Running Rasa with Docker

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:

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

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. 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.

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. See Choosing a Pipeline for more information about dependencies.

The tags are:

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. After, run the trained model with:

docker run \
  -v $(pwd)/models:/app/models \
  rasa/rasa:latest-full \
  run

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

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()
    joke = request['value']['joke']
    dispatcher.utter_message(joke)
    return []

Adding the Action Server

The custom actions are run by 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 a Custom Tracker Store

By default, all conversations are saved in memory. 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 MongoDB as Tracker Store

Start by adding MongoDB to your docker-compose file:

mongo:
  image: mongo
  environment:
    MONGO_INITDB_ROOT_USERNAME: rasa
    MONGO_INITDB_ROOT_PASSWORD: example

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

tracker_store:
  type: mongod
  url: mongodb://mongo:27017
  username: rasa
  password: example

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:

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