1391 lines
57 KiB
Python
1391 lines
57 KiB
Python
"""
|
||
表格模板填写服务
|
||
|
||
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, field
|
||
from pathlib import Path
|
||
from typing import Any, Dict, List, Optional
|
||
|
||
from app.core.database import mongodb
|
||
from app.services.llm_service import llm_service
|
||
from app.core.document_parser import ParserFactory
|
||
|
||
logger = logging.getLogger(__name__)
|
||
|
||
|
||
@dataclass
|
||
class TemplateField:
|
||
"""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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||
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||
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||
@dataclass
|
||
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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||
ai_structured_data: Optional[Dict[str, Any]] = None # AI 结构化分析结果缓存
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||
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||
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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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||
values: List[Any] = None # 支持多个值
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||
value: Any = "" # 保留兼容
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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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||
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||
def __post_init__(self):
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||
if self.values is None:
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self.values = []
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||
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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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||
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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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||
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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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||
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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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logger.info(f"开始填表: {len(template_fields)} 个字段, {len(source_doc_ids or [])} 个源文档, {len(source_file_paths or [])} 个文件路径")
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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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logger.info(f"加载了 {len(source_docs)} 个源文档")
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for doc in source_docs:
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logger.info(f" - 文档: {doc.filename}, 类型: {doc.doc_type}, 内容长度: {len(doc.content)}, AI分析: {bool(doc.ai_structured_data)}")
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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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||
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# 2. 对每个字段进行提取
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for idx, field in enumerate(template_fields):
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try:
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logger.info(f"提取字段 [{idx+1}/{len(template_fields)}]: {field.name}")
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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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||
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# 存储结果 - 使用 values 数组
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||
filled_data[field.name] = result.values if result.values else [""]
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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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||
"values": result.values,
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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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||
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||
logger.info(f"字段 {field.name} 填写完成: {len(result.values)} 个值")
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||
|
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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)}", exc_info=True)
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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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||
"values": [f"[提取失败]"],
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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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||
|
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# 计算最大行数
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max_rows = max(len(v) for v in filled_data.values()) if filled_data else 1
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logger.info(f"填表完成: {len(filled_data)} 个字段, 最大行数: {max_rows}")
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||
|
||
return {
|
||
"success": True,
|
||
"filled_data": filled_data,
|
||
"fill_details": fill_details,
|
||
"source_doc_count": len(source_docs),
|
||
"max_rows": max_rows
|
||
}
|
||
|
||
async def _load_source_documents(
|
||
self,
|
||
source_doc_ids: Optional[List[str]] = None,
|
||
source_file_paths: Optional[List[str]] = None
