AA
A. Alimin
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Piping and Instrumentation Diagrams (P&IDs) are central to process engineering workflows, yet extracting information from them remains a tedious and time-consuming task. This work introduces ChatP&ID, a framework enabling natural-language interaction with smart P&IDs through Graph Retrieval-Augmented Generation (GraphRAG), to our knowledge, the first application and benchmark of GraphRAG to structured engineering diagrams. DEXPI-encoded P&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&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&ID lays practical groundwork for AI-assisted process engineering tasks, including Hazard and Operability Studies (HAZOP)
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Piping and Instrumentation Diagrams (P&IDs) are central to process engineering workflows, yet extracting information from them remains a tedious and time-consuming task. This work introduces ChatP&ID, a framework enabling natural-language interaction with smart P&IDs through Graph Retrieval-Augmented Generation (GraphRAG), to our knowledge, the first application and benchmark of GraphRAG to structured engineering diagrams. DEXPI-encoded P&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&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&ID lays practical groundwork for AI-assisted process engineering tasks, including Hazard and Operability Studies (HAZOP)