zyh

backend/app/api/endpoints/templates.py

@@ -13,7 +13,7 @@ import pandas as pd
 from pydantic import BaseModel
 
 from app.services.template_fill_service import template_fill_service, TemplateField
-from app.services.excel_storage_service import excel_storage_service
+from app.services.file_service import file_service
 
 logger = logging.getLogger(__name__)
 
@@ -28,13 +28,15 @@ class TemplateFieldRequest(BaseModel):
     name: str
     field_type: str = "text"
     required: bool = True
+    hint: str = ""
 
 
 class FillRequest(BaseModel):
     """填写请求"""
     template_id: str
     template_fields: List[TemplateFieldRequest]
-    source_doc_ids: Optional[List[str]] = None
+    source_doc_ids: Optional[List[str]] = None  # MongoDB 文档 ID 列表
+    source_file_paths: Optional[List[str]] = None  # 源文档文件路径列表
     user_hint: Optional[str] = None
 
 
@@ -71,7 +73,6 @@ async def upload_template(
 
     try:
         # 保存文件
-        from app.services.file_service import file_service
         content = await file.read()
         saved_path = file_service.save_uploaded_file(
             content,
@@ -87,7 +88,7 @@ async def upload_template(
 
         return {
             "success": True,
-            "template_id": saved_path,  # 使用文件路径作为ID
+            "template_id": saved_path,
             "filename": file.filename,
             "file_type": file_ext,
             "fields": [
@@ -95,7 +96,8 @@ async def upload_template(
                     "cell": f.cell,
                     "name": f.name,
                     "field_type": f.field_type,
-                    "required": f.required
+                    "required": f.required,
+                    "hint": f.hint
                 }
                 for f in template_fields
             ],
@@ -135,7 +137,8 @@ async def extract_template_fields(
                     "cell": f.cell,
                     "name": f.name,
                     "field_type": f.field_type,
-                    "required": f.required
+                    "required": f.required,
+                    "hint": f.hint
                 }
                 for f in fields
             ]
@@ -153,7 +156,7 @@ async def fill_template(
     """
     执行表格填写
 
-    根据提供的字段定义，从已上传的文档中检索信息并填写
+    根据提供的字段定义，从源文档中检索信息并填写
 
     Args:
         request: 填写请求
@@ -168,7 +171,8 @@ async def fill_template(
                 cell=f.cell,
                 name=f.name,
                 field_type=f.field_type,
-                required=f.required
+                required=f.required,
+                hint=f.hint
             )
             for f in request.template_fields
         ]
@@ -177,6 +181,7 @@ async def fill_template(
         result = await template_fill_service.fill_template(
             template_fields=fields,
             source_doc_ids=request.source_doc_ids,
+            source_file_paths=request.source_file_paths,
             user_hint=request.user_hint
         )
 
@@ -194,6 +199,8 @@ async def export_filled_template(
     """
     导出填写后的表格
 
+    支持 Excel (.xlsx) 和 Word (.docx) 格式
+
     Args:
         request: 导出请求
 
@@ -201,25 +208,124 @@ async def export_filled_template(
         文件流
     """
     try:
-        # 创建 DataFrame
-        df = pd.DataFrame([request.filled_data])
-
-        # 导出为 Excel
-        output = io.BytesIO()
-        with pd.ExcelWriter(output, engine='openpyxl') as writer:
-            df.to_excel(writer, index=False, sheet_name='填写结果')
-
-        output.seek(0)
-
-        # 生成文件名
-        filename = f"filled_template.{request.format}"
-
-        return StreamingResponse(
-            io.BytesIO(output.getvalue()),
-            media_type="application/vnd.openxmlformats-officedocument.spreadsheetml.sheet",
-            headers={"Content-Disposition": f"attachment; filename={filename}"}
-        )
+        if request.format == "xlsx":
+            return await _export_to_excel(request.filled_data, request.template_id)
+        elif request.format == "docx":
+            return await _export_to_word(request.filled_data, request.template_id)
+        else:
+            raise HTTPException(
+                status_code=400,
+                detail=f"不支持的导出格式: {request.format}，仅支持 xlsx/docx"
+            )
 
