修改打分bug

AITrain/dialogue_scorer.py

@@ -0,0 +1,497 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+'''
+对话质量评分系统
+使用AI模型对生成的对话进行多维度质量评分
+'''
+
+import json
+import re
+from typing import Dict, List, Tuple, Optional
+from dataclasses import dataclass
+from datetime import datetime
+
+# 尝试导入numpy，如果失败则跳过
+try:
+    import numpy as np
+    NUMPY_AVAILABLE = True
+except ImportError:
+    NUMPY_AVAILABLE = False
+
+@dataclass
+class ScoreResult:
+    """单次打分结果"""
+    dialogue_id: str
+    session_id: str
+    speaker: str
+    content: str
+    timestamp: str
+    scores: Dict[str, float]  # 各维度分数
+    overall_score: float     # 总分
+    feedback: str           # 反馈意见
+    scorer_type: str        # 打分器类型 (ai/human)
+
+class DialogueAIScorer:
+    """AI对话质量评分器"""
+    
+    def __init__(self, base_model_path: str, tokenizer=None, model=None):
+        """
+        初始化AI评分器
+        
+        Args:
+            base_model_path: 基础模型路径
+            tokenizer: 分词器（可选，复用现有的）
+            model: 模型（可选，复用现有的）
+        """
+        self.base_model_path = base_model_path
+        self.tokenizer = tokenizer
+        self.model = model
+        
+        # 如果没有传入模型，则加载
+        if self.tokenizer is None or self.model is None:
+            self._load_model()
+        
+        # 评分维度定义
+        self.score_dimensions = {
+            "coherence": {
+                "name": "连贯性",
+                "description": "对话是否与上下文逻辑连贯",
+                "weight": 0.25
+            },
+            "character_consistency": {
+                "name": "角色一致性", 
+                "description": "是否符合角色设定和人格特征",
+                "weight": 0.25
+            },
+            "naturalness": {
+                "name": "自然度",
+                "description": "语言表达是否自然流畅",
+                "weight": 0.20
+            },
+            "information_density": {
+                "name": "信息密度",
+                "description": "是否包含有意义的信息，避免废话",
+                "weight": 0.15
+            },
+            "creativity": {
+                "name": "创意性",
+                "description": "内容是否有趣、有创意",
+                "weight": 0.15
+            }
+        }
+    
+    def _load_model(self):
+        """加载模型和分词器"""
+        try:
+            from transformers import AutoModelForCausalLM, AutoTokenizer
+            import torch
+            
+            print(f"Loading scorer tokenizer from: {self.base_model_path}")
+            self.tokenizer = AutoTokenizer.from_pretrained(
+                self.base_model_path, 
+                use_fast=False, 
+                trust_remote_code=True
+            )
+            
+            if self.tokenizer.pad_token is None:
+                self.tokenizer.pad_token = self.tokenizer.eos_token
+            
+            print(f"Loading scorer model from: {self.base_model_path}")
+            self.model = AutoModelForCausalLM.from_pretrained(
+                self.base_model_path,
+                device_map="auto",
+                torch_dtype=torch.bfloat16,
+                trust_remote_code=True
+            )
+            
+        except Exception as e:
+            print(f"✗ AI评分器模型加载失败: {e}")
+            raise
+    
+    def score_dialogue(self, 
+                      dialogue_content: str,
+                      speaker: str,
+                      character_data: Dict,
+                      dialogue_history: List[Dict] = None,
+                      context_info: List[Dict] = None) -> ScoreResult:
+        """
+        对单条对话进行AI评分
+        
+        Args:
+            dialogue_content: 对话内容
+            speaker: 说话者
+            character_data: 角色数据
+            dialogue_history: 对话历史
+            context_info: 上下文信息
+            
+        Returns:
+            ScoreResult: 评分结果
+        """
+        # 构建评分提示
+        scoring_prompt = self._build_scoring_prompt(
+            dialogue_content, speaker, character_data, dialogue_history, context_info
+        )
+        
+        # 使用AI模型生成评分
+        try:
+            scores, feedback = self._generate_ai_scores(scoring_prompt)
+            
+            # 计算总分
+            overall_score = self._calculate_overall_score(scores)
+            
+            # 创建评分结果
+            result = ScoreResult(
+                dialogue_id=f"{speaker}_{datetime.now().strftime('%Y%m%d_%H%M%S')}",
+                session_id="",  # 由调用方设置
+                speaker=speaker,
+                content=dialogue_content,
+                timestamp=datetime.now().isoformat(),
+                scores=scores,
+                overall_score=overall_score,
+                feedback=feedback,
+                scorer_type="ai"
+            )
+            
+            return result
+            
+        except Exception as e:
+            print(f"✗ AI评分失败: {e}")
+            # 返回默认评分
+            return self._create_default_score(dialogue_content, speaker)
+    
+    def _build_scoring_prompt(self, 
+                             dialogue_content: str,
+                             speaker: str,
+                             character_data: Dict,
+                             dialogue_history: List[Dict] = None,
+                             context_info: List[Dict] = None) -> str:
+        """构建评分提示"""
+        
+        # 基础角色信息
+        character_info = ""
+        if character_data:
+            personality = character_data.get('personality', {})
+            traits = personality.get('core_traits', [])
