Similarity Search Patterns

About

Similarity Search Patterns provides a comprehensive framework for building and scaling semantic search systems and RAG pipelines. It offers expert guidance on selecting distance metrics like Cosine or Euclidean, choosing index types such as HNSW or IVF for performance optimization, and implementing production-ready code for popular vector stores including Pinecone, Qdrant, and pgvector. This skill is essential for developers building LLM-powered applications that require efficient, high-performance nearest-neighbor queries across millions of high-dimensional vectors.

Key Features

• Performance optimization strategies for scaling to millions of vectors
• 3 GitHub stars
• Comparative analysis of distance metrics including Cosine, L2, and Dot Product
• Implementation templates for Pinecone, Qdrant, and pgvector
• Index selection guidance for Flat, HNSW, and IVF+PQ types
• Advanced retrieval patterns for hybrid search and cross-encoder reranking

Use Cases

• Developing semantic search engines that understand intent beyond keyword matching
• Building robust Retrieval-Augmented Generation (RAG) systems for LLM applications
• Creating high-performance recommendation systems based on user behavior embeddings