KiriAky 107
3b82103e87
当pandas无法解析某些包含非标准元素的Excel文件时， 添加了XML直接解析功能来提取工作表名称和数据。 - 实现了`_extract_sheet_names_from_xml`方法从XML提取工作表名称 - 实现了`_read_excel_sheet_xml`方法直接解析Excel XML数据 - 添加多种命名空间支持以处理不同Excel格式 - 在pandas解析失败时自动回退到XML解析方式 fix(excel-storage-service): 修复XML解析中的命名空间问题 改进了XML解析逻辑，添加对多种命名空间的支持， 使用通配符查找元素以兼容不同Excel文件格式。 refactor(table-rag-service): 优化XML解析逻辑提高兼容性 统一了XML解析的命名空间处理方式， 改进了元素查找逻辑以更好地支持不同Excel格式。 feat(frontend): 添加RAG向量检索和索引重建功能 - 实现了RAG状态查看、搜索和索引重建接口 - 添加了前端RAG检索界面组件 - 增加了错误处理和加载状态提示
723 lines
25 KiB
Python
723 lines
25 KiB
Python
"""
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Excel 存储服务
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将 Excel 数据转换为 MySQL 表结构并存储
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"""
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import logging
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import re
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from datetime import datetime
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from typing import Any, Dict, List, Optional
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import pandas as pd
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from sqlalchemy import (
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Column,
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DateTime,
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Float,
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Integer,
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String,
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Text,
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inspect,
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text,
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)
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from sqlalchemy.ext.asyncio import AsyncSession
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from app.core.database.mysql import Base, mysql_db
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logger = logging.getLogger(__name__)
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# 设置该模块的日志级别
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logger.setLevel(logging.DEBUG)
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class ExcelStorageService:
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"""Excel 数据存储服务"""
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def __init__(self):
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self.mysql_db = mysql_db
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def _extract_sheet_names_from_xml(self, file_path: str) -> list:
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"""从 Excel 文件的 XML 中提取工作表名称"""
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import zipfile
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from xml.etree import ElementTree as ET
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try:
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with zipfile.ZipFile(file_path, 'r') as z:
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if 'xl/workbook.xml' not in z.namelist():
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return []
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content = z.read('xl/workbook.xml')
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root = ET.fromstring(content)
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# 尝试多种命名空间
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namespaces = [
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'http://schemas.openxmlformats.org/spreadsheetml/2006/main',
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'http://purl.oclc.org/ooxml/spreadsheetml/main',
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]
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for ns_uri in namespaces:
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ns = {'main': ns_uri}
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sheets = root.findall('.//main:sheet', ns)
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if sheets:
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names = [s.get('name') for s in sheets if s.get('name')]
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if names:
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return names
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# 尝试通配符
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sheets = root.findall('.//{*}sheet')
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if not sheets:
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sheets = root.findall('.//sheet')
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return [s.get('name') for s in sheets if s.get('name')]
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except Exception:
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return []
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def _read_excel_sheet(self, file_path: str, sheet_name: str = None, header_row: int = 0) -> pd.DataFrame:
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"""读取 Excel 工作表，支持 pandas 无法解析的特殊 Excel 文件"""
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import zipfile
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from xml.etree import ElementTree as ET
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try:
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df = pd.read_excel(file_path, sheet_name=sheet_name, header=header_row)
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if df is not None and not df.empty:
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return df
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except Exception:
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pass
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# pandas 读取失败，从 XML 直接解析
