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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},
}