AgentSkillsCN

vector-index-tuning

针对延迟、召回率与内存占用，优化向量索引性能。适用于HNSW参数调优、量化策略选择，或向量检索基础设施的规模化扩展。

SKILL.md
--- frontmatter
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.

Use this skill when

  • •Tuning HNSW parameters
  • •Implementing quantization
  • •Optimizing memory usage
  • •Reducing search latency
  • •Balancing recall vs speed
  • •Scaling to billions of vectors

Do not use this skill when

  • •You only need exact search on small datasets (use a flat index)
  • •You lack workload metrics or ground truth to validate recall
  • •You need end-to-end retrieval system design beyond index tuning

Instructions

  1. •Gather workload targets (latency, recall, QPS), data size, and memory budget.
  2. •Choose an index type and establish a baseline with default parameters.
  3. •Benchmark parameter sweeps using real queries and track recall, latency, and memory.
  4. •Validate changes on a staging dataset before rolling out to production.

Refer to resources/implementation-playbook.md for detailed patterns, checklists, and templates.

Safety

  • •Avoid reindexing in production without a rollback plan.
  • •Validate changes under realistic load before applying globally.
  • •Track recall regressions and revert if quality drops.

Resources

  • •resources/implementation-playbook.md for detailed patterns, checklists, and templates.