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. 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")

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.

The return value of this function is parsed_data.

Parameters:

• message_data (Text) – Contain the received message in text or intent payload format.
• tracker (DialogueStateTracker) – Contains the tracker to be used by the interpreter.

Returns: The parsed message.

Example:

{ "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
• kwargs – additional arguments passed to the underlying ML trainer (e.g. keras parameters)

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