155 lines
5.0 KiB
Python
155 lines
5.0 KiB
Python
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import re
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from openai import OpenAI
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from typing import List, Dict
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import json
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import time
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import os
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from kg_build_v2.extract_html import extract_repairproject_tables_html,extract_operation_tables_html,extract_tables
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from kg_build_v2.Prompt import prompt_extract_repair_node_rela
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from modelsAPI.model_api import OpenaiAPI
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def read_markdown_file(file_path):
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"""
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读取 Markdown 文件并返回其全部内容作为字符串。
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参数:
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file_path (str): Markdown 文件的路径（支持相对或绝对路径）
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返回:
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str 或 None: 成功时返回文件内容；出错时返回 None
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"""
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try:
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with open(file_path, 'r', encoding='utf-8') as f:
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content = f.read()
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return content
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except FileNotFoundError:
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print(f"错误：文件未找到 - {file_path}")
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except PermissionError:
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print(f"错误：没有权限读取文件 - {file_path}")
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except UnicodeDecodeError:
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print(f"错误：文件编码不是 UTF-8，无法读取 - {file_path}")
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except Exception as e:
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print(f"读取文件时发生未知错误: {e}")
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return None
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def safe_json_loads(s: str):
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try:
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return json.loads(s)
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except json.JSONDecodeError as e:
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# 尝试修复常见问题
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s = s.replace("\\n", "\\\n").replace("\r", "\\r").replace("\t", "\\t")
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# 或者直接清理非法字符
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s = re.sub(r'[\x00-\x1f\x7f]', '', s) # 移除控制字符
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try:
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return json.loads(s)
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except:
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raise e
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def extract_wxindextable(chunks, skip_first=True):
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"""
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批量处理一个文档的多个切片（chunks），每个切片调用大模型结构化，并返回全部结果。
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:param chunks: List[str] - 同一文档的多个文本切片
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:param skip_first: bool - 是否跳过第一个表格块（默认True）
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:return: List[Dict] - 结构化结果列表
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"""
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all_results = []
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# 确定起始索引
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start_idx = 1 if skip_first else 0
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for idx, block in enumerate(chunks, 1):
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# 跳过第一个表格
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if skip_first and idx == 1:
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print(f"=== 跳过第 {idx} 个表格块（首个表格） ===\n")
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continue
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print(f"=== 维修项目 Block {idx} ===")
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print(block[:100]) # 只打印前500字符
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print("\n" + "="*60 + "\n")
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final_prompt = prompt_extract_repair_node_rela.format(text=block)
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raw_response = OpenaiAPI.openai_chat(final_prompt,timeout=180)
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if raw_response is None:
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print(f" ⚠️ 第 {idx} 切片 API 调用失败")
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continue
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# 尝试解析 JSON
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try:
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cleaned = raw_response.strip()
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if cleaned.startswith("```json"):
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cleaned = cleaned[7:].lstrip()
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if cleaned.endswith("```"):
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cleaned = cleaned[:-3].rstrip()
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json_result = safe_json_loads(cleaned)
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if "properties" in json_result:
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json_result["properties"]["切片"] = block # 添加原始文本块
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else:
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print(f" ⚠️ 第 {idx} 切片的 JSON 中缺少 'properties' 字段，跳过添加切片")
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# 打印美化版
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all_results.append(json_result)
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except Exception as e:
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print(f" ⚠️ 第 {idx} 切片 JSON 解析失败: {e}")
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print(f" 原始响应: {repr(raw_response[:500])}")
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json_result = None
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continue
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return all_results
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# def openai_chat(query: str, model="Qwen3-32B"):
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# """调用OpenAI API"""
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# try:
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# client = OpenAI(
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# api_key="none",
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# base_url="http://192.168.0.46:59800/v1"
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# )
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# response = client.chat.completions.create(
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# model=model,
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# messages=[
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# {"role": "user", "content": query},
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# ],
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# temperature=0.1,
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# stream=False,
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# response_format={"type": "json_object"},
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# extra_body={"chat_template_kwargs": {"enable_thinking": False}}
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# )
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# return response.choices[0].message.content
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# except Exception as e:
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# print(f"调用OpenAI API时出错: {e}")
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# return None
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def get_repair_node_rela(mdcontent):
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print("开始提取维修项目表实体和关系")
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chunks, cleaned_content = extract_repairproject_tables_html(mdcontent)
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print(f"找到 {len(chunks)} 个维修表格块")
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if len(chunks) > 0:
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result = extract_wxindextable(chunks=chunks)
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return result
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else:
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return []
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# if __name__ == "__main__":
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# input_directory = "F:\zklnlp\HJ\hj_kg_code\kgrag\extract_html_json\测试wx手册_shenghna_output11_full_content.md"
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# # 调用目录处理函数
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# mdcontent = read_markdown_file(input_directory)
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# if mdcontent is None:
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# print("无法读取文件，程序退出")
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# exit(1)
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# result = get_repair_node_rela(mdcontent=mdcontent)
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# print(len(result))
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