blackinkkkxi/RAG_langchain

Everything you need to know to build your own RAG application

RAGs: Simple implementations of Retrieval Augmented Generation (RAG) Systems

Demystify RAG by building it from scratch. Local LLMs, no black boxes - real understanding of embeddings, vector search, retrieval, and context-augmented generation.

本仓库囊括了 RAG 中各技术点的学习案例。包括各种调优方法示例：chunk / query enhancement / retrieval / rerank / compression； 可靠性增强：相关性过滤、groundedness 检查、证据高亮；高级架构：RAPTOR / GraphRAG / Self-RAG / CRAG / MemoRAG 等。

A collection of some RAG tutorials where i share what i learned in my own way.

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。

This is a repository for the course "From Beginner to LLM Developer" by Towards AI.