XRAG and RAG-evaluation-harnesses
About XRAG
DocAILab/XRAG
XRAG: eXamining the Core - Benchmarking Foundational Component Modules in Advanced Retrieval-Augmented Generation
This project helps developers and researchers evaluate different components of Retrieval-Augmented Generation (RAG) systems. It takes various RAG configurations, such as different retrievers, embeddings, and Large Language Models, and outputs performance metrics and visualizations. The primary users are AI/ML engineers and researchers building or optimizing RAG applications.
About RAG-evaluation-harnesses
RulinShao/RAG-evaluation-harnesses
An evaluation suite for Retrieval-Augmented Generation (RAG).
This project helps evaluate how well your Retrieval-Augmented Generation (RAG) system performs on various question-answering tasks. You provide your RAG model's retrieved documents and the questions, and it outputs performance scores. This tool is for researchers, developers, or MLOps engineers who are building and fine-tuning RAG systems and need to rigorously benchmark their effectiveness.
Related comparisons
Scores updated daily from GitHub, PyPI, and npm data. How scores work