mirror of
https://github.com/catlog22/Claude-Code-Workflow.git
synced 2026-02-05 01:50:27 +08:00
5e91ba6c60
- Added ANNIndex class for approximate nearest neighbor search using HNSW. - Integrated ANN index with VectorStore for enhanced search capabilities. - Updated test suite for ANN index, including tests for adding, searching, saving, and loading vectors. - Modified existing tests to accommodate changes in search performance expectations. - Improved error handling for file operations in tests to ensure compatibility with Windows file locks. - Adjusted hybrid search performance assertions for increased stability in CI environments.
424 lines
14 KiB
Python
424 lines
14 KiB
Python
"""Tests for ANN (Approximate Nearest Neighbor) index using HNSW."""
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import tempfile
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from pathlib import Path
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from unittest.mock import patch
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import pytest
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# Skip all tests if semantic dependencies not available
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pytest.importorskip("numpy")
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def _hnswlib_available() -> bool:
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"""Check if hnswlib is available."""
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try:
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import hnswlib
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return True
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except ImportError:
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return False
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class TestANNIndex:
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"""Test suite for ANNIndex class."""
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@pytest.fixture
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def temp_db(self):
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"""Create a temporary database file."""
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with tempfile.TemporaryDirectory() as tmpdir:
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yield Path(tmpdir) / "_index.db"
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@pytest.fixture
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def sample_vectors(self):
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"""Generate sample vectors for testing."""
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import numpy as np
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np.random.seed(42)
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# 100 vectors of dimension 384 (matches fast model)
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return np.random.randn(100, 384).astype(np.float32)
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@pytest.fixture
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def sample_ids(self):
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"""Generate sample IDs."""
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return list(range(1, 101))
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def test_import_check(self):
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"""Test that HNSWLIB_AVAILABLE flag is set correctly."""
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try:
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from codexlens.semantic.ann_index import HNSWLIB_AVAILABLE
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# Should be True if hnswlib is installed, False otherwise
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assert isinstance(HNSWLIB_AVAILABLE, bool)
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except ImportError:
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pytest.skip("ann_index module not available")
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@pytest.mark.skipif(
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not _hnswlib_available(),
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reason="hnswlib not installed"
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)
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def test_create_index(self, temp_db):
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"""Test creating a new ANN index."""
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from codexlens.semantic.ann_index import ANNIndex
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index = ANNIndex(temp_db, dim=384)
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assert index.dim == 384
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assert index.count() == 0
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assert not index.is_loaded
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@pytest.mark.skipif(
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not _hnswlib_available(),
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reason="hnswlib not installed"
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)
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def test_add_vectors(self, temp_db, sample_vectors, sample_ids):
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"""Test adding vectors to the index."""
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from codexlens.semantic.ann_index import ANNIndex
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index = ANNIndex(temp_db, dim=384)
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index.add_vectors(sample_ids, sample_vectors)
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assert index.count() == 100
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assert index.is_loaded
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@pytest.mark.skipif(
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not _hnswlib_available(),
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reason="hnswlib not installed"
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)
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def test_search(self, temp_db, sample_vectors, sample_ids):
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"""Test searching for similar vectors."""
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from codexlens.semantic.ann_index import ANNIndex
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index = ANNIndex(temp_db, dim=384)
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index.add_vectors(sample_ids, sample_vectors)
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# Search for the first vector - should find itself
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query = sample_vectors[0]
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ids, distances = index.search(query, top_k=5)
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assert len(ids) == 5
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assert len(distances) == 5
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# First result should be the query vector itself (or very close)
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assert ids[0] == 1 # ID of first vector
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assert distances[0] < 0.01 # Very small distance (almost identical)
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@pytest.mark.skipif(
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not _hnswlib_available(),
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reason="hnswlib not installed"
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)
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def test_save_and_load(self, temp_db, sample_vectors, sample_ids):
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"""Test saving and loading index from disk."""
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from codexlens.semantic.ann_index import ANNIndex
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# Create and save index
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index1 = ANNIndex(temp_db, dim=384)
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index1.add_vectors(sample_ids, sample_vectors)
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index1.save()
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# Check that file was created (new naming: {db_stem}_vectors.hnsw)
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hnsw_path = temp_db.parent / f"{temp_db.stem}_vectors.hnsw"
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assert hnsw_path.exists()
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# Load in new instance
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index2 = ANNIndex(temp_db, dim=384)
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loaded = index2.load()
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assert loaded is True
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assert index2.count() == 100
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assert index2.is_loaded
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# Verify search still works
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query = sample_vectors[0]
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ids, distances = index2.search(query, top_k=5)
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assert ids[0] == 1
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@pytest.mark.skipif(
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not _hnswlib_available(),
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reason="hnswlib not installed"
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)
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def test_load_nonexistent(self, temp_db):
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"""Test loading when index file doesn't exist."""
