RAG From Scratch - Implementing Retrieval-Augmented Generation with LangChain

Dive into a comprehensive tutorial on implementing Retrieval-Augmented Generation (RAG) from scratch, led by a LangChain software engineer. Master the art of combining custom data with Large Language Models (LLMs) through RAG techniques. Explore key concepts including indexing, retrieval, generation, and various query translation methods such as Multi-Query, RAG Fusion, Decomposition, Step Back, and HyDE. Delve into advanced topics like routing, query construction, multi-representation indexing, RAPTOR, ColBERT, CRAG, and Adaptive RAG. Gain hands-on experience with provided code examples and engage in a thought-provoking discussion on the future of RAG technology. Perfect for developers seeking to enhance their skills in leveraging custom data with LLMs for more powerful and context-aware applications.

⌨️ Overview
⌨️ Indexing
⌨️ Retrieval
⌨️ Generation
⌨️ Query Translation Multi-Query
⌨️ Query Translation RAG Fusion
⌨️ Query Translation Decomposition
⌨️ Query Translation Step Back
⌨️ Query Translation HyDE
⌨️ Routing
⌨️ Query Construction
⌨️ Indexing Multi Representation
⌨️ Indexing RAPTOR
⌨️ Indexing ColBERT
⌨️ CRAG
⌨️ Adaptive RAG
⌨️ Is RAG Really Dead?