RAGAS

Automatic RAG Evaluation System - a framework for assessing RAG system quality through automated evaluation of retrieval relevance and generation accuracy without human labels.

Comprehensive overview of frameworks and tools for evaluating RAG systems including RAGAS, TruLens, LangSmith, and ARES with metrics for retrieval quality, generation accuracy, and end-to-end performance.

Comprehensive LLM evaluation framework offering 50+ ready-to-use metrics for RAG, agents, and chatbots, featuring G-Eval for custom criteria and multi-turn conversation evaluation with human-like accuracy.

Industry-standard metrics for evaluating Retrieval-Augmented Generation systems, including Answer Relevancy, Faithfulness, Context Relevance, Context Recall, and Context Precision to ensure quality and reliability.

RAG Assessment framework for Python providing reference-free evaluation of RAG pipelines using LLM-as-a-judge, measuring context relevancy, context recall, faithfulness, and answer relevancy with automatic test data generation.

RAG evaluation metric assessing retriever's ability to rank relevant chunks higher than irrelevant ones, measuring context relevance and ranking quality for optimal retrieval.