添加对话内容 ai打分功能

AITrain/dual_ai_dialogue_system.py

@@ -286,6 +286,9 @@ class ConversationManager:
                     timestamp TEXT,
                     context_used TEXT,
                     relevance_score REAL,
+                    dialogue_score REAL DEFAULT 0.0,
+                    score_details TEXT,
+                    score_feedback TEXT,
                     FOREIGN KEY (session_id) REFERENCES conversations (session_id)
                 )
             ''')
@@ -305,7 +308,9 @@ class ConversationManager:
         print(f"✓ 创建对话会话: {session_id}")
         return session_id
     
-    def add_dialogue_turn(self, session_id: str, speaker: str, content: str, context_used: List[str] = None, relevance_score: float = 0.0):
+    def add_dialogue_turn(self, session_id: str, speaker: str, content: str, context_used: List[str] = None, 
+                         relevance_score: float = 0.0, dialogue_score: float = 0.0, 
+                         score_details: str = None, score_feedback: str = None):
         """添加对话轮次"""
         if context_used is None:
             context_used = []
@@ -318,10 +323,11 @@ class ConversationManager:
             # 插入对话轮次
             conn.execute(
                 """INSERT INTO dialogue_turns 
-                   (session_id, turn_number, speaker, content, timestamp, context_used, relevance_score) 
+                   (session_id, turn_number, speaker, content, timestamp, context_used, relevance_score, 
-                   VALUES (?, ?, ?, ?, ?, ?, ?)""",
+                    dialogue_score, score_details, score_feedback) 
+                   VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?)""",
                 (session_id, turn_number, speaker, content, datetime.now().isoformat(), 
-                 json.dumps(context_used), relevance_score)
+                 json.dumps(context_used), relevance_score, dialogue_score, score_details, score_feedback)
             )
             
             # 更新会话最后更新时间
@@ -335,7 +341,7 @@ class ConversationManager:
         """获取对话历史"""
         with sqlite3.connect(self.db_path) as conn:
             cursor = conn.execute(
-                """SELECT speaker, content, timestamp, context_used, relevance_score 
+                """SELECT speaker, content, timestamp, context_used, relevance_score, dialogue_score, score_feedback 
                    FROM dialogue_turns 
                    WHERE session_id = ? 
                    ORDER BY turn_number DESC LIMIT ?""",
@@ -344,14 +350,20 @@ class ConversationManager:
             
             turns = []
             for row in cursor.fetchall():
-                speaker, content, timestamp, context_used, relevance_score = row
+                speaker, content, timestamp, context_used, relevance_score, dialogue_score, score_feedback = row
-                turns.append(DialogueTurn(
+                turn = DialogueTurn(
                     speaker=speaker,
                     content=content,
                     timestamp=timestamp,
                     context_used=json.loads(context_used or "[]"),
                     relevance_score=relevance_score
-                ))
+                )
+                # 添加评分信息到turn对象
+                if dialogue_score:
+                    turn.dialogue_score = dialogue_score
+                if score_feedback:
+                    turn.score_feedback = score_feedback
+                turns.append(turn)
             
             return list(reversed(turns))  # 按时间正序返回
     
@@ -378,10 +390,70 @@ class ConversationManager:
 class DualAIDialogueEngine:
     """双AI对话引擎"""
     
-    def __init__(self, knowledge_base: RAGKnowledgeBase, conversation_manager: ConversationManager, llm_generator):
+    def __init__(self, knowledge_base: RAGKnowledgeBase, conversation_manager: ConversationManager, llm_generator, 
+                 enable_scoring: bool = True, base_model_path: str = None):
         self.kb = knowledge_base
         self.conv_mgr = conversation_manager
         self.llm_generator = llm_generator
+        self.enable_scoring = enable_scoring
+        self.scorer = None
+        
+        # 初始化评分器
+        if enable_scoring and base_model_path:
+            try:
+                from dialogue_scorer import DialogueAIScorer
+                print("正在初始化对话评分系统...")
+                self.scorer = DialogueAIScorer(
+                    base_model_path=base_model_path,
+                    tokenizer=getattr(llm_generator, 'tokenizer', None),
+                    model=getattr(llm_generator, 'model', None)
+                )
+                print("✓ 对话评分系统初始化成功")
+            except Exception as e:
+                print(f"⚠ 对话评分系统初始化失败: {e}")
+                self.enable_scoring = False
+    
+    def score_dialogue_turn(self, dialogue_content: str, speaker: str, dialogue_history: List[DialogueTurn]) -> Tuple[float, str, str]:
+        """对单条对话进行评分
+        
+        Args:
+            dialogue_content: 对话内容
+            speaker: 说话者
+            dialogue_history: 对话历史
+            
+        Returns:
+            tuple: (总分, 详细分数JSON, 反馈意见)
+        """
+        if not self.enable_scoring or not self.scorer:
+            return 0.0, "{}", "评分系统未启用"
+        
+        try:
+            # 获取角色数据
+            character_data = self.kb.character_data.get(speaker, {})
+            
+            # 转换对话历史格式
+            history_for_scoring = []
+            for turn in dialogue_history[-5:]:  # 最近5轮对话
+                history_for_scoring.append({
+                    'speaker': turn.speaker,
+                    'content': turn.content
+                })
+            
+            # 进行AI评分
+            score_result = self.scorer.score_dialogue(
+                dialogue_content=dialogue_content,
+                speaker=speaker,
+                character_data=character_data,
+                dialogue_history=history_for_scoring,
+                context_info=[]
+            )
+            
+            # 返回评分结果
+            return score_result.overall_score, json.dumps(score_result.scores), score_result.feedback
+            
+        except Exception as e:
+            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:
@@ -494,9 +566,17 @@ class DualAIDialogueEngine:
             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
             
-            # 保存对话轮次
+            # 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
+                session_id, current_speaker, response, context_used, avg_relevance,
+                dialogue_score, score_details, score_feedback
             )
             
             return response, context_used
@@ -605,14 +685,29 @@ class DualAIDialogueEngine:
                 max_new_tokens=150
             )
     
-            # 保存对话到数据库
+            # 保存对话到数据库并进行评分
             for result in conversation_results:
+                # 获取当前对话历史进行评分
+                current_dialogue_history = self.conv_mgr.get_conversation_history(session_id)
+                
+                # 对对话进行评分
+                if self.enable_scoring:
+                    dialogue_score, score_details, score_feedback = self.score_dialogue_turn(
+                        result['dialogue'], result['speaker'], current_dialogue_history
+                    )
+                    print(f"  [评分: {dialogue_score:.2f}] {score_feedback[:100]}...")
+                else:
+                    dialogue_score, score_details, score_feedback = 0.0, "{}", ""
+                
                 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
+                    0.8,  # 默认相关性分数
+                    dialogue_score,
+                    score_details, 
+                    score_feedback
                 )