增强 Word 文档 AI 解析和模板填充功能
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@@ -59,7 +59,13 @@ class DocxParser(BaseParser):
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paragraphs = []
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for para in doc.paragraphs:
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if para.text.strip():
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paragraphs.append(para.text)
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paragraphs.append({
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"text": para.text,
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"style": str(para.style.name) if para.style else "Normal"
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})
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# 提取段落纯文本（用于 AI 解析）
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paragraphs_text = [p["text"] for p in paragraphs if p["text"].strip()]
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# 提取表格内容
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tables_data = []
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@@ -77,8 +83,25 @@ class DocxParser(BaseParser):
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"column_count": len(table_rows[0]) if table_rows else 0
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})
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# 合并所有文本
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full_text = "\n".join(paragraphs)
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# 提取图片/嵌入式对象信息
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images_info = self._extract_images_info(doc, path)
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# 合并所有文本（包括图片描述）
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full_text_parts = []
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full_text_parts.append("【文档正文】")
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full_text_parts.extend(paragraphs_text)
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if tables_data:
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full_text_parts.append("\n【文档表格】")
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for idx, table in enumerate(tables_data):
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full_text_parts.append(f"--- 表格 {idx + 1} ---")
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for row in table["rows"]:
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full_text_parts.append(" | ".join(str(cell) for cell in row))
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if images_info.get("image_count", 0) > 0:
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full_text_parts.append(f"\n【文档图片】文档包含 {images_info['image_count']} 张图片/图表")
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full_text = "\n".join(full_text_parts)
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# 构建元数据
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metadata = {
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@@ -89,7 +112,9 @@ class DocxParser(BaseParser):
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"table_count": len(tables_data),
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"word_count": len(full_text),
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"char_count": len(full_text.replace("\n", "")),
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"has_tables": len(tables_data) > 0
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"has_tables": len(tables_data) > 0,
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"has_images": images_info.get("image_count", 0) > 0,
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"image_count": images_info.get("image_count", 0)
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}
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# 返回结果
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@@ -97,12 +122,16 @@ class DocxParser(BaseParser):
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success=True,
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data={
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"content": full_text,
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"paragraphs": paragraphs,
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"paragraphs": paragraphs_text,
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"paragraphs_with_style": paragraphs,
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"tables": tables_data,
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"images": images_info,
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"word_count": len(full_text),
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"structured_data": {
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"paragraphs": paragraphs,
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"tables": tables_data
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"paragraphs_text": paragraphs_text,
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"tables": tables_data,
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"images": images_info
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}
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},
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metadata=metadata
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@@ -115,6 +144,59 @@ class DocxParser(BaseParser):
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error=f"解析 Word 文档失败: {str(e)}"
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)
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def extract_images_as_base64(self, file_path: str) -> List[Dict[str, str]]:
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"""
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提取 Word 文档中的所有图片，返回 base64 编码列表
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Args:
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file_path: Word 文件路径
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Returns:
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图片列表，每项包含 base64 编码和图片类型
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"""
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import zipfile
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import base64
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from io import BytesIO
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images = []
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try:
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with zipfile.ZipFile(file_path, 'r') as zf:
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# 查找 word/media 目录下的图片文件
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for filename in zf.namelist():
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if filename.startswith('word/media/'):
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# 获取图片类型
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ext = filename.split('.')[-1].lower()
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mime_types = {
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'png': 'image/png',
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'jpg': 'image/jpeg',
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'jpeg': 'image/jpeg',
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'gif': 'image/gif',
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'bmp': 'image/bmp'
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}
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mime_type = mime_types.get(ext, 'image/png')
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try:
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# 读取图片数据并转为 base64
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image_data = zf.read(filename)
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base64_data = base64.b64encode(image_data).decode('utf-8')
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images.append({
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"filename": filename,
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"mime_type": mime_type,
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"base64": base64_data,
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"size": len(image_data)
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})
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logger.info(f"提取图片: {filename}, 大小: {len(image_data)} bytes")
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except Exception as e:
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logger.warning(f"提取图片失败 {filename}: {str(e)}")
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except Exception as e:
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logger.error(f"打开 Word 文档提取图片失败: {str(e)}")
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logger.info(f"共提取 {len(images)} 张图片")
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return images
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def extract_key_sentences(self, text: str, max_sentences: int = 10) -> List[str]:
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"""
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从文本中提取关键句子
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@@ -268,6 +350,60 @@ class DocxParser(BaseParser):
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return fields
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def _extract_images_info(self, doc: Document, path: Path) -> Dict[str, Any]:
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"""
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提取 Word 文档中的图片/嵌入式对象信息
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Args:
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doc: Document 对象
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path: 文件路径
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Returns:
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图片信息字典
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"""
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import zipfile
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from io import BytesIO
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image_count = 0
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image_descriptions = []
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inline_shapes_count = 0
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try:
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# 方法1: 通过 inline shapes 统计图片
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try:
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inline_shapes_count = len(doc.inline_shapes)
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if inline_shapes_count > 0:
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image_count = inline_shapes_count
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image_descriptions.append(f"文档包含 {inline_shapes_count} 个嵌入式图形/图片")
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except Exception:
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pass
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# 方法2: 通过 ZIP 分析 document.xml 获取图片引用
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try:
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with zipfile.ZipFile(path, 'r') as zf:
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# 查找 word/media 目录下的图片文件
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media_files = [f for f in zf.namelist() if f.startswith('word/media/')]
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if media_files and not inline_shapes_count:
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image_count = len(media_files)
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image_descriptions.append(f"文档包含 {image_count} 个嵌入图片")
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# 检查是否有页眉页脚中的图片
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header_images = [f for f in zf.namelist() if 'header' in f.lower() and f.endswith(('.png', '.jpg', '.jpeg', '.gif', '.bmp'))]
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if header_images:
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image_descriptions.append(f"页眉/页脚包含 {len(header_images)} 个图片")
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except Exception:
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pass
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except Exception as e:
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logger.warning(f"提取图片信息失败: {str(e)}")
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return {
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"image_count": image_count,
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"inline_shapes_count": inline_shapes_count,
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"descriptions": image_descriptions,
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"has_images": image_count > 0
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}
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def _infer_field_type_from_hint(self, hint: str) -> str:
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"""
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从提示词推断字段类型
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