+            occupation = character_data.get('basic_info', {}).get('occupation', '未知')
+            character_info = f"角色职业: {occupation}, 性格特点: {', '.join(traits[:3])}"
+        
+        # 对话历史
+        history_text = ""
+        if dialogue_history:
+            history_text = "对话历史:\n"
+            for turn in dialogue_history[-3:]:  # 只取最近3轮
+                history_text += f"{turn.get('speaker', '未知')}: {turn.get('content', '')}\n"
+        
+        # 构建完整提示
+        prompt = f"""请对以下对话内容进行质量评分。
+
+角色设定:
+{character_info}
+
+{history_text}
+
+当前对话:
+{speaker}: {dialogue_content}
+
+请从以下5个维度评分（1-10分）：
+1. 连贯性 - 对话是否与上下文逻辑连贯
+2. 角色一致性 - 是否符合角色设定和人格特征  
+3. 自然度 - 语言表达是否自然流畅
+4. 信息密度 - 是否包含有意义的信息，避免废话
+5. 创意性 - 内容是否有趣、有创意
+
+请按以下格式输出：
+连贯性: X分
+角色一致性: X分
+自然度: X分
+信息密度: X分
+创意性: X分
+
+总体评价: [具体的改进建议和优点分析]"""
+
+        return prompt
+    
+    def _generate_ai_scores(self, prompt: str) -> Tuple[Dict[str, float], str]:
+        """使用AI模型生成评分"""
+        import torch
+        
+        # 准备消息
+        messages = [
+            {"role": "system", "content": "你是一个专业的对话质量评估专家，请客观公正地评分。"},
+            {"role": "user", "content": prompt}
+        ]
+        
+        # 应用对话模板
+        inputs = self.tokenizer.apply_chat_template(
+            messages,
+            add_generation_prompt=True,
+            tokenize=True,
+            return_tensors="pt",
+            return_dict=True,
+            enable_thinking=False
+        )
+        
+        # 移动到设备
+        inputs = {k: v.to(self.model.device) for k, v in inputs.items()}
+        
+        # 生成评分
+        with torch.no_grad():
+            outputs = self.model.generate(
+                **inputs,
+                max_new_tokens=300,
+                do_sample=True,
+                temperature=0.3,  # 较低温度确保评分稳定
+                top_p=0.8,
+                pad_token_id=self.tokenizer.eos_token_id,
+                repetition_penalty=1.1
+            )
+        
+        # 解码输出
+        response = outputs[0][inputs['input_ids'].shape[1]:]
+        result_text = self.tokenizer.decode(response, skip_special_tokens=True).strip()
+        
+        # 解析评分结果
+        scores, feedback = self._parse_score_response(result_text)
+        
+        return scores, feedback
+    
+    def _parse_score_response(self, response: str) -> Tuple[Dict[str, float], str]:
+        """解析AI评分响应"""
+        scores = {}
+        feedback = ""
+        
+        # 定义维度映射
+        dimension_map = {
+            "连贯性": "coherence",
+            "角色一致性": "character_consistency", 
+            "自然度": "naturalness",
+            "信息密度": "information_density",
+            "创意性": "creativity"
+        }
+        
+        try:
+            lines = response.split('\n')
+            feedback_start = False
+            
+            for line in lines:
+                line = line.strip()
+                
+                # 查找评分
+                for chinese_name, english_key in dimension_map.items():
+                    if chinese_name in line and ':' in line:
+                        # 提取分数
+                        score_match = re.search(r'(\d+(?:\.\d+)?)', line)
+                        if score_match:
+                            score = float(score_match.group(1))
+                            # 确保分数在1-10范围内
+                            score = max(1.0, min(10.0, score))
+                            scores[english_key] = score
+                
+                # 查找总体评价
+                if '总体评价' in line or '评价' in line:
+                    feedback_start = True
+                    feedback_content = line.split(':', 1)
+                    if len(feedback_content) > 1:
+                        feedback += feedback_content[1].strip()
+                elif feedback_start and line:
+                    feedback += " " + line
+            
+            # 确保所有维度都有分数
+            for english_key in dimension_map.values():
+                if english_key not in scores:
+                    scores[english_key] = 5.0  # 默认中等分数
+            
+            if not feedback:
+                feedback = "AI评分完成，建议根据各维度分数进行改进。"
+                
+        except Exception as e:
+            print(f"解析评分响应失败: {e}")
+            # 使用默认分数
+            for english_key in dimension_map.values():
+                scores[english_key] = 5.0
+            feedback = "评分解析失败，使用默认分数。"
+        
+        return scores, feedback
+    
+    def _calculate_overall_score(self, scores: Dict[str, float]) -> float:
+        """计算总分"""
+        total_score = 0.0
+        total_weight = 0.0
+        
+        for dimension, score in scores.items():
+            if dimension in self.score_dimensions:
+                weight = self.score_dimensions[dimension]["weight"]
+                total_score += score * weight
+                total_weight += weight
+        
+        if total_weight > 0:
+            return round(total_score / total_weight, 2)
+        else:
+            return 5.0
+    
+    def _create_default_score(self, dialogue_content: str, speaker: str) -> ScoreResult:
+        """创建默认评分结果"""
+        default_scores = {
+            "coherence": 5.0,
+            "character_consistency": 5.0,
+            "naturalness": 5.0,
+            "information_density": 5.0,
+            "creativity": 5.0
+        }
+        
+        return ScoreResult(
+            dialogue_id=f"{speaker}_{datetime.now().strftime('%Y%m%d_%H%M%S')}",
