Agent
Agent
classrasa.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)
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.
create_processor( preprocessor=None)
Instantiates a processor based on the set state of the agent.
Return type
MessageProcessor
asyncexecute_action( sender_id, action, output_channel, policy, confidence)
Handle a single message.
Return type
handle_channels( channels, http_port=5005, route='/webhooks/', cors=None)
Start a webserver attaching the input channels and handling msgs.
Return type
Sanic
asynchandle_message( message, message_preprocessor=None, **kwargs)
Handle a single message.
Return type
Optional[List[Dict[str, Any]]]
asynchandle_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
classmethodload( 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
asyncload_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]
asynclog_message( message, message_preprocessor=None, **kwargs)
Append a message to a dialogue - does not predict actions.
Return type
asyncparse_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.
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
asyncpredict_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
asynctrigger_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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