From d9cf5cd48025c2579b5149c595df5bd351c2d465 Mon Sep 17 00:00:00 2001 From: Ashwin Bharambe Date: Wed, 5 Nov 2025 12:14:02 -0800 Subject: [PATCH 01/10] fix(ci): use --no-cache instead of --no-cache-dir (#4081) This is necessary to make sure GPU dockers can be built on CI without running out of space. --- containers/Containerfile | 20 ++++++++++---------- 1 file changed, 10 insertions(+), 10 deletions(-) diff --git a/containers/Containerfile b/containers/Containerfile index d2d066845..4993d3273 100644 --- a/containers/Containerfile +++ b/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 From 84a84ee85c0aee02db1c485381103445789fdb43 Mon Sep 17 00:00:00 2001 From: ehhuang Date: Wed, 5 Nov 2025 14:10:10 -0800 Subject: [PATCH 02/10] fix: last_id when listing files in vector store (#4079) # What does this PR do? the last_id should be the id of the last item in the returned list, not the unfiltered list. ## Test Plan fixed test --- .../providers/utils/memory/openai_vector_store_mixin.py | 4 ++-- tests/integration/vector_io/test_openai_vector_stores.py | 2 +- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/src/llama_stack/providers/utils/memory/openai_vector_store_mixin.py b/src/llama_stack/providers/utils/memory/openai_vector_store_mixin.py index 41d4cb2d7..dc305e74e 100644 --- a/src/llama_stack/providers/utils/memory/openai_vector_store_mixin.py +++ b/src/llama_stack/providers/utils/memory/openai_vector_store_mixin.py @@ -886,8 +886,8 @@ 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, diff --git a/tests/integration/vector_io/test_openai_vector_stores.py b/tests/integration/vector_io/test_openai_vector_stores.py index 9da5dd25b..8ca29ee0c 100644 --- a/tests/integration/vector_io/test_openai_vector_stores.py +++ b/tests/integration/vector_io/test_openai_vector_stores.py @@ -825,7 +825,7 @@ def test_openai_vector_store_list_files( assert first_page.has_more assert len(first_page.data) == 2 assert first_page.first_id == first_page.data[0].id - assert first_page.last_id != first_page.data[-1].id + assert first_page.last_id == first_page.data[-1].id next_page = compat_client.vector_stores.files.list( vector_store_id=vector_store.id, limit=2, after=first_page.data[-1].id From 9d5c34af275ffadfc506cc26efa5804b8fed57a7 Mon Sep 17 00:00:00 2001 From: ehhuang Date: Wed, 5 Nov 2025 15:01:48 -0800 Subject: [PATCH 03/10] fix!: BREAKING CHANGE: vector_store: search API response fix (#4080) # What does this PR do? - search_query in the vector store search API should be a list, according to https://github.com/openai/openai-openapi ## Test Plan modified tests --- [//]: # (BEGIN SAPLING FOOTER) Stack created with [Sapling](https://sapling-scm.com). Best reviewed with [ReviewStack](https://reviewstack.dev/llamastack/llama-stack/pull/4080). * #4086 * __->__ #4080 --- client-sdks/stainless/openapi.yml | 142 +++++++++--------- docs/static/llama-stack-spec.yaml | 142 +++++++++--------- docs/static/stainless-llama-stack-spec.yaml | 142 +++++++++--------- src/llama_stack/apis/vector_io/vector_io.py | 4 +- .../utils/memory/openai_vector_store_mixin.py | 4 +- .../vector_io/test_openai_vector_stores.py | 2 +- 6 files changed, 209 insertions(+), 227 deletions(-) diff --git a/client-sdks/stainless/openapi.yml b/client-sdks/stainless/openapi.yml index 5d9917bfd..448b08908 100644 --- a/client-sdks/stainless/openapi.yml +++ b/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: diff --git a/docs/static/llama-stack-spec.yaml b/docs/static/llama-stack-spec.yaml index a705f499a..cc720ad18 100644 --- a/docs/static/llama-stack-spec.yaml +++ b/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: diff --git a/docs/static/stainless-llama-stack-spec.yaml b/docs/static/stainless-llama-stack-spec.yaml index 5d9917bfd..448b08908 100644 --- a/docs/static/stainless-llama-stack-spec.yaml +++ b/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: diff --git a/src/llama_stack/apis/vector_io/vector_io.py b/src/llama_stack/apis/vector_io/vector_io.py index cbb16287b..9148d10e5 100644 --- a/src/llama_stack/apis/vector_io/vector_io.py +++ b/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 diff --git a/src/llama_stack/providers/utils/memory/openai_vector_store_mixin.py b/src/llama_stack/providers/utils/memory/openai_vector_store_mixin.py index dc305e74e..f3c9a3140 100644 --- a/src/llama_stack/providers/utils/memory/openai_vector_store_mixin.py +++ b/src/llama_stack/providers/utils/memory/openai_vector_store_mixin.py @@ -637,7 +637,7 @@ 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 +647,7 @@ 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, diff --git a/tests/integration/vector_io/test_openai_vector_stores.py b/tests/integration/vector_io/test_openai_vector_stores.py index 8ca29ee0c..b05728ae2 100644 --- a/tests/integration/vector_io/test_openai_vector_stores.py +++ b/tests/integration/vector_io/test_openai_vector_stores.py @@ -350,7 +350,7 @@ def test_openai_vector_store_search_empty( assert search_response is not None assert hasattr(search_response, "data") assert len(search_response.data) == 0 # Empty store should return no results - assert search_response.search_query == "test query" + assert search_response.search_query == ["test query"] assert search_response.has_more is False From c672a5d7924f1bdefd977b1f7f41ae8edb384528 Mon Sep 17 00:00:00 2001 From: Roy Belio <34023431+r-bit-rry@users.noreply.github.com> Date: Thu, 6 Nov 2025 01:37:06 +0200 Subject: [PATCH 04/10] feat: ability to use postgres as store for starter distro (#4076) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit ## What does this PR do? The starter distribution now comes with all the required packages to support persistent stores—like the agent store, metadata, and inference—using PostgreSQL. Users can enable PostgreSQL support by setting the `ENABLE_POSTGRES_STORE=1` environment variable. This PR consolidates the functionality from the removed `postgres-demo` distribution into the starter distribution, reducing maintenance overhead. **Closes: #2619** **Supersedes: #2851** (rebased and updated) ## Changes Made 1. **Added PostgreSQL support to starter distribution** - New `run-with-postgres-store.yaml` configuration - Automatic config switching via `ENABLE_POSTGRES_STORE` environment variable - Removed separate `postgres-demo` distribution 2. **Updated to new build system** - Integrated postgres switching logic into Containerfile entrypoint - Uses new `storage_backends` and `storage_stores` API - Properly configured both PostgreSQL KV store and SQL store 3. **Updated dependencies** - Added `psycopg2-binary` and `asyncpg` to starter distribution - All postgres-related dependencies automatically included ## How to Use ### With Docker (PostgreSQL): ```bash docker run \ -e ENABLE_POSTGRES_STORE=1 \ -e POSTGRES_HOST=your_postgres_host \ -e POSTGRES_PORT=5432 \ -e POSTGRES_DB=llamastack \ -e POSTGRES_USER=llamastack \ -e POSTGRES_PASSWORD=llamastack \ -e OPENAI_API_KEY=your_key \ llamastack/distribution-starter ``` ### PostgreSQL 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`) ## Test Plan All pre-commit hooks pass (mypy, ruff, distro-codegen) `llama stack list-deps starter` confirms psycopg2-binary is included Storage configuration correctly uses PostgreSQL backends Container builds successfully with postgres support ## Credits Original work by @leseb in #2851. Rebased and updated by @r-bit-rry to work with latest main. