完善双AI对话

AITrain/dual_ai_dialogue_system.py

@@ -593,24 +593,30 @@ class DualAIDialogueEngine:
         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(
+        for turn in range(turns):
-            topic=topic,
+            # 获取对话历史
-            turns=turns,
+            dialogue_history = self.conv_mgr.get_conversation_history(session_id)
-            context=context_str,
+            conversation_results = self.llm_generator.run_dual_character_conversation(
-            temperature=0.8,
+                topic=topic,
-            max_new_tokens=150
+                turn_index = turn,
-        )
+                context=context_str,
-        
+                dialogue_history = dialogue_history,
-        # 保存对话到数据库
+                history_context_count = history_context_count,
-        for result in conversation_results:
+                temperature=0.8,
-            self.conv_mgr.add_dialogue_turn(
+                max_new_tokens=150
-                session_id, 
-                result['speaker'], 
-                result['dialogue'], 
-                [result.get('context_used', '')], 
-                0.8  # 默认相关性分数
             )
     
+            # 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():

AITrain/npc_dialogue_generator.py

@@ -141,10 +141,11 @@ class NPCDialogueGenerator:
         self, 
         character_name: str, 
         context: str = "", 
-        user_input: str = "", 
         temperature: float = 0.8,
         max_new_tokens: int = 150,
-        top_p: float = 0.9
+        top_p: float = 0.9,
+        dialogue_history: List[Dict] = None,
+        history_context_count: int = 3
     ) -> str:
         """
         生成指定角色的对话
@@ -156,6 +157,8 @@ class NPCDialogueGenerator:
             temperature: 采样温度
             max_new_tokens: 最大生成token数
             top_p: 核采样参数
+            dialogue_history: 对话历史记录列表，每个元素包含speaker和content
+            history_context_count: 使用的历史对话轮数（默认3轮）
             
         Returns:
             生成的对话内容
@@ -165,12 +168,12 @@ class NPCDialogueGenerator:
         
         profile = self.character_profiles[character_name]
         
-        # 构建系统提示
+        # 构建系统提示，包含历史对话数据
-        system_prompt = self._build_system_prompt(profile, context)
+        system_prompt = self._build_system_prompt(profile, context, dialogue_history, history_context_count)
         
         # 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。"
-            user_input = "请说一段符合你角色设定的话。"
+            
         
         # 准备消息
         messages = [
@@ -209,8 +212,15 @@ class NPCDialogueGenerator:
         
         return dialogue
     
-    def _build_system_prompt(self, profile: Dict, context: str = "") -> str:
+    def _build_system_prompt(self, profile: Dict, context: str = "", dialogue_history: List[Dict] = None, history_context_count: int = 3) -> str:
-        """构建系统提示"""
+        """构建系统提示
+        
+        Args:
+            profile: 角色配置信息
+            context: 当前情境
+            dialogue_history: 对话历史记录列表，每个元素包含speaker和content
+            history_context_count: 使用的历史对话轮数（默认3轮）
+        """
         personality_str = "、".join(profile["personality"])
         speech_pattern_str = "；".join(profile["speech_patterns"])
         
@@ -219,9 +229,24 @@ class NPCDialogueGenerator:
                     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。"""
+        
+        # 添加当前情境
         if context:
             system_prompt += f"\n\n当前情境：{context}"
-            return system_prompt
+        
+        # 添加历史对话数据（参考generate_character_prompt的实现）
+        if dialogue_history:
+            system_prompt += "\n\n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:
+                system_prompt += f"{turn.speaker}: {turn.content}"
+                # speaker = turn.get('speaker', '未知')
+                # content = turn.get('content', '')
+                # if content:
+                #     system_prompt += f"\n{speaker}: {content}"
+        
+        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
@@ -327,9 +352,10 @@ class DualModelDialogueGenerator:
     def generate_dual_character_dialogue(self, 
                                        character_name: str,
                                        context: str = "",
-                                       user_input: str = "",
                                        temperature: float = 0.8,
-                                       max_new_tokens: int = 150) -> str:
+                                       max_new_tokens: int = 150,
+                                       dialogue_history: str = "",
+                                       history_context_count: int = 3) -> str:
         """
         生成指定角色的对话（使用对应的模型）
         
@@ -345,19 +371,21 @@ class DualModelDialogueGenerator:
         """
         if character_name == self.character1_config['name']:
             return self.character1_generator.generate_character_dialogue(
-                character_name, context, user_input, temperature, max_new_tokens
+                character_name, context,  temperature, max_new_tokens, dialogue_history = dialogue_history, history_context_count=history_context_count,
             )
         elif character_name == self.character2_config['name']:
             return self.character2_generator.generate_character_dialogue(
-                character_name, context, user_input, temperature, max_new_tokens
+                character_name, context,  temperature, max_new_tokens, dialogue_history = dialogue_history, history_context_count=history_context_count,
             )
         else:
             raise ValueError(f"Unknown character: {character_name}")
     
     def run_dual_character_conversation(self, 
                                       topic: str = "",
-                                      turns: int = 4,
+                                      turn_index: int = 4,
                                       context: str = "",
+                                      dialogue_history: str = "",
+                                      history_context_count: int = 3,
                                       temperature: float = 0.8,
                                       max_new_tokens: int = 150) -> List[Dict]:
         """
@@ -380,45 +408,43 @@ class DualModelDialogueGenerator:
         # 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"\n=== 开始双角色对话 ===")
-        print(f"主题: {topic}")
+        # print(f"主题: {topic}")
-        print(f"角色: {char1_name} vs {char2_name}")
+        # print(f"角色: {char1_name} vs {char2_name}")
-        print(f"轮数: {turns}")
+        # print("-" * 50)
-        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...")
+        if turn_index % 2 == 0:
+            # 角色1说话
+            speaker = char1_name
+            # if turn_index == 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。"
         
-            # 使用对应角色的模型生成对话
+        print(f"\n[第{turn_index+1}轮] {speaker}正在思考...")
-            dialogue = self.generate_dual_character_dialogue(
-                speaker, full_context, user_input, temperature, max_new_tokens
-            )
         
-            conversation.append({
+        # 使用对应角色的模型生成对话
-                "turn": turn + 1,
+        dialogue = self.generate_dual_character_dialogue(
-                "speaker": speaker,
+            speaker, full_context,  temperature, max_new_tokens, dialogue_history, history_context_count
-                "dialogue": dialogue,
+        )
-                "context_used": full_context[:100] + "..." if len(full_context) > 100 else full_context
+        
-            })
+        conversation.append({
+            "turn": turn_index + 1,
+            "speaker": speaker,
+            "dialogue": dialogue,
+            "context_used": full_context[:100] + "..." if len(full_context) > 100 else full_context
+        })
+        
+        print(f"{speaker}: {dialogue}")
         
-            print(f"{speaker}: {dialogue}")
       
-        print("-" * 50)
-        print("✓ 双角色对话完成")
         
         return conversation