wshobson 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
Source: wshobson/agents → plugins/llm-application-dev/skills/vector-index-tuning/SKILL.md