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.

Methods

• create_processor(preprocessor=None)
  Instantiates a processor based on the set state of the agent. Return type: MessageProcessor

• _async_ execute_action(sender_id, action, output_channel, policy, confidence)
  Handle a single message. Return type: DialogueStateTracker

• handle_channels(channels, http_port=5005, route='/webhooks/', cors=None)
  Start a webserver attaching the input channels and handling msgs. Return type: Sanic

• _async_ handle_message(message, message_preprocessor=None, **kwargs)
  Handle a single message. Return type: Optional[List[Dict[str, Any]]

• _async_ handle_text(text_message, message_preprocessor=None, output_channel=None, sender_id='default')
  Handle a single message.
  If a message preprocessor is passed, the message will be passed to that function first and the return value is then used as the input for the dialogue engine.

Return type: Optional[List[Dict[str, Any]]`

Example

>>> from rasa.core.agent import Agent
>>> from rasa.core.interpreter import RasaNLUInterpreter
>>> agent = Agent.load("examples/restaurantbot/models/current")
>>> await agent.handle_text("hello")
[u'how can I help you?']

• is_core_ready()
  Check if all necessary components and policies are ready to use the agent. Return type: bool

• is_ready()
  Check if all necessary components are instantiated to use agent. Return type: bool

• _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)
  Load a persisted model from the passed path. Return type: Agent

• _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)
  Load training data from a resource. Return type: List DialogueStateTracker

• _async_ train(training_trackers, **kwargs)
  Train the policies / policy ensemble using dialogue data from file.

Other

• 👋 I can help you get started with Rasa and answer your technical questions.