先看看示例
作者提供的示例，实际效果取决于你的输入和使用工具。
输入
百万级向量检索延迟偏高：对照规模表确认 HNSW 适用，评估提高 efSearch 对召回与延迟的影响，并比较 INT8 量化后的内存节省。
输出
预期交付：索引类型选择建议、HNSW 参数调整方向、量化方案对比，以及基准测试与监控建议。（编辑说明，非实际运行结果）
方法说明
--- name: vector-index-tuning description: Optimize vector index performance for latency, recall, and memory. Use when tuning HNSW parameters, selecting quantization strategies, or scaling vector search infrastructure. --- # 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