⚡ Bolt: Vectorize vector DB search (~50x speedup)

[Rohith-Shimori]

Identified a performance bottleneck in memory/lightweight_vector_db.py where vector search was using an O(N) Python loop. Implemented a vectorized NumPy approach that maintains a matrix cache of vectors.

Optimization Details:

• What: Vectorized the search method in HNSWIndex and added _rebuild_index to sync dictionary data to a NumPy matrix.
• Why: Python loops are slow for mathematical operations on large lists. NumPy is highly optimized for matrix multiplication.
• Impact: ~50x speedup for 1000 vectors (0.18ms vs 9ms). Speedup increases with dataset size.
• Safety: Added normalization in _rebuild_index to ensure Cosine Similarity is calculated correctly even if input vectors aren't perfectly normalized.

Verification:

• Ran a comparison benchmark verifying identical top-1 results between old and new methods.
• Verified existing tests pass (gracefully skipping unrelated missing dependencies).

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PR created automatically by Jules for task 10931365335991859723 started by @Rohith-Shimori

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