---
title: "all-in-rag"
type: "tool"
slug: "datawhalechina-all-in-rag"
canonical_url: "https://www.graphcanon.com/tools/datawhalechina-all-in-rag"
github_url: "https://github.com/datawhalechina/all-in-rag"
homepage_url: "https://datawhalechina.github.io/all-in-rag/"
stars: 9294
forks: 4625
primary_language: "Python"
license: null
categories: ["llm-frameworks", "data-retrieval"]
tags: ["neo4j", "deepseek", "llm", "ai", "embedding", "langchain", "milvus", "multimodal"]
updated_at: "2026-07-07T18:35:26.326673+00:00"
---

# all-in-rag

> 大模型应用开发实战一：RAG技术全栈指南

All-in-RAG 提供了从理论到实践，涵盖基础与进阶的 RAG 技术知识体系。

## Facts

- Repository: https://github.com/datawhalechina/all-in-rag
- Homepage: https://datawhalechina.github.io/all-in-rag/
- Stars: 9,294 · Forks: 4,625 · Open issues: 17 · Watchers: 23
- Primary language: Python
- Last pushed: 2026-06-05T08:29:26+00:00

## Categories

- [LLM Frameworks](/categories/llm-frameworks.md)
- [Data & Retrieval](/categories/data-retrieval.md)

