GraphRAG for Engineering Diagrams: ChatP&ID Enables LLM Interaction with P&IDs

TL;DR

ChatP&ID introduces a graph-based retrieval framework enabling cost-effective, accurate natural-language interaction with engineering diagrams like P&IDs, significantly reducing costs and improving reliability over raw image processing.

Contribution

This work presents GraphRAG for engineering diagrams, transforming smart P&IDs into knowledge graphs for improved LLM querying and reasoning, reducing costs and increasing accuracy.

Findings

GraphRAG improves accuracy by 18% over raw images.

Token costs are reduced by 85% with graph-based representations.

GPT-5-mini with ContextRAG achieves 91% accuracy at low cost.

Abstract

Large Language Models (LLMs) combined with Retrieval-Augmented Generation (RAG) and knowledge graphs offer new opportunities for interacting with engineering diagrams such as Piping and Instrumentation Diagrams (P&IDs). However, directly processing raw images or smart P&ID files with LLMs is often costly, inefficient, and prone to hallucinations. This work introduces ChatP&ID, an agentic framework that enables grounded and cost-effective natural-language interaction with P&IDs using Graph Retrieval-Augmented Generation (GraphRAG), a paradigm we refer to as GraphRAG for engineering diagrams. Smart P&IDs encoded in the DEXPI standard are transformed into structured knowledge graphs, which serve as the basis for graph-based retrieval and reasoning by LLM agents. This approach enables reliable querying of engineering diagrams while significantly reducing computational cost. Benchmarking across commercial LLM APIs (OpenAI, Anthropic) demonstrates that graph-based representations improve accuracy by 18% over raw image inputs and reduce token costs by 85% compared to directly ingesting smart P&ID files. While small open-source models still struggle to interpret knowledge graph formats and structured engineering data, integrating them with VectorRAG and PathRAG improves response accuracy by up to 40%. Notably, GPT-5-mini combined with ContextRAG achieves 91% accuracy at a cost of only $0.004 per task. The resulting ChatP&ID interface enables intuitive natural-language interaction with complex engineering diagrams and lays the groundwork for AI-assisted process engineering tasks such as Hazard and Operability Studies (HAZOP) and multi-agent analysis.