Implement ANN index using HNSW algorithm and update related tests

- 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.

codex-lens/tests/test_vector_search_full.py

@@ -435,6 +435,10 @@ class TestVectorStoreCache:
         chunk.embedding = embedder.embed_single(chunk.content)
         vector_store.add_chunk(chunk, "/test/a.py")
 
+        # Force brute-force mode to populate cache (disable ANN)
+        original_ann = vector_store._ann_index
+        vector_store._ann_index = None
+
         # Trigger cache population
         query_embedding = embedder.embed_single("function")
         vector_store.search_similar(query_embedding)
@@ -445,6 +449,9 @@ class TestVectorStoreCache:
 
         assert vector_store._embedding_matrix is None
 
+        # Restore ANN index
+        vector_store._ann_index = original_ann
+
 
 # === Semantic Search Accuracy Tests ===