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feat: rebase and implement file API methods

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Signed-off-by: Varsha Prasad Narsing <varshaprasad96@gmail.com>
This commit is contained in:
Varsha Prasad Narsing 2025-06-25 16:59:29 -07:00
parent 918e68548f
commit dfafa5bbae
15 changed files with 212 additions and 214 deletions
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60
tests/unit/providers/vector_io/conftest.py
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@ -16,10 +16,12 @@ from llama_stack.providers.inline.vector_io.chroma.config import ChromaVectorIOC
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.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
from llama_stack.providers.remote.vector_io.milvus.milvus import MilvusIndex, MilvusVectorIOAdapter
from llama_stack.providers.remote.vector_io.qdrant.qdrant import QdrantVectorIOAdapter
EMBEDDING_DIMENSION = 384
COLLECTION_PREFIX = "test_collection"
@ -94,7 +96,7 @@ 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"])
@pytest.fixture(params=["milvus", "sqlite_vec", "faiss", "chroma", "qdrant"])
def vector_provider(request):
return request.param
@ -133,7 +135,7 @@ async def sqlite_vec_vec_index(embedding_dimension, tmp_path_factory):
await index.initialize()
index.db_path = db_path
yield index
index.delete()
await index.delete()
@pytest.fixture
@ -276,14 +278,66 @@ async def chroma_vec_adapter(chroma_vec_db_path, mock_inference_api, embedding_d
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, mock_inference_api, embedding_dimension):
import uuid
config = QdrantVectorIOConfig(
path=qdrant_vec_db_path,
kvstore=SqliteKVStoreConfig(),
)
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 vector_io_adapter(vector_provider, request):
"""Returns the appropriate vector IO adapter based on the provider parameter."""
vector_provider_dict = {
"milvus": "milvus_vec_adapter",
"faiss": "faiss_vec_adapter",
"sqlite_vec": "sqlite_vec_adapter",
"qdrant": "qdrant_vec_adapter",
"chroma": "chroma_vec_adapter",
"sqlite_vec": "sqlite_vec_adapter",
}
return request.getfixturevalue(vector_provider_dict[vector_provider])