ipIterPrompt

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.

wshobson · agentsUpdated 2026-07-084,077 copies

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. Imported from wshobson/agents (MIT).

SKILL.md

---
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

Run this skill on a real model without leaving the page. Every run is saved to your history for this skill.

How to use

  1. 1Save the content below as SKILL.md in your agent's skills directory (e.g. .claude/skills/<name>/SKILL.md).
  2. 2Or paste it directly into the conversation as context before asking the agent to do the task.
  3. 3Adjust any project-specific paths or conventions mentioned in the skill to match your setup.

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