📘 QuantiLearn

QuantiLearn is an adaptive e-learning platform that dynamically adjusts lesson difficulty and study plans using AI. The backend is powered by Spring Boot microservices with Kafka, Redis, Docker, and AWS for scalable deployment.

🧩 Microservices Overview

✅ auth-service

• Handles user authentication using JWT.
• Issues and validates tokens for secure communication.

👤 user-service

• Stores user profile data.
• Integrates Redis caching to reduce latency and improve performance.

📊 progress-service

• Tracks user progression (lesson completion, time spent, scores).
• Will use Kafka to asynchronously process and broadcast progress updates.

🔔 notification-service

• Sends reminders and alerts via email or push notifications.

📚 lesson-service

• Stores and serves educational lessons and metadata.
• AI Integration: Generates personalized lesson plans based on user progress, performance history, and learning preferences.
• Can utilize models via Spring AI or integrate external LLM APIs (e.g., OpenAI, Claude, or Hugging Face).

🌐 System Architecture

graph LR
  subgraph External
    UI[Frontend]
    User[End Users]
  end

  subgraph API_Gateway
    GW[Gateway Service]
  end

  subgraph Discovery_and_Config
    EUREKA[Eureka Server]
    CONFIG[Config Server]
  end

  subgraph Core_Services
    AUTH[auth-service]
    USER[user-service]
    PROG[progress-service]
    LES[lesson-service]
    NOTIF[notification-service]
  end

  subgraph Infrastructure
    KAFKA[Kafka Topics]
    REDIS[Redis Cache]
    DB_User[(User DB)]
    DB_Progress[(Progress DB)]
    DB_Lesson[(Lesson DB)]
    EMAIL[Email/Push System]
    AIModel[AI Engine / LLM API]
  end

  User --> UI
  UI --> GW
  GW --> EUREKA
  GW --> AUTH
  GW --> USER
  GW --> PROG
  GW --> LES
  GW --> NOTIF

  AUTH --> USER
  USER --> REDIS
  USER --> DB_User

  PROG --> KAFKA
  PROG --> DB_Progress

  LES --> DB_Lesson
  LES --> AIModel

  NOTIF --> EMAIL
  NOTIF --> USER

  CONFIG --> AUTH
  CONFIG --> USER
  CONFIG --> PROG
  CONFIG --> LES
  CONFIG --> NOTIF

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🛠️ Tech Stack

Component	Technology
Frontend	Angular, HTML, CSS
Backend	Spring Boot (Java)
Auth	JWT
Database	PostGreSQL, MongoDb
Messaging	Apache Kafka
Caching	Redis
Service Discovery	Eureka Server
API Gateway	Spring Cloud Gateway
Config	Spring Cloud Config Server
Containerization	Docker, Kubernetes
Deployment Platform	AWS (EC2, RDS, S3, EKS)
AI Integration	Spring AI (OpenAI or Hugging Face)

🚧 Development Roadmap

• Build and deploy auth-service, user-service, progress-service

• Redis integration in user-service

• Kafka event handling in progress-service

• Develop lesson-service and notification-service

• Setup service-register, config-server, and gateway

• Create Angular frontend

• Dockerize all microservices

• Deploy using Kubernetes

• Migrate entire stack to AWS Cloud

🚀 Getting Started

Prerequisites

• Java 17+
• Maven
• Docker & Docker Compose
• Kafka, Redis
• Spring Cloud dependencies

Clone Repository

https://github.com/Manishnemade12/QuantiLearn.git
cd EventManager

Run Microservices Each service is in its own directory. Navigate to the folder and run:

./mvnw spring-boot:run -Dspring-boot.run.profiles=dev

Or use Docker:

docker-compose up --build

API Access Use the gateway once integrated:

http://localhost:8080/api/{service}/...

💡 Contribution Guidelines

• Fork and clone the repository.

• Make changes in feature branches.

• Submit pull requests with proper documentation.

• Report bugs and request features via GitHub Issues.

📜 License

This project is licensed under the MIT License.