Tutorial: Building Assistants

After following the basics of setting up an assistant in the Rasa Tutorial, we’ll now walk through building a basic FAQ chatbot and then build a bot that can handle contextual conversations.

Building a simple FAQ assistant

FAQ assistants are the simplest assistants to build and a good place to get started. These assistants allow the user to ask a simple question and get a response. We’re going to build a basic FAQ assistant using features of Rasa designed specifically for this type of assistant.

In this section we’re going to cover the following topics:

• Responding to simple intents with the MemoizationPolicy
• Handling FAQs using the ResponseSelector

You should first install Rasa using the Step-by-step Installation Guide and then follow the Rasa Tutorial to make sure you know the basics.

To prepare for this tutorial, we’re going to create a new directory and start a new Rasa project.

mkdir rasa-assistant rasa init

Let’s remove the default content from this bot, so that the nlu.md, stories.md and domain.yml are empty.

Memoization Policy

The MemoizationPolicy remembers examples from training stories for up to a max_history of turns. The number of “turns” includes messages the user sent, and actions the assistant performed. For the purpose of a simple, context-less FAQ bot, we only need to pay attention to the last message the user sent, and therefore we’ll set that to 1.

You can do this by editing your config.yml file as follows:

policies: - name: MemoizationPolicy max_history: 1 - name: MappingPolicy

Note The MappingPolicy is there because it handles the logic of the /restart intent, which allows you to clear the conversation history and start fresh.

Now that we’ve defined our policies, we can add some stories for the goodbye, thank and greet intents to the stories.md file:

greet

* greet
  - utter_greet

thank

* thank
  - utter_noworries

goodbye

* bye
  - utter_bye

We’ll also need to add the intents, actions and templates to our domain.yml file in the following sections:

intents: - greet - bye - thank

actions: - utter_greet - utter_noworries - utter_bye

templates: utter_noworries: - text: No worries! utter_greet: - text: Hi utter_bye: - text: Bye!

Finally, we’ll copy over some NLU data from Sara into our nlu.md (more can be found here):

intent:greet

- Hi
- Hey
- Hi bot
- Hey bot
- Hello
- Good morning
- hi again
- hi folks

intent:bye

- goodbye
- goodnight
- good bye
- good night
- see ya
- toodle-oo
- bye bye
- gotta go
- farewell

intent:thank

- Thanks
- Thank you
- Thank you so much
- Thanks bot
- Thanks for that
- cheers

You can now train a first model and test the bot, by running the following commands:

rasa train rasa shell

This bot should now be able to reply to the intents we defined consistently, and in any order.

Response Selectors

The Response Selector NLU component is designed to make it easier to handle dialogue elements like Small Talk and FAQ messages in a simple manner. By using the ResponseSelector, you only need one story to handle all FAQs, instead of adding new stories every time you want to increase your bot’s scope.

People often ask Sara different questions surrounding the Rasa products, so let’s start with three intents: ask_channels, ask_languages, and ask_rasax. We’re going to copy over some NLU data from the Sara training data into our nlu.md. It’s important that these intents have an faq/ prefix, so they’re recognised as the faq intent by the ResponseSelector:

intent: faq/ask_channels

- What channels of communication does rasa support?
- what channels do you support?
- what chat channels does rasa uses
- channels supported by Rasa
- which messaging channels does rasa support?

intent: faq/ask_languages

- what language does rasa support?
- which language do you support?
- which languages supports rasa
- can I use rasa also for another laguage?
- languages supported

intent: faq/ask_rasax

- I want information about rasa x
- i want to learn more about Rasa X
- what is rasa x?
- Can you tell me about rasa x?
- Tell me about rasa x
- tell me what is rasa x

Next, we’ll need to define the responses associated with these FAQs in a new file called responses.md in the data/ directory:

ask channels

* faq/ask_channels
  - We have a comprehensive list of [supported connectors](/content/docs/core/connectors/index.html), but if you don't see the one you're looking for, you can always create a custom connector by following [this guide](/content/docs/rasa/user-guide/connectors/custom-connectors/index.html).

