fed-rag and FlexRAG
These are competitors offering different approaches to RAG system development—fed-rag emphasizes fine-tuning existing RAG architectures while FlexRAG provides a flexible framework for building information retrieval and generation pipelines from scratch.
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.
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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