From 5ba1d0dbddd2e75a0fa71364e0c9dbfe3984c31e Mon Sep 17 00:00:00 2001 From: 997146918 <997146918@qq.com> Date: Sat, 23 Aug 2025 17:27:01 +0800 Subject: [PATCH] =?UTF-8?q?=E5=88=A0=E9=99=A4=E5=A4=9A=E4=BD=99=E4=BB=A3?= =?UTF-8?q?=E7=A0=81?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- AITrain/conversation_data/conversations.db | Bin 36864 -> 40960 bytes AITrain/dual_ai_dialogue_system.py | 401 +-------------------- AITrain/main_controller.py | 74 +--- AITrain/npc_dialogue_generator.py | 98 ----- 4 files changed, 7 insertions(+), 566 deletions(-) diff --git a/AITrain/conversation_data/conversations.db b/AITrain/conversation_data/conversations.db index 5cd331772eecc6c470bd0435faeeaef9ad132093..a4ed8570963599e7c2f8aa3d1d78db2931fc2fed 100644 GIT binary patch delta 2683 zcmZuyS#KNX71d(H4graB7o@G*T2NphFIFjuG9{rv;Fs<|Tc9tF4a98|0|^kn6afkZ zDQCEEA|;O~iHk(h<|fPJkRmOS94_e}$REfX?%i^%EiSFu?ejL<(!!E`(YCmJ!?to` zap_MBc(c4_x6LoF*z5}q`R$4SW_ovZW%<5s$?@lR?SFld=}pHPLToGZcE|FnAzqX7c*@D8fncVDzF1PMT2lW>{n|x`>+Ll`84R5lmtQlG_FXF1dMPlLV^78|LAdaV)d^K3Dhj zuEOe?-YnD1hTcvM5vVO*$+1kHX$sxpwT=p7HB!gYMS29 zQdu4Hp?0{x0WxT!jq;4dI!c;5?*wQtOe22Y)oA>fW+R4k?-7!*RE2nh#?}T29FlaE zRI-2)oK>nchzT#xy)Q<@Fv+R12Px~A#fxJf_y$!l36bx3cA z1zRL=grWc*waksGdm3_GS+gz9FF0=6mv7eZ-TuwQ#K)i1i{9H7%M>&=e({5`iyzeF zdl#?8zk7K`h$-}r5Yr|3Z_2``R!un>nF5s$i8sKuG6I~dG>V&M4^7-GTOpNw6CXXT zfp;2x#12n{Y(@mh+I`j#3_aJO-s$MADDj?>oFAQM#dVZNDr7qYUorLE;@31c|tYQ#;J8^0D{@_nJzH?WZ`q=hUy|c@TlDHY>6RqlP zA1lf*N4*Cpc%(eId*k~ezf`0L9Z_db#w~Cf<)0t^U@#t{Uuv0`B;#=uXUM|{mG+p{ zFiq7s+JzuUev@njV6A#L4N`CeshpTMh;VSPbpe4?b2uYhEnOOR|7C$A`DpX$&IxyB zOG0ISp;*1C=*=Xs;+&}mD#@lEp*uJ@Z!~~|7a#KAF|YJUC5d8Yg>Uxvc|%1cj9dVL z=c;==(S=ayaTcwoyHU*d;0`!_7y{t#2#o&xu3*k*NJ+S-u!UIYOiG)Ea3<}2DarWx zeA3OiHi!7d+m~y^b5;%#ubVXvl!Jf#=sihqJ|UF|jW=*Tb$fZp#XUi$r3KK`0h}6! zi0uYqSMU!N@o*k@0FB;D(Nu%vk|tQw&Jp&aTqr!%K$DcjgC9H1KJuPh>_iMuM{EVs z6dMKwlX3yHk?!FjK?1A|J6O|5EP<&Ki%V>z0OSg&I0k0)R))p%Vz+K(SoSF%KA#q3 zzI|Bm)=R^g2M)r*fOZeFs`IQ;?l{X72Nb*lsmVXH3e_-JMuvyUI zHvhx`?#UnYMfjNcKLYhIaPt4=|Hl6jD#gC}i#@jhBg>`$E`yDYb6GbpZ;j*v0A`*Z A3IG5A diff --git a/AITrain/dual_ai_dialogue_system.py b/AITrain/dual_ai_dialogue_system.py index c6dbf2c..5906a24 100644 --- a/AITrain/dual_ai_dialogue_system.py +++ b/AITrain/dual_ai_dialogue_system.py @@ -455,177 +455,7 @@ class DualAIDialogueEngine: print(f"⚠ 对话评分失败: {e}") return 0.0, "{}", f"评分失败: {str(e)}" - def generate_character_prompt(self, character_name: str, context_info: List[Dict], dialogue_history: List[DialogueTurn], - history_context_count: int = 3, context_info_count: int = 2) -> str: - """为角色生成对话提示 - - Args: - character_name: 角色名称 - context_info: 相关上下文信息 - dialogue_history: 对话历史 - history_context_count: 使用的历史对话轮数（默认3轮） - context_info_count: 使用的上下文信息数量（默认2个） - """ - char_data = self.kb.character_data.get(character_name, {}) - - # 基础角色设定 - prompt_parts = [] - prompt_parts.append(f"你是{character_name}，具有以下设定：") - - if char_data.get('personality', {}).get('core_traits'): - traits = ", ".join(char_data['personality']['core_traits']) - prompt_parts.append(f"性格特点：{traits}") - - if char_data.get('speech_patterns', {}).get('sample_phrases'): - phrases = char_data['speech_patterns']['sample_phrases'][:3] - prompt_parts.append(f"说话风格示例：{'; '.join(phrases)}") - - # 当前情境 - if char_data.get('current_situation'): - situation = char_data['current_situation'] - prompt_parts.append(f"当前状态：{situation.get('current_mood', '')}") - - # 相关世界观信息（可控制数量） - if context_info: - prompt_parts.append("相关背景信息：") - for info in context_info[:context_info_count]: - content = info['content'][:200] + "..." if len(info['content']) > 200 else info['content'] - prompt_parts.append(f"- {content}") - - # 对话历史（可控制数量） - if dialogue_history: - prompt_parts.append("最近的对话：") - # 使用参数控制历史对话轮数 - history_to_use = dialogue_history[-history_context_count:] if history_context_count > 0 else [] - for turn in history_to_use: - prompt_parts.append(f"{turn.speaker}: {turn.content}") - - prompt_parts.append("\n请根据角色设定和上下文，生成符合角色特点的自然对话。