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

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 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

similarity-search-patterns