diff --git a/backend/app/api/endpoints/templates.py b/backend/app/api/endpoints/templates.py index 572d56e..706f281 100644 --- a/backend/app/api/endpoints/templates.py +++ b/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]) + 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" + ) - # 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') + 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)}") - output.seek(0) - # 生成文件名 - filename = f"filled_template.{request.format}" +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]) - return StreamingResponse( - io.BytesIO(output.getvalue()), - media_type="application/vnd.openxmlformats-officedocument.spreadsheetml.sheet", - headers={"Content-Disposition": f"attachment; filename={filename}"} - ) + output = io.BytesIO() + with pd.ExcelWriter(output, engine='openpyxl') as writer: + df.to_excel(writer, index=False, sheet_name='填写结果') - 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)}") + 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) diff --git a/backend/app/core/document_parser/docx_parser.py b/backend/app/core/document_parser/docx_parser.py index 75e79da..03c341d 100644 --- a/backend/app/core/document_parser/docx_parser.py +++ b/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" diff --git a/backend/app/services/template_fill_service.py b/backend/app/services/template_fill_service.py index 2612354..94930fb 100644 --- a/backend/app/services/template_fill_service.py +++ b/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) - - 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 + # 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 + ) + + # 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}" - - # 检索相关文档片段 - results = self.rag.retrieve(query=query, top_k=5) - - return results + source_docs = [] + + # 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)}") + + # 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 """ + if not source_docs: + return FillResult( + field=field.name, + value="", + source="无源文档", + confidence=0.0 + ) + # 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) - ]) + 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}" - # 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。 + 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) + fields = await self._get_template_fields_from_excel(file_path) + elif file_type == "docx": + fields = await self._get_template_fields_from_docx(file_path) - for idx, col in enumerate(df.columns): - # 获取单元格位置 (A, B, C, ...) - cell = self._column_to_cell(idx) + except Exception as e: + logger.error(f"提取模板字段失败: {str(e)}") - fields.append(TemplateField( - cell=cell, - name=str(col), - field_type=self._infer_field_type(df[col]), - required=True - )) + return fields - 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 - )) + 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"提取模板字段失败: {str(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 ====================