rasa-nlu-integration

name	rasa-nlu-integration
description	Rasa NLU pipeline configuration and training for intent and entity extraction
allowed-tools	["Read","Write","Edit","Bash","Glob","Grep"]
graph	{"domains":["domain:software-engineering"],"specializations":["specialization:ai-agents-conversational"],"skillAreas":["skill-area:natural-language-processing","skill-area:ml-fine-tuning"],"roles":["role:ml-engineer","role:backend-engineer"],"workflows":["workflow:ml-model-lifecycle","workflow:feature-development"]}

Rasa NLU Integration Skill

Capabilities

• Configure Rasa NLU pipelines
• Design training data in Rasa format
• Set up intent classification components
• Configure entity extraction (DIETClassifier)
• Implement pipeline optimization
• Set up model evaluation and testing

Target Processes

• intent-classification-system
• chatbot-design-implementation

Implementation Details

Pipeline Components

1. Tokenizers: WhitespaceTokenizer, SpacyTokenizer
2. Featurizers: CountVectorsFeaturizer, SpacyFeaturizer
3. Classifiers: DIETClassifier, FallbackClassifier
4. Entity Extractors: DIETClassifier, SpacyEntityExtractor

Configuration Files

• config.yml: Pipeline configuration
• nlu.yml: Training data
• domain.yml: Intents and entities

Configuration Options

• Pipeline component selection
• Featurizer settings
• Classifier parameters
• Entity extraction rules
• Fallback thresholds

Best Practices

• Start with recommended pipelines
• Tune based on domain
• Balance complexity vs performance
• Regular model retraining

Dependencies

• rasa