Merge branch 'main' into fix/issue-4005-installer-macos-permissions

.github/actions/run-and-record-tests/action.yml

@@ -72,7 +72,8 @@ runs:
           echo "New recordings detected, committing and pushing"
           git add tests/integration/
 
-          git commit -m "Recordings update from CI (suite: ${{ inputs.suite }})"
+          git commit -m "Recordings update from CI (setup: ${{ inputs.setup }}, suite: ${{ inputs.suite }})"
+
           git fetch origin ${{ github.ref_name }}
           git rebase origin/${{ github.ref_name }}
           echo "Rebased successfully"
@@ -88,6 +89,8 @@ runs:
       run: |
         # Ollama logs (if ollama container exists)
         sudo docker logs ollama > ollama-${{ inputs.inference-mode }}.log 2>&1 || true
+        # vllm logs (if vllm container exists)
+        sudo docker logs vllm > vllm-${{ inputs.inference-mode }}.log 2>&1 || true
         # Note: distro container logs are now dumped in integration-tests.sh before container is removed
 
     - name: Upload logs

.github/actions/setup-vllm/action.yml

@@ -11,13 +11,14 @@ runs:
           --name vllm \
           -p 8000:8000 \
           --privileged=true \
-          quay.io/higginsd/vllm-cpu:65393ee064 \
+          quay.io/higginsd/vllm-cpu:65393ee064-qwen3 \
           --host 0.0.0.0 \
           --port 8000 \
           --enable-auto-tool-choice \
-          --tool-call-parser llama3_json \
-          --model /root/.cache/Llama-3.2-1B-Instruct \
-          --served-model-name meta-llama/Llama-3.2-1B-Instruct
+          --tool-call-parser hermes \
+          --model /root/.cache/Qwen3-0.6B \
+          --served-model-name Qwen/Qwen3-0.6B \
+          --max-model-len 8192
 
           # Wait for vllm to be ready
           echo "Waiting for vllm to be ready..."

.github/workflows/integration-tests.yml

@@ -23,10 +23,10 @@ on:
       - '.github/actions/setup-test-environment/action.yml'
       - '.github/actions/run-and-record-tests/action.yml'
       - 'scripts/integration-tests.sh'
+      - 'scripts/generate_ci_matrix.py'
   schedule:
     # If changing the cron schedule, update the provider in the test-matrix job
     - cron: '0 0 * * *'  # (test latest client) Daily at 12 AM UTC
-    - cron: '1 0 * * 0'  # (test vllm) Weekly on Sunday at 1 AM UTC
   workflow_dispatch:
     inputs:
       test-all-client-versions:
@@ -44,8 +44,27 @@ concurrency:
   cancel-in-progress: true
 
 jobs:
+  generate-matrix:
+    runs-on: ubuntu-latest
+    outputs:
+      matrix: ${{ steps.set-matrix.outputs.matrix }}
+    steps:
+      - name: Checkout repository
+        uses: actions/checkout@08c6903cd8c0fde910a37f88322edcfb5dd907a8 # v5.0.0
+
+      - name: Generate test matrix
+        id: set-matrix
+        run: |
+          # Generate matrix from CI_MATRIX in tests/integration/suites.py
+          # Supports schedule-based and manual input overrides
+          MATRIX=$(PYTHONPATH=. python3 scripts/generate_ci_matrix.py \
+            --schedule "${{ github.event.schedule }}" \
+            --test-setup "${{ github.event.inputs.test-setup }}")
+          echo "matrix=$MATRIX" >> $GITHUB_OUTPUT
+          echo "Generated matrix: $MATRIX"
 
   run-replay-mode-tests:
+    needs: generate-matrix
     runs-on: ubuntu-latest
     name: ${{ format('Integration Tests ({0}, {1}, {2}, client={3}, {4})', matrix.client-type, matrix.config.setup, matrix.python-version, matrix.client-version, matrix.config.suite) }}
 
@@ -56,18 +75,9 @@ jobs:
         # Use Python 3.13 only on nightly schedule (daily latest client test), otherwise use 3.12
         python-version: ${{ github.event.schedule == '0 0 * * *' && fromJSON('["3.12", "3.13"]') || fromJSON('["3.12"]') }}
         client-version: ${{ (github.event.schedule == '0 0 * * *' || github.event.inputs.test-all-client-versions == 'true') && fromJSON('["published", "latest"]') || fromJSON('["latest"]') }}
-        # Define (setup, suite) pairs - they are always matched and cannot be independent
-        # Weekly schedule (Sun 1 AM): vllm+base
-        # Input test-setup=ollama-vision: ollama-vision+vision
-        # Default (including test-setup=ollama): ollama+base, ollama-vision+vision, gpt+responses
-        config: >-
-          ${{
-            github.event.schedule == '1 0 * * 0'
-              && fromJSON('[{"setup": "vllm", "suite": "base"}]')
-            || github.event.inputs.test-setup == 'ollama-vision'
-              && fromJSON('[{"setup": "ollama-vision", "suite": "vision"}]')
-            || fromJSON('[{"setup": "ollama", "suite": "base"}, {"setup": "ollama-vision", "suite": "vision"}, {"setup": "gpt", "suite": "responses"}]')
-          }}
+        # Test configurations: Generated from CI_MATRIX in tests/integration/suites.py
+        # See scripts/generate_ci_matrix.py for generation logic
+        config: ${{ fromJSON(needs.generate-matrix.outputs.matrix).include }}
 
     steps:
       - name: Checkout repository

.github/workflows/pre-commit.yml

@@ -165,3 +165,14 @@ jobs:
             echo "::error::Full mypy failed. Reproduce locally with 'uv run pre-commit run mypy-full --hook-stage manual --all-files'."
           fi
           exit $status
+
+      - name: Check if any unused recordings
+        run: |
+          set -e
+          PYTHONPATH=$PWD uv run ./scripts/cleanup_recordings.py --delete
+          changes=$(git status --short tests/integration | grep 'recordings' || true)
+          if [ -n "$changes" ]; then
+            echo "::error::Unused integration recordings detected. Run 'PYTHONPATH=$(pwd) uv run ./scripts/cleanup_recordings.py --delete' locally and commit the deletions."
+            echo "$changes"
+            exit 1
+          fi

client-sdks/stainless/openapi.yml

@@ -9976,6 +9976,70 @@ components:
         - metadata
       title: VectorStoreObject
       description: OpenAI Vector Store object.
+    VectorStoreChunkingStrategy:
+      oneOf:
+        - $ref: '#/components/schemas/VectorStoreChunkingStrategyAuto'
+        - $ref: '#/components/schemas/VectorStoreChunkingStrategyStatic'
+      discriminator:
+        propertyName: type
+        mapping:
+          auto: '#/components/schemas/VectorStoreChunkingStrategyAuto'
+          static: '#/components/schemas/VectorStoreChunkingStrategyStatic'
+    VectorStoreChunkingStrategyAuto:
+      type: object
+      properties:
+        type:
+          type: string
+          const: auto
+          default: auto
+          description: >-
+            Strategy type, always "auto" for automatic chunking
+      additionalProperties: false
+      required:
+        - type
+      title: VectorStoreChunkingStrategyAuto
+      description: >-
+        Automatic chunking strategy for vector store files.
+    VectorStoreChunkingStrategyStatic:
+      type: object
+      properties:
+        type:
+          type: string
+          const: static
+          default: static
+          description: >-
+            Strategy type, always "static" for static chunking
+        static:
+          $ref: '#/components/schemas/VectorStoreChunkingStrategyStaticConfig'
+          description: >-
+            Configuration parameters for the static chunking strategy
+      additionalProperties: false
+      required:
+        - type
+        - static
+      title: VectorStoreChunkingStrategyStatic
+      description: >-
+        Static chunking strategy with configurable parameters.
+    VectorStoreChunkingStrategyStaticConfig:
+      type: object
+      properties:
+        chunk_overlap_tokens:
+          type: integer
+          default: 400
+          description: >-
+            Number of tokens to overlap between adjacent chunks
+        max_chunk_size_tokens:
+          type: integer
+          default: 800
+          description: >-
+            Maximum number of tokens per chunk, must be between 100 and 4096
+      additionalProperties: false
+      required:
+        - chunk_overlap_tokens
+        - max_chunk_size_tokens
+      title: VectorStoreChunkingStrategyStaticConfig
+      description: >-
+        Configuration for static chunking strategy.
     "OpenAICreateVectorStoreRequestWithExtraBody":
       type: object
       properties:
@@ -10001,15 +10065,7 @@ components:
           description: >-
             (Optional) Expiration policy for the vector store
         chunking_strategy:
-          type: object
-          additionalProperties:
-            oneOf:
-              - type: 'null'
-              - type: boolean
-              - type: number
-              - type: string
-              - type: array
-              - type: object
+          $ref: '#/components/schemas/VectorStoreChunkingStrategy'
           description: >-
             (Optional) Strategy for splitting files into chunks
         metadata:
@@ -10085,70 +10141,6 @@ components:
         - deleted
       title: VectorStoreDeleteResponse
       description: Response from deleting a vector store.
-    VectorStoreChunkingStrategy:
-      oneOf:
-        - $ref: '#/components/schemas/VectorStoreChunkingStrategyAuto'
-        - $ref: '#/components/schemas/VectorStoreChunkingStrategyStatic'
-      discriminator:
-        propertyName: type
-        mapping:
-          auto: '#/components/schemas/VectorStoreChunkingStrategyAuto'
-          static: '#/components/schemas/VectorStoreChunkingStrategyStatic'
-    VectorStoreChunkingStrategyAuto:
-      type: object
-      properties:
-        type:
-          type: string
-          const: auto
-          default: auto
-          description: >-
-            Strategy type, always "auto" for automatic chunking
-      additionalProperties: false
-      required:
-        - type
-      title: VectorStoreChunkingStrategyAuto
-      description: >-
-        Automatic chunking strategy for vector store files.
-    VectorStoreChunkingStrategyStatic:
-      type: object
-      properties:
-        type:
-          type: string
-          const: static
-          default: static
-          description: >-
-            Strategy type, always "static" for static chunking
-        static:
-          $ref: '#/components/schemas/VectorStoreChunkingStrategyStaticConfig'
-          description: >-
-            Configuration parameters for the static chunking strategy
-      additionalProperties: false
-      required:
-        - type
-        - static
-      title: VectorStoreChunkingStrategyStatic
-      description: >-
-        Static chunking strategy with configurable parameters.
-    VectorStoreChunkingStrategyStaticConfig:
-      type: object
-      properties:
-        chunk_overlap_tokens:
-          type: integer
-          default: 400
-          description: >-
-            Number of tokens to overlap between adjacent chunks
-        max_chunk_size_tokens:
-          type: integer
-          default: 800
-          description: >-
-            Maximum number of tokens per chunk, must be between 100 and 4096
-      additionalProperties: false
-      required:
-        - chunk_overlap_tokens
-        - max_chunk_size_tokens
-      title: VectorStoreChunkingStrategyStaticConfig
-      description: >-
-        Configuration for static chunking strategy.
     "OpenAICreateVectorStoreFileBatchRequestWithExtraBody":
       type: object
       properties:
@@ -10606,7 +10598,9 @@ components:
           description: >-
             Object type identifier for the search results page
         search_query:
-          type: string
+          type: array
+          items:
+            type: string
           description: >-
             The original search query that was executed
         data:

containers/Containerfile

@@ -47,7 +47,7 @@ RUN set -eux; \
         exit 1; \
     fi
 
-RUN pip install --no-cache-dir uv
+RUN pip install --no-cache uv
 ENV UV_SYSTEM_PYTHON=1
 
 ENV INSTALL_MODE=${INSTALL_MODE}
@@ -72,7 +72,7 @@ RUN set -eux; \
             echo "LLAMA_STACK_CLIENT_DIR is set but $LLAMA_STACK_CLIENT_DIR does not exist" >&2; \
             exit 1; \
         fi; \
-        uv pip install --no-cache-dir -e "$LLAMA_STACK_CLIENT_DIR"; \
+        uv pip install --no-cache -e "$LLAMA_STACK_CLIENT_DIR"; \
     fi;
 
 # Install llama-stack
@@ -88,22 +88,22 @@ RUN set -eux; \
         fi; \
         if [ -n "$SAVED_UV_EXTRA_INDEX_URL" ] && [ -n "$SAVED_UV_INDEX_STRATEGY" ]; then \
             UV_EXTRA_INDEX_URL="$SAVED_UV_EXTRA_INDEX_URL" UV_INDEX_STRATEGY="$SAVED_UV_INDEX_STRATEGY" \
-                uv pip install --no-cache-dir -e "$LLAMA_STACK_DIR"; \
+                uv pip install --no-cache -e "$LLAMA_STACK_DIR"; \
         else \
-            uv pip install --no-cache-dir -e "$LLAMA_STACK_DIR"; \
+            uv pip install --no-cache -e "$LLAMA_STACK_DIR"; \
         fi; \
     elif [ "$INSTALL_MODE" = "test-pypi" ]; then \
-        uv pip install --no-cache-dir fastapi libcst; \
+        uv pip install --no-cache fastapi libcst; \
         if [ -n "$TEST_PYPI_VERSION" ]; then \
-            uv pip install --no-cache-dir --extra-index-url https://test.pypi.org/simple/ --index-strategy unsafe-best-match "llama-stack==$TEST_PYPI_VERSION"; \
+            uv pip install --no-cache --extra-index-url https://test.pypi.org/simple/ --index-strategy unsafe-best-match "llama-stack==$TEST_PYPI_VERSION"; \
         else \
-            uv pip install --no-cache-dir --extra-index-url https://test.pypi.org/simple/ --index-strategy unsafe-best-match llama-stack; \
+            uv pip install --no-cache --extra-index-url https://test.pypi.org/simple/ --index-strategy unsafe-best-match llama-stack; \
         fi; \
     else \
         if [ -n "$PYPI_VERSION" ]; then \
-            uv pip install --no-cache-dir "llama-stack==$PYPI_VERSION"; \
+            uv pip install --no-cache "llama-stack==$PYPI_VERSION"; \
         else \
-            uv pip install --no-cache-dir llama-stack; \
+            uv pip install --no-cache llama-stack; \
         fi; \
     fi;
 
@@ -117,7 +117,7 @@ RUN set -eux; \
     fi; \
     deps="$(llama stack list-deps "$DISTRO_NAME")"; \
     if [ -n "$deps" ]; then \
-        printf '%s\n' "$deps" | xargs -L1 uv pip install --no-cache-dir; \
+        printf '%s\n' "$deps" | xargs -L1 uv pip install --no-cache; \
     fi
 
 # Cleanup

docs/docs/distributions/self_hosted_distro/starter.md

@@ -163,7 +163,41 @@ docker run \
   --port $LLAMA_STACK_PORT
 ```
 
-### Via venv
+The container will run the distribution with a SQLite store by default. This store is used for the following components:
+
+- Metadata store: store metadata about the models, providers, etc.
+- Inference store: collect of responses from the inference provider
+- Agents store: store agent configurations (sessions, turns, etc.)
+- Agents Responses store: store responses from the agents
+
+However, you can use PostgreSQL instead by running the `starter::run-with-postgres-store.yaml` configuration:
+
+```bash
+docker run \
+  -it \
+  --pull always \
+  -p $LLAMA_STACK_PORT:$LLAMA_STACK_PORT \
+  -e OPENAI_API_KEY=your_openai_key \
+  -e FIREWORKS_API_KEY=your_fireworks_key \
+  -e TOGETHER_API_KEY=your_together_key \
+  -e POSTGRES_HOST=your_postgres_host \
+  -e POSTGRES_PORT=your_postgres_port \
+  -e POSTGRES_DB=your_postgres_db \
+  -e POSTGRES_USER=your_postgres_user \
+  -e POSTGRES_PASSWORD=your_postgres_password \
+  llamastack/distribution-starter \
+  starter::run-with-postgres-store.yaml
+```
+
+Postgres environment variables:
+
+- `POSTGRES_HOST`: Postgres host (default: `localhost`)
+- `POSTGRES_PORT`: Postgres port (default: `5432`)
+- `POSTGRES_DB`: Postgres database name (default: `llamastack`)
+- `POSTGRES_USER`: Postgres username (default: `llamastack`)
+- `POSTGRES_PASSWORD`: Postgres password (default: `llamastack`)
+
+### Via Conda or venv
 
 Ensure you have configured the starter distribution using the environment variables explained above.
 
@@ -171,8 +205,11 @@ Ensure you have configured the starter distribution using the environment variab
 # Install dependencies for the starter distribution
 uv run --with llama-stack llama stack list-deps starter | xargs -L1 uv pip install
 
-# Run the server
+# Run the server (with SQLite - default)
 uv run --with llama-stack llama stack run starter
+
+# Or run with PostgreSQL
+uv run --with llama-stack llama stack run starter::run-with-postgres-store.yaml
 ```
 
 ## Example Usage

docs/docs/providers/inference/remote_passthrough.mdx

@@ -16,7 +16,7 @@ Passthrough inference provider for connecting to any external inference service
 |-------|------|----------|---------|-------------|
 | `allowed_models` | `list[str \| None` | No |  | List of models that should be registered with the model registry. If None, all models are allowed. |
 | `refresh_models` | `<class 'bool'>` | No | False | Whether to refresh models periodically from the provider |
-| `api_key` | `pydantic.types.SecretStr \| None` | No |  | API Key for the passthrouth endpoint |
+| `api_key` | `pydantic.types.SecretStr \| None` | No |  | Authentication credential for the provider |
 | `url` | `<class 'str'>` | No |  | The URL for the passthrough endpoint |
 
