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ml-projects-service

This is ml-projects-services, the managed-learn component responsible for creating projects within the managed learn services.

Setup Guide

Pre-Requisite

Basic understanding of git and github is recommended.

Setup ml-projects-services

Clone the service repository onto your system

  • Create a new folder where you want to clone the repository.
  • Navigate to that directory using the terminal.
  • Execute the git commands to clone the repository using the provided link from the code tab.

Git link

https://github.com/shikshalokam/ml-projects-service.git

command to clone

git clone https://github.com/shikshalokam/ml-projects-service.git

Create .env file

Create a file named .env and copy the environment-specific data corresponding to that service into the .env file.

# Ml project service configurations file

APPLICATION_PORT = "3000"                                                       // Application port number
APPLICATION_ENV = "development"                                                 // Application running environment

INTERNAL_ACCESS_TOKEN = "Fg*************yr"                                     // Internal access token for accessing internal service APIs

# DB
MONGODB_URL = "mongodb://localhost:27017/sl-assessment"                         // Mongodb connection url

# ML Core Service
ML_CORE_SERVICE_URL = "http://ml-core-service:3000"                             // ML Core Service URL

# ML Reports Service
ML_REPORTS_SERVICE_URL = "http://ml-reports-service:3000"                       // ML Reports Service URL

# ML Survey Service
ML_SURVEY_SERVICE_URL = "http://ml-survey-service:3000"                         // ML Survey Service URL

# OFFLINE VALIDATION
KEYCLOAK_PUBLIC_KEY_PATH = "keycloak-public-keys"                               // Keycloak public key path

# KAFKA Configurations
KAFKA_COMMUNICATIONS_ON_OFF = "ON/OFF"                                          // Kafka enable or disable communication flag
KAFKA_URL = "172.31.0.4:9092"                                                   // IP address of kafka server with port without HTTP
KAFKA_GROUP_ID = "projects"                                                     // Kafka group id

# SUBMISSION TOPIC
SUBMISSION_TOPIC = "dev.sl.projects.submissions"                                // Kafka topic name for pushing projects submissions
PROJECT_SUBMISSION_TOPIC = "dev.sl.projects.submissions"                        // project submission topic

# SUNBIRD LOCATION AND USER READ
USER_SERVICE_URL = "http://user-service:3000"                                   // service used for user profile read location search are using this base url

#service name
SERVICE_NAME = ml-project-service                                               // ml-project service name

# sunbird-rc service
CERTIFICATE_SERVICE_URL = http://registry-service:8081                          // sunbird-RC registry service URL

PROJECT_CERTIFICATE_ON_OFF = "ON/OFF"                                           // Project certificate enable or disable flag

Install Dependencies

To install dependencies from a package.json file in Visual Studio Code, you can use the integrated terminal. Here are the steps:

  • Open the integrated terminal by going to View > Terminal or using the shortcut Ctrl+` (backtick).
  • In the terminal, navigate to the directory where the package.json file is located.
  • Run the command npm install or yarn install, depending on your preferred package manager.
  • The package manager will read the package.json file and install all the dependencies specified in it.
  • Wait for the installation process to complete. You should see progress indicators or a success message for each installed dependency.
  • Once the installation is finished, the dependencies listed in the package.json file will be installed in a node_modules directory in your project.

Setting the keycloak

  • Create a folder on service directory named: keycloak-public-keys
  • Inside that folder create a file GRxxx....................xxxxx60fA

for keycloak file please contact Backend Team

Setup Database

Before proceeding with these steps, ensure that you have MongoDB installed on your computer. For a graphical user interface (GUI) for MongoDB, you can choose to install Robo 3T.

  • Obtain the latest database dump from the backend team.

  • Restore the database in your local environment using the following command:

    For Windows/Linux: mongorestore <name you want to give the db> <directory or file to restore>

    For macOS: mongorestore -d <name you want to give the db> <directory or file to restore>

Note: Add <name you want to give the db> to mongoDB url in .env file.

DB Schema

The schema serves as a blueprint for creating and maintaining the database that supports the ML projects services data storage and retrieval operations.

ML-Projects Service

Click here for DB schema and corresponding examples in a PDF format.

Postman Collection

The ML Projects Service Postman Collection is a comprehensive resource for interacting with the ML Core Service. It includes organized endpoints, detailed documentation, and example workflows, providing a valuable reference for developers. Leverage this collection to enhance productivity and collaboration in ML Services.

Click here

IMPORTANT:

Always work on branches. Never make changes to master.

Creating a branch from master.

For more information on git you can use :
https://education.github.com/git-cheat-sheet-education.pdf