Abstract
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.
Abstract (translated)
大规模的机器通常由经验丰富的操作员团队运行。在粒子加速器的情况下，这些操作员对加速器物理和机器的组成部分都具有适当的背景知识。由于机器的复杂性，特别关注机器的子系统由专家处理，操作员可以向他们寻求帮助。在这项工作中，使用了推理和动作（ReAct）提示模式将带有开放标签的大型语言模型（LLM）与高级机器控制系统框架和其他工具（例如电子日志或机器设计文档）相结合。通过这样做，实现了一个多专家检索增强生成（RAG）系统，该系统有助于操作员在知识检索任务中查找信息，在需要时直接与机器交互，或编写高级控制系统脚本。这一专家知识和机器交互的整合可以简化并加速新经验和经验丰富的操作员的机器操作任务。
URL
https://arxiv.org/abs/2405.01359