 ## Sample Configuration

docs/static/llama-stack-spec.yaml

@@ -9260,6 +9260,70 @@ components:
         - metadata
       title: VectorStoreObject
       description: OpenAI Vector Store object.
+    VectorStoreChunkingStrategy:
+      oneOf:
+        - $ref: '#/components/schemas/VectorStoreChunkingStrategyAuto'
+        - $ref: '#/components/schemas/VectorStoreChunkingStrategyStatic'
+      discriminator:
+        propertyName: type
+        mapping:
+          auto: '#/components/schemas/VectorStoreChunkingStrategyAuto'
+          static: '#/components/schemas/VectorStoreChunkingStrategyStatic'
+    VectorStoreChunkingStrategyAuto:
+      type: object
+      properties:
+        type:
+          type: string
+          const: auto
+          default: auto
+          description: >-
+            Strategy type, always "auto" for automatic chunking
+      additionalProperties: false
+      required:
+        - type
+      title: VectorStoreChunkingStrategyAuto
+      description: >-
+        Automatic chunking strategy for vector store files.
+    VectorStoreChunkingStrategyStatic:
+      type: object
+      properties:
+        type:
+          type: string
+          const: static
+          default: static
+          description: >-
+            Strategy type, always "static" for static chunking
+        static:
+          $ref: '#/components/schemas/VectorStoreChunkingStrategyStaticConfig'
+          description: >-
+            Configuration parameters for the static chunking strategy
+      additionalProperties: false
+      required:
+        - type
+        - static
+      title: VectorStoreChunkingStrategyStatic
+      description: >-
+        Static chunking strategy with configurable parameters.
+    VectorStoreChunkingStrategyStaticConfig:
+      type: object
+      properties:
+        chunk_overlap_tokens:
+          type: integer
+          default: 400
+          description: >-
+            Number of tokens to overlap between adjacent chunks
+        max_chunk_size_tokens:
+          type: integer
+          default: 800
+          description: >-
+            Maximum number of tokens per chunk, must be between 100 and 4096
+      additionalProperties: false
+      required:
+        - chunk_overlap_tokens
+        - max_chunk_size_tokens
+      title: VectorStoreChunkingStrategyStaticConfig
+      description: >-
+        Configuration for static chunking strategy.
     "OpenAICreateVectorStoreRequestWithExtraBody":
       type: object
       properties:
@@ -9285,15 +9349,7 @@ components:
           description: >-
             (Optional) Expiration policy for the vector store
         chunking_strategy:
-          type: object
-          additionalProperties:
-            oneOf:
-              - type: 'null'
-              - type: boolean
-              - type: number
-              - type: string
-              - type: array
-              - type: object
+          $ref: '#/components/schemas/VectorStoreChunkingStrategy'
           description: >-
             (Optional) Strategy for splitting files into chunks
         metadata:
@@ -9369,70 +9425,6 @@ components:
         - deleted
       title: VectorStoreDeleteResponse
       description: Response from deleting a vector store.
-    VectorStoreChunkingStrategy:
-      oneOf:
-        - $ref: '#/components/schemas/VectorStoreChunkingStrategyAuto'
-        - $ref: '#/components/schemas/VectorStoreChunkingStrategyStatic'
-      discriminator:
-        propertyName: type
-        mapping:
-          auto: '#/components/schemas/VectorStoreChunkingStrategyAuto'
-          static: '#/components/schemas/VectorStoreChunkingStrategyStatic'
-    VectorStoreChunkingStrategyAuto:
-      type: object
-      properties:
-        type:
-          type: string
-          const: auto
-          default: auto
-          description: >-
-            Strategy type, always "auto" for automatic chunking
-      additionalProperties: false
-      required:
-        - type
-      title: VectorStoreChunkingStrategyAuto
-      description: >-
-        Automatic chunking strategy for vector store files.
-    VectorStoreChunkingStrategyStatic:
-      type: object
-      properties:
-        type:
-          type: string
-          const: static
-          default: static
-          description: >-
-            Strategy type, always "static" for static chunking
-        static:
-          $ref: '#/components/schemas/VectorStoreChunkingStrategyStaticConfig'
-          description: >-
-            Configuration parameters for the static chunking strategy
-      additionalProperties: false
-      required:
-        - type
-        - static
-      title: VectorStoreChunkingStrategyStatic
-      description: >-
-        Static chunking strategy with configurable parameters.
-    VectorStoreChunkingStrategyStaticConfig:
-      type: object
-      properties:
-        chunk_overlap_tokens:
-          type: integer
-          default: 400
-          description: >-
-            Number of tokens to overlap between adjacent chunks
-        max_chunk_size_tokens:
-          type: integer
-          default: 800
-          description: >-
-            Maximum number of tokens per chunk, must be between 100 and 4096
-      additionalProperties: false
-      required:
-        - chunk_overlap_tokens
-        - max_chunk_size_tokens
-      title: VectorStoreChunkingStrategyStaticConfig
-      description: >-
-        Configuration for static chunking strategy.
     "OpenAICreateVectorStoreFileBatchRequestWithExtraBody":
       type: object
       properties:
@@ -9890,7 +9882,9 @@ components:
           description: >-
             Object type identifier for the search results page
         search_query:
-          type: string
+          type: array
+          items:
+            type: string
           description: >-
             The original search query that was executed
         data:

docs/static/stainless-llama-stack-spec.yaml

@@ -9976,6 +9976,70 @@ components:
         - metadata
       title: VectorStoreObject
       description: OpenAI Vector Store object.
+    VectorStoreChunkingStrategy:
+      oneOf:
+        - $ref: '#/components/schemas/VectorStoreChunkingStrategyAuto'
+        - $ref: '#/components/schemas/VectorStoreChunkingStrategyStatic'
+      discriminator:
+        propertyName: type
+        mapping:
+          auto: '#/components/schemas/VectorStoreChunkingStrategyAuto'
+          static: '#/components/schemas/VectorStoreChunkingStrategyStatic'
+    VectorStoreChunkingStrategyAuto:
+      type: object
+      properties:
+        type:
+          type: string
+          const: auto
+          default: auto
+          description: >-
+            Strategy type, always "auto" for automatic chunking
+      additionalProperties: false
+      required:
+        - type
+      title: VectorStoreChunkingStrategyAuto
+      description: >-
+        Automatic chunking strategy for vector store files.
+    VectorStoreChunkingStrategyStatic:
+      type: object
+      properties:
+        type:
+          type: string
+          const: static
+          default: static
+          description: >-
+            Strategy type, always "static" for static chunking
+        static:
+          $ref: '#/components/schemas/VectorStoreChunkingStrategyStaticConfig'
+          description: >-
+            Configuration parameters for the static chunking strategy
+      additionalProperties: false
+      required:
+        - type
+        - static
+      title: VectorStoreChunkingStrategyStatic
+      description: >-
+        Static chunking strategy with configurable parameters.
+    VectorStoreChunkingStrategyStaticConfig:
+      type: object
+      properties:
+        chunk_overlap_tokens:
+          type: integer
+          default: 400
+          description: >-
+            Number of tokens to overlap between adjacent chunks
+        max_chunk_size_tokens:
+          type: integer
+          default: 800
+          description: >-
+            Maximum number of tokens per chunk, must be between 100 and 4096
+      additionalProperties: false
+      required:
+        - chunk_overlap_tokens
+        - max_chunk_size_tokens
+      title: VectorStoreChunkingStrategyStaticConfig
+      description: >-
+        Configuration for static chunking strategy.
     "OpenAICreateVectorStoreRequestWithExtraBody":
       type: object
       properties:
@@ -10001,15 +10065,7 @@ components:
           description: >-
             (Optional) Expiration policy for the vector store
         chunking_strategy:
-          type: object
-          additionalProperties:
-            oneOf:
-              - type: 'null'
-              - type: boolean
-              - type: number
-              - type: string
-              - type: array
-              - type: object
+          $ref: '#/components/schemas/VectorStoreChunkingStrategy'
           description: >-
             (Optional) Strategy for splitting files into chunks
         metadata:
@@ -10085,70 +10141,6 @@ components:
         - deleted
       title: VectorStoreDeleteResponse
       description: Response from deleting a vector store.
-    VectorStoreChunkingStrategy:
-      oneOf:
-        - $ref: '#/components/schemas/VectorStoreChunkingStrategyAuto'
-        - $ref: '#/components/schemas/VectorStoreChunkingStrategyStatic'
-      discriminator:
-        propertyName: type
-        mapping:
-          auto: '#/components/schemas/VectorStoreChunkingStrategyAuto'
-          static: '#/components/schemas/VectorStoreChunkingStrategyStatic'
-    VectorStoreChunkingStrategyAuto:
-      type: object
-      properties:
-        type:
-          type: string
-          const: auto
-          default: auto
-          description: >-
-            Strategy type, always "auto" for automatic chunking
-      additionalProperties: false
-      required:
-        - type
-      title: VectorStoreChunkingStrategyAuto
-      description: >-
-        Automatic chunking strategy for vector store files.
-    VectorStoreChunkingStrategyStatic:
-      type: object
-      properties:
-        type:
-          type: string
-          const: static
-          default: static
-          description: >-
-            Strategy type, always "static" for static chunking
-        static:
-          $ref: '#/components/schemas/VectorStoreChunkingStrategyStaticConfig'
-          description: >-
-            Configuration parameters for the static chunking strategy
-      additionalProperties: false
-      required:
-        - type
-        - static
-      title: VectorStoreChunkingStrategyStatic
-      description: >-
-        Static chunking strategy with configurable parameters.
-    VectorStoreChunkingStrategyStaticConfig:
-      type: object
-      properties:
-        chunk_overlap_tokens:
-          type: integer
-          default: 400
-          description: >-
-            Number of tokens to overlap between adjacent chunks
-        max_chunk_size_tokens:
-          type: integer
-          default: 800
-          description: >-
-            Maximum number of tokens per chunk, must be between 100 and 4096
-      additionalProperties: false
-      required:
-        - chunk_overlap_tokens
-        - max_chunk_size_tokens
-      title: VectorStoreChunkingStrategyStaticConfig
-      description: >-
-        Configuration for static chunking strategy.
     "OpenAICreateVectorStoreFileBatchRequestWithExtraBody":
       type: object
       properties:
@@ -10606,7 +10598,9 @@ components:
           description: >-
             Object type identifier for the search results page
         search_query:
-          type: string
+          type: array
+          items:
+            type: string
           description: >-
             The original search query that was executed
         data:

scripts/cleanup_recordings.py

@@ -0,0 +1,272 @@
+#!/usr/bin/env python3
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+
+"""
+Clean up unused test recordings based on CI test collection.
+
+This script:
+1. Reads CI matrix definitions from tests/integration/ci_matrix.json (default + scheduled overrides)
+2. Uses pytest --collect-only with --json-report to gather all test IDs that run in CI
+3. Compares against existing recordings to identify unused ones
+4. Optionally deletes unused recordings
+
+Usage:
+    # Dry run - see what would be deleted
+    ./scripts/cleanup_recordings.py
+
+    # Save manifest of CI test IDs for inspection
+    ./scripts/cleanup_recordings.py --manifest ci_tests.txt
+
+    # Actually delete unused recordings
+    ./scripts/cleanup_recordings.py --delete
+"""
+
+import argparse
+import json
+import os
+import subprocess
+import tempfile
+from collections import defaultdict
+from pathlib import Path
+
+REPO_ROOT = Path(__file__).parent.parent
+
+# Load CI matrix from JSON file
+CI_MATRIX_FILE = REPO_ROOT / "tests/integration/ci_matrix.json"
+with open(CI_MATRIX_FILE) as f:
+    _matrix_config = json.load(f)
+
+DEFAULT_CI_MATRIX: list[dict[str, str]] = _matrix_config["default"]
+SCHEDULED_MATRICES: dict[str, list[dict[str, str]]] = _matrix_config.get("schedules", {})
+
+
+def _unique_configs(entries):
+    seen: set[tuple[str, str]] = set()
+    for entry in entries:
+        suite = entry["suite"]
+        setup = entry["setup"]
+        key = (suite, setup)
+        if key in seen:
+            continue
+        seen.add(key)
+        yield {"suite": suite, "setup": setup}
+
+
+def iter_all_ci_configs() -> list[dict[str, str]]:
+    """Return unique CI configs across default and scheduled matrices."""
+    combined = list(DEFAULT_CI_MATRIX)
+    for configs in SCHEDULED_MATRICES.values():
+        combined.extend(configs)
+    return list(_unique_configs(combined))
+
+
+def collect_ci_tests():
+    """Collect all test IDs that would run in CI using --collect-only with JSON output."""
+
+    all_test_ids = set()
+    configs = iter_all_ci_configs()
+
+    for config in configs:
+        print(f"Collecting tests for suite={config['suite']}, setup={config['setup']}...")
+
+        # Create a temporary file for JSON report
+        with tempfile.NamedTemporaryFile(mode="w", suffix=".json", delete=False) as f:
+            json_report_file = f.name
+
+        try:
+            # Configure environment for collection run
+            env = os.environ.copy()
+            env["PYTEST_ADDOPTS"] = f"--json-report --json-report-file={json_report_file}"
+            repo_path = str(REPO_ROOT)
+            existing_path = env.get("PYTHONPATH", "")
+            env["PYTHONPATH"] = f"{repo_path}{os.pathsep}{existing_path}" if existing_path else repo_path
+
+            result = subprocess.run(
+                [
+                    "./scripts/integration-tests.sh",
+                    "--collect-only",
+                    "--suite",
+                    config["suite"],
+                    "--setup",
+                    config["setup"],
+                ],
+                capture_output=True,
+                text=True,
+                cwd=REPO_ROOT,
+                env=env,
+            )
+
+            if result.returncode != 0:
+                raise RuntimeError(
+                    "Test collection failed.\n"
+                    f"Command: {' '.join(result.args)}\n"
+                    f"stdout:\n{result.stdout}\n"
+                    f"stderr:\n{result.stderr}"
+                )
+
+            # Parse JSON report to extract test IDs
+            try:
+                with open(json_report_file) as f:
+                    report = json.load(f)
+
+                # The "collectors" field contains collected test items
+                # Each collector has a "result" array with test node IDs
+                for collector in report.get("collectors", []):
+                    for item in collector.get("result", []):
+                        # The "nodeid" field is the test ID
+                        if "nodeid" in item:
+                            all_test_ids.add(item["nodeid"])
+
+                print(f"  Collected {len(all_test_ids)} test IDs so far")
+
+            except (json.JSONDecodeError, FileNotFoundError) as e:
+                print(f"  Warning: Failed to parse JSON report: {e}")
+                continue
+
+        finally:
+            # Clean up temp file
+            if os.path.exists(json_report_file):
+                os.unlink(json_report_file)
+
+    print(f"\nTotal unique test IDs collected: {len(all_test_ids)}")
+    return all_test_ids, configs
+
+
+def get_base_test_id(test_id: str) -> str:
+    """Extract base test ID without parameterization.
+
+    Example:
+      'tests/integration/inference/test_foo.py::test_bar[param1-param2]'
+      -> 'tests/integration/inference/test_foo.py::test_bar'
+    """
+    return test_id.split("[")[0] if "[" in test_id else test_id
+
+
+def find_all_recordings():
+    """Find all recording JSON files."""
+    return list((REPO_ROOT / "tests/integration").rglob("recordings/*.json"))
+
+
+def analyze_recordings(ci_test_ids, dry_run=True):
+    """Analyze recordings and identify unused ones."""
+
+    # Use full test IDs with parameterization for exact matching
+    all_recordings = find_all_recordings()
+    print(f"\nTotal recording files: {len(all_recordings)}")
+
+    # Categorize recordings
+    used_recordings = []
+    unused_recordings = []
+    shared_recordings = []  # model-list endpoints without test_id
+    parse_errors = []
+
+    for json_file in all_recordings:
+        try:
+            with open(json_file) as f:
+                data = json.load(f)
+
+            test_id = data.get("test_id", "")
+
+            if not test_id:
+                # Shared/infrastructure recordings (model lists, etc)
+                shared_recordings.append(json_file)
+                continue
+
+            # Match exact test_id (with full parameterization)
+            if test_id in ci_test_ids:
+                used_recordings.append(json_file)
+            else:
+                unused_recordings.append((json_file, test_id))
+
+        except Exception as e:
+            parse_errors.append((json_file, str(e)))
+
+    # Print summary
+    print("\nRecording Analysis:")
+    print(f"  Used in CI:     {len(used_recordings)}")
+    print(f"  Shared (no ID): {len(shared_recordings)}")
+    print(f"  UNUSED:         {len(unused_recordings)}")
+    print(f"  Parse errors:   {len(parse_errors)}")
+
+    if unused_recordings:
+        print("\nUnused recordings by test:")
+
+        # Group by base test ID
+        by_test = defaultdict(list)
+        for file, test_id in unused_recordings:
+            base = get_base_test_id(test_id)
+            by_test[base].append(file)
+
+        for base_test, files in sorted(by_test.items()):
+            print(f"\n  {base_test}")
+            print(f"    ({len(files)} recording(s))")
+            for f in files[:3]:
+                print(f"      - {f.relative_to(REPO_ROOT / 'tests/integration')}")
+            if len(files) > 3:
+                print(f"      ... and {len(files) - 3} more")
+
+    if parse_errors:
+        print("\nParse errors:")
+        for file, error in parse_errors[:5]:
+            print(f"  {file.relative_to(REPO_ROOT)}: {error}")
+        if len(parse_errors) > 5:
+            print(f"  ... and {len(parse_errors) - 5} more")
+
+    # Perform cleanup
+    if not dry_run:
+        print(f"\nDeleting {len(unused_recordings)} unused recordings...")
+        for file, _ in unused_recordings:
+            file.unlink()
+            print(f"  Deleted: {file.relative_to(REPO_ROOT / 'tests/integration')}")
+        print("✅ Cleanup complete")
+    else:
+        print("\n(Dry run - no files deleted)")
+        print("\nTo delete these files, run with --delete")
+
+    return len(unused_recordings)
+
+
+def main():
+    parser = argparse.ArgumentParser(
+        description="Clean up unused test recordings based on CI test collection",
+        formatter_class=argparse.RawDescriptionHelpFormatter,
+        epilog=__doc__,
+    )
+    parser.add_argument("--delete", action="store_true", help="Actually delete unused recordings (default is dry-run)")
+    parser.add_argument("--manifest", help="Save collected test IDs to file (optional)")
+
+    args = parser.parse_args()
+
+    print("=" * 60)
+    print("Recording Cleanup Utility")
+    print("=" * 60)
+
+    ci_configs = iter_all_ci_configs()
+
+    print(f"\nDetected CI configurations: {len(ci_configs)}")
+    for config in ci_configs:
+        print(f"  - suite={config['suite']}, setup={config['setup']}")
+
+    # Collect test IDs from CI configurations
+    ci_test_ids, _ = collect_ci_tests()
+
+    if args.manifest:
+        with open(args.manifest, "w") as f:
+            for test_id in sorted(ci_test_ids):
+                f.write(f"{test_id}\n")
+        print(f"\nSaved test IDs to: {args.manifest}")
+
+    # Analyze and cleanup
+    unused_count = analyze_recordings(ci_test_ids, dry_run=not args.delete)
+
+    print("\n" + "=" * 60)
+    if unused_count > 0 and not args.delete:
+        print("Run with --delete to remove unused recordings")
+
+
+if __name__ == "__main__":
+    main()

scripts/generate_ci_matrix.py

@@ -0,0 +1,61 @@
+#!/usr/bin/env python3
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+
+"""
+Generate CI test matrix from ci_matrix.json with schedule/input overrides.
+
+This script is used by .github/workflows/integration-tests.yml to generate
+the test matrix dynamically based on the CI_MATRIX definition.
+"""
+
+import json
+from pathlib import Path
+
+CI_MATRIX_FILE = Path(__file__).parent.parent / "tests/integration/ci_matrix.json"
+
+with open(CI_MATRIX_FILE) as f:
+    matrix_config = json.load(f)
+
+DEFAULT_MATRIX = matrix_config["default"]
+SCHEDULE_MATRICES: dict[str, list[dict[str, str]]] = matrix_config.get("schedules", {})
+
+
+def generate_matrix(schedule="", test_setup=""):
+    """
+    Generate test matrix based on schedule or manual input.
+
+    Args:
+        schedule: GitHub cron schedule string (e.g., "1 0 * * 0" for weekly)
+        test_setup: Manual test setup input (e.g., "ollama-vision")
+
+    Returns:
+        Matrix configuration as JSON string
+    """
+    # Weekly scheduled test matrices
+    if schedule and schedule in SCHEDULE_MATRICES:
+        matrix = SCHEDULE_MATRICES[schedule]
+    # Manual input for specific setup
+    elif test_setup == "ollama-vision":
+        matrix = [{"suite": "vision", "setup": "ollama-vision"}]
+    # Default: use JSON-defined matrix
+    else:
+        matrix = DEFAULT_MATRIX
+
+    # GitHub Actions expects {"include": [...]} format
+    return json.dumps({"include": matrix})
+
+
+if __name__ == "__main__":
+    import argparse
+
+    parser = argparse.ArgumentParser(description="Generate CI test matrix")
+    parser.add_argument("--schedule", default="", help="GitHub schedule cron string")
+    parser.add_argument("--test-setup", default="", help="Manual test setup input")
+
+    args = parser.parse_args()
+
+    print(generate_matrix(args.schedule, args.test_setup))

scripts/integration-tests.sh

@@ -227,14 +227,16 @@ if [[ "$STACK_CONFIG" == *"server:"* && "$COLLECT_ONLY" == false ]]; then
     echo "=== Starting Llama Stack Server ==="
     export LLAMA_STACK_LOG_WIDTH=120
 
