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. 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.
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: 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]
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"}] }
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. If a memoization policy is present in the ensemble, this will toggle the prediction of that policy.
Return type: None
train( training_trackers, **kwargs)
Train the policies using dialogue data from file.
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