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rebase and incorporate PR feedback

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Signed-off-by: Francisco Javier Arceo <farceo@redhat.com>
This commit is contained in:
Francisco Javier Arceo 2025-11-10 22:38:17 -05:00
parent 726bdc414d
commit 765f7623c1
7 changed files with 49 additions and 64 deletions
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17
client-sdks/stainless/openapi.yml
View file

@ -2691,16 +2691,8 @@ paths:
responses:
'200':
description: >-
<<<<<<< HEAD
<<<<<<< HEAD
A VectorStoreFileContentResponse representing the file contents.
=======
File contents, optionally with embeddings and metadata based on extra_query
=======
File contents, optionally with embeddings and metadata based on query
>>>>>>> c192529c (use FastAPI Query class instead of custom middlware)
parameters.
>>>>>>> 639f0daa (feat: Adding optional embeddings to content)
content:
application/json:
schema:
@ -10132,11 +10124,10 @@ components:
items:
type: number
description: >-
Optional embedding vector for this content chunk (when requested via extra_body)
Optional embedding vector for this content chunk
chunk_metadata:
$ref: '#/components/schemas/ChunkMetadata'
description: >-
Optional chunk metadata (when requested via extra_body)
description: Optional chunk metadata
metadata:
type: object
additionalProperties:
@ -10147,8 +10138,7 @@ components:
- type: string
- type: array
- type: object
description: >-
Optional user-defined metadata (when requested via extra_body)
description: Optional user-defined metadata
additionalProperties: false
required:
- type
@ -10172,6 +10162,7 @@ components:
description: Parsed content of the file
has_more:
type: boolean
default: false
description: >-
Indicates if there are more content pages to fetch
next_page:

17
docs/static/llama-stack-spec.yaml vendored
View file

@ -2688,16 +2688,8 @@ paths:
responses:
'200':
description: >-
<<<<<<< HEAD
<<<<<<< HEAD
A VectorStoreFileContentResponse representing the file contents.
=======
File contents, optionally with embeddings and metadata based on extra_query
=======
File contents, optionally with embeddings and metadata based on query
>>>>>>> c192529c (use FastAPI Query class instead of custom middlware)
parameters.
>>>>>>> 639f0daa (feat: Adding optional embeddings to content)
content:
application/json:
schema:
@ -9416,11 +9408,10 @@ components:
items:
type: number
description: >-
Optional embedding vector for this content chunk (when requested via extra_body)
Optional embedding vector for this content chunk
chunk_metadata:
$ref: '#/components/schemas/ChunkMetadata'
description: >-
Optional chunk metadata (when requested via extra_body)
description: Optional chunk metadata
metadata:
type: object
additionalProperties:
@ -9431,8 +9422,7 @@ components:
- type: string
- type: array
- type: object
description: >-
Optional user-defined metadata (when requested via extra_body)
description: Optional user-defined metadata
additionalProperties: false
required:
- type
@ -9456,6 +9446,7 @@ components:
description: Parsed content of the file
has_more:
type: boolean
default: false
description: >-
Indicates if there are more content pages to fetch
next_page:

17
docs/static/stainless-llama-stack-spec.yaml vendored
View file

@ -2691,16 +2691,8 @@ paths:
responses:
'200':
description: >-
<<<<<<< HEAD
<<<<<<< HEAD
A VectorStoreFileContentResponse representing the file contents.
=======
File contents, optionally with embeddings and metadata based on extra_query
=======
File contents, optionally with embeddings and metadata based on query
>>>>>>> c192529c (use FastAPI Query class instead of custom middlware)
parameters.
>>>>>>> 639f0daa (feat: Adding optional embeddings to content)
content:
application/json:
schema:
@ -10132,11 +10124,10 @@ components:
items:
type: number
description: >-
Optional embedding vector for this content chunk (when requested via extra_body)
Optional embedding vector for this content chunk
chunk_metadata:
$ref: '#/components/schemas/ChunkMetadata'
description: >-
Optional chunk metadata (when requested via extra_body)
description: Optional chunk metadata
metadata:
type: object
additionalProperties:
@ -10147,8 +10138,7 @@ components:
- type: string
- type: array
- type: object
description: >-
Optional user-defined metadata (when requested via extra_body)
description: Optional user-defined metadata
additionalProperties: false
required:
- type
@ -10172,6 +10162,7 @@ components:
description: Parsed content of the file
has_more:
type: boolean
default: false
description: >-
Indicates if there are more content pages to fetch
next_page:

24
src/llama_stack/apis/vector_io/vector_io.py
View file

@ -224,9 +224,9 @@ class VectorStoreContent(BaseModel):
:param type: Content type, currently only "text" is supported
:param text: The actual text content
:param embedding: Optional embedding vector for this content chunk (when requested via extra_body)
:param chunk_metadata: Optional chunk metadata (when requested via extra_body)
:param metadata: Optional user-defined metadata (when requested via extra_body)
:param embedding: Optional embedding vector for this content chunk
:param chunk_metadata: Optional chunk metadata
:param metadata: Optional user-defined metadata
"""
type: Literal["text"]
@ -286,6 +286,22 @@ class VectorStoreDeleteResponse(BaseModel):
deleted: bool = True
@json_schema_type
class VectorStoreFileContentResponse(BaseModel):
"""Represents the parsed content of a vector store file.
:param object: The object type, which is always `vector_store.file_content.page`
:param data: Parsed content of the file
:param has_more: Indicates if there are more content pages to fetch
:param next_page: The token for the next page, if any
"""
object: Literal["vector_store.file_content.page"] = "vector_store.file_content.page"
data: list[VectorStoreContent]
has_more: bool = False
next_page: str | None = None
@json_schema_type
class VectorStoreChunkingStrategyAuto(BaseModel):
"""Automatic chunking strategy for vector store files.
@ -724,7 +740,7 @@ class VectorIO(Protocol):
file_id: str,
include_embeddings: Annotated[bool | None, Query(default=False)] = False,
include_metadata: Annotated[bool | None, Query(default=False)] = False,
) -> VectorStoreFileContentsResponse:
) -> VectorStoreFileContentResponse:
"""Retrieves the contents of a vector store file.
:param vector_store_id: The ID of the vector store containing the file to retrieve.

