RAGAS

RAGAS is an open-source framework specifically designed for evaluating the performance of Retrieval-Augmented Generation (RAG) systems. It provides a comprehensive set of evaluation metrics and tools to help developers understand and improve various aspects of RAG systems, including retrieval quality, answer relevance, context precision, and more.

The core philosophy of RAGAS is to provide a standardized evaluation framework that makes the evaluation process of RAG systems more systematic and comparable. It not only focuses on the quality of final generated answers but also evaluates the effectiveness of the retrieval process and the quality of intermediate results, helping developers identify bottlenecks and improvement points in their systems.

Compared to traditional evaluation methods, RAGAS particularly focuses on specific challenges of RAG systems, such as hallucination (generating information not present in retrieved content), retrieval relevance, and answer completeness. This makes it an important tool for RAG developers, helping them build more reliable and efficient systems.