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feat: Adding support for get, update, delete for Vector Stores API

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Signed-off-by: Francisco Javier Arceo <farceo@redhat.com>
This commit is contained in:
Francisco Javier Arceo 2025-08-01 17:03:43 -04:00
parent 0527c0fb15
commit 4c0eb47fc7
8 changed files with 1818 additions and 1 deletions
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454
docs/_static/llama-stack-spec.html vendored
View file

@ -3793,6 +3793,195 @@
]
}
},
"/v1/openai/v1/vector_stores/{vector_store_id}/files/{file_id}/chunks/{chunk_id}": {
"get": {
"responses": {
"200": {
"description": "A VectorStoreChunkObject representing the chunk.",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/VectorStoreChunkObject"
}
}
}
},
"400": {
"$ref": "#/components/responses/BadRequest400"
},
"429": {
"$ref": "#/components/responses/TooManyRequests429"
},
"500": {
"$ref": "#/components/responses/InternalServerError500"
},
"default": {
"$ref": "#/components/responses/DefaultError"
}
},
"tags": [
"VectorIO"
],
"description": "Retrieve a specific chunk from a vector store file.",
"parameters": [
{
"name": "vector_store_id",
"in": "path",
"description": "The ID of the vector store containing the chunk.",
"required": true,
"schema": {
"type": "string"
}
},
{
"name": "file_id",
"in": "path",
"description": "The ID of the file containing the chunk.",
"required": true,
"schema": {
"type": "string"
}
},
{
"name": "chunk_id",
"in": "path",
"description": "The ID of the chunk to retrieve.",
"required": true,
"schema": {
"type": "string"
}
}
]
},
"post": {
"responses": {
"200": {
"description": "A VectorStoreChunkObject representing the updated chunk.",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/VectorStoreChunkObject"
}
}
}
},
"400": {
"$ref": "#/components/responses/BadRequest400"
},
"429": {
"$ref": "#/components/responses/TooManyRequests429"
},
"500": {
"$ref": "#/components/responses/InternalServerError500"
},
"default": {
"$ref": "#/components/responses/DefaultError"
}
},
"tags": [
"VectorIO"
],
"description": "Update a specific chunk in a vector store file.",
"parameters": [
{
"name": "vector_store_id",
"in": "path",
"description": "The ID of the vector store containing the chunk.",
"required": true,
"schema": {
"type": "string"
}
},
{
"name": "file_id",
"in": "path",
"description": "The ID of the file containing the chunk.",
"required": true,
"schema": {
"type": "string"
}
},
{
"name": "chunk_id",
"in": "path",
"description": "The ID of the chunk to update.",
"required": true,
"schema": {
"type": "string"
}
}
],
"requestBody": {
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/OpenaiUpdateVectorStoreChunkRequest"
}
}
},
"required": true
}
},
"delete": {
"responses": {
"200": {
"description": "A VectorStoreChunkDeleteResponse indicating the deletion status.",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/VectorStoreChunkDeleteResponse"
}
}
}
},
"400": {
"$ref": "#/components/responses/BadRequest400"
},
"429": {
"$ref": "#/components/responses/TooManyRequests429"
},
"500": {
"$ref": "#/components/responses/InternalServerError500"
},
"default": {
"$ref": "#/components/responses/DefaultError"
}
},
"tags": [
"VectorIO"
],
"description": "Delete a specific chunk from a vector store file.",
"parameters": [
{
"name": "vector_store_id",
"in": "path",
"description": "The ID of the vector store containing the chunk.",
"required": true,
"schema": {
"type": "string"
}
},
{
"name": "file_id",
"in": "path",
"description": "The ID of the file containing the chunk.",
"required": true,
"schema": {
"type": "string"
}
},
{
"name": "chunk_id",
"in": "path",
"description": "The ID of the chunk to delete.",
"required": true,
"schema": {
"type": "string"
}
}
]
}
},
"/v1/openai/v1/vector_stores/{vector_store_id}/files/{file_id}": {
"get": {
"responses": {
@ -4155,6 +4344,94 @@
"parameters": []
}
},
"/v1/openai/v1/vector_stores/{vector_store_id}/files/{file_id}/chunks": {
"get": {
"responses": {
"200": {
"description": "A VectorStoreListChunksResponse with the list of chunks.",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/VectorStoreListChunksResponse"
}
}
}
},
"400": {
"$ref": "#/components/responses/BadRequest400"
},
"429": {
"$ref": "#/components/responses/TooManyRequests429"
},
"500": {
"$ref": "#/components/responses/InternalServerError500"
},
"default": {
"$ref": "#/components/responses/DefaultError"
}
},
"tags": [
"VectorIO"
],
"description": "List chunks in a vector store file.",
"parameters": [
{
"name": "vector_store_id",
"in": "path",
"description": "The ID of the vector store.",
"required": true,
"schema": {
"type": "string"
}
},
{
"name": "file_id",
"in": "path",
"description": "The ID of the file.",
"required": true,
"schema": {
"type": "string"
}
},
{
"name": "limit",
"in": "query",
"description": "Max number of chunks to return.",
"required": false,
"schema": {
"type": "integer"
}
},
{
"name": "order",
"in": "query",
"description": "Sort order.",
"required": false,
"schema": {
"type": "string"
}
},
{
"name": "after",
"in": "query",
"description": "Pagination cursor.",
"required": false,
"schema": {
"type": "string"
}
},
{
"name": "before",
"in": "query",
"description": "Pagination cursor.",
"required": false,
"schema": {
"type": "string"
}
}
]
}
},
"/v1/openai/v1/files/{file_id}/content": {
"get": {
"responses": {
@ -14351,6 +14628,33 @@
"title": "VectorStoreDeleteResponse",
"description": "Response from deleting a vector store."
},
"VectorStoreChunkDeleteResponse": {
"type": "object",
"properties": {
"id": {
"type": "string",
"description": "Unique identifier of the deleted chunk"
},
"object": {
"type": "string",
"default": "vector_store.file.chunk.deleted",
"description": "Object type identifier for the deletion response"
},
"deleted": {
"type": "boolean",
"default": true,
"description": "Whether the deletion operation was successful"
}
},
"additionalProperties": false,
"required": [
"id",
"object",
"deleted"
],
"title": "VectorStoreChunkDeleteResponse",
"description": "Response from deleting a vector store chunk."
},
"VectorStoreFileDeleteResponse": {
"type": "object",
"properties": {
@ -14691,6 +14995,119 @@
],
"title": "OpenAIListModelsResponse"
},
"VectorStoreChunkObject": {
"type": "object",
"properties": {
"id": {
"type": "string",
"description": "Unique identifier for the chunk"
},
"object": {
"type": "string",
"default": "vector_store.file.chunk",
"description": "Object type identifier, always \"vector_store.file.chunk\""
},
"created_at": {
"type": "integer",
"description": "Timestamp when the chunk was created"
},
"vector_store_id": {
"type": "string",
"description": "ID of the vector store containing this chunk"
},
"file_id": {
"type": "string",
"description": "ID of the file containing this chunk"
},
"content": {
"$ref": "#/components/schemas/InterleavedContent",
"description": "The content of the chunk, using the same format as Chunk class"
},
"metadata": {
"type": "object",
"additionalProperties": {
"oneOf": [
{
"type": "null"
},
{
"type": "boolean"
},
{
"type": "number"
},
{
"type": "string"
},
{
"type": "array"
},
{
"type": "object"
}
]
},
"description": "Metadata associated with the chunk"
},
"embedding": {
"type": "array",
"items": {
"type": "number"
},
"description": "The embedding vector for the chunk"
}
},
"additionalProperties": false,
"required": [
"id",
"object",
"created_at",
"vector_store_id",
"file_id",
"content",
"metadata"
],
"title": "VectorStoreChunkObject",
"description": "OpenAI Vector Store Chunk object."
},
"VectorStoreListChunksResponse": {
"type": "object",
"properties": {
"object": {
"type": "string",
"default": "list",
"description": "Object type identifier, always \"list\""
},
"data": {
"type": "array",
"items": {
"$ref": "#/components/schemas/VectorStoreChunkObject"
},
"description": "List of vector store chunk objects"
},
"first_id": {
"type": "string",
"description": "(Optional) ID of the first chunk in the list for pagination"
},
"last_id": {
"type": "string",
"description": "(Optional) ID of the last chunk in the list for pagination"
},
"has_more": {
"type": "boolean",
"default": false,
"description": "Whether there are more chunks available beyond this page"
}
},
"additionalProperties": false,
"required": [
"object",
"data",
"has_more"
],
"title": "VectorStoreListChunksResponse",
"description": "Response from listing chunks in a vector store file."
},
"VectorStoreListResponse": {
"type": "object",
"properties": {
@ -15039,6 +15456,43 @@
"additionalProperties": false,
"title": "OpenaiUpdateVectorStoreRequest"
},
"OpenaiUpdateVectorStoreChunkRequest": {
"type": "object",
"properties": {
"content": {
"$ref": "#/components/schemas/InterleavedContent",
"description": "Updated content for the chunk."
},
"metadata": {
"type": "object",
"additionalProperties": {
"oneOf": [
{
"type": "null"
},
{
"type": "boolean"
},
{
"type": "number"
},
{
"type": "string"
},
{
"type": "array"
},
{
"type": "object"
}
]
},
"description": "Updated metadata for the chunk."
}
},
"additionalProperties": false,
"title": "OpenaiUpdateVectorStoreChunkRequest"
},
"OpenaiUpdateVectorStoreFileRequest": {
"type": "object",
"properties": {

