important fix

llama_stack/core/routing_tables/vector_stores.py

@@ -67,44 +67,16 @@ class VectorStoresRoutingTable(CommonRoutingTableImpl):
             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")
 
-        try:
+        vector_store = VectorStoreWithOwner(
-            provider = self.impls_by_provider_id[provider_id]
+            identifier=vector_store_id,
-        except KeyError:
+            type=ResourceType.vector_store.value,
-            available_providers = list(self.impls_by_provider_id.keys())
-            raise ValueError(
-                f"Provider '{provider_id}' not found in routing table. Available providers: {available_providers}"
-            ) from None
-        logger.warning(
-            "VectorStore is being deprecated in future releases in favor of VectorStore. Please migrate your usage accordingly."
-        )
-        request = OpenAICreateVectorStoreRequestWithExtraBody(
-            name=vector_store_name or vector_store_id,
-            embedding_model=embedding_model,
-            embedding_dimension=model.metadata["embedding_dimension"],
             provider_id=provider_id,
-            provider_vector_store_id=provider_vector_store_id,
+            provider_resource_id=provider_vector_store_id,
+            embedding_model=embedding_model,
+            embedding_dimension=embedding_dimension,
+            vector_store_name=vector_store_name,
         )
-        vector_store = await provider.openai_create_vector_store(request)
-
-        vector_store_id = vector_store.id
-        actual_provider_vector_store_id = provider_vector_store_id or vector_store_id
-        logger.warning(
-            f"Ignoring vector_store_id {vector_store_id} and using vector_store_id {vector_store_id} instead. Setting VectorStore {vector_store_id} to VectorStore.vector_store_name"
-        )
-
-        vector_store_data = {
-            "identifier": vector_store_id,
-            "type": ResourceType.vector_store.value,
-            "provider_id": provider_id,
-            "provider_resource_id": actual_provider_vector_store_id,
-            "embedding_model": embedding_model,
-            "embedding_dimension": model.metadata["embedding_dimension"],
-            "vector_store_name": vector_store.name,
-        }
-        vector_store = TypeAdapter(VectorStoreWithOwner).validate_python(vector_store_data)
         await self.register_object(vector_store)
         return vector_store
 

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

@@ -242,7 +242,6 @@ class FaissVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorStoresProtoco
         assert self.kvstore is not None
 
         if vector_store_id not in self.cache:
-            logger.warning(f"Vector DB {vector_store_id} not found")
             return
 
         await self.cache[vector_store_id].index.delete()

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

@@ -443,7 +443,6 @@ class SQLiteVecVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorStoresPro
 
     async def unregister_vector_store(self, vector_store_id: str) -> None:
         if vector_store_id not in self.cache:
-            logger.warning(f"Vector DB {vector_store_id} not found")
             return
         await self.cache[vector_store_id].index.delete()
         del self.cache[vector_store_id]

llama_stack/providers/utils/memory/openai_vector_store_mixin.py

@@ -333,7 +333,7 @@ class OpenAIVectorStoreMixin(ABC):
     @abstractmethod
     async def insert_chunks(
         self,
-        vector_store_id: str,
+        vector_db_id: str,
         chunks: list[Chunk],
         ttl_seconds: int | None = None,
     ) -> None:
@@ -342,7 +342,7 @@ class OpenAIVectorStoreMixin(ABC):
 
     @abstractmethod
     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:
         """Query chunks from a vector database (provider-specific implementation)."""
         pass
@@ -619,7 +619,7 @@ class OpenAIVectorStoreMixin(ABC):
             # TODO: Add support for ranking_options.ranker
 
             response = await self.query_chunks(
-                vector_store_id=vector_store_id,
+                vector_db_id=vector_store_id,
                 query=search_query,
                 params=params,
             )
@@ -813,7 +813,7 @@ class OpenAIVectorStoreMixin(ABC):
                 )
             else:
                 await self.insert_chunks(
-                    vector_store_id=vector_store_id,
+                    vector_db_id=vector_store_id,
                     chunks=chunks,
                 )
                 vector_store_file_object.status = "completed"

tests/integration/conftest.py

@@ -37,6 +37,9 @@ def pytest_sessionstart(session):
     if "LLAMA_STACK_TEST_INFERENCE_MODE" not in os.environ:
         os.environ["LLAMA_STACK_TEST_INFERENCE_MODE"] = "replay"
 
+    if "LLAMA_STACK_LOGGING" not in os.environ:
+        os.environ["LLAMA_STACK_LOGGING"] = "all=warning"
+
     if "SQLITE_STORE_DIR" not in os.environ:
         os.environ["SQLITE_STORE_DIR"] = tempfile.mkdtemp()