+    except HTTPException:
+        raise
     except Exception as e:
         logger.error(f"导出失败: {str(e)}")
         raise HTTPException(status_code=500, detail=f"导出失败: {str(e)}")
+
+
+async def _export_to_excel(filled_data: dict, template_id: str) -> StreamingResponse:
+    """导出为 Excel 格式"""
+    # 将字典转换为单行 DataFrame
+    df = pd.DataFrame([filled_data])
+
+    output = io.BytesIO()
+    with pd.ExcelWriter(output, engine='openpyxl') as writer:
+        df.to_excel(writer, index=False, sheet_name='填写结果')
+
+    output.seek(0)
+
+    filename = f"filled_template.xlsx"
+
+    return StreamingResponse(
+        io.BytesIO(output.getvalue()),
+        media_type="application/vnd.openxmlformats-officedocument.spreadsheetml.sheet",
+        headers={"Content-Disposition": f"attachment; filename={filename}"}
+    )
+
+
+async def _export_to_word(filled_data: dict, template_id: str) -> StreamingResponse:
+    """导出为 Word 格式"""
+    from docx import Document
+    from docx.shared import Pt, RGBColor
+    from docx.enum.text import WD_ALIGN_PARAGRAPH
+
+    doc = Document()
+
+    # 添加标题
+    title = doc.add_heading('填写结果', level=1)
+    title.alignment = WD_ALIGN_PARAGRAPH.CENTER
+
+    # 添加填写时间和模板信息
+    from datetime import datetime
+    info_para = doc.add_paragraph()
+    info_para.add_run(f"模板ID: {template_id}\n").bold = True
+    info_para.add_run(f"导出时间: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}")
+
+    doc.add_paragraph()  # 空行
+
+    # 添加字段表格
+    table = doc.add_table(rows=1, cols=3)
+    table.style = 'Light Grid Accent 1'
+
+    # 表头
+    header_cells = table.rows[0].cells
+    header_cells[0].text = '字段名'
+    header_cells[1].text = '填写值'
+    header_cells[2].text = '状态'
+
+    for field_name, field_value in filled_data.items():
+        row_cells = table.add_row().cells
+        row_cells[0].text = field_name
+        row_cells[1].text = str(field_value) if field_value else ''
+        row_cells[2].text = '已填写' if field_value else '为空'
+
+    # 保存到 BytesIO
+    output = io.BytesIO()
+    doc.save(output)
+    output.seek(0)
+
+    filename = f"filled_template.docx"
+
+    return StreamingResponse(
+        io.BytesIO(output.getvalue()),
+        media_type="application/vnd.openxmlformats-officedocument.wordprocessingml.document",
+        headers={"Content-Disposition": f"attachment; filename={filename}"}
+    )
+
+
+@router.post("/export/excel")
+async def export_to_excel(
+    filled_data: dict,
+    template_id: str = Query(..., description="模板ID")
+):
+    """
+    专门导出为 Excel 格式
+
+    Args:
+        filled_data: 填写数据
+        template_id: 模板ID
+
+    Returns:
+        Excel 文件流
+    """
+    return await _export_to_excel(filled_data, template_id)
+
+
+@router.post("/export/word")
+async def export_to_word(
+    filled_data: dict,
+    template_id: str = Query(..., description="模板ID")
+):
+    """
+    专门导出为 Word 格式
+
+    Args:
+        filled_data: 填写数据
+        template_id: 模板ID
+
+    Returns:
+        Word 文件流
+    """
+    return await _export_to_word(filled_data, template_id)

backend/app/core/document_parser/docx_parser.py

@@ -161,3 +161,133 @@ class DocxParser(BaseParser):
                 fields[field_name] = match.group(1)
 
