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fix(faiss): handle case where distance is 0 by setting d to minimum positive… (#2387)
# What does this PR do? Adds try-catch to faiss `query_vector` function for when the distance between the query embedding and an embedding within the vector db is 0 (identical vectors). Catches `ZeroDivisionError` and then appends `(1.0 / sys.float_info.min)` to `scores` to represent maximum similarity. <!-- If resolving an issue, uncomment and update the line below --> Closes [#2381] ## Test Plan Checkout this PR Execute this code and there will no longer be a `ZeroDivisionError` exception ``` from llama_stack_client import LlamaStackClient base_url = "http://localhost:8321" client = LlamaStackClient(base_url=base_url) models = client.models.list() embedding_model = ( em := next(m for m in models if m.model_type == "embedding") ).identifier embedding_dimension = 384 _ = client.vector_dbs.register( vector_db_id="foo_db", embedding_model=embedding_model, embedding_dimension=embedding_dimension, provider_id="faiss", ) chunk = { "content": "foo", "mime_type": "text/plain", "metadata": { "document_id": "foo-id" } } client.vector_io.insert(vector_db_id="foo_db", chunks=[chunk]) client.vector_io.query(vector_db_id="foo_db", query="foo") ``` ### Running unit tests `uv run pytest tests/unit/rag/test_rag_query.py -v` --------- Signed-off-by: Ben Browning <bbrownin@redhat.com> Co-authored-by: Ben Browning <bbrownin@redhat.com>
This commit is contained in:
Ibrahim Haroon
GitHub
parent
33ecefd284
commit
a34cef925b
4 changed files with 156 additions and 1 deletions
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@ -112,7 +112,7 @@ class FaissIndex(EmbeddingIndex):
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if i < 0:
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continue
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chunks.append(self.chunk_by_index[int(i)])
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scores.append(1.0 / float(d))
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scores.append(1.0 / float(d) if d != 0 else float("inf"))
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return QueryChunksResponse(chunks=chunks, scores=scores)
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@ -84,6 +84,7 @@ unit = [
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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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]
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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
tests/unit/providers/vector_io/test_faiss.py
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120
tests/unit/providers/vector_io/test_faiss.py
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@ -0,0 +1,120 @@
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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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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.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.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 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) -> FaissVectorIOAdapter:
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adapter = FaissVectorIOAdapter(config=faiss_config, inference_api=mock_inference_api)
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await adapter.initialize()
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yield adapter
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await adapter.shutdown()
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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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34
uv.lock
generated
34
uv.lock
generated
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@ -715,6 +715,38 @@ wheels = [
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{ url = "https://files.pythonhosted.org/packages/7b/8f/c4d9bafc34ad7ad5d8dc16dd1347ee0e507a52c3adb6bfa8887e1c6a26ba/executing-2.2.0-py2.py3-none-any.whl", hash = "sha256:11387150cad388d62750327a53d3339fad4888b39a6fe233c3afbb54ecffd3aa", size = 26702, upload-time = "2025-01-22T15:41:25.929Z" },
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]
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[[package]]
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name = "faiss-cpu"
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version = "1.11.0"
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source = { registry = "https://pypi.org/simple" }
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dependencies = [
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{ name = "numpy" },
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{ name = "packaging" },
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]
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wheels = [
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[[package]]
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name = "fastapi"
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version = "0.115.8"
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@ -1549,6 +1581,7 @@ unit = [
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{ name = "aiosqlite" },
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{ name = "blobfile" },
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{ name = "chardet" },
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{ name = "faiss-cpu" },
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{ name = "mcp" },
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{ name = "openai" },
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{ name = "opentelemetry-exporter-otlp-proto-http" },
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@ -1649,6 +1682,7 @@ unit = [
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{ name = "aiosqlite" },
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{ name = "blobfile" },
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{ name = "chardet" },
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{ name = "faiss-cpu" },
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{ name = "mcp" },
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{ name = "openai" },
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{ name = "opentelemetry-exporter-otlp-proto-http" },
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|
|
|
|||
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