Large-scale machines like particle accelerators are usually run by a team of experienced operators. In case of a particle accelerator, these operators possess suitable background knowledge on both accelerator physics and the technology comprising the machine. Due to the complexity of the machine, particular subsystems of the machine are taken care of by experts, who the operators can turn to. In this work the reasoning and action (ReAct) prompting paradigm is used to couple an open-weights large language model (LLM) with a high-level machine control system framework and other tools, e.g. the electronic logbook or machine design documentation. By doing so, a multi-expert retrieval augmented generation (RAG) system is implemented, which assists operators in knowledge retrieval tasks, interacts with the machine directly if needed, or writes high level control system scripts. This consolidation of expert knowledge and machine interaction can simplify and speed up machine operation tasks for both new and experienced human operators.

翻译：大型机器如粒子加速器通常由经验丰富的操作团队运行。以粒子加速器为例，这些操作人员需兼具加速器物理及机器构成技术的相关背景知识。鉴于机器的高度复杂性，其特定子系统由专家负责维护，操作人员可随时向专家求助。本研究采用推理与行动（ReAct）提示范式，将开源大语言模型（LLM）与高级机器控制系统框架及其他工具（如电子日志或机器设计文档）相结合，由此实现了一个多专家检索增强生成（RAG）系统。该系统可辅助操作人员完成知识检索任务，必要时直接与机器交互，或编写高级控制系统脚本。这种将专家知识与机器交互整合的方式，能够简化和加速新老操作人员的机器运维任务。