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fix(tests): remove chroma and qdrant from vector io unit tests (#3759)

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These vector databases are already thoroughly tested in integration
tests.
Unit tests now focus on sqlite_vec, faiss, and pgvector with mocked
dependencies, removing the need for external service dependencies.

## Changes:
- Deleted test_qdrant.py unit test file
- Removed chroma/qdrant fixtures and parametrization from conftest.py
- Fixed SqliteKVStoreConfig import to use correct location
- Removed chromadb, qdrant-client, pymilvus, milvus-lite, and
  weaviate-client from unit test dependencies in pyproject.toml
This commit is contained in:
Ashwin Bharambe 2025-10-09 14:36:34 -07:00 • committed by GitHub
parent f50ce11a3b
commit a055a32ee4
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5 changed files with 222 additions and 424 deletions
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103
tests/unit/providers/vector_io/conftest.py
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@ -9,27 +9,22 @@ from unittest.mock import AsyncMock, MagicMock, patch
import numpy as np
import pytest
from chromadb import PersistentClient
from llama_stack.apis.vector_dbs import VectorDB
from llama_stack.apis.vector_io import Chunk, ChunkMetadata, QueryChunksResponse
from llama_stack.providers.inline.vector_io.chroma.config import ChromaVectorIOConfig
from llama_stack.providers.inline.vector_io.faiss.config import FaissVectorIOConfig
from llama_stack.providers.inline.vector_io.faiss.faiss import FaissIndex, FaissVectorIOAdapter
from llama_stack.providers.inline.vector_io.milvus.config import SqliteKVStoreConfig
from llama_stack.providers.inline.vector_io.qdrant import QdrantVectorIOConfig
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, maybe_await
from llama_stack.providers.remote.vector_io.pgvector.config import PGVectorVectorIOConfig
from llama_stack.providers.remote.vector_io.pgvector.pgvector import PGVectorIndex, PGVectorVectorIOAdapter
from llama_stack.providers.remote.vector_io.qdrant.qdrant import QdrantVectorIOAdapter
from llama_stack.providers.utils.kvstore.config import SqliteKVStoreConfig
EMBEDDING_DIMENSION = 384
COLLECTION_PREFIX = "test_collection"
@pytest.fixture(params=["sqlite_vec", "faiss", "chroma", "pgvector"])
@pytest.fixture(params=["sqlite_vec", "faiss", "pgvector"])
def vector_provider(request):
return request.param
@ -201,98 +196,6 @@ async def faiss_vec_adapter(unique_kvstore_config, mock_inference_api, embedding
await adapter.shutdown()
@pytest.fixture
def chroma_vec_db_path(tmp_path_factory):
persist_dir = tmp_path_factory.mktemp(f"chroma_{np.random.randint(1e6)}")
return str(persist_dir)
@pytest.fixture
async def chroma_vec_index(chroma_vec_db_path, embedding_dimension):
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()
@pytest.fixture
async def chroma_vec_adapter(chroma_vec_db_path, unique_kvstore_config, mock_inference_api, embedding_dimension):
config = ChromaVectorIOConfig(
db_path=chroma_vec_db_path,
kvstore=unique_kvstore_config,
)
adapter = ChromaVectorIOAdapter(
config=config,
inference_api=mock_inference_api,
files_api=None,
)
await adapter.initialize()
await adapter.register_vector_db(
VectorDB(
identifier=f"chroma_test_collection_{random.randint(1, 1_000_000)}",
provider_id="test_provider",
embedding_model="test_model",
embedding_dimension=embedding_dimension,
)
)
yield adapter
await adapter.shutdown()
@pytest.fixture
def qdrant_vec_db_path(tmp_path_factory):
import uuid
db_path = str(tmp_path_factory.getbasetemp() / f"test_qdrant_{uuid.uuid4()}.db")
return db_path
@pytest.fixture
async def qdrant_vec_adapter(qdrant_vec_db_path, unique_kvstore_config, mock_inference_api, embedding_dimension):
import uuid
config = QdrantVectorIOConfig(
db_path=qdrant_vec_db_path,
kvstore=unique_kvstore_config,
)
adapter = QdrantVectorIOAdapter(
config=config,
inference_api=mock_inference_api,
files_api=None,
)
collection_id = f"qdrant_test_collection_{uuid.uuid4()}"
await adapter.initialize()
await adapter.register_vector_db(
VectorDB(
identifier=collection_id,
provider_id="test_provider",
embedding_model="test_model",
embedding_dimension=embedding_dimension,
)
)
adapter.test_collection_id = collection_id
yield adapter
await adapter.shutdown()
@pytest.fixture
async def qdrant_vec_index(qdrant_vec_db_path, embedding_dimension):
import uuid
from qdrant_client import AsyncQdrantClient
from llama_stack.providers.remote.vector_io.qdrant.qdrant import QdrantIndex
client = AsyncQdrantClient(path=qdrant_vec_db_path)
collection_name = f"qdrant_test_collection_{uuid.uuid4()}"
index = QdrantIndex(client, collection_name)
yield index
await index.delete()
@pytest.fixture
def mock_psycopg2_connection():
connection = MagicMock()
@ -410,8 +313,6 @@ def vector_io_adapter(vector_provider, request):
vector_provider_dict = {
"faiss": "faiss_vec_adapter",
"sqlite_vec": "sqlite_vec_adapter",
"chroma": "chroma_vec_adapter",
"qdrant": "qdrant_vec_adapter",
"pgvector": "pgvector_vec_adapter",
}
return request.getfixturevalue(vector_provider_dict[vector_provider])