Enhancing Operations at Col-CC by Utilizing LLMs, KGs, and RAG

Abstract

This poster presents a hybrid system, which combines Large Language Models (LLMs) with Knowledge Graphs (KGs) and Retrieval-Augmented Generation (RAG) to enhance the operational efficiency of the flight control team at the Columbus Control-Center (Col-CC). Col-CC is responsible for the operations of the Columbus module of the International Space Station (ISS), and is part of German Aerospace Center's (DLR e.V.) German Space Operations Center (GSOC). LLMs have demonstrated a remarkable capacity to comprehend and produce human-like text, positioning themselves as an effective and efficient solution for automating routine tasks and delivering real-time support. However, their effectiveness can be constrained by a lack of domain-specific knowledge and the need for accurate, up-to-date information. To address these limitations, we propose a combination of LLMs with KGs and RAG. KGs offer a structured representation of domain-specific information, enabling more effective access to and utilization of specialized knowledge, while RAG enhances LLMs by retrieving relevant documents and data snippets, ensuring that the generated responses are grounded in current information. By leveraging the strengths of LLMs, KGs, and RAG, this approach aims to create a more intelligent and responsive support system for space missions, ultimately contributing to the safety and success of ISS and Columbus operations.