Ragas is an evaluation framework and performance benchmark designed to quantify the quality of retrieval augmented generation pipelines. It functions as an application optimizer to identify bottlenecks in language model workflows using automated metrics and model-based scoring.
The framework includes a system for generating synthetic datasets that mimic production scenarios and edge cases to create realistic test cases. It enables reference-free assessment, allowing the evaluation of response quality by analyzing grounding in the provided context without requiring gold-standard labels.
The system covers several analytical areas, including retrieval quality assessment, model accuracy measurement, and the optimization of application performance through the analysis of live usage data.