Implements efficient semantic search and vector database patterns for RAG and recommendation systems.
This skill provides standardized patterns for implementing similarity search using popular vector databases like Pinecone, Qdrant, and pgvector. It equips developers with optimized code templates for upserting embeddings, performing nearest-neighbor queries, and executing hybrid search strategies. By covering essential concepts like distance metrics (Cosine, L2) and index types (HNSW, IVF), it helps scale AI-driven applications from initial prototypes to production-grade retrieval systems with optimized latency and recall.
주요 기능
01Multi-database support for Pinecone, Qdrant, and pgvector
02Advanced reranking logic using Cross-Encoder models
03Hybrid search implementations combining dense and sparse vectors
04Optimized HNSW and IVF index configuration templates
05Guidance on distance metrics including Cosine, Euclidean, and Dot Product
