Vector Index Tuning

Benchmarks HNSW parameters (M, efConstruction, efSearch) against your actual data to find the sweet spot between recall and latency. Implements scalar quantization, product quantization, and binary compression with memory usage estimates. The parameter tuning function runs systematic tests across different configurations and calculates recall@k, which beats guessing at settings. Also includes quantization strategies that can cut memory usage by 75% with minimal recall loss. Most useful when you're hitting memory limits or search latency SLAs with large vector datasets over 100K embeddings.