/2134 +* feat: scaffolding for Llama Stack UI by @ehhuang in https://github.com/meta-llama/llama-stack/pull/2149, more to come in the coming releases. + + +--- + +# v0.2.6 +Published on: 2025-05-12T18:06:52Z + + + +--- + # v0.2.5 Published on: 2025-05-04T20:16:49Z diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index d7c3e3e2f..10e3f6cee 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -167,14 +167,11 @@ If you have made changes to a provider's configuration in any form (introducing If you are making changes to the documentation at [https://llama-stack.readthedocs.io/en/latest/](https://llama-stack.readthedocs.io/en/latest/), you can use the following command to build the documentation and preview your changes. You will need [Sphinx](https://www.sphinx-doc.org/en/master/) and the readthedocs theme. ```bash -cd docs -uv sync --extra docs - # This rebuilds the documentation pages. -uv run make html +uv run --group docs make -C docs/ html # This will start a local server (usually at http://127.0.0.1:8000) that automatically rebuilds and refreshes when you make changes to the documentation. -uv run sphinx-autobuild source build/html --write-all +uv run --group docs sphinx-autobuild docs/source docs/build/html --write-all ``` ### Update API Documentation @@ -182,7 +179,7 @@ uv run sphinx-autobuild source build/html --write-all If you modify or add new API endpoints, update the API documentation accordingly. You can do this by running the following command: ```bash -uv run --with ".[dev]" ./docs/openapi_generator/run_openapi_generator.sh +uv run ./docs/openapi_generator/run_openapi_generator.sh ``` The generated API documentation will be available in `docs/_static/`. Make sure to review the changes before committing. diff --git a/MANIFEST.in b/MANIFEST.in index 879a9cbd4..88bd11767 100644 --- a/MANIFEST.in +++ b/MANIFEST.in @@ -1,5 +1,4 @@ include pyproject.toml -include llama_stack/templates/dependencies.json include llama_stack/models/llama/llama3/tokenizer.model include llama_stack/models/llama/llama4/tokenizer.model include llama_stack/distribution/*.sh diff --git a/README.md b/README.md index 5dfe3577a..37f1aa0f3 100644 --- a/README.md +++ b/README.md @@ -107,26 +107,29 @@ By reducing friction and complexity, Llama Stack empowers developers to focus on ### API Providers Here is a list of the various API providers and available distributions that can help developers get started easily with Llama Stack. -| **API Provider Builder** | **Environments** | **Agents** | **Inference** | **Memory** | **Safety** | **Telemetry** | -|:------------------------:|:----------------------:|:----------:|:-------------:|:----------:|:----------:|:-------------:| -| Meta Reference | Single Node | ✅ | ✅ | ✅ | ✅ | ✅ | -| SambaNova | Hosted | | ✅ | | | | -| Cerebras | Hosted | | ✅ | | | | -| Fireworks | Hosted | ✅ | ✅ | ✅ | | | -| AWS Bedrock | Hosted | | ✅ | | ✅ | | -| Together | Hosted | ✅ | ✅ | | ✅ | | -| Groq | Hosted | | ✅ | | | | -| Ollama | Single Node | | ✅ | | | | -| TGI | Hosted and Single Node | | ✅ | | | | -| NVIDIA NIM | Hosted and Single Node | | ✅ | | | | -| Chroma | Single Node | | | ✅ | | | -| PG Vector | Single Node | | | ✅ | | | -| PyTorch ExecuTorch | On-device iOS | ✅ | ✅ | | | | -| vLLM | Hosted and Single Node | | ✅ | | | | -| OpenAI | Hosted | | ✅ | | | | -| Anthropic | Hosted | | ✅ | | | | -| Gemini | Hosted | | ✅ | | | | -| watsonx | Hosted | | ✅ | | | | +| **API Provider Builder** | **Environments** | **Agents** | **Inference** | **Memory** | **Safety** | **Telemetry** | **Post Training** | +|:------------------------:|:----------------------:|:----------:|:-------------:|:----------:|:----------:|:-------------:|:-----------------:| +| Meta Reference | Single Node | ✅ | ✅ | ✅ | ✅ | ✅ | | +| SambaNova | Hosted | | ✅ | | ✅ | | | +| Cerebras | Hosted | | ✅ | | | | | +| Fireworks | Hosted | ✅ | ✅ | ✅ | | | | +| AWS Bedrock | Hosted | | ✅ | | ✅ | | | +| Together | Hosted | ✅ | ✅ | | ✅ | | | +| Groq | Hosted | | ✅ | | | | | +| Ollama | Single Node | | ✅ | | | | | +| TGI | Hosted and Single Node | | ✅ | | | | | +| NVIDIA NIM | Hosted and Single Node | | ✅ | | | | | +| Chroma | Single Node | | | ✅ | | | | +| PG Vector | Single Node | | | ✅ | | | | +| PyTorch ExecuTorch | On-device iOS | ✅ | ✅ | | | | | +| vLLM | Hosted and Single Node | | ✅ | | | | | +| OpenAI | Hosted | | ✅ | | | | | +| Anthropic | Hosted | | ✅ | | | | | +| Gemini | Hosted | | ✅ | | | | | +| watsonx | Hosted | | ✅ | | | | | +| HuggingFace | Single Node | | | | | | ✅ | +| TorchTune | Single Node | | | | | | ✅ | +| NVIDIA NEMO | Hosted | | | | | | ✅ | ### Distributions diff --git a/docs/_static/llama-stack-spec.html b/docs/_static/llama-stack-spec.html index 9032e5968..d88462909 100644 --- a/docs/_static/llama-stack-spec.html +++ b/docs/_static/llama-stack-spec.html @@ -518,6 +518,74 @@ } }, "/v1/openai/v1/responses": { + "get": { + "responses": { + "200": { + "description": "A ListOpenAIResponseObject.", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/ListOpenAIResponseObject" + } + } + } + }, + "400": { + "$ref": "#/components/responses/BadRequest400" + }, + "429": { + "$ref": "#/components/responses/TooManyRequests429" + }, + "500": { + "$ref": "#/components/responses/InternalServerError500" + }, + "default": { + "$ref": "#/components/responses/DefaultError" + } + }, + "tags": [ + "Agents" + ], + "description": "List all OpenAI responses.", + "parameters": [ + { + "name": "after", + "in": "query", + "description": "The ID of the last response to return.", + "required": false, + "schema": { + "type": "string" + } + }, + { + "name": "limit", + "in": "query", + "description": "The number of responses to return.", + "required": false, + "schema": { + "type": "integer" + } + }, + { + "name": "model", + "in": "query", + "description": "The model to filter responses by.", + "required": false, + "schema": { + "type": "string" + } + }, + { + "name": "order", + "in": "query", + "description": "The order to sort responses by when sorted by created_at ('asc' or 'desc').", + "required": false, + "schema": { + "$ref": "#/components/schemas/Order" + } + } + ] + }, "post": { "responses": { "200": { @@ -1198,6 +1266,49 @@ ] } }, + "/v1/openai/v1/chat/completions/{completion_id}": { + "get": { + "responses": { + "200": { + "description": "A OpenAICompletionWithInputMessages.", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/OpenAICompletionWithInputMessages" + } + } + } + }, + "400": { + "$ref": "#/components/responses/BadRequest400" + }, + "429": { + "$ref": "#/components/responses/TooManyRequests429" + }, + "500": { + "$ref": "#/components/responses/InternalServerError500" + }, + "default": { + "$ref": "#/components/responses/DefaultError" + } + }, + "tags": [ + "Inference" + ], + "description": "Describe a chat completion by its ID.", + "parameters": [ + { + "name": "completion_id", + "in": "path", + "description": "ID of the chat completion.", + "required": true, + "schema": { + "type": "string" + } + } + ] + } + }, "/v1/datasets/{dataset_id}": { "get": { "responses": { @@ -1352,7 +1463,7 @@ ] } }, - "/v1/openai/v1/responses/{id}": { + "/v1/openai/v1/responses/{response_id}": { "get": { "responses": { "200": { @@ -1384,7 +1495,7 @@ "description": "Retrieve an OpenAI response by its ID.", "parameters": [ { - "name": "id", + "name": "response_id", "in": "path", "description": "The ID of the OpenAI response to retrieve.", "required": true, @@ -2574,6 +2685,124 @@ } } }, + "/v1/openai/v1/chat/completions": { + "get": { + "responses": { + "200": { + "description": "A ListOpenAIChatCompletionResponse.", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/ListOpenAIChatCompletionResponse" + } + } + } + }, + "400": { + "$ref": "#/components/responses/BadRequest400" + }, + "429": { + "$ref": "#/components/responses/TooManyRequests429" + }, + "500": { + "$ref": "#/components/responses/InternalServerError500" + }, + "default": { + "$ref": "#/components/responses/DefaultError" + } + }, + "tags": [ + "Inference" + ], + "description": "List all chat completions.", + "parameters": [ + { + "name": "after", + "in": "query", + "description": "The ID of the last chat completion to return.", + "required": false, + "schema": { + "type": "string" + } + }, + { + "name": "limit", + "in": "query", + "description": "The maximum number of chat completions to return.", + "required": false, + "schema": { + "type": "integer" + } + }, + { + "name": "model", + "in": "query", + "description": "The model to filter by.", + "required": false, + "schema": { + "type": "string" + } + }, + { + "name": "order", + "in": "query", + "description": "The order to sort the chat completions by: \"asc\" or \"desc\". Defaults to \"desc\".", + "required": false, + "schema": { + "$ref": "#/components/schemas/Order" + } + } + ] + }, + "post": { + "responses": { + "200": { + "description": "An OpenAIChatCompletion.", + "content": { + "application/json": { + "schema": { + "oneOf": [ + { + "$ref": "#/components/schemas/OpenAIChatCompletion" + }, + { + "$ref": "#/components/schemas/OpenAIChatCompletionChunk" + } + ] + } + } + } + }, + "400": { + "$ref": "#/components/responses/BadRequest400" + }, + "429": { + "$ref": "#/components/responses/TooManyRequests429" + }, + "500": { + "$ref": "#/components/responses/InternalServerError500" + }, + "default": { + "$ref": "#/components/responses/DefaultError" + } + }, + "tags": [ + "Inference" + ], + "description": "Generate an OpenAI-compatible chat completion for the given messages using the specified model.", + "parameters": [], + "requestBody": { + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/OpenaiChatCompletionRequest" + } + } + }, + "required": true + } + } + }, "/v1/datasets": { "get": { "responses": { @@ -2765,6 +2994,97 @@ } } }, + "/v1/openai/v1/responses/{response_id}/input_items": { + "get": { + "responses": { + "200": { + "description": "An ListOpenAIResponseInputItem.", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/ListOpenAIResponseInputItem" + } + } + } + }, + "400": { + "$ref": "#/components/responses/BadRequest400" + }, + "429": { + "$ref": "#/components/responses/TooManyRequests429" + }, + "500": { + "$ref": "#/components/responses/InternalServerError500" + }, + "default": { + "$ref": "#/components/responses/DefaultError" + } + }, + "tags": [ + "Agents" + ], + "description": "List input items for a given OpenAI response.", + "parameters": [ + { + "name": "response_id", + "in": "path", + "description": "The ID of the response to retrieve input items for.", + "required": true, + "schema": { + "type": "string" + } + }, + { + "name": "after", + "in": "query", + "description": "An item ID to list items after, used for pagination.", + "required": false, + "schema": { + "type": "string" + } + }, + { + "name": "before", + "in": "query", + "description": "An item ID to list items before, used for pagination.", + "required": false, + "schema": { + "type": "string" + } + }, + { + "name": "include", + "in": "query", + "description": "Additional fields to include in the response.", + "required": false, + "schema": { + "type": "array", + "items": { + "type": "string" + } + } + }, + { + "name": "limit", + "in": "query", + "description": "A limit on the number of objects to be returned. Limit can range between 1 and 100, and the default is 20.", + "required": false, + "schema": { + "type": "integer" + } + }, + { + "name": "order", + "in": "query", + "description": "The order to return the input items in. Default is desc.", + "required": false, + "schema": { + "$ref": "#/components/schemas/Order" + } + } + ] + } + }, "/v1/providers": { "get": { "responses": { @@ -3244,56 +3564,6 @@ } } }, - "/v1/openai/v1/chat/completions": { - "post": { - "responses": { - "200": { - "description": "An OpenAIChatCompletion.", - "content": { - "application/json": { - "schema": { - "oneOf": [ - { - "$ref": "#/components/schemas/OpenAIChatCompletion" - }, - { - "$ref": "#/components/schemas/OpenAIChatCompletionChunk" - } - ] - } - } - } - }, - "400": { - "$ref": "#/components/responses/BadRequest400" - }, - "429": { - "$ref": "#/components/responses/TooManyRequests429" - }, - "500": { - "$ref": "#/components/responses/InternalServerError500" - }, - "default": { - "$ref": "#/components/responses/DefaultError" - } - }, - "tags": [ - "Inference" - ], - "description": "Generate an OpenAI-compatible chat completion for the given messages using the specified model.", - "parameters": [], - "requestBody": { - "content": { - "application/json": { - "schema": { - "$ref": "#/components/schemas/OpenaiChatCompletionRequest" - } - } - }, - "required": true - } - } - }, "/v1/openai/v1/completions": { "post": { "responses": { @@ -3337,6 +3607,49 @@ } } }, + "/v1/openai/v1/embeddings": { + "post": { + "responses": { + "200": { + "description": "An OpenAIEmbeddingsResponse containing the embeddings.", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/OpenAIEmbeddingsResponse" + } + } + } + }, + "400": { + "$ref": "#/components/responses/BadRequest400" + }, + "429": { + "$ref": "#/components/responses/TooManyRequests429" + }, + "500": { + "$ref": "#/components/responses/InternalServerError500" + }, + "default": { + "$ref": "#/components/responses/DefaultError" + } + }, + "tags": [ + "Inference" + ], + "description": "Generate OpenAI-compatible embeddings for the given input using the specified model.", + "parameters": [], + "requestBody": { + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/OpenaiEmbeddingsRequest" + } + } + }, + "required": true + } + } + }, "/v1/openai/v1/models": { "get": { "responses": { @@ -6631,6 +6944,9 @@ }, { "$ref": "#/components/schemas/OpenAIResponseInputToolFunction" + }, + { + "$ref": "#/components/schemas/OpenAIResponseInputToolMCP" } ], "discriminator": { @@ -6638,7 +6954,8 @@ "mapping": { "web_search": "#/components/schemas/OpenAIResponseInputToolWebSearch", "file_search": "#/components/schemas/OpenAIResponseInputToolFileSearch", - "function": "#/components/schemas/OpenAIResponseInputToolFunction" + "function": "#/components/schemas/OpenAIResponseInputToolFunction", + "mcp": "#/components/schemas/OpenAIResponseInputToolMCP" } } }, @@ -6728,6 +7045,110 @@ ], "title": "OpenAIResponseInputToolFunction" }, + "OpenAIResponseInputToolMCP": { + "type": "object", + "properties": { + "type": { + "type": "string", + "const": "mcp", + "default": "mcp" + }, + "server_label": { + "type": "string" + }, + "server_url": { + "type": "string" + }, + "headers": { + "type": "object", + "additionalProperties": { + "oneOf": [ + { + "type": "null" + }, + { + "type": "boolean" + }, + { + "type": "number" + }, + { + "type": "string" + }, + { + "type": "array" + }, + { + "type": "object" + } + ] + } + }, + "require_approval": { + "oneOf": [ + { + "type": "string", + "const": "always" + }, + { + "type": "string", + "const": "never" + }, + { + "type": "object", + "properties": { + "always": { + "type": "array", + "items": { + "type": "string" + } + }, + "never": { + "type": "array", + "items": { + "type": "string" + } + } + }, + "additionalProperties": false, + "title": "ApprovalFilter" + } + ], + "default": "never" + }, + "allowed_tools": { + "oneOf": [ + { + "type": "array", + "items": { + "type": "string" + } + }, + { + "type": "object", + "properties": { + "tool_names": { + "type": "array", + "items": { + "type": "string" + } + } + }, + "additionalProperties": false, + "title": "AllowedToolsFilter" + } + ] + } + }, + "additionalProperties": false, + "required": [ + "type", + "server_label", + "server_url", + "require_approval" + ], + "title": "OpenAIResponseInputToolMCP" + }, "OpenAIResponseInputToolWebSearch": { "type": "object", "properties": { @@ -6840,15 +7261,15 @@ "OpenAIResponseOutputMessageFunctionToolCall": { "type": "object", "properties": { - "arguments": { - "type": "string" - }, "call_id": { "type": "string" }, "name": { "type": "string" }, + "arguments": { + "type": "string" + }, "type": { "type": "string", "const": "function_call", @@ -6863,12 +7284,10 @@ }, "additionalProperties": false, "required": [ - "arguments", "call_id", "name", - "type", - "id", - "status" + "arguments", + "type" ], "title": "OpenAIResponseOutputMessageFunctionToolCall" }, @@ -6916,6 +7335,9 @@ "type": "string", "description": "The underlying LLM used for completions." }, + "instructions": { + "type": "string" + }, "previous_response_id": { "type": "string", "description": "(Optional) if specified, the new response will be a continuation of the previous response. This can be used to easily fork-off new responses from existing responses." @@ -7031,6 +7453,12 @@ }, { "$ref": "#/components/schemas/OpenAIResponseOutputMessageFunctionToolCall" + }, + { + "$ref": "#/components/schemas/OpenAIResponseOutputMessageMCPCall" + }, + { + "$ref": "#/components/schemas/OpenAIResponseOutputMessageMCPListTools" } ], "discriminator": { @@ -7038,15 +7466,126 @@ "mapping": { "message": "#/components/schemas/OpenAIResponseMessage", "web_search_call": "#/components/schemas/OpenAIResponseOutputMessageWebSearchToolCall", - "function_call": "#/components/schemas/OpenAIResponseOutputMessageFunctionToolCall" + "function_call": "#/components/schemas/OpenAIResponseOutputMessageFunctionToolCall", + "mcp_call": "#/components/schemas/OpenAIResponseOutputMessageMCPCall", + "mcp_list_tools": "#/components/schemas/OpenAIResponseOutputMessageMCPListTools" } } }, + "OpenAIResponseOutputMessageMCPCall": { + "type": "object", + "properties": { + "id": { + "type": "string" + }, + "type": { + "type": "string", + "const": "mcp_call", + "default": "mcp_call" + }, + "arguments": { + "type": "string" + }, + "name": { + "type": "string" + }, + "server_label": { + "type": "string" + }, + "error": { + "type": "string" + }, + "output": { + "type": "string" + } + }, + "additionalProperties": false, + "required": [ + "id", + "type", + "arguments", + "name", + "server_label" + ], + "title": "OpenAIResponseOutputMessageMCPCall" + }, + "OpenAIResponseOutputMessageMCPListTools": { + "type": "object", + "properties": { + "id": { + "type": "string" + }, + "type": { + "type": "string", + "const": "mcp_list_tools", + "default": "mcp_list_tools" + }, + "server_label": { + "type": "string" + }, + "tools": { + "type": "array", + "items": { + "type": "object", + "properties": { + "input_schema": { + "type": "object", + "additionalProperties": { + "oneOf": [ + { + "type": "null" + }, + { + "type": "boolean" + }, + { + "type": "number" + }, + { + "type": "string" + }, + { + "type": "array" + }, + { + "type": "object" + } + ] + } + }, + "name": { + "type": "string" + }, + "description": { + "type": "string" + } + }, + "additionalProperties": false, + "required": [ + "input_schema", + "name" + ], + "title": "MCPListToolsTool" + } + } + }, + "additionalProperties": false, + "required": [ + "id", + "type", + "server_label", + "tools" + ], + "title": "OpenAIResponseOutputMessageMCPListTools" + }, "OpenAIResponseObjectStream": { "oneOf": [ { "$ref": "#/components/schemas/OpenAIResponseObjectStreamResponseCreated" }, + { + "$ref": "#/components/schemas/OpenAIResponseObjectStreamResponseOutputTextDelta" + }, { "$ref": "#/components/schemas/OpenAIResponseObjectStreamResponseCompleted" } @@ -7055,6 +7594,7 @@ "propertyName": "type", "mapping": { "response.created": "#/components/schemas/OpenAIResponseObjectStreamResponseCreated", + "response.output_text.delta": "#/components/schemas/OpenAIResponseObjectStreamResponseOutputTextDelta", "response.completed": "#/components/schemas/OpenAIResponseObjectStreamResponseCompleted" } } @@ -7097,6 +7637,41 @@ ], "title": "OpenAIResponseObjectStreamResponseCreated" }, + "OpenAIResponseObjectStreamResponseOutputTextDelta": { + "type": "object", + "properties": { + "content_index": { + "type": "integer" + }, + "delta": { + "type": "string" + }, + "item_id": { + "type": "string" + }, + "output_index": { + "type": "integer" + }, + "sequence_number": { + "type": "integer" + }, + "type": { + "type": "string", + "const": "response.output_text.delta", + "default": "response.output_text.delta" + } + }, + "additionalProperties": false, + "required": [ + "content_index", + "delta", + "item_id", + "output_index", + "sequence_number", + "type" + ], + "title": "OpenAIResponseObjectStreamResponseOutputTextDelta" + }, "CreateUploadSessionRequest": { "type": "object", "properties": { @@ -7781,6 +8356,482 @@ ], "title": "Benchmark" }, + "OpenAIAssistantMessageParam": { + "type": "object", + "properties": { + "role": { + "type": "string", + "const": "assistant", + "default": "assistant", + "description": "Must be \"assistant\" to identify this as the model's response" + }, + "content": { + "oneOf": [ + { + "type": "string" + }, + { + "type": "array", + "items": { + "$ref": "#/components/schemas/OpenAIChatCompletionContentPartParam" + } + } + ], + "description": "The content of the model's response" + }, + "name": { + "type": "string", + "description": "(Optional) The name of the assistant message participant." + }, + "tool_calls": { + "type": "array", + "items": { + "$ref": "#/components/schemas/OpenAIChatCompletionToolCall" + }, + "description": "List of tool calls. Each tool call is an OpenAIChatCompletionToolCall object." + } + }, + "additionalProperties": false, + "required": [ + "role" + ], + "title": "OpenAIAssistantMessageParam", + "description": "A message containing the model's (assistant) response in an OpenAI-compatible chat completion request." + }, + "OpenAIChatCompletionContentPartImageParam": { + "type": "object", + "properties": { + "type": { + "type": "string", + "const": "image_url", + "default": "image_url" + }, + "image_url": { + "$ref": "#/components/schemas/OpenAIImageURL" + } + }, + "additionalProperties": false, + "required": [ + "type", + "image_url" + ], + "title": "OpenAIChatCompletionContentPartImageParam" + }, + "OpenAIChatCompletionContentPartParam": { + "oneOf": [ + { + "$ref": "#/components/schemas/OpenAIChatCompletionContentPartTextParam" + }, + { + "$ref": "#/components/schemas/OpenAIChatCompletionContentPartImageParam" + } + ], + "discriminator": { + "propertyName": "type", + "mapping": { + "text": "#/components/schemas/OpenAIChatCompletionContentPartTextParam", + "image_url": "#/components/schemas/OpenAIChatCompletionContentPartImageParam" + } + } + }, + "OpenAIChatCompletionContentPartTextParam": { + "type": "object", + "properties": { + "type": { + "type": "string", + "const": "text", + "default": "text" + }, + "text": { + "type": "string" + } + }, + "additionalProperties": false, + "required": [ + "type", + "text" + ], + "title": "OpenAIChatCompletionContentPartTextParam" + }, + "OpenAIChatCompletionToolCall": { + "type": "object", + "properties": { + "index": { + "type": "integer" + }, + "id": { + "type": "string" + }, + "type": { + "type": "string", + "const": "function", + "default": "function" + }, + "function": { + "$ref": "#/components/schemas/OpenAIChatCompletionToolCallFunction" + } + }, + "additionalProperties": false, + "required": [ + "type" + ], + "title": "OpenAIChatCompletionToolCall" + }, + "OpenAIChatCompletionToolCallFunction": { + "type": "object", + "properties": { + "name": { + "type": "string" + }, + "arguments": { + "type": "string" + } + }, + "additionalProperties": false, + "title": "OpenAIChatCompletionToolCallFunction" + }, + "OpenAIChoice": { + "type": "object", + "properties": { + "message": { + "$ref": "#/components/schemas/OpenAIMessageParam", + "description": "The message from the model" + }, + "finish_reason": { + "type": "string", + "description": "The reason the model stopped generating" + }, + "index": { + "type": "integer", + "description": "The index of the choice" + }, + "logprobs": { + "$ref": "#/components/schemas/OpenAIChoiceLogprobs", + "description": "(Optional) The log probabilities for the tokens in the message" + } + }, + "additionalProperties": false, + "required": [ + "message", + "finish_reason", + "index" + ], + "title": "OpenAIChoice", + "description": "A choice from an OpenAI-compatible chat completion response." + }, + "OpenAIChoiceLogprobs": { + "type": "object", + "properties": { + "content": { + "type": "array", + "items": { + "$ref": "#/components/schemas/OpenAITokenLogProb" + }, + "description": "(Optional) The log probabilities for the tokens in the message" + }, + "refusal": { + "type": "array", + "items": { + "$ref": "#/components/schemas/OpenAITokenLogProb" + }, + "description": "(Optional) The log probabilities for the tokens in the message" + } + }, + "additionalProperties": false, + "title": "OpenAIChoiceLogprobs", + "description": "The log probabilities for the tokens in the message from an OpenAI-compatible chat completion response." + }, + "OpenAIDeveloperMessageParam": { + "type": "object", + "properties": { + "role": { + "type": "string", + "const": "developer", + "default": "developer", + "description": "Must be \"developer\" to identify this as a developer message" + }, + "content": { + "oneOf": [ + { + "type": "string" + }, + { + "type": "array", + "items": { + "$ref": "#/components/schemas/OpenAIChatCompletionContentPartParam" + } + } + ], + "description": "The content of the developer message" + }, + "name": { + "type": "string", + "description": "(Optional) The name of the developer message participant." + } + }, + "additionalProperties": false, + "required": [ + "role", + "content" + ], + "title": "OpenAIDeveloperMessageParam", + "description": "A message from the developer in an OpenAI-compatible chat completion request." + }, + "OpenAIImageURL": { + "type": "object", + "properties": { + "url": { + "type": "string" + }, + "detail": { + "type": "string" + } + }, + "additionalProperties": false, + "required": [ + "url" + ], + "title": "OpenAIImageURL" + }, + "OpenAIMessageParam": { + "oneOf": [ + { + "$ref": "#/components/schemas/OpenAIUserMessageParam" + }, + { + "$ref": "#/components/schemas/OpenAISystemMessageParam" + }, + { + "$ref": "#/components/schemas/OpenAIAssistantMessageParam" + }, + { + "$ref": "#/components/schemas/OpenAIToolMessageParam" + }, + { + "$ref": "#/components/schemas/OpenAIDeveloperMessageParam" + } + ], + "discriminator": { + "propertyName": "role", + "mapping": { + "user": "#/components/schemas/OpenAIUserMessageParam", + "system": "#/components/schemas/OpenAISystemMessageParam", + "assistant": "#/components/schemas/OpenAIAssistantMessageParam", + "tool": "#/components/schemas/OpenAIToolMessageParam", + "developer": "#/components/schemas/OpenAIDeveloperMessageParam" + } + } + }, + "OpenAISystemMessageParam": { + "type": "object", + "properties": { + "role": { + "type": "string", + "const": "system", + "default": "system", + "description": "Must be \"system\" to identify this as a system message" + }, + "content": { + "oneOf": [ + { + "type": "string" + }, + { + "type": "array", + "items": { + "$ref": "#/components/schemas/OpenAIChatCompletionContentPartParam" + } + } + ], + "description": "The content of the \"system prompt\". If multiple system messages are provided, they are concatenated. The underlying Llama Stack code may also add other system messages (for example, for formatting tool definitions)." + }, + "name": { + "type": "string", + "description": "(Optional) The name of the system message participant." + } + }, + "additionalProperties": false, + "required": [ + "role", + "content" + ], + "title": "OpenAISystemMessageParam", + "description": "A system message providing instructions or context to the model." + }, + "OpenAITokenLogProb": { + "type": "object", + "properties": { + "token": { + "type": "string" + }, + "bytes": { + "type": "array", + "items": { + "type": "integer" + } + }, + "logprob": { + "type": "number" + }, + "top_logprobs": { + "type": "array", + "items": { + "$ref": "#/components/schemas/OpenAITopLogProb" + } + } + }, + "additionalProperties": false, + "required": [ + "token", + "logprob", + "top_logprobs" + ], + "title": "OpenAITokenLogProb", + "description": "The log probability for a token from an OpenAI-compatible chat completion response." + }, + "OpenAIToolMessageParam": { + "type": "object", + "properties": { + "role": { + "type": "string", + "const": "tool", + "default": "tool", + "description": "Must be \"tool\" to identify this as a tool response" + }, + "tool_call_id": { + "type": "string", + "description": "Unique identifier for the tool call this response is for" + }, + "content": { + "oneOf": [ + { + "type": "string" + }, + { + "type": "array", + "items": { + "$ref": "#/components/schemas/OpenAIChatCompletionContentPartParam" + } + } + ], + "description": "The response content from the tool" + } + }, + "additionalProperties": false, + "required": [ + "role", + "tool_call_id", + "content" + ], + "title": "OpenAIToolMessageParam", + "description": "A message representing the result of a tool invocation in an OpenAI-compatible chat completion request." + }, + "OpenAITopLogProb": { + "type": "object", + "properties": { + "token": { + "type": "string" + }, + "bytes": { + "type": "array", + "items": { + "type": "integer" + } + }, + "logprob": { + "type": "number" + } + }, + "additionalProperties": false, + "required": [ + "token", + "logprob" + ], + "title": "OpenAITopLogProb", + "description": "The top log probability for a token from an OpenAI-compatible chat completion response." + }, + "OpenAIUserMessageParam": { + "type": "object", + "properties": { + "role": { + "type": "string", + "const": "user", + "default": "user", + "description": "Must be \"user\" to identify this as a user message" + }, + "content": { + "oneOf": [ + { + "type": "string" + }, + { + "type": "array", + "items": { + "$ref": "#/components/schemas/OpenAIChatCompletionContentPartParam" + } + } + ], + "description": "The content of the message, which can include text and other media" + }, + "name": { + "type": "string", + "description": "(Optional) The name of the user message participant." + } + }, + "additionalProperties": false, + "required": [ + "role", + "content" + ], + "title": "OpenAIUserMessageParam", + "description": "A message from the user in an OpenAI-compatible chat completion request." + }, + "OpenAICompletionWithInputMessages": { + "type": "object", + "properties": { + "id": { + "type": "string", + "description": "The ID of the chat completion" + }, + "choices": { + "type": "array", + "items": { + "$ref": "#/components/schemas/OpenAIChoice" + }, + "description": "List of choices" + }, + "object": { + "type": "string", + "const": "chat.completion", + "default": "chat.completion", + "description": "The object type, which will be \"chat.completion\"" + }, + "created": { + "type": "integer", + "description": "The Unix timestamp in seconds when the chat completion was created" + }, + "model": { + "type": "string", + "description": "The model that was used to generate the chat completion" + }, + "input_messages": { + "type": "array", + "items": { + "$ref": "#/components/schemas/OpenAIMessageParam" + } + } + }, + "additionalProperties": false, + "required": [ + "id", + "choices", + "object", + "created", + "model", + "input_messages" + ], + "title": "OpenAICompletionWithInputMessages" + }, "DataSource": { "oneOf": [ { @@ -8586,9 +9637,6 @@ "toolgroup_id": { "type": "string" }, - "tool_host": { - "$ref": "#/components/schemas/ToolHost" - }, "description": { "type": "string" }, @@ -8630,21 +9678,11 @@ "provider_id", "type", "toolgroup_id", - "tool_host", "description", "parameters" ], "title": "Tool" }, - "ToolHost": { - "type": "string", - "enum": [ - "distribution", - "client", - "model_context_protocol" - ], - "title": "ToolHost" - }, "ToolGroup": { "type": "object", "properties": { @@ -9025,7 +10063,8 @@ "type": "object", "properties": { "content": { - "$ref": "#/components/schemas/InterleavedContent" + "$ref": "#/components/schemas/InterleavedContent", + "description": "The content of the chunk, which can be interleaved text, images, or other types." }, "metadata": { "type": "object", @@ -9050,7 +10089,15 @@ "type": "object" } ] - } + }, + "description": "Metadata associated with the chunk, such as document ID, source, or other relevant information." + }, + "embedding": { + "type": "array", + "items": { + "type": "number" + }, + "description": "Optional embedding for the chunk. If not provided, it will be computed later." } }, "additionalProperties": false, @@ -9058,9 +10105,10 @@ "content", "metadata" ], - "title": "Chunk" + "title": "Chunk", + "description": "A chunk of content that can be inserted into a vector database." }, - "description": "The chunks to insert." + "description": "The chunks to insert. Each `Chunk` should contain content which can be interleaved text, images, or other types. `metadata`: `dict[str, Any]` and `embedding`: `List[float]` are optional. If `metadata` is provided, you configure how Llama Stack formats the chunk during generation. If `embedding` is not provided, it will be computed later." }, "ttl_seconds": { "type": "integer", @@ -9346,6 +10394,91 @@ ], "title": "ListBenchmarksResponse" }, + "Order": { + "type": "string", + "enum": [ + "asc", + "desc" + ], + "title": "Order" + }, + "ListOpenAIChatCompletionResponse": { + "type": "object", + "properties": { + "data": { + "type": "array", + "items": { + "type": "object", + "properties": { + "id": { + "type": "string", + "description": "The ID of the chat completion" + }, + "choices": { + "type": "array", + "items": { + "$ref": "#/components/schemas/OpenAIChoice" + }, + "description": "List of choices" + }, + "object": { + "type": "string", + "const": "chat.completion", + "default": "chat.completion", + "description": "The object type, which will be \"chat.completion\"" + }, + "created": { + "type": "integer", + "description": "The Unix timestamp in seconds when the chat completion was created" + }, + "model": { + "type": "string", + "description": "The model that was used to generate the chat completion" + }, + "input_messages": { + "type": "array", + "items": { + "$ref": "#/components/schemas/OpenAIMessageParam" + } + } + }, + "additionalProperties": false, + "required": [ + "id", + "choices", + "object", + "created", + "model", + "input_messages" + ], + "title": "OpenAICompletionWithInputMessages" + } + }, + "has_more": { + "type": "boolean" + }, + "first_id": { + "type": "string" + }, + "last_id": { + "type": "string" + }, + "object": { + "type": "string", + "const": "list", + "default": "list" + } + }, + "additionalProperties": false, + "required": [ + "data", + "has_more", + "first_id", + "last_id", + "object" + ], + "title": "ListOpenAIChatCompletionResponse" + }, "ListDatasetsResponse": { "type": "object", "properties": { @@ -9396,6 +10529,130 @@ ], "title": "ListModelsResponse" }, + "ListOpenAIResponseInputItem": { + "type": "object", + "properties": { + "data": { + "type": "array", + "items": { + "$ref": "#/components/schemas/OpenAIResponseInput" + } + }, + "object": { + "type": "string", + "const": "list", + "default": "list" + } + }, + "additionalProperties": false, + "required": [ + "data", + "object" + ], + "title": "ListOpenAIResponseInputItem" + }, + "ListOpenAIResponseObject": { + "type": "object", + "properties": { + "data": { + "type": "array", + "items": { + "$ref": "#/components/schemas/OpenAIResponseObjectWithInput" + } + }, + "has_more": { + "type": "boolean" + }, + "first_id": { + "type": "string" + }, + "last_id": { + "type": "string" + }, + "object": { + "type": "string", + "const": "list", + "default": "list" + } + }, + "additionalProperties": false, + "required": [ + "data", + "has_more", + "first_id", + "last_id", + "object" + ], + "title": "ListOpenAIResponseObject" + }, + "OpenAIResponseObjectWithInput": { + "type": "object", + "properties": { + "created_at": { + "type": "integer" + }, + "error": { + "$ref": "#/components/schemas/OpenAIResponseError" + }, + "id": { + "type": "string" + }, + "model": { + "type": "string" + }, + "object": { + "type": "string", + "const": "response", + "default": "response" + }, + "output": { + "type": "array", + "items": { + "$ref": "#/components/schemas/OpenAIResponseOutput" + } + }, + "parallel_tool_calls": { + "type": "boolean", + "default": false + }, + "previous_response_id": { + "type": "string" + }, + "status": { + "type": "string" + }, + "temperature": { + "type": "number" + }, + "top_p": { + "type": "number" + }, + "truncation": { + "type": "string" + }, + "user": { + "type": "string" + }, + "input": { + "type": "array", + "items": { + "$ref": "#/components/schemas/OpenAIResponseInput" + } + } + }, + "additionalProperties": false, + "required": [ + "created_at", + "id", + "model", + "object", + "output", + "parallel_tool_calls", + "status", + "input" + ], + "title": "OpenAIResponseObjectWithInput" + }, "ListProvidersResponse": { "type": "object", "properties": { @@ -9856,192 +11113,6 @@ ], "title": "LogEventRequest" }, - "OpenAIAssistantMessageParam": { - "type": "object", - "properties": { - "role": { - "type": "string", - "const": "assistant", - "default": "assistant", - "description": "Must be \"assistant\" to identify this as the model's response" - }, - "content": { - "oneOf": [ - { - "type": "string" - }, - { - "type": "array", - "items": { - "$ref": "#/components/schemas/OpenAIChatCompletionContentPartParam" - } - } - ], - "description": "The content of the model's response" - }, - "name": { - "type": "string", - "description": "(Optional) The name of the assistant message participant." - }, - "tool_calls": { - "type": "array", - "items": { - "$ref": "#/components/schemas/OpenAIChatCompletionToolCall" - }, - "description": "List of tool calls. Each tool call is an OpenAIChatCompletionToolCall object." - } - }, - "additionalProperties": false, - "required": [ - "role" - ], - "title": "OpenAIAssistantMessageParam", - "description": "A message containing the model's (assistant) response in an OpenAI-compatible chat completion request." - }, - "OpenAIChatCompletionContentPartImageParam": { - "type": "object", - "properties": { - "type": { - "type": "string", - "const": "image_url", - "default": "image_url" - }, - "image_url": { - "$ref": "#/components/schemas/OpenAIImageURL" - } - }, - "additionalProperties": false, - "required": [ - "type", - "image_url" - ], - "title": "OpenAIChatCompletionContentPartImageParam" - }, - "OpenAIChatCompletionContentPartParam": { - "oneOf": [ - { - "$ref": "#/components/schemas/OpenAIChatCompletionContentPartTextParam" - }, - { - "$ref": "#/components/schemas/OpenAIChatCompletionContentPartImageParam" - } - ], - "discriminator": { - "propertyName": "type", - "mapping": { - "text": "#/components/schemas/OpenAIChatCompletionContentPartTextParam", - "image_url": "#/components/schemas/OpenAIChatCompletionContentPartImageParam" - } - } - }, - "OpenAIChatCompletionContentPartTextParam": { - "type": "object", - "properties": { - "type": { - "type": "string", - "const": "text", - "default": "text" - }, - "text": { - "type": "string" - } - }, - "additionalProperties": false, - "required": [ - "type", - "text" - ], - "title": "OpenAIChatCompletionContentPartTextParam" - }, - "OpenAIChatCompletionToolCall": { - "type": "object", - "properties": { - "index": { - "type": "integer" - }, - "id": { - "type": "string" - }, - "type": { - "type": "string", - "const": "function", - "default": "function" - }, - "function": { - "$ref": "#/components/schemas/OpenAIChatCompletionToolCallFunction" - } - }, - "additionalProperties": false, - "required": [ - "type" - ], - "title": "OpenAIChatCompletionToolCall" - }, - "OpenAIChatCompletionToolCallFunction": { - "type": "object", - "properties": { - "name": { - "type": "string" - }, - "arguments": { - "type": "string" - } - }, - "additionalProperties": false, - "title": "OpenAIChatCompletionToolCallFunction" - }, - "OpenAIDeveloperMessageParam": { - "type": "object", - "properties": { - "role": { - "type": "string", - "const": "developer", - "default": "developer", - "description": "Must be \"developer\" to identify this as a developer message" - }, - "content": { - "oneOf": [ - { - "type": "string" - }, - { - "type": "array", - "items": { - "$ref": "#/components/schemas/OpenAIChatCompletionContentPartParam" - } - } - ], - "description": "The content of the developer message" - }, - "name": { - "type": "string", - "description": "(Optional) The name of the developer message participant." - } - }, - "additionalProperties": false, - "required": [ - "role", - "content" - ], - "title": "OpenAIDeveloperMessageParam", - "description": "A message from the developer in an OpenAI-compatible chat completion request." - }, - "OpenAIImageURL": { - "type": "object", - "properties": { - "url": { - "type": "string" - }, - "detail": { - "type": "string" - } - }, - "additionalProperties": false, - "required": [ - "url" - ], - "title": "OpenAIImageURL" - }, "OpenAIJSONSchema": { "type": "object", "properties": { @@ -10086,35 +11157,6 @@ ], "title": "OpenAIJSONSchema" }, - "OpenAIMessageParam": { - "oneOf": [ - { - "$ref": "#/components/schemas/OpenAIUserMessageParam" - }, - { - "$ref": "#/components/schemas/OpenAISystemMessageParam" - }, - { - "$ref": "#/components/schemas/OpenAIAssistantMessageParam" - }, - { - "$ref": "#/components/schemas/OpenAIToolMessageParam" - }, - { - "$ref": "#/components/schemas/OpenAIDeveloperMessageParam" - } - ], - "discriminator": { - "propertyName": "role", - "mapping": { - "user": "#/components/schemas/OpenAIUserMessageParam", - "system": "#/components/schemas/OpenAISystemMessageParam", - "assistant": "#/components/schemas/OpenAIAssistantMessageParam", - "tool": "#/components/schemas/OpenAIToolMessageParam", - "developer": "#/components/schemas/OpenAIDeveloperMessageParam" - } - } - }, "OpenAIResponseFormatJSONObject": { "type": "object", "properties": { @@ -10185,115 +11227,6 @@ ], "title": "OpenAIResponseFormatText" }, - "OpenAISystemMessageParam": { - "type": "object", - "properties": { - "role": { - "type": "string", - "const": "system", - "default": "system", - "description": "Must be \"system\" to identify this as a system message" - }, - "content": { - "oneOf": [ - { - "type": "string" - }, - { - "type": "array", - "items": { - "$ref": "#/components/schemas/OpenAIChatCompletionContentPartParam" - } - } - ], - "description": "The content of the \"system prompt\". If multiple system messages are provided, they are concatenated. The underlying Llama Stack code may also add other system messages (for example, for formatting tool definitions)." - }, - "name": { - "type": "string", - "description": "(Optional) The name of the system message participant." - } - }, - "additionalProperties": false, - "required": [ - "role", - "content" - ], - "title": "OpenAISystemMessageParam", - "description": "A system message providing instructions or context to the model." - }, - "OpenAIToolMessageParam": { - "type": "object", - "properties": { - "role": { - "type": "string", - "const": "tool", - "default": "tool", - "description": "Must be \"tool\" to identify this as a tool response" - }, - "tool_call_id": { - "type": "string", - "description": "Unique identifier for the tool call this response is for" - }, - "content": { - "oneOf": [ - { - "type": "string" - }, - { - "type": "array", - "items": { - "$ref": "#/components/schemas/OpenAIChatCompletionContentPartParam" - } - } - ], - "description": "The response content from the tool" - } - }, - "additionalProperties": false, - "required": [ - "role", - "tool_call_id", - "content" - ], - "title": "OpenAIToolMessageParam", - "description": "A message representing the result of a tool invocation in an OpenAI-compatible chat completion request." - }, - "OpenAIUserMessageParam": { - "type": "object", - "properties": { - "role": { - "type": "string", - "const": "user", - "default": "user", - "description": "Must be \"user\" to identify this as a user message" - }, - "content": { - "oneOf": [ - { - "type": "string" - }, - { - "type": "array", - "items": { - "$ref": "#/components/schemas/OpenAIChatCompletionContentPartParam" - } - } - ], - "description": "The content of the message, which can include text and other media" - }, - "name": { - "type": "string", - "description": "(Optional) The name of the user message participant." - } - }, - "additionalProperties": false, - "required": [ - "role", - "content" - ], - "title": "OpenAIUserMessageParam", - "description": "A message from the user in an OpenAI-compatible chat completion request." - }, "OpenaiChatCompletionRequest": { "type": "object", "properties": { @@ -10623,35 +11556,6 @@ "title": "OpenAIChatCompletionChunk", "description": "Chunk from a streaming response to an OpenAI-compatible chat completion request." }, - "OpenAIChoice": { - "type": "object", - "properties": { - "message": { - "$ref": "#/components/schemas/OpenAIMessageParam", - "description": "The message from the model" - }, - "finish_reason": { - "type": "string", - "description": "The reason the model stopped generating" - }, - "index": { - "type": "integer", - "description": "The index of the choice" - }, - "logprobs": { - "$ref": "#/components/schemas/OpenAIChoiceLogprobs", - "description": "(Optional) The log probabilities for the tokens in the message" - } - }, - "additionalProperties": false, - "required": [ - "message", - "finish_reason", - "index" - ], - "title": "OpenAIChoice", - "description": "A choice from an OpenAI-compatible chat completion response." - }, "OpenAIChoiceDelta": { "type": "object", "properties": { @@ -10679,28 +11583,6 @@ "title": "OpenAIChoiceDelta", "description": "A delta from an OpenAI-compatible chat completion streaming response." }, - "OpenAIChoiceLogprobs": { - "type": "object", - "properties": { - "content": { - "type": "array", - "items": { - "$ref": "#/components/schemas/OpenAITokenLogProb" - }, - "description": "(Optional) The log probabilities for the tokens in the message" - }, - "refusal": { - "type": "array", - "items": { - "$ref": "#/components/schemas/OpenAITokenLogProb" - }, - "description": "(Optional) The log probabilities for the tokens in the message" - } - }, - "additionalProperties": false, - "title": "OpenAIChoiceLogprobs", - "description": "The log probabilities for the tokens in the message from an OpenAI-compatible chat completion response." - }, "OpenAIChunkChoice": { "type": "object", "properties": { @@ -10730,61 +11612,6 @@ "title": "OpenAIChunkChoice", "description": "A chunk choice from an OpenAI-compatible chat completion streaming response." }, - "OpenAITokenLogProb": { - "type": "object", - "properties": { - "token": { - "type": "string" - }, - "bytes": { - "type": "array", - "items": { - "type": "integer" - } - }, - "logprob": { - "type": "number" - }, - "top_logprobs": { - "type": "array", - "items": { - "$ref": "#/components/schemas/OpenAITopLogProb" - } - } - }, - "additionalProperties": false, - "required": [ - "token", - "logprob", - "top_logprobs" - ], - "title": "OpenAITokenLogProb", - "description": "The log probability for a token from an OpenAI-compatible chat completion response." - }, - "OpenAITopLogProb": { - "type": "object", - "properties": { - "token": { - "type": "string" - }, - "bytes": { - "type": "array", - "items": { - "type": "integer" - } - }, - "logprob": { - "type": "number" - } - }, - "additionalProperties": false, - "required": [ - "token", - "logprob" - ], - "title": "OpenAITopLogProb", - "description": "The top log probability for a token from an OpenAI-compatible chat completion response." - }, "OpenaiCompletionRequest": { "type": "object", "properties": { @@ -10993,6 +11820,139 @@ "title": "OpenAICompletionChoice", "description": "A choice from an OpenAI-compatible completion response." }, + "OpenaiEmbeddingsRequest": { + "type": "object", + "properties": { + "model": { + "type": "string", + "description": "The identifier of the model to use. The model must be an embedding model registered with Llama Stack and available via the /models endpoint." + }, + "input": { + "oneOf": [ + { + "type": "string" + }, + { + "type": "array", + "items": { + "type": "string" + } + } + ], + "description": "Input text to embed, encoded as a string or array of strings. To embed multiple inputs in a single request, pass an array of strings." + }, + "encoding_format": { + "type": "string", + "description": "(Optional) The format to return the embeddings in. Can be either \"float\" or \"base64\". Defaults to \"float\"." + }, + "dimensions": { + "type": "integer", + "description": "(Optional) The number of dimensions the resulting output embeddings should have. Only supported in text-embedding-3 and later models." + }, + "user": { + "type": "string", + "description": "(Optional) A unique identifier representing your end-user, which can help OpenAI to monitor and detect abuse." + } + }, + "additionalProperties": false, + "required": [ + "model", + "input" + ], + "title": "OpenaiEmbeddingsRequest" + }, + "OpenAIEmbeddingData": { + "type": "object", + "properties": { + "object": { + "type": "string", + "const": "embedding", + "default": "embedding", + "description": "The object type, which will be \"embedding\"" + }, + "embedding": { + "oneOf": [ + { + "type": "array", + "items": { + "type": "number" + } + }, + { + "type": "string" + } + ], + "description": "The embedding vector as a list of floats (when encoding_format=\"float\") or as a base64-encoded string (when encoding_format=\"base64\")" + }, + "index": { + "type": "integer", + "description": "The index of the embedding in the input list" + } + }, + "additionalProperties": false, + "required": [ + "object", + "embedding", + "index" + ], + "title": "OpenAIEmbeddingData", + "description": "A single embedding data object from an OpenAI-compatible embeddings response." + }, + "OpenAIEmbeddingUsage": { + "type": "object", + "properties": { + "prompt_tokens": { + "type": "integer", + "description": "The number of tokens in the input" + }, + "total_tokens": { + "type": "integer", + "description": "The total number of tokens used" + } + }, + "additionalProperties": false, + "required": [ + "prompt_tokens", + "total_tokens" + ], + "title": "OpenAIEmbeddingUsage", + "description": "Usage information for an OpenAI-compatible embeddings response." + }, + "OpenAIEmbeddingsResponse": { + "type": "object", + "properties": { + "object": { + "type": "string", + "const": "list", + "default": "list", + "description": "The object type, which will be \"list\"" + }, + "data": { + "type": "array", + "items": { + "$ref": "#/components/schemas/OpenAIEmbeddingData" + }, + "description": "List of embedding data objects" + }, + "model": { + "type": "string", + "description": "The model that was used to generate the embeddings" + }, + "usage": { + "$ref": "#/components/schemas/OpenAIEmbeddingUsage", + "description": "Usage information" + } + }, + "additionalProperties": false, + "required": [ + "object", + "data", + "model", + "usage" + ], + "title": "OpenAIEmbeddingsResponse", + "description": "Response from an OpenAI-compatible embeddings request." + }, "OpenAIModel": { "type": "object", "properties": { @@ -11363,6 +12323,10 @@ "type": "string", "default": "Result {index}\nContent: {chunk.content}\nMetadata: {metadata}\n", "description": "Template for formatting each retrieved chunk in the context. Available placeholders: {index} (1-based chunk ordinal), {chunk.content} (chunk content string), {metadata} (chunk metadata dict). Default: \"Result {index}\\nContent: {chunk.content}\\nMetadata: {metadata}\\n\"" + }, + "mode": { + "type": "string", + "description": "Search mode for retrieval—either \"vector\" or \"keyword\". Default \"vector\"." } }, "additionalProperties": false, @@ -11507,7 +12471,8 @@ "type": "object", "properties": { "content": { - "$ref": "#/components/schemas/InterleavedContent" + "$ref": "#/components/schemas/InterleavedContent", + "description": "The content of the chunk, which can be interleaved text, images, or other types." }, "metadata": { "type": "object", @@ -11532,7 +12497,15 @@ "type": "object" } ] - } + }, + "description": "Metadata associated with the chunk, such as document ID, source, or other relevant information." + }, + "embedding": { + "type": "array", + "items": { + "type": "number" + }, + "description": "Optional embedding for the chunk. If not provided, it will be computed later." } }, "additionalProperties": false, @@ -11540,7 +12513,8 @@ "content", "metadata" ], - "title": "Chunk" + "title": "Chunk", + "description": "A chunk of content that can be inserted into a vector database." } }, "scores": { diff --git a/docs/_static/llama-stack-spec.yaml b/docs/_static/llama-stack-spec.yaml index a988e0eab..7638c3cbd 100644 --- a/docs/_static/llama-stack-spec.yaml +++ b/docs/_static/llama-stack-spec.yaml @@ -349,6 +349,53 @@ paths: $ref: '#/components/schemas/CreateAgentTurnRequest' required: true /v1/openai/v1/responses: + get: + responses: + '200': + description: A ListOpenAIResponseObject. + content: + application/json: + schema: + $ref: '#/components/schemas/ListOpenAIResponseObject' + '400': + $ref: '#/components/responses/BadRequest400' + '429': + $ref: >- + #/components/responses/TooManyRequests429 + '500': + $ref: >- + #/components/responses/InternalServerError500 + default: + $ref: '#/components/responses/DefaultError' + tags: + - Agents + description: List all OpenAI responses. + parameters: + - name: after + in: query + description: The ID of the last response to return. + required: false + schema: + type: string + - name: limit + in: query + description: The number of responses to return. + required: false + schema: + type: integer + - name: model + in: query + description: The model to filter responses by. + required: false + schema: + type: string + - name: order + in: query + description: >- + The order to sort responses by when sorted by created_at ('asc' or 'desc'). + required: false + schema: + $ref: '#/components/schemas/Order' post: responses: '200': @@ -827,6 +874,35 @@ paths: required: true schema: type: string + /v1/openai/v1/chat/completions/{completion_id}: + get: + responses: + '200': + description: A OpenAICompletionWithInputMessages. + content: + application/json: + schema: + $ref: '#/components/schemas/OpenAICompletionWithInputMessages' + '400': + $ref: '#/components/responses/BadRequest400' + '429': + $ref: >- + #/components/responses/TooManyRequests429 + '500': + $ref: >- + #/components/responses/InternalServerError500 + default: + $ref: '#/components/responses/DefaultError' + tags: + - Inference + description: Describe a chat completion by its ID. + parameters: + - name: completion_id + in: path + description: ID of the chat completion. + required: true + schema: + type: string /v1/datasets/{dataset_id}: get: responses: @@ -934,7 +1010,7 @@ paths: required: true schema: type: string - /v1/openai/v1/responses/{id}: + /v1/openai/v1/responses/{response_id}: get: responses: '200': @@ -957,7 +1033,7 @@ paths: - Agents description: Retrieve an OpenAI response by its ID. parameters: - - name: id + - name: response_id in: path description: >- The ID of the OpenAI response to retrieve. @@ -1795,6 +1871,89 @@ paths: schema: $ref: '#/components/schemas/RegisterBenchmarkRequest' required: true + /v1/openai/v1/chat/completions: + get: + responses: + '200': + description: A ListOpenAIChatCompletionResponse. + content: + application/json: + schema: + $ref: '#/components/schemas/ListOpenAIChatCompletionResponse' + '400': + $ref: '#/components/responses/BadRequest400' + '429': + $ref: >- + #/components/responses/TooManyRequests429 + '500': + $ref: >- + #/components/responses/InternalServerError500 + default: + $ref: '#/components/responses/DefaultError' + tags: + - Inference + description: List all chat completions. + parameters: + - name: after + in: query + description: >- + The ID of the last chat completion to return. + required: false + schema: + type: string + - name: limit + in: query + description: >- + The maximum number of chat completions to return. + required: false + schema: + type: integer + - name: model + in: query + description: The model to filter by. + required: false + schema: + type: string + - name: order + in: query + description: >- + The order to sort the chat completions by: "asc" or "desc". Defaults to + "desc". + required: false + schema: + $ref: '#/components/schemas/Order' + post: + responses: + '200': + description: An OpenAIChatCompletion. + content: + application/json: + schema: + oneOf: + - $ref: '#/components/schemas/OpenAIChatCompletion' + - $ref: '#/components/schemas/OpenAIChatCompletionChunk' + '400': + $ref: '#/components/responses/BadRequest400' + '429': + $ref: >- + #/components/responses/TooManyRequests429 + '500': + $ref: >- + #/components/responses/InternalServerError500 + default: + $ref: '#/components/responses/DefaultError' + tags: + - Inference + description: >- + Generate an OpenAI-compatible chat completion for the given messages using + the specified model. + parameters: [] + requestBody: + content: + application/json: + schema: + $ref: '#/components/schemas/OpenaiChatCompletionRequest' + required: true /v1/datasets: get: responses: @@ -1926,6 +2085,75 @@ paths: schema: $ref: '#/components/schemas/RegisterModelRequest' required: true + /v1/openai/v1/responses/{response_id}/input_items: + get: + responses: + '200': + description: An ListOpenAIResponseInputItem. + content: + application/json: + schema: + $ref: '#/components/schemas/ListOpenAIResponseInputItem' + '400': + $ref: '#/components/responses/BadRequest400' + '429': + $ref: >- + #/components/responses/TooManyRequests429 + '500': + $ref: >- + #/components/responses/InternalServerError500 + default: + $ref: '#/components/responses/DefaultError' + tags: + - Agents + description: >- + List input items for a given OpenAI response. + parameters: + - name: response_id + in: path + description: >- + The ID of the response to retrieve input items for. + required: true + schema: + type: string + - name: after + in: query + description: >- + An item ID to list items after, used for pagination. + required: false + schema: + type: string + - name: before + in: query + description: >- + An item ID to list items before, used for pagination. + required: false + schema: + type: string + - name: include + in: query + description: >- + Additional fields to include in the response. + required: false + schema: + type: array + items: + type: string + - name: limit + in: query + description: >- + A limit on the number of objects to be returned. Limit can range between + 1 and 100, and the default is 20. + required: false + schema: + type: integer + - name: order + in: query + description: >- + The order to return the input items in. Default is desc. + required: false + schema: + $ref: '#/components/schemas/Order' /v1/providers: get: responses: @@ -2261,39 +2489,6 @@ paths: schema: $ref: '#/components/schemas/LogEventRequest' required: true - /v1/openai/v1/chat/completions: - post: - responses: - '200': - description: An OpenAIChatCompletion. - content: - application/json: - schema: - oneOf: - - $ref: '#/components/schemas/OpenAIChatCompletion' - - $ref: '#/components/schemas/OpenAIChatCompletionChunk' - '400': - $ref: '#/components/responses/BadRequest400' - '429': - $ref: >- - #/components/responses/TooManyRequests429 - '500': - $ref: >- - #/components/responses/InternalServerError500 - default: - $ref: '#/components/responses/DefaultError' - tags: - - Inference - description: >- - Generate an OpenAI-compatible chat completion for the given messages using - the specified model. - parameters: [] - requestBody: - content: - application/json: - schema: - $ref: '#/components/schemas/OpenaiChatCompletionRequest' - required: true /v1/openai/v1/completions: post: responses: @@ -2325,6 +2520,38 @@ paths: schema: $ref: '#/components/schemas/OpenaiCompletionRequest' required: true + /v1/openai/v1/embeddings: + post: + responses: + '200': + description: >- + An OpenAIEmbeddingsResponse containing the embeddings. + content: + application/json: + schema: + $ref: '#/components/schemas/OpenAIEmbeddingsResponse' + '400': + $ref: '#/components/responses/BadRequest400' + '429': + $ref: >- + #/components/responses/TooManyRequests429 + '500': + $ref: >- + #/components/responses/InternalServerError500 + default: + $ref: '#/components/responses/DefaultError' + tags: + - Inference + description: >- + Generate OpenAI-compatible embeddings for the given input using the specified + model. + parameters: [] + requestBody: + content: + application/json: + schema: + $ref: '#/components/schemas/OpenaiEmbeddingsRequest' + required: true /v1/openai/v1/models: get: responses: @@ -4683,12 +4910,14 @@ components: - $ref: '#/components/schemas/OpenAIResponseInputToolWebSearch' - $ref: '#/components/schemas/OpenAIResponseInputToolFileSearch' - $ref: '#/components/schemas/OpenAIResponseInputToolFunction' + - $ref: '#/components/schemas/OpenAIResponseInputToolMCP' discriminator: propertyName: type mapping: web_search: '#/components/schemas/OpenAIResponseInputToolWebSearch' file_search: '#/components/schemas/OpenAIResponseInputToolFileSearch' function: '#/components/schemas/OpenAIResponseInputToolFunction' + mcp: '#/components/schemas/OpenAIResponseInputToolMCP' OpenAIResponseInputToolFileSearch: type: object properties: @@ -4743,6 +4972,66 @@ components: - type - name title: OpenAIResponseInputToolFunction + OpenAIResponseInputToolMCP: + type: object + properties: + type: + type: string + const: mcp + default: mcp + server_label: + type: string + server_url: + type: string + headers: + type: object + additionalProperties: + oneOf: + - type: 'null' + - type: boolean + - type: number + - type: string + - type: array + - type: object + require_approval: + oneOf: + - type: string + const: always + - type: string + const: never + - type: object + properties: + always: + type: array + items: + type: string + never: + type: array + items: + type: string + additionalProperties: false + title: ApprovalFilter + default: never + allowed_tools: + oneOf: + - type: array + items: + type: string + - type: object + properties: + tool_names: + type: array + items: + type: string + additionalProperties: false + title: AllowedToolsFilter + additionalProperties: false + required: + - type + - server_label + - server_url + - require_approval + title: OpenAIResponseInputToolMCP OpenAIResponseInputToolWebSearch: type: object properties: @@ -4818,12 +5107,12 @@ components: "OpenAIResponseOutputMessageFunctionToolCall": type: object properties: - arguments: - type: string call_id: type: string name: type: string + arguments: + type: string type: type: string const: function_call @@ -4834,12 +5123,10 @@ components: type: string additionalProperties: false required: - - arguments - call_id - name + - arguments - type - - id - - status title: >- OpenAIResponseOutputMessageFunctionToolCall "OpenAIResponseOutputMessageWebSearchToolCall": @@ -4873,6 +5160,8 @@ components: model: type: string description: The underlying LLM used for completions. + instructions: + type: string previous_response_id: type: string description: >- @@ -4955,20 +5244,95 @@ components: - $ref: '#/components/schemas/OpenAIResponseMessage' - $ref: '#/components/schemas/OpenAIResponseOutputMessageWebSearchToolCall' - $ref: '#/components/schemas/OpenAIResponseOutputMessageFunctionToolCall' + - $ref: '#/components/schemas/OpenAIResponseOutputMessageMCPCall' + - $ref: '#/components/schemas/OpenAIResponseOutputMessageMCPListTools' discriminator: propertyName: type mapping: message: '#/components/schemas/OpenAIResponseMessage' web_search_call: '#/components/schemas/OpenAIResponseOutputMessageWebSearchToolCall' function_call: '#/components/schemas/OpenAIResponseOutputMessageFunctionToolCall' + mcp_call: '#/components/schemas/OpenAIResponseOutputMessageMCPCall' + mcp_list_tools: '#/components/schemas/OpenAIResponseOutputMessageMCPListTools' + OpenAIResponseOutputMessageMCPCall: + type: object + properties: + id: + type: string + type: + type: string + const: mcp_call + default: mcp_call + arguments: + type: string + name: + type: string + server_label: + type: string + error: + type: string + output: + type: string + additionalProperties: false + required: + - id + - type + - arguments + - name + - server_label + title: OpenAIResponseOutputMessageMCPCall + OpenAIResponseOutputMessageMCPListTools: + type: object + properties: + id: + type: string + type: + type: string + const: mcp_list_tools + default: mcp_list_tools + server_label: + type: string + tools: + type: array + items: + type: object + properties: + input_schema: + type: object + additionalProperties: + oneOf: + - type: 'null' + - type: boolean + - type: number + - type: string + - type: array + - type: object + name: + type: string + description: + type: string + additionalProperties: false + required: + - input_schema + - name + title: MCPListToolsTool + additionalProperties: false + required: + - id + - type + - server_label + - tools + title: OpenAIResponseOutputMessageMCPListTools OpenAIResponseObjectStream: oneOf: - $ref: '#/components/schemas/OpenAIResponseObjectStreamResponseCreated' + - $ref: '#/components/schemas/OpenAIResponseObjectStreamResponseOutputTextDelta' - $ref: '#/components/schemas/OpenAIResponseObjectStreamResponseCompleted' discriminator: propertyName: type mapping: response.created: '#/components/schemas/OpenAIResponseObjectStreamResponseCreated' + response.output_text.delta: '#/components/schemas/OpenAIResponseObjectStreamResponseOutputTextDelta' response.completed: '#/components/schemas/OpenAIResponseObjectStreamResponseCompleted' "OpenAIResponseObjectStreamResponseCompleted": type: object @@ -5000,6 +5364,33 @@ components: - type title: >- OpenAIResponseObjectStreamResponseCreated + "OpenAIResponseObjectStreamResponseOutputTextDelta": + type: object + properties: + content_index: + type: integer + delta: + type: string + item_id: + type: string + output_index: + type: integer + sequence_number: + type: integer + type: + type: string + const: response.output_text.delta + default: response.output_text.delta + additionalProperties: false + required: + - content_index + - delta + - item_id + - output_index + - sequence_number + - type + title: >- + OpenAIResponseObjectStreamResponseOutputTextDelta CreateUploadSessionRequest: type: object properties: @@ -5479,6 +5870,369 @@ components: - scoring_functions - metadata title: Benchmark + OpenAIAssistantMessageParam: + type: object + properties: + role: + type: string + const: assistant + default: assistant + description: >- + Must be "assistant" to identify this as the model's response + content: + oneOf: + - type: string + - type: array + items: + $ref: '#/components/schemas/OpenAIChatCompletionContentPartParam' + description: The content of the model's response + name: + type: string + description: >- + (Optional) The name of the assistant message participant. + tool_calls: + type: array + items: + $ref: '#/components/schemas/OpenAIChatCompletionToolCall' + description: >- + List of tool calls. Each tool call is an OpenAIChatCompletionToolCall + object. + additionalProperties: false + required: + - role + title: OpenAIAssistantMessageParam + description: >- + A message containing the model's (assistant) response in an OpenAI-compatible + chat completion request. + "OpenAIChatCompletionContentPartImageParam": + type: object + properties: + type: + type: string + const: image_url + default: image_url + image_url: + $ref: '#/components/schemas/OpenAIImageURL' + additionalProperties: false + required: + - type + - image_url + title: >- + OpenAIChatCompletionContentPartImageParam + OpenAIChatCompletionContentPartParam: + oneOf: + - $ref: '#/components/schemas/OpenAIChatCompletionContentPartTextParam' + - $ref: '#/components/schemas/OpenAIChatCompletionContentPartImageParam' + discriminator: + propertyName: type + mapping: + text: '#/components/schemas/OpenAIChatCompletionContentPartTextParam' + image_url: '#/components/schemas/OpenAIChatCompletionContentPartImageParam' + OpenAIChatCompletionContentPartTextParam: + type: object + properties: + type: + type: string + const: text + default: text + text: + type: string + additionalProperties: false + required: + - type + - text + title: OpenAIChatCompletionContentPartTextParam + OpenAIChatCompletionToolCall: + type: object + properties: + index: + type: integer + id: + type: string + type: + type: string + const: function + default: function + function: + $ref: '#/components/schemas/OpenAIChatCompletionToolCallFunction' + additionalProperties: false + required: + - type + title: OpenAIChatCompletionToolCall + OpenAIChatCompletionToolCallFunction: + type: object + properties: + name: + type: string + arguments: + type: string + additionalProperties: false + title: OpenAIChatCompletionToolCallFunction + OpenAIChoice: + type: object + properties: + message: + $ref: '#/components/schemas/OpenAIMessageParam' + description: The message from the model + finish_reason: + type: string + description: The reason the model stopped generating + index: + type: integer + description: The index of the choice + logprobs: + $ref: '#/components/schemas/OpenAIChoiceLogprobs' + description: >- + (Optional) The log probabilities for the tokens in the message + additionalProperties: false + required: + - message + - finish_reason + - index + title: OpenAIChoice + description: >- + A choice from an OpenAI-compatible chat completion response. + OpenAIChoiceLogprobs: + type: object + properties: + content: + type: array + items: + $ref: '#/components/schemas/OpenAITokenLogProb' + description: >- + (Optional) The log probabilities for the tokens in the message + refusal: + type: array + items: + $ref: '#/components/schemas/OpenAITokenLogProb' + description: >- + (Optional) The log probabilities for the tokens in the message + additionalProperties: false + title: OpenAIChoiceLogprobs + description: >- + The log probabilities for the tokens in the message from an OpenAI-compatible + chat completion response. + OpenAIDeveloperMessageParam: + type: object + properties: + role: + type: string + const: developer + default: developer + description: >- + Must be "developer" to identify this as a developer message + content: + oneOf: + - type: string + - type: array + items: + $ref: '#/components/schemas/OpenAIChatCompletionContentPartParam' + description: The content of the developer message + name: + type: string + description: >- + (Optional) The name of the developer message participant. + additionalProperties: false + required: + - role + - content + title: OpenAIDeveloperMessageParam + description: >- + A message from the developer in an OpenAI-compatible chat completion request. + OpenAIImageURL: + type: object + properties: + url: + type: string + detail: + type: string + additionalProperties: false + required: + - url + title: OpenAIImageURL + OpenAIMessageParam: + oneOf: + - $ref: '#/components/schemas/OpenAIUserMessageParam' + - $ref: '#/components/schemas/OpenAISystemMessageParam' + - $ref: '#/components/schemas/OpenAIAssistantMessageParam' + - $ref: '#/components/schemas/OpenAIToolMessageParam' + - $ref: '#/components/schemas/OpenAIDeveloperMessageParam' + discriminator: + propertyName: role + mapping: + user: '#/components/schemas/OpenAIUserMessageParam' + system: '#/components/schemas/OpenAISystemMessageParam' + assistant: '#/components/schemas/OpenAIAssistantMessageParam' + tool: '#/components/schemas/OpenAIToolMessageParam' + developer: '#/components/schemas/OpenAIDeveloperMessageParam' + OpenAISystemMessageParam: + type: object + properties: + role: + type: string + const: system + default: system + description: >- + Must be "system" to identify this as a system message + content: + oneOf: + - type: string + - type: array + items: + $ref: '#/components/schemas/OpenAIChatCompletionContentPartParam' + description: >- + The content of the "system prompt". If multiple system messages are provided, + they are concatenated. The underlying Llama Stack code may also add other + system messages (for example, for formatting tool definitions). + name: + type: string + description: >- + (Optional) The name of the system message participant. + additionalProperties: false + required: + - role + - content + title: OpenAISystemMessageParam + description: >- + A system message providing instructions or context to the model. + OpenAITokenLogProb: + type: object + properties: + token: + type: string + bytes: + type: array + items: + type: integer + logprob: + type: number + top_logprobs: + type: array + items: + $ref: '#/components/schemas/OpenAITopLogProb' + additionalProperties: false + required: + - token + - logprob + - top_logprobs + title: OpenAITokenLogProb + description: >- + The log probability for a token from an OpenAI-compatible chat completion + response. + OpenAIToolMessageParam: + type: object + properties: + role: + type: string + const: tool + default: tool + description: >- + Must be "tool" to identify this as a tool response + tool_call_id: + type: string + description: >- + Unique identifier for the tool call this response is for + content: + oneOf: + - type: string + - type: array + items: + $ref: '#/components/schemas/OpenAIChatCompletionContentPartParam' + description: The response content from the tool + additionalProperties: false + required: + - role + - tool_call_id + - content + title: OpenAIToolMessageParam + description: >- + A message representing the result of a tool invocation in an OpenAI-compatible + chat completion request. + OpenAITopLogProb: + type: object + properties: + token: + type: string + bytes: + type: array + items: + type: integer + logprob: + type: number + additionalProperties: false + required: + - token + - logprob + title: OpenAITopLogProb + description: >- + The top log probability for a token from an OpenAI-compatible chat completion + response. + OpenAIUserMessageParam: + type: object + properties: + role: + type: string + const: user + default: user + description: >- + Must be "user" to identify this as a user message + content: + oneOf: + - type: string + - type: array + items: + $ref: '#/components/schemas/OpenAIChatCompletionContentPartParam' + description: >- + The content of the message, which can include text and other media + name: + type: string + description: >- + (Optional) The name of the user message participant. + additionalProperties: false + required: + - role + - content + title: OpenAIUserMessageParam + description: >- + A message from the user in an OpenAI-compatible chat completion request. + OpenAICompletionWithInputMessages: + type: object + properties: + id: + type: string + description: The ID of the chat completion + choices: + type: array + items: + $ref: '#/components/schemas/OpenAIChoice' + description: List of choices + object: + type: string + const: chat.completion + default: chat.completion + description: >- + The object type, which will be "chat.completion" + created: + type: integer + description: >- + The Unix timestamp in seconds when the chat completion was created + model: + type: string + description: >- + The model that was used to generate the chat completion + input_messages: + type: array + items: + $ref: '#/components/schemas/OpenAIMessageParam' + additionalProperties: false + required: + - id + - choices + - object + - created + - model + - input_messages + title: OpenAICompletionWithInputMessages DataSource: oneOf: - $ref: '#/components/schemas/URIDataSource' @@ -6020,8 +6774,6 @@ components: default: tool toolgroup_id: type: string - tool_host: - $ref: '#/components/schemas/ToolHost' description: type: string parameters: @@ -6044,17 +6796,9 @@ components: - provider_id - type - toolgroup_id - - tool_host - description - parameters title: Tool - ToolHost: - type: string - enum: - - distribution - - client - - model_context_protocol - title: ToolHost ToolGroup: type: object properties: @@ -6312,6 +7056,9 @@ components: properties: content: $ref: '#/components/schemas/InterleavedContent' + description: >- + The content of the chunk, which can be interleaved text, images, + or other types. metadata: type: object additionalProperties: @@ -6322,12 +7069,29 @@ components: - type: string - type: array - type: object + description: >- + Metadata associated with the chunk, such as document ID, source, + or other relevant information. + embedding: + type: array + items: + type: number + description: >- + Optional embedding for the chunk. If not provided, it will be computed + later. additionalProperties: false required: - content - metadata title: Chunk - description: The chunks to insert. + description: >- + A chunk of content that can be inserted into a vector database. + description: >- + The chunks to insert. Each `Chunk` should contain content which can be + interleaved text, images, or other types. `metadata`: `dict[str, Any]` + and `embedding`: `List[float]` are optional. If `metadata` is provided, + you configure how Llama Stack formats the chunk during generation. If + `embedding` is not provided, it will be computed later. ttl_seconds: type: integer description: The time to live of the chunks. @@ -6497,6 +7261,73 @@ components: required: - data title: ListBenchmarksResponse + Order: + type: string + enum: + - asc + - desc + title: Order + ListOpenAIChatCompletionResponse: + type: object + properties: + data: + type: array + items: + type: object + properties: + id: + type: string + description: The ID of the chat completion + choices: + type: array + items: + $ref: '#/components/schemas/OpenAIChoice' + description: List of choices + object: + type: string + const: chat.completion + default: chat.completion + description: >- + The object type, which will be "chat.completion" + created: + type: integer + description: >- + The Unix timestamp in seconds when the chat completion was created + model: + type: string + description: >- + The model that was used to generate the chat completion + input_messages: + type: array + items: + $ref: '#/components/schemas/OpenAIMessageParam' + additionalProperties: false + required: + - id + - choices + - object + - created + - model + - input_messages + title: OpenAICompletionWithInputMessages + has_more: + type: boolean + first_id: + type: string + last_id: + type: string + object: + type: string + const: list + default: list + additionalProperties: false + required: + - data + - has_more + - first_id + - last_id + - object + title: ListOpenAIChatCompletionResponse ListDatasetsResponse: type: object properties: @@ -6533,6 +7364,96 @@ components: required: - data title: ListModelsResponse + ListOpenAIResponseInputItem: + type: object + properties: + data: + type: array + items: + $ref: '#/components/schemas/OpenAIResponseInput' + object: + type: string + const: list + default: list + additionalProperties: false + required: + - data + - object + title: ListOpenAIResponseInputItem + ListOpenAIResponseObject: + type: object + properties: + data: + type: array + items: + $ref: '#/components/schemas/OpenAIResponseObjectWithInput' + has_more: + type: boolean + first_id: + type: string + last_id: + type: string + object: + type: string + const: list + default: list + additionalProperties: false + required: + - data + - has_more + - first_id + - last_id + - object + title: ListOpenAIResponseObject + OpenAIResponseObjectWithInput: + type: object + properties: + created_at: + type: integer + error: + $ref: '#/components/schemas/OpenAIResponseError' + id: + type: string + model: + type: string + object: + type: string + const: response + default: response + output: + type: array + items: + $ref: '#/components/schemas/OpenAIResponseOutput' + parallel_tool_calls: + type: boolean + default: false + previous_response_id: + type: string + status: + type: string + temperature: + type: number + top_p: + type: number + truncation: + type: string + user: + type: string + input: + type: array + items: + $ref: '#/components/schemas/OpenAIResponseInput' + additionalProperties: false + required: + - created_at + - id + - model + - object + - output + - parallel_tool_calls + - status + - input + title: OpenAIResponseObjectWithInput ListProvidersResponse: type: object properties: @@ -6835,142 +7756,6 @@ components: - event - ttl_seconds title: LogEventRequest - OpenAIAssistantMessageParam: - type: object - properties: - role: - type: string - const: assistant - default: assistant - description: >- - Must be "assistant" to identify this as the model's response - content: - oneOf: - - type: string - - type: array - items: - $ref: '#/components/schemas/OpenAIChatCompletionContentPartParam' - description: The content of the model's response - name: - type: string - description: >- - (Optional) The name of the assistant message participant. - tool_calls: - type: array - items: - $ref: '#/components/schemas/OpenAIChatCompletionToolCall' - description: >- - List of tool calls. Each tool call is an OpenAIChatCompletionToolCall - object. - additionalProperties: false - required: - - role - title: OpenAIAssistantMessageParam - description: >- - A message containing the model's (assistant) response in an OpenAI-compatible - chat completion request. - "OpenAIChatCompletionContentPartImageParam": - type: object - properties: - type: - type: string - const: image_url - default: image_url - image_url: - $ref: '#/components/schemas/OpenAIImageURL' - additionalProperties: false - required: - - type - - image_url - title: >- - OpenAIChatCompletionContentPartImageParam - OpenAIChatCompletionContentPartParam: - oneOf: - - $ref: '#/components/schemas/OpenAIChatCompletionContentPartTextParam' - - $ref: '#/components/schemas/OpenAIChatCompletionContentPartImageParam' - discriminator: - propertyName: type - mapping: - text: '#/components/schemas/OpenAIChatCompletionContentPartTextParam' - image_url: '#/components/schemas/OpenAIChatCompletionContentPartImageParam' - OpenAIChatCompletionContentPartTextParam: - type: object - properties: - type: - type: string - const: text - default: text - text: - type: string - additionalProperties: false - required: - - type - - text - title: OpenAIChatCompletionContentPartTextParam - OpenAIChatCompletionToolCall: - type: object - properties: - index: - type: integer - id: - type: string - type: - type: string - const: function - default: function - function: - $ref: '#/components/schemas/OpenAIChatCompletionToolCallFunction' - additionalProperties: false - required: - - type - title: OpenAIChatCompletionToolCall - OpenAIChatCompletionToolCallFunction: - type: object - properties: - name: - type: string - arguments: - type: string - additionalProperties: false - title: OpenAIChatCompletionToolCallFunction - OpenAIDeveloperMessageParam: - type: object - properties: - role: - type: string - const: developer - default: developer - description: >- - Must be "developer" to identify this as a developer message - content: - oneOf: - - type: string - - type: array - items: - $ref: '#/components/schemas/OpenAIChatCompletionContentPartParam' - description: The content of the developer message - name: - type: string - description: >- - (Optional) The name of the developer message participant. - additionalProperties: false - required: - - role - - content - title: OpenAIDeveloperMessageParam - description: >- - A message from the developer in an OpenAI-compatible chat completion request. - OpenAIImageURL: - type: object - properties: - url: - type: string - detail: - type: string - additionalProperties: false - required: - - url - title: OpenAIImageURL OpenAIJSONSchema: type: object properties: @@ -6994,21 +7779,6 @@ components: required: - name title: OpenAIJSONSchema - OpenAIMessageParam: - oneOf: - - $ref: '#/components/schemas/OpenAIUserMessageParam' - - $ref: '#/components/schemas/OpenAISystemMessageParam' - - $ref: '#/components/schemas/OpenAIAssistantMessageParam' - - $ref: '#/components/schemas/OpenAIToolMessageParam' - - $ref: '#/components/schemas/OpenAIDeveloperMessageParam' - discriminator: - propertyName: role - mapping: - user: '#/components/schemas/OpenAIUserMessageParam' - system: '#/components/schemas/OpenAISystemMessageParam' - assistant: '#/components/schemas/OpenAIAssistantMessageParam' - tool: '#/components/schemas/OpenAIToolMessageParam' - developer: '#/components/schemas/OpenAIDeveloperMessageParam' OpenAIResponseFormatJSONObject: type: object properties: @@ -7056,93 +7826,6 @@ components: required: - type title: OpenAIResponseFormatText - OpenAISystemMessageParam: - type: object - properties: - role: - type: string - const: system - default: system - description: >- - Must be "system" to identify this as a system message - content: - oneOf: - - type: string - - type: array - items: - $ref: '#/components/schemas/OpenAIChatCompletionContentPartParam' - description: >- - The content of the "system prompt". If multiple system messages are provided, - they are concatenated. The underlying Llama Stack code may also add other - system messages (for example, for formatting tool definitions). - name: - type: string - description: >- - (Optional) The name of the system message participant. - additionalProperties: false - required: - - role - - content - title: OpenAISystemMessageParam - description: >- - A system message providing instructions or context to the model. - OpenAIToolMessageParam: - type: object - properties: - role: - type: string - const: tool - default: tool - description: >- - Must be "tool" to identify this as a tool response - tool_call_id: - type: string - description: >- - Unique identifier for the tool call this response is for - content: - oneOf: - - type: string - - type: array - items: - $ref: '#/components/schemas/OpenAIChatCompletionContentPartParam' - description: The response content from the tool - additionalProperties: false - required: - - role - - tool_call_id - - content - title: OpenAIToolMessageParam - description: >- - A message representing the result of a tool invocation in an OpenAI-compatible - chat completion request. - OpenAIUserMessageParam: - type: object - properties: - role: - type: string - const: user - default: user - description: >- - Must be "user" to identify this as a user message - content: - oneOf: - - type: string - - type: array - items: - $ref: '#/components/schemas/OpenAIChatCompletionContentPartParam' - description: >- - The content of the message, which can include text and other media - name: - type: string - description: >- - (Optional) The name of the user message participant. - additionalProperties: false - required: - - role - - content - title: OpenAIUserMessageParam - description: >- - A message from the user in an OpenAI-compatible chat completion request. OpenaiChatCompletionRequest: type: object properties: @@ -7356,30 +8039,6 @@ components: title: OpenAIChatCompletionChunk description: >- Chunk from a streaming response to an OpenAI-compatible chat completion request. - OpenAIChoice: - type: object - properties: - message: - $ref: '#/components/schemas/OpenAIMessageParam' - description: The message from the model - finish_reason: - type: string - description: The reason the model stopped generating - index: - type: integer - description: The index of the choice - logprobs: - $ref: '#/components/schemas/OpenAIChoiceLogprobs' - description: >- - (Optional) The log probabilities for the tokens in the message - additionalProperties: false - required: - - message - - finish_reason - - index - title: OpenAIChoice - description: >- - A choice from an OpenAI-compatible chat completion response. OpenAIChoiceDelta: type: object properties: @@ -7401,26 +8060,6 @@ components: title: OpenAIChoiceDelta description: >- A delta from an OpenAI-compatible chat completion streaming response. - OpenAIChoiceLogprobs: - type: object - properties: - content: - type: array - items: - $ref: '#/components/schemas/OpenAITokenLogProb' - description: >- - (Optional) The log probabilities for the tokens in the message - refusal: - type: array - items: - $ref: '#/components/schemas/OpenAITokenLogProb' - description: >- - (Optional) The log probabilities for the tokens in the message - additionalProperties: false - title: OpenAIChoiceLogprobs - description: >- - The log probabilities for the tokens in the message from an OpenAI-compatible - chat completion response. OpenAIChunkChoice: type: object properties: @@ -7445,49 +8084,6 @@ components: title: OpenAIChunkChoice description: >- A chunk choice from an OpenAI-compatible chat completion streaming response. - OpenAITokenLogProb: - type: object - properties: - token: - type: string - bytes: - type: array - items: - type: integer - logprob: - type: number - top_logprobs: - type: array - items: - $ref: '#/components/schemas/OpenAITopLogProb' - additionalProperties: false - required: - - token - - logprob - - top_logprobs - title: OpenAITokenLogProb - description: >- - The log probability for a token from an OpenAI-compatible chat completion - response. - OpenAITopLogProb: - type: object - properties: - token: - type: string - bytes: - type: array - items: - type: integer - logprob: - type: number - additionalProperties: false - required: - - token - - logprob - title: OpenAITopLogProb - description: >- - The top log probability for a token from an OpenAI-compatible chat completion - response. OpenaiCompletionRequest: type: object properties: @@ -7633,6 +8229,118 @@ components: title: OpenAICompletionChoice description: >- A choice from an OpenAI-compatible completion response. + OpenaiEmbeddingsRequest: + type: object + properties: + model: + type: string + description: >- + The identifier of the model to use. The model must be an embedding model + registered with Llama Stack and available via the /models endpoint. + input: + oneOf: + - type: string + - type: array + items: + type: string + description: >- + Input text to embed, encoded as a string or array of strings. To embed + multiple inputs in a single request, pass an array of strings. + encoding_format: + type: string + description: >- + (Optional) The format to return the embeddings in. Can be either "float" + or "base64". Defaults to "float". + dimensions: + type: integer + description: >- + (Optional) The number of dimensions the resulting output embeddings should + have. Only supported in text-embedding-3 and later models. + user: + type: string + description: >- + (Optional) A unique identifier representing your end-user, which can help + OpenAI to monitor and detect abuse. + additionalProperties: false + required: + - model + - input + title: OpenaiEmbeddingsRequest + OpenAIEmbeddingData: + type: object + properties: + object: + type: string + const: embedding + default: embedding + description: >- + The object type, which will be "embedding" + embedding: + oneOf: + - type: array + items: + type: number + - type: string + description: >- + The embedding vector as a list of floats (when encoding_format="float") + or as a base64-encoded string (when encoding_format="base64") + index: + type: integer + description: >- + The index of the embedding in the input list + additionalProperties: false + required: + - object + - embedding + - index + title: OpenAIEmbeddingData + description: >- + A single embedding data object from an OpenAI-compatible embeddings response. + OpenAIEmbeddingUsage: + type: object + properties: + prompt_tokens: + type: integer + description: The number of tokens in the input + total_tokens: + type: integer + description: The total number of tokens used + additionalProperties: false + required: + - prompt_tokens + - total_tokens + title: OpenAIEmbeddingUsage + description: >- + Usage information for an OpenAI-compatible embeddings response. + OpenAIEmbeddingsResponse: + type: object + properties: + object: + type: string + const: list + default: list + description: The object type, which will be "list" + data: + type: array + items: + $ref: '#/components/schemas/OpenAIEmbeddingData' + description: List of embedding data objects + model: + type: string + description: >- + The model that was used to generate the embeddings + usage: + $ref: '#/components/schemas/OpenAIEmbeddingUsage' + description: Usage information + additionalProperties: false + required: + - object + - data + - model + - usage + title: OpenAIEmbeddingsResponse + description: >- + Response from an OpenAI-compatible embeddings request. OpenAIModel: type: object properties: @@ -7900,6 +8608,10 @@ components: 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 + description: >- + Search mode for retrieval—either "vector" or "keyword". Default "vector". additionalProperties: false required: - query_generator_config @@ -7989,6 +8701,9 @@ components: properties: content: $ref: '#/components/schemas/InterleavedContent' + description: >- + The content of the chunk, which can be interleaved text, images, + or other types. metadata: type: object additionalProperties: @@ -7999,11 +8714,23 @@ components: - type: string - type: array - type: object + description: >- + Metadata associated with the chunk, such as document ID, source, + or other relevant information. + embedding: + type: array + items: + type: number + description: >- + Optional embedding for the chunk. If not provided, it will be computed + later. additionalProperties: false required: - content - metadata title: Chunk + description: >- + A chunk of content that can be inserted into a vector database. scores: type: array items: diff --git a/docs/notebooks/Llama_Stack_Benchmark_Evals.ipynb b/docs/notebooks/Llama_Stack_Benchmark_Evals.ipynb index 413b693d1..93f78d268 100644 --- a/docs/notebooks/Llama_Stack_Benchmark_Evals.ipynb +++ b/docs/notebooks/Llama_Stack_Benchmark_Evals.ipynb @@ -38,12 +38,8 @@ "cell_type": "code", "execution_count": null, "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, "collapsed": true, - "id": "O9pGVlPIjpix", - "outputId": "e1fbe723-ae31-4630-eb80-4c4f6476d56f" + "id": "O9pGVlPIjpix" }, "outputs": [], "source": [ @@ -55,12 +51,8 @@ "cell_type": "code", "execution_count": null, "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, "collapsed": true, - "id": "JQpLUSNjlGAM", - "outputId": "2f7fec97-5511-4cae-d51e-6d262fbca19c" + "id": "JQpLUSNjlGAM" }, "outputs": [], "source": [ @@ -70,7 +62,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -701,7 +693,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": { "id": "TC_IwIAQo4q-" }, @@ -714,116 +706,10 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 305, - "referenced_widgets": [ - "feb82e061ee44283b4a46be858ef4cd7", - "78a2d2d4ee3f42f3be42ef4baa298561", - "ba5e6ca09f174ef3a348453cf5cfc24a", - "74b58e4647644c9daf9af488942fdaf4", - "d56e218958a041e286e80f24e400ab0b", - "cab80632b7564a9eb59583e09573c1ee", - "10c0d50d7c204de0b4c8e8f4d3ec0af5", - "626ef2f811ae4e119a0e85cebe92b91d", - "aef4172d916f40b0ab4ed09104e10f24", - "25529e7fd57049d2816d31f696eab1fd", - "093bdcb608cf4b4fa37b0032a3915187", - "c788d4e9e1e24dca9b6503689df9b631", - "d1587e2144bf46299c1bdec3ea96e4e7", - "500a072c09da41759cb2c942a16d8429", - "9785009392934e3bbb229e8781667cbc", - "84570fe2c2a54a068fb9b8cbc8b041a1", - "f9e579c58e3f4ae0bbb721dffa33bf0a", - "737116977f474ec0b68d88a40fd1086c", - "e6d6e516cd03452297d80c36376855dd", - "6ae0fadb3aeb4be18a9ab3279fb23145", - "fa4800a506ac480984d58933580df086", - "117468099dbc42fdaafc08207eaac7ab", - "44f585990aa244d8ba61f892dc1ccc1c", - "4fc59928a0544f95a4438b37d19ca437", - "fb644d47049f495397d0e60597c86ea3", - "78632694ff694442bc3fefc2cac2cbf5", - "083fd2549abd4b03bd41d8b92ec28f42", - "611d6472a58d419583acc416767a4c90", - "98c5ce434cff454eaaa3f0fd3498183a", - "3d0344a9cc744e369da1b6b7ea1b3be8", - "c452ccbf47a44073aee710175f707a7d", - "0218397c573e4b28bfb4ffa66464d50f", - "9b01bcd6e5174be2af19f457047017c8", - "4fed5720f30b4b3cbbc606a4f25e223b", - "6fa866b9971542739b0ed26d90ceac80", - "fe7553b513954cc68c427b5d9d260b33", - "4bc266d49a6741a88350e029d101425b", - "da57445f98e7427589962836c2b4287e", - "ad1fb86cc1f94fd9911eda03cf4a3783", - "fdefb51ad4c4418b98c5826126558011", - "179d41b80dc841e8a440482516b8bca5", - "22b1ecd2eff14770bcfb0c62d3d4213f", - "47f876cf41484d55b645e1e99337423a", - "340fbbb4982c460992c88885e79b47db", - "9659140487ca4d3ea799196d2c1ecf61", - "52150fd494d24eea89b5232077509355", - "04acde771d0a46699e1de07d9733d1a3", - "7b98103300814f3caea84266263b95a2", - "75f06408071c494f934bb909b84110d1", - "b09b2690894749339a9172e5ad0a9b75", - "cbed38801163438d891879b756f5baab", - "399a6417b23e4593bb244ec3abb6b46d", - "53a321f36b0d4e08a74a5bcfbd04434b", - "b8c0c8aaac0d4032bf5c673a43d084ab", - "d1f32499fa3f4795b92361637e23a9bb", - "c06f9a090fb54c74b947634bf6d11fa8", - "82991dcc80f14af9bd2e95f705980676", - "cd832e3842b945aabbb327856053f261", - "93ee645d54f34acdb0d15092d4a6f0d1", - "b77fe05bbcf84cdc8ef85b264ccd35f6", - "e17d286a965a49cfb8d5bf885865cb1e", - "ca015c1a0c1449e68edb282462435a3f", - "2932b06afde9468a976eb6bfb072b80e", - "d027c807ddc04f89bec41dc05fde7718", - "4ff3a6aaf706460bbba01b248b93000e", - "bfd75a39f0154c30adbaad1e2ca0f1e2", - "4f788a7920c346f3b42900825bd6711a", - "8e9358ec7d474808bb96c13e13489c67", - "f0dfeee2a8d64dedbc8ef55ad4e69932", - "9437b707bf1a4847a50aafeb4252dab5", - "f255707788704a76bd1651f26a22402d", - "3b70fa4e43ef4951862e119378c3c501", - "6c0a6a7fa8ca4e1c961a36305f0e7638", - "201bd914f9884e46b8e6df9d9900a6e8", - "f53b7ada01084e73bba6e14a95e2a534", - "d2029292327b488db02fd123ee2b75af", - "3e26bc24a3e44b4582f57913bdf98de4", - "9d2b6eabf7e14436b72bbf374b4a2a0a", - "b5d7cb5a6157449a850ef0e12e3d3eb7", - "c245d316bf9e44dabe5bfd1e47fc8d2e", - "963cf422ca894d82b0dd94c6165d41bf", - "78d0e2aa93674bbeb42bff87a23cce9b", - "12c6f1180eeb4e9eb9037ea5dd24ec8e", - "017a81d7160240a398947545963856f5", - "1cf8eeb8d81c4e8a8e95dd43296a78b9", - "5b0b5a3f79e94c51aae48fe0dd34ba0e", - "f5b34a743ce54fb591f25b04a2651d65", - "dec6399e2c5341aead66e1674d3e6c72", - "24e48376a72940679989a39a40bbe7f6", - "484df732051540859bc7ac9cecadc83c", - "4b33b1db50c34a2fa957d81a71a2a47f", - "e51d501e2f994baba40345ad632eabee", - "631a85e420b64e8cb6915af59c5ce08a", - "70af9cb2838c4a92bd67f8cb5c98d97f", - "158115266c284c4f8dbce3586151cbf1", - "ce5019b36cde44c58c5f596dbb59a2f8", - "b90d660ca8584ba1815a3c66b420c079", - "7c4d1de626784a59a7e0a33c24086186", - "21cf0e35ecd845a8b5e7c5ce241cf177" - ] - }, "collapsed": true, - "id": "DJkmoG2kq1_P", - "outputId": "8493ee59-c6ff-4bb6-d787-f295944db1cf" + "id": "DJkmoG2kq1_P" }, "outputs": [], "source": [ @@ -848,7 +734,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -949,7 +835,7 @@ "\n", "client.benchmarks.register(\n", " benchmark_id=\"meta-reference::mmmu\",\n", - " # Note: we can use any value as `dataset_id` because we'll be using the `evaluate_rows` API which accepts the \n", + " # Note: we can use any value as `dataset_id` because we'll be using the `evaluate_rows` API which accepts the\n", " # `input_rows` argument and does not fetch data from the dataset.\n", " dataset_id=f\"mmmu-{subset}-{split}\",\n", " # Note: for the same reason as above, we can use any value as `scoring_functions`.