324
docs/_static/llama-stack-spec.yaml vendored
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@ -2674,6 +2674,142 @@ paths:
required: true
schema:
type: string
/v1/openai/v1/vector_stores/{vector_store_id}/files/{file_id}/chunks/{chunk_id}:
get:
responses:
'200':
description: >-
A VectorStoreChunkObject representing the chunk.
content:
application/json:
schema:
$ref: '#/components/schemas/VectorStoreChunkObject'
'400':
$ref: '#/components/responses/BadRequest400'
'429':
$ref: >-
#/components/responses/TooManyRequests429
'500':
$ref: >-
#/components/responses/InternalServerError500
default:
$ref: '#/components/responses/DefaultError'
tags:
- VectorIO
description: >-
Retrieve a specific chunk from a vector store file.
parameters:
- name: vector_store_id
in: path
description: >-
The ID of the vector store containing the chunk.
required: true
schema:
type: string
- name: file_id
in: path
description: The ID of the file containing the chunk.
required: true
schema:
type: string
- name: chunk_id
in: path
description: The ID of the chunk to retrieve.
required: true
schema:
type: string
post:
responses:
'200':
description: >-
A VectorStoreChunkObject representing the updated chunk.
content:
application/json:
schema:
$ref: '#/components/schemas/VectorStoreChunkObject'
'400':
$ref: '#/components/responses/BadRequest400'
'429':
$ref: >-
#/components/responses/TooManyRequests429
'500':
$ref: >-
#/components/responses/InternalServerError500
default:
$ref: '#/components/responses/DefaultError'
tags:
- VectorIO
description: >-
Update a specific chunk in a vector store file.
parameters:
- name: vector_store_id
in: path
description: >-
The ID of the vector store containing the chunk.
required: true
schema:
type: string
- name: file_id
in: path
description: The ID of the file containing the chunk.
required: true
schema:
type: string
- name: chunk_id
in: path
description: The ID of the chunk to update.
required: true
schema:
type: string
requestBody:
content:
application/json:
schema:
$ref: '#/components/schemas/OpenaiUpdateVectorStoreChunkRequest'
required: true
delete:
responses:
'200':
description: >-
A VectorStoreChunkDeleteResponse indicating the deletion status.
content:
application/json:
schema:
$ref: '#/components/schemas/VectorStoreChunkDeleteResponse'
'400':
$ref: '#/components/responses/BadRequest400'
'429':
$ref: >-
#/components/responses/TooManyRequests429
'500':
$ref: >-
#/components/responses/InternalServerError500
default:
$ref: '#/components/responses/DefaultError'
tags:
- VectorIO
description: >-
Delete a specific chunk from a vector store file.
parameters:
- name: vector_store_id
in: path
description: >-
The ID of the vector store containing the chunk.
required: true
schema:
type: string
- name: file_id
in: path
description: The ID of the file containing the chunk.
required: true
schema:
type: string
- name: chunk_id
in: path
description: The ID of the chunk to delete.
required: true
schema:
type: string
/v1/openai/v1/vector_stores/{vector_store_id}/files/{file_id}:
get:
responses:
@ -2947,6 +3083,66 @@ paths:
- Models
description: List models using the OpenAI API.
parameters: []
/v1/openai/v1/vector_stores/{vector_store_id}/files/{file_id}/chunks:
get:
responses:
'200':
description: >-
A VectorStoreListChunksResponse with the list of chunks.
content:
application/json:
schema:
$ref: '#/components/schemas/VectorStoreListChunksResponse'
'400':
$ref: '#/components/responses/BadRequest400'
'429':
$ref: >-
#/components/responses/TooManyRequests429
'500':
$ref: >-
#/components/responses/InternalServerError500
default:
$ref: '#/components/responses/DefaultError'
tags:
- VectorIO
description: List chunks in a vector store file.
parameters:
- name: vector_store_id
in: path
description: The ID of the vector store.
required: true
schema:
type: string
- name: file_id
in: path
description: The ID of the file.
required: true
schema:
type: string
- name: limit
in: query
description: Max number of chunks to return.
required: false
schema:
type: integer
- name: order
in: query
description: Sort order.
required: false
schema:
type: string
- name: after
in: query
description: Pagination cursor.
required: false
schema:
type: string
- name: before
in: query
description: Pagination cursor.
required: false
schema:
type: string
/v1/openai/v1/files/{file_id}/content:
get:
responses:
@ -10609,6 +10805,30 @@ components:
- deleted
title: VectorStoreDeleteResponse
description: Response from deleting a vector store.
VectorStoreChunkDeleteResponse:
type: object
properties:
id:
type: string
description: Unique identifier of the deleted chunk
object:
type: string
default: vector_store.file.chunk.deleted
description: >-
Object type identifier for the deletion response
deleted:
type: boolean
default: true
description: >-
Whether the deletion operation was successful
additionalProperties: false
required:
- id
- object
- deleted
title: VectorStoreChunkDeleteResponse
description: >-
Response from deleting a vector store chunk.
VectorStoreFileDeleteResponse:
type: object
properties:
@ -10895,6 +11115,91 @@ components:
required:
- data
title: OpenAIListModelsResponse
VectorStoreChunkObject:
type: object
properties:
id:
type: string
description: Unique identifier for the chunk
object:
type: string
default: vector_store.file.chunk
description: >-
Object type identifier, always "vector_store.file.chunk"
created_at:
type: integer
description: Timestamp when the chunk was created
vector_store_id:
type: string
description: >-
ID of the vector store containing this chunk
file_id:
type: string
description: ID of the file containing this chunk
content:
$ref: '#/components/schemas/InterleavedContent'
description: >-
The content of the chunk, using the same format as Chunk class
metadata:
type: object
additionalProperties:
oneOf:
- type: 'null'
- type: boolean
- type: number
- type: string
- type: array
- type: object
description: Metadata associated with the chunk
embedding:
type: array
items:
type: number
description: The embedding vector for the chunk
additionalProperties: false
required:
- id
- object
- created_at
- vector_store_id
- file_id
- content
- metadata
title: VectorStoreChunkObject
description: OpenAI Vector Store Chunk object.
VectorStoreListChunksResponse:
type: object
properties:
object:
type: string
default: list
description: Object type identifier, always "list"
data:
type: array
items:
$ref: '#/components/schemas/VectorStoreChunkObject'
description: List of vector store chunk objects
first_id:
type: string
description: >-
(Optional) ID of the first chunk in the list for pagination
last_id:
type: string
description: >-
(Optional) ID of the last chunk in the list for pagination
has_more:
type: boolean
default: false
description: >-
Whether there are more chunks available beyond this page
additionalProperties: false
required:
- object
- data
- has_more
title: VectorStoreListChunksResponse
description: >-
Response from listing chunks in a vector store file.
VectorStoreListResponse:
type: object
properties:
@ -11141,6 +11446,25 @@ components:
Set of 16 key-value pairs that can be attached to an object.
additionalProperties: false
title: OpenaiUpdateVectorStoreRequest
OpenaiUpdateVectorStoreChunkRequest:
type: object
properties:
content:
$ref: '#/components/schemas/InterleavedContent'
description: Updated content for the chunk.
metadata:
type: object
additionalProperties:
oneOf:
- type: 'null'
- type: boolean
- type: number
- type: string
- type: array
- type: object
description: Updated metadata for the chunk.
additionalProperties: false
title: OpenaiUpdateVectorStoreChunkRequest
OpenaiUpdateVectorStoreFileRequest:
type: object
properties:

