AI Study Notes Backend

A serverless backend service for generating AI-powered study notes and research summaries. This project uses AWS Lambda functions to create an asynchronous processing pipeline that generates comprehensive study materials from topic inputs.

🚀 Features

• Asynchronous Processing: Submit study topics and receive AI-generated notes when ready
• RESTful API: Clean HTTP endpoints for task management
• Queue-based Architecture: Uses AWS SQS for reliable message processing
• Real-time Notifications: SNS integration for completion alerts
• Scalable Storage: DynamoDB for persistent data storage
• CORS Support: Ready for frontend integration

🏗️ Architecture

The system consists of two main Lambda functions:

API Function (api-function/)

Handles HTTP requests and provides the following endpoints:

• POST /tasks - Create a new study note task
• GET /tasks - List all tasks with pagination
• GET /tasks/{id} - Get specific task details including generated content
• DELETE /tasks/{id} - Delete a task

Worker Function (worker-function/)

Processes tasks from the SQS queue:

• Consumes messages from SQS
• Calls OpenAI API to generate study notes
• Updates task status in DynamoDB
• Sends completion notifications via SNS

📋 API Endpoints

Create Task

POST /tasks
Content-Type: application/json

{
  "topic": "Machine Learning Fundamentals"
}

Response:

{
  "id": "uuid",
  "topic": "Machine Learning Fundamentals",
  "status": "QUEUED",
  "createdAt": "2024-01-01T00:00:00.000Z"
}

List Tasks

GET /tasks?limit=25&cursor=base64_encoded_cursor

Response:

{
  "items": [
    {
      "id": "uuid",
      "topic": "Machine Learning Fundamentals",
      "status": "DONE",
      "createdAt": "2024-01-01T00:00:00.000Z",
      "updatedAt": "2024-01-01T00:01:00.000Z"
    }
  ],
  "cursor": "base64_encoded_cursor"
}

Get Task Details

GET /tasks/{id}

Response:

{
  "id": "uuid",
  "topic": "Machine Learning Fundamentals",
  "status": "DONE",
  "createdAt": "2024-01-01T00:00:00.000Z",
  "updatedAt": "2024-01-01T00:01:00.000Z",
  "researchMd": "# Machine Learning Fundamentals\n\n## Overview\n..."
}

Delete Task

DELETE /tasks/{id}

Response: 204 No Content

🔧 Environment Variables

API Function

• TABLE_NAME - DynamoDB table name for storing tasks
• QUEUE_URL - SQS queue URL for task processing

Worker Function

• TABLE_NAME - DynamoDB table name for storing tasks
• SNS_TOPIC_ARN - SNS topic ARN for completion notifications
• OPENAI_API_KEY - OpenAI API key for generating content
• OPENAI_PROMPT_ID - OpenAI prompt template ID

📊 Task Status Flow

1. QUEUED - Task created and added to processing queue
2. PROCESSING - Worker function is generating the study notes
3. DONE - Study notes generated successfully
4. ERROR - Processing failed (check error field for details)

🛠️ AWS Services Used

• AWS Lambda - Serverless compute for API and worker functions
• Amazon DynamoDB - NoSQL database for task storage
• Amazon SQS - Message queue for asynchronous processing
• Amazon SNS - Notification service for task completion alerts
• OpenAI API - AI service for generating study notes

📁 Project Structure

ai-studynotes-backend/
├── api-function/
│   └── index.js          # API Lambda function
├── worker-function/
│   └── index.js          # Worker Lambda function
├── LICENSE               # GNU GPL v3 License
└── README.md            # This file

🚀 Deployment

This project is designed to be deployed on AWS using serverless frameworks like:

• AWS SAM (Serverless Application Model)
• Serverless Framework
• AWS CDK (Cloud Development Kit)

Prerequisites

• AWS CLI configured
• Node.js runtime
• AWS Lambda deployment tools

Required AWS Resources

• DynamoDB table with GSI byCreatedAt
• SQS queue for task processing
• SNS topic for notifications
• IAM roles with appropriate permissions

🔐 Security

• CORS headers configured for cross-origin requests
• Input validation on all endpoints
• Error handling with appropriate HTTP status codes
• Secure environment variable management

📝 License

This project is licensed under the GNU General Public License v3.0 - see the LICENSE file for details.

🤝 Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Test thoroughly
5. Submit a pull request

📞 Support

For questions or issues, please open an issue in the repository or contact the maintainers.

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Note: This backend service requires proper AWS infrastructure setup and OpenAI API credentials to function correctly.