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logger.info(f"使用 XML 方式读取 Excel: {file_path}")
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try:
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with zipfile.ZipFile(file_path, 'r') as z:
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sheet_names = self._extract_sheet_names_from_xml(file_path)
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if not sheet_names:
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raise ValueError("无法从 Excel 文件中找到工作表")
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target_sheet = sheet_name if sheet_name and sheet_name in sheet_names else sheet_names[0]
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sheet_index = sheet_names.index(target_sheet) + 1
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shared_strings = []
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if 'xl/sharedStrings.xml' in z.namelist():
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ss_content = z.read('xl/sharedStrings.xml')
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ss_root = ET.fromstring(ss_content)
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for si in ss_root.iter():
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if si.tag.endswith('}si') or si.tag == 'si':
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t = si.find('.//{*}t')
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shared_strings.append(t.text if t is not None and t.text else '')
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sheet_file = f'xl/worksheets/sheet{sheet_index}.xml'
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sheet_content = z.read(sheet_file)
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root = ET.fromstring(sheet_content)
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rows_data = []
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headers = {}
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for row in root.iter():
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if row.tag.endswith('}row') or row.tag == 'row':
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row_idx = int(row.get('r', 0))
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# 收集表头行
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if row_idx == header_row + 1:
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for cell in row:
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if cell.tag.endswith('}c') or cell.tag == 'c':
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cell_ref = cell.get('r', '')
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col_letters = ''.join(filter(str.isalpha, cell_ref))
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cell_type = cell.get('t', 'n')
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v = cell.find('{*}v')
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if v is not None and v.text:
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if cell_type == 's':
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try:
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headers[col_letters] = shared_strings[int(v.text)]
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except (ValueError, IndexError):
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headers[col_letters] = v.text
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else:
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headers[col_letters] = v.text
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else:
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headers[col_letters] = col_letters
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continue
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if row_idx <= header_row + 1:
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continue
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row_cells = {}
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for cell in row:
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if cell.tag.endswith('}c') or cell.tag == 'c':
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cell_ref = cell.get('r', '')
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col_letters = ''.join(filter(str.isalpha, cell_ref))
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cell_type = cell.get('t', 'n')
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v = cell.find('{*}v')
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if v is not None and v.text:
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if cell_type == 's':
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try:
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val = shared_strings[int(v.text)]
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except (ValueError, IndexError):
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val = v.text
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elif cell_type == 'b':
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val = v.text == '1'
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else:
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val = v.text
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else:
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val = None
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row_cells[col_letters] = val
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if row_cells:
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rows_data.append(row_cells)
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if not rows_data:
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return pd.DataFrame()
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df = pd.DataFrame(rows_data)