142
llama_stack/apis/vector_io/vector_io.py
View file

@ -426,6 +426,74 @@ class VectorStoreFileDeleteResponse(BaseModel):
deleted: bool = True
@json_schema_type
class VectorStoreChunkObject(BaseModel):
"""OpenAI Vector Store Chunk object.
:param id: Unique identifier for the chunk
:param object: Object type identifier, always "vector_store.file.chunk"
:param created_at: Timestamp when the chunk was created
:param vector_store_id: ID of the vector store containing this chunk
:param file_id: ID of the file containing this chunk
:param content: The content of the chunk, using the same format as Chunk class
:param metadata: Metadata associated with the chunk
:param embedding: The embedding vector for the chunk
"""
id: str
object: str = "vector_store.file.chunk"
created_at: int
vector_store_id: str
file_id: str
content: InterleavedContent
metadata: dict[str, Any] = Field(default_factory=dict)
embedding: list[float] | None = None
@json_schema_type
class VectorStoreListChunksResponse(BaseModel):
"""Response from listing chunks in a vector store file.
:param object: Object type identifier, always "list"
:param data: List of vector store chunk objects
:param first_id: (Optional) ID of the first chunk in the list for pagination
:param last_id: (Optional) ID of the last chunk in the list for pagination
:param has_more: Whether there are more chunks available beyond this page
"""
object: str = "list"
data: list[VectorStoreChunkObject]
first_id: str | None = None
last_id: str | None = None
has_more: bool = False
@json_schema_type
class VectorStoreChunkUpdateRequest(BaseModel):
"""Request to update a vector store chunk.
:param content: Updated content for the chunk
:param metadata: Updated metadata for the chunk
"""
content: InterleavedContent | None = None
metadata: dict[str, Any] | None = None
@json_schema_type
class VectorStoreChunkDeleteResponse(BaseModel):
"""Response from deleting a vector store chunk.
:param id: Unique identifier of the deleted chunk
:param object: Object type identifier for the deletion response
:param deleted: Whether the deletion operation was successful
"""
id: str
object: str = "vector_store.file.chunk.deleted"
deleted: bool = True
class VectorDBStore(Protocol):
def get_vector_db(self, vector_db_id: str) -> VectorDB | None: ...
@ -638,6 +706,28 @@ class VectorIO(Protocol):
"""
...
@webmethod(route="/openai/v1/vector_stores/{vector_store_id}/files/{file_id}/chunks", method="GET")
async def openai_list_vector_store_chunks(
self,
vector_store_id: str,
file_id: str,
limit: int | None = 20,
order: str | None = "desc",
after: str | None = None,
before: str | None = None,
) -> VectorStoreListChunksResponse:
"""List chunks in a vector store file.
:param vector_store_id: The ID of the vector store.
:param file_id: The ID of the file.
:param limit: Max number of chunks to return.
:param order: Sort order.
:param after: Pagination cursor.
:param before: Pagination cursor.
:returns: A VectorStoreListChunksResponse with the list of chunks.
"""
...
@webmethod(route="/openai/v1/vector_stores/{vector_store_id}/files/{file_id}/content", method="GET")
async def openai_retrieve_vector_store_file_contents(
self,
@ -681,3 +771,55 @@ class VectorIO(Protocol):
:returns: A VectorStoreFileDeleteResponse indicating the deletion status.
"""
...
@webmethod(route="/openai/v1/vector_stores/{vector_store_id}/files/{file_id}/chunks/{chunk_id}", method="GET")
async def openai_retrieve_vector_store_chunk(
self,
vector_store_id: str,
file_id: str,
chunk_id: str,
) -> VectorStoreChunkObject:
"""Retrieve a specific chunk from a vector store file.
:param vector_store_id: The ID of the vector store containing the chunk.
:param file_id: The ID of the file containing the chunk.
:param chunk_id: The ID of the chunk to retrieve.
:returns: A VectorStoreChunkObject representing the chunk.
"""
...
@webmethod(route="/openai/v1/vector_stores/{vector_store_id}/files/{file_id}/chunks/{chunk_id}", method="POST")
async def openai_update_vector_store_chunk(
self,
vector_store_id: str,
file_id: str,
chunk_id: str,
content: InterleavedContent | None = None,
metadata: dict[str, Any] | None = None,
) -> VectorStoreChunkObject:
"""Update a specific chunk in a vector store file.
:param vector_store_id: The ID of the vector store containing the chunk.
:param file_id: The ID of the file containing the chunk.
:param chunk_id: The ID of the chunk to update.
:param content: Updated content for the chunk.
:param metadata: Updated metadata for the chunk.
:returns: A VectorStoreChunkObject representing the updated chunk.
"""
...
@webmethod(route="/openai/v1/vector_stores/{vector_store_id}/files/{file_id}/chunks/{chunk_id}", method="DELETE")
async def openai_delete_vector_store_chunk(
self,
vector_store_id: str,
file_id: str,
chunk_id: str,
) -> VectorStoreChunkDeleteResponse:
"""Delete a specific chunk from a vector store file.
:param vector_store_id: The ID of the vector store containing the chunk.
:param file_id: The ID of the file containing the chunk.
:param chunk_id: The ID of the chunk to delete.
:returns: A VectorStoreChunkDeleteResponse indicating the deletion status.
"""
...

