Agent-Skills.md

Agent Skills: Vector Index Tuning

Optimize vector index performance for latency, recall, and memory. Use when tuning HNSW parameters, selecting quantization strategies, or scaling vector search infrastructure.

UncategorizedID: wshobson/agents/vector-index-tuning

Repository

wshobson/agents
wshobsonLicense: MIT
32,1353,507

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pnpm dlx add-skill https://github.com/wshobson/agents/tree/HEAD/plugins/llm-application-dev/skills/vector-index-tuning

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plugins/llm-application-dev/skills/vector-index-tuning/SKILL.md

Skill Metadata

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