Microservice based application with OpenAI API

This project encompasses the source code for a sophisticated e-commerce application structured using a microservices architecture. It features an AI service that leverages the OpenAI API, similar to ChatGPT, to autonomously generate descriptions for newly added products. The system employs MongoDB for robust data management and RabbitMQ for efficient message queuing. The frontend component of the application operates as an independent microservice crafted with React, ensuring a dynamic and responsive user interface. Additionally, the backend microservices are developed using NodeJS and Python, offering a versatile and scalable server-side solution.

Architecture Diagram

Installations/Prerequisites

• Microsoft Azure account
• Azure cli
• terraform
• kubectl
• helm

How to Run

• Setup the AKS cluster

  1. Login into the Azure account using azure cli

     az login
     

  2. Create a new service principal

     az ad sp create-for-rbac --skip-assignment
     

     Copy and save the information returned after executing the command successfully. Information will look something like:

     {
         "appId": "<app_if>",
         "displayName": "<display_name>",
         "password": "<password>",
         "tenant": "<tenant>"
     }
     

  3. From the project folder, go into the terraform folder

     cd terraform
     

  4. Create a new file terraform.tfvars and provide the below details in that file.

     resource_group_name = "<resource_group_name_of_your_choice>"
     location            = "<location_of_the_rg>"
     cluster_name        = "<cluster_name>"
     kubernetes_version  = "1.29"
     system_node_count   = 3
     appId               = "<appId_from_step_2>"
     password            = "<password_from_step_2>"
     dns_prefix          = "<any_name_for_dns_prefix>"
     

  5. Run the below commands

     terraform init
     terraform fmt
     terraform plan
     ## If there are no errors then go ahead ##
     terraform apply -auto-approve
     

  6. Once the provisioning is complete, run the below command to add K8S cluster credentials to your local kubeconfig file so that you can interact with AKS cluster using kubectl from your local system.

     az aks get-credentials --resource-group $(terraform output -raw resource_group_name) --name $(terraform output -raw kubernetes_cluster_name)
     

• Install helm charts for MongoDB, RabbitMQ and Traefik.

  1. Open terminal into the project folder.

  2. Install MongoDB

     helm install mongo --create-namespace -n mongo -f helm/mongo_values.yaml oci://registry-1.docker.io/bitnamicharts/mongodb
     

     This chart will install MongoDB in the mongo namespace according to the setting mentioned in the values file from the ./helm/mongo_values.yaml file.

  3. Install RabbitMQ

     helm install rabbitmq --create-namespace -n rabbitmq -f helm/rabbitmq_values.yaml oci://registry-1.docker.io/bitnamicharts/rabbitmq
     

     This chart will install RabbitMQ in the rabbitmq namespace according to the setting mentioned in the values file from the ./helm/rabbitmq_values.yaml file.

  4. Install Traefik ingress controller

     helm repo add traefik https://helm.traefik.io/traefik
     helm repo update
     kubectl create namespace traefik
     helm install traefik traefik/traefik -n traefik
     

• Install Kubernetes manifests files

  1. Go to the Kubernetes-manifests folder.

  2. Check all the manifest files and fill the appropriate base64 encoded connection strings. Example connection strings(pre base64 encoded)

     CONNECTION_STRING = mongodb://root:[email protected]:27017/auth-service?authSource=admin
     RABBITMQ_CONNECTION_STRING = amqp://user:[email protected]:5672
     

  3. Run the below command to get Load Balance External-IP.

     kubectl get svc -n traefik
     

  4. Input the above External-IP in the environment variable REACT_APP_INGRESS_PUBLIC_IP in admin-front-service-manifest.yaml.

  5. Install Kubernetes manifests for all the microservices.

     kubectl apply -f Kubernetes-manifests
     

• How to run the application

  1. Ensure that all the services are up.
  2. Send a POST request to /auth/register endpoint to create a new user. URL will be http://<External-IP>/auth/register.

        {
           "name": "dba",
           "email": "[email protected]",
           "password": "12341234"
        }
     

  3. Access the application by going to the URL http://<External-IP>.
  4. Login using the email and password registered above.

• Add a new product

  1. Use the application or
  2. Send a POST request to the /product/create endpoint. URL will be http://<External-IP>/product/create

        {
           "name": "PurrfectPan cat litter box",
           "tags": "cat, litter, clean, hygiene",
           "description": "<generate_using_ai_service>",
           "price": 3000,
           "created_by": "[email protected]",
           "img_url": "https://i.imgur.com/iBK83bn.png"
        }
     

• Add a new order

  1. Send a POST request to the /product/buy endpoint. URL will be http://<External-IP>/product/buy.

     {
        "productsWithQuantity": [
           {
              "id": "6646137ef04ba4cf8bb0547e",
              "quantity": 3
           },
           {
              "id": "6646137ef04ba4cf8bb0547e",
              "quantity": 2
           }
        ],
        "userEmail": "[email protected]",
        "userAddress": "11/22, Address_test"
     }
     

• Health check end points

  /generate/health : ai service
  /auth/health : auth service
  /order/health : order service