#!/usr/bin/env python # -*- coding: utf-8 -*- ''' 游戏NPC角色对话生成器 基于微调后的LoRA模型生成角色对话 ''' import torch import json import random from peft import PeftModel from transformers import AutoModelForCausalLM, AutoTokenizer from typing import Dict, List, Optional import platform # Windows multiprocessing兼容性修复 if platform.system() == "Windows": import multiprocessing multiprocessing.set_start_method('spawn', force=True) class NPCDialogueGenerator: def __init__(self, base_model_path: str, lora_model_path: Optional[str] = None, external_character_data: Optional[Dict] = None): """ 初始化NPC对话生成器 Args: base_model_path: 基础模型路径 lora_model_path: LoRA模型路径（可选） external_character_data: 外部角色数据（可选，优先使用） """ self.base_model_path = base_model_path self.lora_model_path = lora_model_path self.model = None self.tokenizer = None # 优先使用外部角色数据，如果没有则使用内置数据 if external_character_data: self.character_profiles = self._process_external_character_data(external_character_data) print(f"✓ 使用外部角色数据: {list(self.character_profiles.keys())}") # else: # self.character_profiles = self._load_character_profiles() # print(f"✓ 使用内置角色数据: {list(self.character_profiles.keys())}") self._load_model() def _process_external_character_data(self, external_data: Dict) -> Dict: """ 处理外部角色数据，转换为对话生成器可用的格式 Args: external_data: 来自knowledge_base的角色数据 Returns: 处理后的角色数据字典 """ processed_profiles = {} for char_name, char_data in external_data.items(): # 提取基本信息 basic_info = char_data.get('basic_info', {}) personality = char_data.get('personality', {}) background = char_data.get('background', {}) skills = char_data.get('skills_and_abilities', {}) speech_patterns = char_data.get('speech_patterns', {}) # 构建角色画像 profile = { "name": char_data.get('character_name', char_name), "title": basic_info.get('occupation', '未知'), "personality": personality.get('core_traits', []) + personality.get('strengths', []), "background": background.get('childhood', '') + ' ' + background.get('education', ''), "speech_patterns": speech_patterns.get('vocabulary', []) + speech_patterns.get('tone', []), "sample_dialogues": self._generate_sample_dialogues(char_data), # 保存完整数据供高级功能使用 "full_data": char_data } processed_profiles[char_name] = profile return processed_profiles def _generate_sample_dialogues(self, char_data: Dict) -> List[str]: """ 基于角色数据生成示例对话 Args: char_data: 角色数据 Returns: 示例对话列表 """ # 这里可以根据角色的性格、背景等生成更合适的示例对话 # 暂时返回一些通用的示例 basic_info = char_data.get('basic_info', {}) occupation = basic_info.get('occupation', '角色') if '侦探' in occupation or '调查员' in occupation: return [ "我需要仔细分析这个案件。", "每个细节都可能很重要。", "让我重新梳理一下线索。" ] elif '教授' in occupation or '博士' in occupation: return [ "根据我的研究，这个现象很特殊。", "我们需要更谨慎地处理这个问题。", "知识就是力量，但也要小心使用。" ] else: return [ "我遇到了一些困难。", "请帮帮我。", "这太奇怪了。" ] # def _load_character_profiles(self) -> Dict: # """加载角色画像数据""" # return { # "维多利亚·布莱克伍德": { # "name": "维多利亚·布莱克伍德", # "title": "神秘学专家", # "personality": ["理性分析", "谨慎小心", "实用主义", "思维缜密"], # "background": "拥有丰富神秘学知识和战斗经验的侦探，既是非凡者也是夏洛克·莫里亚蒂", # "speech_patterns": ["会使用专业术语", "经常进行逻辑分析", "对危险保持警告", "内心独白较多"], # "sample_dialogues": [ # "好奇往往是导致死亡的主要因素。", # "总之，我的任务到此为止。", # "这需要仔细分析才能得出结论。" # ] # }, # "阿奇博尔德·韦恩博士": { # "name": "阿奇博尔德·韦恩博士", # "title": "神秘学导师", # "personality": ["沉稳睿智", "言简意赅", "关怀学生", "经验丰富"], # "background": "神秘学领域的资深专家，经验极其丰富的导师，知识渊博", # "speech_patterns": ["话语简练但信息量大", "给予实用指导", "语调平和但权威", "关心但保持距离"], # "sample_dialogues": [ # "耐心是修炼的基础。", # "不要急于求成，稳扎稳打比什么都重要。", # "这种情况需要格外小心。" # ] # }, # "塔利姆": { # "name": "塔利姆", # "title": "文雅绅士", # "personality": ["礼貌尊敬", "有文化素养", "寻求帮助", "温和友善"], # "background": "受过良好教育的普通人，有一定的文学修养，遇到困难时会寻求专家帮助", # "speech_patterns": ["使用礼貌称谓", "表达困惑时措辞文雅", "会引用文学作品", "语气温和"], # "sample_dialogues": [ # "噢，尊敬的大侦探，你最近在忙碌什么？", # "这不是《罗密欧与朱丽叶》的故事！", # "我有个朋友遇到了困难..." # ] # }, # "艾伦": { # "name": "艾伦", # "title": "困扰的求助者", # "personality": ["焦虑不安", "详细描述", "半信半疑", "急需帮助"], # "background": "普通人，但最近遭遇了一系列神秘的厄运事件，怀疑受到诅咒", # "speech_patterns": ["情绪紧张", "会详细描述遭遇", "语气急切", "表现出恐惧"], # "sample_dialogues": [ # "最近我总是遭遇各种厄运...", # "我怀疑是不是受到了什么诅咒。", # "请帮帮我，我不知道该怎么办！" # ] # }, # "戴莉.西蒙妮": { # "name": "戴莉·西蒙妮", # "title": "专业调查员", # "personality": ["专业简洁", "直接明确", "严谨认真", "目标导向"], # "background": "负责调查神秘事件的专业人员，办事效率高，问题直接", # "speech_patterns": ["问题直接明确", "语气专业", "注重事实", "简洁有力"], # "sample_dialogues": [ # "请详细描述事件经过。", # "有什么证据可以证明？", # "这件事需要立即调查。" # ] # } # } def _load_model(self): """加载模型和分词器""" print(f"Loading tokenizer from: {self.base_model_path}") self.tokenizer = AutoTokenizer.from_pretrained( self.base_model_path, use_fast=False, trust_remote_code=True ) if self.tokenizer.pad_token is None: self.tokenizer.pad_token = self.tokenizer.eos_token print(f"Loading base model from: {self.base_model_path}") self.model = AutoModelForCausalLM.from_pretrained( self.base_model_path, device_map="auto", torch_dtype=torch.bfloat16, trust_remote_code=True ) # 如果有LoRA模型，则加载 if self.lora_model_path: print(f"Loading LoRA weights from: {self.lora_model_path}") self.model = PeftModel.from_pretrained(self.model, self.lora_model_path) def generate_character_dialogue( self, character_name: str, context: str = "", user_input: str = "", temperature: float = 0.8, max_new_tokens: int = 150, top_p: float = 0.9 ) -> str: """ 生成指定角色的对话 Args: character_name: 角色名称 context: 对话上下文 user_input: 用户输入/触发内容 temperature: 采样温度 max_new_tokens: 最大生成token数 top_p: 核采样参数 Returns: 生成的对话内容 """ if character_name not in self.character_profiles: raise ValueError(f"Unknown character: {character_name}") profile = self.character_profiles[character_name] # 构建系统提示 system_prompt = self._build_system_prompt(profile, context) # 构建用户输入 if not user_input: user_input = "请说一段符合你角色设定的话。" # 准备消息 messages = [ {"role": "system", "content": system_prompt}, {"role": "user", "content": user_input} ] # 应用对话模板 inputs = self.tokenizer.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_tensors="pt", return_dict=True, enable_thinking=False ) # 移动到设备 inputs = {k: v.to(self.model.device) for k, v in inputs.items()} # 生成对话 with torch.no_grad(): outputs = self.model.generate( **inputs, max_new_tokens=max_new_tokens, do_sample=True, temperature=temperature, top_p=top_p, pad_token_id=self.tokenizer.eos_token_id, repetition_penalty=1.1 ) # 解码输出 response = outputs[0][inputs['input_ids'].shape[1]:] dialogue = self.tokenizer.decode(response, skip_special_tokens=True).strip() return dialogue def _build_system_prompt(self, profile: Dict, context: str = "") -> str: """构建系统提示""" personality_str = "、".join(profile["personality"]) speech_pattern_str = "；".join(profile["speech_patterns"]) system_prompt = f"""你是游戏中的NPC角色{profile["name"]}（{profile["title"]}）。 