454 lines
15 KiB
Python
454 lines
15 KiB
Python
"""
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表格模板填写服务
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从非结构化文档中检索信息并填写到表格模板
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"""
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import logging
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from dataclasses import dataclass, field
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from typing import Any, Dict, List, Optional
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from app.core.database import mongodb
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from app.services.llm_service import llm_service
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from app.core.document_parser import ParserFactory
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logger = logging.getLogger(__name__)
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@dataclass
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class TemplateField:
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"""模板字段"""
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cell: str # 单元格位置，如 "A1"
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name: str # 字段名称
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field_type: str = "text" # 字段类型: text/number/date
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required: bool = True
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hint: str = "" # 字段提示词
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@dataclass
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class SourceDocument:
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"""源文档"""
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doc_id: str
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filename: str
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doc_type: str
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content: str = ""
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structured_data: Dict[str, Any] = field(default_factory=dict)
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@dataclass
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class FillResult:
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"""填写结果"""
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field: str
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value: Any
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source: str # 来源文档
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confidence: float = 1.0 # 置信度
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class TemplateFillService:
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"""表格填写服务"""
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def __init__(self):
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self.llm = llm_service
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async def fill_template(
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self,
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template_fields: List[TemplateField],
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source_doc_ids: Optional[List[str]] = None,
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source_file_paths: Optional[List[str]] = None,
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user_hint: Optional[str] = None
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) -> Dict[str, Any]:
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"""
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填写表格模板
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Args:
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template_fields: 模板字段列表
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source_doc_ids: 源文档 MongoDB ID 列表
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source_file_paths: 源文档文件路径列表
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user_hint: 用户提示（如"请从合同文档中提取"）
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Returns:
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填写结果
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"""
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filled_data = {}
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fill_details = []
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# 1. 加载源文档内容
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source_docs = await self._load_source_documents(source_doc_ids, source_file_paths)
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if not source_docs:
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logger.warning("没有找到源文档，填表结果将全部为空")
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# 2. 对每个字段进行提取
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for field in template_fields:
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try:
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# 从源文档中提取字段值
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result = await self._extract_field_value(
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field=field,
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source_docs=source_docs,
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user_hint=user_hint
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)
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# 存储结果
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filled_data[field.name] = result.value
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fill_details.append({
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"field": field.name,
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"cell": field.cell,
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"value": result.value,
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"source": result.source,
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"confidence": result.confidence
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})
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logger.info(f"字段 {field.name} 填写完成: {result.value}")
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except Exception as e:
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logger.error(f"填写字段 {field.name} 失败: {str(e)}")
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filled_data[field.name] = f"[提取失败: {str(e)}]"
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fill_details.append({
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"field": field.name,
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"cell": field.cell,
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"value": f"[提取失败]",
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"source": "error",
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"confidence": 0.0
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})
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return {
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"success": True,
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"filled_data": filled_data,
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"fill_details": fill_details,
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"source_doc_count": len(source_docs)
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}
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async def _load_source_documents(
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self,
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source_doc_ids: Optional[List[str]] = None,
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source_file_paths: Optional[List[str]] = None
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) -> List[SourceDocument]:
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"""
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加载源文档内容
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Args:
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source_doc_ids: MongoDB 文档 ID 列表
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source_file_paths: 源文档文件路径列表
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Returns:
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源文档列表
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"""
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source_docs = []
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# 1. 从 MongoDB 加载文档
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if source_doc_ids:
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for doc_id in source_doc_ids:
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try:
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doc = await mongodb.get_document(doc_id)
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if doc:
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source_docs.append(SourceDocument(
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doc_id=doc_id,
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filename=doc.get("metadata", {}).get("original_filename", "unknown"),
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doc_type=doc.get("doc_type", "unknown"),
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content=doc.get("content", ""),
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structured_data=doc.get("structured_data", {})
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))
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logger.info(f"从MongoDB加载文档: {doc_id}")
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except Exception as e:
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logger.error(f"从MongoDB加载文档失败 {doc_id}: {str(e)}")