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if headers:
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df.columns = [headers.get(col, col) for col in df.columns]
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return df
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except Exception as e:
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logger.error(f"XML 解析 Excel 失败: {e}")
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raise
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def _sanitize_table_name(self, filename: str) -> str:
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"""
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将文件名转换为合法的表名
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Args:
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filename: 原始文件名
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Returns:
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合法的表名
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"""
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# 移除扩展名
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name = filename.rsplit('.', 1)[0] if '.' in filename else filename
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# 只保留字母、数字、下划线
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name = re.sub(r'[^a-zA-Z0-9_]', '_', name)
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# 确保以字母开头
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if name and name[0].isdigit():
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name = 't_' + name
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# 限制长度
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return name[:50]
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def _sanitize_column_name(self, col_name: str) -> str:
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"""
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将列名转换为合法的字段名
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Args:
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col_name: 原始列名
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Returns:
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合法的字段名
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"""
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# MySQL 支持 UTF8 编码，中文字符可以直接使用
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# 只处理非法字符（控制字符等）和首字符数字
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name = str(col_name).strip()
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# 移除控制字符
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name = re.sub(r'[\x00-\x1f\x7f]', '', name)
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# 确保以字母或中文开头
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if name and name[0].isdigit():
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name = 'col_' + name
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# 限制长度 (MySQL 字段名最多64字符)
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return name[:64]
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def _get_unique_column_name(self, col_name: str, used_names: set) -> str:
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"""
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获取唯一的列名，避免重复
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Args:
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col_name: 原始列名
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used_names: 已使用的列名集合
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Returns:
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唯一的列名
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"""
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sanitized = self._sanitize_column_name(col_name)
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# "id" 是 MySQL 保留名，作为主键使用
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if sanitized.lower() == "id":
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sanitized = "col_id"
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if sanitized not in used_names:
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used_names.add(sanitized)
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return sanitized
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# 添加数字后缀直到唯一
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base = sanitized if sanitized else "col"
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counter = 1
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while f"{base}_{counter}" in used_names:
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counter += 1
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unique_name = f"{base}_{counter}"
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used_names.add(unique_name)
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return unique_name
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def _infer_column_type(self, series: pd.Series) -> str:
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"""
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根据数据推断列类型
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Args:
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series: pandas Series
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Returns:
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类型名称
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"""
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# 移除空值进行类型检查
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non_null = series.dropna()
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if len(non_null) == 0:
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return "TEXT"
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dtype = series.dtype
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# 整数类型检查