回复应该在50-150字之间。") - - return "\n".join(prompt_parts) - - def generate_dialogue(self, session_id: str, current_speaker: str, topic_hint: str = "", - history_context_count: int = 3, context_info_count: int = 2) -> Tuple[str, List[str]]: - """生成角色对话 - - Args: - session_id: 会话ID - current_speaker: 当前说话者 - topic_hint: 话题提示 - history_context_count: 使用的历史对话轮数（默认3轮） - context_info_count: 使用的上下文信息数量（默认2个） - """ - # 获取对话历史 - dialogue_history = self.conv_mgr.get_conversation_history(session_id) - - # 构建搜索查询 - if dialogue_history: - # 基于最近的对话内容（可控制数量） - 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_hint - else: - # 首次对话 - search_query = f"{current_speaker} {topic_hint} introduction greeting" - - # 搜索相关上下文 - context_info = self.kb.search_relevant_context(search_query, current_speaker, context_info_count) - - # 生成提示（使用参数控制上下文数量） - prompt = self.generate_character_prompt( - current_speaker, - context_info, - dialogue_history, - history_context_count, - context_info_count - ) - - # 生成对话 - 使用双模型系统 - try: - # 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]] - avg_relevance = sum(info['relevance_score'] for info in context_info[:context_info_count]) / len(context_info[:context_info_count]) if context_info else 0.0 - - # 对对话进行评分 - if self.enable_scoring: - dialogue_score, score_details, score_feedback = self.score_dialogue_turn(response, current_speaker, dialogue_history) - print(f" [评分: {dialogue_score:.2f}] {score_feedback}") - else: - dialogue_score, score_details, score_feedback = 0.0, "{}", "" - - # 保存对话轮次（包含评分信息） - self.conv_mgr.add_dialogue_turn( - session_id, current_speaker, response, context_used, avg_relevance, - dialogue_score, score_details, score_feedback - ) - - return response, context_used - - except Exception as e: - print(f"✗ 对话生成失败: {e}") - return f"[{current_speaker}暂时无法回应]", [] - - def run_conversation_turn(self, session_id: str, characters: List[str], turns_count: int = 1, topic: str = "", - history_context_count: int = 3, context_info_count: int = 2): - """运行对话轮次 - - Args: - session_id: 会话ID - characters: 角色列表 - turns_count: 对话轮数 - topic: 对话主题 - history_context_count: 使用的历史对话轮数（默认3轮） - context_info_count: 使用的上下文信息数量（默认2个） - """ - results = [] - print(f" [上下文设置: 历史{history_context_count}轮, 信息{context_info_count}个]") - for i in range(turns_count): - for char in characters: - response, context_used = self.generate_dialogue( - session_id, - char, - topic, - history_context_count, - context_info_count - ) - results.append({ - "speaker": char, - "content": response, - "context_used": context_used, - "turn": i + 1, - "context_settings": { - "history_count": history_context_count, - "context_info_count": context_info_count - } - }) - - print(f"{char}: {response}") - # if context_used: - # print(f" [使用上下文: {', '.join(context_used)}]") - - 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): @@ -713,232 +543,3 @@ class DualAIDialogueEngine: return conversation_results -# def main(): -# """主函数 - 演示系统使用""" -# print("=== RAG增强双AI角色对话系统 ===") - -# # 设置路径 -# knowledge_dir = "./knowledge_base" # 包含世界观和角色文档的目录 - -# # 检查必要文件 -# required_dirs = [knowledge_dir] -# for dir_path in required_dirs: -# if not os.path.exists(dir_path): -# print(f"✗ 目录不存在: {dir_path}") -# print("请确保以下文件存在：") -# print("- ./knowledge_base/worldview_template_coc.json") -# print("- ./knowledge_base/character_template_detective.json") -# print("- ./knowledge_base/character_template_professor.json") -# return - -# try: -# # 初始化系统组件 -# print("\n初始化系统...") -# kb = RAGKnowledgeBase(knowledge_dir) -# conv_mgr = ConversationManager() - -# # 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("⚠ 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) - -# print("✓ 系统初始化完成") - -# # 交互式菜单 -# while True: -# print("\n" + "="*50) -# print("双AI角色对话系统") -# print("1. 