+            session_id="",
+            speaker=speaker,
+            content=dialogue_content,
+            timestamp=datetime.now().isoformat(),
+            scores=default_scores,
+            overall_score=5.0,
+            feedback="使用默认评分",
+            scorer_type="ai"
+        )
+    
+    def batch_score_dialogue(self, dialogue_list: List[Dict]) -> List[ScoreResult]:
+        """批量评分对话"""
+        results = []
+        
+        for i, dialogue_item in enumerate(dialogue_list):
+            print(f"正在评分 {i+1}/{len(dialogue_list)}: {dialogue_item.get('speaker', '未知')}")
+            
+            try:
+                result = self.score_dialogue(
+                    dialogue_content=dialogue_item.get('content', ''),
+                    speaker=dialogue_item.get('speaker', '未知'),
+                    character_data=dialogue_item.get('character_data', {}),
+                    dialogue_history=dialogue_item.get('dialogue_history', []),
+                    context_info=dialogue_item.get('context_info', [])
+                )
+                
+                # 设置session_id
+                result.session_id = dialogue_item.get('session_id', '')
+                results.append(result)
+                
+            except Exception as e:
+                print(f"评分失败: {e}")
+                # 添加默认评分
+                default_result = self._create_default_score(
+                    dialogue_item.get('content', ''),
+                    dialogue_item.get('speaker', '未知')
+                )
+                default_result.session_id = dialogue_item.get('session_id', '')
+                results.append(default_result)
+        
+        return results
+
+class HumanScorer:
+    """人工评分器"""
+    
+    def __init__(self):
+        self.score_dimensions = {
+            "coherence": "连贯性",
+            "character_consistency": "角色一致性", 
+            "naturalness": "自然度",
+            "information_density": "信息密度",
+            "creativity": "创意性"
+        }
+    
+    def score_dialogue_interactive(self, 
+                                 dialogue_content: str,
+                                 speaker: str,
+                                 session_id: str = "") -> ScoreResult:
+        """交互式人工评分"""
+        
+        print(f"\n=== 人工评分 ===")
+        print(f"角色: {speaker}")
+        print(f"对话: {dialogue_content}")
+        print(f"请对以下维度评分 (1-10分):")
+        
+        scores = {}
+        
+        for dimension_key, dimension_name in self.score_dimensions.items():
+            while True:
+                try:
+                    score_input = input(f"{dimension_name} (1-10): ").strip()
+                    score = float(score_input)
+                    if 1 <= score <= 10:
+                        scores[dimension_key] = score
+                        break
+                    else:
+                        print("请输入1-10之间的分数")
+                except ValueError:
+                    print("请输入有效的数字")
+        
+        # 获取反馈
+        feedback = input("请输入评价和建议 (可选): ").strip()
+        if not feedback:
+            feedback = "人工评分完成"
+        
+        # 计算总分
+        overall_score = sum(scores.values()) / len(scores)
+        
+        return ScoreResult(
+            dialogue_id=f"{speaker}_{datetime.now().strftime('%Y%m%d_%H%M%S')}",
+            session_id=session_id,
+            speaker=speaker,
+            content=dialogue_content,
+            timestamp=datetime.now().isoformat(),
+            scores=scores,
+            overall_score=round(overall_score, 2),
+            feedback=feedback,
+            scorer_type="human"
+        )
+
+class QuickScorer:
+    """快速规则评分器（用于实时反馈）"""
+    
+    def __init__(self):
+        pass
+    
+    def quick_score(self, 
+                   dialogue_content: str,
+                   speaker: str,
+                   dialogue_history: List[Dict] = None) -> float:
+        """快速评分（基于规则）"""
+        
+        score = 5.0  # 基础分
+        
+        # 长度检查
+        content_length = len(dialogue_content.strip())
+        if content_length < 10:
+            score -= 2.0  # 太短
+        elif content_length > 200:
+            score -= 1.0  # 太长
+        elif 30 <= content_length <= 100:
+            score += 0.5  # 长度适中
+        
+        # 重复检查
+        if dialogue_history:
+            recent_content = [turn.get('content', '') for turn in dialogue_history[-3:]]
+            for prev_content in recent_content:
+                if dialogue_content == prev_content:
+                    score -= 3.0  # 重复内容
+                elif self._calculate_similarity(dialogue_content, prev_content) > 0.8:
+                    score -= 1.5  # 高度相似
+        
+        # 内容质量检查
+        if any(word in dialogue_content for word in ['...', '呃', '额', '嗯嗯']):
+            score -= 0.5  # 含有填充词
+        
+        if re.search(r'[。！？]', dialogue_content):
+            score += 0.3  # 有标点符号
+        
+        # 确保分数在合理范围内
+        return max(1.0, min(10.0, score))
+    
+    def _calculate_similarity(self, text1: str, text2: str) -> float:
+        """计算文本相似度（简单方法）"""
+        words1 = set(text1)
+        words2 = set(text2)
+        
+        if not words1 and not words2:
+            return 1.0
+        
+        intersection = len(words1.intersection(words2))
+        union = len(words1.union(words2))
+        
+        return intersection / union if union > 0 else 0.0

