LangChain is a platform based on language models designed for building and deploying large-scale applications using RAG (Retrieval Augmented Generation) technology. It encompasses query transformation, hypothesis document embedding (HyDE), routing mechanisms, query construction and indexing strategies, retrieval techniques, and the final generation phase. Lately, LangChain has implemented the guide for RAG (Retrieval-Augmented Generation), providing users with a detailed tutorial on how to build

The RAG system tutorial has been released! This system is based on arXiv papers as a context source and provides links to the source papers used in generating answers. Tutorial link: https://colab.research.google.com/drive/1Lc8eq8P87JjzUhbYb33_c7h7njsWb-hn#scrollTo=eCSBhP4FxOg3 . The tutorial thoroughly demonstrates the process of implementing the RAG system, including obtaining paper text, preprocessing and chunking PDF documents using Unstructured, creating a ChromaDB retriever, setting up RAG

A detailed translation of Claude's prompt design documentation, including what prompts are, the steps to design prompts, prompt optimization, and more, along with numerous examples of prompt design.