【智能助手增强】
- 新增对话历史管理：MongoDB新增conversations集合，存储用户与AI的对话上下文，支持多轮对话意图延续
- 新增对话历史API（conversation.py）：GET/DELETE conversation历史、列出所有会话
- 意图解析增强：支持基于对话历史的意图识别，上下文理解更准确
- 字段提取优化：支持"提取文档中的医院数量"等自然语言模式，智能去除"文档中的"前缀
- 文档对比优化：从指令中提取文件名并精确匹配source_docs，支持"对比A和B两个文档"
- 文档摘要优化：使用LLM生成真实AI摘要而非返回原始文档预览
【Word模板填表核心功能】
- Word模板字段生成：空白Word上传后，自动从源文档（Excel/Word/TXT/MD）内容AI生成字段名
- Word模板填表（_fill_docx）：将提取数据写入Word模板表格，支持精确匹配、模糊匹配、追加新行
- 数据润色（_polish_word_filled_data）：LLM对多行Excel数据进行统计归纳（合计/平均/极值），转化为专业自然语言描述
- 段落格式输出：使用📌字段名+值段落+分隔线（灰色横线）格式，提升可读性
- 导出链打通：fill_template返回filled_file_path，export直接返回已填好的Word文件
【其他修复】
- 修复Word导出Windows文件锁问题：NamedTemporaryFile改为mkstemp+close
- 修复Word方框非法字符：扩展clean_text移除\uFFFD、□等Unicode替代符和零宽字符
- 修复文档对比"需要至少2个文档"：从指令提取具体文件名优先匹配而非取前2个
- 修复导出format硬编码：自动识别docx/xlsx格式
- Docx解析器增加备用解析方法和更完整的段落/表格/标题提取
- RAG服务新增MySQL数据源支持
This commit is contained in:
@@ -44,6 +44,22 @@ class DocxParser(BaseParser):
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error=f"文件不存在: {file_path}"
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)
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# 尝试使用 python-docx 解析，失败则使用备用方法
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try:
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return self._parse_with_docx(path)
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except Exception as e:
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logger.warning(f"python-docx 解析失败，使用备用方法: {e}")
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try:
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return self._parse_fallback(path)
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except Exception as fallback_error:
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logger.error(f"备用解析方法也失败: {fallback_error}")
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return ParseResult(
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success=False,
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error=f"解析 Word 文档失败: {str(e)}"
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)
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def _parse_with_docx(self, path: Path) -> ParseResult:
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"""使用 python-docx 解析文档"""
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# 检查文件扩展名
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if path.suffix.lower() not in self.supported_extensions:
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return ParseResult(
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@@ -51,98 +67,177 @@ class DocxParser(BaseParser):
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error=f"不支持的文件类型: {path.suffix}"
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)
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# 读取 Word 文档
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doc = Document(path)
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# 提取文本内容
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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({
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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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for i, table in enumerate(doc.tables):
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table_rows = []
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for row in table.rows:
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row_data = [cell.text.strip() for cell in row.cells]
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table_rows.append(row_data)
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if table_rows:
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tables_data.append({
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"table_index": i,
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"rows": table_rows,
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"row_count": len(table_rows),
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"column_count": len(table_rows[0]) if table_rows else 0
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})
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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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"filename": path.name,
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"extension": path.suffix.lower(),
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"paragraph_count": len(paragraphs),
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"table_count": len(tables_data),
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"image_count": images_info.get("image_count", 0)
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}
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return ParseResult(
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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_with_style": paragraphs,
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"tables": tables_data,
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"images": images_info
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},
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metadata=metadata
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)
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def _parse_fallback(self, path: Path) -> ParseResult:
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"""备用解析方法：直接解析 docx 的 XML 结构"""
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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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# 读取 Word 文档
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doc = Document(file_path)
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with zipfile.ZipFile(path, 'r') as zf:
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# 读取 document.xml
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if 'word/document.xml' not in zf.namelist():
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return ParseResult(success=False, error="无效的 docx 文件格式")
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# 提取文本内容
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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({
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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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xml_content = zf.read('word/document.xml')
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root = ET.fromstring(xml_content)
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# 命名空间
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namespaces = {
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'w': 'http://schemas.openxmlformats.org/wordprocessingml/2006/main'
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}
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paragraphs = []
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tables = []
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current_table = []
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for elem in root.iter():
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if elem.tag.endswith('}p'): # 段落
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text_parts = []
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for t in elem.iter():
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if t.tag.endswith('}t') and t.text:
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text_parts.append(t.text)
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text = ''.join(text_parts).strip()
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if text:
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paragraphs.append({'text': text, 'style': 'Normal'})
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elif elem.tag.endswith('}tr'): # 表格行
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row_data = []
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for tc in elem.iter():
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if tc.tag.endswith('}tc'): # 单元格
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cell_text = []
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for t in tc.iter():
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if t.tag.endswith('}t') and t.text:
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cell_text.append(t.text)
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row_data.append(''.join(cell_text).strip())
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if row_data:
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current_table.append(row_data)
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else:
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# 表格结束，保存
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if current_table:
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tables.append({
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'table_index': len(tables),
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'rows': current_table,
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'row_count': len(current_table),
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'column_count': len(current_table[0]) if current_table else 0
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})
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current_table = []
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# 保存最后一张表格
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if current_table:
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tables.append({
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'table_index': len(tables),
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'rows': current_table,
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'row_count': len(current_table),
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'column_count': len(current_table[0]) if current_table else 0
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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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# 构建文本