AITrain/main_controller.py

@@ -529,215 +529,75 @@ def run_model_optimization():
         
         conv_mgr = ConversationManager("./conversation_data/conversations.db")
         
-        print("模型优化选项:")
+        # print("模型优化选项:")
-        print("1. 分析优化需求")
+ 
-        print("2. 生成LoRA训练脚本")
+        # print("1. 生成LoRA训练脚本")
-        print("3. 创建提示优化配置")
+        # print("2. 执行增量训练")
-        print("4. 执行增量训练")
+ 
-        print("5. 性能对比验证")
         
-        choice = input("请输入选择 (1-5): ").strip()
+        # choice = input("请输入选择 (1-5): ").strip()
+                
+        # if choice == '1':
+        # 生成LoRA训练脚本
+        print("\n=== 生成LoRA训练脚本 ===")
         
-        if choice == '1':
+        script_content = generate_lora_training_script()
-            # 分析优化需求
+        script_path = "./scripts/iterative_lora_training.py"
-            print("\n=== 优化需求分析 ===")
-            
-            with sqlite3.connect(conv_mgr.db_path) as conn:
-                # 获取性能数据
-                cursor = conn.execute("""
-                    SELECT 
-                        COUNT(*) as total,
-                        AVG(dialogue_score) as avg_score,
-                        AVG(CASE WHEN dialogue_score >= 8.0 THEN 1.0 ELSE 0.0 END) as high_quality_rate
-                    FROM dialogue_turns WHERE dialogue_score > 0
-                """)
-                
-                total, avg_score, hq_rate = cursor.fetchone()
-                
-                print(f"当前性能指标:")
-                print(f"  - 总评分样本: {total}")
-                print(f"  - 平均分数: {avg_score:.2f}")
-                print(f"  - 高质量率: {hq_rate*100:.1f}%")
-                
-                # 分析优化潜力
-                optimization_needs = []
-                
-                if avg_score < 7.0:
-                    optimization_needs.append("整体质量需要提升")
-                
-                if hq_rate < 0.6:
-                    optimization_needs.append("高质量对话比例偏低")
-                
-                # 分析各维度表现
-                cursor = conn.execute("""
-                    SELECT score_details FROM dialogue_turns 
-                    WHERE dialogue_score > 0 AND score_details != '{}'
-                    ORDER BY timestamp DESC LIMIT 100
-                """)
-                
-                dim_scores = {'coherence': [], 'character_consistency': [], 
-                             'naturalness': [], 'information_density': [], 'creativity': []}
-                
-                for (score_details,) in cursor.fetchall():
-                    try:
-                        scores = json.loads(score_details)
-                        for dim, score in scores.items():
-                            if dim in dim_scores:
-                                dim_scores[dim].append(float(score))
-                    except:
-                        continue
-                
-                weak_dimensions = []
-                print(f"\n维度分析:")
-                for dim, scores in dim_scores.items():
-                    if scores:
-                        avg = sum(scores) / len(scores)
-                        print(f"  - {dim}: {avg:.2f}分")
-                        if avg < 7.0:
-                            weak_dimensions.append(dim)
-                
-                if weak_dimensions:
-                    optimization_needs.append(f"薄弱维度: {weak_dimensions}")
-                
-                print(f"\n优化建议:")
-                if optimization_needs:
-                    for i, need in enumerate(optimization_needs, 1):
-                        print(f"  {i}. {need}")
-                else:
-                    print("  当前模型表现良好，可考虑细微调优")
-                
-        elif choice == '2':
-            # 生成LoRA训练脚本
-            print("\n=== 生成LoRA训练脚本 ===")
-            
-            script_content = generate_lora_training_script()
-            script_path = "./scripts/iterative_lora_training.py"
-            
-            os.makedirs("./scripts", exist_ok=True)
-            with open(script_path, 'w', encoding='utf-8') as f:
-                f.write(script_content)
-            
-            print(f"✓ LoRA训练脚本已生成: {script_path}")
-            print("使用方法:")
-            print("  1. 先运行训练数据生成 (选项8)")
-            print("  2. 修改脚本中的路径配置")
-            print(f"  3. 运行: python {script_path}")
-            
-        elif choice == '3':
-            # 创建提示优化配置
-            print("\n=== 创建提示优化配置 ===")
-            
-            config = generate_prompt_optimization_config()
-            config_path = "./config/prompt_optimization.json"
-            
-            os.makedirs("./config", exist_ok=True)
-            with open(config_path, 'w', encoding='utf-8') as f:
-                json.dump(config, f, ensure_ascii=False, indent=2)
-            
-            print(f"✓ 提示优化配置已生成: {config_path}")
-            print("配置包含:")
-            print("  - 动态提示调整规则")
-            print("  - 质量阈值设置")
-            print("  - 生成参数优化")
-            
-        elif choice == '4':
-            # 执行增量训练
-            print("\n=== 执行增量训练 ===")
-            
-            # 检查训练数据
-            training_dir = "./training_data"
-            if not os.path.exists(training_dir):
-                print("❌ 训练数据目录不存在，请先生成训练数据 (选项8)")
-                return
-            
-            training_files = [f for f in os.listdir(training_dir) if f.endswith('.json')]
-            if not training_files:
-                print("❌ 未找到训练数据文件，请先生成训练数据 (选项8)")
-                return
-            
-            print(f"找到训练数据文件:")
-            for i, file in enumerate(training_files, 1):
-                print(f"  {i}. {file}")
-            
-            file_idx = input(f"选择训练数据文件 (1-{len(training_files)}): ").strip()
-            try:
-                selected_file = training_files[int(file_idx) - 1]
-                training_file_path = os.path.join(training_dir, selected_file)
-                
-                print(f"将使用训练文件: {selected_file}")
-                print("⚠ 注意：实际训练需要配置正确的模型路径和计算资源")
-                
-                # 生成训练命令
-                training_command = generate_training_command(training_file_path)
-                print(f"建议训练命令:")
-                print(f"  {training_command}")
-                
-                # 可选：执行训练（需要用户确认）
-                confirm = input("是否现在执行训练？(y/N): ").strip().lower()
-                if confirm == 'y':
-                    print("开始增量训练...")
-                    # 这里可以添加实际的训练执行逻辑
-                    print("⚠ 训练功能需要根据实际环境配置")
-                
-            except (ValueError, IndexError):
-                print("❌ 无效的文件选择")
-                
-        elif choice == '5':
-            # 性能对比验证
-            print("\n=== 性能对比验证 ===")
-            
-            print("验证选项:")
-            print("1. 历史性能趋势对比")
-            print("2. A/B测试配置生成")
-            print("3. 模型版本性能对比")
-            
-            verify_choice = input("请输入选择 (1-3): ").strip()
-            
-            if verify_choice == '1':
-                # 历史性能趋势
-                with sqlite3.connect(conv_mgr.db_path) as conn:
-                    cursor = conn.execute("""
-                        SELECT 
-                            DATE(timestamp) as date,
-                            AVG(dialogue_score) as avg_score,
-                            COUNT(*) as count
-                        FROM dialogue_turns 
-                        WHERE dialogue_score > 0 
-                            AND timestamp >= datetime('now', '-30 days')
-                        GROUP BY DATE(timestamp)
-                        ORDER BY date ASC
-                    """)
-                    
-                    trend_data = cursor.fetchall()
-                    if trend_data:
-                        print(f"30天性能趋势:")
-                        for date, avg_score, count in trend_data:
-                            trend = "📈" if avg_score > 7.5 else "📉" if avg_score < 6.5 else "📊"
-                            print(f"  {date}: {avg_score:.2f}分 {trend} ({count}条对话)")
-                    else:
-                        print("暂无足够的历史数据")
-                        
-            elif verify_choice == '2':
-                # A/B测试配置
-                ab_config = generate_ab_test_config()
-                ab_config_path = "./config/ab_test_config.json"
-                
-                os.makedirs("./config", exist_ok=True)
-                with open(ab_config_path, 'w', encoding='utf-8') as f:
-                    json.dump(ab_config, f, ensure_ascii=False, indent=2)
-                
-                print(f"✓ A/B测试配置已生成: {ab_config_path}")
-                print("配置包含:")
-                print("  - 对照组和实验组设置")
-                print("  - 评估指标定义")
-                print("  - 测试持续时间配置")
-                
-            elif verify_choice == '3':
-                print("模型版本对比功能开发中...")
-                print("建议手动记录不同版本的性能指标进行对比")
         
