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chore!: BREAKING CHANGE: vector_db_id -> vector_store_id (#3923)

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# What does this PR do?


## Test Plan
CI
vector_io tests will fail until next client sync

passed with
https://github.com/llamastack/llama-stack-client-python/pull/286 checked
out locally
This commit is contained in:
ehhuang 2025-10-27 14:26:06 -07:00 • committed by GitHub
parent b6954c9882
commit b7dd3f5c56
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GPG key ID: B5690EEEBB952194
29 changed files with 175 additions and 175 deletions
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20
client-sdks/stainless/openapi.yml
View file

@ -9862,7 +9862,7 @@ components:
$ref: '#/components/schemas/RAGDocument'
description: >-
List of documents to index in the RAG system
vector_db_id:
vector_store_id:
type: string
description: >-
ID of the vector database to store the document embeddings
@ -9873,7 +9873,7 @@ components:
additionalProperties: false
required:
- documents
- vector_db_id
- vector_store_id
- chunk_size_in_tokens
title: InsertRequest
DefaultRAGQueryGeneratorConfig:
@ -10044,7 +10044,7 @@ components:
$ref: '#/components/schemas/InterleavedContent'
description: >-
The query content to search for in the indexed documents
vector_db_ids:
vector_store_ids:
type: array
items:
type: string
@ -10057,7 +10057,7 @@ components:
additionalProperties: false
required:
- content
- vector_db_ids
- vector_store_ids
title: QueryRequest
RAGQueryResult:
type: object
@ -10281,7 +10281,7 @@ components:
InsertChunksRequest:
type: object
properties:
vector_db_id:
vector_store_id:
type: string
description: >-
The identifier of the vector database to insert the chunks into.
@ -10300,13 +10300,13 @@ components:
description: The time to live of the chunks.
additionalProperties: false
required:
- vector_db_id
- vector_store_id
- chunks
title: InsertChunksRequest
QueryChunksRequest:
type: object
properties:
vector_db_id:
vector_store_id:
type: string
description: >-
The identifier of the vector database to query.
@ -10326,7 +10326,7 @@ components:
description: The parameters of the query.
additionalProperties: false
required:
- vector_db_id
- vector_store_id
- query
title: QueryChunksRequest
QueryChunksResponse:
@ -11844,7 +11844,7 @@ components:
description: Type of the step in an agent turn.
const: memory_retrieval
default: memory_retrieval
vector_db_ids:
vector_store_ids:
type: string
description: >-
The IDs of the vector databases to retrieve context from.
@ -11857,7 +11857,7 @@ components:
- turn_id
- step_id
- step_type
- vector_db_ids
- vector_store_ids
- inserted_context
title: MemoryRetrievalStep
description: >-

20
docs/docs/providers/vector_io/inline_sqlite-vec.mdx
View file

@ -72,14 +72,14 @@ description: |
Example with hybrid search:
```python
response = await vector_io.query_chunks(
vector_db_id="my_db",
vector_store_id="my_db",
query="your query here",
params={"mode": "hybrid", "max_chunks": 3, "score_threshold": 0.7},
)
# Using RRF ranker
response = await vector_io.query_chunks(
vector_db_id="my_db",
vector_store_id="my_db",
query="your query here",
params={
"mode": "hybrid",
@ -91,7 +91,7 @@ description: |
# Using weighted ranker
response = await vector_io.query_chunks(
vector_db_id="my_db",
vector_store_id="my_db",
query="your query here",
params={
"mode": "hybrid",
@ -105,7 +105,7 @@ description: |
Example with explicit vector search:
```python
response = await vector_io.query_chunks(
vector_db_id="my_db",
vector_store_id="my_db",
query="your query here",
params={"mode": "vector", "max_chunks": 3, "score_threshold": 0.7},
)
@ -114,7 +114,7 @@ description: |
Example with keyword search:
```python
response = await vector_io.query_chunks(
vector_db_id="my_db",
vector_store_id="my_db",
query="your query here",
params={"mode": "keyword", "max_chunks": 3, "score_threshold": 0.7},
)
@ -277,14 +277,14 @@ The SQLite-vec provider supports three search modes:
Example with hybrid search:
```python
response = await vector_io.query_chunks(
vector_db_id="my_db",
vector_store_id="my_db",
query="your query here",
params={"mode": "hybrid", "max_chunks": 3, "score_threshold": 0.7},
)
# Using RRF ranker
response = await vector_io.query_chunks(
vector_db_id="my_db",
vector_store_id="my_db",
query="your query here",
params={
"mode": "hybrid",
@ -296,7 +296,7 @@ response = await vector_io.query_chunks(
# Using weighted ranker
response = await vector_io.query_chunks(
vector_db_id="my_db",
vector_store_id="my_db",
query="your query here",
params={
"mode": "hybrid",
@ -310,7 +310,7 @@ response = await vector_io.query_chunks(
Example with explicit vector search:
```python
response = await vector_io.query_chunks(
vector_db_id="my_db",
vector_store_id="my_db",
query="your query here",
params={"mode": "vector", "max_chunks": 3, "score_threshold": 0.7},
)
@ -319,7 +319,7 @@ response = await vector_io.query_chunks(
Example with keyword search:
```python
response = await vector_io.query_chunks(
vector_db_id="my_db",
vector_store_id="my_db",
query="your query here",
params={"mode": "keyword", "max_chunks": 3, "score_threshold": 0.7},
)

4
docs/static/deprecated-llama-stack-spec.html vendored
View file

@ -4390,7 +4390,7 @@
"const": "memory_retrieval",
"default": "memory_retrieval"
},
"vector_db_ids": {
"vector_store_ids": {
"type": "string",
"description": "The IDs of the vector databases to retrieve context from."
},
@ -4404,7 +4404,7 @@
"turn_id",
"step_id",
"step_type",
"vector_db_ids",
"vector_store_ids",
"inserted_context"
],
"title": "MemoryRetrievalStep",

