RAG_Techniques and raggenie
The first is an educational reference implementation of RAG techniques and patterns, while the second is a production-oriented low-code platform for deploying RAG applications—making them complementary resources where one teaches concepts and the other operationalizes them.
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 raggenie
sirocco-ventures/raggenie
RAGGENIE: An open-source, low-code platform to build custom Retrieval-Augmented Generation (RAG) Copilets with your own data. Simplify AI development with ease!
This helps non-technical professionals build custom AI assistants or chatbots to answer questions using their own business data. You input your company's documents, website content, or database information, and it produces a conversational AI that can provide relevant answers. This is for business users, content managers, or team leads who want to deploy internal knowledge bots or customer support assistants.
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