角色背景：{profile["background"]} 性格特点：{personality_str} 说话风格：{speech_pattern_str} 请严格按照这个角色的设定来回应，保持角色的一致性和独特性。""" if context: system_prompt += f"\n\n当前情境：{context}" return system_prompt def generate_dialogue_conversation(self, character1: str, character2: str, topic: str, turns: int = 4) -> List[Dict]: """生成两个角色之间的对话 Args: character1: 第一个角色 character2: 第二个角色 topic: 对话主题 turns: 对话轮数 Returns: 对话列表，每个元素包含speaker和dialogue """ conversation = [] context = f"现在{character1}和{character2}在讨论关于{topic}的话题。" for turn in range(turns): if turn % 2 == 0: # character1 说话 speaker = character1 if turn == 0: user_input = f"开始和{character2}讨论{topic}这个话题。" else: # 基于上一轮对话内容 last_dialogue = conversation[-1]["dialogue"] user_input = f"{character2}刚才说：\"{last_dialogue}\"。请回应。" else: # character2 说话 speaker = character2 last_dialogue = conversation[-1]["dialogue"] user_input = f"{character1}刚才说：\"{last_dialogue}\"。请回应。" dialogue = self.generate_character_dialogue( speaker, context, user_input, temperature=0.8 ) conversation.append({ "speaker": speaker, "dialogue": dialogue }) return conversation def get_character_info(self, character_name: str) -> Dict: """获取角色信息""" return self.character_profiles.get(character_name, {}) def list_available_characters(self) -> List[str]: """列出所有可用角色""" return list(self.character_profiles.keys()) def main(): """测试对话生成器""" # 配置路径 base_model_path = '/mnt/g/Project02/AITrain/Qwen/Qwen3-8B-AWQ' lora_model_path = './output/NPC_Dialogue_LoRA/final_model' # 如果没有训练LoRA，设为None # 检查LoRA模型是否存在 import os if not os.path.exists(lora_model_path): print("LoRA模型不存在，使用基础模型") lora_model_path = None # 创建对话生成器 generator = NPCDialogueGenerator(base_model_path, lora_model_path) print("=== 游戏NPC角色对话生成器 ===") print(f"可用角色：{', '.join(generator.list_available_characters())}") # 测试单个角色对话生成 print("\n=== 单角色对话测试 ===") test_scenarios = [ { "character": "克莱恩", "context": "玩家向你咨询神秘学知识", "input": "请告诉我一些关于灵界的注意事项。" }, { "character": "阿兹克", "context": "学生遇到了修炼瓶颈", "input": "导师，我在修炼中遇到了困难。" }, { "character": "塔利姆", "context": "在俱乐部偶遇老朋友", "input": "好久不见，最近怎么样？" } ] for scenario in test_scenarios: print(f"\n--- {scenario['character']} ---") print(f"情境：{scenario['context']}") print(f"输入：{scenario['input']}") dialogue = generator.generate_character_dialogue( scenario["character"], scenario["context"], scenario["input"] ) print(f"回复：{dialogue}") # 测试角色间对话 print("\n=== 角色间对话测试 ===") conversation = generator.generate_dialogue_conversation( "克莱恩", "塔利姆", "最近遇到的神秘事件", turns=4 ) for turn in conversation: print(f"{turn['speaker']}：{turn['dialogue']}") # 交互式对话模式 print("\n=== 交互式对话模式 ===") print("输入格式：角色名 上下文 用户输入") print("例如：克莱恩 在俱乐部 请给我一些建议") print("输入'quit'退出") while True: try: user_command = input("\n请输入指令: ").strip() if user_command.lower() == 'quit': break parts = user_command.split(' ', 2) if len(parts) < 2: print("格式错误，请使用：角色名 上下文 [用户输入]") continue character = parts[0] context = parts[1] user_input = parts[2] if len(parts) > 2 else "" if character not in generator.list_available_characters(): print(f"未知角色：{character}") print(f"可用角色：{', '.join(generator.list_available_characters())}") continue dialogue = generator.generate_character_dialogue( character, context, user_input ) print(f"\n{character}：{dialogue}") except KeyboardInterrupt: break except Exception as e: print(f"生成对话时出错：{e}") print("\n对话生成器已退出") if __name__ == '__main__': main()