Building a Simple Bot

Note

This tutorial will show you the different parts needed to build a bot. Be aware that this is a small example to get started quickly, it doesn’t include a lot of training data, so there is some room for improvement of the final bot performance.

Example Code on GitHub

Here we show how to create your first bot the relevant classes in an application. This might be easier to follow if you also look at Plumbing - How it all fits together.

Goal

We will create a very simple bot that checks our current mood and tries to cheer us up if we are feeling sad. It will query our mood and based on our response will respond with a funny image or a message.

Let’s start by creating a project folder:

mkdir moodbot && cd moodbot

We need to create two data files (dialogue stories as well as nlu examples) as well as two configuration files (dialogue domain and nlu config). The final structure should look like this:

moodbot/
├── data/
│   ├── stories.md            # dialogue training data
│   └── nlu.md                # nlu training data
├── domain.yml                # dialogue configuration
└── nlu_model_config.json     # nlu configuration

Let’s go through each of them!

1. Define a Domain

The first thing we need is a Domain. The domain defines the universe your bot lives in.

Here is an example domain for our moodbot, domain.yml:

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intents:
  - greet
  - goodbye
  - mood_affirm
  - mood_deny
  - mood_great
  - mood_unhappy

templates:
  utter_greet:
  - text: "Hey! How are you?"
    buttons:
    - title: "great"
      payload: "great"
    - title: "super sad"
      payload: "super sad"

  utter_cheer_up:
  - text: "Here is something to cheer you up:"
    image: "https://cdn77.eatliver.com/wp-content/uploads/2017/10/trump-frog.jpg"

  utter_did_that_help:
  - text: "Did that help you?"

  utter_happy:
  - text: "Great carry on!"

  utter_goodbye:
  - text: "Bye"

actions:
  - utter_greet
  - utter_cheer_up
  - utter_did_that_help
  - utter_happy
  - utter_goodbye

So what do the different parts mean?

intents things you expect users to say. See Rasa NLU for details.
entities pieces of info you want to extract from messages. See Rasa NLU for details.
actions things your bot can do and say
slots information to keep track of during a conversation (e.g. a users age)
templates template strings for the things your bot can say

In our simple example we don’t need slots, so that section doesn’t appear in our definition.

How does this fit together? Rasa takes the intent, entities, and the internal state of the dialogue, and selects one of the actions that should executed next. If the action is just to say something to the user, Rasa will look for a matching template in the domain (action name equals the utter template, e.g. as for utter_greeting in the above example), fill in any variables, and respond.

Note

There is one special action, ActionListen which means to stop taking further actions until the user says something else. For more actions which do more than just send a message, you can define them as python classes and reference them in the domain by their module path. See Defining Custom Actions for more information about custom actions.

2. Define an interpreter

An interpreter is responsible for parsing messages. It performs the Natural Language Understanding and transforms the message into structured output. For this example we are going to use Rasa NLU for this purpose.

Since we want to use Rasa NLU, we need to define the user messages our bot should be able to handle in the Rasa NLU training format. Let’s create some intent examples in data/nlu.md:

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## intent:greet
- hey
- hello
- hi
- hello there
- good morning
- good evening
- moin

## intent:goodbye
- bye
- goodbye
- have a nice day
- see you around
- bye bye
- see you later

## intent:mood_affirm
- yes
- indeed
- of course
- that sounds good
- correct

## intent:mood_deny
- no
- never
- I don't think so
- don't like that
- no way

## intent:mood_great
- wonderful
- I am feeling very good
- I am great
- I am amazing
- I am going to save the world

## intent:mood_unhappy
- my day was horrible
- I am sad
- I don't feel very well
- I am disappointed

Furthermore, we need a configuration file nlu_model_config.json for the NLU model:

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{
  "pipeline": "spacy_sklearn",
  "path" : "./models",
  "project": "nlu",
  "data" : "./data/nlu.md"
}

We can now train a NLU model using our examples (make sure to install Rasa NLU first as well as spaCy).

Let’s run

python -m rasa_nlu.train -c nlu_model_config.json --fixed_model_name current

to train our NLU model. A new directory models/nlu/current should have been created containing the NLU model.

Note

To gather more insights about the above configuration and Rasa NLU features head over to the Rasa NLU documentation.

3. Define stories

So far, we’ve got a NLU model, a domain defining the actions our bot can take, and inputs it should handle (intents & entities). We are still missing the central piece, stories to tell our bot what to do at which point in the dialogue. There are two different ways to create stories (and you can mix them):

  • create the stories by hand, writing them directly in a file
  • create stories using the interactive learning (see Interactive Learning).

For this example, we are going to create the stories by writing them directly into stories.md. But be aware, although it is a bit faster to write stories directly by hand instead of using interactive learning, special care needs to be taken when using slots, as they need to be properly set in the stories. But enough talking, let’s get to our stories:

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## happy path               <!-- name of the story - just for debugging -->
* _greet              
  - utter_greet
* _mood_great               <!-- user utterance, in format _intent[entities] -->
  - utter_happy

## sad path 1               <!-- this is already the start of the next story -->
* _greet
  - utter_greet             <!-- action of the bot to execute -->
* _mood_unhappy
  - utter_cheer_up
  - utter_did_that_help
* _mood_affirm
  - utter_happy

## sad path 2
* _greet
  - utter_greet
* _mood_unhappy
  - utter_cheer_up
  - utter_did_that_help
* _mood_deny
  - utter_goodbye

## say goodbye
* _goodbye
  - utter_goodbye

The bot actions are also events, and are specified by lines starting with a dash. The end of a story is denoted by a newline. See Stories - The Training Data for more information about the data format.

4. Put the pieces together

There are two things we still need to do: train the dialogue model and run it.

To train the dialogue model, run:

python -m rasa_core.train -s data/stories.md -d domain.yml -o models/dialogue --epochs 300

This will train the dialogue model for 300 epochs and store it into models/dialogue. Now we can use that trained dialogue model and the previously created NLU model to run our bot:

python -m rasa_core.run -d models/dialogue -u models/nlu/current

And there we have it! A minimal bot containing all the important pieces of Rasa Core.

_images/facebook-run.png

Bonus: Handle messages from facebook

If you want to handle input from facebook instead of the command line, you can specify that as part of the run command, after creating a credentials file containing the information to connect to facebook. Let’s put that into credentials.yml:

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verify: "rasa-bot"
secret: "3e34709d01ea89032asdebfe5a74518"
page-tokens:
  1730621093913654: "EAAbHPa7H9rEBAAuFk4Q3gPKbDedQnx4djJJ1JmQ7CAqO4iJKrQcNT0wtD"

If you are new to facebook messenger bots, head over to Facebook Messenger Setup for an explanation of the different values.

After setting that up, we can now run the bot using

python -m rasa_core.run -d models/dialogue -u models/nlu/current \
   --port 5002 --connector facebook --credentials credentials.yml

and it will now handle messages users send to the facebook page!