         return fields
+
+    def parse_tables_for_template(
+        self,
+        file_path: str
+    ) -> Dict[str, Any]:
+        """
+        解析 Word 文档中的表格，提取模板字段
+
+        专门用于比赛场景：解析表格模板，识别需要填写的字段
+
+        Args:
+            file_path: Word 文件路径
+
+        Returns:
+            包含表格字段信息的字典
+        """
+        from docx import Document
+        from docx.table import Table
+        from docx.oxml.ns import qn
+
+        doc = Document(file_path)
+
+        template_info = {
+            "tables": [],
+            "fields": [],
+            "field_count": 0
+        }
+
+        for table_idx, table in enumerate(doc.tables):
+            table_info = {
+                "table_index": table_idx,
+                "rows": [],
+                "headers": [],
+                "data_rows": [],
+                "field_hints": {}  # 字段名称 -> 提示词/描述
+            }
+
+            # 提取表头（第一行）
+            if table.rows:
+                header_cells = [cell.text.strip() for cell in table.rows[0].cells]
+                table_info["headers"] = header_cells
+
+                # 提取数据行
+                for row_idx, row in enumerate(table.rows[1:], 1):
+                    row_data = [cell.text.strip() for cell in row.cells]
+                    table_info["data_rows"].append(row_data)
+                    table_info["rows"].append({
+                        "row_index": row_idx,
+                        "cells": row_data
+                    })
+
+                # 尝试从第二列/第三列提取提示词
+                # 比赛模板通常格式为：字段名 | 提示词 | 填写值
+                if len(table.rows[0].cells) >= 2:
+                    for row_idx, row in enumerate(table.rows[1:], 1):
+                        cells = [cell.text.strip() for cell in row.cells]
+                        if len(cells) >= 2 and cells[0]:
+                            # 第一列是字段名
+                            field_name = cells[0]
+                            # 第二列可能是提示词或描述
+                            hint = cells[1] if len(cells) > 1 else ""
+                            table_info["field_hints"][field_name] = hint
+
+                            template_info["fields"].append({
+                                "table_index": table_idx,
+                                "row_index": row_idx,
+                                "field_name": field_name,
+                                "hint": hint,
+                                "expected_value": cells[2] if len(cells) > 2 else ""
+                            })
+
+            template_info["tables"].append(table_info)
+
+        template_info["field_count"] = len(template_info["fields"])
+        return template_info
+
+    def extract_template_fields_from_docx(
+        self,
+        file_path: str
+    ) -> List[Dict[str, Any]]:
+        """
+        从 Word 文档中提取模板字段定义
+
+        适用于比赛评分表格：表格第一列是字段名，第二列是提示词/填写示例
+
+        Args:
+            file_path: Word 文件路径
+
+        Returns:
+            字段定义列表
+        """
+        template_info = self.parse_tables_for_template(file_path)
+
+        fields = []
+        for field in template_info["fields"]:
+            fields.append({
+                "cell": f"T{field['table_index']}R{field['row_index']}",  # TableXRowY 格式
+                "name": field["field_name"],
+                "hint": field["hint"],
+                "table_index": field["table_index"],
+                "row_index": field["row_index"],
+                "field_type": self._infer_field_type_from_hint(field["hint"]),
+                "required": True
+            })
+
+        return fields
+
+    def _infer_field_type_from_hint(self, hint: str) -> str:
+        """
+        从提示词推断字段类型
+
+        Args:
+            hint: 字段提示词
+
+        Returns:
+            字段类型 (text/number/date)
+        """
+        hint_lower = hint.lower()
+
+        # 日期关键词
+        date_keywords = ["年", "月", "日", "日期", "时间", "出生"]
+        if any(kw in hint for kw in date_keywords):
+            return "date"
+
+        # 数字关键词
+        number_keywords = ["数量", "金额", "人数", "面积", "增长", "比率", "%", "率"]
+        if any(kw in hint_lower for kw in number_keywords):
+            return "number"
+
+        return "text"

backend/app/services/template_fill_service.py

@@ -4,13 +4,12 @@
 从非结构化文档中检索信息并填写到表格模板
 """
 import logging
-from dataclasses import dataclass
+from dataclasses import dataclass, field
 from typing import Any, Dict, List, Optional
 
 from app.core.database import mongodb
-from app.services.rag_service import rag_service
 from app.services.llm_service import llm_service
-from app.services.excel_storage_service import excel_storage_service
+from app.core.document_parser import ParserFactory
 
 logger = logging.getLogger(__name__)
 
@@ -22,6 +21,17 @@ class TemplateField:
     name: str  # 字段名称
     field_type: str = "text"  # 字段类型: text/number/date
     required: bool = True
+    hint: str = ""  # 字段提示词
+
+
+@dataclass
+class SourceDocument:
+    """源文档"""
+    doc_id: str
+    filename: str
+    doc_type: str
+    content: str = ""
+    structured_data: Dict[str, Any] = field(default_factory=dict)
 
 
 @dataclass
@@ -38,12 +48,12 @@ class TemplateFillService:
 
     def __init__(self):
         self.llm = llm_service
-        self.rag = rag_service
 
     async def fill_template(
         self,
         template_fields: List[TemplateField],
         source_doc_ids: Optional[List[str]] = None,
+        source_file_paths: Optional[List[str]] = None,
         user_hint: Optional[str] = None
     ) -> Dict[str, Any]:
         """
@@ -51,7 +61,8 @@ class TemplateFillService:
 
