The rapid evolution of digital sports media necessitates sophisticated information retrieval systems that can efficiently parse extensive multimodal datasets. This paper demonstrates SoccerRAG, an innovative framework designed to harness the power of Retrieval Augmented Generation (RAG) and Large Language Models (LLMs) to extract soccer-related information through natural language queries. By leveraging a multimodal dataset, SoccerRAG supports dynamic querying and automatic data validation, enhancing user interaction and accessibility to sports archives. We present a novel interactive user interface (UI) based on the Chainlit framework which wraps around the core functionality, and enable users to interact with the SoccerRAG framework in a chatbot-like visual manner.

翻译：数字体育媒体的快速发展，要求信息检索系统能够高效解析海量多模态数据集。本文演示了SoccerRAG，这是一个创新框架，旨在利用检索增强生成（RAG）和大语言模型（LLMs）的能力，通过自然语言查询提取足球相关信息。通过利用多模态数据集，SoccerRAG支持动态查询和自动数据验证，从而增强了用户交互性及对体育档案的可访问性。我们提出了一种基于Chainlit框架的新型交互式用户界面（UI），该界面封装了核心功能，使用户能够以类似聊天机器人的可视化方式与SoccerRAG框架进行交互。