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.

• When to deploy your assistant

• Recommended Deployment Methods

• Kubernetes/Openshift

  • Docker Compose

• Rasa-Only Deployment with Docker Compose

When to deploy your assistant

The best time to deploy your assistant and make it available to test users is once it can handle the most important happy paths or is what we call a minimum viable assistant.

The recommended deployment methods described below make it easy to share your assistant with test users via the share your assistant feature in Rasa X. Then, when you’re ready to make your assistant available via one or more Messaging and Voice Channels, you can easily add them to your existing deployment set up.

Recommended Deployment Methods

The recommended way to deploy an assistant is using either the Docker Compose or Kubernetes/Openshift options we support. Both deploy Rasa X and your assistant. They are the easiest ways to deploy your assistant, allow you to use Rasa X to view conversations and turn them into training data, and are production-ready.

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.

• Default: Read the docs here.
• Custom: Read the docs here and customize the open source Helm charts.

Docker Compose

• Default: Watching this video or read the docs here.
• Custom: Read the docs here.

Rasa-Only Deployment with Docker Compose

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

• Installing Docker

• Building an Assistant with Rasa and Docker

• Setup

  • Talking to Your Assistant

• Customizing your Model

• Choosing a Tag

  • Training a Custom Rasa Model with Docker

• Running the Rasa Server

• Using Docker Compose to Run Multiple Services

• Adding 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

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 []

Next, add the custom action in your stories and your domain file. Continuing with the example bot from rasa init, replace utter_cheer_up in data/stories.md with the custom action action_joke, and add action_joke to the actions in the domain file.

Adding Custom Dependencies

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.