64
llama_stack/core/routers/vector_io.py
View file

@ -17,7 +17,9 @@ from llama_stack.apis.vector_io import (
QueryChunksResponse,
SearchRankingOptions,
VectorIO,
VectorStoreChunkDeleteResponse,
VectorStoreChunkingStrategy,
VectorStoreChunkObject,
VectorStoreDeleteResponse,
VectorStoreFileContentsResponse,
VectorStoreFileDeleteResponse,
@ -341,6 +343,68 @@ class VectorIORouter(VectorIO):
file_id=file_id,
)
async def openai_retrieve_vector_store_chunk(
self,
vector_store_id: str,
file_id: str,
chunk_id: str,
) -> VectorStoreChunkObject:
logger.debug(f"VectorIORouter.openai_retrieve_vector_store_chunk: {vector_store_id}, {file_id}, {chunk_id}")
return await self.routing_table.openai_retrieve_vector_store_chunk(
vector_store_id=vector_store_id,
file_id=file_id,
chunk_id=chunk_id,
)
async def openai_update_vector_store_chunk(
self,
vector_store_id: str,
file_id: str,
chunk_id: str,
content: InterleavedContent | None = None,
metadata: dict[str, Any] | None = None,
) -> VectorStoreChunkObject:
logger.debug(f"VectorIORouter.openai_update_vector_store_chunk: {vector_store_id}, {file_id}, {chunk_id}")
return await self.routing_table.openai_update_vector_store_chunk(
vector_store_id=vector_store_id,
file_id=file_id,
chunk_id=chunk_id,
content=content,
metadata=metadata,
)
async def openai_delete_vector_store_chunk(
self,
vector_store_id: str,
file_id: str,
chunk_id: str,
) -> VectorStoreChunkDeleteResponse:
logger.debug(f"VectorIORouter.openai_delete_vector_store_chunk: {vector_store_id}, {file_id}, {chunk_id}")
return await self.routing_table.openai_delete_vector_store_chunk(
vector_store_id=vector_store_id,
file_id=file_id,
chunk_id=chunk_id,
)
async def openai_list_vector_store_chunks(
self,
vector_store_id: str,
file_id: str,
limit: int | None = 20,
order: str | None = "desc",
after: str | None = None,
before: str | None = None,
):
logger.debug(f"VectorIORouter.openai_list_vector_store_chunks: {vector_store_id}, {file_id}")
return await self.routing_table.openai_list_vector_store_chunks(
vector_store_id=vector_store_id,
file_id=file_id,
limit=limit,
order=order,
after=after,
before=before,
)
async def health(self) -> dict[str, HealthResponse]:
health_statuses = {}
timeout = 1 # increasing the timeout to 1 second for health checks

70
llama_stack/core/routing_tables/vector_dbs.py
View file

@ -13,13 +13,17 @@ from llama_stack.apis.models import ModelType
from llama_stack.apis.resource import ResourceType
from llama_stack.apis.vector_dbs import ListVectorDBsResponse, VectorDB, VectorDBs
from llama_stack.apis.vector_io.vector_io import (
InterleavedContent,
SearchRankingOptions,
VectorStoreChunkDeleteResponse,
VectorStoreChunkingStrategy,
VectorStoreChunkObject,
VectorStoreDeleteResponse,
VectorStoreFileContentsResponse,
VectorStoreFileDeleteResponse,
VectorStoreFileObject,
VectorStoreFileStatus,
VectorStoreListChunksResponse,
VectorStoreObject,
VectorStoreSearchResponsePage,
)
@ -227,3 +231,69 @@ class VectorDBsRoutingTable(CommonRoutingTableImpl, VectorDBs):
vector_store_id=vector_store_id,
file_id=file_id,
)
async def openai_retrieve_vector_store_chunk(
self,
vector_store_id: str,
file_id: str,
chunk_id: str,
) -> VectorStoreChunkObject:
await self.assert_action_allowed("read", "vector_db", vector_store_id)
provider = await self.get_provider_impl(vector_store_id)
return await provider.openai_retrieve_vector_store_chunk(
vector_store_id=vector_store_id,
file_id=file_id,
chunk_id=chunk_id,
)
async def openai_update_vector_store_chunk(
self,
vector_store_id: str,
file_id: str,
chunk_id: str,
content: InterleavedContent | None = None,
metadata: dict[str, Any] | None = None,
) -> VectorStoreChunkObject:
await self.assert_action_allowed("update", "vector_db", vector_store_id)
provider = await self.get_provider_impl(vector_store_id)
return await provider.openai_update_vector_store_chunk(
vector_store_id=vector_store_id,
file_id=file_id,
chunk_id=chunk_id,
content=content,
metadata=metadata,
)
async def openai_delete_vector_store_chunk(
self,
vector_store_id: str,
file_id: str,
chunk_id: str,
) -> VectorStoreChunkDeleteResponse:
await self.assert_action_allowed("delete", "vector_db", vector_store_id)
provider = await self.get_provider_impl(vector_store_id)
return await provider.openai_delete_vector_store_chunk(
vector_store_id=vector_store_id,
file_id=file_id,
chunk_id=chunk_id,
)
async def openai_list_vector_store_chunks(
self,
vector_store_id: str,
file_id: str,
limit: int | None = 20,
order: str | None = "desc",
after: str | None = None,
before: str | None = None,
) -> VectorStoreListChunksResponse:
await self.assert_action_allowed("read", "vector_db", vector_store_id)
provider = await self.get_provider_impl(vector_store_id)
return await provider.openai_list_vector_store_chunks(
vector_store_id=vector_store_id,
file_id=file_id,
limit=limit,
order=order,
after=after,
before=before,
)

