chore: Enabling teste for Weaviate

Signed-off-by: Francisco Javier Arceo <farceo@redhat.com>

chore: Actually enabling Chroma unit tests

Signed-off-by: Francisco Javier Arceo <farceo@redhat.com>

fixed tests

Signed-off-by: Francisco Javier Arceo <farceo@redhat.com>

fix integration test

Signed-off-by: Francisco Javier Arceo <farceo@redhat.com>

remove changes from weavbiate

Signed-off-by: Francisco Javier Arceo <farceo@redhat.com>

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

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")

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,