GI-SERVICE

General Information Service - API Adapter for OpenGIN (Open General Information Network)

A FastAPI-based service that provides data processing and API endpoints for government information management, including entity attributes, organizational charts, and data visualization capabilities.

🚀 Features

• Entity Management: Create and manage government entities (departments, ministries, etc.)
• Attribute Processing: Process and store entity attributes with metadata
• Organizational Charts: Generate timeline-based organizational structures
• Data Visualization: Transform data for chart generation
• Category Management: Hierarchical category creation and relationships
• RESTful APIs: Clean, documented API endpoints

📋 Prerequisites

• Python 3.8 to 3.13
• pip (Python package installer)
• Git

🛠️ Installation & Setup

1. Clone the Repository

git clone <repository-url>
cd GI-SERVICE

2. Create Virtual Environment

# Create virtual environment
python -m venv venv

# Activate virtual environment
# On Windows:
venv\Scripts\activate
# On macOS/Linux:
source venv/bin/activate

3. Install Dependencies

pip install -r requirements.txt

4. Environment Configuration

Create a .env file in the root directory:

# Base URLs for CRUD and Query services
BASE_URL_CRUD=http://0.0.0.0:8080
BASE_URL_QUERY=http://0.0.0.0:8081

# Optional: Add other environment variables as needed

5. Run the Application

# Development server
uvicorn main:app --reload --host 0.0.0.0 --port 8000

# Or using the Procfile (for production)
uvicorn main:app --host 0.0.0.0 --port $PORT

The API will be available at: http://localhost:8000

📚 API Documentation

Once the server is running, you can access:

• Interactive API Docs: http://localhost:8000/docs (Swagger UI)
• Alternative Docs: http://localhost:8000/redoc (ReDoc)

🔌 API Endpoints

Entity Attributes

Method	Endpoint	Description
GET	/allAttributes	Get all available attributes
POST	/data/entity/{entityId}	Get attributes for a specific entity
POST	/data/attribute/{entityId}	Get data for a specific attribute

Data Writing

Method	Endpoint	Description
POST	/data/writeAttributes	Process and write attributes to entities

Organizational Charts

Method	Endpoint	Description
GET	/data/orgchart/timeline	Get timeline for organizational chart
POST	/data/orgchart/ministries	Get ministries for selected date
POST	/data/orgchart/departments	Get departments for selected ministry

🧪 Testing the APIs

1. Test Basic Connectivity

curl http://localhost:8000/docs

2. Test All Attributes Endpoint

curl -X GET "http://localhost:8000/allAttributes" \
     -H "accept: application/json"

3. Test Entity Attributes

curl -X POST "http://localhost:8000/data/entity/your-entity-id" \
     -H "Content-Type: application/json" \
     -d '{
       "entityId": "your-entity-id",
       "filters": {}
     }'

4. Test Data Writing

curl -X POST "http://localhost:8000/data/writeAttributes" \
     -H "Content-Type: application/json" \
     -d '{
       "base_url": "/path/to/your/data"
     }'

📁 Project Structure

GI-SERVICE/
├── src/
│   ├── dependencies/          # Dependency injection
│   ├── models/               # Pydantic models
│   ├── routers/              # API route handlers
│   └── services/             # Business logic
├── chartFactory/             # Chart generation utilities
├── test/                     # Test data and scripts
├── main.py                   # FastAPI application entry point
├── requirements.txt          # Python dependencies
├── Procfile                  # Deployment configuration
└── README.md                # This file

🔧 Configuration

Environment Variables

Variable	Description	Default
BASE_URL_CRUD	CRUD service base URL	http://0.0.0.0:8080
BASE_URL_QUERY	Query service base URL	http://0.0.0.0:8081

Data Processing

The service processes data in the following format:

{
  "attributeName": "example_attribute",
  "relatedEntityName": "Department Name",
  "relation": "2022 - Government - Minister - Department",
  "attributeData": {
    "columns": ["col1", "col2"],
    "rows": [["val1", "val2"]]
  },
  "attributeMetadata": {
    "storage_type": "tabular",
    "dataset_name": "Example Dataset"
  }
}

🚀 Deployment

Using Heroku

1. Install Heroku CLI
2. Login to Heroku: heroku login
3. Create app: heroku create your-app-name
4. Deploy: git push heroku main

Using Docker

FROM python:3.9-slim

WORKDIR /app
COPY requirements.txt .
RUN pip install -r requirements.txt

COPY . .
EXPOSE 8000

CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "8000"]

🐛 Troubleshooting

Common Issues

1. Port already in use: Change the port in the uvicorn command
2. Environment variables not loaded: Ensure .env file is in the root directory
3. Import errors: Make sure virtual environment is activated
4. API not responding: Check if the CRUD and Query services are running

Debug Mode

# Run with debug logging
uvicorn main:app --reload --log-level debug

🤝 Contributing

1. Fork the repository
2. Create a feature branch: git checkout -b feature-name
3. Commit changes: git commit -am 'Add feature'
4. Push to branch: git push origin feature-name
5. Submit a Pull Request

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

📞 Support

For support and questions:

• Create an issue in the repository
• Contact the development team

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Happy Coding! 🎉