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Sébastien Han @leseb --------- Signed-off-by: Sébastien Han Co-authored-by: Sébastien Han --- .../self_hosted_distro/starter.md | 41 ++- .../core/utils/config_resolution.py | 20 +- src/llama_stack/core/utils/exec.py | 9 + .../distributions/ci-tests/build.yaml | 1 + .../distributions/ci-tests/ci_tests.py | 1 + .../distributions/postgres-demo/__init__.py | 7 - .../distributions/postgres-demo/build.yaml | 23 -- .../postgres-demo/postgres_demo.py | 125 -------- .../distributions/starter-gpu/build.yaml | 1 + .../starter-gpu/run-with-postgres-store.yaml | 281 ++++++++++++++++++ .../distributions/starter/build.yaml | 1 + .../starter/run-with-postgres-store.yaml | 278 +++++++++++++++++ .../distributions/starter/starter.py | 169 +++++++---- 13 files changed, 740 insertions(+), 217 deletions(-) delete mode 100644 src/llama_stack/distributions/postgres-demo/__init__.py delete mode 100644 src/llama_stack/distributions/postgres-demo/build.yaml delete mode 100644 src/llama_stack/distributions/postgres-demo/postgres_demo.py create mode 100644 src/llama_stack/distributions/starter-gpu/run-with-postgres-store.yaml create mode 100644 src/llama_stack/distributions/starter/run-with-postgres-store.yaml diff --git a/docs/docs/distributions/self_hosted_distro/starter.md b/docs/docs/distributions/self_hosted_distro/starter.md index f6786a95c..84c35f3d3 100644 --- a/docs/docs/distributions/self_hosted_distro/starter.md +++ b/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 diff --git a/src/llama_stack/core/utils/config_resolution.py b/src/llama_stack/core/utils/config_resolution.py index fcf057db6..2a85837b6 100644 --- a/src/llama_stack/core/utils/config_resolution.py +++ b/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: diff --git a/src/llama_stack/core/utils/exec.py b/src/llama_stack/core/utils/exec.py index 12fb82d01..98964db2c 100644 --- a/src/llama_stack/core/utils/exec.py +++ b/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}") diff --git a/src/llama_stack/distributions/ci-tests/build.yaml b/src/llama_stack/distributions/ci-tests/build.yaml index c01e415a9..f29ac7712 100644 --- a/src/llama_stack/distributions/ci-tests/build.yaml +++ b/src/llama_stack/distributions/ci-tests/build.yaml @@ -56,4 +56,5 @@ image_type: venv additional_pip_packages: - aiosqlite - asyncpg +- psycopg2-binary - sqlalchemy[asyncio] diff --git a/src/llama_stack/distributions/ci-tests/ci_tests.py b/src/llama_stack/distributions/ci-tests/ci_tests.py index ab102f5f3..c06b1b98d 100644 --- a/src/llama_stack/distributions/ci-tests/ci_tests.py +++ b/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 diff --git a/src/llama_stack/distributions/postgres-demo/__init__.py b/src/llama_stack/distributions/postgres-demo/__init__.py deleted file mode 100644 index 81473cb73..000000000 --- a/src/llama_stack/distributions/postgres-demo/__init__.py +++ /dev/null @@ -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 diff --git a/src/llama_stack/distributions/postgres-demo/build.yaml b/src/llama_stack/distributions/postgres-demo/build.yaml deleted file mode 100644 index 063dc3999..000000000 --- a/src/llama_stack/distributions/postgres-demo/build.yaml +++ /dev/null @@ -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] diff --git a/src/llama_stack/distributions/postgres-demo/postgres_demo.py b/src/llama_stack/distributions/postgres-demo/postgres_demo.py deleted file mode 100644 index 876370ef3..000000000 --- a/src/llama_stack/distributions/postgres-demo/postgres_demo.py +++ /dev/null @@ -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", - ), - }, - ) diff --git a/src/llama_stack/distributions/starter-gpu/build.yaml b/src/llama_stack/distributions/starter-gpu/build.yaml index b2e2a0c85..10cbb1389 100644 --- a/src/llama_stack/distributions/starter-gpu/build.yaml +++ b/src/llama_stack/distributions/starter-gpu/build.yaml @@ -57,4 +57,5 @@ image_type: venv additional_pip_packages: - aiosqlite - asyncpg +- psycopg2-binary - sqlalchemy[asyncio] diff --git a/src/llama_stack/distributions/starter-gpu/run-with-postgres-store.yaml b/src/llama_stack/distributions/starter-gpu/run-with-postgres-store.yaml new file mode 100644 index 000000000..6dbbc8716 --- /dev/null +++ b/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 diff --git a/src/llama_stack/distributions/starter/build.yaml b/src/llama_stack/distributions/starter/build.yaml index baa80ef3e..acd51f773 100644 --- a/src/llama_stack/distributions/starter/build.yaml +++ b/src/llama_stack/distributions/starter/build.yaml @@ -57,4 +57,5 @@ image_type: venv additional_pip_packages: - aiosqlite - asyncpg +- psycopg2-binary - sqlalchemy[asyncio] diff --git a/src/llama_stack/distributions/starter/run-with-postgres-store.yaml b/src/llama_stack/distributions/starter/run-with-postgres-store.yaml new file mode 100644 index 000000000..530084bd9 --- /dev/null +++ b/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: ${env.POSTGRES_PASSWORD:=llamastack} + stores: + metadata: + namespace: registry + backend: kv_postgres + inference: + table_name: inference_store + backend: sql_postgres + max_write_queue_size: 10000 + num_writers: 4 + conversations: + table_name: openai_conversations + backend: sql_postgres + prompts: + namespace: prompts + backend: kv_postgres +registered_resources: + models: [] + shields: [] + vector_dbs: [] + datasets: [] + scoring_fns: [] + benchmarks: [] + tool_groups: [] +server: + port: 8321 +telemetry: + enabled: true diff --git a/src/llama_stack/distributions/starter/starter.py b/src/llama_stack/distributions/starter/starter.py index 49b7a2463..88cd3a4fe 100644 --- a/src/llama_stack/distributions/starter/starter.py +++ b/src/llama_stack/distributions/starter/starter.py @@ -17,6 +17,11 @@ from llama_stack.core.datatypes import ( ToolGroupInput, VectorStoresConfig, ) +from llama_stack.core.storage.datatypes import ( + InferenceStoreReference, + KVStoreReference, + SqlStoreReference, +) from llama_stack.core.utils.dynamic import instantiate_class_type from llama_stack.distributions.template import DistributionTemplate, RunConfigSettings from llama_stack.providers.datatypes import RemoteProviderSpec @@ -36,6 +41,7 @@ from llama_stack.providers.remote.vector_io.pgvector.config import ( ) from llama_stack.providers.remote.vector_io.qdrant.config import QdrantVectorIOConfig from llama_stack.providers.remote.vector_io.weaviate.config import WeaviateVectorIOConfig +from llama_stack.providers.utils.kvstore.config import PostgresKVStoreConfig from llama_stack.providers.utils.sqlstore.sqlstore import PostgresSqlStoreConfig @@ -181,6 +187,62 @@ def get_distribution_template(name: str = "starter") -> DistributionTemplate: provider_shield_id="${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": ( From b335419faa846312f271067358acc440b8c08bb7 Mon Sep 17 00:00:00 2001 From: ehhuang Date: Wed, 5 Nov 2025 15:47:54 -0800 Subject: [PATCH 05/10] fix: actualize chunking strategy in vector store create API (#4086) # What does this PR do? - when create vector store is called without chunk strategy, we actually the strategy used so that the value is persisted instead of strategy='None' ## Test Plan updated tests --- src/llama_stack/core/routers/vector_io.py | 11 +++++++++++ .../utils/memory/openai_vector_store_mixin.py | 7 ++++++- .../vector_io/test_openai_vector_stores.py | 6 +++--- 3 files changed, 20 insertions(+), 4 deletions(-) diff --git