\n", @@ -994,7 +880,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": { "id": "HXmZf3Ymw-aX" }, @@ -1014,7 +900,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": { "id": "Gc8azb4Rxr5J" }, @@ -1028,7 +914,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -1168,7 +1054,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -1293,7 +1179,9 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "id": "lxc9-eXYK5Av" + }, "outputs": [], "source": [] } @@ -1322,3088 +1210,6 @@ "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.16" - }, - "widgets": { - "application/vnd.jupyter.widget-state+json": { - "017a81d7160240a398947545963856f5": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "DescriptionStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "description_width": "" - } - }, - "0218397c573e4b28bfb4ffa66464d50f": { - "model_module": "@jupyter-widgets/base", - "model_module_version": "1.2.0", - "model_name": "LayoutModel", - "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "1.2.0", - "_model_name": "LayoutModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "overflow_x": null, - "overflow_y": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null - } - }, - "04acde771d0a46699e1de07d9733d1a3": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "FloatProgressModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "FloatProgressModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_399a6417b23e4593bb244ec3abb6b46d", - "max": 453677660, - "min": 0, - "orientation": "horizontal", - "style": "IPY_MODEL_53a321f36b0d4e08a74a5bcfbd04434b", - "value": 453677660 - } - }, - "083fd2549abd4b03bd41d8b92ec28f42": { - "model_module": "@jupyter-widgets/base", - "model_module_version": "1.2.0", - "model_name": "LayoutModel", - "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "1.2.0", - "_model_name": "LayoutModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "overflow_x": null, - "overflow_y": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null - } - }, - "093bdcb608cf4b4fa37b0032a3915187": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "DescriptionStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "description_width": "" - } - }, - "10c0d50d7c204de0b4c8e8f4d3ec0af5": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "DescriptionStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "description_width": "" - } - }, - "117468099dbc42fdaafc08207eaac7ab": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "DescriptionStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "description_width": "" - } - }, - "12c6f1180eeb4e9eb9037ea5dd24ec8e": { - "model_module": "@jupyter-widgets/base", - "model_module_version": "1.2.0", - "model_name": "LayoutModel", - "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "1.2.0", - "_model_name": "LayoutModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "overflow_x": null, - "overflow_y": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null - } - }, - "158115266c284c4f8dbce3586151cbf1": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "DescriptionStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "description_width": "" - } - }, - "179d41b80dc841e8a440482516b8bca5": { - "model_module": "@jupyter-widgets/base", - "model_module_version": "1.2.0", - "model_name": "LayoutModel", - "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "1.2.0", - "_model_name": "LayoutModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "overflow_x": null, - "overflow_y": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null - } - }, - "1cf8eeb8d81c4e8a8e95dd43296a78b9": { - "model_module": "@jupyter-widgets/base", - "model_module_version": "1.2.0", - "model_name": "LayoutModel", - "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "1.2.0", - "_model_name": "LayoutModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "overflow_x": null, - "overflow_y": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null - } - }, - "201bd914f9884e46b8e6df9d9900a6e8": { - "model_module": "@jupyter-widgets/base", - "model_module_version": "1.2.0", - "model_name": "LayoutModel", - "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "1.2.0", - "_model_name": "LayoutModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "overflow_x": null, - "overflow_y": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null - } - }, - "21cf0e35ecd845a8b5e7c5ce241cf177": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "DescriptionStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "description_width": "" - } - }, - "22b1ecd2eff14770bcfb0c62d3d4213f": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "ProgressStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "ProgressStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" - } - }, - "24e48376a72940679989a39a40bbe7f6": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "HBoxModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HBoxModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_484df732051540859bc7ac9cecadc83c", - "IPY_MODEL_4b33b1db50c34a2fa957d81a71a2a47f", - "IPY_MODEL_e51d501e2f994baba40345ad632eabee" - ], - "layout": "IPY_MODEL_631a85e420b64e8cb6915af59c5ce08a" - } - }, - "25529e7fd57049d2816d31f696eab1fd": { - "model_module": "@jupyter-widgets/base", - "model_module_version": "1.2.0", - "model_name": "LayoutModel", - "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "1.2.0", - "_model_name": "LayoutModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "overflow_x": null, - "overflow_y": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null - } - }, - "2932b06afde9468a976eb6bfb072b80e": { - "model_module": "@jupyter-widgets/base", - "model_module_version": "1.2.0", - "model_name": "LayoutModel", - "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "1.2.0", - "_model_name": "LayoutModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "overflow_x": null, - "overflow_y": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null - } - }, - "340fbbb4982c460992c88885e79b47db": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "DescriptionStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "description_width": "" - } - }, - "399a6417b23e4593bb244ec3abb6b46d": { - "model_module": "@jupyter-widgets/base", - "model_module_version": "1.2.0", - "model_name": "LayoutModel", - "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "1.2.0", - "_model_name": "LayoutModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "overflow_x": null, - "overflow_y": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null - } - }, - "3b70fa4e43ef4951862e119378c3c501": { - "model_module": "@jupyter-widgets/base", - "model_module_version": "1.2.0", - "model_name": "LayoutModel", - "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "1.2.0", - "_model_name": "LayoutModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "overflow_x": null, - "overflow_y": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null - } - }, - "3d0344a9cc744e369da1b6b7ea1b3be8": { - "model_module": "@jupyter-widgets/base", - "model_module_version": "1.2.0", - "model_name": "LayoutModel", - "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "1.2.0", - "_model_name": "LayoutModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "overflow_x": null, - "overflow_y": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null - } - }, - "3e26bc24a3e44b4582f57913bdf98de4": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "DescriptionStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "description_width": "" - } - }, - "44f585990aa244d8ba61f892dc1ccc1c": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "HBoxModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HBoxModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_4fc59928a0544f95a4438b37d19ca437", - "IPY_MODEL_fb644d47049f495397d0e60597c86ea3", - "IPY_MODEL_78632694ff694442bc3fefc2cac2cbf5" - ], - "layout": "IPY_MODEL_083fd2549abd4b03bd41d8b92ec28f42" - } - }, - "47f876cf41484d55b645e1e99337423a": { - "model_module": "@jupyter-widgets/base", - "model_module_version": "1.2.0", - "model_name": "LayoutModel", - "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "1.2.0", - "_model_name": "LayoutModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "overflow_x": null, - "overflow_y": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null - } - }, - "484df732051540859bc7ac9cecadc83c": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HTMLView", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_70af9cb2838c4a92bd67f8cb5c98d97f", - "placeholder": "​", - "style": "IPY_MODEL_158115266c284c4f8dbce3586151cbf1", - "value": "Generating test split: 100%" - } - }, - "4b33b1db50c34a2fa957d81a71a2a47f": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "FloatProgressModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "FloatProgressModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_ce5019b36cde44c58c5f596dbb59a2f8", - "max": 287, - "min": 0, - "orientation": "horizontal", - "style": "IPY_MODEL_b90d660ca8584ba1815a3c66b420c079", - "value": 287 - } - }, - "4bc266d49a6741a88350e029d101425b": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HTMLView", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_47f876cf41484d55b645e1e99337423a", - "placeholder": "​", - "style": "IPY_MODEL_340fbbb4982c460992c88885e79b47db", - "value": " 461M/461M [00:11<00:00, 31.2MB/s]" - } - }, - "4f788a7920c346f3b42900825bd6711a": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "HBoxModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HBoxModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_8e9358ec7d474808bb96c13e13489c67", - "IPY_MODEL_f0dfeee2a8d64dedbc8ef55ad4e69932", - "IPY_MODEL_9437b707bf1a4847a50aafeb4252dab5" - ], - "layout": "IPY_MODEL_f255707788704a76bd1651f26a22402d" - } - }, - "4fc59928a0544f95a4438b37d19ca437": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HTMLView", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_611d6472a58d419583acc416767a4c90", - "placeholder": "​", - "style": "IPY_MODEL_98c5ce434cff454eaaa3f0fd3498183a", - "value": "validation-00000-of-00001.parquet: 100%" - } - }, - "4fed5720f30b4b3cbbc606a4f25e223b": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "HBoxModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HBoxModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_6fa866b9971542739b0ed26d90ceac80", - "IPY_MODEL_fe7553b513954cc68c427b5d9d260b33", - "IPY_MODEL_4bc266d49a6741a88350e029d101425b" - ], - "layout": "IPY_MODEL_da57445f98e7427589962836c2b4287e" - } - }, - "4ff3a6aaf706460bbba01b248b93000e": { - "model_module": "@jupyter-widgets/base", - "model_module_version": "1.2.0", - "model_name": "LayoutModel", - "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "1.2.0", - "_model_name": "LayoutModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "overflow_x": null, - "overflow_y": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null - } - }, - "500a072c09da41759cb2c942a16d8429": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "FloatProgressModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "FloatProgressModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_e6d6e516cd03452297d80c36376855dd", - "max": 29453850, - "min": 0, - "orientation": "horizontal", - "style": "IPY_MODEL_6ae0fadb3aeb4be18a9ab3279fb23145", - "value": 29453850 - } - }, - "52150fd494d24eea89b5232077509355": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HTMLView", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_b09b2690894749339a9172e5ad0a9b75", - "placeholder": "​", - "style": "IPY_MODEL_cbed38801163438d891879b756f5baab", - "value": "test-00001-of-00003.parquet: 100%" - } - }, - "53a321f36b0d4e08a74a5bcfbd04434b": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "ProgressStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "ProgressStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" - } - }, - "5b0b5a3f79e94c51aae48fe0dd34ba0e": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "ProgressStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "ProgressStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" - } - }, - "611d6472a58d419583acc416767a4c90": { - "model_module": "@jupyter-widgets/base", - "model_module_version": "1.2.0", - "model_name": "LayoutModel", - "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "1.2.0", - "_model_name": "LayoutModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "overflow_x": null, - "overflow_y": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null - } - }, - "626ef2f811ae4e119a0e85cebe92b91d": { - "model_module": "@jupyter-widgets/base", - "model_module_version": "1.2.0", - "model_name": "LayoutModel", - "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "1.2.0", - "_model_name": "LayoutModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "overflow_x": null, - "overflow_y": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null - } - }, - "631a85e420b64e8cb6915af59c5ce08a": { - "model_module": "@jupyter-widgets/base", - "model_module_version": "1.2.0", - "model_name": "LayoutModel", - "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "1.2.0", - "_model_name": "LayoutModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "overflow_x": null, - "overflow_y": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null - } - }, - "6ae0fadb3aeb4be18a9ab3279fb23145": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "ProgressStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "ProgressStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" - } - }, - "6c0a6a7fa8ca4e1c961a36305f0e7638": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "DescriptionStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "description_width": "" - } - }, - "6fa866b9971542739b0ed26d90ceac80": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HTMLView", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_ad1fb86cc1f94fd9911eda03cf4a3783", - "placeholder": "​", - "style": "IPY_MODEL_fdefb51ad4c4418b98c5826126558011", - "value": "test-00000-of-00003.parquet: 100%" - } - }, - "70af9cb2838c4a92bd67f8cb5c98d97f": { - "model_module": "@jupyter-widgets/base", - "model_module_version": "1.2.0", - "model_name": "LayoutModel", - "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "1.2.0", - "_model_name": "LayoutModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "overflow_x": null, - "overflow_y": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null - } - }, - "737116977f474ec0b68d88a40fd1086c": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "DescriptionStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "description_width": "" - } - }, - "74b58e4647644c9daf9af488942fdaf4": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HTMLView", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_25529e7fd57049d2816d31f696eab1fd", - "placeholder": "​", - "style": "IPY_MODEL_093bdcb608cf4b4fa37b0032a3915187", - "value": " 36.0k/36.0k [00:00<00:00, 1.29MB/s]" - } - }, - "75f06408071c494f934bb909b84110d1": { - "model_module": "@jupyter-widgets/base", - "model_module_version": "1.2.0", - "model_name": "LayoutModel", - "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "1.2.0", - "_model_name": "LayoutModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "overflow_x": null, - "overflow_y": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null - } - }, - "78632694ff694442bc3fefc2cac2cbf5": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HTMLView", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_0218397c573e4b28bfb4ffa66464d50f", - "placeholder": "​", - "style": "IPY_MODEL_9b01bcd6e5174be2af19f457047017c8", - "value": " 165M/165M [00:03<00:00, 42.9MB/s]" - } - }, - "78a2d2d4ee3f42f3be42ef4baa298561": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HTMLView", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_cab80632b7564a9eb59583e09573c1ee", - "placeholder": "​", - "style": "IPY_MODEL_10c0d50d7c204de0b4c8e8f4d3ec0af5", - "value": "README.md: 100%" - } - }, - "78d0e2aa93674bbeb42bff87a23cce9b": { - "model_module": "@jupyter-widgets/base", - "model_module_version": "1.2.0", - "model_name": "LayoutModel", - "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "1.2.0", - "_model_name": "LayoutModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "overflow_x": null, - "overflow_y": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null - } - }, - "7b98103300814f3caea84266263b95a2": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HTMLView", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_b8c0c8aaac0d4032bf5c673a43d084ab", - "placeholder": "​", - "style": "IPY_MODEL_d1f32499fa3f4795b92361637e23a9bb", - "value": " 454M/454M [00:11<00:00, 40.4MB/s]" - } - }, - "7c4d1de626784a59a7e0a33c24086186": { - "model_module": "@jupyter-widgets/base", - "model_module_version": "1.2.0", - "model_name": "LayoutModel", - "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "1.2.0", - "_model_name": "LayoutModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "overflow_x": null, - "overflow_y": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null - } - }, - "82991dcc80f14af9bd2e95f705980676": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HTMLView", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_e17d286a965a49cfb8d5bf885865cb1e", - "placeholder": "​", - "style": "IPY_MODEL_ca015c1a0c1449e68edb282462435a3f", - "value": "test-00002-of-00003.parquet: 100%" - } - }, - "84570fe2c2a54a068fb9b8cbc8b041a1": { - "model_module": "@jupyter-widgets/base", - "model_module_version": "1.2.0", - "model_name": "LayoutModel", - "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "1.2.0", - "_model_name": "LayoutModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "overflow_x": null, - "overflow_y": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null - } - }, - "8e9358ec7d474808bb96c13e13489c67": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HTMLView", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_3b70fa4e43ef4951862e119378c3c501", - "placeholder": "​", - "style": "IPY_MODEL_6c0a6a7fa8ca4e1c961a36305f0e7638", - "value": "Generating dev split: 100%" - } - }, - "93ee645d54f34acdb0d15092d4a6f0d1": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HTMLView", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_4ff3a6aaf706460bbba01b248b93000e", - "placeholder": "​", - "style": "IPY_MODEL_bfd75a39f0154c30adbaad1e2ca0f1e2", - "value": " 471M/471M [00:11<00:00, 41.5MB/s]" - } - }, - "9437b707bf1a4847a50aafeb4252dab5": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HTMLView", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_d2029292327b488db02fd123ee2b75af", - "placeholder": "​", - "style": "IPY_MODEL_3e26bc24a3e44b4582f57913bdf98de4", - "value": " 5/5 [00:00<00:00,  8.03 examples/s]" - } - }, - "963cf422ca894d82b0dd94c6165d41bf": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HTMLView", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_f5b34a743ce54fb591f25b04a2651d65", - "placeholder": "​", - "style": "IPY_MODEL_dec6399e2c5341aead66e1674d3e6c72", - "value": " 30/30 [00:03<00:00,  8.23 examples/s]" - } - }, - "9659140487ca4d3ea799196d2c1ecf61": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "HBoxModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HBoxModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_52150fd494d24eea89b5232077509355", - "IPY_MODEL_04acde771d0a46699e1de07d9733d1a3", - "IPY_MODEL_7b98103300814f3caea84266263b95a2" - ], - "layout": "IPY_MODEL_75f06408071c494f934bb909b84110d1" - } - }, - "9785009392934e3bbb229e8781667cbc": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HTMLView", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_fa4800a506ac480984d58933580df086", - "placeholder": "​", - "style": "IPY_MODEL_117468099dbc42fdaafc08207eaac7ab", - "value": " 29.5M/29.5M [00:00<00:00, 36.5MB/s]" - } - }, - "98c5ce434cff454eaaa3f0fd3498183a": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "DescriptionStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "description_width": "" - } - }, - "9b01bcd6e5174be2af19f457047017c8": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "DescriptionStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "description_width": "" - } - }, - "9d2b6eabf7e14436b72bbf374b4a2a0a": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "HBoxModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HBoxModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_b5d7cb5a6157449a850ef0e12e3d3eb7", - "IPY_MODEL_c245d316bf9e44dabe5bfd1e47fc8d2e", - "IPY_MODEL_963cf422ca894d82b0dd94c6165d41bf" - ], - "layout": "IPY_MODEL_78d0e2aa93674bbeb42bff87a23cce9b" - } - }, - "ad1fb86cc1f94fd9911eda03cf4a3783": { - "model_module": "@jupyter-widgets/base", - "model_module_version": "1.2.0", - "model_name": "LayoutModel", - "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "1.2.0", - "_model_name": "LayoutModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "overflow_x": null, - "overflow_y": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null - } - }, - "aef4172d916f40b0ab4ed09104e10f24": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "ProgressStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "ProgressStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" - } - }, - "b09b2690894749339a9172e5ad0a9b75": { - "model_module": "@jupyter-widgets/base", - "model_module_version": "1.2.0", - "model_name": "LayoutModel", - "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "1.2.0", - "_model_name": "LayoutModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "overflow_x": null, - "overflow_y": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null - } - }, - "b5d7cb5a6157449a850ef0e12e3d3eb7": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HTMLView", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_12c6f1180eeb4e9eb9037ea5dd24ec8e", - "placeholder": "​", - "style": "IPY_MODEL_017a81d7160240a398947545963856f5", - "value": "Generating validation split: 100%" - } - }, - "b77fe05bbcf84cdc8ef85b264ccd35f6": { - "model_module": "@jupyter-widgets/base", - "model_module_version": "1.2.0", - "model_name": "LayoutModel", - "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "1.2.0", - "_model_name": "LayoutModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "overflow_x": null, - "overflow_y": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null - } - }, - "b8c0c8aaac0d4032bf5c673a43d084ab": { - "model_module": "@jupyter-widgets/base", - "model_module_version": "1.2.0", - "model_name": "LayoutModel", - "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "1.2.0", - "_model_name": "LayoutModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "overflow_x": null, - "overflow_y": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null - } - }, - "b90d660ca8584ba1815a3c66b420c079": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "ProgressStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "ProgressStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" - } - }, - "ba5e6ca09f174ef3a348453cf5cfc24a": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "FloatProgressModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "FloatProgressModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_626ef2f811ae4e119a0e85cebe92b91d", - "max": 36030, - "min": 0, - "orientation": "horizontal", - "style": "IPY_MODEL_aef4172d916f40b0ab4ed09104e10f24", - "value": 36030 - } - }, - "bfd75a39f0154c30adbaad1e2ca0f1e2": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "DescriptionStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "description_width": "" - } - }, - "c06f9a090fb54c74b947634bf6d11fa8": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "HBoxModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HBoxModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_82991dcc80f14af9bd2e95f705980676", - "IPY_MODEL_cd832e3842b945aabbb327856053f261", - "IPY_MODEL_93ee645d54f34acdb0d15092d4a6f0d1" - ], - "layout": "IPY_MODEL_b77fe05bbcf84cdc8ef85b264ccd35f6" - } - }, - "c245d316bf9e44dabe5bfd1e47fc8d2e": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "FloatProgressModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "FloatProgressModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_1cf8eeb8d81c4e8a8e95dd43296a78b9", - "max": 30, - "min": 0, - "orientation": "horizontal", - "style": "IPY_MODEL_5b0b5a3f79e94c51aae48fe0dd34ba0e", - "value": 30 - } - }, - "c452ccbf47a44073aee710175f707a7d": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "ProgressStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "ProgressStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" - } - }, - "c788d4e9e1e24dca9b6503689df9b631": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "HBoxModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HBoxModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_d1587e2144bf46299c1bdec3ea96e4e7", - "IPY_MODEL_500a072c09da41759cb2c942a16d8429", - "IPY_MODEL_9785009392934e3bbb229e8781667cbc" - ], - "layout": "IPY_MODEL_84570fe2c2a54a068fb9b8cbc8b041a1" - } - }, - "ca015c1a0c1449e68edb282462435a3f": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "DescriptionStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "description_width": "" - } - }, - "cab80632b7564a9eb59583e09573c1ee": { - "model_module": "@jupyter-widgets/base", - "model_module_version": "1.2.0", - "model_name": "LayoutModel", - "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "1.2.0", - "_model_name": "LayoutModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "overflow_x": null, - "overflow_y": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null - } - }, - "cbed38801163438d891879b756f5baab": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "DescriptionStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "description_width": "" - } - }, - "cd832e3842b945aabbb327856053f261": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "FloatProgressModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "FloatProgressModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_2932b06afde9468a976eb6bfb072b80e", - "max": 470745176, - "min": 0, - "orientation": "horizontal", - "style": "IPY_MODEL_d027c807ddc04f89bec41dc05fde7718", - "value": 470745176 - } - }, - "ce5019b36cde44c58c5f596dbb59a2f8": { - "model_module": "@jupyter-widgets/base", - "model_module_version": "1.2.0", - "model_name": "LayoutModel", - "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "1.2.0", - "_model_name": "LayoutModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "overflow_x": null, - "overflow_y": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null - } - }, - "d027c807ddc04f89bec41dc05fde7718": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "ProgressStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "ProgressStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" - } - }, - "d1587e2144bf46299c1bdec3ea96e4e7": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HTMLView", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_f9e579c58e3f4ae0bbb721dffa33bf0a", - "placeholder": "​", - "style": "IPY_MODEL_737116977f474ec0b68d88a40fd1086c", - "value": "dev-00000-of-00001.parquet: 100%" - } - }, - "d1f32499fa3f4795b92361637e23a9bb": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "DescriptionStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "description_width": "" - } - }, - "d2029292327b488db02fd123ee2b75af": { - "model_module": "@jupyter-widgets/base", - "model_module_version": "1.2.0", - "model_name": "LayoutModel", - "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "1.2.0", - "_model_name": "LayoutModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "overflow_x": null, - "overflow_y": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null - } - }, - "d56e218958a041e286e80f24e400ab0b": { - "model_module": "@jupyter-widgets/base", - "model_module_version": "1.2.0", - "model_name": "LayoutModel", - "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "1.2.0", - "_model_name": "LayoutModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "overflow_x": null, - "overflow_y": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null - } - }, - "da57445f98e7427589962836c2b4287e": { - "model_module": "@jupyter-widgets/base", - "model_module_version": "1.2.0", - "model_name": "LayoutModel", - "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "1.2.0", - "_model_name": "LayoutModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "overflow_x": null, - "overflow_y": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null - } - }, - "dec6399e2c5341aead66e1674d3e6c72": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "DescriptionStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "description_width": "" - } - }, - "e17d286a965a49cfb8d5bf885865cb1e": { - "model_module": "@jupyter-widgets/base", - "model_module_version": "1.2.0", - "model_name": "LayoutModel", - "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "1.2.0", - "_model_name": "LayoutModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "overflow_x": null, - "overflow_y": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null - } - }, - "e51d501e2f994baba40345ad632eabee": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HTMLView", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_7c4d1de626784a59a7e0a33c24086186", - "placeholder": "​", - "style": "IPY_MODEL_21cf0e35ecd845a8b5e7c5ce241cf177", - "value": " 287/287 [00:23<00:00, 12.48 examples/s]" - } - }, - "e6d6e516cd03452297d80c36376855dd": { - "model_module": "@jupyter-widgets/base", - "model_module_version": "1.2.0", - "model_name": "LayoutModel", - "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "1.2.0", - "_model_name": "LayoutModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "overflow_x": null, - "overflow_y": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null - } - }, - "f0dfeee2a8d64dedbc8ef55ad4e69932": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "FloatProgressModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "FloatProgressModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_201bd914f9884e46b8e6df9d9900a6e8", - "max": 5, - "min": 0, - "orientation": "horizontal", - "style": "IPY_MODEL_f53b7ada01084e73bba6e14a95e2a534", - "value": 5 - } - }, - "f255707788704a76bd1651f26a22402d": { - "model_module": "@jupyter-widgets/base", - "model_module_version": "1.2.0", - "model_name": "LayoutModel", - "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "1.2.0", - "_model_name": "LayoutModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "overflow_x": null, - "overflow_y": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null - } - }, - "f53b7ada01084e73bba6e14a95e2a534": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "ProgressStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "ProgressStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" - } - }, - "f5b34a743ce54fb591f25b04a2651d65": { - "model_module": "@jupyter-widgets/base", - "model_module_version": "1.2.0", - "model_name": "LayoutModel", - "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "1.2.0", - "_model_name": "LayoutModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "overflow_x": null, - "overflow_y": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null - } - }, - "f9e579c58e3f4ae0bbb721dffa33bf0a": { - "model_module": "@jupyter-widgets/base", - "model_module_version": "1.2.0", - "model_name": "LayoutModel", - "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "1.2.0", - "_model_name": "LayoutModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "overflow_x": null, - "overflow_y": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null - } - }, - "fa4800a506ac480984d58933580df086": { - "model_module": "@jupyter-widgets/base", - "model_module_version": "1.2.0", - "model_name": "LayoutModel", - "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "1.2.0", - "_model_name": "LayoutModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "overflow_x": null, - "overflow_y": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null - } - }, - "fb644d47049f495397d0e60597c86ea3": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "FloatProgressModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "FloatProgressModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_3d0344a9cc744e369da1b6b7ea1b3be8", - "max": 165333397, - "min": 0, - "orientation": "horizontal", - "style": "IPY_MODEL_c452ccbf47a44073aee710175f707a7d", - "value": 165333397 - } - }, - "fdefb51ad4c4418b98c5826126558011": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "DescriptionStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "description_width": "" - } - }, - "fe7553b513954cc68c427b5d9d260b33": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "FloatProgressModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "FloatProgressModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_179d41b80dc841e8a440482516b8bca5", - "max": 461411018, - "min": 0, - "orientation": "horizontal", - "style": "IPY_MODEL_22b1ecd2eff14770bcfb0c62d3d4213f", - "value": 461411018 - } - }, - "feb82e061ee44283b4a46be858ef4cd7": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "HBoxModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HBoxModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_78a2d2d4ee3f42f3be42ef4baa298561", - "IPY_MODEL_ba5e6ca09f174ef3a348453cf5cfc24a", - "IPY_MODEL_74b58e4647644c9daf9af488942fdaf4" - ], - "layout": "IPY_MODEL_d56e218958a041e286e80f24e400ab0b" - } - } - } } }, "nbformat": 4, diff --git a/docs/openapi_generator/pyopenapi/generator.py b/docs/openapi_generator/pyopenapi/generator.py index cc594d8d7..5b7a685c1 100644 --- a/docs/openapi_generator/pyopenapi/generator.py +++ b/docs/openapi_generator/pyopenapi/generator.py @@ -759,7 +759,7 @@ class Generator: ) return Operation( - tags=[op.defining_class.__name__], + tags=[getattr(op.defining_class, "API_NAMESPACE", op.defining_class.__name__)], summary=None, # summary=doc_string.short_description, description=description, @@ -805,6 +805,8 @@ class Generator: operation_tags: List[Tag] = [] for cls in endpoint_classes: doc_string = parse_type(cls) + if hasattr(cls, "API_NAMESPACE") and cls.API_NAMESPACE != cls.__name__: + continue operation_tags.append( Tag( name=cls.__name__, diff --git a/docs/readme.md b/docs/readme.md index b88a4738d..c238c4720 100644 --- a/docs/readme.md +++ b/docs/readme.md @@ -3,10 +3,10 @@ Here's a collection of comprehensive guides, examples, and resources for building AI applications with Llama Stack. For the complete documentation, visit our [ReadTheDocs page](https://llama-stack.readthedocs.io/en/latest/index.html). ## Render locally + +From the llama-stack root directory, run the following command to render the docs locally: ```bash -pip install -r requirements.txt -cd docs -python -m sphinx_autobuild source _build +uv run --group docs sphinx-autobuild docs/source docs/build/html --write-all ``` You can open up the docs in your browser at http://localhost:8000 diff --git a/docs/requirements.txt b/docs/requirements.txt deleted file mode 100644 index 6cd45c33b..000000000 --- a/docs/requirements.txt +++ /dev/null @@ -1,16 +0,0 @@ -linkify -myst-parser --e git+https://github.com/pytorch/pytorch_sphinx_theme.git#egg=pytorch_sphinx_theme -sphinx==8.1.3 -sphinx-copybutton -sphinx-design -sphinx-pdj-theme -sphinx-rtd-theme>=1.0.0 -sphinx-tabs -sphinx_autobuild -sphinx_rtd_dark_mode -sphinxcontrib-mermaid -sphinxcontrib-openapi -sphinxcontrib-redoc -sphinxcontrib-video -tomli diff --git a/docs/source/building_applications/rag.md b/docs/source/building_applications/rag.md index dbe90a7fc..289c38991 100644 --- a/docs/source/building_applications/rag.md +++ b/docs/source/building_applications/rag.md @@ -57,6 +57,31 @@ chunks = [ ] client.vector_io.insert(vector_db_id=vector_db_id, chunks=chunks) ``` + +#### Using Precomputed Embeddings +If you decide to precompute embeddings for your documents, you can insert them directly into the vector database by +including the embedding vectors in the chunk data. This is useful if you have a separate embedding service or if you +want to customize the ingestion process. +```python +chunks_with_embeddings = [ + { + "content": "First chunk of text", + "mime_type": "text/plain", + "embedding": [0.1, 0.2, 0.3, ...], # Your precomputed embedding vector + "metadata": {"document_id": "doc1", "section": "introduction"}, + }, + { + "content": "Second chunk of text", + "mime_type": "text/plain", + "embedding": [0.2, 0.3, 0.4, ...], # Your precomputed embedding vector + "metadata": {"document_id": "doc1", "section": "methodology"}, + }, +] +client.vector_io.insert(vector_db_id=vector_db_id, chunks=chunks_with_embeddings) +``` +When providing precomputed embeddings, ensure the embedding dimension matches the embedding_dimension specified when +registering the vector database. + ### Retrieval You can query the vector database to retrieve documents based on their embeddings. ```python diff --git a/docs/source/conf.py b/docs/source/conf.py index 501a923dd..6e59dbdfb 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -22,7 +22,11 @@ from docutils import nodes # Read version from pyproject.toml with Path(__file__).parent.parent.parent.joinpath("pyproject.toml").open("rb") as f: pypi_url = "https://pypi.org/pypi/llama-stack/json" - version_tag = json.loads(requests.get(pypi_url).text)["info"]["version"] + headers = { + 'User-Agent': 'pip/23.0.1 (python 3.11)', # Mimic pip's user agent + 'Accept': 'application/json' + } + version_tag = json.loads(requests.get(pypi_url, headers=headers).text)["info"]["version"] print(f"{version_tag=}") # generate the full link including text and url here @@ -53,14 +57,6 @@ myst_enable_extensions = ["colon_fence"] html_theme = "sphinx_rtd_theme" html_use_relative_paths = True - -# html_theme = "sphinx_pdj_theme" -# html_theme_path = [sphinx_pdj_theme.get_html_theme_path()] - -# html_theme = "pytorch_sphinx_theme" -# html_theme_path = [pytorch_sphinx_theme.get_html_theme_path()] - - templates_path = ["_templates"] exclude_patterns = ["_build", "Thumbs.db", ".DS_Store"] diff --git a/docs/source/distributions/building_distro.md b/docs/source/distributions/building_distro.md index d9b73c910..0dbabf8aa 100644 --- a/docs/source/distributions/building_distro.md +++ b/docs/source/distributions/building_distro.md @@ -338,6 +338,48 @@ INFO: Application startup complete. INFO: Uvicorn running on http://['::', '0.0.0.0']:8321 (Press CTRL+C to quit) INFO: 2401:db00:35c:2d2b:face:0:c9:0:54678 - "GET /models/list HTTP/1.1" 200 OK ``` +### Listing Distributions +Using the list command, you can view all existing Llama Stack distributions, including stacks built from templates, from scratch, or using custom configuration files. + +``` +llama stack list -h +usage: llama stack list [-h] + +list the build stacks + +options: + -h, --help show this help message and exit +``` + +Example Usage + +``` +llama stack list +``` + +### Removing a Distribution +Use the remove command to delete a distribution you've previously built. + +``` +llama stack rm -h +usage: llama stack rm [-h] [--all] [name] + +Remove the build stack + +positional arguments: + name Name of the stack to delete (default: None) + +options: + -h, --help show this help message and exit + --all, -a Delete all stacks (use with caution) (default: False) +``` + +Example +``` +llama stack rm llamastack-test +``` + +To keep your environment organized and avoid clutter, consider using `llama stack list` to review old or unused distributions and `llama stack rm ` to delete them when they’re no longer needed. ### Troubleshooting diff --git a/docs/source/distributions/configuration.md b/docs/source/distributions/configuration.md index b62227a84..de99b6576 100644 --- a/docs/source/distributions/configuration.md +++ b/docs/source/distributions/configuration.md @@ -118,11 +118,6 @@ server: port: 8321 # Port to listen on (default: 8321) tls_certfile: "/path/to/cert.pem" # Optional: Path to TLS certificate for HTTPS tls_keyfile: "/path/to/key.pem" # Optional: Path to TLS key for HTTPS - auth: # Optional: Authentication configuration - provider_type: "kubernetes" # Type of auth provider - config: # Provider-specific configuration - api_server_url: "https://kubernetes.default.svc" - ca_cert_path: "/path/to/ca.crt" # Optional: Path to CA certificate ``` ### Authentication Configuration @@ -135,7 +130,7 @@ Authorization: Bearer The server supports multiple authentication providers: -#### Kubernetes Provider +#### OAuth 2.0/OpenID Connect Provider with Kubernetes The Kubernetes cluster must be configured to use a service account for authentication. @@ -146,14 +141,67 @@ kubectl create rolebinding llama-stack-auth-rolebinding --clusterrole=admin --se kubectl create token llama-stack-auth -n llama-stack > llama-stack-auth-token ``` -Validates tokens against the Kubernetes API server: +Make sure the `kube-apiserver` runs with `--anonymous-auth=true` to allow unauthenticated requests +and that the correct RoleBinding is created to allow the service account to access the necessary +resources. If that is not the case, you can create a RoleBinding for the service account to access +the necessary resources: + +```yaml +# allow-anonymous-openid.yaml +apiVersion: rbac.authorization.k8s.io/v1 +kind: ClusterRole +metadata: + name: allow-anonymous-openid +rules: +- nonResourceURLs: ["/openid/v1/jwks"] + verbs: ["get"] +--- +apiVersion: rbac.authorization.k8s.io/v1 +kind: ClusterRoleBinding +metadata: + name: allow-anonymous-openid +roleRef: + apiGroup: rbac.authorization.k8s.io + kind: ClusterRole + name: allow-anonymous-openid +subjects: +- kind: User + name: system:anonymous + apiGroup: rbac.authorization.k8s.io +``` + +And then apply the configuration: +```bash +kubectl apply -f allow-anonymous-openid.yaml +``` + +Validates tokens against the Kubernetes API server through the OIDC provider: ```yaml server: auth: - provider_type: "kubernetes" + provider_type: "oauth2_token" config: - api_server_url: "https://kubernetes.default.svc" # URL of the Kubernetes API server - ca_cert_path: "/path/to/ca.crt" # Optional: Path to CA certificate + jwks: + uri: "https://kubernetes.default.svc" + key_recheck_period: 3600 + tls_cafile: "/path/to/ca.crt" + issuer: "https://kubernetes.default.svc" + audience: "https://kubernetes.default.svc" +``` + +To find your cluster's audience, run: +```bash +kubectl create token default --duration=1h | cut -d. -f2 | base64 -d | jq .aud +``` + +For the issuer, you can use the OIDC provider's URL: +```bash +kubectl get --raw /.well-known/openid-configuration| jq .issuer +``` + +For the tls_cafile, you can use the CA certificate of the OIDC provider: +```bash +kubectl config view --minify -o jsonpath='{.clusters[0].cluster.certificate-authority}' ``` The provider extracts user information from the JWT token: @@ -208,6 +256,80 @@ And must respond with: If no access attributes are returned, the token is used as a namespace. +### Quota Configuration + +The `quota` section allows you to enable server-side request throttling for both +authenticated and anonymous clients. This is useful for preventing abuse, enforcing +fairness across tenants, and controlling infrastructure costs without requiring +client-side rate limiting or external proxies. + +Quotas are disabled by default. When enabled, each client is tracked using either: + +* Their authenticated `client_id` (derived from the Bearer token), or +* Their IP address (fallback for anonymous requests) + +Quota state is stored in a SQLite-backed key-value store, and rate limits are applied +within a configurable time window (currently only `day` is supported). + +#### Example + +```yaml +server: + quota: + kvstore: + type: sqlite + db_path: ./quotas.db + anonymous_max_requests: 100 + authenticated_max_requests: 1000 + period: day +``` + +#### Configuration Options + +| Field | Description | +| ---------------------------- | -------------------------------------------------------------------------- | +| `kvstore` | Required. Backend storage config for tracking request counts. | +| `kvstore.type` | Must be `"sqlite"` for now. Other backends may be supported in the future. | +| `kvstore.db_path` | File path to the SQLite database. | +| `anonymous_max_requests` | Max requests per period for unauthenticated clients. | +| `authenticated_max_requests` | Max requests per period for authenticated clients. | +| `period` | Time window for quota enforcement. Only `"day"` is supported. | + +> Note: if `authenticated_max_requests` is set but no authentication provider is +configured, the server will fall back to applying `anonymous_max_requests` to all +clients. + +#### Example with Authentication Enabled + +```yaml +server: + port: 8321 + auth: + provider_type: custom + config: + endpoint: https://auth.example.com/validate + quota: + kvstore: + type: sqlite + db_path: ./quotas.db + anonymous_max_requests: 100 + authenticated_max_requests: 1000 + period: day +``` + +If a client exceeds their limit, the server responds with: + +```http +HTTP/1.1 429 Too Many Requests +Content-Type: application/json + +{ + "error": { + "message": "Quota exceeded" + } +} +``` + ## Extending to handle Safety Configuring Safety can be a little involved so it is instructive to go through an example. diff --git a/docs/source/distributions/kubernetes_deployment.md b/docs/source/distributions/kubernetes_deployment.md index 21ec02012..f43039824 100644 --- a/docs/source/distributions/kubernetes_deployment.md +++ b/docs/source/distributions/kubernetes_deployment.md @@ -172,7 +172,7 @@ spec: - name: llama-stack image: localhost/llama-stack-run-k8s:latest imagePullPolicy: IfNotPresent - command: ["python", "-m", "llama_stack.distribution.server.server", "--yaml-config", "/app/config.yaml"] + command: ["python", "-m", "llama_stack.distribution.server.server", "--config", "/app/config.yaml"] ports: - containerPort: 5000 volumeMounts: diff --git a/docs/source/distributions/remote_hosted_distro/watsonx.md b/docs/source/distributions/remote_hosted_distro/watsonx.md index d8d327bb5..ec1b98059 100644 --- a/docs/source/distributions/remote_hosted_distro/watsonx.md +++ b/docs/source/distributions/remote_hosted_distro/watsonx.md @@ -70,7 +70,7 @@ docker run \ -p $LLAMA_STACK_PORT:$LLAMA_STACK_PORT \ -v ./run.yaml:/root/my-run.yaml \ llamastack/distribution-watsonx \ - --yaml-config /root/my-run.yaml \ + --config /root/my-run.yaml \ --port $LLAMA_STACK_PORT \ --env WATSONX_API_KEY=$WATSONX_API_KEY \ --env WATSONX_PROJECT_ID=$WATSONX_PROJECT_ID \ diff --git a/docs/source/distributions/self_hosted_distro/cerebras.md b/docs/source/distributions/self_hosted_distro/cerebras.md index 329c9b802..3c4db1b75 100644 --- a/docs/source/distributions/self_hosted_distro/cerebras.md +++ b/docs/source/distributions/self_hosted_distro/cerebras.md @@ -52,7 +52,7 @@ docker run \ -p $LLAMA_STACK_PORT:$LLAMA_STACK_PORT \ -v ./run.yaml:/root/my-run.yaml \ llamastack/distribution-cerebras \ - --yaml-config /root/my-run.yaml \ + --config /root/my-run.yaml \ --port $LLAMA_STACK_PORT \ --env CEREBRAS_API_KEY=$CEREBRAS_API_KEY ``` diff --git a/docs/source/distributions/self_hosted_distro/dell.md b/docs/source/distributions/self_hosted_distro/dell.md index 2e987985c..eded3bdc4 100644 --- a/docs/source/distributions/self_hosted_distro/dell.md +++ b/docs/source/distributions/self_hosted_distro/dell.md @@ -155,7 +155,7 @@ docker run \ -v $HOME/.llama:/root/.llama \ -v ./llama_stack/templates/tgi/run-with-safety.yaml:/root/my-run.yaml \ llamastack/distribution-dell \ - --yaml-config /root/my-run.yaml \ + --config /root/my-run.yaml \ --port $LLAMA_STACK_PORT \ --env INFERENCE_MODEL=$INFERENCE_MODEL \ --env DEH_URL=$DEH_URL \ diff --git a/docs/source/distributions/self_hosted_distro/nvidia.md b/docs/source/distributions/self_hosted_distro/nvidia.md index a5bbbfdee..e84b5c525 100644 --- a/docs/source/distributions/self_hosted_distro/nvidia.md +++ b/docs/source/distributions/self_hosted_distro/nvidia.md @@ -143,7 +143,7 @@ docker run \ -p $LLAMA_STACK_PORT:$LLAMA_STACK_PORT \ -v ./run.yaml:/root/my-run.yaml \ llamastack/distribution-nvidia \ - --yaml-config /root/my-run.yaml \ + --config /root/my-run.yaml \ --port $LLAMA_STACK_PORT \ --env NVIDIA_API_KEY=$NVIDIA_API_KEY ``` diff --git a/docs/source/distributions/self_hosted_distro/ollama.md b/docs/source/distributions/self_hosted_distro/ollama.md index 5d8935fe2..4d148feda 100644 --- a/docs/source/distributions/self_hosted_distro/ollama.md +++ b/docs/source/distributions/self_hosted_distro/ollama.md @@ -19,6 +19,7 @@ The `llamastack/distribution-ollama` distribution consists of the following prov | datasetio | `remote::huggingface`, `inline::localfs` | | eval | `inline::meta-reference` | | inference | `remote::ollama` | +| post_training | `inline::huggingface` | | safety | `inline::llama-guard` | | scoring | `inline::basic`, `inline::llm-as-judge`, `inline::braintrust` | | telemetry | `inline::meta-reference` | @@ -97,7 +98,7 @@ docker run \ -v ~/.llama:/root/.llama \ -v ./llama_stack/templates/ollama/run-with-safety.yaml:/root/my-run.yaml \ llamastack/distribution-ollama \ - --yaml-config /root/my-run.yaml \ + --config /root/my-run.yaml \ --port $LLAMA_STACK_PORT \ --env INFERENCE_MODEL=$INFERENCE_MODEL \ --env SAFETY_MODEL=$SAFETY_MODEL \ diff --git a/docs/source/distributions/self_hosted_distro/remote-vllm.md b/docs/source/distributions/self_hosted_distro/remote-vllm.md index 2ff4bad5b..6e7cf410d 100644 --- a/docs/source/distributions/self_hosted_distro/remote-vllm.md +++ b/docs/source/distributions/self_hosted_distro/remote-vllm.md @@ -233,7 +233,7 @@ docker run \ -p $LLAMA_STACK_PORT:$LLAMA_STACK_PORT \ -v ./llama_stack/templates/remote-vllm/run.yaml:/root/my-run.yaml \ llamastack/distribution-remote-vllm \ - --yaml-config /root/my-run.yaml \ + --config /root/my-run.yaml \ --port $LLAMA_STACK_PORT \ --env INFERENCE_MODEL=$INFERENCE_MODEL \ --env VLLM_URL=http://host.docker.internal:$INFERENCE_PORT/v1 @@ -255,7 +255,7 @@ docker run \ -v ~/.llama:/root/.llama \ -v ./llama_stack/templates/remote-vllm/run-with-safety.yaml:/root/my-run.yaml \ llamastack/distribution-remote-vllm \ - --yaml-config /root/my-run.yaml \ + --config /root/my-run.yaml \ --port $LLAMA_STACK_PORT \ --env INFERENCE_MODEL=$INFERENCE_MODEL \ --env VLLM_URL=http://host.docker.internal:$INFERENCE_PORT/v1 \ diff --git a/docs/source/distributions/self_hosted_distro/sambanova.md b/docs/source/distributions/self_hosted_distro/sambanova.md index aaa8fd3cc..bb4842362 100644 --- a/docs/source/distributions/self_hosted_distro/sambanova.md +++ b/docs/source/distributions/self_hosted_distro/sambanova.md @@ -17,7 +17,7 @@ The `llamastack/distribution-sambanova` distribution consists of the following p |-----|-------------| | agents | `inline::meta-reference` | | inference | `remote::sambanova`, `inline::sentence-transformers` | -| safety | `inline::llama-guard` | +| safety | `remote::sambanova` | | telemetry | `inline::meta-reference` | | tool_runtime | `remote::brave-search`, `remote::tavily-search`, `inline::rag-runtime`, `remote::model-context-protocol`, `remote::wolfram-alpha` | | vector_io | `inline::faiss`, `remote::chromadb`, `remote::pgvector` | @@ -48,33 +48,44 @@ The following models are available by default: ### Prerequisite: API Keys -Make sure you have access to a SambaNova API Key. You can get one by visiting [SambaNova.ai](https://sambanova.ai/). +Make sure you have access to a SambaNova API Key. You can get one by visiting [SambaNova.ai](http://cloud.sambanova.ai?utm_source=llamastack&utm_medium=external&utm_campaign=cloud_signup). ## Running Llama Stack with SambaNova You can do this via Conda (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. +### Via Docker ```bash LLAMA_STACK_PORT=8321 +llama stack build --template sambanova --image-type container docker run \ -it \ - --pull always \ -p $LLAMA_STACK_PORT:$LLAMA_STACK_PORT \ - llamastack/distribution-sambanova \ + -v ~/.llama:/root/.llama \ + distribution-sambanova \ --port $LLAMA_STACK_PORT \ --env SAMBANOVA_API_KEY=$SAMBANOVA_API_KEY ``` + +### Via Venv + +```bash +llama stack build --template sambanova --image-type venv +llama stack run --image-type venv ~/.llama/distributions/sambanova/sambanova-run.yaml \ + --port $LLAMA_STACK_PORT \ + --env SAMBANOVA_API_KEY=$SAMBANOVA_API_KEY +``` + + ### Via Conda ```bash llama stack build --template sambanova --image-type conda -llama stack run ./run.yaml \ +llama stack run --image-type conda ~/.llama/distributions/sambanova/sambanova-run.yaml \ --port $LLAMA_STACK_PORT \ --env SAMBANOVA_API_KEY=$SAMBANOVA_API_KEY ``` diff --git a/docs/source/distributions/self_hosted_distro/tgi.md b/docs/source/distributions/self_hosted_distro/tgi.md index 7a75aa559..24f9d03ec 100644 --- a/docs/source/distributions/self_hosted_distro/tgi.md +++ b/docs/source/distributions/self_hosted_distro/tgi.md @@ -117,7 +117,7 @@ docker run \ -v ~/.llama:/root/.llama \ -v ./llama_stack/templates/tgi/run-with-safety.yaml:/root/my-run.yaml \ llamastack/distribution-tgi \ - --yaml-config /root/my-run.yaml \ + --config /root/my-run.yaml \ --port $LLAMA_STACK_PORT \ --env INFERENCE_MODEL=$INFERENCE_MODEL \ --env TGI_URL=http://host.docker.internal:$INFERENCE_PORT \ diff --git a/docs/source/providers/index.md b/docs/source/providers/index.md index 1d1a6e081..1f5026479 100644 --- a/docs/source/providers/index.md +++ b/docs/source/providers/index.md @@ -30,6 +30,18 @@ Runs inference with an LLM. ## Post Training Fine-tunes a model. +#### Post Training Providers +The following providers are available for Post Training: + +```{toctree} +:maxdepth: 1 + +external +post_training/huggingface +post_training/torchtune +post_training/nvidia_nemo +``` + ## Safety Applies safety policies to the output at a Systems (not only model) level. diff --git a/docs/source/providers/post_training/huggingface.md b/docs/source/providers/post_training/huggingface.md new file mode 100644 index 000000000..c342203a8 --- /dev/null +++ b/docs/source/providers/post_training/huggingface.md @@ -0,0 +1,122 @@ +--- +orphan: true +--- +# HuggingFace SFTTrainer + +[HuggingFace SFTTrainer](https://huggingface.co/docs/trl/en/sft_trainer) is an inline post training provider for Llama Stack. It allows you to run supervised fine tuning on a variety of models using many datasets + +## Features + +- Simple access through the post_training API +- Fully integrated with Llama Stack +- GPU support, CPU support, and MPS support (MacOS Metal Performance Shaders) + +## Usage + +To use the HF SFTTrainer in your Llama Stack project, follow these steps: + +1. Configure your Llama Stack project to use this provider. +2. Kick off a SFT job using the Llama Stack post_training API. + +## Setup + +You can access the HuggingFace trainer via the `ollama` distribution: + +```bash +llama stack build --template ollama --image-type venv +llama stack run --image-type venv ~/.llama/distributions/ollama/ollama-run.yaml +``` + +## Run Training + +You can access the provider and the `supervised_fine_tune` method via the post_training API: + +```python +import time +import uuid + + +from llama_stack_client.types import ( + post_training_supervised_fine_tune_params, + algorithm_config_param, +) + + +def create_http_client(): + from llama_stack_client import LlamaStackClient + + return LlamaStackClient(base_url="http://localhost:8321") + + +client = create_http_client() + +# Example Dataset +client.datasets.register( + purpose="post-training/messages", + source={ + "type": "uri", + "uri": "huggingface://datasets/llamastack/simpleqa?split=train", + }, + dataset_id="simpleqa", +) + +training_config = post_training_supervised_fine_tune_params.TrainingConfig( + data_config=post_training_supervised_fine_tune_params.TrainingConfigDataConfig( + batch_size=32, + data_format="instruct", + dataset_id="simpleqa", + shuffle=True, + ), + gradient_accumulation_steps=1, + max_steps_per_epoch=0, + max_validation_steps=1, + n_epochs=4, +) + +algorithm_config = algorithm_config_param.LoraFinetuningConfig( # this config is also currently mandatory but should not be + alpha=1, + apply_lora_to_mlp=True, + apply_lora_to_output=False, + lora_attn_modules=["q_proj"], + rank=1, + type="LoRA", +) + +job_uuid = f"test-job{uuid.uuid4()}" + +# Example Model +training_model = "ibm-granite/granite-3.3-8b-instruct" + +start_time = time.time() +response = client.post_training.supervised_fine_tune( + job_uuid=job_uuid, + logger_config={}, + model=training_model, + hyperparam_search_config={}, + training_config=training_config, + algorithm_config=algorithm_config, + checkpoint_dir="output", +) +print("Job: ", job_uuid) + + +# Wait for the job to complete! +while True: + status = client.post_training.job.status(job_uuid=job_uuid) + if not status: + print("Job not found") + break + + print(status) + if status.status == "completed": + break + + print("Waiting for job to complete...") + time.sleep(5) + +end_time = time.time() +print("Job completed in", end_time - start_time, "seconds!") + +print("Artifacts:") +print(client.post_training.job.artifacts(job_uuid=job_uuid)) +``` diff --git a/docs/source/providers/post_training/nvidia_nemo.md b/docs/source/providers/post_training/nvidia_nemo.md new file mode 100644 index 000000000..1a7adbe16 --- /dev/null +++ b/docs/source/providers/post_training/nvidia_nemo.md @@ -0,0 +1,163 @@ +--- +orphan: true +--- +# NVIDIA NEMO + +[NVIDIA NEMO](https://developer.nvidia.com/nemo-framework) is a remote post training provider for Llama Stack. It provides enterprise-grade fine-tuning capabilities through NVIDIA's NeMo Customizer service. + +## Features + +- Enterprise-grade fine-tuning capabilities +- Support for LoRA and SFT fine-tuning +- Integration with NVIDIA's NeMo Customizer service +- Support for various NVIDIA-optimized models +- Efficient training with NVIDIA hardware acceleration + +## Usage + +To use NVIDIA NEMO in your Llama Stack project, follow these steps: + +1. Configure your Llama Stack project to use this provider. +2. Set up your NVIDIA API credentials. +3. Kick off a fine-tuning job using the Llama Stack post_training API. + +## Setup + +You'll need to set the following environment variables: + +```bash +export NVIDIA_API_KEY="your-api-key" +export NVIDIA_DATASET_NAMESPACE="default" +export NVIDIA_CUSTOMIZER_URL="your-customizer-url" +export NVIDIA_PROJECT_ID="your-project-id" +export NVIDIA_OUTPUT_MODEL_DIR="your-output-model-dir" +``` + +## Run Training + +You can access the provider and the `supervised_fine_tune` method via the post_training API: + +```python +import time +import uuid + +from llama_stack_client.types import ( + post_training_supervised_fine_tune_params, + algorithm_config_param, +) + + +def create_http_client(): + from llama_stack_client import LlamaStackClient + + return LlamaStackClient(base_url="http://localhost:8321") + + +client = create_http_client() + +# Example Dataset +client.datasets.register( + purpose="post-training/messages", + source={ + "type": "uri", + "uri": "huggingface://datasets/llamastack/simpleqa?split=train", + }, + dataset_id="simpleqa", +) + +training_config = post_training_supervised_fine_tune_params.TrainingConfig( + data_config=post_training_supervised_fine_tune_params.TrainingConfigDataConfig( + batch_size=8, # Default batch size for NEMO + data_format="instruct", + dataset_id="simpleqa", + shuffle=True, + ), + n_epochs=50, # Default epochs for NEMO + optimizer_config=post_training_supervised_fine_tune_params.TrainingConfigOptimizerConfig( + lr=0.0001, # Default learning rate + weight_decay=0.01, # NEMO-specific parameter + ), + # NEMO-specific parameters + log_every_n_steps=None, + val_check_interval=0.25, + sequence_packing_enabled=False, + hidden_dropout=None, + attention_dropout=None, + ffn_dropout=None, +) + +algorithm_config = algorithm_config_param.LoraFinetuningConfig( + alpha=16, # Default alpha for NEMO + type="LoRA", +) + +job_uuid = f"test-job{uuid.uuid4()}" + +# Example Model - must be a supported NEMO model +training_model = "meta/llama-3.1-8b-instruct" + +start_time = time.time() +response = client.post_training.supervised_fine_tune( + job_uuid=job_uuid, + logger_config={}, + model=training_model, + hyperparam_search_config={}, + training_config=training_config, + algorithm_config=algorithm_config, + checkpoint_dir="output", +) +print("Job: ", job_uuid) + +# Wait for the job to complete! +while True: + status = client.post_training.job.status(job_uuid=job_uuid) + if not status: + print("Job not found") + break + + print(status) + if status.status == "completed": + break + + print("Waiting for job to complete...") + time.sleep(5) + +end_time = time.time() +print("Job completed in", end_time - start_time, "seconds!") + +print("Artifacts:") +print(client.post_training.job.artifacts(job_uuid=job_uuid)) +``` + +## Supported Models + +Currently supports the following models: +- meta/llama-3.1-8b-instruct +- meta/llama-3.2-1b-instruct + +## Supported Parameters + +### TrainingConfig +- n_epochs (default: 50) +- data_config +- optimizer_config +- log_every_n_steps +- val_check_interval (default: 0.25) +- sequence_packing_enabled (default: False) +- hidden_dropout (0.0-1.0) +- attention_dropout (0.0-1.0) +- ffn_dropout (0.0-1.0) + +### DataConfig +- dataset_id +- batch_size (default: 8) + +### OptimizerConfig +- lr (default: 0.0001) +- weight_decay (default: 0.01) + +### LoRA Config +- alpha (default: 16) +- type (must be "LoRA") + +Note: Some parameters from the standard Llama Stack API are not supported and will be ignored with a warning. diff --git a/docs/source/providers/post_training/torchtune.md b/docs/source/providers/post_training/torchtune.md new file mode 100644 index 000000000..ef72505b1 --- /dev/null +++ b/docs/source/providers/post_training/torchtune.md @@ -0,0 +1,125 @@ +--- +orphan: true +--- +# TorchTune + +[TorchTune](https://github.com/pytorch/torchtune) is an inline post training provider for Llama Stack. It provides a simple and efficient way to fine-tune language models using PyTorch. + +## Features + +- Simple access through the post_training API +- Fully integrated with Llama Stack +- GPU support and single device capabilities. +- Support for LoRA + +## Usage + +To use TorchTune in your Llama Stack project, follow these steps: + +1. Configure your Llama Stack project to use this provider. +2. Kick off a fine-tuning job using the Llama Stack post_training API. + +## Setup + +You can access the TorchTune trainer by writing your own yaml pointing to the provider: + +```yaml +post_training: + - provider_id: torchtune + provider_type: inline::torchtune + config: {} +``` + +you can then build and run your own stack with this provider. + +## Run Training + +You can access the provider and the `supervised_fine_tune` method via the post_training API: + +```python +import time +import uuid + +from llama_stack_client.types import ( + post_training_supervised_fine_tune_params, + algorithm_config_param, +) + + +def create_http_client(): + from llama_stack_client import LlamaStackClient + + return LlamaStackClient(base_url="http://localhost:8321") + + +client = create_http_client() + +# Example Dataset +client.datasets.register( + purpose="post-training/messages", + source={ + "type": "uri", + "uri": "huggingface://datasets/llamastack/simpleqa?split=train", + }, + dataset_id="simpleqa", +) + +training_config = post_training_supervised_fine_tune_params.TrainingConfig( + data_config=post_training_supervised_fine_tune_params.TrainingConfigDataConfig( + batch_size=32, + data_format="instruct", + dataset_id="simpleqa", + shuffle=True, + ), + gradient_accumulation_steps=1, + max_steps_per_epoch=0, + max_validation_steps=1, + n_epochs=4, +) + +algorithm_config = algorithm_config_param.LoraFinetuningConfig( + alpha=1, + apply_lora_to_mlp=True, + apply_lora_to_output=False, + lora_attn_modules=["q_proj"], + rank=1, + type="LoRA", +) + +job_uuid = f"test-job{uuid.uuid4()}" + +# Example Model +training_model = "meta-llama/Llama-2-7b-hf" + +start_time = time.time() +response = client.post_training.supervised_fine_tune( + job_uuid=job_uuid, + logger_config={}, + model=training_model, + hyperparam_search_config={}, + training_config=training_config, + algorithm_config=algorithm_config, + checkpoint_dir="output", +) +print("Job: ", job_uuid) + +# Wait for the job to complete! +while True: + status = client.post_training.job.status(job_uuid=job_uuid) + if not status: + print("Job not found") + break + + print(status) + if status.status == "completed": + break + + print("Waiting for job to complete...") + time.sleep(5) + +end_time = time.time() +print("Job completed in", end_time - start_time, "seconds!") + +print("Artifacts:") +print(client.post_training.job.artifacts(job_uuid=job_uuid)) +``` diff --git a/docs/source/providers/vector_io/sqlite-vec.md b/docs/source/providers/vector_io/sqlite-vec.md index 43d10c751..49ba659f7 100644 --- a/docs/source/providers/vector_io/sqlite-vec.md +++ b/docs/source/providers/vector_io/sqlite-vec.md @@ -66,6 +66,25 @@ To use sqlite-vec in your Llama Stack project, follow these steps: 2. Configure your Llama Stack project to use SQLite-Vec. 3. Start storing and querying vectors. +## Supported Search Modes + +The sqlite-vec provider supports both vector-based and keyword-based (full-text) search modes. + +When using the RAGTool interface, you can specify the desired search behavior via the `mode` parameter in +`RAGQueryConfig`. For example: + +```python +from llama_stack.apis.tool_runtime.rag import RAGQueryConfig + +query_config = RAGQueryConfig(max_chunks=6, mode="vector") + +results = client.tool_runtime.rag_tool.query( + vector_db_ids=[vector_db_id], + content="what is torchtune", + query_config=query_config, +) +``` + ## Installation You can install SQLite-Vec using pip: diff --git a/kvant_build_local.sh b/kvant_build_local.sh new file mode 100755 index 000000000..9701c57dc --- /dev/null +++ b/kvant_build_local.sh @@ -0,0 +1,6 @@ +#!/usr/bin/env bash + +export USE_COPY_NOT_MOUNT=true +export LLAMA_STACK_DIR=. + +uvx --from . llama stack build --template kvant --image-type container --image-name kvant diff --git a/kvant_start_local.sh b/kvant_start_local.sh new file mode 100755 index 000000000..db5bff84a --- /dev/null +++ b/kvant_start_local.sh @@ -0,0 +1,17 @@ +#!/usr/bin/env bash + +export LLAMA_STACK_PORT=8321 +# VLLM_API_TOKEN= env file +# KEYCLOAK_CLIENT_SECRET= env file + + +docker run -it \ + -p $LLAMA_STACK_PORT:$LLAMA_STACK_PORT \ + -v $(pwd)/data:/root/.llama \ + --mount type=bind,source="$(pwd)"/llama_stack/templates/kvant/run.yaml,target=/root/.llama/config.yaml,readonly \ + --entrypoint python \ + --env-file ./.env \ + distribution-kvant:dev \ + -m llama_stack.distribution.server.server --config /root/.llama/config.yaml \ + --port $LLAMA_STACK_PORT \ + diff --git a/llama_stack/apis/agents/agents.py b/llama_stack/apis/agents/agents.py index b2f85336c..b79c512b8 100644 --- a/llama_stack/apis/agents/agents.py +++ b/llama_stack/apis/agents/agents.py @@ -13,7 +13,7 @@ from typing import Annotated, Any, Literal, Protocol, runtime_checkable from pydantic import BaseModel, ConfigDict, Field from llama_stack.apis.common.content_types import URL, ContentDelta, InterleavedContent -from llama_stack.apis.common.responses import PaginatedResponse +from llama_stack.apis.common.responses import Order, PaginatedResponse from llama_stack.apis.inference import ( CompletionMessage, ResponseFormat, @@ -31,6 +31,8 @@ from llama_stack.apis.tools import ToolDef from llama_stack.schema_utils import json_schema_type, register_schema, webmethod from .openai_responses import ( + ListOpenAIResponseInputItem, + ListOpenAIResponseObject, OpenAIResponseInput, OpenAIResponseInputTool, OpenAIResponseObject, @@ -579,14 +581,14 @@ class Agents(Protocol): # # Both of these APIs are inherently stateful. - @webmethod(route="/openai/v1/responses/{id}", method="GET") + @webmethod(route="/openai/v1/responses/{response_id}", method="GET") async def get_openai_response( self, - id: str, + response_id: str, ) -> OpenAIResponseObject: """Retrieve an OpenAI response by its ID. - :param id: The ID of the OpenAI response to retrieve. + :param response_id: The ID of the OpenAI response to retrieve. :returns: An OpenAIResponseObject. """ ... @@ -596,6 +598,7 @@ class Agents(Protocol): self, input: str | list[OpenAIResponseInput], model: str, + instructions: str | None = None, previous_response_id: str | None = None, store: bool | None = True, stream: bool | None = False, @@ -610,3 +613,43 @@ class Agents(Protocol): :returns: An OpenAIResponseObject. """ ... + + @webmethod(route="/openai/v1/responses", method="GET") + async def list_openai_responses( + self, + after: str | None = None, + limit: int | None = 50, + model: str | None = None, + order: Order | None = Order.desc, + ) -> ListOpenAIResponseObject: + """List all OpenAI responses. + + :param after: The ID of the last response to return. + :param limit: The number of responses to return. + :param model: The model to filter responses by. + :param order: The order to sort responses by when sorted by created_at ('asc' or 'desc'). + :returns: A ListOpenAIResponseObject. + """ + ... + + @webmethod(route="/openai/v1/responses/{response_id}/input_items", method="GET") + async def list_openai_response_input_items( + self, + response_id: str, + after: str | None = None, + before: str | None = None, + include: list[str] | None = None, + limit: int | None = 20, + order: Order | None = Order.desc, + ) -> ListOpenAIResponseInputItem: + """List input items for a given OpenAI response. + + :param response_id: The ID of the response to retrieve input items for. + :param after: An item ID to list items after, used for pagination. + :param before: An item ID to list items before, used for pagination. + :param include: Additional fields to include in the response. + :param limit: A limit on the number of objects to be returned. Limit can range between 1 and 100, and the default is 20. + :param order: The order to return the input items in. Default is desc. + :returns: An ListOpenAIResponseInputItem. + """ + ... diff --git a/llama_stack/apis/agents/openai_responses.py b/llama_stack/apis/agents/openai_responses.py index dcf0c7f9c..6806e1d3f 100644 --- a/llama_stack/apis/agents/openai_responses.py +++ b/llama_stack/apis/agents/openai_responses.py @@ -10,6 +10,9 @@ from pydantic import BaseModel, Field from llama_stack.schema_utils import json_schema_type, register_schema +# NOTE(ashwin): this file is literally a copy of the OpenAI responses API schema. We should probably +# take their YAML and generate this file automatically. Their YAML is available. + @json_schema_type class OpenAIResponseError(BaseModel): @@ -79,16 +82,45 @@ class OpenAIResponseOutputMessageWebSearchToolCall(BaseModel): @json_schema_type class OpenAIResponseOutputMessageFunctionToolCall(BaseModel): - arguments: str call_id: str name: str + arguments: str type: Literal["function_call"] = "function_call" + id: str | None = None + status: str | None = None + + +@json_schema_type +class OpenAIResponseOutputMessageMCPCall(BaseModel): id: str - status: str + type: Literal["mcp_call"] = "mcp_call" + arguments: str + name: str + server_label: str + error: str | None = None + output: str | None = None + + +class MCPListToolsTool(BaseModel): + input_schema: dict[str, Any] + name: str + description: str | None = None + + +@json_schema_type +class OpenAIResponseOutputMessageMCPListTools(BaseModel): + id: str + type: Literal["mcp_list_tools"] = "mcp_list_tools" + server_label: str + tools: list[MCPListToolsTool] OpenAIResponseOutput = Annotated[ - OpenAIResponseMessage | OpenAIResponseOutputMessageWebSearchToolCall | OpenAIResponseOutputMessageFunctionToolCall, + OpenAIResponseMessage + | OpenAIResponseOutputMessageWebSearchToolCall + | OpenAIResponseOutputMessageFunctionToolCall + | OpenAIResponseOutputMessageMCPCall + | OpenAIResponseOutputMessageMCPListTools, Field(discriminator="type"), ] register_schema(OpenAIResponseOutput, name="OpenAIResponseOutput") @@ -117,6 +149,16 @@ class OpenAIResponseObjectStreamResponseCreated(BaseModel): type: Literal["response.created"] = "response.created" +@json_schema_type +class OpenAIResponseObjectStreamResponseOutputTextDelta(BaseModel): + content_index: int + delta: str + item_id: str + output_index: int + sequence_number: int + type: Literal["response.output_text.delta"] = "response.output_text.delta" + + @json_schema_type class OpenAIResponseObjectStreamResponseCompleted(BaseModel): response: OpenAIResponseObject @@ -124,7 +166,9 @@ class OpenAIResponseObjectStreamResponseCompleted(BaseModel): OpenAIResponseObjectStream = Annotated[ - OpenAIResponseObjectStreamResponseCreated | OpenAIResponseObjectStreamResponseCompleted, + OpenAIResponseObjectStreamResponseCreated + | OpenAIResponseObjectStreamResponseOutputTextDelta + | OpenAIResponseObjectStreamResponseCompleted, Field(discriminator="type"), ] register_schema(OpenAIResponseObjectStream, name="OpenAIResponseObjectStream") @@ -186,13 +230,50 @@ class OpenAIResponseInputToolFileSearch(BaseModel): # TODO: add filters +class ApprovalFilter(BaseModel): + always: list[str] | None = None + never: list[str] | None = None + + +class AllowedToolsFilter(BaseModel): + tool_names: list[str] | None = None + + +@json_schema_type +class OpenAIResponseInputToolMCP(BaseModel): + type: Literal["mcp"] = "mcp" + server_label: str + server_url: str + headers: dict[str, Any] | None = None + + require_approval: Literal["always"] | Literal["never"] | ApprovalFilter = "never" + allowed_tools: list[str] | AllowedToolsFilter | None = None + + OpenAIResponseInputTool = Annotated[ - OpenAIResponseInputToolWebSearch | OpenAIResponseInputToolFileSearch | OpenAIResponseInputToolFunction, + OpenAIResponseInputToolWebSearch + | OpenAIResponseInputToolFileSearch + | OpenAIResponseInputToolFunction + | OpenAIResponseInputToolMCP, Field(discriminator="type"), ] register_schema(OpenAIResponseInputTool, name="OpenAIResponseInputTool") -class OpenAIResponseInputItemList(BaseModel): +class ListOpenAIResponseInputItem(BaseModel): data: list[OpenAIResponseInput] object: Literal["list"] = "list" + + +@json_schema_type +class OpenAIResponseObjectWithInput(OpenAIResponseObject): + input: list[OpenAIResponseInput] + + +@json_schema_type +class ListOpenAIResponseObject(BaseModel): + data: list[OpenAIResponseObjectWithInput] + has_more: bool + first_id: str + last_id: str + object: Literal["list"] = "list" diff --git a/llama_stack/apis/common/deployment_types.py b/llama_stack/apis/common/deployment_types.py deleted file mode 100644 index 4d01d7ad1..000000000 --- a/llama_stack/apis/common/deployment_types.py +++ /dev/null @@ -1,30 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# All rights reserved. -# -# This source code is licensed under the terms described in the LICENSE file in -# the root directory of this source tree. - -from enum import Enum -from typing import Any - -from pydantic import BaseModel - -from llama_stack.apis.common.content_types import URL -from llama_stack.schema_utils import json_schema_type - - -@json_schema_type -class RestAPIMethod(Enum): - GET = "GET" - POST = "POST" - PUT = "PUT" - DELETE = "DELETE" - - -@json_schema_type -class RestAPIExecutionConfig(BaseModel): - url: URL - method: RestAPIMethod - params: dict[str, Any] | None = None - headers: dict[str, Any] | None = None - body: dict[str, Any] | None = None diff --git a/llama_stack/apis/common/responses.py b/llama_stack/apis/common/responses.py index b3bb5cb6b..5cb41e23d 100644 --- a/llama_stack/apis/common/responses.py +++ b/llama_stack/apis/common/responses.py @@ -4,6 +4,7 @@ # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. +from enum import Enum from typing import Any from pydantic import BaseModel @@ -11,6 +12,11 @@ from pydantic import BaseModel from llama_stack.schema_utils import json_schema_type +class Order(Enum): + asc = "asc" + desc = "desc" + + @json_schema_type class PaginatedResponse(BaseModel): """A generic paginated response that follows a simple format. diff --git a/llama_stack/apis/inference/inference.py b/llama_stack/apis/inference/inference.py index 3c91b5a6e..74697dd18 100644 --- a/llama_stack/apis/inference/inference.py +++ b/llama_stack/apis/inference/inference.py @@ -19,6 +19,7 @@ from pydantic import BaseModel, Field, field_validator from typing_extensions import TypedDict from llama_stack.apis.common.content_types import ContentDelta, InterleavedContent, InterleavedContentItem +from llama_stack.apis.common.responses import Order from llama_stack.apis.models import Model from llama_stack.apis.telemetry.telemetry import MetricResponseMixin from llama_stack.models.llama.datatypes import ( @@ -782,6 +783,48 @@ class OpenAICompletion(BaseModel): object: Literal["text_completion"] = "text_completion" +@json_schema_type +class OpenAIEmbeddingData(BaseModel): + """A single embedding data object from an OpenAI-compatible embeddings response. + + :param object: The object type, which will be "embedding" + :param embedding: The embedding vector as a list of floats (when encoding_format="float") or as a base64-encoded string (when encoding_format="base64") + :param index: The index of the embedding in the input list + """ + + object: Literal["embedding"] = "embedding" + embedding: list[float] | str + index: int + + +@json_schema_type +class OpenAIEmbeddingUsage(BaseModel): + """Usage information for an OpenAI-compatible embeddings response. + + :param prompt_tokens: The number of tokens in the input + :param total_tokens: The total number of tokens used + """ + + prompt_tokens: int + total_tokens: int + + +@json_schema_type +class OpenAIEmbeddingsResponse(BaseModel): + """Response from an OpenAI-compatible embeddings request. + + :param object: The object type, which will be "list" + :param data: List of embedding data objects + :param model: The model that was used to generate the embeddings + :param usage: Usage information + """ + + object: Literal["list"] = "list" + data: list[OpenAIEmbeddingData] + model: str + usage: OpenAIEmbeddingUsage + + class ModelStore(Protocol): async def get_model(self, identifier: str) -> Model: ... @@ -820,15 +863,27 @@ class BatchChatCompletionResponse(BaseModel): batch: list[ChatCompletionResponse] +class OpenAICompletionWithInputMessages(OpenAIChatCompletion): + input_messages: list[OpenAIMessageParam] + + +@json_schema_type +class ListOpenAIChatCompletionResponse(BaseModel): + data: list[OpenAICompletionWithInputMessages] + has_more: bool + first_id: str + last_id: str + object: Literal["list"] = "list" + + @runtime_checkable @trace_protocol -class Inference(Protocol): - """Llama Stack Inference API for generating completions, chat completions, and embeddings. - - This API provides the raw interface to the underlying models. Two kinds of models are supported: - - LLM models: these models generate "raw" and "chat" (conversational) completions. - - Embedding models: these models generate embeddings to be used for semantic search. +class InferenceProvider(Protocol): """ + This protocol defines the interface that should be implemented by all inference providers. + """ + + API_NAMESPACE: str = "Inference" model_store: ModelStore | None = None @@ -1062,3 +1117,59 @@ class Inference(Protocol): :returns: An OpenAIChatCompletion. """ ... + + @webmethod(route="/openai/v1/embeddings", method="POST") + async def openai_embeddings( + self, + model: str, + input: str | list[str], + encoding_format: str | None = "float", + dimensions: int | None = None, + user: str | None = None, + ) -> OpenAIEmbeddingsResponse: + """Generate OpenAI-compatible embeddings for the given input using the specified model. + + :param model: The identifier of the model to use. The model must be an embedding model registered with Llama Stack and available via the /models endpoint. + :param input: Input text to embed, encoded as a string or array of strings. To embed multiple inputs in a single request, pass an array of strings. + :param encoding_format: (Optional) The format to return the embeddings in. Can be either "float" or "base64". Defaults to "float". + :param dimensions: (Optional) The number of dimensions the resulting output embeddings should have. Only supported in text-embedding-3 and later models. + :param user: (Optional) A unique identifier representing your end-user, which can help OpenAI to monitor and detect abuse. + :returns: An OpenAIEmbeddingsResponse containing the embeddings. + """ + ... + + +class Inference(InferenceProvider): + """Llama Stack Inference API for generating completions, chat completions, and embeddings. + + This API provides the raw interface to the underlying models. Two kinds of models are supported: + - LLM models: these models generate "raw" and "chat" (conversational) completions. + - Embedding models: these models generate embeddings to be used for semantic search. + """ + + @webmethod(route="/openai/v1/chat/completions", method="GET") + async def list_chat_completions( + self, + after: str | None = None, + limit: int | None = 20, + model: str | None = None, + order: Order | None = Order.desc, + ) -> ListOpenAIChatCompletionResponse: + """List all chat completions. + + :param after: The ID of the last chat completion to return. + :param limit: The maximum number of chat completions to return. + :param model: The model to filter by. + :param order: The order to sort the chat completions by: "asc" or "desc". Defaults to "desc". + :returns: A ListOpenAIChatCompletionResponse. + """ + raise NotImplementedError("List chat completions is not implemented") + + @webmethod(route="/openai/v1/chat/completions/{completion_id}", method="GET") + async def get_chat_completion(self, completion_id: str) -> OpenAICompletionWithInputMessages: + """Describe a chat completion by its ID. + + :param completion_id: ID of the chat completion. + :returns: A OpenAICompletionWithInputMessages. + """ + raise NotImplementedError("Get chat completion is not implemented") diff --git a/llama_stack/apis/tools/rag_tool.py b/llama_stack/apis/tools/rag_tool.py index de3e4c62c..1e3542f74 100644 --- a/llama_stack/apis/tools/rag_tool.py +++ b/llama_stack/apis/tools/rag_tool.py @@ -76,6 +76,7 @@ class RAGQueryConfig(BaseModel): :param chunk_template: 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" + :param mode: Search mode for retrieval—either "vector" or "keyword". Default "vector". """ # This config defines how a query is generated using the messages @@ -84,6 +85,7 @@ 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 @field_validator("chunk_template") def validate_chunk_template(cls, v: str) -> str: diff --git a/llama_stack/apis/tools/tools.py b/llama_stack/apis/tools/tools.py index 2f62b0ba1..0c8d47edf 100644 --- a/llama_stack/apis/tools/tools.py +++ b/llama_stack/apis/tools/tools.py @@ -27,18 +27,10 @@ class ToolParameter(BaseModel): default: Any | None = None -@json_schema_type -class ToolHost(Enum): - distribution = "distribution" - client = "client" - model_context_protocol = "model_context_protocol" - - @json_schema_type class Tool(Resource): type: Literal[ResourceType.tool] = ResourceType.tool toolgroup_id: str - tool_host: ToolHost description: str parameters: list[ToolParameter] metadata: dict[str, Any] | None = None @@ -76,8 +68,8 @@ class ToolInvocationResult(BaseModel): class ToolStore(Protocol): - def get_tool(self, tool_name: str) -> Tool: ... - def get_tool_group(self, toolgroup_id: str) -> ToolGroup: ... + async def get_tool(self, tool_name: str) -> Tool: ... + async def get_tool_group(self, toolgroup_id: str) -> ToolGroup: ... class ListToolGroupsResponse(BaseModel): diff --git a/llama_stack/apis/vector_io/vector_io.py b/llama_stack/apis/vector_io/vector_io.py index 3ac62d42c..44cc8f904 100644 --- a/llama_stack/apis/vector_io/vector_io.py +++ b/llama_stack/apis/vector_io/vector_io.py @@ -19,8 +19,16 @@ from llama_stack.schema_utils import json_schema_type, webmethod class Chunk(BaseModel): + """ + A chunk of content that can be inserted into a vector database. + :param content: The content of the chunk, which can be interleaved text, images, or other types. + :param embedding: Optional embedding for the chunk. If not provided, it will be computed later. + :param metadata: Metadata associated with the chunk, such as document ID, source, or other relevant information. + """ + content: InterleavedContent metadata: dict[str, Any] = Field(default_factory=dict) + embedding: list[float] | None = None @json_schema_type @@ -50,7 +58,10 @@ class VectorIO(Protocol): """Insert chunks into a vector database. :param vector_db_id: The identifier of the vector database to insert the chunks into. - :param chunks: The chunks to insert. + :param chunks: The chunks to insert. Each `Chunk` should contain content which can be interleaved text, images, or other types. + `metadata`: `dict[str, Any]` and `embedding`: `List[float]` are optional. + If `metadata` is provided, you configure how Llama Stack formats the chunk during generation. + If `embedding` is not provided, it will be computed later. :param ttl_seconds: The time to live of the chunks. """ ... diff --git a/llama_stack/cli/download.py b/llama_stack/cli/download.py index 09c753776..b96842119 100644 --- a/llama_stack/cli/download.py +++ b/llama_stack/cli/download.py @@ -9,6 +9,7 @@ import asyncio import json import os import shutil +import sys from dataclasses import dataclass from datetime import datetime, timezone from functools import partial @@ -377,14 +378,15 @@ def _meta_download( downloader = ParallelDownloader(max_concurrent_downloads=max_concurrent_downloads) asyncio.run(downloader.download_all(tasks)) - cprint(f"\nSuccessfully downloaded model to {output_dir}", "green") + cprint(f"\nSuccessfully downloaded model to {output_dir}", color="green", file=sys.stderr) cprint( f"\nView MD5 checksum files at: {output_dir / 'checklist.chk'}", - "white", + file=sys.stderr, ) cprint( f"\n[Optionally] To run MD5 checksums, use the following command: llama model verify-download --model-id {model_id}", - "yellow", + color="yellow", + file=sys.stderr, ) diff --git a/llama_stack/cli/stack/_build.py b/llama_stack/cli/stack/_build.py index 37147e905..f6f72946a 100644 --- a/llama_stack/cli/stack/_build.py +++ b/llama_stack/cli/stack/_build.py @@ -12,6 +12,7 @@ import shutil import sys import textwrap from functools import lru_cache +from importlib.abc import Traversable from pathlib import Path import yaml @@ -78,6 +79,7 @@ def run_stack_build_command(args: argparse.Namespace) -> None: cprint( f"Could not find template {args.template}. Please run `llama stack build --list-templates` to check out the available templates", color="red", + file=sys.stderr, ) sys.exit(1) build_config = available_templates[args.template] @@ -87,6 +89,7 @@ def run_stack_build_command(args: argparse.Namespace) -> None: cprint( f"Please specify a image-type ({' | '.join(e.value for e in ImageType)}) for {args.template}", color="red", + file=sys.stderr, ) sys.exit(1) elif args.providers: @@ -96,6 +99,7 @@ def run_stack_build_command(args: argparse.Namespace) -> None: cprint( "Could not parse `--providers`. Please ensure the list is in the format api1=provider1,api2=provider2", color="red", + file=sys.stderr, ) sys.exit(1) api, provider = api_provider.split("=") @@ -104,6 +108,7 @@ def run_stack_build_command(args: argparse.Namespace) -> None: cprint( f"{api} is not a valid API.", color="red", + file=sys.stderr, ) sys.exit(1) if provider in providers_for_api: @@ -112,6 +117,7 @@ def run_stack_build_command(args: argparse.Namespace) -> None: cprint( f"{provider} is not a valid provider for the {api} API.", color="red", + file=sys.stderr, ) sys.exit(1) distribution_spec = DistributionSpec( @@ -122,6 +128,7 @@ def run_stack_build_command(args: argparse.Namespace) -> None: cprint( f"Please specify a image-type (container | conda | venv) for {args.template}", color="red", + file=sys.stderr, ) sys.exit(1) @@ -150,12 +157,14 @@ def run_stack_build_command(args: argparse.Namespace) -> None: cprint( f"No current conda environment detected or specified, will create a new conda environment with the name `llamastack-{name}`", color="yellow", + file=sys.stderr, ) image_name = f"llamastack-{name}" else: cprint( f"Using conda environment {image_name}", color="green", + file=sys.stderr, ) else: image_name = f"llamastack-{name}" @@ -168,9 +177,10 @@ def run_stack_build_command(args: argparse.Namespace) -> None: """, ), color="green", + file=sys.stderr, ) - print("Tip: use to see options for the providers.\n") + cprint("Tip: use to see options for the providers.\n", color="green", file=sys.stderr) providers = dict() for api, providers_for_api in get_provider_registry().items(): @@ -206,10 +216,13 @@ def run_stack_build_command(args: argparse.Namespace) -> None: contents = yaml.safe_load(f) contents = replace_env_vars(contents) build_config = BuildConfig(**contents) + if args.image_type: + build_config.image_type = args.image_type except Exception as e: cprint( f"Could not parse config file {args.config}: {e}", color="red", + file=sys.stderr, ) sys.exit(1) @@ -236,25 +249,27 @@ def run_stack_build_command(args: argparse.Namespace) -> None: cprint( f"Error building stack: {exc}", color="red", + file=sys.stderr, ) - cprint("Stack trace:", color="red") + cprint("Stack trace:", color="red", file=sys.stderr) traceback.print_exc() sys.exit(1) + if run_config is None: cprint( "Run config path is empty", color="red", + file=sys.stderr, ) sys.exit(1) if args.run: - run_config = Path(run_config) config_dict = yaml.safe_load(run_config.read_text()) config = parse_and_maybe_upgrade_config(config_dict) - if not os.path.exists(str(config.external_providers_dir)): - os.makedirs(str(config.external_providers_dir), exist_ok=True) + if config.external_providers_dir and not config.external_providers_dir.exists(): + config.external_providers_dir.mkdir(exist_ok=True) run_args = formulate_run_args(args.image_type, args.image_name, config, args.template) - run_args.extend([run_config, str(os.getenv("LLAMA_STACK_PORT", 8321))]) + run_args.extend([str(os.getenv("LLAMA_STACK_PORT", 8321)), "--config", run_config]) run_command(run_args) @@ -262,7 +277,7 @@ def _generate_run_config( build_config: BuildConfig, build_dir: Path, image_name: str, -) -> str: +) -> Path: """ Generate a run.yaml template file for user to edit from a build.yaml file """ @@ -302,6 +317,7 @@ def _generate_run_config( cprint( f"Failed to import provider {provider_type} for API {api} - assuming it's external, skipping", color="yellow", + file=sys.stderr, ) # Set config_type to None to avoid UnboundLocalError config_type = None @@ -329,10 +345,7 @@ def _generate_run_config( # For non-container builds, the run.yaml is generated at the very end of the build process so it # makes sense to display this message if build_config.image_type != LlamaStackImageType.CONTAINER.value: - cprint( - f"You can now run your stack with `llama stack run {run_config_file}`", - color="green", - ) + cprint(f"You can now run your stack with `llama stack run {run_config_file}`", color="green", file=sys.stderr) return run_config_file @@ -341,7 +354,7 @@ def _run_stack_build_command_from_build_config( image_name: str | None = None, template_name: str | None = None, config_path: str | None = None, -) -> str: +) -> Path | Traversable: image_name = image_name or build_config.image_name if build_config.image_type == LlamaStackImageType.CONTAINER.value: if template_name: @@ -370,7 +383,7 @@ def _run_stack_build_command_from_build_config( # Generate the run.yaml so it can be included in the container image with the proper entrypoint # Only do this if we're building a container image and we're not using a template if build_config.image_type == LlamaStackImageType.CONTAINER.value and not template_name and config_path: - cprint("Generating run.yaml file", color="green") + cprint("Generating run.yaml file", color="yellow", file=sys.stderr) run_config_file = _generate_run_config(build_config, build_dir, image_name) with open(build_file_path, "w") as f: @@ -394,11 +407,13 @@ def _run_stack_build_command_from_build_config( run_config_file = build_dir / f"{template_name}-run.yaml" shutil.copy(path, run_config_file) - cprint("Build Successful!", color="green") - cprint("You can find the newly-built template here: " + colored(template_path, "light_blue")) + cprint("Build Successful!", color="green", file=sys.stderr) + cprint(f"You can find the newly-built template here: {template_path}", color="light_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}", "light_blue") + + colored(f"llama stack run {template_path} --image-type {build_config.image_type}", "light_blue"), + color="green", + file=sys.stderr, ) return template_path else: diff --git a/llama_stack/cli/stack/build.py b/llama_stack/cli/stack/build.py index 93e7d9b22..2c402beeb 100644 --- a/llama_stack/cli/stack/build.py +++ b/llama_stack/cli/stack/build.py @@ -49,7 +49,7 @@ class StackBuild(Subcommand): type=str, help="Image Type to use for the build. If not specified, will use the image type from the template config.", choices=[e.value for e in ImageType], - default=ImageType.CONDA.value, + default=None, # no default so we can detect if a user specified --image-type and override image_type in the config ) self.parser.add_argument( diff --git a/llama_stack/cli/stack/list_stacks.py b/llama_stack/cli/stack/list_stacks.py new file mode 100644 index 000000000..2ea0fdeea --- /dev/null +++ b/llama_stack/cli/stack/list_stacks.py @@ -0,0 +1,56 @@ +# 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 argparse +from pathlib import Path + +from llama_stack.cli.subcommand import Subcommand +from llama_stack.cli.table import print_table + + +class StackListBuilds(Subcommand): + """List built stacks in .llama/distributions directory""" + + def __init__(self, subparsers: argparse._SubParsersAction): + super().__init__() + self.parser = subparsers.add_parser( + "list", + prog="llama stack list", + description="list the build stacks", + formatter_class=argparse.ArgumentDefaultsHelpFormatter, + ) + self._add_arguments() + self.parser.set_defaults(func=self._list_stack_command) + + def _get_distribution_dirs(self) -> dict[str, Path]: + """Return a dictionary of distribution names and their paths""" + distributions = {} + dist_dir = Path.home() / ".llama" / "distributions" + + if dist_dir.exists(): + for stack_dir in dist_dir.iterdir(): + if stack_dir.is_dir(): + distributions[stack_dir.name] = stack_dir + return distributions + + def _list_stack_command(self, args: argparse.Namespace) -> None: + distributions = self._get_distribution_dirs() + + if not distributions: + print("No stacks found in ~/.llama/distributions") + return + + headers = ["Stack Name", "Path"] + headers.extend(["Build Config", "Run Config"]) + rows = [] + for name, path in distributions.items(): + row = [name, str(path)] + # Check for build and run config files + build_config = "Yes" if (path / f"{name}-build.yaml").exists() else "No" + run_config = "Yes" if (path / f"{name}-run.yaml").exists() else "No" + row.extend([build_config, run_config]) + rows.append(row) + print_table(rows, headers, separate_rows=True) diff --git a/llama_stack/cli/stack/remove.py b/llama_stack/cli/stack/remove.py new file mode 100644 index 000000000..a1796941e --- /dev/null +++ b/llama_stack/cli/stack/remove.py @@ -0,0 +1,115 @@ +# 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 argparse +import shutil +import sys +from pathlib import Path + +from termcolor import cprint + +from llama_stack.cli.subcommand import Subcommand +from llama_stack.cli.table import print_table + + +class StackRemove(Subcommand): + """Remove the build stack""" + + def __init__(self, subparsers: argparse._SubParsersAction): + super().__init__() + self.parser = subparsers.add_parser( + "rm", + prog="llama stack rm", + description="Remove the build stack", + formatter_class=argparse.ArgumentDefaultsHelpFormatter, + ) + self._add_arguments() + self.parser.set_defaults(func=self._remove_stack_build_command) + + def _add_arguments(self) -> None: + self.parser.add_argument( + "name", + type=str, + nargs="?", + help="Name of the stack to delete", + ) + self.parser.add_argument( + "--all", + "-a", + action="store_true", + help="Delete all stacks (use with caution)", + ) + + def _get_distribution_dirs(self) -> dict[str, Path]: + """Return a dictionary of distribution names and their paths""" + distributions = {} + dist_dir = Path.home() / ".llama" / "distributions" + + if dist_dir.exists(): + for stack_dir in dist_dir.iterdir(): + if stack_dir.is_dir(): + distributions[stack_dir.name] = stack_dir + return distributions + + def _list_stacks(self) -> None: + """Display available stacks in a table""" + distributions = self._get_distribution_dirs() + if not distributions: + cprint("No stacks found in ~/.llama/distributions", color="red", file=sys.stderr) + sys.exit(1) + + headers = ["Stack Name", "Path"] + rows = [[name, str(path)] for name, path in distributions.items()] + print_table(rows, headers, separate_rows=True) + + def _remove_stack_build_command(self, args: argparse.Namespace) -> None: + distributions = self._get_distribution_dirs() + + if args.all: + confirm = input("Are you sure you want to delete ALL stacks? [yes-i-really-want/N] ").lower() + if confirm != "yes-i-really-want": + cprint("Deletion cancelled.", color="green", file=sys.stderr) + return + + for name, path in distributions.items(): + try: + shutil.rmtree(path) + cprint(f"Deleted stack: {name}", color="green", file=sys.stderr) + except Exception as e: + cprint( + f"Failed to delete stack {name}: {e}", + color="red", + file=sys.stderr, + ) + sys.exit(1) + + if not args.name: + self._list_stacks() + if not args.name: + return + + if args.name not in distributions: + self._list_stacks() + cprint( + f"Stack not found: {args.name}", + color="red", + file=sys.stderr, + ) + sys.exit(1) + + stack_path = distributions[args.name] + + confirm = input(f"Are you sure you want to delete stack '{args.name}'? [y/N] ").lower() + if confirm != "y": + cprint("Deletion cancelled.", color="green", file=sys.stderr) + return + + try: + shutil.rmtree(stack_path) + cprint(f"Successfully deleted stack: {args.name}", color="green", file=sys.stderr) + except Exception as e: + cprint(f"Failed to delete stack {args.name}: {e}", color="red", file=sys.stderr) + sys.exit(1) diff --git a/llama_stack/cli/stack/run.py b/llama_stack/cli/stack/run.py index 4a44e0366..27745edac 100644 --- a/llama_stack/cli/stack/run.py +++ b/llama_stack/cli/stack/run.py @@ -6,6 +6,7 @@ import argparse import os +import subprocess from pathlib import Path from llama_stack.cli.stack.utils import ImageType @@ -60,6 +61,11 @@ class StackRun(Subcommand): help="Image Type used during the build. This can be either conda or container or venv.", choices=[e.value for e in ImageType], ) + self.parser.add_argument( + "--enable-ui", + action="store_true", + help="Start the UI server", + ) # If neither image type nor image name is provided, but at the same time # the current environment has conda breadcrumbs, then assume what the user @@ -83,6 +89,8 @@ class StackRun(Subcommand): 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) # Check if config is required based on image type @@ -170,3 +178,44 @@ class StackRun(Subcommand): run_args.extend(["--env", f"{key}={value}"]) run_command(run_args) + + def _start_ui_development_server(self, stack_server_port: int): + logger.info("Attempting to start UI development server...") + # Check if npm is available + npm_check = subprocess.run(["npm", "--version"], capture_output=True, text=True, check=False) + if npm_check.returncode != 0: + logger.warning( + f"'npm' command not found or not executable. UI development server will not be started. Error: {npm_check.stderr}" + ) + return + + ui_dir = REPO_ROOT / "llama_stack" / "ui" + logs_dir = Path("~/.llama/ui/logs").expanduser() + try: + # Create logs directory if it doesn't exist + logs_dir.mkdir(parents=True, exist_ok=True) + + ui_stdout_log_path = logs_dir / "stdout.log" + ui_stderr_log_path = logs_dir / "stderr.log" + + # Open log files in append mode + stdout_log_file = open(ui_stdout_log_path, "a") + stderr_log_file = open(ui_stderr_log_path, "a") + + process = subprocess.Popen( + ["npm", "run", "dev"], + cwd=str(ui_dir), + stdout=stdout_log_file, + stderr=stderr_log_file, + env={**os.environ, "NEXT_PUBLIC_LLAMA_STACK_BASE_URL": f"http://localhost:{stack_server_port}"}, + ) + logger.info(f"UI development server process started in {ui_dir} with PID {process.pid}.") + logger.info(f"Logs: stdout -> {ui_stdout_log_path}, stderr -> {ui_stderr_log_path}") + logger.info(f"UI will be available at http://localhost:{os.getenv('LLAMA_STACK_UI_PORT', 8322)}") + + except FileNotFoundError: + logger.error( + "Failed to start UI development server: 'npm' command not found. Make sure npm is installed and in your PATH." + ) + except Exception as e: + logger.error(f"Failed to start UI development server in {ui_dir}: {e}") diff --git a/llama_stack/cli/stack/stack.py b/llama_stack/cli/stack/stack.py index ccf1a5ffc..3aff78e23 100644 --- a/llama_stack/cli/stack/stack.py +++ b/llama_stack/cli/stack/stack.py @@ -7,12 +7,14 @@ import argparse from importlib.metadata import version +from llama_stack.cli.stack.list_stacks import StackListBuilds from llama_stack.cli.stack.utils import print_subcommand_description from llama_stack.cli.subcommand import Subcommand from .build import StackBuild from .list_apis import StackListApis from .list_providers import StackListProviders +from .remove import StackRemove from .run import StackRun @@ -41,5 +43,6 @@ class StackParser(Subcommand): StackListApis.create(subparsers) StackListProviders.create(subparsers) StackRun.create(subparsers) - + StackRemove.create(subparsers) + StackListBuilds.create(subparsers) print_subcommand_description(self.parser, subparsers) diff --git a/llama_stack/distribution/build.py b/llama_stack/distribution/build.py index 1d39063f0..072f9c425 100644 --- a/llama_stack/distribution/build.py +++ b/llama_stack/distribution/build.py @@ -6,6 +6,7 @@ import importlib.resources import logging +import sys from pathlib import Path from pydantic import BaseModel @@ -43,8 +44,20 @@ def get_provider_dependencies( # Extract providers based on config type if isinstance(config, DistributionTemplate): providers = config.providers + + # TODO: This is a hack to get the dependencies for internal APIs into build + # We should have a better way to do this by formalizing the concept of "internal" APIs + # and providers, with a way to specify dependencies for them. + run_configs = config.run_configs + additional_pip_packages: list[str] = [] + if run_configs: + for run_config in run_configs.values(): + run_config_ = run_config.run_config(name="", providers={}, container_image=None) + if run_config_.inference_store: + additional_pip_packages.extend(run_config_.inference_store.pip_packages) elif isinstance(config, BuildConfig): providers = config.distribution_spec.providers + additional_pip_packages = config.additional_pip_packages deps = [] registry = get_provider_registry(config) for api_str, provider_or_providers in providers.items(): @@ -72,6 +85,9 @@ def get_provider_dependencies( else: normal_deps.append(package) + if additional_pip_packages: + normal_deps.extend(additional_pip_packages) + return list(set(normal_deps)), list(set(special_deps)) @@ -80,10 +96,11 @@ def print_pip_install_help(config: BuildConfig): cprint( f"Please install needed dependencies using the following commands:\n\nuv pip install {' '.join(normal_deps)}", - "yellow", + color="yellow", + file=sys.stderr, ) for special_dep in special_deps: - cprint(f"uv pip install {special_dep}", "yellow") + cprint(f"uv pip install {special_dep}", color="yellow", file=sys.stderr) print() diff --git a/llama_stack/distribution/client.py b/llama_stack/distribution/client.py index 9fde8a157..03e4fb051 100644 --- a/llama_stack/distribution/client.py +++ b/llama_stack/distribution/client.py @@ -6,6 +6,7 @@ import inspect import json +import sys from collections.abc import AsyncIterator from enum import Enum from typing import Any, Union, get_args, get_origin @@ -96,13 +97,13 @@ def create_api_client_class(protocol) -> type: try: data = json.loads(data) if "error" in data: - cprint(data, "red") + cprint(data, color="red", file=sys.stderr) continue yield parse_obj_as(return_type, data) except Exception as e: - print(f"Error with parsing or validation: {e}") - print(data) + cprint(f"Error with parsing or validation: {e}", color="red", file=sys.stderr) + cprint(data, color="red", file=sys.stderr) def httpx_request_params(self, method_name: str, *args, **kwargs) -> dict: webmethod, sig = self.routes[method_name] diff --git a/llama_stack/distribution/datatypes.py b/llama_stack/distribution/datatypes.py index 783a48de3..def7048c0 100644 --- a/llama_stack/distribution/datatypes.py +++ b/llama_stack/distribution/datatypes.py @@ -25,7 +25,8 @@ from llama_stack.apis.tools import Tool, ToolGroup, ToolGroupInput, ToolRuntime from llama_stack.apis.vector_dbs import VectorDB, VectorDBInput from llama_stack.apis.vector_io import VectorIO from llama_stack.providers.datatypes import Api, ProviderSpec -from llama_stack.providers.utils.kvstore.config import KVStoreConfig +from llama_stack.providers.utils.kvstore.config import KVStoreConfig, SqliteKVStoreConfig +from llama_stack.providers.utils.sqlstore.sqlstore import SqlStoreConfig LLAMA_STACK_BUILD_CONFIG_VERSION = "2" LLAMA_STACK_RUN_CONFIG_VERSION = "2" @@ -220,21 +221,38 @@ class LoggingConfig(BaseModel): class AuthProviderType(str, Enum): """Supported authentication provider types.""" - KUBERNETES = "kubernetes" + OAUTH2_TOKEN = "oauth2_token" CUSTOM = "custom" class AuthenticationConfig(BaseModel): provider_type: AuthProviderType = Field( ..., - description="Type of authentication provider (e.g., 'kubernetes', 'custom')", + description="Type of authentication provider", ) - config: dict[str, str] = Field( + config: dict[str, Any] = Field( ..., description="Provider-specific configuration", ) +class AuthenticationRequiredError(Exception): + pass + + +class QuotaPeriod(str, Enum): + DAY = "day" + + +class QuotaConfig(BaseModel): + kvstore: SqliteKVStoreConfig = Field(description="Config for KV store backend (SQLite only for now)") + anonymous_max_requests: int = Field(default=100, description="Max requests for unauthenticated clients per period") + authenticated_max_requests: int = Field( + default=1000, description="Max requests for authenticated clients per period" + ) + period: QuotaPeriod = Field(default=QuotaPeriod.DAY, description="Quota period to set") + + class ServerConfig(BaseModel): port: int = Field( default=8321, @@ -262,6 +280,10 @@ class ServerConfig(BaseModel): default=None, description="The host the server should listen on", ) + quota: QuotaConfig | None = Field( + default=None, + description="Per client quota request configuration", + ) class StackRunConfig(BaseModel): @@ -297,6 +319,13 @@ Configuration for the persistence store used by the distribution registry. If no a default SQLite store will be used.""", ) + inference_store: SqlStoreConfig | None = Field( + default=None, + description=""" +Configuration for the persistence store used by the inference API. If not specified, +a default SQLite store will be used.""", + ) + # registry of "resources" in the distribution models: list[ModelInput] = Field(default_factory=list) shields: list[ShieldInput] = Field(default_factory=list) @@ -340,8 +369,21 @@ class BuildConfig(BaseModel): default=None, description="Name of the distribution to build", ) - external_providers_dir: str | None = Field( + external_providers_dir: Path | None = Field( default=None, description="Path to directory containing external provider implementations. The providers packages will be resolved from this directory. " "pip_packages MUST contain the provider package name.", ) + additional_pip_packages: list[str] = Field( + default_factory=list, + description="Additional pip packages to install in the distribution. These packages will be installed in the distribution environment.", + ) + + @field_validator("external_providers_dir") + @classmethod + def validate_external_providers_dir(cls, v): + if v is None: + return None + if isinstance(v, str): + return Path(v) + return v diff --git a/llama_stack/distribution/inspect.py b/llama_stack/distribution/inspect.py index 23f644ec6..5822070ad 100644 --- a/llama_stack/distribution/inspect.py +++ b/llama_stack/distribution/inspect.py @@ -16,7 +16,7 @@ from llama_stack.apis.inspect import ( VersionInfo, ) from llama_stack.distribution.datatypes import StackRunConfig -from llama_stack.distribution.server.endpoints import get_all_api_endpoints +from llama_stack.distribution.server.routes import get_all_api_routes from llama_stack.providers.datatypes import HealthStatus @@ -31,7 +31,7 @@ async def get_provider_impl(config, deps): class DistributionInspectImpl(Inspect): - def __init__(self, config, deps): + def __init__(self, config: DistributionInspectConfig, deps): self.config = config self.deps = deps @@ -39,22 +39,36 @@ class DistributionInspectImpl(Inspect): pass async def list_routes(self) -> ListRoutesResponse: - run_config = self.config.run_config + run_config: StackRunConfig = self.config.run_config ret = [] - all_endpoints = get_all_api_endpoints() + all_endpoints = get_all_api_routes() for api, endpoints in all_endpoints.items(): - providers = run_config.providers.get(api.value, []) - ret.extend( - [ - RouteInfo( - route=e.route, - method=e.method, - provider_types=[p.provider_type for p in providers], + # Always include provider and inspect APIs, filter others based on run config + if api.value in ["providers", "inspect"]: + ret.extend( + [ + RouteInfo( + route=e.path, + method=next(iter([m for m in e.methods if m != "HEAD"])), + provider_types=[], # These APIs don't have "real" providers - they're internal to the stack + ) + for e in endpoints + ] + ) + else: + providers = run_config.providers.get(api.value, []) + if providers: # Only process if there are providers for this API + ret.extend( + [ + RouteInfo( + route=e.path, + method=next(iter([m for m in e.methods if m != "HEAD"])), + provider_types=[p.provider_type for p in providers], + ) + for e in endpoints + ] ) - for e in endpoints - ] - ) return ListRoutesResponse(data=ret) diff --git a/llama_stack/distribution/library_client.py b/llama_stack/distribution/library_client.py index 8e5445874..f32130cf9 100644 --- a/llama_stack/distribution/library_client.py +++ b/llama_stack/distribution/library_client.py @@ -9,6 +9,7 @@ import inspect import json import logging import os +import sys from concurrent.futures import ThreadPoolExecutor from enum import Enum from pathlib import Path @@ -36,10 +37,7 @@ from llama_stack.distribution.request_headers import ( request_provider_data_context, ) from llama_stack.distribution.resolver import ProviderRegistry -from llama_stack.distribution.server.endpoints import ( - find_matching_endpoint, - initialize_endpoint_impls, -) +from llama_stack.distribution.server.routes import find_matching_route, initialize_route_impls from llama_stack.distribution.stack import ( construct_stack, get_stack_run_config_from_template, @@ -207,13 +205,14 @@ class AsyncLlamaStackAsLibraryClient(AsyncLlamaStackClient): async def initialize(self) -> bool: try: - self.endpoint_impls = None + self.route_impls = None self.impls = await construct_stack(self.config, self.custom_provider_registry) except ModuleNotFoundError as _e: - cprint(_e.msg, "red") + cprint(_e.msg, color="red", file=sys.stderr) cprint( "Using llama-stack as a library requires installing dependencies depending on the template (providers) you choose.