vector-index-tuning

Optimize vector index performance across latency, recall, and memory tradeoffs. Guide to optimizing vector indexes for production performance. - Covers HNSW parameter tuning (M, efConstruction, efSearch) with benchmarking templates and automated recommendation logic based on vector count and target recall - Includes quantization strategies: scalar INT8, product quantization, binary quantization, and FP16 compression with memory estimation tools - Provides Qdrant collection configuration templates optimized for three scenarios: recall-focused, speed-focused, balanced, and memory-constrained deployments - Includes search performance monitoring, latency profiling (p50/p95/p99), and recall measurement against ground truth - Tuning HNSW parameters - Implementing quantization - Optimizing memory usage - Reducing search latency - Balancing recall vs speed - Scaling to billions of vectors - Benchmark with real queries - Synthetic may not represent production - Monitor recall continuously - Can degrade with data drift - Start with defaults - Tune only when needed - Use quantization - Significant memory savings - Consider tiered storage - Hot/cold data separation - Don't over-optimize early - Profile first - Don't ignore build time - Index updates have cost - Don't forget reindexing - Plan for maintenance - Don't skip warming - Cold indexes are slow