Speaker
Description
RAON uses EPICS as the primary framework for inte-grating local control systems into the central control sys-tem. Technical notes for control system installation and management are documented in LaTeX format and man-aged with relevant code and files in a Git-based configura-tion management environment on an isolated internal network. As part of efforts to introduce AI technologies into the control infrastructure, this study examines the applicability of Retrieval-Augmented Generation (RAG) to control system technical documents.
LaTeX documents were converted into formats such as Markdown under multiple preprocessing scenarios with different document representations and chunk segmenta-tion conditions. Different embedding models were applied to construct vector databases. Relevant queries were de-signed, and performance was compared in terms of re-trieval accuracy and contextual relevance. This study aims to evaluate the feasibility of applying RAG to EPICS technical documents and to suggest directions for build-ing a retrieval framework for LaTeX-based documentation. BGE-M3 with Structured LaTex and heading-based chunking achieved the best retrieval performance, with an MRR of 0.647 and Hit@5 of 0.777
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