Warning: This document is for an old version of Rasa Core. The latest version is 0.14.5.

Fallback Actions

Sometimes you want to fall back to a fallback action like saying “Sorry, I didn’t understand that”. To do this, add the FallbackPolicy to your policy ensemble. The fallback action will be executed if the intent recognition has a confidence below nlu_threshold or if none of the dialogue policies predict an action with confidence higher than core_threshold

from rasa_core.policies.fallback import FallbackPolicy
from rasa_core.policies.keras_policy import KerasPolicy
from rasa_core.agent import Agent

fallback = FallbackPolicy(fallback_action_name="action_default_fallback",
                          core_threshold=0.3,
                          nlu_threshold=0.3)

agent = Agent("domain.yml",
               policies=[KerasPolicy(), fallback])

action_fallback is a default action in Rasa Core, which will send the utter_default template message to the user. Make sure to specify this template in your domain file. It will also revert back to the state of the conversation before the user message that caused the fallback, so that it will not influence the prediction of future actions. You can take a look at the source of the action below:

class rasa_core.actions.action.ActionDefaultFallback

Executes the fallback action and goes back to the previous state of the dialogue

Note

You can also create your own custom action to use as a fallback. Be aware that if this action does not return a UserUtteranceReverted event, the next predictions of your bot may become inaccurate, as it very likely that the fallback action is not present in your stories

If you have a specific intent that will trigger this, let’s say it’s called out_of_scope, then you should add this as a story:

## fallback story
* out_of_scope
  - action_default_fallback