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all-in-rag
datawhalechina/all-in-rag
🔍大模型应用开发实战一：RAG 技术全栈指南，在线阅读地址：https://datawhalechina.github.io/all-in-rag/
★10.2kstars
Python
Updated: Today
📋 Project at a Glance
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What's this?A open-source Data & Infrastructure project, built with Python, focusing on deepseek and embedding
Who made it?Maintained by datawhalechina team, 10.2K⭐ on GitHub, #194 out of 3133 in Data & Infrastructure
Why does it exist?The datawhalechina team recognized that existing deepseek tools in Data & Infrastructure were hard to use. all-in-rag was designed to make embedding more accessible.
What can it do?Key use cases: kimi-k2, langchain, llama-index
How to install with AI?Use an AI coding assistant (Claude Code, Cursor, Copilot) to automatically set up pip dependencies and virtual env. Follow the README — the AI handles the rest.
🔗 github.com/datawhalechina/all-in-rag | 官网 https://datawhalechina.github.io/all-in-rag/
🔗 github.com/datawhalechina/all-in-rag | 官网 https://datawhalechina.github.io/all-in-rag/
Topics
aideepseekembeddingkimi-k2langchainllama-indexllmmilvusmultimodalneo4jpythonrag