revert insert_chunks and query_chunks

llama_stack/providers/inline/vector_io/faiss/faiss.py

@@ -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:
+    async def insert_chunks(self, vector_db_id: str, chunks: list[Chunk], ttl_seconds: int | None = None) -> None:
-        index = self.cache.get(vector_store_id)
+        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)
 

llama_stack/providers/inline/vector_io/sqlite_vec/sqlite_vec.py

@@ -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:
+    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_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)
         # 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:

llama_stack/providers/remote/vector_io/chroma/chroma.py

@@ -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:
+    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_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")
 
         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)
 

llama_stack/providers/remote/vector_io/milvus/milvus.py

@@ -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:
+    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_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)
 
         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:

llama_stack/providers/remote/vector_io/pgvector/pgvector.py

@@ -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:
+    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_store_id)
+        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:

llama_stack/providers/remote/vector_io/qdrant/qdrant.py

@@ -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:
+    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_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)
 
         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)
 

llama_stack/providers/remote/vector_io/weaviate/weaviate.py

@@ -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:
+    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_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)
 
         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)
 

tests/integration/vector_io/test_openai_vector_stores.py

@@ -367,7 +367,7 @@ def test_openai_vector_store_with_chunks(
 
     # Insert chunks using the native LlamaStack API (since OpenAI API doesn't have direct chunk insertion)
     llama_client.vector_io.insert(
-        vector_store_id=vector_store.id,
+        vector_db_id=vector_store.id,
         chunks=sample_chunks,
     )
 
@@ -434,7 +434,7 @@ def test_openai_vector_store_search_relevance(
 
     # Insert chunks using native API
     llama_client.vector_io.insert(
-        vector_store_id=vector_store.id,
+        vector_db_id=vector_store.id,
         chunks=sample_chunks,
     )
 
@@ -484,7 +484,7 @@ def test_openai_vector_store_search_with_ranking_options(
 
     # Insert chunks
     llama_client.vector_io.insert(
-        vector_store_id=vector_store.id,
+        vector_db_id=vector_store.id,
         chunks=sample_chunks,
     )
 
@@ -544,7 +544,7 @@ def test_openai_vector_store_search_with_high_score_filter(
 
     # Insert chunks
     llama_client.vector_io.insert(
-        vector_store_id=vector_store.id,
+        vector_db_id=vector_store.id,
         chunks=sample_chunks,
     )
 
@@ -610,7 +610,7 @@ def test_openai_vector_store_search_with_max_num_results(
 
     # Insert chunks
     llama_client.vector_io.insert(
-        vector_store_id=vector_store.id,
+        vector_db_id=vector_store.id,
         chunks=sample_chunks,
     )
 
@@ -1175,7 +1175,7 @@ def test_openai_vector_store_search_modes(
     )
 
     client_with_models.vector_io.insert(
-        vector_store_id=vector_store.id,
+        vector_db_id=vector_store.id,
         chunks=sample_chunks,
     )
     query = "Python programming language"

tests/integration/vector_io/test_vector_io.py

@@ -123,12 +123,12 @@ def test_insert_chunks(
     actual_vector_store_id = create_response.id
 
     client_with_empty_registry.vector_io.insert(
-        vector_store_id=actual_vector_store_id,
+        vector_db_id=actual_vector_store_id,
         chunks=sample_chunks,
     )
 
     response = client_with_empty_registry.vector_io.query(
-        vector_store_id=actual_vector_store_id,
+        vector_db_id=actual_vector_store_id,
         query="What is the capital of France?",
     )
     assert response is not None
@@ -137,7 +137,7 @@ def test_insert_chunks(
 
     query, expected_doc_id = test_case
     response = client_with_empty_registry.vector_io.query(
-        vector_store_id=actual_vector_store_id,
+        vector_db_id=actual_vector_store_id,
         query=query,
     )
     assert response is not None
@@ -174,13 +174,13 @@ def test_insert_chunks_with_precomputed_embeddings(
     ]
 
     client_with_empty_registry.vector_io.insert(
-        vector_store_id=actual_vector_store_id,
+        vector_db_id=actual_vector_store_id,
         chunks=chunks_with_embeddings,
     )
 
     provider = [p.provider_id for p in client_with_empty_registry.providers.list() if p.api == "vector_io"][0]
     response = client_with_empty_registry.vector_io.query(
-        vector_store_id=actual_vector_store_id,
+        vector_db_id=actual_vector_store_id,
         query="precomputed embedding test",
         params=vector_io_provider_params_dict.get(provider, None),
     )
@@ -224,13 +224,13 @@ def test_query_returns_valid_object_when_identical_to_embedding_in_vdb(
     ]
 
     client_with_empty_registry.vector_io.insert(
-        vector_store_id=actual_vector_store_id,
+        vector_db_id=actual_vector_store_id,
         chunks=chunks_with_embeddings,
     )
 
     provider = [p.provider_id for p in client_with_empty_registry.providers.list() if p.api == "vector_io"][0]
     response = client_with_empty_registry.vector_io.query(
-        vector_store_id=actual_vector_store_id,
+        vector_db_id=actual_vector_store_id,
         query="duplicate",
         params=vector_io_provider_params_dict.get(provider, None),
     )