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.

The thresholds and fallback action can be adjusted in the policy configuration file as parameters of the FallbackPolicy:

policies:
  - name: "FallbackPolicy"
    # min confidence needed to accept an NLU prediction
    nlu_threshold: 0.3
    # min confidence needed to accept an action prediction from Rasa Core
    core_threshold: 0.3
    # name of the action to be called if the confidence of intent / action
    # is below the threshold
    fallback_action_name: 'action_default_fallback'

If you want to run this from python, use:

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_default_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

You can also create your own custom action to use as a fallback. If you do, then make sure to pass the custom fallback action to FallbackPolicy inside your policy configuration file. For example:

policies:
  - name: "FallbackPolicy"
    nlu_threshold: 0.4
    core_threshold: 0.3
    fallback_action_name: "my_fallback_action"

Note

If your custom fallback action does not return a UserUtteranceReverted event, the next predictions of your bot may become inaccurate, as it is 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

Have questions or feedback?

We have a very active support community on Rasa Community Forum that is happy to help you with your questions. If you have any feedback for us or a specific suggestion for improving the docs, feel free to share it by creating an issue on Rasa Core GitHub repository.