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.
This commit is contained in:
Mimikko-zeus
380
history/task_features.py
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380
history/task_features.py
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"""
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任务特征提取与匹配模块
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用于更精确的相似任务识别
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"""
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import re
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from typing import Dict, List, Set, Optional, Tuple
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from dataclasses import dataclass
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from pathlib import Path
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@dataclass
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class TaskFeatures:
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"""任务结构化特征"""
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# 基础信息
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raw_input: str
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keywords: Set[str]
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# 关键参数
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file_formats: Set[str] # 文件格式（如 .txt, .csv, .json）
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directory_paths: Set[str] # 目录路径
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file_names: Set[str] # 文件名
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naming_patterns: List[str] # 命名规则（如 "按日期", "按序号"）
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# 操作类型
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operations: Set[str] # 操作类型（如 "批量重命名", "文件转换", "数据处理"）
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# 数量/范围参数
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quantities: List[str] # 数量相关（如 "100个", "所有"）
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# 其他约束
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constraints: List[str] # 其他约束条件
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@dataclass
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class TaskDifference:
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"""任务差异描述"""
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category: str # 差异类别
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field: str # 字段名
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current_value: str # 当前任务的值
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history_value: str # 历史任务的值
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importance: str # 重要性：critical/high/medium/low
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class TaskFeatureExtractor:
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"""任务特征提取器"""
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# 文件格式模式
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FILE_FORMAT_PATTERN = r'\.(txt|csv|json|xml|xlsx?|docx?|pdf|png|jpe?g|gif|mp[34]|avi|mov|zip|rar|7z|py|js|java|cpp|html?|css)'
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# 目录路径模式（Windows 和 Unix）
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DIR_PATH_PATTERN = r'(?:[a-zA-Z]:\\[\w\\\s\u4e00-\u9fa5.-]+|/[\w/\s\u4e00-\u9fa5.-]+|[./][\w/\\\s\u4e00-\u9fa5.-]+)'
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# 文件名模式
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FILE_NAME_PATTERN = r'[\w\u4e00-\u9fa5.-]+\.[a-zA-Z0-9]+'
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# 数量模式
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QUANTITY_PATTERN = r'(\d+\s*[个张份条篇页行列]|所有|全部|批量)'
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# 操作关键词映射
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OPERATION_KEYWORDS = {
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'重命名': ['重命名', '改名', '命名', '更名'],
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'转换': ['转换', '转为', '转成', '变成', '改成'],
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'批量处理': ['批量', '批处理', '一次性'],
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'复制': ['复制', '拷贝', 'copy'],
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'移动': ['移动', '转移', 'move'],
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'删除': ['删除', '清理', '移除'],
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'合并': ['合并', '整合', '汇总'],
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'分割': ['分割', '拆分', '切分'],
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'压缩': ['压缩', '打包'],
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'解压': ['解压', '解包', '提取'],
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'排序': ['排序', '排列'],
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'筛选': ['筛选', '过滤', '查找'],
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'统计': ['统计', '计数', '汇总'],
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'生成': ['生成', '创建', '制作'],
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}
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# 命名规则关键词
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NAMING_PATTERNS = {
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'按日期': ['日期', '时间', 'date', 'time'],
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'按序号': ['序号', '编号', '数字', '顺序'],
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'按前缀': ['前缀', '开头'],
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'按后缀': ['后缀', '结尾'],
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'按内容': ['内容', '根据'],
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}
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def extract(self, user_input: str) -> TaskFeatures:
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"""
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从用户输入中提取结构化特征
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Args:
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user_input: 用户输入文本
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Returns:
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TaskFeatures: 提取的特征
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"""
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# 提取关键词
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keywords = self._extract_keywords(user_input)
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# 提取文件格式
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file_formats = self._extract_file_formats(user_input)
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# 提取目录路径
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directory_paths = self._extract_directory_paths(user_input)
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# 提取文件名
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file_names = self._extract_file_names(user_input)
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# 提取命名规则
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naming_patterns = self._extract_naming_patterns(user_input)
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# 提取操作类型
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operations = self._extract_operations(user_input)
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# 提取数量信息
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quantities = self._extract_quantities(user_input)
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# 提取其他约束
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constraints = self._extract_constraints(user_input)
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return TaskFeatures(
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raw_input=user_input,
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keywords=keywords,
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file_formats=file_formats,
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directory_paths=directory_paths,
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file_names=file_names,
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naming_patterns=naming_patterns,
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operations=operations,
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quantities=quantities,
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constraints=constraints
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)
