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From llm-application-dev
Implements efficient similarity search with vector databases for semantic search, nearest neighbor queries, and RAG retrieval.
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Patterns for implementing efficient similarity search in production systems.
Implements efficient similarity search patterns with vector databases for semantic search, RAG retrieval, recommendation engines, nearest neighbor queries, and scaling to millions of vectors.
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Provides patterns and Python templates for similarity search with vector databases, including metrics, indexes, and Pinecone implementation. Use for semantic search, RAG, recommendations, and scaling.
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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.