rageval and RAG-evaluation-harnesses
These are competitors, as both repositories provide an evaluation suite specifically designed for Retrieval-Augmented Generation (RAG) methods, making them alternative choices for the same purpose.
About rageval
gomate-community/rageval
Evaluation tools for Retrieval-augmented Generation (RAG) methods.
This tool helps evaluate the performance of your Retrieval-Augmented Generation (RAG) systems. It takes the outputs from various stages of your RAG pipeline—like rewritten queries, retrieved documents, and generated answers—and provides comprehensive scores on how well your system is performing across aspects like answer correctness, factual consistency, and document relevance. It is designed for AI/ML engineers or researchers building and refining RAG-based 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.
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