\n", - "yellow", + color="yellow", + file=sys.stderr, ) if self.config_path_or_template_name.endswith(".yaml"): # Convert Provider objects to their types @@ -226,6 +225,7 @@ class AsyncLlamaStackAsLibraryClient(AsyncLlamaStackClient): distribution_spec=DistributionSpec( providers=provider_types, ), + external_providers_dir=self.config.external_providers_dir, ) print_pip_install_help(build_config) else: @@ -233,7 +233,13 @@ class AsyncLlamaStackAsLibraryClient(AsyncLlamaStackClient): cprint( f"Please run:\n\n{prefix}llama stack build --template {self.config_path_or_template_name} --image-type venv\n\n", "yellow", + file=sys.stderr, ) + cprint( + "Please check your internet connection and try again.", + "red", + file=sys.stderr, + ) raise _e if Api.telemetry in self.impls: @@ -245,7 +251,7 @@ class AsyncLlamaStackAsLibraryClient(AsyncLlamaStackClient): safe_config = redact_sensitive_fields(self.config.model_dump()) console.print(yaml.dump(safe_config, indent=2)) - self.endpoint_impls = initialize_endpoint_impls(self.impls) + self.route_impls = initialize_route_impls(self.impls) return True async def request( @@ -256,13 +262,15 @@ class AsyncLlamaStackAsLibraryClient(AsyncLlamaStackClient): stream=False, stream_cls=None, ): - if not self.endpoint_impls: + if not self.route_impls: raise ValueError("Client not initialized") # Create headers with provider data if available - headers = {} + headers = options.headers or {} if self.provider_data: - headers["X-LlamaStack-Provider-Data"] = json.dumps(self.provider_data) + keys = ["X-LlamaStack-Provider-Data", "x-llamastack-provider-data"] + if all(key not in headers for key in keys): + headers["X-LlamaStack-Provider-Data"] = json.dumps(self.provider_data) # Use context manager for provider data with request_provider_data_context(headers): @@ -285,11 +293,14 @@ class AsyncLlamaStackAsLibraryClient(AsyncLlamaStackClient): cast_to: Any, options: Any, ): + if self.route_impls is None: + raise ValueError("Client not initialized") + path = options.url body = options.params or {} body |= options.json_data or {} - matched_func, path_params, route = find_matching_endpoint(options.method, path, self.endpoint_impls) + matched_func, path_params, route = find_matching_route(options.method, path, self.route_impls) body |= path_params body = self._convert_body(path, options.method, body) await start_trace(route, {"__location__": "library_client"}) @@ -331,10 +342,13 @@ class AsyncLlamaStackAsLibraryClient(AsyncLlamaStackClient): options: Any, stream_cls: Any, ): + if self.route_impls is None: + raise ValueError("Client not initialized") + path = options.url body = options.params or {} body |= options.json_data or {} - func, path_params, route = find_matching_endpoint(options.method, path, self.endpoint_impls) + func, path_params, route = find_matching_route(options.method, path, self.route_impls) body |= path_params body = self._convert_body(path, options.method, body) @@ -386,7 +400,10 @@ class AsyncLlamaStackAsLibraryClient(AsyncLlamaStackClient): if not body: return {} - func, _, _ = find_matching_endpoint(method, path, self.endpoint_impls) + if self.route_impls is None: + raise ValueError("Client not initialized") + + func, _, _ = find_matching_route(method, path, self.route_impls) sig = inspect.signature(func) # Strip NOT_GIVENs to use the defaults in signature diff --git a/llama_stack/distribution/resolver.py b/llama_stack/distribution/resolver.py index 37588ea64..b7c7cb87f 100644 --- a/llama_stack/distribution/resolver.py +++ b/llama_stack/distribution/resolver.py @@ -13,7 +13,7 @@ from llama_stack.apis.datasetio import DatasetIO from llama_stack.apis.datasets import Datasets from llama_stack.apis.eval import Eval from llama_stack.apis.files import Files -from llama_stack.apis.inference import Inference +from llama_stack.apis.inference import Inference, InferenceProvider from llama_stack.apis.inspect import Inspect from llama_stack.apis.models import Models from llama_stack.apis.post_training import PostTraining @@ -47,7 +47,7 @@ from llama_stack.providers.datatypes import ( RemoteProviderSpec, ScoringFunctionsProtocolPrivate, ShieldsProtocolPrivate, - ToolsProtocolPrivate, + ToolGroupsProtocolPrivate, VectorDBsProtocolPrivate, ) @@ -83,10 +83,17 @@ def api_protocol_map() -> dict[Api, Any]: } +def api_protocol_map_for_compliance_check() -> dict[Api, Any]: + return { + **api_protocol_map(), + Api.inference: InferenceProvider, + } + + def additional_protocols_map() -> dict[Api, Any]: return { Api.inference: (ModelsProtocolPrivate, Models, Api.models), - Api.tool_groups: (ToolsProtocolPrivate, ToolGroups, Api.tool_groups), + Api.tool_groups: (ToolGroupsProtocolPrivate, ToolGroups, Api.tool_groups), Api.vector_io: (VectorDBsProtocolPrivate, VectorDBs, Api.vector_dbs), Api.safety: (ShieldsProtocolPrivate, Shields, Api.shields), Api.datasetio: (DatasetsProtocolPrivate, Datasets, Api.datasets), @@ -133,7 +140,7 @@ async def resolve_impls( sorted_providers = sort_providers_by_deps(providers_with_specs, run_config) - return await instantiate_providers(sorted_providers, router_apis, dist_registry) + return await instantiate_providers(sorted_providers, router_apis, dist_registry, run_config) def specs_for_autorouted_apis(apis_to_serve: list[str] | set[str]) -> dict[str, dict[str, ProviderWithSpec]]: @@ -236,7 +243,10 @@ def sort_providers_by_deps( async def instantiate_providers( - sorted_providers: list[tuple[str, ProviderWithSpec]], router_apis: set[Api], dist_registry: DistributionRegistry + sorted_providers: list[tuple[str, ProviderWithSpec]], + router_apis: set[Api], + dist_registry: DistributionRegistry, + run_config: StackRunConfig, ) -> dict: """Instantiates providers asynchronously while managing dependencies.""" impls: dict[Api, Any] = {} @@ -251,7 +261,7 @@ async def instantiate_providers( if isinstance(provider.spec, RoutingTableProviderSpec): inner_impls = inner_impls_by_provider_id[f"inner-{provider.spec.router_api.value}"] - impl = await instantiate_provider(provider, deps, inner_impls, dist_registry) + impl = await instantiate_provider(provider, deps, inner_impls, dist_registry, run_config) if api_str.startswith("inner-"): inner_impls_by_provider_id[api_str][provider.provider_id] = impl @@ -301,10 +311,8 @@ async def instantiate_provider( deps: dict[Api, Any], inner_impls: dict[str, Any], dist_registry: DistributionRegistry, + run_config: StackRunConfig, ): - protocols = api_protocol_map() - additional_protocols = additional_protocols_map() - provider_spec = provider.spec if not hasattr(provider_spec, "module"): raise AttributeError(f"ProviderSpec of type {type(provider_spec)} does not have a 'module' attribute") @@ -323,7 +331,7 @@ async def instantiate_provider( method = "get_auto_router_impl" config = None - args = [provider_spec.api, deps[provider_spec.routing_table_api], deps] + args = [provider_spec.api, deps[provider_spec.routing_table_api], deps, run_config] elif isinstance(provider_spec, RoutingTableProviderSpec): method = "get_routing_table_impl" @@ -342,6 +350,8 @@ async def instantiate_provider( impl.__provider_spec__ = provider_spec impl.__provider_config__ = config + protocols = api_protocol_map_for_compliance_check() + additional_protocols = additional_protocols_map() # TODO: check compliance for special tool groups # the impl should be for Api.tool_runtime, the name should be the special tool group, the protocol should be the special tool group protocol check_protocol_compliance(impl, protocols[provider_spec.api]) diff --git a/llama_stack/distribution/routers/__init__.py b/llama_stack/distribution/routers/__init__.py index cd2a296f2..1358d5812 100644 --- a/llama_stack/distribution/routers/__init__.py +++ b/llama_stack/distribution/routers/__init__.py @@ -7,18 +7,10 @@ from typing import Any from llama_stack.distribution.datatypes import RoutedProtocol +from llama_stack.distribution.stack import StackRunConfig from llama_stack.distribution.store import DistributionRegistry from llama_stack.providers.datatypes import Api, RoutingTable - -from .routing_tables import ( - BenchmarksRoutingTable, - DatasetsRoutingTable, - ModelsRoutingTable, - ScoringFunctionsRoutingTable, - ShieldsRoutingTable, - ToolGroupsRoutingTable, - VectorDBsRoutingTable, -) +from llama_stack.providers.utils.inference.inference_store import InferenceStore async def get_routing_table_impl( @@ -27,6 +19,14 @@ async def get_routing_table_impl( _deps, dist_registry: DistributionRegistry, ) -> Any: + from ..routing_tables.benchmarks import BenchmarksRoutingTable + from ..routing_tables.datasets import DatasetsRoutingTable + from ..routing_tables.models import ModelsRoutingTable + from ..routing_tables.scoring_functions import ScoringFunctionsRoutingTable + from ..routing_tables.shields import ShieldsRoutingTable + from ..routing_tables.toolgroups import ToolGroupsRoutingTable + from ..routing_tables.vector_dbs import VectorDBsRoutingTable + api_to_tables = { "vector_dbs": VectorDBsRoutingTable, "models": ModelsRoutingTable, @@ -45,16 +45,15 @@ async def get_routing_table_impl( return impl -async def get_auto_router_impl(api: Api, routing_table: RoutingTable, deps: dict[str, Any]) -> Any: - from .routers import ( - DatasetIORouter, - EvalRouter, - InferenceRouter, - SafetyRouter, - ScoringRouter, - ToolRuntimeRouter, - VectorIORouter, - ) +async def get_auto_router_impl( + api: Api, routing_table: RoutingTable, deps: dict[str, Any], run_config: StackRunConfig +) -> Any: + from .datasets import DatasetIORouter + from .eval_scoring import EvalRouter, ScoringRouter + from .inference import InferenceRouter + from .safety import SafetyRouter + from .tool_runtime import ToolRuntimeRouter + from .vector_io import VectorIORouter api_to_routers = { "vector_io": VectorIORouter, @@ -76,6 +75,12 @@ async def get_auto_router_impl(api: Api, routing_table: RoutingTable, deps: dict if dep_api in deps: api_to_dep_impl[dep_name] = deps[dep_api] + # TODO: move pass configs to routers instead + if api == Api.inference and run_config.inference_store: + inference_store = InferenceStore(run_config.inference_store) + await inference_store.initialize() + api_to_dep_impl["store"] = inference_store + impl = api_to_routers[api.value](routing_table, **api_to_dep_impl) await impl.initialize() return impl diff --git a/llama_stack/distribution/routers/datasets.py b/llama_stack/distribution/routers/datasets.py new file mode 100644 index 000000000..6f28756c9 --- /dev/null +++ b/llama_stack/distribution/routers/datasets.py @@ -0,0 +1,71 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the terms described in the LICENSE file in +# the root directory of this source tree. + +from typing import Any + +from llama_stack.apis.common.responses import PaginatedResponse +from llama_stack.apis.datasetio import DatasetIO +from llama_stack.apis.datasets import DatasetPurpose, DataSource +from llama_stack.log import get_logger +from llama_stack.providers.datatypes import RoutingTable + +logger = get_logger(name=__name__, category="core") + + +class DatasetIORouter(DatasetIO): + def __init__( + self, + routing_table: RoutingTable, + ) -> None: + logger.debug("Initializing DatasetIORouter") + self.routing_table = routing_table + + async def initialize(self) -> None: + logger.debug("DatasetIORouter.initialize") + pass + + async def shutdown(self) -> None: + logger.debug("DatasetIORouter.shutdown") + pass + + async def register_dataset( + self, + purpose: DatasetPurpose, + source: DataSource, + metadata: dict[str, Any] | None = None, + dataset_id: str | None = None, + ) -> None: + logger.debug( + f"DatasetIORouter.register_dataset: {purpose=} {source=} {metadata=} {dataset_id=}", + ) + await self.routing_table.register_dataset( + purpose=purpose, + source=source, + metadata=metadata, + dataset_id=dataset_id, + ) + + async def iterrows( + self, + dataset_id: str, + start_index: int | None = None, + limit: int | None = None, + ) -> PaginatedResponse: + logger.debug( + f"DatasetIORouter.iterrows: {dataset_id}, {start_index=} {limit=}", + ) + return await self.routing_table.get_provider_impl(dataset_id).iterrows( + dataset_id=dataset_id, + start_index=start_index, + limit=limit, + ) + + async def append_rows(self, dataset_id: str, rows: list[dict[str, Any]]) -> None: + logger.debug(f"DatasetIORouter.append_rows: {dataset_id}, {len(rows)} rows") + return await self.routing_table.get_provider_impl(dataset_id).append_rows( + dataset_id=dataset_id, + rows=rows, + ) diff --git a/llama_stack/distribution/routers/eval_scoring.py b/llama_stack/distribution/routers/eval_scoring.py new file mode 100644 index 000000000..fd0bb90a7 --- /dev/null +++ b/llama_stack/distribution/routers/eval_scoring.py @@ -0,0 +1,148 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the terms described in the LICENSE file in +# the root directory of this source tree. + +from typing import Any + +from llama_stack.apis.eval import BenchmarkConfig, Eval, EvaluateResponse, Job +from llama_stack.apis.scoring import ( + ScoreBatchResponse, + ScoreResponse, + Scoring, + ScoringFnParams, +) +from llama_stack.log import get_logger +from llama_stack.providers.datatypes import RoutingTable + +logger = get_logger(name=__name__, category="core") + + +class ScoringRouter(Scoring): + def __init__( + self, + routing_table: RoutingTable, + ) -> None: + logger.debug("Initializing ScoringRouter") + self.routing_table = routing_table + + async def initialize(self) -> None: + logger.debug("ScoringRouter.initialize") + pass + + async def shutdown(self) -> None: + logger.debug("ScoringRouter.shutdown") + pass + + async def score_batch( + self, + dataset_id: str, + scoring_functions: dict[str, ScoringFnParams | None] = None, + save_results_dataset: bool = False, + ) -> ScoreBatchResponse: + logger.debug(f"ScoringRouter.score_batch: {dataset_id}") + res = {} + for fn_identifier in scoring_functions.keys(): + score_response = await self.routing_table.get_provider_impl(fn_identifier).score_batch( + dataset_id=dataset_id, + scoring_functions={fn_identifier: scoring_functions[fn_identifier]}, + ) + res.update(score_response.results) + + if save_results_dataset: + raise NotImplementedError("Save results dataset not implemented yet") + + return ScoreBatchResponse( + results=res, + ) + + async def score( + self, + input_rows: list[dict[str, Any]], + scoring_functions: dict[str, ScoringFnParams | None] = None, + ) -> ScoreResponse: + logger.debug(f"ScoringRouter.score: {len(input_rows)} rows, {len(scoring_functions)} functions") + res = {} + # look up and map each scoring function to its provider impl + for fn_identifier in scoring_functions.keys(): + score_response = await self.routing_table.get_provider_impl(fn_identifier).score( + input_rows=input_rows, + scoring_functions={fn_identifier: scoring_functions[fn_identifier]}, + ) + res.update(score_response.results) + + return ScoreResponse(results=res) + + +class EvalRouter(Eval): + def __init__( + self, + routing_table: RoutingTable, + ) -> None: + logger.debug("Initializing EvalRouter") + self.routing_table = routing_table + + async def initialize(self) -> None: + logger.debug("EvalRouter.initialize") + pass + + async def shutdown(self) -> None: + logger.debug("EvalRouter.shutdown") + pass + + async def run_eval( + self, + benchmark_id: str, + benchmark_config: BenchmarkConfig, + ) -> Job: + logger.debug(f"EvalRouter.run_eval: {benchmark_id}") + return await self.routing_table.get_provider_impl(benchmark_id).run_eval( + benchmark_id=benchmark_id, + benchmark_config=benchmark_config, + ) + + async def evaluate_rows( + self, + benchmark_id: str, + input_rows: list[dict[str, Any]], + scoring_functions: list[str], + benchmark_config: BenchmarkConfig, + ) -> EvaluateResponse: + logger.debug(f"EvalRouter.evaluate_rows: {benchmark_id}, {len(input_rows)} rows") + return await self.routing_table.get_provider_impl(benchmark_id).evaluate_rows( + benchmark_id=benchmark_id, + input_rows=input_rows, + scoring_functions=scoring_functions, + benchmark_config=benchmark_config, + ) + + async def job_status( + self, + benchmark_id: str, + job_id: str, + ) -> Job: + logger.debug(f"EvalRouter.job_status: {benchmark_id}, {job_id}") + return await self.routing_table.get_provider_impl(benchmark_id).job_status(benchmark_id, job_id) + + async def job_cancel( + self, + benchmark_id: str, + job_id: str, + ) -> None: + logger.debug(f"EvalRouter.job_cancel: {benchmark_id}, {job_id}") + await self.routing_table.get_provider_impl(benchmark_id).job_cancel( + benchmark_id, + job_id, + ) + + async def job_result( + self, + benchmark_id: str, + job_id: str, + ) -> EvaluateResponse: + logger.debug(f"EvalRouter.job_result: {benchmark_id}, {job_id}") + return await self.routing_table.get_provider_impl(benchmark_id).job_result( + benchmark_id, + job_id, + ) diff --git a/llama_stack/distribution/routers/routers.py b/llama_stack/distribution/routers/inference.py similarity index 65% rename from llama_stack/distribution/routers/routers.py rename to llama_stack/distribution/routers/inference.py index 371d34904..763bd9105 100644 --- a/llama_stack/distribution/routers/routers.py +++ b/llama_stack/distribution/routers/inference.py @@ -14,14 +14,9 @@ from openai.types.chat import ChatCompletionToolParam as OpenAIChatCompletionToo from pydantic import Field, TypeAdapter from llama_stack.apis.common.content_types import ( - URL, InterleavedContent, InterleavedContentItem, ) -from llama_stack.apis.common.responses import PaginatedResponse -from llama_stack.apis.datasetio import DatasetIO -from llama_stack.apis.datasets import DatasetPurpose, DataSource -from llama_stack.apis.eval import BenchmarkConfig, Eval, EvaluateResponse, Job from llama_stack.apis.inference import ( BatchChatCompletionResponse, BatchCompletionResponse, @@ -32,8 +27,11 @@ from llama_stack.apis.inference import ( EmbeddingsResponse, EmbeddingTaskType, Inference, + ListOpenAIChatCompletionResponse, LogProbConfig, Message, + OpenAICompletionWithInputMessages, + Order, ResponseFormat, SamplingParams, StopReason, @@ -47,93 +45,23 @@ from llama_stack.apis.inference.inference import ( OpenAIChatCompletion, OpenAIChatCompletionChunk, OpenAICompletion, + OpenAIEmbeddingsResponse, OpenAIMessageParam, OpenAIResponseFormatParam, ) from llama_stack.apis.models import Model, ModelType -from llama_stack.apis.safety import RunShieldResponse, Safety -from llama_stack.apis.scoring import ( - ScoreBatchResponse, - ScoreResponse, - Scoring, - ScoringFnParams, -) -from llama_stack.apis.shields import Shield from llama_stack.apis.telemetry import MetricEvent, MetricInResponse, Telemetry -from llama_stack.apis.tools import ( - ListToolDefsResponse, - RAGDocument, - RAGQueryConfig, - RAGQueryResult, - RAGToolRuntime, - ToolRuntime, -) -from llama_stack.apis.vector_io import Chunk, QueryChunksResponse, VectorIO from llama_stack.log import get_logger from llama_stack.models.llama.llama3.chat_format import ChatFormat from llama_stack.models.llama.llama3.tokenizer import Tokenizer from llama_stack.providers.datatypes import HealthResponse, HealthStatus, RoutingTable +from llama_stack.providers.utils.inference.inference_store import InferenceStore +from llama_stack.providers.utils.inference.stream_utils import stream_and_store_openai_completion from llama_stack.providers.utils.telemetry.tracing import get_current_span logger = get_logger(name=__name__, category="core") -class VectorIORouter(VectorIO): - """Routes to an provider based on the vector db identifier""" - - def __init__( - self, - routing_table: RoutingTable, - ) -> None: - logger.debug("Initializing VectorIORouter") - self.routing_table = routing_table - - async def initialize(self) -> None: - logger.debug("VectorIORouter.initialize") - pass - - async def shutdown(self) -> None: - logger.debug("VectorIORouter.shutdown") - pass - - async def register_vector_db( - self, - vector_db_id: str, - embedding_model: str, - embedding_dimension: int | None = 384, - provider_id: str | None = None, - provider_vector_db_id: str | None = None, - ) -> None: - logger.debug(f"VectorIORouter.register_vector_db: {vector_db_id}, {embedding_model}") - await self.routing_table.register_vector_db( - vector_db_id, - embedding_model, - embedding_dimension, - provider_id, - provider_vector_db_id, - ) - - async def insert_chunks( - self, - vector_db_id: str, - chunks: list[Chunk], - ttl_seconds: int | None = None, - ) -> None: - logger.debug( - f"VectorIORouter.insert_chunks: {vector_db_id}, {len(chunks)} chunks, ttl_seconds={ttl_seconds}, chunk_ids={[chunk.metadata['document_id'] for chunk in chunks[:3]]}{' and more...' if len(chunks) > 3 else ''}", - ) - return await self.routing_table.get_provider_impl(vector_db_id).insert_chunks(vector_db_id, chunks, ttl_seconds) - - async def query_chunks( - self, - vector_db_id: str, - query: InterleavedContent, - params: dict[str, Any] | None = None, - ) -> QueryChunksResponse: - logger.debug(f"VectorIORouter.query_chunks: {vector_db_id}") - return await self.routing_table.get_provider_impl(vector_db_id).query_chunks(vector_db_id, query, params) - - class InferenceRouter(Inference): """Routes to an provider based on the model""" @@ -141,10 +69,12 @@ class InferenceRouter(Inference): self, routing_table: RoutingTable, telemetry: Telemetry | None = None, + store: InferenceStore | None = None, ) -> None: logger.debug("Initializing InferenceRouter") self.routing_table = routing_table self.telemetry = telemetry + self.store = store if self.telemetry: self.tokenizer = Tokenizer.get_instance() self.formatter = ChatFormat(self.tokenizer) @@ -607,9 +537,59 @@ class InferenceRouter(Inference): provider = self.routing_table.get_provider_impl(model_obj.identifier) if stream: - return await provider.openai_chat_completion(**params) + response_stream = await provider.openai_chat_completion(**params) + if self.store: + return stream_and_store_openai_completion(response_stream, model, self.store, messages) + return response_stream else: - return await self._nonstream_openai_chat_completion(provider, params) + response = await self._nonstream_openai_chat_completion(provider, params) + if self.store: + await self.store.store_chat_completion(response, messages) + return response + + async def openai_embeddings( + self, + model: str, + input: str | list[str], + encoding_format: str | None = "float", + dimensions: int | None = None, + user: str | None = None, + ) -> OpenAIEmbeddingsResponse: + logger.debug( + f"InferenceRouter.openai_embeddings: {model=}, input_type={type(input)}, {encoding_format=}, {dimensions=}", + ) + model_obj = await self.routing_table.get_model(model) + if model_obj is None: + raise ValueError(f"Model '{model}' not found") + if model_obj.model_type != ModelType.embedding: + raise ValueError(f"Model '{model}' is not an embedding model") + + params = dict( + model=model_obj.identifier, + input=input, + encoding_format=encoding_format, + dimensions=dimensions, + user=user, + ) + + provider = self.routing_table.get_provider_impl(model_obj.identifier) + return await provider.openai_embeddings(**params) + + async def list_chat_completions( + self, + after: str | None = None, + limit: int | None = 20, + model: str | None = None, + order: Order | None = Order.desc, + ) -> ListOpenAIChatCompletionResponse: + if self.store: + return await self.store.list_chat_completions(after, limit, model, order) + raise NotImplementedError("List chat completions is not supported: inference store is not configured.") + + async def get_chat_completion(self, completion_id: str) -> OpenAICompletionWithInputMessages: + if self.store: + return await self.store.get_chat_completion(completion_id) + raise NotImplementedError("Get chat completion is not supported: inference store is not configured.") async def _nonstream_openai_chat_completion(self, provider: Inference, params: dict) -> OpenAIChatCompletion: response = await provider.openai_chat_completion(**params) @@ -642,295 +622,3 @@ class InferenceRouter(Inference): status=HealthStatus.ERROR, message=f"Health check failed: {str(e)}" ) return health_statuses - - -class SafetyRouter(Safety): - def __init__( - self, - routing_table: RoutingTable, - ) -> None: - logger.debug("Initializing SafetyRouter") - self.routing_table = routing_table - - async def initialize(self) -> None: - logger.debug("SafetyRouter.initialize") - pass - - async def shutdown(self) -> None: - logger.debug("SafetyRouter.shutdown") - pass - - async def register_shield( - self, - shield_id: str, - provider_shield_id: str | None = None, - provider_id: str | None = None, - params: dict[str, Any] | None = None, - ) -> Shield: - logger.debug(f"SafetyRouter.register_shield: {shield_id}") - return await self.routing_table.register_shield(shield_id, provider_shield_id, provider_id, params) - - async def run_shield( - self, - shield_id: str, - messages: list[Message], - params: dict[str, Any] = None, - ) -> RunShieldResponse: - logger.debug(f"SafetyRouter.run_shield: {shield_id}") - return await self.routing_table.get_provider_impl(shield_id).run_shield( - shield_id=shield_id, - messages=messages, - params=params, - ) - - -class DatasetIORouter(DatasetIO): - def __init__( - self, - routing_table: RoutingTable, - ) -> None: - logger.debug("Initializing DatasetIORouter") - self.routing_table = routing_table - - async def initialize(self) -> None: - logger.debug("DatasetIORouter.initialize") - pass - - async def shutdown(self) -> None: - logger.debug("DatasetIORouter.shutdown") - pass - - async def register_dataset( - self, - purpose: DatasetPurpose, - source: DataSource, - metadata: dict[str, Any] | None = None, - dataset_id: str | None = None, - ) -> None: - logger.debug( - f"DatasetIORouter.register_dataset: {purpose=} {source=} {metadata=} {dataset_id=}", - ) - await self.routing_table.register_dataset( - purpose=purpose, - source=source, - metadata=metadata, - dataset_id=dataset_id, - ) - - async def iterrows( - self, - dataset_id: str, - start_index: int | None = None, - limit: int | None = None, - ) -> PaginatedResponse: - logger.debug( - f"DatasetIORouter.iterrows: {dataset_id}, {start_index=} {limit=}", - ) - return await self.routing_table.get_provider_impl(dataset_id).iterrows( - dataset_id=dataset_id, - start_index=start_index, - limit=limit, - ) - - async def append_rows(self, dataset_id: str, rows: list[dict[str, Any]]) -> None: - logger.debug(f"DatasetIORouter.append_rows: {dataset_id}, {len(rows)} rows") - return await self.routing_table.get_provider_impl(dataset_id).append_rows( - dataset_id=dataset_id, - rows=rows, - ) - - -class ScoringRouter(Scoring): - def __init__( - self, - routing_table: RoutingTable, - ) -> None: - logger.debug("Initializing ScoringRouter") - self.routing_table = routing_table - - async def initialize(self) -> None: - logger.debug("ScoringRouter.initialize") - pass - - async def shutdown(self) -> None: - logger.debug("ScoringRouter.shutdown") - pass - - async def score_batch( - self, - dataset_id: str, - scoring_functions: dict[str, ScoringFnParams | None] = None, - save_results_dataset: bool = False, - ) -> ScoreBatchResponse: - logger.debug(f"ScoringRouter.score_batch: {dataset_id}") - res = {} - for fn_identifier in scoring_functions.keys(): - score_response = await self.routing_table.get_provider_impl(fn_identifier).score_batch( - dataset_id=dataset_id, - scoring_functions={fn_identifier: scoring_functions[fn_identifier]}, - ) - res.update(score_response.results) - - if save_results_dataset: - raise NotImplementedError("Save results dataset not implemented yet") - - return ScoreBatchResponse( - results=res, - ) - - async def score( - self, - input_rows: list[dict[str, Any]], - scoring_functions: dict[str, ScoringFnParams | None] = None, - ) -> ScoreResponse: - logger.debug(f"ScoringRouter.score: {len(input_rows)} rows, {len(scoring_functions)} functions") - res = {} - # look up and map each scoring function to its provider impl - for fn_identifier in scoring_functions.keys(): - score_response = await self.routing_table.get_provider_impl(fn_identifier).score( - input_rows=input_rows, - scoring_functions={fn_identifier: scoring_functions[fn_identifier]}, - ) - res.update(score_response.results) - - return ScoreResponse(results=res) - - -class EvalRouter(Eval): - def __init__( - self, - routing_table: RoutingTable, - ) -> None: - logger.debug("Initializing EvalRouter") - self.routing_table = routing_table - - async def initialize(self) -> None: - logger.debug("EvalRouter.initialize") - pass - - async def shutdown(self) -> None: - logger.debug("EvalRouter.shutdown") - pass - - async def run_eval( - self, - benchmark_id: str, - benchmark_config: BenchmarkConfig, - ) -> Job: - logger.debug(f"EvalRouter.run_eval: {benchmark_id}") - return await self.routing_table.get_provider_impl(benchmark_id).run_eval( - benchmark_id=benchmark_id, - benchmark_config=benchmark_config, - ) - - async def evaluate_rows( - self, - benchmark_id: str, - input_rows: list[dict[str, Any]], - scoring_functions: list[str], - benchmark_config: BenchmarkConfig, - ) -> EvaluateResponse: - logger.debug(f"EvalRouter.evaluate_rows: {benchmark_id}, {len(input_rows)} rows") - return await self.routing_table.get_provider_impl(benchmark_id).evaluate_rows( - benchmark_id=benchmark_id, - input_rows=input_rows, - scoring_functions=scoring_functions, - benchmark_config=benchmark_config, - ) - - async def job_status( - self, - benchmark_id: str, - job_id: str, - ) -> Job: - logger.debug(f"EvalRouter.job_status: {benchmark_id}, {job_id}") - return await self.routing_table.get_provider_impl(benchmark_id).job_status(benchmark_id, job_id) - - async def job_cancel( - self, - benchmark_id: str, - job_id: str, - ) -> None: - logger.debug(f"EvalRouter.job_cancel: {benchmark_id}, {job_id}") - await self.routing_table.get_provider_impl(benchmark_id).job_cancel( - benchmark_id, - job_id, - ) - - async def job_result( - self, - benchmark_id: str, - job_id: str, - ) -> EvaluateResponse: - logger.debug(f"EvalRouter.job_result: {benchmark_id}, {job_id}") - return await self.routing_table.get_provider_impl(benchmark_id).job_result( - benchmark_id, - job_id, - ) - - -class ToolRuntimeRouter(ToolRuntime): - class RagToolImpl(RAGToolRuntime): - def __init__( - self, - routing_table: RoutingTable, - ) -> None: - logger.debug("Initializing ToolRuntimeRouter.RagToolImpl") - self.routing_table = routing_table - - async def query( - self, - content: InterleavedContent, - vector_db_ids: list[str], - query_config: RAGQueryConfig | None = None, - ) -> RAGQueryResult: - logger.debug(f"ToolRuntimeRouter.RagToolImpl.query: {vector_db_ids}") - return await self.routing_table.get_provider_impl("knowledge_search").query( - content, vector_db_ids, query_config - ) - - async def insert( - self, - documents: list[RAGDocument], - vector_db_id: str, - chunk_size_in_tokens: int = 512, - ) -> None: - logger.debug( - f"ToolRuntimeRouter.RagToolImpl.insert: {vector_db_id}, {len(documents)} documents, chunk_size={chunk_size_in_tokens}" - ) - return await self.routing_table.get_provider_impl("insert_into_memory").insert( - documents, vector_db_id, chunk_size_in_tokens - ) - - def __init__( - self, - routing_table: RoutingTable, - ) -> None: - logger.debug("Initializing ToolRuntimeRouter") - self.routing_table = routing_table - - # HACK ALERT this should be in sync with "get_all_api_endpoints()" - self.rag_tool = self.RagToolImpl(routing_table) - for method in ("query", "insert"): - setattr(self, f"rag_tool.{method}", getattr(self.rag_tool, method)) - - async def initialize(self) -> None: - logger.debug("ToolRuntimeRouter.initialize") - pass - - async def shutdown(self) -> None: - logger.debug("ToolRuntimeRouter.shutdown") - pass - - async def invoke_tool(self, tool_name: str, kwargs: dict[str, Any]) -> Any: - logger.debug(f"ToolRuntimeRouter.invoke_tool: {tool_name}") - return await self.routing_table.get_provider_impl(tool_name).invoke_tool( - tool_name=tool_name, - kwargs=kwargs, - ) - - async def list_runtime_tools( - self, tool_group_id: str | None = None, mcp_endpoint: URL | None = None - ) -> ListToolDefsResponse: - logger.debug(f"ToolRuntimeRouter.list_runtime_tools: {tool_group_id}") - return await self.routing_table.get_provider_impl(tool_group_id).list_tools(tool_group_id, mcp_endpoint) diff --git a/llama_stack/distribution/routers/routing_tables.py b/llama_stack/distribution/routers/routing_tables.py deleted file mode 100644 index c04562197..000000000 --- a/llama_stack/distribution/routers/routing_tables.py +++ /dev/null @@ -1,634 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# All rights reserved. -# -# This source code is licensed under the terms described in the LICENSE file in -# the root directory of this source tree. - -import logging -import time -import uuid -from typing import Any - -from pydantic import TypeAdapter - -from llama_stack.apis.benchmarks import Benchmark, Benchmarks, ListBenchmarksResponse -from llama_stack.apis.common.content_types import URL -from llama_stack.apis.common.type_system import ParamType -from llama_stack.apis.datasets import ( - Dataset, - DatasetPurpose, - Datasets, - DatasetType, - DataSource, - ListDatasetsResponse, - RowsDataSource, - URIDataSource, -) -from llama_stack.apis.models import ListModelsResponse, Model, Models, ModelType, OpenAIListModelsResponse, OpenAIModel -from llama_stack.apis.resource import ResourceType -from llama_stack.apis.scoring_functions import ( - ListScoringFunctionsResponse, - ScoringFn, - ScoringFnParams, - ScoringFunctions, -) -from llama_stack.apis.shields import ListShieldsResponse, Shield, Shields -from llama_stack.apis.tools import ( - ListToolGroupsResponse, - ListToolsResponse, - Tool, - ToolGroup, - ToolGroups, - ToolHost, -) -from llama_stack.apis.vector_dbs import ListVectorDBsResponse, VectorDB, VectorDBs -from llama_stack.distribution.access_control import check_access -from llama_stack.distribution.datatypes import ( - AccessAttributes, - BenchmarkWithACL, - DatasetWithACL, - ModelWithACL, - RoutableObject, - RoutableObjectWithProvider, - RoutedProtocol, - ScoringFnWithACL, - ShieldWithACL, - ToolGroupWithACL, - ToolWithACL, - VectorDBWithACL, -) -from llama_stack.distribution.request_headers import get_auth_attributes -from llama_stack.distribution.store import DistributionRegistry -from llama_stack.providers.datatypes import Api, RoutingTable - -logger = logging.getLogger(__name__) - - -def get_impl_api(p: Any) -> Api: - return p.__provider_spec__.api - - -# TODO: this should return the registered object for all APIs -async def register_object_with_provider(obj: RoutableObject, p: Any) -> RoutableObject: - api = get_impl_api(p) - - assert obj.provider_id != "remote", "Remote provider should not be registered" - - if api == Api.inference: - return await p.register_model(obj) - elif api == Api.safety: - return await p.register_shield(obj) - elif api == Api.vector_io: - return await p.register_vector_db(obj) - elif api == Api.datasetio: - return await p.register_dataset(obj) - elif api == Api.scoring: - return await p.register_scoring_function(obj) - elif api == Api.eval: - return await p.register_benchmark(obj) - elif api == Api.tool_runtime: - return await p.register_tool(obj) - else: - raise ValueError(f"Unknown API {api} for registering object with provider") - - -async def unregister_object_from_provider(obj: RoutableObject, p: Any) -> None: - api = get_impl_api(p) - if api == Api.vector_io: - return await p.unregister_vector_db(obj.identifier) - elif api == Api.inference: - return await p.unregister_model(obj.identifier) - elif api == Api.datasetio: - return await p.unregister_dataset(obj.identifier) - elif api == Api.tool_runtime: - return await p.unregister_tool(obj.identifier) - else: - raise ValueError(f"Unregister not supported for {api}") - - -Registry = dict[str, list[RoutableObjectWithProvider]] - - -class CommonRoutingTableImpl(RoutingTable): - def __init__( - self, - impls_by_provider_id: dict[str, RoutedProtocol], - dist_registry: DistributionRegistry, - ) -> None: - self.impls_by_provider_id = impls_by_provider_id - self.dist_registry = dist_registry - - async def initialize(self) -> None: - async def add_objects(objs: list[RoutableObjectWithProvider], provider_id: str, cls) -> None: - for obj in objs: - if cls is None: - obj.provider_id = provider_id - else: - # Create a copy of the model data and explicitly set provider_id - model_data = obj.model_dump() - model_data["provider_id"] = provider_id - obj = cls(**model_data) - await self.dist_registry.register(obj) - - # Register all objects from providers - for pid, p in self.impls_by_provider_id.items(): - api = get_impl_api(p) - if api == Api.inference: - p.model_store = self - elif api == Api.safety: - p.shield_store = self - elif api == Api.vector_io: - p.vector_db_store = self - elif api == Api.datasetio: - p.dataset_store = self - elif api == Api.scoring: - p.scoring_function_store = self - scoring_functions = await p.list_scoring_functions() - await add_objects(scoring_functions, pid, ScoringFn) - elif api == Api.eval: - p.benchmark_store = self - elif api == Api.tool_runtime: - p.tool_store = self - - async def shutdown(self) -> None: - for p in self.impls_by_provider_id.values(): - await p.shutdown() - - def get_provider_impl(self, routing_key: str, provider_id: str | None = None) -> Any: - def apiname_object(): - if isinstance(self, ModelsRoutingTable): - return ("Inference", "model") - elif isinstance(self, ShieldsRoutingTable): - return ("Safety", "shield") - elif isinstance(self, VectorDBsRoutingTable): - return ("VectorIO", "vector_db") - elif isinstance(self, DatasetsRoutingTable): - return ("DatasetIO", "dataset") - elif isinstance(self, ScoringFunctionsRoutingTable): - return ("Scoring", "scoring_function") - elif isinstance(self, BenchmarksRoutingTable): - return ("Eval", "benchmark") - elif isinstance(self, ToolGroupsRoutingTable): - return ("Tools", "tool") - else: - raise ValueError("Unknown routing table type") - - apiname, objtype = apiname_object() - - # Get objects from disk registry - obj = self.dist_registry.get_cached(objtype, routing_key) - if not obj: - provider_ids = list(self.impls_by_provider_id.keys()) - if len(provider_ids) > 1: - provider_ids_str = f"any of the providers: {', '.join(provider_ids)}" - else: - provider_ids_str = f"provider: `{provider_ids[0]}`" - raise ValueError( - f"{objtype.capitalize()} `{routing_key}` not served by {provider_ids_str}. Make sure there is an {apiname} provider serving this {objtype}." - ) - - if not provider_id or provider_id == obj.provider_id: - return self.impls_by_provider_id[obj.provider_id] - - raise ValueError(f"Provider not found for `{routing_key}`") - - async def get_object_by_identifier(self, type: str, identifier: str) -> RoutableObjectWithProvider | None: - # Get from disk registry - obj = await self.dist_registry.get(type, identifier) - if not obj: - return None - - # Check if user has permission to access this object - if not check_access(obj.identifier, getattr(obj, "access_attributes", None), get_auth_attributes()): - logger.debug(f"Access denied to {type} '{identifier}' based on attribute mismatch") - return None - - return obj - - async def unregister_object(self, obj: RoutableObjectWithProvider) -> None: - await self.dist_registry.delete(obj.type, obj.identifier) - await unregister_object_from_provider(obj, self.impls_by_provider_id[obj.provider_id]) - - async def register_object(self, obj: RoutableObjectWithProvider) -> RoutableObjectWithProvider: - # if provider_id is not specified, pick an arbitrary one from existing entries - if not obj.provider_id and len(self.impls_by_provider_id) > 0: - obj.provider_id = list(self.impls_by_provider_id.keys())[0] - - if obj.provider_id not in self.impls_by_provider_id: - raise ValueError(f"Provider `{obj.provider_id}` not found") - - p = self.impls_by_provider_id[obj.provider_id] - - # If object supports access control but no attributes set, use creator's attributes - if not obj.access_attributes: - creator_attributes = get_auth_attributes() - if creator_attributes: - obj.access_attributes = AccessAttributes(**creator_attributes) - logger.info(f"Setting access attributes for {obj.type} '{obj.identifier}' based on creator's identity") - - registered_obj = await register_object_with_provider(obj, p) - # TODO: This needs to be fixed for all APIs once they return the registered object - if obj.type == ResourceType.model.value: - await self.dist_registry.register(registered_obj) - return registered_obj - - else: - await self.dist_registry.register(obj) - return obj - - async def get_all_with_type(self, type: str) -> list[RoutableObjectWithProvider]: - objs = await self.dist_registry.get_all() - filtered_objs = [obj for obj in objs if obj.type == type] - - # Apply attribute-based access control filtering - if filtered_objs: - filtered_objs = [ - obj - for obj in filtered_objs - if check_access(obj.identifier, getattr(obj, "access_attributes", None), get_auth_attributes()) - ] - - return filtered_objs - - -class ModelsRoutingTable(CommonRoutingTableImpl, Models): - async def list_models(self) -> ListModelsResponse: - return ListModelsResponse(data=await self.get_all_with_type("model")) - - async def openai_list_models(self) -> OpenAIListModelsResponse: - models = await self.get_all_with_type("model") - openai_models = [ - OpenAIModel( - id=model.identifier, - object="model", - created=int(time.time()), - owned_by="llama_stack", - ) - for model in models - ] - return OpenAIListModelsResponse(data=openai_models) - - async def get_model(self, model_id: str) -> Model: - model = await self.get_object_by_identifier("model", model_id) - if model is None: - raise ValueError(f"Model '{model_id}' not found") - return model - - async def register_model( - self, - model_id: str, - provider_model_id: str | None = None, - provider_id: str | None = None, - metadata: dict[str, Any] | None = None, - model_type: ModelType | None = None, - ) -> Model: - if provider_model_id is None: - provider_model_id = model_id - if provider_id is None: - # If provider_id not specified, use the only provider if it supports this model - if len(self.impls_by_provider_id) == 1: - provider_id = list(self.impls_by_provider_id.keys())[0] - else: - raise ValueError( - f"No provider specified and multiple providers available. Please specify a provider_id. Available providers: {self.impls_by_provider_id.keys()}" - ) - if metadata is None: - metadata = {} - if model_type is None: - model_type = ModelType.llm - if "embedding_dimension" not in metadata and model_type == ModelType.embedding: - raise ValueError("Embedding model must have an embedding dimension in its metadata") - model = ModelWithACL( - identifier=model_id, - provider_resource_id=provider_model_id, - provider_id=provider_id, - metadata=metadata, - model_type=model_type, - ) - registered_model = await self.register_object(model) - return registered_model - - async def unregister_model(self, model_id: str) -> None: - existing_model = await self.get_model(model_id) - if existing_model is None: - raise ValueError(f"Model {model_id} not found") - await self.unregister_object(existing_model) - - -class ShieldsRoutingTable(CommonRoutingTableImpl, Shields): - async def list_shields(self) -> ListShieldsResponse: - return ListShieldsResponse(data=await self.get_all_with_type(ResourceType.shield.value)) - - async def get_shield(self, identifier: str) -> Shield: - shield = await self.get_object_by_identifier("shield", identifier) - if shield is None: - raise ValueError(f"Shield '{identifier}' not found") - return shield - - async def register_shield( - self, - shield_id: str, - provider_shield_id: str | None = None, - provider_id: str | None = None, - params: dict[str, Any] | None = None, - ) -> Shield: - if provider_shield_id is None: - provider_shield_id = shield_id - if provider_id is None: - # If provider_id not specified, use the only provider if it supports this shield type - if len(self.impls_by_provider_id) == 1: - provider_id = list(self.impls_by_provider_id.keys())[0] - else: - raise ValueError( - "No provider specified and multiple providers available. Please specify a provider_id." - ) - if params is None: - params = {} - shield = ShieldWithACL( - identifier=shield_id, - provider_resource_id=provider_shield_id, - provider_id=provider_id, - params=params, - ) - await self.register_object(shield) - return shield - - -class VectorDBsRoutingTable(CommonRoutingTableImpl, VectorDBs): - async def list_vector_dbs(self) -> ListVectorDBsResponse: - return ListVectorDBsResponse(data=await self.get_all_with_type("vector_db")) - - async def get_vector_db(self, vector_db_id: str) -> VectorDB: - vector_db = await self.get_object_by_identifier("vector_db", vector_db_id) - if vector_db is None: - raise ValueError(f"Vector DB '{vector_db_id}' not found") - return vector_db - - async def register_vector_db( - self, - vector_db_id: str, - embedding_model: str, - embedding_dimension: int | None = 384, - provider_id: str | None = None, - provider_vector_db_id: str | None = None, - ) -> VectorDB: - if provider_vector_db_id is None: - provider_vector_db_id = vector_db_id - if provider_id is None: - if len(self.impls_by_provider_id) > 0: - provider_id = list(self.impls_by_provider_id.keys())[0] - if len(self.impls_by_provider_id) > 1: - logger.warning( - f"No provider specified and multiple providers available. Arbitrarily selected the first provider {provider_id}." - ) - else: - raise ValueError("No provider available. Please configure a vector_io provider.") - model = await self.get_object_by_identifier("model", embedding_model) - if model is None: - raise ValueError(f"Model {embedding_model} not found") - if model.model_type != ModelType.embedding: - raise ValueError(f"Model {embedding_model} is not an embedding model") - if "embedding_dimension" not in model.metadata: - raise ValueError(f"Model {embedding_model} does not have an embedding dimension") - vector_db_data = { - "identifier": vector_db_id, - "type": ResourceType.vector_db.value, - "provider_id": provider_id, - "provider_resource_id": provider_vector_db_id, - "embedding_model": embedding_model, - "embedding_dimension": model.metadata["embedding_dimension"], - } - vector_db = TypeAdapter(VectorDBWithACL).validate_python(vector_db_data) - await self.register_object(vector_db) - return vector_db - - async def unregister_vector_db(self, vector_db_id: str) -> None: - existing_vector_db = await self.get_vector_db(vector_db_id) - if existing_vector_db is None: - raise ValueError(f"Vector DB {vector_db_id} not found") - await self.unregister_object(existing_vector_db) - - -class DatasetsRoutingTable(CommonRoutingTableImpl, Datasets): - async def list_datasets(self) -> ListDatasetsResponse: - return ListDatasetsResponse(data=await self.get_all_with_type(ResourceType.dataset.value)) - - async def get_dataset(self, dataset_id: str) -> Dataset: - dataset = await self.get_object_by_identifier("dataset", dataset_id) - if dataset is None: - raise ValueError(f"Dataset '{dataset_id}' not found") - return dataset - - async def register_dataset( - self, - purpose: DatasetPurpose, - source: DataSource, - metadata: dict[str, Any] | None = None, - dataset_id: str | None = None, - ) -> Dataset: - if isinstance(source, dict): - if source["type"] == "uri": - source = URIDataSource.parse_obj(source) - elif source["type"] == "rows": - source = RowsDataSource.parse_obj(source) - - if not dataset_id: - dataset_id = f"dataset-{str(uuid.uuid4())}" - - provider_dataset_id = dataset_id - - # infer provider from source - if metadata: - if metadata.get("provider_id"): - provider_id = metadata.get("provider_id") # pass through from nvidia datasetio - elif source.type == DatasetType.rows.value: - provider_id = "localfs" - elif source.type == DatasetType.uri.value: - # infer provider from uri - if source.uri.startswith("huggingface"): - provider_id = "huggingface" - else: - provider_id = "localfs" - else: - raise ValueError(f"Unknown data source type: {source.type}") - - if metadata is None: - metadata = {} - - dataset = DatasetWithACL( - identifier=dataset_id, - provider_resource_id=provider_dataset_id, - provider_id=provider_id, - purpose=purpose, - source=source, - metadata=metadata, - ) - - await self.register_object(dataset) - return dataset - - async def unregister_dataset(self, dataset_id: str) -> None: - dataset = await self.get_dataset(dataset_id) - if dataset is None: - raise ValueError(f"Dataset {dataset_id} not found") - await self.unregister_object(dataset) - - -class ScoringFunctionsRoutingTable(CommonRoutingTableImpl, ScoringFunctions): - async def list_scoring_functions(self) -> ListScoringFunctionsResponse: - return ListScoringFunctionsResponse(data=await self.get_all_with_type(ResourceType.scoring_function.value)) - - async def get_scoring_function(self, scoring_fn_id: str) -> ScoringFn: - scoring_fn = await self.get_object_by_identifier("scoring_function", scoring_fn_id) - if scoring_fn is None: - raise ValueError(f"Scoring function '{scoring_fn_id}' not found") - return scoring_fn - - async def register_scoring_function( - self, - scoring_fn_id: str, - description: str, - return_type: ParamType, - provider_scoring_fn_id: str | None = None, - provider_id: str | None = None, - params: ScoringFnParams | None = None, - ) -> None: - if provider_scoring_fn_id is None: - provider_scoring_fn_id = scoring_fn_id - if provider_id is None: - if len(self.impls_by_provider_id) == 1: - provider_id = list(self.impls_by_provider_id.keys())[0] - else: - raise ValueError( - "No provider specified and multiple providers available. Please specify a provider_id." - ) - scoring_fn = ScoringFnWithACL( - identifier=scoring_fn_id, - description=description, - return_type=return_type, - provider_resource_id=provider_scoring_fn_id, - provider_id=provider_id, - params=params, - ) - scoring_fn.provider_id = provider_id - await self.register_object(scoring_fn) - - -class BenchmarksRoutingTable(CommonRoutingTableImpl, Benchmarks): - async def list_benchmarks(self) -> ListBenchmarksResponse: - return ListBenchmarksResponse(data=await self.get_all_with_type("benchmark")) - - async def get_benchmark(self, benchmark_id: str) -> Benchmark: - benchmark = await self.get_object_by_identifier("benchmark", benchmark_id) - if benchmark is None: - raise ValueError(f"Benchmark '{benchmark_id}' not found") - return benchmark - - async def register_benchmark( - self, - benchmark_id: str, - dataset_id: str, - scoring_functions: list[str], - metadata: dict[str, Any] | None = None, - provider_benchmark_id: str | None = None, - provider_id: str | None = None, - ) -> None: - if metadata is None: - metadata = {} - if provider_id is None: - if len(self.impls_by_provider_id) == 1: - provider_id = list(self.impls_by_provider_id.keys())[0] - else: - raise ValueError( - "No provider specified and multiple providers available. Please specify a provider_id." - ) - if provider_benchmark_id is None: - provider_benchmark_id = benchmark_id - benchmark = BenchmarkWithACL( - identifier=benchmark_id, - dataset_id=dataset_id, - scoring_functions=scoring_functions, - metadata=metadata, - provider_id=provider_id, - provider_resource_id=provider_benchmark_id, - ) - await self.register_object(benchmark) - - -class ToolGroupsRoutingTable(CommonRoutingTableImpl, ToolGroups): - async def list_tools(self, toolgroup_id: str | None = None) -> ListToolsResponse: - tools = await self.get_all_with_type("tool") - if toolgroup_id: - tools = [tool for tool in tools if tool.toolgroup_id == toolgroup_id] - return ListToolsResponse(data=tools) - - async def list_tool_groups(self) -> ListToolGroupsResponse: - return ListToolGroupsResponse(data=await self.get_all_with_type("tool_group")) - - async def get_tool_group(self, toolgroup_id: str) -> ToolGroup: - tool_group = await self.get_object_by_identifier("tool_group", toolgroup_id) - if tool_group is None: - raise ValueError(f"Tool group '{toolgroup_id}' not found") - return tool_group - - async def get_tool(self, tool_name: str) -> Tool: - return await self.get_object_by_identifier("tool", tool_name) - - async def register_tool_group( - self, - toolgroup_id: str, - provider_id: str, - mcp_endpoint: URL | None = None, - args: dict[str, Any] | None = None, - ) -> None: - tools = [] - tool_defs = await self.impls_by_provider_id[provider_id].list_runtime_tools(toolgroup_id, mcp_endpoint) - tool_host = ToolHost.model_context_protocol if mcp_endpoint else ToolHost.distribution - - for tool_def in tool_defs.data: - tools.append( - ToolWithACL( - identifier=tool_def.name, - toolgroup_id=toolgroup_id, - description=tool_def.description or "", - parameters=tool_def.parameters or [], - provider_id=provider_id, - provider_resource_id=tool_def.name, - metadata=tool_def.metadata, - tool_host=tool_host, - ) - ) - for tool in tools: - existing_tool = await self.get_tool(tool.identifier) - # Compare existing and new object if one exists - if existing_tool: - existing_dict = existing_tool.model_dump() - new_dict = tool.model_dump() - - if existing_dict != new_dict: - raise ValueError( - f"Object {tool.identifier} already exists in registry. Please use a different identifier." - ) - await self.register_object(tool) - - await self.dist_registry.register( - ToolGroupWithACL( - identifier=toolgroup_id, - provider_id=provider_id, - provider_resource_id=toolgroup_id, - mcp_endpoint=mcp_endpoint, - args=args, - ) - ) - - async def unregister_toolgroup(self, toolgroup_id: str) -> None: - tool_group = await self.get_tool_group(toolgroup_id) - if tool_group is None: - raise ValueError(f"Tool group {toolgroup_id} not found") - tools = await self.list_tools(toolgroup_id) - for tool in getattr(tools, "data", []): - await self.unregister_object(tool) - await self.unregister_object(tool_group) - - async def shutdown(self) -> None: - pass diff --git a/llama_stack/distribution/routers/safety.py b/llama_stack/distribution/routers/safety.py new file mode 100644 index 000000000..9761d2db0 --- /dev/null +++ b/llama_stack/distribution/routers/safety.py @@ -0,0 +1,57 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the terms described in the LICENSE file in +# the root directory of this source tree. + +from typing import Any + +from llama_stack.apis.inference import ( + Message, +) +from llama_stack.apis.safety import RunShieldResponse, Safety +from llama_stack.apis.shields import Shield +from llama_stack.log import get_logger +from llama_stack.providers.datatypes import RoutingTable + +logger = get_logger(name=__name__, category="core") + + +class SafetyRouter(Safety): + def __init__( + self, + routing_table: RoutingTable, + ) -> None: + logger.debug("Initializing SafetyRouter") + self.routing_table = routing_table + + async def initialize(self) -> None: + logger.debug("SafetyRouter.initialize") + pass + + async def shutdown(self) -> None: + logger.debug("SafetyRouter.shutdown") + pass + + async def register_shield( + self, + shield_id: str, + provider_shield_id: str | None = None, + provider_id: str | None = None, + params: dict[str, Any] | None = None, + ) -> Shield: + logger.debug(f"SafetyRouter.register_shield: {shield_id}") + return await self.routing_table.register_shield(shield_id, provider_shield_id, provider_id, params) + + async def run_shield( + self, + shield_id: str, + messages: list[Message], + params: dict[str, Any] = None, + ) -> RunShieldResponse: + logger.debug(f"SafetyRouter.run_shield: {shield_id}") + return await self.routing_table.get_provider_impl(shield_id).run_shield( + shield_id=shield_id, + messages=messages, + params=params, + ) diff --git a/llama_stack/distribution/routers/tool_runtime.py b/llama_stack/distribution/routers/tool_runtime.py new file mode 100644 index 000000000..285843dbc --- /dev/null +++ b/llama_stack/distribution/routers/tool_runtime.py @@ -0,0 +1,92 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the terms described in the LICENSE file in +# the root directory of this source tree. + +from typing import Any + +from llama_stack.apis.common.content_types import ( + URL, + InterleavedContent, +) +from llama_stack.apis.tools import ( + ListToolsResponse, + RAGDocument, + RAGQueryConfig, + RAGQueryResult, + RAGToolRuntime, + ToolRuntime, +) +from llama_stack.log import get_logger + +from ..routing_tables.toolgroups import ToolGroupsRoutingTable + +logger = get_logger(name=__name__, category="core") + + +class ToolRuntimeRouter(ToolRuntime): + class RagToolImpl(RAGToolRuntime): + def __init__( + self, + routing_table: ToolGroupsRoutingTable, + ) -> None: + logger.debug("Initializing ToolRuntimeRouter.RagToolImpl") + self.routing_table = routing_table + + async def query( + self, + content: InterleavedContent, + vector_db_ids: list[str], + query_config: RAGQueryConfig | None = None, + ) -> RAGQueryResult: + logger.debug(f"ToolRuntimeRouter.RagToolImpl.query: {vector_db_ids}") + return await self.routing_table.get_provider_impl("knowledge_search").query( + content, vector_db_ids, query_config + ) + + async def insert( + self, + documents: list[RAGDocument], + vector_db_id: str, + chunk_size_in_tokens: int = 512, + ) -> None: + logger.debug( + f"ToolRuntimeRouter.RagToolImpl.insert: {vector_db_id}, {len(documents)} documents, chunk_size={chunk_size_in_tokens}" + ) + return await self.routing_table.get_provider_impl("insert_into_memory").insert( + documents, vector_db_id, chunk_size_in_tokens + ) + + def __init__( + self, + routing_table: ToolGroupsRoutingTable, + ) -> None: + logger.debug("Initializing ToolRuntimeRouter") + self.routing_table = routing_table + + # HACK ALERT this should be in sync with "get_all_api_endpoints()" + self.rag_tool = self.RagToolImpl(routing_table) + for method in ("query", "insert"): + setattr(self, f"rag_tool.{method}", getattr(self.rag_tool, method)) + + async def initialize(self) -> None: + logger.debug("ToolRuntimeRouter.initialize") + pass + + async def shutdown(self) -> None: + logger.debug("ToolRuntimeRouter.shutdown") + pass + + async def invoke_tool(self, tool_name: str, kwargs: dict[str, Any]) -> Any: + logger.debug(f"ToolRuntimeRouter.invoke_tool: {tool_name}") + return await self.routing_table.get_provider_impl(tool_name).invoke_tool( + tool_name=tool_name, + kwargs=kwargs, + ) + + async def list_runtime_tools( + self, tool_group_id: str | None = None, mcp_endpoint: URL | None = None + ) -> ListToolsResponse: + logger.debug(f"ToolRuntimeRouter.list_runtime_tools: {tool_group_id}") + return await self.routing_table.list_tools(tool_group_id) diff --git a/llama_stack/distribution/routers/vector_io.py b/llama_stack/distribution/routers/vector_io.py new file mode 100644 index 000000000..8c17aa890 --- /dev/null +++ b/llama_stack/distribution/routers/vector_io.py @@ -0,0 +1,72 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the terms described in the LICENSE file in +# the root directory of this source tree. + +from typing import Any + +from llama_stack.apis.common.content_types import ( + InterleavedContent, +) +from llama_stack.apis.vector_io import Chunk, QueryChunksResponse, VectorIO +from llama_stack.log import get_logger +from llama_stack.providers.datatypes import RoutingTable + +logger = get_logger(name=__name__, category="core") + + +class VectorIORouter(VectorIO): + """Routes to an provider based on the vector db identifier""" + + def __init__( + self, + routing_table: RoutingTable, + ) -> None: + logger.debug("Initializing VectorIORouter") + self.routing_table = routing_table + + async def initialize(self) -> None: + logger.debug("VectorIORouter.initialize") + pass + + async def shutdown(self) -> None: + logger.debug("VectorIORouter.shutdown") + pass + + async def register_vector_db( + self, + vector_db_id: str, + embedding_model: str, + embedding_dimension: int | None = 384, + provider_id: str | None = None, + provider_vector_db_id: str | None = None, + ) -> None: + logger.debug(f"VectorIORouter.register_vector_db: {vector_db_id}, {embedding_model}") + await self.routing_table.register_vector_db( + vector_db_id, + embedding_model, + embedding_dimension, + provider_id, + provider_vector_db_id, + ) + + async def insert_chunks( + self, + vector_db_id: str, + chunks: list[Chunk], + ttl_seconds: int | None = None, + ) -> None: + logger.debug( + f"VectorIORouter.insert_chunks: {vector_db_id}, {len(chunks)} chunks, ttl_seconds={ttl_seconds}, chunk_ids={[chunk.metadata['document_id'] for chunk in chunks[:3]]}{' and more...' if len(chunks) > 3 else ''}", + ) + return await self.routing_table.get_provider_impl(vector_db_id).insert_chunks(vector_db_id, chunks, ttl_seconds) + + async def query_chunks( + self, + vector_db_id: str, + query: InterleavedContent, + params: dict[str, Any] | None = None, + ) -> QueryChunksResponse: + logger.debug(f"VectorIORouter.query_chunks: {vector_db_id}") + return await self.routing_table.get_provider_impl(vector_db_id).query_chunks(vector_db_id, query, params) diff --git a/llama_stack/distribution/routing_tables/__init__.py b/llama_stack/distribution/routing_tables/__init__.py new file mode 100644 index 000000000..756f351d8 --- /dev/null +++ b/llama_stack/distribution/routing_tables/__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/llama_stack/distribution/routing_tables/benchmarks.py b/llama_stack/distribution/routing_tables/benchmarks.py new file mode 100644 index 000000000..589a00c02 --- /dev/null +++ b/llama_stack/distribution/routing_tables/benchmarks.py @@ -0,0 +1,58 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the terms described in the LICENSE file in +# the root directory of this source tree. + +from typing import Any + +from llama_stack.apis.benchmarks import Benchmark, Benchmarks, ListBenchmarksResponse +from llama_stack.distribution.datatypes import ( + BenchmarkWithACL, +) +from llama_stack.log import get_logger + +from .common import CommonRoutingTableImpl + +logger = get_logger(name=__name__, category="core") + + +class BenchmarksRoutingTable(CommonRoutingTableImpl, Benchmarks): + async def list_benchmarks(self) -> ListBenchmarksResponse: + return ListBenchmarksResponse(data=await self.get_all_with_type("benchmark")) + + async def get_benchmark(self, benchmark_id: str) -> Benchmark: + benchmark = await self.get_object_by_identifier("benchmark", benchmark_id) + if benchmark is None: + raise ValueError(f"Benchmark '{benchmark_id}' not found") + return benchmark + + async def register_benchmark( + self, + benchmark_id: str, + dataset_id: str, + scoring_functions: list[str], + metadata: dict[str, Any] | None = None, + provider_benchmark_id: str | None = None, + provider_id: str | None = None, + ) -> None: + if metadata is None: + metadata = {} + if provider_id is None: + if len(self.impls_by_provider_id) == 1: + provider_id = list(self.impls_by_provider_id.keys())[0] + else: + raise ValueError( + "No provider specified and multiple providers available. Please specify a provider_id." + ) + if provider_benchmark_id is None: + provider_benchmark_id = benchmark_id + benchmark = BenchmarkWithACL( + identifier=benchmark_id, + dataset_id=dataset_id, + scoring_functions=scoring_functions, + metadata=metadata, + provider_id=provider_id, + provider_resource_id=provider_benchmark_id, + ) + await self.register_object(benchmark) diff --git a/llama_stack/distribution/routing_tables/common.py b/llama_stack/distribution/routing_tables/common.py new file mode 100644 index 000000000..8ec87ca50 --- /dev/null +++ b/llama_stack/distribution/routing_tables/common.py @@ -0,0 +1,218 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the terms described in the LICENSE file in +# the root directory of this source tree. + +from typing import Any + +from llama_stack.apis.resource import ResourceType +from llama_stack.apis.scoring_functions import ScoringFn +from llama_stack.distribution.access_control import check_access +from llama_stack.distribution.datatypes import ( + AccessAttributes, + RoutableObject, + RoutableObjectWithProvider, + RoutedProtocol, +) +from llama_stack.distribution.request_headers import get_auth_attributes +from llama_stack.distribution.store import DistributionRegistry +from llama_stack.log import get_logger +from llama_stack.providers.datatypes import Api, RoutingTable + +logger = get_logger(name=__name__, category="core") + + +def get_impl_api(p: Any) -> Api: + return p.__provider_spec__.api + + +# TODO: this should return the registered object for all APIs +async def register_object_with_provider(obj: RoutableObject, p: Any) -> RoutableObject: + api = get_impl_api(p) + + assert obj.provider_id != "remote", "Remote provider should not be registered" + + if api == Api.inference: + return await p.register_model(obj) + elif api == Api.safety: + return await p.register_shield(obj) + elif api == Api.vector_io: + return await p.register_vector_db(obj) + elif api == Api.datasetio: + return await p.register_dataset(obj) + elif api == Api.scoring: + return await p.register_scoring_function(obj) + elif api == Api.eval: + return await p.register_benchmark(obj) + elif api == Api.tool_runtime: + return await p.register_toolgroup(obj) + else: + raise ValueError(f"Unknown API {api} for registering object with provider") + + +async def unregister_object_from_provider(obj: RoutableObject, p: Any) -> None: + api = get_impl_api(p) + if api == Api.vector_io: + return await p.unregister_vector_db(obj.identifier) + elif api == Api.inference: + return await p.unregister_model(obj.identifier) + elif api == Api.datasetio: + return await p.unregister_dataset(obj.identifier) + elif api == Api.tool_runtime: + return await p.unregister_toolgroup(obj.identifier) + else: + raise ValueError(f"Unregister not supported for {api}") + + +Registry = dict[str, list[RoutableObjectWithProvider]] + + +class CommonRoutingTableImpl(RoutingTable): + def __init__( + self, + impls_by_provider_id: dict[str, RoutedProtocol], + dist_registry: DistributionRegistry, + ) -> None: + self.impls_by_provider_id = impls_by_provider_id + self.dist_registry = dist_registry + + async def initialize(self) -> None: + async def add_objects(objs: list[RoutableObjectWithProvider], provider_id: str, cls) -> None: + for obj in objs: + if cls is None: + obj.provider_id = provider_id + else: + # Create a copy of the model data and explicitly set provider_id + model_data = obj.model_dump() + model_data["provider_id"] = provider_id + obj = cls(**model_data) + await self.dist_registry.register(obj) + + # Register all objects from providers + for pid, p in self.impls_by_provider_id.items(): + api = get_impl_api(p) + if api == Api.inference: + p.model_store = self + elif api == Api.safety: + p.shield_store = self + elif api == Api.vector_io: + p.vector_db_store = self + elif api == Api.datasetio: + p.dataset_store = self + elif api == Api.scoring: + p.scoring_function_store = self + scoring_functions = await p.list_scoring_functions() + await add_objects(scoring_functions, pid, ScoringFn) + elif api == Api.eval: + p.benchmark_store = self + elif api == Api.tool_runtime: + p.tool_store = self + + async def shutdown(self) -> None: + for p in self.impls_by_provider_id.values(): + await p.shutdown() + + def get_provider_impl(self, routing_key: str, provider_id: str | None = None) -> Any: + from .benchmarks import BenchmarksRoutingTable + from .datasets import DatasetsRoutingTable + from .models import ModelsRoutingTable + from .scoring_functions import ScoringFunctionsRoutingTable + from .shields import ShieldsRoutingTable + from .toolgroups import ToolGroupsRoutingTable + from .vector_dbs import VectorDBsRoutingTable + + def apiname_object(): + if isinstance(self, ModelsRoutingTable): + return ("Inference", "model") + elif isinstance(self, ShieldsRoutingTable): + return ("Safety", "shield") + elif isinstance(self, VectorDBsRoutingTable): + return ("VectorIO", "vector_db") + elif isinstance(self, DatasetsRoutingTable): + return ("DatasetIO", "dataset") + elif isinstance(self, ScoringFunctionsRoutingTable): + return ("Scoring", "scoring_function") + elif isinstance(self, BenchmarksRoutingTable): + return ("Eval", "benchmark") + elif isinstance(self, ToolGroupsRoutingTable): + return ("ToolGroups", "tool_group") + else: + raise ValueError("Unknown routing table type") + + apiname, objtype = apiname_object() + + # Get objects from disk registry + obj = self.dist_registry.get_cached(objtype, routing_key) + if not obj: + provider_ids = list(self.impls_by_provider_id.keys()) + if len(provider_ids) > 1: + provider_ids_str = f"any of the providers: {', '.join(provider_ids)}" + else: + provider_ids_str = f"provider: `{provider_ids[0]}`" + raise ValueError( + f"{objtype.capitalize()} `{routing_key}` not served by {provider_ids_str}. Make sure there is an {apiname} provider serving this {objtype}." + ) + + if not provider_id or provider_id == obj.provider_id: + return self.impls_by_provider_id[obj.provider_id] + + raise ValueError(f"Provider not found for `{routing_key}`") + + async def get_object_by_identifier(self, type: str, identifier: str) -> RoutableObjectWithProvider | None: + # Get from disk registry + obj = await self.dist_registry.get(type, identifier) + if not obj: + return None + + # Check if user has permission to access this object + if not check_access(obj.identifier, getattr(obj, "access_attributes", None), get_auth_attributes()): + logger.debug(f"Access denied to {type} '{identifier}' based on attribute mismatch") + return None + + return obj + + async def unregister_object(self, obj: RoutableObjectWithProvider) -> None: + await self.dist_registry.delete(obj.type, obj.identifier) + await unregister_object_from_provider(obj, self.impls_by_provider_id[obj.provider_id]) + + async def register_object(self, obj: RoutableObjectWithProvider) -> RoutableObjectWithProvider: + # if provider_id is not specified, pick an arbitrary one from existing entries + if not obj.provider_id and len(self.impls_by_provider_id) > 0: + obj.provider_id = list(self.impls_by_provider_id.keys())[0] + + if obj.provider_id not in self.impls_by_provider_id: + raise ValueError(f"Provider `{obj.provider_id}` not found") + + p = self.impls_by_provider_id[obj.provider_id] + + # If object supports access control but no attributes set, use creator's attributes + if not obj.access_attributes: + creator_attributes = get_auth_attributes() + if creator_attributes: + obj.access_attributes = AccessAttributes(**creator_attributes) + logger.info(f"Setting access attributes for {obj.type} '{obj.identifier}' based on creator's identity") + + registered_obj = await register_object_with_provider(obj, p) + # TODO: This needs to be fixed for all APIs once they return the registered object + if obj.type == ResourceType.model.value: + await self.dist_registry.register(registered_obj) + return registered_obj + + else: + await self.dist_registry.register(obj) + return obj + + async def get_all_with_type(self, type: str) -> list[RoutableObjectWithProvider]: + objs = await self.dist_registry.get_all() + filtered_objs = [obj for obj in objs if obj.type == type] + + # Apply attribute-based access control filtering + if filtered_objs: + filtered_objs = [ + obj + for obj in filtered_objs + if check_access(obj.identifier, getattr(obj, "access_attributes", None), get_auth_attributes()) + ] + + return filtered_objs diff --git a/llama_stack/distribution/routing_tables/datasets.py b/llama_stack/distribution/routing_tables/datasets.py new file mode 100644 index 000000000..4401ad47e --- /dev/null +++ b/llama_stack/distribution/routing_tables/datasets.py @@ -0,0 +1,93 @@ +# 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 uuid +from typing import Any + +from llama_stack.apis.datasets import ( + Dataset, + DatasetPurpose, + Datasets, + DatasetType, + DataSource, + ListDatasetsResponse, + RowsDataSource, + URIDataSource, +) +from llama_stack.apis.resource import ResourceType +from llama_stack.distribution.datatypes import ( + DatasetWithACL, +) +from llama_stack.log import get_logger + +from .common import CommonRoutingTableImpl + +logger = get_logger(name=__name__, category="core") + + +class DatasetsRoutingTable(CommonRoutingTableImpl, Datasets): + async def list_datasets(self) -> ListDatasetsResponse: + return ListDatasetsResponse(data=await self.get_all_with_type(ResourceType.dataset.value)) + + async def get_dataset(self, dataset_id: str) -> Dataset: + dataset = await self.get_object_by_identifier("dataset", dataset_id) + if dataset is None: + raise ValueError(f"Dataset '{dataset_id}' not found") + return dataset + + async def register_dataset( + self, + purpose: DatasetPurpose, + source: DataSource, + metadata: dict[str, Any] | None = None, + dataset_id: str | None = None, + ) -> Dataset: + if isinstance(source, dict): + if source["type"] == "uri": + source = URIDataSource.parse_obj(source) + elif source["type"] == "rows": + source = RowsDataSource.parse_obj(source) + + if not dataset_id: + dataset_id = f"dataset-{str(uuid.uuid4())}" + + provider_dataset_id = dataset_id + + # infer provider from source + if metadata: + if metadata.get("provider_id"): + provider_id = metadata.get("provider_id") # pass through from nvidia datasetio + elif source.type == DatasetType.rows.value: + provider_id = "localfs" + elif source.type == DatasetType.uri.value: + # infer provider from uri + if source.uri.startswith("huggingface"): + provider_id = "huggingface" + else: + provider_id = "localfs" + else: + raise ValueError(f"Unknown data source type: {source.type}") + + if metadata is None: + metadata = {} + + dataset = DatasetWithACL( + identifier=dataset_id, + provider_resource_id=provider_dataset_id, + provider_id=provider_id, + purpose=purpose, + source=source, + metadata=metadata, + ) + + await self.register_object(dataset) + return dataset + + async def unregister_dataset(self, dataset_id: str) -> None: + dataset = await self.get_dataset(dataset_id) + if dataset is None: + raise ValueError(f"Dataset {dataset_id} not found") + await self.unregister_object(dataset) diff --git a/llama_stack/distribution/routing_tables/models.py b/llama_stack/distribution/routing_tables/models.py new file mode 100644 index 000000000..7216d9935 --- /dev/null +++ b/llama_stack/distribution/routing_tables/models.py @@ -0,0 +1,82 @@ +# 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 time +from typing import Any + +from llama_stack.apis.models import ListModelsResponse, Model, Models, ModelType, OpenAIListModelsResponse, OpenAIModel +from llama_stack.distribution.datatypes import ( + ModelWithACL, +) +from llama_stack.log import get_logger + +from .common import CommonRoutingTableImpl + +logger = get_logger(name=__name__, category="core") + + +class ModelsRoutingTable(CommonRoutingTableImpl, Models): + async def list_models(self) -> ListModelsResponse: + return ListModelsResponse(data=await self.get_all_with_type("model")) + + async def openai_list_models(self) -> OpenAIListModelsResponse: + models = await self.get_all_with_type("model") + openai_models = [ + OpenAIModel( + id=model.identifier, + object="model", + created=int(time.time()), + owned_by="llama_stack", + ) + for model in models + ] + return OpenAIListModelsResponse(data=openai_models) + + async def get_model(self, model_id: str) -> Model: + model = await self.get_object_by_identifier("model", model_id) + if model is None: + raise ValueError(f"Model '{model_id}' not found") + return model + + async def register_model( + self, + model_id: str, + provider_model_id: str | None = None, + provider_id: str | None = None, + metadata: dict[str, Any] | None = None, + model_type: ModelType | None = None, + ) -> Model: + if provider_model_id is None: + provider_model_id = model_id + if provider_id is None: + # If provider_id not specified, use the only provider if it supports this model + if len(self.impls_by_provider_id) == 1: + provider_id = list(self.impls_by_provider_id.keys())[0] + else: + raise ValueError( + f"No provider specified and multiple providers available. Please specify a provider_id. Available providers: {self.impls_by_provider_id.keys()}" + ) + if metadata is None: + metadata = {} + if model_type is None: + model_type = ModelType.llm + if "embedding_dimension" not in metadata and model_type == ModelType.embedding: + raise ValueError("Embedding model must have an embedding dimension in its metadata") + model = ModelWithACL( + identifier=model_id, + provider_resource_id=provider_model_id, + provider_id=provider_id, + metadata=metadata, + model_type=model_type, + ) + registered_model = await self.register_object(model) + return registered_model + + async def unregister_model(self, model_id: str) -> None: + existing_model = await self.get_model(model_id) + if existing_model is None: + raise ValueError(f"Model {model_id} not found") + await self.unregister_object(existing_model) diff --git a/llama_stack/distribution/routing_tables/scoring_functions.py b/llama_stack/distribution/routing_tables/scoring_functions.py new file mode 100644 index 000000000..d85f64b57 --- /dev/null +++ b/llama_stack/distribution/routing_tables/scoring_functions.py @@ -0,0 +1,62 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the terms described in the LICENSE file in +# the root directory of this source tree. + +from llama_stack.apis.common.type_system import ParamType +from llama_stack.apis.resource import ResourceType +from llama_stack.apis.scoring_functions import ( + ListScoringFunctionsResponse, + ScoringFn, + ScoringFnParams, + ScoringFunctions, +) +from llama_stack.distribution.datatypes import ( + ScoringFnWithACL, +) +from llama_stack.log import get_logger + +from .common import CommonRoutingTableImpl + +logger = get_logger(name=__name__, category="core") + + +class ScoringFunctionsRoutingTable(CommonRoutingTableImpl, ScoringFunctions): + async def list_scoring_functions(self) -> ListScoringFunctionsResponse: + return ListScoringFunctionsResponse(data=await self.get_all_with_type(ResourceType.scoring_function.value)) + + async def get_scoring_function(self, scoring_fn_id: str) -> ScoringFn: + scoring_fn = await self.get_object_by_identifier("scoring_function", scoring_fn_id) + if scoring_fn is None: + raise ValueError(f"Scoring function '{scoring_fn_id}' not found") + return scoring_fn + + async def register_scoring_function( + self, + scoring_fn_id: str, + description: str, + return_type: ParamType, + provider_scoring_fn_id: str | None = None, + provider_id: str | None = None, + params: ScoringFnParams | None = None, + ) -> None: + if provider_scoring_fn_id is None: + provider_scoring_fn_id = scoring_fn_id + if provider_id is None: + if len(self.impls_by_provider_id) == 1: + provider_id = list(self.impls_by_provider_id.keys())[0] + else: + raise ValueError( + "No provider specified and multiple providers available. Please specify a provider_id." + ) + scoring_fn = ScoringFnWithACL( + identifier=scoring_fn_id, + description=description, + return_type=return_type, + provider_resource_id=provider_scoring_fn_id, + provider_id=provider_id, + params=params, + ) + scoring_fn.provider_id = provider_id + await self.register_object(scoring_fn) diff --git a/llama_stack/distribution/routing_tables/shields.py b/llama_stack/distribution/routing_tables/shields.py new file mode 100644 index 000000000..7f62596c9 --- /dev/null +++ b/llama_stack/distribution/routing_tables/shields.py @@ -0,0 +1,57 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the terms described in the LICENSE file in +# the root directory of this source tree. + +from typing import Any + +from llama_stack.apis.resource import ResourceType +from llama_stack.apis.shields import ListShieldsResponse, Shield, Shields +from llama_stack.distribution.datatypes import ( + ShieldWithACL, +) +from llama_stack.log import get_logger + +from .common import CommonRoutingTableImpl + +logger = get_logger(name=__name__, category="core") + + +class ShieldsRoutingTable(CommonRoutingTableImpl, Shields): + async def list_shields(self) -> ListShieldsResponse: + return ListShieldsResponse(data=await self.get_all_with_type(ResourceType.shield.value)) + + async def get_shield(self, identifier: str) -> Shield: + shield = await self.get_object_by_identifier("shield", identifier) + if shield is None: + raise ValueError(f"Shield '{identifier}' not found") + return shield + + async def register_shield( + self, + shield_id: str, + provider_shield_id: str | None = None, + provider_id: str | None = None, + params: dict[str, Any] | None = None, + ) -> Shield: + if provider_shield_id is None: + provider_shield_id = shield_id + if provider_id is None: + # If provider_id not specified, use the only provider if it supports this shield type + if len(self.impls_by_provider_id) == 1: + provider_id = list(self.impls_by_provider_id.keys())[0] + else: + raise ValueError( + "No provider specified and multiple providers available. Please specify a provider_id." + ) + if params is None: + params = {} + shield = ShieldWithACL( + identifier=shield_id, + provider_resource_id=provider_shield_id, + provider_id=provider_id, + params=params, + ) + await self.register_object(shield) + return shield diff --git a/llama_stack/distribution/routing_tables/toolgroups.py b/llama_stack/distribution/routing_tables/toolgroups.py new file mode 100644 index 000000000..2f7dc3e06 --- /dev/null +++ b/llama_stack/distribution/routing_tables/toolgroups.py @@ -0,0 +1,132 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the terms described in the LICENSE file in +# the root directory of this source tree. + +from typing import Any + +from llama_stack.apis.common.content_types import URL +from llama_stack.apis.tools import ListToolGroupsResponse, ListToolsResponse, Tool, ToolGroup, ToolGroups +from llama_stack.distribution.datatypes import ToolGroupWithACL +from llama_stack.log import get_logger + +from .common import CommonRoutingTableImpl + +logger = get_logger(name=__name__, category="core") + + +def parse_toolgroup_from_toolgroup_name_pair(toolgroup_name_with_maybe_tool_name: str) -> str | None: + # handle the funny case like "builtin::rag/knowledge_search" + parts = toolgroup_name_with_maybe_tool_name.split("/") + if len(parts) == 2: + return parts[0] + else: + return None + + +class ToolGroupsRoutingTable(CommonRoutingTableImpl, ToolGroups): + toolgroups_to_tools: dict[str, list[Tool]] = {} + tool_to_toolgroup: dict[str, str] = {} + + # overridden + def get_provider_impl(self, routing_key: str, provider_id: str | None = None) -> Any: + # we don't index tools in the registry anymore, but only keep a cache of them by toolgroup_id + # TODO: we may want to invalidate the cache (for a given toolgroup_id) every once in a while? + + toolgroup_id = parse_toolgroup_from_toolgroup_name_pair(routing_key) + if toolgroup_id: + routing_key = toolgroup_id + + if routing_key in self.tool_to_toolgroup: + routing_key = self.tool_to_toolgroup[routing_key] + return super().get_provider_impl(routing_key, provider_id) + + async def list_tools(self, toolgroup_id: str | None = None) -> ListToolsResponse: + if toolgroup_id: + if group_id := parse_toolgroup_from_toolgroup_name_pair(toolgroup_id): + toolgroup_id = group_id + toolgroups = [await self.get_tool_group(toolgroup_id)] + else: + toolgroups = await self.get_all_with_type("tool_group") + + all_tools = [] + for toolgroup in toolgroups: + if toolgroup.identifier not in self.toolgroups_to_tools: + await self._index_tools(toolgroup) + all_tools.extend(self.toolgroups_to_tools[toolgroup.identifier]) + + return ListToolsResponse(data=all_tools) + + async def _index_tools(self, toolgroup: ToolGroup): + provider_impl = super().get_provider_impl(toolgroup.identifier, toolgroup.provider_id) + tooldefs_response = await provider_impl.list_runtime_tools(toolgroup.identifier, toolgroup.mcp_endpoint) + + # TODO: kill this Tool vs ToolDef distinction + tooldefs = tooldefs_response.data + tools = [] + for t in tooldefs: + tools.append( + Tool( + identifier=t.name, + toolgroup_id=toolgroup.identifier, + description=t.description or "", + parameters=t.parameters or [], + metadata=t.metadata, + provider_id=toolgroup.provider_id, + ) + ) + + self.toolgroups_to_tools[toolgroup.identifier] = tools + for tool in tools: + self.tool_to_toolgroup[tool.identifier] = toolgroup.identifier + + async def list_tool_groups(self) -> ListToolGroupsResponse: + return ListToolGroupsResponse(data=await self.get_all_with_type("tool_group")) + + async def get_tool_group(self, toolgroup_id: str) -> ToolGroup: + tool_group = await self.get_object_by_identifier("tool_group", toolgroup_id) + if tool_group is None: + raise ValueError(f"Tool group '{toolgroup_id}' not found") + return tool_group + + async def get_tool(self, tool_name: str) -> Tool: + if tool_name in self.tool_to_toolgroup: + toolgroup_id = self.tool_to_toolgroup[tool_name] + tools = self.toolgroups_to_tools[toolgroup_id] + for tool in tools: + if tool.identifier == tool_name: + return tool + raise ValueError(f"Tool '{tool_name}' not found") + + async def register_tool_group( + self, + toolgroup_id: str, + provider_id: str, + mcp_endpoint: URL | None = None, + args: dict[str, Any] | None = None, + ) -> None: + toolgroup = ToolGroupWithACL( + identifier=toolgroup_id, + provider_id=provider_id, + provider_resource_id=toolgroup_id, + mcp_endpoint=mcp_endpoint, + args=args, + ) + await self.register_object(toolgroup) + + # ideally, indexing of the tools should not be necessary because anyone using + # the tools should first list the tools and then use them. but there are assumptions + # baked in some of the code and tests right now. + if not toolgroup.mcp_endpoint: + await self._index_tools(toolgroup) + return toolgroup + + async def unregister_toolgroup(self, toolgroup_id: str) -> None: + tool_group = await self.get_tool_group(toolgroup_id) + if tool_group is None: + raise ValueError(f"Tool group {toolgroup_id} not found") + await self.unregister_object(tool_group) + + async def shutdown(self) -> None: + pass diff --git a/llama_stack/distribution/routing_tables/vector_dbs.py b/llama_stack/distribution/routing_tables/vector_dbs.py new file mode 100644 index 000000000..dc6c0d0ef --- /dev/null +++ b/llama_stack/distribution/routing_tables/vector_dbs.py @@ -0,0 +1,74 @@ +# 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 pydantic import TypeAdapter + +from llama_stack.apis.models import ModelType +from llama_stack.apis.resource import ResourceType +from llama_stack.apis.vector_dbs import ListVectorDBsResponse, VectorDB, VectorDBs +from llama_stack.distribution.datatypes import ( + VectorDBWithACL, +) +from llama_stack.log import get_logger + +from .common import CommonRoutingTableImpl + +logger = get_logger(name=__name__, category="core") + + +class VectorDBsRoutingTable(CommonRoutingTableImpl, VectorDBs): + async def list_vector_dbs(self) -> ListVectorDBsResponse: + return ListVectorDBsResponse(data=await self.get_all_with_type("vector_db")) + + async def get_vector_db(self, vector_db_id: str) -> VectorDB: + vector_db = await self.get_object_by_identifier("vector_db", vector_db_id) + if vector_db is None: + raise ValueError(f"Vector DB '{vector_db_id}' not found") + return vector_db + + async def register_vector_db( + self, + vector_db_id: str, + embedding_model: str, + embedding_dimension: int | None = 384, + provider_id: str | None = None, + provider_vector_db_id: str | None = None, + ) -> VectorDB: + if provider_vector_db_id is None: + provider_vector_db_id = vector_db_id + if provider_id is None: + if len(self.impls_by_provider_id) > 0: + provider_id = list(self.impls_by_provider_id.keys())[0] + if len(self.impls_by_provider_id) > 1: + logger.warning( + f"No provider specified and multiple providers available. Arbitrarily selected the first provider {provider_id}." + ) + else: + raise ValueError("No provider available. Please configure a vector_io provider.") + model = await self.get_object_by_identifier("model", embedding_model) + if model is None: + raise ValueError(f"Model {embedding_model} not found") + if model.model_type != ModelType.embedding: + raise ValueError(f"Model {embedding_model} is not an embedding model") + if "embedding_dimension" not in model.metadata: + raise ValueError(f"Model {embedding_model} does not have an embedding dimension") + vector_db_data = { + "identifier": vector_db_id, + "type": ResourceType.vector_db.value, + "provider_id": provider_id, + "provider_resource_id": provider_vector_db_id, + "embedding_model": embedding_model, + "embedding_dimension": model.metadata["embedding_dimension"], + } + vector_db = TypeAdapter(VectorDBWithACL).validate_python(vector_db_data) + await self.register_object(vector_db) + return vector_db + + async def unregister_vector_db(self, vector_db_id: str) -> None: + existing_vector_db = await self.get_vector_db(vector_db_id) + if existing_vector_db is None: + raise ValueError(f"Vector DB {vector_db_id} not found") + await self.unregister_object(existing_vector_db) diff --git a/llama_stack/distribution/server/auth.py b/llama_stack/distribution/server/auth.py index 429232ece..fb26b49a7 100644 --- a/llama_stack/distribution/server/auth.py +++ b/llama_stack/distribution/server/auth.py @@ -8,7 +8,8 @@ import json import httpx -from llama_stack.distribution.server.auth_providers import AuthProviderConfig, create_auth_provider +from llama_stack.distribution.datatypes import AuthenticationConfig +from llama_stack.distribution.server.auth_providers import create_auth_provider from llama_stack.log import get_logger logger = get_logger(name=__name__, category="auth") @@ -77,7 +78,7 @@ class AuthenticationMiddleware: access resources that don't have access_attributes defined. """ - def __init__(self, app, auth_config: AuthProviderConfig): + def __init__(self, app, auth_config: AuthenticationConfig): self.app = app self.auth_provider = create_auth_provider(auth_config) @@ -93,7 +94,7 @@ class AuthenticationMiddleware: # Validate token and get access attributes try: - access_attributes = await self.auth_provider.validate_token(token, scope) + validation_result = await self.auth_provider.validate_token(token, scope) except httpx.TimeoutException: logger.exception("Authentication request timed out") return await self._send_auth_error(send, "Authentication service timeout") @@ -105,17 +106,24 @@ class AuthenticationMiddleware: return await self._send_auth_error(send, "Authentication service error") # Store attributes in request scope for access control - if access_attributes: - user_attributes = access_attributes.model_dump(exclude_none=True) + if validation_result.access_attributes: + user_attributes = validation_result.access_attributes.model_dump(exclude_none=True) else: logger.warning("No access attributes, setting namespace to token by default") user_attributes = { - "namespaces": [token], + "roles": [token], } + # Store the client ID in the request scope so that downstream middleware (like QuotaMiddleware) + # can identify the requester and enforce per-client rate limits. + scope["authenticated_client_id"] = token + # Store attributes in request scope scope["user_attributes"] = user_attributes - logger.debug(f"Authentication successful: {len(scope['user_attributes'])} attributes") + scope["principal"] = validation_result.principal + logger.debug( + f"Authentication successful: {validation_result.principal} with {len(scope['user_attributes'])} attributes" + ) return await self.app(scope, receive, send) diff --git a/llama_stack/distribution/server/auth_providers.py b/llama_stack/distribution/server/auth_providers.py index 1b19f8923..723a65b77 100644 --- a/llama_stack/distribution/server/auth_providers.py +++ b/llama_stack/distribution/server/auth_providers.py @@ -4,23 +4,29 @@ # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. -import json +import ssl +import time from abc import ABC, abstractmethod -from enum import Enum +from asyncio import Lock +from pathlib import Path from urllib.parse import parse_qs import httpx -from pydantic import BaseModel, Field +from jose import jwt +from pydantic import BaseModel, Field, field_validator, model_validator +from typing_extensions import Self -from llama_stack.distribution.datatypes import AccessAttributes +from llama_stack.distribution.datatypes import AccessAttributes, AuthenticationConfig, AuthProviderType from llama_stack.log import get_logger logger = get_logger(name=__name__, category="auth") -class AuthResponse(BaseModel): - """The format of the authentication response from the auth endpoint.""" - +class TokenValidationResult(BaseModel): + principal: str | None = Field( + default=None, + description="The principal (username or persistent identifier) of the authenticated user", + ) access_attributes: AccessAttributes | None = Field( default=None, description=""" @@ -43,6 +49,10 @@ class AuthResponse(BaseModel): """, ) + +class AuthResponse(TokenValidationResult): + """The format of the authentication response from the auth endpoint.""" + message: str | None = Field( default=None, description="Optional message providing additional context about the authentication result." ) @@ -64,25 +74,11 @@ class AuthRequest(BaseModel): request: AuthRequestContext = Field(description="Context information about the request being authenticated") -class AuthProviderType(str, Enum): - """Supported authentication provider types.""" - - KUBERNETES = "kubernetes" - CUSTOM = "custom" - - -class AuthProviderConfig(BaseModel): - """Base configuration for authentication providers.""" - - provider_type: AuthProviderType = Field(..., description="Type of authentication provider") - config: dict[str, str] = Field(..., description="Provider-specific configuration") - - class AuthProvider(ABC): """Abstract base class for authentication providers.""" @abstractmethod - async def validate_token(self, token: str, scope: dict | None = None) -> AccessAttributes | None: + async def validate_token(self, token: str, scope: dict | None = None) -> TokenValidationResult: """Validate a token and return access attributes.""" pass @@ -92,88 +88,219 @@ class AuthProvider(ABC): pass -class KubernetesAuthProvider(AuthProvider): - """Kubernetes authentication provider that validates tokens against the Kubernetes API server.""" +def get_attributes_from_claims(claims: dict[str, str], mapping: dict[str, str]) -> AccessAttributes: + attributes = AccessAttributes() + for claim_key, attribute_key in mapping.items(): + if claim_key not in claims or not hasattr(attributes, attribute_key): + continue + claim = claims[claim_key] + if isinstance(claim, list): + values = claim + else: + values = claim.split() - def __init__(self, config: dict[str, str]): - self.api_server_url = config["api_server_url"] - self.ca_cert_path = config.get("ca_cert_path") - self._client = None + current = getattr(attributes, attribute_key) + if current: + current.extend(values) + else: + setattr(attributes, attribute_key, values) + return attributes - async def _get_client(self): - """Get or create a Kubernetes client.""" - if self._client is None: - # kubernetes-client has not async support, see: - # https://github.com/kubernetes-client/python/issues/323 - from kubernetes import client - from kubernetes.client import ApiClient - # Configure the client - configuration = client.Configuration() - configuration.host = self.api_server_url - if self.ca_cert_path: - configuration.ssl_ca_cert = self.ca_cert_path - configuration.verify_ssl = bool(self.ca_cert_path) +class OAuth2JWKSConfig(BaseModel): + # The JWKS URI for collecting public keys + uri: str + key_recheck_period: int = Field(default=3600, description="The period to recheck the JWKS URI for key updates") - # Create API client - self._client = ApiClient(configuration) - return self._client - async def validate_token(self, token: str, scope: dict | None = None) -> AccessAttributes | None: - """Validate a Kubernetes token and return access attributes.""" +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}") + if value not in AccessAttributes.model_fields: + raise ValueError(f"claims_mapping value is not a valid attribute: {value}") + 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. + + This should be the standard authentication provider for most use cases. + """ + + def __init__(self, config: OAuth2TokenAuthProviderConfig): + self.config = config + self._jwks_at: float = 0.0 + self._jwks: dict[str, str] = {} + self._jwks_lock = Lock() + + async def validate_token(self, token: str, scope: dict | None = None) -> TokenValidationResult: + if self.config.jwks: + return await self.validate_jwt_token(token, scope) + if self.config.introspection: + return await self.introspect_token(token, scope) + raise ValueError("One of jwks or introspection must be configured") + + async def validate_jwt_token(self, token: str, scope: dict | None = None) -> TokenValidationResult: + """Validate a token using the JWT token.""" + await self._refresh_jwks() + try: - client = await self._get_client() - - # Set the token in the client - client.set_default_header("Authorization", f"Bearer {token}") - - # Make a request to validate the token - # We use the /api endpoint which requires authentication - from kubernetes.client import CoreV1Api - - api = CoreV1Api(client) - api.get_api_resources(_request_timeout=3.0) # Set timeout for this specific request - - # If we get here, the token is valid - # Extract user info from the token claims - import base64 - - # Decode the token (without verification since we've already validated it) - token_parts = token.split(".") - payload = json.loads(base64.b64decode(token_parts[1] + "=" * (-len(token_parts[1]) % 4))) - - # Extract user information from the token - username = payload.get("sub", "") - groups = payload.get("groups", []) - - return AccessAttributes( - roles=[username], # Use username as a role - teams=groups, # Use Kubernetes groups as teams + header = jwt.get_unverified_header(token) + kid = header["kid"] + if kid not in self._jwks: + raise ValueError(f"Unknown key ID: {kid}") + key_data = self._jwks[kid] + algorithm = header.get("alg", "RS256") + claims = jwt.decode( + token, + key_data, + algorithms=[algorithm], + audience=self.config.audience, + issuer=self.config.issuer, ) + except Exception as exc: + raise ValueError(f"Invalid JWT token: {token}") from exc + # There are other standard claims, the most relevant of which is `scope`. + # We should incorporate these into the access attributes. + principal = claims["sub"] + access_attributes = get_attributes_from_claims(claims, self.config.claims_mapping) + return TokenValidationResult( + principal=principal, + access_attributes=access_attributes, + ) + + async def introspect_token(self, token: str, scope: dict | None = None) -> TokenValidationResult: + """Validate a token using token introspection as defined by RFC 7662.""" + form = { + "token": token, + } + if self.config.introspection is None: + raise ValueError("Introspection is not configured") + + if self.config.introspection.send_secret_in_body: + form["client_id"] = self.config.introspection.client_id + form["client_secret"] = self.config.introspection.client_secret + auth = None + else: + auth = (self.config.introspection.client_id, self.config.introspection.client_secret) + ssl_ctxt = None + if self.config.tls_cafile: + ssl_ctxt = ssl.create_default_context(cafile=self.config.tls_cafile.as_posix()) + try: + async with httpx.AsyncClient(verify=ssl_ctxt) as client: + response = await client.post( + self.config.introspection.url, + data=form, + auth=auth, + timeout=10.0, # Add a reasonable timeout + ) + if response.status_code != 200: + logger.warning(f"Token introspection failed with status code: {response.status_code}") + raise ValueError(f"Token introspection failed: {response.status_code}") + + fields = response.json() + if not fields["active"]: + raise ValueError("Token not active") + principal = fields["sub"] or fields["username"] + access_attributes = get_attributes_from_claims(fields, self.config.claims_mapping) + return TokenValidationResult( + principal=principal, + access_attributes=access_attributes, + ) + except httpx.TimeoutException: + logger.exception("Token introspection request timed out") + raise + except ValueError: + # Re-raise ValueError exceptions to preserve their message + raise except Exception as e: - logger.exception("Failed to validate Kubernetes token") - raise ValueError("Invalid or expired token") from e + logger.exception("Error during token introspection") + raise ValueError("Token introspection error") from e async def close(self): - """Close the HTTP client.""" - if self._client: - self._client.close() - self._client = None + pass + + async def _refresh_jwks(self) -> None: + """ + Refresh the JWKS cache. + + This is a simple cache that expires after a certain amount of time (defined by `key_recheck_period`). + If the cache is expired, we refresh the JWKS from the JWKS URI. + + Notes: for Kubernetes which doesn't fully implement the OIDC protocol: + * It doesn't have user authentication flows + * It doesn't have refresh tokens + """ + async with self._jwks_lock: + if self.config.jwks is None: + raise ValueError("JWKS is not configured") + if time.time() - self._jwks_at > self.config.jwks.key_recheck_period: + verify = self.config.tls_cafile.as_posix() if self.config.tls_cafile else self.config.verify_tls + async with httpx.AsyncClient(verify=verify) as client: + res = await client.get(self.config.jwks.uri, timeout=5) + res.raise_for_status() + jwks_data = res.json()["keys"] + updated = {} + for k in jwks_data: + kid = k["kid"] + # Store the entire key object as it may be needed for different algorithms + updated[kid] = k + self._jwks = updated + 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: dict[str, str]): - self.endpoint = config["endpoint"] + def __init__(self, config: CustomAuthProviderConfig): + self.config = config self._client = None - async def validate_token(self, token: str, scope: dict | None = None) -> AccessAttributes | None: + async def validate_token(self, token: str, scope: dict | None = None) -> TokenValidationResult: """Validate a token using the custom authentication endpoint.""" - if not self.endpoint: - raise ValueError("Authentication endpoint not configured") - if scope is None: scope = {} @@ -202,7 +329,7 @@ class CustomAuthProvider(AuthProvider): try: async with httpx.AsyncClient() as client: response = await client.post( - self.endpoint, + self.config.endpoint, json=auth_request.model_dump(), timeout=10.0, # Add a reasonable timeout ) @@ -214,19 +341,7 @@ class CustomAuthProvider(AuthProvider): try: response_data = response.json() auth_response = AuthResponse(**response_data) - - # Store attributes in request scope for access control - if auth_response.access_attributes: - return auth_response.access_attributes - else: - logger.warning("No access attributes, setting namespace to api_key by default") - user_attributes = { - "namespaces": [token], - } - - scope["user_attributes"] = user_attributes - logger.debug(f"Authentication successful: {len(user_attributes)} attributes") - return auth_response.access_attributes + return auth_response except Exception as e: logger.exception("Error parsing authentication response") raise ValueError("Invalid authentication response format") from e @@ -248,14 +363,14 @@ class CustomAuthProvider(AuthProvider): self._client = None -def create_auth_provider(config: AuthProviderConfig) -> AuthProvider: +def create_auth_provider(config: AuthenticationConfig) -> AuthProvider: """Factory function to create the appropriate auth provider.""" provider_type = config.provider_type.lower() - if provider_type == "kubernetes": - return KubernetesAuthProvider(config.config) - elif provider_type == "custom": - return CustomAuthProvider(config.config) + if provider_type == "custom": + return CustomAuthProvider(CustomAuthProviderConfig.model_validate(config.config)) + elif provider_type == "oauth2_token": + return OAuth2TokenAuthProvider(OAuth2TokenAuthProviderConfig.model_validate(config.