4
docs/static/deprecated-llama-stack-spec.yaml vendored
View file

@ -3252,7 +3252,7 @@ components:
description: Type of the step in an agent turn.
const: memory_retrieval
default: memory_retrieval
vector_db_ids:
vector_store_ids:
type: string
description: >-
The IDs of the vector databases to retrieve context from.
@ -3265,7 +3265,7 @@ components:
- turn_id
- step_id
- step_type
- vector_db_ids
- vector_store_ids
- inserted_context
title: MemoryRetrievalStep
description: >-

4
docs/static/experimental-llama-stack-spec.html vendored
View file

@ -2865,7 +2865,7 @@
"const": "memory_retrieval",
"default": "memory_retrieval"
},
"vector_db_ids": {
"vector_store_ids": {
"type": "string",
"description": "The IDs of the vector databases to retrieve context from."
},
@ -2879,7 +2879,7 @@
"turn_id",
"step_id",
"step_type",
"vector_db_ids",
"vector_store_ids",
"inserted_context"
],
"title": "MemoryRetrievalStep",

4
docs/static/experimental-llama-stack-spec.yaml vendored
View file

@ -2085,7 +2085,7 @@ components:
description: Type of the step in an agent turn.
const: memory_retrieval
default: memory_retrieval
vector_db_ids:
vector_store_ids:
type: string
description: >-
The IDs of the vector databases to retrieve context from.
@ -2098,7 +2098,7 @@ components:
- turn_id
- step_id
- step_type
- vector_db_ids
- vector_store_ids
- inserted_context
title: MemoryRetrievalStep
description: >-

16
docs/static/llama-stack-spec.html vendored
View file

@ -11412,7 +11412,7 @@
},
"description": "List of documents to index in the RAG system"
},
"vector_db_id": {
"vector_store_id": {
"type": "string",
"description": "ID of the vector database to store the document embeddings"
},
@ -11424,7 +11424,7 @@
"additionalProperties": false,
"required": [
"documents",
"vector_db_id",
"vector_store_id",
"chunk_size_in_tokens"
],
"title": "InsertRequest"
@ -11615,7 +11615,7 @@
"$ref": "#/components/schemas/InterleavedContent",
"description": "The query content to search for in the indexed documents"
},
"vector_db_ids": {
"vector_store_ids": {
"type": "array",
"items": {
"type": "string"
@ -11630,7 +11630,7 @@
"additionalProperties": false,
"required": [
"content",
"vector_db_ids"
"vector_store_ids"
],
"title": "QueryRequest"
},
@ -11923,7 +11923,7 @@
"InsertChunksRequest": {
"type": "object",
"properties": {
"vector_db_id": {
"vector_store_id": {
"type": "string",
"description": "The identifier of the vector database to insert the chunks into."
},
@ -11941,7 +11941,7 @@
},
"additionalProperties": false,
"required": [
"vector_db_id",
"vector_store_id",
"chunks"
],
"title": "InsertChunksRequest"
@ -11949,7 +11949,7 @@
"QueryChunksRequest": {
"type": "object",
"properties": {
"vector_db_id": {
"vector_store_id": {
"type": "string",
"description": "The identifier of the vector database to query."
},
@ -11986,7 +11986,7 @@
},
"additionalProperties": false,
"required": [
"vector_db_id",
"vector_store_id",
"query"
],
"title": "QueryChunksRequest"

16
docs/static/llama-stack-spec.yaml vendored
View file

@ -8649,7 +8649,7 @@ components:
$ref: '#/components/schemas/RAGDocument'
description: >-
List of documents to index in the RAG system
vector_db_id:
vector_store_id:
type: string
description: >-
ID of the vector database to store the document embeddings
@ -8660,7 +8660,7 @@ components:
additionalProperties: false
required:
- documents
- vector_db_id
- vector_store_id
- chunk_size_in_tokens
title: InsertRequest
DefaultRAGQueryGeneratorConfig:
@ -8831,7 +8831,7 @@ components:
$ref: '#/components/schemas/InterleavedContent'
description: >-
The query content to search for in the indexed documents
vector_db_ids:
vector_store_ids:
type: array
items:
type: string
@ -8844,7 +8844,7 @@ components:
additionalProperties: false
required:
- content
- vector_db_ids
- vector_store_ids
title: QueryRequest
RAGQueryResult:
type: object
@ -9068,7 +9068,7 @@ components:
InsertChunksRequest:
type: object
properties:
vector_db_id:
vector_store_id:
type: string
description: >-
The identifier of the vector database to insert the chunks into.
@ -9087,13 +9087,13 @@ components:
description: The time to live of the chunks.
additionalProperties: false
required:
- vector_db_id
- vector_store_id
- chunks
title: InsertChunksRequest
QueryChunksRequest:
type: object
properties:
vector_db_id:
vector_store_id:
type: string
description: >-
The identifier of the vector database to query.
@ -9113,7 +9113,7 @@ components:
description: The parameters of the query.
additionalProperties: false
required:
- vector_db_id
- vector_store_id
- query
title: QueryChunksRequest
QueryChunksResponse:

