version: "1.0.1" name: similarity-search-patterns description: Implement efficient similarity search with vector databases. Use when building semantic search, implementing nearest neighbor queries, or optimizing retrieval performance.

Similarity Search Patterns

Patterns for implementing efficient similarity search in production systems.

When to Use This Skill

• Building semantic search systems
• Implementing RAG retrieval
• Creating recommendation engines
• Optimizing search latency
• Scaling to millions of vectors
• Combining semantic and keyword search

Core Concepts

1. Distance Metrics

2. Index Types

┌─────────────────────────────────────────────────┐
│                 Index Types                      │
├─────────────┬───────────────┬───────────────────┤
│    Flat     │     HNSW      │    IVF+PQ         │
│ (Exact)     │ (Graph-based) │ (Quantized)       │
├─────────────┼───────────────┼───────────────────┤
│ O(n) search │ O(log n)      │ O(√n)             │
│ 100% recall │ ~95-99%       │ ~90-95%           │
│ Small data  │ Medium-Large  │ Very Large        │
└─────────────┴───────────────┴───────────────────┘

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

• Use appropriate index - HNSW for most cases
• Tune parameters - ef_search, nprobe for recall/speed
• Implement hybrid search - Combine with keyword search
• Monitor recall - Measure search quality
• Pre-filter when possible - Reduce search space

Don'ts

• Don't skip evaluation - Measure before optimizing
• Don't over-index - Start with flat, scale up
• Don't ignore latency - P99 matters for UX
• Don't forget costs - Vector storage adds up