RAG_Techniques and FlexRAG
FlexRAG is a production-ready RAG implementation framework, while RAG_Techniques is an educational repository of RAG methodologies and approaches—making them complementary resources where practitioners learn techniques from the latter and implement them using the former.
About RAG_Techniques
NirDiamant/RAG_Techniques
This repository showcases various advanced techniques for Retrieval-Augmented Generation (RAG) systems. RAG systems combine information retrieval with generative models to provide accurate and contextually rich responses.
This project helps developers and AI practitioners enhance the accuracy and contextual richness of their RAG (Retrieval-Augmented Generation) systems. It provides advanced techniques for improving how AI models retrieve information and generate responses. Users input their existing RAG system components and learn how to apply cutting-edge methods to get more relevant and comprehensive AI-generated outputs.
About FlexRAG
ictnlp/FlexRAG
FlexRAG: A RAG Framework for Information Retrieval and Generation.
This is a tool for AI researchers and developers who are building Retrieval-Augmented Generation (RAG) systems. It helps quickly reproduce, develop, and evaluate RAG systems, taking various data types like text, images, and web content as input and producing enhanced generative AI models. It's designed for those who need to experiment with different RAG approaches and share their findings efficiently.
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