a/src/llama_stack/core/routers/vector_io.py b/src/llama_stack/core/routers/vector_io.py index 78b38ba95..b54217619 100644 --- a/src/llama_stack/core/routers/vector_io.py +++ b/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, diff --git a/src/llama_stack/providers/utils/memory/openai_vector_store_mixin.py b/src/llama_stack/providers/utils/memory/openai_vector_store_mixin.py index f3c9a3140..d047d9d12 100644 --- a/src/llama_stack/providers/utils/memory/openai_vector_store_mixin.py +++ b/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 diff --git a/tests/integration/vector_io/test_openai_vector_stores.py b/tests/integration/vector_io/test_openai_vector_stores.py index b05728ae2..97ce4abe8 100644 --- a/tests/integration/vector_io/test_openai_vector_stores.py +++ b/tests/integration/vector_io/test_openai_vector_stores.py @@ -679,7 +679,7 @@ def test_openai_vector_store_attach_file( assert file_attach_response.id == file.id assert file_attach_response.vector_store_id == vector_store.id assert file_attach_response.status == "completed" - assert file_attach_response.chunking_strategy.type == "auto" + assert file_attach_response.chunking_strategy.type == "static" assert file_attach_response.created_at > 0 assert not file_attach_response.last_error @@ -815,8 +815,8 @@ def test_openai_vector_store_list_files( assert set(file_ids) == {file.id for file in files_list.data} assert files_list.data[0].object == "vector_store.file" assert files_list.data[0].vector_store_id == vector_store.id - assert files_list.data[0].status == "completed" - assert files_list.data[0].chunking_strategy.type == "auto" + assert files_list.data[0].status in ["completed", "in_progress"] + assert files_list.data[0].chunking_strategy.type == "static" assert files_list.data[0].created_at > 0 assert files_list.first_id == files_list.data[0].id assert not files_list.data[0].last_error From bef1b044bde10fa5a1ef70eb0269c04afeaef817 Mon Sep 17 00:00:00 2001 From: Ashwin Bharambe Date: Wed, 5 Nov 2025 18:15:11 -0800 Subject: [PATCH 06/10] refactor(passthrough): use AsyncOpenAI instead of AsyncLlamaStackClient (#4085) We'd like to remove the dependence of `llama-stack` on `llama-stack-client`. This is a necessary step. A few small cleanups - Enables `embeddings` now also - Remove ModelRegistryHelper dependency (unused) - Consolidate to auth_credential field via RemoteInferenceProviderConfig - Implement list_models() to fetch from downstream /v1/models ## Test Plan Tested using this script https://gist.github.com/ashwinb/6356463d10f989c0682ab3bff8589581 Output: ``` Listing models from downstream server... Available models: ['passthrough/ollama/nomic-embed-text:latest', 'passthrough/ollama/all-minilm:l6-v2', 'passthrough/ollama/llama3.2-vision:11b', 'passthrough/ollama/llama3.2-vision:latest', 'passthrough/ollama/llama-guard3:1b', 'passthrough/o llama/llama3.2:1b', 'passthrough/ollama/all-minilm:latest', 'passthrough/ollama/llama3.2:3b', 'passthrough/ollama/llama3.2:3b-instruct-fp16', 'passthrough/bedrock/meta.llama3-1-8b-instruct-v1:0', 'passthrough/bedrock/meta.llama3-1-70b-instruct -v1:0', 'passthrough/bedrock/meta.llama3-1-405b-instruct-v1:0', 'passthrough/sentence-transformers/nomic-ai/nomic-embed-text-v1.5'] Using LLM model: passthrough/ollama/llama3.2-vision:11b Making inference request... Response: 4. --- Testing streaming --- Streamed response: ChatCompletionChunk(id='chatcmpl-64', choices=[Choice(delta=ChoiceDelta(content='1', reasoning_content=None, refusal=None, role='assistant', tool_calls=None), finish_reason='', index=0, logprobs=None)], created=1762381674, m odel='passthrough/ollama/llama3.2-vision:11b', object='chat.completion.chunk', usage=None) ... 5ChatCompletionChunk(id='chatcmpl-64', choices=[Choice(delta=ChoiceDelta(content='', reasoning_content=None, refusal=None, role='assistant', tool_calls=None), finish_reason='stop', index=0, logprobs=None)], created=1762381674, model='passthrou gh/ollama/llama3.2-vision:11b', object='chat.completion.chunk', usage=None) ``` --- .../inference/remote_passthrough.mdx | 2 +- .../remote/inference/passthrough/__init__.py | 4 +- .../remote/inference/passthrough/config.py | 7 +- .../inference/passthrough/passthrough.py | 155 ++++++++++-------- 4 files changed, 88 insertions(+), 80 deletions(-) diff --git a/docs/docs/providers/inference/remote_passthrough.mdx b/docs/docs/providers/inference/remote_passthrough.mdx index 7a2931690..957cd04da 100644 --- a/docs/docs/providers/inference/remote_passthrough.mdx +++ b/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` | `` | 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` | `` | No | | The URL for the passthrough endpoint | ## Sample Configuration diff --git a/src/llama_stack/providers/remote/inference/passthrough/__init__.py b/src/llama_stack/providers/remote/inference/passthrough/__init__.py index 69dd4c461..1cc46bff1 100644 --- a/src/llama_stack/providers/remote/inference/passthrough/__init__.py +++ b/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): diff --git a/src/llama_stack/providers/remote/inference/passthrough/config.py b/src/llama_stack/providers/remote/inference/passthrough/config.py index f8e8b8ce5..eca28a86a 100644 --- a/src/llama_stack/providers/remote/inference/passthrough/config.py +++ b/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 diff --git a/src/llama_stack/providers/remote/inference/passthrough/passthrough.py b/src/llama_stack/providers/remote/inference/passthrough/passthrough.py index 4d4d4f41d..3c56acfbd 100644 --- a/src/llama_stack/providers/remote/inference/passthrough/passthrough.py +++ b/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": }' - ) - 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": }' - ) - 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": }' + ) + 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": }' + ) + 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 From c62a09ab7678884e337a4dcb10f69abb8ca8eb04 Mon Sep 17 00:00:00 2001 From: Derek Higgins Date: Thu, 6 Nov 2025 09:36:40 +0000 Subject: [PATCH 07/10] ci: Add vLLM support to integration testing infrastructure (with qwen) (#3545) o Introduces vLLM provider support to the record/replay testing framework o Enabling both recording and replay of vLLM API interactions alongside existing Ollama support. The changes enable testing of vLLM functionality. vLLM tests focus on inference capabilities, while Ollama continues to exercise the full API surface including vision features. -- This is an alternative to #3128 , using qwen3 instead of llama 3.2 1B appears to be more capable at structure output and tool calls. --------- Signed-off-by: Derek Higgins Co-authored-by: github-actions[bot] --- .../actions/run-and-record-tests/action.yml | 5 +- .github/actions/setup-vllm/action.yml | 9 +- .github/workflows/integration-tests.yml | 1 - scripts/integration-tests.sh | 5 - tests/integration/ci_matrix.json | 3 +- ...34a95f56931b792d5939f4cebc57-fb68f5a6.json | 45 + ...9ffbde15a1c52adbeea456bb42abdfc931bd1.json | 3010 +++++++++++++++++ ...78a2e184866dafee83186cd84932daae1af42.json | 84 + ...86663d08a5bc31f697d1fc5d9bed1c71f5950.json | 92 + ...a3fb312d2a525cc35e20c181190ddf40793e6.json | 92 + ...b310ebc8afc00aba3946ba498abe2fdbe6a63.json | 2113 ++++++++++++ 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a/.github/actions/run-and-record-tests/action.yml b/.github/actions/run-and-record-tests/action.yml index ec4d7f977..d44cba4ee 100644 --- a/.github/actions/run-and-record-tests/action.yml +++ b/.