Process and Instrumentation Diagrams (P&IDs) represent the backbone of industrial process engineering, containing critical information about equipment relationships, control loops, and process flows.

However, this valuable knowledge typically remains locked in static PDF documents, limiting accessibility and intelligent analysis. In this deep-dive session, Everllence demonstrates how to transform traditional P&IDs into dynamic, queryable knowledge graphs using advanced AI techniques.

We'll explore the complete pipeline from PDF extraction to intelligent graph-based reasoning:

**P&ID Processing & Graph Conversion:**

• Advanced PDF extraction techniques for complex engineering drawings via

template matching and symbol recognition for industrial equipment

• Automated conversion to GraphML format preserving semantic relationships

• Handling multi-page diagrams and cross-references

**AI-Powered Agent System:**

• Building specialized agents for P&ID interpretation using graph representations

• Implementing tool calling patterns for equipment identification and process analysis

• Integration with Large Language Models for natural language queries

**Knowledge Retrieval with LightRAG:**

• Implementing LightRAG for efficient retrieval of related documentation to blend process information with the core documentation

• Real-time query processing for maintenance, troubleshooting, and design optimization