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.

class rasa.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,
)

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")  
# Output: [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: 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.
Return type: None