llama-stack/tests/unit/providers/vector_io/test_qdrant.py
Varsha e92301f2d7
feat(sqlite-vec): enable keyword search for sqlite-vec (#1439)
# What does this PR do?
This PR introduces support for keyword based FTS5 search with BM25
relevance scoring. It makes changes to the existing EmbeddingIndex base
class in order to support a search_mode and query_str parameter, that
can be used for keyword based search implementations.

[//]: # (If resolving an issue, uncomment and update the line below)
[//]: # (Closes #[issue-number])

## Test Plan
run 
```
pytest llama_stack/providers/tests/vector_io/test_sqlite_vec.py -v -s --tb=short --disable-warnings --asyncio-mode=auto
```
Output:
```
pytest llama_stack/providers/tests/vector_io/test_sqlite_vec.py -v -s --tb=short --disable-warnings --asyncio-mode=auto
/Users/vnarsing/miniconda3/envs/stack-client/lib/python3.10/site-packages/pytest_asyncio/plugin.py:207: PytestDeprecationWarning: The configuration option "asyncio_default_fixture_loop_scope" is unset.
The event loop scope for asynchronous fixtures will default to the fixture caching scope. Future versions of pytest-asyncio will default the loop scope for asynchronous fixtures to function scope. Set the default fixture loop scope explicitly in order to avoid unexpected behavior in the future. Valid fixture loop scopes are: "function", "class", "module", "package", "session"

  warnings.warn(PytestDeprecationWarning(_DEFAULT_FIXTURE_LOOP_SCOPE_UNSET))
====================================================== test session starts =======================================================
platform darwin -- Python 3.10.16, pytest-8.3.4, pluggy-1.5.0 -- /Users/vnarsing/miniconda3/envs/stack-client/bin/python
cachedir: .pytest_cache
metadata: {'Python': '3.10.16', 'Platform': 'macOS-14.7.4-arm64-arm-64bit', 'Packages': {'pytest': '8.3.4', 'pluggy': '1.5.0'}, 'Plugins': {'html': '4.1.1', 'metadata': '3.1.1', 'asyncio': '0.25.3', 'anyio': '4.8.0'}}
rootdir: /Users/vnarsing/go/src/github/meta-llama/llama-stack
configfile: pyproject.toml
plugins: html-4.1.1, metadata-3.1.1, asyncio-0.25.3, anyio-4.8.0
asyncio: mode=auto, asyncio_default_fixture_loop_scope=None
collected 7 items                                                                                                                

llama_stack/providers/tests/vector_io/test_sqlite_vec.py::test_add_chunks PASSED
llama_stack/providers/tests/vector_io/test_sqlite_vec.py::test_query_chunks_vector PASSED
llama_stack/providers/tests/vector_io/test_sqlite_vec.py::test_query_chunks_fts PASSED
llama_stack/providers/tests/vector_io/test_sqlite_vec.py::test_chunk_id_conflict PASSED
llama_stack/providers/tests/vector_io/test_sqlite_vec.py::test_register_vector_db PASSED
llama_stack/providers/tests/vector_io/test_sqlite_vec.py::test_unregister_vector_db PASSED
llama_stack/providers/tests/vector_io/test_sqlite_vec.py::test_generate_chunk_id PASSED
```


For reference, with the implementation, the fts table looks like below:
```
Chunk ID: 9fbc39ce-c729-64a2-260f-c5ec9bb2a33e, Content: Sentence 0 from document 0
Chunk ID: 94062914-3e23-44cf-1e50-9e25821ba882, Content: Sentence 1 from document 0
Chunk ID: e6cfd559-4641-33ba-6ce1-7038226495eb, Content: Sentence 2 from document 0
Chunk ID: 1383af9b-f1f0-f417-4de5-65fe9456cc20, Content: Sentence 3 from document 0
Chunk ID: 2db19b1a-de14-353b-f4e1-085e8463361c, Content: Sentence 4 from document 0
Chunk ID: 9faf986a-f028-7714-068a-1c795e8f2598, Content: Sentence 5 from document 0
Chunk ID: ef593ead-5a4a-392f-7ad8-471a50f033e8, Content: Sentence 6 from document 0
Chunk ID: e161950f-021f-7300-4d05-3166738b94cf, Content: Sentence 7 from document 0
Chunk ID: 90610fc4-67c1-e740-f043-709c5978867a, Content: Sentence 8 from document 0
Chunk ID: 97712879-6fff-98ad-0558-e9f42e6b81d3, Content: Sentence 9 from document 0
Chunk ID: aea70411-51df-61ba-d2f0-cb2b5972c210, Content: Sentence 0 from document 1
Chunk ID: b678a463-7b84-92b8-abb2-27e9a1977e3c, Content: Sentence 1 from document 1
Chunk ID: 27bd63da-909c-1606-a109-75bdb9479882, Content: Sentence 2 from document 1
Chunk ID: a2ad49ad-f9be-5372-e0c7-7b0221d0b53e, Content: Sentence 3 from document 1
Chunk ID: cac53bcd-1965-082a-c0f4-ceee7323fc70, Content: Sentence 4 from document 1
```

