mirror of
https://github.com/catlog22/Claude-Code-Workflow.git
synced 2026-02-05 01:50:27 +08:00
c56104c082
Prevents errors when HNSW search returns null/empty results due to race conditions. Adds validation for ids and distances before zip operation. Fixes: ISS-1766921318981-5 Solution-ID: SOL-1735386000-5 Issue-ID: ISS-1766921318981-5 Task-ID: T1
254 lines
8.9 KiB
Python
254 lines
8.9 KiB
Python
import tempfile
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import threading
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import time
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from pathlib import Path
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import numpy as np
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import pytest
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from codexlens.entities import SemanticChunk
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import codexlens.semantic.vector_store as vector_store_module
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from codexlens.semantic.vector_store import VectorStore
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@pytest.fixture()
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def temp_db():
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with tempfile.TemporaryDirectory(ignore_cleanup_errors=True) as tmpdir:
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yield Path(tmpdir) / "semantic.db"
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def test_concurrent_bulk_insert(monkeypatch: pytest.MonkeyPatch, temp_db: Path) -> None:
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"""Concurrent batch inserts in bulk mode should not corrupt accumulation state."""
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store = VectorStore(temp_db)
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monkeypatch.setattr(store, "_ensure_ann_index", lambda dim: True)
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store.begin_bulk_insert()
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errors: list[Exception] = []
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lock = threading.Lock()
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threads: list[threading.Thread] = []
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def make_chunks(count: int, dim: int) -> list[SemanticChunk]:
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chunks: list[SemanticChunk] = []
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for i in range(count):
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chunk = SemanticChunk(content=f"chunk {i}", metadata={})
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chunk.embedding = np.random.randn(dim).astype(np.float32)
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chunks.append(chunk)
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return chunks
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def worker(idx: int) -> None:
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try:
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dim = 8
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if idx % 2 == 0:
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chunks = make_chunks(5, dim)
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store.add_chunks_batch([(c, f"file_{idx}.py") for c in chunks], auto_save_ann=False)
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else:
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chunks = [SemanticChunk(content=f"chunk {i}") for i in range(5)]
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embeddings = np.random.randn(5, dim).astype(np.float32)
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store.add_chunks_batch_numpy(
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[(c, f"file_{idx}.py") for c in chunks],
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embeddings_matrix=embeddings,
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auto_save_ann=False,
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)
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except Exception as exc:
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with lock:
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errors.append(exc)
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for i in range(10):
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threads.append(threading.Thread(target=worker, args=(i,)))
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for t in threads:
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t.start()
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for t in threads:
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t.join()
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assert not errors
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assert len(store._bulk_insert_ids) == 50
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assert len(store._bulk_insert_embeddings) == 50
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assert store.count_chunks() == 50
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def test_bulk_insert_mode_transitions(monkeypatch: pytest.MonkeyPatch, temp_db: Path) -> None:
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"""begin/end bulk insert should be thread-safe with concurrent add operations."""
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store = VectorStore(temp_db)
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class DummyAnn:
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def __init__(self) -> None:
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self.total_added = 0
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self.save_calls = 0
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def add_vectors(self, ids, embeddings) -> None:
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self.total_added += len(ids)
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def save(self) -> None:
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self.save_calls += 1
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dummy_ann = DummyAnn()
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store._ann_index = dummy_ann
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monkeypatch.setattr(store, "_ensure_ann_index", lambda dim: True)
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errors: list[Exception] = []
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lock = threading.Lock()
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stop_event = threading.Event()
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def adder(worker_id: int) -> None:
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try:
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while not stop_event.is_set():
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chunk = SemanticChunk(content=f"chunk {worker_id}", metadata={})
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chunk.embedding = np.random.randn(8).astype(np.float32)
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store.add_chunks_batch([(chunk, f"file_{worker_id}.py")], auto_save_ann=False)
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except Exception as exc:
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with lock:
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errors.append(exc)
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def toggler() -> None:
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try:
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for _ in range(5):
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store.begin_bulk_insert()
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time.sleep(0.05)
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store.end_bulk_insert()
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time.sleep(0.05)
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except Exception as exc:
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with lock:
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errors.append(exc)
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threads = [threading.Thread(target=adder, args=(i,)) for i in range(3)]
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toggle_thread = threading.Thread(target=toggler)
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for t in threads:
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t.start()
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toggle_thread.start()
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toggle_thread.join(timeout=10)
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stop_event.set()
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for t in threads:
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t.join(timeout=10)
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assert not errors
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assert toggle_thread.is_alive() is False
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assert store._bulk_insert_mode is False
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assert store._bulk_insert_ids == []
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assert store._bulk_insert_embeddings == []
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assert dummy_ann.total_added == store.count_chunks()
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def test_search_similar_min_score_validation(monkeypatch: pytest.MonkeyPatch, temp_db: Path) -> None:
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"""search_similar should validate min_score is within [0.0, 1.0]."""