|
||
) -> List[SourceDocument]:
|
||
"""
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||
加载源文档内容，并对 TXT 文件进行 AI 结构化分析
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||
|
||
Args:
|
||
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
|
||
|
||
Returns:
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||
源文档列表
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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:
|
||
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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||
doc_type = doc.get("doc_type", "unknown")
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||
content = doc.get("content", "")
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||
|
||
# 对 TXT 文档进行 AI 结构化分析
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||
ai_structured = None
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||
if doc_type == "txt" and content:
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logger.info(f"MongoDB TXT 文档需要 AI 分析: {doc_id}, 内容长度: {len(content)}")
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ai_structured = await self._analyze_txt_once(content, doc.get("metadata", {}).get("original_filename", "unknown"))
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logger.info(f"AI 分析结果: has_data={ai_structured is not None}")
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||
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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_type,
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||
content=content,
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||
structured_data=doc.get("structured_data", {}),
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||
ai_structured_data=ai_structured
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||
))
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||
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
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if source_file_paths:
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||
logger.info(f"开始从文件路径加载 {len(source_file_paths)} 个文档")
|
||
for file_path in source_file_paths:
|
||
try:
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||
logger.info(f" 加载文件: {file_path}")
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||
parser = ParserFactory.get_parser(file_path)
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result = parser.parse(file_path)
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||
logger.info(f" 解析结果: success={result.success}, error={result.error}")
|
||
if result.success:
|
||
# result.data 的结构取决于解析器类型:
|
||
# - Excel 单 sheet: {columns: [...], rows: [...], row_count, column_count}
|
||
# - Excel 多 sheet: {sheets: {sheet_name: {columns, rows, ...}}}
|
||
# - Word/TXT: {content: "...", structured_data: {...}}
|
||
doc_data = result.data if result.data else {}
|
||
doc_content = doc_data.get("content", "") if isinstance(doc_data, dict) else ""
|
||
doc_structured = doc_data if isinstance(doc_data, dict) and "rows" in doc_data or isinstance(doc_data, dict) and "sheets" in doc_data else {}
|
||
doc_type = result.metadata.get("extension", "unknown").replace(".", "").lower()
|
||
logger.info(f" 文件类型: {doc_type}, 内容长度: {len(doc_content)}")
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||
|
||
# 对 TXT 文件进行 AI 结构化分析
|
||
ai_structured = None
|
||
if doc_type == "txt" and doc_content:
|
||
logger.info(f" 检测到 TXT 文件，内容前100字: {doc_content[:100]}")
|
||
ai_structured = await self._analyze_txt_once(doc_content, result.metadata.get("filename", Path(file_path).name))
|
||
logger.info(f" AI 分析完成: has_result={ai_structured is not None}")
|
||
if ai_structured:
|
||
logger.info(f" AI 结果 keys: {list(ai_structured.keys())}")
|
||
if "table" in ai_structured:
|
||
table = ai_structured.get("table", {})
|
||
logger.info(f" AI 表格: {len(table.get('columns', []))} 列, {len(table.get('rows', []))} 行")
|
||
|
||
source_docs.append(SourceDocument(
|
||
doc_id=file_path,
|
||
filename=result.metadata.get("filename", Path(file_path).name),
|
||
doc_type=doc_type,
|
||
content=doc_content,
|
||
structured_data=doc_structured,
|
||
ai_structured_data=ai_structured
|
||
))
|
||
else:
|
||
logger.warning(f"文档解析失败 {file_path}: {result.error}")
|
||
except Exception as e:
|
||
logger.error(f"从文件加载文档失败 {file_path}: {str(e)}", exc_info=True)
|
||
|
||
return source_docs
|
||
|
||
async def _analyze_txt_once(self, content: str, filename: str) -> Optional[Dict[str, Any]]:
|
||
"""
|
||
对 TXT 内容进行一次性 AI 分析，提取保持行结构的表格数据
|
||
|
||
Args:
|
||
content: 原始文本内容
|
||
filename: 文件名
|
||
|
||
Returns:
|
||
分析结果字典，包含表格数据
|
||
"""
|
||
# 确保 content 是字符串
|
||
if isinstance(content, bytes):
|
||
try:
|
||
content = content.decode('utf-8')
|
||
except:
|
||
content = content.decode('gbk', errors='replace')
|
||
|
||
if not content or len(str(content).strip()) < 10:
|
||
logger.warning(f"TXT 内容过短或为空: {filename}, 类型: {type(content)}")
|
||
return None
|
||
|
||
content = str(content)
|
||
|
||
# 限制内容长度，避免 token 超限
|
||
max_chars = 8000
|
||
truncated_content = content[:max_chars] if len(content) > max_chars else content
|
||
|
||
prompt = f"""你是一个专业的数据提取助手。请从以下文本内容中提取表格数据。
|
||
|
||
文件名：{filename}
|
||
|
||
文本内容：
|
||
{truncated_content}
|
||
|
||
请仔细分析文本中的表格数据，提取所有行。每行是一个完整的数据记录。
|
||
|
||
请严格按以下 JSON 格式输出，不要添加任何解释：
|
||
{{
|
||
"table": {{
|
||
"columns": ["列1", "列2", "列3", ...],
|
||
"rows": [
|
||
["值1", "值2", "值3", ...],
|
||
["值1", "值2", "值3", ...]
