<scp>GraphRAG</scp> for engineering diagrams: <scp>ChatP</scp> &amp; <scp>ID</scp> enables <scp>LLM</scp> interaction with P&amp; <scp>IDs</scp>

Abstract Piping and Instrumentation Diagrams (P&amp;IDs) are central to process engineering workflows, yet extracting information from them remains a tedious and time‐consuming task. This work introduces ChatP&amp;ID, a framework enabling natural‐language interaction with smart P&amp;IDs through Graph Retrieval‐Augmented Generation (GraphRAG), to our knowledge, the first application and benchmark of GraphRAG to structured engineering diagrams. DEXPI‐encoded P&amp;IDs are transformed into structured knowledge graphs, enabling reliable, grounded querying by large language model (LLM) agents. Benchmarking across commercial LLM APIs demonstrates that graph‐based representations improve response accuracy by 18% over raw image inputs and reduce token costs by 85% compared to directly ingesting smart P&amp;ID files. Among the retrieval strategies evaluated, ContextRAG achieves the highest accuracy (91%) at only $0.004 per query using GPT‐5‐mini. For smaller open‐source models, vector‐based retrieval improves accuracy by up to 40%. ChatP&amp;ID lays practical groundwork for AI‐assisted process engineering tasks, including Hazard and Operability Studies (HAZOP).