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# 2. 从文件路径加载文档
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if source_file_paths:
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for file_path in source_file_paths:
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try:
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parser = ParserFactory.get_parser(file_path)
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result = parser.parse(file_path)
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if result.success:
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source_docs.append(SourceDocument(
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doc_id=file_path,
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filename=result.metadata.get("filename", file_path.split("/")[-1]),
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doc_type=result.metadata.get("extension", "unknown").replace(".", ""),
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content=result.data.get("content", ""),
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structured_data=result.data.get("structured_data", {})
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))
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logger.info(f"从文件加载文档: {file_path}")
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except Exception as e:
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logger.error(f"从文件加载文档失败 {file_path}: {str(e)}")
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return source_docs
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async def _extract_field_value(
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self,
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field: TemplateField,
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source_docs: List[SourceDocument],
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user_hint: Optional[str] = None
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) -> FillResult:
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"""
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使用 LLM 从源文档中提取字段值
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Args:
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field: 字段定义
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source_docs: 源文档列表
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user_hint: 用户提示
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Returns:
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提取结果
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"""
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if not source_docs:
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return FillResult(
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field=field.name,
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value="",
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source="无源文档",
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confidence=0.0
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)
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# 构建上下文文本
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context_text = self._build_context_text(source_docs, max_length=8000)
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# 构建提示词
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hint_text = field.hint if field.hint else f"请提取{field.name}的信息"
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if user_hint:
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hint_text = f"{user_hint}。{hint_text}"
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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。
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需要提取的字段：
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- 字段名称：{field.name}
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- 字段类型：{field.field_type}
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- 填写提示：{hint_text}
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- 是否必填：{'是' if field.required else '否'}
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参考文档内容：
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{context_text}
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请严格按照以下 JSON 格式输出，不要添加任何解释：
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{{
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"value": "提取到的值，如果没有找到则填写空字符串",
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"source": "数据来源的文档描述（如：来自xxx文档）",
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"confidence": 0.0到1.0之间的置信度，表示对提取结果的信心程度"
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}}
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"""
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# 调用 LLM
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messages = [
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{"role": "system", "content": "你是一个专业的数据提取助手。请严格按JSON格式输出。"},
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{"role": "user", "content": prompt}
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]
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try:
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response = await self.llm.chat(
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messages=messages,
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temperature=0.1,
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max_tokens=500
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)
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content = self.llm.extract_message_content(response)
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# 解析 JSON 响应
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import json
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import re
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# 尝试提取 JSON
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json_match = re.search(r'\{[\s\S]*\}', content)
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if json_match:
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result = json.loads(json_match.group())
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return FillResult(
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field=field.name,
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value=result.get("value", ""),
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source=result.get("source", "LLM生成"),
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confidence=result.get("confidence", 0.5)
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)
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else:
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# 如果无法解析，返回原始内容
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return FillResult(
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field=field.name,
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value=content.strip(),
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source="直接提取",
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confidence=0.5
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)
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except Exception as e:
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logger.error(f"LLM 提取失败: {str(e)}")
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return FillResult(
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field=field.name,
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value="",
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source=f"提取失败: {str(e)}",
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confidence=0.0
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)
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def _build_context_text(self, source_docs: List[SourceDocument], max_length: int = 8000) -> str:
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"""
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构建上下文文本
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Args:
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source_docs: 源文档列表
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max_length: 最大字符数
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Returns:
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上下文文本
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"""
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contexts = []
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total_length = 0
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for doc in source_docs:
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# 优先使用结构化数据（表格），其次使用文本内容
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doc_content = ""
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if doc.structured_data and doc.structured_data.get("tables"):
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# 如果有表格数据，优先使用
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tables = doc.structured_data.get("tables", [])
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for table in tables:
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if isinstance(table, dict):
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rows = table.get("rows", [])
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if rows:
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doc_content += f"\n【文档: {doc.filename} 表格数据】\n"
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for row in rows[:20]: # 限制每表最多20行
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if isinstance(row, list):