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def _extract_keywords(self, text: str) -> Set[str]:
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"""提取关键词（基础分词）"""
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words = re.findall(r'[\u4e00-\u9fa5]+|[a-zA-Z]+', text.lower())
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return set(w for w in words if len(w) >= 2)
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def _extract_file_formats(self, text: str) -> Set[str]:
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"""提取文件格式"""
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matches = re.findall(self.FILE_FORMAT_PATTERN, text.lower())
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return set(f'.{m}' for m in matches)
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def _extract_directory_paths(self, text: str) -> Set[str]:
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"""提取目录路径"""
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matches = re.findall(self.DIR_PATH_PATTERN, text)
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# 标准化路径
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normalized = set()
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for path in matches:
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try:
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p = Path(path)
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normalized.add(str(p.resolve()))
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except:
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normalized.add(path)
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return normalized
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def _extract_file_names(self, text: str) -> Set[str]:
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"""提取文件名"""
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matches = re.findall(self.FILE_NAME_PATTERN, text)
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return set(matches)
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def _extract_naming_patterns(self, text: str) -> List[str]:
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"""提取命名规则"""
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patterns = []
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for pattern_name, keywords in self.NAMING_PATTERNS.items():
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if any(kw in text for kw in keywords):
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patterns.append(pattern_name)
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return patterns
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def _extract_operations(self, text: str) -> Set[str]:
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"""提取操作类型"""
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operations = set()
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for op_name, keywords in self.OPERATION_KEYWORDS.items():
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if any(kw in text for kw in keywords):
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operations.add(op_name)
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return operations
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def _extract_quantities(self, text: str) -> List[str]:
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"""提取数量信息"""
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matches = re.findall(self.QUANTITY_PATTERN, text)
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return matches
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def _extract_constraints(self, text: str) -> List[str]:
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"""提取其他约束条件"""
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constraints = []
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# 条件关键词
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condition_keywords = ['如果', '当', '满足', '符合', '包含', '不包含', '大于', '小于', '等于']
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for keyword in condition_keywords:
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if keyword in text:
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# 提取包含该关键词的句子片段
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pattern = f'[^。，；]*{keyword}[^。，；]*'
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matches = re.findall(pattern, text)
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constraints.extend(matches)
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return constraints
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class TaskMatcher:
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"""任务匹配器"""
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def __init__(self):
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self.extractor = TaskFeatureExtractor()
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def calculate_similarity(
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self,
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current_input: str,
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history_input: str
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) -> Tuple[float, List[TaskDifference]]:
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"""
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计算两个任务的相似度，并返回差异列表
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Args:
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current_input: 当前任务输入
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history_input: 历史任务输入
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Returns:
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(相似度分数 0-1, 差异列表)
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"""
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# 提取特征
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current_features = self.extractor.extract(current_input)
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history_features = self.extractor.extract(history_input)
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# 计算各维度相似度和差异
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differences = []
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scores = []
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# 1. 关键词相似度（基础权重 0.2）
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keyword_sim = self._jaccard_similarity(
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current_features.keywords,
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history_features.keywords
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)
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scores.append(('keywords', keyword_sim, 0.2))
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# 2. 文件格式相似度（权重 0.15）
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format_sim, format_diffs = self._compare_sets(
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current_features.file_formats,
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history_features.file_formats,
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'file_formats',
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'文件格式',
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'high'
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)
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scores.append(('file_formats', format_sim, 0.15))
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differences.extend(format_diffs)
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# 3. 目录路径相似度（权重 0.15）
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dir_sim, dir_diffs = self._compare_sets(
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current_features.directory_paths,
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history_features.directory_paths,
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'directory_paths',
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'目录路径',
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'critical'
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)
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scores.append(('directory_paths', dir_sim, 0.15))
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differences.extend(dir_diffs)
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# 4. 命名规则相似度（权重 0.15）
