Towards Unlocking Insights from Logbooks Using AI

TL;DR

This paper investigates using a tailored Retrieval Augmented Generation (RAG) model to improve the usability and insight extraction from technical logbooks of particle accelerators, enhancing automation and analysis capabilities.

Contribution

It introduces a novel RAG-based approach tailored for technical logbooks, demonstrating its potential to unlock insights and improve data accessibility in accelerator facilities.

Findings

RAG model effectively retrieves relevant logbook information.

Enhanced usability and analysis of logbooks demonstrated.

Potential for automation and macro-analysis of accelerator data.

Abstract

Electronic logbooks contain valuable information about activities and events concerning their associated particle accelerator facilities. However, the highly technical nature of logbook entries can hinder their usability and automation. As natural language processing (NLP) continues advancing, it offers opportunities to address various challenges that logbooks present. This work explores jointly testing a tailored Retrieval Augmented Generation (RAG) model for enhancing the usability of particle accelerator logbooks at institutes like DESY, BESSY, Fermilab, BNL, SLAC, LBNL, and CERN. The RAG model uses a corpus built on logbook contributions and aims to unlock insights from these logbooks by leveraging retrieval over facility datasets, including discussion about potential multimodal sources. Our goals are to increase the FAIR-ness (findability, accessibility, interoperability, and reusability) of logbooks by exploiting their information content to streamline everyday use, enable macro-analysis for root cause analysis, and facilitate problem-solving automation.