MiniRAG and RAGLAB
About MiniRAG
HKUDS/MiniRAG
"MiniRAG: Making RAG Simpler with Small and Open-Sourced Language Models"
This tool helps you quickly get accurate answers to complex questions from your own documents, even when using smaller, more efficient AI models. You provide your text data, and it processes it into a structured knowledge base, then uses that to generate precise responses. It's designed for anyone who needs to build an efficient question-answering system without relying on very large, expensive AI models.
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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