         Args:
             template_fields: 模板字段列表
-            source_doc_ids: 源文档ID列表，不指定则从所有文档检索
+            source_doc_ids: 源文档 MongoDB ID 列表
+            source_file_paths: 源文档文件路径列表
             user_hint: 用户提示（如"请从合同文档中提取"）
 
         Returns:
@@ -60,28 +71,23 @@ class TemplateFillService:
         filled_data = {}
         fill_details = []
 
+        # 1. 加载源文档内容
+        source_docs = await self._load_source_documents(source_doc_ids, source_file_paths)
+
+        if not source_docs:
+            logger.warning("没有找到源文档，填表结果将全部为空")
+
+        # 2. 对每个字段进行提取
         for field in template_fields:
             try:
-                # 1. 从 RAG 检索相关上下文
-                rag_results = await self._retrieve_context(field.name, user_hint)
+                # 从源文档中提取字段值
+                result = await self._extract_field_value(
+                    field=field,
+                    source_docs=source_docs,
+                    user_hint=user_hint
+                )
 
-                if not rag_results:
-                    # 如果没有检索到结果，尝试直接询问 LLM
-                    result = FillResult(
-                        field=field.name,
-                        value="",
-                        source="未找到相关数据",
-                        confidence=0.0
-                    )
-                else:
-                    # 2. 构建 Prompt 让 LLM 提取信息
-                    result = await self._extract_field_value(
-                        field=field,
-                        rag_context=rag_results,
-                        user_hint=user_hint
-                    )
-
-                # 3. 存储结果
+                # 存储结果
                 filled_data[field.name] = result.value
                 fill_details.append({
                     "field": field.name,
@@ -107,75 +113,113 @@ class TemplateFillService:
         return {
             "success": True,
             "filled_data": filled_data,
-            "fill_details": fill_details
+            "fill_details": fill_details,
+            "source_doc_count": len(source_docs)
         }
 
-    async def _retrieve_context(
+    async def _load_source_documents(
         self,
-        field_name: str,
-        user_hint: Optional[str] = None
-    ) -> List[Dict[str, Any]]:
+        source_doc_ids: Optional[List[str]] = None,
+        source_file_paths: Optional[List[str]] = None
+    ) -> List[SourceDocument]:
         """
-        从 RAG 检索相关上下文
+        加载源文档内容
 
         Args:
-            field_name: 字段名称
-            user_hint: 用户提示
+            source_doc_ids: MongoDB 文档 ID 列表
+            source_file_paths: 源文档文件路径列表
 
         Returns:
-            检索结果列表
+            源文档列表
         """
-        # 构建查询文本
-        query = field_name
-        if user_hint:
-            query = f"{user_hint} {field_name}"
+        source_docs = []
 
-        # 检索相关文档片段
-        results = self.rag.retrieve(query=query, top_k=5)
+        # 1. 从 MongoDB 加载文档
+        if source_doc_ids:
+            for doc_id in source_doc_ids:
+                try:
+                    doc = await mongodb.get_document(doc_id)
+                    if doc:
+                        source_docs.append(SourceDocument(
+                            doc_id=doc_id,
+                            filename=doc.get("metadata", {}).get("original_filename", "unknown"),
+                            doc_type=doc.get("doc_type", "unknown"),
+                            content=doc.get("content", ""),
+                            structured_data=doc.get("structured_data", {})
+                        ))
+                        logger.info(f"从MongoDB加载文档: {doc_id}")
+                except Exception as e:
+                    logger.error(f"从MongoDB加载文档失败 {doc_id}: {str(e)}")
 
-        return results
+        # 2. 从文件路径加载文档
+        if source_file_paths:
+            for file_path in source_file_paths:
+                try:
+                    parser = ParserFactory.get_parser(file_path)
+                    result = parser.parse(file_path)
+                    if result.success:
+                        source_docs.append(SourceDocument(
+                            doc_id=file_path,
+                            filename=result.metadata.get("filename", file_path.split("/")[-1]),
+                            doc_type=result.metadata.get("extension", "unknown").replace(".", ""),
+                            content=result.data.get("content", ""),
+                            structured_data=result.data.get("structured_data", {})
+                        ))
+                        logger.info(f"从文件加载文档: {file_path}")
+                except Exception as e:
+                    logger.error(f"从文件加载文档失败 {file_path}: {str(e)}")
+
+        return source_docs
 
     async def _extract_field_value(
         self,
         field: TemplateField,
-        rag_context: List[Dict[str, Any]],
+        source_docs: List[SourceDocument],
         user_hint: Optional[str] = None
     ) -> FillResult:
         """
-        使用 LLM 从上下文中提取字段值
+        使用 LLM 从源文档中提取字段值
 
