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chore!: BREAKING CHANGE removing VectorDB APIs (#3774)

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# What does this PR do?
Removes VectorDBs from API surface and our tests.

Moves tests to Vector Stores.

<!-- If resolving an issue, uncomment and update the line below -->
<!-- Closes #[issue-number] -->

## Test Plan
<!-- Describe the tests you ran to verify your changes with result
summaries. *Provide clear instructions so the plan can be easily
re-executed.* -->

---------

Signed-off-by: Francisco Javier Arceo <farceo@redhat.com>
Co-authored-by: Ashwin Bharambe <ashwin.bharambe@gmail.com>
This commit is contained in:
Francisco Arceo 2025-10-11 17:07:08 -04:00 • committed by GitHub
parent 06e4cd8e02
commit a165b8b5bb
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111 changed files with 60412 additions and 2765 deletions
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3
llama_stack/core/routing_tables/common.py
View file

@ -134,15 +134,12 @@ class CommonRoutingTableImpl(RoutingTable):
from .scoring_functions import ScoringFunctionsRoutingTable
from .shields import ShieldsRoutingTable
from .toolgroups import ToolGroupsRoutingTable
from .vector_dbs import VectorDBsRoutingTable
def apiname_object():
if isinstance(self, ModelsRoutingTable):
return ("Inference", "model")
elif isinstance(self, ShieldsRoutingTable):
return ("Safety", "shield")
elif isinstance(self, VectorDBsRoutingTable):
return ("VectorIO", "vector_db")
elif isinstance(self, DatasetsRoutingTable):
return ("DatasetIO", "dataset")
elif isinstance(self, ScoringFunctionsRoutingTable):

