# Tutorial: Building Assistants

After following the basics of setting up an assistant in the [Rasa Tutorial](/content/docs/rasa/user-guide/rasa-tutorial/index.html), 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

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` files 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 (you can remove `TEDPolicy` for now):

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

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 responses to our `domain.yml` file in the following sections:

```
intents:
  - greet
  - bye
  - thank

responses:
  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` file:

```
## intent:greet
- Hi
- Hey
...
- hi folks

## intent:bye
- goodbye
- goodnight
...
- gotta go

## intent:thank
- Thanks
- Thank you
- Thank you so much
...
```

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.

For example:

While it’s good to test the bot interactively, we should also add end to end test cases that can later be included as part of a CI/CD system. End-to-end test conversations include NLU data, so that both components of Rasa can be tested. The file `tests/conversation_tests.md` contains example test conversations. Delete all the test conversations and replace them with some test conversations for your assistant so far:
 
```
## greet + goodbye
* greet: Hi!
  - utter_greet
* bye: Bye
  - utter_bye

## greet + thanks
* greet: Hello there
  - utter_greet
* thank: thanks a bunch
  - utter_noworries

## greet + thanks + goodbye
* greet: Hey
  - utter_greet
* thank: thank you
  - utter_noworries
* bye: bye bye
  - utter_bye
```

To test our model against the test file, run the command:

```
rasa test --stories tests/conversation_tests.md
```
 
### Response Selectors

The ResponseSelector 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`.

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.

## 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)
```

The ResponseSelector should already be at the end of the NLU pipeline in our `config.yml`:

```
language: en
pipeline:
  - name: WhitespaceTokenizer
...
- name: ResponseSelector
    epochs: 100
```

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:
 
```
actions:
  - 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 a contextual assistant](https://legacy-docs-v1.rasa.com/1.10.9/user-guide/building-assistants/#tutorial-contextual-assistants).

The document also covers more advanced features such as Handling unexpected user input, Fallback policies, and More complex contextual conversations.

### Conclusion

By following this structured approach to building assistants in Rasa, you create a foundation upon which to build more complex and context-aware chatbots.