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}") diff --git a/llama_stack/distribution/server/quota.py b/llama_stack/distribution/server/quota.py new file mode 100644 index 000000000..ddbffae64 --- /dev/null +++ b/llama_stack/distribution/server/quota.py @@ -0,0 +1,110 @@ +# 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 datetime import datetime, timedelta, timezone + +from starlette.types import ASGIApp, Receive, Scope, Send + +from llama_stack.log import get_logger +from llama_stack.providers.utils.kvstore.api import KVStore +from llama_stack.providers.utils.kvstore.config import KVStoreConfig, SqliteKVStoreConfig +from llama_stack.providers.utils.kvstore.kvstore import kvstore_impl + +logger = get_logger(name=__name__, category="quota") + + +class QuotaMiddleware: + """ + ASGI middleware that enforces separate quotas for authenticated and anonymous clients + within a configurable time window. + + - For authenticated requests, it reads the client ID from the + `Authorization: Bearer ` header. + - For anonymous requests, it falls back to the IP address of the client. + Requests are counted in a KV store (e.g., SQLite), and HTTP 429 is returned + once a client exceeds its quota. + """ + + def __init__( + self, + app: ASGIApp, + kv_config: KVStoreConfig, + anonymous_max_requests: int, + authenticated_max_requests: int, + window_seconds: int = 86400, + ): + self.app = app + self.kv_config = kv_config + self.kv: KVStore | None = None + self.anonymous_max_requests = anonymous_max_requests + self.authenticated_max_requests = authenticated_max_requests + self.window_seconds = window_seconds + + if isinstance(self.kv_config, SqliteKVStoreConfig): + logger.warning( + "QuotaMiddleware: Using SQLite backend. Expiry/TTL is not enforced; cleanup is manual. " + f"window_seconds={self.window_seconds}" + ) + + async def _get_kv(self) -> KVStore: + if self.kv is None: + self.kv = await kvstore_impl(self.kv_config) + return self.kv + + async def __call__(self, scope: Scope, receive: Receive, send: Send): + if scope["type"] == "http": + # pick key & limit based on auth + auth_id = scope.get("authenticated_client_id") + if auth_id: + key_id = auth_id + limit = self.authenticated_max_requests + else: + # fallback to IP + client = scope.get("client") + key_id = client[0] if client else "anonymous" + limit = self.anonymous_max_requests + + current_window = int(time.time() // self.window_seconds) + key = f"quota:{key_id}:{current_window}" + + try: + kv = await self._get_kv() + prev = await kv.get(key) or "0" + count = int(prev) + 1 + + if int(prev) == 0: + # Set with expiration datetime when it is the first request in the window. + expiration = datetime.now(timezone.utc) + timedelta(seconds=self.window_seconds) + await kv.set(key, str(count), expiration=expiration) + else: + await kv.set(key, str(count)) + except Exception: + logger.exception("Failed to access KV store for quota") + return await self._send_error(send, 500, "Quota service error") + + if count > limit: + logger.warning( + "Quota exceeded for client %s: %d/%d", + key_id, + count, + limit, + ) + return await self._send_error(send, 429, "Quota exceeded") + + return await self.app(scope, receive, send) + + async def _send_error(self, send: Send, status: int, message: str): + await send( + { + "type": "http.response.start", + "status": status, + "headers": [[b"content-type", b"application/json"]], + } + ) + body = json.dumps({"error": {"message": message}}).encode() + await send({"type": "http.response.body", "body": body}) diff --git a/llama_stack/distribution/server/endpoints.py b/llama_stack/distribution/server/routes.py similarity index 55% rename from llama_stack/distribution/server/endpoints.py rename to llama_stack/distribution/server/routes.py index ec1f7e083..ea66fec5a 100644 --- a/llama_stack/distribution/server/endpoints.py +++ b/llama_stack/distribution/server/routes.py @@ -6,20 +6,23 @@ import inspect import re +from collections.abc import Callable +from typing import Any -from pydantic import BaseModel +from aiohttp import hdrs +from starlette.routing import Route from llama_stack.apis.tools import RAGToolRuntime, SpecialToolGroup from llama_stack.apis.version import LLAMA_STACK_API_VERSION from llama_stack.distribution.resolver import api_protocol_map from llama_stack.providers.datatypes import Api - -class ApiEndpoint(BaseModel): - route: str - method: str - name: str - descriptive_name: str | None = None +EndpointFunc = Callable[..., Any] +PathParams = dict[str, str] +RouteInfo = tuple[EndpointFunc, str] +PathImpl = dict[str, RouteInfo] +RouteImpls = dict[str, PathImpl] +RouteMatch = tuple[EndpointFunc, PathParams, str] def toolgroup_protocol_map(): @@ -28,13 +31,13 @@ def toolgroup_protocol_map(): } -def get_all_api_endpoints() -> dict[Api, list[ApiEndpoint]]: +def get_all_api_routes() -> dict[Api, list[Route]]: apis = {} protocols = api_protocol_map() toolgroup_protocols = toolgroup_protocol_map() for api, protocol in protocols.items(): - endpoints = [] + routes = [] protocol_methods = inspect.getmembers(protocol, predicate=inspect.isfunction) # HACK ALERT @@ -51,26 +54,28 @@ def get_all_api_endpoints() -> dict[Api, list[ApiEndpoint]]: if not hasattr(method, "__webmethod__"): continue - webmethod = method.__webmethod__ - route = f"/{LLAMA_STACK_API_VERSION}/{webmethod.route.lstrip('/')}" - if webmethod.method == "GET": - method = "get" - elif webmethod.method == "DELETE": - method = "delete" + # The __webmethod__ attribute is dynamically added by the @webmethod decorator + # mypy doesn't know about this dynamic attribute, so we ignore the attr-defined error + webmethod = method.__webmethod__ # type: ignore[attr-defined] + path = f"/{LLAMA_STACK_API_VERSION}/{webmethod.route.lstrip('/')}" + if webmethod.method == hdrs.METH_GET: + http_method = hdrs.METH_GET + elif webmethod.method == hdrs.METH_DELETE: + http_method = hdrs.METH_DELETE else: - method = "post" - endpoints.append( - ApiEndpoint(route=route, method=method, name=name, descriptive_name=webmethod.descriptive_name) - ) + http_method = hdrs.METH_POST + routes.append( + Route(path=path, methods=[http_method], name=name, endpoint=None) + ) # setting endpoint to None since don't use a Router object - apis[api] = endpoints + apis[api] = routes return apis -def initialize_endpoint_impls(impls): - endpoints = get_all_api_endpoints() - endpoint_impls = {} +def initialize_route_impls(impls: dict[Api, Any]) -> RouteImpls: + routes = get_all_api_routes() + route_impls: RouteImpls = {} def _convert_path_to_regex(path: str) -> str: # Convert {param} to named capture groups @@ -83,29 +88,34 @@ def initialize_endpoint_impls(impls): return f"^{pattern}$" - for api, api_endpoints in endpoints.items(): + for api, api_routes in routes.items(): if api not in impls: continue - for endpoint in api_endpoints: + for route in api_routes: impl = impls[api] - func = getattr(impl, endpoint.name) - if endpoint.method not in endpoint_impls: - endpoint_impls[endpoint.method] = {} - endpoint_impls[endpoint.method][_convert_path_to_regex(endpoint.route)] = ( + func = getattr(impl, route.name) + # Get the first (and typically only) method from the set, filtering out HEAD + available_methods = [m for m in route.methods if m != "HEAD"] + if not available_methods: + continue # Skip if only HEAD method is available + method = available_methods[0].lower() + if method not in route_impls: + route_impls[method] = {} + route_impls[method][_convert_path_to_regex(route.path)] = ( func, - endpoint.descriptive_name or endpoint.route, + route.path, ) - return endpoint_impls + return route_impls -def find_matching_endpoint(method, path, endpoint_impls): +def find_matching_route(method: str, path: str, route_impls: RouteImpls) -> RouteMatch: """Find the matching endpoint implementation for a given method and path. Args: method: HTTP method (GET, POST, etc.) path: URL path to match against - endpoint_impls: A dictionary of endpoint implementations + route_impls: A dictionary of endpoint implementations Returns: A tuple of (endpoint_function, path_params, descriptive_name) @@ -113,7 +123,7 @@ def find_matching_endpoint(method, path, endpoint_impls): Raises: ValueError: If no matching endpoint is found """ - impls = endpoint_impls.get(method.lower()) + impls = route_impls.get(method.lower()) if not impls: raise ValueError(f"No endpoint found for {path}") diff --git a/llama_stack/distribution/server/server.py b/llama_stack/distribution/server/server.py index ff0775dd6..6c88bbfe9 100644 --- a/llama_stack/distribution/server/server.py +++ b/llama_stack/distribution/server/server.py @@ -6,6 +6,7 @@ import argparse import asyncio +import functools import inspect import json import os @@ -13,6 +14,7 @@ import ssl import sys import traceback import warnings +from collections.abc import Callable from contextlib import asynccontextmanager from importlib.metadata import version as parse_version from pathlib import Path @@ -20,23 +22,26 @@ from typing import Annotated, Any import rich.pretty import yaml +from aiohttp import hdrs from fastapi import Body, FastAPI, HTTPException, Request from fastapi import Path as FastapiPath from fastapi.exceptions import RequestValidationError +from fastapi.middleware.cors import CORSMiddleware from fastapi.responses import JSONResponse, StreamingResponse from openai import BadRequestError from pydantic import BaseModel, ValidationError -from llama_stack.distribution.datatypes import 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, request_provider_data_context, ) from llama_stack.distribution.resolver import InvalidProviderError -from llama_stack.distribution.server.endpoints import ( - find_matching_endpoint, - initialize_endpoint_impls, +from llama_stack.distribution.server.routes import ( + find_matching_route, + get_all_api_routes, + initialize_route_impls, ) from llama_stack.distribution.stack import ( construct_stack, @@ -59,7 +64,7 @@ from llama_stack.providers.utils.telemetry.tracing import ( ) from .auth import AuthenticationMiddleware -from .endpoints import get_all_api_endpoints +from .quota import QuotaMiddleware REPO_ROOT = Path(__file__).parent.parent.parent.parent @@ -120,6 +125,8 @@ def translate_exception(exc: Exception) -> HTTPException | RequestValidationErro return HTTPException(status_code=504, detail=f"Operation timed out: {str(exc)}") elif isinstance(exc, NotImplementedError): return HTTPException(status_code=501, detail=f"Not implemented: {str(exc)}") + elif isinstance(exc, AuthenticationRequiredError): + return HTTPException(status_code=401, detail=f"Authentication required: {str(exc)}") else: return HTTPException( status_code=500, @@ -205,8 +212,9 @@ async def log_request_pre_validation(request: Request): logger.warning(f"Could not read or log request body for {request.method} {request.url.path}: {e}") -def create_dynamic_typed_route(func: Any, method: str, route: str): - async def endpoint(request: Request, **kwargs): +def create_dynamic_typed_route(func: Any, method: str, route: str) -> Callable: + @functools.wraps(func) + async def route_handler(request: Request, **kwargs): # Get auth attributes from the request scope user_attributes = request.scope.get("user_attributes", {}) @@ -246,9 +254,9 @@ def create_dynamic_typed_route(func: Any, method: str, route: str): for param in new_params[1:] ] - endpoint.__signature__ = sig.replace(parameters=new_params) + route_handler.__signature__ = sig.replace(parameters=new_params) - return endpoint + return route_handler class TracingMiddleware: @@ -270,17 +278,28 @@ class TracingMiddleware: logger.debug(f"Bypassing custom routing for FastAPI built-in path: {path}") return await self.app(scope, receive, send) - if not hasattr(self, "endpoint_impls"): - self.endpoint_impls = initialize_endpoint_impls(self.impls) + if not hasattr(self, "route_impls"): + self.route_impls = initialize_route_impls(self.impls) try: - _, _, trace_path = find_matching_endpoint(scope.get("method", "GET"), path, self.endpoint_impls) + _, _, trace_path = find_matching_route(scope.get("method", hdrs.METH_GET), path, self.route_impls) except ValueError: # If no matching endpoint is found, pass through to FastAPI - logger.debug(f"No matching endpoint found for path: {path}, falling back to FastAPI") + logger.debug(f"No matching route found for path: {path}, falling back to FastAPI") return await self.app(scope, receive, send) - trace_context = await start_trace(trace_path, {"__location__": "server", "raw_path": path}) + trace_attributes = {"__location__": "server", "raw_path": path} + + # Extract W3C trace context headers and store as trace attributes + headers = dict(scope.get("headers", [])) + traceparent = headers.get(b"traceparent", b"").decode() + if traceparent: + trace_attributes["traceparent"] = traceparent + tracestate = headers.get(b"tracestate", b"").decode() + if tracestate: + trace_attributes["tracestate"] = tracestate + + trace_context = await start_trace(trace_path, trace_attributes) async def send_with_trace_id(message): if message["type"] == "http.response.start": @@ -370,14 +389,6 @@ def main(args: argparse.Namespace | None = None): if args is None: args = parser.parse_args() - # Check for deprecated argument usage - if "--yaml-config" in sys.argv: - warnings.warn( - "The '--yaml-config' argument is deprecated and will be removed in a future version. Use '--config' instead.", - DeprecationWarning, - stacklevel=2, - ) - log_line = "" if args.config: # if the user provided a config file, use it, even if template was specified @@ -391,7 +402,7 @@ def main(args: argparse.Namespace | None = None): raise ValueError(f"Template {args.template} does not exist") log_line = f"Using template {args.template} config file: {config_file}" else: - raise ValueError("Either --yaml-config or --template must be provided") + raise ValueError("Either --config or --template must be provided") logger_config = None with open(config_file) as fp: @@ -431,6 +442,46 @@ def main(args: argparse.Namespace | None = None): if config.server.auth: logger.info(f"Enabling authentication with provider: {config.server.auth.provider_type.value}") app.add_middleware(AuthenticationMiddleware, auth_config=config.server.auth) + else: + if config.server.quota: + quota = config.server.quota + logger.warning( + "Configured authenticated_max_requests (%d) but no auth is enabled; " + "falling back to anonymous_max_requests (%d) for all the requests", + quota.authenticated_max_requests, + quota.anonymous_max_requests, + ) + + if config.server.quota: + logger.info("Enabling quota middleware for authenticated and anonymous clients") + + quota = config.server.quota + anonymous_max_requests = quota.anonymous_max_requests + # if auth is disabled, use the anonymous max requests + authenticated_max_requests = quota.authenticated_max_requests if config.server.auth else anonymous_max_requests + + kv_config = quota.kvstore + window_map = {"day": 86400} + window_seconds = window_map[quota.period.value] + + app.add_middleware( + QuotaMiddleware, + kv_config=kv_config, + anonymous_max_requests=anonymous_max_requests, + authenticated_max_requests=authenticated_max_requests, + window_seconds=window_seconds, + ) + + # --- CORS middleware for local development --- + # TODO: move to reverse proxy + ui_port = os.environ.get("LLAMA_STACK_UI_PORT", 8322) + app.add_middleware( + CORSMiddleware, + allow_origins=[f"http://localhost:{ui_port}"], + allow_credentials=True, + allow_methods=["*"], + allow_headers=["*"], + ) try: impls = asyncio.run(construct_stack(config)) @@ -443,7 +494,7 @@ def main(args: argparse.Namespace | None = None): else: setup_logger(TelemetryAdapter(TelemetryConfig(), {})) - all_endpoints = get_all_api_endpoints() + all_routes = get_all_api_routes() if config.apis: apis_to_serve = set(config.apis) @@ -461,24 +512,29 @@ def main(args: argparse.Namespace | None = None): for api_str in apis_to_serve: api = Api(api_str) - endpoints = all_endpoints[api] + routes = all_routes[api] impl = impls[api] - for endpoint in endpoints: - if not hasattr(impl, endpoint.name): + for route in routes: + if not hasattr(impl, route.name): # ideally this should be a typing violation already - raise ValueError(f"Could not find method {endpoint.name} on {impl}!!") + raise ValueError(f"Could not find method {route.name} on {impl}!") - impl_method = getattr(impl, endpoint.name) - logger.debug(f"{endpoint.method.upper()} {endpoint.route}") + impl_method = getattr(impl, route.name) + # Filter out HEAD method since it's automatically handled by FastAPI for GET routes + available_methods = [m for m in route.methods if m != "HEAD"] + if not available_methods: + raise ValueError(f"No methods found for {route.name} on {impl}") + method = available_methods[0] + logger.debug(f"{method} {route.path}") with warnings.catch_warnings(): warnings.filterwarnings("ignore", category=UserWarning, module="pydantic._internal._fields") - getattr(app, endpoint.method)(endpoint.route, response_model=None)( + getattr(app, method.lower())(route.path, response_model=None)( create_dynamic_typed_route( impl_method, - endpoint.method, - endpoint.route, + method.lower(), + route.path, ) ) diff --git a/llama_stack/distribution/start_stack.sh b/llama_stack/distribution/start_stack.sh index bf49e1619..996935a5e 100755 --- a/llama_stack/distribution/start_stack.sh +++ b/llama_stack/distribution/start_stack.sh @@ -54,7 +54,7 @@ other_args="" # Process remaining arguments while [[ $# -gt 0 ]]; do case "$1" in - --config|--yaml-config) + --config) if [[ -n "$2" ]]; then yaml_config="$2" shift 2 @@ -121,7 +121,7 @@ if [[ "$env_type" == "venv" || "$env_type" == "conda" ]]; then set -x if [ -n "$yaml_config" ]; then - yaml_config_arg="--yaml-config $yaml_config" + yaml_config_arg="--config $yaml_config" else yaml_config_arg="" fi @@ -181,9 +181,9 @@ elif [[ "$env_type" == "container" ]]; then # Add yaml config if provided, otherwise use default if [ -n "$yaml_config" ]; then - cmd="$cmd -v $yaml_config:/app/run.yaml --yaml-config /app/run.yaml" + cmd="$cmd -v $yaml_config:/app/run.yaml --config /app/run.yaml" else - cmd="$cmd --yaml-config /app/run.yaml" + cmd="$cmd --config /app/run.yaml" fi # Add any other args diff --git a/llama_stack/distribution/store/registry.py b/llama_stack/distribution/store/registry.py index a6b400136..0e84854c2 100644 --- a/llama_stack/distribution/store/registry.py +++ b/llama_stack/distribution/store/registry.py @@ -36,7 +36,7 @@ class DistributionRegistry(Protocol): REGISTER_PREFIX = "distributions:registry" -KEY_VERSION = "v8" +KEY_VERSION = "v9" KEY_FORMAT = f"{REGISTER_PREFIX}:{KEY_VERSION}::" + "{type}:{identifier}" diff --git a/llama_stack/distribution/ui/Containerfile b/llama_stack/distribution/ui/Containerfile index 0126d1867..5d2dc933b 100644 --- a/llama_stack/distribution/ui/Containerfile +++ b/llama_stack/distribution/ui/Containerfile @@ -5,7 +5,8 @@ FROM python:3.12-slim WORKDIR /app COPY . /app/ RUN /usr/local/bin/python -m pip install --upgrade pip && \ - /usr/local/bin/pip3 install -r requirements.txt + /usr/local/bin/pip3 install -r requirements.txt && \ + /usr/local/bin/pip3 install -r llama_stack/distribution/ui/requirements.txt EXPOSE 8501 -ENTRYPOINT ["streamlit", "run", "app.py", "--server.port=8501", "--server.address=0.0.0.0"] +ENTRYPOINT ["streamlit", "run", "llama_stack/distribution/ui/app.py", "--server.port=8501", "--server.address=0.0.0.0"] diff --git a/llama_stack/distribution/ui/README.md b/llama_stack/distribution/ui/README.md index 51c2d2bc2..0e96690ec 100644 --- a/llama_stack/distribution/ui/README.md +++ b/llama_stack/distribution/ui/README.md @@ -48,3 +48,6 @@ uv run --with ".[ui]" streamlit run llama_stack/distribution/ui/app.py | TOGETHER_API_KEY | API key for Together provider | (empty string) | | SAMBANOVA_API_KEY | API key for SambaNova provider | (empty string) | | OPENAI_API_KEY | API key for OpenAI provider | (empty string) | +| KEYCLOAK_URL | URL for keycloak authentication | (empty string) | +| KEYCLOAK_REALM | Keycloak realm | default | +| KEYCLOAK_CLIENT_ID | Client ID for keycloak auth | (empty string) | \ No newline at end of file diff --git a/llama_stack/distribution/ui/app.py b/llama_stack/distribution/ui/app.py index 441f65d20..a9a28b445 100644 --- a/llama_stack/distribution/ui/app.py +++ b/llama_stack/distribution/ui/app.py @@ -50,6 +50,42 @@ def main(): ) pg.run() +def main2(): + from dataclasses import asdict + st.subheader(f"Welcome {keycloak.user_info['preferred_username']}!") + st.write(f"Here is your user information:") + st.write(asdict(keycloak)) + +def get_access_token() -> str|None: + return st.session_state.get('access_token') if __name__ == "__main__": - main() + + from streamlit_keycloak import login + import os + + keycloak_url = os.environ.get("KEYCLOAK_URL") + keycloak_realm = os.environ.get("KEYCLOAK_REALM", "default") + keycloak_client_id = os.environ.get("KEYCLOAK_CLIENT_ID") + + if keycloak_url and keycloak_client_id: + keycloak = login( + url=keycloak_url, + realm=keycloak_realm, + client_id=keycloak_client_id, + custom_labels={ + "labelButton": "Sign in to kvant", + "labelLogin": "Please sign in to your kvant account.", + "errorNoPopup": "Unable to open the authentication popup. Allow popups and refresh the page to proceed.", + "errorPopupClosed": "Authentication popup was closed manually.", + "errorFatal": "Unable to connect to Keycloak using the current configuration." + }, + auto_refresh=True, + ) + + if keycloak.authenticated: + st.session_state['access_token'] = keycloak.access_token + main() + # TBD - add other authentications + else: + main() diff --git a/llama_stack/distribution/ui/modules/api.py b/llama_stack/distribution/ui/modules/api.py index 11455ed46..a426e59ba 100644 --- a/llama_stack/distribution/ui/modules/api.py +++ b/llama_stack/distribution/ui/modules/api.py @@ -7,11 +7,13 @@ import os from llama_stack_client import LlamaStackClient +from llama_stack.distribution.ui.app import get_access_token class LlamaStackApi: def __init__(self): self.client = LlamaStackClient( + api_key=get_access_token(), base_url=os.environ.get("LLAMA_STACK_ENDPOINT", "http://localhost:8321"), provider_data={ "fireworks_api_key": os.environ.get("FIREWORKS_API_KEY", ""), @@ -28,5 +30,3 @@ class LlamaStackApi: scoring_params = {fn_id: None for fn_id in scoring_function_ids} return self.client.scoring.score(input_rows=[row], scoring_functions=scoring_params) - -llama_stack_api = LlamaStackApi() diff --git a/llama_stack/distribution/ui/page/distribution/datasets.py b/llama_stack/distribution/ui/page/distribution/datasets.py index 6842b29a7..89f645ca8 100644 --- a/llama_stack/distribution/ui/page/distribution/datasets.py +++ b/llama_stack/distribution/ui/page/distribution/datasets.py @@ -6,13 +6,13 @@ import streamlit as st -from llama_stack.distribution.ui.modules.api import llama_stack_api +from llama_stack.distribution.ui.modules.api import LlamaStackApi def datasets(): st.header("Datasets") - datasets_info = {d.identifier: d.to_dict() for d in llama_stack_api.client.datasets.list()} + datasets_info = {d.identifier: d.to_dict() for d in LlamaStackApi().client.datasets.list()} if len(datasets_info) > 0: selected_dataset = st.selectbox("Select a dataset", list(datasets_info.keys())) st.json(datasets_info[selected_dataset], expanded=True) diff --git a/llama_stack/distribution/ui/page/distribution/eval_tasks.py b/llama_stack/distribution/ui/page/distribution/eval_tasks.py index 492be4700..2b70f9202 100644 --- a/llama_stack/distribution/ui/page/distribution/eval_tasks.py +++ b/llama_stack/distribution/ui/page/distribution/eval_tasks.py @@ -6,14 +6,14 @@ import streamlit as st -from llama_stack.distribution.ui.modules.api import llama_stack_api +from llama_stack.distribution.ui.modules.api import LlamaStackApi def benchmarks(): # Benchmarks Section st.header("Benchmarks") - benchmarks_info = {d.identifier: d.to_dict() for d in llama_stack_api.client.benchmarks.list()} + benchmarks_info = {d.identifier: d.to_dict() for d in LlamaStackApi().client.benchmarks.list()} if len(benchmarks_info) > 0: selected_benchmark = st.selectbox("Select an eval task", list(benchmarks_info.keys()), key="benchmark_inspect") diff --git a/llama_stack/distribution/ui/page/distribution/models.py b/llama_stack/distribution/ui/page/distribution/models.py index f29459098..3b96f179f 100644 --- a/llama_stack/distribution/ui/page/distribution/models.py +++ b/llama_stack/distribution/ui/page/distribution/models.py @@ -6,13 +6,13 @@ import streamlit as st -from llama_stack.distribution.ui.modules.api import llama_stack_api +from llama_stack.distribution.ui.modules.api import LlamaStackApi def models(): # Models Section st.header("Models") - models_info = {m.identifier: m.to_dict() for m in llama_stack_api.client.models.list()} + models_info = {m.identifier: m.to_dict() for m in LlamaStackApi().client.models.list()} selected_model = st.selectbox("Select a model", list(models_info.keys())) st.json(models_info[selected_model]) diff --git a/llama_stack/distribution/ui/page/distribution/providers.py b/llama_stack/distribution/ui/page/distribution/providers.py index c660cb986..116237b13 100644 --- a/llama_stack/distribution/ui/page/distribution/providers.py +++ b/llama_stack/distribution/ui/page/distribution/providers.py @@ -6,12 +6,12 @@ import streamlit as st -from llama_stack.distribution.ui.modules.api import llama_stack_api +from llama_stack.distribution.ui.modules.api import LlamaStackApi def providers(): st.header("🔍 API Providers") - apis_providers_lst = llama_stack_api.client.providers.list() + apis_providers_lst = LlamaStackApi().client.providers.list() api_to_providers = {} for api_provider in apis_providers_lst: if api_provider.api in api_to_providers: diff --git a/llama_stack/distribution/ui/page/distribution/scoring_functions.py b/llama_stack/distribution/ui/page/distribution/scoring_functions.py index 193146356..3c3428f44 100644 --- a/llama_stack/distribution/ui/page/distribution/scoring_functions.py +++ b/llama_stack/distribution/ui/page/distribution/scoring_functions.py @@ -6,13 +6,13 @@ import streamlit as st -from llama_stack.distribution.ui.modules.api import llama_stack_api +from llama_stack.distribution.ui.modules.api import LlamaStackApi def scoring_functions(): st.header("Scoring Functions") - scoring_functions_info = {s.identifier: s.to_dict() for s in llama_stack_api.client.scoring_functions.list()} + scoring_functions_info = {s.identifier: s.to_dict() for s in LlamaStackApi().client.scoring_functions.list()} selected_scoring_function = st.selectbox("Select a scoring function", list(scoring_functions_info.keys())) st.json(scoring_functions_info[selected_scoring_function], expanded=True) diff --git a/llama_stack/distribution/ui/page/distribution/shields.py b/llama_stack/distribution/ui/page/distribution/shields.py index 67d66d64f..84b583980 100644 --- a/llama_stack/distribution/ui/page/distribution/shields.py +++ b/llama_stack/distribution/ui/page/distribution/shields.py @@ -6,14 +6,14 @@ import streamlit as st -from llama_stack.distribution.ui.modules.api import llama_stack_api +from llama_stack.distribution.ui.modules.api import LlamaStackApi def shields(): # Shields Section st.header("Shields") - shields_info = {s.identifier: s.to_dict() for s in llama_stack_api.client.shields.list()} + shields_info = {s.identifier: s.to_dict() for s in LlamaStackApi().client.shields.list()} selected_shield = st.selectbox("Select a shield", list(shields_info.keys())) st.json(shields_info[selected_shield]) diff --git a/llama_stack/distribution/ui/page/distribution/vector_dbs.py b/llama_stack/distribution/ui/page/distribution/vector_dbs.py index 49a4f25bb..e7eb7b13b 100644 --- a/llama_stack/distribution/ui/page/distribution/vector_dbs.py +++ b/llama_stack/distribution/ui/page/distribution/vector_dbs.py @@ -6,12 +6,12 @@ import streamlit as st -from llama_stack.distribution.ui.modules.api import llama_stack_api +from llama_stack.distribution.ui.modules.api import LlamaStackApi def vector_dbs(): st.header("Vector Databases") - vector_dbs_info = {v.identifier: v.to_dict() for v in llama_stack_api.client.vector_dbs.list()} + vector_dbs_info = {v.identifier: v.to_dict() for v in LlamaStackApi().client.vector_dbs.list()} if len(vector_dbs_info) > 0: selected_vector_db = st.selectbox("Select a vector database", list(vector_dbs_info.keys())) diff --git a/llama_stack/distribution/ui/page/evaluations/app_eval.py b/llama_stack/distribution/ui/page/evaluations/app_eval.py index d7bc6388c..13da6071e 100644 --- a/llama_stack/distribution/ui/page/evaluations/app_eval.py +++ b/llama_stack/distribution/ui/page/evaluations/app_eval.py @@ -9,7 +9,7 @@ import json import pandas as pd import streamlit as st -from llama_stack.distribution.ui.modules.api import llama_stack_api +from llama_stack.distribution.ui.modules.api import LlamaStackApi from llama_stack.distribution.ui.modules.utils import process_dataset @@ -39,7 +39,7 @@ def application_evaluation_page(): # Select Scoring Functions to Run Evaluation On st.subheader("Select Scoring Functions") - scoring_functions = llama_stack_api.client.scoring_functions.list() + scoring_functions = LlamaStackApi().client.scoring_functions.list() scoring_functions = {sf.identifier: sf for sf in scoring_functions} scoring_functions_names = list(scoring_functions.keys()) selected_scoring_functions = st.multiselect( @@ -48,7 +48,7 @@ def application_evaluation_page(): help="Choose one or more scoring functions.", ) - available_models = llama_stack_api.client.models.list() + available_models = LlamaStackApi().client.models.list() available_models = [m.identifier for m in available_models] scoring_params = {} @@ -108,7 +108,7 @@ def application_evaluation_page(): progress_bar.progress(progress, text=progress_text) # Run evaluation for current row - score_res = llama_stack_api.run_scoring( + score_res = LlamaStackApi().run_scoring( r, scoring_function_ids=selected_scoring_functions, scoring_params=scoring_params, diff --git a/llama_stack/distribution/ui/page/evaluations/native_eval.py b/llama_stack/distribution/ui/page/evaluations/native_eval.py index 97f875e17..133c3b151 100644 --- a/llama_stack/distribution/ui/page/evaluations/native_eval.py +++ b/llama_stack/distribution/ui/page/evaluations/native_eval.py @@ -9,13 +9,13 @@ import json import pandas as pd import streamlit as st -from llama_stack.distribution.ui.modules.api import llama_stack_api +from llama_stack.distribution.ui.modules.api import LlamaStackApi def select_benchmark_1(): # Select Benchmarks st.subheader("1. Choose An Eval Task") - benchmarks = llama_stack_api.client.benchmarks.list() + benchmarks = LlamaStackApi().client.benchmarks.list() benchmarks = {et.identifier: et for et in benchmarks} benchmarks_names = list(benchmarks.keys()) selected_benchmark = st.selectbox( @@ -47,7 +47,7 @@ def define_eval_candidate_2(): # Define Eval Candidate candidate_type = st.radio("Candidate Type", ["model", "agent"]) - available_models = llama_stack_api.client.models.list() + available_models = LlamaStackApi().client.models.list() available_models = [model.identifier for model in available_models] selected_model = st.selectbox( "Choose a model", @@ -167,7 +167,7 @@ def run_evaluation_3(): eval_candidate = st.session_state["eval_candidate"] dataset_id = benchmarks[selected_benchmark].dataset_id - rows = llama_stack_api.client.datasets.iterrows( + rows = LlamaStackApi().client.datasets.iterrows( dataset_id=dataset_id, ) total_rows = len(rows.data) @@ -208,7 +208,7 @@ def run_evaluation_3(): progress = i / len(rows) progress_bar.progress(progress, text=progress_text) # Run evaluation for current row - eval_res = llama_stack_api.client.eval.evaluate_rows( + eval_res = LlamaStackApi().client.eval.evaluate_rows( benchmark_id=selected_benchmark, input_rows=[r], scoring_functions=benchmarks[selected_benchmark].scoring_functions, diff --git a/llama_stack/distribution/ui/page/playground/chat.py b/llama_stack/distribution/ui/page/playground/chat.py index fcaf08795..053ae42de 100644 --- a/llama_stack/distribution/ui/page/playground/chat.py +++ b/llama_stack/distribution/ui/page/playground/chat.py @@ -6,12 +6,12 @@ import streamlit as st -from llama_stack.distribution.ui.modules.api import llama_stack_api +from llama_stack.distribution.ui.modules.api import LlamaStackApi # Sidebar configurations with st.sidebar: st.header("Configuration") - available_models = llama_stack_api.client.models.list() + available_models = LlamaStackApi().client.models.list() available_models = [model.identifier for model in available_models if model.model_type == "llm"] selected_model = st.selectbox( "Choose a model", @@ -103,7 +103,7 @@ if prompt := st.chat_input("Example: What is Llama Stack?"): else: strategy = {"type": "greedy"} - response = llama_stack_api.client.inference.chat_completion( + response = LlamaStackApi().client.inference.chat_completion( messages=[ {"role": "system", "content": system_prompt}, {"role": "user", "content": prompt}, diff --git a/llama_stack/distribution/ui/page/playground/rag.py b/llama_stack/distribution/ui/page/playground/rag.py index 696d89bc2..94e27a255 100644 --- a/llama_stack/distribution/ui/page/playground/rag.py +++ b/llama_stack/distribution/ui/page/playground/rag.py @@ -10,7 +10,7 @@ import streamlit as st from llama_stack_client import Agent, AgentEventLogger, RAGDocument from llama_stack.apis.common.content_types import ToolCallDelta -from llama_stack.distribution.ui.modules.api import llama_stack_api +from llama_stack.distribution.ui.modules.api import LlamaStackApi from llama_stack.distribution.ui.modules.utils import data_url_from_file @@ -57,14 +57,14 @@ def rag_chat_page(): for i, uploaded_file in enumerate(uploaded_files) ] - providers = llama_stack_api.client.providers.list() + providers = LlamaStackApi().client.providers.list() vector_io_provider = None for x in providers: if x.api == "vector_io": vector_io_provider = x.provider_id - llama_stack_api.client.vector_dbs.register( + LlamaStackApi().client.vector_dbs.register( vector_db_id=vector_db_name, # Use the user-provided name embedding_dimension=384, embedding_model="all-MiniLM-L6-v2", @@ -72,7 +72,7 @@ def rag_chat_page(): ) # insert documents using the custom vector db name - llama_stack_api.client.tool_runtime.rag_tool.insert( + LlamaStackApi().client.tool_runtime.rag_tool.insert( vector_db_id=vector_db_name, # Use the user-provided name documents=documents, chunk_size_in_tokens=512, @@ -93,7 +93,7 @@ def rag_chat_page(): ) # select memory banks - vector_dbs = llama_stack_api.client.vector_dbs.list() + vector_dbs = LlamaStackApi().client.vector_dbs.list() vector_dbs = [vector_db.identifier for vector_db in vector_dbs] selected_vector_dbs = st.multiselect( label="Select Document Collections to use in RAG queries", @@ -103,7 +103,7 @@ def rag_chat_page(): ) st.subheader("Inference Parameters", divider=True) - available_models = llama_stack_api.client.models.list() + available_models = LlamaStackApi().client.models.list() available_models = [model.identifier for model in available_models if model.model_type == "llm"] selected_model = st.selectbox( label="Choose a model", @@ -167,7 +167,7 @@ def rag_chat_page(): @st.cache_resource def create_agent(): return Agent( - llama_stack_api.client, + LlamaStackApi().client, model=selected_model, instructions=system_prompt, sampling_params={ @@ -232,7 +232,7 @@ def rag_chat_page(): st.session_state.messages.append({"role": "system", "content": system_prompt}) # Query the vector DB - rag_response = llama_stack_api.client.tool_runtime.rag_tool.query( + rag_response = LlamaStackApi().client.tool_runtime.rag_tool.query( content=prompt, vector_db_ids=list(selected_vector_dbs) ) prompt_context = rag_response.content @@ -251,7 +251,7 @@ def rag_chat_page(): # Run inference directly st.session_state.messages.append({"role": "user", "content": extended_prompt}) - response = llama_stack_api.client.inference.chat_completion( + response = LlamaStackApi().client.inference.chat_completion( messages=st.session_state.messages, model_id=selected_model, sampling_params={ diff --git a/llama_stack/distribution/ui/page/playground/tools.py b/llama_stack/distribution/ui/page/playground/tools.py index 149d8cce9..570bfb366 100644 --- a/llama_stack/distribution/ui/page/playground/tools.py +++ b/llama_stack/distribution/ui/page/playground/tools.py @@ -13,7 +13,7 @@ from llama_stack_client import Agent from llama_stack_client.lib.agents.react.agent import ReActAgent from llama_stack_client.lib.agents.react.tool_parser import ReActOutput -from llama_stack.distribution.ui.modules.api import llama_stack_api +from llama_stack.distribution.ui.modules.api import LlamaStackApi class AgentType(enum.Enum): @@ -24,7 +24,7 @@ class AgentType(enum.Enum): def tool_chat_page(): st.title("🛠 Tools") - client = llama_stack_api.client + client = LlamaStackApi().client models = client.models.list() model_list = [model.identifier for model in models if model.api_model_type == "llm"] @@ -55,7 +55,7 @@ def tool_chat_page(): ) if "builtin::rag" in toolgroup_selection: - vector_dbs = llama_stack_api.client.vector_dbs.list() or [] + vector_dbs = LlamaStackApi().client.vector_dbs.list() or [] if not vector_dbs: st.info("No vector databases available for selection.") vector_dbs = [vector_db.identifier for vector_db in vector_dbs] diff --git a/llama_stack/distribution/ui/requirements.txt b/llama_stack/distribution/ui/requirements.txt index 53a1e7bf3..862f969d6 100644 --- a/llama_stack/distribution/ui/requirements.txt +++ b/llama_stack/distribution/ui/requirements.txt @@ -1,5 +1,5 @@ -llama-stack>=0.2.1 -llama-stack-client>=0.2.1 +llama-stack-client>=0.2.9 pandas streamlit streamlit-option-menu +streamlit-keycloak diff --git a/llama_stack/distribution/utils/exec.py b/llama_stack/distribution/utils/exec.py index 4acce4f5b..7c2e00524 100644 --- a/llama_stack/distribution/utils/exec.py +++ b/llama_stack/distribution/utils/exec.py @@ -8,6 +8,7 @@ import logging import os import signal import subprocess +import sys from termcolor import cprint @@ -33,6 +34,7 @@ def formulate_run_args(image_type, image_name, config, template_name) -> list: cprint( "No current conda environment detected, please specify a conda environment name with --image-name", color="red", + file=sys.stderr, ) return @@ -49,12 +51,13 @@ def formulate_run_args(image_type, image_name, config, template_name) -> list: return envpath return None - print(f"Using conda environment: {env_name}") + cprint(f"Using conda environment: {env_name}", color="green", file=sys.stderr) conda_prefix = get_conda_prefix(env_name) if not conda_prefix: cprint( f"Conda environment {env_name} does not exist.", color="red", + file=sys.stderr, ) return @@ -63,6 +66,7 @@ def formulate_run_args(image_type, image_name, config, template_name) -> list: cprint( f"Build file {build_file} does not exist.\n\nPlease run `llama stack build` or specify the correct conda environment name with --image-name", color="red", + file=sys.stderr, ) return else: @@ -73,9 +77,10 @@ def formulate_run_args(image_type, image_name, config, template_name) -> list: cprint( "No current virtual environment detected, please specify a virtual environment name with --image-name", color="red", + file=sys.stderr, ) return - print(f"Using virtual environment: {env_name}") + cprint(f"Using virtual environment: {env_name}", file=sys.stderr) script = importlib.resources.files("llama_stack") / "distribution/start_stack.sh" run_args = [ diff --git a/llama_stack/log.py b/llama_stack/log.py index 98858d208..f4184710a 100644 --- a/llama_stack/log.py +++ b/llama_stack/log.py @@ -6,6 +6,7 @@ import logging import os +import sys from logging.config import dictConfig from rich.console import Console @@ -234,7 +235,7 @@ def get_logger( env_config = os.environ.get("LLAMA_STACK_LOGGING", "") if env_config: - cprint(f"Environment variable LLAMA_STACK_LOGGING found: {env_config}", "yellow") + cprint(f"Environment variable LLAMA_STACK_LOGGING found: {env_config}", color="yellow", file=sys.stderr) _category_levels.update(parse_environment_config(env_config)) log_file = os.environ.get("LLAMA_STACK_LOG_FILE") diff --git a/llama_stack/models/llama/llama3/generation.py b/llama_stack/models/llama/llama3/generation.py index c6d618818..fe7be5ea9 100644 --- a/llama_stack/models/llama/llama3/generation.py +++ b/llama_stack/models/llama/llama3/generation.py @@ -174,6 +174,7 @@ class Llama3: cprint( "Input to model:\n" + self.tokenizer.decode(tokens_to_print) + "\n", "red", + file=sys.stderr, ) prompt_tokens = [inp.tokens for inp in llm_inputs] @@ -184,7 +185,11 @@ class Llama3: max_prompt_len = max(len(t) for t in prompt_tokens) if max_prompt_len >= params.max_seq_len: - cprint(f"Out of token budget {max_prompt_len} vs {params.max_seq_len}", "red") + cprint( + f"Out of token budget {max_prompt_len} vs {params.max_seq_len}", + color="red", + file=sys.stderr, + ) return total_len = min(max_gen_len + max_prompt_len, params.max_seq_len) diff --git a/llama_stack/models/llama/llama4/generation.py b/llama_stack/models/llama/llama4/generation.py index 476761209..6132d25d4 100644 --- a/llama_stack/models/llama/llama4/generation.py +++ b/llama_stack/models/llama/llama4/generation.py @@ -133,9 +133,9 @@ class Llama4: print_model_input = print_model_input or os.environ.get("LLAMA_MODELS_DEBUG", "0") == "1" if print_model_input: - cprint("Input to model:\n", "yellow") + cprint("Input to model:\n", color="yellow", file=sys.stderr) for inp in llm_inputs: - cprint(self.tokenizer.decode(inp.tokens), "grey") + cprint(self.tokenizer.decode(inp.tokens), color="grey", file=sys.stderr) prompt_tokens = [inp.tokens for inp in llm_inputs] bsz = len(llm_inputs) @@ -145,7 +145,7 @@ class Llama4: max_prompt_len = max(len(t) for t in prompt_tokens) if max_prompt_len >= params.max_seq_len: - cprint(f"Out of token budget {max_prompt_len} vs {params.max_seq_len}", "red") + cprint(f"Out of token budget {max_prompt_len} vs {params.max_seq_len}", color="red", file=sys.stderr) return total_len = min(max_gen_len + max_prompt_len, params.max_seq_len) diff --git a/llama_stack/providers/datatypes.py b/llama_stack/providers/datatypes.py index 3e9806f23..60b05545b 100644 --- a/llama_stack/providers/datatypes.py +++ b/llama_stack/providers/datatypes.py @@ -16,7 +16,7 @@ from llama_stack.apis.datatypes import Api from llama_stack.apis.models import Model from llama_stack.apis.scoring_functions import ScoringFn from llama_stack.apis.shields import Shield -from llama_stack.apis.tools import Tool +from llama_stack.apis.tools import ToolGroup from llama_stack.apis.vector_dbs import VectorDB from llama_stack.schema_utils import json_schema_type @@ -74,10 +74,10 @@ class BenchmarksProtocolPrivate(Protocol): async def register_benchmark(self, benchmark: Benchmark) -> None: ... -class ToolsProtocolPrivate(Protocol): - async def register_tool(self, tool: Tool) -> None: ... +class ToolGroupsProtocolPrivate(Protocol): + async def register_toolgroup(self, toolgroup: ToolGroup) -> None: ... - async def unregister_tool(self, tool_id: str) -> None: ... + async def unregister_toolgroup(self, toolgroup_id: str) -> None: ... @json_schema_type diff --git a/llama_stack/providers/inline/agents/meta_reference/agents.py b/llama_stack/providers/inline/agents/meta_reference/agents.py index 86780fd61..bcbfcbe31 100644 --- a/llama_stack/providers/inline/agents/meta_reference/agents.py +++ b/llama_stack/providers/inline/agents/meta_reference/agents.py @@ -20,9 +20,12 @@ from llama_stack.apis.agents import ( AgentTurnCreateRequest, AgentTurnResumeRequest, Document, + ListOpenAIResponseInputItem, + ListOpenAIResponseObject, OpenAIResponseInput, OpenAIResponseInputTool, OpenAIResponseObject, + Order, Session, Turn, ) @@ -39,6 +42,7 @@ from llama_stack.apis.tools import ToolGroups, ToolRuntime from llama_stack.apis.vector_io import VectorIO from llama_stack.providers.utils.kvstore import InmemoryKVStoreImpl, kvstore_impl from llama_stack.providers.utils.pagination import paginate_records +from llama_stack.providers.utils.responses.responses_store import ResponsesStore from .agent_instance import ChatAgent from .config import MetaReferenceAgentsImplConfig @@ -66,15 +70,17 @@ class MetaReferenceAgentsImpl(Agents): self.tool_groups_api = tool_groups_api self.in_memory_store = InmemoryKVStoreImpl() - self.openai_responses_impl = None + self.openai_responses_impl: OpenAIResponsesImpl | None = None async def initialize(self) -> None: self.persistence_store = await kvstore_impl(self.config.persistence_store) + self.responses_store = ResponsesStore(self.config.responses_store) + await self.responses_store.initialize() self.openai_responses_impl = OpenAIResponsesImpl( - self.persistence_store, inference_api=self.inference_api, tool_groups_api=self.tool_groups_api, tool_runtime_api=self.tool_runtime_api, + responses_store=self.responses_store, ) async def create_agent( @@ -305,14 +311,15 @@ class MetaReferenceAgentsImpl(Agents): # OpenAI responses async def get_openai_response( self, - id: str, + response_id: str, ) -> OpenAIResponseObject: - return await self.openai_responses_impl.get_openai_response(id) + return await self.openai_responses_impl.get_openai_response(response_id) async def create_openai_response( self, input: str | list[OpenAIResponseInput], model: str, + instructions: str | None = None, previous_response_id: str | None = None, store: bool | None = True, stream: bool | None = False, @@ -320,5 +327,27 @@ class MetaReferenceAgentsImpl(Agents): tools: list[OpenAIResponseInputTool] | None = None, ) -> OpenAIResponseObject: return await self.openai_responses_impl.create_openai_response( - input, model, previous_response_id, store, stream, temperature, tools + input, model, instructions, previous_response_id, store, stream, temperature, tools + ) + + async def list_openai_responses( + self, + after: str | None = None, + limit: int | None = 50, + model: str | None = None, + order: Order | None = Order.desc, + ) -> ListOpenAIResponseObject: + return await self.openai_responses_impl.list_openai_responses(after, limit, model, order) + + async def list_openai_response_input_items( + self, + response_id: str, + after: str | None = None, + before: str | None = None, + include: list[str] | None = None, + limit: int | None = 20, + order: Order | None = Order.desc, + ) -> ListOpenAIResponseInputItem: + return await self.openai_responses_impl.list_openai_response_input_items( + response_id, after, before, include, limit, order ) diff --git a/llama_stack/providers/inline/agents/meta_reference/config.py b/llama_stack/providers/inline/agents/meta_reference/config.py index c860e6df1..1c392f29c 100644 --- a/llama_stack/providers/inline/agents/meta_reference/config.py +++ b/llama_stack/providers/inline/agents/meta_reference/config.py @@ -10,10 +10,12 @@ from pydantic import BaseModel from llama_stack.providers.utils.kvstore import KVStoreConfig from llama_stack.providers.utils.kvstore.config import SqliteKVStoreConfig +from llama_stack.providers.utils.sqlstore.sqlstore import SqliteSqlStoreConfig, SqlStoreConfig class MetaReferenceAgentsImplConfig(BaseModel): persistence_store: KVStoreConfig + responses_store: SqlStoreConfig @classmethod def sample_run_config(cls, __distro_dir__: str) -> dict[str, Any]: @@ -21,5 +23,9 @@ class MetaReferenceAgentsImplConfig(BaseModel): "persistence_store": SqliteKVStoreConfig.sample_run_config( __distro_dir__=__distro_dir__, db_name="agents_store.db", - ) + ), + "responses_store": SqliteSqlStoreConfig.sample_run_config( + __distro_dir__=__distro_dir__, + db_name="responses_store.db", + ), } diff --git a/llama_stack/providers/inline/agents/meta_reference/openai_responses.py b/llama_stack/providers/inline/agents/meta_reference/openai_responses.py index 6d9d06109..19d7ea56f 100644 --- a/llama_stack/providers/inline/agents/meta_reference/openai_responses.py +++ b/llama_stack/providers/inline/agents/meta_reference/openai_responses.py @@ -5,6 +5,7 @@ # the root directory of this source tree. import json +import time import uuid from collections.abc import AsyncIterator from typing import Any, cast @@ -12,24 +13,29 @@ from typing import Any, cast from openai.types.chat import ChatCompletionToolParam from pydantic import BaseModel +from llama_stack.apis.agents import Order from llama_stack.apis.agents.openai_responses import ( + AllowedToolsFilter, + ListOpenAIResponseInputItem, + ListOpenAIResponseObject, OpenAIResponseInput, OpenAIResponseInputFunctionToolCallOutput, - OpenAIResponseInputItemList, OpenAIResponseInputMessageContent, OpenAIResponseInputMessageContentImage, OpenAIResponseInputMessageContentText, OpenAIResponseInputTool, - OpenAIResponseInputToolFunction, + OpenAIResponseInputToolMCP, OpenAIResponseMessage, OpenAIResponseObject, OpenAIResponseObjectStream, OpenAIResponseObjectStreamResponseCompleted, OpenAIResponseObjectStreamResponseCreated, + OpenAIResponseObjectStreamResponseOutputTextDelta, OpenAIResponseOutput, OpenAIResponseOutputMessageContent, OpenAIResponseOutputMessageContentOutputText, OpenAIResponseOutputMessageFunctionToolCall, + OpenAIResponseOutputMessageMCPListTools, OpenAIResponseOutputMessageWebSearchToolCall, ) from llama_stack.apis.inference.inference import ( @@ -49,11 +55,12 @@ from llama_stack.apis.inference.inference import ( OpenAIToolMessageParam, OpenAIUserMessageParam, ) -from llama_stack.apis.tools.tools import ToolGroups, ToolInvocationResult, ToolRuntime +from llama_stack.apis.tools.tools import ToolGroups, ToolRuntime from llama_stack.log import get_logger from llama_stack.models.llama.datatypes import ToolDefinition, ToolParamDefinition from llama_stack.providers.utils.inference.openai_compat import convert_tooldef_to_openai_tool -from llama_stack.providers.utils.kvstore import KVStore +from llama_stack.providers.utils.responses.responses_store import ResponsesStore +from llama_stack.providers.utils.tools.mcp import invoke_mcp_tool, list_mcp_tools logger = get_logger(name=__name__, category="openai_responses") @@ -162,41 +169,43 @@ async def _get_message_type_by_role(role: str): class OpenAIResponsePreviousResponseWithInputItems(BaseModel): - input_items: OpenAIResponseInputItemList + input_items: ListOpenAIResponseInputItem response: OpenAIResponseObject +class ChatCompletionContext(BaseModel): + model: str + messages: list[OpenAIMessageParam] + tools: list[ChatCompletionToolParam] | None = None + mcp_tool_to_server: dict[str, OpenAIResponseInputToolMCP] + stream: bool + temperature: float | None + + class OpenAIResponsesImpl: def __init__( self, - persistence_store: KVStore, inference_api: Inference, tool_groups_api: ToolGroups, tool_runtime_api: ToolRuntime, + responses_store: ResponsesStore, ): - self.persistence_store = persistence_store self.inference_api = inference_api self.tool_groups_api = tool_groups_api self.tool_runtime_api = tool_runtime_api - - async def _get_previous_response_with_input(self, id: str) -> OpenAIResponsePreviousResponseWithInputItems: - key = f"{OPENAI_RESPONSES_PREFIX}{id}" - response_json = await self.persistence_store.get(key=key) - if response_json is None: - raise ValueError(f"OpenAI response with id '{id}' not found") - return OpenAIResponsePreviousResponseWithInputItems.model_validate_json(response_json) + self.responses_store = responses_store async def _prepend_previous_response( self, input: str | list[OpenAIResponseInput], previous_response_id: str | None = None ): if previous_response_id: - previous_response_with_input = await self._get_previous_response_with_input(previous_response_id) + previous_response_with_input = await self.responses_store.get_response_object(previous_response_id) # previous response input items - new_input_items = previous_response_with_input.input_items.data + new_input_items = previous_response_with_input.input # previous response output items - new_input_items.extend(previous_response_with_input.response.output) + new_input_items.extend(previous_response_with_input.output) # new input items from the current request if isinstance(input, str): @@ -208,99 +217,60 @@ class OpenAIResponsesImpl: return input + async def _prepend_instructions(self, messages, instructions): + if instructions: + messages.insert(0, OpenAISystemMessageParam(content=instructions)) + async def get_openai_response( self, - id: str, + response_id: str, ) -> OpenAIResponseObject: - response_with_input = await self._get_previous_response_with_input(id) - return response_with_input.response + response_with_input = await self.responses_store.get_response_object(response_id) + return OpenAIResponseObject(**{k: v for k, v in response_with_input.model_dump().items() if k != "input"}) - async def create_openai_response( + async def list_openai_responses( self, - input: str | list[OpenAIResponseInput], - model: str, - previous_response_id: str | None = None, - store: bool | None = True, - stream: bool | None = False, - temperature: float | None = None, - tools: list[OpenAIResponseInputTool] | None = None, - ): - stream = False if stream is None else stream + after: str | None = None, + limit: int | None = 50, + model: str | None = None, + order: Order | None = Order.desc, + ) -> ListOpenAIResponseObject: + return await self.responses_store.list_responses(after, limit, model, order) - input = await self._prepend_previous_response(input, previous_response_id) - messages = await _convert_response_input_to_chat_messages(input) - chat_tools = await self._convert_response_tools_to_chat_tools(tools) if tools else None - chat_response = await self.inference_api.openai_chat_completion( - model=model, - messages=messages, - tools=chat_tools, - stream=stream, - temperature=temperature, - ) + async def list_openai_response_input_items( + self, + response_id: str, + after: str | None = None, + before: str | None = None, + include: list[str] | None = None, + limit: int | None = 20, + order: Order | None = Order.desc, + ) -> ListOpenAIResponseInputItem: + """List input items for a given OpenAI response. - if stream: - # TODO: refactor this into a separate method that handles streaming - chat_response_id = "" - chat_response_content = [] - chat_response_tool_calls: dict[int, OpenAIChatCompletionToolCall] = {} - # TODO: these chunk_ fields are hacky and only take the last chunk into account - chunk_created = 0 - chunk_model = "" - chunk_finish_reason = "" - async for chunk in chat_response: - chat_response_id = chunk.id - chunk_created = chunk.created - chunk_model = chunk.model - for chunk_choice in chunk.choices: - # TODO: this only works for text content - chat_response_content.append(chunk_choice.delta.content or "") - if chunk_choice.finish_reason: - chunk_finish_reason = chunk_choice.finish_reason - - # Aggregate tool call arguments across chunks, using their index as the aggregation key - if chunk_choice.delta.tool_calls: - for tool_call in chunk_choice.delta.tool_calls: - response_tool_call = chat_response_tool_calls.get(tool_call.index, None) - if response_tool_call: - response_tool_call.function.arguments += tool_call.function.arguments - else: - tool_call_dict: dict[str, Any] = tool_call.model_dump() - # Ensure we don't have any empty type field in the tool call dict. - # The OpenAI client used by providers often returns a type=None here. - tool_call_dict.pop("type", None) - response_tool_call = OpenAIChatCompletionToolCall(**tool_call_dict) - chat_response_tool_calls[tool_call.index] = response_tool_call - - # Convert the dict of tool calls by index to a list of tool calls to pass back in our response - if chat_response_tool_calls: - tool_calls = [chat_response_tool_calls[i] for i in sorted(chat_response_tool_calls.keys())] - else: - tool_calls = None - assistant_message = OpenAIAssistantMessageParam( - content="".join(chat_response_content), - tool_calls=tool_calls, - ) - chat_response = OpenAIChatCompletion( - id=chat_response_id, - choices=[ - OpenAIChoice( - message=assistant_message, - finish_reason=chunk_finish_reason, - index=0, - ) - ], - created=chunk_created, - model=chunk_model, - ) - else: - # dump and reload to map to our pydantic types - chat_response = OpenAIChatCompletion(**chat_response.model_dump()) + :param response_id: The ID of the response to retrieve input items for. + :param after: An item ID to list items after, used for pagination. + :param before: An item ID to list items before, used for pagination. + :param include: Additional fields to include in the response. + :param limit: A limit on the number of objects to be returned. + :param order: The order to return the input items in. + :returns: An ListOpenAIResponseInputItem. + """ + return await self.responses_store.list_response_input_items(response_id, after, before, include, limit, order) + async def _process_response_choices( + self, + chat_response: OpenAIChatCompletion, + ctx: ChatCompletionContext, + tools: list[OpenAIResponseInputTool] | None, + ) -> list[OpenAIResponseOutput]: + """Handle tool execution and response message creation.""" output_messages: list[OpenAIResponseOutput] = [] + # Execute tool calls if any for choice in chat_response.choices: if choice.message.tool_calls and tools: # Assume if the first tool is a function, all tools are functions - if isinstance(tools[0], OpenAIResponseInputToolFunction): + if tools[0].type == "function": for tool_call in choice.message.tool_calls: output_messages.append( OpenAIResponseOutputMessageFunctionToolCall( @@ -312,11 +282,132 @@ class OpenAIResponsesImpl: ) ) else: - output_messages.extend( - await self._execute_tool_and_return_final_output(model, stream, choice, messages, temperature) - ) + tool_messages = await self._execute_tool_and_return_final_output(choice, ctx) + output_messages.extend(tool_messages) else: output_messages.append(await _convert_chat_choice_to_response_message(choice)) + + return output_messages + + async def _store_response( + self, + response: OpenAIResponseObject, + input: str | list[OpenAIResponseInput], + ) -> None: + new_input_id = f"msg_{uuid.uuid4()}" + if isinstance(input, str): + # synthesize a message from the input string + input_content = OpenAIResponseInputMessageContentText(text=input) + input_content_item = OpenAIResponseMessage( + role="user", + content=[input_content], + id=new_input_id, + ) + input_items_data = [input_content_item] + else: + # we already have a list of messages + input_items_data = [] + for input_item in input: + if isinstance(input_item, OpenAIResponseMessage): + # These may or may not already have an id, so dump to dict, check for id, and add if missing + input_item_dict = input_item.model_dump() + if "id" not in input_item_dict: + input_item_dict["id"] = new_input_id + input_items_data.append(OpenAIResponseMessage(**input_item_dict)) + else: + input_items_data.append(input_item) + + await self.responses_store.store_response_object( + response_object=response, + input=input_items_data, + ) + + async def create_openai_response( + self, + input: str | list[OpenAIResponseInput], + model: str, + instructions: str | None = None, + previous_response_id: str | None = None, + store: bool | None = True, + stream: bool | None = False, + temperature: float | None = None, + tools: list[OpenAIResponseInputTool] | None = None, + ): + stream = False if stream is None else stream + + output_messages: list[OpenAIResponseOutput] = [] + + # Input preprocessing + input = await self._prepend_previous_response(input, previous_response_id) + messages = await _convert_response_input_to_chat_messages(input) + await self._prepend_instructions(messages, instructions) + + # Tool setup + chat_tools, mcp_tool_to_server, mcp_list_message = ( + await self._convert_response_tools_to_chat_tools(tools) if tools else (None, {}, None) + ) + if mcp_list_message: + output_messages.append(mcp_list_message) + + ctx = ChatCompletionContext( + model=model, + messages=messages, + tools=chat_tools, + mcp_tool_to_server=mcp_tool_to_server, + stream=stream, + temperature=temperature, + ) + + inference_result = await self.inference_api.openai_chat_completion( + model=model, + messages=messages, + tools=chat_tools, + stream=stream, + temperature=temperature, + ) + + if stream: + return self._create_streaming_response( + inference_result=inference_result, + ctx=ctx, + output_messages=output_messages, + input=input, + model=model, + store=store, + tools=tools, + ) + else: + return await self._create_non_streaming_response( + inference_result=inference_result, + ctx=ctx, + output_messages=output_messages, + input=input, + model=model, + store=store, + tools=tools, + ) + + async def _create_non_streaming_response( + self, + inference_result: Any, + ctx: ChatCompletionContext, + output_messages: list[OpenAIResponseOutput], + input: str | list[OpenAIResponseInput], + model: str, + store: bool | None, + tools: list[OpenAIResponseInputTool] | None, + ) -> OpenAIResponseObject: + chat_response = OpenAIChatCompletion(**inference_result.model_dump()) + + # Process response choices (tool execution and message creation) + output_messages.extend( + await self._process_response_choices( + chat_response=chat_response, + ctx=ctx, + tools=tools, + ) + ) + response = OpenAIResponseObject( created_at=chat_response.created, id=f"resp-{uuid.uuid4()}", @@ -327,57 +418,173 @@ class OpenAIResponsesImpl: ) logger.debug(f"OpenAI Responses response: {response}") + # Store response if requested if store: - # Store in kvstore - - new_input_id = f"msg_{uuid.uuid4()}" - if isinstance(input, str): - # synthesize a message from the input string - input_content = OpenAIResponseInputMessageContentText(text=input) - input_content_item = OpenAIResponseMessage( - role="user", - content=[input_content], - id=new_input_id, - ) - input_items_data = [input_content_item] - else: - # we already have a list of messages - input_items_data = [] - for input_item in input: - if isinstance(input_item, OpenAIResponseMessage): - # These may or may not already have an id, so dump to dict, check for id, and add if missing - input_item_dict = input_item.model_dump() - if "id" not in input_item_dict: - input_item_dict["id"] = new_input_id - input_items_data.append(OpenAIResponseMessage(**input_item_dict)) - else: - input_items_data.append(input_item) - - input_items = OpenAIResponseInputItemList(data=input_items_data) - prev_response = OpenAIResponsePreviousResponseWithInputItems( - input_items=input_items, + await self._store_response( response=response, + input=input, ) - key = f"{OPENAI_RESPONSES_PREFIX}{response.id}" - await self.persistence_store.set( - key=key, - value=prev_response.model_dump_json(), - ) - - if stream: - - async def async_response() -> AsyncIterator[OpenAIResponseObjectStream]: - # TODO: response created should actually get emitted much earlier in the process - yield OpenAIResponseObjectStreamResponseCreated(response=response) - yield OpenAIResponseObjectStreamResponseCompleted(response=response) - - return async_response() return response + async def _create_streaming_response( + self, + inference_result: Any, + ctx: ChatCompletionContext, + output_messages: list[OpenAIResponseOutput], + input: str | list[OpenAIResponseInput], + model: str, + store: bool | None, + tools: list[OpenAIResponseInputTool] | None, + ) -> AsyncIterator[OpenAIResponseObjectStream]: + # Create initial response and emit response.created immediately + response_id = f"resp-{uuid.uuid4()}" + created_at = int(time.time()) + + initial_response = OpenAIResponseObject( + created_at=created_at, + id=response_id, + model=model, + object="response", + status="in_progress", + output=output_messages.copy(), + ) + + # Emit response.created immediately + yield OpenAIResponseObjectStreamResponseCreated(response=initial_response) + + # For streaming, inference_result is an async iterator of chunks + # Stream chunks and emit delta events as they arrive + chat_response_id = "" + chat_response_content = [] + chat_response_tool_calls: dict[int, OpenAIChatCompletionToolCall] = {} + chunk_created = 0 + chunk_model = "" + chunk_finish_reason = "" + sequence_number = 0 + + # Create a placeholder message item for delta events + message_item_id = f"msg_{uuid.uuid4()}" + + async for chunk in inference_result: + chat_response_id = chunk.id + chunk_created = chunk.created + chunk_model = chunk.model + for chunk_choice in chunk.choices: + # Emit incremental text content as delta events + if chunk_choice.delta.content: + sequence_number += 1 + yield OpenAIResponseObjectStreamResponseOutputTextDelta( + content_index=0, + delta=chunk_choice.delta.content, + item_id=message_item_id, + output_index=0, + sequence_number=sequence_number, + ) + + # Collect content for final response + chat_response_content.append(chunk_choice.delta.content or "") + if chunk_choice.finish_reason: + chunk_finish_reason = chunk_choice.finish_reason + + # Aggregate tool call arguments across chunks, using their index as the aggregation key + if chunk_choice.delta.tool_calls: + for tool_call in chunk_choice.delta.tool_calls: + response_tool_call = chat_response_tool_calls.get(tool_call.index, None) + if response_tool_call: + # Don't attempt to concatenate arguments if we don't have any new arguments + if tool_call.function.arguments: + # Guard against an initial None argument before we concatenate + response_tool_call.function.arguments = ( + response_tool_call.function.arguments or "" + ) + tool_call.function.arguments + else: + tool_call_dict: dict[str, Any] = tool_call.model_dump() + tool_call_dict.pop("type", None) + response_tool_call = OpenAIChatCompletionToolCall(**tool_call_dict) + chat_response_tool_calls[tool_call.index] = response_tool_call + + # Convert collected chunks to complete response + if chat_response_tool_calls: + tool_calls = [chat_response_tool_calls[i] for i in sorted(chat_response_tool_calls.keys())] + else: + tool_calls = None + assistant_message = OpenAIAssistantMessageParam( + content="".join(chat_response_content), + tool_calls=tool_calls, + ) + chat_response_obj = OpenAIChatCompletion( + id=chat_response_id, + choices=[ + OpenAIChoice( + message=assistant_message, + finish_reason=chunk_finish_reason, + index=0, + ) + ], + created=chunk_created, + model=chunk_model, + ) + + # Process response choices (tool execution and message creation) + output_messages.extend( + await self._process_response_choices( + chat_response=chat_response_obj, + ctx=ctx, + tools=tools, + ) + ) + + # Create final response + final_response = OpenAIResponseObject( + created_at=created_at, + id=response_id, + model=model, + object="response", + status="completed", + output=output_messages, + ) + + if store: + await self._store_response( + response=final_response, + input=input, + ) + + # Emit response.completed + yield OpenAIResponseObjectStreamResponseCompleted(response=final_response) + async def _convert_response_tools_to_chat_tools( self, tools: list[OpenAIResponseInputTool] - ) -> list[ChatCompletionToolParam]: + ) -> tuple[ + list[ChatCompletionToolParam], + dict[str, OpenAIResponseInputToolMCP], + OpenAIResponseOutput | None, + ]: + from llama_stack.apis.agents.openai_responses import ( + MCPListToolsTool, + ) + from llama_stack.apis.tools.tools import Tool + + mcp_tool_to_server = {} + + def make_openai_tool(tool_name: str, tool: Tool) -> ChatCompletionToolParam: + tool_def = ToolDefinition( + tool_name=tool_name, + description=tool.description, + parameters={ + param.name: ToolParamDefinition( + param_type=param.parameter_type, + description=param.description, + required=param.required, + default=param.default, + ) + for param in tool.parameters + }, + ) + return convert_tooldef_to_openai_tool(tool_def) + + mcp_list_message = None chat_tools: list[ChatCompletionToolParam] = [] for input_tool in tools: # TODO: Handle other tool types @@ -386,91 +593,95 @@ class OpenAIResponsesImpl: elif input_tool.type == "web_search": tool_name = "web_search" tool = await self.tool_groups_api.get_tool(tool_name) - tool_def = ToolDefinition( - tool_name=tool_name, - description=tool.description, - parameters={ - param.name: ToolParamDefinition( - param_type=param.parameter_type, - description=param.description, - required=param.required, - default=param.default, - ) - for param in tool.parameters - }, + if not tool: + raise ValueError(f"Tool {tool_name} not found") + chat_tools.append(make_openai_tool(tool_name, tool)) + elif input_tool.type == "mcp": + always_allowed = None + never_allowed = None + if input_tool.allowed_tools: + if isinstance(input_tool.allowed_tools, list): + always_allowed = input_tool.allowed_tools + elif isinstance(input_tool.allowed_tools, AllowedToolsFilter): + always_allowed = input_tool.allowed_tools.always + never_allowed = input_tool.allowed_tools.never + + tool_defs = await list_mcp_tools( + endpoint=input_tool.server_url, + headers=input_tool.headers or {}, ) - chat_tool = convert_tooldef_to_openai_tool(tool_def) - chat_tools.append(chat_tool) + + mcp_list_message = OpenAIResponseOutputMessageMCPListTools( + id=f"mcp_list_{uuid.uuid4()}", + status="completed", + server_label=input_tool.server_label, + tools=[], + ) + for t in tool_defs.data: + if never_allowed and t.name in never_allowed: + continue + if not always_allowed or t.name in always_allowed: + chat_tools.append(make_openai_tool(t.name, t)) + if t.name in mcp_tool_to_server: + raise ValueError(f"Duplicate tool name {t.name} found for server {input_tool.server_label}") + mcp_tool_to_server[t.name] = input_tool + mcp_list_message.tools.append( + MCPListToolsTool( + name=t.name, + description=t.description, + input_schema={ + "type": "object", + "properties": { + p.name: { + "type": p.parameter_type, + "description": p.description, + } + for p in t.parameters + }, + "required": [p.name for p in t.parameters if p.required], + }, + ) + ) else: raise ValueError(f"Llama Stack OpenAI Responses does not yet support tool type: {input_tool.type}") - return chat_tools + return chat_tools, mcp_tool_to_server, mcp_list_message async def _execute_tool_and_return_final_output( self, - model_id: str, - stream: bool, choice: OpenAIChoice, - messages: list[OpenAIMessageParam], - temperature: float, + ctx: ChatCompletionContext, ) -> list[OpenAIResponseOutput]: output_messages: list[OpenAIResponseOutput] = [] - # If the choice is not an assistant message, we don't need to execute any tools if not isinstance(choice.message, OpenAIAssistantMessageParam): return output_messages - # If the assistant message doesn't have any tool calls, we don't need to execute any tools if not choice.message.tool_calls: return output_messages - # Copy the messages list to avoid mutating the original list - messages = messages.copy() + next_turn_messages = ctx.messages.copy() # Add the assistant message with tool_calls response to the messages list - messages.append(choice.message) + next_turn_messages.append(choice.message) for tool_call in choice.message.tool_calls: - tool_call_id = tool_call.id - function = tool_call.function - - # If for some reason the tool call doesn't have a function or id, we can't execute it - if not function or not tool_call_id: - continue - # TODO: telemetry spans for tool calls - result = await self._execute_tool_call(function) - - # Handle tool call failure - if not result: - output_messages.append( - OpenAIResponseOutputMessageWebSearchToolCall( - id=tool_call_id, - status="failed", - ) - ) - continue - - output_messages.append( - OpenAIResponseOutputMessageWebSearchToolCall( - id=tool_call_id, - status="completed", - ), - ) - - result_content = "" - # TODO: handle other result content types and lists - if isinstance(result.content, str): - result_content = result.content - messages.append(OpenAIToolMessageParam(content=result_content, tool_call_id=tool_call_id)) + tool_call_log, further_input = await self._execute_tool_call(tool_call, ctx) + if tool_call_log: + output_messages.append(tool_call_log) + if further_input: + next_turn_messages.append(further_input) tool_results_chat_response = await self.inference_api.openai_chat_completion( - model=model_id, - messages=messages, - stream=stream, - temperature=temperature, + model=ctx.model, + messages=next_turn_messages, + stream=ctx.stream, + temperature=ctx.temperature, ) - # type cast to appease mypy + # type cast to appease mypy: this is needed because we don't handle streaming properly :) tool_results_chat_response = cast(OpenAIChatCompletion, tool_results_chat_response) + + # Huge TODO: these are NOT the final outputs, we must keep the loop going tool_final_outputs = [ await _convert_chat_choice_to_response_message(choice) for choice in tool_results_chat_response.choices ] @@ -480,15 +691,86 @@ class OpenAIResponsesImpl: async def _execute_tool_call( self, - function: OpenAIChatCompletionToolCallFunction, - ) -> ToolInvocationResult | None: - if not function.name: - return None - function_args = json.loads(function.arguments) if function.arguments else {} - logger.info(f"executing tool call: {function.name} with args: {function_args}") - result = await self.tool_runtime_api.invoke_tool( - tool_name=function.name, - kwargs=function_args, + tool_call: OpenAIChatCompletionToolCall, + ctx: ChatCompletionContext, + ) -> tuple[OpenAIResponseOutput | None, OpenAIMessageParam | None]: + from llama_stack.providers.utils.inference.prompt_adapter import ( + interleaved_content_as_str, ) - logger.debug(f"tool call {function.name} completed with result: {result}") - return result + + tool_call_id = tool_call.id + function = tool_call.function + + if not function or not tool_call_id or not function.name: + return None, None + + error_exc = None + result = None + try: + if function.name in ctx.mcp_tool_to_server: + mcp_tool = ctx.mcp_tool_to_server[function.name] + result = await invoke_mcp_tool( + endpoint=mcp_tool.server_url, + headers=mcp_tool.headers or {}, + tool_name=function.name, + kwargs=json.loads(function.arguments) if function.arguments else {}, + ) + else: + result = await self.tool_runtime_api.invoke_tool( + tool_name=function.name, + kwargs=json.loads(function.arguments) if function.arguments else {}, + ) + except Exception as e: + error_exc = e + + if function.name in ctx.mcp_tool_to_server: + from llama_stack.apis.agents.openai_responses import OpenAIResponseOutputMessageMCPCall + + message = OpenAIResponseOutputMessageMCPCall( + id=tool_call_id, + arguments=function.arguments, + name=function.name, + server_label=ctx.mcp_tool_to_server[function.name].server_label, + ) + if error_exc: + message.error = str(error_exc) + elif (result.error_code and result.error_code > 0) or result.error_message: + message.error = f"Error (code {result.error_code}): {result.error_message}" + elif result.content: + message.output = interleaved_content_as_str(result.content) + else: + if function.name == "web_search": + message = OpenAIResponseOutputMessageWebSearchToolCall( + id=tool_call_id, + status="completed", + ) + if error_exc or (result.error_code and result.error_code > 0) or result.error_message: + message.status = "failed" + else: + raise ValueError(f"Unknown tool {function.name} called") + + input_message = None + if result and result.content: + if isinstance(result.content, str): + content = result.content + elif isinstance(result.content, list): + from llama_stack.apis.common.content_types import ImageContentItem, TextContentItem + + content = [] + for item in result.content: + if isinstance(item, TextContentItem): + part = OpenAIChatCompletionContentPartTextParam(text=item.text) + elif isinstance(item, ImageContentItem): + if item.image.data: + url = f"data:image;base64,{item.image.data}" + else: + url = item.image.url + part = OpenAIChatCompletionContentPartImageParam(image_url=OpenAIImageURL(url=url)) + else: + raise ValueError(f"Unknown result content type: {type(item)}") + content.append(part) + else: + raise ValueError(f"Unknown result content type: {type(result.content)}") + input_message = OpenAIToolMessageParam(content=content, tool_call_id=tool_call_id) + + return message, input_message diff --git a/llama_stack/providers/inline/inference/meta_reference/inference.py b/llama_stack/providers/inline/inference/meta_reference/inference.py index 8dd594869..e238e1b78 100644 --- a/llama_stack/providers/inline/inference/meta_reference/inference.py +++ b/llama_stack/providers/inline/inference/meta_reference/inference.py @@ -6,6 +6,7 @@ import asyncio import os +import sys from collections.abc import AsyncGenerator from pydantic import BaseModel @@ -28,7 +29,7 @@ from llama_stack.apis.inference import ( CompletionRequest, CompletionResponse, CompletionResponseStreamChunk, - Inference, + InferenceProvider, InterleavedContent, LogProbConfig, Message, @@ -86,7 +87,7 @@ class MetaReferenceInferenceImpl( OpenAICompletionToLlamaStackMixin, OpenAIChatCompletionToLlamaStackMixin, SentenceTransformerEmbeddingMixin, - Inference, + InferenceProvider, ModelsProtocolPrivate, ): def __init__(self, config: MetaReferenceInferenceConfig) -> None: @@ -455,9 +456,9 @@ class MetaReferenceInferenceImpl( first = token_results[0] if not first.finished and not first.ignore_token: if os.environ.get("LLAMA_MODELS_DEBUG", "0") in ("1", "2"): - cprint(first.text, "cyan", end="") + cprint(first.text, color="cyan", end="", file=sys.stderr) if os.environ.get("LLAMA_MODELS_DEBUG", "0") == "2": - cprint(f"<{first.token}>", "magenta", end="") + cprint(f"<{first.token}>", color="magenta", end="", file=sys.stderr) for result in token_results: idx = result.batch_idx @@ -519,9 +520,9 @@ class MetaReferenceInferenceImpl( for token_results in self.generator.chat_completion([request]): token_result = token_results[0] if os.environ.get("LLAMA_MODELS_DEBUG", "0") == "1": - cprint(token_result.text, "cyan", end="") + cprint(token_result.text, color="cyan", end="", file=sys.stderr) if os.environ.get("LLAMA_MODELS_DEBUG", "0") == "2": - cprint(f"<{token_result.token}>", "magenta", end="") + cprint(f"<{token_result.token}>", color="magenta", end="", file=sys.stderr) if token_result.token == tokenizer.eot_id: stop_reason = StopReason.end_of_turn diff --git a/llama_stack/providers/inline/inference/sentence_transformers/sentence_transformers.py b/llama_stack/providers/inline/inference/sentence_transformers/sentence_transformers.py index 7b36b0997..890c526f5 100644 --- a/llama_stack/providers/inline/inference/sentence_transformers/sentence_transformers.py +++ b/llama_stack/providers/inline/inference/sentence_transformers/sentence_transformers.py @@ -9,7 +9,7 @@ from collections.abc import AsyncGenerator from llama_stack.apis.inference import ( CompletionResponse, - Inference, + InferenceProvider, InterleavedContent, LogProbConfig, Message, @@ -38,7 +38,7 @@ class SentenceTransformersInferenceImpl( OpenAIChatCompletionToLlamaStackMixin, OpenAICompletionToLlamaStackMixin, SentenceTransformerEmbeddingMixin, - Inference, + InferenceProvider, ModelsProtocolPrivate, ): def __init__(self, config: SentenceTransformersInferenceConfig) -> None: diff --git a/llama_stack/providers/inline/inference/vllm/vllm.py b/llama_stack/providers/inline/inference/vllm/vllm.py index 438cb14a0..bf54462b5 100644 --- a/llama_stack/providers/inline/inference/vllm/vllm.py +++ b/llama_stack/providers/inline/inference/vllm/vllm.py @@ -40,6 +40,7 @@ from llama_stack.apis.inference import ( JsonSchemaResponseFormat, LogProbConfig, Message, + OpenAIEmbeddingsResponse, ResponseFormat, SamplingParams, TextTruncation, @@ -410,6 +411,16 @@ class VLLMInferenceImpl( ) -> EmbeddingsResponse: raise NotImplementedError() + async def openai_embeddings( + self, + model: str, + input: str | list[str], + encoding_format: str | None = "float", + dimensions: int | None = None, + user: str | None = None, + ) -> OpenAIEmbeddingsResponse: + raise NotImplementedError() + async def chat_completion( self, model_id: str, diff --git a/llama_stack/providers/inline/post_training/common/utils.py b/llama_stack/providers/inline/post_training/common/utils.py new file mode 100644 index 000000000..7840b21e8 --- /dev/null +++ b/llama_stack/providers/inline/post_training/common/utils.py @@ -0,0 +1,35 @@ +# 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 gc + + +def evacuate_model_from_device(model, device: str): + """Safely clear a model from memory and free device resources. + This function handles the proper cleanup of a model by: + 1. Moving the model to CPU if it's on a non-CPU device + 2. Deleting the model object to free memory + 3. Running garbage collection + 4. Clearing CUDA cache if the model was on a CUDA device + Args: + model: The PyTorch model to clear + device: The device type the model is currently on ('cuda', 'mps', 'cpu') + Note: + - For CUDA devices, this will clear the CUDA cache after moving the model to CPU + - For MPS devices, only moves the model to CPU (no cache clearing available) + - For CPU devices, only deletes the model object and runs garbage collection + """ + if device != "cpu": + model.to("cpu") + + del model + gc.collect() + + if device == "cuda": + # we need to import such that this is only imported when the method is called + import torch + + torch.cuda.empty_cache() diff --git a/llama_stack/providers/inline/post_training/huggingface/__init__.py b/llama_stack/providers/inline/post_training/huggingface/__init__.py new file mode 100644 index 000000000..cc1a671c1 --- /dev/null +++ b/llama_stack/providers/inline/post_training/huggingface/__init__.py @@ -0,0 +1,27 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the terms described in the LICENSE file in +# the root directory of this source tree. + +from typing import Any + +from llama_stack.distribution.datatypes import Api + +from .config import HuggingFacePostTrainingConfig + +# post_training api and the huggingface provider is still experimental and under heavy development + + +async def get_provider_impl( + config: HuggingFacePostTrainingConfig, + deps: dict[Api, Any], +): + from .post_training import HuggingFacePostTrainingImpl + + impl = HuggingFacePostTrainingImpl( + config, + deps[Api.datasetio], + deps[Api.datasets], + ) + return impl diff --git a/llama_stack/providers/inline/post_training/huggingface/config.py b/llama_stack/providers/inline/post_training/huggingface/config.py new file mode 100644 index 000000000..06c6d8073 --- /dev/null +++ b/llama_stack/providers/inline/post_training/huggingface/config.py @@ -0,0 +1,72 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the terms described in the LICENSE file in +# the root directory of this source tree. + +from typing import Any, Literal + +from pydantic import BaseModel + + +class HuggingFacePostTrainingConfig(BaseModel): + # Device to run training on (cuda, cpu, mps) + device: str = "cuda" + + # Distributed training backend if using multiple devices + # fsdp: Fully Sharded Data Parallel + # deepspeed: DeepSpeed ZeRO optimization + distributed_backend: Literal["fsdp", "deepspeed"] | None = None + + # Format for saving model checkpoints + # full_state: Save complete model state + # huggingface: Save in HuggingFace format (recommended for compatibility) + checkpoint_format: Literal["full_state", "huggingface"] | None = "huggingface" + + # Template for formatting chat inputs and outputs + # Used to structure the conversation format for training + chat_template: str = "<|user|>\n{input}\n<|assistant|>\n{output}" + + # Model-specific configuration parameters + # trust_remote_code: Allow execution of custom model code + # attn_implementation: Use SDPA (Scaled Dot Product Attention) for better performance + model_specific_config: dict = { + "trust_remote_code": True, + "attn_implementation": "sdpa", + } + + # Maximum sequence length for training + # Set to 2048 as this is the maximum that works reliably on MPS (Apple Silicon) + # Longer sequences may cause memory issues on MPS devices + max_seq_length: int = 2048 + + # Enable gradient checkpointing to reduce memory usage + # Trades computation for memory by recomputing activations + gradient_checkpointing: bool = False + + # Maximum number of checkpoints to keep + # Older checkpoints are deleted when this limit is reached + save_total_limit: int = 3 + + # Number of training steps between logging updates + logging_steps: int = 10 + + # Ratio of training steps used for learning rate warmup + # Helps stabilize early training + warmup_ratio: float = 0.1 + + # L2 regularization coefficient + # Helps prevent overfitting + weight_decay: float = 0.01 + + # Number of worker processes for data loading + # Higher values can improve data loading speed but increase memory usage + dataloader_num_workers: int = 4 + + # Whether to pin memory in data loader + # Can improve data transfer speed to GPU but uses more memory + dataloader_pin_memory: bool = True + + @classmethod + def sample_run_config(cls, __distro_dir__: str, **kwargs: Any) -> dict[str, Any]: + return {"checkpoint_format": "huggingface", "distributed_backend": None, "device": "cpu"} diff --git a/llama_stack/providers/inline/post_training/huggingface/post_training.py b/llama_stack/providers/inline/post_training/huggingface/post_training.py new file mode 100644 index 000000000..0b2760792 --- /dev/null +++ b/llama_stack/providers/inline/post_training/huggingface/post_training.py @@ -0,0 +1,176 @@ +# 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 enum import Enum +from typing import Any + +from llama_stack.apis.datasetio import DatasetIO +from llama_stack.apis.datasets import Datasets +from llama_stack.apis.post_training import ( + AlgorithmConfig, + Checkpoint, + DPOAlignmentConfig, + JobStatus, + ListPostTrainingJobsResponse, + PostTrainingJob, + PostTrainingJobArtifactsResponse, + PostTrainingJobStatusResponse, + TrainingConfig, +) +from llama_stack.providers.inline.post_training.huggingface.config import ( + HuggingFacePostTrainingConfig, +) +from llama_stack.providers.inline.post_training.huggingface.recipes.finetune_single_device import ( + HFFinetuningSingleDevice, +) +from llama_stack.providers.utils.scheduler import JobArtifact, Scheduler +from llama_stack.providers.utils.scheduler import JobStatus as SchedulerJobStatus +from llama_stack.schema_utils import webmethod + + +class TrainingArtifactType(Enum): + CHECKPOINT = "checkpoint" + RESOURCES_STATS = "resources_stats" + + +_JOB_TYPE_SUPERVISED_FINE_TUNE = "supervised-fine-tune" + + +class HuggingFacePostTrainingImpl: + def __init__( + self, + config: HuggingFacePostTrainingConfig, + datasetio_api: DatasetIO, + datasets: Datasets, + ) -> None: + self.config = config + self.datasetio_api = datasetio_api + self.datasets_api = datasets + self._scheduler = Scheduler() + + async def shutdown(self) -> None: + await self._scheduler.shutdown() + + @staticmethod + def _checkpoint_to_artifact(checkpoint: Checkpoint) -> JobArtifact: + return JobArtifact( + type=TrainingArtifactType.CHECKPOINT.value, + name=checkpoint.identifier, + uri=checkpoint.path, + metadata=dict(checkpoint), + ) + + @staticmethod + def _resources_stats_to_artifact(resources_stats: dict[str, Any]) -> JobArtifact: + return JobArtifact( + type=TrainingArtifactType.RESOURCES_STATS.value, + name=TrainingArtifactType.RESOURCES_STATS.value, + metadata=resources_stats, + ) + + async def supervised_fine_tune( + self, + job_uuid: str, + training_config: TrainingConfig, + hyperparam_search_config: dict[str, Any], + logger_config: dict[str, Any], + model: str, + checkpoint_dir: str | None = None, + algorithm_config: AlgorithmConfig | None = None, + ) -> PostTrainingJob: + async def handler(on_log_message_cb, on_status_change_cb, on_artifact_collected_cb): + on_log_message_cb("Starting HF finetuning") + + recipe = HFFinetuningSingleDevice( + job_uuid=job_uuid, + datasetio_api=self.datasetio_api, + datasets_api=self.datasets_api, + ) + + resources_allocated, checkpoints = await recipe.train( + model=model, + output_dir=checkpoint_dir, + job_uuid=job_uuid, + lora_config=algorithm_config, + config=training_config, + provider_config=self.config, + ) + + on_artifact_collected_cb(self._resources_stats_to_artifact(resources_allocated)) + if checkpoints: + for checkpoint in checkpoints: + artifact = self._checkpoint_to_artifact(checkpoint) + on_artifact_collected_cb(artifact) + + on_status_change_cb(SchedulerJobStatus.completed) + on_log_message_cb("HF finetuning completed") + + job_uuid = self._scheduler.schedule(_JOB_TYPE_SUPERVISED_FINE_TUNE, job_uuid, handler) + return PostTrainingJob(job_uuid=job_uuid) + + async def preference_optimize( + self, + job_uuid: str, + finetuned_model: str, + algorithm_config: DPOAlignmentConfig, + training_config: TrainingConfig, + hyperparam_search_config: dict[str, Any], + logger_config: dict[str, Any], + ) -> PostTrainingJob: + raise NotImplementedError("DPO alignment is not implemented yet") + + async def get_training_jobs(self) -> ListPostTrainingJobsResponse: + return ListPostTrainingJobsResponse( + data=[PostTrainingJob(job_uuid=job.id) for job in self._scheduler.get_jobs()] + ) + + @staticmethod + def _get_artifacts_metadata_by_type(job, artifact_type): + return [artifact.metadata for artifact in job.artifacts if artifact.type == artifact_type] + + @classmethod + def _get_checkpoints(cls, job): + return cls._get_artifacts_metadata_by_type(job, TrainingArtifactType.CHECKPOINT.value) + + @classmethod + def _get_resources_allocated(cls, job): + data = cls._get_artifacts_metadata_by_type(job, TrainingArtifactType.RESOURCES_STATS.value) + return data[0] if data else None + + @webmethod(route="/post-training/job/status") + async def get_training_job_status(self, job_uuid: str) -> PostTrainingJobStatusResponse | None: + job = self._scheduler.get_job(job_uuid) + + match job.status: + # TODO: Add support for other statuses to API + case SchedulerJobStatus.new | SchedulerJobStatus.scheduled: + status = JobStatus.scheduled + case SchedulerJobStatus.running: + status = JobStatus.in_progress + case SchedulerJobStatus.completed: + status = JobStatus.completed + case SchedulerJobStatus.failed: + status = JobStatus.failed + case _: + raise NotImplementedError() + + return PostTrainingJobStatusResponse( + job_uuid=job_uuid, + status=status, + scheduled_at=job.scheduled_at, + started_at=job.started_at, + completed_at=job.completed_at, + checkpoints=self._get_checkpoints(job), + resources_allocated=self._get_resources_allocated(job), + ) + + @webmethod(route="/post-training/job/cancel") + async def cancel_training_job(self, job_uuid: str) -> None: + self._scheduler.cancel(job_uuid) + + @webmethod(route="/post-training/job/artifacts") + async def get_training_job_artifacts(self, job_uuid: str) -> PostTrainingJobArtifactsResponse | None: + job = self._scheduler.get_job(job_uuid) + return PostTrainingJobArtifactsResponse(job_uuid=job_uuid, checkpoints=self._get_checkpoints(job)) diff --git a/llama_stack/providers/inline/post_training/huggingface/recipes/finetune_single_device.py b/llama_stack/providers/inline/post_training/huggingface/recipes/finetune_single_device.py new file mode 100644 index 000000000..b6d13b029 --- /dev/null +++ b/llama_stack/providers/inline/post_training/huggingface/recipes/finetune_single_device.py @@ -0,0 +1,683 @@ +# 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 gc +import json +import logging +import multiprocessing +import os +import signal +import sys +from datetime import datetime, timezone +from pathlib import Path +from typing import Any + +import psutil + +from llama_stack.providers.inline.post_training.common.utils import evacuate_model_from_device + +# Set tokenizer parallelism environment variable +os.environ["TOKENIZERS_PARALLELISM"] = "false" + +# Force PyTorch to use OpenBLAS instead of MKL +os.environ["MKL_THREADING_LAYER"] = "GNU" +os.environ["MKL_SERVICE_FORCE_INTEL"] = "0" +os.environ["MKL_NUM_THREADS"] = "1" + +import torch +from datasets import Dataset +from peft import LoraConfig +from transformers import ( + AutoConfig, + AutoModelForCausalLM, + AutoTokenizer, +) +from trl import SFTConfig, SFTTrainer + +from llama_stack.apis.datasetio import DatasetIO +from llama_stack.apis.datasets import Datasets +from llama_stack.apis.post_training import ( + Checkpoint, + DataConfig, + LoraFinetuningConfig, + TrainingConfig, +) + +from ..config import HuggingFacePostTrainingConfig + +logger = logging.getLogger(__name__) + + +def get_gb(to_convert: int) -> str: + """Converts memory stats to GB and formats to 2 decimal places. + Args: + to_convert: Memory value in bytes + Returns: + str: Memory value in GB formatted to 2 decimal places + """ + return f"{(to_convert / (1024**3)):.2f}" + + +def get_memory_stats(device: torch.device) -> dict[str, Any]: + """Get memory statistics for the given device.""" + stats = { + "system_memory": { + "total": get_gb(psutil.virtual_memory().total), + "available": get_gb(psutil.virtual_memory().available), + "used": get_gb(psutil.virtual_memory().used), + "percent": psutil.virtual_memory().percent, + } + } + + if device.type == "cuda": + stats["device_memory"] = { + "allocated": get_gb(torch.cuda.memory_allocated(device)), + "reserved": get_gb(torch.cuda.memory_reserved(device)), + "max_allocated": get_gb(torch.cuda.max_memory_allocated(device)), + } + elif device.type == "mps": + # MPS doesn't provide direct memory stats, but we can track system memory + stats["device_memory"] = { + "note": "MPS memory stats not directly available", + "system_memory_used": get_gb(psutil.virtual_memory().used), + } + elif device.type == "cpu": + # For CPU, we track process memory usage + process = psutil.Process() + stats["device_memory"] = { + "process_rss": get_gb(process.memory_info().rss), + "process_vms": get_gb(process.memory_info().vms), + "process_percent": process.memory_percent(), + } + + return stats + + +def setup_torch_device(device_str: str) -> torch.device: + """Initialize and validate a PyTorch device. + This function handles device initialization and validation for different device types: + - CUDA: Validates CUDA availability and handles device selection + - MPS: Validates MPS availability for Apple Silicon + - CPU: Basic validation + - HPU: Raises error as it's not supported + Args: + device_str: String specifying the device ('cuda', 'cpu', 'mps') + Returns: + torch.device: The initialized and validated device + Raises: + RuntimeError: If device