## Tags

neo4j, deepseek, llm, ai, embedding, langchain, milvus, multimodal

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- [transformers](/tools/huggingface-transformers.md) - 🤗 Transformers: the model-definition framework for state-of-the-art machine learning models (★ 162,347)
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## README (excerpt)

```text
# All-in-RAG | 大模型应用开发实战一：RAG技术全栈指南

<div align='center'>
  <img src="./docs/logo.svg" alt="All-in-RAG Logo" width="70%">
</div>

<div align="center">
  <h2>🔍 检索增强生成 (RAG) 技术全栈指南</h2>
  <p><em>从理论到实践，从基础到进阶，构建你的RAG技术体系</em></p>
</div>

<div align="center">
  <img src="https://img.shields.io/github/stars/datawhalechina/all-in-rag?style=for-the-badge&logo=github&color=ff6b6b" alt="GitHub stars"/>
  <img src="https://img.shields.io/github/forks/datawhalechina/all-in-rag?style=for-the-badge&logo=github&color=4ecdc4" alt="GitHub forks"/>
  <img src="https://img.shields.io/badge/Python-3.12.7-blue?style=for-the-badge&logo=python&logoColor=white" alt="Python"/>
  <a href="https://zread.ai/datawhalechina/all-in-rag">
    <img src="https://img.shields.io/badge/Ask_Zread-_.svg?style=for-the-badge&color=00b0aa&labelColor=000000&logo=data%3Aimage%2Fsvg%2Bxml%3Bbase64%2CPHN2ZyB3aWR0aD0iMTYiIGhlaWdodD0iMTYiIHZpZXdCb3g9IjAgMCAxNiAxNiIgZmlsbD0ibm9uZSIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KPHBhdGggZD0iTTQuOTYxNTYgMS42MDAxSDIuMjQxNTZDMS44ODgxIDEuNjAwMSAxLjYwMTU2IDEuODg2NjQgMS42MDE1NiAyLjI0MDFWNC45NjAxQzEuNjAxNTYgNS4zMTM1NiAxLjg4ODEgNS42MDAxIDIuMjQxNTYgNS42MDAxSDQuOTYxNTZDNS4zMTUwMiA1LjYwMDEgNS42MDE1NiA1LjMxMzU2IDUuNjAxNTYgNC45NjAxVjIuMjQwMUM1LjYwMTU2IDEuODg2NjQgNS4zMTUwMiAxLjYwMDEgNC45NjE1NiAxLjYwMDFaIiBmaWxsPSIjZmZmIi8%2BCjxwYXRoIGQ9Ik00Ljk2MTU2IDEwLjM5OTlIMi4yNDE1NkMxLjg4ODEgMTAuMzk5OSAxLjYwMTU2IDEwLjY4NjQgMS42MDE1NiAxMS4wMzk5VjEzLjc1OTlDMS42MDE1NiAxNC4xMTM0IDEuODg4MSAxNC4zOTk5IDIuMjQxNTYgMTQuMzk5OUg0Ljk2MTU2QzUuMzE1MDIgMTQuMzk5OSA1LjYwMTU2IDE0LjExMzQgNS42MDE1NiAxMy43NTk5VjExLjAzOTlDNS42MDE1NiAxMC42ODY0IDUuMzE1MDIgMTAuMzk5OSA0Ljk2MTU2IDEwLjM5OTlaIiBmaWxsPSIjZmZmIi8%2BCjxwYXRoIGQ9Ik0xMy43NTg0IDEuNjAwMUgxMS4wMzg0QzEwLjY4NSAxLjYwMDEgMTAuMzk4NCAxLjg4NjY0IDEwLjM5ODQgMi4yNDAxVjQuOTYwMUMxMC4zOTg0IDUuMzEzNTYgMTAuNjg1IDUuNjAwMSAxMS4wMzg0IDUuNjAwMUgxMy43NTg0QzE0LjExMTkgNS42MDAxIDE0LjM5ODQgNS4zMTM1NiAxNC4zOTg0IDQuOTYwMVYyLjI0MDFDMTQuMzk4NCAxLjg4NjY0IDE0LjExMTkgMS42MDAxIDEzLjc1ODQgMS42MDAxWiIgZmlsbD0iI2ZmZiIvPgo8cGF0aCBkPSJNNCAxMkwxMiA0TDQgMTJaIiBmaWxsPSIjZmZmIi8%2BCjxwYXRoIGQ9Ik00IDEyTDEyIDQiIHN0cm9rZT0iI2ZmZiIgc3Ryb2tlLXdpZHRoPSIxLjUiIHN0cm9rZS1saW5lY2FwPSJyb3VuZCIvPgo8L3N2Zz4K&logoColor=ffffff" alt="zread"/>
  </a>
</div>

<div align="center">
  <a href="https://datawhalechina.github.io/all-in-rag/">
    <img src="https://img.shields.io/badge/📖_在线阅读-立即开始-success?style=for-the-badge&logoColor=white" alt="在线阅读"/>
  </a>
  <a href="README_en.md">
    <img src="https://img.shields.io/badge/🌍_English-Version-blue?style=for-the-badge&logoColor=white" alt="English Version"/>
  </a>
  <a href="https://github.com/datawhalechina">
    <img src="https://img.shields.io/badge/💬_讨论交流-加入我们-purple?style=for-the-badge&logoColor=white" alt="讨论交流"/>
  </a>
</div>

<div align="center">
  <br>
  <table>
    <tr>
      <td align="center">🎯 <strong>系统化学习</strong><br>完整的RAG技术体系</td>
      <td align="center">🛠️ <strong>动手实践</strong><br>丰富的项目案例</td>
      <td align="center">🚀 <strong>生产就绪</strong><br>工程化最佳实践</td>
      <td align="center">📊 <strong>多模态支持</strong><br>文本+图像检索</td>
    </tr>
  </table>
</div>

## 项目简介（中文 | [English](README_en.md)）

本项目是一个面向大模型应用开发者的RAG（检索增强生成）技术全栈教程，旨在通过体系化的学习路径和动手实践项目，帮助开发者掌握基于大语言模型的RAG应用开发技能，构建生产级的智能问答和知识检索系统。

**主要内容包括：**

1. **RAG技术基础**：深入浅出地介绍RAG的核心概念、技术原理和应用场景
2. **数据处理全流程**：从数据加载、清洗到文本分块的完整数据准备流程
3. **索引构建与优化**：向量嵌入、多模态嵌入、向量数据库构建及索引优化技术
4. **检索技术进阶**：混合检索、查询构建、Text2SQL等高级检索技术
5. **生成集成与评估**：格式化生成、系统评估与优化方法
6. **项目实战**：从基础到进阶的完整RAG应用开发实践

## 项目意义

随着大语言模型的快速发展，RAG技术已成为构建智能问答系统、知识检索应用的核心技术。然而，现有的RAG教程往往零散且缺乏系统性，初学者难以形成完整的技术体系认知。

本项目从实践出发，结合最新的RAG技术发展趋势，构建了一套完整的RAG学习体系，帮助开发者：
- 系统掌握RAG技术的理论基础和实践技能
- 理解RAG系统的完整架构和各组件的作用
- 具备独立开发RAG应用的能力
- 掌握RAG系统的评估和优化方法

## 项目受众

**本项目适合以下人群学习：**
- 具备Python编程基础，对RAG技术感兴趣的开发者
- 希望系统学习RAG技术的AI工程师
- 想要构建智能问答系统的产品开发者
- 对检索增强生成技术有学习需求的研究人员

**前置要求：**
- 掌握Python基础语法和常用库的使用
- 能够简单使用docker
- 了解基本的LLM概念（推荐但非必需）
- 具备基础的Linux命令行操作能力

## 项目亮点

1. **体系化学习路径**：从基础概念到高级应用，构建完整的RAG技术学习体系
2. **理论与实践并重**：每个章节
```

---

**Machine-readable endpoints**

- JSON: [`/api/graphcanon/tools/datawhalechina-all-in-rag`](/api/graphcanon/tools/datawhalechina-all-in-rag)
- LLM index: [/llms.txt](/llms.txt)
- Full corpus: [/llms-full.txt](/llms-full.txt)

_GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_