RAG-Survey facilitates comprehensive exploration of Retrieval-Augmented Generation (RAG) by aggregating publicly available knowledge. It aims to streamline access to academic papers, benchmarks, datasets, and tools, resolving inefficiencies found in traditional survey paper organization. The repository offers a dynamically updated view of the RAG landscape, supporting customized analysis and summarization.
OpenRAG Base provides a highly flexible and intuitive platform for RAG knowledge, moving beyond static lists. The database structure enables efficient analysis and summarization of RAG resources. It offers a user-friendly interface, utilizing Notion's database features for organization and search. It provides a dynamic overview of RAG developments, updating regularly with new resources.
- Comprehensive Resource Aggregation: Collects academic papers, benchmarks, datasets, and tools related to RAG.
- Dynamic and Updated Content: Regularly updated with the latest information in the field.
- Flexible Database Structure: Utilizes Notion databases with linked relations for enhanced data organization and analysis.
- User-Friendly Interface: Leverages Notion's interface with filtering, sorting, and custom views for easy navigation.
- Detailed Paper Pages: Provides detailed pages for each paper with key information and resources.
- Integrated Tools: Offers links to resources like Papers.cool and Kimi Chat for enhanced paper reading and understanding.
- Duplicate Functionality: Allows users to duplicate the entire knowledge base or individual pages for local use.
The project is actively maintained with recent updates and new additions, including the introduction of OpenRAG Base. Regular updates and the inclusion of a dedicated News section indicate ongoing development. The use of Notion as a platform contributes to its collaborative and accessible nature. A growing number of stars and forks demonstrate community interest and potential.
RAG-Survey benefits researchers, practitioners, and anyone interested in understanding RAG. It addresses the need for a more efficient and flexible way to access and analyze RAG resources, supporting tasks like comparing papers, identifying open-sourced code, and tracking conference publications. By providing a curated and dynamic knowledge base, it simplifies the exploration of this rapidly evolving field and delivers value over manual searching and disparate information sources.