huangjia2019/rag-in-action

End-to-end RAG system design, evaluation, and optimization. 极客时间RAG训练营，RAG 10大组件全面拆解，4个实操项目吃透 RAG 全流程。RAG的落地，往往是面向业务做RAG，而不是反过来面向RAG做业务。这就是为什么我们需要针对不同场景、不同问题做针对性的调整、优化和定制化。魔鬼全在细节中，我们深入进去探究。

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This project helps AI developers build sophisticated Retrieval-Augmented Generation (RAG) systems. It guides you through integrating your documents into large language models to generate accurate, context-aware responses. You provide raw data like PDFs and get a fully functional RAG application tailored for specific business needs.

654 stars. No commits in the last 6 months.

Use this if you are an AI developer looking to build, evaluate, and optimize an end-to-end RAG system for a specific business application.

Not ideal if you are a business user looking for a ready-to-use RAG application without any development work.

AI Development NLP Engineering Knowledge Retrieval Large Language Models System Integration

No License Stale 6m No Package No Dependents

Maintenance 2 / 25

Adoption 10 / 25

Maturity 8 / 25

Community 25 / 25

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654

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266

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