moar

llama_stack/core/build.py

@@ -91,7 +91,7 @@ def get_provider_dependencies(
 
 
 def print_pip_install_help(config: BuildConfig):
-    normal_deps, special_deps = get_provider_dependencies(config)
+    normal_deps, special_deps, _ = get_provider_dependencies(config)
 
     cprint(
         f"Please install needed dependencies using the following commands:\n\nuv pip install {' '.join(normal_deps)}",

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

@@ -266,7 +266,9 @@ class QdrantVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorDBsProtocolP
     ) -> VectorStoreFileObject:
         # Qdrant doesn't allow multiple clients to access the same storage path simultaneously.
         async with self._qdrant_lock:
-            await super().openai_attach_file_to_vector_store(vector_store_id, file_id, attributes, chunking_strategy)
+            return await super().openai_attach_file_to_vector_store(
+                vector_store_id, file_id, attributes, chunking_strategy
+            )
 
     async def delete_chunks(self, store_id: str, chunks_for_deletion: list[ChunkForDeletion]) -> None:
         """Delete chunks from a Qdrant vector store."""

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

@@ -68,6 +68,7 @@ class WeaviateIndex(EmbeddingIndex):
             data_objects.append(
                 wvc.data.DataObject(
                     properties={
+                        "chunk_id": chunk.chunk_id,
                         "chunk_content": chunk.model_dump_json(),
                     },
                     vector=embeddings[i].tolist(),
@@ -84,7 +85,7 @@ class WeaviateIndex(EmbeddingIndex):
         sanitized_collection_name = sanitize_collection_name(self.collection_name, weaviate_format=True)
         collection = self.client.collections.get(sanitized_collection_name)
         chunk_ids = [chunk.chunk_id for chunk in chunks_for_deletion]
-        collection.data.delete_many(where=Filter.by_property("id").contains_any(chunk_ids))
+        collection.data.delete_many(where=Filter.by_property("chunk_id").contains_any(chunk_ids))
 
     async def query_vector(self, embedding: NDArray, k: int, score_threshold: float) -> QueryChunksResponse:
         sanitized_collection_name = sanitize_collection_name(self.collection_name, weaviate_format=True)

llama_stack/providers/utils/memory/openai_vector_store_mixin.py

@@ -37,6 +37,7 @@ from llama_stack.apis.vector_io import (
     VectorStoreSearchResponse,
     VectorStoreSearchResponsePage,
 )
+from llama_stack.log import get_logger
 from llama_stack.providers.utils.kvstore.api import KVStore
 from llama_stack.providers.utils.memory.vector_store import (
     ChunkForDeletion,
@@ -44,7 +45,7 @@ from llama_stack.providers.utils.memory.vector_store import (
     make_overlapped_chunks,
 )
 
-logger = logging.getLogger(__name__)
+logger = get_logger(__name__, category="vector_io")
 
 # Constants for OpenAI vector stores
 CHUNK_MULTIPLIER = 5

tests/integration/vector_io/test_openai_vector_stores.py

@@ -592,6 +592,7 @@ def test_openai_vector_store_list_files(compat_client_with_empty_stores, client_
             vector_store_id=vector_store.id,
             file_id=file.id,
         )
+        assert response is not None
         assert response.status == "completed", (
             f"Failed to attach file {file.id} to vector store {vector_store.id}: {response=}"
         )