|
||
]
|
||
}},
|
||
"summary": "简要说明数据内容"
|
||
}}"""
|
||
|
||
messages = [
|
||
{"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，只输出纯JSON。"},
|
||
{"role": "user", "content": prompt}
|
||
]
|
||
|
||
try:
|
||
logger.info(f"开始 AI 分析 TXT 文件: {filename}, 内容长度: {len(truncated_content)}")
|
||
response = await self.llm.chat(
|
||
messages=messages,
|
||
temperature=0.1,
|
||
max_tokens=2000
|
||
)
|
||
|
||
ai_content = self.llm.extract_message_content(response)
|
||
logger.info(f"LLM 返回内容长度: {len(ai_content)}, 内容前200字: {ai_content[:200]}")
|
||
|
||
# 解析 JSON
|
||
import json
|
||
import re
|
||
|
||
cleaned = ai_content.strip()
|
||
cleaned = re.sub(r'^```json\s*', '', cleaned, flags=re.MULTILINE)
|
||
cleaned = re.sub(r'^```\s*', '', cleaned, flags=re.MULTILINE)
|
||
cleaned = cleaned.strip()
|
||
|
||
logger.info(f"清理后内容前200字: {cleaned[:200]}")
|
||
|
||
# 查找 JSON
|
||
json_start = cleaned.find('{')
|
||
json_end = cleaned.rfind('}') + 1
|
||
|
||
if json_start >= 0 and json_end > json_start:
|
||
json_str = cleaned[json_start:json_end]
|
||
logger.info(f"提取的JSON字符串: {json_str[:200]}")
|
||
try:
|
||
result = json.loads(json_str)
|
||
# 兼容不同格式的返回
|
||
if "table" in result:
|
||
table = result["table"]
|
||
elif "data" in result:
|
||
table = result["data"]
|
||
elif "rows" in result:
|
||
table = {"columns": result.get("columns", []), "rows": result.get("rows", [])}
|
||
else:
|
||
# 尝试直接使用根级别的数据
|
||
table = result
|
||
|
||
if isinstance(table, dict) and ("columns" in table or "rows" in table):
|
||
columns = table.get("columns", [])
|
||
rows = table.get("rows", [])
|
||
logger.info(f"TXT AI 分析成功: {filename}, 列数: {len(columns)}, 行数: {len(rows)}")
|
||
return {"table": {"columns": columns, "rows": rows}, "summary": result.get("summary", "")}
|
||
else:
|
||
logger.warning(f"JSON 中没有找到有效的表格数据: {filename}, result keys: {list(result.keys())}")
|
||
except json.JSONDecodeError as e:
|
||
logger.warning(f"JSON 解析失败: {e}, json_str: {json_str[:200]}")
|
||
|
||
logger.warning(f"无法解析 AI 返回的 JSON: {filename}, ai_content: {ai_content[:500]}")
|
||
return None
|
||
|
||
except Exception as e:
|
||
logger.error(f"AI 分析 TXT 失败: {str(e)}, 文件: {filename}", exc_info=True)
|
||
return None
|
||
|
||
async def _extract_field_value(
|
||
self,
|
||
field: TemplateField,
|
||
source_docs: List[SourceDocument],
|
||
user_hint: Optional[str] = None
|
||
) -> FillResult:
|
||
"""
|
||
使用 LLM 从源文档中提取字段值
|
||
|
||
Args:
|
||
field: 字段定义
|
||
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
|
||
)
|
||
|
||
# 优先尝试直接从结构化数据中提取列值（适用于 Excel 等有 rows 的数据）
|
||
direct_values = self._extract_values_from_structured_data(source_docs, field.name)
|
||
if direct_values:
|
||
logger.info(f"✅ 字段 {field.name} 直接从结构化数据提取到 {len(direct_values)} 个值")
|
||
return FillResult(
|
||
field=field.name,
|
||
values=direct_values,
|
||
value=direct_values[0] if direct_values else "",
|
||
source="结构化数据直接提取",
|
||
confidence=1.0
|
||
)
|
||
|
||
# 无法从结构化数据提取，使用 LLM
|
||
logger.info(f"字段 {field.name} 无法直接从结构化数据提取，使用 LLM...")
