⚡ Bolt: Optimize LightweightVectorDB search with vectorization - #28
⚡ Bolt: Optimize LightweightVectorDB search with vectorization#28Rohith-Shimori wants to merge 1 commit into
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- Refactored HNSWIndex to use cached numpy matrix for search - Replaced iterative cosine similarity with matrix multiplication - Added lazy index rebuilding - Added benchmark script (tests/benchmark_vector_db.py) - Added correctness test (tests/test_vector_db_correctness.py) - Updated .jules/bolt.md with learnings Co-authored-by: Rohith-Shimori <228351330+Rohith-Shimori@users.noreply.github.com>
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💡 What: Refactored HNSWIndex.search to use vectorized NumPy matrix operations instead of a Python loop. Added lazy matrix rebuilding.
🎯 Why: The previous implementation iterated over every vector and computed norms repeatedly, causing slow search times (O(N) with high constant).
📊 Impact: Reduces search time for 2000 vectors from ~17.6ms to ~0.4ms (~44x speedup).
🔬 Measurement: Run tests/benchmark_vector_db.py to verify.
PR created automatically by Jules for task 9576782051231160173 started by @Rohith-Shimori