G.O.D Framework

Introduction

The api_server.py is a core module within the G.O.D Framework. It serves as the primary server to expose RESTful APIs, enabling secure and structured interaction between different components and external systems. This component acts as the backbone for interconnectivity across the framework.

Purpose

The key objectives of this script are:

• Support the interaction of external systems with various modules in the G.O.D Framework.
• Serve as the endpoint hub for system operations such as data retrieval, model predictions, and monitoring.
• Enable modular and scalable API management to facilitate reusable functionality.
• Provide responsive communication with built-in error handling and status monitoring.

Key Features

• Modular API Design: Hosts a variety of API endpoints mapped to G.O.D Framework tasks.
• Scalability: Designed to handle high user/API call volumes efficiently.
• Authentication: Supports secure interactions using token-based authentication (e.g., JWT).
• Error Handling: Detects, logs, and reports improper API usage or system errors.
• Real-Time Response: Returns accurate and prompt results to clients with minimal latency.

Logic and Implementation

The api_server.py module uses the Flask library for RESTful API development. Below is a minimal example of how it works:

from flask import Flask, request, jsonify
from ai_inference_service import InferenceService

app = Flask(__name__)
inference_service = InferenceService()

@app.route("/predict", methods=["POST"])
def predict():
    """
    Handle prediction requests.

    Returns:
        JSON: Predicted results or error message.
    """
    data = request.json
    try:
        model_name = data.get("model")
        input_data = data.get("data")
        prediction = inference_service.predict(model_name, input_data)
        return jsonify({"status": "success", "prediction": prediction})
    except Exception as e:
        return jsonify({"status": "error", "message": str(e)}), 500

@app.route("/health", methods=["GET"])
def health_check():
    """
    Performs a basic health check for the API server.

    Returns:
        JSON: Health status response.
    """
    return jsonify({"status": "healthy"})

if __name__ == "__main__":
    app.run(host="0.0.0.0", port=8000)
        

This code represents a lightweight server exposing APIs for prediction (/predict) and health monitoring (/health).

Dependencies

• Flask: A microframework for building web applications and RESTful APIs.
• Inference Service: Depends on the ai_inference_service.py module for performing AI predictions.
• JSON Libraries: Used for API request and response payloads.

Integration with the G.O.D Framework

The api_server.py module integrates seamlessly with the following components:

• ai_inference_service.py: Provides predictive results for requested models.
• ai_monitoring.py: Shares real-time operational metrics via APIs.
• ai_alerting.py: Exposes alert notification services for external clients.

Future Enhancements

• Implementing support for GraphQL to optimize API calls.
• Adding role-based access control (RBAC) for API security.
• Improved API response time using asynchronous request handling with FastAPI.
• Extending API server to support WebSockets for real-time communication.
• Integration with deployment management tools like Kubernetes for auto-scaling.