AgentSkillsCN

vector-index-tuning

优化向量索引的性能，提升延迟、召回率与内存利用率。适用于调整HNSW参数、选择量化策略，或扩展向量搜索基础设施时使用。

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. 
category: AI & Agents
source: antigravity
tags: [ai, template, design]
url: https://github.com/sickn33/antigravity-awesome-skills/tree/main/skills/vector-index-tuning

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.