Implement NPC memory and interaction system with SQLite database; add NPC data structure and dynamic NPC handling; integrate LLM for NPC conversations and quest generation.

LLM NPC/talkingAI.py

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+import requests
+import time
+
+# ---- CONFIG ----
+OLLAMA_HOST = "http://100.103.117.14:11434"  # Tailscale IP
+MODEL_NAME = "neural-chat"
+NUM_EXCHANGES = 10  # number of exchanges
+
+# Names / personalities
+AI1_NAME = "Alice"
+AI2_NAME = "Bob"
+AI1_PERSONALITY = "Alice is cheerful, curious, and friendly."
+AI2_PERSONALITY = "Bob is witty, thoughtful, and calm."
+
+# ---- HELPER FUNCTION ----
+def generate_response(prompt, model="neural-chat", url="http://100.103.117.14:11434/api/generate"):
+    # Add instruction for short answers
+    payload = {
+        "model": model,
+        "prompt": prompt,
+        "stream": False
+    }
+    try:
+        response = requests.post(url, json=payload, timeout=120)
+        response.raise_for_status()
+        data = response.json()
+        return data.get("response", "").strip()
+    except Exception as e:
+        print(f"LLM error: {e}")
+        return None
+
+# ---- INITIAL PROMPT ----
+conversation_history = f"{AI1_NAME}: Hello, my name is {AI1_NAME}. {AI1_PERSONALITY}\n" \
+                       f"{AI2_NAME}: Hi, I'm {AI2_NAME}. {AI2_PERSONALITY}\n"
+
+print("=== Conversation Start ===")
+for i in range(NUM_EXCHANGES):
+    # AI1 speaks
+    ai1_prompt = conversation_history + f"{AI1_NAME}:"
+    ai1_msg = generate_response(ai1_prompt)
+    ai1_msg = ai1_msg.strip()
+    print(f"{AI1_NAME}: {ai1_msg}")
+    conversation_history += f"{AI1_NAME}: {ai1_msg}\n"
+
+
+
+    # AI2 speaks
+    ai2_prompt = conversation_history + f"{AI2_NAME}:"
+    ai2_msg = generate_response(ai2_prompt)
+    ai2_msg = ai2_msg.strip()
+    print(f"{AI2_NAME}: {ai2_msg}")
+    conversation_history += f"{AI2_NAME}: {ai2_msg}\n"
+
+
+print("=== Conversation End ===")