Implements efficient semantic search and vector database patterns for production-grade retrieval systems.
This skill provides standardized patterns for implementing similarity search across leading vector databases including Pinecone, Qdrant, and pgvector. It equips Claude with the expertise to select appropriate distance metrics such as Cosine or Euclidean, choose optimal indexing strategies like HNSW or IVF+PQ, and implement advanced retrieval techniques including cross-encoder reranking and hybrid search. Whether you are building a RAG pipeline, a recommendation engine, or a large-scale semantic search tool, this skill ensures scalable, low-latency implementations optimized for both performance and recall.
主な機能
01Multi-provider support for Pinecone, Qdrant, and PostgreSQL (pgvector)
02Implementation patterns for major distance metrics and indexing types
03Hybrid search patterns combining dense and sparse vector lookups
040 GitHub stars
05Advanced retrieval strategies including cross-encoder reranking
06Optimization guidance for HNSW and quantized indexing strategies
ユースケース
01Building high-performance Retrieval Augmented Generation (RAG) systems
02Implementing personalized recommendation systems using vector embeddings
03Developing semantic search engines for large-scale document collections
