Agent

classrasa.core.agent.``Agent( domain=None, policies=None, interpreter=None, generator=None, tracker_store=None, lock_store=None, action_endpoint=None, fingerprint=None, model_directory=None, model_server=None, remote_storage=None, path_to_model_archive=None)

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.

create_processor( preprocessor=None)

Instantiates a processor based on the set state of the agent.
Return type: MessageProcessor

asyncexecute_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

asynchandle_message( message, message_preprocessor=None, **kwargs)

Handle a single message.
Return type: Optional List[Dict[str, Any]]

asynchandle_text( text_message, message_preprocessor=None, output_channel=None, _sender_id='default')

Handle a single message.

>>> 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?']

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

classmethodload( 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

asyncload_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

asynclog_message( message, message_preprocessor=None, **kwargs)

Append a message to a dialogue - does not predict actions.
Return type: DialogueStateTracker

asyncparse_message_using_nlu_interpreter( message_data, tracker=None)

Handles message text and intent payload input messages.

The return value of this function is parsed_data.