OpenSCAD MCP Server

# Natural Language Processing for 3D Modeling <metadata> author: devin-ai-integration timestamp: 2025-03-21T01:30:00Z version: 1.0.0 tags: [nlp, 3d-modeling, parameter-extraction, pattern-matching] </metadata> ## Overview Natural Language Processing (NLP) techniques can be applied to extract 3D modeling parameters and intentions from user descriptions. This knowledge document outlines approaches for translating natural language into structured data for 3D model generation. ## Approaches ### Pattern Matching Regular expression pattern matching is effective for identifying: - Dimensions and measurements - Shape types and primitives - Operations (union, difference, etc.) - Transformations (rotate, scale, etc.) - Material properties and colors Example patterns: ```python # Dimension pattern dimension_pattern = r'(\d+(?:\.\d+)?)\s*(mm|cm|m|inch|in)' # Shape pattern shape_pattern = r'\b(cube|box|sphere|ball|cylinder|tube|cone|pyramid)\b' ``` ### Contextual Understanding Beyond simple pattern matching, contextual understanding involves: - Identifying relationships between objects - Understanding relative positioning - Resolving ambiguous references - Maintaining dialog state for multi-turn interactions ### Hybrid Approaches Combining pattern matching with contextual rules provides: - Better accuracy than pure pattern matching - Lower computational requirements than full ML approaches - More maintainable and debuggable systems - Flexibility to handle diverse descriptions ## Parameter Extraction Key parameters to extract include: - **Dimensions**: Width, height, depth, radius, diameter - **Positions**: Coordinates, relative positions - **Operations**: Boolean operations, transformations - **Features**: Holes, fillets, chamfers, text - **Properties**: Color, material, finish ## Implementation Considerations - **Ambiguity Resolution**: Handle cases where measurements could apply to multiple dimensions - **Default Values**: Provide sensible defaults for unspecified parameters - **Unit Conversion**: Convert between different measurement units - **Error Handling**: Gracefully handle unparseable or contradictory descriptions - **Dialog Management**: Maintain state for multi-turn interactions to refine models ## Evaluation Metrics Effective NLP for 3D modeling can be evaluated by: - **Accuracy**: Correctness of extracted parameters - **Completeness**: Percentage of required parameters successfully extracted - **Robustness**: Ability to handle diverse phrasings and descriptions - **User Satisfaction**: Subjective evaluation of the resulting models