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DTSTART;TZID=America/Chicago:20251116T163000
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UID:submissions.supercomputing.org_SC25_sess209_ws_fgai125@linklings.com
SUMMARY:ChatEED: An agentic retrieval assistant for accelerator operators
DESCRIPTION:Aaron Reed, Claudio Bisegni, Sandesh Shrestha, Michelle Huang,
  and Daniel Ratner (SLAC National Accelerator Laboratory)\n\nScientists an
 d operators at SLAC National Accelerator Laboratory rely on electronic log
 books (ELOGs) to record and share information surrounding accelerator oper
 ations. However, since creating log entries is time-consuming and complex,
  they are often brief, incomplete, jargonized, and inconsistent. With thou
 sands of records spanning decades, this makes it difficult for operators t
 o search for and interpret information. Through interviews with operators,
  we identified two critical gaps: the lack of automated shift summarizatio
 n and the difficulty of real-time ELOG information retrieval. We introduce
  ChatEED, a novel agentic retrieval-augmented generation (RAG) system that
  addresses these two needs while also prioritizing security, modularity, e
 fficiency, and transparency. In this paper, we analyze the operator needs 
 and workflow that guide the system design, detail the system architecture 
 and deployment, and outline future directions for expansion and evaluation
 . This ongoing work demonstrates the potential for AI systems to improve c
 ontinuity, communication, and efficiency in high-performance science facil
 ities.\n\nRecording: Livestreamed, Recorded\n\nRegistration Category: Tech
 nical Program Reg Pass, Workshop Reg Pass\n\nSession Chairs: Charlie Catle
 tt (Argonne National Laboratory (ANL), University of Chicago); Javier Aula
 -Blasco (Barcelona Supercomputing Center (BSC)); Valerie Taylor (Argonne N
 ational Laboratory (ANL), University of Chicago); Laura Morselli (CINECA);
  and Rio Yokota (Institute of Science Tokyo)\n\n
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