241
llama_stack/providers/utils/memory/openai_vector_store_mixin.py
View file

@ -15,14 +15,17 @@ from typing import Any
from llama_stack.apis.common.errors import VectorStoreNotFoundError
from llama_stack.apis.files import Files, OpenAIFileObject
from llama_stack.apis.inference import InterleavedContent
from llama_stack.apis.vector_dbs import VectorDB
from llama_stack.apis.vector_io import (
Chunk,
QueryChunksResponse,
SearchRankingOptions,
VectorStoreChunkDeleteResponse,
VectorStoreChunkingStrategy,
VectorStoreChunkingStrategyAuto,
VectorStoreChunkingStrategyStatic,
VectorStoreChunkObject,
VectorStoreContent,
VectorStoreDeleteResponse,
VectorStoreFileContentsResponse,
@ -31,6 +34,7 @@ from llama_stack.apis.vector_io import (
VectorStoreFileLastError,
VectorStoreFileObject,
VectorStoreFileStatus,
VectorStoreListChunksResponse,
VectorStoreListFilesResponse,
VectorStoreListResponse,
VectorStoreObject,
@ -109,7 +113,14 @@ class OpenAIVectorStoreMixin(ABC):
assert self.kvstore
meta_key = f"{OPENAI_VECTOR_STORES_FILES_PREFIX}{store_id}:{file_id}"
await self.kvstore.set(key=meta_key, value=json.dumps(file_info))
# delete old file data to properly update content
contents_prefix = f"{OPENAI_VECTOR_STORES_FILES_CONTENTS_PREFIX}{store_id}:{file_id}:"
end_key = f"{contents_prefix}\xff"
old_keys = await self.kvstore.keys_in_range(contents_prefix, end_key)
for old_key in old_keys:
await self.kvstore.delete(old_key)
for idx, chunk in enumerate(file_contents):
await self.kvstore.set(key=f"{contents_prefix}{idx}", value=json.dumps(chunk))
@ -791,3 +802,233 @@ class OpenAIVectorStoreMixin(ABC):
id=file_id,
deleted=True,
)
async def openai_retrieve_vector_store_chunk(
self,
vector_store_id: str,
file_id: str,
chunk_id: str,
) -> VectorStoreChunkObject:
"""Retrieve a specific chunk from a vector store file."""
if vector_store_id not in self.openai_vector_stores:
raise VectorStoreNotFoundError(vector_store_id)
store_info = self.openai_vector_stores[vector_store_id]
if file_id not in store_info["file_ids"]:
raise ValueError(f"File {file_id} not found in vector store {vector_store_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]
target_chunk = None
for chunk in chunks:
if chunk.chunk_id == chunk_id:
target_chunk = chunk
break
if target_chunk is None:
raise ValueError(f"Chunk {chunk_id} not found in file {file_id}")
file_info = await self._load_openai_vector_store_file(vector_store_id, file_id)
return VectorStoreChunkObject(
id=chunk_id,
created_at=file_info.get("created_at", int(time.time())),
vector_store_id=vector_store_id,
file_id=file_id,
content=target_chunk.content,
metadata=target_chunk.metadata,
embedding=target_chunk.embedding,
)
async def openai_update_vector_store_chunk(
self,
vector_store_id: str,
file_id: str,
chunk_id: str,
content: InterleavedContent | None = None,
metadata: dict[str, Any] | None = None,
) -> VectorStoreChunkObject:
"""Update a specific chunk in a vector store file."""
if vector_store_id not in self.openai_vector_stores:
raise VectorStoreNotFoundError(vector_store_id)
store_info = self.openai_vector_stores[vector_store_id]
if file_id not in store_info["file_ids"]:
raise ValueError(f"File {file_id} not found in vector store {vector_store_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]
target_chunk_index = None
for i, chunk in enumerate(chunks):
if chunk.chunk_id == chunk_id:
target_chunk_index = i
break
if target_chunk_index is None:
raise ValueError(f"Chunk {chunk_id} not found in file {file_id}")
# updating content
target_chunk = chunks[target_chunk_index]
if content is not None:
target_chunk.content = content
# delete old chunk and update
await self.delete_chunks(vector_store_id, [chunk_id])
await self.insert_chunks(vector_store_id, [target_chunk])
if metadata is not None:
target_chunk.metadata.update(metadata)
chunks[target_chunk_index] = target_chunk
dict_chunks = [c.model_dump() for c in chunks]
file_info = await self._load_openai_vector_store_file(vector_store_id, file_id)
await self._save_openai_vector_store_file(vector_store_id, file_id, file_info, dict_chunks)
return VectorStoreChunkObject(
id=chunk_id,
created_at=file_info.get("created_at", int(time.time())),
vector_store_id=vector_store_id,
file_id=file_id,
content=target_chunk.content,
metadata=target_chunk.metadata,
embedding=target_chunk.embedding,
)
async def openai_delete_vector_store_chunk(
self,
vector_store_id: str,
file_id: str,
chunk_id: str,
) -> VectorStoreChunkDeleteResponse:
"""Delete a specific chunk from a vector store file."""
if vector_store_id not in self.openai_vector_stores:
raise VectorStoreNotFoundError(vector_store_id)
store_info = self.openai_vector_stores[vector_store_id]
if file_id not in store_info["file_ids"]:
raise ValueError(f"File {file_id} not found in vector store {vector_store_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]
target_chunk_index = None
for i, chunk in enumerate(chunks):
if chunk.chunk_id == chunk_id:
target_chunk_index = i
break
if target_chunk_index is None:
raise ValueError(f"Chunk {chunk_id} not found in file {file_id}")
await self.delete_chunks(vector_store_id, [chunk_id])
dict_chunks.pop(target_chunk_index)
file_info = await self._load_openai_vector_store_file(vector_store_id, file_id)
await self._save_openai_vector_store_file(vector_store_id, file_id, file_info, dict_chunks)
return VectorStoreChunkDeleteResponse(
id=chunk_id,
deleted=True,
)
async def openai_list_vector_store_chunks(
self,
vector_store_id: str,
file_id: str,
limit: int | None = 20,
order: str | None = "desc",
after: str | None = None,
before: str | None = None,
) -> VectorStoreListChunksResponse:
"""List chunks in a vector store file."""
if vector_store_id not in self.openai_vector_stores:
raise VectorStoreNotFoundError(vector_store_id)
store_info = self.openai_vector_stores[vector_store_id]
if file_id not in store_info["file_ids"]:
raise ValueError(f"File {file_id} not found in vector store {vector_store_id}")
dict_chunks = await self._load_openai_vector_store_file_contents(vector_store_id, file_id)
file_info = await self._load_openai_vector_store_file(vector_store_id, file_id)
chunk_ids = []
for dict_chunk in dict_chunks:
chunk = Chunk.model_validate(dict_chunk)
if chunk.chunk_id:
chunk_ids.append(chunk.chunk_id)
# TODO: Add abstract method query_all_chunks() to properly filter by file_id and vector_db_id, this is a temporary hack
chunks = []
if chunk_ids:
try:
file_filter = {"type": "eq", "key": "file_id", "value": file_id}
query_result = await self.query_chunks(
vector_db_id=vector_store_id,
query="*", # wildcard query to get all chunks
params={
"max_chunks": len(chunk_ids) * 2,
"score_threshold": 0.0,
"filters": file_filter,
},
)
chunk_id_set = set(chunk_ids)
chunks = [chunk for chunk in query_result.chunks if chunk.chunk_id in chunk_id_set]
except Exception as e:
logger.warning(f"Failed to query vector database for chunks: {e}")
# Fallback to KV store chunks if vector DB query fails
chunks = [Chunk.model_validate(c) for c in dict_chunks]
chunk_objects = []
for chunk in chunks:
chunk_obj = VectorStoreChunkObject(
id=chunk.chunk_id,
created_at=file_info.get("created_at", int(time.time())),
vector_store_id=vector_store_id,
file_id=file_id,
content=chunk.content,
metadata=chunk.metadata,
embedding=chunk.embedding,
)
chunk_objects.append(chunk_obj)
if order == "desc":
chunk_objects.sort(key=lambda x: x.created_at, reverse=True)
else:
chunk_objects.sort(key=lambda x: x.created_at)
start_idx = 0
end_idx = len(chunk_objects)
if after:
# find index after 'after' chunk
for i, chunk_obj in enumerate(chunk_objects):
if chunk_obj.id == after:
start_idx = i + 1
break
if before:
# find index before 'before' chunk
for i, chunk_obj in enumerate(chunk_objects):
if chunk_obj.id == before:
end_idx = i
break
if limit:
if end_idx - start_idx > limit:
end_idx = start_idx + limit
paginated_chunks = chunk_objects[start_idx:end_idx]
first_id = paginated_chunks[0].id if paginated_chunks else None
last_id = paginated_chunks[-1].id if paginated_chunks else None
has_more = end_idx < len(chunk_objects)
return VectorStoreListChunksResponse(
data=paginated_chunks,
first_id=first_id,
last_id=last_id,
has_more=has_more,
)