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if pd.api.types.is_integer_dtype(dtype):
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# 检查是否所有值都能放入 INT 范围
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try:
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int_values = non_null.astype('int64')
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if int_values.min() >= -2147483648 and int_values.max() <= 2147483647:
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return "INTEGER"
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else:
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# 超出 INT 范围，使用 TEXT
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return "TEXT"
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except (ValueError, OverflowError):
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return "TEXT"
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elif pd.api.types.is_float_dtype(dtype):
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# 检查是否所有值都能放入 FLOAT
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try:
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float_values = non_null.astype('float64')
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if float_values.min() >= -1e308 and float_values.max() <= 1e308:
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return "FLOAT"
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else:
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return "TEXT"
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except (ValueError, OverflowError):
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return "TEXT"
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elif pd.api.types.is_datetime64_any_dtype(dtype):
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return "DATETIME"
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elif pd.api.types.is_bool_dtype(dtype):
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return "BOOLEAN"
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else:
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return "TEXT"
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def _create_table_model(
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self,
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table_name: str,
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columns: List[str],
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column_types: Dict[str, str]
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) -> type:
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"""
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动态创建 SQLAlchemy 模型类
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Args:
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table_name: 表名
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columns: 列名列表
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column_types: 列类型字典
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Returns:
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SQLAlchemy 模型类
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"""
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# 创建属性字典
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attrs = {
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'__tablename__': table_name,
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'__table_args__': {'extend_existing': True},
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}
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# 添加主键列
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attrs['id'] = Column(Integer, primary_key=True, autoincrement=True)
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# 添加数据列
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for col in columns:
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col_name = self._sanitize_column_name(col)
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col_type = column_types.get(col, "TEXT")
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if col_type == "INTEGER":
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attrs[col_name] = Column(Integer, nullable=True)
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elif col_type == "FLOAT":
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attrs[col_name] = Column(Float, nullable=True)
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elif col_type == "DATETIME":
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attrs[col_name] = Column(DateTime, nullable=True)
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elif col_type == "BOOLEAN":
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attrs[col_name] = Column(Integer, nullable=True) # MySQL 没有原生 BOOLEAN
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else:
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attrs[col_name] = Column(Text, nullable=True)
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# 添加元数据列
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attrs['created_at'] = Column(DateTime, default=datetime.utcnow)
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attrs['updated_at'] = Column(DateTime, default=datetime.utcnow, onupdate=datetime.utcnow)
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# 创建类
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return type(table_name, (Base,), attrs)
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async def store_excel(
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||
self,
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file_path: str,
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filename: str,
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sheet_name: Optional[str] = None,
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header_row: int = 0
