rasa-nlu-integration

# 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