RAGHub is a community-driven directory cataloging the rapidly expanding landscape of Retrieval-Augmented Generation (RAG) tools, frameworks, and resources. It aims to help developers and researchers navigate the evolving RAG ecosystem by providing a curated list of projects, their descriptions, and relevant links, fostering a centralized knowledge base for this fast-moving field.

How It Works

RAGHub functions as a curated, community-contributed list. It categorizes RAG-related projects into distinct areas such as Frameworks, Evaluation and Optimization, Engines, Data Preparation, and specific Projects. Each entry includes a brief description, website, GitHub repository link, and recent activity, allowing users to quickly assess the relevance and maturity of different RAG solutions.

Quick Start & Requirements

This repository is a directory and does not require installation or execution. It serves as a reference guide to other RAG projects.

Highlighted Details

• Comprehensive categorization of RAG tools into frameworks, evaluation, engines, data preparation, and projects.
• Includes links to GitHub repositories, project websites, and recent activity for each listed tool.
• Features a dedicated section for RAG Resources and Sites, including articles and leaderboards for model evaluation.
• Actively maintained with frequent updates reflecting the fast-paced development in the RAG space.

Maintenance & Community

RAGHub is a community-driven project associated with the r/RAG community. Contributions are welcomed via their contribution guidelines. A Discord server is available for discussion and feedback.

Licensing & Compatibility

This project is licensed under the MIT License, permitting commercial use and integration into closed-source projects.

Limitations & Caveats

As a curated directory, RAGHub relies on community contributions for accuracy and completeness. The rapid pace of RAG development means the directory may not always be perfectly up-to-date with the very latest emerging tools.