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Similarity Search Patterns

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Implement efficient similarity search with vector databases. Use when building semantic search, implementing nearest neighbor queries, or optimizing retrieval performance.

Originally by wshobson · MIT

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Implement efficient similarity search with vector databases. Use when building semantic search, implementing nearest neighbor queries, or optimizing retrieval performance.

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Frontmatter — 2 properties
namesimilarity-search-patterns
descriptionImplement efficient similarity search with vector databases. Use when building semantic search, implementing nearest neighbor queries, or optimizing retrieval performance.
1---
2name: similarity-search-patterns
3description: Implement efficient similarity search with vector databases. Use when building semantic search, implementing nearest neighbor queries, or optimizing retrieval performance.
4---A5 — No allowed-tools declared — no way to tell what this skill may touch
5 
6# Similarity Search Patterns
7 
8Patterns for implementing efficient similarity search in production systems.
9 
10## When to Use This Skill
11 
12- Building semantic search systems
13- Implementing RAG retrieval
14- Creating recommendation engines
15- Optimizing search latency
16- Scaling to millions of vectors
17- Combining semantic and keyword search
18 
19## Core Concepts
20 
21### 1. Distance Metrics
22 
23| Metric | Formula | Best For |
24| ------------------ | ------------------ | --------------------- | --- | -------------- |
25| **Cosine** | 1 - (A·B)/(‖A‖‖B‖) | Normalized embeddings |
26| **Euclidean (L2)** | √Σ(a-b)² | Raw embeddings |
27| **Dot Product** | A·B | Magnitude matters |
28| **Manhattan (L1)** | Σ | a-b | | Sparse vectors |
29 
30### 2. Index Types
31 
32```
33┌─────────────────────────────────────────────────┐
34│ Index Types │
35├─────────────┬───────────────┬───────────────────┤
36│ Flat │ HNSW │ IVF+PQ │
37│ (Exact) │ (Graph-based) │ (Quantized) │
38├─────────────┼───────────────┼───────────────────┤
39│ O(n) search │ O(log n) │ O(√n) │
40│ 100% recall │ ~95-99% │ ~90-95% │
41│ Small data │ Medium-Large │ Very Large │
42└─────────────┴───────────────┴───────────────────┘
43```
44 
45## Templates and detailed worked examples
46 
47Full template library and detailed worked examples live in `references/details.md`. Read that file when you need the concrete templates.
48 
49## Best Practices
50 
51### Do's
52 
53- **Use appropriate index** - HNSW for most cases
54- **Tune parameters** - ef_search, nprobe for recall/speed
55- **Implement hybrid search** - Combine with keyword search
56- **Monitor recall** - Measure search quality
57- **Pre-filter when possible** - Reduce search space
58 
59### Don'ts
60 
61- **Don't skip evaluation** - Measure before optimizing
62- **Don't over-index** - Start with flat, scale up
63- **Don't ignore latency** - P99 matters for UX
64- **Don't forget costs** - Vector storage adds up
65 

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