17
llama_stack/ui/components/vector-stores/vector-store-detail.tsx
View file

@ -1,9 +1,11 @@
"use client";
import { useRouter } from "next/navigation";
import type { VectorStore } from "llama-stack-client/resources/vector-stores/vector-stores";
import type { VectorStoreFile } from "llama-stack-client/resources/vector-stores/files";
import { Card, CardContent, CardHeader, CardTitle } from "@/components/ui/card";
import { Skeleton } from "@/components/ui/skeleton";
import { Button } from "@/components/ui/button";
import {
DetailLoadingView,
DetailErrorView,
@ -42,6 +44,11 @@ export function VectorStoreDetailView({
id,
}: VectorStoreDetailViewProps) {
const title = "Vector Store Details";
const router = useRouter();
const handleFileClick = (fileId: string) => {
router.push(`/logs/vector-stores/${id}/files/${fileId}`);
};
if (errorStore) {
return <DetailErrorView title={title} id={id} error={errorStore} />;
@ -80,7 +87,15 @@ export function VectorStoreDetailView({
<TableBody>
{files.map((file) => (
<TableRow key={file.id}>
<TableCell>{file.id}</TableCell>
<TableCell>
<Button
variant="link"
className="p-0 h-auto font-mono text-blue-600 hover:text-blue-800 dark:text-blue-400 dark:hover:text-blue-300"
onClick={() => handleFileClick(file.id)}
>
{file.id}
</Button>
</TableCell>
<TableCell>{file.status}</TableCell>
<TableCell>
{new Date(file.created_at * 1000).toLocaleString()}

507
tests/unit/providers/vector_io/test_vector_io_openai_vector_stores.py
View file

