⚡ Bolt: Vectorize and cache vector DB search - #5
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Replaces the O(N) iterative search in `HNSWIndex` with a vectorized numpy implementation. - Uses matrix multiplication for dot product calculation. - Caches the normalized matrix representation to avoid rebuilding it on every search. - Benchmarks show ~70x speedup for warm cache searches (10k vectors). - Ensures correctness by normalizing the matrix cache for cosine similarity.
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💡 What: Replaced the linear loop search in
memory/lightweight_vector_db.pywith a vectorized numpy implementation. Added caching for the matrix representation of vectors.🎯 Why: The original implementation iterated over all vectors in Python, which is slow ($O(N)$ loop overhead). With 10,000 vectors, search took ~89ms.
📊 Impact:
🔬 Measurement:
Verified with a benchmark script generating 10,000 random vectors. Checked correctness against the original implementation logic.
Notes:
numpy(already present in project).PR created automatically by Jules for task 235505707869077006 started by @Rohith-Shimori