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")
[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

`_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: ListDialogueStateTracker

`_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.

Returns:

{ "text": '/greet{"name":"Rasa"}', "intent": {"name": "greet", "confidence": 1.0}, "intent_ranking": [{"name": "greet", "confidence": 1.0}], "entities": [{"entity": "name", "start": 6, "end": 21, "value": "Rasa"}] }

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.

Parameters:

training_trackers – trackers to train on

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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