-    # Configure telemetry collector for server mode
-    # Use a fixed port for the OTEL collector so the server can connect to it
-    COLLECTOR_PORT=4317
-    export LLAMA_STACK_TEST_COLLECTOR_PORT="${COLLECTOR_PORT}"
-    export OTEL_EXPORTER_OTLP_ENDPOINT="http://127.0.0.1:${COLLECTOR_PORT}"
-    export OTEL_EXPORTER_OTLP_PROTOCOL="http/protobuf"
-    export OTEL_BSP_SCHEDULE_DELAY="200"
-    export OTEL_BSP_EXPORT_TIMEOUT="2000"
+        # Configure telemetry collector for server mode
+        # Use a fixed port for the OTEL collector so the server can connect to it
+        COLLECTOR_PORT=4317
+        export LLAMA_STACK_TEST_COLLECTOR_PORT="${COLLECTOR_PORT}"
+        # Disabled: https://github.com/llamastack/llama-stack/issues/4089
+        #export OTEL_EXPORTER_OTLP_ENDPOINT="http://127.0.0.1:${COLLECTOR_PORT}"
+        export OTEL_EXPORTER_OTLP_PROTOCOL="http/protobuf"
+        export OTEL_BSP_SCHEDULE_DELAY="200"
+        export OTEL_BSP_EXPORT_TIMEOUT="2000"
+        export OTEL_METRIC_EXPORT_INTERVAL="200"
 
     # remove "server:" from STACK_CONFIG
     stack_config=$(echo "$STACK_CONFIG" | sed 's/^server://')
@@ -336,7 +338,11 @@ if [[ "$STACK_CONFIG" == *"docker:"* && "$COLLECT_ONLY" == false ]]; then
     DOCKER_ENV_VARS=""
     DOCKER_ENV_VARS="$DOCKER_ENV_VARS -e LLAMA_STACK_TEST_INFERENCE_MODE=$INFERENCE_MODE"
     DOCKER_ENV_VARS="$DOCKER_ENV_VARS -e LLAMA_STACK_TEST_STACK_CONFIG_TYPE=server"
-    DOCKER_ENV_VARS="$DOCKER_ENV_VARS -e OTEL_EXPORTER_OTLP_ENDPOINT=http://localhost:${COLLECTOR_PORT}"
+    # Disabled: https://github.com/llamastack/llama-stack/issues/4089
+    #DOCKER_ENV_VARS="$DOCKER_ENV_VARS -e OTEL_EXPORTER_OTLP_ENDPOINT=http://localhost:${COLLECTOR_PORT}"
+    DOCKER_ENV_VARS="$DOCKER_ENV_VARS -e OTEL_METRIC_EXPORT_INTERVAL=200"
+    DOCKER_ENV_VARS="$DOCKER_ENV_VARS -e OTEL_BSP_SCHEDULE_DELAY=200"
+    DOCKER_ENV_VARS="$DOCKER_ENV_VARS -e OTEL_BSP_EXPORT_TIMEOUT=2000"
 
     # Pass through API keys if they exist
     [ -n "${TOGETHER_API_KEY:-}" ] && DOCKER_ENV_VARS="$DOCKER_ENV_VARS -e TOGETHER_API_KEY=$TOGETHER_API_KEY"
@@ -349,6 +355,10 @@ if [[ "$STACK_CONFIG" == *"docker:"* && "$COLLECT_ONLY" == false ]]; then
     [ -n "${OLLAMA_URL:-}" ] && DOCKER_ENV_VARS="$DOCKER_ENV_VARS -e OLLAMA_URL=$OLLAMA_URL"
     [ -n "${SAFETY_MODEL:-}" ] && DOCKER_ENV_VARS="$DOCKER_ENV_VARS -e SAFETY_MODEL=$SAFETY_MODEL"
 
+    if [[ "$TEST_SETUP" == "vllm" ]]; then
+        DOCKER_ENV_VARS="$DOCKER_ENV_VARS -e VLLM_URL=http://localhost:8000/v1"
+    fi
+
     # Determine the actual image name (may have localhost/ prefix)
     IMAGE_NAME=$(docker images --format "{{.Repository}}:{{.Tag}}" | grep "distribution-$DISTRO:dev$" | head -1)
     if [[ -z "$IMAGE_NAME" ]]; then
@@ -401,11 +411,6 @@ fi
 echo "=== Running Integration Tests ==="
 EXCLUDE_TESTS="builtin_tool or safety_with_image or code_interpreter or test_rag"
 
-# Additional exclusions for vllm setup
-if [[ "$TEST_SETUP" == "vllm" ]]; then
-    EXCLUDE_TESTS="${EXCLUDE_TESTS} or test_inference_store_tool_calls"
-fi
-
 PYTEST_PATTERN="not( $EXCLUDE_TESTS )"
 if [[ -n "$TEST_PATTERN" ]]; then
     PYTEST_PATTERN="${PYTEST_PATTERN} and $TEST_PATTERN"

src/llama_stack/apis/conversations/__init__.py

@@ -6,26 +6,22 @@
 
 from .conversations import (
     Conversation,
-    ConversationCreateRequest,
     ConversationDeletedResource,
     ConversationItem,
     ConversationItemCreateRequest,
     ConversationItemDeletedResource,
     ConversationItemList,
     Conversations,
-    ConversationUpdateRequest,
     Metadata,
 )
 
 __all__ = [
     "Conversation",
-    "ConversationCreateRequest",
     "ConversationDeletedResource",
     "ConversationItem",
     "ConversationItemCreateRequest",
     "ConversationItemDeletedResource",
     "ConversationItemList",
     "Conversations",
-    "ConversationUpdateRequest",
     "Metadata",
 ]

src/llama_stack/apis/conversations/conversations.py

@@ -102,32 +102,6 @@ register_schema(ConversationItem, name="ConversationItem")
 # ]
 
 
-@json_schema_type
-class ConversationCreateRequest(BaseModel):
-    """Request body for creating a conversation."""
-
-    items: list[ConversationItem] | None = Field(
-        default=[],
-        description="Initial items to include in the conversation context. You may add up to 20 items at a time.",
-        max_length=20,
-    )
-    metadata: Metadata | None = Field(
-        default={},
-        description="Set of 16 key-value pairs that can be attached to an object. Useful for storing additional information",
-        max_length=16,
-    )
-
-
-@json_schema_type
-class ConversationUpdateRequest(BaseModel):
-    """Request body for updating a conversation."""
-
-    metadata: Metadata = Field(
-        ...,
-        description="Set of 16 key-value pairs that can be attached to an object. This can be useful for storing additional information about the object in a structured format, and querying for objects via API or the dashboard. Keys are strings with a maximum length of 64 characters. Values are strings with a maximum length of 512 characters.",
-    )
-
-
 @json_schema_type
 class ConversationDeletedResource(BaseModel):
     """Response for deleted conversation."""

src/llama_stack/apis/vector_io/vector_io.py

@@ -260,7 +260,7 @@ class VectorStoreSearchResponsePage(BaseModel):
     """
 
     object: str = "vector_store.search_results.page"
-    search_query: str
+    search_query: list[str]
     data: list[VectorStoreSearchResponse]
     has_more: bool = False
     next_page: str | None = None
@@ -478,7 +478,7 @@ class OpenAICreateVectorStoreRequestWithExtraBody(BaseModel, extra="allow"):
     name: str | None = None
     file_ids: list[str] | None = None
     expires_after: dict[str, Any] | None = None
-    chunking_strategy: dict[str, Any] | None = None
+    chunking_strategy: VectorStoreChunkingStrategy | None = None
     metadata: dict[str, Any] | None = None
 
 

src/llama_stack/cli/stack/list_deps.py

@@ -46,6 +46,10 @@ class StackListDeps(Subcommand):
     def _run_stack_list_deps_command(self, args: argparse.Namespace) -> None:
         # always keep implementation completely silo-ed away from CLI so CLI
         # can be fast to load and reduces dependencies
+        if not args.config and not args.providers:
+            self.parser.print_help()
+            self.parser.exit()
+
         from ._list_deps import run_stack_list_deps_command
 
         return run_stack_list_deps_command(args)

src/llama_stack/cli/stack/list_stacks.py

@@ -9,48 +9,69 @@ from pathlib import Path
 
 from llama_stack.cli.subcommand import Subcommand
 from llama_stack.cli.table import print_table
+from llama_stack.core.utils.config_dirs import DISTRIBS_BASE_DIR
 
 
 class StackListBuilds(Subcommand):
-    """List built stacks in .llama/distributions directory"""
+    """List available distributions (both built-in and custom)"""
 
     def __init__(self, subparsers: argparse._SubParsersAction):
         super().__init__()
         self.parser = subparsers.add_parser(
             "list",
             prog="llama stack list",
-            description="list the build stacks",
+            description="list available distributions",
             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"
+    def _get_distribution_dirs(self) -> dict[str, tuple[Path, str]]:
+        """Return a dictionary of distribution names and their paths with source type
+
+        Returns:
+            dict mapping distro name to (path, source_type) where source_type is 'built-in' or 'custom'
+        """
+        distributions = {}
+
+        # Get built-in distributions from source code
+        distro_dir = Path(__file__).parent.parent.parent / "distributions"
+        if distro_dir.exists():
+            for stack_dir in distro_dir.iterdir():
+                if stack_dir.is_dir() and not stack_dir.name.startswith(".") and not stack_dir.name.startswith("__"):
+                    distributions[stack_dir.name] = (stack_dir, "built-in")
+
+        # Get custom/run distributions from ~/.llama/distributions
+        # These override built-in ones if they have the same name
+        if DISTRIBS_BASE_DIR.exists():
+            for stack_dir in DISTRIBS_BASE_DIR.iterdir():
+                if stack_dir.is_dir() and not stack_dir.name.startswith("."):
+                    # Clean up the name (remove llamastack- prefix if present)
+                    name = stack_dir.name.replace("llamastack-", "")
+                    distributions[name] = (stack_dir, "custom")
 
-        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")
+            print("No distributions found")
             return
 
-        headers = ["Stack Name", "Path"]
-        headers.extend(["Build Config", "Run Config"])
+        headers = ["Stack Name", "Source", "Path", "Build Config", "Run Config"]
         rows = []
-        for name, path in distributions.items():
-            row = [name, str(path)]
+        for name, (path, source_type) in sorted(distributions.items()):
+            row = [name, source_type, 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"
+            # For built-in distributions, configs are named build.yaml and run.yaml
+            # For custom distributions, configs are named {name}-build.yaml and {name}-run.yaml
+            if source_type == "built-in":
+                build_config = "Yes" if (path / "build.yaml").exists() else "No"
+                run_config = "Yes" if (path / "run.yaml").exists() else "No"
+            else:
+                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)

src/llama_stack/core/routers/vector_io.py

@@ -20,6 +20,8 @@ from llama_stack.apis.vector_io import (
     SearchRankingOptions,
     VectorIO,
     VectorStoreChunkingStrategy,
+    VectorStoreChunkingStrategyStatic,
+    VectorStoreChunkingStrategyStaticConfig,
     VectorStoreDeleteResponse,
     VectorStoreFileBatchObject,
     VectorStoreFileContentsResponse,
@@ -167,6 +169,13 @@ class VectorIORouter(VectorIO):
         if embedding_dimension is not None:
             params.model_extra["embedding_dimension"] = embedding_dimension
 
+        # Set chunking strategy explicitly if not provided
+        if params.chunking_strategy is None or params.chunking_strategy.type == "auto":
+            # actualize the chunking strategy to static
+            params.chunking_strategy = VectorStoreChunkingStrategyStatic(
+                static=VectorStoreChunkingStrategyStaticConfig()
+            )
+
         return await provider.openai_create_vector_store(params)
 
     async def openai_list_vector_stores(
@@ -283,6 +292,8 @@ class VectorIORouter(VectorIO):
         chunking_strategy: VectorStoreChunkingStrategy | None = None,
     ) -> VectorStoreFileObject:
         logger.debug(f"VectorIORouter.openai_attach_file_to_vector_store: {vector_store_id}, {file_id}")
+        if chunking_strategy is None or chunking_strategy.type == "auto":
+            chunking_strategy = VectorStoreChunkingStrategyStatic(static=VectorStoreChunkingStrategyStaticConfig())
         provider = await self.routing_table.get_provider_impl(vector_store_id)
         return await provider.openai_attach_file_to_vector_store(
             vector_store_id=vector_store_id,

src/llama_stack/core/telemetry/telemetry.py

@@ -427,6 +427,7 @@ _GLOBAL_STORAGE: dict[str, dict[str | int, Any]] = {
     "counters": {},
     "gauges": {},
     "up_down_counters": {},
+    "histograms": {},
 }
 _global_lock = threading.Lock()
 _TRACER_PROVIDER = None
@@ -540,6 +541,16 @@ class Telemetry:
             )
         return cast(metrics.ObservableGauge, _GLOBAL_STORAGE["gauges"][name])
 
+    def _get_or_create_histogram(self, name: str, unit: str) -> metrics.Histogram:
+        assert self.meter is not None
+        if name not in _GLOBAL_STORAGE["histograms"]:
+            _GLOBAL_STORAGE["histograms"][name] = self.meter.create_histogram(
+                name=name,
+                unit=unit,
+                description=f"Histogram for {name}",
+            )
+        return cast(metrics.Histogram, _GLOBAL_STORAGE["histograms"][name])
+
     def _log_metric(self, event: MetricEvent) -> None:
         # Add metric as an event to the current span
         try:
@@ -571,7 +582,16 @@ class Telemetry:
         # Log to OpenTelemetry meter if available
         if self.meter is None:
             return
-        if isinstance(event.value, int):
+
+        # Use histograms for token-related metrics (per-request measurements)
+        # Use counters for other cumulative metrics
+        token_metrics = {"prompt_tokens", "completion_tokens", "total_tokens"}
+
+        if event.metric in token_metrics:
+            # Token metrics are per-request measurements, use histogram
+            histogram = self._get_or_create_histogram(event.metric, event.unit)
+            histogram.record(event.value, attributes=_clean_attributes(event.attributes))
+        elif isinstance(event.value, int):
             counter = self._get_or_create_counter(event.metric, event.unit)
             counter.add(event.value, attributes=_clean_attributes(event.attributes))
         elif isinstance(event.value, float):

src/llama_stack/core/utils/config_resolution.py

@@ -52,7 +52,17 @@ def resolve_config_or_distro(
             logger.debug(f"Using distribution: {distro_config}")
             return distro_config
 
-    # Strategy 3: Try as built distribution name
+    # Strategy 3: Try as distro config path (if no .yaml extension and contains a slash)
+    # eg: starter::run-with-postgres-store.yaml
+    # Use :: to avoid slash and confusion with a filesystem path
+    if "::" in config_or_distro:
+        distro_name, config_name = config_or_distro.split("::")
+        distro_config = _get_distro_config_path(distro_name, config_name)
+        if distro_config.exists():
+            logger.info(f"Using distribution: {distro_config}")
+            return distro_config
+
+    # Strategy 4: Try as built distribution name
     distrib_config = DISTRIBS_BASE_DIR / f"llamastack-{config_or_distro}" / f"{config_or_distro}-{mode}.yaml"
     if distrib_config.exists():
         logger.debug(f"Using built distribution: {distrib_config}")
@@ -63,13 +73,15 @@ def resolve_config_or_distro(
         logger.debug(f"Using built distribution: {distrib_config}")
         return distrib_config
 
-    # Strategy 4: Failed - provide helpful error
+    # Strategy 5: Failed - provide helpful error
     raise ValueError(_format_resolution_error(config_or_distro, mode))
 
