G.O.D Framework

Introduction

The ai_life_connection.py module is a critical part of the G.O.D Framework’s interaction system. It bridges AI modules with dynamic real-world data sources, such as IoT devices and live APIs. This module plays a central role in integrating AI systems into environments that demand constant and secure real-time updates.

With its design, this script facilitates connections between various external systems and internal AI components for effective decision-making grounded in real-world data.

Purpose

• Create secure channels between AI algorithms and real-world data streams.
• Process incoming life data for analysis by predictive AI engines.
• Support integration workflows with IoT devices, cloud APIs, and more.
• Handle dynamic inputs and ensure resilience under unstable conditions.

Key Features

• Real-World Data Pipelines: Streamline the flow of data from external devices to AI modules.
• Dynamic Input Parsing: Preprocess and filter incoming raw data dynamically.
• Stability and Monitoring: Ensure constant uptime in complex environments.
• Configurable Protocols: Integrate easily with HTTP, MQTT, WebSocket, and other protocols.

Logic and Implementation

The ai_life_connection.py module operates as a middleware layer that:

1. Establishes secure connections to external systems (e.g., IoT devices, live data feeds).
2. Buffers or preprocesses data for downstream AI ingestion.
3. Monitors connection health and implements reconnect logic if needed.

Below is a high-level implementation of its key components:

            import requests
            import time
            import logging

            class LifeConnectionManager:
                """
                Handles real-world data connections and routes incoming data to AI modules.
                """
                def __init__(self, endpoint_url, auth_token=None, api_key=None, retry_attempts=3):
                    self.endpoint_url = endpoint_url
                    self.auth_token = auth_token
                    self.api_key = api_key
                    self.retry_attempts = retry_attempts
                    self.logger = logging.getLogger("LifeConnectionManager")
                    self.logger.setLevel(logging.INFO)

                def connect(self):
                    """
                    Attempt to establish connection with the given endpoint.
                    Optionally handles auth_tokens and API keys if required.
                    """
                    self.logger.info(f"Connecting to {self.endpoint_url}...")
                    # Simulate a connection check
                    response = requests.get(self.endpoint_url, headers=self._get_headers())
                    if response.status_code == 200:
                        self.logger.info("Successfully connected.")
                        return True
                    else:
                        self.logger.error("Connection failed!")
                        return False

                def _get_headers(self):
                    """
                    Build request headers.
                    """
                    headers = {}
                    if self.auth_token:
                        headers["Authorization"] = f"Bearer {self.auth_token}"
                    if self.api_key:
                        headers["x-api-key"] = self.api_key
                    return headers

                def stream_life_data(self, process_function, interval=10):
                    """
                    Continually stream data from the endpoint URL.
                    Pass incoming data to process_function for handling.
                    """
                    while True:
                        try:
                            response = requests.get(self.endpoint_url, headers=self._get_headers())
                            if response.status_code == 200:
                                data = response.json()
                                process_function(data)
                            else:
                                self.logger.warning(f"Non-200 response ({response.status_code}) received.")
                        except Exception as e:
                            self.logger.error(f"Error while streaming data: {str(e)}")
                        time.sleep(interval)

            # Usage Example:
            if __name__ == "__main__":
                # Define URL and credentials
                API_URL = "https://api.openweathermap.org/data/2.5/weather"
                API_KEY = "MY_API_KEY"

                manager = LifeConnectionManager(endpoint_url=API_URL, api_key=API_KEY)

                # Define custom function to process data
                def handle_weather_data(data):
                    print("Received weather data:", data)

                # Start streaming
                if manager.connect():
                    manager.stream_life_data(process_function=handle_weather_data)
            

Dependencies

• requests: Handles HTTP requests to external APIs.
• logging: Provides structured logging for debugging and monitoring.
• time: Enables periodic retry and reconnection logic.

Usage Example

Here is an end-to-end example of setting up the AI Life Connection module:

            from ai_life_connection import LifeConnectionManager

            # Initialize Manager
            connection_manager = LifeConnectionManager(endpoint_url="https://example.com/data", auth_token="YOUR_TOKEN")

            # Define a handler for incoming real-world data
            def handle_data(data):
                print("Processed Real-World Data:", data)

            # Start the real-world data connection
            connection_manager.stream_life_data(process_function=handle_data)
            

System Integration

• IoT and Smart Devices: Monitor live feeds from temperature sensors, cameras, or other IoT devices.
• Cloud Service Integration: Fetch live app or service analytics from APIs.
• Dynamic AI Inputs: Use real-world data to adjust predictive models or generate alerts.

Future Enhancements

• Add WebSocket and MQTT protocol support to handle non-HTTP communication.
• Implement failover and redundancy mechanisms for highly unstable endpoints.
• Enable integration with edge computing devices for faster data preprocessing.