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chore(cleanup)!: kill vector_db references as far as possible (#3864)

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There should not be "vector db" anywhere.
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Ashwin Bharambe 2025-10-20 20:06:16 -07:00 • committed by GitHub
parent 444f6c88f3
commit 122de785c4
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46 changed files with 701 additions and 822 deletions
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70
llama_stack/providers/remote/vector_io/weaviate/weaviate.py
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@ -16,11 +16,11 @@ from llama_stack.apis.common.content_types import InterleavedContent
from llama_stack.apis.common.errors import VectorStoreNotFoundError
from llama_stack.apis.files import Files
from llama_stack.apis.inference import Inference
from llama_stack.apis.vector_dbs import VectorDB
from llama_stack.apis.vector_io import Chunk, QueryChunksResponse, VectorIO
from llama_stack.apis.vector_stores import VectorStore
from llama_stack.core.request_headers import NeedsRequestProviderData
from llama_stack.log import get_logger
from llama_stack.providers.datatypes import VectorDBsProtocolPrivate
from llama_stack.providers.datatypes import VectorStoresProtocolPrivate
from llama_stack.providers.utils.kvstore import kvstore_impl
from llama_stack.providers.utils.kvstore.api import KVStore
from llama_stack.providers.utils.memory.openai_vector_store_mixin import OpenAIVectorStoreMixin
@ -28,7 +28,7 @@ from llama_stack.providers.utils.memory.vector_store import (
RERANKER_TYPE_RRF,
ChunkForDeletion,
EmbeddingIndex,
VectorDBWithIndex,
VectorStoreWithIndex,
)
from llama_stack.providers.utils.vector_io.vector_utils import sanitize_collection_name
@ -37,7 +37,7 @@ from .config import WeaviateVectorIOConfig
log = get_logger(name=__name__, category="vector_io::weaviate")
VERSION = "v3"
VECTOR_DBS_PREFIX = f"vector_dbs:weaviate:{VERSION}::"
VECTOR_DBS_PREFIX = f"vector_stores:weaviate:{VERSION}::"
VECTOR_INDEX_PREFIX = f"vector_index:weaviate:{VERSION}::"
OPENAI_VECTOR_STORES_PREFIX = f"openai_vector_stores:weaviate:{VERSION}::"
OPENAI_VECTOR_STORES_FILES_PREFIX = f"openai_vector_stores_files:weaviate:{VERSION}::"
@ -257,14 +257,14 @@ class WeaviateIndex(EmbeddingIndex):
return QueryChunksResponse(chunks=chunks, scores=scores)
class WeaviateVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, NeedsRequestProviderData, VectorDBsProtocolPrivate):
class WeaviateVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, NeedsRequestProviderData, VectorStoresProtocolPrivate):
def __init__(self, config: WeaviateVectorIOConfig, inference_api: Inference, files_api: Files | None) -> None:
super().__init__(files_api=files_api, kvstore=None)
self.config = config
self.inference_api = inference_api
self.client_cache = {}
self.cache = {}
self.vector_db_store = None
self.vector_store_table = None
self.metadata_collection_name = "openai_vector_stores_metadata"
def _get_client(self) -> weaviate.WeaviateClient:
@ -300,11 +300,11 @@ class WeaviateVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, NeedsRequestProv
end_key = f"{VECTOR_DBS_PREFIX}\xff"
stored = await self.kvstore.values_in_range(start_key, end_key)
for raw in stored:
vector_db = VectorDB.model_validate_json(raw)
vector_store = VectorStore.model_validate_json(raw)
client = self._get_client()
idx = WeaviateIndex(client=client, collection_name=vector_db.identifier, kvstore=self.kvstore)
self.cache[vector_db.identifier] = VectorDBWithIndex(
vector_db=vector_db, index=idx, inference_api=self.inference_api
idx = WeaviateIndex(client=client, collection_name=vector_store.identifier, kvstore=self.kvstore)
self.cache[vector_store.identifier] = VectorStoreWithIndex(
vector_store=vector_store, index=idx, inference_api=self.inference_api
)
# Load OpenAI vector stores metadata into cache
@ -316,9 +316,9 @@ class WeaviateVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, NeedsRequestProv
# Clean up mixin resources (file batch tasks)
await super().shutdown()
