wshobson/vector-index-tuning
plugins/llm-application-dev/skills/vector-index-tuning/SKILL.md
Optimize vector index performance for latency, recall, and memory. Use when tuning HNSW parameters, selecting quantization strategies, or scaling vector search infrastructure.
$ install --global
skillsauthnpx skillsauth add wshobson/agents vector-index-tuningInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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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
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