 
-def _get_distro_config_path(distro_name: str, mode: Mode) -> Path:
+def _get_distro_config_path(distro_name: str, mode: str) -> Path:
     """Get the config file path for a distro."""
-    return DISTRO_DIR / distro_name / f"{mode}.yaml"
+    if not mode.endswith(".yaml"):
+        mode = f"{mode}.yaml"
+    return DISTRO_DIR / distro_name / mode
 
 
 def _format_resolution_error(config_or_distro: str, mode: Mode) -> str:

src/llama_stack/core/utils/exec.py

@@ -84,6 +84,15 @@ def run_command(command: list[str]) -> int:
             text=True,
             check=False,
         )
+
+        # Print stdout and stderr if command failed
+        if result.returncode != 0:
+            log.error(f"Command {' '.join(command)} failed with returncode {result.returncode}")
+            if result.stdout:
+                log.error(f"STDOUT: {result.stdout}")
+            if result.stderr:
+                log.error(f"STDERR: {result.stderr}")
+
         return result.returncode
     except subprocess.SubprocessError as e:
         log.error(f"Subprocess error: {e}")

src/llama_stack/distributions/ci-tests/build.yaml

@@ -56,4 +56,5 @@ image_type: venv
 additional_pip_packages:
 - aiosqlite
 - asyncpg
+- psycopg2-binary
 - sqlalchemy[asyncio]

src/llama_stack/distributions/ci-tests/ci_tests.py

@@ -13,5 +13,6 @@ from ..starter.starter import get_distribution_template as get_starter_distribut
 def get_distribution_template() -> DistributionTemplate:
     template = get_starter_distribution_template(name="ci-tests")
     template.description = "CI tests for Llama Stack"
+    template.run_configs.pop("run-with-postgres-store.yaml", None)
 
     return template

src/llama_stack/distributions/postgres-demo/__init__.py

@@ -1,7 +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 .postgres_demo import get_distribution_template  # noqa: F401

src/llama_stack/distributions/postgres-demo/build.yaml

@@ -1,23 +0,0 @@
-version: 2
-distribution_spec:
-  description: Quick start template for running Llama Stack with several popular providers
-  providers:
-    inference:
-    - provider_type: remote::vllm
-    - provider_type: inline::sentence-transformers
-    vector_io:
-    - provider_type: remote::chromadb
-    safety:
-    - provider_type: inline::llama-guard
-    agents:
-    - provider_type: inline::meta-reference
-    tool_runtime:
-    - provider_type: remote::brave-search
-    - provider_type: remote::tavily-search
-    - provider_type: inline::rag-runtime
-    - provider_type: remote::model-context-protocol
-image_type: venv
-additional_pip_packages:
-- asyncpg
-- psycopg2-binary
-- sqlalchemy[asyncio]

src/llama_stack/distributions/postgres-demo/postgres_demo.py

@@ -1,125 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the terms described in the LICENSE file in
-# the root directory of this source tree.
-
-
-from llama_stack.apis.models import ModelType
-from llama_stack.core.datatypes import (
-    BuildProvider,
-    ModelInput,
-    Provider,
-    ShieldInput,
-    ToolGroupInput,
-)
-from llama_stack.distributions.template import (
-    DistributionTemplate,
-    RunConfigSettings,
-)
-from llama_stack.providers.inline.inference.sentence_transformers import SentenceTransformersInferenceConfig
-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.kvstore.config import PostgresKVStoreConfig
-from llama_stack.providers.utils.sqlstore.sqlstore import PostgresSqlStoreConfig
-
-
-def get_distribution_template() -> DistributionTemplate:
-    inference_providers = [
-        Provider(
-            provider_id="vllm-inference",
-            provider_type="remote::vllm",
-            config=VLLMInferenceAdapterConfig.sample_run_config(
-                url="${env.VLLM_URL:=http://localhost:8000/v1}",
-            ),
-        ),
-    ]
-    providers = {
-        "inference": [
-            BuildProvider(provider_type="remote::vllm"),
-            BuildProvider(provider_type="inline::sentence-transformers"),
-        ],
-        "vector_io": [BuildProvider(provider_type="remote::chromadb")],
-        "safety": [BuildProvider(provider_type="inline::llama-guard")],
-        "agents": [BuildProvider(provider_type="inline::meta-reference")],
-        "tool_runtime": [
-            BuildProvider(provider_type="remote::brave-search"),
-            BuildProvider(provider_type="remote::tavily-search"),
-            BuildProvider(provider_type="inline::rag-runtime"),
-            BuildProvider(provider_type="remote::model-context-protocol"),
-        ],
-    }
-    name = "postgres-demo"
-
-    vector_io_providers = [
-        Provider(
-            provider_id="${env.ENABLE_CHROMADB:+chromadb}",
-            provider_type="remote::chromadb",
-            config=ChromaVectorIOConfig.sample_run_config(
-                f"~/.llama/distributions/{name}",
-                url="${env.CHROMADB_URL:=}",
-            ),
-        ),
-    ]
-    default_tool_groups = [
-        ToolGroupInput(
-            toolgroup_id="builtin::websearch",
-            provider_id="tavily-search",
-        ),
-        ToolGroupInput(
-            toolgroup_id="builtin::rag",
-            provider_id="rag-runtime",
-        ),
-    ]
-
-    default_models = [
-        ModelInput(
-            model_id="${env.INFERENCE_MODEL}",
-            provider_id="vllm-inference",
-        )
-    ]
-    embedding_provider = Provider(
-        provider_id="sentence-transformers",
-        provider_type="inline::sentence-transformers",
-        config=SentenceTransformersInferenceConfig.sample_run_config(),
-    )
-    embedding_model = ModelInput(
-        model_id="nomic-embed-text-v1.5",
-        provider_id=embedding_provider.provider_id,
-        model_type=ModelType.embedding,
-        metadata={
-            "embedding_dimension": 768,
-        },
-    )
-    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={},
-        run_configs={
-            "run.yaml": RunConfigSettings(
-                provider_overrides={
-                    "inference": inference_providers + [embedding_provider],
-                    "vector_io": vector_io_providers,
-                },
-                default_models=default_models + [embedding_model],
-                default_tool_groups=default_tool_groups,
-                default_shields=[ShieldInput(shield_id="meta-llama/Llama-Guard-3-8B")],
-                storage_backends={
-                    "kv_default": PostgresKVStoreConfig.sample_run_config(
-                        table_name="llamastack_kvstore",
-                    ),
-                    "sql_default": PostgresSqlStoreConfig.sample_run_config(),
-                },
-            ),
-        },
-        run_config_env_vars={
-            "LLAMA_STACK_PORT": (
-                "8321",
-                "Port for the Llama Stack distribution server",
-            ),
-        },
-    )

src/llama_stack/distributions/starter-gpu/build.yaml

@@ -57,4 +57,5 @@ image_type: venv
 additional_pip_packages:
 - aiosqlite
 - asyncpg
+- psycopg2-binary
 - sqlalchemy[asyncio]

src/llama_stack/distributions/starter-gpu/run-with-postgres-store.yaml

@@ -0,0 +1,281 @@
+version: 2
+image_name: starter-gpu
+apis:
+- agents
+- batches
+- datasetio
+- eval
+- files
+- inference
+- post_training
+- safety
+- scoring
+- tool_runtime
+- vector_io
+providers:
+  inference:
+  - provider_id: ${env.CEREBRAS_API_KEY:+cerebras}
+    provider_type: remote::cerebras
+    config:
+      base_url: https://api.cerebras.ai
+      api_key: ${env.CEREBRAS_API_KEY:=}
+  - provider_id: ${env.OLLAMA_URL:+ollama}
+    provider_type: remote::ollama
+    config:
+      url: ${env.OLLAMA_URL:=http://localhost:11434}
+  - provider_id: ${env.VLLM_URL:+vllm}
+    provider_type: remote::vllm
+    config:
+      url: ${env.VLLM_URL:=}
+      max_tokens: ${env.VLLM_MAX_TOKENS:=4096}
+      api_token: ${env.VLLM_API_TOKEN:=fake}
+      tls_verify: ${env.VLLM_TLS_VERIFY:=true}
+  - provider_id: ${env.TGI_URL:+tgi}
+    provider_type: remote::tgi
+    config:
+      url: ${env.TGI_URL:=}
+  - provider_id: fireworks
+    provider_type: remote::fireworks
+    config:
+      url: https://api.fireworks.ai/inference/v1
+      api_key: ${env.FIREWORKS_API_KEY:=}
+  - provider_id: together
+    provider_type: remote::together
+    config:
+      url: https://api.together.xyz/v1
+      api_key: ${env.TOGETHER_API_KEY:=}
+  - provider_id: bedrock
+    provider_type: remote::bedrock
+  - provider_id: ${env.NVIDIA_API_KEY:+nvidia}
+    provider_type: remote::nvidia
+    config:
+      url: ${env.NVIDIA_BASE_URL:=https://integrate.api.nvidia.com}
+      api_key: ${env.NVIDIA_API_KEY:=}
+      append_api_version: ${env.NVIDIA_APPEND_API_VERSION:=True}
+  - provider_id: openai
+    provider_type: remote::openai
+    config:
+      api_key: ${env.OPENAI_API_KEY:=}
+      base_url: ${env.OPENAI_BASE_URL:=https://api.openai.com/v1}
+  - provider_id: anthropic
+    provider_type: remote::anthropic
+    config:
+      api_key: ${env.ANTHROPIC_API_KEY:=}
+  - provider_id: gemini
+    provider_type: remote::gemini
+    config:
+      api_key: ${env.GEMINI_API_KEY:=}
+  - provider_id: ${env.VERTEX_AI_PROJECT:+vertexai}
+    provider_type: remote::vertexai
+    config:
+      project: ${env.VERTEX_AI_PROJECT:=}
+      location: ${env.VERTEX_AI_LOCATION:=us-central1}
+  - provider_id: groq
+    provider_type: remote::groq
+    config:
+      url: https://api.groq.com
+      api_key: ${env.GROQ_API_KEY:=}
+  - provider_id: sambanova
+    provider_type: remote::sambanova
+    config:
+      url: https://api.sambanova.ai/v1
+      api_key: ${env.SAMBANOVA_API_KEY:=}
+  - provider_id: ${env.AZURE_API_KEY:+azure}
+    provider_type: remote::azure
+    config:
+      api_key: ${env.AZURE_API_KEY:=}
+      api_base: ${env.AZURE_API_BASE:=}
+      api_version: ${env.AZURE_API_VERSION:=}
+      api_type: ${env.AZURE_API_TYPE:=}
+  - provider_id: sentence-transformers
+    provider_type: inline::sentence-transformers
+  vector_io:
+  - provider_id: faiss
+    provider_type: inline::faiss
+    config:
+      persistence:
+        namespace: vector_io::faiss
+        backend: kv_default
+  - provider_id: sqlite-vec
+    provider_type: inline::sqlite-vec
+    config:
+      db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/starter-gpu}/sqlite_vec.db
+      persistence:
+        namespace: vector_io::sqlite_vec
+        backend: kv_default
+  - provider_id: ${env.MILVUS_URL:+milvus}
+    provider_type: inline::milvus
+    config:
+      db_path: ${env.MILVUS_DB_PATH:=~/.llama/distributions/starter-gpu}/milvus.db
+      persistence:
+        namespace: vector_io::milvus
+        backend: kv_default
+  - provider_id: ${env.CHROMADB_URL:+chromadb}
+    provider_type: remote::chromadb
+    config:
+      url: ${env.CHROMADB_URL:=}
+      persistence:
+        namespace: vector_io::chroma_remote
+        backend: kv_default
+  - provider_id: ${env.PGVECTOR_DB:+pgvector}
+    provider_type: remote::pgvector
+    config:
+      host: ${env.PGVECTOR_HOST:=localhost}
+      port: ${env.PGVECTOR_PORT:=5432}
+      db: ${env.PGVECTOR_DB:=}
+      user: ${env.PGVECTOR_USER:=}
+      password: ${env.PGVECTOR_PASSWORD:=}
+      persistence:
+        namespace: vector_io::pgvector
+        backend: kv_default
+  - provider_id: ${env.QDRANT_URL:+qdrant}
+    provider_type: remote::qdrant
+    config:
+      api_key: ${env.QDRANT_API_KEY:=}
+      persistence:
+        namespace: vector_io::qdrant_remote
+        backend: kv_default
+  - provider_id: ${env.WEAVIATE_CLUSTER_URL:+weaviate}
+    provider_type: remote::weaviate
+    config:
+      weaviate_api_key: null
+      weaviate_cluster_url: ${env.WEAVIATE_CLUSTER_URL:=localhost:8080}
+      persistence:
+        namespace: vector_io::weaviate
+        backend: kv_default
+  files:
+  - provider_id: meta-reference-files
+    provider_type: inline::localfs
+    config:
+      storage_dir: ${env.FILES_STORAGE_DIR:=~/.llama/distributions/starter-gpu/files}
+      metadata_store:
+        table_name: files_metadata
+        backend: sql_default
+  safety:
+  - provider_id: llama-guard
+    provider_type: inline::llama-guard
+    config:
+      excluded_categories: []
+  - provider_id: code-scanner
+    provider_type: inline::code-scanner
+  agents:
+  - provider_id: meta-reference
+    provider_type: inline::meta-reference
+    config:
+      persistence_store:
+        type: sql_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: sql_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}
+  post_training:
+  - provider_id: huggingface-gpu
+    provider_type: inline::huggingface-gpu
+    config:
+      checkpoint_format: huggingface
+      distributed_backend: null
+      device: cpu
+      dpo_output_dir: ~/.llama/distributions/starter-gpu/dpo_output
+  eval:
+  - provider_id: meta-reference
+    provider_type: inline::meta-reference
+    config:
+      kvstore:
+        namespace: eval
+        backend: kv_default
+  datasetio:
+  - provider_id: huggingface
+    provider_type: remote::huggingface
+    config:
+      kvstore:
+        namespace: datasetio::huggingface
+        backend: kv_default
+  - provider_id: localfs
+    provider_type: inline::localfs
+    config:
+      kvstore:
+        namespace: datasetio::localfs
+        backend: kv_default
+  scoring:
+  - provider_id: basic
+    provider_type: inline::basic
+  - provider_id: llm-as-judge
+    provider_type: inline::llm-as-judge
+  - 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: rag-runtime
+    provider_type: inline::rag-runtime
+  - provider_id: model-context-protocol
+    provider_type: remote::model-context-protocol
+  batches:
+  - provider_id: reference
+    provider_type: inline::reference
+    config:
+      kvstore:
+        namespace: batches
+        backend: kv_postgres
+storage:
+  backends:
+    kv_postgres:
+      type: kv_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: ${env.POSTGRES_TABLE_NAME:=llamastack_kvstore}
+    sql_postgres:
+      type: sql_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}
+  stores:
+    metadata:
+      namespace: registry
+      backend: kv_postgres
+    inference:
+      table_name: inference_store
+      backend: sql_postgres
+      max_write_queue_size: 10000
+      num_writers: 4
+    conversations:
+      table_name: openai_conversations
+      backend: sql_postgres
+    prompts:
+      namespace: prompts
+      backend: kv_postgres
+registered_resources:
+  models: []
+  shields: []
+  vector_dbs: []
+  datasets: []
+  scoring_fns: []
+  benchmarks: []
+  tool_groups: []
+server:
+  port: 8321
+telemetry:
+  enabled: true

src/llama_stack/distributions/starter/build.yaml

@@ -57,4 +57,5 @@ image_type: venv
 additional_pip_packages:
 - aiosqlite
 - asyncpg
+- psycopg2-binary
 - sqlalchemy[asyncio]

