Enhance memory management system

Local Client Tutorial.ipynb

@@ -356,7 +356,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "07cf336c-de93-449d-ab4e-440ccfbbc81e",
+   "id": "07cf336c-de93-449d-ab4e-440ccfbbc81a",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -560,13 +560,59 @@
     "nb_print(response.messages)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "d5f9a5c9-4e3b-4b8e-8b8e-5f1c9a4cb9d",
+   "metadata": {},
+   "source": [
+    "## Section 6: Demonstrating Memory Categorization and Prioritization\n",
+    "In this section, we will demonstrate how to categorize and prioritize memories using the new memory management system."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "9f854c2d-fe1a-460f-9c55-392b14947e8c",
+   "id": "d5f9a5c9-4e3b-4b8e-8b8e-5f1c9a4cb9d",
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "from helper import Memory, MemoryManager\n",
+    "\n",
+    "memory_manager = MemoryManager()\n",
+    "\n",
+    "memory1 = Memory(\"Learned about AI\", 10)\n",
+    "memory2 = Memory(\"Had lunch\", 5)\n",
+    "memory3 = Memory(\"Went for a walk\", 7)\n",
+    "\n",
+    "memory_manager.add_memory(memory1, \"short_term\")\n",
+    "memory_manager.add_memory(memory2, \"long_term\")\n",
+    "memory_manager.add_memory(memory3, \"episodic\")\n",
+    "\n",
+    "memory_manager.prioritize_memories()\n",
+    "print(\"Prioritized Memories:\", memory_manager.get_memories(\"short_term\"))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0106a5c9-4e3b-4b8e-8b8e-5f1c9a4cb9d",
+   "metadata": {},
+   "source": [
+    "## Section 7: Demonstrating Memory Decay Mechanism\n",
+    "In this section, we will demonstrate how the decay mechanism works to gradually forget less important memories over time."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0106a5c9-4e3b-4b8e-8b8e-5f1c9a4cb9d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import time\n",
+    "\n",
+    "memory_manager.decay_memories()\n",
+    "print(\"Memories after decay:\", memory_manager.get_memories(\"short_term\"))"
+   ]
   }
  ],
  "metadata": {

helper.py

@@ -4,6 +4,7 @@
 import json
 import os
 import re
+import time
 
 from dotenv import find_dotenv, load_dotenv
 from IPython.display import HTML, display
@@ -130,4 +131,76 @@ def format_json(json_str):
         formatted = re.sub(r": (true|false)", r': <span class="json-boolean">\1</span>', formatted)
         return formatted
     except json.JSONDecodeError:
-        return html.escape(json_str)
+        return html.escape(json_str)
+
+
+# Memory management functions
+
+class Memory:
+    def __init__(self, content, importance, timestamp=None):
+        self.content = content
+        self.importance = importance
+        self.timestamp = timestamp or time.time()
+
+    def __repr__(self):
+        return f"Memory(content={self.content}, importance={self.importance}, timestamp={self.timestamp})"
+
+
+class MemoryManager:
+    def __init__(self):
+        self.short_term_memory = []
+        self.long_term_memory = []
+        self.episodic_memory = []
+
+    def add_memory(self, memory, category):
+        if category == "short_term":
+            self.short_term_memory.append(memory)
+        elif category == "long_term":
+            self.long_term_memory.append(memory)
+        elif category == "episodic":
+            self.episodic_memory.append(memory)
+
+    def prioritize_memories(self):
+        self.short_term_memory.sort(key=lambda x: x.importance, reverse=True)
+        self.long_term_memory.sort(key=lambda x: x.importance, reverse=True)
+        self.episodic_memory.sort(key=lambda x: x.importance, reverse=True)
+
+    def decay_memories(self, decay_rate=0.01):
+        current_time = time.time()
+        for memory in self.short_term_memory:
+            memory.importance -= decay_rate * (current_time - memory.timestamp)
+        for memory in self.long_term_memory:
+            memory.importance -= decay_rate * (current_time - memory.timestamp)
+        for memory in self.episodic_memory:
+            memory.importance -= decay_rate * (current_time - memory.timestamp)
+
+        self.short_term_memory = [m for m in self.short_term_memory if m.importance > 0]
+        self.long_term_memory = [m for m in self.long_term_memory if m.importance > 0]
+        self.episodic_memory = [m for m in self.episodic_memory if m.importance > 0]
+
+    def get_memories(self, category):
+        if category == "short_term":
+            return self.short_term_memory
+        elif category == "long_term":
+            return self.long_term_memory
+        elif category == "episodic":
+            return self.episodic_memory
+
+
+# Example usage
+if __name__ == "__main__":
+    memory_manager = MemoryManager()
+
+    memory1 = Memory("Learned about AI", 10)
+    memory2 = Memory("Had lunch", 5)
+    memory3 = Memory("Went for a walk", 7)
+
+    memory_manager.add_memory(memory1, "short_term")
+    memory_manager.add_memory(memory2, "long_term")
+    memory_manager.add_memory(memory3, "episodic")
+
+    memory_manager.prioritize_memories()
+    print("Prioritized Memories:", memory_manager.get_memories("short_term"))
+
+    memory_manager.decay_memories()
+    print("Memories after decay:", memory_manager.get_memories("short_term"))