20
docs/static/stainless-llama-stack-spec.html vendored
View file

@ -13084,7 +13084,7 @@
},
"description": "List of documents to index in the RAG system"
},
"vector_db_id": {
"vector_store_id": {
"type": "string",
"description": "ID of the vector database to store the document embeddings"
},
@ -13096,7 +13096,7 @@
"additionalProperties": false,
"required": [
"documents",
"vector_db_id",
"vector_store_id",
"chunk_size_in_tokens"
],
"title": "InsertRequest"
@ -13287,7 +13287,7 @@
"$ref": "#/components/schemas/InterleavedContent",
"description": "The query content to search for in the indexed documents"
},
"vector_db_ids": {
"vector_store_ids": {
"type": "array",
"items": {
"type": "string"
@ -13302,7 +13302,7 @@
"additionalProperties": false,
"required": [
"content",
"vector_db_ids"
"vector_store_ids"
],
"title": "QueryRequest"
},
@ -13595,7 +13595,7 @@
"InsertChunksRequest": {
"type": "object",
"properties": {
"vector_db_id": {
"vector_store_id": {
"type": "string",
"description": "The identifier of the vector database to insert the chunks into."
},
@ -13613,7 +13613,7 @@
},
"additionalProperties": false,
"required": [
"vector_db_id",
"vector_store_id",
"chunks"
],
"title": "InsertChunksRequest"
@ -13621,7 +13621,7 @@
"QueryChunksRequest": {
"type": "object",
"properties": {
"vector_db_id": {
"vector_store_id": {
"type": "string",
"description": "The identifier of the vector database to query."
},
@ -13658,7 +13658,7 @@
},
"additionalProperties": false,
"required": [
"vector_db_id",
"vector_store_id",
"query"
],
"title": "QueryChunksRequest"
@ -15719,7 +15719,7 @@
"const": "memory_retrieval",
"default": "memory_retrieval"
},
"vector_db_ids": {
"vector_store_ids": {
"type": "string",
"description": "The IDs of the vector databases to retrieve context from."
},
@ -15733,7 +15733,7 @@
"turn_id",
"step_id",
"step_type",
"vector_db_ids",
"vector_store_ids",
"inserted_context"
],
"title": "MemoryRetrievalStep",

20
docs/static/stainless-llama-stack-spec.yaml vendored
View file

@ -9862,7 +9862,7 @@ components:
$ref: '#/components/schemas/RAGDocument'
description: >-
List of documents to index in the RAG system
vector_db_id:
vector_store_id:
type: string
description: >-
ID of the vector database to store the document embeddings
@ -9873,7 +9873,7 @@ components:
additionalProperties: false
required:
- documents
- vector_db_id
- vector_store_id
- chunk_size_in_tokens
title: InsertRequest
DefaultRAGQueryGeneratorConfig:
@ -10044,7 +10044,7 @@ components:
$ref: '#/components/schemas/InterleavedContent'
description: >-
The query content to search for in the indexed documents
vector_db_ids:
vector_store_ids:
type: array
items:
type: string
@ -10057,7 +10057,7 @@ components:
additionalProperties: false
required:
- content
- vector_db_ids
- vector_store_ids
title: QueryRequest
RAGQueryResult:
type: object
@ -10281,7 +10281,7 @@ components:
InsertChunksRequest:
type: object
properties:
vector_db_id:
vector_store_id:
type: string
description: >-
The identifier of the vector database to insert the chunks into.
@ -10300,13 +10300,13 @@ components:
description: The time to live of the chunks.
additionalProperties: false
required:
- vector_db_id
- vector_store_id
- chunks
title: InsertChunksRequest
QueryChunksRequest:
type: object
properties:
vector_db_id:
vector_store_id:
type: string
description: >-
The identifier of the vector database to query.
@ -10326,7 +10326,7 @@ components:
description: The parameters of the query.
additionalProperties: false
required:
- vector_db_id
- vector_store_id
- query
title: QueryChunksRequest
QueryChunksResponse:
@ -11844,7 +11844,7 @@ components:
description: Type of the step in an agent turn.
const: memory_retrieval
default: memory_retrieval
vector_db_ids:
vector_store_ids:
type: string
description: >-
The IDs of the vector databases to retrieve context from.
@ -11857,7 +11857,7 @@ components:
- turn_id
- step_id
- step_type
- vector_db_ids
- vector_store_ids
- inserted_context
title: MemoryRetrievalStep
description: >-

4
src/llama_stack/apis/agents/agents.py
View file

@ -149,13 +149,13 @@ class ShieldCallStep(StepCommon):
class MemoryRetrievalStep(StepCommon):
"""A memory retrieval step in an agent turn.
:param vector_db_ids: The IDs of the vector databases to retrieve context from.
:param vector_store_ids: The IDs of the vector databases to retrieve context from.
:param inserted_context: The context retrieved from the vector databases.
"""
step_type: Literal[StepType.memory_retrieval] = StepType.memory_retrieval
# TODO: should this be List[str]?
vector_db_ids: str
vector_store_ids: str
inserted_context: InterleavedContent

8
src/llama_stack/apis/tools/rag_tool.py
View file

@ -190,13 +190,13 @@ class RAGToolRuntime(Protocol):
async def insert(
self,
documents: list[RAGDocument],
vector_db_id: str,
vector_store_id: str,
chunk_size_in_tokens: int = 512,
) -> None:
"""Index documents so they can be used by the RAG system.
:param documents: List of documents to index in the RAG system
:param vector_db_id: ID of the vector database to store the document embeddings
:param vector_store_id: ID of the vector database to store the document embeddings
:param chunk_size_in_tokens: (Optional) Size in tokens for document chunking during indexing
"""
...
@ -205,13 +205,13 @@ class RAGToolRuntime(Protocol):
async def query(
self,
content: InterleavedContent,
vector_db_ids: list[str],
vector_store_ids: list[str],
query_config: RAGQueryConfig | None = None,
) -> RAGQueryResult:
"""Query the RAG system for context; typically invoked by the agent.
:param content: The query content to search for in the indexed documents
:param vector_db_ids: List of vector database IDs to search within
:param vector_store_ids: List of vector database IDs to search within
:param query_config: (Optional) Configuration parameters for the query operation
:returns: RAGQueryResult containing the retrieved content and metadata
"""

