Deploying your Rasa Assistant

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

Deploying your Rasa Assistant

This page explains when and how to deploy an assistant built with Rasa. It will allow you to make your assistant available to users and set you up with a production-ready environment.

Kubernetes/Openshift

Kubernetes/Openshift is the best option if you need a scalable architecture. It’s straightforward to deploy if you use the helm charts we provide. However, you can also customize the Helm charts if you have specific requirements.

Docker Compose

Rasa-Only Deployment with Docker Compose

It is also possible to deploy a Rasa assistant using Docker Compose without Rasa X.

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.

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

Note: By default Docker runs containers as root user. Hence, all files created by these containers will be owned by root. See the documentation of docker and docker-compose if you want to run the containers with a different user.

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. All tags start with a version – the latest tag corresponds to the current master build. 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

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
touch actions/__init__.py
touch actions/actions.py

Then build a custom action using the Rasa SDK, e.g.:

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 []

Then add the custom action in your stories and your domain file.

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 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

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

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

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 it is described in Tracker Stores. If you want the tracker store component (e.g. a certain database) to be part of your Docker Compose file, add a corresponding service and configuration there.