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) -> Dict[str, Any]:
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||
"""
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将 Excel 文件存储到 MySQL
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||
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||
Args:
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||
file_path: Excel 文件路径
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filename: 原始文件名
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sheet_name: 工作表名称
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||
header_row: 表头行号
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||
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||
Returns:
|
||
存储结果
|
||
"""
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||
table_name = self._sanitize_table_name(filename)
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results = {
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||
"success": True,
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"table_name": table_name,
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||
"row_count": 0,
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"columns": []
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||
}
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||
|
||
try:
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||
logger.info(f"开始读取Excel文件: {file_path}")
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||
# 读取 Excel（使用 fallback 方式支持特殊格式文件）
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df = self._read_excel_sheet(file_path, sheet_name=sheet_name, header_row=header_row)
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||
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||
logger.info(f"Excel读取完成，行数: {len(df)}, 列数: {len(df.columns)}")
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||
|
||
if df.empty:
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||
return {"success": False, "error": "Excel 文件为空"}
|
||
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||
# 清理列名
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||
df.columns = [str(c) for c in df.columns]
|
||
|
||
# 推断列类型，并生成唯一的列名
|
||
column_types = {}
|
||
column_name_map = {} # 原始列名 -> 唯一合法列名
|
||
used_names = set()
|
||
for col in df.columns:
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||
col_name = self._get_unique_column_name(col, used_names)
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||
col_type = self._infer_column_type(df[col])
|
||
column_types[col] = col_type
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||
column_name_map[col] = col_name
|
||
results["columns"].append({
|
||
"original_name": col,
|
||
"sanitized_name": col_name,
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||
"type": col_type
|
||
})
|
||
|
||
# 创建表 - 使用原始 SQL 以兼容异步
|
||
logger.info(f"正在创建MySQL表: {table_name}")
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||
sql_columns = ["id INT AUTO_INCREMENT PRIMARY KEY"]
|
||
for col in df.columns:
|
||
col_name = column_name_map[col]
|
||
col_type = column_types.get(col, "TEXT")
|
||
sql_type = "INT" if col_type == "INTEGER" else "FLOAT" if col_type == "FLOAT" else "DATETIME" if col_type == "DATETIME" else "TEXT"
|
||
sql_columns.append(f"`{col_name}` {sql_type}")
|
||
sql_columns.append("created_at DATETIME DEFAULT CURRENT_TIMESTAMP")
|
||
sql_columns.append("updated_at DATETIME DEFAULT CURRENT_TIMESTAMP ON UPDATE CURRENT_TIMESTAMP")
|
||
create_sql = text(f"CREATE TABLE IF NOT EXISTS `{table_name}` ({', '.join(sql_columns)})")
|
||
await self.mysql_db.execute_raw_sql(str(create_sql))
|
||
logger.info(f"MySQL表创建完成: {table_name}")
|
||
|
||
# 插入数据
|
||
records = []
|
||
for _, row in df.iterrows():
|
||
record = {}
|
||
for col in df.columns:
|
||
col_name = column_name_map[col]
|
||
value = row[col]
|
||
|
||
# 处理 NaN 值
|
||
if pd.isna(value):
|
||
record[col_name] = None
|
||
elif column_types[col] == "INTEGER":
|
||
try:
|
||
record[col_name] = int(value)
|
||
except (ValueError, TypeError):
|
||
record[col_name] = None
|
||
elif column_types[col] == "FLOAT":
|
||
try:
|
||
record[col_name] = float(value)
|
||
except (ValueError, TypeError):
|
||
record[col_name] = None
|
||
else:
|
||
record[col_name] = str(value)
|
||
|
||
records.append(record)
|
||
|
||
logger.info(f"正在插入 {len(records)} 条数据到 MySQL (使用批量插入)...")
|
||
# 使用 pymysql 直接插入以避免 SQLAlchemy 异步问题
|
||
import pymysql
|
||
from app.config import settings
|
||
|
||
connection = pymysql.connect(
|
||
host=settings.MYSQL_HOST,
|
||
port=settings.MYSQL_PORT,
|
||
user=settings.MYSQL_USER,
|
||
password=settings.MYSQL_PASSWORD,
|
||
database=settings.MYSQL_DATABASE,
|
||
charset=settings.MYSQL_CHARSET
|
||
)
|
||
try:
|
||
columns_str = ', '.join(['`' + column_name_map[col] + '`' for col in df.columns])
|
||
placeholders = ', '.join(['%s' for _ in df.columns])
|
||
insert_sql = f"INSERT INTO `{table_name}` ({columns_str}) VALUES ({placeholders})"
|
||
|
||
# 转换为元组列表 (使用映射后的列名)
|
||
param_list = [tuple(record.get(column_name_map[col]) for col in df.columns) for record in records]
|
||
|
||
with connection.cursor() as cursor:
|
||
cursor.executemany(insert_sql, param_list)
|
||
connection.commit()
|
||
logger.info(f"数据插入完成: {len(records)} 条")
|
||
finally:
|
||
connection.close()
|
||
|
||
results["row_count"] = len(records)
|
||
logger.info(f"Excel 数据已存储到 MySQL 表 {table_name}，共 {len(records)} 行")
|
||
|
||
return results
|
||
|
||
except Exception as e:
|
||
logger.error(f"存储 Excel 到 MySQL 失败: {str(e)}", exc_info=True)
|
||
return {"success": False, "error": str(e)}
|
||
|
||
async def store_structured_data(
|
||
self,
|
||
table_name: str,
|
||
data: Dict[str, Any],
|
||
source_doc_id: str = None
|
||