12
src/llama_stack/apis/vector_io/vector_io.py
View file

@ -529,17 +529,17 @@ class VectorIO(Protocol):
# this will just block now until chunks are inserted, but it should
# probably return a Job instance which can be polled for completion
# TODO: rename vector_db_id to vector_store_id once Stainless is working
# TODO: rename vector_store_id to vector_store_id once Stainless is working
@webmethod(route="/vector-io/insert", method="POST", level=LLAMA_STACK_API_V1)
async def insert_chunks(
self,
vector_db_id: str,
vector_store_id: str,
chunks: list[Chunk],
ttl_seconds: int | None = None,
) -> None:
"""Insert chunks into a vector database.
:param vector_db_id: The identifier of the vector database to insert the chunks into.
:param vector_store_id: The identifier of the vector database to insert the chunks into.
:param chunks: The chunks to insert. Each `Chunk` should contain content which can be interleaved text, images, or other types.
`metadata`: `dict[str, Any]` and `embedding`: `List[float]` are optional.
If `metadata` is provided, you configure how Llama Stack formats the chunk during generation.
@ -548,17 +548,17 @@ class VectorIO(Protocol):
"""
...
# TODO: rename vector_db_id to vector_store_id once Stainless is working
# TODO: rename vector_store_id to vector_store_id once Stainless is working
@webmethod(route="/vector-io/query", method="POST", level=LLAMA_STACK_API_V1)
async def query_chunks(
self,
vector_db_id: str,
vector_store_id: str,
query: InterleavedContent,
params: dict[str, Any] | None = None,
) -> QueryChunksResponse:
"""Query chunks from a vector database.
:param vector_db_id: The identifier of the vector database to query.
:param vector_store_id: The identifier of the vector database to query.
:param query: The query to search for.
:param params: The parameters of the query.
:returns: A QueryChunksResponse.

16
src/llama_stack/core/routers/vector_io.py
View file

@ -73,27 +73,27 @@ class VectorIORouter(VectorIO):
async def insert_chunks(
self,
vector_db_id: str,
vector_store_id: str,
chunks: list[Chunk],
ttl_seconds: int | None = None,
) -> None:
doc_ids = [chunk.document_id for chunk in chunks[:3]]
logger.debug(
f"VectorIORouter.insert_chunks: {vector_db_id}, {len(chunks)} chunks, "
f"VectorIORouter.insert_chunks: {vector_store_id}, {len(chunks)} chunks, "
f"ttl_seconds={ttl_seconds}, chunk_ids={doc_ids}{' and more...' if len(chunks) > 3 else ''}"
)
provider = await self.routing_table.get_provider_impl(vector_db_id)
return await provider.insert_chunks(vector_db_id, chunks, ttl_seconds)
provider = await self.routing_table.get_provider_impl(vector_store_id)
return await provider.insert_chunks(vector_store_id, chunks, ttl_seconds)
async def query_chunks(
self,
vector_db_id: str,
vector_store_id: str,
query: InterleavedContent,
params: dict[str, Any] | None = None,
) -> QueryChunksResponse:
logger.debug(f"VectorIORouter.query_chunks: {vector_db_id}")
provider = await self.routing_table.get_provider_impl(vector_db_id)
return await provider.query_chunks(vector_db_id, query, params)
logger.debug(f"VectorIORouter.query_chunks: {vector_store_id}")
provider = await self.routing_table.get_provider_impl(vector_store_id)
return await provider.query_chunks(vector_store_id, query, params)
# OpenAI Vector Stores API endpoints
async def openai_create_vector_store(

8
src/llama_stack/providers/inline/agents/meta_reference/agent_instance.py
View file

@ -488,13 +488,13 @@ class ChatAgent(ShieldRunnerMixin):
session_info = await self.storage.get_session_info(session_id)
# if the session has a memory bank id, let the memory tool use it
if session_info and session_info.vector_db_id:
if session_info and session_info.vector_store_id:
for tool_name in self.tool_name_to_args.keys():
if tool_name == MEMORY_QUERY_TOOL:
if "vector_db_ids" not in self.tool_name_to_args[tool_name]:
self.tool_name_to_args[tool_name]["vector_db_ids"] = [session_info.vector_db_id]
if "vector_store_ids" not in self.tool_name_to_args[tool_name]:
self.tool_name_to_args[tool_name]["vector_store_ids"] = [session_info.vector_store_id]
else:
self.tool_name_to_args[tool_name]["vector_db_ids"].append(session_info.vector_db_id)
self.tool_name_to_args[tool_name]["vector_store_ids"].append(session_info.vector_store_id)
output_attachments = []

6
src/llama_stack/providers/inline/agents/meta_reference/persistence.py
View file

@ -22,7 +22,7 @@ log = get_logger(name=__name__, category="agents::meta_reference")
class AgentSessionInfo(Session):
# TODO: is this used anywhere?
vector_db_id: str | None = None
vector_store_id: str | None = None
started_at: datetime
owner: User | None = None
identifier: str | None = None
@ -93,12 +93,12 @@ class AgentPersistence:
return session_info
async def add_vector_db_to_session(self, session_id: str, vector_db_id: str):
async def add_vector_db_to_session(self, session_id: str, vector_store_id: str):
session_info = await self.get_session_if_accessible(session_id)
if session_info is None:
raise SessionNotFoundError(session_id)
session_info.vector_db_id = vector_db_id
session_info.vector_store_id = vector_store_id
await self.kvstore.set(
key=f"session:{self.agent_id}:{session_id}",
value=session_info.model_dump_json(),