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from codexlens.semantic.ann_index import ANNIndex
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index = ANNIndex(temp_db, dim=384)
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loaded = index.load()
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assert loaded is False
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assert not index.is_loaded
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@pytest.mark.skipif(
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not _hnswlib_available(),
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reason="hnswlib not installed"
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)
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def test_remove_vectors(self, temp_db, sample_vectors, sample_ids):
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"""Test removing vectors from the index."""
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from codexlens.semantic.ann_index import ANNIndex
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index = ANNIndex(temp_db, dim=384)
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index.add_vectors(sample_ids, sample_vectors)
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# Remove first 10 vectors
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index.remove_vectors(list(range(1, 11)))
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# Search for removed vector - should not be in results
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query = sample_vectors[0]
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ids, distances = index.search(query, top_k=5)
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# ID 1 should not be in results (soft deleted)
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assert 1 not in ids
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@pytest.mark.skipif(
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not _hnswlib_available(),
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reason="hnswlib not installed"
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)
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def test_incremental_add(self, temp_db):
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"""Test adding vectors incrementally."""
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import numpy as np
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from codexlens.semantic.ann_index import ANNIndex
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index = ANNIndex(temp_db, dim=384)
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# Add first batch
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vectors1 = np.random.randn(50, 384).astype(np.float32)
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index.add_vectors(list(range(1, 51)), vectors1)
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assert index.count() == 50
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# Add second batch
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vectors2 = np.random.randn(50, 384).astype(np.float32)
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index.add_vectors(list(range(51, 101)), vectors2)
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assert index.count() == 100
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@pytest.mark.skipif(
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not _hnswlib_available(),
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reason="hnswlib not installed"
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)
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def test_search_empty_index(self, temp_db):
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"""Test searching an empty index."""
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import numpy as np
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from codexlens.semantic.ann_index import ANNIndex
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index = ANNIndex(temp_db, dim=384)
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query = np.random.randn(384).astype(np.float32)
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ids, distances = index.search(query, top_k=5)
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assert ids == []
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assert distances == []
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@pytest.mark.skipif(
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not _hnswlib_available(),
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reason="hnswlib not installed"
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)
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def test_invalid_dimension(self, temp_db, sample_vectors, sample_ids):
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"""Test adding vectors with wrong dimension."""
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import numpy as np
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from codexlens.semantic.ann_index import ANNIndex
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index = ANNIndex(temp_db, dim=384)
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# Try to add vectors with wrong dimension
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wrong_vectors = np.random.randn(10, 768).astype(np.float32)
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with pytest.raises(ValueError, match="dimension"):
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index.add_vectors(list(range(1, 11)), wrong_vectors)
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@pytest.mark.skipif(
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not _hnswlib_available(),
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reason="hnswlib not installed"
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)
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def test_auto_resize(self, temp_db):
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"""Test that index automatically resizes when capacity is exceeded."""
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import numpy as np
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from codexlens.semantic.ann_index import ANNIndex
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index = ANNIndex(temp_db, dim=384)
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# Override initial capacity to test resize
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index._max_elements = 100
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# Add more vectors than initial capacity
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vectors = np.random.randn(150, 384).astype(np.float32)
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index.add_vectors(list(range(1, 151)), vectors)
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assert index.count() == 150
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assert index._max_elements >= 150
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class TestVectorStoreWithANN:
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"""Test VectorStore integration with ANN index."""
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@pytest.fixture
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def temp_db(self):
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"""Create a temporary database file."""
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with tempfile.TemporaryDirectory(ignore_cleanup_errors=True) as tmpdir:
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yield Path(tmpdir) / "_index.db"
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@pytest.fixture
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def sample_chunks(self):
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"""Create sample semantic chunks with embeddings."""
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import numpy as np
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from codexlens.entities import SemanticChunk
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np.random.seed(42)
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chunks = []
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for i in range(10):
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chunk = SemanticChunk(
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content=f"def function_{i}(): pass",
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metadata={"symbol_name": f"function_{i}", "symbol_kind": "function"},
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)
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chunk.embedding = np.random.randn(384).astype(np.float32).tolist()
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chunks.append(chunk)
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return chunks
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def test_vector_store_with_ann(self, temp_db, sample_chunks):
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"""Test VectorStore using ANN index for search."""
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from codexlens.semantic.vector_store import VectorStore, HNSWLIB_AVAILABLE
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store = VectorStore(temp_db)
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# Add chunks
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ids = store.add_chunks(sample_chunks, "test.py")
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assert len(ids) == 10
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# Check ANN status
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if HNSWLIB_AVAILABLE:
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assert store.ann_available or store.ann_count >= 0
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# Search
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query_embedding = sample_chunks[0].embedding
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results = store.search_similar(query_embedding, top_k=5)
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assert len(results) <= 5
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if results:
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# First result should have high similarity
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assert results[0].score > 0.9
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def test_vector_store_rebuild_ann(self, temp_db, sample_chunks):
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"""Test rebuilding ANN index from SQLite data."""