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 diff --git a/.github/actions/setup-vllm/action.yml b/.github/actions/setup-vllm/action.yml index 17ebd42f2..34ced0998 100644 --- a/.github/actions/setup-vllm/action.yml +++ b/.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..." diff --git a/.github/workflows/integration-tests.yml b/.github/workflows/integration-tests.yml index 41822fa79..2c797e906 100644 --- a/.github/workflows/integration-tests.yml +++ b/.github/workflows/integration-tests.yml @@ -27,7 +27,6 @@ on: 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: diff --git a/scripts/integration-tests.sh b/scripts/integration-tests.sh index 372e97d8c..2d088f3df 100755 --- a/scripts/integration-tests.sh +++ b/scripts/integration-tests.sh @@ -405,11 +405,6 @@ 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" diff --git a/tests/integration/ci_matrix.json 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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\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": {} +} diff --git a/tests/integration/inference/recordings/77cf218283607bfac37623e1bb4e6f33cae670df7d6995d432bca34c5dfb0e43.json b/tests/integration/inference/recordings/77cf218283607bfac37623e1bb4e6f33cae670df7d6995d432bca34c5dfb0e43.json new file mode 100644 index 000000000..05b4e2609 --- /dev/null +++ b/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": "\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\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 + } + ], + "created": 0, + "model": "Qwen/Qwen3-0.6B", + "object": "chat.completion", + "service_tier": null, + "system_fingerprint": null, + "usage": { + "completion_tokens": 154, + "prompt_tokens": 33, + "total_tokens": 187, + "completion_tokens_details": null, + "prompt_tokens_details": null + }, + "prompt_logprobs": null, + "kv_transfer_params": null + } + }, + "is_streaming": false + }, + "id_normalization_mapping": {} +} diff --git a/tests/integration/inference/recordings/853f6a700b98d71d390b7d366e27133a22772fbdf11863158349c1b0625bbc72.json b/tests/integration/inference/recordings/853f6a700b98d71d390b7d366e27133a22772fbdf11863158349c1b0625bbc72.json new file mode 100644 index 000000000..2f6e4d3fc --- /dev/null +++ b/tests/integration/inference/recordings/853f6a700b98d71d390b7d366e27133a22772fbdf11863158349c1b0625bbc72.json @@ -0,0 +1,128 @@ +{ + "test_id": "tests/integration/inference/test_tools_with_schemas.py::TestEdgeCases::test_multiple_tools_with_different_schemas[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": "Use one of the available tools" + } + ], + "max_tokens": 4096, + "tools": [ + { + "type": "function", + "function": { + "name": "simple", + "parameters": { + "type": "object", + "properties": { + "x": { + "type": "string" + } + } + } + } + }, + { + "type": "function", + "function": { + "name": "complex", + "parameters": { + "type": "object", + "properties": { + "data": { + "$ref": "#/$defs/Complex" + } + }, + "$defs": { + "Complex": { + "type": "object", + "properties": { + "nested": { + "type": "array", + "items": { + "type": "number" + } + } + } + } + } + } + } + }, + { + "type": "function", + "function": { + "name": "with_output", + "parameters": { + "type": "object", + "properties": { + "input": { + "type": "string" + } + } + } + } + } + ] + }, + "endpoint": "/v1/chat/completions", + "model": "Qwen/Qwen3-0.6B" + }, + "response": { + "body": { + "__type__": "openai.types.chat.chat_completion.ChatCompletion", + "__data__": { + "id": "rec-853f6a700b98", + "choices": [ + { + "finish_reason": "tool_calls", + "index": 0, + "logprobs": null, + "message": { + "content": "\nOkay, let's see. The user wants me to use one of the available tools. The tools provided are simple, complex, and with_output. The simple function takes an argument 'x' of type string. The complex function requires a 'data' parameter that's an object with a nested array of numbers. The with_output function takes an input string.\n\nThe user's query is about using a tool, but there's no specific function name mentioned. Wait, maybe the user expects me to choose one based on the context. Since the tools are available, but the query is a general instruction, perhaps I should ask for clarification. However, the instructions say to use one of the tools if possible. Since the user hasn't specified a particular function, maybe I should check if there's any implicit function needed. But looking at the tools, none are directly related to the query. The user might need to specify which tool to use. Alternatively, maybe the answer is to call the simple function with an example input. But without more context, it's hard to tell. Wait, maybe the user expects me to choose the simplest one. Let's go with the simple function first. So the tool call would be to the simple function with x set to some value. But the user hasn't provided a specific value. Maybe I should state that the tool requires a value. But according to the instructions, if possible, use one of the tools. Since the user hasn't given a value, perhaps the answer is to call the simple function with an example. But the parameters for the simple function require 'x' which is a string. Maybe the user expects me to proceed without needing more info. So I'll proceed by calling the simple function with x as \"example\".\n\n\n", + "refusal": null, + "role": "assistant", + "annotations": null, + "audio": null, + "function_call": null, + "tool_calls": [ + { + "id": "chatcmpl-tool-12e2ba0189cf484bb936cbb254a5c32a", + "function": { + "arguments": "{\"x\": \"example\"}", + "name": "simple" + }, + "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": 378, + "prompt_tokens": 265, + "total_tokens": 643, + "completion_tokens_details": null, + "prompt_tokens_details": null + }, + "prompt_logprobs": null, + "kv_transfer_params": null + } + }, + "is_streaming": false + }, + "id_normalization_mapping": {} +} diff --git a/tests/integration/inference/recordings/cfb292c0f41dbc4a2c0fb39016760f6c23c248a7bbffea19ac6bcab7bf25292d.json b/tests/integration/inference/recordings/cfb292c0f41dbc4a2c0fb39016760f6c23c248a7bbffea19ac6bcab7bf25292d.json new file mode 100644 index 000000000..fdec100bb --- /dev/null +++ b/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" + }, + "to": { + "type": "string" + }, + "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": "\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\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": {} +} diff --git a/tests/integration/inference/recordings/df353403c7fb59ed88c52269261b3dd9b75f681f8bb5431b4f07006d6c08aa7c.json b/tests/integration/inference/recordings/df353403c7fb59ed88c52269261b3dd9b75f681f8bb5431b4f07006d6c08aa7c.json new file mode 100644 index 000000000..eb6eb8eb2 --- /dev/null +++ b/tests/integration/inference/recordings/df353403c7fb59ed88c52269261b3dd9b75f681f8bb5431b4f07006d6c08aa7c.json @@ -0,0 +1,96 @@ +{ + "test_id": "tests/integration/inference/test_openai_completion.py::test_openai_chat_completion_structured_output[txt=vllm/Qwen/Qwen3-0.6B-inference:chat_completion:structured_output]", + "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": { + "completion_tokens": 28, + "prompt_tokens": 66, + "total_tokens": 94, + "completion_tokens_details": null, + "prompt_tokens_details": null + }, + "prompt_logprobs": null, + "kv_transfer_params": null + } + }, + "is_streaming": false + }, + "id_normalization_mapping": {} +} diff --git a/tests/integration/inference/recordings/e89112e7735fccc5ad9ebe6a96454953aed0ba2501cabfaa80b742c2bf371cbc.json b/tests/integration/inference/recordings/e89112e7735fccc5ad9ebe6a96454953aed0ba2501cabfaa80b742c2bf371cbc.json new file mode 100644 index 000000000..856684a55 --- /dev/null +++ b/tests/integration/inference/recordings/e89112e7735fccc5ad9ebe6a96454953aed0ba2501cabfaa80b742c2bf371cbc.json @@ -0,0 +1,92 @@ +{ + "test_id": "tests/integration/inference/test_tools_with_schemas.py::TestChatCompletionWithTools::test_simple_tool_call[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": "What's the weather in San Francisco?" + } + ], + "max_tokens": 4096, + "tools": [ + { + "type": "function", + "function": { + "name": "get_weather", + "description": "Get weather for a location", + "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-e89112e7735f", + "choices": [ + { + "finish_reason": "tool_calls", + "index": 0, + "logprobs": null, + "message": { + "content": "\nOkay, the user is asking for the weather in San Francisco. I need to check if there's a function available for that. Looking at the tools provided, there's a function called get_weather that requires a location parameter. The description says it gets weather for a location, and the parameter is the city name. The user provided \"San Francisco\" as the location, so I should call the get_weather function with \"San Francisco\" as the argument. I don't see any other parameters needed here, so the tool call should be straightforward. Just make sure the city name is correctly formatted in JSON.\n\n\n", + "refusal": null, + "role": "assistant", + "annotations": null, + "audio": null, + "function_call": null, + "tool_calls": [ + { + "id": "chatcmpl-tool-feead29842dc40b2831c41ed397f555f", + "function": { + "arguments": "{\"location\": \"San Francisco\"}", + "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": 146, + "prompt_tokens": 161, + "total_tokens": 307, + "completion_tokens_details": null, + "prompt_tokens_details": null + }, + "prompt_logprobs": null, + "kv_transfer_params": null + } + }, + "is_streaming": false + }, + "id_normalization_mapping": {} +} diff --git a/tests/integration/inference/recordings/f02f1bfd75adaea87b91dedc59430b99015b5ed0e2bbf24418a31146ffcbca9b.json 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"text": "joy", + "stop_reason": null, + "prompt_logprobs": null + } + ], + "created": 0, + "model": "Qwen/Qwen3-0.6B", + "object": "text_completion", + "system_fingerprint": null, + "usage": { + "completion_tokens": 2, + "prompt_tokens": 7, + "total_tokens": 9, + "completion_tokens_details": null, + "prompt_tokens_details": null + }, + "kv_transfer_params": null + } + }, + "is_streaming": false + }, + "id_normalization_mapping": {} +} diff --git a/tests/integration/inference/recordings/models-0037f2d2065a360cfcc36c35f138318cfc6508e743ff9423da4b7b1d7bfd4f3f-fb68f5a6.json b/tests/integration/inference/recordings/models-0037f2d2065a360cfcc36c35f138318cfc6508e743ff9423da4b7b1d7bfd4f3f-fb68f5a6.json new file mode 100644 index 000000000..7256ae75d --- /dev/null +++ b/tests/integration/inference/recordings/models-0037f2d2065a360cfcc36c35f138318cfc6508e743ff9423da4b7b1d7bfd4f3f-fb68f5a6.json @@ -0,0 +1,45 @@ +{ + "test_id": 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false + }, + "id_normalization_mapping": {} +} diff --git a/tests/integration/inference/recordings/models-f936269fe152d95db3fb80fb10482e3cc79cfd6a28ebdf1a7a8b220ba2de641b-fb68f5a6.json b/tests/integration/inference/recordings/models-f936269fe152d95db3fb80fb10482e3cc79cfd6a28ebdf1a7a8b220ba2de641b-fb68f5a6.json new file mode 100644 index 000000000..c561e0df0 --- /dev/null +++ b/tests/integration/inference/recordings/models-f936269fe152d95db3fb80fb10482e3cc79cfd6a28ebdf1a7a8b220ba2de641b-fb68f5a6.json @@ -0,0 +1,45 @@ +{ + "test_id": "tests/integration/inference/test_openai_completion.py::test_inference_store[client_with_models-txt=vllm/Qwen/Qwen3-0.6B-False]", + "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": 1762375285, + "object": "model", + "owned_by": "vllm", + "root": "/root/.cache/Qwen3-0.6B", + "parent": null, + "max_model_len": 8192, + "permission": [ + { + "id": "modelperm-e0640be42b814b3394545ebe92d844b3", + "object": "model_permission", + "created": 1762375285, + "allow_create_engine": false, + "allow_sampling": true, + "allow_logprobs": true, + "allow_search_indices": false, + "allow_view": true, + "allow_fine_tuning": false, + "organization": "*", + "group": null, + "is_blocking": false + } + ] + } + } + ], + "is_streaming": false + }, + "id_normalization_mapping": {} +} diff --git a/tests/integration/suites.py b/tests/integration/suites.py index e1fb6a1c7..0cec66afe 100644 --- a/tests/integration/suites.py +++ b/tests/integration/suites.py @@ -78,7 +78,7 @@ SETUP_DEFINITIONS: dict[str, Setup] = { "VLLM_URL": "http://localhost:8000/v1", }, defaults={ - "text_model": "vllm/meta-llama/Llama-3.2-1B-Instruct", + "text_model": "vllm/Qwen/Qwen3-0.6B", "embedding_model": "sentence-transformers/nomic-embed-text-v1.5", }, ), @@ -169,6 +169,11 @@ SUITE_DEFINITIONS: dict[str, Suite] = { roots=base_roots, default_setup="ollama", ), + "base-vllm-subset": Suite( + name="base-vllm-subset", + roots=["tests/integration/inference"], + default_setup="vllm", + ), "responses": Suite( name="responses", roots=["tests/integration/responses"], From 03d23db910a97fd5387d25652c3a2a31170b58d4 Mon Sep 17 00:00:00 2001 From: Derek Higgins Date: Thu, 6 Nov 2025 15:59:55 +0000 Subject: [PATCH 08/10] ci: vllm ci job update (#4088) Add missing recording for vllm in library mode Add Docker env (missed during rebase) Signed-off-by: Derek Higgins --- scripts/integration-tests.sh | 4 + ...62a706ebe85e2a5fe637ddad558cbaafe92d8.json | 103 ++++++++++++++++++ 2 files changed, 107 insertions(+) create mode 100644 tests/integration/inference/recordings/99bf0054f11a9c58c13a44f9cf962a706ebe85e2a5fe637ddad558cbaafe92d8.json diff --git a/scripts/integration-tests.sh b/scripts/integration-tests.sh index 2d088f3df..e21f73f99 100755 --- a/scripts/integration-tests.sh +++ b/scripts/integration-tests.sh @@ -353,6 +353,10 @@ 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 diff --git a/tests/integration/inference/recordings/99bf0054f11a9c58c13a44f9cf962a706ebe85e2a5fe637ddad558cbaafe92d8.json b/tests/integration/inference/recordings/99bf0054f11a9c58c13a44f9cf962a706ebe85e2a5fe637ddad558cbaafe92d8.json new file mode 100644 index 000000000..250e91c68 --- /dev/null +++ b/tests/integration/inference/recordings/99bf0054f11a9c58c13a44f9cf962a706ebe85e2a5fe637ddad558cbaafe92d8.json @@ -0,0 +1,103 @@ +{ + "test_id": "tests/integration/inference/test_tools_with_schemas.py::TestMCPToolsInChatCompletion::test_mcp_tools_in_inference[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": "Calculate 5 + 3" + } + ], + "max_tokens": 4096, + "tools": [ + { + "type": "function", + "function": { + "name": "calculate", + "description": "", + "parameters": { + "properties": { + "x": { + "title": "X", + "type": "number" + }, + "y": { + "title": "Y", + "type": "number" + }, + "operation": { + "title": "Operation", + "type": "string" + } + }, + "required": [ + "x", + "y", + "operation" + ], + "title": "calculateArguments", + "type": "object" + } + } + } + ] + }, + "endpoint": "/v1/chat/completions", + "model": "Qwen/Qwen3-0.6B" + }, + "response": { + "body": { + "__type__": "openai.types.chat.chat_completion.ChatCompletion", + "__data__": { + "id": "rec-99bf0054f11a", + "choices": [ + { + "finish_reason": "tool_calls", + "index": 0, + "logprobs": null, + "message": { + "content": "\nOkay, the user wants to calculate 5 plus 3. Let me check the tools provided. The only function available is 'calculate', which requires x, y, and operation. The parameters are numbers and an operation. The user input is straightforward: 5 + 3. So I need to call the 'calculate' function with x=5, y=3, and operation='+'. That should give the correct result. I don't see any other parameters needed here. Just make sure the JSON is correctly formatted with the required fields.