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

Using Slots

Here is the full list of slot types defined by Rasa Core.

Slots are your bot’s memory. They act as a key-value store which can be used to store information the user provided (e.g their home city) as well as information gathered about the outside world (e.g. the result of a database query).

Most of the time, you want slots to influence how the dialogue progresses. There are different slot types for different behaviors.

For example, if your user has provided their home city, you might have a text slot called home_city. If the user asks for the weather, and you don’t know their home city, you will have to ask them for it. A text slot only tells Rasa Core whether the slot has a value. The specific value of a text slot (e.g. Bangalore or New York or Hong Kong) doesn’t make any difference.

If the value itself is important, use a categorical slot. There are also boolean, float, and list slots. If you just want to store some data, but don’t want it to affect the flow of the conversation, use an unfeaturized slot.

How Rasa Uses Slots

The rasa_core.policies.Policy doesn’t have access to the value of your slots. It receives a featurized representation. As mentioned above, for a text slot the value is irrelevant. The policy just sees a 1 or 0 depending on whether it is set.

You should choose your slot types carefully!

How Slots Get Set

You can provide an initial value for a slot in your domain file:

    type: text
    initial_value: "human"

There are multiple ways that slots are set during a conversation:

Slots Set from NLU

If your NLU model picks up an entity, and your domain contains a slot with the same name, the slot will be set automatically. For example:

# story_01
* greet{"name": "Ali"}
  - slot{"name": "Ali"}
  - utter_greet

In this case, you don’t have to include the - slot{} part in the story, because it is automatically picked up.

Slots Set By Clicking Buttons

You can use buttons as a shortcut. Rasa Core will send messages starting with a / to the RegexInterpreter, which expects NLU input in the same format as in story files, e.g. /intent{entities}. For example, if you let users choose a color by clicking a button, the button payloads might be /choose{"color": "blue"} and /choose{"color": "red"}.

You can specify this in your domain file like this: (see details in Domain Format)

- text: "what color would you like?"
  - title: "blue"
    payload: '/choose{"color": "blue"}'
  - title: "red"
    payload: '/choose{"color": "red"}'

Slots Set by Actions

The second option is to set slots by returning events in Actions. In this case, your stories need to include the slots. For example, you have a custom action to fetch a user’s profile, and you have a categorical slot called account_type. When the fetch_profile action is run, it returns a rasa_core.events.SlotSet event:

      type: categorical
      - premium
      - basic
from rasa_core_sdk.actions import Action
from rasa_core_sdk.events import SlotSet
import requests

class FetchProfileAction(Action):
    def name(self):
        return "fetch_profile"

    def run(self, dispatcher, tracker, domain):
        url = "http://myprofileurl.com"
        data = requests.get(url).json
        return [SlotSet("account_type", data["account_type"])]
# story_01
* greet
  - action_fetch_profile
  - slot{"account_type" : "premium"}
  - utter_welcome_premium

# story_02
* greet
  - action_fetch_profile
  - slot{"account_type" : "basic"}
  - utter_welcome_basic

In this case you do have to include the - slot{} part in your stories. Rasa Core will learn to use this information to decide on the correct action to take (in this case, utter_welcome_premuim or utter_welcome_basic).


It is very easy to forget about slots if you are writing stories by hand. We strongly recommend that you build up these stories using Interactive Learning rather than writing them.

Custom Slot Types

Maybe your restaurant booking system can only handle bookings for up to 6 people. In this case you want the value of the slot to influence the next selected action (and not just whether it’s been specified). You can do this by defining a custom slot class.

In the code below, we define a slot class called NumberOfPeopleSlot. The featurization defines how the value of this slot gets converted to a vector to our machine learning model can deal with. Our slot has three possible “values”, which we can represent with a vector of length 2.

(0,0) not yet set
(1,0) between 1 and 6
(0,1) more than 6
from rasa_core.slots import Slot

class NumberOfPeopleSlot(Slot):

    def feature_dimensionality(self):
        return 2

    def as_feature(self):
        r = [0.0] * self.feature_dimensionality()
        if self.value:
            if self.value <= 6:
                r[0] = 1.0
                r[1] = 1.0
    return r

Now we also need some training stories, so that Rasa Core can learn from these how to handle the different situations:

# story1
* inform{"people": "3"}
- action_book_table
# story2
* inform{"people": "9"}
- action_explain_table_limit

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.