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from codexlens.semantic.vector_store import VectorStore, HNSWLIB_AVAILABLE
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if not HNSWLIB_AVAILABLE:
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pytest.skip("hnswlib not installed")
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store = VectorStore(temp_db)
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# Add chunks
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store.add_chunks(sample_chunks, "test.py")
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# Rebuild ANN index
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count = store.rebuild_ann_index()
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assert count == 10
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# Verify search works
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query_embedding = sample_chunks[0].embedding
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results = store.search_similar(query_embedding, top_k=5)
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assert len(results) > 0
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def test_vector_store_delete_updates_ann(self, temp_db, sample_chunks):
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"""Test that deleting chunks updates ANN index."""
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from codexlens.semantic.vector_store import VectorStore, HNSWLIB_AVAILABLE
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if not HNSWLIB_AVAILABLE:
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pytest.skip("hnswlib not installed")
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store = VectorStore(temp_db)
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# Add chunks for two files
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store.add_chunks(sample_chunks[:5], "file1.py")
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store.add_chunks(sample_chunks[5:], "file2.py")
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initial_count = store.count_chunks()
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assert initial_count == 10
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# Delete one file's chunks
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deleted = store.delete_file_chunks("file1.py")
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assert deleted == 5
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# Verify count
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assert store.count_chunks() == 5
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def test_vector_store_batch_add(self, temp_db, sample_chunks):
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"""Test batch adding chunks from multiple files."""
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from codexlens.semantic.vector_store import VectorStore
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store = VectorStore(temp_db)
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# Prepare chunks with paths
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chunks_with_paths = [
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(chunk, f"file{i % 3}.py")
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for i, chunk in enumerate(sample_chunks)
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]
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# Batch add
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ids = store.add_chunks_batch(chunks_with_paths)
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assert len(ids) == 10
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# Verify
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assert store.count_chunks() == 10
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def test_vector_store_fallback_search(self, temp_db, sample_chunks):
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"""Test that search falls back to brute-force when ANN unavailable."""
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from codexlens.semantic.vector_store import VectorStore
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store = VectorStore(temp_db)
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store.add_chunks(sample_chunks, "test.py")
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# Force disable ANN
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store._ann_index = None
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# Search should still work (brute-force fallback)
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query_embedding = sample_chunks[0].embedding
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results = store.search_similar(query_embedding, top_k=5)
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assert len(results) > 0
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assert results[0].score > 0.9
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class TestSearchAccuracy:
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"""Test search accuracy comparing ANN vs brute-force."""
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@pytest.fixture
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def temp_db(self):
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"""Create a temporary database file."""
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with tempfile.TemporaryDirectory(ignore_cleanup_errors=True) as tmpdir:
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yield Path(tmpdir) / "_index.db"
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@pytest.mark.skipif(
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not _hnswlib_available(),
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reason="hnswlib not installed"
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)
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def test_ann_vs_brute_force_recall(self, temp_db):
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"""Test that ANN search has high recall compared to brute-force."""
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import numpy as np
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from codexlens.entities import SemanticChunk
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from codexlens.semantic.vector_store import VectorStore
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np.random.seed(42)
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# Create larger dataset
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chunks = []
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for i in range(100):
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chunk = SemanticChunk(
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content=f"code block {i}",
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metadata={"chunk_id": i},
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)
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chunk.embedding = np.random.randn(384).astype(np.float32).tolist()
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chunks.append(chunk)
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store = VectorStore(temp_db)
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store.add_chunks(chunks, "test.py")
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# Get brute-force results
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store._ann_index = None # Force brute-force
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store._invalidate_cache() # Clear cache to force refresh
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query = chunks[0].embedding
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bf_results = store.search_similar(query, top_k=10)
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# Use chunk_id from metadata for comparison (more reliable than path+score)
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bf_chunk_ids = {r.metadata.get("chunk_id") for r in bf_results}
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# Rebuild ANN and get ANN results
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store.rebuild_ann_index()
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ann_results = store.search_similar(query, top_k=10)
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ann_chunk_ids = {r.metadata.get("chunk_id") for r in ann_results}
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# Calculate recall (how many brute-force results are in ANN results)
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# ANN should find at least 80% of the same results
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overlap = len(bf_chunk_ids & ann_chunk_ids)
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recall = overlap / len(bf_chunk_ids) if bf_chunk_ids else 1.0
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assert recall >= 0.8, f"ANN recall too low: {recall} (overlap: {overlap}, bf: {bf_chunk_ids}, ann: {ann_chunk_ids})"
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