\n\n\n", + "refusal": null, + "role": "assistant", + "annotations": null, + "audio": null, + "function_call": null, + "tool_calls": [ + { + "id": "chatcmpl-tool-6d1a92899a8246bb8fae5682dc08590c", + "function": { + "arguments": "{\"x\": 5, \"y\": 3, \"operation\": \"+\"}", + "name": "calculate" + }, + "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": 144, + "prompt_tokens": 193, + "total_tokens": 337, + "completion_tokens_details": null, + "prompt_tokens_details": null + }, + "prompt_logprobs": null, + "kv_transfer_params": null + } + }, + "is_streaming": false + }, + "id_normalization_mapping": {} +} From dc9497a3b245769e3caf615e34d28b014ed7e0f0 Mon Sep 17 00:00:00 2001 From: Derek Higgins Date: Thu, 6 Nov 2025 16:53:02 +0000 Subject: [PATCH 09/10] ci: Temperarily disable Telemetry during tests (#4090) Closes: #4089 Signed-off-by: Derek Higgins --- scripts/integration-tests.sh | 6 ++++-- tests/integration/telemetry/test_completions.py | 5 +++++ 2 files changed, 9 insertions(+), 2 deletions(-) diff --git a/scripts/integration-tests.sh b/scripts/integration-tests.sh index e21f73f99..0951feb14 100755 --- a/scripts/integration-tests.sh +++ b/scripts/integration-tests.sh @@ -231,7 +231,8 @@ if [[ "$STACK_CONFIG" == *"server:"* && "$COLLECT_ONLY" == false ]]; then # Use a fixed port for the OTEL collector so the server can connect to it COLLECTOR_PORT=4317 export LLAMA_STACK_TEST_COLLECTOR_PORT="${COLLECTOR_PORT}" - export OTEL_EXPORTER_OTLP_ENDPOINT="http://127.0.0.1:${COLLECTOR_PORT}" + # Disabled: https://github.com/llamastack/llama-stack/issues/4089 + #export OTEL_EXPORTER_OTLP_ENDPOINT="http://127.0.0.1:${COLLECTOR_PORT}" export OTEL_EXPORTER_OTLP_PROTOCOL="http/protobuf" export OTEL_BSP_SCHEDULE_DELAY="200" export OTEL_BSP_EXPORT_TIMEOUT="2000" @@ -337,7 +338,8 @@ 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" diff --git a/tests/integration/telemetry/test_completions.py b/tests/integration/telemetry/test_completions.py index 2b8835f6c..af073d8bc 100644 --- a/tests/integration/telemetry/test_completions.py +++ b/tests/integration/telemetry/test_completions.py @@ -12,9 +12,13 @@ before and after each test, ensuring test isolation. import json +import pytest + def test_streaming_chunk_count(mock_otlp_collector, llama_stack_client, text_model_id): """Verify streaming adds chunk_count and __type__=async_generator.""" + + pytest.skip("Disabled: See https://github.com/llamastack/llama-stack/issues/4089") stream = llama_stack_client.chat.completions.create( model=text_model_id, messages=[{"role": "user", "content": "Test trace openai 1"}], @@ -50,6 +54,7 @@ def test_streaming_chunk_count(mock_otlp_collector, llama_stack_client, text_mod def test_telemetry_format_completeness(mock_otlp_collector, llama_stack_client, text_model_id): """Comprehensive validation of telemetry data format including spans and metrics.""" + pytest.skip("Disabled: See https://github.com/llamastack/llama-stack/issues/4089") response = llama_stack_client.chat.completions.create( model=text_model_id, messages=[{"role": "user", "content": "Test trace openai with temperature 0.7"}], From 9df073450f5eb7eab167ee557352b25babd3d521 Mon Sep 17 00:00:00 2001 From: Charlie Doern Date: Thu, 6 Nov 2025 13:58:30 -0500 Subject: [PATCH 10/10] feat: remove core.telemetry as a dependency of llama_stack.apis (#4064) # What does this PR do? Remove circular dependency by moving tracing from API protocol definitions to router implementation layer. This gets us closer to having a self contained API package with no other cross-cutting dependencies to other parts of the llama stack codebase. To the best of our ability, the llama_stack.api should only be type and protocol definitions. Changes: - Create apis/common/tracing.py with marker decorator (zero core dependencies) - Add the _new_ `@telemetry_traceable` marker decorator to 11 protocol classes - Apply actual tracing in core/resolver.py in `instantiate_provider` based on protocol marker - Move MetricResponseMixin from core to apis (it's an API response type) - APIs package is now self-contained with zero core dependencies The tracing functionality remains identical - actual trace_protocol from core is applied to router implementations at runtime when both telemetry is enabled and the protocol has the `__marked_for_tracing__` marker. ## Test Plan Manual integration test confirms identical behavior to main branch: ```bash llama stack list-deps --format uv starter | sh export OLLAMA_URL=http://localhost:11434 llama stack run starter curl -X POST http://localhost:8321/v1/chat/completions \ -H "Content-Type: application/json" \ -d '{"model": "ollama/gpt-oss:20b", "messages": [{"role": "user", "content": "Say hello"}], "max_tokens": 10}' ``` Verified identical between main and this branch: - trace_id present in response - metrics array with prompt_tokens, completion_tokens, total_tokens - Server logs show trace_protocol applied to all routers Existing telemetry integration tests (tests/integration/telemetry/) validate trace context propagation and span attributes. relates to #3895 --------- Signed-off-by: Charlie Doern --- src/llama_stack/apis/common/responses.py | 41 +++++++++++++++++++ src/llama_stack/apis/common/tracing.py | 22 ++++++++++ .../apis/conversations/conversations.py | 4 +- src/llama_stack/apis/files/files.py | 4 +- src/llama_stack/apis/inference/inference.py | 7 ++-- src/llama_stack/apis/models/models.py | 4 +- src/llama_stack/apis/prompts/prompts.py | 4 +- src/llama_stack/apis/safety/safety.py | 4 +- src/llama_stack/apis/shields/shields.py | 4 +- src/llama_stack/apis/tools/tools.py | 6 +-- src/llama_stack/apis/vector_io/vector_io.py | 4 +- src/llama_stack/core/resolver.py | 12 ++++++ src/llama_stack/core/routers/__init__.py | 2 + src/llama_stack/core/telemetry/telemetry.py | 41 ------------------- .../core/telemetry/trace_protocol.py | 9 ++++ 15 files changed, 106 insertions(+), 62 deletions(-) create mode 100644 src/llama_stack/apis/common/tracing.py diff --git a/src/llama_stack/apis/common/responses.py b/src/llama_stack/apis/common/responses.py index 616bee73a..53a290eea 100644 --- a/src/llama_stack/apis/common/responses.py +++ b/src/llama_stack/apis/common/responses.py @@ -34,3 +34,44 @@ class PaginatedResponse(BaseModel): data: list[dict[str, Any]] has_more: bool url: str | None = None + + +# This is a short term solution to allow inference API to return metrics +# The ideal way to do this is to have a way for all response types to include metrics +# and all metric events logged to the telemetry API to be included with the response +# To do this, we will need to augment all response types with a metrics field. +# We have