228 lines
7.9 KiB
Python
228 lines
7.9 KiB
Python
#
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# Copyright 2025 The InfiniFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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from docx import Document
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import re
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import pandas as pd
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from collections import Counter
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from rag.nlp import rag_tokenizer
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from io import BytesIO
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class RAGFlowDocxParser:
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"""
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Word文档(.docx)解析器，用于提取文档中的文本内容和表格。
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该解析器能够：
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1. 按页面范围提取文档中的段落文本及其样式
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2. 识别文档中的表格并将其转换为结构化文本
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3. 智能处理表格头部和内容，生成语义化的文本描述
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"""
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def __extract_table_content(self, tb):
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"""
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从Word表格对象中提取内容并转换为DataFrame
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参数:
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tb: docx库的Table对象
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返回:
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处理后的表格内容文本列表
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"""
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df = []
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for row in tb.rows:
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df.append([c.text for c in row.cells])
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return self.__compose_table_content(pd.DataFrame(df))
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def __compose_table_content(self, df):
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"""
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将表格DataFrame转换为语义化的文本描述
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通过识别表格的结构特征(如表头、数据类型等)，将表格转换为更易于理解的文本形式
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参数:
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df: 包含表格内容的DataFrame
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返回:
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表格内容的文本表示列表
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"""
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def blockType(b):
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"""
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识别单元格内容的类型
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通过正则表达式和文本特征分析，将单元格内容分类为不同类型：
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- Dt: 日期类型
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- Nu: 数字类型
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- Ca: 代码/ID类型
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- En: 英文文本
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- NE: 数字和文本混合
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- Sg: 单字符
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- Tx: 短文本
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- Lx: 长文本
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- Nr: 人名
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- Ot: 其他类型
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参数:
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b: 单元格文本内容
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返回:
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内容类型的字符串标识
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"""
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patt = [
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("^(20|19)[0-9]{2}[年/-][0-9]{1,2}[月/-][0-9]{1,2}日*$", "Dt"),
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(r"^(20|19)[0-9]{2}年$", "Dt"),
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(r"^(20|19)[0-9]{2}[年/-][0-9]{1,2}月*$", "Dt"),
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("^[0-9]{1,2}[月/-][0-9]{1,2}日*$", "Dt"),
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(r"^第*[一二三四1-4]季度$", "Dt"),
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(r"^(20|19)[0-9]{2}年*[一二三四1-4]季度$", "Dt"),
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(r"^(20|19)[0-9]{2}[ABCDE]$", "DT"),
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("^[0-9.,+%/ -]+$", "Nu"),
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(r"^[0-9A-Z/\._~-]+$", "Ca"),
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(r"^[A-Z]*[a-z' -]+$", "En"),
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(r"^[0-9.,+-]+[0-9A-Za-z/$￥%<>（）()' -]+$", "NE"),
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(r"^.{1}$", "Sg")
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]
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for p, n in patt:
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if re.search(p, b):
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return n
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tks = [t for t in rag_tokenizer.tokenize(b).split() if len(t) > 1]
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if len(tks) > 3:
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if len(tks) < 12:
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return "Tx"
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else:
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return "Lx"
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if len(tks) == 1 and rag_tokenizer.tag(tks[0]) == "nr":
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return "Nr"
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return "Ot"
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# 表格至少需要两行才能处理
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if len(df) < 2:
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return []
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# 统计表格中最常见的内容类型
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max_type = Counter([blockType(str(df.iloc[i, j])) for i in range(
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1, len(df)) for j in range(len(df.iloc[i, :]))])
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max_type = max(max_type.items(), key=lambda x: x[1])[0]
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# 获取表格列数
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colnm = len(df.iloc[0, :])
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# 默认第一行为表头
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hdrows = [0] # 表头不一定出现在第一行
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# 如果表格主要是数字类型，则识别非数字行作为表头
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if max_type == "Nu":
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for r in range(1, len(df)):
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tys = Counter([blockType(str(df.iloc[r, j]))
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for j in range(len(df.iloc[r, :]))])
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tys = max(tys.items(), key=lambda x: x[1])[0]
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if tys != max_type:
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hdrows.append(r)
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# 处理表格内容，将每行转换为文本
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lines = []
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for i in range(1, len(df)):
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# 跳过表头行
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if i in hdrows:
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continue
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# 计算当前行之前的表头行
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hr = [r - i for r in hdrows]
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hr = [r for r in hr if r < 0]
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# 找到最近的连续表头行
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t = len(hr) - 1
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while t > 0:
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if hr[t] - hr[t - 1] > 1:
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hr = hr[t:]
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break
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t -= 1
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# 为每列构建表头描述
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headers = []
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for j in range(len(df.iloc[i, :])):
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t = []
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for h in hr:
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x = str(df.iloc[i + h, j]).strip()
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if x in t:
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continue
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t.append(x)
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t = ",".join(t)
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if t:
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t += ": "
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headers.append(t)
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# 构建每行的文本表示
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cells = []
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for j in range(len(df.iloc[i, :])):
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if not str(df.iloc[i, j]):
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continue
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cells.append(headers[j] + str(df.iloc[i, j]))
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lines.append(";".join(cells))
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# 根据列数决定返回格式
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if colnm > 3:
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return lines
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return ["\n".join(lines)]
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def __call__(self, fnm, from_page=0, to_page=100000000):
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"""
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解析Word文档，提取指定页面范围内的文本和表格
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参数:
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fnm: 文件名或二进制内容
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from_page: 起始页码(从0开始)
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to_page: 结束页码
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返回:
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元组(secs, tbls)，其中:
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- secs: 段落内容列表，每项为(文本内容, 样式名称)的元组
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- tbls: 表格内容列表
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"""
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# 根据输入类型创建Document对象
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self.doc = Document(fnm) if isinstance(
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fnm, str) else Document(BytesIO(fnm))
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pn = 0 # 当前解析页码
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secs = [] # 存储解析的段落内容
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# 遍历文档中的所有段落
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for p in self.doc.paragraphs:
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# 如果超出指定页码范围，停止解析
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if pn > to_page:
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break
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runs_within_single_paragraph = [] # 保存在页面范围内的文本片段
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# 遍历段落中的所有文本片段(run)
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for run in p.runs:
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if pn > to_page:
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break
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# 如果当前页码在指定范围内且段落有内容，则添加文本
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if from_page <= pn < to_page and p.text.strip():
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runs_within_single_paragraph.append(run.text) # 先添加文本片段
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# 检查页面分隔符
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if 'lastRenderedPageBreak' in run._element.xml:
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pn += 1
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# 将段落文本和样式添加到结果列表
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secs.append(("".join(runs_within_single_paragraph), p.style.name if hasattr(p.style, 'name') else '')) # 然后将文本片段连接为段落的一部分
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# 提取所有表格内容
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tbls = [self.__extract_table_content(tb) for tb in self.doc.tables]
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return secs, tbls
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