Building a Rasa Assistant in Docker

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

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

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:1.10.18-full init --no-prompt

What does this command mean?

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

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:1.10.18-full shell

This will start a shell where you can chat to your assistant. Note that this command includes the flags -it, which means that you are running Docker interactively, and you are able to give input via the command line.

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:1.10.18-full train --domain domain.yml --data data --out models

Here’s what’s happening in that command:

In this case, we’ve also passed values for the location of the domain file, training data, and the models output directory to show how these can be customized.

Customizing your Model

Choosing a Tag

All rasa/rasa image tags start with a version number. The current version is 1.10.18. The tags are:

The {version}-full tag includes all possible pipeline dependencies, allowing you to change your config.yml as you like without worrying about missing dependencies. The plain {version} tag includes all the dependencies you need to run the default pipeline created by rasa init.

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. You can do this by either mounting the file or by including it in your own custom image (e.g. if the custom component or policy has extra dependencies). Make sure that your module is in the Python module search path by setting the environment variable PYTHONPATH=$PYTHONPATH:<directory of your module>.

Adding Custom Actions

To create more sophisticated assistants, you will want to use Custom Actions. Continuing the example from above, you might want to add an action which tells the user a joke to cheer them up.

Start by creating the custom actions in a directory actions in your working directory:

mkdir actions
> mv actions.py actions/actions.py
> # Rasa SDK expects a python module.
> # Therefore, make sure that you have this file in the directory.
> touch actions/__init__.py

Then build a custom action using the Rasa SDK by editing actions/actions.py, for example:

import requests
import json
from rasa_sdk import Action

class ActionJoke(Action):
  def name(self):
    return "action_joke"

  def run(self, dispatcher, tracker, domain):
    request = requests.get('http://api.icndb.com/jokes/random').json()  # make an api call
    joke = request['value']['joke']  # extract a joke from returned json response
    dispatcher.utter_message(text=joke)  # send the message back to the user
    return []

In data/stories.md, replace utter_cheer_up with the custom action action_joke to tell your bot to use this new action.

In domain.yml, add a section for custom actions, including your new action:

actions:
  - action_joke

After updating your domain and stories, you have to retrain your model:

docker run -v $(pwd):/app rasa/rasa:1.10.18-full train

Your actions will run on a separate server from your Rasa server. First create a network to connect the two containers:

docker network create my-project

You can then run the actions with the following command:

docker run -d -v $(pwd)/actions:/app/actions --net my-project --name action-server rasa/rasa-sdk:1.10.3

To instruct the Rasa server to use the action server, you have to tell Rasa its location. Add this endpoint to your endpoints.yml, referencing the --name you gave the server:

action_endpoint:
  url: "http://action-server:5055/webhook"

Now you can talk to your bot again via the shell command:

docker run -it -v $(pwd):/app -p 5005:5005 --net my-project rasa/rasa:1.10.18-full shell

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. To do so, you can deploy the model you created with Rasa X via one of our recommended deployment methods or do a Rasa-only deployment in Docker Compose.