src/llama_stack/distributions/starter/run-with-postgres-store.yaml

@@ -0,0 +1,278 @@
+version: 2
+image_name: starter
+apis:
+- agents
+- batches
+- datasetio
+- eval
+- files
+- inference
+- post_training
+- safety
+- scoring
+- tool_runtime
+- vector_io
+providers:
+  inference:
+  - provider_id: ${env.CEREBRAS_API_KEY:+cerebras}
+    provider_type: remote::cerebras
+    config:
+      base_url: https://api.cerebras.ai
+      api_key: ${env.CEREBRAS_API_KEY:=}
+  - provider_id: ${env.OLLAMA_URL:+ollama}
+    provider_type: remote::ollama
+    config:
+      url: ${env.OLLAMA_URL:=http://localhost:11434}
+  - provider_id: ${env.VLLM_URL:+vllm}
+    provider_type: remote::vllm
+    config:
+      url: ${env.VLLM_URL:=}
+      max_tokens: ${env.VLLM_MAX_TOKENS:=4096}
+      api_token: ${env.VLLM_API_TOKEN:=fake}
+      tls_verify: ${env.VLLM_TLS_VERIFY:=true}
+  - provider_id: ${env.TGI_URL:+tgi}
+    provider_type: remote::tgi
+    config:
+      url: ${env.TGI_URL:=}
+  - provider_id: fireworks
+    provider_type: remote::fireworks
+    config:
+      url: https://api.fireworks.ai/inference/v1
+      api_key: ${env.FIREWORKS_API_KEY:=}
+  - provider_id: together
+    provider_type: remote::together
+    config:
+      url: https://api.together.xyz/v1
+      api_key: ${env.TOGETHER_API_KEY:=}
+  - provider_id: bedrock
+    provider_type: remote::bedrock
+  - provider_id: ${env.NVIDIA_API_KEY:+nvidia}
+    provider_type: remote::nvidia
+    config:
+      url: ${env.NVIDIA_BASE_URL:=https://integrate.api.nvidia.com}
+      api_key: ${env.NVIDIA_API_KEY:=}
+      append_api_version: ${env.NVIDIA_APPEND_API_VERSION:=True}
+  - provider_id: openai
+    provider_type: remote::openai
+    config:
+      api_key: ${env.OPENAI_API_KEY:=}
+      base_url: ${env.OPENAI_BASE_URL:=https://api.openai.com/v1}
+  - provider_id: anthropic
+    provider_type: remote::anthropic
+    config:
+      api_key: ${env.ANTHROPIC_API_KEY:=}
+  - provider_id: gemini
+    provider_type: remote::gemini
+    config:
+      api_key: ${env.GEMINI_API_KEY:=}
+  - provider_id: ${env.VERTEX_AI_PROJECT:+vertexai}
+    provider_type: remote::vertexai
+    config:
+      project: ${env.VERTEX_AI_PROJECT:=}
+      location: ${env.VERTEX_AI_LOCATION:=us-central1}
+  - provider_id: groq
+    provider_type: remote::groq
+    config:
+      url: https://api.groq.com
+      api_key: ${env.GROQ_API_KEY:=}
+  - provider_id: sambanova
+    provider_type: remote::sambanova
+    config:
+      url: https://api.sambanova.ai/v1
+      api_key: ${env.SAMBANOVA_API_KEY:=}
+  - provider_id: ${env.AZURE_API_KEY:+azure}
+    provider_type: remote::azure
+    config:
+      api_key: ${env.AZURE_API_KEY:=}
+      api_base: ${env.AZURE_API_BASE:=}
+      api_version: ${env.AZURE_API_VERSION:=}
+      api_type: ${env.AZURE_API_TYPE:=}
+  - provider_id: sentence-transformers
+    provider_type: inline::sentence-transformers
+  vector_io:
+  - provider_id: faiss
+    provider_type: inline::faiss
+    config:
+      persistence:
+        namespace: vector_io::faiss
+        backend: kv_default
+  - provider_id: sqlite-vec
+    provider_type: inline::sqlite-vec
+    config:
+      db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/starter}/sqlite_vec.db
+      persistence:
+        namespace: vector_io::sqlite_vec
+        backend: kv_default
+  - provider_id: ${env.MILVUS_URL:+milvus}
+    provider_type: inline::milvus
+    config:
+      db_path: ${env.MILVUS_DB_PATH:=~/.llama/distributions/starter}/milvus.db
+      persistence:
+        namespace: vector_io::milvus
+        backend: kv_default
+  - provider_id: ${env.CHROMADB_URL:+chromadb}
+    provider_type: remote::chromadb
+    config:
+      url: ${env.CHROMADB_URL:=}
+      persistence:
+        namespace: vector_io::chroma_remote
+        backend: kv_default
+  - provider_id: ${env.PGVECTOR_DB:+pgvector}
+    provider_type: remote::pgvector
+    config:
+      host: ${env.PGVECTOR_HOST:=localhost}
+      port: ${env.PGVECTOR_PORT:=5432}
+      db: ${env.PGVECTOR_DB:=}
+      user: ${env.PGVECTOR_USER:=}
+      password: ${env.PGVECTOR_PASSWORD:=}
+      persistence:
+        namespace: vector_io::pgvector
+        backend: kv_default
+  - provider_id: ${env.QDRANT_URL:+qdrant}
+    provider_type: remote::qdrant
+    config:
+      api_key: ${env.QDRANT_API_KEY:=}
+      persistence:
+        namespace: vector_io::qdrant_remote
+        backend: kv_default
+  - provider_id: ${env.WEAVIATE_CLUSTER_URL:+weaviate}
+    provider_type: remote::weaviate
+    config:
+      weaviate_api_key: null
+      weaviate_cluster_url: ${env.WEAVIATE_CLUSTER_URL:=localhost:8080}
+      persistence:
+        namespace: vector_io::weaviate
+        backend: kv_default
+  files:
+  - provider_id: meta-reference-files
+    provider_type: inline::localfs
+    config:
+      storage_dir: ${env.FILES_STORAGE_DIR:=~/.llama/distributions/starter/files}
+      metadata_store:
+        table_name: files_metadata
+        backend: sql_default
+  safety:
+  - provider_id: llama-guard
+    provider_type: inline::llama-guard
+    config:
+      excluded_categories: []
+  - provider_id: code-scanner
+    provider_type: inline::code-scanner
+  agents:
+  - provider_id: meta-reference
+    provider_type: inline::meta-reference
+    config:
+      persistence_store:
+        type: sql_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: sql_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}
+  post_training:
+  - provider_id: torchtune-cpu
+    provider_type: inline::torchtune-cpu
+    config:
+      checkpoint_format: meta
+  eval:
+  - provider_id: meta-reference
+    provider_type: inline::meta-reference
+    config:
+      kvstore:
+        namespace: eval
+        backend: kv_default
+  datasetio:
+  - provider_id: huggingface
+    provider_type: remote::huggingface
+    config:
+      kvstore:
+        namespace: datasetio::huggingface
+        backend: kv_default
+  - provider_id: localfs
+    provider_type: inline::localfs
+    config:
+      kvstore:
+        namespace: datasetio::localfs
+        backend: kv_default
+  scoring:
+  - provider_id: basic
+    provider_type: inline::basic
+  - provider_id: llm-as-judge
+    provider_type: inline::llm-as-judge
+  - 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: rag-runtime
+    provider_type: inline::rag-runtime
+  - provider_id: model-context-protocol
+    provider_type: remote::model-context-protocol
+  batches:
+  - provider_id: reference
+    provider_type: inline::reference
+    config:
+      kvstore:
+        namespace: batches
+        backend: kv_postgres
+storage:
+  backends:
+    kv_postgres:
+      type: kv_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: ${env.POSTGRES_TABLE_NAME:=llamastack_kvstore}
+    sql_postgres:
+      type: sql_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}
+  stores:
+    metadata:
+      namespace: registry
+      backend: kv_postgres
+    inference:
+      table_name: inference_store
+      backend: sql_postgres
+      max_write_queue_size: 10000
+      num_writers: 4
+    conversations:
+      table_name: openai_conversations
+      backend: sql_postgres
+    prompts:
+      namespace: prompts
+      backend: kv_postgres
+registered_resources:
+  models: []
+  shields: []
+  vector_dbs: []
+  datasets: []
+  scoring_fns: []
+  benchmarks: []
+  tool_groups: []
+server:
+  port: 8321
+telemetry:
+  enabled: true

src/llama_stack/distributions/starter/starter.py

@@ -17,6 +17,11 @@ from llama_stack.core.datatypes import (
     ToolGroupInput,
     VectorStoresConfig,
 )
+from llama_stack.core.storage.datatypes import (
+    InferenceStoreReference,
+    KVStoreReference,
+    SqlStoreReference,
+)
 from llama_stack.core.utils.dynamic import instantiate_class_type
 from llama_stack.distributions.template import DistributionTemplate, RunConfigSettings
 from llama_stack.providers.datatypes import RemoteProviderSpec
@@ -36,6 +41,7 @@ from llama_stack.providers.remote.vector_io.pgvector.config import (
 )
 from llama_stack.providers.remote.vector_io.qdrant.config import QdrantVectorIOConfig
 from llama_stack.providers.remote.vector_io.weaviate.config import WeaviateVectorIOConfig
+from llama_stack.providers.utils.kvstore.config import PostgresKVStoreConfig
 from llama_stack.providers.utils.sqlstore.sqlstore import PostgresSqlStoreConfig
 
 
@@ -181,6 +187,62 @@ def get_distribution_template(name: str = "starter") -> DistributionTemplate:
             provider_shield_id="${env.CODE_SCANNER_MODEL:=}",
         ),
     ]
+    postgres_config = PostgresSqlStoreConfig.sample_run_config()
+    default_overrides = {
+        "inference": remote_inference_providers + [embedding_provider],
+        "vector_io": [
+            Provider(
+                provider_id="faiss",
+                provider_type="inline::faiss",
+                config=FaissVectorIOConfig.sample_run_config(f"~/.llama/distributions/{name}"),
+            ),
+            Provider(
+                provider_id="sqlite-vec",
+                provider_type="inline::sqlite-vec",
+                config=SQLiteVectorIOConfig.sample_run_config(f"~/.llama/distributions/{name}"),
+            ),
+            Provider(
+                provider_id="${env.MILVUS_URL:+milvus}",
+                provider_type="inline::milvus",
+                config=MilvusVectorIOConfig.sample_run_config(f"~/.llama/distributions/{name}"),
+            ),
+            Provider(
+                provider_id="${env.CHROMADB_URL:+chromadb}",
+                provider_type="remote::chromadb",
+                config=ChromaVectorIOConfig.sample_run_config(
+                    f"~/.llama/distributions/{name}/",
+                    url="${env.CHROMADB_URL:=}",
+                ),
+            ),
+            Provider(
+                provider_id="${env.PGVECTOR_DB:+pgvector}",
+                provider_type="remote::pgvector",
+                config=PGVectorVectorIOConfig.sample_run_config(
+                    f"~/.llama/distributions/{name}",
+                    db="${env.PGVECTOR_DB:=}",
+                    user="${env.PGVECTOR_USER:=}",
+                    password="${env.PGVECTOR_PASSWORD:=}",
+                ),
+            ),
+            Provider(
+                provider_id="${env.QDRANT_URL:+qdrant}",
+                provider_type="remote::qdrant",
+                config=QdrantVectorIOConfig.sample_run_config(
+                    f"~/.llama/distributions/{name}",
+                    url="${env.QDRANT_URL:=}",
+                ),
+            ),
+            Provider(
+                provider_id="${env.WEAVIATE_CLUSTER_URL:+weaviate}",
+                provider_type="remote::weaviate",
+                config=WeaviateVectorIOConfig.sample_run_config(
+                    f"~/.llama/distributions/{name}",
+                    cluster_url="${env.WEAVIATE_CLUSTER_URL:=}",
+                ),
+            ),
+        ],
+        "files": [files_provider],
+    }
 
     return DistributionTemplate(
         name=name,
@@ -189,64 +251,10 @@ def get_distribution_template(name: str = "starter") -> DistributionTemplate:
         container_image=None,
         template_path=None,
         providers=providers,
-        additional_pip_packages=PostgresSqlStoreConfig.pip_packages(),
+        additional_pip_packages=list(set(PostgresSqlStoreConfig.pip_packages() + PostgresKVStoreConfig.pip_packages())),
         run_configs={
             "run.yaml": RunConfigSettings(
-                provider_overrides={
-                    "inference": remote_inference_providers + [embedding_provider],
-                    "vector_io": [
-                        Provider(
-                            provider_id="faiss",
-                            provider_type="inline::faiss",
-                            config=FaissVectorIOConfig.sample_run_config(f"~/.llama/distributions/{name}"),
-                        ),
-                        Provider(
-                            provider_id="sqlite-vec",
-                            provider_type="inline::sqlite-vec",
-                            config=SQLiteVectorIOConfig.sample_run_config(f"~/.llama/distributions/{name}"),
-                        ),
-                        Provider(
-                            provider_id="${env.MILVUS_URL:+milvus}",
-                            provider_type="inline::milvus",
-                            config=MilvusVectorIOConfig.sample_run_config(f"~/.llama/distributions/{name}"),
-                        ),
-                        Provider(
-                            provider_id="${env.CHROMADB_URL:+chromadb}",
-                            provider_type="remote::chromadb",
-                            config=ChromaVectorIOConfig.sample_run_config(
-                                f"~/.llama/distributions/{name}/",
-                                url="${env.CHROMADB_URL:=}",
-                            ),
-                        ),
-                        Provider(
-                            provider_id="${env.PGVECTOR_DB:+pgvector}",
-                            provider_type="remote::pgvector",
-                            config=PGVectorVectorIOConfig.sample_run_config(
-                                f"~/.llama/distributions/{name}",
-                                db="${env.PGVECTOR_DB:=}",
-                                user="${env.PGVECTOR_USER:=}",
-                                password="${env.PGVECTOR_PASSWORD:=}",
-                            ),
-                        ),
-                        Provider(
-                            provider_id="${env.QDRANT_URL:+qdrant}",
-                            provider_type="remote::qdrant",
-                            config=QdrantVectorIOConfig.sample_run_config(
-                                f"~/.llama/distributions/{name}",
-                                url="${env.QDRANT_URL:=}",
-                            ),
-                        ),
-                        Provider(
-                            provider_id="${env.WEAVIATE_CLUSTER_URL:+weaviate}",
-                            provider_type="remote::weaviate",
-                            config=WeaviateVectorIOConfig.sample_run_config(
-                                f"~/.llama/distributions/{name}",
-                                cluster_url="${env.WEAVIATE_CLUSTER_URL:=}",
-                            ),
-                        ),
-                    ],
-                    "files": [files_provider],
-                },
+                provider_overrides=default_overrides,
                 default_models=[],
                 default_tool_groups=default_tool_groups,
                 default_shields=default_shields,
@@ -261,6 +269,55 @@ def get_distribution_template(name: str = "starter") -> DistributionTemplate:
                     default_shield_id="llama-guard",
                 ),
             ),
+            "run-with-postgres-store.yaml": RunConfigSettings(
+                provider_overrides={
+                    **default_overrides,
+                    "agents": [
+                        Provider(
+                            provider_id="meta-reference",
+                            provider_type="inline::meta-reference",
+                            config=dict(
+                                persistence_store=postgres_config,
+                                responses_store=postgres_config,
+                            ),
+                        )
+                    ],
+                    "batches": [
+                        Provider(
+                            provider_id="reference",
+                            provider_type="inline::reference",
+                            config=dict(
+                                kvstore=KVStoreReference(
+                                    backend="kv_postgres",
+                                    namespace="batches",
+                                ).model_dump(exclude_none=True),
+                            ),
+                        )
+                    ],
+                },
+                storage_backends={
+                    "kv_postgres": PostgresKVStoreConfig.sample_run_config(),
+                    "sql_postgres": postgres_config,
+                },
+                storage_stores={
+                    "metadata": KVStoreReference(
+                        backend="kv_postgres",
+                        namespace="registry",
+                    ).model_dump(exclude_none=True),
+                    "inference": InferenceStoreReference(
+                        backend="sql_postgres",
+                        table_name="inference_store",
+                    ).model_dump(exclude_none=True),
+                    "conversations": SqlStoreReference(
+                        backend="sql_postgres",
+                        table_name="openai_conversations",
+                    ).model_dump(exclude_none=True),
+                    "prompts": KVStoreReference(
+                        backend="kv_postgres",
+                        namespace="prompts",
+                    ).model_dump(exclude_none=True),
+                },
+            ),
         },
         run_config_env_vars={
             "LLAMA_STACK_PORT": (

src/llama_stack/providers/remote/inference/passthrough/__init__.py

@@ -10,8 +10,8 @@ from .config import PassthroughImplConfig
 
 
 class PassthroughProviderDataValidator(BaseModel):
-    url: str
-    api_key: str
+    passthrough_url: str
+    passthrough_api_key: str
 
 
 async def get_adapter_impl(config: PassthroughImplConfig, _deps):

src/llama_stack/providers/remote/inference/passthrough/config.py

@@ -6,7 +6,7 @@
 
 from typing import Any
 
-from pydantic import Field, SecretStr
+from pydantic import Field
 
 from llama_stack.providers.utils.inference.model_registry import RemoteInferenceProviderConfig
 from llama_stack.schema_utils import json_schema_type
@@ -19,11 +19,6 @@ class PassthroughImplConfig(RemoteInferenceProviderConfig):
         description="The URL for the passthrough endpoint",
     )
 
-    api_key: SecretStr | None = Field(
-        default=None,
-        description="API Key for the passthrouth endpoint",
-    )
-
     @classmethod
     def sample_run_config(
         cls, url: str = "${env.PASSTHROUGH_URL}", api_key: str = "${env.PASSTHROUGH_API_KEY}", **kwargs

src/llama_stack/providers/remote/inference/passthrough/passthrough.py

@@ -5,9 +5,8 @@
 # the root directory of this source tree.
 
 from collections.abc import AsyncIterator
-from typing import Any
 
-from llama_stack_client import AsyncLlamaStackClient
+from openai import AsyncOpenAI
 
 from llama_stack.apis.inference import (
     Inference,
@@ -20,103 +19,117 @@ from llama_stack.apis.inference import (
     OpenAIEmbeddingsResponse,
 )
 from llama_stack.apis.models import Model
-from llama_stack.core.library_client import convert_pydantic_to_json_value
-from llama_stack.providers.utils.inference.model_registry import ModelRegistryHelper
+from llama_stack.core.request_headers import NeedsRequestProviderData
 
 from .config import PassthroughImplConfig
 
 
-class PassthroughInferenceAdapter(Inference):
+class PassthroughInferenceAdapter(NeedsRequestProviderData, Inference):
     def __init__(self, config: PassthroughImplConfig) -> None:
-        ModelRegistryHelper.__init__(self)
         self.config = config
 
+    async def initialize(self) -> None:
+        pass
+
+    async def shutdown(self) -> None:
+        pass
+
     async def unregister_model(self, model_id: str) -> None:
         pass
 
     async def register_model(self, model: Model) -> Model:
         return model
 
-    def _get_client(self) -> AsyncLlamaStackClient:
-        passthrough_url = None
-        passthrough_api_key = None
-        provider_data = None
+    async def list_models(self) -> list[Model]:
+        """List models by calling the downstream /v1/models endpoint."""
+        client = self._get_openai_client()
 
-        if self.config.url is not None:
-            passthrough_url = self.config.url
-        else:
-            provider_data = self.get_request_provider_data()
-            if provider_data is None or not provider_data.passthrough_url:
-                raise ValueError(
-                    'Pass url of the passthrough endpoint in the header X-LlamaStack-Provider-Data as { "passthrough_url": <your passthrough url>}'
-                )
-            passthrough_url = provider_data.passthrough_url
+        response = await client.models.list()
 
-        if self.config.api_key is not None:
-            passthrough_api_key = self.config.api_key.get_secret_value()
-        else:
-            provider_data = self.get_request_provider_data()
-            if provider_data is None or not provider_data.passthrough_api_key:
-                raise ValueError(
-                    'Pass API Key for the passthrough endpoint in the header X-LlamaStack-Provider-Data as { "passthrough_api_key": <your api key>}'
-                )
-            passthrough_api_key = provider_data.passthrough_api_key
+        # Convert from OpenAI format to Llama Stack Model format
+        models = []
+        for model_data in response.data:
+            downstream_model_id = model_data.id
+            custom_metadata = getattr(model_data, "custom_metadata", {}) or {}
 
-        return AsyncLlamaStackClient(
-            base_url=passthrough_url,
-            api_key=passthrough_api_key,
-            provider_data=provider_data,
+            # Prefix identifier with provider ID for local registry
+            local_identifier = f"{self.__provider_id__}/{downstream_model_id}"
+
+            model = Model(
+                identifier=local_identifier,
+                provider_id=self.__provider_id__,
+                provider_resource_id=downstream_model_id,
+                model_type=custom_metadata.get("model_type", "llm"),
+                metadata=custom_metadata,
+            )
+            models.append(model)
+
+        return models
+
+    async def should_refresh_models(self) -> bool:
+        """Passthrough should refresh models since they come from downstream dynamically."""
+        return self.config.refresh_models
+
+    def _get_openai_client(self) -> AsyncOpenAI:
+        """Get an AsyncOpenAI client configured for the downstream server."""
+        base_url = self._get_passthrough_url()
+        api_key = self._get_passthrough_api_key()
+
+        return AsyncOpenAI(
+            base_url=f"{base_url.rstrip('/')}/v1",
+            api_key=api_key,
         )
 
