Building a Rasa Assistant in Docker

If you don’t have a Rasa project yet, you can build one in Docker without having to install Rasa Open Source on your local machine. If you already have a model you’re satisfied with, see Deploying Your Rasa Assistant to learn how to deploy your model.

• Installing Docker
• Setting up your Rasa Project
• Talking to Your Assistant
• Training a Model
• Customizing your Model
  • Choosing a Tag
  • Adding Custom Components
  • Adding Custom Actions
• Deploying your Assistant

Installing Docker

If you’re not sure if you have Docker installed, you can check by running:

docker -v
# Docker version 18.09.2, build 6247962

If Docker is installed on your machine, the output should show you your installed versions of Docker. If the command doesn’t work, you’ll have to install Docker. See Docker Installation for details.

Setting up your Rasa Project

Just like in the tutorial, you’ll use the rasa init command to create a project. The only difference is that you’ll be running Rasa inside a Docker container, using the image rasa/rasa. To initialize your project, run:

docker run -v $(pwd):/app rasa/rasa:1.10.24-full init --no-prompt

What does this command mean?

• -v $(pwd):/app mounts your current working directory to the working directory in the Docker container.
• rasa/rasa is the name of the docker image to run.

Running this command will produce a lot of output. What happens is:

• a Rasa project is created
• an initial model is trained using the project’s training data.

To check that the command completed correctly, look at the contents of your working directory:

ls -1

The initial project files should all be there, as well as a models directory that contains your trained model.

Talking to Your Assistant

To talk to your newly-trained assistant, run this command:

docker run -it -v $(pwd):/app rasa/rasa:1.10.24-full shell

Training a Model

If you edit the NLU or Core training data or edit the config.yml file, you’ll need to retrain your Rasa model. You can do so by running:

docker run -v $(pwd):/app rasa/rasa:1.10.24-full train --domain domain.yml --data data --out models

Customizing your Model

Choosing a Tag

All rasa/rasa image tags start with a version number. The current version is 1.10.24. The tags are:

• {version}
• {version}-full
• {version}-spacy-en
• {version}-spacy-de
• {version}-mitie-en

Adding Custom Components

If you are using a custom NLU component or policy in your config.yml, you have to add the module file to your Docker container.

Adding Custom Actions

To create more sophisticated assistants, you will want to use Custom Actions.

Deploying your Assistant

Work on your bot until you have a minimum viable assistant that can handle your happy paths. After that, you’ll want to deploy your model to get feedback from real test users.