Running Rasa with Docker

This is a guide on how to build a Rasa assistant with Docker. If you haven’t used Rasa before, we’d recommend that you start with the Rasa Tutorial.

Installing Docker

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

docker -v && docker-compose -v
# Docker version 18.09.2, build 6247962
# docker-compose version 1.23.2, build 1110ad01

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

Building an Assistant with Rasa and Docker

This section will cover the following:

• Setting up your Rasa project and training an initial model
• Talking to your AI assistant via Docker
• Choosing a Docker image tag
• Training your Rasa models using Docker
• Talking to your assistant using Docker
• Running a Rasa server with Docker

Setup

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 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.

Talking to Your Assistant

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

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

This will start a shell where you can chat to your assistant.

Customizing your Model

Choosing a Tag

To keep images as small as possible, we publish different tags of the rasa/rasa image with different dependencies installed.

The tags are:

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

Training a Custom Rasa Model with Docker

Edit the config.yml file to use the pipeline you want, and place your NLU and Core data into the data/ directory. Now you can train your Rasa model by running:

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

Running the Rasa Server

To run your AI assistant in production, configure your required Messaging and Voice Channels in credentials.yml. If this file does not exist, create it using:

touch credentials.yml

Then edit it according to your connected channels.

Using Docker Compose to Run Multiple Services

To run Rasa together with other services, such as a server for custom actions, it is recommended to use Docker Compose.

Start by creating a file called docker-compose.yml:

touch docker-compose.yml

Add the following content to the file:

version: '3.0'
services:
  rasa:
    image: rasa/rasa:latest-full
    ports:
      - 5005:5005
    volumes:
      - ./:/app
    command:
      - run

To run the services configured in your docker-compose.yml execute:

docker-compose up

Adding Custom Actions

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

Creating a Custom Action

Start by creating the custom actions in a directory actions:

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

Adding the Action Server

To spin it up together with the Rasa instance, add a service action_server to the docker-compose.yml:

version: '3.0'
services:
  rasa:
    image: rasa/rasa:latest-full
    ports:
      - 5005:5005
    volumes:
      - ./:/app
    command:
      - run
  action_server:
    image: rasa/rasa-sdk:latest
    volumes:
      - ./actions:/app/actions

Adding Custom Dependencies

If your custom action has additional dependencies of systems or Python libraries, you can add these by extending the official image.

Adding a Custom Tracker Store

By default, all conversations are saved in memory. This means that all conversations are lost as soon as you restart the Rasa server. If you want to persist your conversations, you can use a different Tracker Store.

Using PostgreSQL as Tracker Store

Start by adding PostgreSQL to your docker-compose file:

postgres:
  image: postgres:latest

Then add PostgreSQL to the tracker_store section of your endpoint configuration config/endpoints.yml:

tracker_store:
  type: sql
  dialect: "postgresql"
  url: postgres
  db: rasa

Using Redis as Tracker Store

Start by adding Redis to your docker-compose file:

redis:
  image: redis:latest

Then add Redis to the tracker_store section of your endpoint configuration endpoints.yml:

tracker_store:
  type: redis
  url: redis

Using a Custom Tracker Store Implementation

If you have a custom implementation of a tracker store you have two options to add this store to Rasa:

• extending the Rasa image
• mounting it as volume

Then add the required configuration to your endpoint configuration endpoints.yml as it is described in Tracker Stores.