-    async def openai_embeddings(
-        self,
-        params: OpenAIEmbeddingsRequestWithExtraBody,
-    ) -> OpenAIEmbeddingsResponse:
-        raise NotImplementedError()
+    def _get_passthrough_url(self) -> str:
+        """Get the passthrough URL from config or provider data."""
+        if self.config.url is not None:
+            return self.config.url
+
+        provider_data = self.get_request_provider_data()
+        if provider_data is None:
+            raise ValueError(
+                'Pass url of the passthrough endpoint in the header X-LlamaStack-Provider-Data as { "passthrough_url": <your passthrough url>}'
+            )
+        return provider_data.passthrough_url
+
+    def _get_passthrough_api_key(self) -> str:
+        """Get the passthrough API key from config or provider data."""
+        if self.config.auth_credential is not None:
+            return self.config.auth_credential.get_secret_value()
+
+        provider_data = self.get_request_provider_data()
+        if provider_data is None:
+            raise ValueError(
+                'Pass API Key for the passthrough endpoint in the header X-LlamaStack-Provider-Data as { "passthrough_api_key": <your api key>}'
+            )
+        return provider_data.passthrough_api_key
 
     async def openai_completion(
         self,
         params: OpenAICompletionRequestWithExtraBody,
     ) -> OpenAICompletion:
-        client = self._get_client()
-        model_obj = await self.model_store.get_model(params.model)
-
-        params = params.model_copy()
-        params.model = model_obj.provider_resource_id
-
+        """Forward completion request to downstream using OpenAI client."""
+        client = self._get_openai_client()
         request_params = params.model_dump(exclude_none=True)
-
-        return await client.inference.openai_completion(**request_params)
+        response = await client.completions.create(**request_params)
+        return response  # type: ignore
 
     async def openai_chat_completion(
         self,
         params: OpenAIChatCompletionRequestWithExtraBody,
     ) -> OpenAIChatCompletion | AsyncIterator[OpenAIChatCompletionChunk]:
-        client = self._get_client()
-        model_obj = await self.model_store.get_model(params.model)
-
-        params = params.model_copy()
-        params.model = model_obj.provider_resource_id
-
+        """Forward chat completion request to downstream using OpenAI client."""
+        client = self._get_openai_client()
         request_params = params.model_dump(exclude_none=True)
+        response = await client.chat.completions.create(**request_params)
+        return response  # type: ignore
 
-        return await client.inference.openai_chat_completion(**request_params)
-
-    def cast_value_to_json_dict(self, request_params: dict[str, Any]) -> dict[str, Any]:
-        json_params = {}
-        for key, value in request_params.items():
-            json_input = convert_pydantic_to_json_value(value)
-            if isinstance(json_input, dict):
-                json_input = {k: v for k, v in json_input.items() if v is not None}
-            elif isinstance(json_input, list):
-                json_input = [x for x in json_input if x is not None]
-                new_input = []
-                for x in json_input:
-                    if isinstance(x, dict):
-                        x = {k: v for k, v in x.items() if v is not None}
-                    new_input.append(x)
-                json_input = new_input
-
-            json_params[key] = json_input
-
-        return json_params
+    async def openai_embeddings(
+        self,
+        params: OpenAIEmbeddingsRequestWithExtraBody,
+    ) -> OpenAIEmbeddingsResponse:
+        """Forward embeddings request to downstream using OpenAI client."""
+        client = self._get_openai_client()
+        request_params = params.model_dump(exclude_none=True)
+        response = await client.embeddings.create(**request_params)
+        return response  # type: ignore

src/llama_stack/providers/remote/inference/watsonx/watsonx.py

@@ -283,8 +283,8 @@ class WatsonXInferenceAdapter(LiteLLMOpenAIMixin):
             # ...
             provider_resource_id = f"{self.__provider_id__}/{model_spec['model_id']}"
             if "embedding" in functions:
-                embedding_dimension = model_spec["model_limits"]["embedding_dimension"]
-                context_length = model_spec["model_limits"]["max_sequence_length"]
+                embedding_dimension = model_spec.get("model_limits", {}).get("embedding_dimension", 0)
+                context_length = model_spec.get("model_limits", {}).get("max_sequence_length", 0)
                 embedding_metadata = {
                     "embedding_dimension": embedding_dimension,
                     "context_length": context_length,
@@ -306,10 +306,6 @@ class WatsonXInferenceAdapter(LiteLLMOpenAIMixin):
                     metadata={},
                     model_type=ModelType.llm,
                 )
-                # In theory, I guess it is possible that a model could be both an embedding model and a text chat model.
-                # In that case, the cache will record the generator Model object, and the list which we return will have
-                # both the generator Model object and the text chat Model object.  That's fine because the cache is
-                # only used for check_model_availability() anyway.
                 self._model_cache[provider_resource_id] = model
                 models.append(model)
         return models

src/llama_stack/providers/utils/memory/openai_vector_store_mixin.py

@@ -26,6 +26,7 @@ from llama_stack.apis.vector_io import (
     VectorStoreChunkingStrategy,
     VectorStoreChunkingStrategyAuto,
     VectorStoreChunkingStrategyStatic,
+    VectorStoreChunkingStrategyStaticConfig,
     VectorStoreContent,
     VectorStoreDeleteResponse,
     VectorStoreFileBatchObject,
@@ -414,6 +415,10 @@ class OpenAIVectorStoreMixin(ABC):
             in_progress=0,
             total=0,
         )
+        if not params.chunking_strategy or params.chunking_strategy.type == "auto":
+            chunking_strategy = VectorStoreChunkingStrategyStatic(static=VectorStoreChunkingStrategyStaticConfig())
+        else:
+            chunking_strategy = params.chunking_strategy
         store_info: dict[str, Any] = {
             "id": vector_store_id,
             "object": "vector_store",
@@ -426,7 +431,7 @@ class OpenAIVectorStoreMixin(ABC):
             "expires_at": None,
             "last_active_at": created_at,
             "file_ids": [],
-            "chunking_strategy": params.chunking_strategy,
+            "chunking_strategy": chunking_strategy.model_dump(),
         }
 
         # Add provider information to metadata if provided
@@ -637,7 +642,7 @@ class OpenAIVectorStoreMixin(ABC):
                     break
 
             return VectorStoreSearchResponsePage(
-                search_query=search_query,
+                search_query=query if isinstance(query, list) else [query],
                 data=data,
                 has_more=False,  # For simplicity, we don't implement pagination here
                 next_page=None,
@@ -647,7 +652,7 @@ class OpenAIVectorStoreMixin(ABC):
             logger.error(f"Error searching vector store {vector_store_id}: {e}")
             # Return empty results on error
             return VectorStoreSearchResponsePage(
-                search_query=search_query,
+                search_query=query if isinstance(query, list) else [query],
                 data=[],
                 has_more=False,
                 next_page=None,
@@ -886,8 +891,8 @@ class OpenAIVectorStoreMixin(ABC):
 
         # Determine pagination info
         has_more = len(file_objects) > limit
-        first_id = file_objects[0].id if file_objects else None
-        last_id = file_objects[-1].id if file_objects else None
+        first_id = limited_files[0].id if file_objects else None
+        last_id = limited_files[-1].id if file_objects else None
 
         return VectorStoreListFilesResponse(
             data=limited_files,

tests/integration/batches/recordings/37b8d062efc39eaa4ac3438f5102044a29fd1e9b844ead2f066a1fb25c85672c.json

@@ -1,422 +0,0 @@
-{
-  "test_id": "tests/integration/batches/test_batches.py::TestBatchesIntegration::test_batch_e2e_embeddings[emb=ollama/all-minilm:l6-v2]",
-  "request": {
-    "method": "POST",
-    "url": "http://0.0.0.0:11434/v1/v1/embeddings",
-    "headers": {},
-    "body": {
-      "model": "all-minilm:l6-v2",
-      "input": "Hello world",
-      "encoding_format": "float"
-    },
-    "endpoint": "/v1/embeddings",
-    "model": "all-minilm:l6-v2"
-  },
-  "response": {
-    "body": {
-      "__type__": "openai.types.create_embedding_response.CreateEmbeddingResponse",
-      "__data__": {
-        "data": [
-          {
-            "embedding": [
-              -0.034477483,
-              0.030899182,
-              0.0066526434,
-              0.026075281,
-              -0.039411988,
-              -0.16037956,
-              0.06692074,
-              -0.006511468,
-              -0.047467157,
-              0.014774274,
-              0.07094562,
-              0.055527706,
-              0.019183245,
-              -0.026297163,
-              -0.010018651,
-              -0.02694715,
-              0.0223884,
-              -0.02220693,
-              -0.14977267,
-              -0.017530814,
-              0.0075938613,
-              0.054253556,
-              0.0032258728,
-              0.031724673,
-              -0.08466085,
-              -0.029342307,
-              0.05155048,
-              0.048105717,
-              -0.0032670307,
-              -0.05822795,
-              0.041971523,
-              0.022229431,
-              0.1281518,
-              -0.022270948,
-              -0.011725874,
-              0.06294936,
-              -0.032847952,
-              -0.09124354,
-              -0.031128692,
-              0.05274829,
-              0.047067728,
-              -0.08414196,
-              -0.029979317,
-              -0.020692566,
-              0.00949804,
-              -0.0035992558,
-              0.0074442336,
-              0.03928378,
-              0.09326073,
-              -0.0037437282,
-              -0.052663893,
-              -0.058101393,
-              -0.006925679,
-              0.0052269334,
-              0.08290669,
-              0.019312402,
-              0.0062818974,
-              -0.010331665,
-              0.008930684,
-              -0.037712026,
-              -0.045175705,
-              0.023950849,
-              -0.006926045,
-              0.013429504,
-              0.100098,
-              -0.0715888,
-              -0.021700105,
-              0.031693522,
-              -0.05161389,
-              -0.08224763,
-              -0.06577986,
-              -0.009853981,
-              0.005808086,
-              0.07364217,
-              -0.034008067,
-              0.024907362,
-              0.014441484,
-              0.02645124,
-              0.009659713,
-              0.030284341,
-              0.052878983,
-              -0.07536944,
-              0.009890014,
-              0.029907802,
-              0.017498897,
-              0.02313779,
-              0.0018918256,
-              0.0013156217,
-              -0.047173936,
-              -0.011251131,
-              -0.11422648,
-              -0.019960148,
-              0.040278148,
-              0.0022633963,
-              -0.07986738,
-              -0.025357265,
-              0.094500035,
-              -0.029062947,
-              -0.14495483,
-              0.2309815,
-              0.027703581,
-              0.03208736,
-              0.031073036,
-              0.042917974,
-              0.064246915,
-              0.032118786,
-              -0.004844535,
-              0.055775862,
-              -0.03756279,
-              -0.021487191,
-              -0.028432492,
-              -0.028887685,
-              0.03842892,
-              -0.017359573,
-              0.052465834,
-              -0.07493626,
-              -0.031175744,
-              0.021936033,
-              -0.039823197,
-              -0.008681939,
-              0.026978256,
-              -0.048551314,
-              0.011414809,
-              0.029628372,
-              -0.020587107,
-              0.013077965,
-              0.028824588,
-              -3.1978743e-33,
-              0.06475607,
-              -0.018065408,
-              0.05190019,
-              0.12193858,
-              0.028755108,
-              0.008794777,
-              -0.07044016,
-              -0.016856866,
-              0.040675826,
-              0.04222898,
-              0.025450956,
-              0.035772353,
-              -0.049134083,
-              0.0021395232,
-              -0.015527445,
-              0.05065655,
-              -0.04814189,
-              0.03586998,
-              -0.004134139,
-              0.10165314,
-              -0.055980552,
-              -0.010677752,
-              0.011231545,
-              0.09068785,
-              0.004311188,
-              0.035094332,
-              -0.009658399,
-              -0.09383056,
-              0.092755266,
-              0.00799794,
-              -0.0077075018,
-              -0.052119244,
-              -0.01259255,
-              0.0032277475,
-              0.005989667,
-              0.0075889886,
-              0.010571857,
-              -0.08629758,
-              -0.06985891,
-              -0.002511263,
-              -0.091053724,
-              0.0468712,
-              0.05203361,
-              0.0072902967,
-              0.010906411,
-              -0.0052922186,
-              0.013883815,
-              0.021929385,
-              0.0341257,
-              0.060227357,
-              0.00018942523,
-              0.0146624865,
-              -0.07000342,
-              0.028425341,
-              -0.027542787,
-              0.01082086,
-              0.03491755,
-              -0.022430921,
-              0.0096813915,
-              0.07725412,
-              0.021618832,
-              0.114911504,
-              -0.06805403,
-              0.023872944,
-              -0.015999107,
-              -0.017794114,
-              0.06442477,
-              0.03206309,
-              0.050293576,
-              -0.005988605,
-              -0.03376946,
-              0.017821673,
-              0.016567992,
-              0.063335925,
-              0.034753703,
-              0.046586752,
-              0.09789875,
-              -0.006560692,
-              0.025039855,
-              -0.07780643,
-              0.016878096,
-              -0.0010056288,
-              0.02257608,
-              -0.0382721,
-              0.09572481,
-              -0.005296001,
-              0.010567662,
-              -0.11538674,
-              -0.013233586,
-              -0.010786205,
-              -0.083147496,
-              0.073254965,
-              0.049377624,
-              -0.009025328,
-              -0.0957893,
-              3.3687185e-33,
-              0.12494067,
-              0.019226579,
-              -0.058172084,
-              -0.035952393,
-              -0.050862074,
-              -0.045700952,
-              -0.0826631,
-              0.14819908,
-              -0.088347495,
-              0.060315337,
-              0.05109269,
-              0.010308115,
-              0.1411753,
-              0.030833788,
-              0.06101746,
-              -0.052806143,
-              0.13661332,
-              0.00917483,
-              -0.017295862,
-              -0.0128495265,
-              -0.007851698,
-              -0.051084496,
-              -0.05235087,
-              0.0076632234,
-              -0.015217299,
-              0.017015414,
-              0.021324545,
-              0.020506723,
-              -0.12004153,
-              0.014523494,
-              0.026743378,
-              0.025221687,
-              -0.04270567,
-              0.00676352,
-              -0.014453511,
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-              -0.019459482,
-              -0.017806036,
-              -0.055010412,
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-              -0.010370778,
-              -0.052053526,
-              0.020918628,
-              -0.080037735,
-              -0.012147244,
-              -0.057777684,
-              0.023249507,
-              -0.007838778,
-              -0.025807643,
-              -0.07987164,
-              -0.020683115,
-              0.04888083,
-              -0.020459235,
-              -0.049192864,
-              0.01407799,
-              -0.063744746,
-              -0.0077936463,
-              0.016429903,
-              -0.025707569,
-              0.013326097,
-              0.026210392,
-              0.009855086,
-              0.06317218,
-              0.0026150644,
-              -0.0065879063,
-              0.0166049,
-              0.032400407,
-              0.038005095,
-              -0.036269873,
-              -0.0069020875,
-              0.00019545198,
-              -0.0017537851,
-              -0.027427403,
-              -0.02801922,
-              0.049696837,
-              -0.028842367,
-              -0.0023814398,
-              0.01481421,
-              0.00976869,
-              0.0057697925,
-              0.01341087,
-              0.00551593,
-              0.037237898,
-              0.007291808,
-              0.040068958,
-              0.08141818,
-              0.07197348,
-              -0.013163506,
-              -0.042782705,
-              -0.010938265,
-              0.0049547236,
-              -0.00923014,
-              0.035068717,
-              -0.051007,
-              -1.5708556e-08,
-              -0.088558294,
-              0.02391312,
-              -0.016132735,
-              0.03169382,
-              0.027184812,
-              0.052484553,
-              -0.047118798,
-              -0.058789898,
-              -0.063239954,
-              0.040775288,
-              0.049807984,
-              0.106462926,
-              -0.07448737,
-              -0.012401869,
-              0.018361589,
-              0.039486438,
-              -0.024830224,
-              0.014500051,
-              -0.03712332,
-              0.020043189,
-              8.399218e-05,
-              0.009852795,
-              0.024823224,
-              -0.05252818,
-              0.02932855,
-              -0.0871494,
-              -0.01447227,
-              0.025996566,
-              -0.018731978,
-              -0.07618361,
-              0.03505914,
-              0.10363578,
-              -0.0280213,
-              0.012769872,
-              -0.076482065,
-              -0.018743375,
-              0.024961015,
-              0.08152011,
-              0.06866303,
-              -0.06411612,
-              -0.08387694,
-              0.061479986,
-              -0.03345598,
-              -0.10615398,
-              -0.040166635,
-              0.032536518,
-              0.076652974,
-              -0.07297006,
-              0.00039833272,
-              -0.0409393,
-              -0.07580284,
-              0.027465926,
-              0.07468789,
-              0.017779494,
-              0.09106629,
-              0.11033428,
-              0.00065298256,
-              0.051472265,
-              -0.01461242,
-              0.033237122,
-              0.023671487,
-              -0.022980422,
-              0.038988944,
-              0.030206418
-            ],
-            "index": 0,
-            "object": "embedding"
-          }
-        ],
-        "model": "all-minilm:l6-v2",
-        "object": "list",
-        "usage": {
-          "prompt_tokens": 2,
-          "total_tokens": 2
-        }
-      }
-    },
-    "is_streaming": false
-  },
-  "id_normalization_mapping": {}
-}

tests/integration/ci_matrix.json

@@ -0,0 +1,13 @@
+{
+  "default": [
+    {"suite": "base", "setup": "ollama"},
+    {"suite": "vision", "setup": "ollama-vision"},
+    {"suite": "responses", "setup": "gpt"},
+    {"suite": "base-vllm-subset", "setup": "vllm"}
+  ],
+  "schedules": {
+    "1 0 * * 0": [
+      {"suite": "base", "setup": "vllm"}
+    ]
+  }
+}

tests/integration/common/recordings/models-64a2277c90f0f42576f60c1030e3a020403d34a95f56931b792d5939f4cebc57-826d44c3.json

@@ -0,0 +1,89 @@
+{
+  "test_id": null,
+  "request": {
+    "method": "POST",
+    "url": "http://0.0.0.0:11434/v1/v1/models",
+    "headers": {},
+    "body": {},
+    "endpoint": "/v1/models",
+    "model": ""
+  },
+  "response": {
+    "body": [
+      {
+        "__type__": "openai.types.model.Model",
+        "__data__": {
+          "id": "llama3.2:3b-instruct-fp16",
+          "created": 1760453641,
+          "object": "model",
+          "owned_by": "library"
+        }
+      },
+      {
+        "__type__": "openai.types.model.Model",
+        "__data__": {
+          "id": "qwen3:4b",
+          "created": 1757615302,
+          "object": "model",
+          "owned_by": "library"
+        }
+      },
+      {
+        "__type__": "openai.types.model.Model",
+        "__data__": {
+          "id": "gpt-oss:latest",
+          "created": 1756395223,
+          "object": "model",
+          "owned_by": "library"
+        }
+      },
+      {
+        "__type__": "openai.types.model.Model",
+        "__data__": {
+          "id": "nomic-embed-text:latest",
+          "created": 1756318548,
+          "object": "model",
+          "owned_by": "library"
+        }
+      },
+      {
+        "__type__": "openai.types.model.Model",
+        "__data__": {
+          "id": "llama3.2:3b",
+          "created": 1755191039,
+          "object": "model",
+          "owned_by": "library"
+        }
+      },
+      {
+        "__type__": "openai.types.model.Model",
+        "__data__": {
+          "id": "all-minilm:l6-v2",
+          "created": 1753968177,
+          "object": "model",
+          "owned_by": "library"
+        }
+      },
+      {
+        "__type__": "openai.types.model.Model",
+        "__data__": {
+          "id": "llama3.2:1b",
+          "created": 1746124735,
+          "object": "model",
+          "owned_by": "library"
+        }
+      },
+      {
+        "__type__": "openai.types.model.Model",
+        "__data__": {
+          "id": "llama3.2:latest",
+          "created": 1746044170,
+          "object": "model",
+          "owned_by": "library"
+        }
+      }
+    ],
+    "is_streaming": false
+  },
+  "id_normalization_mapping": {}
+}

tests/integration/common/recordings/models-64a2277c90f0f42576f60c1030e3a020403d34a95f56931b792d5939f4cebc57-fb68f5a6.json

