[2603.22528] GraphRAG for Engineering Diagrams: ChatP&ID Enables LLM Interaction with P&IDs

Computer Science > Information Retrieval arXiv:2603.22528 (cs) [Submitted on 23 Mar 2026] Title:GraphRAG for Engineering Diagrams: ChatP&ID Enables LLM Interaction with P&IDs Authors:Achmad Anggawirya Alimin, Artur M. Schweidtmann View a PDF of the paper titled GraphRAG for Engineering Diagrams: ChatP&ID Enables LLM Interaction with P&IDs, by Achmad Anggawirya Alimin and Artur M. Schweidtmann View PDF 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 direct...