修复Excel解析和智能填表功能
- 增强Excel解析器支持多种命名空间和路径格式，解决英文表头Excel无法读取问题 - 当MongoDB中structured_data为空时，尝试用file_path重新解析文件 - 改进AI分析提示词，明确要求返回纯数值不要单位 - 修复max_tokens值(5000→4000)避免DeepSeek API报错 Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
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@@ -317,24 +317,70 @@ class XlsxParser(BaseParser):
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import zipfile
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from xml.etree import ElementTree as ET
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# 常见的命名空间
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COMMON_NAMESPACES = [
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'http://schemas.openxmlformats.org/spreadsheetml/2006/main',
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'http://schemas.openxmlformats.org/spreadsheetml/2005/main',
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'http://schemas.openxmlformats.org/spreadsheetml/2004/main',
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'http://schemas.openxmlformats.org/spreadsheetml/2003/main',
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]
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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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# 尝试多种可能的 workbook.xml 路径
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possible_paths = ['xl/workbook.xml', 'xl\\workbook.xml', 'workbook.xml']
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content = None
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for path in possible_paths:
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if path in z.namelist():
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content = z.read(path)
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logger.info(f"找到 workbook.xml at: {path}")
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break
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if content is None:
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logger.warning(f"未找到 workbook.xml，文件列表: {z.namelist()[:10]}")
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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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# 命名空间
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ns = {'main': 'http://schemas.openxmlformats.org/spreadsheetml/2006/main'}
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sheet_names = []
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for sheet in root.findall('.//main:sheet', ns):
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name = sheet.get('name')
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if name:
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sheet_names.append(name)
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# 方法1：尝试带命名空间的查找
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for ns in COMMON_NAMESPACES:
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sheet_elements = root.findall(f'.//{{{ns}}}sheet')
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if sheet_elements:
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for sheet in sheet_elements:
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name = sheet.get('name')
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if name:
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sheet_names.append(name)
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if sheet_names:
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logger.info(f"使用命名空间 {ns} 提取工作表: {sheet_names}")
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return sheet_names
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# 方法2：不使用命名空间，直接查找所有 sheet 元素
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if not sheet_names:
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for elem in root.iter():
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if elem.tag.endswith('sheet') and elem.tag != 'sheets':
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name = elem.get('name')
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if name:
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sheet_names.append(name)
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for child in elem:
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if child.tag.endswith('sheet') or child.tag == 'sheet':
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name = child.get('name')
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if name and name not in sheet_names:
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sheet_names.append(name)
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# 方法3：直接从 XML 文本中正则匹配 sheet name
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if not sheet_names:
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import re
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xml_str = content.decode('utf-8', errors='ignore')
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matches = re.findall(r'<sheet\s+[^>]*name=["\']([^"\']+)["\']', xml_str, re.IGNORECASE)
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if matches:
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sheet_names = matches
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logger.info(f"使用正则提取工作表: {sheet_names}")
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logger.info(f"从 XML 提取工作表: {sheet_names}")
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return sheet_names
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except Exception as e:
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logger.error(f"从 XML 提取工作表名称失败: {e}")
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return []
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@@ -356,6 +402,32 @@ class XlsxParser(BaseParser):
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import zipfile
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from xml.etree import ElementTree as ET
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# 常见的命名空间
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COMMON_NAMESPACES = [
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'http://schemas.openxmlformats.org/spreadsheetml/2006/main',
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'http://schemas.openxmlformats.org/spreadsheetml/2005/main',
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'http://schemas.openxmlformats.org/spreadsheetml/2004/main',
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'http://schemas.openxmlformats.org/spreadsheetml/2003/main',
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]
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def find_elements_with_ns(root, tag_name):
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"""灵活查找元素，支持任意命名空间"""
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results = []
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# 方法1：用固定命名空间
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for ns in COMMON_NAMESPACES:
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try:
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elems = root.findall(f'.//{{{ns}}}{tag_name}')
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if elems:
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results.extend(elems)
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except:
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pass
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# 方法2：不带命名空间查找
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if not results:
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for elem in root.iter():
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if elem.tag.endswith('}' + tag_name):
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results.append(elem)
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return results
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with zipfile.ZipFile(file_path, 'r') as z:
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# 获取工作表名称
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sheet_names = self._extract_sheet_names_from_xml(file_path)
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@@ -366,57 +438,68 @@ class XlsxParser(BaseParser):
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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 # sheet1.xml, sheet2.xml, ...
