cadling — Overview

cadling is a docling-inspired toolkit for CAD document processing. It parses STEP / STL / BRep / IGES files (and rendered CAD images), builds a structured document model, and exposes that model for RAG chunking, synthetic Q&A generation, and JSON/Markdown export — making CAD files first-class citizens in LLM/ML pipelines.

What it does

Dual-modality processing — text-based parsing of CAD file contents (STEP entities, STL vertices) and vision-based recognition from rendered images, with hybrid fusion of both.
Multi-format support — STEP (ISO 10303-21), STL (ASCII + binary), BRep (OpenCASCADE), IGES, and rendered CAD images.
Topology analysis — graph-based analysis of entity relationships, assembly hierarchies, and geometric connectivity.
RAG-ready chunking — hybrid, hierarchical, and topology-aware chunkers with 3D metadata for vector databases.
Synthetic data generation (SDG) — LLM-powered Q&A pair generation from CAD documents, with sampling, generation, and critique stages.
Enrichment models — pluggable geometry analysis, topology validation, mesh quality, interference checking, surface analysis, and GNN segmentation.

Architecture at a glance

DocumentConverter (entry point)
  → Format Detection → Backend Selection
  → Backend (format-specific parsing)
  → Pipeline (Build → Assemble → Enrich)
  → CADlingDocument (central data model)
  → Chunking (RAG) · SDG (Q&A) · Export (JSON / Markdown)

The processing flow mirrors docling’s Backend / Pipeline / Enrichment layering, adapted for 3D geometry. See Usage (in the sidebar) for the layer details and worked examples.

A first taste

from cadling import DocumentConverter, ConversionStatus

converter = DocumentConverter()
result = converter.convert("part.step")

if result.status == ConversionStatus.SUCCESS:
    doc = result.document
    print(f"Parsed {len(doc.items)} items")
    markdown = doc.export_to_markdown()

Status

Use the sidebar for Installation, Usage, and the API Reference.