RAGVUE：一种用于可解释和自动评估检索增强生成的诊断视图
RAGVUE: A Diagnostic View for Explainable and Automated Evaluation of Retrieval-Augmented Generation
AI总结：
RAGVUE通过分解RAG行为并提供结构化解释，实现无参考的自动评估，揭示RAG流程中的细粒度失败。
摘要：
评估检索增强生成（RAG）系统仍是一项具有挑战性的任务：现有的度量标准往往将异质行为压缩成单一分数，并且提供很少的洞察力，无法确定错误是否源于检索、推理或基础。在本文中，我们引入了RAGVUE，一种用于自动、无参考评估RAG流程的诊断和可解释框架。RAGVUE将RAG行为分解为检索质量、答案相关性和完整性、严格断言层面的忠实度以及判断校准。每个指标均包含结构化的解释，使评估过程透明。我们的框架支持手动度量选择和完全自动化的代理评估。它还提供了一个Python API、CLI以及本地Streamlit接口用于交互式使用。在比较实验中，RAGVUE揭示了现有工具如RAGAS常忽视的细粒度失败。我们展示了完整的RAGVUE工作流程，并说明了如何将其集成到研究流程和实际RAG开发中。源代码和详细的使用说明已公开在GitHub上。
英文摘要：
Evaluating Retrieval-Augmented Generation (RAG) systems remains a challenging task: existing metrics often collapse heterogeneous behaviors into single scores and provide little insight into whether errors arise from retrieval,reasoning, or grounding. In this paper, we introduce RAGVUE, a diagnostic and explainable framework for automated, reference-free evaluation of RAG pipelines. RAGVUE decomposes RAG behavior into retrieval quality, answer relevance and completeness, strict claim-level faithfulness, and judge calibration. Each metric includes a structured explanation, making the evaluation process transparent. Our framework supports both manual metric selection and fully automated agentic evaluation. It also provides a Python API, CLI, and a local Streamlit interface for interactive usage. In comparative experiments, RAGVUE surfaces fine-grained failures that existing tools such as RAGAS often overlook. We showcase the full RAGVUE workflow and illustrate how it can be integrated into research pipelines and practical RAG development. The source code and detailed instructions on usage are publicly available on GitHub
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
