mirror of
https://github.com/meta-llama/llama-stack.git
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fix(tests): remove chroma and qdrant from vector io unit tests (#3759)
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
GitHub
parent
f50ce11a3b
commit
a055a32ee4
5 changed files with 222 additions and 424 deletions
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@ -87,19 +87,14 @@ unit = [
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"pypdf",
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"mcp",
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"chardet",
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"qdrant-client",
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"sqlalchemy",
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"sqlalchemy[asyncio]>=2.0.41",
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"blobfile",
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"faiss-cpu",
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"pymilvus>=2.6.1",
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"milvus-lite>=2.5.0",
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"litellm",
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"together",
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"coverage",
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"chromadb>=1.0.15",
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"moto[s3]>=5.1.10",
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"weaviate-client>=4.16.4",
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]
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# These are the core dependencies required for running integration tests. They are shared across all
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# providers. If a provider requires additional dependencies, please add them to your environment
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@ -120,6 +115,8 @@ test = [
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"sqlalchemy",
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"sqlalchemy[asyncio]>=2.0.41",
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"requests",
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"chromadb>=1.0.15",
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"qdrant-client",
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"pymilvus>=2.6.1",
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"milvus-lite>=2.5.0",
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"weaviate-client>=4.16.4",
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@ -9,27 +9,22 @@ from unittest.mock import AsyncMock, MagicMock, patch
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import numpy as np
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import pytest
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from chromadb import PersistentClient
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from llama_stack.apis.vector_dbs import VectorDB
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from llama_stack.apis.vector_io import Chunk, ChunkMetadata, QueryChunksResponse
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from llama_stack.providers.inline.vector_io.chroma.config import ChromaVectorIOConfig
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from llama_stack.providers.inline.vector_io.faiss.config import FaissVectorIOConfig
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from llama_stack.providers.inline.vector_io.faiss.faiss import FaissIndex, FaissVectorIOAdapter
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from llama_stack.providers.inline.vector_io.milvus.config import SqliteKVStoreConfig
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from llama_stack.providers.inline.vector_io.qdrant import QdrantVectorIOConfig
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from llama_stack.providers.inline.vector_io.sqlite_vec import SQLiteVectorIOConfig
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from llama_stack.providers.inline.vector_io.sqlite_vec.sqlite_vec import SQLiteVecIndex, SQLiteVecVectorIOAdapter
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from llama_stack.providers.remote.vector_io.chroma.chroma import ChromaIndex, ChromaVectorIOAdapter, maybe_await
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from llama_stack.providers.remote.vector_io.pgvector.config import PGVectorVectorIOConfig
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from llama_stack.providers.remote.vector_io.pgvector.pgvector import PGVectorIndex, PGVectorVectorIOAdapter
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from llama_stack.providers.remote.vector_io.qdrant.qdrant import QdrantVectorIOAdapter
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from llama_stack.providers.utils.kvstore.config import SqliteKVStoreConfig
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EMBEDDING_DIMENSION = 384
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COLLECTION_PREFIX = "test_collection"
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@pytest.fixture(params=["sqlite_vec", "faiss", "chroma", "pgvector"])
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@pytest.fixture(params=["sqlite_vec", "faiss", "pgvector"])
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def vector_provider(request):
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return request.param
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@ -201,98 +196,6 @@ async def faiss_vec_adapter(unique_kvstore_config, mock_inference_api, embedding
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await adapter.shutdown()
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@pytest.fixture
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def chroma_vec_db_path(tmp_path_factory):
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persist_dir = tmp_path_factory.mktemp(f"chroma_{np.random.randint(1e6)}")
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return str(persist_dir)
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@pytest.fixture
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async def chroma_vec_index(chroma_vec_db_path, embedding_dimension):
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client = PersistentClient(path=chroma_vec_db_path)
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name = f"{COLLECTION_PREFIX}_{np.random.randint(1e6)}"
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collection = await maybe_await(client.get_or_create_collection(name))
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index = ChromaIndex(client=client, collection=collection)
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await index.initialize()
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yield index
