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similarity-search-patterns【数据与研究】Implement efficient similarity search with vector databases. Use when building semantic search, implementing nearest neighbor queries, or optimizing retrieval performance.
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Patterns for implementing efficient similarity search in production systems.
| 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 |
┌─────────────────────────────────────────────────┐
│ 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 │
└─────────────┴───────────────┴───────────────────┘
Full template library and detailed worked examples live in references/details.md. Read that file when you need the concrete templates.