initialization fails or device is not supported + """ + try: + device = torch.device(device_str) + except RuntimeError as e: + raise RuntimeError(f"Error getting Torch Device {str(e)}") from e + + # Validate device capabilities + if device.type == "cuda": + if not torch.cuda.is_available(): + raise RuntimeError( + f"{device.type}: Torch has no CUDA/ROCm support or could not detect a compatible device." + ) + if device.index is None: + device = torch.device(device.type, torch.cuda.current_device()) + elif device.type == "mps": + if not torch.backends.mps.is_available(): + raise RuntimeError(f"{device.type}: Torch has no MPS support or could not detect a compatible device.") + elif device.type == "hpu": + raise RuntimeError(f"{device.type}: training does not support Intel Gaudi.") + + return device + + +class HFFinetuningSingleDevice: + def __init__( + self, + job_uuid: str, + datasetio_api: DatasetIO, + datasets_api: Datasets, + ): + self.datasetio_api = datasetio_api + self.datasets_api = datasets_api + self.job_uuid = job_uuid + + def validate_dataset_format(self, rows: list[dict]) -> bool: + """Validate that the dataset has the required fields.""" + required_fields = ["input_query", "expected_answer", "chat_completion_input"] + return all(field in row for row in rows for field in required_fields) + + def _process_instruct_format(self, row: dict) -> tuple[str | None, str | None]: + """Process a row in instruct format.""" + if "chat_completion_input" in row and "expected_answer" in row: + try: + messages = json.loads(row["chat_completion_input"]) + if not isinstance(messages, list) or len(messages) != 1: + logger.warning(f"Invalid chat_completion_input format: {row['chat_completion_input']}") + return None, None + if "content" not in messages[0]: + logger.warning(f"Message missing content: {messages[0]}") + return None, None + return messages[0]["content"], row["expected_answer"] + except json.JSONDecodeError: + logger.warning(f"Failed to parse chat_completion_input: {row['chat_completion_input']}") + return None, None + return None, None + + def _process_dialog_format(self, row: dict) -> tuple[str | None, str | None]: + """Process a row in dialog format.""" + if "dialog" in row: + try: + dialog = json.loads(row["dialog"]) + if not isinstance(dialog, list) or len(dialog) < 2: + logger.warning(f"Dialog must have at least 2 messages: {row['dialog']}") + return None, None + if dialog[0].get("role") != "user": + logger.warning(f"First message must be from user: {dialog[0]}") + return None, None + if not any(msg.get("role") == "assistant" for msg in dialog): + logger.warning("Dialog must have at least one assistant message") + return None, None + + # Convert to human/gpt format + role_map = {"user": "human", "assistant": "gpt"} + conversations = [] + for msg in dialog: + if "role" not in msg or "content" not in msg: + logger.warning(f"Message missing role or content: {msg}") + continue + conversations.append({"from": role_map[msg["role"]], "value": msg["content"]}) + + # Format as a single conversation + return conversations[0]["value"], conversations[1]["value"] + except json.JSONDecodeError: + logger.warning(f"Failed to parse dialog: {row['dialog']}") + return None, None + return None, None + + def _process_fallback_format(self, row: dict) -> tuple[str | None, str | None]: + """Process a row using fallback formats.""" + if "input" in row and "output" in row: + return row["input"], row["output"] + elif "prompt" in row and "completion" in row: + return row["prompt"], row["completion"] + elif "question" in row and "answer" in row: + return row["question"], row["answer"] + return None, None + + def _format_text(self, input_text: str, output_text: str, provider_config: HuggingFacePostTrainingConfig) -> str: + """Format input and output text based on model requirements.""" + if hasattr(provider_config, "chat_template"): + return provider_config.chat_template.format(input=input_text, output=output_text) + return f"{input_text}\n{output_text}" + + def _create_dataset( + self, rows: list[dict], config: TrainingConfig, provider_config: HuggingFacePostTrainingConfig + ) -> Dataset: + """Create and preprocess the dataset.""" + formatted_rows = [] + for row in rows: + input_text = None + output_text = None + + # Process based on format + assert isinstance(config.data_config, DataConfig), "DataConfig must be initialized" + if config.data_config.data_format.value == "instruct": + input_text, output_text = self._process_instruct_format(row) + elif config.data_config.data_format.value == "dialog": + input_text, output_text = self._process_dialog_format(row) + else: + input_text, output_text = self._process_fallback_format(row) + + if input_text and output_text: + formatted_text = self._format_text(input_text, output_text, provider_config) + formatted_rows.append({"text": formatted_text}) + + if not formatted_rows: + assert isinstance(config.data_config, DataConfig), "DataConfig must be initialized" + raise ValueError( + f"No valid input/output pairs found in the dataset for format: {config.data_config.data_format.value}" + ) + + return Dataset.from_list(formatted_rows) + + def _preprocess_dataset( + self, ds: Dataset, tokenizer: AutoTokenizer, provider_config: HuggingFacePostTrainingConfig + ) -> Dataset: + """Preprocess the dataset with tokenizer.""" + + def tokenize_function(examples): + return tokenizer( + examples["text"], + padding=True, + truncation=True, + max_length=provider_config.max_seq_length, + return_tensors=None, + ) + + return ds.map( + tokenize_function, + batched=True, + remove_columns=ds.column_names, + ) + + async def _setup_data(self, dataset_id: str) -> list[dict[str, Any]]: + """Load dataset from llama stack dataset provider""" + try: + all_rows = await self.datasetio_api.iterrows( + dataset_id=dataset_id, + limit=-1, + ) + if not isinstance(all_rows.data, list): + raise RuntimeError("Expected dataset data to be a list") + return all_rows.data + except Exception as e: + raise RuntimeError(f"Failed to load dataset: {str(e)}") from e + + def _run_training_sync( + self, + model: str, + provider_config: dict[str, Any], + peft_config: LoraConfig | None, + config: dict[str, Any], + output_dir_path: Path | None, + ) -> None: + """Synchronous wrapper for running training process. + This method serves as a bridge between the multiprocessing Process and the async training function. + It creates a new event loop to run the async training process. + Args: + model: The model identifier to load + dataset_id: ID of the dataset to use for training + provider_config: Configuration specific to the HuggingFace provider + peft_config: Optional LoRA configuration + config: General training configuration + output_dir_path: Optional path to save the model + """ + import asyncio + + logger.info("Starting training process with async wrapper") + asyncio.run( + self._run_training( + model=model, + provider_config=provider_config, + peft_config=peft_config, + config=config, + output_dir_path=output_dir_path, + ) + ) + + async def load_dataset( + self, + model: str, + config: TrainingConfig, + provider_config: HuggingFacePostTrainingConfig, + ) -> tuple[Dataset, Dataset, AutoTokenizer]: + """Load and prepare the dataset for training. + Args: + model: The model identifier to load + config: Training configuration + provider_config: Provider-specific configuration + Returns: + tuple: (train_dataset, eval_dataset, tokenizer) + """ + # Validate data config + if not config.data_config: + raise ValueError("DataConfig is required for training") + + # Load dataset + logger.info(f"Loading dataset: {config.data_config.dataset_id}") + rows = await self._setup_data(config.data_config.dataset_id) + if not self.validate_dataset_format(rows): + raise ValueError("Dataset is missing required fields: input_query, expected_answer, chat_completion_input") + logger.info(f"Loaded {len(rows)} rows from dataset") + + # Initialize tokenizer + logger.info(f"Initializing tokenizer for model: {model}") + try: + tokenizer = AutoTokenizer.from_pretrained(model, **provider_config.model_specific_config) + + # Set pad token to eos token if not present + # This is common for models that don't have a dedicated pad token + if not tokenizer.pad_token: + tokenizer.pad_token = tokenizer.eos_token + + # Set padding side to right for causal language modeling + # This ensures that padding tokens don't interfere with the model's ability + # to predict the next token in the sequence + tokenizer.padding_side = "right" + + # Set truncation side to right to keep the beginning of the sequence + # This is important for maintaining context and instruction format + tokenizer.truncation_side = "right" + + # Set model max length to match provider config + # This ensures consistent sequence lengths across the training process + tokenizer.model_max_length = provider_config.max_seq_length + + logger.info("Tokenizer initialized successfully") + except Exception as e: + raise RuntimeError(f"Failed to initialize tokenizer: {str(e)}") from e + + # Create and preprocess dataset + logger.info("Creating and preprocessing dataset") + try: + ds = self._create_dataset(rows, config, provider_config) + ds = self._preprocess_dataset(ds, tokenizer, provider_config) + logger.info(f"Dataset created with {len(ds)} examples") + except Exception as e: + raise ValueError(f"Failed to create dataset: {str(e)}") from e + + # Split dataset + logger.info("Splitting dataset into train and validation sets") + train_val_split = ds.train_test_split(test_size=0.1, seed=42) + train_dataset = train_val_split["train"] + eval_dataset = train_val_split["test"] + logger.info(f"Split dataset into {len(train_dataset)} training and {len(eval_dataset)} validation examples") + + return train_dataset, eval_dataset, tokenizer + + def load_model( + self, + model: str, + device: torch.device, + provider_config: HuggingFacePostTrainingConfig, + ) -> AutoModelForCausalLM: + """Load and initialize the model for training. + Args: + model: The model identifier to load + device: The device to load the model onto + provider_config: Provider-specific configuration + Returns: + The loaded and initialized model + Raises: + RuntimeError: If model loading fails + """ + logger.info("Loading the base model") + try: + model_config = AutoConfig.from_pretrained(model, **provider_config.model_specific_config) + model_obj = AutoModelForCausalLM.from_pretrained( + model, + torch_dtype="auto" if device.type != "cpu" else "float32", + quantization_config=None, + config=model_config, + **provider_config.model_specific_config, + ) + # Always move model to specified device + model_obj = model_obj.to(device) + logger.info(f"Model loaded and moved to device: {model_obj.device}") + return model_obj + except Exception as e: + raise RuntimeError(f"Failed to load model: {str(e)}") from e + + def setup_training_args( + self, + config: TrainingConfig, + provider_config: HuggingFacePostTrainingConfig, + device: torch.device, + output_dir_path: Path | None, + steps_per_epoch: int, + ) -> SFTConfig: + """Setup training arguments. + Args: + config: Training configuration + provider_config: Provider-specific configuration + device: The device to train on + output_dir_path: Optional path to save the model + steps_per_epoch: Number of steps per epoch + Returns: + Configured SFTConfig object + """ + logger.info("Configuring training arguments") + lr = 2e-5 + if config.optimizer_config: + lr = config.optimizer_config.lr + logger.info(f"Using custom learning rate: {lr}") + + # Validate data config + if not config.data_config: + raise ValueError("DataConfig is required for training") + data_config = config.data_config + + # Calculate steps + total_steps = steps_per_epoch * config.n_epochs + max_steps = min(config.max_steps_per_epoch, total_steps) + eval_steps = max(1, steps_per_epoch // 10) # Evaluate 10 times per epoch + save_steps = max(1, steps_per_epoch // 5) # Save 5 times per epoch + logging_steps = max(1, steps_per_epoch // 50) # Log 50 times per epoch + + logger.info("Training configuration:") + logger.info(f"- Steps per epoch: {steps_per_epoch}") + logger.info(f"- Total steps: {total_steps}") + logger.info(f"- Max steps: {max_steps}") + logger.info(f"- Eval steps: {eval_steps}") + logger.info(f"- Save steps: {save_steps}") + logger.info(f"- Logging steps: {logging_steps}") + + # Configure save strategy + save_strategy = "no" + if output_dir_path: + save_strategy = "steps" + logger.info(f"Will save checkpoints to {output_dir_path}") + + return SFTConfig( + max_steps=max_steps, + output_dir=str(output_dir_path) if output_dir_path is not None else None, + num_train_epochs=config.n_epochs, + per_device_train_batch_size=data_config.batch_size, + fp16=device.type == "cuda", + bf16=False, # Causes CPU issues. + eval_strategy="steps", + use_cpu=True if device.type == "cpu" and not torch.backends.mps.is_available() else False, + save_strategy=save_strategy, + report_to="none", + max_seq_length=provider_config.max_seq_length, + gradient_accumulation_steps=config.gradient_accumulation_steps, + gradient_checkpointing=provider_config.gradient_checkpointing, + learning_rate=lr, + warmup_ratio=provider_config.warmup_ratio, + weight_decay=provider_config.weight_decay, + remove_unused_columns=False, + dataloader_pin_memory=provider_config.dataloader_pin_memory, + dataloader_num_workers=provider_config.dataloader_num_workers, + dataset_text_field="text", + packing=False, + load_best_model_at_end=True if output_dir_path else False, + metric_for_best_model="eval_loss", + greater_is_better=False, + eval_steps=eval_steps, + save_steps=save_steps, + logging_steps=logging_steps, + ) + + def save_model( + self, + model_obj: AutoModelForCausalLM, + trainer: SFTTrainer, + peft_config: LoraConfig | None, + output_dir_path: Path, + ) -> None: + """Save the trained model. + Args: + model_obj: The model to save + trainer: The trainer instance + peft_config: Optional LoRA configuration + output_dir_path: Path to save the model + """ + logger.info("Saving final model") + model_obj.config.use_cache = True + + if peft_config: + logger.info("Merging LoRA weights with base model") + model_obj = trainer.model.merge_and_unload() + else: + model_obj = trainer.model + + save_path = output_dir_path / "merged_model" + logger.info(f"Saving model to {save_path}") + model_obj.save_pretrained(save_path) + + async def _run_training( + self, + model: str, + provider_config: dict[str, Any], + peft_config: LoraConfig | None, + config: dict[str, Any], + output_dir_path: Path | None, + ) -> None: + """Run the training process with signal handling.""" + + def signal_handler(signum, frame): + """Handle termination signals gracefully.""" + logger.info(f"Received signal {signum}, initiating graceful shutdown") + sys.exit(0) + + signal.signal(signal.SIGTERM, signal_handler) + signal.signal(signal.SIGINT, signal_handler) + + # Convert config dicts back to objects + logger.info("Initializing configuration objects") + provider_config_obj = HuggingFacePostTrainingConfig(**provider_config) + config_obj = TrainingConfig(**config) + + # Initialize and validate device + device = setup_torch_device(provider_config_obj.device) + logger.info(f"Using device '{device}'") + + # Load dataset and tokenizer + train_dataset, eval_dataset, tokenizer = await self.load_dataset(model, config_obj, provider_config_obj) + + # Calculate steps per epoch + if not config_obj.data_config: + raise ValueError("DataConfig is required for training") + steps_per_epoch = len(train_dataset) // config_obj.data_config.batch_size + + # Setup training arguments + training_args = self.setup_training_args( + config_obj, + provider_config_obj, + device, + output_dir_path, + steps_per_epoch, + ) + + # Load model + model_obj = self.load_model(model, device, provider_config_obj) + + # Initialize trainer + logger.info("Initializing SFTTrainer") + trainer = SFTTrainer( + model=model_obj, + train_dataset=train_dataset, + eval_dataset=eval_dataset, + peft_config=peft_config, + args=training_args, + ) + + try: + # Train + logger.info("Starting training") + trainer.train() + logger.info("Training completed successfully") + + # Save final model if output directory is provided + if output_dir_path: + self.save_model(model_obj, trainer, peft_config, output_dir_path) + + finally: + # Clean up resources + logger.info("Cleaning up resources") + if hasattr(trainer, "model"): + evacuate_model_from_device(trainer.model, device.type) + del trainer + gc.collect() + logger.info("Cleanup completed") + + async def train( + self, + model: str, + output_dir: str | None, + job_uuid: str, + lora_config: LoraFinetuningConfig, + config: TrainingConfig, + provider_config: HuggingFacePostTrainingConfig, + ) -> tuple[dict[str, Any], list[Checkpoint] | None]: + """Train a model using HuggingFace's SFTTrainer""" + # Initialize and validate device + device = setup_torch_device(provider_config.device) + logger.info(f"Using device '{device}'") + + output_dir_path = None + if output_dir: + output_dir_path = Path(output_dir) + + # Track memory stats + memory_stats = { + "initial": get_memory_stats(device), + "after_training": None, + "final": None, + } + + # Configure LoRA + peft_config = None + if lora_config: + peft_config = LoraConfig( + lora_alpha=lora_config.alpha, + lora_dropout=0.1, + r=lora_config.rank, + bias="none", + task_type="CAUSAL_LM", + target_modules=lora_config.lora_attn_modules, + ) + + # Validate data config + if not config.data_config: + raise ValueError("DataConfig is required for training") + + # Train in a separate process + logger.info("Starting training in separate process") + try: + # Set multiprocessing start method to 'spawn' for CUDA/MPS compatibility + if device.type in ["cuda", "mps"]: + multiprocessing.set_start_method("spawn", force=True) + + process = multiprocessing.Process( + target=self._run_training_sync, + kwargs={ + "model": model, + "provider_config": provider_config.model_dump(), + "peft_config": peft_config, + "config": config.model_dump(), + "output_dir_path": output_dir_path, + }, + ) + process.start() + + # Monitor the process + while process.is_alive(): + process.join(timeout=1) # Check every second + if not process.is_alive(): + break + + # Get the return code + if process.exitcode != 0: + raise RuntimeError(f"Training failed with exit code {process.exitcode}") + + memory_stats["after_training"] = get_memory_stats(device) + + checkpoints = None + if output_dir_path: + # Create checkpoint + checkpoint = Checkpoint( + identifier=f"{model}-sft-{config.n_epochs}", + created_at=datetime.now(timezone.utc), + epoch=config.n_epochs, + post_training_job_id=job_uuid, + path=str(output_dir_path / "merged_model"), + ) + checkpoints = [checkpoint] + + return memory_stats, checkpoints + finally: + memory_stats["final"] = get_memory_stats(device) + gc.collect() diff --git a/llama_stack/providers/inline/post_training/torchtune/recipes/lora_finetuning_single_device.py b/llama_stack/providers/inline/post_training/torchtune/recipes/lora_finetuning_single_device.py index b5a495935..f56dd2499 100644 --- a/llama_stack/providers/inline/post_training/torchtune/recipes/lora_finetuning_single_device.py +++ b/llama_stack/providers/inline/post_training/torchtune/recipes/lora_finetuning_single_device.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 gc import logging import os import time @@ -47,6 +46,7 @@ from llama_stack.apis.post_training import ( from llama_stack.distribution.utils.config_dirs import DEFAULT_CHECKPOINT_DIR from llama_stack.distribution.utils.model_utils import model_local_dir from llama_stack.models.llama.sku_list import resolve_model +from llama_stack.providers.inline.post_training.common.utils import evacuate_model_from_device from llama_stack.providers.inline.post_training.torchtune.common import utils from llama_stack.providers.inline.post_training.torchtune.common.checkpointer import ( TorchtuneCheckpointer, @@ -554,11 +554,7 @@ class LoraFinetuningSingleDevice: checkpoints.append(checkpoint) # clean up the memory after training finishes - if self._device.type != "cpu": - self._model.to("cpu") - torch.cuda.empty_cache() - del self._model - gc.collect() + evacuate_model_from_device(self._model, self._device.type) return (memory_stats, checkpoints) diff --git a/llama_stack/providers/inline/safety/prompt_guard/prompt_guard.py b/llama_stack/providers/inline/safety/prompt_guard/prompt_guard.py index 56ce8285f..ff87889ea 100644 --- a/llama_stack/providers/inline/safety/prompt_guard/prompt_guard.py +++ b/llama_stack/providers/inline/safety/prompt_guard/prompt_guard.py @@ -75,7 +75,9 @@ class PromptGuardShield: self.temperature = temperature self.threshold = threshold - self.device = "cuda" + self.device = "cpu" + if torch.cuda.is_available(): + self.device = "cuda" # load model and tokenizer self.tokenizer = AutoTokenizer.from_pretrained(model_dir) diff --git a/llama_stack/providers/inline/telemetry/meta_reference/telemetry.py b/llama_stack/providers/inline/telemetry/meta_reference/telemetry.py index 67362dd36..0f6cf8619 100644 --- a/llama_stack/providers/inline/telemetry/meta_reference/telemetry.py +++ b/llama_stack/providers/inline/telemetry/meta_reference/telemetry.py @@ -16,6 +16,7 @@ from opentelemetry.sdk.resources import Resource from opentelemetry.sdk.trace import TracerProvider from opentelemetry.sdk.trace.export import BatchSpanProcessor from opentelemetry.semconv.resource import ResourceAttributes +from opentelemetry.trace.propagation.tracecontext import TraceContextTextMapPropagator from llama_stack.apis.telemetry import ( Event, @@ -44,6 +45,7 @@ from llama_stack.providers.inline.telemetry.meta_reference.sqlite_span_processor ) from llama_stack.providers.utils.telemetry.dataset_mixin import TelemetryDatasetMixin from llama_stack.providers.utils.telemetry.sqlite_trace_store import SQLiteTraceStore +from llama_stack.providers.utils.telemetry.tracing import ROOT_SPAN_MARKERS from .config import TelemetryConfig, TelemetrySink @@ -146,7 +148,7 @@ class TelemetryAdapter(TelemetryDatasetMixin, Telemetry): if span: timestamp_ns = int(event.timestamp.timestamp() * 1e9) span.add_event( - name=event.type, + name=event.type.value, attributes={ "message": event.message, "severity": event.severity.value, @@ -206,6 +208,15 @@ class TelemetryAdapter(TelemetryDatasetMixin, Telemetry): event.attributes = {} event.attributes["__ttl__"] = ttl_seconds + # Extract these W3C trace context attributes so they are not written to + # underlying storage, as we just need them to propagate the trace context. + traceparent = event.attributes.pop("traceparent", None) + tracestate = event.attributes.pop("tracestate", None) + if traceparent: + # If we have a traceparent header value, we're not the root span. + for root_attribute in ROOT_SPAN_MARKERS: + event.attributes.pop(root_attribute, None) + if isinstance(event.payload, SpanStartPayload): # Check if span already exists to prevent duplicates if span_id in _GLOBAL_STORAGE["active_spans"]: @@ -216,8 +227,12 @@ class TelemetryAdapter(TelemetryDatasetMixin, Telemetry): parent_span_id = int(event.payload.parent_span_id, 16) parent_span = _GLOBAL_STORAGE["active_spans"].get(parent_span_id) context = trace.set_span_in_context(parent_span) - else: - event.attributes["__root_span__"] = "true" + elif traceparent: + carrier = { + "traceparent": traceparent, + "tracestate": tracestate, + } + context = TraceContextTextMapPropagator().extract(carrier=carrier) span = tracer.start_span( name=event.payload.name, diff --git a/llama_stack/providers/inline/tool_runtime/rag/memory.py b/llama_stack/providers/inline/tool_runtime/rag/memory.py index 39f752297..4776d47d0 100644 --- a/llama_stack/providers/inline/tool_runtime/rag/memory.py +++ b/llama_stack/providers/inline/tool_runtime/rag/memory.py @@ -25,14 +25,14 @@ from llama_stack.apis.tools import ( RAGQueryConfig, RAGQueryResult, RAGToolRuntime, - Tool, ToolDef, + ToolGroup, ToolInvocationResult, ToolParameter, ToolRuntime, ) from llama_stack.apis.vector_io import QueryChunksResponse, VectorIO -from llama_stack.providers.datatypes import ToolsProtocolPrivate +from llama_stack.providers.datatypes import ToolGroupsProtocolPrivate from llama_stack.providers.utils.inference.prompt_adapter import interleaved_content_as_str from llama_stack.providers.utils.memory.vector_store import ( content_from_doc, @@ -49,7 +49,7 @@ def make_random_string(length: int = 8): return "".join(secrets.choice(string.ascii_letters + string.digits) for _ in range(length)) -class MemoryToolRuntimeImpl(ToolsProtocolPrivate, ToolRuntime, RAGToolRuntime): +class MemoryToolRuntimeImpl(ToolGroupsProtocolPrivate, ToolRuntime, RAGToolRuntime): def __init__( self, config: RagToolRuntimeConfig, @@ -66,10 +66,10 @@ class MemoryToolRuntimeImpl(ToolsProtocolPrivate, ToolRuntime, RAGToolRuntime): async def shutdown(self): pass - async def register_tool(self, tool: Tool) -> None: + async def register_toolgroup(self, toolgroup: ToolGroup) -> None: pass - async def unregister_tool(self, tool_id: str) -> None: + async def unregister_toolgroup(self, toolgroup_id: str) -> None: return async def insert( @@ -122,6 +122,7 @@ class MemoryToolRuntimeImpl(ToolsProtocolPrivate, ToolRuntime, RAGToolRuntime): query=query, params={ "max_chunks": query_config.max_chunks, + "mode": query_config.mode, }, ) for vector_db_id in vector_db_ids @@ -145,8 +146,8 @@ class MemoryToolRuntimeImpl(ToolsProtocolPrivate, ToolRuntime, RAGToolRuntime): ] for i, chunk in enumerate(chunks): metadata = chunk.metadata - tokens += metadata["token_count"] - tokens += metadata["metadata_token_count"] + tokens += metadata.get("token_count", 0) + tokens += metadata.get("metadata_token_count", 0) if tokens > query_config.max_tokens_in_context: log.error( diff --git a/llama_stack/providers/inline/vector_io/faiss/faiss.py b/llama_stack/providers/inline/vector_io/faiss/faiss.py index d3dc7e694..47256d88d 100644 --- a/llama_stack/providers/inline/vector_io/faiss/faiss.py +++ b/llama_stack/providers/inline/vector_io/faiss/faiss.py @@ -99,9 +99,13 @@ class FaissIndex(EmbeddingIndex): # Save updated index await self._save_index() - async def query(self, embedding: NDArray, k: int, score_threshold: float) -> QueryChunksResponse: + async def query_vector( + self, + embedding: NDArray, + k: int, + score_threshold: float, + ) -> QueryChunksResponse: distances, indices = await asyncio.to_thread(self.index.search, embedding.reshape(1, -1).astype(np.float32), k) - chunks = [] scores = [] for d, i in zip(distances[0], indices[0], strict=False): @@ -112,6 +116,14 @@ class FaissIndex(EmbeddingIndex): return QueryChunksResponse(chunks=chunks, scores=scores) + async def query_keyword( + self, + query_string: str, + k: int, + score_threshold: float, + ) -> QueryChunksResponse: + raise NotImplementedError("Keyword search is not supported in FAISS") + class FaissVectorIOAdapter(VectorIO, VectorDBsProtocolPrivate): def __init__(self, config: FaissVectorIOConfig, inference_api: Inference) -> None: 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 ab4384021..fc1a8ddb0 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 @@ -24,6 +24,11 @@ from llama_stack.providers.utils.memory.vector_store import EmbeddingIndex, Vect logger = logging.getLogger(__name__) +# Specifying search mode is dependent on the VectorIO provider. +VECTOR_SEARCH = "vector" +KEYWORD_SEARCH = "keyword" +SEARCH_MODES = {VECTOR_SEARCH, KEYWORD_SEARCH} + def serialize_vector(vector: list[float]) -> bytes: """Serialize a list of floats into a compact binary representation.""" @@ -45,6 +50,7 @@ class SQLiteVecIndex(EmbeddingIndex): Two tables are used: - A metadata table (chunks_{bank_id}) that holds the chunk JSON. - A virtual table (vec_chunks_{bank_id}) that holds the serialized vector. + - An FTS5 table (fts_chunks_{bank_id}) for full-text keyword search. """ def __init__(self, dimension: int, db_path: str, bank_id: str): @@ -53,6 +59,7 @@ class SQLiteVecIndex(EmbeddingIndex): 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("-", "_") @classmethod async def create(cls, dimension: int, db_path: str, bank_id: str): @@ -78,6 +85,14 @@ class SQLiteVecIndex(EmbeddingIndex): USING vec0(embedding FLOAT[{self.dimension}], id TEXT); """) connection.commit() + # FTS5 table (for keyword search) - creating both the tables by default. Will use the relevant one + # 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} + USING fts5(id, content); + """) + connection.commit() finally: cur.close() connection.close() @@ -91,6 +106,7 @@ class SQLiteVecIndex(EmbeddingIndex): 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};") connection.commit() finally: cur.close() @@ -104,6 +120,7 @@ class SQLiteVecIndex(EmbeddingIndex): For each chunk, we insert its JSON into the metadata table and then insert its embedding (serialized to raw bytes) into the virtual table using the assigned rowid. If any insert fails, the transaction is rolled back to maintain consistency. + Also inserts chunk content into FTS table for keyword search support. """ assert all(isinstance(chunk.content, str) for chunk in chunks), "SQLiteVecIndex only supports text chunks" @@ -112,18 +129,16 @@ class SQLiteVecIndex(EmbeddingIndex): cur = connection.cursor() try: - # Start transaction a single transcation for all batches cur.execute("BEGIN TRANSACTION") for i in range(0, len(chunks), batch_size): batch_chunks = chunks[i : i + batch_size] batch_embeddings = embeddings[i : i + batch_size] - # Prepare metadata inserts + + # Insert metadata metadata_data = [ (generate_chunk_id(chunk.metadata["document_id"], chunk.content), chunk.model_dump_json()) for chunk in batch_chunks - if isinstance(chunk.content, str) ] - # Insert metadata (ON CONFLICT to avoid duplicates) cur.executemany( f""" INSERT INTO {self.metadata_table} (id, chunk) @@ -132,21 +147,43 @@ class SQLiteVecIndex(EmbeddingIndex): """, metadata_data, ) - # Prepare embeddings inserts + + # Insert vector embeddings embedding_data = [ ( - generate_chunk_id(chunk.metadata["document_id"], chunk.content), - serialize_vector(emb.tolist()), + ( + generate_chunk_id(chunk.metadata["document_id"], chunk.content), + serialize_vector(emb.tolist()), + ) ) for chunk, emb in zip(batch_chunks, batch_embeddings, strict=True) - if isinstance(chunk.content, str) ] - # Insert embeddings in batch - cur.executemany(f"INSERT INTO {self.vector_table} (id, embedding) VALUES (?, ?);", embedding_data) + cur.executemany( + f"INSERT INTO {self.vector_table} (id, embedding) VALUES (?, ?);", + embedding_data, + ) + + # Insert FTS content + fts_data = [ + (generate_chunk_id(chunk.metadata["document_id"], chunk.content), 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 = ?;", + [(row[0],) for row in fts_data], + ) + + # INSERT new entries + cur.executemany( + f"INSERT INTO {self.fts_table} (id, content) VALUES (?, ?);", + fts_data, + ) + connection.commit() except sqlite3.Error as e: - connection.rollback() # Rollback on failure + connection.rollback() logger.error(f"Error inserting into {self.vector_table}: {e}") raise @@ -154,22 +191,25 @@ class SQLiteVecIndex(EmbeddingIndex): cur.close() connection.close() - # Process all batches in a single thread + # Run batch insertion in a background thread await asyncio.to_thread(_execute_all_batch_inserts) - async def query(self, embedding: NDArray, k: int, score_threshold: float) -> QueryChunksResponse: + async def query_vector( + self, + embedding: NDArray, + k: int, + score_threshold: float, + ) -> QueryChunksResponse: """ - Query for the k most similar chunks. We convert the query embedding to a blob and run a SQL query - against the virtual table. The SQL joins the metadata table to recover the chunk JSON. + Performs vector-based search using a virtual table for vector similarity. """ - emb_list = embedding.tolist() if isinstance(embedding, np.ndarray) else list(embedding) - emb_blob = serialize_vector(emb_list) def _execute_query(): connection = _create_sqlite_connection(self.db_path) cur = connection.cursor() - try: + emb_list = embedding.tolist() if isinstance(embedding, np.ndarray) else list(embedding) + emb_blob = serialize_vector(emb_list) query_sql = f""" SELECT m.id, m.chunk, v.distance FROM {self.vector_table} AS v @@ -184,17 +224,66 @@ class SQLiteVecIndex(EmbeddingIndex): connection.close() rows = await asyncio.to_thread(_execute_query) - chunks, scores = [], [] - for _id, chunk_json, distance in rows: + for row in rows: + _id, chunk_json, distance = row + score = 1.0 / distance if distance != 0 else float("inf") + if score < score_threshold: + continue + try: + chunk = Chunk.model_validate_json(chunk_json) + except Exception as e: + logger.error(f"Error parsing chunk JSON for id {_id}: {e}") + continue + chunks.append(chunk) + scores.append(score) + return QueryChunksResponse(chunks=chunks, scores=scores) + + async def query_keyword( + self, + query_string: str, + k: int, + score_threshold: float, + ) -> QueryChunksResponse: + """ + Performs keyword-based search using SQLite FTS5 for relevance-ranked full-text search. + """ + if query_string is None: + raise ValueError("query_string is required for keyword search.") + + def _execute_query(): + connection = _create_sqlite_connection(self.db_path) + 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 + WHERE f.content MATCH ? + ORDER BY score ASC + LIMIT ?; + """ + cur.execute(query_sql, (query_string, k)) + return cur.fetchall() + finally: + cur.close() + connection.close() + + rows = await asyncio.to_thread(_execute_query) + chunks, scores = [], [] + for row in rows: + _id, chunk_json, score = row + # BM25 scores returned by sqlite-vec are NEGATED (i.e., more relevant = more negative). + # This design is intentional to simplify sorting by ascending score. + # Reference: https://alexgarcia.xyz/blog/2024/sqlite-vec-hybrid-search/index.html + if score > -score_threshold: + continue try: chunk = Chunk.model_validate_json(chunk_json) except Exception as e: logger.error(f"Error parsing chunk JSON for id {_id}: {e}") continue chunks.append(chunk) - # Mimic the Faiss scoring: score = 1/distance (avoid division by zero) - score = 1.0 / distance if distance != 0 else float("inf") scores.append(score) return QueryChunksResponse(chunks=chunks, scores=scores) diff --git a/llama_stack/providers/registry/post_training.py b/llama_stack/providers/registry/post_training.py index 35567c07d..d752b8819 100644 --- a/llama_stack/providers/registry/post_training.py +++ b/llama_stack/providers/registry/post_training.py @@ -21,6 +21,17 @@ def available_providers() -> list[ProviderSpec]: Api.datasets, ], ), + InlineProviderSpec( + api=Api.post_training, + provider_type="inline::huggingface", + pip_packages=["torch", "trl", "transformers", "peft", "datasets"], + module="llama_stack.providers.inline.post_training.huggingface", + config_class="llama_stack.providers.inline.post_training.huggingface.HuggingFacePostTrainingConfig", + api_dependencies=[ + Api.datasetio, + Api.datasets, + ], + ), remote_provider_spec( api=Api.post_training, adapter=AdapterSpec( diff --git a/llama_stack/providers/registry/safety.py b/llama_stack/providers/registry/safety.py index c209da092..e0a04be48 100644 --- a/llama_stack/providers/registry/safety.py +++ b/llama_stack/providers/registry/safety.py @@ -63,4 +63,14 @@ def available_providers() -> list[ProviderSpec]: config_class="llama_stack.providers.remote.safety.nvidia.NVIDIASafetyConfig", ), ), + remote_provider_spec( + api=Api.safety, + adapter=AdapterSpec( + adapter_type="sambanova", + pip_packages=["litellm"], + module="llama_stack.providers.remote.safety.sambanova", + config_class="llama_stack.providers.remote.safety.sambanova.SambaNovaSafetyConfig", + provider_data_validator="llama_stack.providers.remote.safety.sambanova.config.SambaNovaProviderDataValidator", + ), + ), ] diff --git a/llama_stack/providers/registry/tool_runtime.py b/llama_stack/providers/registry/tool_runtime.py index b9194810e..277914df2 100644 --- a/llama_stack/providers/registry/tool_runtime.py +++ b/llama_stack/providers/registry/tool_runtime.py @@ -80,8 +80,9 @@ def available_providers() -> list[ProviderSpec]: adapter=AdapterSpec( adapter_type="model-context-protocol", module="llama_stack.providers.remote.tool_runtime.model_context_protocol", - config_class="llama_stack.providers.remote.tool_runtime.model_context_protocol.config.ModelContextProtocolConfig", + config_class="llama_stack.providers.remote.tool_runtime.model_context_protocol.config.MCPProviderConfig", pip_packages=["mcp"], + provider_data_validator="llama_stack.providers.remote.tool_runtime.model_context_protocol.config.MCPProviderDataValidator", ), ), ] diff --git a/llama_stack/providers/remote/inference/bedrock/bedrock.py b/llama_stack/providers/remote/inference/bedrock/bedrock.py index 0404a578f..952d86f1a 100644 --- a/llama_stack/providers/remote/inference/bedrock/bedrock.py +++ b/llama_stack/providers/remote/inference/bedrock/bedrock.py @@ -22,6 +22,7 @@ from llama_stack.apis.inference import ( Inference, LogProbConfig, Message, + OpenAIEmbeddingsResponse, ResponseFormat, SamplingParams, TextTruncation, @@ -197,3 +198,13 @@ class BedrockInferenceAdapter( response_body = json.loads(response.get("body").read()) embeddings.append(response_body.get("embedding")) return EmbeddingsResponse(embeddings=embeddings) + + async def openai_embeddings( + self, + model: str, + input: str | list[str], + encoding_format: str | None = "float", + dimensions: int | None = None, + user: str | None = None, + ) -> OpenAIEmbeddingsResponse: + raise NotImplementedError() diff --git a/llama_stack/providers/remote/inference/cerebras/cerebras.py b/llama_stack/providers/remote/inference/cerebras/cerebras.py index 685375346..952118e24 100644 --- a/llama_stack/providers/remote/inference/cerebras/cerebras.py +++ b/llama_stack/providers/remote/inference/cerebras/cerebras.py @@ -21,6 +21,7 @@ from llama_stack.apis.inference import ( Inference, LogProbConfig, Message, + OpenAIEmbeddingsResponse, ResponseFormat, SamplingParams, TextTruncation, @@ -194,3 +195,13 @@ class CerebrasInferenceAdapter( task_type: EmbeddingTaskType | None = None, ) -> EmbeddingsResponse: raise NotImplementedError() + + async def openai_embeddings( + self, + model: str, + input: str | list[str], + encoding_format: str | None = "float", + dimensions: int | None = None, + user: str | None = None, + ) -> OpenAIEmbeddingsResponse: + raise NotImplementedError() diff --git a/llama_stack/providers/remote/inference/cerebras_openai_compat/__init__.py b/llama_stack/providers/remote/inference/cerebras_openai_compat/__init__.py index a5f07edd2..523a8dfe7 100644 --- a/llama_stack/providers/remote/inference/cerebras_openai_compat/__init__.py +++ b/llama_stack/providers/remote/inference/cerebras_openai_compat/__init__.py @@ -4,12 +4,12 @@ # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. -from llama_stack.apis.inference import Inference +from llama_stack.apis.inference import InferenceProvider from .config import CerebrasCompatConfig -async def get_adapter_impl(config: CerebrasCompatConfig, _deps) -> Inference: +async def get_adapter_impl(config: CerebrasCompatConfig, _deps) -> InferenceProvider: # import dynamically so the import is used only when it is needed from .cerebras import CerebrasCompatInferenceAdapter diff --git a/llama_stack/providers/remote/inference/databricks/databricks.py b/llama_stack/providers/remote/inference/databricks/databricks.py index 5c36eac3e..1dc18b97f 100644 --- a/llama_stack/providers/remote/inference/databricks/databricks.py +++ b/llama_stack/providers/remote/inference/databricks/databricks.py @@ -20,6 +20,7 @@ from llama_stack.apis.inference import ( Inference, LogProbConfig, Message, + OpenAIEmbeddingsResponse, ResponseFormat, SamplingParams, TextTruncation, @@ -152,3 +153,13 @@ class DatabricksInferenceAdapter( task_type: EmbeddingTaskType | None = None, ) -> EmbeddingsResponse: raise NotImplementedError() + + async def openai_embeddings( + self, + model: str, + input: str | list[str], + encoding_format: str | None = "float", + dimensions: int | None = None, + user: str | None = None, + ) -> OpenAIEmbeddingsResponse: + raise NotImplementedError() diff --git a/llama_stack/providers/remote/inference/fireworks/fireworks.py b/llama_stack/providers/remote/inference/fireworks/fireworks.py index b6d3984c6..fe21685dd 100644 --- a/llama_stack/providers/remote/inference/fireworks/fireworks.py +++ b/llama_stack/providers/remote/inference/fireworks/fireworks.py @@ -37,6 +37,7 @@ from llama_stack.apis.inference.inference import ( OpenAIChatCompletion, OpenAIChatCompletionChunk, OpenAICompletion, + OpenAIEmbeddingsResponse, OpenAIMessageParam, OpenAIResponseFormatParam, ) @@ -286,6 +287,16 @@ class FireworksInferenceAdapter(ModelRegistryHelper, Inference, NeedsRequestProv embeddings = [data.embedding for data in response.data] return EmbeddingsResponse(embeddings=embeddings) + async def openai_embeddings( + self, + model: str, + input: str | list[str], + encoding_format: str | None = "float", + dimensions: int | None = None, + user: str | None = None, + ) -> OpenAIEmbeddingsResponse: + raise NotImplementedError() + async def openai_completion( self, model: str, diff --git a/llama_stack/providers/remote/inference/fireworks_openai_compat/__init__.py b/llama_stack/providers/remote/inference/fireworks_openai_compat/__init__.py index f78f218b5..15a666cb6 100644 --- a/llama_stack/providers/remote/inference/fireworks_openai_compat/__init__.py +++ b/llama_stack/providers/remote/inference/fireworks_openai_compat/__init__.py @@ -4,12 +4,12 @@ # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. -from llama_stack.apis.inference import Inference +from llama_stack.apis.inference import InferenceProvider from .config import FireworksCompatConfig -async def get_adapter_impl(config: FireworksCompatConfig, _deps) -> Inference: +async def get_adapter_impl(config: FireworksCompatConfig, _deps) -> InferenceProvider: # import dynamically so the import is used only when it is needed from .fireworks import FireworksCompatInferenceAdapter diff --git a/llama_stack/providers/remote/inference/groq_openai_compat/__init__.py b/llama_stack/providers/remote/inference/groq_openai_compat/__init__.py index 8161df20d..794cdebd7 100644 --- a/llama_stack/providers/remote/inference/groq_openai_compat/__init__.py +++ b/llama_stack/providers/remote/inference/groq_openai_compat/__init__.py @@ -4,12 +4,12 @@ # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. -from llama_stack.apis.inference import Inference +from llama_stack.apis.inference import InferenceProvider from .config import GroqCompatConfig -async def get_adapter_impl(config: GroqCompatConfig, _deps) -> Inference: +async def get_adapter_impl(config: GroqCompatConfig, _deps) -> InferenceProvider: # import dynamically so the import is used only when it is needed from .groq import GroqCompatInferenceAdapter diff --git a/llama_stack/providers/remote/inference/llama_openai_compat/__init__.py b/llama_stack/providers/remote/inference/llama_openai_compat/__init__.py index a6fb37cad..be48d1067 100644 --- a/llama_stack/providers/remote/inference/llama_openai_compat/__init__.py +++ b/llama_stack/providers/remote/inference/llama_openai_compat/__init__.py @@ -4,12 +4,12 @@ # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. -from llama_stack.apis.inference import Inference +from llama_stack.apis.inference import InferenceProvider from .config import LlamaCompatConfig -async def get_adapter_impl(config: LlamaCompatConfig, _deps) -> Inference: +async def get_adapter_impl(config: LlamaCompatConfig, _deps) -> InferenceProvider: # import dynamically so the import is used only when it is needed from .llama import LlamaCompatInferenceAdapter diff --git a/llama_stack/providers/remote/inference/nvidia/nvidia.py b/llama_stack/providers/remote/inference/nvidia/nvidia.py index 333486fe4..4c68322e0 100644 --- a/llama_stack/providers/remote/inference/nvidia/nvidia.py +++ b/llama_stack/providers/remote/inference/nvidia/nvidia.py @@ -29,6 +29,7 @@ from llama_stack.apis.inference import ( Inference, LogProbConfig, Message, + OpenAIEmbeddingsResponse, ResponseFormat, SamplingParams, TextTruncation, @@ -238,6 +239,16 @@ class NVIDIAInferenceAdapter(Inference, ModelRegistryHelper): # return EmbeddingsResponse(embeddings=[embedding.embedding for embedding in response.data]) + async def openai_embeddings( + self, + model: str, + input: str | list[str], + encoding_format: str | None = "float", + dimensions: int | None = None, + user: str | None = None, + ) -> OpenAIEmbeddingsResponse: + raise NotImplementedError() + async def chat_completion( self, model_id: str, diff --git a/llama_stack/providers/remote/inference/ollama/ollama.py b/llama_stack/providers/remote/inference/ollama/ollama.py index 72cf0d129..8863e0edc 100644 --- a/llama_stack/providers/remote/inference/ollama/ollama.py +++ b/llama_stack/providers/remote/inference/ollama/ollama.py @@ -28,10 +28,11 @@ from llama_stack.apis.inference import ( EmbeddingsResponse, EmbeddingTaskType, GrammarResponseFormat, - Inference, + InferenceProvider, JsonSchemaResponseFormat, LogProbConfig, Message, + OpenAIEmbeddingsResponse, ResponseFormat, SamplingParams, TextTruncation, @@ -82,7 +83,7 @@ logger = get_logger(name=__name__, category="inference") class OllamaInferenceAdapter( - Inference, + InferenceProvider, ModelsProtocolPrivate, ): def __init__(self, url: str) -> None: @@ -370,6 +371,16 @@ class OllamaInferenceAdapter( return model + async def openai_embeddings( + self, + model: str, + input: str | list[str], + encoding_format: str | None = "float", + dimensions: int | None = None, + user: str | None = None, + ) -> OpenAIEmbeddingsResponse: + raise NotImplementedError() + async def openai_completion( self, model: str, diff --git a/llama_stack/providers/remote/inference/openai/openai.py b/llama_stack/providers/remote/inference/openai/openai.py index 9a1ec7ee0..6f3a686a8 100644 --- a/llama_stack/providers/remote/inference/openai/openai.py +++ b/llama_stack/providers/remote/inference/openai/openai.py @@ -14,6 +14,9 @@ from llama_stack.apis.inference.inference import ( OpenAIChatCompletion, OpenAIChatCompletionChunk, OpenAICompletion, + OpenAIEmbeddingData, + OpenAIEmbeddingsResponse, + OpenAIEmbeddingUsage, OpenAIMessageParam, OpenAIResponseFormatParam, ) @@ -38,6 +41,7 @@ logger = logging.getLogger(__name__) # | batch_chat_completion | LiteLLMOpenAIMixin | # | openai_completion | AsyncOpenAI | # | openai_chat_completion | AsyncOpenAI | +# | openai_embeddings | AsyncOpenAI | # class OpenAIInferenceAdapter(LiteLLMOpenAIMixin): def __init__(self, config: OpenAIConfig) -> None: @@ -92,8 +96,11 @@ class OpenAIInferenceAdapter(LiteLLMOpenAIMixin): if prompt_logprobs is not None: logging.warning("prompt_logprobs is not supported by the OpenAI API. Ignoring.") + model_id = (await self.model_store.get_model(model)).provider_resource_id + if model_id.startswith("openai/"): + model_id = model_id[len("openai/") :] params = await prepare_openai_completion_params( - model=(await self.model_store.get_model(model)).provider_resource_id, + model=model_id, prompt=prompt, best_of=best_of, echo=echo, @@ -139,8 +146,11 @@ class OpenAIInferenceAdapter(LiteLLMOpenAIMixin): top_p: float | None = None, user: str | None = None, ) -> OpenAIChatCompletion | AsyncIterator[OpenAIChatCompletionChunk]: + model_id = (await self.model_store.get_model(model)).provider_resource_id + if model_id.startswith("openai/"): + model_id = model_id[len("openai/") :] params = await prepare_openai_completion_params( - model=(await self.model_store.get_model(model)).provider_resource_id, + model=model_id, messages=messages, frequency_penalty=frequency_penalty, function_call=function_call, @@ -165,3 +175,51 @@ class OpenAIInferenceAdapter(LiteLLMOpenAIMixin): user=user, ) return await self._openai_client.chat.completions.create(**params) + + async def openai_embeddings( + self, + model: str, + input: str | list[str], + encoding_format: str | None = "float", + dimensions: int | None = None, + user: str | None = None, + ) -> OpenAIEmbeddingsResponse: + model_id = (await self.model_store.get_model(model)).provider_resource_id + if model_id.startswith("openai/"): + model_id = model_id[len("openai/") :] + + # Prepare parameters for OpenAI embeddings API + params = { + "model": model_id, + "input": input, + } + + if encoding_format is not None: + params["encoding_format"] = encoding_format + if dimensions is not None: + params["dimensions"] = dimensions + if user is not None: + params["user"] = user + + # Call OpenAI embeddings API + response = await self._openai_client.embeddings.create(**params) + + data = [] + for i, embedding_data in enumerate(response.data): + data.append( + OpenAIEmbeddingData( + embedding=embedding_data.embedding, + index=i, + ) + ) + + usage = OpenAIEmbeddingUsage( + prompt_tokens=response.usage.prompt_tokens, + total_tokens=response.usage.total_tokens, + ) + + return OpenAIEmbeddingsResponse( + data=data, + model=response.model, + usage=usage, + ) diff --git a/llama_stack/providers/remote/inference/passthrough/passthrough.py b/llama_stack/providers/remote/inference/passthrough/passthrough.py index 78ee52641..6cf4680e2 100644 --- a/llama_stack/providers/remote/inference/passthrough/passthrough.py +++ b/llama_stack/providers/remote/inference/passthrough/passthrough.py @@ -19,6 +19,7 @@ from llama_stack.apis.inference import ( Inference, LogProbConfig, Message, + OpenAIEmbeddingsResponse, ResponseFormat, SamplingParams, TextTruncation, @@ -210,6 +211,16 @@ class PassthroughInferenceAdapter(Inference): task_type=task_type, ) + async def openai_embeddings( + self, + model: str, + input: str | list[str], + encoding_format: str | None = "float", + dimensions: int | None = None, + user: str | None = None, + ) -> OpenAIEmbeddingsResponse: + raise NotImplementedError() + async def openai_completion( self, model: str, diff --git a/llama_stack/providers/remote/inference/runpod/runpod.py b/llama_stack/providers/remote/inference/runpod/runpod.py index 2706aa15e..f8c98893e 100644 --- a/llama_stack/providers/remote/inference/runpod/runpod.py +++ b/llama_stack/providers/remote/inference/runpod/runpod.py @@ -8,6 +8,7 @@ from collections.abc import AsyncGenerator from openai import OpenAI from llama_stack.apis.inference import * # noqa: F403 +from llama_stack.apis.inference.inference import OpenAIEmbeddingsResponse # from llama_stack.providers.datatypes import ModelsProtocolPrivate from llama_stack.providers.utils.inference.model_registry import ModelRegistryHelper @@ -134,3 +135,13 @@ class RunpodInferenceAdapter( task_type: Optional[EmbeddingTaskType] = None, ) -> EmbeddingsResponse: raise NotImplementedError() + + async def openai_embeddings( + self, + model: str, + input: str | list[str], + encoding_format: str | None = "float", + dimensions: int | None = None, + user: str | None = None, + ) -> OpenAIEmbeddingsResponse: + raise NotImplementedError() diff --git a/llama_stack/providers/remote/inference/sambanova/sambanova.py b/llama_stack/providers/remote/inference/sambanova/sambanova.py index d182aa1dc..20f863665 100644 --- a/llama_stack/providers/remote/inference/sambanova/sambanova.py +++ b/llama_stack/providers/remote/inference/sambanova/sambanova.py @@ -218,7 +218,7 @@ class SambaNovaInferenceAdapter(LiteLLMOpenAIMixin): "json_schema": { "name": name, "schema": fmt, - "strict": True, + "strict": False, }, } if request.tools: diff --git a/llama_stack/providers/remote/inference/sambanova_openai_compat/__init__.py b/llama_stack/providers/remote/inference/sambanova_openai_compat/__init__.py index e31a3364c..60afe91ca 100644 --- a/llama_stack/providers/remote/inference/sambanova_openai_compat/__init__.py +++ b/llama_stack/providers/remote/inference/sambanova_openai_compat/__init__.py @@ -4,12 +4,12 @@ # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. -from llama_stack.apis.inference import Inference +from llama_stack.apis.inference import InferenceProvider from .config import SambaNovaCompatConfig -async def get_adapter_impl(config: SambaNovaCompatConfig, _deps) -> Inference: +async def get_adapter_impl(config: SambaNovaCompatConfig, _deps) -> InferenceProvider: # import dynamically so the import is used only when it is needed from .sambanova import SambaNovaCompatInferenceAdapter diff --git a/llama_stack/providers/remote/inference/tgi/tgi.py b/llama_stack/providers/remote/inference/tgi/tgi.py index 8f6666462..292d74ef8 100644 --- a/llama_stack/providers/remote/inference/tgi/tgi.py +++ b/llama_stack/providers/remote/inference/tgi/tgi.py @@ -23,6 +23,7 @@ from llama_stack.apis.inference import ( Inference, LogProbConfig, Message, + OpenAIEmbeddingsResponse, ResponseFormat, ResponseFormatType, SamplingParams, @@ -291,6 +292,16 @@ class _HfAdapter( ) -> EmbeddingsResponse: raise NotImplementedError() + async def openai_embeddings( + self, + model: str, + input: str | list[str], + encoding_format: str | None = "float", + dimensions: int | None = None, + user: str | None = None, + ) -> OpenAIEmbeddingsResponse: + raise NotImplementedError() + class TGIAdapter(_HfAdapter): async def initialize(self, config: TGIImplConfig) -> None: diff --git a/llama_stack/providers/remote/inference/together/together.py b/llama_stack/providers/remote/inference/together/together.py index 562e6e0ff..7305a638d 100644 --- a/llama_stack/providers/remote/inference/together/together.py +++ b/llama_stack/providers/remote/inference/together/together.py @@ -23,6 +23,7 @@ from llama_stack.apis.inference import ( Inference, LogProbConfig, Message, + OpenAIEmbeddingsResponse, ResponseFormat, ResponseFormatType, SamplingParams, @@ -267,6 +268,16 @@ class TogetherInferenceAdapter(ModelRegistryHelper, Inference, NeedsRequestProvi embeddings = [item.embedding for item in r.data] return EmbeddingsResponse(embeddings=embeddings) + async def openai_embeddings( + self, + model: str, + input: str | list[str], + encoding_format: str | None = "float", + dimensions: int | None = None, + user: str | None = None, + ) -> OpenAIEmbeddingsResponse: + raise NotImplementedError() + async def openai_completion( self, model: str, diff --git a/llama_stack/providers/remote/inference/together_openai_compat/__init__.py b/llama_stack/providers/remote/inference/together_openai_compat/__init__.py index 6fdf05b7e..8213fc5f4 100644 --- a/llama_stack/providers/remote/inference/together_openai_compat/__init__.py +++ b/llama_stack/providers/remote/inference/together_openai_compat/__init__.py @@ -4,12 +4,12 @@ # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. -from llama_stack.apis.inference import Inference +from llama_stack.apis.inference import InferenceProvider from .config import TogetherCompatConfig -async def get_adapter_impl(config: TogetherCompatConfig, _deps) -> Inference: +async def get_adapter_impl(config: TogetherCompatConfig, _deps) -> InferenceProvider: # import dynamically so the import is used only when it is needed from .together import TogetherCompatInferenceAdapter diff --git a/llama_stack/providers/remote/inference/vllm/config.py b/llama_stack/providers/remote/inference/vllm/config.py index 8530594b6..99abddf51 100644 --- a/llama_stack/providers/remote/inference/vllm/config.py +++ b/llama_stack/providers/remote/inference/vllm/config.py @@ -4,8 +4,9 @@ # 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 pydantic import BaseModel, Field +from pydantic import BaseModel, Field, field_validator from llama_stack.schema_utils import json_schema_type @@ -24,11 +25,27 @@ class VLLMInferenceAdapterConfig(BaseModel): default="fake", description="The API token", ) - tls_verify: bool = Field( + tls_verify: bool | str = Field( default=True, - description="Whether to verify TLS certificates", + description="Whether to verify TLS certificates. Can be a boolean or a path to a CA certificate file.", ) + @field_validator("tls_verify") + @classmethod + def validate_tls_verify(cls, v): + if isinstance(v, str): + # Check if it's a boolean string + if v.lower() in ("true", "false"): + return v.lower() == "true" + # Otherwise, treat it as a cert path + cert_path = Path(v).expanduser().resolve() + if not cert_path.exists(): + raise ValueError(f"TLS certificate file does not exist: {v}") + if not cert_path.is_file(): + raise ValueError(f"TLS certificate path is not a file: {v}") + return v + return v + @classmethod def sample_run_config( cls, diff --git a/llama_stack/providers/remote/inference/vllm/vllm.py b/llama_stack/providers/remote/inference/vllm/vllm.py index d00218dd5..9f38d9abf 100644 --- a/llama_stack/providers/remote/inference/vllm/vllm.py +++ b/llama_stack/providers/remote/inference/vllm/vllm.py @@ -38,6 +38,7 @@ from llama_stack.apis.inference import ( JsonSchemaResponseFormat, LogProbConfig, Message, + OpenAIEmbeddingsResponse, ResponseFormat, SamplingParams, TextTruncation, @@ -313,7 +314,7 @@ class VLLMInferenceAdapter(Inference, ModelsProtocolPrivate): return AsyncOpenAI( base_url=self.config.url, api_key=self.config.api_token, - http_client=None if self.config.tls_verify else httpx.AsyncClient(verify=False), + http_client=httpx.AsyncClient(verify=self.config.tls_verify), ) async def completion( @@ -507,6 +508,16 @@ class VLLMInferenceAdapter(Inference, ModelsProtocolPrivate): embeddings = [data.embedding for data in response.data] return EmbeddingsResponse(embeddings=embeddings) + async def openai_embeddings( + self, + model: str, + input: str | list[str], + encoding_format: str | None = "float", + dimensions: int | None = None, + user: str | None = None, + ) -> OpenAIEmbeddingsResponse: + raise NotImplementedError() + async def openai_completion( self, model: str, diff --git a/llama_stack/providers/remote/inference/watsonx/watsonx.py b/llama_stack/providers/remote/inference/watsonx/watsonx.py index c1299e11f..59f5f5562 100644 --- a/llama_stack/providers/remote/inference/watsonx/watsonx.py +++ b/llama_stack/providers/remote/inference/watsonx/watsonx.py @@ -21,6 +21,7 @@ from llama_stack.apis.inference import ( Inference, LogProbConfig, Message, + OpenAIEmbeddingsResponse, ResponseFormat, SamplingParams, TextTruncation, @@ -260,6 +261,16 @@ class WatsonXInferenceAdapter(Inference, ModelRegistryHelper): ) -> EmbeddingsResponse: raise NotImplementedError("embedding is not supported for watsonx") + async def openai_embeddings( + self, + model: str, + input: str | list[str], + encoding_format: str | None = "float", + dimensions: int | None = None, + user: str | None = None, + ) -> OpenAIEmbeddingsResponse: + raise NotImplementedError() + async def openai_completion( self, model: str, diff --git a/llama_stack/providers/remote/post_training/nvidia/post_training.py b/llama_stack/providers/remote/post_training/nvidia/post_training.py index 409818cb3..d839ffd6f 100644 --- a/llama_stack/providers/remote/post_training/nvidia/post_training.py +++ b/llama_stack/providers/remote/post_training/nvidia/post_training.py @@ -224,7 +224,7 @@ class NvidiaPostTrainingAdapter(ModelRegistryHelper): Parameters: training_config: TrainingConfig - Configuration for training - model: str - Model identifier + model: str - NeMo Customizer configuration name algorithm_config: Optional[AlgorithmConfig] - Algorithm-specific configuration checkpoint_dir: Optional[str] - Directory containing model checkpoints, ignored atm job_uuid: str - Unique identifier for the job, ignored atm @@ -299,9 +299,6 @@ class NvidiaPostTrainingAdapter(ModelRegistryHelper): User is informed about unsupported parameters via warnings. """ - # Map model to nvidia model name - # See `_MODEL_ENTRIES` for supported models - nvidia_model = self.get_provider_model_id(model) # Check for unsupported method parameters unsupported_method_params = [] @@ -347,7 +344,7 @@ class NvidiaPostTrainingAdapter(ModelRegistryHelper): # Prepare base job configuration job_config = { - "config": nvidia_model, + "config": model, "dataset": { "name": training_config["data_config"]["dataset_id"], "namespace": self.config.dataset_namespace, diff --git a/llama_stack/providers/remote/safety/sambanova/__init__.py b/llama_stack/providers/remote/safety/sambanova/__init__.py new file mode 100644 index 000000000..bb9d15374 --- /dev/null +++ b/llama_stack/providers/remote/safety/sambanova/__init__.py @@ -0,0 +1,18 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the terms described in the LICENSE file in +# the root directory of this source tree. + + +from typing import Any + +from .config import SambaNovaSafetyConfig + + +async def get_adapter_impl(config: SambaNovaSafetyConfig, _deps) -> Any: + from .sambanova import SambaNovaSafetyAdapter + + impl = SambaNovaSafetyAdapter(config) + await impl.initialize() + return impl diff --git a/llama_stack/providers/remote/safety/sambanova/config.py b/llama_stack/providers/remote/safety/sambanova/config.py new file mode 100644 index 000000000..383cea244 --- /dev/null +++ b/llama_stack/providers/remote/safety/sambanova/config.py @@ -0,0 +1,37 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the terms described in the LICENSE file in +# the root directory of this source tree. + +from typing import Any + +from pydantic import BaseModel, Field, SecretStr + +from llama_stack.schema_utils import json_schema_type + + +class SambaNovaProviderDataValidator(BaseModel): + sambanova_api_key: str | None = Field( + default=None, + description="Sambanova Cloud API key", + ) + + +@json_schema_type +class SambaNovaSafetyConfig(BaseModel): + url: str = Field( + default="https://api.sambanova.ai/v1", + description="The URL for the SambaNova AI server", + ) + api_key: SecretStr | None = Field( + default=None, + description="The SambaNova cloud API Key", + ) + + @classmethod + def sample_run_config(cls, api_key: str = "${env.SAMBANOVA_API_KEY}", **kwargs) -> dict[str, Any]: + return { + "url": "https://api.sambanova.ai/v1", + "api_key": api_key, + } diff --git a/llama_stack/providers/remote/safety/sambanova/sambanova.py b/llama_stack/providers/remote/safety/sambanova/sambanova.py new file mode 100644 index 000000000..84c8267ae --- /dev/null +++ b/llama_stack/providers/remote/safety/sambanova/sambanova.py @@ -0,0 +1,100 @@ +# 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 logging +from typing import Any + +import litellm +import requests + +from llama_stack.apis.inference import Message +from llama_stack.apis.safety import ( + RunShieldResponse, + Safety, + SafetyViolation, + ViolationLevel, +) +from llama_stack.apis.shields import Shield +from llama_stack.distribution.request_headers import NeedsRequestProviderData +from llama_stack.providers.datatypes import ShieldsProtocolPrivate +from llama_stack.providers.utils.inference.openai_compat import convert_message_to_openai_dict_new + +from .config import SambaNovaSafetyConfig + +logger = logging.getLogger(__name__) + +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 + + async def initialize(self) -> None: + pass + + async def shutdown(self) -> None: + pass + + def _get_api_key(self) -> str: + config_api_key = self.config.api_key if self.config.api_key else None + if config_api_key: + return config_api_key.get_secret_value() + else: + provider_data = self.get_request_provider_data() + if provider_data is None or not provider_data.sambanova_api_key: + raise ValueError( + 'Pass Sambanova API Key in the header X-LlamaStack-Provider-Data as { "sambanova_api_key": }' + ) + return provider_data.sambanova_api_key + + 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(available_models) == 0 + or "guard" not in shield.provider_resource_id.lower() + or shield.provider_resource_id.split("sambanova/")[-1] not in available_models + ): + raise ValueError(f"Shield {shield.provider_resource_id} not found in SambaNova") + + async def run_shield( + self, shield_id: str, messages: list[Message], params: dict[str, Any] | None = None + ) -> RunShieldResponse: + shield = await self.shield_store.get_shield(shield_id) + if not shield: + raise ValueError(f"Shield {shield_id} not found") + + shield_params = shield.params + logger.debug(f"run_shield::{shield_params}::messages={messages}") + content_messages = [await convert_message_to_openai_dict_new(m) for m in messages] + logger.debug(f"run_shield::final:messages::{json.dumps(content_messages, indent=2)}:") + + response = litellm.completion( + model=shield.provider_resource_id, messages=content_messages, api_key=self._get_api_key() + ) + shield_message = response.choices[0].message.content + + if "unsafe" in shield_message.lower(): + user_message = CANNED_RESPONSE_TEXT + violation_type = shield_message.split("\n")[-1] + metadata = {"violation_type": violation_type} + + return RunShieldResponse( + violation=SafetyViolation( + user_message=user_message, + violation_level=ViolationLevel.ERROR, + metadata=metadata, + ) + ) + + return RunShieldResponse() diff --git a/llama_stack/providers/remote/tool_runtime/bing_search/bing_search.py b/llama_stack/providers/remote/tool_runtime/bing_search/bing_search.py index 18bec463f..7e82cb6d4 100644 --- a/llama_stack/providers/remote/tool_runtime/bing_search/bing_search.py +++ b/llama_stack/providers/remote/tool_runtime/bing_search/bing_search.py @@ -12,19 +12,19 @@ import httpx from llama_stack.apis.common.content_types import URL from llama_stack.apis.tools import ( ListToolDefsResponse, - Tool, ToolDef, + ToolGroup, ToolInvocationResult, ToolParameter, ToolRuntime, ) from llama_stack.distribution.request_headers import NeedsRequestProviderData -from llama_stack.providers.datatypes import ToolsProtocolPrivate +from llama_stack.providers.datatypes import ToolGroupsProtocolPrivate from .config import BingSearchToolConfig -class BingSearchToolRuntimeImpl(ToolsProtocolPrivate, ToolRuntime, NeedsRequestProviderData): +class BingSearchToolRuntimeImpl(ToolGroupsProtocolPrivate, ToolRuntime, NeedsRequestProviderData): def __init__(self, config: BingSearchToolConfig): self.config = config self.url = "https://api.bing.microsoft.com/v7.0/search" @@ -32,10 +32,10 @@ class BingSearchToolRuntimeImpl(ToolsProtocolPrivate, ToolRuntime, NeedsRequestP async def initialize(self): pass - async def register_tool(self, tool: Tool) -> None: + async def register_toolgroup(self, toolgroup: ToolGroup) -> None: pass - async def unregister_tool(self, tool_id: str) -> None: + async def unregister_toolgroup(self, toolgroup_id: str) -> None: return def _get_api_key(self) -> str: diff --git a/llama_stack/providers/remote/tool_runtime/brave_search/brave_search.py b/llama_stack/providers/remote/tool_runtime/brave_search/brave_search.py index 355cb98b6..b96b9e59c 100644 --- a/llama_stack/providers/remote/tool_runtime/brave_search/brave_search.py +++ b/llama_stack/providers/remote/tool_runtime/brave_search/brave_search.py @@ -11,30 +11,30 @@ import httpx from llama_stack.apis.common.content_types import URL from llama_stack.apis.tools import ( ListToolDefsResponse, - Tool, ToolDef, + ToolGroup, ToolInvocationResult, ToolParameter, ToolRuntime, ) from llama_stack.distribution.request_headers import NeedsRequestProviderData from llama_stack.models.llama.datatypes import BuiltinTool -from llama_stack.providers.datatypes import ToolsProtocolPrivate +from llama_stack.providers.datatypes import ToolGroupsProtocolPrivate from .config import BraveSearchToolConfig -class BraveSearchToolRuntimeImpl(ToolsProtocolPrivate, ToolRuntime, NeedsRequestProviderData): +class BraveSearchToolRuntimeImpl(ToolGroupsProtocolPrivate, ToolRuntime, NeedsRequestProviderData): def __init__(self, config: BraveSearchToolConfig): self.config = config async def initialize(self): pass - async def register_tool(self, tool: Tool) -> None: + async def register_toolgroup(self, toolgroup: ToolGroup) -> None: pass - async def unregister_tool(self, tool_id: str) -> None: + async def unregister_toolgroup(self, toolgroup_id: str) -> None: return def _get_api_key(self) -> str: diff --git a/llama_stack/providers/remote/tool_runtime/model_context_protocol/__init__.py b/llama_stack/providers/remote/tool_runtime/model_context_protocol/__init__.py index fb1f558e5..051a880a7 100644 --- a/llama_stack/providers/remote/tool_runtime/model_context_protocol/__init__.py +++ b/llama_stack/providers/remote/tool_runtime/model_context_protocol/__init__.py @@ -4,18 +4,12 @@ # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. -from pydantic import BaseModel - -from .config import ModelContextProtocolConfig +from .config import MCPProviderConfig -class ModelContextProtocolToolProviderDataValidator(BaseModel): - api_key: str - - -async def get_adapter_impl(config: ModelContextProtocolConfig, _deps): +async def get_adapter_impl(config: MCPProviderConfig, _deps): from .model_context_protocol import ModelContextProtocolToolRuntimeImpl - impl = ModelContextProtocolToolRuntimeImpl(config) + impl = ModelContextProtocolToolRuntimeImpl(config, _deps) await impl.initialize() return impl diff --git a/llama_stack/providers/remote/tool_runtime/model_context_protocol/config.py b/llama_stack/providers/remote/tool_runtime/model_context_protocol/config.py index d509074fc..b8c5e77fd 100644 --- a/llama_stack/providers/remote/tool_runtime/model_context_protocol/config.py +++ b/llama_stack/providers/remote/tool_runtime/model_context_protocol/config.py @@ -9,7 +9,12 @@ from typing import Any from pydantic import BaseModel -class ModelContextProtocolConfig(BaseModel): +class MCPProviderDataValidator(BaseModel): + # mcp_endpoint => dict of headers to send + mcp_headers: dict[str, dict[str, str]] | None = None + + +class MCPProviderConfig(BaseModel): @classmethod def sample_run_config(cls, __distro_dir__: str, **kwargs: Any) -> dict[str, Any]: return {} diff --git a/llama_stack/providers/remote/tool_runtime/model_context_protocol/model_context_protocol.py b/llama_stack/providers/remote/tool_runtime/model_context_protocol/model_context_protocol.py index 142730e89..a9b252dfe 100644 --- a/llama_stack/providers/remote/tool_runtime/model_context_protocol/model_context_protocol.py +++ b/llama_stack/providers/remote/tool_runtime/model_context_protocol/model_context_protocol.py @@ -7,61 +7,45 @@ from typing import Any from urllib.parse import urlparse -from mcp import ClientSession -from mcp.client.sse import sse_client - from llama_stack.apis.common.content_types import URL +from llama_stack.apis.datatypes import Api from llama_stack.apis.tools import ( ListToolDefsResponse, - ToolDef, + ToolGroup, ToolInvocationResult, - ToolParameter, ToolRuntime, ) -from llama_stack.providers.datatypes import ToolsProtocolPrivate +from llama_stack.distribution.request_headers import NeedsRequestProviderData +from llama_stack.log import get_logger +from llama_stack.providers.datatypes import ToolGroupsProtocolPrivate +from llama_stack.providers.utils.tools.mcp import invoke_mcp_tool, list_mcp_tools -from .config import ModelContextProtocolConfig +from .config import MCPProviderConfig + +logger = get_logger(__name__, category="tools") -class ModelContextProtocolToolRuntimeImpl(ToolsProtocolPrivate, ToolRuntime): - def __init__(self, config: ModelContextProtocolConfig): +class ModelContextProtocolToolRuntimeImpl(ToolGroupsProtocolPrivate, ToolRuntime, NeedsRequestProviderData): + def __init__(self, config: MCPProviderConfig, _deps: dict[Api, Any]): self.config = config async def initialize(self): pass + async def register_toolgroup(self, toolgroup: ToolGroup) -> None: + pass + + async def unregister_toolgroup(self, toolgroup_id: str) -> None: + return + async def list_runtime_tools( self, tool_group_id: str | None = None, mcp_endpoint: URL | None = None ) -> ListToolDefsResponse: + # this endpoint should be retrieved by getting the tool group right? if mcp_endpoint is None: raise ValueError("mcp_endpoint is required") - - tools = [] - async with sse_client(mcp_endpoint.uri) as streams: - async with ClientSession(*streams) as session: - await session.initialize() - tools_result = await session.list_tools() - for tool in tools_result.tools: - parameters = [] - for param_name, param_schema in tool.inputSchema.get("properties", {}).items(): - parameters.append( - ToolParameter( - name=param_name, - parameter_type=param_schema.get("type", "string"), - description=param_schema.get("description", ""), - ) - ) - tools.append( - ToolDef( - name=tool.name, - description=tool.description, - parameters=parameters, - metadata={ - "endpoint": mcp_endpoint.uri, - }, - ) - ) - return ListToolDefsResponse(data=tools) + headers = await self.get_headers_from_request(mcp_endpoint.uri) + return await list_mcp_tools(mcp_endpoint.uri, headers) async def invoke_tool(self, tool_name: str, kwargs: dict[str, Any]) -> ToolInvocationResult: tool = await self.tool_store.get_tool(tool_name) @@ -71,12 +55,19 @@ class ModelContextProtocolToolRuntimeImpl(ToolsProtocolPrivate, ToolRuntime): if urlparse(endpoint).scheme not in ("http", "https"): raise ValueError(f"Endpoint {endpoint} is not a valid HTTP(S) URL") - async with sse_client(endpoint) as streams: - async with ClientSession(*streams) as session: - await session.initialize() - result = await session.call_tool(tool.identifier, kwargs) + headers = await self.get_headers_from_request(endpoint) + return await invoke_mcp_tool(endpoint, headers, tool_name, kwargs) - return ToolInvocationResult( - content="\n".join([result.model_dump_json() for result in result.content]), - error_code=1 if result.isError else 0, - ) + async def get_headers_from_request(self, mcp_endpoint_uri: str) -> dict[str, str]: + def canonicalize_uri(uri: str) -> str: + return f"{urlparse(uri).netloc or ''}/{urlparse(uri).path or ''}" + + headers = {} + + provider_data = self.get_request_provider_data() + if provider_data and provider_data.mcp_headers: + for uri, values in provider_data.mcp_headers.items(): + if canonicalize_uri(uri) != canonicalize_uri(mcp_endpoint_uri): + continue + headers.update(values) + return headers diff --git a/llama_stack/providers/remote/tool_runtime/tavily_search/tavily_search.py b/llama_stack/providers/remote/tool_runtime/tavily_search/tavily_search.py index 9d6fcd951..1fe91fd7f 100644 --- a/llama_stack/providers/remote/tool_runtime/tavily_search/tavily_search.py +++ b/llama_stack/providers/remote/tool_runtime/tavily_search/tavily_search.py @@ -12,29 +12,29 @@ import httpx from llama_stack.apis.common.content_types import URL from llama_stack.apis.tools import ( ListToolDefsResponse, - Tool, ToolDef, + ToolGroup, ToolInvocationResult, ToolParameter, ToolRuntime, ) from llama_stack.distribution.request_headers import NeedsRequestProviderData -from llama_stack.providers.datatypes import ToolsProtocolPrivate +from llama_stack.providers.datatypes import ToolGroupsProtocolPrivate from .config import TavilySearchToolConfig -class TavilySearchToolRuntimeImpl(ToolsProtocolPrivate, ToolRuntime, NeedsRequestProviderData): +class TavilySearchToolRuntimeImpl(ToolGroupsProtocolPrivate, ToolRuntime, NeedsRequestProviderData): def __init__(self, config: TavilySearchToolConfig): self.config = config async def initialize(self): pass - async def register_tool(self, tool: Tool) -> None: + async def register_toolgroup(self, toolgroup: ToolGroup) -> None: pass - async def unregister_tool(self, tool_id: str) -> None: + async def unregister_toolgroup(self, toolgroup_id: str) -> None: return def _get_api_key(self) -> str: diff --git a/llama_stack/providers/remote/tool_runtime/wolfram_alpha/wolfram_alpha.py b/llama_stack/providers/remote/tool_runtime/wolfram_alpha/wolfram_alpha.py index a3724e4b4..6e1d0f61d 100644 --- a/llama_stack/providers/remote/tool_runtime/wolfram_alpha/wolfram_alpha.py +++ b/llama_stack/providers/remote/tool_runtime/wolfram_alpha/wolfram_alpha.py @@ -12,19 +12,19 @@ import httpx from llama_stack.apis.common.content_types import URL from llama_stack.apis.tools import ( ListToolDefsResponse, - Tool, ToolDef, + ToolGroup, ToolInvocationResult, ToolParameter, ToolRuntime, ) from llama_stack.distribution.request_headers import NeedsRequestProviderData -from llama_stack.providers.datatypes import ToolsProtocolPrivate +from llama_stack.providers.datatypes import ToolGroupsProtocolPrivate from .config import WolframAlphaToolConfig -class WolframAlphaToolRuntimeImpl(ToolsProtocolPrivate, ToolRuntime, NeedsRequestProviderData): +class WolframAlphaToolRuntimeImpl(ToolGroupsProtocolPrivate, ToolRuntime, NeedsRequestProviderData): def __init__(self, config: WolframAlphaToolConfig): self.config = config self.url = "https://api.wolframalpha.com/v2/query" @@ -32,10 +32,10 @@ class WolframAlphaToolRuntimeImpl(ToolsProtocolPrivate, ToolRuntime, NeedsReques async def initialize(self): pass - async def register_tool(self, tool: Tool) -> None: + async def register_toolgroup(self, toolgroup: ToolGroup) -> None: pass - async def unregister_tool(self, tool_id: str) -> None: + async def unregister_toolgroup(self, toolgroup_id: str) -> None: return def _get_api_key(self) -> str: diff --git a/llama_stack/providers/remote/vector_io/chroma/chroma.py b/llama_stack/providers/remote/vector_io/chroma/chroma.py index a919963ab..a59a38573 100644 --- a/llama_stack/providers/remote/vector_io/chroma/chroma.py +++ b/llama_stack/providers/remote/vector_io/chroma/chroma.py @@ -84,6 +84,14 @@ class ChromaIndex(EmbeddingIndex): async def delete(self): await maybe_await(self.client.delete_collection(self.collection.name)) + async def query_keyword( + self, + query_string: str, + k: int, + score_threshold: float, + ) -> QueryChunksResponse: + raise NotImplementedError("Keyword search is not supported in Chroma") + class ChromaVectorIOAdapter(VectorIO, VectorDBsProtocolPrivate): def __init__( diff --git a/llama_stack/providers/remote/vector_io/milvus/milvus.py b/llama_stack/providers/remote/vector_io/milvus/milvus.py index c98417b56..6628292db 100644 --- a/llama_stack/providers/remote/vector_io/milvus/milvus.py +++ b/llama_stack/providers/remote/vector_io/milvus/milvus.py @@ -73,7 +73,7 @@ class MilvusIndex(EmbeddingIndex): logger.error(f"Error inserting chunks into Milvus collection {self.collection_name}: {e}") raise e - async def query(self, embedding: NDArray, k: int, score_threshold: float) -> QueryChunksResponse: + async def query_vector(self, embedding: NDArray, k: int, score_threshold: float) -> QueryChunksResponse: search_res = await asyncio.to_thread( self.client.search, collection_name=self.collection_name, @@ -86,6 +86,14 @@ class MilvusIndex(EmbeddingIndex): scores = [res["distance"] for res in search_res[0]] return QueryChunksResponse(chunks=chunks, scores=scores) + async def query_keyword( + self, + query_string: str, + k: int, + score_threshold: float, + ) -> QueryChunksResponse: + raise NotImplementedError("Keyword search is not supported in Milvus") + class MilvusVectorIOAdapter(VectorIO, VectorDBsProtocolPrivate): def __init__( diff --git a/llama_stack/providers/remote/vector_io/pgvector/pgvector.py b/llama_stack/providers/remote/vector_io/pgvector/pgvector.py index 94546c6cf..ea918c552 100644 --- a/llama_stack/providers/remote/vector_io/pgvector/pgvector.py +++ b/llama_stack/providers/remote/vector_io/pgvector/pgvector.py @@ -99,7 +99,7 @@ class PGVectorIndex(EmbeddingIndex): with self.conn.cursor(cursor_factory=psycopg2.extras.DictCursor) as cur: execute_values(cur, query, values, template="(%s, %s, %s::vector)") - async def query(self, embedding: NDArray, k: int, score_threshold: float) -> QueryChunksResponse: + async def query_vector(self, embedding: NDArray, k: int, score_threshold: float) -> QueryChunksResponse: with self.conn.cursor(cursor_factory=psycopg2.extras.DictCursor) as cur: cur.execute( f""" @@ -120,6 +120,14 @@ class PGVectorIndex(EmbeddingIndex): return QueryChunksResponse(chunks=chunks, scores=scores) + async def query_keyword( + self, + query_string: str, + k: int, + score_threshold: float, + ) -> QueryChunksResponse: + raise NotImplementedError("Keyword search is not supported in PGVector") + async def delete(self): with self.conn.cursor(cursor_factory=psycopg2.extras.DictCursor) as cur: cur.execute(f"DROP TABLE IF EXISTS {self.table_name}") diff --git a/llama_stack/providers/remote/vector_io/qdrant/qdrant.py b/llama_stack/providers/remote/vector_io/qdrant/qdrant.py index 514a6c70d..ff0690083 100644 --- a/llama_stack/providers/remote/vector_io/qdrant/qdrant.py +++ b/llama_stack/providers/remote/vector_io/qdrant/qdrant.py @@ -68,7 +68,7 @@ class QdrantIndex(EmbeddingIndex): await self.client.upsert(collection_name=self.collection_name, points=points) - async def query(self, embedding: NDArray, k: int, score_threshold: float) -> QueryChunksResponse: + async def query_vector(self, embedding: NDArray, k: int, score_threshold: float) -> QueryChunksResponse: results = ( await self.client.query_points( collection_name=self.collection_name, @@ -95,6 +95,14 @@ class QdrantIndex(EmbeddingIndex): return QueryChunksResponse(chunks=chunks, scores=scores) + async def query_keyword( + self, + query_string: str, + k: int, + score_threshold: float, + ) -> QueryChunksResponse: + raise NotImplementedError("Keyword search is not supported in Qdrant") + async def delete(self): await self.client.delete_collection(collection_name=self.collection_name) diff --git a/llama_stack/providers/remote/vector_io/weaviate/weaviate.py b/llama_stack/providers/remote/vector_io/weaviate/weaviate.py index 308d2eb3d..e6fe8ccd3 100644 --- a/llama_stack/providers/remote/vector_io/weaviate/weaviate.py +++ b/llama_stack/providers/remote/vector_io/weaviate/weaviate.py @@ -55,7 +55,7 @@ class WeaviateIndex(EmbeddingIndex): # TODO: make this async friendly collection.data.insert_many(data_objects) - async def query(self, embedding: NDArray, k: int, score_threshold: float) -> QueryChunksResponse: + async def query_vector(self, embedding: NDArray, k: int, score_threshold: float) -> QueryChunksResponse: collection = self.client.collections.get(self.collection_name) results = collection.query.near_vector( @@ -84,6 +84,14 @@ class WeaviateIndex(EmbeddingIndex): collection = self.client.collections.get(self.collection_name) collection.data.delete_many(where=Filter.by_property("id").contains_any(chunk_ids)) + async def query_keyword( + self, + query_string: str, + k: int, + score_threshold: float, + ) -> QueryChunksResponse: + raise NotImplementedError("Keyword search is not supported in Weaviate") + class WeaviateVectorIOAdapter( VectorIO, diff --git a/llama_stack/providers/utils/inference/embedding_mixin.py b/llama_stack/providers/utils/inference/embedding_mixin.py index 7c8144c62..97cf87360 100644 --- a/llama_stack/providers/utils/inference/embedding_mixin.py +++ b/llama_stack/providers/utils/inference/embedding_mixin.py @@ -4,7 +4,9 @@ # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. +import base64 import logging +import struct from typing import TYPE_CHECKING if TYPE_CHECKING: @@ -15,6 +17,9 @@ from llama_stack.apis.inference import ( EmbeddingTaskType, InterleavedContentItem, ModelStore, + OpenAIEmbeddingData, + OpenAIEmbeddingsResponse, + OpenAIEmbeddingUsage, TextTruncation, ) from llama_stack.providers.utils.inference.prompt_adapter import interleaved_content_as_str @@ -43,6 +48,50 @@ class SentenceTransformerEmbeddingMixin: ) return EmbeddingsResponse(embeddings=embeddings) + async def openai_embeddings( + self, + model: str, + input: str | list[str], + encoding_format: str | None = "float", + dimensions: int | None = None, + user: str | None = None, + ) -> OpenAIEmbeddingsResponse: + # Convert input to list format if it's a single string + input_list = [input] if isinstance(input, str) else input + if not input_list: + raise ValueError("Empty list not supported") + + # Get the model and generate embeddings + model_obj = await self.model_store.get_model(model) + embedding_model = self._load_sentence_transformer_model(model_obj.provider_resource_id) + embeddings = embedding_model.encode(input_list, show_progress_bar=False) + + # Convert embeddings to the requested format + data = [] + for i, embedding in enumerate(embeddings): + if encoding_format == "base64": + # Convert float array to base64 string + float_bytes = struct.pack(f"{len(embedding)}f", *embedding) + embedding_value = base64.b64encode(float_bytes).decode("ascii") + else: + # Default to float format + embedding_value = embedding.tolist() + + data.append( + OpenAIEmbeddingData( + embedding=embedding_value, + index=i, + ) + ) + + # Not returning actual token usage + usage = OpenAIEmbeddingUsage(prompt_tokens=-1, total_tokens=-1) + return OpenAIEmbeddingsResponse( + data=data, + model=model_obj.provider_resource_id, + usage=usage, + ) + def _load_sentence_transformer_model(self, model: str) -> "SentenceTransformer": global EMBEDDING_MODELS diff --git a/llama_stack/providers/utils/inference/inference_store.py b/llama_stack/providers/utils/inference/inference_store.py new file mode 100644 index 000000000..7b6bc2e3d --- /dev/null +++ b/llama_stack/providers/utils/inference/inference_store.py @@ -0,0 +1,123 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the terms described in the LICENSE file in +# the root directory of this source tree. +from llama_stack.apis.inference import ( + ListOpenAIChatCompletionResponse, + OpenAIChatCompletion, + OpenAICompletionWithInputMessages, + OpenAIMessageParam, + Order, +) +from llama_stack.distribution.utils.config_dirs import RUNTIME_BASE_DIR + +from ..sqlstore.api import ColumnDefinition, ColumnType +from ..sqlstore.sqlstore import SqliteSqlStoreConfig, SqlStoreConfig, sqlstore_impl + + +class InferenceStore: + def __init__(self, sql_store_config: SqlStoreConfig): + if not sql_store_config: + sql_store_config = SqliteSqlStoreConfig( + db_path=(RUNTIME_BASE_DIR / "sqlstore.db").as_posix(), + ) + self.sql_store_config = sql_store_config + self.sql_store = None + + async def initialize(self): + """Create the necessary tables if they don't exist.""" + self.sql_store = sqlstore_impl(self.sql_store_config) + await self.sql_store.create_table( + "chat_completions", + { + "id": ColumnDefinition(type=ColumnType.STRING, primary_key=True), + "created": ColumnType.INTEGER, + "model": ColumnType.STRING, + "choices": ColumnType.JSON, + "input_messages": ColumnType.JSON, + }, + ) + + async def store_chat_completion( + self, chat_completion: OpenAIChatCompletion, input_messages: list[OpenAIMessageParam] + ) -> None: + if not self.sql_store: + raise ValueError("Inference store is not initialized") + + data = chat_completion.model_dump() + + await self.sql_store.insert( + "chat_completions", + { + "id": data["id"], + "created": data["created"], + "model": data["model"], + "choices": data["choices"], + "input_messages": [message.model_dump() for message in input_messages], + }, + ) + + async def list_chat_completions( + self, + after: str | None = None, + limit: int | None = 50, + model: str | None = None, + order: Order | None = Order.desc, + ) -> ListOpenAIChatCompletionResponse: + """ + List chat completions from the database. + + :param after: The ID of the last chat completion to return. + :param limit: The maximum number of chat completions to return. + :param model: The model to filter by. + :param order: The order to sort the chat completions by. + """ + if not self.sql_store: + raise ValueError("Inference store is not initialized") + + # TODO: support after + if after: + raise NotImplementedError("After is not supported for SQLite") + if not order: + order = Order.desc + + rows = await self.sql_store.fetch_all( + "chat_completions", + where={"model": model} if model else None, + order_by=[("created", order.value)], + limit=limit, + ) + + data = [ + OpenAICompletionWithInputMessages( + id=row["id"], + created=row["created"], + model=row["model"], + choices=row["choices"], + input_messages=row["input_messages"], + ) + for row in rows + ] + return ListOpenAIChatCompletionResponse( + data=data, + # TODO: implement has_more + has_more=False, + first_id=data[0].id if data else "", + last_id=data[-1].id if data else "", + ) + + async def get_chat_completion(self, completion_id: str) -> OpenAICompletionWithInputMessages: + if not self.sql_store: + raise ValueError("Inference store is not initialized") + + row = await self.sql_store.fetch_one("chat_completions", where={"id": completion_id}) + if not row: + raise ValueError(f"Chat completion with id {completion_id} not found") from None + return OpenAICompletionWithInputMessages( + id=row["id"], + created=row["created"], + model=row["model"], + choices=row["choices"], + input_messages=row["input_messages"], + ) diff --git a/llama_stack/providers/utils/inference/litellm_openai_mixin.py b/llama_stack/providers/utils/inference/litellm_openai_mixin.py index 0a5c5e4f4..dab10bc55 100644 --- a/llama_stack/providers/utils/inference/litellm_openai_mixin.py +++ b/llama_stack/providers/utils/inference/litellm_openai_mixin.py @@ -4,6 +4,8 @@ # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. +import base64 +import struct from collections.abc import AsyncGenerator, AsyncIterator from typing import Any @@ -19,7 +21,7 @@ from llama_stack.apis.inference import ( ChatCompletionResponseStreamChunk, EmbeddingsResponse, EmbeddingTaskType, - Inference, + InferenceProvider, JsonSchemaResponseFormat, LogProbConfig, Message, @@ -35,6 +37,9 @@ from llama_stack.apis.inference.inference import ( OpenAIChatCompletion, OpenAIChatCompletionChunk, OpenAICompletion, + OpenAIEmbeddingData, + OpenAIEmbeddingsResponse, + OpenAIEmbeddingUsage, OpenAIMessageParam, OpenAIResponseFormatParam, ) @@ -59,7 +64,7 @@ logger = get_logger(name=__name__, category="inference") class LiteLLMOpenAIMixin( ModelRegistryHelper, - Inference, + InferenceProvider, NeedsRequestProviderData, ): # TODO: avoid exposing the litellm specific model names to the user. @@ -264,6 +269,52 @@ class LiteLLMOpenAIMixin( embeddings = [data["embedding"] for data in response["data"]] return EmbeddingsResponse(embeddings=embeddings) + async def openai_embeddings( + self, + model: str, + input: str | list[str], + encoding_format: str | None = "float", + dimensions: int | None = None, + user: str | None = None, + ) -> OpenAIEmbeddingsResponse: + model_obj = await self.model_store.get_model(model) + + # Convert input to list if it's a string + input_list = [input] if isinstance(input, str) else input + + # Call litellm embedding function + # litellm.drop_params = True + response = litellm.embedding( + model=self.get_litellm_model_name(model_obj.provider_resource_id), + input=input_list, + api_key=self.get_api_key(), + api_base=self.api_base, + dimensions=dimensions, + ) + + # Convert response to OpenAI format + data = [] + for i, embedding_data in enumerate(response["data"]): + # we encode to base64 if the encoding format is base64 in the request + if encoding_format == "base64": + byte_data = b"".join(struct.pack("f", f) for f in embedding_data["embedding"]) + embedding = base64.b64encode(byte_data).decode("utf-8") + else: + embedding = embedding_data["embedding"] + + data.append(OpenAIEmbeddingData(embedding=embedding, index=i)) + + usage = OpenAIEmbeddingUsage( + prompt_tokens=response["usage"]["prompt_tokens"], + total_tokens=response["usage"]["total_tokens"], + ) + + return OpenAIEmbeddingsResponse( + data=data, + model=model_obj.provider_resource_id, + usage=usage, + ) + async def openai_completion( self, model: str, diff --git a/llama_stack/providers/utils/inference/openai_compat.py b/llama_stack/providers/utils/inference/openai_compat.py index cc0000528..049f06fdb 100644 --- a/llama_stack/providers/utils/inference/openai_compat.py +++ b/llama_stack/providers/utils/inference/openai_compat.py @@ -1402,9 +1402,8 @@ class OpenAIChatCompletionToLlamaStackMixin: outstanding_responses: list[Awaitable[AsyncIterator[ChatCompletionResponseStreamChunk]]], ): id = f"chatcmpl-{uuid.uuid4()}" - for outstanding_response in outstanding_responses: + for i, outstanding_response in enumerate(outstanding_responses): response = await outstanding_response - i = 0 async for chunk in response: event = chunk.event finish_reason = _convert_stop_reason_to_openai_finish_reason(event.stop_reason) @@ -1459,7 +1458,6 @@ class OpenAIChatCompletionToLlamaStackMixin: model=model, object="chat.completion.chunk", ) - i = i + 1 async def _process_non_stream_response( self, model: str, outstanding_responses: list[Awaitable[ChatCompletionResponse]] diff --git a/llama_stack/providers/utils/inference/stream_utils.py b/llama_stack/providers/utils/inference/stream_utils.py new file mode 100644 index 000000000..a2edbb9c8 --- /dev/null +++ b/llama_stack/providers/utils/inference/stream_utils.py @@ -0,0 +1,129 @@ +# 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 collections.abc import AsyncIterator +from datetime import datetime, timezone +from typing import Any + +from llama_stack.apis.inference import ( + OpenAIAssistantMessageParam, + OpenAIChatCompletion, + OpenAIChatCompletionChunk, + OpenAIChatCompletionToolCall, + OpenAIChatCompletionToolCallFunction, + OpenAIChoice, + OpenAIChoiceLogprobs, + OpenAIMessageParam, +) +from llama_stack.providers.utils.inference.inference_store import InferenceStore + + +async def stream_and_store_openai_completion( + provider_stream: AsyncIterator[OpenAIChatCompletionChunk], + model: str, + store: InferenceStore, + input_messages: list[OpenAIMessageParam], +) -> AsyncIterator[OpenAIChatCompletionChunk]: + """ + Wraps a provider's stream, yields chunks, and stores the full completion at the end. + """ + id = None + created = None + choices_data: dict[int, dict[str, Any]] = {} + + try: + async for chunk in provider_stream: + if id is None and chunk.id: + id = chunk.id + if created is None and chunk.created: + created = chunk.created + + if chunk.choices: + for choice_delta in chunk.choices: + idx = choice_delta.index + if idx not in choices_data: + choices_data[idx] = { + "content_parts": [], + "tool_calls_builder": {}, + "finish_reason": None, + "logprobs_content_parts": [], + } + current_choice_data = choices_data[idx] + + if choice_delta.delta: + delta = choice_delta.delta + if delta.content: + current_choice_data["content_parts"].append(delta.content) + if delta.tool_calls: + for tool_call_delta in delta.tool_calls: + tc_idx = tool_call_delta.index + if tc_idx not in current_choice_data["tool_calls_builder"]: + # Initialize with correct structure for _ToolCallBuilderData + current_choice_data["tool_calls_builder"][tc_idx] = { + "id": None, + "type": "function", + "function_name_parts": [], + "function_arguments_parts": [], + } + builder = current_choice_data["tool_calls_builder"][tc_idx] + if tool_call_delta.id: + builder["id"] = tool_call_delta.id + if tool_call_delta.type: + builder["type"] = tool_call_delta.type + if tool_call_delta.function: + if tool_call_delta.function.name: + builder["function_name_parts"].append(tool_call_delta.function.name) + if tool_call_delta.function.arguments: + builder["function_arguments_parts"].append(tool_call_delta.function.arguments) + if choice_delta.finish_reason: + current_choice_data["finish_reason"] = choice_delta.finish_reason + if choice_delta.logprobs and choice_delta.logprobs.content: + # Ensure that we are extending with the correct type + current_choice_data["logprobs_content_parts"].extend(choice_delta.logprobs.content) + yield chunk + finally: + if id: + assembled_choices: list[OpenAIChoice] = [] + for choice_idx, choice_data in choices_data.items(): + content_str = "".join(choice_data["content_parts"]) + assembled_tool_calls: list[OpenAIChatCompletionToolCall] = [] + if choice_data["tool_calls_builder"]: + for tc_build_data in choice_data["tool_calls_builder"].values(): + if tc_build_data["id"]: + func_name = "".join(tc_build_data["function_name_parts"]) + func_args = "".join(tc_build_data["function_arguments_parts"]) + assembled_tool_calls.append( + OpenAIChatCompletionToolCall( + id=tc_build_data["id"], + type=tc_build_data["type"], # No or "function" needed, already set + function=OpenAIChatCompletionToolCallFunction(name=func_name, arguments=func_args), + ) + ) + message = OpenAIAssistantMessageParam( + role="assistant", + content=content_str if content_str else None, + tool_calls=assembled_tool_calls if assembled_tool_calls else None, + ) + logprobs_content = choice_data["logprobs_content_parts"] + final_logprobs = OpenAIChoiceLogprobs(content=logprobs_content) if logprobs_content else None + + assembled_choices.append( + OpenAIChoice( + finish_reason=choice_data["finish_reason"], + index=choice_idx, + message=message, + logprobs=final_logprobs, + ) + ) + + final_response = OpenAIChatCompletion( + id=id, + choices=assembled_choices, + created=created or int(datetime.now(timezone.utc).timestamp()), + model=model, + object="chat.completion", + ) + await store.store_chat_completion(final_response, input_messages) diff --git a/llama_stack/providers/utils/kvstore/config.py b/llama_stack/providers/utils/kvstore/config.py index e9aac6e8c..bbb0c5c0a 100644 --- a/llama_stack/providers/utils/kvstore/config.py +++ b/llama_stack/providers/utils/kvstore/config.py @@ -65,7 +65,7 @@ class SqliteKVStoreConfig(CommonConfig): class PostgresKVStoreConfig(CommonConfig): type: Literal[KVStoreType.postgres.value] = KVStoreType.postgres.value host: str = "localhost" - port: int = 5432 + port: str = "5432" db: str = "llamastack" user: str password: str | None = None diff --git a/llama_stack/providers/utils/memory/vector_store.py b/llama_stack/providers/utils/memory/vector_store.py index e0e9d0679..4cd15860b 100644 --- a/llama_stack/providers/utils/memory/vector_store.py +++ b/llama_stack/providers/utils/memory/vector_store.py @@ -171,13 +171,33 @@ def make_overlapped_chunks( return chunks +def _validate_embedding(embedding: NDArray, index: int, expected_dimension: int): + """Helper method to validate embedding format and dimensions""" + if not isinstance(embedding, (list | np.ndarray)): + raise ValueError(f"Embedding at index {index} must be a list or numpy array, got {type(embedding)}") + + if isinstance(embedding, np.ndarray): + if not np.issubdtype(embedding.dtype, np.number): + raise ValueError(f"Embedding at index {index} contains non-numeric values") + else: + if not all(isinstance(e, (float | int | np.number)) for e in embedding): + raise ValueError(f"Embedding at index {index} contains non-numeric values") + + if len(embedding) != expected_dimension: + raise ValueError(f"Embedding at index {index} has dimension {len(embedding)}, expected {expected_dimension}") + + class EmbeddingIndex(ABC): @abstractmethod async def add_chunks(self, chunks: list[Chunk], embeddings: NDArray): raise NotImplementedError() @abstractmethod - async def query(self, embedding: NDArray, k: int, score_threshold: float) -> QueryChunksResponse: + async def query_vector(self, embedding: NDArray, k: int, score_threshold: float) -> QueryChunksResponse: + raise NotImplementedError() + + @abstractmethod + async def query_keyword(self, query_string: str, k: int, score_threshold: float) -> QueryChunksResponse: raise NotImplementedError() @abstractmethod @@ -195,11 +215,22 @@ class VectorDBWithIndex: self, chunks: list[Chunk], ) -> None: - embeddings_response = await self.inference_api.embeddings( - self.vector_db.embedding_model, [x.content for x in chunks] - ) - embeddings = np.array(embeddings_response.embeddings) + chunks_to_embed = [] + for i, c in enumerate(chunks): + if c.embedding is None: + chunks_to_embed.append(c) + else: + _validate_embedding(c.embedding, i, self.vector_db.embedding_dimension) + if chunks_to_embed: + resp = await self.inference_api.embeddings( + self.vector_db.embedding_model, + [c.content for c in chunks_to_embed], + ) + for c, embedding in zip(chunks_to_embed, resp.embeddings, strict=False): + c.embedding = embedding + + embeddings = np.array([c.embedding for c in chunks], dtype=np.float32) await self.index.add_chunks(chunks, embeddings) async def query_chunks( @@ -210,9 +241,12 @@ class VectorDBWithIndex: if params is None: params = {} k = params.get("max_chunks", 3) + mode = params.get("mode") score_threshold = params.get("score_threshold", 0.0) - - query_str = interleaved_content_as_str(query) - embeddings_response = await self.inference_api.embeddings(self.vector_db.embedding_model, [query_str]) - query_vector = np.array(embeddings_response.embeddings[0], dtype=np.float32) - return await self.index.query(query_vector, k, score_threshold) + query_string = interleaved_content_as_str(query) + if mode == "keyword": + return await self.index.query_keyword(query_string, k, score_threshold) + else: + embeddings_response = await self.inference_api.embeddings(self.vector_db.embedding_model, [query_string]) + query_vector = np.array(embeddings_response.embeddings[0], dtype=np.float32) + return await self.index.query_vector(query_vector, k, score_threshold) diff --git a/llama_stack/providers/utils/responses/responses_store.py b/llama_stack/providers/utils/responses/responses_store.py new file mode 100644 index 000000000..15354e3e2 --- /dev/null +++ b/llama_stack/providers/utils/responses/responses_store.py @@ -0,0 +1,135 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the terms described in the LICENSE file in +# the root directory of this source tree. +from llama_stack.apis.agents import ( + Order, +) +from llama_stack.apis.agents.openai_responses import ( + ListOpenAIResponseInputItem, + ListOpenAIResponseObject, + OpenAIResponseInput, + OpenAIResponseObject, + OpenAIResponseObjectWithInput, +) +from llama_stack.distribution.utils.config_dirs import RUNTIME_BASE_DIR + +from ..sqlstore.api import ColumnDefinition, ColumnType +from ..sqlstore.sqlstore import SqliteSqlStoreConfig, SqlStoreConfig, sqlstore_impl + + +class ResponsesStore: + def __init__(self, sql_store_config: SqlStoreConfig): + if not sql_store_config: + sql_store_config = SqliteSqlStoreConfig( + db_path=(RUNTIME_BASE_DIR / "sqlstore.db").as_posix(), + ) + self.sql_store = sqlstore_impl(sql_store_config) + + async def initialize(self): + """Create the necessary tables if they don't exist.""" + await self.sql_store.create_table( + "openai_responses", + { + "id": ColumnDefinition(type=ColumnType.STRING, primary_key=True), + "created_at": ColumnType.INTEGER, + "response_object": ColumnType.JSON, + "model": ColumnType.STRING, + }, + ) + + async def store_response_object( + self, response_object: OpenAIResponseObject, input: