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chore(tests): fix responses and vector_io tests (#3119)
Some fixes to MCP tests. And a bunch of fixes for Vector providers. I also enabled a bunch of Vector IO tests to be used with `LlamaStackLibraryClient` ## Test Plan Run Responses tests with llama stack library client: ``` pytest -s -v tests/integration/non_ci/responses/ --stack-config=server:starter \ --text-model openai/gpt-4o \ --embedding-model=sentence-transformers/all-MiniLM-L6-v2 \ -k "client_with_models" ``` Do the same with `-k openai_client` The rest should be taken care of by CI.
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25 changed files with 175 additions and 112 deletions
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@ -29,6 +29,7 @@ from llama_stack.providers.inline.vector_io.qdrant import QdrantVectorIOConfig a
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from llama_stack.providers.utils.kvstore import KVStore, kvstore_impl
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from llama_stack.providers.utils.memory.openai_vector_store_mixin import OpenAIVectorStoreMixin
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from llama_stack.providers.utils.memory.vector_store import (
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ChunkForDeletion,
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EmbeddingIndex,
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VectorDBWithIndex,
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)
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@ -88,15 +89,16 @@ class QdrantIndex(EmbeddingIndex):
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await self.client.upsert(collection_name=self.collection_name, points=points)
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async def delete_chunk(self, chunk_id: str) -> None:
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async def delete_chunks(self, chunks_for_deletion: list[ChunkForDeletion]) -> None:
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"""Remove a chunk from the Qdrant collection."""
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chunk_ids = [convert_id(c.chunk_id) for c in chunks_for_deletion]
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try:
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await self.client.delete(
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collection_name=self.collection_name,
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points_selector=models.PointIdsList(points=[convert_id(chunk_id)]),
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points_selector=models.PointIdsList(points=chunk_ids),
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)
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except Exception as e:
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log.error(f"Error deleting chunk {chunk_id} from Qdrant collection {self.collection_name}: {e}")
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log.error(f"Error deleting chunks from Qdrant collection {self.collection_name}: {e}")
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raise
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async def query_vector(self, embedding: NDArray, k: int, score_threshold: float) -> QueryChunksResponse:
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@ -264,12 +266,14 @@ class QdrantVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorDBsProtocolP
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) -> VectorStoreFileObject:
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# Qdrant doesn't allow multiple clients to access the same storage path simultaneously.
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async with self._qdrant_lock:
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await super().openai_attach_file_to_vector_store(vector_store_id, file_id, attributes, chunking_strategy)
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return await super().openai_attach_file_to_vector_store(
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vector_store_id, file_id, attributes, chunking_strategy
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)
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async def delete_chunks(self, store_id: str, chunk_ids: list[str]) -> None:
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async def delete_chunks(self, store_id: str, chunks_for_deletion: list[ChunkForDeletion]) -> None:
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"""Delete chunks from a Qdrant vector store."""
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index = await self._get_and_cache_vector_db_index(store_id)
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if not index:
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raise ValueError(f"Vector DB {store_id} not found")
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for chunk_id in chunk_ids:
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await index.index.delete_chunk(chunk_id)
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await index.index.delete_chunks(chunks_for_deletion)
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