) -> Dict[str, Any]:
|
||
"""
|
||
将结构化数据（从非结构化文档提取的表格）存储到 MySQL
|
||
|
||
Args:
|
||
table_name: 表名
|
||
data: 结构化数据，格式为:
|
||
{
|
||
"columns": ["col1", "col2"], # 列名
|
||
"rows": [["val1", "val2"], ["val3", "val4"]] # 数据行
|
||
}
|
||
source_doc_id: 源文档 ID
|
||
|
||
Returns:
|
||
存储结果
|
||
"""
|
||
results = {
|
||
"success": True,
|
||
"table_name": table_name,
|
||
"row_count": 0,
|
||
"columns": []
|
||
}
|
||
|
||
try:
|
||
columns = data.get("columns", [])
|
||
rows = data.get("rows", [])
|
||
|
||
if not columns or not rows:
|
||
return {"success": False, "error": "数据为空"}
|
||
|
||
# 清理列名
|
||
sanitized_columns = [self._sanitize_column_name(c) for c in columns]
|
||
|
||
# 推断列类型
|
||
column_types = {}
|
||
for i, col in enumerate(columns):
|
||
col_values = [row[i] for row in rows if i < len(row)]
|
||
# 根据数据推断类型
|
||
col_type = self._infer_type_from_values(col_values)
|
||
column_types[col] = col_type
|
||
results["columns"].append({
|
||
"original_name": col,
|
||
"sanitized_name": self._sanitize_column_name(col),
|
||
"type": col_type
|
||
})
|
||
|
||
# 创建表
|
||
model_class = self._create_table_model(table_name, columns, column_types)
|
||
|
||
# 创建表结构
|
||
async with self.mysql_db.get_session() as session:
|
||
model_class.__table__.create(session.bind, checkfirst=True)
|
||
|
||
# 插入数据
|
||
records = []
|
||
for row in rows:
|
||
record = {}
|
||
for i, col in enumerate(columns):
|
||
if i >= len(row):
|
||
continue
|
||
col_name = self._sanitize_column_name(col)
|
||
value = row[i]
|
||
col_type = column_types.get(col, "TEXT")
|
||
|
||
# 处理空值
|
||
if value is None or str(value).strip() == '':
|
||
record[col_name] = None
|
||
elif col_type == "INTEGER":
|
||
try:
|
||
record[col_name] = int(value)
|
||
except (ValueError, TypeError):
|
||
record[col_name] = None
|
||
elif col_type == "FLOAT":
|
||
try:
|
||
record[col_name] = float(value)
|
||
except (ValueError, TypeError):
|
||
record[col_name] = None
|
||
else:
|
||
record[col_name] = str(value)
|
||
|
||
records.append(record)
|
||
|
||
# 批量插入
|
||
async with self.mysql_db.get_session() as session:
|
||
for record in records:
|
||
session.add(model_class(**record))
|
||
await session.commit()
|
||
|
||
results["row_count"] = len(records)
|
||
logger.info(f"结构化数据已存储到 MySQL 表 {table_name}，共 {len(records)} 行")
|
||
|
||
return results
|
||
|
||
except Exception as e:
|
||
logger.error(f"存储结构化数据到 MySQL 失败: {str(e)}")
|
||
return {"success": False, "error": str(e)}
|
||
|
||
def _infer_type_from_values(self, values: List[Any]) -> str:
|
||
"""
|
||
根据值列表推断列类型
|
||
|
||
Args:
|
||
values: 值列表
|
||
|
||
Returns:
|
||
类型名称
|
||
"""
|
||
non_null_values = [v for v in values if v is not None and str(v).strip() != '']
|
||
if not non_null_values:
|
||
return "TEXT"
|
||
|
||
# 检查是否全是整数
|
||
is_integer = all(self._is_integer(v) for v in non_null_values)
|
||
if is_integer:
|
||
return "INTEGER"
|
||
|
||
# 检查是否全是浮点数
|
||
is_float = all(self._is_float(v) for v in non_null_values)
|
||
if is_float:
|
||
return "FLOAT"
|
||
|
||
return "TEXT"
|
||
|
||
def _is_integer(self, value: Any) -> bool:
|
||
"""判断值是否可以转为整数"""
|
||
try:
|
||
int(value)
|
||
return True
|
||
except (ValueError, TypeError):
|
||
return False
|
||
|
||
def _is_float(self, value: Any) -> bool:
|
||
"""判断值是否可以转为浮点数"""
|
||
try:
|
||
float(value)
|
||
return True
|
||
except (ValueError, TypeError):
|
||
return False
|
||
|
||
async def query_table(
|
||
self,
|
||
table_name: str,
|
||
columns: Optional[List[str]] = None,
|
||
where: Optional[str] = None,
|
||
limit: int = 100
|
||
) -> List[Dict[str, Any]]:
|
||
"""
|
||
查询 MySQL 表数据
|
||
|
||
Args:
|
||
table_name: 表名
|
||
columns: 要查询的列
|
||
where: WHERE 条件
|
||
limit: 限制返回行数
|
||
|
||
Returns:
|
||
查询结果
|
||
"""
|
||
try:
|
||
# 构建查询
|
||
sql = f"SELECT * FROM `{table_name}`"
|
||
if where:
|
||
sql += f" WHERE {where}"
|
||
sql += f" LIMIT {limit}"
|
||
|
||
results = await self.mysql_db.execute_query(sql)
|
||
return results
|
||
|
||
except Exception as e:
|
||
logger.error(f"查询表失败: {str(e)}")
|
||
return []
|
||
|
||
async def get_table_schema(self, table_name: str) -> Optional[Dict[str, Any]]:
|
||
"""
|
||
获取表结构信息
|
||
|
||
Args:
|
||
table_name: 表名
|
||
|
||
Returns:
|
||
表结构信息
|
||
"""
|
||
try:
|
||
sql = f"""
|
||
SELECT COLUMN_NAME, DATA_TYPE, IS_NULLABLE, COLUMN_KEY, COLUMN_COMMENT
|
||
FROM INFORMATION_SCHEMA.COLUMNS
|
||
WHERE TABLE_SCHEMA = DATABASE() AND TABLE_NAME = '{table_name}'
|
||
ORDER BY ORDINAL_POSITION
|
||
"""
|
||
results = await self.mysql_db.execute_query(sql)
|
||
return results
|
||
|
||
except Exception as e:
|
||
logger.error(f"获取表结构失败: {str(e)}")
|
||
return None
|
||
|
||
async def delete_table(self, table_name: str) -> bool:
|
||
"""
|
||
删除表
|
||
|
||
Args:
|
||
table_name: 表名
|
||
|
||
Returns:
|
||
是否成功
|
||
"""
|
||
try:
|
||
# 安全检查：表名必须包含下划线（避免删除系统表）
|
||
if '_' not in table_name and not table_name.startswith('t_'):
|
||
raise ValueError("不允许删除此表")
|
||
|
||
sql = f"DROP TABLE IF EXISTS `{table_name}`"
|
||
await self.mysql_db.execute_raw_sql(sql)
|
||
logger.info(f"表 {table_name} 已删除")
|
||
return True
|
||
|
||
except Exception as e:
|
||
logger.error(f"删除表失败: {str(e)}")
|
||
return False
|
||
|
||
async def list_tables(self) -> List[str]:
|
||
"""
|
||
列出所有用户表
|
||
|
||
Returns:
|
||
表名列表
|
||
"""
|
||
try:
|
||
sql = """
|
||
SELECT TABLE_NAME FROM INFORMATION_SCHEMA.TABLES
|
||
WHERE TABLE_SCHEMA = DATABASE() AND TABLE_TYPE = 'BASE TABLE'
|
||
"""
|
||
results = await self.mysql_db.execute_query(sql)
|
||
return [r['TABLE_NAME'] for r in results]
|
||
|
||
except Exception as e:
|
||
logger.error(f"列出表失败: {str(e)}")
|
||
return []
|
||
|
||
|
||
# ==================== 全局单例 ====================
|
||
|
||
excel_storage_service = ExcelStorageService()
|