4
src/llama_stack/core/routers/vector_io.py
View file

@ -24,7 +24,7 @@ from llama_stack.apis.vector_io import (
VectorStoreChunkingStrategyStaticConfig,
VectorStoreDeleteResponse,
VectorStoreFileBatchObject,
VectorStoreFileContentsResponse,
VectorStoreFileContentResponse,
VectorStoreFileDeleteResponse,
VectorStoreFileObject,
VectorStoreFilesListInBatchResponse,
@ -347,7 +347,7 @@ class VectorIORouter(VectorIO):
file_id: str,
include_embeddings: bool | None = False,
include_metadata: bool | None = False,
) -> VectorStoreFileContentsResponse:
) -> VectorStoreFileContentResponse:
logger.debug(
f"VectorIORouter.openai_retrieve_vector_store_file_contents: {vector_store_id}, {file_id}, "
f"include_embeddings={include_embeddings}, include_metadata={include_metadata}"

4
src/llama_stack/core/routing_tables/vector_stores.py
View file

@ -15,7 +15,7 @@ from llama_stack.apis.vector_io.vector_io import (
SearchRankingOptions,
VectorStoreChunkingStrategy,
VectorStoreDeleteResponse,
VectorStoreFileContentsResponse,
VectorStoreFileContentResponse,
VectorStoreFileDeleteResponse,
VectorStoreFileObject,
VectorStoreFileStatus,
@ -197,7 +197,7 @@ class VectorStoresRoutingTable(CommonRoutingTableImpl):
file_id: str,
include_embeddings: bool | None = False,
include_metadata: bool | None = False,
) -> VectorStoreFileContentsResponse:
) -> VectorStoreFileContentResponse:
await self.assert_action_allowed("read", "vector_store", vector_store_id)
provider = await self.get_provider_impl(vector_store_id)

30
src/llama_stack/providers/utils/memory/openai_vector_store_mixin.py
View file

@ -30,7 +30,7 @@ from llama_stack.apis.vector_io import (
VectorStoreContent,
VectorStoreDeleteResponse,
VectorStoreFileBatchObject,
VectorStoreFileContentsResponse,
VectorStoreFileContentResponse,
VectorStoreFileCounts,
VectorStoreFileDeleteResponse,
VectorStoreFileLastError,
@ -704,18 +704,18 @@ class OpenAIVectorStoreMixin(ABC):
# Unknown filter type, default to no match
raise ValueError(f"Unsupported filter type: {filter_type}")
def _extract_chunk_fields(self, chunk: Chunk, include_embeddings: bool, include_metadata: bool) -> dict:
"""Extract embedding and metadata fields from chunk based on include flags."""
return {
"embedding": chunk.embedding if include_embeddings else None,
"chunk_metadata": chunk.chunk_metadata if include_metadata else None,
"metadata": chunk.metadata if include_metadata else None,
}
def _chunk_to_vector_store_content(
self, chunk: Chunk, include_embeddings: bool = False, include_metadata: bool = False
) -> list[VectorStoreContent]:
fields = self._extract_chunk_fields(chunk, include_embeddings, include_metadata)
def extract_fields() -> dict:
"""Extract embedding and metadata fields from chunk based on include flags."""
return {
"embedding": chunk.embedding if include_embeddings else None,
"chunk_metadata": chunk.chunk_metadata if include_metadata else None,
"metadata": chunk.metadata if include_metadata else None,
}
fields = extract_fields()
if isinstance(chunk.content, str):
content_item = VectorStoreContent(type="text", text=chunk.content, **fields)
@ -923,7 +923,7 @@ class OpenAIVectorStoreMixin(ABC):
file_id: str,
include_embeddings: bool | None = False,
include_metadata: bool | None = False,
) -> VectorStoreFileContentsResponse:
) -> VectorStoreFileContentResponse:
"""Retrieves the contents of a vector store file."""
if vector_store_id not in self.openai_vector_stores:
raise VectorStoreNotFoundError(vector_store_id)
@ -931,7 +931,6 @@ class OpenAIVectorStoreMixin(ABC):
# Parameters are already provided directly
# include_embeddings and include_metadata are now function parameters
file_info = await self._load_openai_vector_store_file(vector_store_id, file_id)
dict_chunks = await self._load_openai_vector_store_file_contents(vector_store_id, file_id)
chunks = [Chunk.model_validate(c) for c in dict_chunks]
content = []
@ -941,11 +940,8 @@ class OpenAIVectorStoreMixin(ABC):
chunk, include_embeddings=include_embeddings or False, include_metadata=include_metadata or False
)
)
return VectorStoreFileContentsResponse(
file_id=file_id,
filename=file_info.get("filename", ""),
attributes=file_info.get("attributes", {}),
content=content,
return VectorStoreFileContentResponse(
data=content,
)
async def openai_update_vector_store_file(