Agent
These docs are for version 1.x of Rasa Open Source.
User Guide
- Installation
- Tutorial: Rasa Basics
- Tutorial: Building Assistants
- Command Line Interface
- Architecture
- Messaging and Voice Channels
- Testing Your Assistant
- Setting up CI/CD
- Validate Data
- Configuring the HTTP API
- Deploying Your Rasa Assistant
- Cloud Storage
NLU
- About
- Using NLU Only
- Training Data Format
- Language Support
- Choosing a Pipeline
- Components
- Entity Extraction
Core
- About
- Stories
- Domains
- Responses
- Actions
- Reminders and External Events
- Policies
- Slots
- Forms
- Retrieval Actions
- Interactive Learning
- Fallback Actions
- Knowledge Base Actions
Conversation Design
API Reference
- Action Server
- HTTP API
- Jupyter Notebooks
- Agent
- Custom NLU Components
- Rasa SDK
- Events
- Tracker
- Tracker Stores
- Event Brokers
- Lock Stores
- Training Data Importers
- Featurization of Conversations
- TensorFlow Configuration
- Migration Guide
- Rasa Open Source Change Log
Migrate from (beta)
Reference
Versions
viewing: 1.10.6
Agent
The Agent class provides a convenient interface for the most important Rasa functionality.
This includes training, handling messages, loading a dialogue model, getting the next action, and handling a channel.
Function definitions
create_processor(preprocessor=None)async execute_action(sender_id, action, output_channel, policy, confidence)handle_channels(channels, http_port=5005, route='/webhooks/', cors=None)async handle_message(message, message_preprocessor=None, **kwargs)async handle_text(text_message, message_preprocessor=None, output_channel=None, sender_id='default')is_core_ready()is_ready()classmethod load(model_path, interpreter=None, generator=None, tracker_store=None, lock_store=None, action_endpoint=None, model_server=None, remote_storage=None, path_to_model_archive=None)async load_data(training_resource, remove_duplicates=True, unique_last_num_states=None, augmentation_factor=50, tracker_limit=None, use_story_concatenation=True, debug_plots=False, exclusion_percentage=None)async log_message(message, message_preprocessor=None, **kwargs)async parse_message_using_nlu_interpreter(message_data, tracker=None)persist(model_path)async predict_next(sender_id, **kwargs)toggle_memoization(activate)train(training_trackers, **kwargs)async trigger_intent(intent_name, entities, output_channel, tracker)