fate-ubw/RAGLAB

[EMNLP 2024: Demo Oral] RAGLAB: A Modular and Research-Oriented Unified Framework for Retrieval-Augmented Generation

/ 100

Emerging

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.

310 stars. No commits in the last 6 months.

Use this if you are developing or researching new RAG algorithms and need a robust framework for fair comparison, evaluation, and reproduction against existing methods.

Not ideal if you are looking for a simple, out-of-the-box RAG solution for immediate application development without deep algorithmic research.

AI research NLP development Generative AI Language model evaluation Information retrieval

Stale 6m No Package No Dependents

Maintenance 0 / 25

Adoption 10 / 25

Maturity 16 / 25

Community 17 / 25

How are scores calculated?

Stars

310

Forks

Language

Python

License

MIT

Compare

RAGLAB and RAG_Techniques RAGLAB and fed-rag RAGLAB and FlexRAG RAGLAB and RAGHub RAGLAB and MiniRAG RAGLAB and HiRAG RAGLAB and MRAG RAGLAB and RAG-ARC

Higher-rated alternatives

NirDiamant/RAG_Techniques

This repository showcases various advanced techniques for Retrieval-Augmented Generation (RAG)...

VectorInstitute/fed-rag

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

RUC-NLPIR/FlashRAG

⚡FlashRAG: A Python Toolkit for Efficient RAG Research (WWW2025 Resource)

ictnlp/FlexRAG

FlexRAG: A RAG Framework for Information Retrieval and Generation.

Andrew-Jang/RAGHub

A community-driven collection of RAG (Retrieval-Augmented Generation) frameworks, projects, and...

Explore RAG Tools

All categories Trending RAG directory Insights