async def register_vector_db(self, vector_db: VectorDB) -> None:
async def register_vector_store(self, vector_store: VectorStore) -> None:
client = self._get_client()
sanitized_collection_name = sanitize_collection_name(vector_db.identifier, weaviate_format=True)
sanitized_collection_name = sanitize_collection_name(vector_store.identifier, weaviate_format=True)
# Create collection if it doesn't exist
if not client.collections.exists(sanitized_collection_name):
client.collections.create(
@ -329,45 +329,45 @@ class WeaviateVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, NeedsRequestProv
],
)
self.cache[vector_db.identifier] = VectorDBWithIndex(
vector_db, WeaviateIndex(client=client, collection_name=sanitized_collection_name), self.inference_api
self.cache[vector_store.identifier] = VectorStoreWithIndex(
vector_store, WeaviateIndex(client=client, collection_name=sanitized_collection_name), self.inference_api
)
async def unregister_vector_db(self, vector_db_id: str) -> None:
async def unregister_vector_store(self, vector_store_id: str) -> None:
client = self._get_client()
sanitized_collection_name = sanitize_collection_name(vector_db_id, weaviate_format=True)
if vector_db_id not in self.cache or client.collections.exists(sanitized_collection_name) is False:
sanitized_collection_name = sanitize_collection_name(vector_store_id, weaviate_format=True)
if vector_store_id not in self.cache or client.collections.exists(sanitized_collection_name) is False:
return
client.collections.delete(sanitized_collection_name)
await self.cache[vector_db_id].index.delete()
del self.cache[vector_db_id]
await self.cache[vector_store_id].index.delete()
del self.cache[vector_store_id]
async def _get_and_cache_vector_db_index(self, vector_db_id: str) -> VectorDBWithIndex | None:
if vector_db_id in self.cache:
return self.cache[vector_db_id]
async def _get_and_cache_vector_store_index(self, vector_store_id: str) -> VectorStoreWithIndex | None:
if vector_store_id in self.cache:
return self.cache[vector_store_id]
if self.vector_db_store is None:
raise VectorStoreNotFoundError(vector_db_id)
if self.vector_store_table is None:
raise VectorStoreNotFoundError(vector_store_id)
vector_db = await self.vector_db_store.get_vector_db(vector_db_id)
if not vector_db:
raise VectorStoreNotFoundError(vector_db_id)
vector_store = await self.vector_store_table.get_vector_store(vector_store_id)
if not vector_store:
raise VectorStoreNotFoundError(vector_store_id)
client = self._get_client()
sanitized_collection_name = sanitize_collection_name(vector_db.identifier, weaviate_format=True)
sanitized_collection_name = sanitize_collection_name(vector_store.identifier, weaviate_format=True)
if not client.collections.exists(sanitized_collection_name):
raise ValueError(f"Collection with name `{sanitized_collection_name}` not found")
index = VectorDBWithIndex(
vector_db=vector_db,
index=WeaviateIndex(client=client, collection_name=vector_db.identifier),
index = VectorStoreWithIndex(
vector_store=vector_store,
index=WeaviateIndex(client=client, collection_name=vector_store.identifier),
inference_api=self.inference_api,
)
self.cache[vector_db_id] = index
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_db_index(vector_db_id)
index = await self._get_and_cache_vector_store_index(vector_db_id)
if not index:
raise VectorStoreNotFoundError(vector_db_id)
@ -376,14 +376,14 @@ class WeaviateVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, NeedsRequestProv
async def query_chunks(
self, vector_db_id: str, query: InterleavedContent, params: dict[str, Any] | None = None
) -> QueryChunksResponse:
index = await self._get_and_cache_vector_db_index(vector_db_id)
index = await self._get_and_cache_vector_store_index(vector_db_id)
if not index:
raise VectorStoreNotFoundError(vector_db_id)
return await index.query_chunks(query, params)
async def delete_chunks(self, store_id: str, chunks_for_deletion: list[ChunkForDeletion]) -> None:
index = await self._get_and_cache_vector_db_index(store_id)
index = await self._get_and_cache_vector_store_index(store_id)
if not index:
raise ValueError(f"Vector DB {store_id} not found")