XGraphRAG通过可视化帮助开发者分析图结构RAG的召回问题。
XGraphRAG: Interactive Visual Analysis for Graph-based Retrieval-Augmented Generation
- 构建交互式可视化框架，追踪图结构RAG全流程
- 支持发现失败案例并定位问题环节，提升可解释性
- 适合需要调试和优化RAG系统的开发者使用
基于图的检索增强生成（GraphRAG）通过引入图作为中间表示，捕捉语料中更结构化的关联知识，显著提升了大语言模型生成结果的精度与全面性。然而，由于图构建与查询过程中涉及复杂的处理流程和大量LLM调用，开发者难以分析GraphRAG在特定数据集上的表现，限制了其可解释性与易用性。本文提出一种可视化分析框架，帮助开发者识别GraphRAG的关键召回问题，并追踪这些召回在管道中的演变过程。基于此框架，我们开发了原型系统XGraphRAG，集成多组交互式可视化工具，辅助用户高效分析，促进故障案例收集与改进机会识别。评估表明该方法有效且实用。相关代码已开源，地址为https://github.com/Gk0Wk/XGraphRAG。
原文摘要 · Abstract (English)
Graph-based Retrieval-Augmented Generation (RAG) has shown great capability in enhancing Large Language Model (LLM)'s answer with an external knowledge base. Compared to traditional RAG, it introduces a graph as an intermediate representation to capture better structured relational knowledge in the corpus, elevating the precision and comprehensiveness of generation results. However, developers usually face challenges in analyzing the effectiveness of GraphRAG on their dataset due to GraphRAG's complex information processing pipeline and the overwhelming amount of LLM invocations involved during graph construction and query, which limits GraphRAG interpretability and accessibility. This research proposes a visual analysis framework that helps RAG developers identify critical recalls of GraphRAG and trace these recalls through the GraphRAG pipeline. Based on this framework, we develop XGraphRAG, a prototype system incorporating a set of interactive visualizations to facilitate users' analysis process, boosting failure cases collection and improvement opportunities identification. Our evaluation demonstrates the effectiveness and usability of our approach. Our work is open-sourced and available at https://github.com/Gk0Wk/XGraphRAG.
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