@ -11,6 +11,7 @@ from unittest.mock import AsyncMock
import numpy as np
import pytest
from llama_stack.apis.common.errors import VectorStoreNotFoundError
from llama_stack.apis.vector_dbs import VectorDB
from llama_stack.apis.vector_io import Chunk, QueryChunksResponse
from llama_stack.providers.remote.vector_io.milvus.milvus import VECTOR_DBS_PREFIX
@ -294,3 +295,509 @@ async def test_delete_openai_vector_store_file_from_storage(vector_io_adapter, t
assert loaded_file_info == {}
loaded_contents = await vector_io_adapter._load_openai_vector_store_file_contents(store_id, file_id)
assert loaded_contents == []
async def test_openai_retrieve_vector_store_chunk(vector_io_adapter):
"""Test retrieving a specific chunk from a vector store file."""
store_id = "vs_1234"
file_id = "file_1234"
chunk_id = "chunk_001"
store_info = {
"id": store_id,
"file_ids": [file_id],
"created_at": int(time.time()),
}
vector_io_adapter.openai_vector_stores[store_id] = store_info
file_info = {
"id": file_id,
"status": "completed",
"vector_store_id": store_id,
"filename": "test_file.txt",
"created_at": int(time.time()),
}
file_contents = [
{
"content": "First chunk content",
"stored_chunk_id": chunk_id,
"metadata": {"file_id": file_id, "position": 0},
"chunk_metadata": {"chunk_id": chunk_id},
},
{
"content": "Second chunk content",
"stored_chunk_id": "chunk_002",
"metadata": {"file_id": file_id, "position": 1},
"chunk_metadata": {"chunk_id": "chunk_002"},
},
]
await vector_io_adapter._save_openai_vector_store_file(store_id, file_id, file_info, file_contents)
chunk_object = await vector_io_adapter.openai_retrieve_vector_store_chunk(
vector_store_id=store_id, file_id=file_id, chunk_id=chunk_id
)
assert chunk_object.id == chunk_id
assert chunk_object.vector_store_id == store_id
assert chunk_object.file_id == file_id
assert chunk_object.object == "vector_store.file.chunk"
assert len(chunk_object.content) > 0
assert chunk_object.content[0].type == "text"
assert chunk_object.content[0].text == "First chunk content"
assert chunk_object.metadata["file_id"] == file_id
assert chunk_object.metadata["position"] == 0
async def test_openai_retrieve_vector_store_chunk_not_found(vector_io_adapter):
"""Test retrieving a non-existent chunk raises appropriate error."""
store_id = "vs_1234"
file_id = "file_1234"
chunk_id = "nonexistent_chunk"
store_info = {
"id": store_id,
"file_ids": [file_id],
"created_at": int(time.time()),
}
vector_io_adapter.openai_vector_stores[store_id] = store_info
file_info = {"id": file_id, "created_at": int(time.time())}
await vector_io_adapter._save_openai_vector_store_file(store_id, file_id, file_info, [])
with pytest.raises(ValueError, match="Chunk nonexistent_chunk not found"):
await vector_io_adapter.openai_retrieve_vector_store_chunk(
vector_store_id=store_id, file_id=file_id, chunk_id=chunk_id
)
async def test_openai_update_vector_store_chunk_metadata_only(vector_io_adapter):
"""Test updating only the metadata of a chunk."""
store_id = "vs_1234"
file_id = "file_1234"
chunk_id = "chunk_001"
store_info = {
"id": store_id,
"file_ids": [file_id],
"created_at": int(time.time()),
}
vector_io_adapter.openai_vector_stores[store_id] = store_info
file_info = {
"id": file_id,
"status": "completed",
"vector_store_id": store_id,
"filename": "test_file.txt",
"created_at": int(time.time()),
}
original_content = "Original chunk content"
file_contents = [
{
"content": original_content,
"stored_chunk_id": chunk_id,
"metadata": {"file_id": file_id, "version": 1},
"chunk_metadata": {"chunk_id": chunk_id},
}
]
await vector_io_adapter._save_openai_vector_store_file(store_id, file_id, file_info, file_contents)
vector_io_adapter.delete_chunks = AsyncMock()
vector_io_adapter.insert_chunks = AsyncMock()
new_metadata = {"file_id": file_id, "version": 2, "updated": True}
updated_chunk = await vector_io_adapter.openai_update_vector_store_chunk(
vector_store_id=store_id, file_id=file_id, chunk_id=chunk_id, metadata=new_metadata
)
vector_io_adapter.delete_chunks.assert_not_called()
vector_io_adapter.insert_chunks.assert_not_called()
assert updated_chunk.id == chunk_id
assert updated_chunk.metadata["version"] == 2
assert updated_chunk.metadata["updated"] is True
assert updated_chunk.content[0].text == original_content
async def test_openai_update_vector_store_chunk_content(vector_io_adapter):
"""Test updating the content of a chunk."""
store_id = "vs_1234"
file_id = "file_1234"
chunk_id = "chunk_001"
store_info = {
"id": store_id,
"file_ids": [file_id],
"created_at": int(time.time()),
}
vector_io_adapter.openai_vector_stores[store_id] = store_info
file_info = {
"id": file_id,
"status": "completed",
"vector_store_id": store_id,
"filename": "test_file.txt",
"created_at": int(time.time()),
}
file_contents = [
{
"content": "Original chunk content",
"stored_chunk_id": chunk_id,
"metadata": {"file_id": file_id},
"chunk_metadata": {"chunk_id": chunk_id},
}
]
await vector_io_adapter._save_openai_vector_store_file(store_id, file_id, file_info, file_contents)
vector_io_adapter.delete_chunks = AsyncMock()
vector_io_adapter.insert_chunks = AsyncMock()
new_content = "Updated chunk content"
updated_chunk = await vector_io_adapter.openai_update_vector_store_chunk(
vector_store_id=store_id, file_id=file_id, chunk_id=chunk_id, content=new_content
)
vector_io_adapter.delete_chunks.assert_awaited_once_with(store_id, [chunk_id])
vector_io_adapter.insert_chunks.assert_awaited_once()
assert updated_chunk.id == chunk_id
assert updated_chunk.content[0].text == new_content
async def test_openai_update_vector_store_chunk_both_content_and_metadata(vector_io_adapter):
"""Test updating both content and metadata of a chunk."""
store_id = "vs_1234"
file_id = "file_1234"
chunk_id = "chunk_001"
store_info = {
"id": store_id,
"file_ids": [file_id],
"created_at": int(time.time()),
}
vector_io_adapter.openai_vector_stores[store_id] = store_info
file_info = {
"id": file_id,
"status": "completed",
"vector_store_id": store_id,
"filename": "test_file.txt",
"created_at": int(time.time()),
}
file_contents = [
{
"content": "Original chunk content",
"stored_chunk_id": chunk_id,
"metadata": {"file_id": file_id, "version": 1},
"chunk_metadata": {"chunk_id": chunk_id},
}
]
await vector_io_adapter._save_openai_vector_store_file(store_id, file_id, file_info, file_contents)
vector_io_adapter.delete_chunks = AsyncMock()
vector_io_adapter.insert_chunks = AsyncMock()
new_content = "Updated chunk content"
new_metadata = {"file_id": file_id, "version": 2, "updated": True}
updated_chunk = await vector_io_adapter.openai_update_vector_store_chunk(
vector_store_id=store_id, file_id=file_id, chunk_id=chunk_id, content=new_content, metadata=new_metadata
)
vector_io_adapter.delete_chunks.assert_awaited_once_with(store_id, [chunk_id])
vector_io_adapter.insert_chunks.assert_awaited_once()
assert updated_chunk.id == chunk_id
assert updated_chunk.content[0].text == new_content
assert updated_chunk.metadata["version"] == 2
assert updated_chunk.metadata["updated"] is True
async def test_openai_delete_vector_store_chunk(vector_io_adapter):
"""Test deleting a specific chunk from a vector store file."""
store_id = "vs_1234"
file_id = "file_1234"
chunk_id_to_delete = "chunk_001"
chunk_id_to_keep = "chunk_002"
store_info = {
"id": store_id,
"file_ids": [file_id],
"created_at": int(time.time()),
}
vector_io_adapter.openai_vector_stores[store_id] = store_info
file_info = {
"id": file_id,
"status": "completed",
"vector_store_id": store_id,
"filename": "test_file.txt",
"created_at": int(time.time()),
}
file_contents = [
{
"content": "First chunk content",
"stored_chunk_id": chunk_id_to_delete,
"metadata": {"file_id": file_id, "position": 0},
"chunk_metadata": {"chunk_id": chunk_id_to_delete},
},
{
"content": "Second chunk content",
"stored_chunk_id": chunk_id_to_keep,
"metadata": {"file_id": file_id, "position": 1},
"chunk_metadata": {"chunk_id": chunk_id_to_keep},
},
]
await vector_io_adapter._save_openai_vector_store_file(store_id, file_id, file_info, file_contents)