26
src/llama_stack/providers/inline/tool_runtime/rag/memory.py
View file

@ -119,7 +119,7 @@ class MemoryToolRuntimeImpl(ToolGroupsProtocolPrivate, ToolRuntime, RAGToolRunti
async def insert(
self,
documents: list[RAGDocument],
vector_db_id: str,
vector_store_id: str,
chunk_size_in_tokens: int = 512,
) -> None:
if not documents:
@ -158,14 +158,14 @@ class MemoryToolRuntimeImpl(ToolGroupsProtocolPrivate, ToolRuntime, RAGToolRunti
try:
await self.vector_io_api.openai_attach_file_to_vector_store(
vector_store_id=vector_db_id,
vector_store_id=vector_store_id,
file_id=created_file.id,
attributes=doc.metadata,
chunking_strategy=chunking_strategy,
)
except Exception as e:
log.error(
f"Failed to attach file {created_file.id} to vector store {vector_db_id} for document {doc.document_id}: {e}"
f"Failed to attach file {created_file.id} to vector store {vector_store_id} for document {doc.document_id}: {e}"
)
continue
@ -176,10 +176,10 @@ class MemoryToolRuntimeImpl(ToolGroupsProtocolPrivate, ToolRuntime, RAGToolRunti
async def query(
self,
content: InterleavedContent,
vector_db_ids: list[str],
vector_store_ids: list[str],
query_config: RAGQueryConfig | None = None,
) -> RAGQueryResult:
if not vector_db_ids:
if not vector_store_ids:
raise ValueError(
"No vector DBs were provided to the knowledge search tool. Please provide at least one vector DB ID."
)
@ -192,7 +192,7 @@ class MemoryToolRuntimeImpl(ToolGroupsProtocolPrivate, ToolRuntime, RAGToolRunti
)
tasks = [
self.vector_io_api.query_chunks(
vector_db_id=vector_db_id,
vector_store_id=vector_store_id,
query=query,
params={
"mode": query_config.mode,
@ -201,18 +201,18 @@ class MemoryToolRuntimeImpl(ToolGroupsProtocolPrivate, ToolRuntime, RAGToolRunti
"ranker": query_config.ranker,
},
)
for vector_db_id in vector_db_ids
for vector_store_id in vector_store_ids
]
results: list[QueryChunksResponse] = await asyncio.gather(*tasks)
chunks = []
scores = []
for vector_db_id, result in zip(vector_db_ids, results, strict=False):
for vector_store_id, result in zip(vector_store_ids, results, strict=False):
for chunk, score in zip(result.chunks, result.scores, strict=False):
if not hasattr(chunk, "metadata") or chunk.metadata is None:
chunk.metadata = {}
chunk.metadata["vector_db_id"] = vector_db_id
chunk.metadata["vector_store_id"] = vector_store_id
chunks.append(chunk)
scores.append(score)
@ -250,7 +250,7 @@ class MemoryToolRuntimeImpl(ToolGroupsProtocolPrivate, ToolRuntime, RAGToolRunti
metadata_keys_to_exclude_from_context = [
"token_count",
"metadata_token_count",
"vector_db_id",
"vector_store_id",
]
metadata_for_context = {}
for k in chunk_metadata_keys_to_include_from_context:
@ -275,7 +275,7 @@ class MemoryToolRuntimeImpl(ToolGroupsProtocolPrivate, ToolRuntime, RAGToolRunti
"document_ids": [c.document_id for c in chunks[: len(picked)]],
"chunks": [c.content for c in chunks[: len(picked)]],
"scores": scores[: len(picked)],
"vector_db_ids": [c.metadata["vector_db_id"] for c in chunks[: len(picked)]],
"vector_store_ids": [c.metadata["vector_store_id"] for c in chunks[: len(picked)]],
},
)
@ -309,7 +309,7 @@ class MemoryToolRuntimeImpl(ToolGroupsProtocolPrivate, ToolRuntime, RAGToolRunti
)
async def invoke_tool(self, tool_name: str, kwargs: dict[str, Any]) -> ToolInvocationResult:
vector_db_ids = kwargs.get("vector_db_ids", [])
vector_store_ids = kwargs.get("vector_store_ids", [])
query_config = kwargs.get("query_config")
if query_config:
query_config = TypeAdapter(RAGQueryConfig).validate_python(query_config)
@ -319,7 +319,7 @@ class MemoryToolRuntimeImpl(ToolGroupsProtocolPrivate, ToolRuntime, RAGToolRunti
query = kwargs["query"]
result = await self.query(
content=query,
vector_db_ids=vector_db_ids,
vector_store_ids=vector_store_ids,
query_config=query_config,
)

12
src/llama_stack/providers/inline/vector_io/faiss/faiss.py
View file

@ -248,19 +248,19 @@ class FaissVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorStoresProtoco
del self.cache[vector_store_id]
await self.kvstore.delete(f"{VECTOR_DBS_PREFIX}{vector_store_id}")
async def insert_chunks(self, vector_db_id: str, chunks: list[Chunk], ttl_seconds: int | None = None) -> None:
index = self.cache.get(vector_db_id)
async def insert_chunks(self, vector_store_id: str, chunks: list[Chunk], ttl_seconds: int | None = None) -> None:
index = self.cache.get(vector_store_id)
if index is None:
raise ValueError(f"Vector DB {vector_db_id} not found. found: {self.cache.keys()}")
raise ValueError(f"Vector DB {vector_store_id} not found. found: {self.cache.keys()}")
await index.insert_chunks(chunks)
async def query_chunks(
self, vector_db_id: str, query: InterleavedContent, params: dict[str, Any] | None = None
self, vector_store_id: str, query: InterleavedContent, params: dict[str, Any] | None = None
) -> QueryChunksResponse:
index = self.cache.get(vector_db_id)
index = self.cache.get(vector_store_id)
if index is None:
raise VectorStoreNotFoundError(vector_db_id)
raise VectorStoreNotFoundError(vector_store_id)
return await index.query_chunks(query, params)

