GAIA: A General AI Assistant for Intelligent Accelerator Operations

TL;DR

GAIA leverages a large language model combined with a retrieval system and control tools to assist operators in managing complex particle accelerators more efficiently and accurately.

Contribution

This work introduces GAIA, a novel AI assistant that integrates LLMs with machine control and knowledge retrieval for accelerator operations.

Findings

Enhanced operator support in complex machine management

Improved speed and accuracy in operation tasks

Facilitated knowledge access and automation

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.