rebase and incorporate PR feedback

Signed-off-by: Francisco Javier Arceo <farceo@redhat.com>

client-sdks/stainless/openapi.yml

@@ -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: >-
+          description: Optional chunk metadata
-            Optional chunk metadata (when requested via extra_body)
         metadata:
           type: object
           additionalProperties:
@@ -10147,8 +10138,7 @@ components:
               - type: string
               - type: array
               - type: object
-          description: >-
+          description: Optional user-defined metadata
-            Optional user-defined metadata (when requested via extra_body)
       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:

docs/static/llama-stack-spec.yaml

@@ -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: >-
+          description: Optional chunk metadata
-            Optional chunk metadata (when requested via extra_body)
         metadata:
           type: object
           additionalProperties:
@@ -9431,8 +9422,7 @@ components:
               - type: string
               - type: array
               - type: object
-          description: >-
+          description: Optional user-defined metadata
-            Optional user-defined metadata (when requested via extra_body)
       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:

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

@@ -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: >-
+          description: Optional chunk metadata
-            Optional chunk metadata (when requested via extra_body)
         metadata:
           type: object
           additionalProperties:
@@ -10147,8 +10138,7 @@ components:
               - type: string
               - type: array
               - type: object
-          description: >-
+          description: Optional user-defined metadata
-            Optional user-defined metadata (when requested via extra_body)
       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:

src/llama_stack/apis/vector_io/vector_io.py

@@ -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 embedding: Optional embedding vector for this content chunk
-    :param chunk_metadata: Optional chunk metadata (when requested via extra_body)
+    :param chunk_metadata: Optional chunk metadata
-    :param metadata: Optional user-defined metadata (when requested via extra_body)
+    :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.

src/llama_stack/core/routers/vector_io.py

@@ -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}"

src/llama_stack/core/routing_tables/vector_stores.py

@@ -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)

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

@@ -30,7 +30,7 @@ from llama_stack.apis.vector_io import (
     VectorStoreContent,
     VectorStoreDeleteResponse,
     VectorStoreFileBatchObject,
-    VectorStoreFileContentsResponse,
+    VectorStoreFileContentResponse,
     VectorStoreFileCounts,
     VectorStoreFileDeleteResponse,
     VectorStoreFileLastError,
@@ -704,7 +704,10 @@ 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:
+    def _chunk_to_vector_store_content(
+        self, chunk: Chunk, include_embeddings: bool = False, include_metadata: bool = False
+    ) -> list[VectorStoreContent]:
+        def extract_fields() -> dict:
             """Extract embedding and metadata fields from chunk based on include flags."""
             return {
                 "embedding": chunk.embedding if include_embeddings else None,
@@ -712,10 +715,7 @@ class OpenAIVectorStoreMixin(ABC):
                 "metadata": chunk.metadata if include_metadata else None,
             }
 
-    def _chunk_to_vector_store_content(
+        fields = extract_fields()
-        self, chunk: Chunk, include_embeddings: bool = False, include_metadata: bool = False
-    ) -> list[VectorStoreContent]:
-        fields = self._extract_chunk_fields(chunk, include_embeddings, include_metadata)
 
         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(
+        return VectorStoreFileContentResponse(
-            file_id=file_id,
+            data=content,
-            filename=file_info.get("filename", ""),
-            attributes=file_info.get("attributes", {}),
-            content=content,
         )
 
     async def openai_update_vector_store_file(