import numpy as np import json from datetime import datetime from config import MEMORY_SIZE, LOCAL_MEMORY_SIZE class MemoryManager: def __init__(self): self.memory = np.memmap('jairvismkv_memory.bin', dtype='uint8', mode='w+', shape=(MEMORY_SIZE,)) self.local_memory_size = LOCAL_MEMORY_SIZE self.last_backup_date = None def store(self, input_data, output_data): data = json.dumps({ 'timestamp': datetime.now().isoformat(), 'input': input_data, 'output': output_data }) data_bytes = data.encode('utf-8') data_size = len(data_bytes) available_index = self.find_available_space(data_size) if available_index is not None: self.memory[available_index:available_index+data_size] = np.frombuffer(data_bytes, dtype='uint8') self.memory.flush() self.manage_local_memory() else: print("Not enough space in memory to store the data") def find_available_space(self, size): for i in range(0, len(self.memory) - size): if np.all(self.memory[i:i+size] == 0): return i return None def manage_local_memory(self): current_size = self.memory.size * self.memory.itemsize if current_size > self.local_memory_size: excess_size = current_size - self.local_memory_size self.memory = self.memory[:self.local_memory_size] self.memory.flush() print(f"Local memory optimized. Current size: {self.local_memory_size / (1024**3):.2f} GB") def retrieve(self, query): # Implement retrieval logic here # This could involve searching through the memory for relevant information pass def create_backup(self): current_date = datetime.now() if self.last_backup_date is None or (current_date - self.last_backup_date).days >= 30: backup_file = f"jairvismkv_backup_{current_date.strftime('%Y%m%d')}.bin" self.memory.flush() # Implement backup logic here (e.g., using Hugging Face's file storage) self.last_backup_date = current_date print(f"Backup created: {backup_file}") def load_from_backup(self, backup_file): # Implement logic to load memory from a backup file pass