Workshops Archive

ChatEED: An agentic retrieval assistant for accelerator operators

Workshop: Frontiers in Generative AI for HPC Science and Engineering: Foundations, Challenges, and Opportunities

Authors: Aaron Reed, Claudio Bisegni, Sandesh Shrestha, Michelle Huang, and Daniel Ratner (SLAC National Accelerator Laboratory)

Abstract: Scientists and operators at SLAC National Accelerator Laboratory rely on electronic logbooks (ELOGs) to record and share information surrounding accelerator operations. However, since creating log entries is time-consuming and complex, they are often brief, incomplete, jargonized, and inconsistent. With thousands of records spanning decades, this makes it difficult for operators to search for and interpret information. Through interviews with operators, we identified two critical gaps: the lack of automated shift summarization 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, efficiency, 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 continuity, communication, and efficiency in high-performance science facilities.

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