Introduction to Rasa stories

Stories are the building blocks of conversation flows. It's a symbolic language used to describe conversations a user can have with a bot. In their simplest form, stories are made of user messages, starting with a *, and bot responses, starting with a -.

* chitchat.greet
  - utter_hi_there
* chitchat.bye
  - utter_bye

As you can see this is not real natural language: the user message is expressed in the form of an intent, and the bot response with a response name. The content of this intent (the many ways to say hi) and of the bot response (something like Hello my human friend) will be defined later

This has an important implication: stories are language agnostic. The stories you write will work in any language.

Stories and context

The context of a conversation is the knowledge of all the passed events of this conversation.

In the story above (previous section), if you say Hi three times to the bot it will reply three times the same thing. Consider this one:

* chitchat.greet
  - utter_hi_there
* chitchat.greet
  - utter_hi_again
* chitchat.greet
  - utter_hmm_really

Branching conversations

Conversations are often designed as tree-like flow charts. Stories are real conversations examples. Let's see how you can use stories to branch your flow:.

Branching with intents

The simplest way to branch a conversation is to use different intents at some point



 


* chitchat.greet
  - utter_hi_how_are_you
* chitchat.i_am_happy
  - utter_awesome


 


* chitchat.greet
  - utter_hi_how_are_you
* chitchat.i_am_sad
  - utter_i_have_a_bad_day_myself

In other words, to represent a conversation branching in two different scenarios you need two stories.

Branching with entity values

Another way is to use entity values:

* book{"class":"business"}
- utter_business
* book{"class":"eco"}
- utter_eco
* book
- utter_which_class

But wait, that doesn't work!

If you train and try those stories, you'll see that if you type /book the agent will utter utter_which_class as expected, but if you type book{"class":"business"} or book{"class":"business"} the response will be random. The reason is that if the value of the entity is not stored somewhere, Rasa only differentiates flow looking at ifthe entity class exists or not in the user utterance

Solution: store entity values in slots

If you want the above stories to work, you need create a slot. In that case we're going to create a categorical slot, and add the categories business and eco. Then retrain and it should work

Branching with slots

Once you define a slot with the same name as an entity, any entity value extracted from a user message will be set as the slot value, and this value will persist accross the conversation until it is changed or reset. It means that if a user said one of the above sentences (book{"class":"business"} or book{"class":"eco"}) you can still use that information to branch your conversation in other stories.

Use case: a user wants to cancel a booking, but only business bookings are cancellable.

Let's add 2 new stories


 


* cancel_booking
- slot{"class":"eco"}
- utter_booking_not_cancellable

 


* cancel_booking
- slot{"class":"business"}
- utter_booking_canceled

As you can see, the - slot{"class":"..."} branch the conversation in different paths.

What if the class has not been set yet?

You can add a 3rd category not_set to the class slot, and set the initial value to not_set. Then you can handle the case where no class is set gracefully like this:


 


* cancel_booking
- slot{"class":"not_set"}
- utter_which_class