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monkeypatch.setattr(vector_store_module, "HNSWLIB_AVAILABLE", False)
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store = VectorStore(temp_db)
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chunk_a = SemanticChunk(content="chunk A", metadata={})
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chunk_a.embedding = np.array([1.0, 0.0, 0.0], dtype=np.float32)
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chunk_b = SemanticChunk(content="chunk B", metadata={})
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chunk_b.embedding = np.array([0.0, 1.0, 0.0], dtype=np.float32)
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store.add_chunks_batch([(chunk_a, "a.py"), (chunk_b, "b.py")])
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query = [1.0, 0.0, 0.0]
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with pytest.raises(ValueError, match=r"min_score.*\[0\.0, 1\.0\].*cosine"):
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store.search_similar(query, min_score=-0.5)
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with pytest.raises(ValueError, match=r"min_score.*\[0\.0, 1\.0\].*cosine"):
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store.search_similar(query, min_score=1.5)
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store.search_similar(query, min_score=0.0)
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store.search_similar(query, min_score=1.0)
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results = store.search_similar(query, min_score=0.5, return_full_content=False)
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assert [r.path for r in results] == ["a.py"]
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def test_search_similar(monkeypatch: pytest.MonkeyPatch, temp_db: Path) -> None:
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"""search_similar returns results ordered by descending similarity."""
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monkeypatch.setattr(vector_store_module, "HNSWLIB_AVAILABLE", False)
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store = VectorStore(temp_db)
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chunk_a = SemanticChunk(content="chunk A", metadata={})
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chunk_a.embedding = np.array([1.0, 0.0, 0.0], dtype=np.float32)
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chunk_b = SemanticChunk(content="chunk B", metadata={})
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chunk_b.embedding = np.array([0.0, 1.0, 0.0], dtype=np.float32)
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store.add_chunks_batch([(chunk_a, "a.py"), (chunk_b, "b.py")])
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results = store.search_similar([1.0, 0.0, 0.0], top_k=10, min_score=0.0, return_full_content=False)
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assert [r.path for r in results] == ["a.py", "b.py"]
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assert results[0].score == pytest.approx(1.0)
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assert results[1].score == pytest.approx(0.0)
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def test_search_with_ann_null_results(monkeypatch: pytest.MonkeyPatch, temp_db: Path) -> None:
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"""_search_with_ann should return [] when ANN search returns null results."""
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monkeypatch.setattr(vector_store_module, "HNSWLIB_AVAILABLE", False)
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store = VectorStore(temp_db)
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class DummyAnn:
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def count(self) -> int:
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return 1
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def search(self, query_vec: np.ndarray, top_k: int):
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return None, None
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store._ann_index = DummyAnn()
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results = store._search_with_ann(np.array([1.0, 0.0, 0.0], dtype=np.float32), top_k=10, min_score=0.0, return_full_content=False)
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assert results == []
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def test_search_with_ann_empty_results(monkeypatch: pytest.MonkeyPatch, temp_db: Path) -> None:
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"""_search_with_ann should return [] when ANN search returns empty results."""
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monkeypatch.setattr(vector_store_module, "HNSWLIB_AVAILABLE", False)
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store = VectorStore(temp_db)
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class DummyAnn:
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def count(self) -> int:
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return 1
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def search(self, query_vec: np.ndarray, top_k: int):
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return [], []
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store._ann_index = DummyAnn()
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results = store._search_with_ann(np.array([1.0, 0.0, 0.0], dtype=np.float32), top_k=10, min_score=0.0, return_full_content=False)
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assert results == []
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def test_search_with_ann_mismatched_results(monkeypatch: pytest.MonkeyPatch, temp_db: Path) -> None:
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"""_search_with_ann should return [] when ANN search returns mismatched results."""
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monkeypatch.setattr(vector_store_module, "HNSWLIB_AVAILABLE", False)
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store = VectorStore(temp_db)
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class DummyAnn:
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def count(self) -> int:
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return 2
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def search(self, query_vec: np.ndarray, top_k: int):
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return [1, 2], [0.5]
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store._ann_index = DummyAnn()
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results = store._search_with_ann(np.array([1.0, 0.0, 0.0], dtype=np.float32), top_k=10, min_score=0.0, return_full_content=False)
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assert results == []
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def test_search_with_ann_valid_results(monkeypatch: pytest.MonkeyPatch, temp_db: Path) -> None:
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"""_search_with_ann should return results for valid ANN outputs."""
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monkeypatch.setattr(vector_store_module, "HNSWLIB_AVAILABLE", False)
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store = VectorStore(temp_db)
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chunk = SemanticChunk(content="chunk A", metadata={})
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chunk.embedding = np.array([1.0, 0.0, 0.0], dtype=np.float32)
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chunk_id = store.add_chunk(chunk, "a.py")
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class DummyAnn:
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def count(self) -> int:
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return 1
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def search(self, query_vec: np.ndarray, top_k: int):
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return [chunk_id], [0.0]
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store._ann_index = DummyAnn()
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results = store._search_with_ann(np.array([1.0, 0.0, 0.0], dtype=np.float32), top_k=10, min_score=0.0, return_full_content=False)
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assert [r.path for r in results] == ["a.py"]
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assert results[0].score == pytest.approx(1.0)
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