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

DialogueStateTracker

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.

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

Agent

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.

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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