ask languages

* faq/ask_languages
  - You can use Rasa to build assistants in any language you want!

ask rasa x

* faq/ask_rasax
  - Rasa X is a tool to learn from real conversations and improve your assistant. Read more [here](/content/docs/rasa-x/index.html)

To use the Response Selector we need to add it to the end of the expanded supervised_embeddings NLU pipeline in our config.yml:

pipeline: - name: "WhitespaceTokenizer" - name: "RegexFeaturizer" - name: "CRFEntityExtractor" - name: "EntitySynonymMapper" - name: "CountVectorsFeaturizer" - name: "CountVectorsFeaturizer" analyzer: "char_wb" min_ngram: 1 max_ngram: 4 - name: "EmbeddingIntentClassifier" - name: "ResponseSelector"

Now that we’ve defined the NLU side, we need to make Core aware of these changes. Open your domain.yml file and add the faq intent:

intents: - greet - bye - thank - faq

We’ll also need to add a retrieval action, which takes care of sending the response predicted from the ResponseSelector back to the user, to the list of actions. These actions always have to start with the respond_ prefix:

actions: - utter_greet - utter_noworries - utter_bye - respond_faq

Next we’ll write a story so that Core knows which action to predict:

Some question from FAQ

* faq
    - respond_faq

After all of the changes are done, train a new model and test the modified FAQs:

rasa train rasa shell

At this stage it makes sense to add a few test cases to your test_stories.md file again:

ask channels

* faq: What messaging channels does Rasa support?
  - respond_faq

ask languages

* faq: Which languages can I build assistants in?
  - respond_faq

ask rasa x

* faq: What’s Rasa X?
  - respond_faq

Using the features we described in this tutorial, you can easily build a context-less assistant. When you’re ready to enhance your assistant with context, check out Building contextual assistants.

Building contextual assistants

Whether you’ve just created an FAQ bot or are starting from scratch, the next step is to expand your bot to handle contextual conversations.

In this tutorial we’re going to cover a variety of topics:

• Handling business logic
• Handling unexpected user input
• Failing gracefully
• More complex contextual conversations

Please make sure you’ve got all the data from the Building a simple FAQ assistant section before starting this part.

Business logic

A lot of conversational assistants have user goals that involve collecting a bunch of information from the user before being able to do something for them. This is called slot filling. For example, in the banking industry you may have a user goal of transferring money, where you need to collect information about which account to transfer from, whom to transfer to and the amount to transfer. This type of behaviour can and should be handled in a rule-based way, as it is clear how this information should be collected.

For this type of use case, we can use Forms and our FormPolicy. The FormPolicy works by predicting the form as the next action until all information is gathered from the user.

As an example, we will build out the SalesForm from Sara. The user wants to contact our sales team, and for this we need to gather the following pieces of information:

• Their job
• Their bot use case
• Their name
• Their email
• Their budget
• Their company

We will start by defining the SalesForm as a new class in the file called actions.py.

from rasa_sdk.forms import FormAction

class SalesForm(FormAction): """Collects sales information and adds it to the spreadsheet"""

def name(self): return "sales_form"

Next we have to define the required_slots method which specifies which pieces of information to ask for, i.e. which slots to fill.

@staticmethod def required_slots(tracker): return [
"job_function",
"use_case",
"budget",
"person_name",
"company",
"business_email",
]

Once you’ve done that, you’ll need to specify how the bot should ask for this information. This is done by specifying utter_ask_{slotname} templates in your domain file. For the above we’ll need to specify the following:

utter_ask_business_email: - text: What's your business email? utter_ask_company: - text: What company do you work for? utter_ask_budget: - text: "What's your annual budget for conversational AI? 💸" utter_ask_job_function: - text: "What's your job? 🕴" utter_ask_person_name: - text: What's your name? utter_ask_use_case: - text: What's your use case?

