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.

tests/test_task_features.py

@@ -0,0 +1,142 @@
+"""
+任务特征提取与匹配的测试用例
+"""
+
+import sys
+from pathlib import Path
+
+# 添加项目根目录到路径
+project_root = Path(__file__).parent.parent
+sys.path.insert(0, str(project_root))
+
+from history.task_features import TaskFeatureExtractor, TaskMatcher
+
+
+def test_feature_extraction():
+    """测试特征提取"""
+    print("=" * 60)
+    print("测试 1: 特征提取")
+    print("=" * 60)
+    
+    extractor = TaskFeatureExtractor()
+    
+    # 测试用例 1
+    input1 = "将 D:/photos 目录下的所有 .jpg 图片按日期重命名"
+    features1 = extractor.extract(input1)
+    
+    print(f"\n输入: {input1}")
+    print(f"文件格式: {features1.file_formats}")
+    print(f"目录路径: {features1.directory_paths}")
+    print(f"命名规则: {features1.naming_patterns}")
+    print(f"操作类型: {features1.operations}")
+    print(f"数量信息: {features1.quantities}")
+    
+    # 测试用例 2
+    input2 = "批量转换 C:/documents 下的 100 个 .docx 文件为 .pdf"
+    features2 = extractor.extract(input2)
+    
+    print(f"\n输入: {input2}")
+    print(f"文件格式: {features2.file_formats}")
+    print(f"目录路径: {features2.directory_paths}")
+    print(f"命名规则: {features2.naming_patterns}")
+    print(f"操作类型: {features2.operations}")
+    print(f"数量信息: {features2.quantities}")
+
+
+def test_similarity_matching():
+    """测试相似度匹配"""
+    print("\n" + "=" * 60)
+    print("测试 2: 相似度匹配")
+    print("=" * 60)
+    
+    matcher = TaskMatcher()
+    
+    # 测试场景 1: 高度相似（仅目录不同）
+    print("\n场景 1: 高度相似任务（仅目录不同）")
+    current1 = "将 D:/photos 目录下的所有 .jpg 图片按日期重命名"
+    history1 = "将 C:/images 目录下的所有 .jpg 图片按日期重命名"
+    
+    score1, diffs1 = matcher.calculate_similarity(current1, history1)
+    print(f"当前任务: {current1}")
+    print(f"历史任务: {history1}")
+    print(f"相似度: {score1:.2%}")
+    print(f"差异数量: {len(diffs1)}")
+    for diff in diffs1:
+        print(f"  - {diff.category} [{diff.importance}]: 当前={diff.current_value}, 历史={diff.history_value}")
+    
+    # 测试场景 2: 中等相似（格式和操作不同）
+    print("\n场景 2: 中等相似任务（格式和操作不同）")
+    current2 = "将 D:/photos 目录下的所有 .jpg 图片转换为 .png"
+    history2 = "将 D:/photos 目录下的所有 .jpg 图片按日期重命名"
+    
+    score2, diffs2 = matcher.calculate_similarity(current2, history2)
+    print(f"当前任务: {current2}")
+    print(f"历史任务: {history2}")
+    print(f"相似度: {score2:.2%}")
+    print(f"差异数量: {len(diffs2)}")
+    for diff in diffs2:
+        print(f"  - {diff.category} [{diff.importance}]: 当前={diff.current_value}, 历史={diff.history_value}")
+    
+    # 测试场景 3: 低相似度（完全不同的任务）
+    print("\n场景 3: 低相似度任务（完全不同）")
+    current3 = "将 D:/photos 目录下的所有 .jpg 图片按日期重命名"
+    history3 = "统计 C:/documents 下所有 .txt 文件的行数"
+    
+    score3, diffs3 = matcher.calculate_similarity(current3, history3)
+    print(f"当前任务: {current3}")
+    print(f"历史任务: {history3}")
+    print(f"相似度: {score3:.2%}")
+    print(f"差异数量: {len(diffs3)}")
+    for diff in diffs3:
+        print(f"  - {diff.category} [{diff.importance}]: 当前={diff.current_value}, 历史={diff.history_value}")
+    
+    # 测试场景 4: 关键参数差异（数量不同）
+    print("\n场景 4: 关键参数差异（数量不同）")
+    current4 = "批量转换 100 个 .docx 文件为 .pdf"
+    history4 = "批量转换所有 .docx 文件为 .pdf"
+    
+    score4, diffs4 = matcher.calculate_similarity(current4, history4)
+    print(f"当前任务: {current4}")
+    print(f"历史任务: {history4}")
+    print(f"相似度: {score4:.2%}")
+    print(f"差异数量: {len(diffs4)}")
+    for diff in diffs4:
+        print(f"  - {diff.category} [{diff.importance}]: 当前={diff.current_value}, 历史={diff.history_value}")
+
+
+def test_edge_cases():
+    """测试边界情况"""
+    print("\n" + "=" * 60)
+    print("测试 3: 边界情况")
+    print("=" * 60)
+    
+    matcher = TaskMatcher()
+    
+    # 空输入
+    print("\n边界 1: 空输入")
+    score, diffs = matcher.calculate_similarity("", "")
+    print(f"相似度: {score:.2%}, 差异数: {len(diffs)}")
+    
+    # 完全相同
+    print("\n边界 2: 完全相同")
+    same_input = "将 D:/photos 目录下的所有 .jpg 图片按日期重命名"
+    score, diffs = matcher.calculate_similarity(same_input, same_input)
+    print(f"相似度: {score:.2%}, 差异数: {len(diffs)}")
+    
+    # 仅标点不同
+    print("\n边界 3: 仅标点不同")
+    input_a = "将D:/photos目录下的所有.jpg图片按日期重命名"
+    input_b = "将 D:/photos 目录下的所有 .jpg 图片按日期重命名"
+    score, diffs = matcher.calculate_similarity(input_a, input_b)
+    print(f"相似度: {score:.2%}, 差异数: {len(diffs)}")
+
+
+if __name__ == "__main__":
+    test_feature_extraction()
+    test_similarity_matching()
+    test_edge_cases()
+    
+    print("\n" + "=" * 60)
+    print("所有测试完成！")
+    print("=" * 60)
+