parameter_extractor.py.md•2.59 kB
<metadata>
author: devin-ai-integration
timestamp: 2025-03-21T01:30:00Z
version: 1.0.0
related-files: [/src/models/code_generator.py]
prompt: "Enhance parameter extractor with expanded shape recognition"
</metadata>
<exploration>
The parameter extractor was designed to parse natural language descriptions and extract structured parameters for 3D model generation. Several approaches were considered:
1. Using a full NLP pipeline with named entity recognition
2. Implementing regex-based pattern matching
3. Creating a hybrid approach with contextual understanding
The regex-based approach with contextual enhancements was selected for its balance of simplicity and effectiveness.
</exploration>
<mental-model>
The parameter extractor operates on a "pattern recognition and extraction" paradigm, where common phrases and patterns in natural language are mapped to specific parameter types. This mental model allows for intuitive parameter extraction from diverse descriptions.
</mental-model>
<pattern-recognition>
The implementation uses the Strategy pattern for different parameter extraction strategies based on shape type. This pattern allows for specialized extraction logic for each shape type while maintaining a consistent interface.
</pattern-recognition>
<trade-off>
Options considered:
1. Machine learning-based approach with trained models
2. Pure regex pattern matching
3. Hybrid approach with contextual rules
The regex approach with contextual rules was chosen because:
- Simpler implementation with good accuracy
- No training data required
- Easier to debug and maintain
- More predictable behavior
</trade-off>
<domain-knowledge>
The implementation required understanding of:
- Natural language processing concepts
- Regular expression pattern matching
- 3D modeling terminology
- Parameter types for different geometric shapes
</domain-knowledge>
<technical-debt>
The current implementation has some limitations:
- Limited support for complex nested descriptions
- Regex patterns may need maintenance as language evolves
- Only supports millimeters as per project requirements
Future improvements planned:
- Enhanced contextual understanding
- Support for more complex descriptions
- Better handling of ambiguous parameters
</technical-debt>
<knowledge-refs>
[Parameter Extraction](/rtfmd/knowledge/nlp/parameter-extraction.md) - Last updated 2025-03-21
[Natural Language Processing](/rtfmd/knowledge/ai/natural-language-processing.md) - Last updated 2025-03-21
</knowledge-refs>