Query results:
Result 1: Sentence 5 from document 0
Result 2: Sentence 5 from document 1
Result 3: Sentence 5 from document 2

[//]: # (## Documentation)

---------

Signed-off-by: Varsha Prasad Narsing <varshaprasad96@gmail.com>
2025-05-21 15:24:24 -04:00

135 lines
4.4 KiB
Python

# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the terms described in the LICENSE file in
# the root directory of this source tree.
import asyncio
import os
from typing import Any
from unittest.mock import AsyncMock, MagicMock, patch
import pytest
import pytest_asyncio
from llama_stack.apis.inference import EmbeddingsResponse, Inference
from llama_stack.apis.vector_io import (
QueryChunksResponse,
VectorDB,
VectorDBStore,
)
from llama_stack.providers.inline.vector_io.qdrant.config import (
QdrantVectorIOConfig as InlineQdrantVectorIOConfig,
)
from llama_stack.providers.remote.vector_io.qdrant.qdrant import (
QdrantVectorIOAdapter,
)
# This test is a unit test for the QdrantVectorIOAdapter class. This should only contain
# tests which are specific to this class. More general (API-level) tests should be placed in
# tests/integration/vector_io/
#
# How to run this test:
#
# pytest tests/unit/providers/vector_io/test_qdrant.py \
# -v -s --tb=short --disable-warnings --asyncio-mode=auto
@pytest.fixture
def qdrant_config(tmp_path) -> InlineQdrantVectorIOConfig:
return InlineQdrantVectorIOConfig(path=os.path.join(tmp_path, "qdrant.db"))
@pytest.fixture(scope="session")
def loop():
return asyncio.new_event_loop()
@pytest.fixture
def mock_vector_db(vector_db_id) -> MagicMock:
mock_vector_db = MagicMock(spec=VectorDB)
mock_vector_db.embedding_model = "embedding_model"
mock_vector_db.identifier = vector_db_id
return mock_vector_db
@pytest.fixture
def mock_vector_db_store(mock_vector_db) -> MagicMock:
mock_store = MagicMock(spec=VectorDBStore)
mock_store.get_vector_db = AsyncMock(return_value=mock_vector_db)
return mock_store
@pytest.fixture
def mock_api_service(sample_embeddings):
mock_api_service = MagicMock(spec=Inference)
mock_api_service.embeddings = AsyncMock(return_value=EmbeddingsResponse(embeddings=sample_embeddings))
return mock_api_service
@pytest_asyncio.fixture
async def qdrant_adapter(qdrant_config, mock_vector_db_store, mock_api_service, loop) -> QdrantVectorIOAdapter:
adapter = QdrantVectorIOAdapter(config=qdrant_config, inference_api=mock_api_service)
adapter.vector_db_store = mock_vector_db_store
await adapter.initialize()
yield adapter
await adapter.shutdown()
__QUERY = "Sample query"
@pytest.mark.asyncio
@pytest.mark.parametrize("max_query_chunks, expected_chunks", [(2, 2), (100, 30)])
async def test_qdrant_adapter_returns_expected_chunks(
qdrant_adapter: QdrantVectorIOAdapter,
vector_db_id,
sample_chunks,
sample_embeddings,
max_query_chunks,
expected_chunks,
) -> None:
assert qdrant_adapter is not None
await qdrant_adapter.insert_chunks(vector_db_id, sample_chunks)
index = await qdrant_adapter._get_and_cache_vector_db_index(vector_db_id=vector_db_id)
assert index is not None
response = await qdrant_adapter.query_chunks(
query=__QUERY,
vector_db_id=vector_db_id,
params={"max_chunks": max_query_chunks, "mode": "vector"},
)
assert isinstance(response, QueryChunksResponse)
assert len(response.chunks) == expected_chunks
# To by-pass attempt to convert a Mock to JSON
def _prepare_for_json(value: Any) -> str:
return str(value)
@patch("llama_stack.providers.utils.telemetry.trace_protocol._prepare_for_json", new=_prepare_for_json)
@pytest.mark.asyncio
async def test_qdrant_register_and_unregister_vector_db(
qdrant_adapter: QdrantVectorIOAdapter,
mock_vector_db,
sample_chunks,
) -> None:
# Initially, no collections
vector_db_id = mock_vector_db.identifier
assert len((await qdrant_adapter.client.get_collections()).collections) == 0
# Register does not create a collection
assert not (await qdrant_adapter.client.collection_exists(vector_db_id))
await qdrant_adapter.register_vector_db(mock_vector_db)
assert not (await qdrant_adapter.client.collection_exists(vector_db_id))
# First insert creates the collection
await qdrant_adapter.insert_chunks(vector_db_id, sample_chunks)
assert await qdrant_adapter.client.collection_exists(vector_db_id)
# Unregister deletes the collection
await qdrant_adapter.unregister_vector_db(vector_db_id)
assert not (await qdrant_adapter.client.collection_exists(vector_db_id))
assert len((await qdrant_adapter.client.get_collections()).collections) == 0