Speckle2Graph 0.0.5

Enable graph-based analysis of any Speckle model.

Speckle2Graph

⚠️ Currently in alpha - bug reports and feedback are appreciated!

Purpose of the Library

speckle2graph is a framework for establishing pipelines from BIM models to graph databases with just a few lines of code.

The semantics of a BIM model are locked within the modelling software, which creates a bottleneck for implementing AI agents that truly understand the model. Converting a BIM model to a knowledge graph with a custom ontology is an ideal foundation for building AI-driven applications.

Currently we support Neo4j only.

Key Features

• Convert BIM models to Neo4j graph databases in 3 lines of code
• Support for Revit models via Speckle
• Preserves model relationships and hierarchies
• Extensible for custom ontologies

What Gets Created

The library traverses your Speckle BIM model and creates:

• Nodes: Representing BIM elements with their properties
• Relationships: Spatial and hierarchical connections between elements
• Graph structure: Ready for AI agents, path-finding, and semantic queries

Prerequisites

• Ensure that the Speckle models were uploaded using the latest versions of the connectors.

Usage

# Install the library
pip install speckle2graph

# To follow latest developments install it from the github
pip install git+https://github.com/2twenity/Speckle2Graph.git

# Authorize to Speckle 
client = SpeckleClient()
client.authenticate_with_token(SPECKLE_TOKEN)
transport = ServerTransport(PROJECT_ID, client)

# Receive the root object from Speckle (must be the model root for geometry to build correctly)
root = operations.receive(ROOT, remote_transport = transport) 

# Build a Graph in 3 lines of code
traversed_object = TraverseRevitDAG(root)
graph_builder = DataGraphBuilder(traversed_speckle_object=traversed_object)
graph_builder.build()

# Write the graph to a neo4j database
from speckle2graph import Neo4jRevitLabelAssigner
from speckle2graph import Neo4jClientDriverWrapper
from neo4j import GraphDatabase

# Authorize and write the graph to Neo4j for further analysis. 
with GraphDatabase.driver(URI, auth=auth) as driver:
    driver.verify_connectivity()
    neo4j_client_wrapper = Neo4jClientDriverWrapper(
        driver=driver,
        graph_builder_object=graph_builder,
        label_assigner=Neo4jRevitLabelAssigner()
    )
    neo4j_client_wrapper.write_graph()

Example