In the above example, set_volume is a FormAction, and inform_volume is used to answer slot (volume).
For this kind of form intent (set_volume) that can contain inform intent (inform_volume), when nlu classifies the intent, it will mistakenly classify the form intent as inform intent
set_volume intent will trigger your form. one of the slot in your form is volume, and the response to utter_ask_volume will be inform_volume.
Mute, 40 and maximum are entity values, which will be extracted from the set_volume. If the slot and entity names are same then rasa fill automatically fill the slot for you. or you can specify them in the slot_mappings method of your FormAction.
I can understand what you mean, but there is a problem. Assuming that the value of volume entity is in the tens of thousands, will all entities be listed in the corpus of set_volume intent? If not, the intent classifier will misclassify entities that do not appear in the corpus.Does the data in the database serve as a training corpus for entity recognition?