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await index.delete()
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@pytest.fixture
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async def chroma_vec_adapter(chroma_vec_db_path, unique_kvstore_config, mock_inference_api, embedding_dimension):
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config = ChromaVectorIOConfig(
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db_path=chroma_vec_db_path,
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kvstore=unique_kvstore_config,
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)
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adapter = ChromaVectorIOAdapter(
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config=config,
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inference_api=mock_inference_api,
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files_api=None,
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)
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await adapter.initialize()
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await adapter.register_vector_db(
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VectorDB(
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identifier=f"chroma_test_collection_{random.randint(1, 1_000_000)}",
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provider_id="test_provider",
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embedding_model="test_model",
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embedding_dimension=embedding_dimension,
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)
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)
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yield adapter
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await adapter.shutdown()
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@pytest.fixture
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def qdrant_vec_db_path(tmp_path_factory):
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import uuid
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db_path = str(tmp_path_factory.getbasetemp() / f"test_qdrant_{uuid.uuid4()}.db")
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return db_path
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@pytest.fixture
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async def qdrant_vec_adapter(qdrant_vec_db_path, unique_kvstore_config, mock_inference_api, embedding_dimension):
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import uuid
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config = QdrantVectorIOConfig(
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db_path=qdrant_vec_db_path,
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kvstore=unique_kvstore_config,
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)
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adapter = QdrantVectorIOAdapter(
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config=config,
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inference_api=mock_inference_api,
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files_api=None,
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)
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collection_id = f"qdrant_test_collection_{uuid.uuid4()}"
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await adapter.initialize()
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await adapter.register_vector_db(
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VectorDB(
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identifier=collection_id,
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provider_id="test_provider",
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embedding_model="test_model",
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embedding_dimension=embedding_dimension,
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)
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)
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adapter.test_collection_id = collection_id
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yield adapter
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await adapter.shutdown()
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@pytest.fixture
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async def qdrant_vec_index(qdrant_vec_db_path, embedding_dimension):
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import uuid
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from qdrant_client import AsyncQdrantClient
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from llama_stack.providers.remote.vector_io.qdrant.qdrant import QdrantIndex
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client = AsyncQdrantClient(path=qdrant_vec_db_path)
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collection_name = f"qdrant_test_collection_{uuid.uuid4()}"
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index = QdrantIndex(client, collection_name)
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yield index
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await index.delete()
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@pytest.fixture
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def mock_psycopg2_connection():
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connection = MagicMock()
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@ -410,8 +313,6 @@ def vector_io_adapter(vector_provider, request):
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vector_provider_dict = {
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"faiss": "faiss_vec_adapter",
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"sqlite_vec": "sqlite_vec_adapter",
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"chroma": "chroma_vec_adapter",
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"qdrant": "qdrant_vec_adapter",
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"pgvector": "pgvector_vec_adapter",
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}
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return request.getfixturevalue(vector_provider_dict[vector_provider])
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@ -1,147 +0,0 @@
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# Copyright (c) Meta Platforms, Inc. and affiliates.
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# All rights reserved.
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#
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# This source code is licensed under the terms described in the LICENSE file in
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# the root directory of this source tree.