12
src/llama_stack/providers/inline/vector_io/sqlite_vec/sqlite_vec.py
View file

@ -447,20 +447,20 @@ class SQLiteVecVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorStoresPro
await self.cache[vector_store_id].index.delete()
del self.cache[vector_store_id]
async def insert_chunks(self, vector_db_id: str, chunks: list[Chunk], ttl_seconds: int | None = None) -> None:
index = await self._get_and_cache_vector_store_index(vector_db_id)
async def insert_chunks(self, vector_store_id: str, chunks: list[Chunk], ttl_seconds: int | None = None) -> None:
index = await self._get_and_cache_vector_store_index(vector_store_id)
if not index:
raise VectorStoreNotFoundError(vector_db_id)
raise VectorStoreNotFoundError(vector_store_id)
# The VectorStoreWithIndex helper is expected to compute embeddings via the inference_api
# and then call our index's add_chunks.
await index.insert_chunks(chunks)
async def query_chunks(
self, vector_db_id: str, query: Any, params: dict[str, Any] | None = None
self, vector_store_id: str, query: Any, params: dict[str, Any] | None = None
) -> QueryChunksResponse:
index = await self._get_and_cache_vector_store_index(vector_db_id)
index = await self._get_and_cache_vector_store_index(vector_store_id)
if not index:
raise VectorStoreNotFoundError(vector_db_id)
raise VectorStoreNotFoundError(vector_store_id)
return await index.query_chunks(query, params)
async def delete_chunks(self, store_id: str, chunks_for_deletion: list[ChunkForDeletion]) -> None:

