“Hey Google” with Rasa – complete CI/CD solution for multilingual chatbots

old phone

In this article I will show how to build the complete CI/CD solution for building, training and deploying multilingual chatbots. I will use Rasa core framework, Gitlab pipelines, Minio and Redis to build simple two language google assistant.

The project code is available on my github https://github.com/qooba/heygoogle-with-rasa

Before you will continue reading please watch quick introduction:


architecture diagram

The solution contains several components thus I will describe each of them.

Google actions

To build google assistant we need to create and configure the google action project. google actions create

We will build our own nlu engine thus we will start with the blank project. Then we need to install gactions CLI to manage project from command line. To access your projects you need to authenticate using command:

gactions login

if you want you can create the project using templates:

gactions init

to simplify the tutorial I have included configuration in the repository. You will need to set your project id in settings.yaml and webhook configuration using your ngrok address. Configuration can be deployed using command:

gactions push


As mentioned before for development purposes I have used the ngrok to proxy the traffic from public endpoint (used for webhook destination) to localhost:8081:

ngrok http 8081


Currently in google action project is not possible to set different webhook addresses for different languages thus I have used NGINX and LuaJIT to route the traffic to proper language container. The information about language context is included in the request body which can be handled using Lua script:

server {
        listen 80;
        resolver ipv6=off;
        location / {
            set $target '';
            access_by_lua '
                local cjson = require("cjson")
                local text = ngx.var.request_body
                local value = cjson.new().decode(text)
                local lang = string.sub(value["user"]["locale"],1,2)
                ngx.var.target = "http://heygoogle-" .. lang
            proxy_pass $target;

Rasa application

The rasa core is one of the famous framework for building chatbots. I have decided to create separate docker container for each language which gives flexibility in terms of scalability and deployment. Dockerfile (development version with watchdog) for rasa application (qooba/rasa:1.10.10_app):

FROM rasa/rasa:1.10.10
USER root
RUN pip3 install python-jose watchdog[watchmedo]
ENTRYPOINT watchmedo auto-restart -d . -p '*.py' --recursive -- python3 app.py

Using default rasa engine you have to restart the container when you want to deploy new retrained model thus I have decided to wrap it with simple python application which additionally listen the redis PubSub topic and waits for event which automatically reloads the model without restarting the whole application. Additionally there are separate topics for different languages thus we can simply deploy and reload model for specific language.


In this solution the redis has two responsibilities:

We can simply run Redis using command:

docker run --name redis -d --rm --network gitlab redis


Minio is used as a Rasa Model Store (Rasa supports the S3 protocol). The GitLab pipeline worker after model training uploads the model package to Minio. Each language has separate bucket:

model store

To run minio we will use command (for whole solution setup use run.sh where environment variables are set) :

docker run -d --rm -p 9000:9000 --network gitlab --name minio \
  -v $(pwd)/minio/data:/data \
  minio/minio server /data

Gitlab pipelines

In this solution I have used the gitlab as a git repository and CI/CD engine. You can simply run the GitLab locally using gitlab docker image:

docker run -d --rm -p 80:80 -p 8022:22 -p 443:443 --name gitlab --network gitlab \
  --hostname gitlab \
  -v $(pwd)/gitlab/config:/etc/gitlab:Z \
  -v $(pwd)/gitlab/logs:/var/log/gitlab:Z \
  -v $(pwd)/gitlab/data:/var/opt/gitlab:Z \

Notice that I have used gitlab hostname (without this pipelines does not work correctly on localhost) thus you will need to add appropriate entry to /etc/hosts:	gitlab

Now you can create new project (in my case I called it heygoogle). Likely you already use 22 port thus for ssh I used 8022. You can clone the project using command (remember to setup ssh keys):

git clone ssh://git@localhost:8022/root/heygoogle.git

Before you can use the gitlab runner you have to configure at least one worker. First you get registration token (Settings -> CI/CD -> Runners):

gitlab runners

and run once:

docker run --rm --network gitlab -v /srv/gitlab-runner/config:/etc/gitlab-runner gitlab/gitlab-runner register \
  --non-interactive \
  --docker-network-mode gitlab \
  --executor "docker" \
  --docker-image ubuntu:latest \
  --url "http://gitlab/" \
  --registration-token "TWJABbyzkVWVAbJc9bSx" \
  --description "docker-runner" \
  --tag-list "docker,aws" \
  --run-untagged="true" \
  --locked="false" \

Now you can run the gitlab-runner container:

docker run -d --rm --name gitlab-runner --network gitlab \
     -v /srv/gitlab-runner/config:/etc/gitlab-runner \
     -v /var/run/docker.sock:/var/run/docker.sock \

To create pipeline you simply commit the .gitlab-ci.yml into your repository. In our case it contains two steps (one for each language):

  PROJECT_NAME: heygoogle

  - process_en
  - process_pl

  image: qooba/rasa:1.10.10
  stage: process_en
    - ./pipeline.sh en
  interruptible: true

  image: qooba/rasa:1.10.10
  stage: process_pl
    - ./pipeline.sh pl
  interruptible: true

Gitlab pipeline steps use qooba/rasa:1.10.10 docker image:

FROM rasa/rasa:1.10.10
USER root
RUN apt update && apt install git -yq
ENTRYPOINT /bin/bash

thus they have complete rasa environment.

The pipeline.sh script:


echo "Processing $lang"

if (($(git diff-tree --no-commit-id --name-only -r $CI_COMMIT_SHA | grep ^$lang/ | wc -l) > 0)); then
   echo "Training $lang"
   cd $lang
   rasa train
   rasa test
   cd ..
   python3 pipeline.py --language $lang

checks if something have changed in chosen language directory, trains and tests the model and finally uploads trained model to Minio and publish event to Redis using pipeline.py:

import os
import boto3
import redis
from botocore.client import Config

def upload_model(project_name: str, language: str, model: str):
    print(f"BUCKET NAME: {bucket_name}") 
    bucket_exists=s3.Bucket(bucket_name) in s3.buckets.all() or s3.create_bucket(Bucket=bucket_name)

def publish_event(project_name: str, language: str, model: str):
    print(f"TOPIC NAME: {topic_name}") 
    client=redis.Redis(host="redis", port=6379, db=0);
    client.publish(topic_name, model)

if __name__ == '__main__':
    import argparse


    parser = argparse.ArgumentParser(description='Create a ArcHydro schema')
    parser.add_argument('--language', metavar='path', required=True,
                        help='the model language')

    args = parser.parse_args()
    print("Uploading model")
    upload_model(project_name=project_name, language=args.language, model=model)

    print("Publishing event")
    publish_event(project_name=project_name, language=args.language, model=model)

Now after each change in the repository the gitlab starts the pipeline run: gitlab pipeline
gitlab step


We have built complete solution for creating, training, testing and deploying the chatbots. Additionally the solution supports multi language chatbots keeping scalability and deployment flexibility. Moreover trained models can be continuously deployed without chatbot downtime (for Kubernetes environments the Canary Deployment could be another solution). Finally we have integrated solution with the google actions and created simple chatbot.