Rasa Addons

A set of power tools to 🚀🚀🚀 your productivity with Rasa

Automated tests
Web chat: a channel to use with our open source web chat widget
Input validation: if you expect Yes or No, make sure your users anser Yes or No
Intent Substitution: avoid random intents when users enter data without semantic consistency (names, brands, time,...)
Custom dispatchers: need to store your Rasa Core templates out of domain file? We've got you covered

Installation

pip install rasa-addons

Web chat channel

from rasa_addons.webchat import WebChatInput, SocketInputChannel

agent = Agent.load(...)
input_channel = WebChatInput(static_assets_path=os.path.join(os.path.dirname(os.path.realpath(__file__)), 'static'))
agent.handle_channel(SocketInputChannel(5500, "/bot", input_channel))

In static you could have an index.html containing the widget snippet that you could access to http://localhost:5500/bot

Validate user input

Use the SuperAgent instead of the native Agent and specify a rules file.

from rasa_addons.superagent import SuperAgent
agent = SuperAgent.load(...,rules_file='rules.yml')

In rules.yml you can add input validation rules

input_validation:
  - after: utter_when_do_you_want_a_wake_up_call
    expected:
      - intents:
        - cancel
      - intents:
        - skeak_to_human
      - intents:
        - enter_time
        entities:
        - time
    error_template: utter_please_provide_time

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":"..."} 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 you can ask the user to pick from a list of likely intents.

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:

An intro message (if the optional field intro_template is present)
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.

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.

disambiguation_policy:
  trigger: $0 < 2 * $1
  max_suggestions: 2
  display:
    intro_template: utter_disamb_intro
    text_template: utter_disamb_text
    button_title_template_prefix: utter_disamb
    fallback_button:
      title: utter_fallback_yes
      payload: /fallback
    exclude:
      - chitchat\..*
      - basics\..*
      - cancel

Note about the 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.

Fallback policy

You may want to make the bot go straight to suggesting fallback (e.g when the top intent ranking is low).

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

The bot will utter:

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

fallback_policy:
  trigger: $0 < 0.5
  display:
    text: utter_fallback_intro
    buttons:
      - 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:
      (...disambiguation policy definition...)

fallback_policy:
      (...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.

Swap 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...

intent_substitutions:
  - 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:

allowed_entities:
  book: # intent
    - origin # entity
    - destination
  buy:
    - color
    - product

Get templates out of domain files

If you want to get your templates from another source than the domain, you can do it like this:

Create your dispatcher

class MyDispatcher(Dispatcher):
    def retrieve_template(self, template_name, filled_slots=None, **kwargs):
        """Retrieve a named template from the domain."""

        response = requests.get('api/{template_key}/'.format(...))
        if response.status_code == 200:
            r = response.json()
            if r is not None:
                return self._fill_template_text(r, filled_slots, **kwargs)
            
        else:
            print("error")

Then load your agent

agent = SuperAgent.load(POLICY_PATH,
                        interpreter=interpreter,
                        create_dispatcher=lambda sender_id, output_channel, domain: MyDispatcher(sender_id, output_channel, domain))

Run automated tests (experimental)

You can write test cases as you would write stories, except you should only have utter_... actions.

## chitchat.greet
* chitchat.greet
  - utter_reply_to_greet

## chitchat.how_are_you
* chitchat.how_are_you
  - utter_reply_to_how_are_you

## chitchat.are_you_a_robot
* chitchat.are_you_a_robot
  - utter_reply_to_are_you_a_robot

Then you can run your tests with:

python -m rasa_addons.tests -d domains.yml -m models/dialogue/ -t test_cases/ -r rules.yml

You can put your test cases in different files starting with test (e.g. test_chitchat.md)in a directory.
At this time, it only runs the test and outputs dialogues in the console (errors in red). There is no report (Help wanted). You can also use --distinct to change the sender_id at every test case and --shuffle to shuffle test cases before running the tests.

mmxmb / rasa-addons