10
src/llama_stack/providers/registry/vector_io.py
View file

@ -163,14 +163,14 @@ The SQLite-vec provider supports three search modes:
Example with hybrid search:
```python
response = await vector_io.query_chunks(
vector_db_id="my_db",
vector_store_id="my_db",
query="your query here",
params={"mode": "hybrid", "max_chunks": 3, "score_threshold": 0.7},
)
# Using RRF ranker
response = await vector_io.query_chunks(
vector_db_id="my_db",
vector_store_id="my_db",
query="your query here",
params={
"mode": "hybrid",
@ -182,7 +182,7 @@ response = await vector_io.query_chunks(
# Using weighted ranker
response = await vector_io.query_chunks(
vector_db_id="my_db",
vector_store_id="my_db",
query="your query here",
params={
"mode": "hybrid",
@ -196,7 +196,7 @@ response = await vector_io.query_chunks(
Example with explicit vector search:
```python
response = await vector_io.query_chunks(
vector_db_id="my_db",
vector_store_id="my_db",
query="your query here",
params={"mode": "vector", "max_chunks": 3, "score_threshold": 0.7},
)
@ -205,7 +205,7 @@ response = await vector_io.query_chunks(
Example with keyword search:
```python
response = await vector_io.query_chunks(
vector_db_id="my_db",
vector_store_id="my_db",
query="your query here",
params={"mode": "keyword", "max_chunks": 3, "score_threshold": 0.7},
)

12
src/llama_stack/providers/remote/vector_io/chroma/chroma.py
View file

@ -169,20 +169,20 @@ class ChromaVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorStoresProtoc
await self.cache[vector_store_id].index.delete()
del self.cache[vector_store_id]
async def insert_chunks(self, vector_db_id: str, chunks: list[Chunk], ttl_seconds: int | None = None) -> None:
index = await self._get_and_cache_vector_store_index(vector_db_id)
async def insert_chunks(self, vector_store_id: str, chunks: list[Chunk], ttl_seconds: int | None = None) -> None:
index = await self._get_and_cache_vector_store_index(vector_store_id)
if index is None:
raise ValueError(f"Vector DB {vector_db_id} not found in Chroma")
raise ValueError(f"Vector DB {vector_store_id} not found in Chroma")
await index.insert_chunks(chunks)
async def query_chunks(
self, vector_db_id: str, query: InterleavedContent, params: dict[str, Any] | None = None
self, vector_store_id: str, query: InterleavedContent, params: dict[str, Any] | None = None
) -> QueryChunksResponse:
index = await self._get_and_cache_vector_store_index(vector_db_id)
index = await self._get_and_cache_vector_store_index(vector_store_id)
if index is None:
raise ValueError(f"Vector DB {vector_db_id} not found in Chroma")
raise ValueError(f"Vector DB {vector_store_id} not found in Chroma")
return await index.query_chunks(query, params)

12
src/llama_stack/providers/remote/vector_io/milvus/milvus.py
View file

@ -348,19 +348,19 @@ class MilvusVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorStoresProtoc
await self.cache[vector_store_id].index.delete()
del self.cache[vector_store_id]
async def insert_chunks(self, vector_db_id: str, chunks: list[Chunk], ttl_seconds: int | None = None) -> None:
index = await self._get_and_cache_vector_store_index(vector_db_id)
async def insert_chunks(self, vector_store_id: str, chunks: list[Chunk], ttl_seconds: int | None = None) -> None:
index = await self._get_and_cache_vector_store_index(vector_store_id)
if not index:
raise VectorStoreNotFoundError(vector_db_id)
raise VectorStoreNotFoundError(vector_store_id)
await index.insert_chunks(chunks)
async def query_chunks(
self, vector_db_id: str, query: InterleavedContent, params: dict[str, Any] | None = None
self, vector_store_id: str, query: InterleavedContent, params: dict[str, Any] | None = None
) -> QueryChunksResponse:
index = await self._get_and_cache_vector_store_index(vector_db_id)
index = await self._get_and_cache_vector_store_index(vector_store_id)
if not index:
raise VectorStoreNotFoundError(vector_db_id)
raise VectorStoreNotFoundError(vector_store_id)
return await index.query_chunks(query, params)
async def delete_chunks(self, store_id: str, chunks_for_deletion: list[ChunkForDeletion]) -> None:

8
src/llama_stack/providers/remote/vector_io/pgvector/pgvector.py
View file

@ -399,14 +399,14 @@ class PGVectorVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorStoresProt
assert self.kvstore is not None
await self.kvstore.delete(key=f"{VECTOR_DBS_PREFIX}{vector_store_id}")
async def insert_chunks(self, vector_db_id: str, chunks: list[Chunk], ttl_seconds: int | None = None) -> None:
index = await self._get_and_cache_vector_store_index(vector_db_id)
async def insert_chunks(self, vector_store_id: str, chunks: list[Chunk], ttl_seconds: int | None = None) -> None:
index = await self._get_and_cache_vector_store_index(vector_store_id)
await index.insert_chunks(chunks)
async def query_chunks(
self, vector_db_id: str, query: InterleavedContent, params: dict[str, Any] | None = None
self, vector_store_id: str, query: InterleavedContent, params: dict[str, Any] | None = None
) -> QueryChunksResponse:
index = await self._get_and_cache_vector_store_index(vector_db_id)
index = await self._get_and_cache_vector_store_index(vector_store_id)
return await index.query_chunks(query, params)
async def _get_and_cache_vector_store_index(self, vector_store_id: str) -> VectorStoreWithIndex:

12
src/llama_stack/providers/remote/vector_io/qdrant/qdrant.py
View file

@ -222,19 +222,19 @@ class QdrantVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorStoresProtoc
self.cache[vector_store_id] = index
return index
async def insert_chunks(self, vector_db_id: str, chunks: list[Chunk], ttl_seconds: int | None = None) -> None:
index = await self._get_and_cache_vector_store_index(vector_db_id)
async def insert_chunks(self, vector_store_id: str, chunks: list[Chunk], ttl_seconds: int | None = None) -> None:
index = await self._get_and_cache_vector_store_index(vector_store_id)
if not index:
raise VectorStoreNotFoundError(vector_db_id)
raise VectorStoreNotFoundError(vector_store_id)
await index.insert_chunks(chunks)
async def query_chunks(
self, vector_db_id: str, query: InterleavedContent, params: dict[str, Any] | None = None
self, vector_store_id: str, query: InterleavedContent, params: dict[str, Any] | None = None
) -> QueryChunksResponse:
index = await self._get_and_cache_vector_store_index(vector_db_id)
index = await self._get_and_cache_vector_store_index(vector_store_id)
if not index:
raise VectorStoreNotFoundError(vector_db_id)
raise VectorStoreNotFoundError(vector_store_id)
return await index.query_chunks(query, params)

12
src/llama_stack/providers/remote/vector_io/weaviate/weaviate.py
View file

@ -366,19 +366,19 @@ class WeaviateVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, NeedsRequestProv
self.cache[vector_store_id] = index
return index
async def insert_chunks(self, vector_db_id: str, chunks: list[Chunk], ttl_seconds: int | None = None) -> None:
index = await self._get_and_cache_vector_store_index(vector_db_id)
async def insert_chunks(self, vector_store_id: str, chunks: list[Chunk], ttl_seconds: int | None = None) -> None:
index = await self._get_and_cache_vector_store_index(vector_store_id)
if not index:
raise VectorStoreNotFoundError(vector_db_id)
raise VectorStoreNotFoundError(vector_store_id)
await index.insert_chunks(chunks)
async def query_chunks(
self, vector_db_id: str, query: InterleavedContent, params: dict[str, Any] | None = None
