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chore: Removing Weaviate, PGVector, and Milvus from unit tests (#3742)
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# What does this PR do?
Removing Weaviate, PostGres, and Milvus unit tests

<!-- If resolving an issue, uncomment and update the line below -->
<!-- Closes #[issue-number] -->

## Test Plan
<!-- Describe the tests you ran to verify your changes with result
summaries. *Provide clear instructions so the plan can be easily
re-executed.* -->

Signed-off-by: Francisco Javier Arceo <farceo@redhat.com>
This commit is contained in:
Francisco Arceo 2025-10-08 15:25:51 -04:00 • committed by GitHub
parent 79bed44b04
commit b96640eca3
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4 changed files with 3 additions and 579 deletions
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116
tests/unit/providers/vector_io/conftest.py
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@ -10,31 +10,26 @@ from unittest.mock import AsyncMock, MagicMock, patch
import numpy as np
import pytest
from chromadb import PersistentClient
from pymilvus import MilvusClient, connections
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 MilvusVectorIOConfig, SqliteKVStoreConfig
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.milvus.milvus import MilvusIndex, MilvusVectorIOAdapter
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.remote.vector_io.weaviate.config import WeaviateVectorIOConfig
from llama_stack.providers.remote.vector_io.weaviate.weaviate import WeaviateIndex, WeaviateVectorIOAdapter
EMBEDDING_DIMENSION = 384
COLLECTION_PREFIX = "test_collection"
MILVUS_ALIAS = "test_milvus"
@pytest.fixture(params=["milvus", "sqlite_vec", "faiss", "chroma", "pgvector", "weaviate"])
@pytest.fixture(params=["sqlite_vec", "faiss", "chroma", "pgvector"])
def vector_provider(request):
return request.param
@ -170,46 +165,6 @@ async def sqlite_vec_adapter(sqlite_vec_db_path, unique_kvstore_config, mock_inf
await adapter.shutdown()
@pytest.fixture(scope="session")
def milvus_vec_db_path(tmp_path_factory):
db_path = str(tmp_path_factory.getbasetemp() / "test_milvus.db")
return db_path
@pytest.fixture
async def milvus_vec_index(milvus_vec_db_path, embedding_dimension):
client = MilvusClient(milvus_vec_db_path)
name = f"{COLLECTION_PREFIX}_{np.random.randint(1e6)}"
connections.connect(alias=MILVUS_ALIAS, uri=milvus_vec_db_path)
index = MilvusIndex(client, name, consistency_level="Strong")
index.db_path = milvus_vec_db_path
yield index
@pytest.fixture
async def milvus_vec_adapter(milvus_vec_db_path, unique_kvstore_config, mock_inference_api):
config = MilvusVectorIOConfig(
db_path=milvus_vec_db_path,
kvstore=unique_kvstore_config,
)
adapter = MilvusVectorIOAdapter(
config=config,
inference_api=mock_inference_api,
files_api=None,
)
await adapter.initialize()
await adapter.register_vector_db(
VectorDB(
identifier=adapter.metadata_collection_name,
provider_id="test_provider",
embedding_model="test_model",
embedding_dimension=128,
)
)
yield adapter
await adapter.shutdown()
@pytest.fixture
def faiss_vec_db_path(tmp_path_factory):
db_path = str(tmp_path_factory.getbasetemp() / "test_faiss.db")
@ -450,81 +405,14 @@ async def pgvector_vec_adapter(unique_kvstore_config, mock_inference_api, embedd
await adapter.shutdown()
@pytest.fixture(scope="session")
def weaviate_vec_db_path(tmp_path_factory):
db_path = str(tmp_path_factory.getbasetemp() / "test_weaviate.db")
return db_path
@pytest.fixture
async def weaviate_vec_index(weaviate_vec_db_path):
import pytest_socket
import weaviate
pytest_socket.enable_socket()
client = weaviate.connect_to_embedded(
hostname="localhost",
port=8080,
grpc_port=50051,
persistence_data_path=weaviate_vec_db_path,
)
index = WeaviateIndex(client=client, collection_name="Testcollection")
await index.initialize()
yield index
await index.delete()
client.close()
@pytest.fixture
async def weaviate_vec_adapter(weaviate_vec_db_path, unique_kvstore_config, mock_inference_api, embedding_dimension):
import pytest_socket
import weaviate
pytest_socket.enable_socket()
client = weaviate.connect_to_embedded(
hostname="localhost",
port=8080,
grpc_port=50051,
persistence_data_path=weaviate_vec_db_path,
)
config = WeaviateVectorIOConfig(
weaviate_cluster_url="localhost:8080",
weaviate_api_key=None,
kvstore=unique_kvstore_config,
)
adapter = WeaviateVectorIOAdapter(
config=config,
inference_api=mock_inference_api,
files_api=None,
)
collection_id = f"weaviate_test_collection_{random.randint(1, 1_000_000)}"
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()
client.close()
@pytest.fixture
def vector_io_adapter(vector_provider, request):
vector_provider_dict = {
"milvus": "milvus_vec_adapter",
"faiss": "faiss_vec_adapter",
"sqlite_vec": "sqlite_vec_adapter",
"chroma": "chroma_vec_adapter",
"qdrant": "qdrant_vec_adapter",
"pgvector": "pgvector_vec_adapter",
"weaviate": "weaviate_vec_adapter",
}
return request.getfixturevalue(vector_provider_dict[vector_provider])