similarity-search-patterns

SKILL.md

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
| Metric             | Formula            | Best For              |
| ------------------ | ------------------ | --------------------- | --- | -------------- |
| Cosine         | 1 - (A·B)/(‖A‖‖B‖) | Normalized embeddings |
| Euclidean (L2) | √Σ(a-b)²           | Raw embeddings        |
| Dot Product    | A·B                | Magnitude matters     |
| Manhattan (L1) | Σ                  | a-b                   |     | Sparse vectors |

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