vector-index-tuning skill

by wshobson·wshobson/agents·40k stars·MIT

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

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Install the vector-index-tuning skill

A skill is a folder. Copy it into your agent's skills folder and the agent loads it when the task matches its description.

git clone --depth 1 https://github.com/wshobson/agents.git /tmp/agents
mkdir -p ~/.claude/skills
cp -r /tmp/agents/plugins/llm-application-dev/skills/vector-index-tuning ~/.claude/skills/vector-index-tuning

available in every project

In the Claude apps, zip the folder and upload it from the Skills settings. The folder on GitHub

The instructions your agent would load

SKILL.md as published, without the frontmatter. Read it on GitHub

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

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

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