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chore: Migrating VectorDB to use OpenAI compatible identifier

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Signed-off-by: Francisco Javier Arceo <farceo@redhat.com>
This commit is contained in:
Francisco Javier Arceo 2025-07-03 22:02:06 -04:00
parent b094960199
commit ddb29b306c
9 changed files with 44 additions and 22 deletions
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10
docs/_static/llama-stack-spec.html vendored
View file

@ -11285,6 +11285,9 @@
},
"embedding_dimension": {
"type": "integer"
},
"provider_vector_db_name": {
"type": "string"
}
},
"additionalProperties": false,
@ -13543,7 +13546,8 @@
},
"additionalProperties": false,
"required": [
"name"
"name",
"provider_vector_db_id"
],
"title": "OpenaiCreateVectorStoreRequest"
},
@ -15571,6 +15575,10 @@
"provider_vector_db_id": {
"type": "string",
"description": "The identifier of the vector database in the provider."
},
"provider_vector_db_name": {
"type": "string",
"description": "The name of the vector database."
}
},
"additionalProperties": false,

6
docs/_static/llama-stack-spec.yaml vendored
View file

@ -7944,6 +7944,8 @@ components:
type: string
embedding_dimension:
type: integer
provider_vector_db_name:
type: string
additionalProperties: false
required:
- identifier
@ -9461,6 +9463,7 @@ components:
additionalProperties: false
required:
- name
- provider_vector_db_id
title: OpenaiCreateVectorStoreRequest
VectorStoreFileCounts:
type: object
@ -10897,6 +10900,9 @@ components:
type: string
description: >-
The identifier of the vector database in the provider.
provider_vector_db_name:
type: string
description: The name of the vector database.
additionalProperties: false
required:
- vector_db_id

3
llama_stack/apis/vector_dbs/vector_dbs.py
View file

@ -19,6 +19,7 @@ class VectorDB(Resource):
embedding_model: str
embedding_dimension: int
provider_vector_db_name: str | None = None
@property
def vector_db_id(self) -> str:
@ -71,6 +72,7 @@ class VectorDBs(Protocol):
embedding_dimension: int | None = 384,
provider_id: str | None = None,
provider_vector_db_id: str | None = None,
provider_vector_db_name: str | None = None,
) -> VectorDB:
"""Register a vector database.
@ -79,6 +81,7 @@ class VectorDBs(Protocol):
:param embedding_dimension: The dimension of the embedding model.
:param provider_id: The identifier of the provider.
:param provider_vector_db_id: The identifier of the vector database in the provider.
:param provider_vector_db_name: The name of the vector database.
:returns: A VectorDB.
"""
...

2
llama_stack/apis/vector_io/vector_io.py
View file

@ -346,7 +346,7 @@ class VectorIO(Protocol):
embedding_model: str | None = None,
embedding_dimension: int | None = 384,
provider_id: str | None = None,
provider_vector_db_id: str | None = None,
provider_vector_db_id: str = "",
) -> VectorStoreObject:
"""Creates a vector store.

11
llama_stack/distribution/routers/vector_io.py
View file

@ -5,6 +5,7 @@
# the root directory of this source tree.
import asyncio
import uuid
from typing import Any
from llama_stack.apis.common.content_types import (
@ -82,6 +83,7 @@ class VectorIORouter(VectorIO):
embedding_dimension: int | None = 384,
provider_id: str | None = None,
provider_vector_db_id: str | None = None,
provider_vector_db_name: str | None = None,
) -> None:
logger.debug(f"VectorIORouter.register_vector_db: {vector_db_id}, {embedding_model}")
await self.routing_table.register_vector_db(
@ -90,6 +92,7 @@ class VectorIORouter(VectorIO):
embedding_dimension,
provider_id,
provider_vector_db_id,
provider_vector_db_name,
)
async def insert_chunks(
@ -123,7 +126,7 @@ class VectorIORouter(VectorIO):
embedding_model: str | None = None,
embedding_dimension: int | None = None,
provider_id: str | None = None,
provider_vector_db_id: str | None = None,
provider_vector_db_id: str = "",
) -> VectorStoreObject:
logger.debug(f"VectorIORouter.openai_create_vector_store: name={name}, provider_id={provider_id}")
@ -135,17 +138,17 @@ class VectorIORouter(VectorIO):
embedding_model, embedding_dimension = embedding_model_info
logger.info(f"No embedding model specified, using first available: {embedding_model}")
vector_db_id = name
vector_db_id = f"vs_{uuid.uuid4()}"
registered_vector_db = await self.routing_table.register_vector_db(
vector_db_id,
embedding_model,
embedding_dimension,
provider_id,
provider_vector_db_id,
name,
)
return await self.routing_table.get_provider_impl(registered_vector_db.identifier).openai_create_vector_store(
vector_db_id,
name,
file_ids=file_ids,
expires_after=expires_after,
chunking_strategy=chunking_strategy,

