Vector Database Optimisation — Faster Queries at Scale

I will optimise your vector database query performance — tuning HNSW index parameters (ef_construction, M, ef_search), implementing approximate nearest neighbour configuration for your precision/speed tradeoff, optimising metadata filter pushdown before vector search, and implementing query caching for frequent identical queries. Vector database query performance degrades invisibly as index size grows — HNSW parameters that deliver acceptable latency at 100k vectors may produce unacceptable latency at 10M vectors, and metadata filters that aren't pushed down to the index level force post-retrieval filtering that scales linearly with index size; performance tuning at scale prevents the latency degradation that production systems encounter without it.

The optimisation covers HNSW parameter tuning at your scale, precision vs. speed tradeoff calibration, metadata filter pushdown verification, query caching layer implementation, latency benchmarking before and after, and a performance monitoring setup.