vector-index-tuning – DevOps & Infrastructure skill for AI agents

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