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