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@ARTICLE{Mayet:606679,
author = {Mayet, Frank},
title = {{GAIA}: {A} {G}eneral {AI} {A}ssistant for {I}ntelligent
{A}ccelerator {O}perations},
reportid = {PUBDB-2024-01615, arXiv:2405.01359},
year = {2024},
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.},
cin = {MPY1},
cid = {I:(DE-H253)MPY1-20170908},
pnm = {621 - Accelerator Research and Development (POF4-621)},
pid = {G:(DE-HGF)POF4-621},
experiment = {EXP:(DE-H253)ARES-20200101},
typ = {PUB:(DE-HGF)25},
eprint = {2405.01359},
howpublished = {arXiv:2405.01359},
archivePrefix = {arXiv},
SLACcitation = {$\%\%CITATION$ = $arXiv:2405.01359;\%\%$},
url = {https://bib-pubdb1.desy.de/record/606679},
}