6
llama_stack/distribution/routing_tables/vector_dbs.py
View file

@ -35,9 +35,10 @@ class VectorDBsRoutingTable(CommonRoutingTableImpl, VectorDBs):
embedding_model: str,
embedding_dimension: int | None = 384,
provider_id: str | None = None,
provider_vector_db_id: str | None = None,
provider_vector_db_id: str = "",
provider_vector_db_name: str | None = None,
) -> VectorDB:
if provider_vector_db_id is None:
if provider_vector_db_id == "":
provider_vector_db_id = vector_db_id
if provider_id is None:
if len(self.impls_by_provider_id) > 0:
@ -62,6 +63,7 @@ class VectorDBsRoutingTable(CommonRoutingTableImpl, VectorDBs):
"provider_resource_id": provider_vector_db_id,
"embedding_model": embedding_model,
"embedding_dimension": model.metadata["embedding_dimension"],
"vector_db_name": provider_vector_db_name,
}
vector_db = TypeAdapter(VectorDBWithOwner).validate_python(vector_db_data)
await self.register_object(vector_db)

2
llama_stack/providers/remote/vector_io/chroma/chroma.py
View file

@ -217,7 +217,7 @@ class ChromaVectorIOAdapter(VectorIO, VectorDBsProtocolPrivate):
embedding_model: str | None = None,
embedding_dimension: int | None = 384,
provider_id: str | None = None,
provider_vector_db_id: str | None = None,
provider_vector_db_id: str = "",
) -> VectorStoreObject:
raise NotImplementedError("OpenAI Vector Stores API is not supported in Chroma")

2
llama_stack/providers/remote/vector_io/pgvector/pgvector.py
View file

@ -247,7 +247,7 @@ class PGVectorVectorIOAdapter(VectorIO, VectorDBsProtocolPrivate):
embedding_model: str | None = None,
embedding_dimension: int | None = 384,
provider_id: str | None = None,
provider_vector_db_id: str | None = None,
provider_vector_db_id: str = "",
) -> VectorStoreObject:
raise NotImplementedError("OpenAI Vector Stores API is not supported in PGVector")

24
llama_stack/providers/utils/memory/openai_vector_store_mixin.py
View file

@ -8,7 +8,6 @@ import asyncio
import logging
import mimetypes
import time
import uuid
from abc import ABC, abstractmethod
from typing import Any
@ -144,11 +143,12 @@ class OpenAIVectorStoreMixin(ABC):
embedding_model: str | None = None,
embedding_dimension: int | None = 384,
provider_id: str | None = None,
provider_vector_db_id: str | None = None,
provider_vector_db_id: str = "",
) -> VectorStoreObject:
"""Creates a vector store."""
store_id = name or str(uuid.uuid4())
created_at = int(time.time())
if provider_vector_db_id is None:
raise ValueError("Provider vector DB ID is required")
if provider_id is None:
raise ValueError("Provider ID is required")
@ -160,13 +160,13 @@ class OpenAIVectorStoreMixin(ABC):
if embedding_dimension is None:
raise ValueError("Embedding dimension is required")
provider_vector_db_id = provider_vector_db_id or store_id
vector_db = VectorDB(
identifier=store_id,
identifier=provider_vector_db_id,
embedding_dimension=embedding_dimension,
embedding_model=embedding_model,
provider_id=provider_id,
provider_resource_id=provider_vector_db_id,
provider_vector_db_name=name,
)
# Register the vector DB
await self.register_vector_db(vector_db)
@ -182,11 +182,11 @@ class OpenAIVectorStoreMixin(ABC):
in_progress=0,
total=0,
)
store_info = {
"id": store_id,
store_info: dict[str, Any] = {
"id": provider_vector_db_id,
"object": "vector_store",
"created_at": created_at,
"name": store_id,
"name": name,
"usage_bytes": 0,
"file_counts": file_counts.model_dump(),
"status": status,
@ -206,18 +206,18 @@ class OpenAIVectorStoreMixin(ABC):
store_info["metadata"] = metadata
# Save to persistent storage (provider-specific)
await self._save_openai_vector_store(store_id, store_info)
await self._save_openai_vector_store(provider_vector_db_id, store_info)
# Store in memory cache
self.openai_vector_stores[store_id] = store_info
self.openai_vector_stores[provider_vector_db_id] = store_info
# Now that our vector store is created, attach any files that were provided
file_ids = file_ids or []
tasks = [self.openai_attach_file_to_vector_store(store_id, file_id) for file_id in file_ids]
tasks = [self.openai_attach_file_to_vector_store(provider_vector_db_id, file_id) for file_id in file_ids]
await asyncio.gather(*tasks)
# Get the updated store info and return it
store_info = self.openai_vector_stores[store_id]
store_info = self.openai_vector_stores[provider_vector_db_id]
return VectorStoreObject.model_validate(store_info)
async def openai_list_vector_stores(