-        else:
+        os.makedirs("./scripts", exist_ok=True)
-            print("❌ 无效选择")
+        with open(script_path, 'w', encoding='utf-8') as f:
+            f.write(script_content)
+        
+        print(f"✓ LoRA训练脚本已生成: {script_path}")
+        print("使用方法:")
+        print("  1. 先运行训练数据生成 (选项8)")
+        print("  2. 修改脚本中的路径配置")
+        print(f"  3. 运行: python {script_path}")
+            
+        # elif choice == '2':
+        #     # 执行增量训练
+        #     print("\n=== 执行增量训练 ===")
+            
+        #     # 检查训练数据
+        #     training_dir = "./training_data"
+        #     if not os.path.exists(training_dir):
+        #         print("❌ 训练数据目录不存在，请先生成训练数据 (选项8)")
+        #         return
+            
+        #     training_files = [f for f in os.listdir(training_dir) if f.endswith('.json')]
+        #     if not training_files:
+        #         print("❌ 未找到训练数据文件，请先生成训练数据 (选项8)")
+        #         return
+            
+        #     print(f"找到训练数据文件:")
+        #     for i, file in enumerate(training_files, 1):
+        #         print(f"  {i}. {file}")
+            
+        #     file_idx = input(f"选择训练数据文件 (1-{len(training_files)}): ").strip()
+        #     try:
+        #         selected_file = training_files[int(file_idx) - 1]
+        #         training_file_path = os.path.join(training_dir, selected_file)
+                
+        #         print(f"将使用训练文件: {selected_file}")
+        #         print("⚠ 注意：实际训练需要配置正确的模型路径和计算资源")
+                
+        #         # 生成训练命令
+        #         training_command = generate_training_command(training_file_path)
+        #         print(f"建议训练命令:")
+        #         print(f"  {training_command}")
+                
+        #         # 可选：执行训练（需要用户确认）
+        #         confirm = input("是否现在执行训练？(y/N): ").strip().lower()
+        #         if confirm == 'y':
+        #             print("开始增量训练...")
+        #             # 这里可以添加实际的训练执行逻辑
+        #             print("⚠ 训练功能需要根据实际环境配置")
+                
+        #     except (ValueError, IndexError):
+        #         print("❌ 无效的文件选择")
+        
+        # else:
+        #     print("❌ 无效选择")
             
     except Exception as e:
         print(f"✗ 模型优化失败: {e}")
@@ -837,102 +697,8 @@ if __name__ == '__main__':
     trainer.train()
 '''
 
-def generate_prompt_optimization_config():
-    """生成提示优化配置"""
-    return {
-        "optimization_rules": {
-            "quality_thresholds": {
-                "excellent": 9.0,
-                "good": 8.0,
-                "acceptable": 7.0,
-                "needs_improvement": 6.0
-            },
-            "adaptive_adjustments": {
-                "low_coherence": {
-                    "add_context_emphasis": True,
-                    "increase_history_weight": 1.2,
-                    "add_logical_constraints": True
-                },
-                "low_character_consistency": {
-                    "enhance_character_description": True,
-                    "add_personality_reminders": True,
-                    "increase_character_weight": 1.5
-                },
-                "low_creativity": {
-                    "add_creativity_prompts": True,
-                    "increase_temperature": 0.1,
-                    "diversify_examples": True
-                }
-            }
-        },
-        "dynamic_prompts": {
-            "quality_boost_templates": [
-                "请生成一个富有创意且符合角色特征的高质量对话",
-                "确保对话逻辑连贯，信息丰富，表达自然",
-                "体现角色的独特性格和说话风格"
-            ],
-            "problem_specific_guidance": {
-                "repetitive": "避免重复之前的内容，提供新的信息和观点",
-                "inconsistent": "严格遵循角色设定，保持人格一致性",
-                "dull": "增加对话的趣味性和深度，使用生动的表达"
-            }
-        },
-        "generation_parameters": {
-            "adaptive_temperature": {
-                "high_creativity_needed": 0.9,
-                "normal": 0.8,
-                "high_consistency_needed": 0.7
-            },
-            "adaptive_top_p": {
-                "creative_mode": 0.9,
-                "balanced_mode": 0.8,
-                "conservative_mode": 0.7
-            }
-        }
-    }
 
-def generate_ab_test_config():
+
-    """生成A/B测试配置"""
-    return {
-        "test_name": "model_optimization_validation",
-        "created_at": datetime.now().isoformat(),
-        "groups": {
-            "control": {
-                "name": "原始模型",
-                "description": "未优化的基础模型",
-                "model_path": "/path/to/base/model",
-                "sample_ratio": 0.5
-            },
-            "experimental": {
-                "name": "优化模型",
-                "description": "经过迭代优化的模型",
-                "model_path": "/path/to/optimized/model",
-                "sample_ratio": 0.5
-            }
-        },
-        "evaluation_metrics": {
-            "primary": [
-                "overall_dialogue_score",
-                "character_consistency_score",
-                "creativity_score"
-            ],
-            "secondary": [
-                "user_satisfaction",
-                "response_time",
-                "coherence_score"
-            ]
-        },
-        "test_duration": {
-            "target_samples": 200,
-            "max_duration_days": 7,
-            "min_samples_per_group": 50
-        },
-        "statistical_settings": {
-            "confidence_level": 0.95,
-            "minimum_effect_size": 0.3,
-            "power": 0.8
-        }
-    }
 
 def generate_training_command(training_file_path):
     """生成训练命令"""

