  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.