|
||
|
||
# 构建上下文文本 - 传入字段名，只提取该列数据
|
||
context_text = await self._build_context_text(source_docs, field_name=field.name, max_length=6000)
|
||
|
||
# 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。请从以下文档内容中提取"{field.name}"字段的值。
|
||
|
||
参考文档内容：
|
||
{context_text}
|
||
|
||
请仔细阅读上述内容，找到所有与"{field.name}"相关的值。
|
||
如果内容是表格格式，请找到对应的列，提取该列所有行的值。
|
||
每一行对应数组中的一个元素，保持行与行的对应关系。
|
||
如果找不到对应的值，返回空数组。
|
||
|
||
请严格按以下JSON格式输出（只输出纯JSON，不要任何解释）：
|
||
{{"values": ["值1", "值2", "值3", ...], "source": "来源说明", "confidence": 0.9}}
|
||
"""
|
||
|
||
# 调用 LLM
|
||
messages = [
|
||
{"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。"},
|
||
{"role": "user", "content": prompt}
|
||
]
|
||
|
||
try:
|
||
response = await self.llm.chat(
|
||
messages=messages,
|
||
temperature=0.1,
|
||
max_tokens=2000
|
||
)
|
||
|
||
content = self.llm.extract_message_content(response)
|
||
|
||
# 解析 JSON 响应
|
||
import json
|
||
import re
|
||
|
||
extracted_values = []
|
||
extracted_source = "LLM生成"
|
||
confidence = 0.5
|
||
|
||
logger.info(f"原始 LLM 返回: {content[:500]}")
|
||
|
||
# ========== 步骤1: 彻底清理 markdown 和各种格式问题 ==========
|
||
# 移除 ```json 和 ``` 标记
|
||
cleaned = content.strip()
|
||
cleaned = re.sub(r'^```json\s*', '', cleaned, flags=re.MULTILINE)
|
||
cleaned = re.sub(r'^```\s*', '', cleaned, flags=re.MULTILINE)
|
||
cleaned = cleaned.strip()
|
||
|
||
logger.info(f"清理后: {cleaned[:500]}")
|
||
|
||
# ========== 步骤2: 定位 JSON 开始位置 ==========
|
||
json_start = -1
|
||
# 找到第一个 { 或 [
|
||
for i, c in enumerate(cleaned):
|
||
if c == '{' or c == '[':
|
||
json_start = i
|
||
break
|
||
|
||
if json_start == -1:
|
||
logger.warning(f"无法找到 JSON 开始位置")
|
||
extracted_values = self._extract_values_from_text(cleaned, field.name)
|
||
else:
|
||
json_text = cleaned[json_start:]
|
||
logger.info(f"JSON 开始位置: {json_start}, 内容: {json_text[:200]}")
|
||
|
||
# ========== 步骤3: 尝试解析 JSON ==========
|
||
# 3a. 尝试直接解析整个字符串
|
||
try:
|
||
result = json.loads(json_text)
|
||
extracted_values = self._extract_values_from_json(result)
|
||
if extracted_values:
|
||
logger.info(f"✅ 直接解析成功，得到 {len(extracted_values)} 个值")
|
||
else:
|
||
logger.warning(f"直接解析成功但未提取到值")
|
||
except json.JSONDecodeError as e:
|
||
logger.warning(f"直接解析失败: {e}, 尝试修复...")
|
||
|
||
# 3b. 尝试修复常见的 JSON 问题
|
||
# 尝试1: 找到配对的闭合括号
|
||
fixed_json = self._fix_json(json_text)
|
||
if fixed_json:
|
||
try:
|
||
result = json.loads(fixed_json)
|
||
extracted_values = self._extract_values_from_json(result)
|
||
if extracted_values:
|
||
logger.info(f"✅ 修复后解析成功，得到 {len(extracted_values)} 个值")
|
||
except json.JSONDecodeError as e2:
|
||
logger.warning(f"修复后仍然失败: {e2}")
|
||
|
||
# 3c. 如果以上都失败，使用正则直接从文本提取 values 数组
|
||
if not extracted_values:
|
||
extracted_values = self._extract_values_by_regex(cleaned)
|
||
if extracted_values:
|
||
logger.info(f"✅ 正则提取成功，得到 {len(extracted_values)} 个值")
|
||
else:
|
||
# 最后的备选：使用旧的文本提取
|
||
extracted_values = self._extract_values_from_text(cleaned, field.name)
|
||
|
||
# 如果仍然没有提取到值
|
||
if not extracted_values:
|
||
extracted_values = [""]
|
||
logger.warning(f"❌ 字段 {field.name} 没有提取到值")
|
||
|
||