AITrain/score_data_manager.py

@@ -0,0 +1,617 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+'''
+对话评分数据存储管理系统
+管理评分结果、统计分析、数据导出等功能
+'''
+
+import sqlite3
+import json
+import pandas as pd
+from typing import Dict, List, Optional, Tuple
+from datetime import datetime, timedelta
+from dataclasses import asdict
+import numpy as np
+import os
+
+from dialogue_scorer import ScoreResult
+
+class ScoreDataManager:
+    """评分数据管理器"""
+    
+    def __init__(self, db_path: str = "score_data.db"):
+        """
+        初始化评分数据管理器
+        
+        Args:
+            db_path: 数据库文件路径
+        """
+        self.db_path = db_path
+        self._init_database()
+    
+    def _init_database(self):
+        """初始化数据库"""
+        with sqlite3.connect(self.db_path) as conn:
+            # 评分结果表
+            conn.execute('''
+                CREATE TABLE IF NOT EXISTS score_results (
+                    id INTEGER PRIMARY KEY AUTOINCREMENT,
+                    dialogue_id TEXT UNIQUE,
+                    session_id TEXT,
+                    speaker TEXT,
+                    content TEXT,
+                    timestamp TEXT,
+                    scorer_type TEXT,
+                    coherence_score REAL,
+                    character_consistency_score REAL,
+                    naturalness_score REAL,
+                    information_density_score REAL,
+                    creativity_score REAL,
+                    overall_score REAL,
+                    feedback TEXT,
+                    created_at TEXT DEFAULT CURRENT_TIMESTAMP
+                )
+            ''')
+            
+            # 评分统计表
+            conn.execute('''
+                CREATE TABLE IF NOT EXISTS score_statistics (
+                    id INTEGER PRIMARY KEY AUTOINCREMENT,
+                    session_id TEXT,
+                    speaker TEXT,
+                    date_range TEXT,
+                    total_dialogues INTEGER,
+                    avg_overall_score REAL,
+                    avg_coherence REAL,
+                    avg_character_consistency REAL,
+                    avg_naturalness REAL,
+                    avg_information_density REAL,
+                    avg_creativity REAL,
+                    created_at TEXT DEFAULT CURRENT_TIMESTAMP
+                )
+            ''')
+            
+            # 模型训练数据表
+            conn.execute('''
+                CREATE TABLE IF NOT EXISTS training_data (
+                    id INTEGER PRIMARY KEY AUTOINCREMENT,
+                    dialogue_content TEXT,
+                    speaker TEXT,
+                    character_data TEXT,
+                    context_info TEXT,
+                    dialogue_history TEXT,
+                    score_overall REAL,
+                    score_dimensions TEXT,
+                    feedback TEXT,
+                    is_high_quality INTEGER,
+                    created_at TEXT DEFAULT CURRENT_TIMESTAMP
+                )
+            ''')
+            
+            # 优化记录表
+            conn.execute('''
+                CREATE TABLE IF NOT EXISTS optimization_records (
+                    id INTEGER PRIMARY KEY AUTOINCREMENT,
+                    optimization_type TEXT,
+                    before_score REAL,
+                    after_score REAL,
+                    improvement REAL,
+                    parameters_changed TEXT,
+                    description TEXT,
+                    created_at TEXT DEFAULT CURRENT_TIMESTAMP
+                )
+            ''')
+            
+            conn.commit()
+    
+    def save_score_result(self, score_result: ScoreResult) -> bool:
+        """
+        保存评分结果
+        
+        Args:
+            score_result: 评分结果对象
+            
+        Returns:
+            bool: 保存是否成功
+        """
+        try:
+            with sqlite3.connect(self.db_path) as conn:
+                conn.execute('''
+                    INSERT OR REPLACE INTO score_results 
+                    (dialogue_id, session_id, speaker, content, timestamp, scorer_type,
+                     coherence_score, character_consistency_score, naturalness_score,
+                     information_density_score, creativity_score, overall_score, feedback)
+                    VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)
+                ''', (
+                    score_result.dialogue_id,
+                    score_result.session_id,
+                    score_result.speaker,
+                    score_result.content,
+                    score_result.timestamp,
+                    score_result.scorer_type,
+                    score_result.scores.get('coherence', 0),
+                    score_result.scores.get('character_consistency', 0),
+                    score_result.scores.get('naturalness', 0),
+                    score_result.scores.get('information_density', 0),
+                    score_result.scores.get('creativity', 0),
+                    score_result.overall_score,
+                    score_result.feedback
+                ))
+                conn.commit()
+            return True
+        except Exception as e:
+            print(f"保存评分结果失败: {e}")
+            return False
+    
+    def get_score_results(self, 
+                         session_id: Optional[str] = None,
+                         speaker: Optional[str] = None,
+                         min_score: Optional[float] = None,
+                         max_score: Optional[float] = None,
+                         limit: int = 100) -> List[Dict]:
+        """
+        获取评分结果
+        
+        Args:
+            session_id: 会话ID过滤
+            speaker: 角色过滤
+            min_score: 最低分数过滤
+            max_score: 最高分数过滤
+            limit: 返回数量限制
+            
+        Returns:
+            List[Dict]: 评分结果列表
+        """
+        query = "SELECT * FROM score_results WHERE 1=1"
+        params = []
+        
+        if session_id:
+            query += " AND session_id = ?"
+            params.append(session_id)
+        
+        if speaker:
+            query += " AND speaker = ?"
+            params.append(speaker)
+        
+        if min_score is not None:
+            query += " AND overall_score >= ?"
+            params.append(min_score)
+        
+        if max_score is not None:
+            query += " AND overall_score <= ?"
+            params.append(max_score)
+        
+        query += " ORDER BY created_at DESC LIMIT ?"
+        params.append(limit)
+        
+        with sqlite3.connect(self.db_path) as conn:
+            conn.row_factory = sqlite3.Row
+            cursor = conn.execute(query, params)
+            return [dict(row) for row in cursor.fetchall()]
+    
+    def calculate_statistics(self, 
+                           session_id: Optional[str] = None,
+                           speaker: Optional[str] = None,
+                           days: int = 7) -> Dict:
+        """
+        计算评分统计信息
+        
+        Args:
+            session_id: 会话ID过滤
+            speaker: 角色过滤
+            days: 统计天数
+            
+        Returns:
+            Dict: 统计信息
+        """
+        # 计算时间范围
+        end_date = datetime.now()
+        start_date = end_date - timedelta(days=days)
+        
+        query = '''
+            SELECT 
+                COUNT(*) as total_dialogues,
