RAG_Techniques and fed-rag
Fed-RAG provides a federated fine-tuning framework for optimizing RAG systems, while RAG_Techniques is an educational repository demonstrating implementation patterns—making them **complements** where techniques from the latter could inform the training approaches in 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 fed-rag
VectorInstitute/fed-rag
A framework for fine-tuning retrieval-augmented generation (RAG) systems.
This is a framework for developers and machine learning engineers to improve the accuracy and relevance of their Retrieval-Augmented Generation (RAG) systems. It helps fine-tune these systems to produce better responses by integrating external data sources, whether that data is stored centrally or distributed across different locations. Users provide their RAG models and data, and the framework outputs an enhanced RAG system.
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