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similarity-search-patterns

Build searches that find similar meanings using vector databases, which store numerical representations of data. Choose search indexes, combine semantic and keyword search, and tune retrieval quality and response time for search, recommendations, or AI answer systems.

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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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适用场景

  • Build search that finds documents with similar meanings
  • Improve document retrieval for AI answers
  • Design a recommendation engine
  • Tune search quality and speed across millions of vectors

包含的文件(2)

  • SKILL.md2.7 KB
  • references/details.md15.0 KB

SKILL.md(原文)

安装前可以查看交给 AI 代理的指令内容。

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

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