logger.info(f"✅✅ 字段 {field.name} 最终返回: {len(extracted_values)} 个值, 示例: {extracted_values[:3]}")
|
||
|
||
return FillResult(
|
||
field=field.name,
|
||
values=extracted_values,
|
||
value=extracted_values[0] if extracted_values else "",
|
||
source=extracted_source,
|
||
confidence=confidence
|
||
)
|
||
|
||
except Exception as e:
|
||
logger.error(f"LLM 提取失败: {str(e)}")
|
||
return FillResult(
|
||
field=field.name,
|
||
values=[""],
|
||
value="",
|
||
source=f"提取失败: {str(e)}",
|
||
confidence=0.0
|
||
)
|
||
|
||
async def _build_context_text(self, source_docs: List[SourceDocument], field_name: str = None, max_length: int = 8000) -> str:
|
||
"""
|
||
构建上下文文本
|
||
|
||
Args:
|
||
source_docs: 源文档列表
|
||
field_name: 需要提取的字段名（可选，用于只提取特定列）
|
||
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 = ""
|
||
row_count = 0
|
||
|
||
if doc.structured_data and doc.structured_data.get("sheets"):
|
||
# parse_all_sheets 格式: {sheets: {sheet_name: {columns, rows}}}
|
||
sheets = doc.structured_data.get("sheets", {})
|
||
for sheet_name, sheet_data in sheets.items():
|
||
if isinstance(sheet_data, dict):
|
||
columns = sheet_data.get("columns", [])
|
||
rows = sheet_data.get("rows", [])
|
||
if rows and columns:
|
||
doc_content += f"\n【文档: {doc.filename} - {sheet_name}，共 {len(rows)} 行】\n"
|
||
# 如果指定了字段名，只提取该列数据
|
||
if field_name:
|
||
# 查找匹配的列（模糊匹配）
|
||
target_col = None
|
||
for col in columns:
|
||
if field_name.lower() in str(col).lower() or str(col).lower() in field_name.lower():
|
||
target_col = col
|
||
break
|
||
if target_col:
|
||
doc_content += f"列名: {target_col}\n"
|
||
for row_idx, row in enumerate(rows):
|
||
if isinstance(row, dict):
|
||
val = row.get(target_col, "")
|
||
elif isinstance(row, list) and target_col in columns:
|
||
val = row[columns.index(target_col)]
|
||
else:
|
||
val = ""
|
||
doc_content += f"行{row_idx+1}: {val}\n"
|
||
row_count += 1
|
||
else:
|
||
# 列名不匹配，输出所有列（但只输出关键列）
|
||
doc_content += " | ".join(str(col) for col in columns) + "\n"
|
||
for row in rows:
|
||
if isinstance(row, dict):
|
||
doc_content += " | ".join(str(row.get(col, "")) for col in columns) + "\n"
|
||
elif isinstance(row, list):
|
||
doc_content += " | ".join(str(cell) for cell in row) + "\n"
|
||
row_count += 1
|
||
else:
|
||
# 输出所有列和行
|
||
doc_content += " | ".join(str(col) for col in columns) + "\n"
|
||
for row in rows:
|
||
if isinstance(row, dict):
|
||
doc_content += " | ".join(str(row.get(col, "")) for col in columns) + "\n"
|
||
elif isinstance(row, list):
|
||
doc_content += " | ".join(str(cell) for cell in row) + "\n"
|
||
row_count += 1
|
||
elif doc.structured_data and doc.structured_data.get("rows"):
|
||
# Excel 单 sheet 格式: {columns: [...], rows: [...], ...}
|
||
columns = doc.structured_data.get("columns", [])
|
||
rows = doc.structured_data.get("rows", [])
|
||
if rows and columns:
|
||
doc_content += f"\n【文档: {doc.filename}，共 {len(rows)} 行】\n"
|
||
if field_name:
|
||
target_col = None
|
||
for col in columns:
|
||
if field_name.lower() in str(col).lower() or str(col).lower() in field_name.lower():
|
||
target_col = col
|
||
break
|
||
if target_col:
|
||
doc_content += f"列名: {target_col}\n"
