Agent

Class rasa.core.agent.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.

  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")
  

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

• 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

• class 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 log_message(message, message_preprocessor=None, **kwargs)
  Append a message to a dialogue - does not predict actions.

  Return type: DialogueStateTracker

• async parse_message_using_nlu_interpreter(message_data, tracker=None)
  Handles message text and intent payload input messages.

Return type: Dict[str, Any]

• persist(model_path)
  Persists this agent into a directory for later loading and usage.

  Return type: None

• async predict_next(sender_id, **kwargs)
  Handle a single message.

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

• toggle_memoization(activate)
  Toggles the memoization on and off.

  Return type: None

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

  Return type: None

• async trigger_intent(intent_name, entities, output_channel, tracker)
  Trigger a user intent, e.g. triggered by an external event.

  Return type: None

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