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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.
This commit is contained in:
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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74
llama_stack/providers/remote/vector_io/milvus/milvus.py
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@ -14,10 +14,10 @@ from pymilvus import AnnSearchRequest, DataType, Function, FunctionType, MilvusC
from llama_stack.apis.common.errors import VectorStoreNotFoundError
from llama_stack.apis.files import Files
from llama_stack.apis.inference import Inference, InterleavedContent
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.log import get_logger
from llama_stack.providers.datatypes import VectorDBsProtocolPrivate
from llama_stack.providers.datatypes import VectorStoresProtocolPrivate
from llama_stack.providers.inline.vector_io.milvus import MilvusVectorIOConfig as InlineMilvusVectorIOConfig
from llama_stack.providers.utils.kvstore import kvstore_impl
from llama_stack.providers.utils.kvstore.api import KVStore
@ -26,7 +26,7 @@ from llama_stack.providers.utils.memory.vector_store import (
RERANKER_TYPE_WEIGHTED,
ChunkForDeletion,
EmbeddingIndex,
VectorDBWithIndex,
VectorStoreWithIndex,
)
from llama_stack.providers.utils.vector_io.vector_utils import sanitize_collection_name
@ -35,7 +35,7 @@ from .config import MilvusVectorIOConfig as RemoteMilvusVectorIOConfig
logger = get_logger(name=__name__, category="vector_io::milvus")
VERSION = "v3"
VECTOR_DBS_PREFIX = f"vector_dbs:milvus:{VERSION}::"
VECTOR_DBS_PREFIX = f"vector_stores:milvus:{VERSION}::"
VECTOR_INDEX_PREFIX = f"vector_index:milvus:{VERSION}::"
OPENAI_VECTOR_STORES_PREFIX = f"openai_vector_stores:milvus:{VERSION}::"
OPENAI_VECTOR_STORES_FILES_PREFIX = f"openai_vector_stores_files:milvus:{VERSION}::"
@ -261,7 +261,7 @@ class MilvusIndex(EmbeddingIndex):
raise
class MilvusVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorDBsProtocolPrivate):
class MilvusVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorStoresProtocolPrivate):
def __init__(
self,
config: RemoteMilvusVectorIOConfig | InlineMilvusVectorIOConfig,
@ -273,28 +273,28 @@ class MilvusVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorDBsProtocolP
self.cache = {}
self.client = None
self.inference_api = inference_api
self.vector_db_store = None
self.vector_store_table = None
self.metadata_collection_name = "openai_vector_stores_metadata"
async def initialize(self) -> None:
self.kvstore = await kvstore_impl(self.config.persistence)
start_key = VECTOR_DBS_PREFIX
end_key = f"{VECTOR_DBS_PREFIX}\xff"
stored_vector_dbs = await self.kvstore.values_in_range(start_key, end_key)
stored_vector_stores = await self.kvstore.values_in_range(start_key, end_key)
for vector_db_data in stored_vector_dbs:
vector_db = VectorDB.model_validate_json(vector_db_data)
index = VectorDBWithIndex(
vector_db,
for vector_store_data in stored_vector_stores:
vector_store = VectorStore.model_validate_json(vector_store_data)
index = VectorStoreWithIndex(
vector_store,
index=MilvusIndex(
client=self.client,
collection_name=vector_db.identifier,
collection_name=vector_store.identifier,
consistency_level=self.config.consistency_level,
kvstore=self.kvstore,
),
inference_api=self.inference_api,
)
self.cache[vector_db.identifier] = index
self.cache[vector_store.identifier] = index
if isinstance(self.config, RemoteMilvusVectorIOConfig):
logger.info(f"Connecting to Milvus server at {self.config.uri}")
self.client = MilvusClient(**self.config.model_dump(exclude_none=True))
@ -311,45 +311,45 @@ class MilvusVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorDBsProtocolP
# 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:
if isinstance(self.config, RemoteMilvusVectorIOConfig):
consistency_level = self.config.consistency_level
else:
consistency_level = "Strong"
index = VectorDBWithIndex(
vector_db=vector_db,
index=MilvusIndex(self.client, vector_db.identifier, consistency_level=consistency_level),
index = VectorStoreWithIndex(
vector_store=vector_store,
index=MilvusIndex(self.client, vector_store.identifier, consistency_level=consistency_level),
inference_api=self.inference_api,
)
self.cache[vector_db.identifier] = index
self.cache[vector_store.identifier] = index
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)
index = VectorDBWithIndex(
vector_db=vector_db,
index=MilvusIndex(client=self.client, collection_name=vector_db.identifier, kvstore=self.kvstore),
index = VectorStoreWithIndex(
vector_store=vector_store,
index=MilvusIndex(client=self.client, collection_name=vector_store.identifier, kvstore=self.kvstore),
inference_api=self.inference_api,
)
self.cache[vector_db_id] = index
self.cache[vector_store_id] = index
return index
async def unregister_vector_db(self, vector_db_id: str) -> None:
if vector_db_id in self.cache:
await self.cache[vector_db_id].index.delete()
del self.cache[vector_db_id]
async def unregister_vector_store(self, vector_store_id: str) -> None:
if vector_store_id in self.cache:
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_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)
@ -358,14 +358,14 @@ class MilvusVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorDBsProtocolP
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:
"""Delete a chunk from a milvus vector store."""
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 VectorStoreNotFoundError(store_id)