// 传统硬编码对话系统
public class OldDialogueSystem
{
    private string[] preScriptedDialogues = {
        "你好，旅行者！",
        "听说北方的洞穴里有宝藏。",
        "你要去冒险吗？(是/否)"
    };
    private int currentLine = 0;
    
    public void OnPlayerInteract()
    {
        if (currentLine < preScriptedDialogues.Length)
        {
            ShowDialogue(preScriptedDialogues[currentLine]);
            currentLine++;
        }
    }
}

 dialogue_fsm.py - AI对话游戏的状态机定义

from enum import Enum
from typing import Dict, Optional, Callable
import json

 定义对话状态
class DialogueState(Enum):
    GREETING = "greeting"           问候状态
    INFO_GATHERING = "info_gather"  信息收集状态
    QUEST_OFFER = "quest_offer"     任务发布状态
    RESPONDING = "responding"       响应状态
    END = "end"                     结束状态

 定义状态转移规则
class DialogueFSM:
    def __init__(self):
        self.current_state = DialogueState.GREETING
        self.context: Dict = {}           对话上下文（记忆）
        self.transitions = {
            DialogueState.GREETING: {
                "greet": DialogueState.INFO_GATHERING,
                "skip": DialogueState.QUEST_OFFER
            },
            DialogueState.INFO_GATHERING: {
                "info_collected": DialogueState.QUEST_OFFER,
                "info_missing": DialogueState.INFO_GATHERING
            },
            DialogueState.QUEST_OFFER: {
                "accept": DialogueState.RESPONDING,
                "decline": DialogueState.END
            },
            DialogueState.RESPONDING: {
                "complete": DialogueState.END
            }
        }
    
    def transition(self, trigger: str) -> bool:
        """根据触发条件进行状态转移"""
        if trigger in self.transitions.get(self.current_state, {}):
            self.current_state = self.transitions[self.current_state][trigger]
            return True
        return False
    
    def get_state_json(self) -> str:
        """将当前状态序列化为JSON，供LLM使用"""
        return json.dumps({
            "state": self.current_state.value,
            "context": self.context
        })

 llm_dialogue_engine.py - 核心对话引擎

import requests
from typing import List, Dict

class LLMDialogueEngine:
    """
    LLM-FSM混合对话引擎
    FSM负责状态管理，LLM负责自然语言理解和生成
    """
    
    def __init__(self, llm_api_endpoint: str, npc_personality: str):
        self.fsm = DialogueFSM()
        self.api_endpoint = llm_api_endpoint
        self.npc_personality = npc_personality
        self.conversation_history: List[Dict] = []
    
    def _build_system_prompt(self) -> str:
        """构建包含状态信息的系统提示"""
        return f"""
        你是一个RPG游戏中的NPC，性格设定：{self.npc_personality}
        
        当前对话状态：{self.fsm.get_state_json()}
        
        规则：
        1. 保持角色一致性，不要跳出角色设定
        2. 根据玩家的输入，判断应该触发什么状态转移
        3. 在回答末尾，用 [TRANSITION:xxx] 标注建议的下一个状态
        4. 如果玩家提供的信息对任务推进有帮助，记录到上下文中
        """
    
    def process_input(self, player_input: str) -> str:
        """处理玩家输入，生成AI回复"""
        
         构建完整提示（包含历史对话）
        messages = [
            {"role": "system", "content": self._build_system_prompt()},
            self.conversation_history,
            {"role": "user", "content": player_input}
        ]
        
         调用LLM API
        response = requests.post(
            self.api_endpoint,
            json={"messages": messages, "max_tokens": 150}
        )
        ai_response = response.json()["choices"][0]["message"]["content"]
        
         解析LLM建议的状态转移（简化实现）
        if "[TRANSITION:" in ai_response:
            trigger = ai_response.split("[TRANSITION:")[1].split("]")[0]
            self.fsm.transition(trigger)
        
         记录对话历史
        self.conversation_history.append({"role": "user", "content": player_input})
        self.conversation_history.append({"role": "assistant", "content": ai_response})
        
         清理历史（防止超出上下文窗口）
        if len(self.conversation_history) > 20:
            self.conversation_history = self.conversation_history[-20:]
        
        return ai_response.split("[TRANSITION:")[0].strip()

// UnityNPCController.cs - 在Unity中集成AI对话NPC

using UnityEngine;
using UnityEngine.UI;
using System.Net.Http;
using System.Threading.Tasks;

public class UnityNPCController : MonoBehaviour
{
    [Header("NPC设置")]
    public string npcName = "老铁匠";
    public string npcPersonality = "经验丰富但性格暴躁的老铁匠，喜欢讲冷笑话";
    
    [Header("UI组件")]
    public Text dialogueText;      // 对话显示区域
    public InputField playerInput; // 玩家输入框
    public Button sendButton;      // 发送按钮
    
    private LLMDialogueEngine dialogueEngine;
    private string llmEndpoint = "http://localhost:11434/api/generate"; // Ollama本地端点
    
    void Start()
    {
        dialogueEngine = new LLMDialogueEngine(llmEndpoint, npcPersonality);
        sendButton.onClick.AddListener(OnSendMessage);
        
        // 开场问候
        StartCoroutine(ProcessInputAsync("你好"));
    }
    
    async void OnSendMessage()
    {
        string playerMsg = playerInput.text;
        if (string.IsNullOrEmpty(playerMsg)) return;
        
        playerInput.text = "";
        await ProcessInputAsync(playerMsg);
    }
    
    async Task ProcessInputAsync(string playerMsg)
    {
        string aiResponse = await dialogueEngine.ProcessInputAsync(playerMsg);
        dialogueText.text = $"{npcName}: {aiResponse}";
        
        // 触发Unity动画事件
        GetComponent<Animator>()?.SetTrigger("Talk");
    }
}