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revert insert_chunks and query_chunks

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This commit is contained in:
Ashwin Bharambe 2025-10-20 19:40:59 -07:00
parent 04b9954a8b
commit 86926ee339
9 changed files with 53 additions and 53 deletions
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12
llama_stack/providers/inline/vector_io/faiss/faiss.py
View file

@ -249,19 +249,19 @@ 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_store_id: str, chunks: list[Chunk], ttl_seconds: int | None = None) -> None:
index = self.cache.get(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)
if index is None:
raise ValueError(f"Vector DB {vector_store_id} not found. found: {self.cache.keys()}")
raise ValueError(f"Vector DB {vector_db_id} not found. found: {self.cache.keys()}")
await index.insert_chunks(chunks)
async def query_chunks(
self, vector_store_id: str, query: InterleavedContent, params: dict[str, Any] | None = None
self, vector_db_id: str, query: InterleavedContent, params: dict[str, Any] | None = None
) -> QueryChunksResponse:
index = self.cache.get(vector_store_id)
index = self.cache.get(vector_db_id)
if index is None:
raise VectorStoreNotFoundError(vector_store_id)
raise VectorStoreNotFoundError(vector_db_id)
return await index.query_chunks(query, params)

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

@ -448,20 +448,20 @@ 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_store_id: str, chunks: list[Chunk], ttl_seconds: int | None = None) -> None:
index = await self._get_and_cache_vector_store_index(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)
if not index:
raise VectorStoreNotFoundError(vector_store_id)
raise VectorStoreNotFoundError(vector_db_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_store_id: str, query: Any, params: dict[str, Any] | None = None
self, vector_db_id: str, query: Any, params: dict[str, Any] | None = None
) -> QueryChunksResponse:
index = await self._get_and_cache_vector_store_index(vector_store_id)
index = await self._get_and_cache_vector_store_index(vector_db_id)
if not index:
raise VectorStoreNotFoundError(vector_store_id)
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:

12
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_store_id: str, chunks: list[Chunk], ttl_seconds: int | None = None) -> None:
index = await self._get_and_cache_vector_store_index(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)
if index is None:
raise ValueError(f"Vector DB {vector_store_id} not found in Chroma")
raise ValueError(f"Vector DB {vector_db_id} not found in Chroma")
await index.insert_chunks(chunks)
async def query_chunks(
self, vector_store_id: str, query: InterleavedContent, params: dict[str, Any] | None = None
self, vector_db_id: str, query: InterleavedContent, params: dict[str, Any] | None = None
) -> QueryChunksResponse:
index = await self._get_and_cache_vector_store_index(vector_store_id)
index = await self._get_and_cache_vector_store_index(vector_db_id)
if index is None:
raise ValueError(f"Vector DB {vector_store_id} not found in Chroma")
raise ValueError(f"Vector DB {vector_db_id} not found in Chroma")
return await index.query_chunks(query, params)

12
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_store_id: str, chunks: list[Chunk], ttl_seconds: int | None = None) -> None:
index = await self._get_and_cache_vector_store_index(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)
if not index:
raise VectorStoreNotFoundError(vector_store_id)
raise VectorStoreNotFoundError(vector_db_id)
await index.insert_chunks(chunks)
async def query_chunks(
self, vector_store_id: str, query: InterleavedContent, params: dict[str, Any] | None = None
self, vector_db_id: str, query: InterleavedContent, params: dict[str, Any] | None = None
) -> QueryChunksResponse:
index = await self._get_and_cache_vector_store_index(vector_store_id)
index = await self._get_and_cache_vector_store_index(vector_db_id)
if not index:
raise VectorStoreNotFoundError(vector_store_id)
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:

8
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_store_id: str, chunks: list[Chunk], ttl_seconds: int | None = None) -> None:
index = await self._get_and_cache_vector_store_index(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)
await index.insert_chunks(chunks)
async def query_chunks(
self, vector_store_id: str, query: InterleavedContent, params: dict[str, Any] | None = None
self, vector_db_id: str, query: InterleavedContent, params: dict[str, Any] | None = None
) -> QueryChunksResponse:
index = await self._get_and_cache_vector_store_index(vector_store_id)
index = await self._get_and_cache_vector_store_index(vector_db_id)
return await index.query_chunks(query, params)
async def _get_and_cache_vector_store_index(self, vector_store_id: str) -> VectorStoreWithIndex:

12
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_store_id: str, chunks: list[Chunk], ttl_seconds: int | None = None) -> None:
index = await self._get_and_cache_vector_store_index(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)
if not index:
raise VectorStoreNotFoundError(vector_store_id)
raise VectorStoreNotFoundError(vector_db_id)
await index.insert_chunks(chunks)
async def query_chunks(
self, vector_store_id: str, query: InterleavedContent, params: dict[str, Any] | None = None
self, vector_db_id: str, query: InterleavedContent, params: dict[str, Any] | None = None
) -> QueryChunksResponse:
index = await self._get_and_cache_vector_store_index(vector_store_id)
index = await self._get_and_cache_vector_store_index(vector_db_id)
if not index:
raise VectorStoreNotFoundError(vector_store_id)
raise VectorStoreNotFoundError(vector_db_id)
return await index.query_chunks(query, params)

12
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_store_id: str, chunks: list[Chunk], ttl_seconds: int | None = None) -> None:
index = await self._get_and_cache_vector_store_index(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)
if not index:
raise VectorStoreNotFoundError(vector_store_id)
raise VectorStoreNotFoundError(vector_db_id)
await index.insert_chunks(chunks)
async def query_chunks(
self, vector_store_id: str, query: InterleavedContent, params: dict[str, Any] | None = None
self, vector_db_id: str, query: InterleavedContent, params: dict[str, Any] | None = None
) -> QueryChunksResponse:
index = await self._get_and_cache_vector_store_index(vector_store_id)
index = await self._get_and_cache_vector_store_index(vector_db_id)
if not index:
raise VectorStoreNotFoundError(vector_store_id)
raise VectorStoreNotFoundError(vector_db_id)
return await index.query_chunks(query, params)