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# 读取 shared strings
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# 读取 shared strings - 尝试多种路径
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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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ns = {'main': 'http://schemas.openxmlformats.org/spreadsheetml/2006/main'}
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for si in ss_root.findall('.//main:si', ns):
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t = si.find('.//main:t', ns)
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if t is not None:
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shared_strings.append(t.text or '')
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else:
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shared_strings.append('')
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ss_paths = ['xl/sharedStrings.xml', 'xl\\sharedStrings.xml', 'sharedStrings.xml']
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for ss_path in ss_paths:
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if ss_path in z.namelist():
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try:
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ss_content = z.read(ss_path)
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ss_root = ET.fromstring(ss_content)
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for si in find_elements_with_ns(ss_root, 'si'):
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t_elements = [c for c in si if c.tag.endswith('}t') or c.tag == 't']
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if t_elements:
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shared_strings.append(t_elements[0].text or '')
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else:
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shared_strings.append('')
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break
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except Exception as e:
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logger.warning(f"读取 sharedStrings 失败: {e}")
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# 读取工作表
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sheet_file = f'xl/worksheets/sheet{sheet_index}.xml'
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if sheet_file not in z.namelist():
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raise ValueError(f"工作表文件 {sheet_file} 不存在")
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# 读取工作表 - 尝试多种可能的路径
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sheet_content = None
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sheet_paths = [
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f'xl/worksheets/sheet{sheet_index}.xml',
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f'xl\\worksheets\\sheet{sheet_index}.xml',
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f'worksheets/sheet{sheet_index}.xml',
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]
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for sp in sheet_paths:
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if sp in z.namelist():
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sheet_content = z.read(sp)
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break
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if sheet_content is None:
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raise ValueError(f"工作表文件 sheet{sheet_index}.xml 不存在")
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sheet_content = z.read(sheet_file)
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root = ET.fromstring(sheet_content)
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ns = {'main': 'http://schemas.openxmlformats.org/spreadsheetml/2006/main'}
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# 收集所有行数据
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all_rows = []
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headers = {}
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for row in root.findall('.//main:row', ns):
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for row in find_elements_with_ns(root, 'row'):
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row_idx = int(row.get('r', 0))
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row_cells = {}
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for cell in row.findall('main:c', ns):
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for cell in find_elements_with_ns(row, '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('main:v', ns)
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v_elements = find_elements_with_ns(cell, 'v')
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v = v_elements[0] if v_elements else None
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if v is not None and v.text:
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if cell_type == 's':
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# shared string
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try:
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row_cells[col_letters] = shared_strings[int(v.text)]
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except (ValueError, IndexError):
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row_cells[col_letters] = v.text
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elif cell_type == 'b':
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# boolean
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row_cells[col_letters] = v.text == '1'
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else:
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row_cells[col_letters] = v.text
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else:
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row_cells[col_letters] = None
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# 处理表头行
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if row_idx == header_row + 1:
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headers = {**row_cells}
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elif row_idx > header_row + 1:
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@@ -424,7 +507,6 @@ class XlsxParser(BaseParser):
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# 构建 DataFrame
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if headers:
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# 按原始列顺序排列
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col_order = list(headers.keys())
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df = pd.DataFrame(all_rows)
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if not df.empty:
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@@ -168,16 +168,44 @@ class TemplateFillService:
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sd = doc.get("structured_data", {})
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sd_keys = list(sd.keys()) if sd else []
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logger.info(f"从MongoDB加载文档: {doc_id}, doc_type={doc.get('doc_type')}, structured_data keys={sd_keys}")
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# 如果 structured_data 为空，但有 file_path，尝试重新解析文件
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doc_content = doc.get("content", "")
