RAGViz: Diagnose and Visualize Retrieval-Augmented Generation

• URL: http://arxiv.org/abs/2411.01751v1
• Date: Mon, 04 Nov 2024 02:30:05 GMT
• Title: RAGViz: Diagnose and Visualize Retrieval-Augmented Generation
• Authors: Tevin Wang, Jingyuan He, Chenyan Xiong,
• Abstract summary: Retrieval-augmented generation (RAG) combines knowledge from domain-specific sources into large language models. We propose RAGViz, a RAG diagnosis tool that visualizes the attentiveness of the generated tokens in retrieved documents. RAGViz provides two main functionalities: (1) token and document-level attention visualization, and (2) generation comparison upon context document addition and removal.
• Score: 16.91653397201039
• License:
• Abstract: Retrieval-augmented generation (RAG) combines knowledge from domain-specific sources into large language models to ground answer generation. Current RAG systems lack customizable visibility on the context documents and the model's attentiveness towards such documents. We propose RAGViz, a RAG diagnosis tool that visualizes the attentiveness of the generated tokens in retrieved documents. With a built-in user interface, retrieval index, and Large Language Model (LLM) backbone, RAGViz provides two main functionalities: (1) token and document-level attention visualization, and (2) generation comparison upon context document addition and removal. As an open-source toolkit, RAGViz can be easily hosted with a custom embedding model and HuggingFace-supported LLM backbone. Using a hybrid ANN (Approximate Nearest Neighbor) index, memory-efficient LLM inference tool, and custom context snippet method, RAGViz operates efficiently with a median query time of about 5 seconds on a moderate GPU node. Our code is available at https://github.com/cxcscmu/RAGViz. A demo video of RAGViz can be found at https://youtu.be/cTAbuTu6ur4.

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