ml-survey-service

This is ml-survey-services, the managed-learn component responsible for creating observation and surveys within the managed learn services.

Setup Guide

Pre-Requisite

Install any IDE in your system(eg: VScode etc..)
Install nodejs from : https://nodejs.org/en/download/
Install mongoDB: https://docs.mongodb.com/manual/installation/
Install Robo 3T: https://robomongo.org/

Basic understanding of git and github is recommended.

Setup ml-survey-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-survey-service.git

command to clone

git clone https://github.com/shikshalokam/ml-survey-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 Survey Service Config

APPLICATION_PORT = 3000                                                         // Application port number
APPLICATION_ENV = 'development'                                                 // Application running enviornment

# Setting for custom request timeout for reports
MONGODB_URL = mongodb://localhost:27017/sl-assessment                           // Mongodb connection url
USER_SERVICE_URL = "http://user-service:3000"                                   // Base url of the sunbird enviornment
INTERNAL_ACCESS_TOKEN = "Internal access token to access reports"               // Internal access token for accessing Admin specific APIs

# Kafka Configuration
KAFKA_COMMUNICATIONS_ON_OFF = "ON/OFF"                                          // Kafka enable or disable communication flag
KAFKA_URL = "100.0.0.1:9092"                                                    // IP address of kafka server with port without HTTP
SUBMISSION_RATING_QUEUE_TOPIC = "dev.sl.submission.rating.raw"                                // Kafka topic name for pushing submissions for which rating has to be done.
COMPLETED_SURVEY_SUBMISSION_TOPIC = "dev.sl.survey.raw"                            // Kafka topic name for completed survey submission
INCOMPLETE_SURVEY_SUBMISSION_TOPIC = "dev.sl.incomplete.survey.raw"                           // Kafka topic name for incomplete survey submission
KAFKA_GROUP_ID = "survey"                                                       // Kafka consumer group for ML Survey Service
IMPROVEMENT_PROJECT_SUBMISSION_TOPIC = "dev.sl.improvement.project.submission"                                       // Kafka topic name for pushing project submission related data
OBSERVATION_SUBMISSION_TOPIC = "dev.sl.observation.raw"                         // Kafka topic name for pushing observation submission

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

# IMPROVEMENT PROJECT SERVICE
ML_PROJECT_SERVICE_URL = "http://ml-project-service:3000"                       // Project Service URL

KEYCLOAK_PUBLIC_KEY_PATH = "keycloak-public-keys"                               // Keycloak public keys path

DISABLE_LEARNER_SERVICE_ON_OFF = "ON"                                          // Disable learner service check

FORM_SERVICE_URL = "http://player:3000"                                         // Base url for form search

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 survey services data storage and retrieval operations.

ML-Survey Service

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

Postman Collection

The ML Survey 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

shikshalokam / ml-survey-service

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