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About Ragas

Ragas is an open-source framework designed for evaluating the performance and robustness of Large Language Model (LLM) applications, particularly those leveraging Retrieval Augmented Generation (RAG) systems. It provides a suite of automatic metrics to assess various aspects of RAG quality, such as faithfulness, answer relevancy, context precision, and context recall. This allows developers to understand how well their LLM applications are performing and identify areas for improvement. The platform also offers the capability to synthetically generate high-quality and diverse evaluation data tailored to specific application requirements. This addresses the challenge of acquiring sufficient and relevant test data for LLM evaluation. Furthermore, Ragas supports online monitoring, enabling continuous evaluation of LLM applications in production environments to ensure ongoing quality and provide actionable insights for iterative enhancement. It is built by a team with expertise in applied AI research and is integrated with popular LLM frameworks like LlamaIndex and LangChain.