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paragraphs_text = [p["text"] for p in paragraphs]
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full_text_parts = ["【文档正文】"] + paragraphs_text
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# 提取表格内容
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tables_data = []
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for i, table in enumerate(doc.tables):
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table_rows = []
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for row in table.rows:
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row_data = [cell.text.strip() for cell in row.cells]
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table_rows.append(row_data)
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if tables:
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full_text_parts.append("\n【文档表格】")
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for idx, table in enumerate(tables):
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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 table_rows:
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tables_data.append({
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"table_index": i,
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"rows": table_rows,
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"row_count": len(table_rows),
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"column_count": len(table_rows[0]) if table_rows else 0
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})
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full_text = "\n".join(full_text_parts)
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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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"filename": path.name,
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"extension": path.suffix.lower(),
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"file_size": path.stat().st_size,
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"paragraph_count": len(paragraphs),
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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_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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return ParseResult(
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success=True,
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data={
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"content": full_text,
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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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return ParseResult(
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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_text": paragraphs_text,
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"tables": tables_data,
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"images": images_info
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"paragraphs_with_style": paragraphs,
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"tables": tables,
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"images": {"image_count": 0, "descriptions": []}
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},
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metadata={
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"filename": path.name,
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"extension": path.suffix.lower(),
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"paragraph_count": len(paragraphs),
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"table_count": len(tables),
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"image_count": 0,
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"parse_method": "fallback_xml"
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}
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},
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metadata=metadata
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)
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)
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except zipfile.BadZipFile:
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return ParseResult(success=False, error="无效的 ZIP/文档文件")
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except Exception as e:
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logger.error(f"解析 Word 文档失败: {str(e)}")
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return ParseResult(
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success=False,
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error=f"解析 Word 文档失败: {str(e)}"
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)
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return ParseResult(success=False, error=f"备用解析失败: {str(e)}")
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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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@@ -197,6 +292,83 @@ class DocxParser(BaseParser):
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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_text_from_images(self, file_path: str, lang: str = 'chi_sim+eng') -> Dict[str, Any]:
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"""
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对 Word 文档中的图片进行 OCR 文字识别
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Args:
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file_path: Word 文件路径
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lang: Tesseract 语言代码，默认简体中文+英文 (chi_sim+eng)
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Returns:
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包含识别结果的字典
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"""
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import zipfile
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from io import BytesIO
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from PIL import Image
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try:
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import pytesseract
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except ImportError:
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logger.warning("pytesseract 未安装，OCR 功能不可用")
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return {
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"success": False,
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"error": "pytesseract 未安装，请运行: pip install pytesseract",
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"image_count": 0,
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"extracted_text": []
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}
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results = {
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"success": True,
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"image_count": 0,
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"extracted_text": [],
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"total_chars": 0
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}
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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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media_files = [f for f in zf.namelist() if f.startswith('word/media/')]
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for idx, filename in enumerate(media_files):
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ext = filename.split('.')[-1].lower()
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if ext not in ['png', 'jpg', 'jpeg', 'gif', 'bmp']:
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continue
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try:
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# 读取图片数据
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image_data = zf.read(filename)
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image = Image.open(BytesIO(image_data))
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# 使用 Tesseract OCR 提取文字
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text = pytesseract.image_to_string(image, lang=lang)
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text = text.strip()
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if text:
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results["extracted_text"].append({
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"image_index": idx,
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"filename": filename,
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"text": text,
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"char_count": len(text)
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})
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results["total_chars"] += len(text)
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logger.info(f"图片 {filename} OCR 识别完成，提取 {len(text)} 字符")
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except Exception as e:
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logger.warning(f"图片 {filename} OCR 识别失败: {str(e)}")
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results["image_count"] = len(results["extracted_text"])
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except zipfile.BadZipFile:
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results["success"] = False
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results["error"] = "无效的 Word 文档文件"
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except Exception as e:
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results["success"] = False
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results["error"] = f"OCR 处理失败: {str(e)}"
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return results
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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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Reference in New Issue
Block a user