RAGHub and RAGLAB
About RAGHub
Andrew-Jang/RAGHub
A community-driven collection of RAG (Retrieval-Augmented Generation) frameworks, projects, and resources. Contribute and explore the evolving RAG ecosystem.
This is a living directory of tools, frameworks, and resources for Retrieval-Augmented Generation (RAG). It helps you navigate the rapidly changing landscape of RAG by providing a curated list of new and emerging solutions. You'll find frameworks for building RAG applications, evaluation tools, and data preparation frameworks. Developers and AI engineers who are building or evaluating RAG systems would use this to stay informed and choose appropriate tools.
About RAGLAB
fate-ubw/RAGLAB
[EMNLP 2024: Demo Oral] RAGLAB: A Modular and Research-Oriented Unified Framework for Retrieval-Augmented Generation
This project helps researchers and developers evaluate and compare different Retrieval-Augmented Generation (RAG) algorithms for large language models. It takes in various RAG algorithms and benchmark datasets, then outputs comprehensive evaluation results. It is ideal for AI researchers, NLP scientists, and machine learning engineers who need to understand, reproduce, and extend state-of-the-art RAG techniques.
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