The Domain defines the universe in which your bot operates.It specifies the intents, entities, slots, and actionsyour bot should know about. Optionally, it can also include templatesfor the things your bot can say.

1. By default, Rasa Open Source fills a slot with an entity that has the same name. So if you’ve defined a name slot in your domain file, and you also have a name entity defined in your domain and training data, when the NLU model extracts the name entity, it’ll be saved to the name slot automatically.
2. 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.
3. Rasa.db file is a database where all the conversation with your bot Is stored. Domain.yml file: In which you are storing all the actions, intents, entities, templates and slots.
4. In the example above, an entity date uniquely sets the slot arrivaldate, an entity city with a role from uniquely sets the slot departurecity and an entity city with a role to uniquely sets the slot arrivalcity, therefore they can be used to fit corresponding slots even if these slots were not requested.

From rasasdk.events import SlotSet. Then in you run method, you can set your value in relevant slot. Imagine you slot name for humidity is weatherhumidity. Then imagine your humidity value from the API is extracted for a variable called humidity. Then in your custom action run method, simply set the slot value with below line.

As an example, the DefaultDomain has the following yaml definition:

What does this mean?

Your NLU model will define the intents and entities that youneed to include in the domain.

slots are the things you want to keep track of during a conversation,see Using Slots . A categorical slot called risk_level would bedefined like this:

Here is the full list of slot types defined byRasa Core, along with syntax for including them in your domain file.

actions are the things your bot can actually do.For example, an action can:

• respond to a user
• make an external API call
• query a database

see Actions

For a more complete example domain, check the Quickstart.

Custom Actions and Slots¶

To reference slots in your domain, you need to reference them bytheir module path. To reference custom actions, use their name.For example, if you have a module called my_actions containinga class MyAwesomeAction, and module my_slots containingMyAwesomeSlot, you would add these lines to the domain file:

The name function of MyAwesomeAction needs to returnmy_custom_action in this example (for more details,see Actions).

Utterance templates¶

Utterance templates are messages the bot will send back to the user. There aretwo ways to use these templates:

1. if the name of the template starts with utter_, the utterance candirectly be used like an action. You would add the utterance templateto the domain

   Afterwards, you can use the template as if it were an action in thestories:

   When utter_greet is run as an action, it will send the message fromthe template back to the user.

2. You can use the templates to generate response messages from yourcustom actions using the dispatcher:dispatcher.utter_template('utter_greet').This allows you to separate the logic of generatingthe messages from the actual copy. In you custom action code, you cansend a message based on the template like this:

Images and Buttons¶

Templates defined in a domains yaml file can contain images andbuttons as well:

Note

Please keep in mind that it is up to the implementation of the outputchannel on how to display the defined buttons. E.g. the cmdlineinterface can not display buttons or images, but tries to mimic them inthe command line.

Variables¶

You can also use variables in your templates to insert informationcollected during the dialogue. You can either do that in your custom pythoncode or by using the automatic slot filling mechanism. E.g if yougot a template like this:

Rasa will automatically fill that variable with a value found in a slot calledname.

In custom code, you can retrieve a template by using:

If the template contains variables denoted with {my_variable}you can supply values for the fields by passing them as key wordarguments to utter_template:

Variations¶

If you want to randomly vary the response sent to the user, you can listmultiple responses and Rasa will randomly pick one of them, e.g.:

Ignoring entities for certain intents¶

If you want entities to be ignored for certain intents, you canadd the use_entities:false parameter to the intent in your domainfile like this:

This means that entities for those intents will be unfeaturized and thereforewill not impact the next action predictions. This is useful when you havean intent where you don’t care about the entities being picked up. If you listyour intents as normal without this parameter, the entities will befeaturized as normal.

Note

If you really want these entities not to influence action prediction wesuggest you make the slots with the same name of type unfeaturized

Have questions or feedback?¶

We have a very active support community on Rasa Community Forumthat is happy to help you with your questions. If you have any feedback for us or a specificsuggestion for improving the docs, feel free to share it by creating an issue on Rasa CoreGitHub repository.

Slots are your bot’s memory. They act as a key-value storewhich 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 adatabase 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 mighthave a text slot called home_city. If the user asks for theweather, and you don’t know their home city, you will have to askthem for it. A text slot only tells Rasa Core whether the slothas a value. The specific value of a text slot (e.g. Bangaloreor New York or Hong Kong) doesn’t make any difference.

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

The Policy doesn’t have access to thevalue 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!

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

You can get the value of a slot using .get_slot() inside actions.py for example:

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

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

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

To disable this behavior for a particular slot, you can set theauto_fill attribute to False in the domain file:

You can use buttons as a shortcut.Rasa Core will send messages starting with a / to theRegexInterpreter, which expects NLU input in the same formatas in story files, e.g. /intent{entities}. For example, if you letusers choose a color by clicking a button, the button payloads mightbe /choose{'color':'blue'} and /choose{'color':'red'}.

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

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

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 totake (in this case, utter_welcome_premium or utter_welcome_basic).

Note

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

User preferences where you only care whether or not they’vebeen specified.

Results in the feature of the slot being set to 1 if any value is set.Otherwise the feature will be set to 0 (no value is set).

True or False

Checks if slot is set and if True

Slots which can take one of N values

Creates a one-hot encoding describing which of the values matched.A default value __other__ is automatically added to the user-definedvalues. All values encountered which are not explicitly defined in thedomain are mapped to __other__ for featurization. The value__other__ should not be used as a user-defined value; if it is, itwill still behave as the default to which all unseen values are mapped.

Continuous values

max_value=1.0, min_value=0.0

All values below min_value will be treated as min_value, the samehappens for values above max_value. Hence, if max_value is set to1, there is no difference between the slot values 2 and 3.5 interms of featurization (e.g. both values will influence the dialogue inthe same way and the model can not learn to differentiate between them).

Lists of values

The feature of this slot is set to 1 if a value with a list is set,where the list is not empty. If no value is set, or the empty list is theset value, the feature will be 0. The length of the list stored inthe slot does not influence the dialogue.

Data you want to store which shouldn’t influence the dialogue flow

Slot Example Rasa Al

There will not be any featurization of this slot, hence its value doesnot influence the dialogue flow and is ignored when predicting the nextaction the bot should run.

Maybe your restaurant booking system can only handle bookingsfor up to 6 people. In this case you want the value of theslot to influence the next selected action (and not just whetherit’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 vectorto our machine learning model can deal with.Our slot has three possible “values”, which we can represent witha vector of length 2.

Slot List Rasa Example

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