Forms

Note
There is an in-depth tutorial here about how to use Rasa Forms for slot filling.

• Configuration File
• Form Basics
• Custom slot mappings
• Validating user input
• Handling unhappy paths
• The requested_slot slot
• Handling conditional slot logic
• Debugging

Overview of Form Actions

One of the most common conversation patterns is to collect a few pieces of information from a user in order to do something (book a restaurant, call an API, search a database, etc.). This is also called slot filling.

If you need to collect multiple pieces of information in a row, we recommended that you create a FormAction. This is a single action which contains the logic to loop over the required slots and ask the user for this information. There is a full example using forms in the examples/formbot directory of Rasa Core.

Defining a Form

When you define a form, you need to add it to your domain file. If your form’s name is restaurant_form, your domain would look like this:

forms:
  - restaurant_form
actions:
  ...

Configuration File

To use forms, you also need to include the FormPolicy in your policy configuration file. For example:

policies:
  - name: "FormPolicy"

Form Basics

Using a FormAction, you can describe all of the happy paths with a single story. By “happy path”, we mean that whenever you ask a user for some information, they respond with the information you asked for.

If we take the example of the restaurant bot, this single story describes all of the happy paths.

## happy path
* request_restaurant
    - restaurant_form
    - form{"name": "restaurant_form"}
    - form{"name": null}

In this story the user intent is request_restaurant, which is followed by the form action restaurant_form. With form{"name": "restaurant_form"} the form is activated and with form{"name": null} the form is deactivated again.

The FormAction will only request slots which haven’t already been set. If a user starts the conversation with I’d like a vegetarian Chinese restaurant for 8 people, then they won’t be asked about the cuisine and num_people slots.

Custom Slot Mappings

If you do not define slot mappings, slots will be only filled by entities with the same name as the slot that are picked up from the user input.

Example for the Restaurant Bot

Here’s an example for the restaurant bot:

def slot_mappings(self) -> Dict[Text, Union[Dict, List[Dict]]]:
    return {
        "cuisine": self.from_entity(entity="cuisine", not_intent="chitchat"),
        "num_people": [
            self.from_entity(
                entity="number", intent=["inform", "request_restaurant"]
            ),
        ],
        "outdoor_seating": [
            self.from_entity(entity="seating"),
            self.from_intent(intent="affirm", value=True),
            self.from_intent(intent="deny", value=False),
        ],
        "preferences": [
            self.from_intent(intent="deny", value="no additional preferences"),
            self.from_text(not_intent="affirm"),
        ],
        "feedback": [self.from_entity(entity="feedback"), self.from_text()],
    }

Validating User Input

After extracting a slot value from user input, the form will try to validate the value of the slot.

def validate_cuisine(
    self,
    value: Text,
    dispatcher: CollectingDispatcher,
    tracker: Tracker,
    domain: Dict[Text, Any],
) -> Dict[Text, Any]:
    if value.lower() in self.cuisine_db():
        return {"cuisine": value}
    else:
        dispatcher.utter_message(template="utter_wrong_cuisine")
        return {"cuisine": None}

The helper validation functions return dictionaries of slot names and values to set.

Handling Unhappy Paths

Of course your users will not always respond with the information you ask of them. Typically, users will ask questions, make chitchat, change their mind, or otherwise stray from the happy path. The way this works with forms is that a form will raise an ActionExecutionRejection if the user didn’t provide the requested information.

Use Interactive Learning with Forms to build these stories effectively.

Debugging

The first thing to try is to run your bot with the --debug flag, see Command Line Interface for details.