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import asyncio
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import os
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from typing import Any
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from unittest.mock import AsyncMock, MagicMock, patch
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import pytest
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from llama_stack.apis.inference import Inference
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from llama_stack.apis.inference.inference import OpenAIEmbeddingData, OpenAIEmbeddingsResponse, OpenAIEmbeddingUsage
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from llama_stack.apis.vector_io import (
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QueryChunksResponse,
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VectorDB,
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VectorDBStore,
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)
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from llama_stack.providers.inline.vector_io.qdrant.config import (
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QdrantVectorIOConfig as InlineQdrantVectorIOConfig,
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)
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from llama_stack.providers.remote.vector_io.qdrant.qdrant import (
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QdrantVectorIOAdapter,
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)
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from llama_stack.providers.utils.kvstore.config import SqliteKVStoreConfig
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# This test is a unit test for the QdrantVectorIOAdapter class. This should only contain
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# tests which are specific to this class. More general (API-level) tests should be placed in
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# tests/integration/vector_io/
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#
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# How to run this test:
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#
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# pytest tests/unit/providers/vector_io/test_qdrant.py \
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# -v -s --tb=short --disable-warnings --asyncio-mode=auto
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@pytest.fixture
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def qdrant_config(tmp_path) -> InlineQdrantVectorIOConfig:
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kvstore_config = SqliteKVStoreConfig(db_name=os.path.join(tmp_path, "test_kvstore.db"))
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return InlineQdrantVectorIOConfig(path=os.path.join(tmp_path, "qdrant.db"), kvstore=kvstore_config)
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@pytest.fixture(scope="session")
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def loop():
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return asyncio.new_event_loop()
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@pytest.fixture
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def mock_vector_db(vector_db_id) -> MagicMock:
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mock_vector_db = MagicMock(spec=VectorDB)
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mock_vector_db.embedding_model = "embedding_model"
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mock_vector_db.identifier = vector_db_id
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mock_vector_db.embedding_dimension = 384
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mock_vector_db.model_dump_json.return_value = (
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'{"identifier": "'
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+ vector_db_id
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+ '", "provider_id": "qdrant", "embedding_model": "embedding_model", "embedding_dimension": 384}'
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)
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return mock_vector_db
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@pytest.fixture
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def mock_vector_db_store(mock_vector_db) -> MagicMock:
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mock_store = MagicMock(spec=VectorDBStore)
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mock_store.get_vector_db = AsyncMock(return_value=mock_vector_db)
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return mock_store
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@pytest.fixture
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def mock_api_service(sample_embeddings):
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mock_api_service = MagicMock(spec=Inference)
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mock_api_service.openai_embeddings = AsyncMock(
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return_value=OpenAIEmbeddingsResponse(
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model="mock-embedding-model",
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data=[OpenAIEmbeddingData(embedding=sample, index=i) for i, sample in enumerate(sample_embeddings)],
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usage=OpenAIEmbeddingUsage(prompt_tokens=10, total_tokens=10),
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)
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)
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return mock_api_service
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@pytest.fixture
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async def qdrant_adapter(qdrant_config, mock_vector_db_store, mock_api_service, loop) -> QdrantVectorIOAdapter:
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adapter = QdrantVectorIOAdapter(config=qdrant_config, inference_api=mock_api_service, files_api=None)
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adapter.vector_db_store = mock_vector_db_store
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await adapter.initialize()
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yield adapter
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await adapter.shutdown()
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__QUERY = "Sample query"
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@pytest.mark.parametrize("max_query_chunks, expected_chunks", [(2, 2), (100, 60)])
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async def test_qdrant_adapter_returns_expected_chunks(
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qdrant_adapter: QdrantVectorIOAdapter,
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vector_db_id,
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sample_chunks,
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sample_embeddings,
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max_query_chunks,
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expected_chunks,
