The rapid expansion of space activities has led to an unprecedented accumulation of technical documentation, operational guidelines, and scientific literature, creating challenges for timely decision-making in space operations. Effective management in space operations requires tools capable of efficiently processing vast and heterogeneous information sources. This paper systematically evaluates the performance of Retrieval Augmented Generation (RAG) pipelines, combining Large Language Models (LLMs) with information retrieval techniques for extracting and synthesizing actionable knowledge from domain-specific documents. We compare various retrieval strategies, embedding models, and LLM answers to assess their impact on information accuracy, relevance, and reliability. Our results demonstrate that RAG pipelines can significantly enhance knowledge access, reduce uncertainty, and support decision-making in complex space operations.

翻译：空间活动的快速扩展导致技术文档、操作指南及科学文献以前所未有的速度累积，为空间任务中的实时决策带来了挑战。空间任务的有效管理需借助能够高效处理海量异构信息源的工具。本文系统评估了检索增强生成（RAG）管线的性能，该管线结合了大语言模型（LLMs）与信息检索技术，用于从领域专业文档中提取并综合可操作知识。我们通过比较多种检索策略、嵌入模型及大语言模型输出结果，评估其对信息准确性、相关性与可靠性的影响。结果表明，RAG管线能够显著提升知识获取效率、降低不确定性，并支持复杂空间任务中的决策过程。