@@ -0,0 +1,45 @@
+{
+  "test_id": null,
+  "request": {
+    "method": "POST",
+    "url": "http://localhost:8000/v1/v1/models",
+    "headers": {},
+    "body": {},
+    "endpoint": "/v1/models",
+    "model": ""
+  },
+  "response": {
+    "body": [
+      {
+        "__type__": "openai.types.model.Model",
+        "__data__": {
+          "id": "Qwen/Qwen3-0.6B",
+          "created": 1762374291,
+          "object": "model",
+          "owned_by": "vllm",
+          "root": "/root/.cache/Qwen3-0.6B",
+          "parent": null,
+          "max_model_len": 8192,
+          "permission": [
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-          }
-        ],
-        "model": "all-minilm:l6-v2",
-        "object": "list",
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tests/integration/inference/recordings/2efc033e9a1b66980245aabc44b6d7fe2509aafa52d6aa19eee2cde5245cd3b9.json

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-{
-  "test_id": "tests/integration/inference/test_openai_embeddings.py::test_openai_embeddings_with_user_parameter[openai_client-emb=ollama/all-minilm:l6-v2]",
-  "request": {
-    "method": "POST",
-    "url": "http://0.0.0.0:11434/v1/v1/embeddings",
-    "headers": {},
-    "body": {
-      "model": "all-minilm:l6-v2",
-      "input": "Test user parameter",
-      "encoding_format": "base64",
-      "user": "test-user-123"
-    },
-    "endpoint": "/v1/embeddings",
-    "model": "all-minilm:l6-v2"
-  },
-  "response": {
-    "body": {
-      "__type__": "openai.types.create_embedding_response.CreateEmbeddingResponse",
-      "__data__": {
-        "data": [
-          {
-            "embedding": [
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-            ],
-            "index": 0,
-            "object": "embedding"
-          }
-        ],
-        "model": "all-minilm:l6-v2",
-        "object": "list",
-        "usage": {
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-      }
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-    "is_streaming": false
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-}

tests/integration/inference/recordings/452805c3c85951c86e4e5dfeef078a2e184866dafee83186cd84932daae1af42.json

@@ -0,0 +1,84 @@
+{
+  "test_id": "tests/integration/inference/test_tools_with_schemas.py::TestEdgeCases::test_tool_without_schema[txt=vllm/Qwen/Qwen3-0.6B]",
+  "request": {
+    "method": "POST",
+    "url": "http://localhost:8000/v1/v1/chat/completions",
+    "headers": {},
+    "body": {
+      "model": "Qwen/Qwen3-0.6B",
+      "messages": [
+        {
+          "role": "user",
+          "content": "Call the no args tool"
+        }
+      ],
+      "max_tokens": 4096,
+      "tools": [
+        {
+          "type": "function",
+          "function": {
+            "name": "no_args_tool",
+            "description": "Tool with no arguments",
+            "parameters": {
+              "type": "object",
+              "properties": {}
+            }
+          }
+        }
+      ]
+    },
+    "endpoint": "/v1/chat/completions",
+    "model": "Qwen/Qwen3-0.6B"
+  },
+  "response": {
+    "body": {
+      "__type__": "openai.types.chat.chat_completion.ChatCompletion",
+      "__data__": {
+        "id": "rec-452805c3c859",
+        "choices": [
+          {
+            "finish_reason": "tool_calls",
+            "index": 0,
+            "logprobs": null,
+            "message": {
+              "content": "<think>\nOkay, the user wants me to call the no args tool. Let me check the available functions. There's only one tool provided, which is the no_args_tool with no arguments. Since the user didn't specify any parameters, I should just return the tool call as instructed. I need to make sure the JSON is correctly formatted and within the XML tags. Alright, that's all I need.\n</think>\n\n",
+              "refusal": null,
+              "role": "assistant",
+              "annotations": null,
+              "audio": null,
+              "function_call": null,
+              "tool_calls": [
+                {
+                  "id": "chatcmpl-tool-7a67269afe214c85924c5171612bbdbd",
+                  "function": {
+                    "arguments": "{}",
+                    "name": "no_args_tool"
+                  },
+                  "type": "function"
+                }
+              ],
+              "reasoning_content": null
+            },
+            "stop_reason": null
+          }
+        ],
+        "created": 0,
+        "model": "Qwen/Qwen3-0.6B",
+        "object": "chat.completion",
+        "service_tier": null,
+        "system_fingerprint": null,
+        "usage": {
+          "completion_tokens": 101,
+          "prompt_tokens": 136,
+          "total_tokens": 237,
+          "completion_tokens_details": null,
+          "prompt_tokens_details": null
+        },
+        "prompt_logprobs": null,
+        "kv_transfer_params": null
+      }
+    },
+    "is_streaming": false
+  },
+  "id_normalization_mapping": {}
+}

tests/integration/inference/recordings/45de0b7bfdc88fd9a7b76b6f18c0a1b2c4953aa4791012e2f372430d5362c50b.json

@@ -1,421 +0,0 @@
-{
-  "test_id": "tests/integration/inference/test_openai_embeddings.py::test_openai_embeddings_different_inputs_different_outputs[openai_client-emb=ollama/all-minilm:l6-v2]",
-  "request": {
-    "method": "POST",
-    "url": "http://0.0.0.0:11434/v1/v1/embeddings",
-    "headers": {},
-    "body": {
-      "model": "all-minilm:l6-v2",
-      "input": "This is completely different content",
-      "encoding_format": "float"
-    },
-    "endpoint": "/v1/embeddings",
-    "model": "all-minilm:l6-v2"
-  },
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tests/integration/inference/recordings/496035259763c1bddb1a3148c2586663d08a5bc31f697d1fc5d9bed1c71f5950.json

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+  "request": {
+    "method": "POST",
+    "url": "http://localhost:8000/v1/v1/chat/completions",
+    "headers": {},
+    "body": {
+      "model": "Qwen/Qwen3-0.6B",
+      "messages": [
+        {
+          "role": "user",
+          "content": "What's the weather in Tokyo?"
+        }
+      ],
+      "max_tokens": 4096,
+      "tools": [
+        {
+          "type": "function",
+          "function": {
+            "name": "get_weather",
+            "description": "Get weather information",
+            "parameters": {
+              "type": "object",
+              "properties": {
+                "location": {
+                  "type": "string",
+                  "description": "City name"
+                }
+              },
+              "required": [
+                "location"
+              ]
+            }
+          }
+        }
+      ]
+    },
+    "endpoint": "/v1/chat/completions",
+    "model": "Qwen/Qwen3-0.6B"
+  },
+  "response": {
+    "body": {
+      "__type__": "openai.types.chat.chat_completion.ChatCompletion",
+      "__data__": {
+        "id": "rec-496035259763",
+        "choices": [
+          {
+            "finish_reason": "tool_calls",
+            "index": 0,
+            "logprobs": null,
+            "message": {
+              "content": "<think>\nOkay, the user is asking about the weather in Tokyo. I need to use the get_weather function for that. The function requires the location parameter, which in this case is Tokyo. I should make sure to specify \"Tokyo\" as the location. Let me check if there are any other parameters needed, but no, the function only needs the location. So the tool call should be straightforward. I'll format the JSON correctly inside the tool_call tags.\n</think>\n\n",
+              "refusal": null,
+              "role": "assistant",
+              "annotations": null,
+              "audio": null,
+              "function_call": null,
+              "tool_calls": [
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+                  "function": {
+                    "arguments": "{\"location\": \"Tokyo\"}",
+                    "name": "get_weather"
+                  },
+                  "type": "function"
+                }
+              ],
+              "reasoning_content": null
+            },
+            "stop_reason": null
+          }
+        ],
+        "created": 0,
+        "model": "Qwen/Qwen3-0.6B",
+        "object": "chat.completion",
+        "service_tier": null,
+        "system_fingerprint": null,
+        "usage": {
+          "completion_tokens": 117,
+          "prompt_tokens": 158,
+          "total_tokens": 275,
+          "completion_tokens_details": null,
+          "prompt_tokens_details": null
+        },
+        "prompt_logprobs": null,
+        "kv_transfer_params": null
+      }
+    },
+    "is_streaming": false
+  },
+  "id_normalization_mapping": {}
+}

tests/integration/inference/recordings/524ead18daaddb6228284820adaa3fb312d2a525cc35e20c181190ddf40793e6.json

@@ -0,0 +1,92 @@
+{
+  "test_id": "tests/integration/inference/test_tools_with_schemas.py::TestOpenAICompatibility::test_openai_format_preserves_complex_schemas[openai_client-txt=vllm/Qwen/Qwen3-0.6B]",
+  "request": {
+    "method": "POST",
+    "url": "http://localhost:8000/v1/v1/chat/completions",
+    "headers": {},
+    "body": {
+      "model": "Qwen/Qwen3-0.6B",
+      "messages": [
+        {
+          "role": "user",
+          "content": "Process this data"
+        }
+      ],
+      "max_tokens": 4096,
+      "tools": [
+        {
+          "type": "function",
+          "function": {
+            "name": "process_data",
+            "description": "Process structured data",
+            "parameters": {
+              "type": "object",
+              "properties": {
+                "data": {
+                  "$ref": "#/$defs/DataObject"
+                }
+              },
+              "$defs": {
+                "DataObject": {
+                  "type": "object",
+                  "properties": {
+                    "values": {
+                      "type": "array",
+                      "items": {
+                        "type": "number"
+                      }
+                    }
+                  }
+                }
+              }
+            }
+          }
+        }
+      ]
+    },
+    "endpoint": "/v1/chat/completions",
+    "model": "Qwen/Qwen3-0.6B"
+  },
+  "response": {
+    "body": {
+      "__type__": "openai.types.chat.chat_completion.ChatCompletion",
+      "__data__": {
+        "id": "rec-524ead18daad",
+        "choices": [
+          {
+            "finish_reason": "stop",
+            "index": 0,
+            "logprobs": null,
+            "message": {
+              "content": "<think>\nOkay, the user wants me to process the data. Let me check the available tools. There's a function called process_data that takes an object with a 'data' parameter. The data is an array of numbers. But the user hasn't provided any specific data yet. They just said \"Process this data.\" Hmm, maybe they expect me to prompt them for the data first. Wait, maybe there's a misunderstanding. Did they include the data in the conversation history? Let me look back. The user's message is \"Process this data.\" No data provided. Oh, maybe they made a mistake and forgot to include it. I need to ask them to provide the data so I can proceed. Let me confirm if there's any data mentioned. No, the current input is just the instruction. So I should ask the user to supply the data array of numbers to process.\n</think>\n\nPlease provide the structured data you'd like me to process. For example, an array of numbers like `[1, 2, 3]`.",
+              "refusal": null,
+              "role": "assistant",
+              "annotations": null,
+              "audio": null,
+              "function_call": null,
+              "tool_calls": [],
+              "reasoning_content": null
+            },
+            "stop_reason": null
+          }
+        ],
+        "created": 0,
+        "model": "Qwen/Qwen3-0.6B",
+        "object": "chat.completion",
+        "service_tier": null,
+        "system_fingerprint": null,
+        "usage": {
+          "completion_tokens": 212,
+          "prompt_tokens": 180,
+          "total_tokens": 392,
+          "completion_tokens_details": null,
+          "prompt_tokens_details": null
+        },
+        "prompt_logprobs": null,
+        "kv_transfer_params": null
+      }
+    },
+    "is_streaming": false
+  },
+  "id_normalization_mapping": {}
+}

tests/integration/inference/recordings/54f1997f5ea8c22d5655cd5718f3c5c19da209925e3df713287db28e7830f02b.json

@@ -1,75 +0,0 @@
-{
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-  "request": {
-    "method": "POST",
-    "url": "http://localhost:11434/api/ps",
-    "headers": {},
-    "body": {},
-    "endpoint": "/api/ps",
-    "model": ""
-  },
-  "response": {
-    "body": {
-      "__type__": "ollama._types.ProcessResponse",
-      "__data__": {
-        "models": [
-          {
-            "model": "llama3.2:3b",
-            "name": "llama3.2:3b",
-            "digest": "a80c4f17acd55265feec403c7aef86be0c25983ab279d83f3bcd3abbcb5b8b72",
-            "expires_at": "2025-10-08T16:14:05.423042-07:00",
-            "size": 3367856128,
-            "size_vram": 3367856128,
-            "details": {
-              "parent_model": "",
-              "format": "gguf",
-              "family": "llama",
-              "families": [
-                "llama"
-              ],
-              "parameter_size": "3.2B",
-              "quantization_level": "Q4_K_M"
-            }
-          },
-          {
-            "model": "all-minilm:l6-v2",
-            "name": "all-minilm:l6-v2",
-            "digest": "1b226e2802dbb772b5fc32a58f103ca1804ef7501331012de126ab22f67475ef",
-            "expires_at": "2025-10-08T11:35:05.205358-07:00",
-            "size": 585846784,
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-              "format": "gguf",
-              "family": "bert",
-              "families": [
-                "bert"
-              ],
-              "parameter_size": "23M",
-              "quantization_level": "F16"
-            }
-          },
-          {
-            "model": "llama3.2:3b-instruct-fp16",
-            "name": "llama3.2:3b-instruct-fp16",
-            "digest": "195a8c01d91ec3cb1e0aad4624a51f2602c51fa7d96110f8ab5a20c84081804d",
-            "expires_at": "2025-10-08T11:35:04.346635-07:00",
-            "size": 7919570944,
-            "size_vram": 7919570944,
-            "details": {
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-              "format": "gguf",
-              "family": "llama",
-              "families": [
-                "llama"
-              ],
-              "parameter_size": "3.2B",
-              "quantization_level": "F16"
-            }
-          }
-        ]
-      }
-    },
-    "is_streaming": false
-  }
-}

tests/integration/inference/recordings/640984c9321e94cb3c6d24ab4af1e83e628af419d09d77948f2480651fa472f2.json

@@ -1,75 +0,0 @@
-{
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-  "request": {
-    "method": "POST",
-    "url": "http://localhost:11434/api/ps",
-    "headers": {},
-    "body": {},
-    "endpoint": "/api/ps",
-    "model": ""
-  },
-  "response": {
-    "body": {
-      "__type__": "ollama._types.ProcessResponse",
-      "__data__": {
-        "models": [
-          {
-            "model": "llama3.2:3b",
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tests/integration/inference/recordings/6ea16771b09b4966fa1dfb0349a83f6e11a6716cf3bd9d6e711ed7497232c152.json

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-{
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tests/integration/inference/recordings/73901e939ef12a9e015f47ad3886700de695975e8d1f43a22b580d788aa8e642.json

@@ -1,54 +0,0 @@
-{
-  "test_id": "tests/integration/inference/test_openai_embeddings.py::test_openai_embeddings_with_dimensions[openai_client-emb=ollama/all-minilm:l6-v2]",
-  "request": {
-    "method": "POST",
-    "url": "http://0.0.0.0:11434/v1/v1/embeddings",
-    "headers": {},
-    "body": {
-      "model": "all-minilm:l6-v2",
-      "input": "Test dimensions parameter",
-      "encoding_format": "base64",
-      "dimensions": 16
-    },
-    "endpoint": "/v1/embeddings",
-    "model": "all-minilm:l6-v2"
-  },
-  "response": {
-    "body": {
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-      "__data__": {
-        "data": [
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-      }
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-}

tests/integration/inference/recordings/744052775cf90e30dac587e6b809d41a8cc37adc29c500eecee2727f428cbf5a.json

@@ -0,0 +1,98 @@
+{
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+  "request": {
+    "method": "POST",
+    "url": "http://localhost:8000/v1/v1/chat/completions",
+    "headers": {},
+    "body": {
+      "model": "Qwen/Qwen3-0.6B",
+      "messages": [
+        {
+          "role": "system",
+          "content": "Pretend you are a weather assistant."
+        },
+        {
+          "role": "user",
+          "content": "What's the weather like in San Francisco, CA?"
+        }
+      ],
+      "max_tokens": 4096,
+      "stream": false,
+      "tool_choice": "auto",
+      "tools": [
+        {
+          "type": "function",
+          "function": {
+            "name": "get_weather",
+            "description": "Get the current weather",
+            "parameters": {
+              "type": "object",
+              "properties": {
+                "location": {
+                  "type": "string",
+                  "description": "The city and state (both required), e.g. San Francisco, CA."
+                }
+              },
+              "required": [
+                "location"
+              ]
+            }
+          }
+        }
+      ]
+    },
+    "endpoint": "/v1/chat/completions",
+    "model": "Qwen/Qwen3-0.6B"
+  },
+  "response": {
+    "body": {
+      "__type__": "openai.types.chat.chat_completion.ChatCompletion",
+      "__data__": {
+        "id": "rec-744052775cf9",
+        "choices": [
+          {
+            "finish_reason": "tool_calls",
+            "index": 0,
+            "logprobs": null,
+            "message": {
+              "content": "<think>\nOkay, the user is asking about the weather in San Francisco, CA. I need to use the get_weather function. The function requires the location parameter, which is provided as San Francisco, CA. I should make sure to format the arguments correctly as a JSON object. Let me check the required parameters again. The location is required, so I can't omit it. I'll structure the tool call with the name \"get_weather\" and the arguments including \"location\": \"San Francisco, CA\". That should get the current weather information for the user.\n</think>\n\n",
+              "refusal": null,
+              "role": "assistant",
+              "annotations": null,
+              "audio": null,
+              "function_call": null,
+              "tool_calls": [
+                {
+                  "id": "chatcmpl-tool-b59dc311dd914d3dbd6d455b122bc39c",
+                  "function": {
+                    "arguments": "{\"location\": \"San Francisco, CA\"}",
+                    "name": "get_weather"
+                  },
+                  "type": "function"
+                }
+              ],
+              "reasoning_content": null
+            },
+            "stop_reason": null
+          }
+        ],
+        "created": 0,
+        "model": "Qwen/Qwen3-0.6B",
+        "object": "chat.completion",
+        "service_tier": null,
+        "system_fingerprint": null,
+        "usage": {
+          "completion_tokens": 138,
+          "prompt_tokens": 185,
+          "total_tokens": 323,
+          "completion_tokens_details": null,
+          "prompt_tokens_details": null
+        },
+        "prompt_logprobs": null,
+        "kv_transfer_params": null
+      }
+    },
+    "is_streaming": false
+  },
+  "id_normalization_mapping": {}
+}

tests/integration/inference/recordings/77cf218283607bfac37623e1bb4e6f33cae670df7d6995d432bca34c5dfb0e43.json

