diff --git a/AITrain/dual_ai_dialogue_system.py b/AITrain/dual_ai_dialogue_system.py index f156e05..2e9e43c 100644 --- a/AITrain/dual_ai_dialogue_system.py +++ b/AITrain/dual_ai_dialogue_system.py @@ -468,15 +468,27 @@ class DualAIDialogueEngine: context_info_count ) - # 生成对话 + # 生成对话 - 使用双模型系统 try: - response = self.llm_generator.generate_character_dialogue( - current_speaker, - prompt, - topic_hint or "请继续对话", - temperature=0.8, - max_new_tokens=150 - ) + # 检查是否为双模型对话系统 + if hasattr(self.llm_generator, 'generate_dual_character_dialogue'): + # 使用双模型系统 + response = self.llm_generator.generate_dual_character_dialogue( + current_speaker, + prompt, + topic_hint or "请继续对话", + temperature=0.8, + max_new_tokens=150 + ) + else: + # 兼容旧的单模型系统 + response = self.llm_generator.generate_character_dialogue( + current_speaker, + prompt, + topic_hint or "请继续对话", + temperature=0.8, + max_new_tokens=150 + ) # 记录使用的上下文 context_used = [f"{info['section']}.{info['subsection']}" for info in context_info[:context_info_count]] @@ -534,6 +546,72 @@ class DualAIDialogueEngine: print() return results + + def run_dual_model_conversation(self, session_id: str, topic: str = "", turns: int = 4, + history_context_count: int = 3, context_info_count: int = 2): + """使用双模型系统运行对话 + + Args: + session_id: 会话ID + topic: 对话主题 + turns: 对话轮数 + history_context_count: 使用的历史对话轮数 + context_info_count: 使用的上下文信息数量 + """ + # 检查是否为双模型对话系统 + if not hasattr(self.llm_generator, 'run_dual_character_conversation'): + print("⚠ 当前系统不支持双模型对话") + return self.run_conversation_turn(session_id, self.llm_generator.list_characters(), turns, topic, + history_context_count, context_info_count) + + # 获取对话历史 + dialogue_history = self.conv_mgr.get_conversation_history(session_id) + + # 构建上下文信息 + if dialogue_history: + recent_turns = dialogue_history[-history_context_count:] if history_context_count > 0 else [] + recent_content = " ".join([turn.content for turn in recent_turns]) + search_query = recent_content + " " + topic + else: + search_query = f"{topic} introduction greeting" + + # 搜索相关上下文 + context_info = self.kb.search_relevant_context(search_query, top_k=context_info_count) + + # 构建上下文字符串 + context_str = "" + if context_info: + context_str = "相关背景信息：" + for info in context_info[:context_info_count]: + content = info['content'][:150] + "..." if len(info['content']) > 150 else info['content'] + context_str += f"\n- {content}" + + print(f"\n=== 双模型对话系统 ===") + print(f"主题: {topic}") + print(f"角色: {', '.join(self.llm_generator.list_characters())}") + print(f"轮数: {turns}") + print(f"上下文设置: 历史{history_context_count}轮, 信息{context_info_count}个") + + # 使用双模型系统生成对话 + conversation_results = self.llm_generator.run_dual_character_conversation( + topic=topic, + turns=turns, + context=context_str, + temperature=0.8, + max_new_tokens=150 + ) + + # 保存对话到数据库 + for result in conversation_results: + self.conv_mgr.add_dialogue_turn( + session_id, + result['speaker'], + result['dialogue'], + [result.get('context_used', '')], + 0.8 # 默认相关性分数 + ) + + return conversation_results def main(): """主函数 - 演示系统使用""" @@ -559,21 +637,43 @@ def main(): kb = RAGKnowledgeBase(knowledge_dir) conv_mgr = ConversationManager() - # 这里需要你的LLM生成器，使用现有的NPCDialogueGenerator - from npc_dialogue_generator import NPCDialogueGenerator + # 这里需要你的LLM生成器，使用新的双模型对话系统 + from npc_dialogue_generator import DualModelDialogueGenerator base_model_path = '/mnt/g/Project02/AITrain/Qwen/Qwen3-4B' # 根据你的路径调整 lora_model_path = './output/NPC_Dialogue_LoRA/final_model' if not os.path.exists(lora_model_path): lora_model_path = None - # 创建对话生成器并传入角色数据 - if hasattr(kb, 'character_data') and kb.character_data: - print("✓ 使用knowledge_base角色数据创建对话生成器") - llm_generator = NPCDialogueGenerator(base_model_path, lora_model_path, kb.character_data) + # 创建双模型对话生成器 + if hasattr(kb, 'character_data') and len(kb.character_data) >= 2: + print("✓ 使用knowledge_base角色数据创建双模型对话系统") + # 获取前两个角色 + character_names = list(kb.character_data.keys())[:2] + char1_name = character_names[0] + char2_name = character_names[1] + + # 配置两个角色的模型 + character1_config = { + "name": char1_name, + "lora_path": lora_model_path, # 可以为每个角色设置不同的LoRA + "character_data": kb.character_data[char1_name] + } + + character2_config = { + "name": char2_name, + "lora_path": lora_model_path, # 可以为每个角色设置不同的LoRA + "character_data": kb.character_data[char2_name] + } + + llm_generator = DualModelDialogueGenerator( + base_model_path, + character1_config, + character2_config + ) else: - print("⚠ 使用内置角色数据创建对话生成器") - llm_generator = NPCDialogueGenerator(base_model_path, lora_model_path) + print("⚠ 角色数据不足，无法创建双模型对话系统") + return # 创建对话引擎 dialogue_engine = DualAIDialogueEngine(kb, conv_mgr, llm_generator) @@ -621,7 +721,15 @@ def main(): print(f"\n开始对话 - 会话ID: {session_id}") print(f"上下文设置: 历史{history_count}轮, 信息{context_info_count}个") - dialogue_engine.run_conversation_turn(session_id, characters, turns, topic, history_count, context_info_count) + + # 询问是否使用双模型对话 + use_dual_model = input("是否使用双模型对话系统？(y/n，默认y): ").strip().lower() + if use_dual_model != 'n': + print("使用双模型对话系统...") + dialogue_engine.run_dual_model_conversation(session_id, topic, turns, history_count, context_info_count) + else: + print("使用传统对话系统...") + dialogue_engine.run_conversation_turn(session_id, characters, turns, topic, history_count, context_info_count) elif choice == '2': # 继续已有对话 @@ -662,7 +770,15 @@ def main(): print(f"\n继续对话 - 会话ID: {session_id}") print(f"上下文设置: 历史{history_count}轮, 信息{context_info_count}个") - dialogue_engine.run_conversation_turn(session_id, characters, turns, topic, history_count, context_info_count) + + # 询问是否使用双模型对话 + use_dual_model = input("是否使用双模型对话系统？(y/n，默认y): ").strip().lower() + if use_dual_model != 'n': + print("使用双模型对话系统...") + dialogue_engine.run_dual_model_conversation(session_id, topic, turns, history_count, context_info_count) + else: + print("使用传统对话系统...") + dialogue_engine.run_conversation_turn(session_id, characters, turns, topic, history_count, context_info_count) else: print("❌ 无效的会话编号") except ValueError: diff --git a/AITrain/main_controller.py b/AITrain/main_controller.py index cdf3117..e0b697b 100644 --- a/AITrain/main_controller.py +++ b/AITrain/main_controller.py @@ -122,14 +122,133 @@ def show_character_info(): print(f"✗ 读取角色文件失败: {char_file} - {e}") def run_dialogue_system(): - """运行对话系统""" + """运行双AI对话系统""" print("\n" + "="*60) print("启动双AI角色对话系统") print("="*60) try: - from dual_ai_dialogue_system import main as dialogue_main - dialogue_main() + + # 直接启动双模型对话 + print("\n正在初始化双模型对话系统...") + + from dual_ai_dialogue_system import RAGKnowledgeBase, ConversationManager, DualAIDialogueEngine + from npc_dialogue_generator import DualModelDialogueGenerator + + # 初始化组件 + kb = RAGKnowledgeBase("./knowledge_base") + conv_mgr = ConversationManager("./conversation_data/conversations.db") + + # 检查模型路径 + base_model_path = '/mnt/g/Project02/AITrain/Qwen/Qwen3-4B' + lora_model_path = './output/NPC_Dialogue_LoRA/final_model' + + if not os.path.exists(base_model_path): + print(f"✗ 基础模型路径不存在: {base_model_path}") + print("请修改 main_controller.py 中的模型路径") + return + + if not os.path.exists(lora_model_path): + lora_model_path = None + print("⚠ LoRA模型不存在，使用基础模型") + + # 检查角色数据 + if not hasattr(kb, 'character_data') or len(kb.character_data) < 2: + print("✗ 角色数据不足，无法创建双模型对话系统") + print("请确保knowledge_base目录中有至少两个角色文件") + return + + # 获取前两个角色 + character_names = list(kb.character_data.keys())[:2] + char1_name = character_names[0] + char2_name = character_names[1] + + print(f"✓ 使用角色: {char1_name} 和 {char2_name}") + + # 配置两个角色的模型 + character1_config = { + "name": char1_name, + "lora_path": lora_model_path, + "character_data": kb.character_data[char1_name] + } + + character2_config = { + "name": char2_name, + "lora_path": lora_model_path, + "character_data": kb.character_data[char2_name] + } + + # 创建双模型对话生成器 + print("正在初始化双模型对话生成器...") + dual_generator = DualModelDialogueGenerator( + base_model_path, + character1_config, + character2_config + ) + + # 创建对话引擎 + dialogue_engine = DualAIDialogueEngine(kb, conv_mgr, dual_generator) + + # 创建对话会话 + characters = [char1_name, char2_name] + worldview = kb.worldview_data.get('worldview_name', '未知世界观') if kb.worldview_data else '未知世界观' + + session_id = conv_mgr.create_session(characters, worldview) + print(f"✓ 创建对话会话: {session_id}") + + # 交互式对话循环 + print(f"\n=== 双AI模型对话系统 ===") + print(f"角色: {char1_name} vs {char2_name}") + print(f"世界观: {worldview}") + print("输入 'quit' 退出对话") + print("-" * 50) + + while True: + try: + # 获取用户输入 + user_input = input("\n请输入对话主题或指令: ").strip() + + if user_input.lower() == 'quit': + print("退出双AI对话系统") + break + + if not user_input: + print("请输入有效的对话主题") + continue + + # 询问对话轮数 + turns_input = input("请输入对话轮数 (默认4): ").strip() + turns = int(turns_input) if turns_input.isdigit() else 4 + + # 询问历史上下文设置 + history_input = input("使用历史对话轮数 (默认3): ").strip() + history_count = int(history_input) if history_input.isdigit() else 3 + + context_input = input("使用上下文信息数量 (默认2): ").strip() + context_info_count = int(context_input) if context_input.isdigit() else 2 + + print(f"\n开始对话 - 主题: {user_input}") + print(f"轮数: {turns}, 历史: {history_count}, 上下文: {context_info_count}") + print("-" * 50) + + # 运行双模型对话 + dialogue_engine.run_dual_model_conversation( + session_id, user_input, turns, history_count, context_info_count + ) + + print("-" * 50) + print("对话完成！") + + except KeyboardInterrupt: + print("\n\n用户中断对话") + break + except Exception as e: + print(f"对话过程中出现错误: {e}") + import traceback + traceback.print_exc() + + + except Exception as e: print(f"✗ 对话系统启动失败: {e}") import traceback @@ -260,7 +379,7 @@ def main(): print("主菜单 - 请选择操作:") print("1. 处理PDF世界观文档 (转换为RAG格式)") print("2. 查看角色设定信息") - print("3. 启动双AI对话系统") + print("3. 启动双AI对话系统 (支持双模型对话)") print("4. 创建演示对话场景") print("5. 系统状态检查") print("6. 查看使用说明") diff --git a/AITrain/npc_dialogue_generator.py b/AITrain/npc_dialogue_generator.py index 9d602b3..fe08db9 100644 --- a/AITrain/npc_dialogue_generator.py +++ b/AITrain/npc_dialogue_generator.py @@ -3,6 +3,7 @@ ''' 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 +支持双模型对话系统，每个模型扮演一个角色 ''' import torch @@ -10,8 +11,9 @@ import json import random from peft import PeftModel from transformers import AutoModelForCausalLM, AutoTokenizer -from typing import Dict, List, Optional +from typing import Dict, List, Optional, Tuple import platform +import os # Windows multiprocessing兼容性修复 if platform.system() == "Windows": @@ -37,9 +39,6 @@ class NPCDialogueGenerator: 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() @@ -113,71 +112,6 @@ class NPCDialogueGenerator: "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}") @@ -339,102 +273,263 @@ class NPCDialogueGenerator: """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 +class DualModelDialogueGenerator: + """双模型对话生成器 - 每个模型扮演一个角色""" + + def __init__(self, + base_model_path: str, + character1_config: Dict, + character2_config: Dict, + lora_model_path: Optional[str] = None): + """ + 初始化双模型对话生成器 - # 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 + Args: + base_model_path: 基础模型路径 + character1_config: 角色1配置 {"name": "角色名", "lora_path": "LoRA路径", "character_data": 角色数据} + character2_config: 角色2配置 {"name": "角色名", "lora_path": "LoRA路径", "character_data": 角色数据} + lora_model_path: 通用LoRA模型路径（可选） + """ + self.base_model_path = base_model_path + self.character1_config = character1_config + self.character2_config = character2_config - # 创建对话生成器 - generator = NPCDialogueGenerator(base_model_path, lora_model_path) + # 为每个角色创建独立的模型实例 + self.character1_generator = None + self.character2_generator = None - print("=== 游戏NPC角色对话生成器 ===") - print(f"可用角色：{', '.join(generator.list_available_characters())}") + self._initialize_character_models() + + def _initialize_character_models(self): + """初始化两个角色的模型""" + print("=== 初始化双模型对话系统 ===") - # 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 + # 初始化角色1的模型 + print(f"\n初始化角色1: {self.character1_config['name']}") + char1_lora_path = self.character1_config.get('lora_path') or self.character1_config.get('lora_model_path') + self.character1_generator = NPCDialogueGenerator( + self.base_model_path, + char1_lora_path, + {self.character1_config['name']: self.character1_config['character_data']} ) - for turn in conversation: - print(f"{turn['speaker']}：{turn['dialogue']}") + # 初始化角色2的模型 + print(f"\n初始化角色2: {self.character2_config['name']}") + char2_lora_path = self.character2_config.get('lora_path') or self.character2_config.get('lora_model_path') + self.character2_generator = NPCDialogueGenerator( + self.base_model_path, + char2_lora_path, + {self.character2_config['name']: self.character2_config['character_data']} + ) - # 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") + print("✓ 双模型对话系统初始化完成") + + def generate_dual_character_dialogue(self, + character_name: str, + context: str = "", + user_input: str = "", + temperature: float = 0.8, + max_new_tokens: int = 150) -> str: + """ + 生成指定角色的对话（使用对应的模型） - 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}") + Args: + character_name: 角色名称 + context: 对话上下文 + user_input: 用户输入 + temperature: 采样温度 + max_new_tokens: 最大生成token数 + + Returns: + 生成的对话内容 + """ + if character_name == self.character1_config['name']: + return self.character1_generator.generate_character_dialogue( + character_name, context, user_input, temperature, max_new_tokens + ) + elif character_name == self.character2_config['name']: + return self.character2_generator.generate_character_dialogue( + character_name, context, user_input, temperature, max_new_tokens + ) + else: + raise ValueError(f"Unknown character: {character_name}") + + def run_dual_character_conversation(self, + topic: str = "", + turns: int = 4, + context: str = "", + temperature: float = 0.8, + max_new_tokens: int = 150) -> List[Dict]: + """ + 运行双角色对话 - print("\n对话生成器已退出") + Args: + topic: 对话主题 + turns: 对话轮数 + context: 额外上下文 + temperature: 采样温度 + max_new_tokens: 最大生成token数 + + Returns: + 对话列表 + """ + conversation = [] + char1_name = self.character1_config['name'] + char2_name = self.character2_config['name'] + + # 构建完整上下文 + full_context = f"现在{char1_name}和{char2_name}在讨论关于{topic}的话题。{context}" + + print(f"\n=== 开始双角色对话 ===") + print(f"主题: {topic}") + print(f"角色: {char1_name} vs {char2_name}") + print(f"轮数: {turns}") + print("-" * 50) + + for turn in range(turns): + if turn % 2 == 0: + # 角色1说话 + speaker = char1_name + if turn == 0: + user_input = f"开始和{char2_name}讨论{topic}这个话题。" + else: + last_dialogue = conversation[-1]["dialogue"] + user_input = f"{char2_name}刚才说：\"{last_dialogue}\"。请回应。" + else: + # 角色2说话 + speaker = char2_name + last_dialogue = conversation[-1]["dialogue"] + user_input = f"{char1_name}刚才说：\"{last_dialogue}\"。请回应。" + + print(f"\n[第{turn+1}轮] {speaker}正在思考...") + + # 使用对应角色的模型生成对话 + dialogue = self.generate_dual_character_dialogue( + speaker, full_context, user_input, temperature, max_new_tokens + ) + + conversation.append({ + "turn": turn + 1, + "speaker": speaker, + "dialogue": dialogue, + "context_used": full_context[:100] + "..." if len(full_context) > 100 else full_context + }) + + print(f"{speaker}: {dialogue}") + + print("-" * 50) + print("✓ 双角色对话完成") + + return conversation + + def get_character_info(self, character_name: str) -> Dict: + """获取角色信息""" + if character_name == self.character1_config['name']: + return self.character1_generator.get_character_info(character_name) + elif character_name == self.character2_config['name']: + return self.character2_generator.get_character_info(character_name) + else: + return {} + + def list_characters(self) -> List[str]: + """列出两个角色名称""" + return [self.character1_config['name'], self.character2_config['name']] - if __name__ == '__main__': - main() \ No newline at end of file +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 + 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() \ No newline at end of file