G.O.D Framework

Introduction

The ai_infinite_memory.py module introduces an advanced memory management system for AI models. It provides features for storing, updating, and retrieving knowledge across sessions, effectively simulating a persistent memory. This capability allows the AI system to maintain context over long periods, making it suitable for conversational AI, real-time learning, and decision-making.

The module utilizes hybrid memory techniques, combining real-time memory for ongoing interactions and long-term memory storage for knowledge retention. The stored knowledge can be used for self-adaptive improvements, debugging, and auditing.

Purpose

• Simulate continuous and persistent states of memory in AI systems.
• Enable context-based interactions by storing knowledge over sessions.
• Reduce computation costs by utilizing previously learned or saved data.
• Bridge the gap between isolated AI processes and stateful systems.
• Aid in debugging and system auditing with historical memory access.

Key Features

• Real-Time Memory: Capture and store memories during a live session for immediate access.
• Long-Term Persistence: Use distributed databases or file storage to persist memory across sessions.
• Memory Optimization: Remove or compress obsolete data to avoid performance bottlenecks.
• Contextual Memory Retrieval: Retrieve memories based on contextual triggers, enhancing decision-making processes.
• Self-Maintenance: Validate and update memory contents dynamically.

Logic and Implementation

This module introduces a memory handler that seamlessly integrates real-time and persistent storage for knowledge management. Below is a simplified implementation for handling AI memory:

            import json
            import os

            class InfiniteMemory:
                """
                AI memory management system for persistent and real-time context.
                """

                def __init__(self, memory_file="memory.json"):
                    """
                    Initialize the memory handler.
                    :param memory_file: Path to the persistent memory file.
                    """
                    self.memory = {}  # Real-time memory
                    self.memory_file = memory_file
                    self._load_memory()

                def _load_memory(self):
                    """
                    Load long-term memory from a file.
                    """
                    if os.path.exists(self.memory_file):
                        with open(self.memory_file, "r") as file:
                            self.memory = json.load(file)
                        print("[INFO] Memory loaded successfully.")
                    else:
                        print("[INFO] No persistent memory found. Initializing with empty memory.")

                def save_memory(self):
                    """
                    Save the current real-time memory to the persistent file.
                    """
                    with open(self.memory_file, "w") as file:
                        json.dump(self.memory, file, indent=4)
                    print("[INFO] Memory saved successfully.")

                def store(self, key, value):
                    """
                    Store a key-value pair in real-time memory.
                    :param key: Memory key.
                    :param value: Memory value.
                    """
                    self.memory[key] = value

                def retrieve(self, key):
                    """
                    Retrieve a value from memory using a key.
                    :param key: Memory key to search.
                    :return: Memory value if found, else None.
                    """
                    return self.memory.get(key)

                def forget(self, key):
                    """
                    Delete a memory entry using a key.
                    :param key: Memory key to delete.
                    """
                    if key in self.memory:
                        del self.memory[key]
                        print(f"[INFO] '{key}' removed from memory.")
                    else:
                        print(f"[WARNING] Key '{key}' not found in memory.")

            # Example Usage
            if __name__ == "__main__":
                memory_system = InfiniteMemory()

                # Storing and retrieving memory
                memory_system.store("favorite_color", "blue")
                print("Favorite color:", memory_system.retrieve("favorite_color"))

                # Forgetting memory
                memory_system.forget("favorite_color")
                print("Favorite color after forgetting:", memory_system.retrieve("favorite_color"))

                # Save current memory to disk
                memory_system.save_memory()
            

Dependencies

• json: Handles serialization and deserialization of memory data.
• os: Facilitates file operations (loading and saving persistent memory).

Usage

The InfiniteMemory class is designed for flexibility, allowing developers to integrate it into any AI system requiring memory persistence. Below is a step-by-step usage guide:

            # Initialize InfiniteMemory
            memory = InfiniteMemory(memory_file="ai_memory.json")

            # Store data
            memory.store("task_status", "Completed")

            # Retrieve data
            print(memory.retrieve("task_status"))

            # Forget a memory
            memory.forget("task_status")

            # Save memory to persistent storage
            memory.save_memory()
            

System Integration

• Conversational AI: Retain the state of user interactions for contextually-aware responses.
• Autonomous Agents: Store mission-critical data for use during long-running operations.
• Decision Support Systems: Use memory snapshots for analyzing system performance trends.
• Audit Trails: Maintain memory history to support auditing and debugging.

Future Enhancements

• Introduce encryption for secure memory storage.
• Enable memory distribution to support distributed AI systems.
• Incorporate machine learning models for prioritizing data retention dynamically.
• Implement time-based memory erasure policies to ensure system scalability.