+                AVG(overall_score) as avg_overall,
+                AVG(coherence_score) as avg_coherence,
+                AVG(character_consistency_score) as avg_character_consistency,
+                AVG(naturalness_score) as avg_naturalness,
+                AVG(information_density_score) as avg_information_density,
+                AVG(creativity_score) as avg_creativity,
+                MIN(overall_score) as min_score,
+                MAX(overall_score) as max_score,
+                speaker
+            FROM score_results 
+            WHERE created_at >= ? AND created_at <= ?
+        '''
+        
+        params = [start_date.isoformat(), end_date.isoformat()]
+        
+        if session_id:
+            query += " AND session_id = ?"
+            params.append(session_id)
+        
+        if speaker:
+            query += " AND speaker = ?"
+            params.append(speaker)
+        else:
+            query += " GROUP BY speaker"
+        
+        with sqlite3.connect(self.db_path) as conn:
+            conn.row_factory = sqlite3.Row
+            cursor = conn.execute(query, params)
+            results = [dict(row) for row in cursor.fetchall()]
+        
+        return {
+            "period": f"{start_date.strftime('%Y-%m-%d')} 到 {end_date.strftime('%Y-%m-%d')}",
+            "statistics": results
+        }
+    
+    def get_high_quality_samples(self, threshold: float = 8.0, limit: int = 50) -> List[Dict]:
+        """
+        获取高质量对话样本
+        
+        Args:
+            threshold: 分数阈值
+            limit: 返回数量限制
+            
+        Returns:
+            List[Dict]: 高质量样本列表
+        """
+        query = '''
+            SELECT dialogue_id, session_id, speaker, content, overall_score, feedback
+            FROM score_results 
+            WHERE overall_score >= ?
+            ORDER BY overall_score DESC
+            LIMIT ?
+        '''
+        
+        with sqlite3.connect(self.db_path) as conn:
+            conn.row_factory = sqlite3.Row
+            cursor = conn.execute(query, [threshold, limit])
+            return [dict(row) for row in cursor.fetchall()]
+    
+    def get_low_quality_samples(self, threshold: float = 4.0, limit: int = 50) -> List[Dict]:
+        """
+        获取低质量对话样本
+        
+        Args:
+            threshold: 分数阈值
+            limit: 返回数量限制
+            
+        Returns:
+            List[Dict]: 低质量样本列表
+        """
+        query = '''
+            SELECT dialogue_id, session_id, speaker, content, overall_score, feedback
+            FROM score_results 
+            WHERE overall_score <= ?
+            ORDER BY overall_score ASC
+            LIMIT ?
+        '''
+        
+        with sqlite3.connect(self.db_path) as conn:
+            conn.row_factory = sqlite3.Row
+            cursor = conn.execute(query, [threshold, limit])
+            return [dict(row) for row in cursor.fetchall()]
+    
+    def save_training_data(self, 
+                          dialogue_content: str,
+                          speaker: str,
+                          character_data: Dict,
+                          context_info: List[Dict],
+                          dialogue_history: List[Dict],
+                          score_result: ScoreResult) -> bool:
+        """
+        保存用于模型训练的数据
+        
+        Args:
+            dialogue_content: 对话内容
+            speaker: 说话者
+            character_data: 角色数据
+            context_info: 上下文信息
+            dialogue_history: 对话历史
+            score_result: 评分结果
+            
+        Returns:
+            bool: 保存是否成功
+        """
+        try:
+            # 判断是否为高质量样本
+            is_high_quality = 1 if score_result.overall_score >= 7.0 else 0
+            
+            with sqlite3.connect(self.db_path) as conn:
+                conn.execute('''
+                    INSERT INTO training_data 
+                    (dialogue_content, speaker, character_data, context_info, 
+                     dialogue_history, score_overall, score_dimensions, feedback, is_high_quality)
+                    VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?)
+                ''', (
+                    dialogue_content,
+                    speaker,
+                    json.dumps(character_data, ensure_ascii=False),
+                    json.dumps(context_info, ensure_ascii=False),
+                    json.dumps([asdict(turn) if hasattr(turn, '__dict__') else turn for turn in dialogue_history], ensure_ascii=False),
+                    score_result.overall_score,
+                    json.dumps(score_result.scores, ensure_ascii=False),
+                    score_result.feedback,
+                    is_high_quality
+                ))
+                conn.commit()
+            return True
+        except Exception as e:
+            print(f"保存训练数据失败: {e}")
+            return False
+    
+    def export_to_csv(self, output_path: str = "score_data_export.csv") -> bool:
+        """
+        导出评分数据到CSV文件
+        
+        Args:
+            output_path: 输出文件路径
+            
+        Returns:
+            bool: 导出是否成功
+        """
+        try:
+            query = '''
+                SELECT dialogue_id, session_id, speaker, content, timestamp, scorer_type,
+                       coherence_score, character_consistency_score, naturalness_score,
+                       information_density_score, creativity_score, overall_score, feedback
+                FROM score_results 
+                ORDER BY created_at DESC
+            '''
+            
+            with sqlite3.connect(self.db_path) as conn:
+                df = pd.read_sql_query(query, conn)
+                df.to_csv(output_path, index=False, encoding='utf-8-sig')
+            
+            print(f"✓ 评分数据已导出到: {output_path}")
+            return True
+        except Exception as e:
+            print(f"导出数据失败: {e}")
+            return False
+    
+    def get_score_trends(self, speaker: str, days: int = 30) -> Dict:
+        """
+        获取评分趋势数据
+        
+        Args:
+            speaker: 角色名称
+            days: 统计天数
+            
+        Returns:
+            Dict: 趋势数据
+        """
+        end_date = datetime.now()
+        start_date = end_date - timedelta(days=days)
+        
+        query = '''
+            SELECT DATE(created_at) as date,
+                   AVG(overall_score) as avg_score,
+                   COUNT(*) as count
+            FROM score_results 
+            WHERE speaker = ? AND created_at >= ? AND created_at <= ?
+            GROUP BY DATE(created_at)
+            ORDER BY date
+        '''
+        
+        with sqlite3.connect(self.db_path) as conn:
+            conn.row_factory = sqlite3.Row
+            cursor = conn.execute(query, [speaker, start_date.isoformat(), end_date.isoformat()])
+            results = [dict(row) for row in cursor.fetchall()]
+        
+        return {
+            "speaker": speaker,
+            "period": f"{start_date.strftime('%Y-%m-%d')} 到 {end_date.strftime('%Y-%m-%d')}",
+            "trend_data": results
+        }
+    
+    def save_optimization_record(self, 
+                               optimization_type: str,
+                               before_score: float,
+                               after_score: float,
