Rag

A comprehensive reference guide for selecting and optimizing embedding models for vector search and RAG applications. Covers model comparisons (OpenAI, Voyage, BGE, E5), chunking strategies (token-based, sentence-based, semantic), domain-specific pipelines, and retrieval quality evaluation metrics.

This skill provides comprehensive documentation for designing LLM applications using the LangChain framework. It covers agent architectures (ReAct, OpenAI Functions), chain compositions, memory management patterns, document processing pipelines, and callback systems with production-ready examples and best practices.

This skill provides comprehensive documentation for building RAG (Retrieval-Augmented Generation) systems. It covers vector database selection (Pinecone, Weaviate, Chroma), embedding models, document chunking strategies, retrieval optimization techniques, and prompt engineering patterns for knowledge-grounded LLM applications.