hit a blocker from stainless SDK that prevents us from doing this. +# The blocker is that if we were to augment the response types that have a data field +# in them like so +# class ListModelsResponse(BaseModel): +# metrics: Optional[List[MetricEvent]] = None +# data: List[Models] +# ... +# The client SDK will need to access the data by using a .data field, which is not +# ergonomic. Stainless SDK does support unwrapping the response type, but it +# requires that the response type to only have a single field. + +# We will need a way in the client SDK to signal that the metrics are needed +# and if they are needed, the client SDK has to return the full response type +# without unwrapping it. + + +@json_schema_type +class MetricInResponse(BaseModel): + """A metric value included in API responses. + :param metric: The name of the metric + :param value: The numeric value of the metric + :param unit: (Optional) The unit of measurement for the metric value + """ + + metric: str + value: int | float + unit: str | None = None + + +class MetricResponseMixin(BaseModel): + """Mixin class for API responses that can include metrics. + :param metrics: (Optional) List of metrics associated with the API response + """ + + metrics: list[MetricInResponse] | None = None diff --git a/src/llama_stack/apis/common/tracing.py b/src/llama_stack/apis/common/tracing.py new file mode 100644 index 000000000..830c2945a --- /dev/null +++ b/src/llama_stack/apis/common/tracing.py @@ -0,0 +1,22 @@ +# 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. + + +def telemetry_traceable(cls): + """ + Mark a protocol for automatic tracing when telemetry is enabled. + + This is a metadata-only decorator with no dependencies on core. + Actual tracing is applied by core routers at runtime if telemetry is enabled. + + Usage: + @runtime_checkable + @telemetry_traceable + class MyProtocol(Protocol): + ... + """ + cls.__marked_for_tracing__ = True + return cls diff --git a/src/llama_stack/apis/conversations/conversations.py b/src/llama_stack/apis/conversations/conversations.py index 6ec7e67d6..3fdd3b47e 100644 --- a/src/llama_stack/apis/conversations/conversations.py +++ b/src/llama_stack/apis/conversations/conversations.py @@ -20,8 +20,8 @@ from llama_stack.apis.agents.openai_responses import ( OpenAIResponseOutputMessageMCPListTools, OpenAIResponseOutputMessageWebSearchToolCall, ) +from llama_stack.apis.common.tracing import telemetry_traceable from llama_stack.apis.version import LLAMA_STACK_API_V1 -from llama_stack.core.telemetry.trace_protocol import trace_protocol from llama_stack.schema_utils import json_schema_type, register_schema, webmethod Metadata = dict[str, str] @@ -157,7 +157,7 @@ class ConversationItemDeletedResource(BaseModel): @runtime_checkable -@trace_protocol +@telemetry_traceable class Conversations(Protocol): """Conversations diff --git a/src/llama_stack/apis/files/files.py b/src/llama_stack/apis/files/files.py index 657e9f500..f0ea2f892 100644 --- a/src/llama_stack/apis/files/files.py +++ b/src/llama_stack/apis/files/files.py @@ -11,8 +11,8 @@ from fastapi import File, Form, Response, UploadFile from pydantic import BaseModel, Field from llama_stack.apis.common.responses import Order +from llama_stack.apis.common.tracing import telemetry_traceable from llama_stack.apis.version import LLAMA_STACK_API_V1 -from llama_stack.core.telemetry.trace_protocol import trace_protocol from llama_stack.schema_utils import json_schema_type, webmethod @@ -102,7 +102,7 @@ class OpenAIFileDeleteResponse(BaseModel): @runtime_checkable -@trace_protocol +@telemetry_traceable class Files(Protocol): """Files diff --git a/src/llama_stack/apis/inference/inference.py b/src/llama_stack/apis/inference/inference.py index f39957190..1a865ce5f 100644 --- a/src/llama_stack/apis/inference/inference.py +++ b/src/llama_stack/apis/inference/inference.py @@ -19,11 +19,10 @@ from pydantic import BaseModel, Field, field_validator from typing_extensions import TypedDict from llama_stack.apis.common.content_types import ContentDelta, InterleavedContent -from llama_stack.apis.common.responses import Order +from llama_stack.apis.common.responses import MetricResponseMixin, Order +from llama_stack.apis.common.tracing import telemetry_traceable from llama_stack.apis.models import Model from llama_stack.apis.version import LLAMA_STACK_API_V1, LLAMA_STACK_API_V1ALPHA -from llama_stack.core.telemetry.telemetry import MetricResponseMixin -from llama_stack.core.telemetry.trace_protocol import trace_protocol from llama_stack.models.llama.datatypes import ( BuiltinTool, StopReason, @@ -1160,7 +1159,7 @@ class OpenAIEmbeddingsRequestWithExtraBody(BaseModel, extra="allow"): @runtime_checkable -@trace_protocol +@telemetry_traceable class InferenceProvider(Protocol): """ This protocol defines the interface that should be implemented by all inference providers. diff --git a/src/llama_stack/apis/models/models.py b/src/llama_stack/apis/models/models.py index 552f47c30..5c976886c 100644 --- a/src/llama_stack/apis/models/models.py +++ b/src/llama_stack/apis/models/models.py @@ -9,9 +9,9 @@ from typing import Any, Literal, Protocol, runtime_checkable from pydantic import BaseModel, ConfigDict, Field, field_validator +from llama_stack.apis.common.tracing import telemetry_traceable from llama_stack.apis.resource import Resource, ResourceType from llama_stack.apis.version import LLAMA_STACK_API_V1 -from llama_stack.core.telemetry.trace_protocol import trace_protocol from llama_stack.schema_utils import json_schema_type, webmethod @@ -105,7 +105,7 @@ class OpenAIListModelsResponse(BaseModel): @runtime_checkable -@trace_protocol +@telemetry_traceable class Models(Protocol): async def list_models(self) -> ListModelsResponse: """List all models. diff --git a/src/llama_stack/apis/prompts/prompts.py b/src/llama_stack/apis/prompts/prompts.py index 4651b9294..406ae529c 100644 --- a/src/llama_stack/apis/prompts/prompts.py +++ b/src/llama_stack/apis/prompts/prompts.py @@ -10,8 +10,8 @@ from typing import Protocol, runtime_checkable from pydantic import BaseModel, Field, field_validator, model_validator +from llama_stack.apis.common.tracing import telemetry_traceable from llama_stack.apis.version import LLAMA_STACK_API_V1 -from llama_stack.core.telemetry.trace_protocol import trace_protocol from llama_stack.schema_utils import json_schema_type, webmethod @@ -92,7 +92,7 @@ class ListPromptsResponse(BaseModel): @runtime_checkable -@trace_protocol +@telemetry_traceable class Prompts(Protocol): """Prompts diff --git a/src/llama_stack/apis/safety/safety.py b/src/llama_stack/apis/safety/safety.py index 97fffcff1..8872cc518 100644 --- a/src/llama_stack/apis/safety/safety.py +++ b/src/llama_stack/apis/safety/safety.py @@ -9,10 +9,10 @@ from typing import Any, Protocol, runtime_checkable from pydantic import BaseModel, Field +from llama_stack.apis.common.tracing import telemetry_traceable from llama_stack.apis.inference import OpenAIMessageParam from llama_stack.apis.shields import Shield from llama_stack.apis.version import LLAMA_STACK_API_V1 -from llama_stack.core.telemetry.trace_protocol import trace_protocol from llama_stack.schema_utils import json_schema_type, webmethod @@ -94,7 +94,7 @@ class ShieldStore(Protocol): @runtime_checkable -@trace_protocol +@telemetry_traceable class Safety(Protocol): """Safety diff --git a/src/llama_stack/apis/shields/shields.py b/src/llama_stack/apis/shields/shields.py