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