self, vector_store_id: str, query: InterleavedContent, params: dict[str, Any] | None = None
) -> QueryChunksResponse:
index = await self._get_and_cache_vector_store_index(vector_db_id)
index = await self._get_and_cache_vector_store_index(vector_store_id)
if not index:
raise VectorStoreNotFoundError(vector_db_id)
raise VectorStoreNotFoundError(vector_store_id)
return await index.query_chunks(query, params)

8
src/llama_stack/providers/utils/memory/openai_vector_store_mixin.py
View file

@ -333,7 +333,7 @@ class OpenAIVectorStoreMixin(ABC):
@abstractmethod
async def insert_chunks(
self,
vector_db_id: str,
vector_store_id: str,
chunks: list[Chunk],
ttl_seconds: int | None = None,
) -> None:
@ -342,7 +342,7 @@ class OpenAIVectorStoreMixin(ABC):
@abstractmethod
async def query_chunks(
self, vector_db_id: str, query: Any, params: dict[str, Any] | None = None
self, vector_store_id: str, query: Any, params: dict[str, Any] | None = None
) -> QueryChunksResponse:
"""Query chunks from a vector database (provider-specific implementation)."""
pass
@ -609,7 +609,7 @@ class OpenAIVectorStoreMixin(ABC):
# TODO: Add support for ranking_options.ranker
response = await self.query_chunks(
vector_db_id=vector_store_id,
vector_store_id=vector_store_id,
query=search_query,
params=params,
)
@ -803,7 +803,7 @@ class OpenAIVectorStoreMixin(ABC):
)
else:
await self.insert_chunks(
vector_db_id=vector_store_id,
vector_store_id=vector_store_id,
chunks=chunks,
)
vector_store_file_object.status = "completed"

12
tests/integration/vector_io/test_openai_vector_stores.py
View file

@ -367,7 +367,7 @@ def test_openai_vector_store_with_chunks(
# Insert chunks using the native LlamaStack API (since OpenAI API doesn't have direct chunk insertion)
llama_client.vector_io.insert(
vector_db_id=vector_store.id,
vector_store_id=vector_store.id,
chunks=sample_chunks,
)
@ -434,7 +434,7 @@ def test_openai_vector_store_search_relevance(
# Insert chunks using native API
llama_client.vector_io.insert(
vector_db_id=vector_store.id,
vector_store_id=vector_store.id,
chunks=sample_chunks,
)
@ -484,7 +484,7 @@ def test_openai_vector_store_search_with_ranking_options(
# Insert chunks
llama_client.vector_io.insert(
vector_db_id=vector_store.id,
vector_store_id=vector_store.id,
chunks=sample_chunks,
)
@ -544,7 +544,7 @@ def test_openai_vector_store_search_with_high_score_filter(
# Insert chunks
llama_client.vector_io.insert(
vector_db_id=vector_store.id,
vector_store_id=vector_store.id,
chunks=sample_chunks,
)
@ -610,7 +610,7 @@ def test_openai_vector_store_search_with_max_num_results(
# Insert chunks
llama_client.vector_io.insert(
vector_db_id=vector_store.id,
vector_store_id=vector_store.id,
chunks=sample_chunks,
)
@ -1175,7 +1175,7 @@ def test_openai_vector_store_search_modes(
)
client_with_models.vector_io.insert(
vector_db_id=vector_store.id,
vector_store_id=vector_store.id,
chunks=sample_chunks,
)
query = "Python programming language"

14
tests/integration/vector_io/test_vector_io.py
View file

@ -123,12 +123,12 @@ def test_insert_chunks(
actual_vector_store_id = create_response.id
client_with_empty_registry.vector_io.insert(
vector_db_id=actual_vector_store_id,
vector_store_id=actual_vector_store_id,
chunks=sample_chunks,
)
response = client_with_empty_registry.vector_io.query(
vector_db_id=actual_vector_store_id,
vector_store_id=actual_vector_store_id,
query="What is the capital of France?",
)
assert response is not None
@ -137,7 +137,7 @@ def test_insert_chunks(
query, expected_doc_id = test_case
response = client_with_empty_registry.vector_io.query(
vector_db_id=actual_vector_store_id,
vector_store_id=actual_vector_store_id,
query=query,
)
assert response is not None
@ -174,13 +174,13 @@ def test_insert_chunks_with_precomputed_embeddings(
]
client_with_empty_registry.vector_io.insert(
vector_db_id=actual_vector_store_id,
vector_store_id=actual_vector_store_id,
chunks=chunks_with_embeddings,
)
provider = [p.provider_id for p in client_with_empty_registry.providers.list() if p.api == "vector_io"][0]
response = client_with_empty_registry.vector_io.query(
vector_db_id=actual_vector_store_id,
vector_store_id=actual_vector_store_id,
query="precomputed embedding test",
params=vector_io_provider_params_dict.get(provider, None),
)
@ -224,13 +224,13 @@ def test_query_returns_valid_object_when_identical_to_embedding_in_vdb(
]
client_with_empty_registry.vector_io.insert(
vector_db_id=actual_vector_store_id,
vector_store_id=actual_vector_store_id,
chunks=chunks_with_embeddings,
)
provider = [p.provider_id for p in client_with_empty_registry.providers.list() if p.api == "vector_io"][0]
response = client_with_empty_registry.vector_io.query(
vector_db_id=actual_vector_store_id,
vector_store_id=actual_vector_store_id,
query="duplicate",
params=vector_io_provider_params_dict.get(provider, None),
)

18
tests/unit/rag/test_rag_query.py
View file

@ -23,14 +23,14 @@ class TestRagQuery:
config=MagicMock(), vector_io_api=MagicMock(), inference_api=MagicMock(), files_api=MagicMock()
)
with pytest.raises(ValueError):
await rag_tool.query(content=MagicMock(), vector_db_ids=[])
await rag_tool.query(content=MagicMock(), vector_store_ids=[])
async def test_query_chunk_metadata_handling(self):
rag_tool = MemoryToolRuntimeImpl(
config=MagicMock(), vector_io_api=MagicMock(), inference_api=MagicMock(), files_api=MagicMock()
)
content = "test query content"
vector_db_ids = ["db1"]
vector_store_ids = ["db1"]
chunk_metadata = ChunkMetadata(
document_id="doc1",
@ -55,7 +55,7 @@ class TestRagQuery:
query_response = QueryChunksResponse(chunks=[chunk], scores=[1.0])
rag_tool.vector_io_api.query_chunks = AsyncMock(return_value=query_response)
result = await rag_tool.query(content=content, vector_db_ids=vector_db_ids)
result = await rag_tool.query(content=content, vector_store_ids=vector_store_ids)
assert result is not None
expected_metadata_string = (
@ -90,7 +90,7 @@ class TestRagQuery:
files_api=MagicMock(),
)
vector_db_ids = ["db1", "db2"]
vector_store_ids = ["db1", "db2"]
# Fake chunks from each DB
chunk_metadata1 = ChunkMetadata(
@ -101,7 +101,7 @@ class TestRagQuery:
)
chunk1 = Chunk(
content="chunk from db1",
metadata={"vector_db_id": "db1", "document_id": "doc1"},
metadata={"vector_store_id": "db1", "document_id": "doc1"},
stored_chunk_id="c1",
chunk_metadata=chunk_metadata1,
)
@ -114,7 +114,7 @@ class TestRagQuery:
)
chunk2 = Chunk(
content="chunk from db2",
metadata={"vector_db_id": "db2", "document_id": "doc2"},
metadata={"vector_store_id": "db2", "document_id": "doc2"},
stored_chunk_id="c2",
chunk_metadata=chunk_metadata2,
)
@ -126,13 +126,13 @@ class TestRagQuery:
]
)
result = await rag_tool.query(content="test", vector_db_ids=vector_db_ids)
result = await rag_tool.query(content="test", vector_store_ids=vector_store_ids)
returned_chunks = result.metadata["chunks"]
returned_scores = result.metadata["scores"]
returned_doc_ids = result.metadata["document_ids"]
returned_vector_db_ids = result.metadata["vector_db_ids"]
returned_vector_store_ids = result.metadata["vector_store_ids"]
assert returned_chunks == ["chunk from db1", "chunk from db2"]
assert returned_scores == (0.9, 0.8)
assert returned_doc_ids == ["doc1", "doc2"]
assert returned_vector_db_ids == ["db1", "db2"]
assert returned_vector_store_ids == ["db1", "db2"]