@@ -0,0 +1,67 @@
+{
+  "test_id": "tests/integration/inference/test_openai_completion.py::test_openai_chat_completion_with_tool_choice_none[txt=vllm/Qwen/Qwen3-0.6B-inference:chat_completion:tool_calling]",
+  "request": {
+    "method": "POST",
+    "url": "http://localhost:8000/v1/v1/chat/completions",
+    "headers": {},
+    "body": {
+      "model": "Qwen/Qwen3-0.6B",
+      "messages": [
+        {
+          "role": "system",
+          "content": "Pretend you are a weather assistant."
+        },
+        {
+          "role": "user",
+          "content": "What's the weather like in San Francisco, CA?"
+        }
+      ],
+      "max_tokens": 4096,
+      "stream": false
+    },
+    "endpoint": "/v1/chat/completions",
+    "model": "Qwen/Qwen3-0.6B"
+  },
+  "response": {
+    "body": {
+      "__type__": "openai.types.chat.chat_completion.ChatCompletion",
+      "__data__": {
+        "id": "rec-77cf21828360",
+        "choices": [
+          {
+            "finish_reason": "stop",
+            "index": 0,
+            "logprobs": null,
+            "message": {
+              "content": "<think>\nOkay, the user is asking about the weather in San Francisco, CA. I need to check the current weather conditions. But wait, I can't access real-time data. I should mention that I can't provide the current weather forecast and ask them to check a reliable source like the National Weather Service or a weather app. Also, maybe suggest they can provide more details if they need help with something else related to the weather.\n</think>\n\nI'm sorry, but I can't provide real-time weather information. However, you can check the current weather for San Francisco, CA using the National Weather Service (NWS) website, weather apps like Weather.com, or local meteorological services. Let me know if there's anything else I can assist with!",
+              "refusal": null,
+              "role": "assistant",
+              "annotations": null,
+              "audio": null,
+              "function_call": null,
+              "tool_calls": [],
+              "reasoning_content": null
+            },
+            "stop_reason": null
+          }
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tests/integration/inference/recordings/853f6a700b98d71d390b7d366e27133a22772fbdf11863158349c1b0625bbc72.json

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tests/integration/inference/recordings/98552969974e8929e79a8311fad2fc801ea4789d4bb75c5b9639ec61c1abbaa5.json

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tests/integration/inference/recordings/bf1b0d36e3c797174ca99e405ef09f32f61fe765095889f233ecf5fd23ed710b.json

@@ -1,57 +0,0 @@
-{
-  "test_id": "tests/integration/inference/test_openai_embeddings.py::test_openai_embeddings_single_string[openai_client-emb=ollama/all-minilm:l6-v2]",
-  "request": {
-    "method": "POST",
-    "url": "http://localhost:11434/api/ps",
-    "headers": {},
-    "body": {},
-    "endpoint": "/api/ps",
-    "model": ""
-  },
-  "response": {
-    "body": {
-      "__type__": "ollama._types.ProcessResponse",
-      "__data__": {
-        "models": [
-          {
-            "model": "llama3.2:3b",
-            "name": "llama3.2:3b",
-            "digest": "a80c4f17acd55265feec403c7aef86be0c25983ab279d83f3bcd3abbcb5b8b72",
-            "expires_at": "2025-10-08T16:14:05.423042-07:00",
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-            "details": {
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-              "format": "gguf",
-              "family": "llama",
-              "families": [
-                "llama"
-              ],
-              "parameter_size": "3.2B",
-              "quantization_level": "Q4_K_M"
-            }
-          },
-          {
-            "model": "llama3.2:3b-instruct-fp16",
-            "name": "llama3.2:3b-instruct-fp16",
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-              "format": "gguf",
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-              "quantization_level": "F16"
-            }
-          }
-        ]
-      }
-    },
-    "is_streaming": false
-  }
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tests/integration/inference/recordings/c1f8c21e8bb0fdc82aa895363d223f58755d0723c39f52d16cfa8de573ec888f.json

@@ -1,75 +0,0 @@
-{
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-  "request": {
-    "method": "POST",
-    "url": "http://localhost:11434/api/ps",
-    "headers": {},
-    "body": {},
-    "endpoint": "/api/ps",
-    "model": ""
-  },
-  "response": {
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-              "parameter_size": "3.2B",
-              "quantization_level": "Q4_K_M"
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tests/integration/inference/recordings/cfb292c0f41dbc4a2c0fb39016760f6c23c248a7bbffea19ac6bcab7bf25292d.json

@@ -0,0 +1,114 @@
+{
+  "test_id": "tests/integration/inference/test_tools_with_schemas.py::TestChatCompletionWithTools::test_tool_with_complex_schema[txt=vllm/Qwen/Qwen3-0.6B]",
+  "request": {
+    "method": "POST",
+    "url": "http://localhost:8000/v1/v1/chat/completions",
+    "headers": {},
+    "body": {
+      "model": "Qwen/Qwen3-0.6B",
+      "messages": [
+        {
+          "role": "user",
+          "content": "Book a flight from SFO to JFK for John Doe"
+        }
+      ],
+      "max_tokens": 4096,
+      "tools": [
+        {
+          "type": "function",
+          "function": {
+            "name": "book_flight",
+            "description": "Book a flight",
+            "parameters": {
+              "type": "object",
+              "properties": {
+                "flight": {
+                  "$ref": "#/$defs/FlightInfo"
+                },
+                "passenger": {
+                  "$ref": "#/$defs/Passenger"
+                }
+              },
+              "required": [
+                "flight",
+                "passenger"
+              ],
+              "$defs": {
+                "FlightInfo": {
+                  "type": "object",
+                  "properties": {
+                    "from": {
+                      "type": "string"
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+                    "to": {
+                      "type": "string"
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+                    "date": {
+                      "type": "string",
+                      "format": "date"
+                    }
+                  }
+                },
+                "Passenger": {
+                  "type": "object",
+                  "properties": {
+                    "name": {
+                      "type": "string"
+                    },
+                    "age": {
+                      "type": "integer"
+                    }
+                  }
+                }
+              }
+            }
+          }
+        }
+      ]
+    },
+    "endpoint": "/v1/chat/completions",
+    "model": "Qwen/Qwen3-0.6B"
+  },
+  "response": {
+    "body": {
+      "__type__": "openai.types.chat.chat_completion.ChatCompletion",
+      "__data__": {
+        "id": "rec-cfb292c0f41d",
+        "choices": [
+          {
+            "finish_reason": "stop",
+            "index": 0,
+            "logprobs": null,
+            "message": {
+              "content": "<think>\nOkay, the user wants to book a flight from SFO to JFK for John Doe. Let me check the tools available. The provided function is book_flight, which requires flight information and a passenger. The parameters needed are flight (as a FlightInfo object) and passenger (with name and age). The user mentioned SFO to JFK, so the flight details are from and to. The passenger's name is John Doe, but the age isn't provided. Wait, the function parameters require the passenger's name and age, but the user only mentioned the name. Maybe the age is missing? But the user didn't specify it, so perhaps I should note that the age is required. However, the function's required parameters are flight and passenger, so even if age is missing, the function can't be called without it. So I need to include both flight info and passenger details. The user's message only gives the name and destination, not the flight details or age. Therefore, I need to ask for the flight details and the passenger's age. But the user hasn't provided those. So I can't proceed with the function call. Wait, but maybe the user expects me to assume some default? No, the function requires all parameters. Since the user hasn't provided flight details or age, I can't call the function. So the correct response is to prompt the user for those details.\n</think>\n\nThe booking requires the flight details and passenger's age. Could you provide the flight number and John Doe's age?",
+              "refusal": null,
+              "role": "assistant",
+              "annotations": null,
+              "audio": null,
+              "function_call": null,
+              "tool_calls": [],
+              "reasoning_content": null
+            },
+            "stop_reason": null
+          }
+        ],
+        "created": 0,
+        "model": "Qwen/Qwen3-0.6B",
+        "object": "chat.completion",
+        "service_tier": null,
+        "system_fingerprint": null,
+        "usage": {
+          "completion_tokens": 310,
+          "prompt_tokens": 261,
+          "total_tokens": 571,
+          "completion_tokens_details": null,
+          "prompt_tokens_details": null
+        },
+        "prompt_logprobs": null,
+        "kv_transfer_params": null
+      }
+    },
+    "is_streaming": false
+  },
+  "id_normalization_mapping": {}
+}

tests/integration/inference/recordings/d28e69b9ec21c644f32ac1a57ac99371068a68248f010f146e8a744cc8eba9af.json

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-{
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-      "input": "Hello, world!",
-      "encoding_format": "float"
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-    "model": "all-minilm:l6-v2"
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tests/integration/inference/recordings/d2ba309413e85d6166f7543a879b890b4e65a5f9917a2d75c5795782ab7cbfff.json

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-{
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tests/integration/inference/recordings/d5b0723b5cea73527f33a5f3c0508ca5ff9df6fb940311653cfab6d01a56d155.json

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-{
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tests/integration/inference/recordings/df353403c7fb59ed88c52269261b3dd9b75f681f8bb5431b4f07006d6c08aa7c.json

@@ -0,0 +1,96 @@
+{
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+  "request": {
+    "method": "POST",
+    "url": "http://localhost:8000/v1/v1/chat/completions",
+    "headers": {},
+    "body": {
+      "model": "Qwen/Qwen3-0.6B",
+      "messages": [
+        {
+          "role": "system",
+          "content": "You are a helpful assistant. Michael Jordan was born in 1963. His first name is \"Michael\", He played basketball for the Chicago Bulls for 15 seasons and was drafted in 1984"
+        },
+        {
+          "role": "user",
+          "content": "Please give me information about Michael Jordan."
+        }
+      ],
+      "max_tokens": 4096,
+      "response_format": {
+        "type": "json_schema",
+        "json_schema": {
+          "name": "AnswerFormat",
+          "schema": {
+            "properties": {
+              "first_name": {
+                "title": "First Name",
+                "type": "string"
+              },
+              "last_name": {
+                "title": "Last Name",
+                "type": "string"
+              },
+              "year_of_birth": {
+                "title": "Year Of Birth",
+                "type": "integer"
+              }
+            },
+            "required": [
+              "first_name",
+              "last_name",
+              "year_of_birth"
+            ],
+            "title": "AnswerFormat",
+            "type": "object"
+          }
+        }
+      },
+      "stream": false
+    },
+    "endpoint": "/v1/chat/completions",
+    "model": "Qwen/Qwen3-0.6B"
+  },
+  "response": {
+    "body": {
+      "__type__": "openai.types.chat.chat_completion.ChatCompletion",
+      "__data__": {
+        "id": "rec-df353403c7fb",
+        "choices": [
+          {
+            "finish_reason": "stop",
+            "index": 0,
+            "logprobs": null,
+            "message": {
+              "content": "{\"first_name\": \"Michael\", \"last_name\": \"Jordan\", \"year_of_birth\": 1963}",
+              "refusal": null,
+              "role": "assistant",
+              "annotations": null,
+              "audio": null,
+              "function_call": null,
+              "tool_calls": [],
+              "reasoning_content": null
+            },
+            "stop_reason": null
+          }
+        ],
+        "created": 0,
+        "model": "Qwen/Qwen3-0.6B",
+        "object": "chat.completion",
+        "service_tier": null,
+        "system_fingerprint": null,
+        "usage": {
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+          "prompt_tokens": 66,
+          "total_tokens": 94,
+          "completion_tokens_details": null,
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+        },
+        "prompt_logprobs": null,
+        "kv_transfer_params": null
+      }
+    },
+    "is_streaming": false
+  },
+  "id_normalization_mapping": {}
+}

tests/integration/inference/recordings/e3727f6c749ab8bdee2f581300092002485023b937d72b7aa8d4c15c9204fc5c.json

@@ -1,54 +0,0 @@
-{
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-  "request": {
-    "method": "POST",
-    "url": "http://localhost:8000/v1/v1/completions",
-    "headers": {},
-    "body": {
-      "model": "Qwen/Qwen3-0.6B",
-      "prompt": "I am feeling really sad today.",
-      "stream": false,
-      "extra_body": {
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-        "choices": [
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-            "finish_reason": "length",
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-            "text": " I feel that I am not good enough, and I feel like I have no",
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tests/integration/inference/recordings/e780faa88ebc38e9a74c5d5fa942921d291f73653719e73301196c0258732bf7.json

@@ -1,421 +0,0 @@
-{
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-  "request": {
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tests/integration/inference/recordings/f02f1bfd75adaea87b91dedc59430b99015b5ed0e2bbf24418a31146ffcbca9b.json

@@ -28,7 +28,7 @@
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tests/integration/inference/recordings/f8f023b96c521ba3f3f3f06156012dc518c00dbcb1bff697ba2bb7e18b7476ac.json

@@ -1,75 +0,0 @@
-{
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tests/integration/inference/recordings/f9f69a2cc8ad22a4cba6d5dc359f41d497fb277b08a7dd6085545ddbc7dbb1bd.json

@@ -1,75 +0,0 @@
-{
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tests/integration/inference/recordings/models-0037f2d2065a360cfcc36c35f138318cfc6508e743ff9423da4b7b1d7bfd4f3f-fb68f5a6.json

@@ -0,0 +1,45 @@
+{
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+  "request": {
+    "method": "POST",
+    "url": "http://localhost:8000/v1/v1/models",
+    "headers": {},
+    "body": {},
+    "endpoint": "/v1/models",
+    "model": ""
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+  "response": {
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+              "organization": "*",
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+            }
+          ]
+        }
+      }
+    ],
+    "is_streaming": false
+  },
+  "id_normalization_mapping": {}
+}

tests/integration/inference/recordings/models-009fb75503cf565d6c97f70deb8235432b0020b93d55e3b33ea093664c4bbc82-fb68f5a6.json

@@ -0,0 +1,45 @@
+{
+  "test_id": "tests/integration/inference/test_openai_embeddings.py::test_openai_embeddings_with_dimensions[llama_stack_client-emb=sentence-transformers/nomic-ai/nomic-embed-text-v1.5]",
+  "request": {
+    "method": "POST",
+    "url": "http://localhost:8000/v1/v1/models",
+    "headers": {},
+    "body": {},
+    "endpoint": "/v1/models",
+    "model": ""
+  },
+  "response": {
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+            }
+          ]
+        }
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+}

tests/integration/inference/recordings/models-01e6ee9852f532d9b0d82dde2e7c831d698e81dea1be69433050d42643f35edc-fb68f5a6.json

@@ -0,0 +1,45 @@
+{
+  "test_id": "tests/integration/inference/test_openai_completion.py::test_openai_completion_stop_sequence[txt=vllm/Qwen/Qwen3-0.6B-inference:completion:stop_sequence]",
+  "request": {
+    "method": "POST",
+    "url": "http://localhost:8000/v1/v1/models",
+    "headers": {},
+    "body": {},
+    "endpoint": "/v1/models",
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+            }
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+      }
+    ],
+    "is_streaming": false
+  },
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+}

tests/integration/inference/recordings/models-10370bf5307b2fc971b8e53bdcc4e9eb4d3d76fe8ecdb31231b59576a612e972-fb68f5a6.json

@@ -0,0 +1,45 @@
+{
+  "test_id": "tests/integration/inference/test_openai_embeddings.py::test_openai_embeddings_base64_batch_processing[llama_stack_client-emb=sentence-transformers/nomic-ai/nomic-embed-text-v1.5]",
+  "request": {
+    "method": "POST",
+    "url": "http://localhost:8000/v1/v1/models",
+    "headers": {},
+    "body": {},
+    "endpoint": "/v1/models",
+    "model": ""
+  },
+  "response": {
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+}

tests/integration/inference/recordings/models-1312e0d8579e9b0e6dcb222272de34115277db71c6c560872fa13722197f881f-fb68f5a6.json

@@ -0,0 +1,45 @@
+{
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+  "request": {
+    "method": "POST",
+    "url": "http://localhost:8000/v1/v1/models",
+    "headers": {},
+    "body": {},
+    "endpoint": "/v1/models",
+    "model": ""
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+  "response": {
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+}

tests/integration/inference/recordings/models-134e731d073e9e07eb9782bbe292167f8ad08157a15150ce92135854d04050fc-fb68f5a6.json

@@ -0,0 +1,45 @@
+{
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+  "request": {
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+    "url": "http://localhost:8000/v1/v1/models",
+    "headers": {},
+    "body": {},
+    "endpoint": "/v1/models",
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tests/integration/inference/recordings/models-14c0905df1b177d2f85b30b0285b0ffdc88d1a7b290e2155fb7a01f3c1436ca0-fb68f5a6.json

@@ -0,0 +1,45 @@
+{
+  "test_id": "tests/integration/inference/test_openai_completion.py::test_inference_store_tool_calls[openai_client-txt=vllm/Qwen/Qwen3-0.6B-True]",
+  "request": {
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+    "url": "http://localhost:8000/v1/v1/models",
+    "headers": {},
+    "body": {},
+    "endpoint": "/v1/models",
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+}

tests/integration/inference/recordings/models-1bc879637162ba23badeea66c4c25a638869a3e90d16ef3e84dea1a613e7192e-fb68f5a6.json

@@ -0,0 +1,45 @@
+{
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+  "request": {
+    "method": "POST",
+    "url": "http://localhost:8000/v1/v1/models",
+    "headers": {},
+    "body": {},
+    "endpoint": "/v1/models",
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tests/integration/inference/recordings/models-2b9bac5da1a03c0b572bc019cc0c50904d49e6193990ca245908f4535bcaab43-fb68f5a6.json

@@ -0,0 +1,45 @@
+{
+  "test_id": "tests/integration/inference/test_openai_completion.py::test_openai_completion_guided_choice[txt=vllm/Qwen/Qwen3-0.6B]",
+  "request": {
+    "method": "POST",
+    "url": "http://localhost:8000/v1/v1/models",
+    "headers": {},
+    "body": {},
+    "endpoint": "/v1/models",
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+}