back
loading skill details...
Optimize vector index performance for latency, recall, and memory. Use when tuning HNSW parameters, selecting quantization strategies, or scaling vector search…
Vector Index Tuning Guide to optimizing vector indexes for production performance. When to Use This Skill Tuning HNSW parameters Implementing quantization Optimizing memory usage Reducing search latency Balancing recall vs speed Scaling to billions of vectors Core Concepts 1. Index Type Selection Data Size Recommended Index ──────────────────────────────────────── < 10K vectors → Flat (exact search) 10K - 1M → HNSW 1M - 100M → HNSW + Quantization > 100M → IVF + PQ or DiskANN 2. HNSW Parameters Parameter Default Effect M 16 Connections per node, ↑ = better recall, more memory efConstruction 100 Build quality, ↑ = better index, slower build efSearch 50 Search quality, ↑ = better recall, slower search 3. Quantization Types Full Precision (FP32): 4 bytes × dimensions Half Precision (FP16): 2 bytes × dimensions INT8 Scalar: 1 byte × dimensions Product Quantization: ~32-64 bytes total Binary: dimensions/8 bytes Templates and detailed worked examples Full template library and detailed worked examples live in references/details.md. Read that file when you need the concrete templates. Best Practices Do's 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'ts 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
don't have the plugin yet? install it then click "run inline in claude" again.