创建新对话") -# print("2. 继续已有对话") -# print("3. 查看对话历史") -# print("4. 列出所有会话") -# print("0. 退出") -# print("="*50) - -# choice = input("请选择操作: ").strip() - -# if choice == '0': -# break - -# elif choice == '1': -# # 创建新对话 -# print(f"可用角色: {list(kb.character_data.keys())}") -# characters = input("请输入两个角色名称（用空格分隔）: ").strip().split() - -# if len(characters) != 2: -# print("❌ 请输入正好两个角色名称") -# continue - -# 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) - -# topic = input("请输入对话主题（可选）: ").strip() -# turns = int(input("请输入对话轮次数量（默认2）: ").strip() or "2") - -# # 历史上下文控制选项 -# print("\n历史上下文设置:") -# history_count = input("使用历史对话轮数（默认3，0表示不使用）: ").strip() -# history_count = int(history_count) if history_count.isdigit() else 3 - -# context_info_count = input("使用上下文信息数量（默认2）: ").strip() -# context_info_count = int(context_info_count) if context_info_count.isdigit() else 2 - -# 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") - -# # 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 -# sessions = conv_mgr.list_sessions() -# if not sessions: -# print("❌ 没有已有对话") -# continue - -# print("已有会话:") -# for i, session in enumerate(sessions[:5]): -# chars = ", ".join(session['characters']) -# print(f"{i+1}. {session['session_id'][:8]}... ({chars}) - {session['last_update'][:16]}") - -# try: -# idx = int(input("请选择会话编号: ").strip()) - 1 -# if 0 <= idx < len(sessions): -# session = sessions[idx] -# session_id = session['session_id'] -# characters = session['characters'] - -# # 显示最近的对话 -# history = conv_mgr.get_conversation_history(session_id, 4) -# if history: -# print("\n最近的对话:") -# for turn in history: -# print(f"{turn.speaker}: {turn.content}") - -# topic = input("请输入对话主题（可选）: ").strip() -# turns = int(input("请输入对话轮次数量（默认1）: ").strip() or "1") - -# # 历史上下文控制选项 -# print("\n历史上下文设置:") -# history_count = input("使用历史对话轮数（默认3，0表示不使用）: ").strip() -# history_count = int(history_count) if history_count.isdigit() else 3 - -# context_info_count = input("使用上下文信息数量（默认2）: ").strip() -# context_info_count = int(context_info_count) if context_info_count.isdigit() else 2 - -# 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") - -# # 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: -# print("❌ 请输入有效的数字") - -# elif choice == '3': -# # 查看对话历史 -# session_id = input("请输入会话ID（前8位即可）: ").strip() - -# # 查找匹配的会话 -# sessions = conv_mgr.list_sessions() -# matching_session = None -# for session in sessions: -# if session['session_id'].startswith(session_id): -# matching_session = session -# break - -# if matching_session: -# full_session_id = matching_session['session_id'] -# history = conv_mgr.get_conversation_history(full_session_id, 20) - -# if history: -# print(f"\n对话历史 - {full_session_id}") -# print(f"角色: {', '.join(matching_session['characters'])}") -# print(f"世界观: {matching_session['worldview']}") -# print("-" * 50) - -# for turn in history: -# print(f"[{turn.timestamp[:16]}] {turn.speaker}:") -# print(f" {turn.content}") -# if turn.context_used: -# print(f" 使用上下文: {', '.join(turn.context_used)}") -# print() -# else: -# print("该会话暂无对话历史") -# else: -# print("❌ 未找到匹配的会话") - -# elif choice == '4': -# # 列出所有会话 -# sessions = conv_mgr.list_sessions() -# if sessions: -# print(f"\n共有 {len(sessions)} 个对话会话:") -# for session in sessions: -# chars = ", ".join(session['characters']) -# print(f"ID: {session['session_id']}") -# print(f" 角色: {chars}") -# print(f" 世界观: {session['worldview']}") -# print(f" 最后更新: {session['last_update']}") -# print() -# else: -# print("暂无对话会话") - -# else: -# print("❌ 无效选择") - -# except Exception as e: -# print(f"✗ 系统运行出错: {e}") -# import traceback -# traceback.print_exc() - -# if __name__ == '__main__': -# main() \ No newline at end of file diff --git a/AITrain/main_controller.py b/AITrain/main_controller.py index 904febd..2e98479 100644 --- a/AITrain/main_controller.py +++ b/AITrain/main_controller.py @@ -121,7 +121,7 @@ def show_character_info(): except Exception as e: print(f"✗ 读取角色文件失败: {char_file} - {e}") -def run_dialogue_system(): +def run_dialogue_system(enableScore: bool): """运行双AI对话系统""" print("\n" + "="*60) print("启动双AI角色对话系统") @@ -191,7 +191,7 @@ def run_dialogue_system(): kb, conv_mgr, dual_generator, - enable_scoring=True, + enable_scoring=enableScore, base_model_path=base_model_path ) @@ -260,68 +260,6 @@ def run_dialogue_system(): import traceback traceback.print_exc() -def create_demo_scenario(): - """创建演示场景""" - print("\n创建演示对话场景...") - - try: - from dual_ai_dialogue_system import RAGKnowledgeBase, ConversationManager, DualAIDialogueEngine - from npc_dialogue_generator import NPCDialogueGenerator - - # 初始化组件 - kb = RAGKnowledgeBase("./knowledge_base") - conv_mgr = ConversationManager("./conversation_data/demo_conversations.db") - - # 检查模型路径 - base_model_path = '/mnt/e/AI/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") - - llm_generator = NPCDialogueGenerator(base_model_path, lora_model_path, kb.character_data) - dialogue_engine = DualAIDialogueEngine(kb, conv_mgr, llm_generator) - - # 创建演示对话 - characters = ["维多利亚·布莱克伍德", "阿奇博尔德·韦恩"] - worldview = "克苏鲁的呼唤" - - session_id = conv_mgr.create_session(characters, worldview) - print(f"✓ 创建演示会话: {session_id}") - - # 运行几轮对话 - topic = "最近发生的神秘事件" - print(f"\n开始演示对话 - 主题: {topic}") - print("-" * 40) - - # 演示不同的历史上下文设置 - # print("演示1: 使用默认上下文设置（历史3轮，信息2个）") - # dialogue_engine.run_conversation_turn(session_id, characters, 6, topic) - - - session_id = conv_mgr.create_session(characters, worldview) - print(f"✓ 创建演示会话: {session_id}") - print("\n演示3: 使用最少历史上下文（历史1轮，信息1个）") - dialogue_engine.run_conversation_turn(session_id, characters, 6, topic, 1, 10) - - session_id = conv_mgr.create_session(characters, worldview) - print(f"✓ 创建演示会话: {session_id}") - print("\n演示2: 使用更多历史上下文（历史10轮，信息10个）") - dialogue_engine.run_conversation_turn(session_id, characters, 6, topic, 5, 10) - - print(f"\n✓ 演示完成！会话ID: {session_id}") - print("你可以通过主对话系统继续这个对话") - - except Exception as e: - print(f"✗ 演示场景创建失败: {e}") - import traceback - traceback.print_exc() def analyze_model_performance(): """分析模型性能""" @@ -1333,8 +1271,8 @@ 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 (支持双模型对话)") - print("4. 创建演示对话场景") + print("3. 启动双AI对话系统 (开启ai打分)") + print("4. 启动双AI对话系统 (关闭ai打分)") print("5. 系统状态检查") print("6. 查看对话评分统计") print("7. 模型性能分析与优化") @@ -1357,10 +1295,10 @@ def main(): show_character_info() elif choice == '3': - run_dialogue_system() + run_dialogue_system(enableScore = True) elif choice == '4': - create_demo_scenario() + run_dialogue_system(enableScore = False) elif choice == '5': show_system_status() diff --git a/AITrain/npc_dialogue_generator.py b/AITrain/npc_dialogue_generator.py index bac40aa..1289128 100644 --- a/AITrain/npc_dialogue_generator.py +++ b/AITrain/npc_dialogue_generator.py @@ -471,101 +471,3 @@ class DualModelDialogueGenerator: """列出两个角色名称""" return [self.character1_config['name'], self.character2_config['name']] -# 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