IfcOpenShell Python Skill for Graph-RAG IFC Parser

Parsing IFC files and extracting geometry for BIM data analysis in Python involves reading the IFC model using the IfcOpenShell library to access element properties, relationships, and 3D spatial geometry. This process transforms complex building data into queryable structures.

Converting IFC BIM data into a knowledge graph is best achieved by using IfcOpenShell to parse entities and relationships, then mapping them into a NetworkX graph structure. This geometry-enriched representation enables semantic and spatial querying for digital twins.

You can use IfcOpenShell with NetworkX to query spatial relationships in BIM models by transforming IFC entities and their spatial geometry into a graph structure. This allows detailed extraction and querying of how building components relate.

This IFC parsing approach supports integration with a Graph RAG system by providing geometry-enriched knowledge graphs built from IFC files. Facilitating semantic and spatial querying of BIM data enables AI-driven analysis and natural language queries.

Extracting element properties and relationships from an IFC file using Python requires the IfcOpenShell library to interpret the model. It supports detailed extraction of element attributes and their connections, which can then be structured as graph nodes and edges.

Building a graph from IFC data for digital twins requires dependencies including ifcopenshell for parsing, networkx for graph representation, numpy for geometry processing, and pydantic-ai for AI integration. These tools collectively enable spatial querying and analysis.