Botfront integrates the Rasa Addons library. Rules allow to deal quickly with common problems without having to retrain policies.

  • Input validation: if you expect Yes or No, make sure your users answer Yes or No and provide an error message
  • Disambiguation and fallback: automatically display dismabiguation options to users based on custom triggers
  • Intent Substitution: avoid random intents when users enter data without semantic consistency (names, brands, time,...)
  • Filter entities: define entities allowed for each intent


Changes to rules will be automatically applied to your current Core server in the next 60 seconds. Rules are not enforced by machine learning and are not related to your Rasa Core policy, so you don't need to retrain.


Because Rules intercepts messages before they are actually handled by the policy, some interactions (e.g. disambiguation messages) may not appear in the conversation history

Validate user input

The following rule will utter the error_template if the user does not reply to utter_when_do_you_want_a_wake_up_call with either /cancel OR /speak_to_human OR /enter_time{"time":"..."}

  - after: utter_when_do_you_want_a_wake_up_call
    # !!WARNING!! If regex is set true then the validation will trigger for
    #             all actions which includes the above text. It is encouraged
    #             to set regex to false for matching the validation to a
    #             specific action.
    regex: false # optional (default: True)
      - intents:
        - cancel
      - intents:
        - skeak_to_human
      - intents:
        - enter_time
        - time
    error_template: utter_please_provide_time

Rules are enforced at the tracker level, so there is no need to retrain when changing them.

Disambiguate user input and fallback

Disambiguation policy

Help your users when your NLU struggles to identify the right intent. Instead of just going with the highest scoring intent or just going with a fallback you can ask the user to confirm the question or to pick from a list of likely intents.


One way to disambiguate is to provide the user with buttons, each button corresponding to one intent. In the example below, the disambiguation is triggered when the score of the highest scoring intent is below twice the score of the second highest scoring intent.

The bot will utter:

  1. An intro message (if the optional field intro_template is present)
  2. A text with buttons (or quick replies) message where:
  • the text is the template defined as text_template,
  • the button titles will be the concatenation of "utter_disamb" and the intent name. For example, utter_disamb_greet."
  • the buttons payloads will be the corresponding intents (e.g. /greet). Entities found in parse_data are passed on.
  1. A fallback button to go along with disambiguation buttons (if the optional field fallback_button is present)

It's also possible to exclude certain intents from being displayed as a disambiguation option by using optional exclude list field. In the example below, all intents that match regex chitchat\..* and basics\..*, as well as intent cancel will not be displayed as an option. The next highest scoring intents will be displayed in place of excluded ones.

  trigger: $0 < 2 * $1
  type: suggest
  max_suggestions: 2
  slot_name: parse_data # optional slot name to store the parse data originating a disambiguation
    intro_template: utter_disamb_intro # optional: will not be rendered if not set
    text_template: utter_disamb_text
    button_title_template_prefix: utter_disamb
      title: utter_fallback_yes
      payload: /fallback
      - chitchat\..*
      - basics\..*
      - cancel


  • trigger: $0 corresponds to parse_data['intent_ranking'][0]["confidence"]. You can set any rule based on intent ranking. Intent scores are checked against the trigger before any intent is excluded with exclude.
  • slot_name: you need to set the slot in the Core domain to get it from the tracker. E.g. tracker.get_slot(slot_name)You may want to make the bot go straight to suggesting fallback (e.g when the top intent ranking is low).

The bot will utter:

  1. An intro message utter_fallback_intro
  2. Optional buttons (if buttons list with at least one item - a pair of title and payload - is defined).


Another way to disambiguate is to rephrase. When triggered, the bot asks "Did you mean [something related to the intent]"? followed by two buttons (titles in yes_template and no_template). no_payload is the payload to trigger when the user clicks the no button.

  trigger: $0 < 2 * $1
  type: rephrase
    rephrase_template: utter_rephrase
    yes_template: utter_yes
    no_template: utter_no
    no_payload: /fallback
      - chitchat\..*
      - basics\..*
      - cancel

Fallback policy

In the example below, fallback is triggered when the top scoring intent's confidence is below 0.5.

  trigger: $0 < 0.5
  slot_name: parse_data # optional slot name to store the parse data originating a disambiguation
    text: utter_fallback_intro
      - title: utter_fallback_yes
        payload: /fallback
      - title: utter_fallback_no
        payload: /restart

There is no limit on the number of buttons you can define for fallback. If no buttons are defined, this policy will simply make the bot utter some default message (e.g utter_fallback_intro) when the top intent confidence is lower than the trigger.

Using both disambiguation and fallback policies

It's easy to combine both disambiguation and fallback policies. It can be done by filling in policy definitions from two previous examples as follows:

      (...disambiguation policy definition...)

      (...fallback policy definition...)

In cases when intent confidence scores in parsed data are such that would cause both policies to trigger, only fallback policy is trigerred. In other words, fallback policy has precedence over disambiguation policy.

Substitute intents

Some intents are hard to catch. For example when the user is asked to fill arbitrary data such as a date or a proper noun. The following rule swaps any intent caught after utter_when_do_you_want_a_wake_up_call with enter_data unless...

  - after: utter_when_do_you_want_a_wake_up_call
    intent: enter_data
    unless: frustration|cancel|speak_to_human

Filter entities

Sometimes Rasa NLU CRF extractor will return unexpected entities and those can perturbate your Rasa Core dialogue model because it has never seen this particular combination of intent and entity.

This helper lets you define precisely the entities allowed for every intent in a yaml file. Entities not in the list for a given intent will be cleared. It will only remove entities for intents specifically listed in this section:

  book: # intent
    - origin # entity
    - destination
    - color
    - product
Last Updated: 5/3/2019, 1:22:17 PM