rag-evaluator and RAG-evaluation-harnesses
These two libraries are competitors, as both provide frameworks for evaluating RAG systems, suggesting a user would likely choose one over the other for their evaluation needs.
About rag-evaluator
AIAnytime/rag-evaluator
A library for evaluating Retrieval-Augmented Generation (RAG) systems (The traditional ways).
This tool helps you check the quality of answers generated by AI systems, especially those that combine information retrieval with text generation (RAG systems). You provide an AI's answer, the original question, and a perfect reference answer, and it tells you how good the AI's answer is. This is ideal for AI developers, researchers, and anyone building or testing conversational AI 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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