list[OpenAIResponseInput] + ) -> None: + data = response_object.model_dump() + data["input"] = [input_item.model_dump() for input_item in input] + + await self.sql_store.insert( + "openai_responses", + { + "id": data["id"], + "created_at": data["created_at"], + "model": data["model"], + "response_object": data, + }, + ) + + async def list_responses( + self, + after: str | None = None, + limit: int | None = 50, + model: str | None = None, + order: Order | None = Order.desc, + ) -> ListOpenAIResponseObject: + """ + List responses from the database. + + :param after: The ID of the last response to return. + :param limit: The maximum number of responses to return. + :param model: The model to filter by. + :param order: The order to sort the responses by. + """ + # TODO: support after + if after: + raise NotImplementedError("After is not supported for SQLite") + if not order: + order = Order.desc + + rows = await self.sql_store.fetch_all( + "openai_responses", + where={"model": model} if model else None, + order_by=[("created_at", order.value)], + limit=limit, + ) + + data = [OpenAIResponseObjectWithInput(**row["response_object"]) for row in rows] + return ListOpenAIResponseObject( + data=data, + # TODO: implement has_more + has_more=False, + first_id=data[0].id if data else "", + last_id=data[-1].id if data else "", + ) + + async def get_response_object(self, response_id: str) -> OpenAIResponseObjectWithInput: + row = await self.sql_store.fetch_one("openai_responses", where={"id": response_id}) + if not row: + raise ValueError(f"Response with id {response_id} not found") from None + return OpenAIResponseObjectWithInput(**row["response_object"]) + + async def list_response_input_items( + self, + response_id: str, + after: str | None = None, + before: str | None = None, + include: list[str] | None = None, + limit: int | None = 20, + order: Order | None = Order.desc, + ) -> ListOpenAIResponseInputItem: + """ + List input items for a given response. + + :param response_id: The ID of the response to retrieve input items for. + :param after: An item ID to list items after, used for pagination. + :param before: An item ID to list items before, used for pagination. + :param include: Additional fields to include in the response. + :param limit: A limit on the number of objects to be returned. + :param order: The order to return the input items in. + """ + # TODO: support after/before pagination + if after or before: + raise NotImplementedError("After/before pagination is not supported yet") + if include: + raise NotImplementedError("Include is not supported yet") + + response_with_input = await self.get_response_object(response_id) + input_items = response_with_input.input + + if order == Order.desc: + input_items = list(reversed(input_items)) + + if limit is not None and len(input_items) > limit: + input_items = input_items[:limit] + + return ListOpenAIResponseInputItem(data=input_items) diff --git a/llama_stack/providers/utils/sqlstore/api.py b/llama_stack/providers/utils/sqlstore/api.py new file mode 100644 index 000000000..ace40e4c4 --- /dev/null +++ b/llama_stack/providers/utils/sqlstore/api.py @@ -0,0 +1,90 @@ +# 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 collections.abc import Mapping +from enum import Enum +from typing import Any, Literal, Protocol + +from pydantic import BaseModel + + +class ColumnType(Enum): + INTEGER = "INTEGER" + STRING = "STRING" + TEXT = "TEXT" + FLOAT = "FLOAT" + BOOLEAN = "BOOLEAN" + JSON = "JSON" + DATETIME = "DATETIME" + + +class ColumnDefinition(BaseModel): + type: ColumnType + primary_key: bool = False + nullable: bool = True + default: Any = None + + +class SqlStore(Protocol): + """ + A protocol for a SQL store. + """ + + async def create_table(self, table: str, schema: Mapping[str, ColumnType | ColumnDefinition]) -> None: + """ + Create a table. + """ + pass + + async def insert(self, table: str, data: Mapping[str, Any]) -> None: + """ + Insert a row into a table. + """ + pass + + async def fetch_all( + self, + table: str, + where: Mapping[str, Any] | None = None, + limit: int | None = None, + order_by: list[tuple[str, Literal["asc", "desc"]]] | None = None, + ) -> list[dict[str, Any]]: + """ + Fetch all rows from a table. + """ + pass + + async def fetch_one( + self, + table: str, + where: Mapping[str, Any] | None = None, + order_by: list[tuple[str, Literal["asc", "desc"]]] | None = None, + ) -> dict[str, Any] | None: + """ + Fetch one row from a table. + """ + pass + + async def update( + self, + table: str, + data: Mapping[str, Any], + where: Mapping[str, Any], + ) -> None: + """ + Update a row in a table. + """ + pass + + async def delete( + self, + table: str, + where: Mapping[str, Any], + ) -> None: + """ + Delete a row from a table. + """ + pass diff --git a/llama_stack/providers/utils/sqlstore/sqlalchemy_sqlstore.py b/llama_stack/providers/utils/sqlstore/sqlalchemy_sqlstore.py new file mode 100644 index 000000000..825220679 --- /dev/null +++ b/llama_stack/providers/utils/sqlstore/sqlalchemy_sqlstore.py @@ -0,0 +1,163 @@ +# 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 collections.abc import Mapping +from typing import Any, Literal + +from sqlalchemy import ( + JSON, + Boolean, + Column, + DateTime, + Float, + Integer, + MetaData, + String, + Table, + Text, + select, +) +from sqlalchemy.ext.asyncio import async_sessionmaker, create_async_engine + +from .api import ColumnDefinition, ColumnType, SqlStore +from .sqlstore import SqlAlchemySqlStoreConfig + +TYPE_MAPPING: dict[ColumnType, Any] = { + ColumnType.INTEGER: Integer, + ColumnType.STRING: String, + ColumnType.FLOAT: Float, + ColumnType.BOOLEAN: Boolean, + ColumnType.DATETIME: DateTime, + ColumnType.TEXT: Text, + ColumnType.JSON: JSON, +} + + +class SqlAlchemySqlStoreImpl(SqlStore): + def __init__(self, config: SqlAlchemySqlStoreConfig): + self.config = config + self.async_session = async_sessionmaker(create_async_engine(config.engine_str)) + self.metadata = MetaData() + + async def create_table( + self, + table: str, + schema: Mapping[str, ColumnType | ColumnDefinition], + ) -> None: + if not schema: + raise ValueError(f"No columns defined for table '{table}'.") + + sqlalchemy_columns: list[Column] = [] + + for col_name, col_props in schema.items(): + col_type = None + is_primary_key = False + is_nullable = True # Default to nullable + + if isinstance(col_props, ColumnType): + col_type = col_props + elif isinstance(col_props, ColumnDefinition): + col_type = col_props.type + is_primary_key = col_props.primary_key + is_nullable = col_props.nullable + + sqlalchemy_type = TYPE_MAPPING.get(col_type) + if not sqlalchemy_type: + raise ValueError(f"Unsupported column type '{col_type}' for column '{col_name}'.") + + sqlalchemy_columns.append( + Column(col_name, sqlalchemy_type, primary_key=is_primary_key, nullable=is_nullable) + ) + + # Check if table already exists in metadata, otherwise define it + if table not in self.metadata.tables: + sqlalchemy_table = Table(table, self.metadata, *sqlalchemy_columns) + else: + sqlalchemy_table = self.metadata.tables[table] + + # Create the table in the database if it doesn't exist + # checkfirst=True ensures it doesn't try to recreate if it's already there + engine = create_async_engine(self.config.engine_str) + async with engine.begin() as conn: + await conn.run_sync(self.metadata.create_all, tables=[sqlalchemy_table], checkfirst=True) + + async def insert(self, table: str, data: Mapping[str, Any]) -> None: + async with self.async_session() as session: + await session.execute(self.metadata.tables[table].insert(), data) + await session.commit() + + async def fetch_all( + self, + table: str, + where: Mapping[str, Any] | None = None, + limit: int | None = None, + order_by: list[tuple[str, Literal["asc", "desc"]]] | None = None, + ) -> list[dict[str, Any]]: + async with self.async_session() as session: + query = select(self.metadata.tables[table]) + if where: + for key, value in where.items(): + query = query.where(self.metadata.tables[table].c[key] == value) + if limit: + query = query.limit(limit) + if order_by: + if not isinstance(order_by, list): + raise ValueError( + f"order_by must be a list of tuples (column, order={['asc', 'desc']}), got {order_by}" + ) + for order in order_by: + if not isinstance(order, tuple): + raise ValueError( + f"order_by must be a list of tuples (column, order={['asc', 'desc']}), got {order_by}" + ) + name, order_type = order + if order_type == "asc": + query = query.order_by(self.metadata.tables[table].c[name].asc()) + elif order_type == "desc": + query = query.order_by(self.metadata.tables[table].c[name].desc()) + else: + raise ValueError(f"Invalid order '{order_type}' for column '{name}'") + result = await session.execute(query) + if result.rowcount == 0: + return [] + return [dict(row._mapping) for row in result] + + async def fetch_one( + self, + table: str, + where: Mapping[str, Any] | None = None, + order_by: list[tuple[str, Literal["asc", "desc"]]] | None = None, + ) -> dict[str, Any] | None: + rows = await self.fetch_all(table, where, limit=1, order_by=order_by) + if not rows: + return None + return rows[0] + + async def update( + self, + table: str, + data: Mapping[str, Any], + where: Mapping[str, Any], + ) -> None: + if not where: + raise ValueError("where is required for update") + + async with self.async_session() as session: + stmt = self.metadata.tables[table].update() + for key, value in where.items(): + stmt = stmt.where(self.metadata.tables[table].c[key] == value) + await session.execute(stmt, data) + await session.commit() + + async def delete(self, table: str, where: Mapping[str, Any]) -> None: + if not where: + raise ValueError("where is required for delete") + + async with self.async_session() as session: + stmt = self.metadata.tables[table].delete() + for key, value in where.items(): + stmt = stmt.where(self.metadata.tables[table].c[key] == value) + await session.execute(stmt) + await session.commit() diff --git a/llama_stack/providers/utils/sqlstore/sqlstore.py b/llama_stack/providers/utils/sqlstore/sqlstore.py new file mode 100644 index 000000000..3091e8f96 --- /dev/null +++ b/llama_stack/providers/utils/sqlstore/sqlstore.py @@ -0,0 +1,90 @@ +# 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 abc import abstractmethod +from enum import Enum +from pathlib import Path +from typing import Annotated, Literal + +from pydantic import BaseModel, Field + +from llama_stack.distribution.utils.config_dirs import RUNTIME_BASE_DIR + +from .api import SqlStore + + +class SqlStoreType(Enum): + sqlite = "sqlite" + postgres = "postgres" + + +class SqlAlchemySqlStoreConfig(BaseModel): + @property + @abstractmethod + def engine_str(self) -> str: ... + + # TODO: move this when we have a better way to specify dependencies with internal APIs + @property + def pip_packages(self) -> list[str]: + return ["sqlalchemy[asyncio]"] + + +class SqliteSqlStoreConfig(SqlAlchemySqlStoreConfig): + type: Literal["sqlite"] = SqlStoreType.sqlite.value + db_path: str = Field( + default=(RUNTIME_BASE_DIR / "sqlstore.db").as_posix(), + description="Database path, e.g. ~/.llama/distributions/ollama/sqlstore.db", + ) + + @property + def engine_str(self) -> str: + return "sqlite+aiosqlite:///" + Path(self.db_path).expanduser().as_posix() + + @classmethod + def sample_run_config(cls, __distro_dir__: str, db_name: str = "sqlstore.db"): + return cls( + type="sqlite", + db_path="${env.SQLITE_STORE_DIR:" + __distro_dir__ + "}/" + db_name, + ) + + @property + def pip_packages(self) -> list[str]: + return super().pip_packages + ["aiosqlite"] + + +class PostgresSqlStoreConfig(SqlAlchemySqlStoreConfig): + type: Literal["postgres"] = SqlStoreType.postgres.value + host: str = "localhost" + port: str = "5432" + db: str = "llamastack" + user: str + password: str | None = None + + @property + def engine_str(self) -> str: + return f"postgresql+asyncpg://{self.user}:{self.password}@{self.host}:{self.port}/{self.db}" + + @property + def pip_packages(self) -> list[str]: + return super().pip_packages + ["asyncpg"] + + +SqlStoreConfig = Annotated[ + SqliteSqlStoreConfig | PostgresSqlStoreConfig, + Field(discriminator="type", default=SqlStoreType.sqlite.value), +] + + +def sqlstore_impl(config: SqlStoreConfig) -> SqlStore: + if config.type in [SqlStoreType.sqlite.value, SqlStoreType.postgres.value]: + from .sqlalchemy_sqlstore import SqlAlchemySqlStoreImpl + + impl = SqlAlchemySqlStoreImpl(config) + else: + raise ValueError(f"Unknown sqlstore type {config.type}") + + return impl diff --git a/llama_stack/providers/utils/telemetry/tracing.py b/llama_stack/providers/utils/telemetry/tracing.py index 0f4fdd0d8..4edfa6516 100644 --- a/llama_stack/providers/utils/telemetry/tracing.py +++ b/llama_stack/providers/utils/telemetry/tracing.py @@ -34,6 +34,8 @@ logger = get_logger(__name__, category="core") INVALID_SPAN_ID = 0x0000000000000000 INVALID_TRACE_ID = 0x00000000000000000000000000000000 +ROOT_SPAN_MARKERS = ["__root__", "__root_span__"] + def trace_id_to_str(trace_id: int) -> str: """Convenience trace ID formatting method @@ -178,7 +180,8 @@ async def start_trace(name: str, attributes: dict[str, Any] = None) -> TraceCont trace_id = generate_trace_id() context = TraceContext(BACKGROUND_LOGGER, trace_id) - context.push_span(name, {"__root__": True, **(attributes or {})}) + attributes = {marker: True for marker in ROOT_SPAN_MARKERS} | (attributes or {}) + context.push_span(name, attributes) CURRENT_TRACE_CONTEXT.set(context) return context diff --git a/llama_stack/providers/utils/tools/mcp.py b/llama_stack/providers/utils/tools/mcp.py new file mode 100644 index 000000000..f024693a0 --- /dev/null +++ b/llama_stack/providers/utils/tools/mcp.py @@ -0,0 +1,100 @@ +# 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 contextlib import asynccontextmanager +from typing import Any + +try: + # for python < 3.11 + import exceptiongroup + + BaseExceptionGroup = exceptiongroup.BaseExceptionGroup +except ImportError: + pass + +import httpx +from mcp import ClientSession +from mcp import types as mcp_types +from mcp.client.sse import sse_client + +from llama_stack.apis.common.content_types import ImageContentItem, InterleavedContentItem, TextContentItem +from llama_stack.apis.tools import ( + ListToolDefsResponse, + ToolDef, + ToolInvocationResult, + ToolParameter, +) +from llama_stack.distribution.datatypes import AuthenticationRequiredError +from llama_stack.log import get_logger + +logger = get_logger(__name__, category="tools") + + +@asynccontextmanager +async def sse_client_wrapper(endpoint: str, headers: dict[str, str]): + try: + async with sse_client(endpoint, headers=headers) as streams: + async with ClientSession(*streams) as session: + await session.initialize() + yield session + except BaseException as e: + if isinstance(e, BaseExceptionGroup): + for exc in e.exceptions: + if isinstance(exc, httpx.HTTPStatusError) and exc.response.status_code == 401: + raise AuthenticationRequiredError(exc) from exc + elif isinstance(e, httpx.HTTPStatusError) and e.response.status_code == 401: + raise AuthenticationRequiredError(e) from e + + raise + + +async def list_mcp_tools(endpoint: str, headers: dict[str, str]) -> ListToolDefsResponse: + tools = [] + async with sse_client_wrapper(endpoint, headers) as session: + tools_result = await session.list_tools() + for tool in tools_result.tools: + parameters = [] + for param_name, param_schema in tool.inputSchema.get("properties", {}).items(): + parameters.append( + ToolParameter( + name=param_name, + parameter_type=param_schema.get("type", "string"), + description=param_schema.get("description", ""), + ) + ) + tools.append( + ToolDef( + name=tool.name, + description=tool.description, + parameters=parameters, + metadata={ + "endpoint": endpoint, + }, + ) + ) + return ListToolDefsResponse(data=tools) + + +async def invoke_mcp_tool( + endpoint: str, headers: dict[str, str], tool_name: str, kwargs: dict[str, Any] +) -> ToolInvocationResult: + async with sse_client_wrapper(endpoint, headers) as session: + result = await session.call_tool(tool_name, kwargs) + + content: list[InterleavedContentItem] = [] + for item in result.content: + if isinstance(item, mcp_types.TextContent): + content.append(TextContentItem(text=item.text)) + elif isinstance(item, mcp_types.ImageContent): + content.append(ImageContentItem(image=item.data)) + elif isinstance(item, mcp_types.EmbeddedResource): + logger.warning(f"EmbeddedResource is not supported: {item}") + else: + raise ValueError(f"Unknown content type: {type(item)}") + return ToolInvocationResult( + content=content, + error_code=1 if result.isError else 0, + ) diff --git a/llama_stack/templates/bedrock/build.yaml b/llama_stack/templates/bedrock/build.yaml index 46d5b9c69..97a06f77a 100644 --- a/llama_stack/templates/bedrock/build.yaml +++ b/llama_stack/templates/bedrock/build.yaml @@ -29,3 +29,6 @@ distribution_spec: - inline::rag-runtime - remote::model-context-protocol image_type: conda +additional_pip_packages: +- aiosqlite +- sqlalchemy[asyncio] diff --git a/llama_stack/templates/bedrock/run.yaml b/llama_stack/templates/bedrock/run.yaml index 30599a6c0..a58068a60 100644 --- a/llama_stack/templates/bedrock/run.yaml +++ b/llama_stack/templates/bedrock/run.yaml @@ -35,6 +35,9 @@ providers: type: sqlite namespace: null db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/bedrock}/agents_store.db + responses_store: + type: sqlite + db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/bedrock}/responses_store.db telemetry: - provider_id: meta-reference provider_type: inline::meta-reference @@ -96,6 +99,9 @@ providers: metadata_store: type: sqlite db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/bedrock}/registry.db +inference_store: + type: sqlite + db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/bedrock}/inference_store.db models: - metadata: {} model_id: meta.llama3-1-8b-instruct-v1:0 diff --git a/llama_stack/templates/cerebras/build.yaml b/llama_stack/templates/cerebras/build.yaml index 0498da1cd..f26f4ed9b 100644 --- a/llama_stack/templates/cerebras/build.yaml +++ b/llama_stack/templates/cerebras/build.yaml @@ -29,3 +29,6 @@ distribution_spec: - remote::tavily-search - inline::rag-runtime image_type: conda +additional_pip_packages: +- aiosqlite +- sqlalchemy[asyncio] diff --git a/llama_stack/templates/cerebras/doc_template.md b/llama_stack/templates/cerebras/doc_template.md index 76f8c34ad..5cae2b2da 100644 --- a/llama_stack/templates/cerebras/doc_template.md +++ b/llama_stack/templates/cerebras/doc_template.md @@ -46,7 +46,7 @@ docker run \ -p $LLAMA_STACK_PORT:$LLAMA_STACK_PORT \ -v ./run.yaml:/root/my-run.yaml \ llamastack/distribution-{{ name }} \ - --yaml-config /root/my-run.yaml \ + --config /root/my-run.yaml \ --port $LLAMA_STACK_PORT \ --env CEREBRAS_API_KEY=$CEREBRAS_API_KEY ``` diff --git a/llama_stack/templates/cerebras/run.yaml b/llama_stack/templates/cerebras/run.yaml index 0731b1df9..c080536b7 100644 --- a/llama_stack/templates/cerebras/run.yaml +++ b/llama_stack/templates/cerebras/run.yaml @@ -41,6 +41,9 @@ providers: type: sqlite namespace: null db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/cerebras}/agents_store.db + responses_store: + type: sqlite + db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/cerebras}/responses_store.db eval: - provider_id: meta-reference provider_type: inline::meta-reference @@ -99,6 +102,9 @@ providers: metadata_store: type: sqlite db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/cerebras}/registry.db +inference_store: + type: sqlite + db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/cerebras}/inference_store.db models: - metadata: {} model_id: llama3.1-8b diff --git a/llama_stack/templates/ci-tests/build.yaml b/llama_stack/templates/ci-tests/build.yaml index a4c5893c4..9f4fbbdda 100644 --- a/llama_stack/templates/ci-tests/build.yaml +++ b/llama_stack/templates/ci-tests/build.yaml @@ -30,3 +30,6 @@ distribution_spec: - inline::rag-runtime - remote::model-context-protocol image_type: conda +additional_pip_packages: +- aiosqlite +- sqlalchemy[asyncio] diff --git a/llama_stack/templates/ci-tests/run.yaml b/llama_stack/templates/ci-tests/run.yaml index d9ee5b3cf..368187d3a 100644 --- a/llama_stack/templates/ci-tests/run.yaml +++ b/llama_stack/templates/ci-tests/run.yaml @@ -38,6 +38,9 @@ providers: type: sqlite namespace: null db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/ci-tests}/agents_store.db + responses_store: + type: sqlite + db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/ci-tests}/responses_store.db telemetry: - provider_id: meta-reference provider_type: inline::meta-reference @@ -99,6 +102,9 @@ providers: metadata_store: type: sqlite db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/ci-tests}/registry.db +inference_store: + type: sqlite + db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/ci-tests}/inference_store.db models: - metadata: {} model_id: accounts/fireworks/models/llama-v3p1-8b-instruct diff --git a/llama_stack/templates/dell/build.yaml b/llama_stack/templates/dell/build.yaml index f5beb6c2f..513df16c1 100644 --- a/llama_stack/templates/dell/build.yaml +++ b/llama_stack/templates/dell/build.yaml @@ -30,3 +30,6 @@ distribution_spec: - remote::tavily-search - inline::rag-runtime image_type: conda +additional_pip_packages: +- aiosqlite +- sqlalchemy[asyncio] diff --git a/llama_stack/templates/dell/doc_template.md b/llama_stack/templates/dell/doc_template.md index 26f07130b..6bdd7f81c 100644 --- a/llama_stack/templates/dell/doc_template.md +++ b/llama_stack/templates/dell/doc_template.md @@ -143,7 +143,7 @@ docker run \ -v $HOME/.llama:/root/.llama \ -v ./llama_stack/templates/tgi/run-with-safety.yaml:/root/my-run.yaml \ llamastack/distribution-{{ name }} \ - --yaml-config /root/my-run.yaml \ + --config /root/my-run.yaml \ --port $LLAMA_STACK_PORT \ --env INFERENCE_MODEL=$INFERENCE_MODEL \ --env DEH_URL=$DEH_URL \ diff --git a/llama_stack/templates/dell/run-with-safety.yaml b/llama_stack/templates/dell/run-with-safety.yaml index 24c515112..5c6072245 100644 --- a/llama_stack/templates/dell/run-with-safety.yaml +++ b/llama_stack/templates/dell/run-with-safety.yaml @@ -41,6 +41,9 @@ providers: type: sqlite namespace: null db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/dell}/agents_store.db + responses_store: + type: sqlite + db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/dell}/responses_store.db telemetry: - provider_id: meta-reference provider_type: inline::meta-reference @@ -99,6 +102,9 @@ providers: metadata_store: type: sqlite db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/dell}/registry.db +inference_store: + type: sqlite + db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/dell}/inference_store.db models: - metadata: {} model_id: ${env.INFERENCE_MODEL} diff --git a/llama_stack/templates/dell/run.yaml b/llama_stack/templates/dell/run.yaml index fdece894f..ffaa0bf2f 100644 --- a/llama_stack/templates/dell/run.yaml +++ b/llama_stack/templates/dell/run.yaml @@ -37,6 +37,9 @@ providers: type: sqlite namespace: null db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/dell}/agents_store.db + responses_store: + type: sqlite + db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/dell}/responses_store.db telemetry: - provider_id: meta-reference provider_type: inline::meta-reference @@ -95,6 +98,9 @@ providers: metadata_store: type: sqlite db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/dell}/registry.db +inference_store: + type: sqlite + db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/dell}/inference_store.db models: - metadata: {} model_id: ${env.INFERENCE_MODEL} diff --git a/llama_stack/templates/dependencies.json b/llama_stack/templates/dependencies.json index d1a17e48e..47a35edc0 100644 --- a/llama_stack/templates/dependencies.json +++ b/llama_stack/templates/dependencies.json @@ -31,6 +31,7 @@ "scikit-learn", "scipy", "sentencepiece", + "sqlalchemy[asyncio]", "tqdm", "transformers", "tree_sitter", @@ -67,6 +68,7 @@ "scikit-learn", "scipy", "sentencepiece", + "sqlalchemy[asyncio]", "tqdm", "transformers", "tree_sitter", @@ -105,6 +107,7 @@ "scikit-learn", "scipy", "sentencepiece", + "sqlalchemy[asyncio]", "sqlite-vec", "tqdm", "transformers", @@ -145,6 +148,7 @@ "scikit-learn", "scipy", "sentencepiece", + "sqlalchemy[asyncio]", "tqdm", "transformers", "tree_sitter", @@ -184,6 +188,7 @@ "scikit-learn", "scipy", "sentencepiece", + "sqlalchemy[asyncio]", "tqdm", "transformers", "tree_sitter", @@ -221,6 +226,7 @@ "scikit-learn", "scipy", "sentencepiece", + "sqlalchemy[asyncio]", "tqdm", "transformers", "tree_sitter", @@ -259,6 +265,7 @@ "scikit-learn", "scipy", "sentencepiece", + "sqlalchemy[asyncio]", "tqdm", "transformers", "tree_sitter", @@ -297,6 +304,46 @@ "scikit-learn", "scipy", "sentencepiece", + "sqlalchemy[asyncio]", + "tqdm", + "transformers", + "tree_sitter", + "uvicorn", + "sentence-transformers --no-deps", + "torch torchvision --index-url https://download.pytorch.org/whl/cpu" + ], + "kvant": [ + "aiosqlite", + "autoevals", + "blobfile", + "chardet", + "chromadb-client", + "datasets", + "emoji", + "faiss-cpu", + "fastapi", + "fire", + "httpx", + "langdetect", + "matplotlib", + "mcp", + "nltk", + "numpy", + "openai", + "opentelemetry-exporter-otlp-proto-http", + "opentelemetry-sdk", + "pandas", + "pillow", + "psycopg2-binary", + "pymongo", + "pypdf", + "pythainlp", + "redis", + "requests", + "scikit-learn", + "scipy", + "sentencepiece", + "sqlalchemy[asyncio]", "tqdm", "transformers", "tree_sitter", @@ -335,6 +382,7 @@ "scikit-learn", "scipy", "sentencepiece", + "sqlalchemy[asyncio]", "sqlite-vec", "tqdm", "transformers", @@ -379,6 +427,7 @@ "scipy", "sentence-transformers", "sentencepiece", + "sqlalchemy[asyncio]", "torch", "torchao==0.8.0", "torchvision", @@ -414,6 +463,7 @@ "scikit-learn", "scipy", "sentencepiece", + "sqlalchemy[asyncio]", "tqdm", "transformers", "uvicorn" @@ -441,6 +491,7 @@ "opentelemetry-exporter-otlp-proto-http", "opentelemetry-sdk", "pandas", + "peft", "pillow", "psycopg2-binary", "pymongo", @@ -451,9 +502,12 @@ "scikit-learn", "scipy", "sentencepiece", + "sqlalchemy[asyncio]", + "torch", "tqdm", "transformers", "tree_sitter", + "trl", "uvicorn" ], "open-benchmark": [ @@ -487,6 +541,7 @@ "scikit-learn", "scipy", "sentencepiece", + "sqlalchemy[asyncio]", "sqlite-vec", "together", "tqdm", @@ -525,6 +580,7 @@ "scikit-learn", "scipy", "sentencepiece", + "sqlalchemy[asyncio]", "tqdm", "transformers", "tree_sitter", @@ -563,6 +619,7 @@ "scikit-learn", "scipy", "sentencepiece", + "sqlalchemy[asyncio]", "tqdm", "transformers", "tree_sitter", @@ -596,6 +653,7 @@ "scikit-learn", "scipy", "sentencepiece", + "sqlalchemy[asyncio]", "tqdm", "transformers", "uvicorn", @@ -634,6 +692,7 @@ "scikit-learn", "scipy", "sentencepiece", + "sqlalchemy[asyncio]", "sqlite-vec", "tqdm", "transformers", @@ -675,6 +734,7 @@ "scikit-learn", "scipy", "sentencepiece", + "sqlalchemy[asyncio]", "tqdm", "transformers", "tree_sitter", @@ -713,6 +773,7 @@ "scikit-learn", "scipy", "sentencepiece", + "sqlalchemy[asyncio]", "together", "tqdm", "transformers", @@ -752,6 +813,7 @@ "scikit-learn", "scipy", "sentencepiece", + "sqlalchemy[asyncio]", "sqlite-vec", "tqdm", "transformers", @@ -791,6 +853,7 @@ "scikit-learn", "scipy", "sentencepiece", + "sqlalchemy[asyncio]", "tqdm", "transformers", "tree_sitter", @@ -830,6 +893,7 @@ "scikit-learn", "scipy", "sentencepiece", + "sqlalchemy[asyncio]", "tqdm", "transformers", "tree_sitter", diff --git a/llama_stack/templates/experimental-post-training/build.yaml b/llama_stack/templates/experimental-post-training/build.yaml index b4b5e2203..55cd189c6 100644 --- a/llama_stack/templates/experimental-post-training/build.yaml +++ b/llama_stack/templates/experimental-post-training/build.yaml @@ -13,9 +13,10 @@ distribution_spec: - inline::basic - inline::braintrust post_training: - - inline::torchtune + - inline::huggingface datasetio: - inline::localfs + - remote::huggingface telemetry: - inline::meta-reference agents: diff --git a/llama_stack/templates/experimental-post-training/run.yaml b/llama_stack/templates/experimental-post-training/run.yaml index 2ebdfe1aa..393cba41d 100644 --- a/llama_stack/templates/experimental-post-training/run.yaml +++ b/llama_stack/templates/experimental-post-training/run.yaml @@ -49,16 +49,24 @@ providers: type: sqlite namespace: null db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/experimental-post-training}/localfs_datasetio.db + - provider_id: huggingface + provider_type: remote::huggingface + config: + kvstore: + type: sqlite + namespace: null + db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/huggingface}/huggingface_datasetio.db telemetry: - provider_id: meta-reference provider_type: inline::meta-reference config: {} post_training: - - provider_id: torchtune-post-training - provider_type: inline::torchtune - config: { + - provider_id: huggingface + provider_type: inline::huggingface + config: checkpoint_format: huggingface - } + distributed_backend: null + device: cpu agents: - provider_id: meta-reference provider_type: inline::meta-reference diff --git a/llama_stack/templates/fireworks/build.yaml b/llama_stack/templates/fireworks/build.yaml index 7c74157ee..be19181c0 100644 --- a/llama_stack/templates/fireworks/build.yaml +++ b/llama_stack/templates/fireworks/build.yaml @@ -31,3 +31,6 @@ distribution_spec: - inline::rag-runtime - remote::model-context-protocol image_type: conda +additional_pip_packages: +- aiosqlite +- sqlalchemy[asyncio] diff --git a/llama_stack/templates/fireworks/run-with-safety.yaml b/llama_stack/templates/fireworks/run-with-safety.yaml index 0ab07613e..41500f6f6 100644 --- a/llama_stack/templates/fireworks/run-with-safety.yaml +++ b/llama_stack/templates/fireworks/run-with-safety.yaml @@ -46,6 +46,9 @@ providers: type: sqlite namespace: null db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/fireworks}/agents_store.db + responses_store: + type: sqlite + db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/fireworks}/responses_store.db telemetry: - provider_id: meta-reference provider_type: inline::meta-reference @@ -111,6 +114,9 @@ providers: metadata_store: type: sqlite db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/fireworks}/registry.db +inference_store: + type: sqlite + db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/fireworks}/inference_store.db models: - metadata: {} model_id: accounts/fireworks/models/llama-v3p1-8b-instruct diff --git a/llama_stack/templates/fireworks/run.yaml b/llama_stack/templates/fireworks/run.yaml index 81c293a46..b1fa03306 100644 --- a/llama_stack/templates/fireworks/run.yaml +++ b/llama_stack/templates/fireworks/run.yaml @@ -41,6 +41,9 @@ providers: type: sqlite namespace: null db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/fireworks}/agents_store.db + responses_store: + type: sqlite + db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/fireworks}/responses_store.db telemetry: - provider_id: meta-reference provider_type: inline::meta-reference @@ -106,6 +109,9 @@ providers: metadata_store: type: sqlite db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/fireworks}/registry.db +inference_store: + type: sqlite + db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/fireworks}/inference_store.db models: - metadata: {} model_id: accounts/fireworks/models/llama-v3p1-8b-instruct diff --git a/llama_stack/templates/groq/build.yaml b/llama_stack/templates/groq/build.yaml index 800c3e3ae..819df22f0 100644 --- a/llama_stack/templates/groq/build.yaml +++ b/llama_stack/templates/groq/build.yaml @@ -26,3 +26,6 @@ distribution_spec: - remote::tavily-search - inline::rag-runtime image_type: conda +additional_pip_packages: +- aiosqlite +- sqlalchemy[asyncio] diff --git a/llama_stack/templates/groq/run.yaml b/llama_stack/templates/groq/run.yaml index 79c350c73..db7ebffee 100644 --- a/llama_stack/templates/groq/run.yaml +++ b/llama_stack/templates/groq/run.yaml @@ -41,6 +41,9 @@ providers: type: sqlite namespace: null db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/groq}/agents_store.db + responses_store: + type: sqlite + db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/groq}/responses_store.db telemetry: - provider_id: meta-reference provider_type: inline::meta-reference @@ -99,6 +102,9 @@ providers: metadata_store: type: sqlite db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/groq}/registry.db +inference_store: + type: sqlite + db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/groq}/inference_store.db models: - metadata: {} model_id: groq/llama3-8b-8192 diff --git a/llama_stack/templates/hf-endpoint/build.yaml b/llama_stack/templates/hf-endpoint/build.yaml index 2a40c3909..8ede83694 100644 --- a/llama_stack/templates/hf-endpoint/build.yaml +++ b/llama_stack/templates/hf-endpoint/build.yaml @@ -29,3 +29,6 @@ distribution_spec: - inline::rag-runtime - remote::model-context-protocol image_type: conda +additional_pip_packages: +- aiosqlite +- sqlalchemy[asyncio] diff --git a/llama_stack/templates/hf-endpoint/run-with-safety.yaml b/llama_stack/templates/hf-endpoint/run-with-safety.yaml index 82bcaa3cf..15cf2a47f 100644 --- a/llama_stack/templates/hf-endpoint/run-with-safety.yaml +++ b/llama_stack/templates/hf-endpoint/run-with-safety.yaml @@ -46,6 +46,9 @@ providers: type: sqlite namespace: null db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/hf-endpoint}/agents_store.db + responses_store: + type: sqlite + db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/hf-endpoint}/responses_store.db telemetry: - provider_id: meta-reference provider_type: inline::meta-reference @@ -107,6 +110,9 @@ providers: metadata_store: type: sqlite db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/hf-endpoint}/registry.db +inference_store: + type: sqlite + db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/hf-endpoint}/inference_store.db models: - metadata: {} model_id: ${env.INFERENCE_MODEL} diff --git a/llama_stack/templates/hf-endpoint/run.yaml b/llama_stack/templates/hf-endpoint/run.yaml index ec7c55032..428edf9a2 100644 --- a/llama_stack/templates/hf-endpoint/run.yaml +++ b/llama_stack/templates/hf-endpoint/run.yaml @@ -41,6 +41,9 @@ providers: type: sqlite namespace: null db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/hf-endpoint}/agents_store.db + responses_store: + type: sqlite + db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/hf-endpoint}/responses_store.db telemetry: - provider_id: meta-reference provider_type: inline::meta-reference @@ -102,6 +105,9 @@ providers: metadata_store: type: sqlite db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/hf-endpoint}/registry.db +inference_store: + type: sqlite + db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/hf-endpoint}/inference_store.db models: - metadata: {} model_id: ${env.INFERENCE_MODEL} diff --git a/llama_stack/templates/hf-serverless/build.yaml b/llama_stack/templates/hf-serverless/build.yaml index f77f8773b..d0752db9a 100644 --- a/llama_stack/templates/hf-serverless/build.yaml +++ b/llama_stack/templates/hf-serverless/build.yaml @@ -30,3 +30,6 @@ distribution_spec: - inline::rag-runtime - remote::model-context-protocol image_type: conda +additional_pip_packages: +- aiosqlite +- sqlalchemy[asyncio] diff --git a/llama_stack/templates/hf-serverless/run-with-safety.yaml b/llama_stack/templates/hf-serverless/run-with-safety.yaml index 320976e2c..ab461c6c3 100644 --- a/llama_stack/templates/hf-serverless/run-with-safety.yaml +++ b/llama_stack/templates/hf-serverless/run-with-safety.yaml @@ -46,6 +46,9 @@ providers: type: sqlite namespace: null db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/hf-serverless}/agents_store.db + responses_store: + type: sqlite + db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/hf-serverless}/responses_store.db telemetry: - provider_id: meta-reference provider_type: inline::meta-reference @@ -107,6 +110,9 @@ providers: metadata_store: type: sqlite db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/hf-serverless}/registry.db +inference_store: + type: sqlite + db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/hf-serverless}/inference_store.db models: - metadata: {} model_id: ${env.INFERENCE_MODEL} diff --git a/llama_stack/templates/hf-serverless/run.yaml b/llama_stack/templates/hf-serverless/run.yaml index 2b22b20c6..d238506fb 100644 --- a/llama_stack/templates/hf-serverless/run.yaml +++ b/llama_stack/templates/hf-serverless/run.yaml @@ -41,6 +41,9 @@ providers: type: sqlite namespace: null db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/hf-serverless}/agents_store.db + responses_store: + type: sqlite + db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/hf-serverless}/responses_store.db telemetry: - provider_id: meta-reference provider_type: inline::meta-reference @@ -102,6 +105,9 @@ providers: metadata_store: type: sqlite db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/hf-serverless}/registry.db +inference_store: + type: sqlite + db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/hf-serverless}/inference_store.db models: - metadata: {} model_id: ${env.INFERENCE_MODEL} diff --git a/llama_stack/templates/kvant/__init__.py b/llama_stack/templates/kvant/__init__.py new file mode 100644 index 000000000..61706f7f6 --- /dev/null +++ b/llama_stack/templates/kvant/__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 .kvant import get_distribution_template # noqa: F401 diff --git a/llama_stack/templates/kvant/build.yaml b/llama_stack/templates/kvant/build.yaml new file mode 100644 index 000000000..25afc1f4d --- /dev/null +++ b/llama_stack/templates/kvant/build.yaml @@ -0,0 +1,35 @@ +version: '2' +distribution_spec: + description: distribution for kvant cloud + providers: + inference: + - remote::vllm + - inline::sentence-transformers + vector_io: + - inline::faiss + - remote::chromadb + - remote::pgvector + safety: + - inline::llama-guard + agents: + - inline::meta-reference + telemetry: + - inline::meta-reference + eval: + - inline::meta-reference + datasetio: + - remote::huggingface + - inline::localfs + scoring: + - inline::basic + - inline::llm-as-judge + - inline::braintrust + tool_runtime: + - remote::brave-search + - remote::tavily-search + - remote::wolfram-alpha + - inline::rag-runtime + - remote::model-context-protocol +image_type: conda +additional_pip_packages: +- sqlalchemy[asyncio] diff --git a/llama_stack/templates/kvant/kvant.py b/llama_stack/templates/kvant/kvant.py new file mode 100644 index 000000000..44cfc7016 --- /dev/null +++ b/llama_stack/templates/kvant/kvant.py @@ -0,0 +1,136 @@ +# 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.apis.models.models import ModelType +from llama_stack.distribution.datatypes import ( + ModelInput, + Provider, + ShieldInput, + ToolGroupInput, +) +from llama_stack.providers.inline.inference.sentence_transformers import ( + SentenceTransformersInferenceConfig, +) +from llama_stack.providers.inline.vector_io.faiss.config import FaissVectorIOConfig +from llama_stack.providers.remote.inference.passthrough.config import ( + PassthroughImplConfig, +) +from llama_stack.providers.utils.inference.model_registry import ProviderModelEntry +from llama_stack.templates.template import DistributionTemplate, RunConfigSettings + + +def get_distribution_template() -> DistributionTemplate: + providers = { + "inference": ["remote::openai", "inline::sentence-transformers"], + "vector_io": ["inline::faiss", "remote::chromadb", "remote::pgvector"], + "safety": ["inline::llama-guard"], + "agents": ["inline::meta-reference"], + "telemetry": ["inline::meta-reference"], + "eval": ["inline::meta-reference"], + "datasetio": ["remote::huggingface", "inline::localfs"], + "scoring": ["inline::basic", "inline::llm-as-judge", "inline::braintrust"], + "tool_runtime": [ + "remote::brave-search", + "remote::tavily-search", + "remote::wolfram-alpha", + "inline::rag-runtime", + "remote::model-context-protocol", + ], + } + + name = "kvant" + + inference_provider = Provider( + provider_id="openai", + provider_type="remote::openai", + config=PassthroughImplConfig.sample_run_config(), + ) + embedding_provider = Provider( + provider_id="sentence-transformers", + provider_type="inline::sentence-transformers", + config=SentenceTransformersInferenceConfig.sample_run_config(), + ) + vector_io_provider = Provider( + provider_id="faiss", + provider_type="inline::faiss", + config=FaissVectorIOConfig.sample_run_config(f"~/.llama/distributions/{name}"), + ) + + default_models = [ + ModelInput( + metadata={}, + model_id="inference-llama4-maverick", + provider_id="openai", + provider_model_id="inference-llama4-maverick", + model_type=ModelType.llm, + ), + ] + + embedding_model = ModelInput( + model_id="all-MiniLM-L6-v2", + provider_id="sentence-transformers", + model_type=ModelType.embedding, + metadata={ + "embedding_dimension": 384, + }, + ) + default_tool_groups = [ + ToolGroupInput( + toolgroup_id="builtin::websearch", + provider_id="tavily-search", + ), + ToolGroupInput( + toolgroup_id="builtin::wolfram_alpha", + provider_id="wolfram-alpha", + ), + ToolGroupInput( + toolgroup_id="builtin::rag", + provider_id="rag-runtime", + ), + ] + + return DistributionTemplate( + name=name, + distro_type="self_hosted", + description="Use Passthrough hosted llama-stack endpoint for LLM inference", + container_image=None, + providers=providers, + available_models_by_provider={ + "openai": [ + ProviderModelEntry( + provider_model_id="inference-llama4-maverick", + model_type=ModelType.llm, + ), + ], + }, + run_configs={ + "run.yaml": RunConfigSettings( + provider_overrides={ + "inference": [inference_provider, embedding_provider], + "vector_io": [vector_io_provider], + }, + default_models=default_models + [embedding_model], + default_shields=[ShieldInput(shield_id="meta-llama/Llama-Guard-3-8B")], + default_tool_groups=default_tool_groups, + ), + }, + run_config_env_vars={ + "LLAMA_STACK_PORT": ( + "8321", + "Port for the Llama Stack distribution server", + ), + "OPENAI_API_KEY": ( + "", + "kvant maas API Key", + ), + "OPENAI_BASE_URL": ( + "https://maas.kvant.cloud", + "kvant maas URL", + ), + }, + ) diff --git a/llama_stack/templates/kvant/run.yaml b/llama_stack/templates/kvant/run.yaml new file mode 100644 index 000000000..99fb6f7fa --- /dev/null +++ b/llama_stack/templates/kvant/run.yaml @@ -0,0 +1,170 @@ +version: '2' +image_name: kvant +apis: +- agents +- datasetio +- eval +- inference +- safety +- scoring +- telemetry +- tool_runtime +- vector_io +providers: + inference: + - provider_id: kvant + provider_type: remote::vllm + config: + url: ${env.VLLM_URL:https://maas.ai-2.kvant.cloud/v1} + max_tokens: ${env.VLLM_MAX_TOKENS:400000} + api_token: ${env.VLLM_API_TOKEN:fake} + tls_verify: ${env.VLLM_TLS_VERIFY:true} + - provider_id: sentence-transformers + provider_type: inline::sentence-transformers + config: {} + vector_io: + - provider_id: faiss + provider_type: inline::faiss + config: + kvstore: + type: sqlite + namespace: null + db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/kvant}/faiss_store.db + safety: + - provider_id: llama-guard + provider_type: inline::llama-guard + config: + excluded_categories: [] + agents: + - provider_id: meta-reference + provider_type: inline::meta-reference + config: + persistence_store: + type: sqlite + namespace: null + db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/kvant}/agents_store.db + responses_store: + type: sqlite + db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/kvant}/responses_store.db + telemetry: + - provider_id: meta-reference + provider_type: inline::meta-reference + config: + service_name: ${env.OTEL_SERVICE_NAME:} + sinks: ${env.TELEMETRY_SINKS:console,sqlite} + sqlite_db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/kvant}/trace_store.db + eval: + - provider_id: meta-reference + provider_type: inline::meta-reference + config: + kvstore: + type: sqlite + namespace: null + db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/kvant}/meta_reference_eval.db + datasetio: + - provider_id: huggingface + provider_type: remote::huggingface + config: + kvstore: + type: sqlite + namespace: null + db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/kvant}/huggingface_datasetio.db + - provider_id: localfs + provider_type: inline::localfs + config: + kvstore: + type: sqlite + namespace: null + db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/kvant}/localfs_datasetio.db + scoring: + - provider_id: basic + provider_type: inline::basic + config: {} + - provider_id: llm-as-judge + provider_type: inline::llm-as-judge + config: {} + - provider_id: braintrust + provider_type: inline::braintrust + config: + openai_api_key: ${env.OPENAI_API_KEY:} + tool_runtime: + - provider_id: brave-search + provider_type: remote::brave-search + config: + api_key: ${env.BRAVE_SEARCH_API_KEY:} + max_results: 3 + - provider_id: tavily-search + provider_type: remote::tavily-search + config: + api_key: ${env.TAVILY_SEARCH_API_KEY:} + max_results: 3 + - provider_id: wolfram-alpha + provider_type: remote::wolfram-alpha + config: + api_key: ${env.WOLFRAM_ALPHA_API_KEY:} + - provider_id: rag-runtime + provider_type: inline::rag-runtime + config: {} + - provider_id: model-context-protocol + provider_type: remote::model-context-protocol + config: {} +metadata_store: + type: sqlite + db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/kvant}/registry.db +inference_store: + type: sqlite + db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/kvant}/inference_store.db +models: +- metadata: {} + model_id: Llama-4-Maverick-17B-128E-Instruct-FP8 + provider_id: kvant + provider_model_id: inference-llama4-maverick + model_type: llm +- metadata: + embedding_dimension: 1024 + context_length: 8192 + model_id: inference-bge-m3 + provider_id: kvant + model_type: embedding +- metadata: + embedding_dimension: 384 + model_id: all-MiniLM-L6-v2 + provider_id: sentence-transformers + model_type: embedding +shields: +- shield_id: meta-llama/Llama-Guard-3-8B +vector_dbs: [] +# - vector_db_id: test-bge +# embedding_model: inference-bge-m3 +# embedding_dimension: 1024 +# provider_id: faiss +# - vector_db_id: test-MiniLM-L6-v2 +# embedding_model: all-MiniLM-L6-v2 +# embedding_dimension: 384 +# provider_id: faiss +datasets: [] +scoring_fns: [] +benchmarks: [] +tool_groups: +- toolgroup_id: builtin::websearch + provider_id: tavily-search +- toolgroup_id: builtin::wolfram_alpha + provider_id: wolfram-alpha +- toolgroup_id: builtin::rag + provider_id: rag-runtime +server: + port: 8321 + auth: + provider_type: "oauth2_token" + config: + jwks: + introspection: + url: ${env.KEYCLOAK_INSTROSPECT:https://iam.phoenix-systems.ch/realms/kvant/protocol/openid-connect/token/introspect} + client_id: ${env.KEYCLOAK_CLIENT_ID:llama-stack} + client_secret: ${env.KEYCLOAK_CLIENT_SECRET} + claims_mapping: + sub: projects + scope: roles + #groups: teams + customer/id: teams + aud: namespaces diff --git a/llama_stack/templates/llama_api/build.yaml b/llama_stack/templates/llama_api/build.yaml index f97ee4091..857e5f014 100644 --- a/llama_stack/templates/llama_api/build.yaml +++ b/llama_stack/templates/llama_api/build.yaml @@ -30,3 +30,6 @@ distribution_spec: - inline::rag-runtime - remote::model-context-protocol image_type: conda +additional_pip_packages: +- aiosqlite +- sqlalchemy[asyncio] diff --git a/llama_stack/templates/llama_api/run.yaml b/llama_stack/templates/llama_api/run.yaml index a879482d7..a7f2b0769 100644 --- a/llama_stack/templates/llama_api/run.yaml +++ b/llama_stack/templates/llama_api/run.yaml @@ -50,6 +50,9 @@ providers: type: sqlite namespace: null db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/llama_api}/agents_store.db + responses_store: + type: sqlite + db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/llama_api}/responses_store.db telemetry: - provider_id: meta-reference provider_type: inline::meta-reference @@ -111,6 +114,9 @@ providers: metadata_store: type: sqlite db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/llama_api}/registry.db +inference_store: + type: sqlite + db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/llama_api}/inference_store.db models: - metadata: {} model_id: Llama-3.3-70B-Instruct diff --git a/llama_stack/templates/meta-reference-gpu/build.yaml b/llama_stack/templates/meta-reference-gpu/build.yaml index a9d03490b..53ad411e3 100644 --- a/llama_stack/templates/meta-reference-gpu/build.yaml +++ b/llama_stack/templates/meta-reference-gpu/build.yaml @@ -29,3 +29,6 @@ distribution_spec: - inline::rag-runtime - remote::model-context-protocol image_type: conda +additional_pip_packages: +- aiosqlite +- sqlalchemy[asyncio] diff --git a/llama_stack/templates/meta-reference-gpu/run-with-safety.yaml b/llama_stack/templates/meta-reference-gpu/run-with-safety.yaml index 180d44e0f..2b751a514 100644 --- a/llama_stack/templates/meta-reference-gpu/run-with-safety.yaml +++ b/llama_stack/templates/meta-reference-gpu/run-with-safety.yaml @@ -56,6 +56,9 @@ providers: type: sqlite namespace: null db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/meta-reference-gpu}/agents_store.db + responses_store: + type: sqlite + db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/meta-reference-gpu}/responses_store.db telemetry: - provider_id: meta-reference provider_type: inline::meta-reference @@ -117,6 +120,9 @@ providers: metadata_store: type: sqlite db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/meta-reference-gpu}/registry.db +inference_store: + type: sqlite + db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/meta-reference-gpu}/inference_store.db models: - metadata: {} model_id: ${env.INFERENCE_MODEL} diff --git a/llama_stack/templates/meta-reference-gpu/run.yaml b/llama_stack/templates/meta-reference-gpu/run.yaml index d879667e0..a24c5fec5 100644 --- a/llama_stack/templates/meta-reference-gpu/run.yaml +++ b/llama_stack/templates/meta-reference-gpu/run.yaml @@ -46,6 +46,9 @@ providers: type: sqlite namespace: null db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/meta-reference-gpu}/agents_store.db + responses_store: + type: sqlite + db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/meta-reference-gpu}/responses_store.db telemetry: - provider_id: meta-reference provider_type: inline::meta-reference @@ -107,6 +110,9 @@ providers: metadata_store: type: sqlite db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/meta-reference-gpu}/registry.db +inference_store: + type: sqlite + db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/meta-reference-gpu}/inference_store.db models: - metadata: {} model_id: ${env.INFERENCE_MODEL} diff --git a/llama_stack/templates/nvidia/build.yaml b/llama_stack/templates/nvidia/build.yaml index a05cf97ad..6bd8a0100 100644 --- a/llama_stack/templates/nvidia/build.yaml +++ b/llama_stack/templates/nvidia/build.yaml @@ -24,3 +24,6 @@ distribution_spec: 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 index 068dd7ac3..50c96802f 100644 --- a/llama_stack/templates/nvidia/doc_template.md +++ b/llama_stack/templates/nvidia/doc_template.md @@ -116,7 +116,7 @@ docker run \ -p $LLAMA_STACK_PORT:$LLAMA_STACK_PORT \ -v ./run.yaml:/root/my-run.yaml \ llamastack/distribution-{{ name }} \ - --yaml-config /root/my-run.yaml \ + --config /root/my-run.yaml \ --port $LLAMA_STACK_PORT \ --env NVIDIA_API_KEY=$NVIDIA_API_KEY ``` diff --git a/llama_stack/templates/nvidia/run-with-safety.yaml b/llama_stack/templates/nvidia/run-with-safety.yaml index 3cdb8e3d2..c431e12f2 100644 --- a/llama_stack/templates/nvidia/run-with-safety.yaml +++ b/llama_stack/templates/nvidia/run-with-safety.yaml @@ -46,6 +46,9 @@ providers: type: sqlite namespace: null 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 @@ -92,6 +95,9 @@ providers: 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} diff --git a/llama_stack/templates/nvidia/run.yaml b/llama_stack/templates/nvidia/run.yaml index 3337b7942..5b244081d 100644 --- a/llama_stack/templates/nvidia/run.yaml +++ b/llama_stack/templates/nvidia/run.yaml @@ -41,6 +41,9 @@ providers: type: sqlite namespace: null 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 @@ -80,6 +83,9 @@ providers: 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 diff --git a/llama_stack/templates/ollama/build.yaml b/llama_stack/templates/ollama/build.yaml index 88e61bf8a..36a120897 100644 --- a/llama_stack/templates/ollama/build.yaml +++ b/llama_stack/templates/ollama/build.yaml @@ -23,6 +23,8 @@ distribution_spec: - inline::basic - inline::llm-as-judge - inline::braintrust + post_training: + - inline::huggingface tool_runtime: - remote::brave-search - remote::tavily-search @@ -30,3 +32,6 @@ distribution_spec: - remote::model-context-protocol - remote::wolfram-alpha image_type: conda +additional_pip_packages: +- aiosqlite +- sqlalchemy[asyncio] diff --git a/llama_stack/templates/ollama/doc_template.md b/llama_stack/templates/ollama/doc_template.md index f961ab7ed..aaa65bab2 100644 --- a/llama_stack/templates/ollama/doc_template.md +++ b/llama_stack/templates/ollama/doc_template.md @@ -86,7 +86,7 @@ docker run \ -v ~/.llama:/root/.llama \ -v ./llama_stack/templates/ollama/run-with-safety.yaml:/root/my-run.yaml \ llamastack/distribution-{{ name }} \ - --yaml-config /root/my-run.yaml \ + --config /root/my-run.yaml \ --port $LLAMA_STACK_PORT \ --env INFERENCE_MODEL=$INFERENCE_MODEL \ --env SAFETY_MODEL=$SAFETY_MODEL \ diff --git a/llama_stack/templates/ollama/ollama.py b/llama_stack/templates/ollama/ollama.py index d72d299ec..0b4f05128 100644 --- a/llama_stack/templates/ollama/ollama.py +++ b/llama_stack/templates/ollama/ollama.py @@ -13,6 +13,7 @@ from llama_stack.distribution.datatypes import ( ShieldInput, ToolGroupInput, ) +from llama_stack.providers.inline.post_training.huggingface import HuggingFacePostTrainingConfig from llama_stack.providers.inline.vector_io.faiss.config import FaissVectorIOConfig from llama_stack.providers.remote.inference.ollama import OllamaImplConfig from llama_stack.templates.template import DistributionTemplate, RunConfigSettings @@ -28,6 +29,7 @@ def get_distribution_template() -> DistributionTemplate: "eval": ["inline::meta-reference"], "datasetio": ["remote::huggingface", "inline::localfs"], "scoring": ["inline::basic", "inline::llm-as-judge", "inline::braintrust"], + "post_training": ["inline::huggingface"], "tool_runtime": [ "remote::brave-search", "remote::tavily-search", @@ -47,7 +49,11 @@ def get_distribution_template() -> DistributionTemplate: provider_type="inline::faiss", config=FaissVectorIOConfig.sample_run_config(f"~/.llama/distributions/{name}"), ) - + posttraining_provider = Provider( + provider_id="huggingface", + provider_type="inline::huggingface", + config=HuggingFacePostTrainingConfig.sample_run_config(f"~/.llama/distributions/{name}"), + ) inference_model = ModelInput( model_id="${env.INFERENCE_MODEL}", provider_id="ollama", @@ -92,6 +98,7 @@ def get_distribution_template() -> DistributionTemplate: provider_overrides={ "inference": [inference_provider], "vector_io": [vector_io_provider_faiss], + "post_training": [posttraining_provider], }, default_models=[inference_model, embedding_model], default_tool_groups=default_tool_groups, @@ -100,6 +107,7 @@ def get_distribution_template() -> DistributionTemplate: provider_overrides={ "inference": [inference_provider], "vector_io": [vector_io_provider_faiss], + "post_training": [posttraining_provider], "safety": [ Provider( provider_id="llama-guard", diff --git a/llama_stack/templates/ollama/run-with-safety.yaml b/llama_stack/templates/ollama/run-with-safety.yaml index 651d58117..d63c5e366 100644 --- a/llama_stack/templates/ollama/run-with-safety.yaml +++ b/llama_stack/templates/ollama/run-with-safety.yaml @@ -5,6 +5,7 @@ apis: - datasetio - eval - inference +- post_training - safety - scoring - telemetry @@ -39,6 +40,9 @@ providers: type: sqlite namespace: null db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/ollama}/agents_store.db + responses_store: + type: sqlite + db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/ollama}/responses_store.db telemetry: - provider_id: meta-reference provider_type: inline::meta-reference @@ -80,6 +84,13 @@ providers: provider_type: inline::braintrust config: openai_api_key: ${env.OPENAI_API_KEY:} + post_training: + - provider_id: huggingface + provider_type: inline::huggingface + config: + checkpoint_format: huggingface + distributed_backend: null + device: cpu tool_runtime: - provider_id: brave-search provider_type: remote::brave-search @@ -104,6 +115,9 @@ providers: metadata_store: type: sqlite db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/ollama}/registry.db +inference_store: + type: sqlite + db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/ollama}/inference_store.db models: - metadata: {} model_id: ${env.INFERENCE_MODEL} diff --git a/llama_stack/templates/ollama/run.yaml b/llama_stack/templates/ollama/run.yaml index 1372486fe..d208cd7f0 100644 --- a/llama_stack/templates/ollama/run.yaml +++ b/llama_stack/templates/ollama/run.yaml @@ -5,6 +5,7 @@ apis: - datasetio - eval - inference +- post_training - safety - scoring - telemetry @@ -37,6 +38,9 @@ providers: type: sqlite namespace: null db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/ollama}/agents_store.db + responses_store: + type: sqlite + db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/ollama}/responses_store.db telemetry: - provider_id: meta-reference provider_type: inline::meta-reference @@ -78,6 +82,13 @@ providers: provider_type: inline::braintrust config: openai_api_key: ${env.OPENAI_API_KEY:} + post_training: + - provider_id: huggingface + provider_type: inline::huggingface + config: + checkpoint_format: huggingface + distributed_backend: null + device: cpu tool_runtime: - provider_id: brave-search provider_type: remote::brave-search @@ -102,6 +113,9 @@ providers: metadata_store: type: sqlite db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/ollama}/registry.db +inference_store: + type: sqlite + db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/ollama}/inference_store.db models: - metadata: {} model_id: ${env.INFERENCE_MODEL} diff --git a/llama_stack/templates/open-benchmark/build.yaml b/llama_stack/templates/open-benchmark/build.yaml index b14e96435..840f1e1db 100644 --- a/llama_stack/templates/open-benchmark/build.yaml +++ b/llama_stack/templates/open-benchmark/build.yaml @@ -33,3 +33,6 @@ distribution_spec: - inline::rag-runtime - remote::model-context-protocol image_type: conda +additional_pip_packages: +- aiosqlite +- sqlalchemy[asyncio] diff --git a/llama_stack/templates/open-benchmark/run.yaml b/llama_stack/templates/open-benchmark/run.yaml index 30a27cbd8..0e5edf728 100644 --- a/llama_stack/templates/open-benchmark/run.yaml +++ b/llama_stack/templates/open-benchmark/run.yaml @@ -64,6 +64,9 @@ providers: type: sqlite namespace: null db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/open-benchmark}/agents_store.db + responses_store: + type: sqlite + db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/open-benchmark}/responses_store.db telemetry: - provider_id: meta-reference provider_type: inline::meta-reference @@ -125,6 +128,9 @@ providers: metadata_store: type: sqlite db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/open-benchmark}/registry.db +inference_store: + type: sqlite + db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/open-benchmark}/inference_store.db models: - metadata: {} model_id: openai/gpt-4o diff --git a/llama_stack/templates/passthrough/build.yaml b/llama_stack/templates/passthrough/build.yaml index f8d099070..46b99cb75 100644 --- a/llama_stack/templates/passthrough/build.yaml +++ b/llama_stack/templates/passthrough/build.yaml @@ -31,3 +31,6 @@ distribution_spec: - inline::rag-runtime - remote::model-context-protocol image_type: conda +additional_pip_packages: +- aiosqlite +- sqlalchemy[asyncio] diff --git a/llama_stack/templates/passthrough/run-with-safety.yaml b/llama_stack/templates/passthrough/run-with-safety.yaml index a91b9fc92..bbf5d9a52 100644 --- a/llama_stack/templates/passthrough/run-with-safety.yaml +++ b/llama_stack/templates/passthrough/run-with-safety.yaml @@ -46,6 +46,9 @@ providers: type: sqlite namespace: null db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/passthrough}/agents_store.db + responses_store: + type: sqlite + db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/passthrough}/responses_store.db telemetry: - provider_id: meta-reference provider_type: inline::meta-reference @@ -111,6 +114,9 @@ providers: metadata_store: type: sqlite db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/passthrough}/registry.db +inference_store: + type: sqlite + db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/passthrough}/inference_store.db models: - metadata: {} model_id: meta-llama/Llama-3.1-8B-Instruct diff --git a/llama_stack/templates/passthrough/run.yaml b/llama_stack/templates/passthrough/run.yaml index d1dd3b885..146906d9b 100644 --- a/llama_stack/templates/passthrough/run.yaml +++ b/llama_stack/templates/passthrough/run.yaml @@ -41,6 +41,9 @@ providers: type: sqlite namespace: null db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/passthrough}/agents_store.db + responses_store: + type: sqlite + db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/passthrough}/responses_store.db telemetry: - provider_id: meta-reference provider_type: inline::meta-reference @@ -106,6 +109,9 @@ providers: metadata_store: type: sqlite db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/passthrough}/registry.db +inference_store: + type: sqlite + db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/passthrough}/inference_store.db models: - metadata: {} model_id: meta-llama/Llama-3.1-8B-Instruct diff --git a/llama_stack/templates/postgres-demo/__init__.py b/llama_stack/templates/postgres-demo/__init__.py new file mode 100644 index 000000000..81473cb73 --- /dev/null +++ b/llama_stack/templates/postgres-demo/__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 .postgres_demo import get_distribution_template # noqa: F401 diff --git a/llama_stack/templates/postgres-demo/build.yaml b/llama_stack/templates/postgres-demo/build.yaml new file mode 100644 index 000000000..8f3648abe --- /dev/null +++ b/llama_stack/templates/postgres-demo/build.yaml @@ -0,0 +1,24 @@ +version: '2' +distribution_spec: + description: Quick start template for running Llama Stack with several popular providers + providers: + inference: + - remote::fireworks + - remote::vllm + vector_io: + - remote::chromadb + safety: + - inline::llama-guard + agents: + - inline::meta-reference + telemetry: + - inline::meta-reference + tool_runtime: + - remote::brave-search + - remote::tavily-search + - inline::rag-runtime + - remote::model-context-protocol +image_type: conda +additional_pip_packages: +- asyncpg +- sqlalchemy[asyncio] diff --git a/llama_stack/templates/postgres-demo/postgres_demo.py b/llama_stack/templates/postgres-demo/postgres_demo.py new file mode 100644 index 000000000..d2e352320 --- /dev/null +++ b/llama_stack/templates/postgres-demo/postgres_demo.py @@ -0,0 +1,164 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the terms described in the LICENSE file in +# the root directory of this source tree. + + +from llama_stack.distribution.datatypes import ( + ModelInput, + Provider, + ShieldInput, + ToolGroupInput, +) +from llama_stack.providers.remote.inference.fireworks.config import FireworksImplConfig +from llama_stack.providers.remote.inference.fireworks.models import ( + MODEL_ENTRIES as FIREWORKS_MODEL_ENTRIES, +) +from llama_stack.providers.remote.inference.vllm import VLLMInferenceAdapterConfig +from llama_stack.providers.remote.vector_io.chroma.config import ChromaVectorIOConfig +from llama_stack.providers.utils.inference.model_registry import ProviderModelEntry +from llama_stack.providers.utils.kvstore.config import PostgresKVStoreConfig +from llama_stack.providers.utils.sqlstore.sqlstore import PostgresSqlStoreConfig +from llama_stack.templates.template import ( + DistributionTemplate, + RunConfigSettings, + get_model_registry, +) + + +def get_inference_providers() -> tuple[list[Provider], dict[str, list[ProviderModelEntry]]]: + # in this template, we allow each API key to be optional + providers = [ + ( + "fireworks", + FIREWORKS_MODEL_ENTRIES, + FireworksImplConfig.sample_run_config(api_key="${env.FIREWORKS_API_KEY:}"), + ), + ] + inference_providers = [] + available_models = {} + for provider_id, model_entries, config in providers: + inference_providers.append( + Provider( + provider_id=provider_id, + provider_type=f"remote::{provider_id}", + config=config, + ) + ) + available_models[provider_id] = model_entries + inference_providers.append( + Provider( + provider_id="vllm-inference", + provider_type="remote::vllm", + config=VLLMInferenceAdapterConfig.sample_run_config( + url="${env.VLLM_URL:http://localhost:8000/v1}", + ), + ) + ) + return inference_providers, available_models + + +def get_distribution_template() -> DistributionTemplate: + inference_providers, available_models = get_inference_providers() + providers = { + "inference": ([p.provider_type for p in inference_providers]), + "vector_io": ["remote::chromadb"], + "safety": ["inline::llama-guard"], + "agents": ["inline::meta-reference"], + "telemetry": ["inline::meta-reference"], + "tool_runtime": [ + "remote::brave-search", + "remote::tavily-search", + "inline::rag-runtime", + "remote::model-context-protocol", + ], + } + name = "postgres-demo" + + vector_io_providers = [ + Provider( + provider_id="${env.ENABLE_CHROMADB+chromadb}", + provider_type="remote::chromadb", + config=ChromaVectorIOConfig.sample_run_config(url="${env.CHROMADB_URL:}"), + ), + ] + default_tool_groups = [ + ToolGroupInput( + toolgroup_id="builtin::websearch", + provider_id="tavily-search", + ), + ToolGroupInput( + toolgroup_id="builtin::rag", + provider_id="rag-runtime", + ), + ] + + default_models = get_model_registry(available_models) + default_models.append( + ModelInput( + model_id="${env.INFERENCE_MODEL}", + provider_id="vllm-inference", + ) + ) + postgres_config = { + "type": "postgres", + "host": "${env.POSTGRES_HOST:localhost}", + "port": "${env.POSTGRES_PORT:5432}", + "db": "${env.POSTGRES_DB:llamastack}", + "user": "${env.POSTGRES_USER:llamastack}", + "password": "${env.POSTGRES_PASSWORD:llamastack}", + } + + return DistributionTemplate( + name=name, + distro_type="self_hosted", + description="Quick start template for running Llama Stack with several popular providers", + container_image=None, + template_path=None, + providers=providers, + available_models_by_provider=available_models, + run_configs={ + "run.yaml": RunConfigSettings( + provider_overrides={ + "inference": inference_providers, + "vector_io": vector_io_providers, + "agents": [ + Provider( + provider_id="meta-reference", + provider_type="inline::meta-reference", + config=dict( + persistence_store=postgres_config, + responses_store=postgres_config, + ), + ) + ], + "telemetry": [ + Provider( + provider_id="meta-reference", + provider_type="inline::meta-reference", + config=dict( + service_name="${env.OTEL_SERVICE_NAME:}", + sinks="${env.TELEMETRY_SINKS:console}", + ), + ) + ], + }, + default_models=default_models, + default_tool_groups=default_tool_groups, + default_shields=[ShieldInput(shield_id="meta-llama/Llama-Guard-3-8B")], + metadata_store=PostgresKVStoreConfig.model_validate(postgres_config), + inference_store=PostgresSqlStoreConfig.model_validate(postgres_config), + ), + }, + run_config_env_vars={ + "LLAMA_STACK_PORT": ( + "8321", + "Port for the Llama Stack distribution server", + ), + "FIREWORKS_API_KEY": ( + "", + "Fireworks API Key", + ), + }, + ) diff --git a/llama_stack/templates/postgres-demo/run.yaml b/llama_stack/templates/postgres-demo/run.yaml new file mode 100644 index 000000000..889b8eaa7 --- /dev/null +++ b/llama_stack/templates/postgres-demo/run.yaml @@ -0,0 +1,224 @@ +version: '2' +image_name: postgres-demo +apis: +- agents +- inference +- safety +- telemetry +- tool_runtime +- vector_io +providers: + inference: + - provider_id: fireworks + provider_type: remote::fireworks + config: + url: https://api.fireworks.ai/inference/v1 + api_key: ${env.FIREWORKS_API_KEY:} + - provider_id: vllm-inference + provider_type: remote::vllm + config: + url: ${env.VLLM_URL:http://localhost:8000/v1} + max_tokens: ${env.VLLM_MAX_TOKENS:4096} + api_token: ${env.VLLM_API_TOKEN:fake} + tls_verify: ${env.VLLM_TLS_VERIFY:true} + vector_io: + - provider_id: ${env.ENABLE_CHROMADB+chromadb} + provider_type: remote::chromadb + config: + url: ${env.CHROMADB_URL:} + safety: + - provider_id: llama-guard + provider_type: inline::llama-guard + config: + excluded_categories: [] + agents: + - provider_id: meta-reference + provider_type: inline::meta-reference + config: + persistence_store: + type: postgres + host: ${env.POSTGRES_HOST:localhost} + port: ${env.POSTGRES_PORT:5432} + db: ${env.POSTGRES_DB:llamastack} + user: ${env.POSTGRES_USER:llamastack} + password: ${env.POSTGRES_PASSWORD:llamastack} + responses_store: + type: postgres + host: ${env.POSTGRES_HOST:localhost} + port: ${env.POSTGRES_PORT:5432} + db: ${env.POSTGRES_DB:llamastack} + user: ${env.POSTGRES_USER:llamastack} + password: ${env.POSTGRES_PASSWORD:llamastack} + telemetry: + - provider_id: meta-reference + provider_type: inline::meta-reference + config: + service_name: ${env.OTEL_SERVICE_NAME:} + sinks: ${env.TELEMETRY_SINKS:console} + tool_runtime: + - provider_id: brave-search + provider_type: remote::brave-search + config: + api_key: ${env.BRAVE_SEARCH_API_KEY:} + max_results: 3 + - provider_id: tavily-search + provider_type: remote::tavily-search + config: + api_key: ${env.TAVILY_SEARCH_API_KEY:} + max_results: 3 + - provider_id: rag-runtime + provider_type: inline::rag-runtime + config: {} + - provider_id: model-context-protocol + provider_type: remote::model-context-protocol + config: {} +metadata_store: + type: postgres + host: ${env.POSTGRES_HOST:localhost} + port: ${env.POSTGRES_PORT:5432} + db: ${env.POSTGRES_DB:llamastack} + user: ${env.POSTGRES_USER:llamastack} + password: ${env.POSTGRES_PASSWORD:llamastack} + table_name: llamastack_kvstore +inference_store: + type: postgres + host: ${env.POSTGRES_HOST:localhost} + port: ${env.POSTGRES_PORT:5432} + db: ${env.POSTGRES_DB:llamastack} + user: ${env.POSTGRES_USER:llamastack} + password: ${env.POSTGRES_PASSWORD:llamastack} +models: +- metadata: {} + model_id: accounts/fireworks/models/llama-v3p1-8b-instruct + provider_id: fireworks + provider_model_id: accounts/fireworks/models/llama-v3p1-8b-instruct + model_type: llm +- metadata: {} + model_id: meta-llama/Llama-3.1-8B-Instruct + provider_id: fireworks + provider_model_id: accounts/fireworks/models/llama-v3p1-8b-instruct + model_type: llm +- metadata: {} + model_id: accounts/fireworks/models/llama-v3p1-70b-instruct + provider_id: fireworks + provider_model_id: accounts/fireworks/models/llama-v3p1-70b-instruct + model_type: llm +- metadata: {} + model_id: meta-llama/Llama-3.1-70B-Instruct + provider_id: fireworks + provider_model_id: accounts/fireworks/models/llama-v3p1-70b-instruct + model_type: llm +- metadata: {} + model_id: accounts/fireworks/models/llama-v3p1-405b-instruct + provider_id: fireworks + provider_model_id: accounts/fireworks/models/llama-v3p1-405b-instruct + model_type: llm +- metadata: {} + model_id: meta-llama/Llama-3.1-405B-Instruct-FP8 + provider_id: fireworks + provider_model_id: accounts/fireworks/models/llama-v3p1-405b-instruct + model_type: llm +- metadata: {} + model_id: accounts/fireworks/models/llama-v3p2-3b-instruct + provider_id: fireworks + provider_model_id: accounts/fireworks/models/llama-v3p2-3b-instruct + model_type: llm +- metadata: {} + model_id: meta-llama/Llama-3.2-3B-Instruct + provider_id: fireworks + provider_model_id: accounts/fireworks/models/llama-v3p2-3b-instruct + model_type: llm +- metadata: {} + model_id: accounts/fireworks/models/llama-v3p2-11b-vision-instruct + provider_id: fireworks + provider_model_id: accounts/fireworks/models/llama-v3p2-11b-vision-instruct + model_type: llm +- metadata: {} + model_id: meta-llama/Llama-3.2-11B-Vision-Instruct + provider_id: fireworks + provider_model_id: accounts/fireworks/models/llama-v3p2-11b-vision-instruct + model_type: llm +- metadata: {} + model_id: accounts/fireworks/models/llama-v3p2-90b-vision-instruct + provider_id: fireworks + provider_model_id: accounts/fireworks/models/llama-v3p2-90b-vision-instruct + model_type: llm +- metadata: {} + model_id: meta-llama/Llama-3.2-90B-Vision-Instruct + provider_id: fireworks + provider_model_id: accounts/fireworks/models/llama-v3p2-90b-vision-instruct + model_type: llm +- metadata: {} + model_id: accounts/fireworks/models/llama-v3p3-70b-instruct + provider_id: fireworks + provider_model_id: accounts/fireworks/models/llama-v3p3-70b-instruct + model_type: llm +- metadata: {} + model_id: meta-llama/Llama-3.3-70B-Instruct + provider_id: fireworks + provider_model_id: accounts/fireworks/models/llama-v3p3-70b-instruct + model_type: llm +- metadata: {} + model_id: accounts/fireworks/models/llama-guard-3-8b + provider_id: fireworks + provider_model_id: accounts/fireworks/models/llama-guard-3-8b + model_type: llm +- metadata: {} + model_id: meta-llama/Llama-Guard-3-8B + provider_id: fireworks + provider_model_id: accounts/fireworks/models/llama-guard-3-8b + model_type: llm +- metadata: {} + model_id: accounts/fireworks/models/llama-guard-3-11b-vision + provider_id: fireworks + provider_model_id: accounts/fireworks/models/llama-guard-3-11b-vision + model_type: llm +- metadata: {} + model_id: meta-llama/Llama-Guard-3-11B-Vision + provider_id: fireworks + provider_model_id: accounts/fireworks/models/llama-guard-3-11b-vision + model_type: llm +- metadata: {} + model_id: accounts/fireworks/models/llama4-scout-instruct-basic + provider_id: fireworks + provider_model_id: accounts/fireworks/models/llama4-scout-instruct-basic + model_type: llm +- metadata: {} + model_id: meta-llama/Llama-4-Scout-17B-16E-Instruct + provider_id: fireworks + provider_model_id: accounts/fireworks/models/llama4-scout-instruct-basic + model_type: llm +- metadata: {} + model_id: accounts/fireworks/models/llama4-maverick-instruct-basic + provider_id: fireworks + provider_model_id: accounts/fireworks/models/llama4-maverick-instruct-basic + model_type: llm +- metadata: {} + model_id: meta-llama/Llama-4-Maverick-17B-128E-Instruct + provider_id: fireworks + provider_model_id: accounts/fireworks/models/llama4-maverick-instruct-basic + model_type: llm +- metadata: + embedding_dimension: 768 + context_length: 8192 + model_id: nomic-ai/nomic-embed-text-v1.5 + provider_id: fireworks + provider_model_id: nomic-ai/nomic-embed-text-v1.5 + model_type: embedding +- metadata: {} + model_id: ${env.INFERENCE_MODEL} + provider_id: vllm-inference + model_type: llm +shields: +- shield_id: meta-llama/Llama-Guard-3-8B +vector_dbs: [] +datasets: [] +scoring_fns: [] +benchmarks: [] +tool_groups: +- toolgroup_id: builtin::websearch + provider_id: tavily-search +- toolgroup_id: builtin::rag + provider_id: rag-runtime +server: + port: 8321 diff --git a/llama_stack/templates/remote-vllm/build.yaml b/llama_stack/templates/remote-vllm/build.yaml index 4baaaf9c8..16fe5d4fd 100644 --- a/llama_stack/templates/remote-vllm/build.yaml +++ b/llama_stack/templates/remote-vllm/build.yaml @@ -31,3 +31,6 @@ distribution_spec: - remote::model-context-protocol - remote::wolfram-alpha image_type: conda +additional_pip_packages: +- aiosqlite +- sqlalchemy[asyncio] diff --git a/llama_stack/templates/remote-vllm/doc_template.md b/llama_stack/templates/remote-vllm/doc_template.md index 3cede6080..5684888da 100644 --- a/llama_stack/templates/remote-vllm/doc_template.md +++ b/llama_stack/templates/remote-vllm/doc_template.md @@ -220,7 +220,7 @@ docker run \ -p $LLAMA_STACK_PORT:$LLAMA_STACK_PORT \ -v ./llama_stack/templates/remote-vllm/run.yaml:/root/my-run.yaml \ llamastack/distribution-{{ name }} \ - --yaml-config /root/my-run.yaml \ + --config /root/my-run.yaml \ --port $LLAMA_STACK_PORT \ --env INFERENCE_MODEL=$INFERENCE_MODEL \ --env VLLM_URL=http://host.docker.internal:$INFERENCE_PORT/v1 @@ -242,7 +242,7 @@ docker run \ -v ~/.llama:/root/.llama \ -v ./llama_stack/templates/remote-vllm/run-with-safety.yaml:/root/my-run.yaml \ llamastack/distribution-{{ name }} \ - --yaml-config /root/my-run.yaml \ + --config /root/my-run.yaml \ --port $LLAMA_STACK_PORT \ --env INFERENCE_MODEL=$INFERENCE_MODEL \ --env VLLM_URL=http://host.docker.internal:$INFERENCE_PORT/v1 \ diff --git a/llama_stack/templates/remote-vllm/run-with-safety.yaml b/llama_stack/templates/remote-vllm/run-with-safety.yaml index 6931d4ba9..e83162a4f 100644 --- a/llama_stack/templates/remote-vllm/run-with-safety.yaml +++ b/llama_stack/templates/remote-vllm/run-with-safety.yaml @@ -50,6 +50,9 @@ providers: type: sqlite namespace: null db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/remote-vllm}/agents_store.db + responses_store: + type: sqlite + db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/remote-vllm}/responses_store.db eval: - provider_id: meta-reference provider_type: inline::meta-reference @@ -115,6 +118,9 @@ providers: metadata_store: type: sqlite db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/remote-vllm}/registry.db +inference_store: + type: sqlite + db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/remote-vllm}/inference_store.db models: - metadata: {} model_id: ${env.INFERENCE_MODEL} diff --git a/llama_stack/templates/remote-vllm/run.yaml b/llama_stack/templates/remote-vllm/run.yaml index 05671165d..4cdf88c6b 100644 --- a/llama_stack/templates/remote-vllm/run.yaml +++ b/llama_stack/templates/remote-vllm/run.yaml @@ -43,6 +43,9 @@ providers: type: sqlite namespace: null db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/remote-vllm}/agents_store.db + responses_store: + type: sqlite + db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/remote-vllm}/responses_store.db eval: - provider_id: meta-reference provider_type: inline::meta-reference @@ -108,6 +111,9 @@ providers: metadata_store: type: sqlite db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/remote-vllm}/registry.db +inference_store: + type: sqlite + db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/remote-vllm}/inference_store.db models: - metadata: {} model_id: ${env.INFERENCE_MODEL} diff --git a/llama_stack/templates/sambanova/build.yaml b/llama_stack/templates/sambanova/build.yaml index 81d90f420..14b1c8974 100644 --- a/llama_stack/templates/sambanova/build.yaml +++ b/llama_stack/templates/sambanova/build.yaml @@ -1,6 +1,6 @@ version: '2' distribution_spec: - description: Use SambaNova for running LLM inference + description: Use SambaNova for running LLM inference and safety providers: inference: - remote::sambanova @@ -10,7 +10,7 @@ distribution_spec: - remote::chromadb - remote::pgvector safety: - - inline::llama-guard + - remote::sambanova agents: - inline::meta-reference telemetry: @@ -22,3 +22,6 @@ distribution_spec: - remote::model-context-protocol - remote::wolfram-alpha image_type: conda +additional_pip_packages: +- aiosqlite +- sqlalchemy[asyncio] diff --git a/llama_stack/templates/sambanova/doc_template.md b/llama_stack/templates/sambanova/doc_template.md index 42d9efb66..1dc76fd3f 100644 --- a/llama_stack/templates/sambanova/doc_template.md +++ b/llama_stack/templates/sambanova/doc_template.md @@ -37,33 +37,44 @@ The following models are available by default: ### Prerequisite: API Keys -Make sure you have access to a SambaNova API Key. You can get one by visiting [SambaNova.ai](https://sambanova.ai/). +Make sure you have access to a SambaNova API Key. You can get one by visiting [SambaNova.ai](http://cloud.sambanova.ai?utm_source=llamastack&utm_medium=external&utm_campaign=cloud_signup). ## Running Llama Stack with SambaNova You can do this via Conda (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. +### Via Docker ```bash LLAMA_STACK_PORT=8321 +llama stack build --template sambanova --image-type container docker run \ -it \ - --pull always \ -p $LLAMA_STACK_PORT:$LLAMA_STACK_PORT \ - llamastack/distribution-{{ name }} \ + -v ~/.llama:/root/.llama \ + distribution-{{ name }} \ --port $LLAMA_STACK_PORT \ --env SAMBANOVA_API_KEY=$SAMBANOVA_API_KEY ``` + +### Via Venv + +```bash +llama stack build --template sambanova --image-type venv +llama stack run --image-type venv ~/.llama/distributions/sambanova/sambanova-run.yaml \ + --port $LLAMA_STACK_PORT \ + --env SAMBANOVA_API_KEY=$SAMBANOVA_API_KEY +``` + + ### Via Conda ```bash llama stack build --template sambanova --image-type conda -llama stack run ./run.yaml \ +llama stack run --image-type conda ~/.llama/distributions/sambanova/sambanova-run.yaml \ --port $LLAMA_STACK_PORT \ --env SAMBANOVA_API_KEY=$SAMBANOVA_API_KEY ``` diff --git a/llama_stack/templates/sambanova/run.yaml b/llama_stack/templates/sambanova/run.yaml index 620d50307..8c2a933ab 100644 --- a/llama_stack/templates/sambanova/run.yaml +++ b/llama_stack/templates/sambanova/run.yaml @@ -38,10 +38,11 @@ providers: user: ${env.PGVECTOR_USER:} password: ${env.PGVECTOR_PASSWORD:} safety: - - provider_id: llama-guard - provider_type: inline::llama-guard + - provider_id: sambanova + provider_type: remote::sambanova config: - excluded_categories: [] + url: https://api.sambanova.ai/v1 + api_key: ${env.SAMBANOVA_API_KEY} agents: - provider_id: meta-reference provider_type: inline::meta-reference @@ -50,6 +51,9 @@ providers: type: sqlite namespace: null db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/sambanova}/agents_store.db + responses_store: + type: sqlite + db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/sambanova}/responses_store.db telemetry: - provider_id: meta-reference provider_type: inline::meta-reference @@ -81,6 +85,9 @@ providers: metadata_store: type: sqlite db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/sambanova}/registry.db +inference_store: + type: sqlite + db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/sambanova}/inference_store.db models: - metadata: {} model_id: sambanova/Meta-Llama-3.1-8B-Instruct @@ -189,6 +196,9 @@ models: model_type: embedding shields: - shield_id: meta-llama/Llama-Guard-3-8B + provider_shield_id: sambanova/Meta-Llama-Guard-3-8B +- shield_id: sambanova/Meta-Llama-Guard-3-8B + provider_shield_id: sambanova/Meta-Llama-Guard-3-8B vector_dbs: [] datasets: [] scoring_fns: [] diff --git a/llama_stack/templates/sambanova/sambanova.py b/llama_stack/templates/sambanova/sambanova.py index 2f8a0b08a..54a49423d 100644 --- a/llama_stack/templates/sambanova/sambanova.py +++ b/llama_stack/templates/sambanova/sambanova.py @@ -34,7 +34,7 @@ def get_distribution_template() -> DistributionTemplate: providers = { "inference": ["remote::sambanova", "inline::sentence-transformers"], "vector_io": ["inline::faiss", "remote::chromadb", "remote::pgvector"], - "safety": ["inline::llama-guard"], + "safety": ["remote::sambanova"], "agents": ["inline::meta-reference"], "telemetry": ["inline::meta-reference"], "tool_runtime": [ @@ -110,7 +110,7 @@ def get_distribution_template() -> DistributionTemplate: return DistributionTemplate( name=name, distro_type="self_hosted", - description="Use SambaNova for running LLM inference", + description="Use SambaNova for running LLM inference and safety", container_image=None, template_path=Path(__file__).parent / "doc_template.md", providers=providers, @@ -122,7 +122,15 @@ def get_distribution_template() -> DistributionTemplate: "vector_io": vector_io_providers, }, default_models=default_models + [embedding_model], - default_shields=[ShieldInput(shield_id="meta-llama/Llama-Guard-3-8B")], + default_shields=[ + ShieldInput( + shield_id="meta-llama/Llama-Guard-3-8B", provider_shield_id="sambanova/Meta-Llama-Guard-3-8B" + ), + ShieldInput( + shield_id="sambanova/Meta-Llama-Guard-3-8B", + provider_shield_id="sambanova/Meta-Llama-Guard-3-8B", + ), + ], default_tool_groups=default_tool_groups, ), }, diff --git a/llama_stack/templates/starter/build.yaml b/llama_stack/templates/starter/build.yaml index 35bd0c713..ec97c7d3e 100644 --- a/llama_stack/templates/starter/build.yaml +++ b/llama_stack/templates/starter/build.yaml @@ -35,3 +35,6 @@ distribution_spec: - inline::rag-runtime - remote::model-context-protocol image_type: conda +additional_pip_packages: +- aiosqlite +- sqlalchemy[asyncio] diff --git a/llama_stack/templates/starter/run.yaml b/llama_stack/templates/starter/run.yaml index 402695850..04425ed35 100644 --- a/llama_stack/templates/starter/run.yaml +++ b/llama_stack/templates/starter/run.yaml @@ -72,6 +72,9 @@ providers: type: sqlite namespace: null db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/starter}/agents_store.db + responses_store: + type: sqlite + db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/starter}/responses_store.db telemetry: - provider_id: meta-reference provider_type: inline::meta-reference @@ -133,6 +136,9 @@ providers: metadata_store: type: sqlite db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/starter}/registry.db +inference_store: + type: sqlite + db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/starter}/inference_store.db models: - metadata: {} model_id: openai/gpt-4o diff --git a/llama_stack/templates/template.py b/llama_stack/templates/template.py index e4d28d904..4013f08f9 100644 --- a/llama_stack/templates/template.py +++ b/llama_stack/templates/template.py @@ -28,7 +28,8 @@ from llama_stack.distribution.datatypes import ( from llama_stack.distribution.distribution import get_provider_registry from llama_stack.distribution.utils.dynamic import instantiate_class_type from llama_stack.providers.utils.inference.model_registry import ProviderModelEntry -from llama_stack.providers.utils.kvstore.config import SqliteKVStoreConfig +from llama_stack.providers.utils.kvstore.config import KVStoreConfig, SqliteKVStoreConfig +from llama_stack.providers.utils.sqlstore.sqlstore import SqliteSqlStoreConfig, SqlStoreConfig def get_model_registry( @@ -63,6 +64,8 @@ class RunConfigSettings(BaseModel): default_tool_groups: list[ToolGroupInput] | None = None default_datasets: list[DatasetInput] | None = None default_benchmarks: list[BenchmarkInput] | None = None + metadata_store: KVStoreConfig | None = None + inference_store: SqlStoreConfig | None = None def run_config( self, @@ -113,10 +116,16 @@ class RunConfigSettings(BaseModel): container_image=container_image, apis=apis, providers=provider_configs, - metadata_store=SqliteKVStoreConfig.sample_run_config( + metadata_store=self.metadata_store + or SqliteKVStoreConfig.sample_run_config( __distro_dir__=f"~/.llama/distributions/{name}", db_name="registry.db", ), + inference_store=self.inference_store + or SqliteSqlStoreConfig.sample_run_config( + __distro_dir__=f"~/.llama/distributions/{name}", + db_name="inference_store.db", + ), models=self.default_models or [], shields=self.default_shields or [], tool_groups=self.default_tool_groups or [], @@ -146,14 +155,20 @@ class DistributionTemplate(BaseModel): available_models_by_provider: dict[str, list[ProviderModelEntry]] | None = None def build_config(self) -> BuildConfig: + additional_pip_packages: list[str] = [] + for run_config in self.run_configs.values(): + run_config_ = run_config.run_config(self.name, self.providers, self.container_image) + if run_config_.inference_store: + additional_pip_packages.extend(run_config_.inference_store.pip_packages) + return BuildConfig( - name=self.name, distribution_spec=DistributionSpec( description=self.description, container_image=self.container_image, providers=self.providers, ), image_type="conda", # default to conda, can be overridden + additional_pip_packages=sorted(set(additional_pip_packages)), ) def generate_markdown_docs(self) -> str: diff --git a/llama_stack/templates/tgi/build.yaml b/llama_stack/templates/tgi/build.yaml index d2ba1c3e9..361b0b680 100644 --- a/llama_stack/templates/tgi/build.yaml +++ b/llama_stack/templates/tgi/build.yaml @@ -30,3 +30,6 @@ distribution_spec: - inline::rag-runtime - remote::model-context-protocol image_type: conda +additional_pip_packages: +- aiosqlite +- sqlalchemy[asyncio] diff --git a/llama_stack/templates/tgi/doc_template.md b/llama_stack/templates/tgi/doc_template.md index b69ccaa56..68b475893 100644 --- a/llama_stack/templates/tgi/doc_template.md +++ b/llama_stack/templates/tgi/doc_template.md @@ -105,7 +105,7 @@ docker run \ -v ~/.llama:/root/.llama \ -v ./llama_stack/templates/tgi/run-with-safety.yaml:/root/my-run.yaml \ llamastack/distribution-{{ name }} \ - --yaml-config /root/my-run.yaml \ + --config /root/my-run.yaml \ --port $LLAMA_STACK_PORT \ --env INFERENCE_MODEL=$INFERENCE_MODEL \ --env TGI_URL=http://host.docker.internal:$INFERENCE_PORT \ diff --git a/llama_stack/templates/tgi/run-with-safety.yaml b/llama_stack/templates/tgi/run-with-safety.yaml index 3255e9c0b..c797b93aa 100644 --- a/llama_stack/templates/tgi/run-with-safety.yaml +++ b/llama_stack/templates/tgi/run-with-safety.yaml @@ -41,6 +41,9 @@ providers: type: sqlite namespace: null db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/tgi}/agents_store.db + responses_store: + type: sqlite + db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/tgi}/responses_store.db telemetry: - provider_id: meta-reference provider_type: inline::meta-reference @@ -102,6 +105,9 @@ providers: metadata_store: type: sqlite db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/tgi}/registry.db +inference_store: + type: sqlite + db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/tgi}/inference_store.db models: - metadata: {} model_id: ${env.INFERENCE_MODEL} diff --git a/llama_stack/templates/tgi/run.yaml b/llama_stack/templates/tgi/run.yaml index 179087258..7e91d20bd 100644 --- a/llama_stack/templates/tgi/run.yaml +++ b/llama_stack/templates/tgi/run.yaml @@ -40,6 +40,9 @@ providers: type: sqlite namespace: null db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/tgi}/agents_store.db + responses_store: + type: sqlite + db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/tgi}/responses_store.db telemetry: - provider_id: meta-reference provider_type: inline::meta-reference @@ -101,6 +104,9 @@ providers: metadata_store: type: sqlite db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/tgi}/registry.db +inference_store: + type: sqlite + db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/tgi}/inference_store.db models: - metadata: {} model_id: ${env.INFERENCE_MODEL} diff --git a/llama_stack/templates/together/build.yaml b/llama_stack/templates/together/build.yaml index b7338795c..5ffeac873 100644 --- a/llama_stack/templates/together/build.yaml +++ b/llama_stack/templates/together/build.yaml @@ -31,3 +31,6 @@ distribution_spec: - remote::model-context-protocol - remote::wolfram-alpha image_type: conda +additional_pip_packages: +- aiosqlite +- sqlalchemy[asyncio] diff --git a/llama_stack/templates/together/run-with-safety.yaml b/llama_stack/templates/together/run-with-safety.yaml index fe8c8e397..190a0400b 100644 --- a/llama_stack/templates/together/run-with-safety.yaml +++ b/llama_stack/templates/together/run-with-safety.yaml @@ -46,6 +46,9 @@ providers: type: sqlite namespace: null db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/together}/agents_store.db + responses_store: + type: sqlite + db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/together}/responses_store.db telemetry: - provider_id: meta-reference provider_type: inline::meta-reference @@ -111,6 +114,9 @@ providers: metadata_store: type: sqlite db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/together}/registry.db +inference_store: + type: sqlite + db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/together}/inference_store.db models: - metadata: {} model_id: meta-llama/Meta-Llama-3.1-8B-Instruct-Turbo diff --git a/llama_stack/templates/together/run.yaml b/llama_stack/templates/together/run.yaml index b903fc659..ce9542130 100644 --- a/llama_stack/templates/together/run.yaml +++ b/llama_stack/templates/together/run.yaml @@ -41,6 +41,9 @@ providers: type: sqlite namespace: null db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/together}/agents_store.db + responses_store: + type: sqlite + db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/together}/responses_store.db telemetry: - provider_id: meta-reference provider_type: inline::meta-reference @@ -106,6 +109,9 @@ providers: metadata_store: type: sqlite db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/together}/registry.db +inference_store: + type: sqlite + db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/together}/inference_store.db models: - metadata: {} model_id: meta-llama/Meta-Llama-3.1-8B-Instruct-Turbo diff --git a/llama_stack/templates/verification/build.yaml b/llama_stack/templates/verification/build.yaml index aae24c3ca..ce083dbba 100644 --- a/llama_stack/templates/verification/build.yaml +++ b/llama_stack/templates/verification/build.yaml @@ -35,3 +35,6 @@ distribution_spec: - inline::rag-runtime - remote::model-context-protocol image_type: conda +additional_pip_packages: +- aiosqlite +- sqlalchemy[asyncio] diff --git a/llama_stack/templates/verification/run.yaml b/llama_stack/templates/verification/run.yaml index 11af41da9..58b3c576c 100644 --- a/llama_stack/templates/verification/run.yaml +++ b/llama_stack/templates/verification/run.yaml @@ -74,6 +74,9 @@ providers: type: sqlite namespace: null db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/verification}/agents_store.db + responses_store: + type: sqlite + db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/verification}/responses_store.db telemetry: - provider_id: meta-reference provider_type: inline::meta-reference @@ -135,6 +138,9 @@ providers: metadata_store: type: sqlite db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/verification}/registry.db +inference_store: + type: sqlite + db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/verification}/inference_store.db models: - metadata: {} model_id: openai/gpt-4o diff --git a/llama_stack/templates/vllm-gpu/build.yaml b/llama_stack/templates/vllm-gpu/build.yaml index 53e257f22..d5ff0f1f4 100644 --- a/llama_stack/templates/vllm-gpu/build.yaml +++ b/llama_stack/templates/vllm-gpu/build.yaml @@ -30,3 +30,6 @@ distribution_spec: - inline::rag-runtime - remote::model-context-protocol image_type: conda +additional_pip_packages: +- aiosqlite +- sqlalchemy[asyncio] diff --git a/llama_stack/templates/vllm-gpu/run.yaml b/llama_stack/templates/vllm-gpu/run.yaml index 5d3482528..6937e2bac 100644 --- a/llama_stack/templates/vllm-gpu/run.yaml +++ b/llama_stack/templates/vllm-gpu/run.yaml @@ -45,6 +45,9 @@ providers: type: sqlite namespace: null db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/vllm-gpu}/agents_store.db + responses_store: + type: sqlite + db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/vllm-gpu}/responses_store.db telemetry: - provider_id: meta-reference provider_type: inline::meta-reference @@ -106,6 +109,9 @@ providers: metadata_store: type: sqlite db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/vllm-gpu}/registry.db +inference_store: + type: sqlite + db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/vllm-gpu}/inference_store.db models: - metadata: {} model_id: ${env.INFERENCE_MODEL} diff --git a/llama_stack/templates/watsonx/build.yaml b/llama_stack/templates/watsonx/build.yaml index 638b16029..e68ace183 100644 --- a/llama_stack/templates/watsonx/build.yaml +++ b/llama_stack/templates/watsonx/build.yaml @@ -28,3 +28,6 @@ distribution_spec: - inline::rag-runtime - remote::model-context-protocol image_type: conda +additional_pip_packages: +- aiosqlite +- sqlalchemy[asyncio] diff --git a/llama_stack/templates/watsonx/doc_template.md b/llama_stack/templates/watsonx/doc_template.md index af0ae15a8..f28dbf0bf 100644 --- a/llama_stack/templates/watsonx/doc_template.md +++ b/llama_stack/templates/watsonx/doc_template.md @@ -56,7 +56,7 @@ docker run \ -p $LLAMA_STACK_PORT:$LLAMA_STACK_PORT \ -v ./run.yaml:/root/my-run.yaml \ llamastack/distribution-{{ name }} \ - --yaml-config /root/my-run.yaml \ + --config /root/my-run.yaml \ --port $LLAMA_STACK_PORT \ --env WATSONX_API_KEY=$WATSONX_API_KEY \ --env WATSONX_PROJECT_ID=$WATSONX_PROJECT_ID \ diff --git a/llama_stack/templates/watsonx/run.yaml b/llama_stack/templates/watsonx/run.yaml index 8de6a2b6c..e7222fd57 100644 --- a/llama_stack/templates/watsonx/run.yaml +++ b/llama_stack/templates/watsonx/run.yaml @@ -42,6 +42,9 @@ providers: type: sqlite namespace: null db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/watsonx}/agents_store.db + responses_store: + type: sqlite + db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/watsonx}/responses_store.db telemetry: - provider_id: meta-reference provider_type: inline::meta-reference @@ -103,6 +106,9 @@ providers: metadata_store: type: sqlite db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/watsonx}/registry.db +inference_store: + type: sqlite + db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/watsonx}/inference_store.db models: - metadata: {} model_id: meta-llama/llama-3-3-70b-instruct diff --git a/llama_stack/ui/.prettierignore b/llama_stack/ui/.prettierignore new file mode 100644 index 000000000..1b8ac8894 --- /dev/null +++ b/llama_stack/ui/.prettierignore @@ -0,0 +1,3 @@ +# Ignore artifacts: +build +coverage diff --git a/llama_stack/ui/.prettierrc b/llama_stack/ui/.prettierrc new file mode 100644 index 000000000..0967ef424 --- /dev/null +++ b/llama_stack/ui/.prettierrc @@ -0,0 +1 @@ +{} diff --git a/llama_stack/ui/README.md b/llama_stack/ui/README.md index e3e21bf0b..b6f803509 100644 --- a/llama_stack/ui/README.md +++ b/llama_stack/ui/README.md @@ -1,6 +1,5 @@ ## This is WIP. - We use shadcdn/ui [Shadcn UI](https://ui.shadcn.com/) for the UI components. ## Getting Started @@ -8,7 +7,7 @@ We use shadcdn/ui [Shadcn UI](https://ui.shadcn.com/) for the UI components. First, install dependencies: ```bash -npm install next react react-dom +npm install ``` Then, run the development server: @@ -23,4 +22,4 @@ pnpm dev bun dev ``` -Open [http://localhost:3000](http://localhost:3000) with your browser to see the result. +Open [http://localhost:8322](http://localhost:8322) with your browser to see the result. diff --git a/llama_stack/ui/app/layout.tsx b/llama_stack/ui/app/layout.tsx index f029002dd..ed8a6cd5d 100644 --- a/llama_stack/ui/app/layout.tsx +++ b/llama_stack/ui/app/layout.tsx @@ -20,7 +20,7 @@ export const metadata: Metadata = { }; import { SidebarProvider, SidebarTrigger } from "@/components/ui/sidebar"; -import { AppSidebar } from "@/components/app-sidebar"; +import { AppSidebar } from "@/components/layout/app-sidebar"; export default function Layout({ children }: { children: React.ReactNode }) { return ( diff --git a/llama_stack/ui/app/logs/chat-completions/[id]/page.tsx b/llama_stack/ui/app/logs/chat-completions/[id]/page.tsx new file mode 100644 index 000000000..e6feef363 --- /dev/null +++ b/llama_stack/ui/app/logs/chat-completions/[id]/page.tsx @@ -0,0 +1,58 @@ +"use client"; + +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"; + +export default function ChatCompletionDetailPage() { + const params = useParams(); + const id = params.id as string; + + const [completionDetail, setCompletionDetail] = + useState(null); + const [isLoading, setIsLoading] = useState(true); + const [error, setError] = useState(null); + + useEffect(() => { + if (!id) { + setError(new Error("Completion ID is missing.")); + setIsLoading(false); + return; + } + + const fetchCompletionDetail = async () => { + setIsLoading(true); + setError(null); + setCompletionDetail(null); + try { + const response = await client.chat.completions.retrieve(id); + setCompletionDetail(response as ChatCompletion); + } catch (err) { + console.error( + `Error fetching chat completion detail for ID ${id}:`, + err, + ); + setError( + err instanceof Error + ? err + : new Error("Failed to fetch completion detail"), + ); + } finally { + setIsLoading(false); + } + }; + + fetchCompletionDetail(); + }, [id]); + + return ( + + ); +} diff --git a/llama_stack/ui/app/logs/chat-completions/layout.tsx b/llama_stack/ui/app/logs/chat-completions/layout.tsx new file mode 100644 index 000000000..f4dbfc782 --- /dev/null +++ b/llama_stack/ui/app/logs/chat-completions/layout.tsx @@ -0,0 +1,19 @@ +"use client"; + +import React from "react"; +import LogsLayout from "@/components/layout/logs-layout"; + +export default function ChatCompletionsLayout({ + children, +}: { + children: React.ReactNode; +}) { + return ( + + {children} + + ); +} diff --git a/llama_stack/ui/app/logs/chat-completions/page.tsx b/llama_stack/ui/app/logs/chat-completions/page.tsx index 84cceb8b7..5bbfcce94 100644 --- a/llama_stack/ui/app/logs/chat-completions/page.tsx +++ b/llama_stack/ui/app/logs/chat-completions/page.tsx @@ -1,7 +1,51 @@ -export default function ChatCompletions() { +"use client"; + +import { useEffect, useState } from "react"; +import { ChatCompletion } from "@/lib/types"; +import { ChatCompletionsTable } from "@/components/chat-completions/chat-completions-table"; +import { client } from "@/lib/client"; + +export default function ChatCompletionsPage() { + const [completions, setCompletions] = useState([]); + const [isLoading, setIsLoading] = useState(true); + const [error, setError] = useState(null); + + useEffect(() => { + const fetchCompletions = async () => { + setIsLoading(true); + setError(null); + try { + const response = await client.chat.completions.list(); + const data = Array.isArray(response) + ? response + : (response as { data: ChatCompletion[] }).data; + + if (Array.isArray(data)) { + setCompletions(data); + } else { + console.error("Unexpected response structure:", response); + setError(new Error("Unexpected response structure")); + setCompletions([]); + } + } catch (err) { + console.error("Error fetching chat completions:", err); + setError( + err instanceof Error ? err : new Error("Failed to fetch completions"), + ); + setCompletions([]); + } finally { + setIsLoading(false); + } + }; + + fetchCompletions(); + }, []); + return ( -
-