+                               parameters_changed: Dict,
+                               description: str = "") -> bool:
+        """
+        保存优化记录
+        
+        Args:
+            optimization_type: 优化类型
+            before_score: 优化前分数
+            after_score: 优化后分数
+            parameters_changed: 改变的参数
+            description: 描述
+            
+        Returns:
+            bool: 保存是否成功
+        """
+        try:
+            improvement = after_score - before_score
+            
+            with sqlite3.connect(self.db_path) as conn:
+                conn.execute('''
+                    INSERT INTO optimization_records 
+                    (optimization_type, before_score, after_score, improvement, 
+                     parameters_changed, description)
+                    VALUES (?, ?, ?, ?, ?, ?)
+                ''', (
+                    optimization_type,
+                    before_score,
+                    after_score,
+                    improvement,
+                    json.dumps(parameters_changed, ensure_ascii=False),
+                    description
+                ))
+                conn.commit()
+            return True
+        except Exception as e:
+            print(f"保存优化记录失败: {e}")
+            return False
+    
+    def get_optimization_history(self, limit: int = 20) -> List[Dict]:
+        """
+        获取优化历史记录
+        
+        Args:
+            limit: 返回数量限制
+            
+        Returns:
+            List[Dict]: 优化历史记录
+        """
+        query = '''
+            SELECT * FROM optimization_records 
+            ORDER BY created_at DESC 
+            LIMIT ?
+        '''
+        
+        with sqlite3.connect(self.db_path) as conn:
+            conn.row_factory = sqlite3.Row
+            cursor = conn.execute(query, [limit])
+            return [dict(row) for row in cursor.fetchall()]
+
+class ScoreAnalyzer:
+    """评分分析器"""
+    
+    def __init__(self, score_manager: ScoreDataManager):
+        """
+        初始化评分分析器
+        
+        Args:
+            score_manager: 评分数据管理器
+        """
+        self.score_manager = score_manager
+    
+    def analyze_character_performance(self, character_name: str) -> Dict:
+        """
+        分析角色表现
+        
+        Args:
+            character_name: 角色名称
+            
+        Returns:
+            Dict: 分析结果
+        """
+        # 获取角色的所有评分数据
+        scores = self.score_manager.get_score_results(speaker=character_name, limit=1000)
+        
+        if not scores:
+            return {"error": f"没有找到角色 {character_name} 的评分数据"}
+        
+        # 计算各种统计信息
+        overall_scores = [score['overall_score'] for score in scores]
+        
+        analysis = {
+            "character_name": character_name,
+            "total_dialogues": len(scores),
+            "average_score": round(np.mean(overall_scores), 2),
+            "median_score": round(np.median(overall_scores), 2),
+            "score_std": round(np.std(overall_scores), 2),
+            "min_score": min(overall_scores),
+            "max_score": max(overall_scores),
+            "score_distribution": self._calculate_score_distribution(overall_scores),
+            "dimension_analysis": self._analyze_dimensions(scores),
+            "improvement_suggestions": self._generate_improvement_suggestions(scores)
+        }
+        
+        return analysis
+    
+    def _calculate_score_distribution(self, scores: List[float]) -> Dict:
+        """计算分数分布"""
+        distribution = {
+            "excellent": len([s for s in scores if s >= 8.0]),
+            "good": len([s for s in scores if 6.0 <= s < 8.0]),
+            "average": len([s for s in scores if 4.0 <= s < 6.0]),
+            "poor": len([s for s in scores if s < 4.0])
+        }
+        
+        total = len(scores)
+        if total > 0:
+            distribution = {k: {"count": v, "percentage": round(v/total*100, 1)} 
+                          for k, v in distribution.items()}
+        
+        return distribution
+    
+    def _analyze_dimensions(self, scores: List[Dict]) -> Dict:
+        """分析各维度表现"""
+        dimensions = ['coherence_score', 'character_consistency_score', 'naturalness_score',
+                     'information_density_score', 'creativity_score']
+        
+        dimension_analysis = {}
+        
+        for dim in dimensions:
+            dim_scores = [score[dim] for score in scores if score[dim] is not None]
+            if dim_scores:
+                dimension_analysis[dim] = {
+                    "average": round(np.mean(dim_scores), 2),
+                    "min": min(dim_scores),
+                    "max": max(dim_scores),
+                    "std": round(np.std(dim_scores), 2)
+                }
+        
+        return dimension_analysis
+    
+    def _generate_improvement_suggestions(self, scores: List[Dict]) -> List[str]:
+        """生成改进建议"""
+        suggestions = []
+        
+        # 分析最弱的维度
+        dimensions = {
+            'coherence_score': '连贯性',
+            'character_consistency_score': '角色一致性',
+            'naturalness_score': '自然度',
+            'information_density_score': '信息密度',
+            'creativity_score': '创意性'
+        }
+        
+        dim_averages = {}
+        for dim_key, dim_name in dimensions.items():
+            dim_scores = [score[dim_key] for score in scores if score[dim_key] is not None]
+            if dim_scores:
+                dim_averages[dim_name] = np.mean(dim_scores)
+        
+        # 找出最弱的维度
+        if dim_averages:
+            weakest_dim = min(dim_averages.items(), key=lambda x: x[1])
+            if weakest_dim[1] < 6.0:
+                suggestions.append(f"需要重点改进{weakest_dim[0]}，当前平均分为{weakest_dim[1]:.1f}")
+        
+        # 根据分数分布给建议
+        overall_avg = np.mean([score['overall_score'] for score in scores])
+        if overall_avg < 5.0:
+            suggestions.append("整体表现需要改进，建议调整角色设定或提示词")
+        elif overall_avg < 7.0:
+            suggestions.append("表现良好，可以尝试增加对话的创意性和深度")
+        else:
+            suggestions.append("表现优秀，继续保持当前设定")
+        
+        return suggestions
+    
+    def compare_characters(self, character_names: List[str]) -> Dict:
+        """
+        比较多个角色的表现
+        
+        Args:
+            character_names: 角色名称列表
+            
+        Returns:
+            Dict: 比较结果
+        """
+        comparison = {"characters": {}}
+        
+        for char_name in character_names:
+            scores = self.score_manager.get_score_results(speaker=char_name, limit=500)
+            if scores:
+                overall_scores = [score['overall_score'] for score in scores]
+                comparison["characters"][char_name] = {
+                    "total_dialogues": len(scores),
+                    "average_score": round(np.mean(overall_scores), 2),
+                    "score_range": f"{min(overall_scores):.1f} - {max(overall_scores):.1f}"
+                }
+        
+        # 排序
+        if comparison["characters"]:
+            sorted_chars = sorted(comparison["characters"].items(), 
+                                key=lambda x: x[1]["average_score"], reverse=True)
+            comparison["ranking"] = [{"character": char, **data} for char, data in sorted_chars]
+        
+        return comparison