Structured Metadata Modeling and Usage in Retrieval-Augmented Generation for Data From Electrical Drive Bench Testing

Abstract

In electrical drive bench testing, a large volume of measurement data and unstructured document data are being generated, but are still underutilized in data intelligence. With continuous iterative development, Retrieval-Augmented Generation (RAG) based on Large Language Models provides a powerful automated data integration solution for data with multiple modalities. However, the particular irregularity and heterogeneity of e-drive bench test data poses challenges to the practical implementation of an effective RAG solution in this specific domain and requires practice-oriented adaptations of RAG components and strategies. To address these challenges, the role of metadata in advanced retrieval strategies is investigated. A set-theoretic framework for structuring metadata in this industry vertical domain is proposed. Classical RAG components that incorporate extracted metadata are evaluated to identify the optimal approach for practical e-drive bench test data retrieval scenarios.