llama-stack-mirror/tests/unit/providers/vector_io/test_faiss.py
Sumit Jaiswal 90d03552d4
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feat: To add health check for faiss inline vector_io provider (#2319)
# What does this PR do?
<!-- Provide a short summary of what this PR does and why. Link to
relevant issues if applicable. -->
To add health check for faiss inline vector_io provider.

I tried adding `async def health(self) -> HealthResponse:` like in
inference provider, but it didn't worked for `inline->vector_io->faiss`
provider. And via debug logs, I understood the critical issue, that the
health responses are being stored with the API name as the key, not as a
nested dictionary with provider IDs. This means that all providers of
the same API type (e.g., "vector_io") will share the same health
response, and only the last one processed will be visible in the API
response.
I've created a patch file that fixes this issue by:
- Storing the original get_providers_health method
- Creating a patched version that correctly maps health responses to
providers
- Applying the patch to the `ProviderImpl` class

Not an expert, so please let me know, if there can be any other
workaround using which I can get the health status updated directly from
`faiss.py`.

<!-- 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.* -->
Added unit tests to test the provider patch implementation in the PR.
Adding a screenshot with the FAISS inline vector_io health status as
"OK"


![faiss_health_check](https://github.com/user-attachments/assets/d769e762-890c-41ea-a596-5e90951f79a4)
2025-06-18 17:56:25 +02:00

182 lines
6 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
from unittest.mock import AsyncMock, MagicMock, patch
import numpy as np
import pytest
import pytest_asyncio
from llama_stack.apis.files import Files
from llama_stack.apis.inference import EmbeddingsResponse, Inference
from llama_stack.apis.vector_dbs import VectorDB
from llama_stack.apis.vector_io import Chunk, QueryChunksResponse
from llama_stack.providers.datatypes import HealthStatus
from llama_stack.providers.inline.vector_io.faiss.config import FaissVectorIOConfig
from llama_stack.providers.inline.vector_io.faiss.faiss import (
FaissIndex,
FaissVectorIOAdapter,
)
# This test is a unit test for the FaissVectorIOAdapter 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_faiss.py \
# -v -s --tb=short --disable-warnings --asyncio-mode=auto
FAISS_PROVIDER = "faiss"
@pytest.fixture(scope="session")
def loop():
return asyncio.new_event_loop()
@pytest.fixture
def embedding_dimension():
return 384
@pytest.fixture
def vector_db_id():
return "test_vector_db"
@pytest.fixture
def sample_chunks():
return [
Chunk(content="MOCK text content 1", mime_type="text/plain", metadata={"document_id": "mock-doc-1"}),
Chunk(content="MOCK text content 1", mime_type="text/plain", metadata={"document_id": "mock-doc-2"}),
]
@pytest.fixture
def sample_embeddings(embedding_dimension):
return np.random.rand(2, embedding_dimension).astype(np.float32)
@pytest.fixture
def mock_vector_db(vector_db_id, embedding_dimension) -> MagicMock:
mock_vector_db = MagicMock(spec=VectorDB)
mock_vector_db.embedding_model = "mock_embedding_model"
mock_vector_db.identifier = vector_db_id
mock_vector_db.embedding_dimension = embedding_dimension
return mock_vector_db
@pytest.fixture
def mock_inference_api(sample_embeddings):
mock_api = MagicMock(spec=Inference)
mock_api.embeddings = AsyncMock(return_value=EmbeddingsResponse(embeddings=sample_embeddings))
return mock_api
@pytest.fixture
def mock_files_api():
mock_api = MagicMock(spec=Files)
return mock_api
@pytest.fixture
def faiss_config():
config = MagicMock(spec=FaissVectorIOConfig)
config.kvstore = None
return config
@pytest_asyncio.fixture
async def faiss_index(embedding_dimension):
index = await FaissIndex.create(dimension=embedding_dimension)
yield index
@pytest_asyncio.fixture
async def faiss_adapter(faiss_config, mock_inference_api, mock_files_api) -> FaissVectorIOAdapter:
# Create the adapter
adapter = FaissVectorIOAdapter(config=faiss_config, inference_api=mock_inference_api, files_api=mock_files_api)
# Create a mock KVStore
mock_kvstore = MagicMock()
mock_kvstore.values_in_range = AsyncMock(return_value=[])
# Patch the initialize method to avoid the kvstore_impl call
with patch.object(FaissVectorIOAdapter, "initialize"):
# Set the kvstore directly
adapter.kvstore = mock_kvstore
yield adapter
@pytest.mark.asyncio
async def test_faiss_query_vector_returns_infinity_when_query_and_embedding_are_identical(
faiss_index, sample_chunks, sample_embeddings, embedding_dimension
):
await faiss_index.add_chunks(sample_chunks, sample_embeddings)
query_embedding = np.random.rand(embedding_dimension).astype(np.float32)
with patch.object(faiss_index.index, "search") as mock_search:
mock_search.return_value = (np.array([[0.0, 0.1]]), np.array([[0, 1]]))
response = await faiss_index.query_vector(embedding=query_embedding, k=2, score_threshold=0.0)
assert isinstance(response, QueryChunksResponse)
assert len(response.chunks) == 2
assert len(response.scores) == 2
assert response.scores[0] == float("inf") # infinity (1.0 / 0.0)
assert response.scores[1] == 10.0 # (1.0 / 0.1 = 10.0)
assert response.chunks[0] == sample_chunks[0]
assert response.chunks[1] == sample_chunks[1]
@pytest.mark.asyncio
async def test_health_success():
"""Test that the health check returns OK status when faiss is working correctly."""
# Create a fresh instance of FaissVectorIOAdapter for testing
config = MagicMock()
inference_api = MagicMock()
files_api = MagicMock()
with patch("llama_stack.providers.inline.vector_io.faiss.faiss.faiss.IndexFlatL2") as mock_index_flat:
mock_index_flat.return_value = MagicMock()
adapter = FaissVectorIOAdapter(config=config, inference_api=inference_api, files_api=files_api)
# Calling the health method directly
response = await adapter.health()
# Verifying the response
assert isinstance(response, dict)
assert response["status"] == HealthStatus.OK
assert "message" not in response
# Verifying that IndexFlatL2 was called with the correct dimension
mock_index_flat.assert_called_once_with(128) # VECTOR_DIMENSION is 128
@pytest.mark.asyncio
async def test_health_failure():
"""Test that the health check returns ERROR status when faiss encounters an error."""
# Create a fresh instance of FaissVectorIOAdapter for testing
config = MagicMock()
inference_api = MagicMock()
files_api = MagicMock()
with patch("llama_stack.providers.inline.vector_io.faiss.faiss.faiss.IndexFlatL2") as mock_index_flat:
mock_index_flat.side_effect = Exception("Test error")
adapter = FaissVectorIOAdapter(config=config, inference_api=inference_api, files_api=files_api)
# Calling the health method directly
response = await adapter.health()
# Verifying the response
assert isinstance(response, dict)
assert response["status"] == HealthStatus.ERROR
assert response["message"] == "Health check failed: Test error"