VectorInstitute/fed-rag

A framework for fine-tuning retrieval-augmented generation (RAG) systems.

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Established

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.

141 stars. Available on PyPI.

Use this if you are a machine learning engineer or researcher looking to fine-tune your RAG systems for better performance, especially when dealing with distributed or sensitive datasets.

Not ideal if you are an end-user simply looking to deploy an off-the-shelf RAG application or if you don't have experience with machine learning model development.

Machine Learning Engineering Generative AI Federated Learning Natural Language Processing AI System Optimization

Maintenance 10 / 25

Adoption 10 / 25

Maturity 25 / 25

Community 20 / 25

How are scores calculated?

Stars

141

Forks

Language

Python

License

Apache-2.0

Compare

fed-rag and RAG_Techniques fed-rag and FlexRAG fed-rag and RAGHub fed-rag and RAGLAB

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