This best practice document focuses on a demo application that integrates ThingWorx with ChatGPT-4o. We explain the components of the application and how they collectively facilitate communication between ThingWorx and the LLM through an API. Ultimately natural language questions from the user are answered in a similar fashion. Although our example uses a specific LLM service, the application and concepts it illustrates can be easily adapted to work with other data stores and other LLMs.
The demo data used by the application is stored in aThingWorx Data Table, for the sake of easy importing. The process covers extracting data from the Data Table, preparing it, and creating prompts that let ThingWorx talk with the LLM to deliver useful, natural language answers. The methods outlined here are flexible. Even if your data comes from ThingWorx Value Streams, historians, or other databases, the overall approach remains the same. You simply extract the relevant data, add context, and feed it into the LLM. Likewise, if you choose a different LLM service, the main steps—API communication, data transformation, and response checking—will still apply with only minor changes.
A specific context for using ThingWorx and LLMs is for Statistical Process Control (SPC). In many factories, SPC is used to track when processes go "out of control" over time. With this setup, you can ask questions like, “What were the out-of-control events for Machine 5 last month?” The system can then pull together historical and real-time SPC data to show when and where rules were violated. This helps teams quickly spot problems and make improvements.
David Kessler
Brad Cline
Users are solely responsible for the use of and reliance on the demo application and the accompanying document which integrates ThingWorx with ChatGPT-4o, a generative Artificial Intelligence (AI) tool. The project file for the demo application is provided for demonstration purposes only and should not be used in production without thorough testing.
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