feat: refactor API key configuration and enhance application initialization

- Renamed `check_environment` to `check_api_key_configured` for clarity, simplifying the API key validation logic.
- Removed the blocking behavior of the API key check during application startup, allowing the app to run while providing a prompt for configuration.
- Updated `LocalAgentApp` to accept an `api_configured` parameter, enabling conditional messaging for API key setup.
- Enhanced the `SandboxRunner` to support backup management and improved execution result handling with detailed metrics.
- Integrated data governance strategies into the `HistoryManager`, ensuring compliance and improved data management.
- Added privacy settings and metrics tracking across various components to enhance user experience and application safety.

app/metrics_logger.py

@@ -0,0 +1,165 @@
+"""
+度量指标记录和导出模块
+用于记录需求分析相关的度量指标
+"""
+
+import json
+from pathlib import Path
+from datetime import datetime
+from typing import Dict, Any
+
+
+class MetricsLogger:
+    """度量指标记录器"""
+    
+    def __init__(self, workspace: Path):
+        """
+        Args:
+            workspace: 工作空间路径
+        """
+        self.workspace = workspace
+        self.metrics_file = workspace / "metrics" / "requirement_analysis.json"
+        self.metrics_file.parent.mkdir(exist_ok=True)
+        
+        # 加载现有指标
+        self.metrics = self._load_metrics()
+    
+    def _load_metrics(self) -> Dict[str, Any]:
+        """加载现有指标"""
+        if self.metrics_file.exists():
+            try:
+                with open(self.metrics_file, 'r', encoding='utf-8') as f:
+                    return json.load(f)
+            except Exception:
+                pass
+        
+        # 返回默认指标结构
+        return {
+            'total_tasks': 0,
+            'clarification_triggered': 0,
+            'direct_execution': 0,
+            'user_modifications': 0,
+            'ambiguity_failures': 0,
+            'history': []
+        }
+    
+    def _save_metrics(self):
+        """保存指标到文件"""
+        try:
+            with open(self.metrics_file, 'w', encoding='utf-8') as f:
+                json.dump(self.metrics, f, ensure_ascii=False, indent=2)
+        except Exception as e:
+            print(f"保存度量指标失败: {e}")
+    
+    def record_task(self, task_type: str, details: Dict[str, Any] = None):
+        """
+        记录任务
+        
+        Args:
+            task_type: 任务类型 (clarification/direct_execution/modification/failure)
+            details: 任务详情
+        """
+        self.metrics['total_tasks'] += 1
+        
+        if task_type == 'clarification':
+            self.metrics['clarification_triggered'] += 1
+        elif task_type == 'direct_execution':
+            self.metrics['direct_execution'] += 1
+        elif task_type == 'modification':
+            self.metrics['user_modifications'] += 1
+        elif task_type == 'failure':
+            self.metrics['ambiguity_failures'] += 1
+        
+        # 记录历史
+        record = {
+            'timestamp': datetime.now().isoformat(),
+            'type': task_type,
+            'details': details or {}
+        }
+        self.metrics['history'].append(record)
+        
+        # 限制历史记录数量
+        if len(self.metrics['history']) > 1000:
+            self.metrics['history'] = self.metrics['history'][-1000:]
+        
+        self._save_metrics()
+    
+    def get_summary(self) -> Dict[str, Any]:
+        """获取指标摘要"""
+        total = self.metrics['total_tasks']
+        if total == 0:
+            return {
+                'total_tasks': 0,
+                'clarification_rate': 0.0,
+                'direct_execution_rate': 0.0,
+                'modification_rate': 0.0,
+                'failure_rate': 0.0
+            }
+        
+        return {
+            'total_tasks': total,
+            'clarification_triggered': self.metrics['clarification_triggered'],
+            'direct_execution': self.metrics['direct_execution'],
+            'user_modifications': self.metrics['user_modifications'],
+            'ambiguity_failures': self.metrics['ambiguity_failures'],
+            'clarification_rate': self.metrics['clarification_triggered'] / total,
+            'direct_execution_rate': self.metrics['direct_execution'] / total,
+            'modification_rate': self.metrics['user_modifications'] / total,
+            'failure_rate': self.metrics['ambiguity_failures'] / total
+        }
+    
+    def export_report(self, output_path: Path = None) -> str:
+        """
+        导出度量报告
+        
+        Args:
+            output_path: 输出路径，如果为None则返回字符串
+            
+        Returns:
+            报告内容
+        """
+        summary = self.get_summary()
+        
+        report = f"""# 需求分析度量报告
+
+生成时间: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}
+
+## 总体统计
+
+- 总任务数: {summary['total_tasks']}
+- 澄清触发次数: {summary['clarification_triggered']}
+- 直接执行次数: {summary['direct_execution']}
+- 用户二次修改次数: {summary['user_modifications']}
+- 需求歧义导致失败次数: {summary['ambiguity_failures']}
+
+## 比率分析
+
+- 澄清触发率: {summary['clarification_rate']:.1%}
+- 直接执行率: {summary['direct_execution_rate']:.1%}
+- 用户二次修改率: {summary['modification_rate']:.1%}
+- 需求歧义失败率: {summary['failure_rate']:.1%}
+
+## 建议
+
+"""
+        
+        # 根据指标给出建议
+        if summary['failure_rate'] > 0.2:
+            report += "- ⚠️ 需求歧义失败率较高，建议提高澄清触发阈值\n"
+        
+        if summary['clarification_rate'] < 0.1:
+            report += "- ⚠️ 澄清触发率较低，可能存在模糊需求被直接执行的风险\n"
+        
+        if summary['modification_rate'] > 0.3:
+            report += "- ⚠️ 用户二次修改率较高，说明初次生成的代码质量需要改进\n"
+        
+        if summary['direct_execution_rate'] > 0.8 and summary['failure_rate'] < 0.1:
+            report += "- ✅ 直接执行率高且失败率低，需求分析效果良好\n"
+        
+        if output_path:
+            output_path.parent.mkdir(parents=True, exist_ok=True)
+            with open(output_path, 'w', encoding='utf-8') as f:
+                f.write(report)
+        
+        return report
+