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if not sd or (not sd.get("tables") and not sd.get("headers") and not sd.get("rows")):
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file_path = doc.get("metadata", {}).get("file_path")
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if file_path:
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logger.info(f" structured_data 为空，尝试重新解析文件: {file_path}")
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try:
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parser = ParserFactory.get_parser(file_path)
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result = parser.parse(file_path)
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if result.success and result.data:
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if result.data.get("structured_data"):
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sd = result.data.get("structured_data")
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logger.info(f" 重新解析成功，structured_data keys: {list(sd.keys())}")
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elif result.data.get("tables"):
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sd = {"tables": result.data.get("tables", [])}
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logger.info(f" 使用 data.tables，tables数量: {len(sd.get('tables', []))}")
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elif result.data.get("rows"):
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sd = result.data
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logger.info(f" 使用 data.rows 格式")
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if result.data.get("content"):
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doc_content = result.data.get("content", "")
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else:
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logger.warning(f" 重新解析失败: {result.error if result else 'unknown'}")
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except Exception as parse_err:
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logger.error(f" 重新解析文件异常: {str(parse_err)}")
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if sd.get("tables"):
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logger.info(f" tables数量: {len(sd.get('tables', []))}")
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if sd["tables"]:
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first_table = sd["tables"][0]
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logger.info(f" 第一表格: headers={first_table.get('headers', [])[:3]}..., rows数量={len(first_table.get('rows', []))}")
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source_docs.append(SourceDocument(
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doc_id=doc_id,
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filename=doc.get("metadata", {}).get("original_filename", "unknown"),
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doc_type=doc.get("doc_type", "unknown"),
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content=doc.get("content", ""),
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content=doc_content,
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structured_data=sd
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))
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except Exception as e:
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@@ -1348,27 +1376,37 @@ class TemplateFillService:
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hint_text = f"{user_hint}。{hint_text}"
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# 构建针对字段提取的提示词
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prompt = f"""你是一个专业的数据提取专家。请从以下文档内容中提取与"{field.name}"相关的所有数据。
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prompt = f"""你是一个专业的数据提取专家。请从以下文档内容中提取与"{field.name}"完全匹配的数据。
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字段提示: {hint_text}
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【重要】字段名: "{field.name}"
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【重要】字段提示: {hint_text}
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请严格按照以下步骤操作：
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1. 在文档中搜索与"{field.name}"完全相同或高度相关的关键词
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2. 找到后，提取该关键词后的数值（注意：只要数值，不要单位）
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3. 如果是表格中的数据，直接提取该单元格的数值
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4. 如果是段落描述，在关键词附近找数值
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【重要】返回值规则：
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- 只返回纯数值，不要单位（如 "4.9" 而不是 "4.9万亿元"）
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- 如果原文是"4.9万亿元"，返回 "4.9"
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- 如果原文是"144000万册"，返回 "144000"
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- 如果是百分比如"增长7.7%"，返回 "7.7"
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- 如果没有找到完全匹配的数据，返回空数组
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文档内容：
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{doc.content[:8000] if doc.content else ""}
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请完成以下任务：
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1. 仔细阅读文档，找出所有与"{field.name}"相关的数据
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2. 如果文档中有表格数据，提取表格中的对应列值
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3. 如果文档中是段落描述，提取其中的关键数值或结论
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4. 返回提取的所有值（可能多个，用数组存储）
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{doc.content[:10000] if doc.content else ""}
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请用严格的 JSON 格式返回：
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{{
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"values": ["值1", "值2", ...],
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"values": ["值1", "值2", ...], // 只填数值，不要单位
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"source": "数据来源说明",
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"confidence": 0.0到1.0之间的置信度
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}}
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如果没有找到相关数据，返回空数组 values: []"""
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示例：
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- 如果字段是"图书馆总藏量（万册）"且文档说"图书总藏量14.4亿册"，返回 values: ["144000"]
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- 如果字段是"国内旅游收入（亿元）"且文档说"国内旅游收入4.9万亿元"，返回 values: ["49000"]"""
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messages = [
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{"role": "system", "content": "你是一个专业的数据提取助手，擅长从政府统计公报等文档中提取数据。请严格按JSON格式输出。"},
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@@ -1378,7 +1416,7 @@ class TemplateFillService:
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response = await self.llm.chat(
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messages=messages,
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temperature=0.1,
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max_tokens=5000
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max_tokens=4000
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)
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content = self.llm.extract_message_content(response)
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