index 565e1db15..ca4483828 100644 --- a/src/llama_stack/apis/shields/shields.py +++ b/src/llama_stack/apis/shields/shields.py @@ -8,9 +8,9 @@ from typing import Any, Literal, Protocol, runtime_checkable from pydantic import BaseModel +from llama_stack.apis.common.tracing import telemetry_traceable from llama_stack.apis.resource import Resource, ResourceType from llama_stack.apis.version import LLAMA_STACK_API_V1 -from llama_stack.core.telemetry.trace_protocol import trace_protocol from llama_stack.schema_utils import json_schema_type, webmethod @@ -48,7 +48,7 @@ class ListShieldsResponse(BaseModel): @runtime_checkable -@trace_protocol +@telemetry_traceable class Shields(Protocol): @webmethod(route="/shields", method="GET", level=LLAMA_STACK_API_V1) async def list_shields(self) -> ListShieldsResponse: diff --git a/src/llama_stack/apis/tools/tools.py b/src/llama_stack/apis/tools/tools.py index 29065a713..c9bdfcfb6 100644 --- a/src/llama_stack/apis/tools/tools.py +++ b/src/llama_stack/apis/tools/tools.py @@ -11,9 +11,9 @@ from pydantic import BaseModel from typing_extensions import runtime_checkable from llama_stack.apis.common.content_types import URL, InterleavedContent +from llama_stack.apis.common.tracing import telemetry_traceable from llama_stack.apis.resource import Resource, ResourceType from llama_stack.apis.version import LLAMA_STACK_API_V1 -from llama_stack.core.telemetry.trace_protocol import trace_protocol from llama_stack.schema_utils import json_schema_type, webmethod @@ -107,7 +107,7 @@ class ListToolDefsResponse(BaseModel): @runtime_checkable -@trace_protocol +@telemetry_traceable class ToolGroups(Protocol): @webmethod(route="/toolgroups", method="POST", level=LLAMA_STACK_API_V1) async def register_tool_group( @@ -189,7 +189,7 @@ class SpecialToolGroup(Enum): @runtime_checkable -@trace_protocol +@telemetry_traceable class ToolRuntime(Protocol): tool_store: ToolStore | None = None diff --git a/src/llama_stack/apis/vector_io/vector_io.py b/src/llama_stack/apis/vector_io/vector_io.py index 9148d10e5..26c961db3 100644 --- a/src/llama_stack/apis/vector_io/vector_io.py +++ b/src/llama_stack/apis/vector_io/vector_io.py @@ -13,10 +13,10 @@ from typing import Annotated, Any, Literal, Protocol, runtime_checkable from fastapi import Body from pydantic import BaseModel, Field +from llama_stack.apis.common.tracing import telemetry_traceable from llama_stack.apis.inference import InterleavedContent from llama_stack.apis.vector_stores import VectorStore from llama_stack.apis.version import LLAMA_STACK_API_V1 -from llama_stack.core.telemetry.trace_protocol import trace_protocol from llama_stack.schema_utils import json_schema_type, webmethod from llama_stack.strong_typing.schema import register_schema @@ -502,7 +502,7 @@ class VectorStoreTable(Protocol): @runtime_checkable -@trace_protocol +@telemetry_traceable class VectorIO(Protocol): vector_store_table: VectorStoreTable | None = None diff --git a/src/llama_stack/core/resolver.py b/src/llama_stack/core/resolver.py index 805d260fc..8bf371fed 100644 --- a/src/llama_stack/core/resolver.py +++ b/src/llama_stack/core/resolver.py @@ -397,6 +397,18 @@ async def instantiate_provider( impl.__provider_spec__ = provider_spec impl.__provider_config__ = config + # Apply tracing if telemetry is enabled and any base class has __marked_for_tracing__ marker + if run_config.telemetry.enabled: + traced_classes = [ + base for base in reversed(impl.__class__.__mro__) if getattr(base, "__marked_for_tracing__", False) + ] + + if traced_classes: + from llama_stack.core.telemetry.trace_protocol import trace_protocol + + for cls in traced_classes: + trace_protocol(cls) + protocols = api_protocol_map_for_compliance_check(run_config) additional_protocols = additional_protocols_map() # TODO: check compliance for special tool groups diff --git a/src/llama_stack/core/routers/__init__.py b/src/llama_stack/core/routers/__init__.py index 204cbb87f..729d1c9ea 100644 --- a/src/llama_stack/core/routers/__init__.py +++ b/src/llama_stack/core/routers/__init__.py @@ -45,6 +45,7 @@ async def get_routing_table_impl( raise ValueError(f"API {api.value} not found in router map") impl = api_to_tables[api.value](impls_by_provider_id, dist_registry, policy) + await impl.initialize() return impl @@ -92,5 +93,6 @@ async def get_auto_router_impl( api_to_dep_impl["safety_config"] = run_config.safety impl = api_to_routers[api.value](routing_table, **api_to_dep_impl) + await impl.initialize() return impl diff --git a/src/llama_stack/core/telemetry/telemetry.py b/src/llama_stack/core/telemetry/telemetry.py index 9476c961a..459c1aa1a 100644 --- a/src/llama_stack/core/telemetry/telemetry.py +++ b/src/llama_stack/core/telemetry/telemetry.py @@ -163,47 +163,6 @@ class MetricEvent(EventCommon): unit: str -@json_schema_type -class MetricInResponse(BaseModel): - """A metric value included in API responses. - :param metric: The name of the metric - :param value: The numeric value of the metric - :param unit: (Optional) The unit of measurement for the metric value - """ - - metric: str - value: int | float - unit: str | None = None - - -# This is a short term solution to allow inference API to return metrics -# The ideal way to do this is to have a way for all response types to include metrics -# and all metric events logged to the telemetry API to be included with the response -# To do this, we will need to augment all response types with a metrics field. -# We have hit a blocker from stainless SDK that prevents us from doing this. -# The blocker is that if we were to augment the response types that have a data field -# in them like so -# class ListModelsResponse(BaseModel): -# metrics: Optional[List[MetricEvent]] = None -# data: List[Models] -# ... -# The client SDK will need to access the data by using a .data field, which is not -# ergonomic. Stainless SDK does support unwrapping the response type, but it -# requires that the response type to only have a single field. - -# We will need a way in the client SDK to signal that the metrics are needed -# and if they are needed, the client SDK has to return the full response type -# without unwrapping it. - - -class MetricResponseMixin(BaseModel): - """Mixin class for API responses that can include metrics. - :param metrics: (Optional) List of metrics associated with the API response - """ - - metrics: list[MetricInResponse] | None = None - - @json_schema_type class StructuredLogType(Enum): """The type of structured log event payload. diff --git a/src/llama_stack/core/telemetry/trace_protocol.py b/src/llama_stack/core/telemetry/trace_protocol.py index 807b8e2a9..95b33a4bc 100644 --- a/src/llama_stack/core/telemetry/trace_protocol.py +++ b/src/llama_stack/core/telemetry/trace_protocol.py @@ -129,6 +129,15 @@ def trace_protocol[T: type[Any]](cls: T) -> T: else: return sync_wrapper + # Wrap methods on the class itself (for classes applied at runtime) + # Skip if already wrapped (indicated by __wrapped__ attribute) + for name, method in vars(cls).items(): + if inspect.isfunction(method) and not name.startswith("_"): + if not hasattr(method, "__wrapped__"): + wrapped = trace_method(method) + setattr(cls, name, wrapped) # noqa: B010 + + # Also set up __init_subclass__ for future subclasses original_init_subclass = cast(Callable[..., Any] | None, getattr(cls, "__init_subclass__", None)) def __init_subclass__(cls_child: type[Any], **kwargs: Any) -> None: # noqa: N807