|
||
for row_idx, row in enumerate(rows):
|
||
if isinstance(row, dict):
|
||
val = row.get(target_col, "")
|
||
elif isinstance(row, list) and target_col in columns:
|
||
val = row[columns.index(target_col)]
|
||
else:
|
||
val = ""
|
||
doc_content += f"行{row_idx+1}: {val}\n"
|
||
row_count += 1
|
||
else:
|
||
doc_content += " | ".join(str(col) for col in columns) + "\n"
|
||
for row in rows:
|
||
if isinstance(row, dict):
|
||
doc_content += " | ".join(str(row.get(col, "")) for col in columns) + "\n"
|
||
elif isinstance(row, list):
|
||
doc_content += " | ".join(str(cell) for cell in row) + "\n"
|
||
row_count += 1
|
||
else:
|
||
doc_content += " | ".join(str(col) for col in columns) + "\n"
|
||
for row in rows:
|
||
if isinstance(row, dict):
|
||
doc_content += " | ".join(str(row.get(col, "")) for col in columns) + "\n"
|
||
elif isinstance(row, list):
|
||
doc_content += " | ".join(str(cell) for cell in row) + "\n"
|
||
row_count += 1
|
||
elif doc.content:
|
||
# TXT 文件优先使用 AI 分析后的结构化数据
|
||
if doc.doc_type == "txt" and doc.ai_structured_data:
|
||
# 使用 AI 结构化分析结果
|
||
ai_table = doc.ai_structured_data.get("table", {})
|
||
columns = ai_table.get("columns", [])
|
||
rows = ai_table.get("rows", [])
|
||
|
||
logger.info(f"TXT AI 结构化数据: doc_type={doc.doc_type}, has_ai_data={doc.ai_structured_data is not None}, columns={columns}, rows={len(rows) if rows else 0}")
|
||
|
||
if columns and rows:
|
||
doc_content += f"\n【文档: {doc.filename} - AI 结构化表格，共 {len(rows)} 行】\n"
|
||
if field_name:
|
||
# 查找匹配的列
|
||
target_col = None
|
||
for col in columns:
|
||
if field_name.lower() in str(col).lower() or str(col).lower() in field_name.lower():
|
||
target_col = col
|
||
break
|
||
if target_col:
|
||
doc_content += f"列名: {target_col}\n"
|
||
for row_idx, row in enumerate(rows):
|
||
if isinstance(row, list) and target_col in columns:
|
||
val = row[columns.index(target_col)]
|
||
else:
|
||
val = str(row.get(target_col, "")) if isinstance(row, dict) else ""
|
||
doc_content += f"行{row_idx+1}: {val}\n"
|
||
row_count += 1
|
||
else:
|
||
# 输出表格
|
||
doc_content += " | ".join(str(col) for col in columns) + "\n"
|
||
for row in rows:
|
||
if isinstance(row, list):
|
||
doc_content += " | ".join(str(cell) for cell in row) + "\n"
|
||
else:
|
||
doc_content += " | ".join(str(row.get(col, "")) for col in columns) + "\n"
|
||
row_count += 1
|
||
logger.info(f"使用 TXT AI 结构化表格: {doc.filename}, {len(columns)} 列, {len(rows)} 行")
|
||
else:
|
||
# AI 结果无表格，回退到原始内容
|
||
doc_content = doc.content[:8000]
|
||
logger.warning(f"TXT AI 结果无表格: {doc.filename}, 使用原始内容")
|
||
elif doc.doc_type == "txt" and doc.content:
|
||
# 没有 AI 分析结果，直接使用原始内容
|
||
doc_content = doc.content[:8000]
|
||
logger.info(f"使用 TXT 原始内容: {doc.filename}, 长度: {len(doc_content)}")
|
||
else:
|
||
# 其他文档类型直接使用内容
|
||
doc_content = doc.content[:5000]
|
||
|
||
if doc_content:
|
||
doc_context = f"【文档: {doc.filename} ({doc.doc_type})】\n{doc_content}"
|
||
logger.info(f"文档 {doc.filename} 上下文长度: {len(doc_context)}, 行数: {row_count}")
|
||
if total_length + len(doc_context) <= max_length:
|
||
contexts.append(doc_context)
|
||