         Args:
             field: 字段定义
-            rag_context: RAG 检索到的上下文
+            source_docs: 源文档列表
             user_hint: 用户提示
 
         Returns:
             提取结果
         """
-        # 构建上下文文本
-        context_text = "\n\n".join([
-            f"【文档 {i+1}】\n{doc['content']}"
-            for i, doc in enumerate(rag_context)
-        ])
+        if not source_docs:
+            return FillResult(
+                field=field.name,
+                value="",
+                source="无源文档",
+                confidence=0.0
+            )
 
-        # 构建 Prompt
-        prompt = f"""你是一个数据提取专家。请根据以下文档内容，提取指定字段的信息。
+        # 构建上下文文本
+        context_text = self._build_context_text(source_docs, max_length=8000)
+
+        # 构建提示词
+        hint_text = field.hint if field.hint else f"请提取{field.name}的信息"
+        if user_hint:
+            hint_text = f"{user_hint}。{hint_text}"
+
+        prompt = f"""你是一个专业的数据提取专家。请根据以下文档内容，提取指定字段的信息。
 
 需要提取的字段：
 - 字段名称：{field.name}
 - 字段类型：{field.field_type}
+- 填写提示：{hint_text}
 - 是否必填：{'是' if field.required else '否'}
 
-{'用户提示：' + user_hint if user_hint else ''}
-
 参考文档内容：
 {context_text}
 
 请严格按照以下 JSON 格式输出，不要添加任何解释：
 {{
     "value": "提取到的值，如果没有找到则填写空字符串",
-    "source": "数据来源的文档描述",
-    "confidence": 0.0到1.0之间的置信度
+    "source": "数据来源的文档描述（如：来自xxx文档）",
+    "confidence": 0.0到1.0之间的置信度，表示对提取结果的信心程度"
 }}
 """
 
@@ -226,6 +270,54 @@ class TemplateFillService:
                 confidence=0.0
             )
 
+    def _build_context_text(self, source_docs: List[SourceDocument], max_length: int = 8000) -> str:
+        """
+        构建上下文文本
+
+        Args:
+            source_docs: 源文档列表
+            max_length: 最大字符数
+
+        Returns:
+            上下文文本
+        """
+        contexts = []
+        total_length = 0
+
+        for doc in source_docs:
+            # 优先使用结构化数据（表格），其次使用文本内容
+            doc_content = ""
+
+            if doc.structured_data and doc.structured_data.get("tables"):
+                # 如果有表格数据，优先使用
+                tables = doc.structured_data.get("tables", [])
+                for table in tables:
+                    if isinstance(table, dict):
+                        rows = table.get("rows", [])
+                        if rows:
+                            doc_content += f"\n【文档: {doc.filename} 表格数据】\n"
+                            for row in rows[:20]:  # 限制每表最多20行
+                                if isinstance(row, list):
+                                    doc_content += " | ".join(str(cell) for cell in row) + "\n"
+                                elif isinstance(row, dict):
+                                    doc_content += " | ".join(str(v) for v in row.values()) + "\n"
+            elif doc.content:
+                doc_content = doc.content[:5000]  # 限制文本长度
+
+            if doc_content:
+                doc_context = f"【文档: {doc.filename} ({doc.doc_type})】\n{doc_content}"
+                if total_length + len(doc_context) <= max_length:
+                    contexts.append(doc_context)
+                    total_length += len(doc_context)
+                else:
+                    # 如果超出长度，截断
+                    remaining = max_length - total_length
+                    if remaining > 100:
+                        contexts.append(doc_context[:remaining])
+                    break
+
+        return "\n\n".join(contexts) if contexts else "（源文档内容为空）"
+
     async def get_template_fields_from_file(
         self,
         file_path: str,
@@ -236,7 +328,7 @@ class TemplateFillService:
 
         Args:
             file_path: 模板文件路径
-            file_type: 文件类型
+            file_type: 文件类型 (xlsx/xls/docx)
 