We’ll also need to define all these slots in our domain:

slots: company: type: unfeaturized job_function: type: unfeaturized person_name: type: unfeaturized budget: type: unfeaturized business_email: type: unfeaturized use_case: type: unfeaturized

Going back to our Form definition, we need to define the submit method as well, which will do something with the information the user has provided once the form is complete:

def submit( self, dispatcher: CollectingDispatcher, tracker: Tracker, domain: Dict[Text, Any], ) -> List[Dict]:

dispatcher.utter_message("Thanks for getting in touch, we’ll contact you soon") return []

We’ll need to add the form we just created to a new section in the domain file:

forms: - sales_form

We also need to create an intent to activate the form, as well as an intent for providing all the information the form asks the user for. For the form activation intent, we can create an intent called contact_sales. Add the following training data to your nlu file:

intent:contact_sales

- I wanna talk to your sales people.
- I want to talk to your sales people
- I want to speak with sales
- Sales
- Please schedule a sales call
- Please connect me to someone from sales
- I want to get in touch with your sales guys
- I would like to talk to someone from your sales team
- sales please

We will also create an intent called inform which covers any sort of information the user provides to the bot. The reason we put all this under one intent is because there is no real intent behind providing information; only the entity is important. Add the following data to your NLU file:

intent:inform

- [100k](budget)
- [100k](budget)
- [240k/year](budget)
- [150,000 USD](budget)
- I work for [Rasa](company)
- The name of the company is [ACME](company)
- company: [Rasa Technologies](company)
- it's a small company from the US, the name is [Hooli](company)
- it's a tech company, [Rasa](company)
- [ACME](company)
- [Rasa Technologies](company)
- [maxmeier@firma.de](business_email)
- [bot-fan@bots.com](business_email)
- [maxmeier@firma.de](business_email)
- [bot-fan@bots.com](business_email)
- [my email is email@rasa.com](business_email)
- [engineer](job_function)
- [brand manager](job_function)
- [marketing](job_function)
- [sales manager](job_function)
- [growth manager](job_function)
- [CTO](job_function)
- [CEO](job_function)
- [COO](job_function)
- [John Doe](person_name)
- [Jane Doe](person_name)
- [Max Mustermann](person_name)
- [Max Meier](person_name)
- We plan to build a [sales bot](use_case) to increase our sales by 500%.
- we plan to build a [sales bot](use_case) to increase our revenue by 100%.
- a [insurance tool](use_case) that consults potential customers on the best life insurance to choose.
- we're building a [conversational assistant](use_case) for our employees to book meeting rooms.

The intents and entities will need to be added to your domain as well:

intents: - greet - bye - thank - faq - contact_sales - inform

entities: - company - job_function - person_name - budget - business_email - use_case

A story for a form is very simple, as all the slot collection form happens inside the form, and therefore doesn’t need to be covered in your stories.

sales form

* contact_sales
    - sales_form
    - form{"name": "sales_form"}
    - form{"name": null}

As a final step, let’s add the FormPolicy to our config file:

policies: - name: MemoizationPolicy - name: KerasPolicy - name: MappingPolicy - name: FormPolicy

At this point, you already have a working form, so let’s try it out. Make sure to uncomment the action_endpoint in your endpoints.yml to make Rasa aware of the action server that will run our form:

action_endpoint: url: "http://localhost:5055/webhook"

Then start the action server in a new terminal window:

rasa run actions

Then you can retrain and talk to your bot:

rasa train rasa shell

This simple form will work out of the box, however, you will likely want to add a bit more capability to handle different situations. One example of this is validating slots, to make sure the user provided information correctly (read more about it here).

Handling unexpected user input

All expected user inputs should be handled by the form we defined above, i.e. if the user provides the information the bot asks for. However, in real situations, the user will often behave differently. In this section we’ll go through various forms of “interjections” and how to handle them within Rasa.