This repository provides a comprehensive, full-stack guide to Retrieval-Augmented Generation (RAG) technology, aimed at developers seeking to build production-ready intelligent Q&A and knowledge retrieval systems. It offers a systematic learning path from theoretical foundations to practical implementation, including multi-modal support and engineering best practices.

How It Works

The guide covers the entire RAG pipeline: data processing (loading, chunking), index construction (vector embeddings, multi-modal embeddings, vector databases like Milvus), advanced retrieval techniques (hybrid search, query construction, Text2SQL), and generation integration with evaluation methods. The approach emphasizes both theoretical understanding and hands-on coding practice with rich project examples, including graph RAG.

Quick Start & Requirements

• Prerequisites: Basic Python programming, familiarity with Docker, fundamental Linux command-line operations. Basic understanding of LLMs is recommended but not required.
• Setup: Environment configuration and Python virtual environment deployment are detailed in the documentation.
• Links: https://datawhalechina.github.io/all-in-rag/#/en/

Highlighted Details

• Systematic learning path covering RAG fundamentals to advanced applications.
• Combines theoretical explanations with practical code examples for each chapter.
• Includes multi-modal RAG with support for image and text retrieval.
• Focuses on engineering aspects like performance optimization and system evaluation.
• Features multiple hands-on projects, from basic to advanced graph RAG implementations.

Maintenance & Community

• Led by Yin Dalü, the project welcomes contributions via bug reports, feature suggestions, documentation improvements, and code contributions.
• Links to Datawhale's official WeChat account for more open-source content are provided.

Licensing & Compatibility

• Licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0).
• The non-commercial clause restricts usage in commercial products without explicit permission or alternative licensing.

Limitations & Caveats

• Project Ten is listed as "planned," indicating it is not yet available.
• The license's non-commercial restriction may limit adoption in certain business contexts.