AutoRAG and awesome-rag
AutoRAG is a practical evaluation and optimization framework that would benefit from consulting awesome-rag's curated list of RAG techniques and implementations to inform its benchmarking datasets and baseline comparisons.
About AutoRAG
Marker-Inc-Korea/AutoRAG
AutoRAG: An Open-Source Framework for Retrieval-Augmented Generation (RAG) Evaluation & Optimization with AutoML-Style Automation
This tool helps AI developers and researchers find the best Retrieval-Augmented Generation (RAG) pipeline for their specific data and use case. You provide your documents and an evaluation dataset (questions and their correct answers), and AutoRAG automatically tests various RAG components and configurations. The output is an optimized RAG pipeline that performs best for your application.
About awesome-rag
coree/awesome-rag
A curated list of retrieval-augmented generation (RAG) in large language models
This project offers a curated collection of resources on Retrieval-Augmented Generation (RAG) for large language models. It helps researchers and practitioners explore key papers, lectures, and tools related to building more accurate and factual AI systems. You'll find academic papers detailing various RAG techniques, along with supplementary materials like code repositories and tutorials. This is ideal for AI researchers, machine learning engineers, and data scientists looking to deepen their understanding or implement RAG in their projects.
Related comparisons
Scores updated daily from GitHub, PyPI, and npm data. How scores work