ytt070666/OmniForge-AI

Agentic-RAG explores advanced Retrieval-Augmented Generation systems enhanced with AI LLM agents.

A Low-Code MCP Framework for Building Complex and Innovative RAG Pipelines

Muffakir is a local-first RAG experimentation and optimization framework designed to help developers build, evaluate, compare, and improve RAG pipelines on their own data.

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。

A modular Agentic RAG built with LangGraph — learn Retrieval-Augmented Generation Agents in minutes.

A community-driven collection of RAG (Retrieval-Augmented Generation) frameworks, projects, and resources. Contribute and explore the evolving RAG ecosystem.

一个按路线图逐步实现的 RAG 学习项目：从 Naive RAG → Advanced RAG → Agentic RAG → GraphRAG & Fine-tuning → RAGOps，在本地用 LangChain + 向量库把“文档问答”跑通，并逐步加入语义分块、混合检索、重排序、路由、自反思与工具调用等能力。 从最简单的 Demo 进化到企业级、甚至科研级的 RAG 系统。

RAG (Retrieval Augmented Generation) Framework for building modular, open source applications for production by TrueFoundry

Retrieval-Augmented Generation in 3 Lines of Code!