  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.