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naming_sim, naming_diffs = self._compare_lists(
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current_features.naming_patterns,
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history_features.naming_patterns,
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'naming_patterns',
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'命名规则',
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'high'
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)
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scores.append(('naming_patterns', naming_sim, 0.15))
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differences.extend(naming_diffs)
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# 5. 操作类型相似度（权重 0.2）
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op_sim, op_diffs = self._compare_sets(
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current_features.operations,
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history_features.operations,
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'operations',
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'操作类型',
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'critical'
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)
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scores.append(('operations', op_sim, 0.2))
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differences.extend(op_diffs)
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# 6. 数量信息相似度（权重 0.1）
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qty_sim, qty_diffs = self._compare_lists(
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current_features.quantities,
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history_features.quantities,
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'quantities',
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'数量',
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'medium'
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)
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scores.append(('quantities', qty_sim, 0.1))
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differences.extend(qty_diffs)
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# 7. 约束条件相似度（权重 0.05）
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constraint_sim, constraint_diffs = self._compare_lists(
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current_features.constraints,
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history_features.constraints,
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'constraints',
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'约束条件',
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'medium'
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)
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scores.append(('constraints', constraint_sim, 0.05))
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differences.extend(constraint_diffs)
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# 计算加权总分
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total_score = sum(score * weight for _, score, weight in scores)
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return total_score, differences
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def _jaccard_similarity(self, set1: Set, set2: Set) -> float:
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"""计算 Jaccard 相似度"""
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if not set1 and not set2:
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return 1.0
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if not set1 or not set2:
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return 0.0
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intersection = len(set1 & set2)
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union = len(set1 | set2)
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return intersection / union if union > 0 else 0.0
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def _compare_sets(
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self,
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current: Set[str],
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history: Set[str],
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field: str,
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display_name: str,
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importance: str
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) -> Tuple[float, List[TaskDifference]]:
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"""比较两个集合，返回相似度和差异"""
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similarity = self._jaccard_similarity(current, history)
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differences = []
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# 找出差异
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only_current = current - history
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only_history = history - current
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if only_current or only_history:
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differences.append(TaskDifference(
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category=display_name,
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field=field,
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current_value=', '.join(sorted(only_current)) if only_current else '(无)',
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history_value=', '.join(sorted(only_history)) if only_history else '(无)',
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importance=importance
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))
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return similarity, differences
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def _compare_lists(
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self,
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current: List[str],
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history: List[str],
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field: str,
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display_name: str,
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importance: str
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) -> Tuple[float, List[TaskDifference]]:
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"""比较两个列表，返回相似度和差异"""
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# 转为集合计算相似度
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current_set = set(current)
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history_set = set(history)
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similarity = self._jaccard_similarity(current_set, history_set)
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differences = []
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if current != history:
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differences.append(TaskDifference(
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category=display_name,
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field=field,
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current_value=', '.join(current) if current else '(无)',
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history_value=', '.join(history) if history else '(无)',
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importance=importance
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))
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return similarity, differences
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# 全局单例
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_matcher: Optional[TaskMatcher] = None
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def get_task_matcher() -> TaskMatcher:
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"""获取任务匹配器单例"""
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global _matcher
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if _matcher is None:
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_matcher = TaskMatcher()
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return _matcher
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Reference in New Issue
Block a user