XGraphRAG: Interactive Visual Analysis for Graph-based Retrieval-Augmented Generation
Ke Wang, Bo Pan, Yingchaojie Feng, Yuwei Wu, Jieyi Chen, Minfeng Zhu, and Wei Chen

TL;DR
XGraphRAG is a visual analysis tool designed to help developers understand and improve graph-based retrieval-augmented generation systems by providing interactive visualizations of the complex pipeline and recall processes.
Contribution
The paper introduces XGraphRAG, a novel visual analysis framework and prototype system that enhances interpretability and debugging of GraphRAG models.
Findings
Effective identification of recall issues in GraphRAG
Improved understanding of GraphRAG pipeline processes
Enhanced debugging and model improvement capabilities
Abstract
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…
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Taxonomy
TopicsMultimodal Machine Learning Applications · Image Retrieval and Classification Techniques · Semantic Web and Ontologies
MethodsLinear Warmup With Linear Decay · Refunds@Expedia|||How do I get a full refund from Expedia? · Attention Dropout · Byte Pair Encoding · Dense Connections · Softmax · Layer Normalization · Dropout · BERT · BART
