This sample demo app consists of a group of containerized microservices that can be easily deployed into an Azure Kubernetes Service (AKS) cluster. This is meant to show a realistic scenario using a polyglot architecture, event-driven design, and common open source back-end services (eg - RabbitMQ, MongoDB). The application also leverages OpenAI's GPT-3 models to generate product descriptions. This can be done using either Azure OpenAI or OpenAI.

This application is inspired by another demo app called Red Dog.

Note: This is not meant to be an example of perfect code to be used in production, but more about showing a realistic application running in AKS.

The application has the following services:

To learn how to depoy this app on AKS, see Quickstart: Deploy an Azure Kubernetes Service (AKS) cluster using Azure CLI.

Note: The above article shows a simplified version of the store app with some services removed. For the full application, you can use the aks-store-all-in-one.yaml file in this repo.

This application uses public images stored in GitHub Container Registry and Microsoft Container Registry (MCR). Once your Kubernetes cluster of choice is setup, you can deploy the full app with the below commands.

This deployment deploys everything except the ai-service that integrates OpenAI. If you want to try integrating the OpenAI component, take a look at this article: Deploy an application that uses OpenAI on Azure Kubernetes Service (AKS).

kubectl create ns pets

kubectl apply -f https://raw.githubusercontent.com/Azure-Samples/aks-store-demo/main/aks-store-all-in-one.yaml -n pets

The application is designed to be run in an AKS cluster, but can also be run locally using Docker Compose.

IMPORTANT: You must have Docker Desktop installed to run this app locally.

To run this app locally:

Clone the repo to your development computer and navigate to the directory:

git clone https://github.com/Azure-Samples/aks-store-demo.git
cd aks-store-demo

Configure your Azure OpenAI or OpenAI API keys in docker-compose.yml using the environment variables in the aiservice section:

  aiservice:
    build: src/ai-service
    container_name: 'aiservice'
    ...
    environment:
      - USE_AZURE_OPENAI=True # set to False if you are not using Azure OpenAI
      - AZURE_OPENAI_DEPLOYMENT_NAME= # required if using Azure OpenAI
      - AZURE_OPENAI_ENDPOINT= # required if using Azure OpenAI
      - OPENAI_API_KEY= # always required
      - OPENAI_ORG_ID= # required if using OpenAI
    ...

Alternatively, if you do not have access to Azure OpenAI or OpenAI API keys, you can run the app without the ai-service by commenting out the aiservice section in docker-compose.yml. For example:

#  aiservice:
#    build: src/ai-service
#    container_name: 'aiservice'
...
#    networks:
#      - backend_services

Start the app using docker compose. For example:

docker compose up

To stop the app, you can hit the CTRL+C key combination in the terminal window where the app is running.

This repo also includes DevContainer configuration, so you can open the repo using GitHub Codespaces. This will allow you to run the app in a container in the cloud, without having to install Docker on your local machine. When the Codespace is created, you can run the app using the same instructions as above.

• AKS Documentation. https://learn.microsoft.com/azure/aks
• Kubernetes Learning Path. https://azure.microsoft.com/resources/kubernetes-learning-path