liu673/rag-all-techniques

Implementation of all RAG techniques in a simpler way（以简单的方式实现所有 RAG 技术）

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Established

This project provides practical, framework-agnostic implementations of various advanced Retrieval Augmented Generation (RAG) techniques. It takes unstructured text data, applies different methods for breaking it down and enriching it, and then uses a large language model to generate improved, contextually relevant answers to user queries. This is for AI practitioners, researchers, or anyone building custom question-answering systems who wants to understand and experiment with core RAG components.

453 stars. No commits in the last 6 months.

Use this if you need to deeply understand and implement different RAG techniques from scratch to build highly accurate and robust AI-powered question-answering systems.

Not ideal if you prefer using high-level frameworks like LangChain or FAISS for rapid prototyping without needing to delve into the underlying mechanics.

AI-powered question-answering information retrieval natural language processing text analytics knowledge management

Stale 6m No Package No Dependents

Maintenance 2 / 25

Adoption 10 / 25

Maturity 15 / 25

Community 24 / 25

How are scores calculated?

Stars

453

Forks

114

Language

Jupyter Notebook

License

MIT

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rag-all-techniques and advanced-rag rag-all-techniques and RAG rag-all-techniques and RAG-Arena rag-all-techniques and RAG-for-LLMs-demo

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