Metadata-Version: 2.1
Name: rasa_addons
Version: 0.5.1
Summary: Rasa Addons - Productivity tools for Rasa Core
Home-page: http://mrbot.ai
Author: Nathan Zylbersztejn
Author-email: human@mrbot.ai
License: Apache 2.0
Description: # Rasa Addons
        
        
        ![PyPI](https://img.shields.io/pypi/v/rasa-addons.svg)
        ![Travis](https://img.shields.io/travis/mrbot-ai/rasa-addons.svg)
        
        ## Rasa Core 0.11 Support on the way
        Only automated tests are not working yet. You can have a look and install from https://github.com/mrbot-ai/rasa-addons/tree/rasacore-0.11-support
        
        
        A set of power tools to 🚀🚀🚀 your productivity with Rasa
        
        - Input validation: if you expect Yes or No, make sure your users anser Yes or No
        - 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
        
        
        ## Contents
        
        - [Installation](#installation)
        - [Usage](#usage)
        	- [Validate user input](#validate-user-input)
        	- [Disambiguate user input and fallback](#disambiguate-user-input-and-fallback)
        		- [Disambiguation policy](#disambiguation-policy)
        		- [Fallback policy](#fallback-policy)
        		- [Using both disambiguation and fallback policies](#using-both-disambiguation-and-fallback-policies)
        	- [Substitute intents](#substitute-intents)
        	- [Filter entities](#filter-entities)
        	- [Bonus - Create a FAQ bot with only ONE action and ONE story](#bonus-create-a-faq-bot-with-only-one-action-and-one-story)
        	- [Where are automated tests ?](#where-are-automated-tests-)
        
        
        ## Installation
        
        Rasa core < 0.11.x
        
        ```bash
        pip install rasa-addons==0.4.3
        ```
        
        Rasa core >= 0.11.x
        
        ```bash
        pip install rasa-addons
        ```
        
        ## Usage
        
        You can set rules in a declarative way using a YAML file or a remote endpoint. To do that you must start Rasa Core from a
        [python script](https://rasa.com/docs/core/connectors/#id18) and include the following snippet
        
        To load rules from a YAML file:
        ```python
        from rasa_addons.superagent import SuperAgent
        agent = SuperAgent.load(...,rules='rules.yml')
        ```
        
        To load rules from a remote endpoint:
        ```python
        from rasa_addons.superagent import SuperAgent
        agent = SuperAgent.load(...,rules=EndpointConfig(url="https://my.rules.endpoint/path", ...))
        ```
        
        In the rest of this document we'll assume you are reading from a YAML file
        
        ### Validate user input
        
        
        ```python
        from rasa_addons.superagent import SuperAgent
        agent = SuperAgent.load(...,rules='rules.yml')
        ```
        In `rules.yml` you can add input validation rules
        
        ```yaml
        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:
        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.
        3. 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.
        
        ```yaml
        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:
        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).
        
        ```yaml
        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:
        
        ```yaml
        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**.
        
        ### 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...
        
        ```yaml
        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:
        
        ```yaml
        allowed_entities:
          book: # intent
            - origin # entity
            - destination
          buy:
            - color
            - product
        ```
        
        Then load your agent
        ```python
        agent = SuperAgent.load(POLICY_PATH,
                                interpreter=interpreter,
                                create_dispatcher=lambda sender_id, output_channel, domain: MyDispatcher(sender_id, output_channel, domain))
        
        ```
        
        ### Bonus - Create a FAQ bot with only ONE action and ONE story
        
        
        You create an intent substitution rule like this:
        
        ```yaml
        intent_substitutions:
        
          - intent: (faq.*)
            with: faq
            entities:
              add:
              - name: intent
                value: '{intent}'
        ```
        
        This rule will match all intents starting with `faq` (e.g.: `faq.how_do_i_create_a_faq`)
        This will change the dialog act to `{intent: "faq", entities: [{intent: "faq.how_do_i_create_a_faq"}]}`
        
        In Core, add this story:
        ```md
        ## FAQ
        * faq
          - action_faq
        ```
        
        And this action
        
        ```python
        class ActionFAQ(Action):
        
            def name(self):
                return "action_faq"
        
            def run(self, dispatcher, tracker, domain):
                 # get the original intent from tracker.latest_message and retrieve the correct answer
        ```
        
        The benefit of this approach is you have only ONE story for all your questions, so if your Q&A are stored externally you don't have to retrain your bot when adding/changing questions. Since you have only one story for potentially 100's of questions, this means you can better handle side questions in more complex dialogs.
        
        ### Where are automated tests ?
        
        Rasa Core will have evaluation tools in 0.12.
        
Platform: UNKNOWN
Description-Content-Type: text/markdown
