#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ 合同智能分析模块 自动提取合同文本,识别合同类型和关键条款位置, 为批注提供智能搜索关键词或行号定位。 """ import subprocess import re from pathlib import Path from typing import Dict, List, Optional, Tuple from defusedxml import minidom class ContractAnalyzer: """合同智能分析器""" def __init__(self, contract_path: str): """ 初始化分析器 Args: contract_path: 合同文件路径 """ self.contract_path = Path(contract_path) self.full_text = "" self.paragraphs = [] self.contract_type = "unknown" self.key_clauses = {} def extract_text(self) -> str: """ 使用pandoc提取合同纯文本 Returns: str: 合同的纯文本内容 """ try: result = subprocess.run( ["pandoc", "-f", "docx", "-t", "plain", str(self.contract_path)], capture_output=True, text=True, check=True, timeout=30 ) self.full_text = result.stdout return self.full_text except subprocess.CalledProcessError as e: print(f"⚠️ pandoc提取失败: {e}") return "" except subprocess.TimeoutExpired: print(f"⚠️ pandoc提取超时") return "" except FileNotFoundError: print(f"⚠️ 未找到pandoc命令,请先安装: sudo apt-get install pandoc") return "" def extract_paragraphs_with_line_numbers(self) -> List[Tuple[int, str]]: """ 提取段落文本及对应的行号 Returns: List[Tuple[int, str]]: [(行号, 段落文本), ...]列表 """ if not self.full_text: self.extract_text() lines = self.full_text.split('\n') self.paragraphs = [] for i, line in enumerate(lines, 1): stripped = line.strip() if stripped: # 跳过空行 self.paragraphs.append((i, stripped)) return self.paragraphs def identify_contract_type(self) -> str: """ 识别合同类型 Returns: str: 合同类型 (training/purchase/service/cooperation/unknown) """ if not self.full_text: self.extract_text() text_lower = self.full_text.lower() # 关键词匹配规则 type_keywords = { "training": ["培训", "训练", "课程", "讲师", "学员"], "purchase": ["采购", "购销", "买方", "卖方", "订购", "货物"], "service": ["服务", "提供", "服务费", "维护", "技术支持"], "cooperation": ["合作", "推广", "联合", "协议", "框架"], } # 统计每种类型的关键词出现次数 type_scores = {} for contract_type, keywords in type_keywords.items(): score = sum(1 for keyword in keywords if keyword in text_lower) if score > 0: type_scores[contract_type] = score # 返回得分最高的类型 if type_scores: self.contract_type = max(type_scores, key=type_scores.get) else: self.contract_type = "unknown" return self.contract_type def find_clause_location(self, search_keywords: List[str]) -> Optional[int]: """ 查找包含指定关键词的条款所在行号 Args: search_keywords: 搜索关键词列表 Returns: Optional[int]: 找到的行号,未找到返回None """ if not self.paragraphs: self.extract_paragraphs_with_line_numbers() # 遍历所有段落,查找包含任一关键词的段落 for line_num, paragraph in self.paragraphs: for keyword in search_keywords: if keyword in paragraph: return line_num return None def analyze_common_fields(self) -> Dict[str, List[str]]: """ 分析合同中的常见字段,返回多个可能的搜索关键词 支持中英文标点符号变体 Returns: Dict[str, List[str]]: 字段名到关键词列表的映射 """ if not self.full_text: self.extract_text() # 定义常见字段,支持中英文标点符号变体 common_fields = { "合同编号": [ "合同编号:", "协议编号:", "合同号:", "协议号:", "编号:", "合同编号：", "协议编号：", "合同号：", "协议号：", "编号：", # 中文冒号 ], "合同金额": [ "合同总金额", "协议总金额", "合同价款", "协议价款", "费用总额", "总金额", "¥", "人民币", "元", # 金额符号 ], "签署日期": [ "签署日期", "签订日期", "签约日期", "签署时间", "生效日期", "签署日期：", "签订日期：", # 中文冒号 ], "甲方": [ "甲方:", "甲方（", "需方:", "买方:", "委托方:", "甲方：", "甲方（", "需方：", # 中文冒号 ], "乙方": [ "乙方:", "乙方（", "供方:", "卖方:", "服务方:", "乙方：", "乙方（", "供方：", # 中文冒号 ], "违约责任": [ "违约责任", "违约金", "赔偿责任", "赔偿条款", "违约责任：", "违约金：", # 中文冒号 ], "争议解决": [ "争议解决", "纠纷解决", "管辖", "诉讼", "仲裁", "争议解决：", # 中文冒号 ], "保密条款": [ "保密", "商业秘密", "保密义务", "保密条款", "保密：", # 中文冒号 ], } # 检查每个字段在合同中的实际表述 found_fields = {} for field_name, keywords in common_fields.items(): found_keywords = [] for keyword in keywords: if keyword in self.full_text: found_keywords.append(keyword) if found_keywords: found_fields[field_name] = found_keywords return found_fields def generate_smart_search_keywords(self) -> Dict[str, List[str]]: """ 生成智能搜索关键词 基于合同内容分析,为常见审核点提供最合适的搜索关键词。 Returns: Dict[str, List[str]]: 审核点到关键词列表的映射 """ # 先分析常见字段 common_fields = self.analyze_common_fields() # 生成智能搜索关键词映射 smart_keywords = { "合同编号为空": common_fields.get("合同编号", ["合同编号:", "协议编号:"]), "金额表述不一致": common_fields.get("合同金额", ["合同总金额", "总金额", "¥"]), "签署日期": common_fields.get("签署日期", ["签署日期", "签订日期"]), "甲方信息": common_fields.get("甲方", ["甲方:"]), "乙方信息": common_fields.get("乙方", ["乙方:"]), "违约责任条款": common_fields.get("违约责任", ["违约责任", "违约金"]), "争议解决条款": common_fields.get("争议解决", ["争议解决", "协商"]), "保密条款": common_fields.get("保密条款", ["保密", "商业秘密"]), } return smart_keywords def get_contract_summary(self) -> Dict: """ 获取合同摘要信息 Returns: Dict: 包含合同类型、关键词字段等信息的字典 """ if not self.full_text: self.extract_text() if not self.paragraphs: self.extract_paragraphs_with_line_numbers() if self.contract_type == "unknown": self.identify_contract_type() smart_keywords = self.generate_smart_search_keywords() return { "contract_type": self.contract_type, "total_paragraphs": len(self.paragraphs), "text_length": len(self.full_text), "smart_keywords": smart_keywords, "found_fields": len(smart_keywords) } def demo(): """演示合同分析功能""" # 示例用法 analyzer = ContractAnalyzer("合同.docx") # 1. 提取文本 text = analyzer.extract_text() print(f"提取文本长度: {len(text)}") # 2. 识别合同类型 contract_type = analyzer.identify_contract_type() print(f"合同类型: {contract_type}") # 3. 分析常见字段 common_fields = analyzer.analyze_common_fields() print(f"发现字段: {list(common_fields.keys())}") # 4. 生成智能搜索关键词 smart_keywords = analyzer.generate_smart_search_keywords() print("智能搜索关键词:") for field, keywords in smart_keywords.items(): print(f" {field}: {keywords}") # 5. 获取合同摘要 summary = analyzer.get_contract_summary() print(f"\n合同摘要: {summary}") if __name__ == "__main__": demo()