similarity-search-patterns

Efficient similarity search patterns for vector databases and semantic retrieval systems. 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 | - Covers four major vector database implementations: Pinecone, Qdrant, pgvector with PostgreSQL, and Weaviate, each with production-ready code templates - Explains three index types (Flat, HNSW, IVF+PQ) with trade-offs between search speed, recall accuracy, and data scale - Includes four distance metrics (Cosine, Euclidean, Dot Product, Manhattan) and guidance on when to use each - Demonstrates hybrid search combining dense vectors with keyword search, reranking, and metadata filtering patterns - Provides best practices for index tuning, recall evaluation, and latency optimization - Building semantic search systems - Implementing RAG retrieval - Creating recommendation engines - Optimizing search latency - Scaling to millions of vectors - Combining semantic and keyword search - Use appropriate index - HNSW for most cases - Tune parameters - ef_search, nprobe for recall/speed - Implement hybrid search -...