Under Construction

-
+ ); } diff --git a/llama_stack/ui/app/logs/responses/[id]/page.tsx b/llama_stack/ui/app/logs/responses/[id]/page.tsx new file mode 100644 index 000000000..efe6f0ff3 --- /dev/null +++ b/llama_stack/ui/app/logs/responses/[id]/page.tsx @@ -0,0 +1,125 @@ +"use client"; + +import { useEffect, useState } from "react"; +import { useParams } from "next/navigation"; +import type { ResponseObject } from "llama-stack-client/resources/responses/responses"; +import { OpenAIResponse, InputItemListResponse } from "@/lib/types"; +import { ResponseDetailView } from "@/components/responses/responses-detail"; +import { client } from "@/lib/client"; + +export default function ResponseDetailPage() { + const params = useParams(); + const id = params.id as string; + + const [responseDetail, setResponseDetail] = useState( + null, + ); + const [inputItems, setInputItems] = useState( + null, + ); + const [isLoading, setIsLoading] = useState(true); + const [isLoadingInputItems, setIsLoadingInputItems] = useState(true); + const [error, setError] = useState(null); + const [inputItemsError, setInputItemsError] = useState(null); + + // Helper function to convert ResponseObject to OpenAIResponse + const convertResponseObject = ( + responseData: ResponseObject, + ): OpenAIResponse => { + return { + id: responseData.id, + created_at: responseData.created_at, + model: responseData.model, + object: responseData.object, + status: responseData.status, + output: responseData.output as OpenAIResponse["output"], + input: [], // ResponseObject doesn't include input; component uses inputItems prop instead + error: responseData.error, + parallel_tool_calls: responseData.parallel_tool_calls, + previous_response_id: responseData.previous_response_id, + temperature: responseData.temperature, + top_p: responseData.top_p, + truncation: responseData.truncation, + user: responseData.user, + }; + }; + + useEffect(() => { + if (!id) { + setError(new Error("Response ID is missing.")); + setIsLoading(false); + return; + } + + const fetchResponseDetail = async () => { + setIsLoading(true); + setIsLoadingInputItems(true); + setError(null); + setInputItemsError(null); + setResponseDetail(null); + setInputItems(null); + + try { + const [responseResult, inputItemsResult] = await Promise.allSettled([ + client.responses.retrieve(id), + client.responses.inputItems.list(id, { order: "asc" }), + ]); + + // Handle response detail result + if (responseResult.status === "fulfilled") { + const convertedResponse = convertResponseObject(responseResult.value); + setResponseDetail(convertedResponse); + } else { + console.error( + `Error fetching response detail for ID ${id}:`, + responseResult.reason, + ); + setError( + responseResult.reason instanceof Error + ? responseResult.reason + : new Error("Failed to fetch response detail"), + ); + } + + // Handle input items result + if (inputItemsResult.status === "fulfilled") { + const inputItemsData = + inputItemsResult.value as unknown as InputItemListResponse; + setInputItems(inputItemsData); + } else { + console.error( + `Error fetching input items for response ID ${id}:`, + inputItemsResult.reason, + ); + setInputItemsError( + inputItemsResult.reason instanceof Error + ? inputItemsResult.reason + : new Error("Failed to fetch input items"), + ); + } + } catch (err) { + console.error(`Unexpected error fetching data for ID ${id}:`, err); + setError( + err instanceof Error ? err : new Error("Unexpected error occurred"), + ); + } finally { + setIsLoading(false); + setIsLoadingInputItems(false); + } + }; + + fetchResponseDetail(); + }, [id]); + + return ( + + ); +} diff --git a/llama_stack/ui/app/logs/responses/layout.tsx b/llama_stack/ui/app/logs/responses/layout.tsx new file mode 100644 index 000000000..1fe116e5e --- /dev/null +++ b/llama_stack/ui/app/logs/responses/layout.tsx @@ -0,0 +1,16 @@ +"use client"; + +import React from "react"; +import LogsLayout from "@/components/layout/logs-layout"; + +export default function ResponsesLayout({ + children, +}: { + children: React.ReactNode; +}) { + return ( + + {children} + + ); +} diff --git a/llama_stack/ui/app/logs/responses/page.tsx b/llama_stack/ui/app/logs/responses/page.tsx index cdc165d08..dab0c735f 100644 --- a/llama_stack/ui/app/logs/responses/page.tsx +++ b/llama_stack/ui/app/logs/responses/page.tsx @@ -1,7 +1,66 @@ -export default function Responses() { +"use client"; + +import { useEffect, useState } from "react"; +import type { ResponseListResponse } from "llama-stack-client/resources/responses/responses"; +import { OpenAIResponse } from "@/lib/types"; +import { ResponsesTable } from "@/components/responses/responses-table"; +import { client } from "@/lib/client"; + +export default function ResponsesPage() { + const [responses, setResponses] = useState([]); + const [isLoading, setIsLoading] = useState(true); + const [error, setError] = useState(null); + + // Helper function to convert ResponseListResponse.Data to OpenAIResponse + const convertResponseListData = ( + responseData: ResponseListResponse.Data, + ): OpenAIResponse => { + return { + id: responseData.id, + created_at: responseData.created_at, + model: responseData.model, + object: responseData.object, + status: responseData.status, + output: responseData.output as OpenAIResponse["output"], + input: responseData.input as OpenAIResponse["input"], + error: responseData.error, + parallel_tool_calls: responseData.parallel_tool_calls, + previous_response_id: responseData.previous_response_id, + temperature: responseData.temperature, + top_p: responseData.top_p, + truncation: responseData.truncation, + user: responseData.user, + }; + }; + + useEffect(() => { + const fetchResponses = async () => { + setIsLoading(true); + setError(null); + try { + const response = await client.responses.list(); + const responseListData = response as ResponseListResponse; + + const convertedResponses: OpenAIResponse[] = responseListData.data.map( + convertResponseListData, + ); + + setResponses(convertedResponses); + } catch (err) { + console.error("Error fetching responses:", err); + setError( + err instanceof Error ? err : new Error("Failed to fetch responses"), + ); + setResponses([]); + } finally { + setIsLoading(false); + } + }; + + fetchResponses(); + }, []); + return ( -
-

