LLM-driven Data Service Discovery in the Internet of Production

The Internet of Services paradigm promotes using data services to accomplish diverse analytics tasks, enhancing collaboration amongst the actors of a production network. While domain experts and R&D managers are familiar with the observed system and can identify the data needed for analytics and digital innovation purposes, IT specialists typically handle data service discovery and their combination into analytics pipelines. Recently, Large Language Models (LLMs) have been recognised as valuable tools to support domain experts and R&D managers in specifying service needs and designing preliminary analytics pipelines, which can then be implemented by IT specialists, thus bridging the gap between domain and technical expertise. However, constructing effective prompts tailored to domain experts for interacting with LLM-based systems still demands advanced technical skills, as well as extensive knowledge of the catalog of available services and how they can be combined into analytics pipelines. To address this challenge, we propose an LLM-based approach for data service discovery within the Internet of Production context, which leverages Retrieval-Augmented Generation (RAG) applied to a catalog of atomic data services and relevant analytics pipelines. Preliminary experiments evaluating the effectiveness of the approach were conducted in a real-world case study within a Smart Factory research project.