vector_io_adapter.delete_chunks = AsyncMock()
delete_response = await vector_io_adapter.openai_delete_vector_store_chunk(
vector_store_id=store_id, file_id=file_id, chunk_id=chunk_id_to_delete
)
vector_io_adapter.delete_chunks.assert_awaited_once_with(store_id, [chunk_id_to_delete])
assert delete_response.id == chunk_id_to_delete
assert delete_response.object == "vector_store.file.chunk.deleted"
assert delete_response.deleted is True
remaining_contents = await vector_io_adapter._load_openai_vector_store_file_contents(store_id, file_id)
assert len(remaining_contents) == 1
assert remaining_contents[0]["stored_chunk_id"] == chunk_id_to_keep
async def test_openai_delete_vector_store_chunk_not_found(vector_io_adapter):
"""Test deleting a non-existent chunk raises appropriate error."""
store_id = "vs_1234"
file_id = "file_1234"
chunk_id = "nonexistent_chunk"
store_info = {
"id": store_id,
"file_ids": [file_id],
"created_at": int(time.time()),
}
vector_io_adapter.openai_vector_stores[store_id] = store_info
file_info = {"id": file_id, "created_at": int(time.time())}
await vector_io_adapter._save_openai_vector_store_file(store_id, file_id, file_info, [])
with pytest.raises(ValueError, match="Chunk nonexistent_chunk not found"):
await vector_io_adapter.openai_delete_vector_store_chunk(
vector_store_id=store_id, file_id=file_id, chunk_id=chunk_id
)
async def test_chunk_operations_with_nonexistent_vector_store(vector_io_adapter):
"""Test that chunk operations raise errors for non-existent vector stores."""
store_id = "nonexistent_store"
file_id = "file_1234"
chunk_id = "chunk_001"
with pytest.raises(VectorStoreNotFoundError):
await vector_io_adapter.openai_retrieve_vector_store_chunk(
vector_store_id=store_id, file_id=file_id, chunk_id=chunk_id
)
with pytest.raises(VectorStoreNotFoundError):
await vector_io_adapter.openai_update_vector_store_chunk(
vector_store_id=store_id, file_id=file_id, chunk_id=chunk_id, metadata={"test": "value"}
)
with pytest.raises(VectorStoreNotFoundError):
await vector_io_adapter.openai_delete_vector_store_chunk(
vector_store_id=store_id, file_id=file_id, chunk_id=chunk_id
)
async def test_chunk_operations_with_nonexistent_file(vector_io_adapter):
"""Test that chunk operations raise errors for non-existent files."""
store_id = "vs_1234"
file_id = "nonexistent_file"
chunk_id = "chunk_001"
store_info = {
"id": store_id,
"file_ids": [],
"created_at": int(time.time()),
}
vector_io_adapter.openai_vector_stores[store_id] = store_info
with pytest.raises(ValueError, match=f"File {file_id} not found in vector store"):
await vector_io_adapter.openai_retrieve_vector_store_chunk(
vector_store_id=store_id, file_id=file_id, chunk_id=chunk_id
)
with pytest.raises(ValueError, match=f"File {file_id} not found in vector store"):
await vector_io_adapter.openai_update_vector_store_chunk(
vector_store_id=store_id, file_id=file_id, chunk_id=chunk_id, metadata={"test": "value"}
)
with pytest.raises(ValueError, match=f"File {file_id} not found in vector store"):
await vector_io_adapter.openai_delete_vector_store_chunk(
vector_store_id=store_id, file_id=file_id, chunk_id=chunk_id
)
with pytest.raises(ValueError, match=f"File {file_id} not found in vector store"):
await vector_io_adapter.openai_list_vector_store_chunks(vector_store_id=store_id, file_id=file_id)
async def test_openai_list_vector_store_chunks(vector_io_adapter):
"""Test listing chunks in a vector store file."""
store_id = "test_store_123"
await vector_io_adapter.openai_create_vector_store(
vector_store_id=store_id,
name="Test Store",
embedding_model="test_model",
embedding_dimension=512,
)
test_content = "This is test content for chunk listing."
test_metadata = {"source": "test_file", "chunk_number": 1}
test_embedding = [0.1] * 512
chunk1 = Chunk(
content=test_content + " First chunk.",
metadata={**test_metadata, "chunk_id": 1},
embedding=test_embedding,
chunk_id="chunk_1",
)
chunk2 = Chunk(
content=test_content + " Second chunk.",
metadata={**test_metadata, "chunk_id": 2},
embedding=[0.2] * 512,
chunk_id="chunk_2",
)
chunk3 = Chunk(
content=test_content + " Third chunk.",
metadata={**test_metadata, "chunk_id": 3},
embedding=[0.3] * 512,
chunk_id="chunk_3",
)
await vector_io_adapter.insert_chunks(store_id, [chunk1, chunk2, chunk3])
file_id = "test_file_456"
file_info = {
"id": file_id,
"object": "vector_store.file",
"created_at": int(time.time()),
"vector_store_id": store_id,
"status": "completed",
"usage_bytes": 1024,
"chunking_strategy": {"type": "static", "static": {"max_chunk_size_tokens": 800, "chunk_overlap_tokens": 400}},
"filename": "test_file.txt",
}
dict_chunks = [chunk1.model_dump(), chunk2.model_dump(), chunk3.model_dump()]
await vector_io_adapter._save_openai_vector_store_file(store_id, file_id, file_info, dict_chunks)
vector_io_adapter.openai_vector_stores[store_id]["file_ids"].append(file_id)
response = await vector_io_adapter.openai_list_vector_store_chunks(vector_store_id=store_id, file_id=file_id)
assert response.object == "list"
assert len(response.data) == 3
assert response.has_more is False
assert response.first_id is not None
assert response.last_id is not None
chunk_ids = [chunk.id for chunk in response.data]
assert "chunk_1" in chunk_ids
assert "chunk_2" in chunk_ids
assert "chunk_3" in chunk_ids
for chunk in response.data:
assert chunk.embedding is not None
assert len(chunk.embedding) == 512
assert chunk.vector_store_id == store_id
assert chunk.file_id == file_id
limited_response = await vector_io_adapter.openai_list_vector_store_chunks(
vector_store_id=store_id, file_id=file_id, limit=2
)
assert len(limited_response.data) == 2
assert limited_response.has_more is True
desc_response = await vector_io_adapter.openai_list_vector_store_chunks(
vector_store_id=store_id, file_id=file_id, order="desc"
)
assert len(desc_response.data) == 3
asc_response = await vector_io_adapter.openai_list_vector_store_chunks(
vector_store_id=store_id, file_id=file_id, order="asc"
)
assert len(asc_response.data) == 3
first_chunk_id = response.data[0].id
after_response = await vector_io_adapter.openai_list_vector_store_chunks(
vector_store_id=store_id, file_id=file_id, after=first_chunk_id
)
assert len(after_response.data) <= 2
after_chunk_ids = [chunk.id for chunk in after_response.data]
assert first_chunk_id not in after_chunk_ids
async def test_openai_list_vector_store_chunks_empty_file(vector_io_adapter):
"""Test listing chunks in an empty file."""
store_id = "test_store_empty"
await vector_io_adapter.openai_create_vector_store(
vector_store_id=store_id,
name="Test Store",
embedding_model="test_model",
embedding_dimension=512,
)
file_id = "empty_file"
file_info = {
"id": file_id,
"object": "vector_store.file",
"created_at": int(time.time()),
"vector_store_id": store_id,
"status": "completed",
"usage_bytes": 0,
"chunking_strategy": {"type": "static", "static": {"max_chunk_size_tokens": 800, "chunk_overlap_tokens": 400}},
"filename": "empty_file.txt",
}
await vector_io_adapter._save_openai_vector_store_file(store_id, file_id, file_info, [])
vector_io_adapter.openai_vector_stores[store_id]["file_ids"].append(file_id)
response = await vector_io_adapter.openai_list_vector_store_chunks(vector_store_id=store_id, file_id=file_id)
assert response.object == "list"
assert len(response.data) == 0
assert response.has_more is False
assert response.first_id is None
assert response.last_id is None
async def test_openai_list_vector_store_chunks_nonexistent_resources(vector_io_adapter):
with pytest.raises(VectorStoreNotFoundError):
await vector_io_adapter.openai_list_vector_store_chunks(vector_store_id="nonexistent_store", file_id="any_file")
store_id = "test_store_list"
await vector_io_adapter.openai_create_vector_store(
vector_store_id=store_id,
name="Test Store",
embedding_model="test_model",
embedding_dimension=512,
)
with pytest.raises(ValueError, match="File nonexistent_file not found in vector store"):
await vector_io_adapter.openai_list_vector_store_chunks(vector_store_id=store_id, file_id="nonexistent_file")