ChatEED: An agentic retrieval assistant for accelerator operators
Abstract
Scientists and operators at SLAC National Accelerator Laboratory rely on electronic logbooks (ELOGs) to record and share critical information surrounding accelerator operations. However, since creating log entries is time-consuming and complex, they are often brief, incomplete, filled with jargon, and inconsistently structured. 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. Therefore, we introduce ChatEED, a novel agentic retrieval-augmented generation (RAG) system that is operator-centric and 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.CCS Concepts• Information systems → Question answering; Summarization; Information extraction.
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