Implements high-performance similarity search and vector database patterns for AI-driven applications.
This skill provides standardized patterns and implementation templates for building efficient similarity search systems, which are essential for Retrieval-Augmented Generation (RAG), recommendation engines, and semantic search. It offers production-ready code for popular vector stores like Pinecone, Qdrant, and pgvector, while guiding developers through critical architectural decisions such as selecting distance metrics and optimized indexing strategies like HNSW or IVF+PQ to balance speed and recall in production environments.
주요 기능
01Detailed guidance on distance metrics (Cosine, Euclidean, Dot Product)
020 GitHub stars
03Production-ready templates for Pinecone, Qdrant, and pgvector
04Optimized indexing strategies for graph-based and quantized search
05Scalable vector upsert, filtering, and deletion logic
06Implementation patterns for hybrid search and re-ranking workflows
사용 사례
01Building Retrieval-Augmented Generation (RAG) systems for LLMs
02Implementing high-performance recommendation systems using vector embeddings
03Developing semantic search engines for large-scale document collections
