Tutorial: Rasa Basics

These docs are for version 1.x of Rasa Open Source.

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viewing: 1.9.0

Tutorial: Rasa Basics

This page explains the basics of building an assistant with Rasa and shows the structure of a Rasa project. You can test it out right here without installing anything. You can also install Rasa and follow along in your command line.

The glossary contains an overview of the most common terms you’ll see in the Rasa documentation.

1. Create a New Project

The first step is to create a new Rasa project. To do this, run:

rasa init --no-prompt

The rasa init command creates all the files that a Rasa project needs and trains a simple bot on some sample data.

This creates the following files:
__init__.py an empty file that helps python find your actions
actions.py code for your custom actions
config.yml * configuration of your NLU and Core models
credentials.yml details for connecting to other services
data/nlu.md * your NLU training data
data/stories.md * your stories
domain.yml * your assistant’s domain
endpoints.yml details for connecting to channels like fb messenger
models/<timestamp>.tar.gz your initial model

2. View Your NLU Training Data

The first piece of a Rasa assistant is an NLU model. NLU stands for Natural Language Understanding, which means turning user messages into structured data. To do this with Rasa, you provide training examples that show how Rasa should understand user messages, and then train a model by showing it those examples.

Run the code cell below to see the NLU training data created by the rasa init command:

cat data/nlu.md

The lines starting with ## define the names of your intents, which are groups of messages with the same meaning. Rasa’s job will be to predict the correct intent when your users send new, unseen messages to your assistant.

3. Define Your Model Configuration

The configuration file defines the NLU and Core components that your model will use. In this example, your NLU model will use the supervised_embeddings pipeline.

Let’s take a look at your model configuration file.

cat config.yml

The language and pipeline keys specify how the NLU model should be built.

4. Write Your First Stories

At this stage, you will teach your assistant how to respond to your messages. This is called dialogue management, and is handled by your Core model.

Below is an example of a simple conversation. The user says hello, and the assistant says hello back. This is how it looks as a story:

## story1
* greet
   - utter_greet

5. Define a Domain

The next thing we need to do is define a Domain. The domain defines the universe your assistant lives in: what user inputs it should expect to get, what actions it should be able to predict, how to respond, and what information to store. The domain for our assistant is saved in a file called domain.yml:

cat domain.yml

So what do the different parts mean?
intents things you expect users to say
actions things your assistant can do and say
templates template strings for the things your assistant can say
How does this fit together? Rasa Core’s job is to choose the right action to execute at each step of the conversation.

6. Train a Model

Anytime we add new NLU or Core data, or update the domain or configuration, we need to re-train a neural network on our example stories and NLU data. To do this, run the command below.

rasa train

7. Test Your Assistant

After you train a model, check that your assistant behaves as expected. 

rasa test

8. Talk to Your Assistant

Congratulations! 🚀 You just built an assistant powered entirely by machine learning.

Now that you’ve built your first Rasa bot it’s time to learn about some more advanced Rasa features.