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datawhalechina/all-in-rag

A comprehensive, full-stack guide to Retrieval Augmented Generation (RAG) technology for large language model application development, covering theory, practice, and engineering best practices.

Core Features

Systematic learning path from basics to advanced RAG concepts.

Combines theoretical knowledge with hands-on coding practices.

Supports multimodal RAG, including text and image retrieval.

Focuses on engineering best practices for production-ready systems.

Includes rich practical projects for skill consolidation.

Detailed Introduction

This project serves as a full-stack tutorial for developers building applications with Large Language Models (LLMs) using Retrieval Augmented Generation (RAG). It addresses the fragmented nature of existing RAG resources by offering a structured learning path, enabling developers to master RAG application development from data processing and indexing to advanced retrieval and evaluation. The guide aims to equip learners with the skills to build production-grade intelligent Q&A and knowledge retrieval systems, emphasizing practical application and engineering considerations.