Talking like Piping and Instrumentation Diagrams (P&IDs)

TL;DR

This paper introduces a method for natural language communication with P&IDs by representing them as graph-based knowledge structures integrated with Large Language Models, enhancing understanding and reducing errors.

Contribution

The paper presents a novel pipeline combining DEXPI-based graph representations with LLMs for natural language interaction with P&IDs, including a new Python tool for graph generation.

Findings

Enables natural language queries on P&IDs.

Improves contextual understanding and reduces hallucinations in LLMs.

Facilitates AI-assisted engineering tasks.

Abstract

We propose a methodology that allows communication with Piping and Instrumentation Diagrams (P&IDs) using natural language. In particular, we represent P&IDs through the DEXPI data model as labeled property graphs and integrate them with Large Language Models (LLMs). The approach consists of three main parts: 1) P&IDs are cast into a graph representation from the DEXPI format using our pyDEXPI Python package. 2) A tool for generating P&ID knowledge graphs from pyDEXPI. 3) Integration of the P&ID knowledge graph to LLMs using graph-based retrieval augmented generation (graph-RAG). This approach allows users to communicate with P&IDs using natural language. It extends LLM's ability to retrieve contextual data from P&IDs and mitigate hallucinations. Leveraging the LLM's large corpus, the model is also able to interpret process information in PIDs, which could help engineers in their daily tasks. In the future, this work will also open up opportunities in the context of other generative Artificial Intelligence (genAI) solutions on P&IDs, and AI-assisted HAZOP studies.