309
llama_stack/core/routing_tables/vector_dbs.py
View file

@ -1,309 +0,0 @@
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the terms described in the LICENSE file in
# the root directory of this source tree.
from typing import Any
from pydantic import TypeAdapter
from llama_stack.apis.common.errors import ModelNotFoundError, ModelTypeError, VectorStoreNotFoundError
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 (
SearchRankingOptions,
VectorStoreChunkingStrategy,
VectorStoreDeleteResponse,
VectorStoreFileContentsResponse,
VectorStoreFileDeleteResponse,
VectorStoreFileObject,
VectorStoreFileStatus,
VectorStoreObject,
VectorStoreSearchResponsePage,
)
from llama_stack.core.datatypes import (
VectorDBWithOwner,
)
from llama_stack.log import get_logger
from .common import CommonRoutingTableImpl, lookup_model
logger = get_logger(name=__name__, category="core::routing_tables")
class VectorDBsRoutingTable(CommonRoutingTableImpl, VectorDBs):
async def list_vector_dbs(self) -> ListVectorDBsResponse:
return ListVectorDBsResponse(data=await self.get_all_with_type("vector_db"))
async def get_vector_db(self, vector_db_id: str) -> VectorDB:
vector_db = await self.get_object_by_identifier("vector_db", vector_db_id)
if vector_db is None:
raise VectorStoreNotFoundError(vector_db_id)
return vector_db
async def register_vector_db(
self,
vector_db_id: str,
embedding_model: str,
embedding_dimension: int | None = 384,
provider_id: str | None = None,
provider_vector_db_id: str | None = None,
vector_db_name: str | None = None,
) -> VectorDB:
if provider_id is None:
if len(self.impls_by_provider_id) > 0:
provider_id = list(self.impls_by_provider_id.keys())[0]
if len(self.impls_by_provider_id) > 1:
logger.warning(
f"No provider specified and multiple providers available. Arbitrarily selected the first provider {provider_id}."
)
else:
raise ValueError("No provider available. Please configure a vector_io provider.")
model = await lookup_model(self, embedding_model)
if model is None:
raise ModelNotFoundError(embedding_model)
if model.model_type != ModelType.embedding:
raise ModelTypeError(embedding_model, model.model_type, ModelType.embedding)
if "embedding_dimension" not in model.metadata:
raise ValueError(f"Model {embedding_model} does not have an embedding dimension")
provider = self.impls_by_provider_id[provider_id]
logger.warning(
"VectorDB is being deprecated in future releases in favor of VectorStore. Please migrate your usage accordingly."
)
vector_store = await provider.openai_create_vector_store(
name=vector_db_name or vector_db_id,
embedding_model=embedding_model,
embedding_dimension=model.metadata["embedding_dimension"],
provider_id=provider_id,
provider_vector_db_id=provider_vector_db_id,
)
vector_store_id = vector_store.id
actual_provider_vector_db_id = provider_vector_db_id or vector_store_id
logger.warning(
f"Ignoring vector_db_id {vector_db_id} and using vector_store_id {vector_store_id} instead. Setting VectorDB {vector_db_id} to VectorDB.vector_db_name"
)
vector_db_data = {
"identifier": vector_store_id,
"type": ResourceType.vector_db.value,
"provider_id": provider_id,
"provider_resource_id": actual_provider_vector_db_id,
"embedding_model": embedding_model,
"embedding_dimension": model.metadata["embedding_dimension"],
"vector_db_name": vector_store.name,
}
vector_db = TypeAdapter(VectorDBWithOwner).validate_python(vector_db_data)
await self.register_object(vector_db)
return vector_db
async def unregister_vector_db(self, vector_db_id: str) -> None:
existing_vector_db = await self.get_vector_db(vector_db_id)
await self.unregister_object(existing_vector_db)
async def openai_retrieve_vector_store(
self,
vector_store_id: str,
) -> VectorStoreObject:
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(vector_store_id)
async def openai_update_vector_store(
self,
vector_store_id: str,
name: str | None = None,
expires_after: dict[str, Any] | None = None,
metadata: dict[str, Any] | None = None,
) -> VectorStoreObject:
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(
vector_store_id=vector_store_id,
name=name,
expires_after=expires_after,
metadata=metadata,
)
async def openai_delete_vector_store(
self,
vector_store_id: str,
) -> VectorStoreDeleteResponse:
await self.assert_action_allowed("delete", "vector_db", vector_store_id)
provider = await self.get_provider_impl(vector_store_id)
result = await provider.openai_delete_vector_store(vector_store_id)
await self.unregister_vector_db(vector_store_id)
return result
async def openai_search_vector_store(
self,
vector_store_id: str,
query: str | list[str],
filters: dict[str, Any] | None = None,
max_num_results: int | None = 10,
ranking_options: SearchRankingOptions | None = None,
rewrite_query: bool | None = False,
search_mode: str | None = "vector",
) -> VectorStoreSearchResponsePage:
await self.assert_action_allowed("read", "vector_db", vector_store_id)
provider = await self.get_provider_impl(vector_store_id)
return await provider.openai_search_vector_store(
vector_store_id=vector_store_id,
query=query,
filters=filters,
max_num_results=max_num_results,
ranking_options=ranking_options,
rewrite_query=rewrite_query,
search_mode=search_mode,
)
async def openai_attach_file_to_vector_store(
self,
vector_store_id: str,
file_id: str,
attributes: dict[str, Any] | None = None,
chunking_strategy: VectorStoreChunkingStrategy | None = None,
) -> VectorStoreFileObject:
await self.assert_action_allowed("update", "vector_db", vector_store_id)
provider = await self.get_provider_impl(vector_store_id)
return await provider.openai_attach_file_to_vector_store(
vector_store_id=vector_store_id,
file_id=file_id,
attributes=attributes,
chunking_strategy=chunking_strategy,
)
async def openai_list_files_in_vector_store(
self,
vector_store_id: str,
limit: int | None = 20,
order: str | None = "desc",
after: str | None = None,
before: str | None = None,
filter: VectorStoreFileStatus | None = None,
) -> list[VectorStoreFileObject]:
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_files_in_vector_store(
vector_store_id=vector_store_id,
limit=limit,
order=order,
after=after,
before=before,
filter=filter,
)
async def openai_retrieve_vector_store_file(
self,
vector_store_id: str,
file_id: str,
) -> VectorStoreFileObject:
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_file(
vector_store_id=vector_store_id,
file_id=file_id,
)
async def openai_retrieve_vector_store_file_contents(
self,
vector_store_id: str,
file_id: str,
) -> VectorStoreFileContentsResponse:
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_file_contents(
vector_store_id=vector_store_id,
file_id=file_id,
)
async def openai_update_vector_store_file(
self,
vector_store_id: str,
file_id: str,
attributes: dict[str, Any],
) -> VectorStoreFileObject:
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_file(
vector_store_id=vector_store_id,
file_id=file_id,
attributes=attributes,
)
async def openai_delete_vector_store_file(
self,
vector_store_id: str,
file_id: str,
) -> VectorStoreFileDeleteResponse:
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_file(
vector_store_id=vector_store_id,
file_id=file_id,
)
async def openai_create_vector_store_file_batch(
self,
vector_store_id: str,
file_ids: list[str],
attributes: dict[str, Any] | None = None,
chunking_strategy: Any | None = None,
):
await self.assert_action_allowed("update", "vector_db", vector_store_id)
provider = await self.get_provider_impl(vector_store_id)
return await provider.openai_create_vector_store_file_batch(
vector_store_id=vector_store_id,
file_ids=file_ids,
attributes=attributes,
chunking_strategy=chunking_strategy,
)
async def openai_retrieve_vector_store_file_batch(
self,
batch_id: str,
vector_store_id: str,
):
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_file_batch(
batch_id=batch_id,
vector_store_id=vector_store_id,
)
async def openai_list_files_in_vector_store_file_batch(
self,
batch_id: str,
vector_store_id: str,
after: str | None = None,
before: str | None = None,
filter: str | None = None,
limit: int | None = 20,
order: str | None = "desc",
):
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_files_in_vector_store_file_batch(
batch_id=batch_id,
vector_store_id=vector_store_id,
after=after,
before=before,
filter=filter,
limit=limit,
order=order,
)
async def openai_cancel_vector_store_file_batch(
self,
batch_id: str,
vector_store_id: str,
):
await self.assert_action_allowed("update", "vector_db", vector_store_id)
provider = await self.get_provider_impl(vector_store_id)
return await provider.openai_cancel_vector_store_file_batch(
batch_id=batch_id,
vector_store_id=vector_store_id,
)