RAG-IoE: IoT context-aware information retrieval with Large Language Models in Industry 5.0

Human-centric design, intelligence, and seamless interconnectivity are key pillars of the Industry 5.0. A critical challenge in these scenarios is the efficient retrieval of relevant, context-aware information for workers within Internet of Everything (IoE) networks. Traditional information retrieval techniques struggle with the heterogeneous, dynamic data generated in industrial settings. To address this, we define a context-aware data model for IoE scenarios, on top of which we propose RAG-IoE, a novel Retrieval-Augmented Generation (RAG) solution to enable adaptive, scalable, and context-based information retrieval from both structured and unstructured data sources. Our approach organizes IoE data within a semantic framework, integrating hybrid retrieval methods. It combines structured search on a Knowledge Graph with unstructured data retrieval using embeddings stored in a vector database, followed by LLM-driven reasoning to refine results. This architecture enhances decision-making, reduces cognitive overload, and ensures precise guidance for industrial operators. We validate the efficiency and effectiveness of RAG-IoE using a novel dataset through both a user study and quantitative analysis, demonstrating its potential to optimize human-machine collaboration in Industry 5.0 environments.