diff --git a/llama_stack/providers/remote/vector_io/chroma/chroma.py b/llama_stack/providers/remote/vector_io/chroma/chroma.py index bd968d96d..656762373 100644 --- a/llama_stack/providers/remote/vector_io/chroma/chroma.py +++ b/llama_stack/providers/remote/vector_io/chroma/chroma.py @@ -57,12 +57,16 @@ class ChromaIndex(EmbeddingIndex): self.collection = collection self.kvstore = kvstore + async def initialize(self): + # Chroma does not require explicit initialization, this is just a helper for unit tests + pass + async def add_chunks(self, chunks: list[Chunk], embeddings: NDArray): assert len(chunks) == len(embeddings), ( f"Chunk length {len(chunks)} does not match embedding length {len(embeddings)}" ) - ids = [f"{c.metadata['document_id']}:chunk-{i}" for i, c in enumerate(chunks)] + ids = [f"{c.metadata.get('document_id', '')}:{c.chunk_id}" for c in chunks] await maybe_await( self.collection.add( documents=[chunk.model_dump_json() for chunk in chunks], @@ -137,9 +141,12 @@ class ChromaVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorDBsProtocolP self.client = None self.cache = {} self.kvstore: KVStore | None = None + self.vector_db_store = None async def initialize(self) -> None: self.kvstore = await kvstore_impl(self.config.kvstore) + self.vector_db_store = self.kvstore + if isinstance(self.config, RemoteChromaVectorIOConfig): log.info(f"Connecting to Chroma server at: {self.config.url}") url = self.config.url.rstrip("/") @@ -172,6 +179,10 @@ class ChromaVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorDBsProtocolP ) async def unregister_vector_db(self, vector_db_id: str) -> None: + if vector_db_id not in self.cache: + log.warning(f"Vector DB {vector_db_id} not found") + return + await self.cache[vector_db_id].index.delete() del self.cache[vector_db_id] @@ -182,6 +193,8 @@ class ChromaVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorDBsProtocolP ttl_seconds: int | None = None, ) -> None: index = await self._get_and_cache_vector_db_index(vector_db_id) + if index is None: + raise ValueError(f"Vector DB {vector_db_id} not found in Chroma") await index.insert_chunks(chunks) @@ -193,18 +206,27 @@ class ChromaVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorDBsProtocolP ) -> QueryChunksResponse: index = await self._get_and_cache_vector_db_index(vector_db_id) + if index is None: + raise ValueError(f"Vector DB {vector_db_id} not found in Chroma") + return await index.query_chunks(query, params) async def _get_and_cache_vector_db_index(self, vector_db_id: str) -> VectorDBWithIndex: if vector_db_id in self.cache: return self.cache[vector_db_id] - vector_db = await self.vector_db_store.get_vector_db(vector_db_id) - if not vector_db: - raise ValueError(f"Vector DB {vector_db_id} not found in Llama Stack") - collection = await maybe_await(self.client.get_collection(vector_db_id)) - if not collection: - raise ValueError(f"Vector DB {vector_db_id} not found in Chroma") - index = VectorDBWithIndex(vector_db, ChromaIndex(self.client, collection), self.inference_api) - self.cache[vector_db_id] = index - return index + try: + collection = await maybe_await(self.client.get_collection(vector_db_id)) + if not collection: + raise ValueError(f"Vector DB {vector_db_id} not found in Chroma") + + vector_db = await self.vector_db_store.get_vector_db(vector_db_id) + if not vector_db: + raise ValueError(f"Vector DB {vector_db_id} not found in Llama Stack") + + index = VectorDBWithIndex(vector_db, ChromaIndex(self.client, collection), self.inference_api) + self.cache[vector_db_id] = index + return index + + except Exception as exc: + raise ValueError(f"Vector DB {vector_db_id} not found in Chroma") from exc diff --git a/tests/integration/vector_io/test_openai_vector_stores.py b/tests/integration/vector_io/test_openai_vector_stores.py index 71d2bc55e..dcb62a931 100644 --- a/tests/integration/vector_io/test_openai_vector_stores.py +++ b/tests/integration/vector_io/test_openai_vector_stores.py @@ -22,7 +22,14 @@ logger = logging.getLogger(__name__) def skip_if_provider_doesnt_support_openai_vector_stores(client_with_models): vector_io_providers = [p for p in client_with_models.providers.list() if p.api == "vector_io"] for p in vector_io_providers: - if p.provider_type in ["inline::faiss", "inline::sqlite-vec", "inline::milvus", "inline::chromadb"]: + if p.provider_type in [ + "inline::faiss", + "inline::sqlite-vec", + "inline::milvus", + "inline::chromadb", + "remote::pgvector", + "remote::chromadb", + ]: return pytest.skip("OpenAI vector stores are not supported by any provider") @@ -31,12 +38,7 @@ def skip_if_provider_doesnt_support_openai_vector_stores(client_with_models): def skip_if_provider_doesnt_support_openai_vector_store_files_api(client_with_models): vector_io_providers = [p for p in client_with_models.providers.list() if p.api == "vector_io"] for p in vector_io_providers: - if p.provider_type in [ - "inline::faiss", - "inline::sqlite-vec", - "inline::milvus", - "remote::pgvector", - ]: + if p.provider_type in []: return pytest.skip("OpenAI vector stores are not supported by any provider") diff --git a/tests/unit/providers/vector_io/conftest.py b/tests/unit/providers/vector_io/conftest.py index 45e37d6ff..bcba06140 100644 --- a/tests/unit/providers/vector_io/conftest.py +++ b/tests/unit/providers/vector_io/conftest.py @@ -8,6 +8,7 @@ import random import numpy as np import pytest +from chromadb import PersistentClient from pymilvus import MilvusClient, connections from llama_stack.apis.vector_dbs import VectorDB @@ -18,7 +19,7 @@ from llama_stack.providers.inline.vector_io.faiss.faiss import FaissIndex, Faiss from llama_stack.providers.inline.vector_io.milvus.config import MilvusVectorIOConfig, SqliteKVStoreConfig from llama_stack.providers.inline.vector_io.sqlite_vec import SQLiteVectorIOConfig from llama_stack.providers.inline.vector_io.sqlite_vec.sqlite_vec import SQLiteVecIndex, SQLiteVecVectorIOAdapter -from llama_stack.providers.remote.vector_io.chroma.chroma import ChromaIndex, ChromaVectorIOAdapter +from llama_stack.providers.remote.vector_io.chroma.chroma import ChromaIndex, ChromaVectorIOAdapter, maybe_await from llama_stack.providers.remote.vector_io.milvus.milvus import MilvusIndex, MilvusVectorIOAdapter EMBEDDING_DIMENSION = 384 @@ -26,6 +27,11 @@ COLLECTION_PREFIX = "test_collection" MILVUS_ALIAS = "test_milvus" +@pytest.fixture(params=["milvus", "sqlite_vec", "faiss", "chroma"]) +def vector_provider(request): + return request.param + + @pytest.fixture def vector_db_id() -> str: return f"test-vector-db-{random.randint(1, 100)}" @@ -94,11 +100,6 @@ def sample_embeddings_with_metadata(sample_chunks_with_metadata): return np.array([np.random.rand(EMBEDDING_DIMENSION).astype(np.float32) for _ in sample_chunks_with_metadata]) -@pytest.fixture(params=["milvus", "sqlite_vec", "faiss"]) -def vector_provider(request): - return request.param - - @pytest.fixture(scope="session") def mock_inference_api(embedding_dimension): class MockInferenceAPI: @@ -246,10 +247,10 @@ def chroma_vec_db_path(tmp_path_factory): @pytest.fixture async def chroma_vec_index(chroma_vec_db_path, embedding_dimension): - index = ChromaIndex( - embedding_dimension=embedding_dimension, - persist_directory=chroma_vec_db_path, - ) + client = PersistentClient(path=chroma_vec_db_path) + name = f"{COLLECTION_PREFIX}_{np.random.randint(1e6)}" + collection = await maybe_await(client.get_or_create_collection(name)) + index = ChromaIndex(client=client, collection=collection) await index.initialize() yield index await index.delete() @@ -257,7 +258,10 @@ async def chroma_vec_index(chroma_vec_db_path, embedding_dimension): @pytest.fixture async def chroma_vec_adapter(chroma_vec_db_path, mock_inference_api, embedding_dimension): - config = ChromaVectorIOConfig(persist_directory=chroma_vec_db_path) + config = ChromaVectorIOConfig( + db_path=chroma_vec_db_path, + kvstore=SqliteKVStoreConfig(), + ) adapter = ChromaVectorIOAdapter( config=config, inference_api=mock_inference_api,