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doc_content += " | ".join(str(cell) for cell in row) + "\n"
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elif isinstance(row, dict):
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doc_content += " | ".join(str(v) for v in row.values()) + "\n"
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elif doc.content:
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doc_content = doc.content[:5000] # 限制文本长度
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if doc_content:
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doc_context = f"【文档: {doc.filename} ({doc.doc_type})】\n{doc_content}"
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if total_length + len(doc_context) <= max_length:
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contexts.append(doc_context)
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total_length += len(doc_context)
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else:
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# 如果超出长度，截断
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remaining = max_length - total_length
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if remaining > 100:
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contexts.append(doc_context[:remaining])
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break
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return "\n\n".join(contexts) if contexts else "（源文档内容为空）"
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async def get_template_fields_from_file(
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self,
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file_path: str,
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file_type: str = "xlsx"
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) -> List[TemplateField]:
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"""
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从模板文件提取字段定义
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Args:
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file_path: 模板文件路径
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file_type: 文件类型 (xlsx/xls/docx)
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Returns:
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字段列表
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"""
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fields = []
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try:
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if file_type in ["xlsx", "xls"]:
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fields = await self._get_template_fields_from_excel(file_path)
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elif file_type == "docx":
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fields = await self._get_template_fields_from_docx(file_path)
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except Exception as e:
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logger.error(f"提取模板字段失败: {str(e)}")
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return fields
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async def _get_template_fields_from_excel(self, file_path: str) -> List[TemplateField]:
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"""从 Excel 模板提取字段"""
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fields = []
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try:
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import pandas as pd
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df = pd.read_excel(file_path, nrows=5)
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for idx, col in enumerate(df.columns):
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cell = self._column_to_cell(idx)
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col_str = str(col)
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fields.append(TemplateField(
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cell=cell,
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name=col_str,
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field_type=self._infer_field_type_from_value(df[col].iloc[0] if len(df) > 0 else ""),
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required=True,
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hint=""
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))
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except Exception as e:
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logger.error(f"从Excel提取字段失败: {str(e)}")
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return fields
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async def _get_template_fields_from_docx(self, file_path: str) -> List[TemplateField]:
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"""从 Word 模板提取字段"""
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fields = []
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try:
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from docx import Document
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doc = Document(file_path)
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for table_idx, table in enumerate(doc.tables):
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for row_idx, row in enumerate(table.rows):
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cells = [cell.text.strip() for cell in row.cells]
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# 假设第一列是字段名
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if cells and cells[0]:
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field_name = cells[0]
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hint = cells[1] if len(cells) > 1 else ""
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# 跳过空行或标题行
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if field_name and field_name not in ["", "字段名", "名称", "项目"]:
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fields.append(TemplateField(
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cell=f"T{table_idx}R{row_idx}",
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name=field_name,
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field_type=self._infer_field_type_from_hint(hint),
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required=True,
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hint=hint
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))
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except Exception as e:
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logger.error(f"从Word提取字段失败: {str(e)}")
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return fields
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def _infer_field_type_from_hint(self, hint: str) -> str:
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"""从提示词推断字段类型"""
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hint_lower = hint.lower()
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date_keywords = ["年", "月", "日", "日期", "时间", "出生"]
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if any(kw in hint for kw in date_keywords):
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return "date"
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number_keywords = ["数量", "金额", "人数", "面积", "增长", "比率", "%", "率", "总计", "合计"]
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if any(kw in hint_lower for kw in number_keywords):
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return "number"
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return "text"
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def _infer_field_type_from_value(self, value: Any) -> str:
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"""从示例值推断字段类型"""
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if value is None or value == "":
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return "text"
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value_str = str(value)
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# 检查日期模式
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import re
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if re.search(r'\d{4}[年/-]\d{1,2}[月/-]\d{1,2}', value_str):
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return "date"
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# 检查数值
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try:
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float(value_str.replace(',', '').replace('%', ''))
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return "number"
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except ValueError:
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pass
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return "text"
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def _column_to_cell(self, col_idx: int) -> str:
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"""将列索引转换为单元格列名 (0 -> A, 1 -> B, ...)"""
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result = ""
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while col_idx >= 0:
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result = chr(65 + (col_idx % 26)) + result
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col_idx = col_idx // 26 - 1
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return result
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# ==================== 全局单例 ====================
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template_fill_service = TemplateFillService()
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