Under Construction

-
+ ); } diff --git a/llama_stack/ui/components/chat-completions/chat-completion-detail.test.tsx b/llama_stack/ui/components/chat-completions/chat-completion-detail.test.tsx new file mode 100644 index 000000000..5348dbc3a --- /dev/null +++ b/llama_stack/ui/components/chat-completions/chat-completion-detail.test.tsx @@ -0,0 +1,193 @@ +import React from "react"; +import { render, screen } from "@testing-library/react"; +import "@testing-library/jest-dom"; +import { ChatCompletionDetailView } from "./chat-completion-detail"; +import { ChatCompletion } from "@/lib/types"; + +// Initial test file setup for ChatCompletionDetailView + +describe("ChatCompletionDetailView", () => { + test("renders skeleton UI when isLoading is true", () => { + const { container } = render( + , + ); + // Use the data-slot attribute for Skeletons + const skeletons = container.querySelectorAll('[data-slot="skeleton"]'); + expect(skeletons.length).toBeGreaterThan(0); + }); + + test("renders error message when error prop is provided", () => { + render( + , + ); + expect( + screen.getByText(/Error loading details for ID err-id: Network Error/), + ).toBeInTheDocument(); + }); + + test("renders default error message when error.message is empty", () => { + render( + , + ); + // Use regex to match the error message regardless of whitespace + expect( + screen.getByText(/Error loading details for ID\s*err-id\s*:/), + ).toBeInTheDocument(); + }); + + test("renders error message when error prop is an object without message", () => { + render( + , + ); + // Use regex to match the error message regardless of whitespace + expect( + screen.getByText(/Error loading details for ID\s*err-id\s*:/), + ).toBeInTheDocument(); + }); + + test("renders not found message when completion is null and not loading/error", () => { + render( + , + ); + expect( + screen.getByText("No details found for ID: notfound-id."), + ).toBeInTheDocument(); + }); + + test("renders input, output, and properties for valid completion", () => { + const mockCompletion: ChatCompletion = { + id: "comp_123", + object: "chat.completion", + created: 1710000000, + model: "llama-test-model", + choices: [ + { + index: 0, + message: { role: "assistant", content: "Test output" }, + finish_reason: "stop", + }, + ], + input_messages: [{ role: "user", content: "Test input" }], + }; + render( + , + ); + // Input + expect(screen.getByText("Input")).toBeInTheDocument(); + expect(screen.getByText("Test input")).toBeInTheDocument(); + // Output + expect(screen.getByText("Output")).toBeInTheDocument(); + expect(screen.getByText("Test output")).toBeInTheDocument(); + // Properties + expect(screen.getByText("Properties")).toBeInTheDocument(); + expect(screen.getByText("Created:")).toBeInTheDocument(); + expect( + screen.getByText(new Date(1710000000 * 1000).toLocaleString()), + ).toBeInTheDocument(); + expect(screen.getByText("ID:")).toBeInTheDocument(); + expect(screen.getByText("comp_123")).toBeInTheDocument(); + expect(screen.getByText("Model:")).toBeInTheDocument(); + expect(screen.getByText("llama-test-model")).toBeInTheDocument(); + expect(screen.getByText("Finish Reason:")).toBeInTheDocument(); + expect(screen.getByText("stop")).toBeInTheDocument(); + }); + + test("renders tool call in output and properties when present", () => { + const toolCall = { + function: { name: "search", arguments: '{"query":"llama"}' }, + }; + const mockCompletion: ChatCompletion = { + id: "comp_tool", + object: "chat.completion", + created: 1710001000, + model: "llama-tool-model", + choices: [ + { + index: 0, + message: { + role: "assistant", + content: "Tool output", + tool_calls: [toolCall], + }, + finish_reason: "stop", + }, + ], + input_messages: [{ role: "user", content: "Tool input" }], + }; + render( + , + ); + // Output should include the tool call block (should be present twice: input and output) + const toolCallLabels = screen.getAllByText("Tool Call"); + expect(toolCallLabels.length).toBeGreaterThanOrEqual(1); // At least one, but could be two + // The tool call block should contain the formatted tool call string in both input and output + const toolCallBlocks = screen.getAllByText('search({"query":"llama"})'); + expect(toolCallBlocks.length).toBe(2); + // Properties should include the tool call name + expect(screen.getByText("Functions/Tools Called:")).toBeInTheDocument(); + expect(screen.getByText("search")).toBeInTheDocument(); + }); + + test("handles missing/empty fields gracefully", () => { + const mockCompletion: ChatCompletion = { + id: "comp_edge", + object: "chat.completion", + created: 1710002000, + model: "llama-edge-model", + choices: [], // No choices + input_messages: [], // No input messages + }; + render( + , + ); + // Input section should be present but empty + expect(screen.getByText("Input")).toBeInTheDocument(); + // Output section should show fallback message + expect( + screen.getByText("No message found in assistant's choice."), + ).toBeInTheDocument(); + // Properties should show N/A for finish reason + expect(screen.getByText("Finish Reason:")).toBeInTheDocument(); + expect(screen.getByText("N/A")).toBeInTheDocument(); + }); +}); diff --git a/llama_stack/ui/components/chat-completions/chat-completion-detail.tsx b/llama_stack/ui/components/chat-completions/chat-completion-detail.tsx new file mode 100644 index 000000000..200807864 --- /dev/null +++ b/llama_stack/ui/components/chat-completions/chat-completion-detail.tsx @@ -0,0 +1,145 @@ +"use client"; + +import { ChatMessage, ChatCompletion } from "@/lib/types"; +import { ChatMessageItem } from "@/components/chat-completions/chat-messasge-item"; +import { Card, CardContent, CardHeader, CardTitle } from "@/components/ui/card"; +import { + DetailLoadingView, + DetailErrorView, + DetailNotFoundView, + DetailLayout, + PropertiesCard, + PropertyItem, +} from "@/components/layout/detail-layout"; + +interface ChatCompletionDetailViewProps { + completion: ChatCompletion | null; + isLoading: boolean; + error: Error | null; + id: string; +} + +export function ChatCompletionDetailView({ + completion, + isLoading, + error, + id, +}: ChatCompletionDetailViewProps) { + const title = "Chat Completion Details"; + + if (error) { + return ; + } + + if (isLoading) { + return ; + } + + if (!completion) { + return ; + } + + // Main content cards + const mainContent = ( + <> + + + Input + + + {completion.input_messages?.map((msg, index) => ( + + ))} + {completion.choices?.[0]?.message?.tool_calls && + Array.isArray(completion.choices[0].message.tool_calls) && + !completion.input_messages?.some( + (im) => + im.role === "assistant" && + im.tool_calls && + Array.isArray(im.tool_calls) && + im.tool_calls.length > 0, + ) + ? completion.choices[0].message.tool_calls.map( + (toolCall: any, index: number) => { + const assistantToolCallMessage: ChatMessage = { + role: "assistant", + tool_calls: [toolCall], + content: "", // Ensure content is defined, even if empty + }; + return ( + + ); + }, + ) + : null} + + + + + + Output + + + {completion.choices?.[0]?.message ? ( + + ) : ( +

+ No message found in assistant's choice. +

+ )} + + + + ); + + // Properties sidebar + const sidebar = ( + + + + + + {(() => { + const toolCalls = completion.choices?.[0]?.message?.tool_calls; + if (toolCalls && Array.isArray(toolCalls) && toolCalls.length > 0) { + return ( + +
    + {toolCalls.map((toolCall: any, index: number) => ( +
  • + + {toolCall.function?.name || "N/A"} + +
    • + ))} +
+ + } + hasBorder + /> + ); + } + return null; + })()} + + ); + + return ( + + ); +} 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 new file mode 100644 index 000000000..c8a55b100 --- /dev/null +++ b/llama_stack/ui/components/chat-completions/chat-completion-table.test.tsx @@ -0,0 +1,347 @@ +import React from "react"; +import { render, screen, fireEvent } from "@testing-library/react"; +import "@testing-library/jest-dom"; +import { ChatCompletionsTable } from "./chat-completions-table"; +import { ChatCompletion } from "@/lib/types"; + +// Mock next/navigation +const mockPush = jest.fn(); +jest.mock("next/navigation", () => ({ + useRouter: () => ({ + push: mockPush, + }), +})); + +// Mock helper functions +jest.mock("@/lib/truncate-text"); +jest.mock("@/lib/format-message-content"); + +// Import the mocked functions to set up default or specific implementations +import { truncateText as originalTruncateText } from "@/lib/truncate-text"; +import { + extractTextFromContentPart as originalExtractTextFromContentPart, + extractDisplayableText as originalExtractDisplayableText, +} from "@/lib/format-message-content"; + +// Cast to jest.Mock for typings +const truncateText = originalTruncateText as jest.Mock; +const extractTextFromContentPart = + originalExtractTextFromContentPart as jest.Mock; +const extractDisplayableText = originalExtractDisplayableText as jest.Mock; + +describe("ChatCompletionsTable", () => { + const defaultProps = { + data: [] as ChatCompletion[], + isLoading: false, + error: null, + }; + + beforeEach(() => { + // Reset all mocks before each test + mockPush.mockClear(); + truncateText.mockClear(); + extractTextFromContentPart.mockClear(); + extractDisplayableText.mockClear(); + + // Default pass-through implementations + truncateText.mockImplementation((text: string | undefined) => text); + extractTextFromContentPart.mockImplementation((content: unknown) => + typeof content === "string" ? content : "extracted text", + ); + extractDisplayableText.mockImplementation( + (message: unknown) => + (message as { content?: string })?.content || "extracted output", + ); + }); + + test("renders without crashing with default props", () => { + render(); + expect(screen.getByText("No chat completions found.")).toBeInTheDocument(); + }); + + test("click on a row navigates to the correct URL", () => { + const mockCompletion: ChatCompletion = { + id: "comp_123", + object: "chat.completion", + created: Math.floor(Date.now() / 1000), + model: "llama-test-model", + choices: [ + { + index: 0, + message: { role: "assistant", content: "Test output" }, + finish_reason: "stop", + }, + ], + input_messages: [{ role: "user", content: "Test input" }], + }; + + // Set up mocks to return expected values + extractTextFromContentPart.mockReturnValue("Test input"); + extractDisplayableText.mockReturnValue("Test output"); + + render(); + + const row = screen.getByText("Test input").closest("tr"); + if (row) { + fireEvent.click(row); + expect(mockPush).toHaveBeenCalledWith("/logs/chat-completions/comp_123"); + } else { + throw new Error('Row with "Test input" not found for router mock test.'); + } + }); + + describe("Loading State", () => { + test("renders skeleton UI when isLoading is true", () => { + const { container } = render( + , + ); + + // Check for skeleton in the table caption + const tableCaption = container.querySelector("caption"); + expect(tableCaption).toBeInTheDocument(); + if (tableCaption) { + const captionSkeleton = tableCaption.querySelector( + '[data-slot="skeleton"]', + ); + expect(captionSkeleton).toBeInTheDocument(); + } + + // Check for skeletons in the table body cells + const tableBody = container.querySelector("tbody"); + expect(tableBody).toBeInTheDocument(); + if (tableBody) { + const bodySkeletons = tableBody.querySelectorAll( + '[data-slot="skeleton"]', + ); + expect(bodySkeletons.length).toBeGreaterThan(0); + } + }); + }); + + describe("Error State", () => { + test("renders error message when error prop is provided", () => { + const errorMessage = "Network Error"; + render( + , + ); + expect( + screen.getByText(`Error fetching data: ${errorMessage}`), + ).toBeInTheDocument(); + }); + + test("renders default error message when error.message is not available", () => { + render( + , + ); + expect( + screen.getByText("Error fetching data: An unknown error occurred"), + ).toBeInTheDocument(); + }); + + test("renders default error message when error prop is an object without message", () => { + render(); + expect( + screen.getByText("Error fetching data: An unknown error occurred"), + ).toBeInTheDocument(); + }); + }); + + describe("Empty State", () => { + test('renders "No chat completions found." and no table when data array is empty', () => { + render(); + expect( + screen.getByText("No chat completions found."), + ).toBeInTheDocument(); + + // Ensure that the table structure is NOT rendered in the empty state + const table = screen.queryByRole("table"); + expect(table).not.toBeInTheDocument(); + }); + }); + + describe("Data Rendering", () => { + test("renders table caption, headers, and completion data correctly", () => { + const mockCompletions = [ + { + id: "comp_1", + object: "chat.completion", + created: 1710000000, + model: "llama-test-model", + choices: [ + { + index: 0, + message: { role: "assistant", content: "Test output" }, + finish_reason: "stop", + }, + ], + input_messages: [{ role: "user", content: "Test input" }], + }, + { + id: "comp_2", + object: "chat.completion", + created: 1710001000, + model: "llama-another-model", + choices: [ + { + index: 0, + message: { role: "assistant", content: "Another output" }, + finish_reason: "stop", + }, + ], + input_messages: [{ role: "user", content: "Another input" }], + }, + ]; + + // Set up mocks to return expected values + extractTextFromContentPart.mockImplementation((content: unknown) => { + if (content === "Test input") return "Test input"; + if (content === "Another input") return "Another input"; + return "extracted text"; + }); + extractDisplayableText.mockImplementation((message: unknown) => { + const msg = message as { content?: string }; + if (msg?.content === "Test output") return "Test output"; + if (msg?.content === "Another output") return "Another output"; + return "extracted output"; + }); + + render( + , + ); + + // Table caption + expect( + screen.getByText("A list of your recent chat completions."), + ).toBeInTheDocument(); + + // Table headers + expect(screen.getByText("Input")).toBeInTheDocument(); + expect(screen.getByText("Output")).toBeInTheDocument(); + expect(screen.getByText("Model")).toBeInTheDocument(); + expect(screen.getByText("Created")).toBeInTheDocument(); + + // Data rows + expect(screen.getByText("Test input")).toBeInTheDocument(); + expect(screen.getByText("Test output")).toBeInTheDocument(); + expect(screen.getByText("llama-test-model")).toBeInTheDocument(); + expect( + screen.getByText(new Date(1710000000 * 1000).toLocaleString()), + ).toBeInTheDocument(); + + expect(screen.getByText("Another input")).toBeInTheDocument(); + expect(screen.getByText("Another output")).toBeInTheDocument(); + expect(screen.getByText("llama-another-model")).toBeInTheDocument(); + expect( + screen.getByText(new Date(1710001000 * 1000).toLocaleString()), + ).toBeInTheDocument(); + }); + }); + + describe("Text Truncation and Content Extraction", () => { + test("truncates long input and output text", () => { + // Specific mock implementation for this test + truncateText.mockImplementation( + (text: string | undefined, maxLength?: number) => { + const defaultTestMaxLength = 10; + const effectiveMaxLength = maxLength ?? defaultTestMaxLength; + return typeof text === "string" && text.length > effectiveMaxLength + ? text.slice(0, effectiveMaxLength) + "..." + : text; + }, + ); + + const longInput = + "This is a very long input message that should be truncated."; + const longOutput = + "This is a very long output message that should also be truncated."; + + extractTextFromContentPart.mockReturnValue(longInput); + extractDisplayableText.mockReturnValue(longOutput); + + const mockCompletions = [ + { + id: "comp_trunc", + object: "chat.completion", + created: 1710002000, + model: "llama-trunc-model", + choices: [ + { + index: 0, + message: { role: "assistant", content: longOutput }, + finish_reason: "stop", + }, + ], + input_messages: [{ role: "user", content: longInput }], + }, + ]; + + render( + , + ); + + // The truncated text should be present for both input and output + const truncatedTexts = screen.getAllByText( + longInput.slice(0, 10) + "...", + ); + expect(truncatedTexts.length).toBe(2); // one for input, one for output + truncatedTexts.forEach((textElement) => + expect(textElement).toBeInTheDocument(), + ); + }); + + test("uses content extraction functions correctly", () => { + const mockCompletion = { + id: "comp_extract", + object: "chat.completion", + created: 1710003000, + model: "llama-extract-model", + choices: [ + { + index: 0, + message: { role: "assistant", content: "Extracted output" }, + finish_reason: "stop", + }, + ], + input_messages: [{ role: "user", content: "Extracted input" }], + }; + + extractTextFromContentPart.mockReturnValue("Extracted input"); + extractDisplayableText.mockReturnValue("Extracted output"); + + render( + , + ); + + // Verify the extraction functions were called + expect(extractTextFromContentPart).toHaveBeenCalledWith( + "Extracted input", + ); + expect(extractDisplayableText).toHaveBeenCalledWith({ + role: "assistant", + content: "Extracted output", + }); + + // Verify the extracted content is displayed + expect(screen.getByText("Extracted input")).toBeInTheDocument(); + expect(screen.getByText("Extracted output")).toBeInTheDocument(); + }); + }); +}); diff --git a/llama_stack/ui/components/chat-completions/chat-completions-table.tsx b/llama_stack/ui/components/chat-completions/chat-completions-table.tsx new file mode 100644 index 000000000..5f1d2f03d --- /dev/null +++ b/llama_stack/ui/components/chat-completions/chat-completions-table.tsx @@ -0,0 +1,43 @@ +"use client"; + +import { ChatCompletion } from "@/lib/types"; +import { LogsTable, LogTableRow } from "@/components/logs/logs-table"; +import { + extractTextFromContentPart, + extractDisplayableText, +} from "@/lib/format-message-content"; + +interface ChatCompletionsTableProps { + data: ChatCompletion[]; + isLoading: boolean; + error: Error | null; +} + +function formatChatCompletionToRow(completion: ChatCompletion): LogTableRow { + return { + id: completion.id, + input: extractTextFromContentPart(completion.input_messages?.[0]?.content), + output: extractDisplayableText(completion.choices?.[0]?.message), + model: completion.model, + createdTime: new Date(completion.created * 1000).toLocaleString(), + detailPath: `/logs/chat-completions/${completion.id}`, + }; +} + +export function ChatCompletionsTable({ + data, + isLoading, + error, +}: ChatCompletionsTableProps) { + const formattedData = data.map(formatChatCompletionToRow); + + return ( + + ); +} diff --git a/llama_stack/ui/components/chat-completions/chat-messasge-item.tsx b/llama_stack/ui/components/chat-completions/chat-messasge-item.tsx new file mode 100644 index 000000000..2e8593bfb --- /dev/null +++ b/llama_stack/ui/components/chat-completions/chat-messasge-item.tsx @@ -0,0 +1,76 @@ +"use client"; + +import { ChatMessage } from "@/lib/types"; +import React from "react"; +import { formatToolCallToString } from "@/lib/format-tool-call"; +import { extractTextFromContentPart } from "@/lib/format-message-content"; +import { + MessageBlock, + ToolCallBlock, +} from "@/components/ui/message-components"; + +interface ChatMessageItemProps { + message: ChatMessage; +} +export function ChatMessageItem({ message }: ChatMessageItemProps) { + switch (message.role) { + case "system": + return ( + + ); + case "user": + return ( + + ); + + case "assistant": + if ( + message.tool_calls && + Array.isArray(message.tool_calls) && + message.tool_calls.length > 0 + ) { + return ( + <> + {message.tool_calls.map((toolCall: any, index: number) => { + const formattedToolCall = formatToolCallToString(toolCall); + const toolCallContent = ( + + {formattedToolCall || "Error: Could not display tool call"} + + ); + return ( + + ); + })} + + ); + } else { + return ( + + ); + } + case "tool": + const toolOutputContent = ( + + {extractTextFromContentPart(message.content)} + + ); + return ( + + ); + } + return null; +} diff --git a/llama_stack/ui/components/app-sidebar.tsx b/llama_stack/ui/components/layout/app-sidebar.tsx similarity index 50% rename from llama_stack/ui/components/app-sidebar.tsx rename to llama_stack/ui/components/layout/app-sidebar.tsx index 3d541856f..1c53d6cc5 100644 --- a/llama_stack/ui/components/app-sidebar.tsx +++ b/llama_stack/ui/components/layout/app-sidebar.tsx @@ -1,5 +1,9 @@ +"use client"; + import { MessageSquareText, MessagesSquare, MoveUpRight } from "lucide-react"; import Link from "next/link"; +import { usePathname } from "next/navigation"; +import { cn } from "@/lib/utils"; import { Sidebar, @@ -32,6 +36,8 @@ const logItems = [ ]; export function AppSidebar() { + const pathname = usePathname(); + return ( @@ -42,16 +48,31 @@ export function AppSidebar() { Logs - {logItems.map((item) => ( - - - - - {item.title} - - - - ))} + {logItems.map((item) => { + const isActive = pathname.startsWith(item.url); + return ( + + + + + {item.title} + + + + ); + })} diff --git a/llama_stack/ui/components/layout/detail-layout.tsx b/llama_stack/ui/components/layout/detail-layout.tsx new file mode 100644 index 000000000..58b912703 --- /dev/null +++ b/llama_stack/ui/components/layout/detail-layout.tsx @@ -0,0 +1,141 @@ +import React from "react"; +import { Card, CardContent, CardHeader, CardTitle } from "@/components/ui/card"; +import { Skeleton } from "@/components/ui/skeleton"; + +export function DetailLoadingView({ title }: { title: string }) { + return ( + <> + {/* Title Skeleton */} +
+
+ {[...Array(2)].map((_, i) => ( + + + + + + + + + + + + + ))} +
+
+
+ {" "} + {/* Properties Title Skeleton */} + {[...Array(5)].map((_, i) => ( +
+ + +
+ ))} +
+
+
+ + ); +} + +export function DetailErrorView({ + title, + id, + error, +}: { + title: string; + id: string; + error: Error; +}) { + return ( + <> +

{title}

+

+ Error loading details for ID {id}: {error.message} +

+ + ); +} + +export function DetailNotFoundView({ + title, + id, +}: { + title: string; + id: string; +}) { + return ( + <> +

{title}

+

No details found for ID: {id}.

+ + ); +} + +export interface PropertyItemProps { + label: string; + value: React.ReactNode; + className?: string; + hasBorder?: boolean; +} + +export function PropertyItem({ + label, + value, + className = "", + hasBorder = false, +}: PropertyItemProps) { + return ( +
  • + {label}:{" "} + {typeof value === "string" || typeof value === "number" ? ( + {value} + ) : ( + value + )} +
    • + ); +} + +export interface PropertiesCardProps { + children: React.ReactNode; +} + +export function PropertiesCard({ children }: PropertiesCardProps) { + return ( + + + Properties + + +
      {children}
    +     +     + ); +} + +export interface DetailLayoutProps { + title: string; + mainContent: React.ReactNode; + sidebar: React.ReactNode; +} + +export function DetailLayout({ + title, + mainContent, + sidebar, +}: DetailLayoutProps) { + return ( + <> +

    {title}

    +
    +
    {mainContent}
    +
    {sidebar}
    +
    + + ); +} diff --git a/llama_stack/ui/components/layout/logs-layout.tsx b/llama_stack/ui/components/layout/logs-layout.tsx new file mode 100644 index 000000000..468ad6e9a --- /dev/null +++ b/llama_stack/ui/components/layout/logs-layout.tsx @@ -0,0 +1,49 @@ +"use client"; + +import React from "react"; +import { usePathname, useParams } from "next/navigation"; +import { + PageBreadcrumb, + BreadcrumbSegment, +} from "@/components/layout/page-breadcrumb"; +import { truncateText } from "@/lib/truncate-text"; + +interface LogsLayoutProps { + children: React.ReactNode; + sectionLabel: string; + basePath: string; +} + +export default function LogsLayout({ + children, + sectionLabel, + basePath, +}: LogsLayoutProps) { + const pathname = usePathname(); + const params = useParams(); + + let segments: BreadcrumbSegment[] = []; + + if (pathname === basePath) { + segments = [{ label: sectionLabel }]; + } + + const idParam = params?.id; + if (idParam && typeof idParam === "string") { + segments = [ + { label: sectionLabel, href: basePath }, + { label: `Details (${truncateText(idParam, 20)})` }, + ]; + } + + return ( +
    + <> + {segments.length > 0 && ( + + )} + {children} + +
    + ); +} diff --git a/llama_stack/ui/components/layout/page-breadcrumb.tsx b/llama_stack/ui/components/layout/page-breadcrumb.tsx new file mode 100644 index 000000000..fdb561d68 --- /dev/null +++ b/llama_stack/ui/components/layout/page-breadcrumb.tsx @@ -0,0 +1,49 @@ +"use client"; + +import Link from "next/link"; +import React from "react"; +import { + Breadcrumb, + BreadcrumbItem, + BreadcrumbLink, + BreadcrumbList, + BreadcrumbPage, + BreadcrumbSeparator, +} from "@/components/ui/breadcrumb"; + +export interface BreadcrumbSegment { + label: string; + href?: string; +} + +interface PageBreadcrumbProps { + segments: BreadcrumbSegment[]; + className?: string; +} + +export function PageBreadcrumb({ segments, className }: PageBreadcrumbProps) { + if (!segments || segments.length === 0) { + return null; + } + + return ( + + + {segments.map((segment, index) => ( + + + {segment.href ? ( + + {segment.label} + + ) : ( + {segment.label} + )} + + {index < segments.length - 1 && } + + ))} + + + ); +} diff --git a/llama_stack/ui/components/logs/logs-table.test.tsx b/llama_stack/ui/components/logs/logs-table.test.tsx new file mode 100644 index 000000000..88263b2fc --- /dev/null +++ b/llama_stack/ui/components/logs/logs-table.test.tsx @@ -0,0 +1,350 @@ +import React from "react"; +import { render, screen, fireEvent } from "@testing-library/react"; +import "@testing-library/jest-dom"; +import { LogsTable, LogTableRow } from "./logs-table"; + +// Mock next/navigation +const mockPush = jest.fn(); +jest.mock("next/navigation", () => ({ + useRouter: () => ({ + push: mockPush, + }), +})); + +// Mock helper functions +jest.mock("@/lib/truncate-text"); + +// Import the mocked functions +import { truncateText as originalTruncateText } from "@/lib/truncate-text"; + +// Cast to jest.Mock for typings +const truncateText = originalTruncateText as jest.Mock; + +describe("LogsTable", () => { + const defaultProps = { + data: [] as LogTableRow[], + isLoading: false, + error: null, + caption: "Test table caption", + emptyMessage: "No data found", + }; + + beforeEach(() => { + // Reset all mocks before each test + mockPush.mockClear(); + truncateText.mockClear(); + + // Default pass-through implementation + truncateText.mockImplementation((text: string | undefined) => text); + }); + + test("renders without crashing with default props", () => { + render(); + expect(screen.getByText("No data found")).toBeInTheDocument(); + }); + + test("click on a row navigates to the correct URL", () => { + const mockData: LogTableRow[] = [ + { + id: "row_123", + input: "Test input", + output: "Test output", + model: "test-model", + createdTime: "2024-01-01 12:00:00", + detailPath: "/test/path/row_123", + }, + ]; + + render(); + + const row = screen.getByText("Test input").closest("tr"); + if (row) { + fireEvent.click(row); + expect(mockPush).toHaveBeenCalledWith("/test/path/row_123"); + } else { + throw new Error('Row with "Test input" not found for router mock test.'); + } + }); + + describe("Loading State", () => { + test("renders skeleton UI when isLoading is true", () => { + const { container } = render( + , + ); + + // Check for skeleton in the table caption + const tableCaption = container.querySelector("caption"); + expect(tableCaption).toBeInTheDocument(); + if (tableCaption) { + const captionSkeleton = tableCaption.querySelector( + '[data-slot="skeleton"]', + ); + expect(captionSkeleton).toBeInTheDocument(); + } + + // Check for skeletons in the table body cells + const tableBody = container.querySelector("tbody"); + expect(tableBody).toBeInTheDocument(); + if (tableBody) { + const bodySkeletons = tableBody.querySelectorAll( + '[data-slot="skeleton"]', + ); + expect(bodySkeletons.length).toBeGreaterThan(0); + } + + // Check that table headers are still rendered + expect(screen.getByText("Input")).toBeInTheDocument(); + expect(screen.getByText("Output")).toBeInTheDocument(); + expect(screen.getByText("Model")).toBeInTheDocument(); + expect(screen.getByText("Created")).toBeInTheDocument(); + }); + + test("renders correct number of skeleton rows", () => { + const { container } = render( + , + ); + + const skeletonRows = container.querySelectorAll("tbody tr"); + expect(skeletonRows.length).toBe(3); // Should render 3 skeleton rows + }); + }); + + describe("Error State", () => { + test("renders error message when error prop is provided", () => { + const errorMessage = "Network Error"; + render( + , + ); + expect( + screen.getByText(`Error fetching data: ${errorMessage}`), + ).toBeInTheDocument(); + }); + + test("renders default error message when error.message is not available", () => { + render( + , + ); + expect( + screen.getByText("Error fetching data: An unknown error occurred"), + ).toBeInTheDocument(); + }); + + test("renders default error message when error prop is an object without message", () => { + render(); + expect( + screen.getByText("Error fetching data: An unknown error occurred"), + ).toBeInTheDocument(); + }); + + test("does not render table when in error state", () => { + render( + , + ); + const table = screen.queryByRole("table"); + expect(table).not.toBeInTheDocument(); + }); + }); + + describe("Empty State", () => { + test("renders custom empty message when data array is empty", () => { + render( + , + ); + expect(screen.getByText("Custom empty message")).toBeInTheDocument(); + + // Ensure that the table structure is NOT rendered in the empty state + const table = screen.queryByRole("table"); + expect(table).not.toBeInTheDocument(); + }); + }); + + describe("Data Rendering", () => { + test("renders table caption, headers, and data correctly", () => { + const mockData: LogTableRow[] = [ + { + id: "row_1", + input: "First input", + output: "First output", + model: "model-1", + createdTime: "2024-01-01 12:00:00", + detailPath: "/path/1", + }, + { + id: "row_2", + input: "Second input", + output: "Second output", + model: "model-2", + createdTime: "2024-01-02 13:00:00", + detailPath: "/path/2", + }, + ]; + + render( + , + ); + + // Table caption + expect(screen.getByText("Custom table caption")).toBeInTheDocument(); + + // Table headers + expect(screen.getByText("Input")).toBeInTheDocument(); + expect(screen.getByText("Output")).toBeInTheDocument(); + expect(screen.getByText("Model")).toBeInTheDocument(); + expect(screen.getByText("Created")).toBeInTheDocument(); + + // Data rows + expect(screen.getByText("First input")).toBeInTheDocument(); + expect(screen.getByText("First output")).toBeInTheDocument(); + expect(screen.getByText("model-1")).toBeInTheDocument(); + expect(screen.getByText("2024-01-01 12:00:00")).toBeInTheDocument(); + + expect(screen.getByText("Second input")).toBeInTheDocument(); + expect(screen.getByText("Second output")).toBeInTheDocument(); + expect(screen.getByText("model-2")).toBeInTheDocument(); + expect(screen.getByText("2024-01-02 13:00:00")).toBeInTheDocument(); + }); + + test("applies correct CSS classes to table rows", () => { + const mockData: LogTableRow[] = [ + { + id: "row_1", + input: "Test input", + output: "Test output", + model: "test-model", + createdTime: "2024-01-01 12:00:00", + detailPath: "/test/path", + }, + ]; + + render(); + + const row = screen.getByText("Test input").closest("tr"); + expect(row).toHaveClass("cursor-pointer"); + expect(row).toHaveClass("hover:bg-muted/50"); + }); + + test("applies correct alignment to Created column", () => { + const mockData: LogTableRow[] = [ + { + id: "row_1", + input: "Test input", + output: "Test output", + model: "test-model", + createdTime: "2024-01-01 12:00:00", + detailPath: "/test/path", + }, + ]; + + render(); + + const createdCell = screen.getByText("2024-01-01 12:00:00").closest("td"); + expect(createdCell).toHaveClass("text-right"); + }); + }); + + describe("Text Truncation", () => { + test("truncates input and output text using truncateText function", () => { + // Mock truncateText to return truncated versions + truncateText.mockImplementation((text: string | undefined) => { + if (typeof text === "string" && text.length > 10) { + return text.slice(0, 10) + "..."; + } + return text; + }); + + const longInput = + "This is a very long input text that should be truncated"; + const longOutput = + "This is a very long output text that should be truncated"; + + const mockData: LogTableRow[] = [ + { + id: "row_1", + input: longInput, + output: longOutput, + model: "test-model", + createdTime: "2024-01-01 12:00:00", + detailPath: "/test/path", + }, + ]; + + render(); + + // Verify truncateText was called + expect(truncateText).toHaveBeenCalledWith(longInput); + expect(truncateText).toHaveBeenCalledWith(longOutput); + + // Verify truncated text is displayed + const truncatedTexts = screen.getAllByText("This is a ..."); + expect(truncatedTexts).toHaveLength(2); // one for input, one for output + truncatedTexts.forEach((textElement) => + expect(textElement).toBeInTheDocument(), + ); + }); + + test("does not truncate model names", () => { + const mockData: LogTableRow[] = [ + { + id: "row_1", + input: "Test input", + output: "Test output", + model: "very-long-model-name-that-should-not-be-truncated", + createdTime: "2024-01-01 12:00:00", + detailPath: "/test/path", + }, + ]; + + render(); + + // Model name should not be passed to truncateText + expect(truncateText).not.toHaveBeenCalledWith( + "very-long-model-name-that-should-not-be-truncated", + ); + + // Full model name should be displayed + expect( + screen.getByText("very-long-model-name-that-should-not-be-truncated"), + ).toBeInTheDocument(); + }); + }); + + describe("Accessibility", () => { + test("table has proper role and structure", () => { + const mockData: LogTableRow[] = [ + { + id: "row_1", + input: "Test input", + output: "Test output", + model: "test-model", + createdTime: "2024-01-01 12:00:00", + detailPath: "/test/path", + }, + ]; + + render(); + + const table = screen.getByRole("table"); + expect(table).toBeInTheDocument(); + + const columnHeaders = screen.getAllByRole("columnheader"); + expect(columnHeaders).toHaveLength(4); + + const rows = screen.getAllByRole("row"); + expect(rows).toHaveLength(2); // 1 header row + 1 data row + }); + }); +}); diff --git a/llama_stack/ui/components/logs/logs-table.tsx b/llama_stack/ui/components/logs/logs-table.tsx new file mode 100644 index 000000000..33afea61b --- /dev/null +++ b/llama_stack/ui/components/logs/logs-table.tsx @@ -0,0 +1,113 @@ +"use client"; + +import { useRouter } from "next/navigation"; +import { truncateText } from "@/lib/truncate-text"; +import { + Table, + TableBody, + TableCaption, + TableCell, + TableHead, + TableHeader, + TableRow, +} from "@/components/ui/table"; +import { Skeleton } from "@/components/ui/skeleton"; + +// Generic table row data interface +export interface LogTableRow { + id: string; + input: string; + output: string; + model: string; + createdTime: string; + detailPath: string; +} + +interface LogsTableProps { + data: LogTableRow[]; + isLoading: boolean; + error: Error | null; + caption: string; + emptyMessage: string; +} + +export function LogsTable({ + data, + isLoading, + error, + caption, + emptyMessage, +}: LogsTableProps) { + const router = useRouter(); + + const tableHeader = ( + + + Input + Output + Model + Created + + + ); + + if (isLoading) { + return ( + + + + + {tableHeader} + + {[...Array(3)].map((_, i) => ( + + + + + + + + + + + + + + + ))} + +
    + ); + } + + if (error) { + return ( +

    Error fetching data: {error.message || "An unknown error occurred"}

    + ); + } + + if (data.length === 0) { + return

    {emptyMessage}

    ; + } + + return ( + + {caption} + {tableHeader} + + {data.map((row) => ( + router.push(row.detailPath)} + className="cursor-pointer hover:bg-muted/50" + > + {truncateText(row.input)} + {truncateText(row.output)} + {row.model} + {row.createdTime} + + ))} + +
    + ); +} diff --git a/llama_stack/ui/components/responses/grouping/grouped-items-display.tsx b/llama_stack/ui/components/responses/grouping/grouped-items-display.tsx new file mode 100644 index 000000000..6ddc0eacc --- /dev/null +++ b/llama_stack/ui/components/responses/grouping/grouped-items-display.tsx @@ -0,0 +1,56 @@ +import { useFunctionCallGrouping } from "../hooks/function-call-grouping"; +import { ItemRenderer } from "../items/item-renderer"; +import { GroupedFunctionCallItemComponent } from "../items/grouped-function-call-item"; +import { + isFunctionCallItem, + isFunctionCallOutputItem, + AnyResponseItem, +} from "../utils/item-types"; + +interface GroupedItemsDisplayProps { + items: AnyResponseItem[]; + keyPrefix: string; + defaultRole?: string; +} + +export function GroupedItemsDisplay({ + items, + keyPrefix, + defaultRole = "unknown", +}: GroupedItemsDisplayProps) { + const groupedItems = useFunctionCallGrouping(items); + + return ( + <> + {groupedItems.map((groupedItem) => { + // If this is a function call with an output, render the grouped component + if ( + groupedItem.outputItem && + isFunctionCallItem(groupedItem.item) && + isFunctionCallOutputItem(groupedItem.outputItem) + ) { + return ( + + ); + } + + // Otherwise, render the individual item + return ( + + ); + })} + + ); +} diff --git a/llama_stack/ui/components/responses/hooks/function-call-grouping.ts b/llama_stack/ui/components/responses/hooks/function-call-grouping.ts new file mode 100644 index 000000000..2994354d5 --- /dev/null +++ b/llama_stack/ui/components/responses/hooks/function-call-grouping.ts @@ -0,0 +1,92 @@ +import { useMemo } from "react"; +import { + isFunctionCallOutputItem, + AnyResponseItem, + FunctionCallOutputItem, +} from "../utils/item-types"; + +export interface GroupedItem { + item: AnyResponseItem; + index: number; + outputItem?: AnyResponseItem; + outputIndex?: number; +} + +/** + * Hook to group function calls with their corresponding outputs + * @param items Array of items to group + * @returns Array of grouped items with their outputs + */ +export function useFunctionCallGrouping( + items: AnyResponseItem[], +): GroupedItem[] { + return useMemo(() => { + const groupedItems: GroupedItem[] = []; + const processedIndices = new Set(); + + // Build a map of call_id to indices for function_call_output items + const callIdToIndices = new Map(); + + for (let i = 0; i < items.length; i++) { + const item = items[i]; + if (isFunctionCallOutputItem(item)) { + if (!callIdToIndices.has(item.call_id)) { + callIdToIndices.set(item.call_id, []); + } + callIdToIndices.get(item.call_id)!.push(i); + } + } + + // Process items and group function calls with their outputs + for (let i = 0; i < items.length; i++) { + if (processedIndices.has(i)) { + continue; + } + + const currentItem = items[i]; + + if ( + currentItem.type === "function_call" && + "name" in currentItem && + "call_id" in currentItem + ) { + const functionCallId = currentItem.call_id as string; + let outputIndex = -1; + let outputItem: FunctionCallOutputItem | null = null; + + const relatedIndices = callIdToIndices.get(functionCallId) || []; + for (const idx of relatedIndices) { + const potentialOutput = items[idx]; + outputIndex = idx; + outputItem = potentialOutput as FunctionCallOutputItem; + break; + } + + if (outputItem && outputIndex !== -1) { + // Group function call with its function_call_output + groupedItems.push({ + item: currentItem, + index: i, + outputItem, + outputIndex, + }); + + // Mark both items as processed + processedIndices.add(i); + processedIndices.add(outputIndex); + + // Matching function call and output found, skip to next item + continue; + } + } + // render normally + groupedItems.push({ + item: currentItem, + index: i, + }); + processedIndices.add(i); + } + + return groupedItems; + }, [items]); +} diff --git a/llama_stack/ui/components/responses/items/function-call-item.tsx b/llama_stack/ui/components/responses/items/function-call-item.tsx new file mode 100644 index 000000000..beca935f0 --- /dev/null +++ b/llama_stack/ui/components/responses/items/function-call-item.tsx @@ -0,0 +1,29 @@ +import { + MessageBlock, + ToolCallBlock, +} from "@/components/ui/message-components"; +import { FunctionCallItem } from "../utils/item-types"; + +interface FunctionCallItemProps { + item: FunctionCallItem; + index: number; + keyPrefix: string; +} + +export function FunctionCallItemComponent({ + item, + index, + keyPrefix, +}: FunctionCallItemProps) { + const name = item.name || "unknown"; + const args = item.arguments || "{}"; + const formattedFunctionCall = `${name}(${args})`; + + return ( + {formattedFunctionCall}} + /> + ); +} diff --git a/llama_stack/ui/components/responses/items/generic-item.tsx b/llama_stack/ui/components/responses/items/generic-item.tsx new file mode 100644 index 000000000..6b6f56603 --- /dev/null +++ b/llama_stack/ui/components/responses/items/generic-item.tsx @@ -0,0 +1,37 @@ +import { + MessageBlock, + ToolCallBlock, +} from "@/components/ui/message-components"; +import { BaseItem } from "../utils/item-types"; + +interface GenericItemProps { + item: BaseItem; + index: number; + keyPrefix: string; +} + +export function GenericItemComponent({ + item, + index, + keyPrefix, +}: GenericItemProps) { + // Handle other types like function calls, tool outputs, etc. + const itemData = item as Record; + + const content = itemData.content + ? typeof itemData.content === "string" + ? itemData.content + : JSON.stringify(itemData.content, null, 2) + : JSON.stringify(itemData, null, 2); + + const label = keyPrefix === "input" ? "Input" : "Output"; + + return ( + {content}} + /> + ); +} diff --git a/llama_stack/ui/components/responses/items/grouped-function-call-item.tsx b/llama_stack/ui/components/responses/items/grouped-function-call-item.tsx new file mode 100644 index 000000000..ded0ced71 --- /dev/null +++ b/llama_stack/ui/components/responses/items/grouped-function-call-item.tsx @@ -0,0 +1,54 @@ +import { + MessageBlock, + ToolCallBlock, +} from "@/components/ui/message-components"; +import { FunctionCallItem, FunctionCallOutputItem } from "../utils/item-types"; + +interface GroupedFunctionCallItemProps { + functionCall: FunctionCallItem; + output: FunctionCallOutputItem; + index: number; + keyPrefix: string; +} + +export function GroupedFunctionCallItemComponent({ + functionCall, + output, + index, + keyPrefix, +}: GroupedFunctionCallItemProps) { + const name = functionCall.name || "unknown"; + const args = functionCall.arguments || "{}"; + + // Extract the output content from function_call_output + let outputContent = ""; + if (output.output) { + outputContent = + typeof output.output === "string" + ? output.output + : JSON.stringify(output.output); + } else { + outputContent = JSON.stringify(output, null, 2); + } + + const functionCallContent = ( +
    +
    + Arguments + {`${name}(${args})`} +
    +
    + Output + {outputContent} +
    +
    + ); + + return ( + + ); +} diff --git a/llama_stack/ui/components/responses/items/index.ts b/llama_stack/ui/components/responses/items/index.ts new file mode 100644 index 000000000..d7bcc2ea4 --- /dev/null +++ b/llama_stack/ui/components/responses/items/index.ts @@ -0,0 +1,6 @@ +export { MessageItemComponent } from "./message-item"; +export { FunctionCallItemComponent } from "./function-call-item"; +export { WebSearchItemComponent } from "./web-search-item"; +export { GenericItemComponent } from "./generic-item"; +export { GroupedFunctionCallItemComponent } from "./grouped-function-call-item"; +export { ItemRenderer } from "./item-renderer"; diff --git a/llama_stack/ui/components/responses/items/item-renderer.tsx b/llama_stack/ui/components/responses/items/item-renderer.tsx new file mode 100644 index 000000000..8f65d50c4 --- /dev/null +++ b/llama_stack/ui/components/responses/items/item-renderer.tsx @@ -0,0 +1,60 @@ +import { + isMessageItem, + isFunctionCallItem, + isWebSearchCallItem, + AnyResponseItem, +} from "../utils/item-types"; +import { MessageItemComponent } from "./message-item"; +import { FunctionCallItemComponent } from "./function-call-item"; +import { WebSearchItemComponent } from "./web-search-item"; +import { GenericItemComponent } from "./generic-item"; + +interface ItemRendererProps { + item: AnyResponseItem; + index: number; + keyPrefix: string; + defaultRole?: string; +} + +export function ItemRenderer({ + item, + index, + keyPrefix, + defaultRole = "unknown", +}: ItemRendererProps) { + if (isMessageItem(item)) { + return ( + + ); + } + + if (isFunctionCallItem(item)) { + return ( + + ); + } + + if (isWebSearchCallItem(item)) { + return ( + + ); + } + + // Fallback to generic item for unknown types + return ( + + ); +} diff --git a/llama_stack/ui/components/responses/items/message-item.tsx b/llama_stack/ui/components/responses/items/message-item.tsx new file mode 100644 index 000000000..532fddfaa --- /dev/null +++ b/llama_stack/ui/components/responses/items/message-item.tsx @@ -0,0 +1,41 @@ +import { MessageBlock } from "@/components/ui/message-components"; +import { MessageItem } from "../utils/item-types"; + +interface MessageItemProps { + item: MessageItem; + index: number; + keyPrefix: string; + defaultRole?: string; +} + +export function MessageItemComponent({ + item, + index, + keyPrefix, + defaultRole = "unknown", +}: MessageItemProps) { + let content = ""; + + if (typeof item.content === "string") { + content = item.content; + } else if (Array.isArray(item.content)) { + content = item.content + .map((c) => { + return c.type === "input_text" || c.type === "output_text" + ? c.text + : JSON.stringify(c); + }) + .join(" "); + } + + const role = item.role || defaultRole; + const label = role.charAt(0).toUpperCase() + role.slice(1); + + return ( + + ); +} diff --git a/llama_stack/ui/components/responses/items/web-search-item.tsx b/llama_stack/ui/components/responses/items/web-search-item.tsx new file mode 100644 index 000000000..aaa5741ce --- /dev/null +++ b/llama_stack/ui/components/responses/items/web-search-item.tsx @@ -0,0 +1,28 @@ +import { + MessageBlock, + ToolCallBlock, +} from "@/components/ui/message-components"; +import { WebSearchCallItem } from "../utils/item-types"; + +interface WebSearchItemProps { + item: WebSearchCallItem; + index: number; + keyPrefix: string; +} + +export function WebSearchItemComponent({ + item, + index, + keyPrefix, +}: WebSearchItemProps) { + const formattedWebSearch = `web_search_call(status: ${item.status})`; + + return ( + {formattedWebSearch}} + /> + ); +} diff --git a/llama_stack/ui/components/responses/responses-detail.test.tsx b/llama_stack/ui/components/responses/responses-detail.test.tsx new file mode 100644 index 000000000..f426dc059 --- /dev/null +++ b/llama_stack/ui/components/responses/responses-detail.test.tsx @@ -0,0 +1,777 @@ +import React from "react"; +import { render, screen } from "@testing-library/react"; +import "@testing-library/jest-dom"; +import { ResponseDetailView } from "./responses-detail"; +import { OpenAIResponse, InputItemListResponse } from "@/lib/types"; + +describe("ResponseDetailView", () => { + const defaultProps = { + response: null, + inputItems: null, + isLoading: false, + isLoadingInputItems: false, + error: null, + inputItemsError: null, + id: "test_id", + }; + + describe("Loading State", () => { + test("renders loading skeleton when isLoading is true", () => { + const { container } = render( + , + ); + + // Check for skeleton elements + const skeletons = container.querySelectorAll('[data-slot="skeleton"]'); + expect(skeletons.length).toBeGreaterThan(0); + + // The title is replaced by a skeleton when loading, so we shouldn't expect the text + }); + }); + + describe("Error State", () => { + test("renders error message when error prop is provided", () => { + const errorMessage = "Network Error"; + render( + , + ); + + expect(screen.getByText("Responses Details")).toBeInTheDocument(); + // The error message is split across elements, so we check for parts + expect( + screen.getByText(/Error loading details for ID/), + ).toBeInTheDocument(); + expect(screen.getByText(/test_id/)).toBeInTheDocument(); + expect(screen.getByText(/Network Error/)).toBeInTheDocument(); + }); + + test("renders default error message when error.message is not available", () => { + render( + , + ); + + expect( + screen.getByText(/Error loading details for ID/), + ).toBeInTheDocument(); + expect(screen.getByText(/test_id/)).toBeInTheDocument(); + }); + }); + + describe("Not Found State", () => { + test("renders not found message when response is null and not loading/error", () => { + render(); + + expect(screen.getByText("Responses Details")).toBeInTheDocument(); + // The message is split across elements + expect(screen.getByText(/No details found for ID:/)).toBeInTheDocument(); + expect(screen.getByText(/test_id/)).toBeInTheDocument(); + }); + }); + + describe("Response Data Rendering", () => { + const mockResponse: OpenAIResponse = { + id: "resp_123", + object: "response", + created_at: 1710000000, + model: "llama-test-model", + status: "completed", + output: [ + { + type: "message", + role: "assistant", + content: "Test response output", + }, + ], + input: [ + { + type: "message", + role: "user", + content: "Test input message", + }, + ], + temperature: 0.7, + top_p: 0.9, + parallel_tool_calls: true, + previous_response_id: "prev_resp_456", + }; + + test("renders response data with input and output sections", () => { + render(); + + // Check main sections + expect(screen.getByText("Responses Details")).toBeInTheDocument(); + expect(screen.getByText("Input")).toBeInTheDocument(); + expect(screen.getByText("Output")).toBeInTheDocument(); + + // Check input content + expect(screen.getByText("Test input message")).toBeInTheDocument(); + expect(screen.getByText("User")).toBeInTheDocument(); + + // Check output content + expect(screen.getByText("Test response output")).toBeInTheDocument(); + expect(screen.getByText("Assistant")).toBeInTheDocument(); + }); + + test("renders properties sidebar with all response metadata", () => { + render(); + + // Check properties - use regex to handle text split across elements + expect(screen.getByText(/Created/)).toBeInTheDocument(); + expect( + screen.getByText(new Date(1710000000 * 1000).toLocaleString()), + ).toBeInTheDocument(); + + // Check for the specific ID label (not Previous Response ID) + expect( + screen.getByText((content, element) => { + return element?.tagName === "STRONG" && content === "ID:"; + }), + ).toBeInTheDocument(); + expect(screen.getByText("resp_123")).toBeInTheDocument(); + + expect(screen.getByText(/Model/)).toBeInTheDocument(); + expect(screen.getByText("llama-test-model")).toBeInTheDocument(); + + expect(screen.getByText(/Status/)).toBeInTheDocument(); + expect(screen.getByText("completed")).toBeInTheDocument(); + + expect(screen.getByText(/Temperature/)).toBeInTheDocument(); + expect(screen.getByText("0.7")).toBeInTheDocument(); + + expect(screen.getByText(/Top P/)).toBeInTheDocument(); + expect(screen.getByText("0.9")).toBeInTheDocument(); + + expect(screen.getByText(/Parallel Tool Calls/)).toBeInTheDocument(); + expect(screen.getByText("Yes")).toBeInTheDocument(); + + expect(screen.getByText(/Previous Response ID/)).toBeInTheDocument(); + expect(screen.getByText("prev_resp_456")).toBeInTheDocument(); + }); + + test("handles optional properties correctly", () => { + const minimalResponse: OpenAIResponse = { + id: "resp_minimal", + object: "response", + created_at: 1710000000, + model: "test-model", + status: "completed", + output: [], + input: [], + }; + + render( + , + ); + + // Should show required properties + expect(screen.getByText("resp_minimal")).toBeInTheDocument(); + expect(screen.getByText("test-model")).toBeInTheDocument(); + expect(screen.getByText("completed")).toBeInTheDocument(); + + // Should not show optional properties + expect(screen.queryByText("Temperature")).not.toBeInTheDocument(); + expect(screen.queryByText("Top P")).not.toBeInTheDocument(); + expect(screen.queryByText("Parallel Tool Calls")).not.toBeInTheDocument(); + expect( + screen.queryByText("Previous Response ID"), + ).not.toBeInTheDocument(); + }); + + test("renders error information when response has error", () => { + const errorResponse: OpenAIResponse = { + ...mockResponse, + error: { + code: "invalid_request", + message: "The request was invalid", + }, + }; + + render(); + + // The error is shown in the properties sidebar, not as a separate "Error" label + expect( + screen.getByText("invalid_request: The request was invalid"), + ).toBeInTheDocument(); + }); + }); + + describe("Input Items Handling", () => { + const mockResponse: OpenAIResponse = { + id: "resp_123", + object: "response", + created_at: 1710000000, + model: "test-model", + status: "completed", + output: [{ type: "message", role: "assistant", content: "output" }], + input: [{ type: "message", role: "user", content: "fallback input" }], + }; + + test("shows loading state for input items", () => { + render( + , + ); + + // Check for skeleton loading in input items section + const { container } = render( + , + ); + + const skeletons = container.querySelectorAll('[data-slot="skeleton"]'); + expect(skeletons.length).toBeGreaterThan(0); + }); + + test("shows error message for input items with fallback", () => { + render( + , + ); + + expect( + screen.getByText( + "Error loading input items: Failed to load input items", + ), + ).toBeInTheDocument(); + expect( + screen.getByText("Falling back to response input data."), + ).toBeInTheDocument(); + + // Should still show fallback input data + expect(screen.getByText("fallback input")).toBeInTheDocument(); + }); + + test("uses input items data when available", () => { + const mockInputItems: InputItemListResponse = { + object: "list", + data: [ + { + type: "message", + role: "user", + content: "input from items API", + }, + ], + }; + + render( + , + ); + + // Should show input items data, not response.input + expect(screen.getByText("input from items API")).toBeInTheDocument(); + expect(screen.queryByText("fallback input")).not.toBeInTheDocument(); + }); + + test("falls back to response.input when input items is empty", () => { + const emptyInputItems: InputItemListResponse = { + object: "list", + data: [], + }; + + render( + , + ); + + // Should show fallback input data + expect(screen.getByText("fallback input")).toBeInTheDocument(); + }); + + test("shows no input message when no data available", () => { + const responseWithoutInput: OpenAIResponse = { + ...mockResponse, + input: [], + }; + + render( + , + ); + + expect(screen.getByText("No input data available.")).toBeInTheDocument(); + }); + }); + + describe("Input Display Components", () => { + test("renders string content input correctly", () => { + const mockResponse: OpenAIResponse = { + id: "resp_123", + object: "response", + created_at: 1710000000, + model: "test-model", + status: "completed", + output: [], + input: [ + { + type: "message", + role: "user", + content: "Simple string input", + }, + ], + }; + + render(); + + expect(screen.getByText("Simple string input")).toBeInTheDocument(); + expect(screen.getByText("User")).toBeInTheDocument(); + }); + + test("renders array content input correctly", () => { + const mockResponse: OpenAIResponse = { + id: "resp_123", + object: "response", + created_at: 1710000000, + model: "test-model", + status: "completed", + output: [], + input: [ + { + type: "message", + role: "user", + content: [ + { type: "input_text", text: "First part" }, + { type: "output_text", text: "Second part" }, + ], + }, + ], + }; + + render(); + + expect(screen.getByText("First part Second part")).toBeInTheDocument(); + expect(screen.getByText("User")).toBeInTheDocument(); + }); + + test("renders non-message input types correctly", () => { + const mockResponse: OpenAIResponse = { + id: "resp_123", + object: "response", + created_at: 1710000000, + model: "test-model", + status: "completed", + output: [], + input: [ + { + type: "function_call", + content: "function call content", + }, + ], + }; + + render(); + + expect(screen.getByText("function call content")).toBeInTheDocument(); + // Use getAllByText to find the specific "Input" with the type detail + const inputElements = screen.getAllByText("Input"); + expect(inputElements.length).toBeGreaterThan(0); + expect(screen.getByText("(function_call)")).toBeInTheDocument(); + }); + + test("handles input with object content", () => { + const mockResponse: OpenAIResponse = { + id: "resp_123", + object: "response", + created_at: 1710000000, + model: "test-model", + status: "completed", + output: [], + input: [ + { + type: "custom_type", + content: JSON.stringify({ key: "value", nested: { data: "test" } }), + }, + ], + }; + + render(); + + // Should show JSON stringified content (without quotes around keys in the rendered output) + expect(screen.getByText(/key.*value/)).toBeInTheDocument(); + // Use getAllByText to find the specific "Input" with the type detail + const inputElements = screen.getAllByText("Input"); + expect(inputElements.length).toBeGreaterThan(0); + expect(screen.getByText("(custom_type)")).toBeInTheDocument(); + }); + + test("renders function call input correctly", () => { + const mockResponse: OpenAIResponse = { + id: "resp_123", + object: "response", + created_at: 1710000000, + model: "test-model", + status: "completed", + output: [], + input: [ + { + type: "function_call", + id: "call_456", + status: "completed", + name: "input_function", + arguments: '{"param": "value"}', + }, + ], + }; + + render(); + + expect( + screen.getByText('input_function({"param": "value"})'), + ).toBeInTheDocument(); + expect(screen.getByText("Function Call")).toBeInTheDocument(); + }); + + test("renders web search call input correctly", () => { + const mockResponse: OpenAIResponse = { + id: "resp_123", + object: "response", + created_at: 1710000000, + model: "test-model", + status: "completed", + output: [], + input: [ + { + type: "web_search_call", + id: "search_789", + status: "completed", + }, + ], + }; + + render(); + + expect( + screen.getByText("web_search_call(status: completed)"), + ).toBeInTheDocument(); + expect(screen.getByText("Function Call")).toBeInTheDocument(); + expect(screen.getByText("(Web Search)")).toBeInTheDocument(); + }); + }); + + describe("Output Display Components", () => { + test("renders message output with string content", () => { + const mockResponse: OpenAIResponse = { + id: "resp_123", + object: "response", + created_at: 1710000000, + model: "test-model", + status: "completed", + output: [ + { + type: "message", + role: "assistant", + content: "Simple string output", + }, + ], + input: [], + }; + + render(); + + expect(screen.getByText("Simple string output")).toBeInTheDocument(); + expect(screen.getByText("Assistant")).toBeInTheDocument(); + }); + + test("renders message output with array content", () => { + const mockResponse: OpenAIResponse = { + id: "resp_123", + object: "response", + created_at: 1710000000, + model: "test-model", + status: "completed", + output: [ + { + type: "message", + role: "assistant", + content: [ + { type: "output_text", text: "First output" }, + { type: "input_text", text: "Second output" }, + ], + }, + ], + input: [], + }; + + render(); + + expect( + screen.getByText("First output Second output"), + ).toBeInTheDocument(); + expect(screen.getByText("Assistant")).toBeInTheDocument(); + }); + + test("renders function call output correctly", () => { + const mockResponse: OpenAIResponse = { + id: "resp_123", + object: "response", + created_at: 1710000000, + model: "test-model", + status: "completed", + output: [ + { + type: "function_call", + id: "call_123", + status: "completed", + name: "search_function", + arguments: '{"query": "test"}', + }, + ], + input: [], + }; + + render(); + + expect( + screen.getByText('search_function({"query": "test"})'), + ).toBeInTheDocument(); + expect(screen.getByText("Function Call")).toBeInTheDocument(); + }); + + test("renders function call output without arguments", () => { + const mockResponse: OpenAIResponse = { + id: "resp_123", + object: "response", + created_at: 1710000000, + model: "test-model", + status: "completed", + output: [ + { + type: "function_call", + id: "call_123", + status: "completed", + name: "simple_function", + }, + ], + input: [], + }; + + render(); + + expect(screen.getByText("simple_function({})")).toBeInTheDocument(); + expect(screen.getByText(/Function Call/)).toBeInTheDocument(); + }); + + test("renders web search call output correctly", () => { + const mockResponse: OpenAIResponse = { + id: "resp_123", + object: "response", + created_at: 1710000000, + model: "test-model", + status: "completed", + output: [ + { + type: "web_search_call", + id: "search_123", + status: "completed", + }, + ], + input: [], + }; + + render(); + + expect( + screen.getByText("web_search_call(status: completed)"), + ).toBeInTheDocument(); + expect(screen.getByText(/Function Call/)).toBeInTheDocument(); + expect(screen.getByText("(Web Search)")).toBeInTheDocument(); + }); + + test("renders unknown output types with JSON fallback", () => { + const mockResponse: OpenAIResponse = { + id: "resp_123", + object: "response", + created_at: 1710000000, + model: "test-model", + status: "completed", + output: [ + { + type: "unknown_type", + custom_field: "custom_value", + data: { nested: "object" }, + } as any, + ], + input: [], + }; + + render(); + + // Should show JSON stringified content + expect( + screen.getByText(/custom_field.*custom_value/), + ).toBeInTheDocument(); + expect(screen.getByText("(unknown_type)")).toBeInTheDocument(); + }); + + test("shows no output message when output array is empty", () => { + const mockResponse: OpenAIResponse = { + id: "resp_123", + object: "response", + created_at: 1710000000, + model: "test-model", + status: "completed", + output: [], + input: [], + }; + + render(); + + expect(screen.getByText("No output data available.")).toBeInTheDocument(); + }); + + test("groups function call with its output correctly", () => { + const mockResponse: OpenAIResponse = { + id: "resp_123", + object: "response", + created_at: 1710000000, + model: "test-model", + status: "completed", + output: [ + { + type: "function_call", + id: "call_123", + status: "completed", + name: "get_weather", + arguments: '{"city": "Tokyo"}', + }, + { + type: "message", + role: "assistant", + call_id: "call_123", + content: "sunny and warm", + } as any, // Using any to bypass the type restriction for this test + ], + input: [], + }; + + render(); + + // Should show the function call and message as separate items (not grouped) + expect(screen.getByText("Function Call")).toBeInTheDocument(); + expect( + screen.getByText('get_weather({"city": "Tokyo"})'), + ).toBeInTheDocument(); + expect(screen.getByText("Assistant")).toBeInTheDocument(); + expect(screen.getByText("sunny and warm")).toBeInTheDocument(); + + // Should NOT have the grouped "Arguments" and "Output" labels + expect(screen.queryByText("Arguments")).not.toBeInTheDocument(); + }); + + test("groups function call with function_call_output correctly", () => { + const mockResponse: OpenAIResponse = { + id: "resp_123", + object: "response", + created_at: 1710000000, + model: "test-model", + status: "completed", + output: [ + { + type: "function_call", + call_id: "call_123", + status: "completed", + name: "get_weather", + arguments: '{"city": "Tokyo"}', + }, + { + type: "function_call_output", + id: "fc_68364957013081...", + status: "completed", + call_id: "call_123", + output: "sunny and warm", + } as any, // Using any to bypass the type restriction for this test + ], + input: [], + }; + + render(); + + // Should show the function call grouped with its clean output + expect(screen.getByText("Function Call")).toBeInTheDocument(); + expect(screen.getByText("Arguments")).toBeInTheDocument(); + expect( + screen.getByText('get_weather({"city": "Tokyo"})'), + ).toBeInTheDocument(); + // Use getAllByText since there are multiple "Output" elements (card title and output label) + const outputElements = screen.getAllByText("Output"); + expect(outputElements.length).toBeGreaterThan(0); + expect(screen.getByText("sunny and warm")).toBeInTheDocument(); + }); + }); + + describe("Edge Cases and Error Handling", () => { + test("handles missing role in message input", () => { + const mockResponse: OpenAIResponse = { + id: "resp_123", + object: "response", + created_at: 1710000000, + model: "test-model", + status: "completed", + output: [], + input: [ + { + type: "message", + content: "Message without role", + }, + ], + }; + + render(); + + expect(screen.getByText("Message without role")).toBeInTheDocument(); + expect(screen.getByText("Unknown")).toBeInTheDocument(); // Default role + }); + + test("handles missing name in function call output", () => { + const mockResponse: OpenAIResponse = { + id: "resp_123", + object: "response", + created_at: 1710000000, + model: "test-model", + status: "completed", + output: [ + { + type: "function_call", + id: "call_123", + status: "completed", + }, + ], + input: [], + }; + + render(); + + // When name is missing, it falls back to JSON.stringify of the entire output + const functionCallElements = screen.getAllByText(/function_call/); + expect(functionCallElements.length).toBeGreaterThan(0); + expect(screen.getByText(/call_123/)).toBeInTheDocument(); + }); + }); +}); diff --git a/llama_stack/ui/components/responses/responses-detail.tsx b/llama_stack/ui/components/responses/responses-detail.tsx new file mode 100644 index 000000000..c8c447ba4 --- /dev/null +++ b/llama_stack/ui/components/responses/responses-detail.tsx @@ -0,0 +1,171 @@ +"use client"; + +import { OpenAIResponse, InputItemListResponse } from "@/lib/types"; +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 { GroupedItemsDisplay } from "./grouping/grouped-items-display"; + +interface ResponseDetailViewProps { + response: OpenAIResponse | null; + inputItems: InputItemListResponse | null; + isLoading: boolean; + isLoadingInputItems: boolean; + error: Error | null; + inputItemsError: Error | null; + id: string; +} + +export function ResponseDetailView({ + response, + inputItems, + isLoading, + isLoadingInputItems, + error, + inputItemsError, + id, +}: ResponseDetailViewProps) { + const title = "Responses Details"; + + if (error) { + return ; + } + + if (isLoading) { + return ; + } + + if (!response) { + return ; + } + + // Main content cards + const mainContent = ( + <> + + + Input + + + {/* Show loading state for input items */} + {isLoadingInputItems ? ( +
    + + + +
    + ) : inputItemsError ? ( +
    + Error loading input items: {inputItemsError.message} +
    + + Falling back to response input data. + +
    + ) : null} + + {/* Display input items if available, otherwise fall back to response.input */} + {(() => { + const dataToDisplay = + inputItems?.data && inputItems.data.length > 0 + ? inputItems.data + : response.input; + + if (dataToDisplay && dataToDisplay.length > 0) { + return ( + + ); + } else { + return ( +

    + No input data available. +

    + ); + } + })()} +     +     + + + + Output + + + {response.output?.length > 0 ? ( + + ) : ( +

    + No output data available. +

    + )} +     +     + + ); + + // Properties sidebar + const sidebar = ( + + + + + + {response.temperature && ( + + )} + {response.top_p && } + {response.parallel_tool_calls && ( + + )} + {response.previous_response_id && ( + {response.previous_response_id} + } + hasBorder + /> + )} + {response.error && ( + + {response.error.code}: {response.error.message} + + } + className="pt-1 mt-1 border-t border-red-200" + /> + )} + + ); + + return ( + + ); +} diff --git a/llama_stack/ui/components/responses/responses-table.test.tsx b/llama_stack/ui/components/responses/responses-table.test.tsx new file mode 100644 index 000000000..7c45c57d3 --- /dev/null +++ b/llama_stack/ui/components/responses/responses-table.test.tsx @@ -0,0 +1,537 @@ +import React from "react"; +import { render, screen, fireEvent } from "@testing-library/react"; +import "@testing-library/jest-dom"; +import { ResponsesTable } from "./responses-table"; +import { OpenAIResponse } from "@/lib/types"; + +// Mock next/navigation +const mockPush = jest.fn(); +jest.mock("next/navigation", () => ({ + useRouter: () => ({ + push: mockPush, + }), +})); + +// Mock helper functions +jest.mock("@/lib/truncate-text"); + +// Import the mocked functions +import { truncateText as originalTruncateText } from "@/lib/truncate-text"; + +// Cast to jest.Mock for typings +const truncateText = originalTruncateText as jest.Mock; + +describe("ResponsesTable", () => { + const defaultProps = { + data: [] as OpenAIResponse[], + isLoading: false, + error: null, + }; + + beforeEach(() => { + // Reset all mocks before each test + mockPush.mockClear(); + truncateText.mockClear(); + + // Default pass-through implementation + truncateText.mockImplementation((text: string | undefined) => text); + }); + + test("renders without crashing with default props", () => { + render(); + expect(screen.getByText("No responses found.")).toBeInTheDocument(); + }); + + test("click on a row navigates to the correct URL", () => { + const mockResponse: OpenAIResponse = { + id: "resp_123", + object: "response", + created_at: Math.floor(Date.now() / 1000), + model: "llama-test-model", + status: "completed", + output: [ + { + type: "message", + role: "assistant", + content: "Test output", + }, + ], + input: [ + { + type: "message", + role: "user", + content: "Test input", + }, + ], + }; + + render(); + + const row = screen.getByText("Test input").closest("tr"); + if (row) { + fireEvent.click(row); + expect(mockPush).toHaveBeenCalledWith("/logs/responses/resp_123"); + } else { + throw new Error('Row with "Test input" not found for router mock test.'); + } + }); + + describe("Loading State", () => { + test("renders skeleton UI when isLoading is true", () => { + const { container } = render( + , + ); + + // Check for skeleton in the table caption + const tableCaption = container.querySelector("caption"); + expect(tableCaption).toBeInTheDocument(); + if (tableCaption) { + const captionSkeleton = tableCaption.querySelector( + '[data-slot="skeleton"]', + ); + expect(captionSkeleton).toBeInTheDocument(); + } + + // Check for skeletons in the table body cells + const tableBody = container.querySelector("tbody"); + expect(tableBody).toBeInTheDocument(); + if (tableBody) { + const bodySkeletons = tableBody.querySelectorAll( + '[data-slot="skeleton"]', + ); + expect(bodySkeletons.length).toBeGreaterThan(0); + } + }); + }); + + describe("Error State", () => { + test("renders error message when error prop is provided", () => { + const errorMessage = "Network Error"; + render( + , + ); + expect( + screen.getByText(`Error fetching data: ${errorMessage}`), + ).toBeInTheDocument(); + }); + + test("renders default error message when error.message is not available", () => { + render( + , + ); + expect( + screen.getByText("Error fetching data: An unknown error occurred"), + ).toBeInTheDocument(); + }); + + test("renders default error message when error prop is an object without message", () => { + render(); + expect( + screen.getByText("Error fetching data: An unknown error occurred"), + ).toBeInTheDocument(); + }); + }); + + describe("Empty State", () => { + test('renders "No responses found." and no table when data array is empty', () => { + render(); + expect(screen.getByText("No responses found.")).toBeInTheDocument(); + + // Ensure that the table structure is NOT rendered in the empty state + const table = screen.queryByRole("table"); + expect(table).not.toBeInTheDocument(); + }); + }); + + describe("Data Rendering", () => { + test("renders table caption, headers, and response data correctly", () => { + const mockResponses = [ + { + id: "resp_1", + object: "response" as const, + created_at: 1710000000, + model: "llama-test-model", + status: "completed", + output: [ + { + type: "message" as const, + role: "assistant" as const, + content: "Test output", + }, + ], + input: [ + { + type: "message", + role: "user", + content: "Test input", + }, + ], + }, + { + id: "resp_2", + object: "response" as const, + created_at: 1710001000, + model: "llama-another-model", + status: "completed", + output: [ + { + type: "message" as const, + role: "assistant" as const, + content: "Another output", + }, + ], + input: [ + { + type: "message", + role: "user", + content: "Another input", + }, + ], + }, + ]; + + render( + , + ); + + // Table caption + expect( + screen.getByText("A list of your recent responses."), + ).toBeInTheDocument(); + + // Table headers + expect(screen.getByText("Input")).toBeInTheDocument(); + expect(screen.getByText("Output")).toBeInTheDocument(); + expect(screen.getByText("Model")).toBeInTheDocument(); + expect(screen.getByText("Created")).toBeInTheDocument(); + + // Data rows + expect(screen.getByText("Test input")).toBeInTheDocument(); + expect(screen.getByText("Test output")).toBeInTheDocument(); + expect(screen.getByText("llama-test-model")).toBeInTheDocument(); + expect( + screen.getByText(new Date(1710000000 * 1000).toLocaleString()), + ).toBeInTheDocument(); + + expect(screen.getByText("Another input")).toBeInTheDocument(); + expect(screen.getByText("Another output")).toBeInTheDocument(); + expect(screen.getByText("llama-another-model")).toBeInTheDocument(); + expect( + screen.getByText(new Date(1710001000 * 1000).toLocaleString()), + ).toBeInTheDocument(); + }); + }); + + describe("Input Text Extraction", () => { + test("extracts text from string content", () => { + const mockResponse: OpenAIResponse = { + id: "resp_string", + object: "response", + created_at: 1710000000, + model: "test-model", + status: "completed", + output: [{ type: "message", role: "assistant", content: "output" }], + input: [ + { + type: "message", + role: "user", + content: "Simple string input", + }, + ], + }; + + render( + , + ); + expect(screen.getByText("Simple string input")).toBeInTheDocument(); + }); + + test("extracts text from array content with input_text type", () => { + const mockResponse: OpenAIResponse = { + id: "resp_array", + object: "response", + created_at: 1710000000, + model: "test-model", + status: "completed", + output: [{ type: "message", role: "assistant", content: "output" }], + input: [ + { + type: "message", + role: "user", + content: [ + { type: "input_text", text: "Array input text" }, + { type: "input_text", text: "Should not be used" }, + ], + }, + ], + }; + + render( + , + ); + expect(screen.getByText("Array input text")).toBeInTheDocument(); + }); + + test("returns empty string when no message input found", () => { + const mockResponse: OpenAIResponse = { + id: "resp_no_input", + object: "response", + created_at: 1710000000, + model: "test-model", + status: "completed", + output: [{ type: "message", role: "assistant", content: "output" }], + input: [ + { + type: "other_type", + content: "Not a message", + }, + ], + }; + + const { container } = render( + , + ); + + // Find the input cell (first cell in the data row) and verify it's empty + const inputCell = container.querySelector("tbody tr td:first-child"); + expect(inputCell).toBeInTheDocument(); + expect(inputCell).toHaveTextContent(""); + }); + }); + + describe("Output Text Extraction", () => { + test("extracts text from string message content", () => { + const mockResponse: OpenAIResponse = { + id: "resp_string_output", + object: "response", + created_at: 1710000000, + model: "test-model", + status: "completed", + output: [ + { + type: "message", + role: "assistant", + content: "Simple string output", + }, + ], + input: [{ type: "message", content: "input" }], + }; + + render( + , + ); + expect(screen.getByText("Simple string output")).toBeInTheDocument(); + }); + + test("extracts text from array message content with output_text type", () => { + const mockResponse: OpenAIResponse = { + id: "resp_array_output", + object: "response", + created_at: 1710000000, + model: "test-model", + status: "completed", + output: [ + { + type: "message", + role: "assistant", + content: [ + { type: "output_text", text: "Array output text" }, + { type: "output_text", text: "Should not be used" }, + ], + }, + ], + input: [{ type: "message", content: "input" }], + }; + + render( + , + ); + expect(screen.getByText("Array output text")).toBeInTheDocument(); + }); + + test("formats function call output", () => { + const mockResponse: OpenAIResponse = { + id: "resp_function_call", + object: "response", + created_at: 1710000000, + model: "test-model", + status: "completed", + output: [ + { + type: "function_call", + id: "call_123", + status: "completed", + name: "search_function", + arguments: '{"query": "test"}', + }, + ], + input: [{ type: "message", content: "input" }], + }; + + render( + , + ); + expect( + screen.getByText('search_function({"query": "test"})'), + ).toBeInTheDocument(); + }); + + test("formats function call output without arguments", () => { + const mockResponse: OpenAIResponse = { + id: "resp_function_no_args", + object: "response", + created_at: 1710000000, + model: "test-model", + status: "completed", + output: [ + { + type: "function_call", + id: "call_123", + status: "completed", + name: "simple_function", + }, + ], + input: [{ type: "message", content: "input" }], + }; + + render( + , + ); + expect(screen.getByText("simple_function({})")).toBeInTheDocument(); + }); + + test("formats web search call output", () => { + const mockResponse: OpenAIResponse = { + id: "resp_web_search", + object: "response", + created_at: 1710000000, + model: "test-model", + status: "completed", + output: [ + { + type: "web_search_call", + id: "search_123", + status: "completed", + }, + ], + input: [{ type: "message", content: "input" }], + }; + + render( + , + ); + expect( + screen.getByText("web_search_call(status: completed)"), + ).toBeInTheDocument(); + }); + + test("falls back to JSON.stringify for unknown tool call types", () => { + const mockResponse: OpenAIResponse = { + id: "resp_unknown_tool", + object: "response", + created_at: 1710000000, + model: "test-model", + status: "completed", + output: [ + { + type: "unknown_call", + id: "unknown_123", + status: "completed", + custom_field: "custom_value", + } as any, + ], + input: [{ type: "message", content: "input" }], + }; + + render( + , + ); + // Should contain the JSON stringified version + expect(screen.getByText(/unknown_call/)).toBeInTheDocument(); + }); + + test("falls back to JSON.stringify for entire output when no message or tool call found", () => { + const mockResponse: OpenAIResponse = { + id: "resp_fallback", + object: "response", + created_at: 1710000000, + model: "test-model", + status: "completed", + output: [ + { + type: "unknown_type", + data: "some data", + } as any, + ], + input: [{ type: "message", content: "input" }], + }; + + render( + , + ); + // Should contain the JSON stringified version of the output array + expect(screen.getByText(/unknown_type/)).toBeInTheDocument(); + }); + }); + + describe("Text Truncation", () => { + test("truncates long input and output text", () => { + // Specific mock implementation for this test + truncateText.mockImplementation( + (text: string | undefined, maxLength?: number) => { + const defaultTestMaxLength = 10; + const effectiveMaxLength = maxLength ?? defaultTestMaxLength; + return typeof text === "string" && text.length > effectiveMaxLength + ? text.slice(0, effectiveMaxLength) + "..." + : text; + }, + ); + + const longInput = + "This is a very long input message that should be truncated."; + const longOutput = + "This is a very long output message that should also be truncated."; + + const mockResponse: OpenAIResponse = { + id: "resp_trunc", + object: "response", + created_at: 1710002000, + model: "llama-trunc-model", + status: "completed", + output: [ + { + type: "message", + role: "assistant", + content: longOutput, + }, + ], + input: [ + { + type: "message", + role: "user", + content: longInput, + }, + ], + }; + + render( + , + ); + + // The truncated text should be present for both input and output + const truncatedTexts = screen.getAllByText( + longInput.slice(0, 10) + "...", + ); + expect(truncatedTexts.length).toBe(2); // one for input, one for output + truncatedTexts.forEach((textElement) => + expect(textElement).toBeInTheDocument(), + ); + }); + }); +}); diff --git a/llama_stack/ui/components/responses/responses-table.tsx b/llama_stack/ui/components/responses/responses-table.tsx new file mode 100644 index 000000000..352450d18 --- /dev/null +++ b/llama_stack/ui/components/responses/responses-table.tsx @@ -0,0 +1,117 @@ +"use client"; + +import { + OpenAIResponse, + ResponseInput, + ResponseInputMessageContent, +} from "@/lib/types"; +import { LogsTable, LogTableRow } from "@/components/logs/logs-table"; +import { + isMessageInput, + isMessageItem, + isFunctionCallItem, + isWebSearchCallItem, + MessageItem, + FunctionCallItem, + WebSearchCallItem, +} from "./utils/item-types"; + +interface ResponsesTableProps { + data: OpenAIResponse[]; + isLoading: boolean; + error: Error | null; +} + +function getInputText(response: OpenAIResponse): string { + const firstInput = response.input.find(isMessageInput); + if (firstInput) { + return extractContentFromItem(firstInput); + } + return ""; +} + +function getOutputText(response: OpenAIResponse): string { + const firstMessage = response.output.find((item) => + isMessageItem(item as any), + ); + if (firstMessage) { + const content = extractContentFromItem(firstMessage as MessageItem); + if (content) { + return content; + } + } + + const functionCall = response.output.find((item) => + isFunctionCallItem(item as any), + ); + if (functionCall) { + return formatFunctionCall(functionCall as FunctionCallItem); + } + + const webSearchCall = response.output.find((item) => + isWebSearchCallItem(item as any), + ); + if (webSearchCall) { + return formatWebSearchCall(webSearchCall as WebSearchCallItem); + } + + return JSON.stringify(response.output); +} + +function extractContentFromItem(item: { + content?: string | ResponseInputMessageContent[]; +}): string { + if (!item.content) { + return ""; + } + + if (typeof item.content === "string") { + return item.content; + } else if (Array.isArray(item.content)) { + const textContent = item.content.find( + (c: ResponseInputMessageContent) => + c.type === "input_text" || c.type === "output_text", + ); + return textContent?.text || ""; + } + return ""; +} + +function formatFunctionCall(functionCall: FunctionCallItem): string { + const args = functionCall.arguments || "{}"; + const name = functionCall.name || "unknown"; + return `${name}(${args})`; +} + +function formatWebSearchCall(webSearchCall: WebSearchCallItem): string { + return `web_search_call(status: ${webSearchCall.status})`; +} + +function formatResponseToRow(response: OpenAIResponse): LogTableRow { + return { + id: response.id, + input: getInputText(response), + output: getOutputText(response), + model: response.model, + createdTime: new Date(response.created_at * 1000).toLocaleString(), + detailPath: `/logs/responses/${response.id}`, + }; +} + +export function ResponsesTable({ + data, + isLoading, + error, +}: ResponsesTableProps) { + const formattedData = data.map(formatResponseToRow); + + return ( + + ); +} diff --git a/llama_stack/ui/components/responses/utils/item-types.ts b/llama_stack/ui/components/responses/utils/item-types.ts new file mode 100644 index 000000000..2bde49119 --- /dev/null +++ b/llama_stack/ui/components/responses/utils/item-types.ts @@ -0,0 +1,61 @@ +/** + * Type guards for different item types in responses + */ + +import type { + ResponseInput, + ResponseOutput, + ResponseMessage, + ResponseToolCall, +} from "@/lib/types"; + +export interface BaseItem { + type: string; + [key: string]: unknown; +} + +export type MessageItem = ResponseMessage; +export type FunctionCallItem = ResponseToolCall & { type: "function_call" }; +export type WebSearchCallItem = ResponseToolCall & { type: "web_search_call" }; +export type FunctionCallOutputItem = BaseItem & { + type: "function_call_output"; + call_id: string; + output?: string | object; +}; + +export type AnyResponseItem = + | ResponseInput + | ResponseOutput + | FunctionCallOutputItem; + +export function isMessageInput( + item: ResponseInput, +): item is ResponseInput & { type: "message" } { + return item.type === "message"; +} + +export function isMessageItem(item: AnyResponseItem): item is MessageItem { + return item.type === "message" && "content" in item; +} + +export function isFunctionCallItem( + item: AnyResponseItem, +): item is FunctionCallItem { + return item.type === "function_call" && "name" in item; +} + +export function isWebSearchCallItem( + item: AnyResponseItem, +): item is WebSearchCallItem { + return item.type === "web_search_call"; +} + +export function isFunctionCallOutputItem( + item: AnyResponseItem, +): item is FunctionCallOutputItem { + return ( + item.type === "function_call_output" && + "call_id" in item && + typeof (item as any).call_id === "string" + ); +} diff --git a/llama_stack/ui/components/ui/breadcrumb.tsx b/llama_stack/ui/components/ui/breadcrumb.tsx new file mode 100644 index 000000000..f63ae19af --- /dev/null +++ b/llama_stack/ui/components/ui/breadcrumb.tsx @@ -0,0 +1,109 @@ +import * as React from "react"; +import { Slot } from "@radix-ui/react-slot"; +import { ChevronRight, MoreHorizontal } from "lucide-react"; + +import { cn } from "@/lib/utils"; + +function Breadcrumb({ ...props }: React.ComponentProps<"nav">) { + return