fix(vector-store): add lock protection for bulk insert mode flag

Protect _bulk_insert_mode flag and accumulation lists with
_ann_write_lock to prevent corruption during concurrent access.

Solution-ID: SOL-1735392000003
Issue-ID: ISS-1766921318981-12
Task-ID: T1

codex-lens/src/codexlens/semantic/vector_store.py

@@ -500,13 +500,13 @@ class VectorStore:
 
         # Handle ANN index updates
         if embeddings_list and update_ann and self._ensure_ann_index(len(embeddings_list[0])):
-            # In bulk insert mode, accumulate for later batch update
-            if self._bulk_insert_mode:
-                self._bulk_insert_ids.extend(ids)
-                self._bulk_insert_embeddings.extend(embeddings_list)
-            else:
-                # Normal mode: update immediately
-                with self._ann_write_lock:
+            with self._ann_write_lock:
+                # In bulk insert mode, accumulate for later batch update
+                if self._bulk_insert_mode:
+                    self._bulk_insert_ids.extend(ids)
+                    self._bulk_insert_embeddings.extend(embeddings_list)
+                else:
+                    # Normal mode: update immediately
                     try:
                         embeddings_matrix = np.vstack(embeddings_list)
                         self._ann_index.add_vectors(ids, embeddings_matrix)
@@ -579,14 +579,14 @@ class VectorStore:
 
         # Handle ANN index updates
         if update_ann and self._ensure_ann_index(embeddings_matrix.shape[1]):
-            # In bulk insert mode, accumulate for later batch update
-            if self._bulk_insert_mode:
-                self._bulk_insert_ids.extend(ids)
-                # Split matrix into individual arrays for accumulation
-                self._bulk_insert_embeddings.extend([embeddings_matrix[i] for i in range(len(ids))])
-            else:
-                # Normal mode: update immediately
-                with self._ann_write_lock:
+            with self._ann_write_lock:
+                # In bulk insert mode, accumulate for later batch update
+                if self._bulk_insert_mode:
+                    self._bulk_insert_ids.extend(ids)
+                    # Split matrix into individual arrays for accumulation
+                    self._bulk_insert_embeddings.extend([embeddings_matrix[i] for i in range(len(ids))])
+                else:
+                    # Normal mode: update immediately
                     try:
                         self._ann_index.add_vectors(ids, embeddings_matrix)
                         if auto_save_ann:

codex-lens/tests/test_vector_store.py

@@ -0,0 +1,67 @@
+import tempfile
+import threading
+from pathlib import Path
+
+import numpy as np
+import pytest
+
+from codexlens.entities import SemanticChunk
+from codexlens.semantic.vector_store import VectorStore
+
+
+@pytest.fixture()
+def temp_db():
+    with tempfile.TemporaryDirectory(ignore_cleanup_errors=True) as tmpdir:
+        yield Path(tmpdir) / "semantic.db"
+
+
+def test_concurrent_bulk_insert(monkeypatch: pytest.MonkeyPatch, temp_db: Path) -> None:
+    """Concurrent batch inserts in bulk mode should not corrupt accumulation state."""
+    store = VectorStore(temp_db)
+    monkeypatch.setattr(store, "_ensure_ann_index", lambda dim: True)
+
+    store.begin_bulk_insert()
+
+    errors: list[Exception] = []
+    lock = threading.Lock()
+    threads: list[threading.Thread] = []
+
+    def make_chunks(count: int, dim: int) -> list[SemanticChunk]:
+        chunks: list[SemanticChunk] = []
+        for i in range(count):
+            chunk = SemanticChunk(content=f"chunk {i}", metadata={})
+            chunk.embedding = np.random.randn(dim).astype(np.float32)
+            chunks.append(chunk)
+        return chunks
+
+    def worker(idx: int) -> None:
+        try:
+            dim = 8
+            if idx % 2 == 0:
+                chunks = make_chunks(5, dim)
+                store.add_chunks_batch([(c, f"file_{idx}.py") for c in chunks], auto_save_ann=False)
+            else:
+                chunks = [SemanticChunk(content=f"chunk {i}") for i in range(5)]
+                embeddings = np.random.randn(5, dim).astype(np.float32)
+                store.add_chunks_batch_numpy(
+                    [(c, f"file_{idx}.py") for c in chunks],
+                    embeddings_matrix=embeddings,
+                    auto_save_ann=False,
+                )
+        except Exception as exc:
+            with lock:
+                errors.append(exc)
+
+    for i in range(10):
+        threads.append(threading.Thread(target=worker, args=(i,)))
+
+    for t in threads:
+        t.start()
+    for t in threads:
+        t.join()
+
+    assert not errors
+    assert len(store._bulk_insert_ids) == 50
+    assert len(store._bulk_insert_embeddings) == 50
+    assert store.count_chunks() == 50
+