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 rasa_core.train
scripts provides parameters to adjust these
thresholds:
--nlu_threshold |
min confidence needed to accept an NLU prediction |
--core_threshold |
min confidence needed to accept an action prediction from Rasa Core |
--fallback_action |
name of the action to be called if the confidence of intent / action prediction is below the threshold |
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:
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
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.