添加双ai 模型互相对话

AITrain/dual_ai_dialogue_system.py

@@ -468,8 +468,20 @@ class DualAIDialogueEngine:
             context_info_count
         )
         
-        # 生成对话
+        # 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, 
@@ -535,6 +547,72 @@ class DualAIDialogueEngine:
         
         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"""
     print("=== RAG增强双AI角色对话系统 ===")
@@ -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
+        # 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 NPCDialogueGenerator
+        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
+        # 创建双模型对话生成器
-        if hasattr(kb, 'character_data') and kb.character_data:
+        if hasattr(kb, 'character_data') and len(kb.character_data) >= 2:
-            print("✓ 使用knowledge_base角色数据创建对话生成器")
+            print("✓ 使用knowledge_base角色数据创建双模型对话系统")
-            llm_generator = NPCDialogueGenerator(base_model_path, lora_model_path, kb.character_data)
+            # 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")
+            print("⚠ 角色数据不足，无法创建双模型对话系统")
-            llm_generator = NPCDialogueGenerator(base_model_path, lora_model_path)
+            return
         
         # 创建对话引擎
         dialogue_engine = DualAIDialogueEngine(kb, conv_mgr, llm_generator)
@@ -621,6 +721,14 @@ 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")
+                
+                # 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':
@@ -662,6 +770,14 @@ 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")
+                        
+                        # 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")

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")

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}")
@@ -339,14 +273,175 @@ class NPCDialogueGenerator:
         """列出所有可用角色"""
         return list(self.character_profiles.keys())
 
-    def main():
+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
+        
+        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
+        
+        # 为每个角色创建独立的模型实例
+        self.character1_generator = None
+        self.character2_generator = None
+        
+        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 ===")
+        
+        # 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']}
+        )
+        
+        # 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']}
+        )
+        
+        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）
+        
+        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
+        
+        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']]
+
+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
@@ -436,5 +531,5 @@ class NPCDialogueGenerator:
     
     print("\n对话生成器已退出")
 
-    if __name__ == '__main__':
+if __name__ == '__main__':
     main()