         Returns:
             字段列表
@@ -245,43 +337,108 @@ class TemplateFillService:
 
         try:
             if file_type in ["xlsx", "xls"]:
-                # 从 Excel 读取表头
-                import pandas as pd
-                df = pd.read_excel(file_path, nrows=5)
-
-                for idx, col in enumerate(df.columns):
-                    # 获取单元格位置 (A, B, C, ...)
-                    cell = self._column_to_cell(idx)
-
-                    fields.append(TemplateField(
-                        cell=cell,
-                        name=str(col),
-                        field_type=self._infer_field_type(df[col]),
-                        required=True
-                    ))
-
+                fields = await self._get_template_fields_from_excel(file_path)
             elif file_type == "docx":
-                # 从 Word 表格读取
-                from docx import Document
-                doc = Document(file_path)
-
-                for table_idx, table in enumerate(doc.tables):
-                    for row_idx, row in enumerate(table.rows):
-                        for col_idx, cell in enumerate(row.cells):
-                            cell_text = cell.text.strip()
-                            if cell_text:
-                                fields.append(TemplateField(
-                                    cell=self._column_to_cell(col_idx),
-                                    name=cell_text,
-                                    field_type="text",
-                                    required=True
-                                ))
+                fields = await self._get_template_fields_from_docx(file_path)
 
         except Exception as e:
             logger.error(f"提取模板字段失败: {str(e)}")
 
         return fields
 
+    async def _get_template_fields_from_excel(self, file_path: str) -> List[TemplateField]:
+        """从 Excel 模板提取字段"""
+        fields = []
+
+        try:
+            import pandas as pd
+            df = pd.read_excel(file_path, nrows=5)
+
+            for idx, col in enumerate(df.columns):
+                cell = self._column_to_cell(idx)
+                col_str = str(col)
+
+                fields.append(TemplateField(
+                    cell=cell,
+                    name=col_str,
+                    field_type=self._infer_field_type_from_value(df[col].iloc[0] if len(df) > 0 else ""),
+                    required=True,
+                    hint=""
+                ))
+
+        except Exception as e:
+            logger.error(f"从Excel提取字段失败: {str(e)}")
+
+        return fields
+
+    async def _get_template_fields_from_docx(self, file_path: str) -> List[TemplateField]:
+        """从 Word 模板提取字段"""
+        fields = []
+
+        try:
+            from docx import Document
+
+            doc = Document(file_path)
+
+            for table_idx, table in enumerate(doc.tables):
+                for row_idx, row in enumerate(table.rows):
+                    cells = [cell.text.strip() for cell in row.cells]
+
+                    # 假设第一列是字段名
+                    if cells and cells[0]:
+                        field_name = cells[0]
+                        hint = cells[1] if len(cells) > 1 else ""
+
+                        # 跳过空行或标题行
+                        if field_name and field_name not in ["", "字段名", "名称", "项目"]:
+                            fields.append(TemplateField(
+                                cell=f"T{table_idx}R{row_idx}",
+                                name=field_name,
+                                field_type=self._infer_field_type_from_hint(hint),
+                                required=True,
+                                hint=hint
+                            ))
+
+        except Exception as e:
+            logger.error(f"从Word提取字段失败: {str(e)}")
+
+        return fields
+
+    def _infer_field_type_from_hint(self, hint: str) -> str:
+        """从提示词推断字段类型"""
+        hint_lower = hint.lower()
+
+        date_keywords = ["年", "月", "日", "日期", "时间", "出生"]
+        if any(kw in hint for kw in date_keywords):
+            return "date"
+
+        number_keywords = ["数量", "金额", "人数", "面积", "增长", "比率", "%", "率", "总计", "合计"]
+        if any(kw in hint_lower for kw in number_keywords):
+            return "number"
+
+        return "text"
+
+    def _infer_field_type_from_value(self, value: Any) -> str:
+        """从示例值推断字段类型"""
+        if value is None or value == "":
+            return "text"
+
+        value_str = str(value)
+
+        # 检查日期模式
+        import re
+        if re.search(r'\d{4}[年/-]\d{1,2}[月/-]\d{1,2}', value_str):
+            return "date"
+
+        # 检查数值
+        try:
+            float(value_str.replace(',', '').replace('%', ''))
+            return "number"
+        except ValueError:
+            pass
+
+        return "text"
+
     def _column_to_cell(self, col_idx: int) -> str:
         """将列索引转换为单元格列名 (0 -> A, 1 -> B, ...)"""
         result = ""
@@ -290,17 +447,6 @@ class TemplateFillService:
             col_idx = col_idx // 26 - 1
         return result
 
-    def _infer_field_type(self, series) -> str:
-        """推断字段类型"""
-        import pandas as pd
-
-        if pd.api.types.is_numeric_dtype(series):
-            return "number"
-        elif pd.api.types.is_datetime64_any_dtype(series):
-            return "date"
-        else:
-            return "text"
-
 
 # ==================== 全局单例 ====================