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) -> None:
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assert qdrant_adapter is not None
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await qdrant_adapter.insert_chunks(vector_db_id, sample_chunks)
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index = await qdrant_adapter._get_and_cache_vector_db_index(vector_db_id=vector_db_id)
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assert index is not None
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response = await qdrant_adapter.query_chunks(
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query=__QUERY,
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vector_db_id=vector_db_id,
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params={"max_chunks": max_query_chunks, "mode": "vector"},
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)
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assert isinstance(response, QueryChunksResponse)
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assert len(response.chunks) == expected_chunks
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# To by-pass attempt to convert a Mock to JSON
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def _prepare_for_json(value: Any) -> str:
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return str(value)
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@patch("llama_stack.providers.utils.telemetry.trace_protocol._prepare_for_json", new=_prepare_for_json)
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async def test_qdrant_register_and_unregister_vector_db(
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qdrant_adapter: QdrantVectorIOAdapter,
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mock_vector_db,
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sample_chunks,
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) -> None:
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# Initially, no collections
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vector_db_id = mock_vector_db.identifier
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assert len((await qdrant_adapter.client.get_collections()).collections) == 0
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# Register does not create a collection
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assert not (await qdrant_adapter.client.collection_exists(vector_db_id))
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await qdrant_adapter.register_vector_db(mock_vector_db)
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assert not (await qdrant_adapter.client.collection_exists(vector_db_id))
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# First insert creates the collection
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await qdrant_adapter.insert_chunks(vector_db_id, sample_chunks)
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assert await qdrant_adapter.client.collection_exists(vector_db_id)
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# Unregister deletes the collection
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await qdrant_adapter.unregister_vector_db(vector_db_id)
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assert not (await qdrant_adapter.client.collection_exists(vector_db_id))
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assert len((await qdrant_adapter.client.get_collections()).collections) == 0
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@ -104,10 +104,6 @@ async def test_register_and_unregister_vector_db(vector_io_adapter):
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async def test_query_unregistered_raises(vector_io_adapter, vector_provider):
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fake_emb = np.zeros(8, dtype=np.float32)
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if vector_provider == "chroma":
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with pytest.raises(AttributeError):
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await vector_io_adapter.query_chunks("no_such_db", fake_emb)
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else:
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with pytest.raises(ValueError):
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await vector_io_adapter.query_chunks("no_such_db", fake_emb)
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381
uv.lock
generated
381
uv.lock
generated
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@ -242,52 +242,68 @@ wheels = [
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[[package]]
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name = "bcrypt"
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version = "4.3.0"
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version = "5.0.0"
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source = { registry = "https://pypi.org/simple" }
|
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sdist = { url = "https://files.pythonhosted.org/packages/bb/5d/6d7433e0f3cd46ce0b43cd65e1db465ea024dbb8216fb2404e919c2ad77b/bcrypt-4.3.0.tar.gz", hash = "sha256:3a3fd2204178b6d2adcf09cb4f6426ffef54762577a7c9b54c159008cb288c18", size = 25697, upload-time = "2025-02-28T01:24:09.174Z" }
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/d4/36/3329e2518d70ad8e2e5817d5a4cac6bba05a47767ec416c7d020a965f408/bcrypt-5.0.0.tar.gz", hash = "sha256:f748f7c2d6fd375cc93d3fba7ef4a9e3a092421b8dbf34d8d4dc06be9492dfdd", size = 25386, upload-time = "2025-09-25T19:50:47.829Z" }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/bf/2c/3d44e853d1fe969d229bd58d39ae6902b3d924af0e2b5a60d17d4b809ded/bcrypt-4.3.0-cp313-cp313t-macosx_10_12_universal2.whl", hash = "sha256:f01e060f14b6b57bbb72fc5b4a83ac21c443c9a2ee708e04a10e9192f90a6281", size = 483719, upload-time = "2025-02-28T01:22:34.539Z" },
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[[package]]
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]
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||||
[[package]]
|
||||
|
|
@ -3820,7 +3871,7 @@ wheels = [
|
|||
|
||||
[[package]]
|
||||
name = "qdrant-client"
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||||
version = "1.15.0"
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||||
version = "1.15.1"
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||||
source = { registry = "https://pypi.org/simple" }
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dependencies = [
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{ name = "grpcio" },
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|
|
@ -3831,9 +3882,9 @@ dependencies = [
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{ name = "pydantic" },
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{ name = "urllib3" },
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]
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wheels = [
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]
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|
||||
[[package]]
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue