A Go implementation of the textbook management and Gemini AI integration API using Gin framework and MongoDB.

• Textbook Management: Create and manage textbooks with detailed table of contents
• Google Gemini AI Integration: Query AI with specific textbook sections for educational assistance
• Role-based Authentication: JWT-based authentication with teacher and student roles
• MongoDB Integration: Efficient document storage and retrieval
• RESTful API: Clean and consistent API design with proper HTTP status codes

• Go 1.21 or higher
• MongoDB running locally or connection string
• Google Gemini API key

1. Clone and setup:

cd go-api
go mod tidy

2. Environment Setup:

cp .env.example .env
# Edit .env file with your configuration

3. Run the server:

go run main.go

The server will start on http://localhost:8080

POST /api/v1/auth/register
Content-Type: application/json

{
  "email": "teacher@example.com",
  "full_name": "John Doe",
  "password": "password123",
  "role": "teacher"
}

POST /api/v1/auth/login
Content-Type: application/json

{
  "email": "teacher@example.com",
  "password": "password123"
}

POST /api/v1/teacher/textbooks
Authorization: Bearer <jwt_token>
Content-Type: application/json

{
  "class": 10,
  "subject": "Science",
  "medium": "English",
  "chapters": [
    {
      "chapter_number": 1,
      "title": "Chemical Reactions and Equations",
      "sections": [
        "1.1 Chemical Equation",
        "1.2 Balanced Chemical Equation"
      ]
    }
  ]
}

GET /api/v1/teacher/textbooks
Authorization: Bearer <jwt_token>

GET /api/v1/teacher/textbooks/{textbook_id}
Authorization: Bearer <jwt_token>

GET /api/v1/common/textbooks
Authorization: Bearer <jwt_token>

GET /api/v1/common/textbooks/{textbook_id}
Authorization: Bearer <jwt_token>

POST /api/v1/common/textbooks/query-sections
Authorization: Bearer <jwt_token>
Content-Type: application/json

{
  "textbook_id": "textbook_id_here",
  "selected_sections": [
    "1.1 Chemical Equation",
    "1.2 Balanced Chemical Equation"
  ],
  "custom_prompt": "Explain the difference between chemical equations and balanced equations for Class 10 students"
}

go-api/
├── main.go                     # Application entry point
├── go.mod                      # Go module definition
├── go.sum                      # Go module checksums
├── .env.example               # Environment variables template
├── sample_textbook_data.json  # Sample data for testing
├── README.md                  # This file
├── internal/                  # Private application code
│   ├── config/               # Configuration management
│   │   └── config.go
│   ├── database/             # Database connection
│   │   └── database.go
│   ├── handlers/             # HTTP request handlers
│   │   ├── auth.go
│   │   └── textbook.go
│   ├── middleware/           # HTTP middleware
│   │   └── auth.go
│   ├── models/               # Data models
│   │   ├── textbook.go
│   │   └── user.go
│   └── services/             # Business logic
│       └── gemini.go
└── pkg/                      # Public utility packages
    └── utils/
        └── auth.go

Environment variables:

• Gin: HTTP web framework
• MongoDB Driver: Official MongoDB Go driver
• JWT: JWT token handling
• Bcrypt: Password hashing
• Godotenv: Environment variable loading

curl http://localhost:8080/health

curl -X POST http://localhost:8080/api/v1/auth/register \
  -H "Content-Type: application/json" \
  -d '{
    "email": "teacher@example.com",
    "full_name": "Test Teacher",
    "password": "password123",
    "role": "teacher"
  }'

# Use the token from registration
curl -X POST http://localhost:8080/api/v1/teacher/textbooks \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer YOUR_JWT_TOKEN" \
  -d @sample_textbook_data.json

• Go Gin instead of Python FastAPI
• Standard library JSON instead of Pydantic
• MongoDB Driver instead of Motor (async)
• Native HTTP client for Gemini API instead of google-generativeai library

• Compiled binary - faster startup and execution
• Built-in concurrency - better handling of concurrent requests
• Lower memory footprint - more efficient resource usage
• Static typing - compile-time error checking

• Same endpoints - identical API structure
• Same request/response format - compatible with existing clients
• Same authentication - JWT-based auth with same claims
• Same database schema - uses identical MongoDB collections

1. Go to Google AI Studio
2. Create a new API key
3. Add it to your .env file as GEMINI_API_KEY=your_key_here

The API returns standard HTTP status codes:

• 200 - Success
• 201 - Created
• 400 - Bad Request (validation errors)
• 401 - Unauthorized (authentication required)
• 403 - Forbidden (insufficient permissions)
• 404 - Not Found
• 409 - Conflict (duplicate resource)
• 500 - Internal Server Error
• 503 - Service Unavailable (external service error)

• JWT Authentication - Secure token-based authentication
• Password Hashing - Bcrypt for secure password storage
• Role-based Access - Teacher/student role separation
• CORS Support - Configurable cross-origin resource sharing
• Input Validation - Request validation using Gin binding

1. Set environment to production:

export ENVIRONMENT=production

2. Use production MongoDB:

export MONGODB_URL=mongodb://your-production-mongo

3. Build binary:

go build -o textbook-api main.go

4. Run binary:

./textbook-api

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

This project is licensed under the MIT License.