total_length += len(doc_context)
|
||
else:
|
||
remaining = max_length - total_length
|
||
if remaining > 100:
|
||
doc_context = doc_context[:remaining] + f"\n...（内容被截断）"
|
||
contexts.append(doc_context)
|
||
logger.warning(f"上下文被截断: {doc.filename}, 总长度: {total_length + len(doc_context)}")
|
||
break
|
||
|
||
result = "\n\n".join(contexts) if contexts else "（源文档内容为空）"
|
||
logger.info(f"最终上下文长度: {len(result)}")
|
||
return result
|
||
|
||
async def analyze_txt_with_ai(self, content: str, filename: str = "") -> Dict[str, Any]:
|
||
"""
|
||
使用 AI 分析 TXT 文本内容，提取结构化数据
|
||
|
||
Args:
|
||
content: 原始文本内容
|
||
filename: 文件名（用于日志）
|
||
|
||
Returns:
|
||
结构化数据，包含:
|
||
- key_value_pairs: 键值对列表
|
||
- tables: 表格数据列表
|
||
- numeric_data: 数值数据列表
|
||
- text_summary: 文本摘要
|
||
"""
|
||
if not content or len(content.strip()) < 10:
|
||
logger.warning(f"TXT 内容过短或为空，跳过 AI 分析: {filename}")
|
||
return {}
|
||
|
||
# 截断过长的文本，避免 token 超限
|
||
max_chars = 15000
|
||
truncated_content = content[:max_chars] if len(content) > max_chars else content
|
||
|
||
system_prompt = """你是一个专业的数据提取专家。请分析提供的文本内容，提取其中包含的结构化信息。
|
||
|
||
请提取以下类型的数据：
|
||
|
||
1. **键值对信息**：从文本中提取的名词-值对，如"姓名: 张三"、"年龄: 25"等
|
||
2. **表格数据**：如果文本中包含表格或列表形式的数据，提取出来
|
||
3. **数值数据**：包含数值、金额、百分比、统计数字等
|
||
4. **关键描述**：文本的核心内容摘要
|
||
|
||
请严格按照以下 JSON 格式输出，不要添加任何 Markdown 标记或解释：
|
||
{
|
||
"key_value_pairs": [
|
||
{"key": "键名1", "value": "值1"},
|
||
{"key": "键名2", "value": "值2"}
|
||
],
|
||
"tables": [
|
||
{
|
||
"description": "表格描述",
|
||
"columns": ["列1", "列2"],
|
||
"rows": [["值1", "值2"], ["值3", "值4"]]
|
||
}
|
||
],
|
||
"numeric_data": [
|
||
{"name": "数据项名称", "value": 123.45, "unit": "单位"}
|
||
],
|
||
"text_summary": "一段简洁的文本摘要，不超过200字"
|
||
}"""
|
||
|
||
user_message = f"""请分析以下文本内容，提取结构化数据：
|
||
|
||
文件名：{filename}
|
||
|
||
文本内容：
|
||
{truncated_content}
|
||
|
||
请严格按 JSON 格式输出。"""
|
||
|
||
messages = [
|
||
{"role": "system", "content": system_prompt},
|
||
{"role": "user", "content": user_message}
|
||
]
|
||
|
||
try:
|
||
logger.info(f"开始 AI 分析 TXT 文件: {filename}, 内容长度: {len(truncated_content)}")
|
||
response = await self.llm.chat(
|
||
messages=messages,
|
||
temperature=0.1,
|
||
max_tokens=2000
|
||
)
|
||
|
||
ai_content = self.llm.extract_message_content(response)
|
||
logger.info(f"AI 返回内容长度: {len(ai_content)}")
|
||
|
||
# 解析 JSON
|
||
import json
|
||
import re
|
||
|
||
# 清理 markdown 格式
|
||
cleaned = ai_content.strip()
|
||
cleaned = re.sub(r'^```json\s*', '', cleaned, flags=re.MULTILINE)
|
||
cleaned = re.sub(r'^```\s*', '', cleaned, flags=re.MULTILINE)
|
||
cleaned = cleaned.strip()
|
||
|
||
# 提取 JSON
|
||
json_start = -1
|
||
for i, c in enumerate(cleaned):
|
||
if c == '{':
|
||
json_start = i
|
||
break
|
||
|
||
if json_start >= 0:
|
||
brace_count = 0
|
||
json_end = -1
|
||
for i in range(json_start, len(cleaned)):
|
||
if cleaned[i] == '{':
|
||
brace_count += 1
|
||
elif cleaned[i] == '}':
|
||
brace_count -= 1
|
||
if brace_count == 0:
|
||
json_end = i + 1
|
||
break
|
||
|
||
if json_end > json_start:
|
||
json_str = cleaned[json_start:json_end]
|
||
result = json.loads(json_str)
|
||
logger.info(f"TXT AI 分析成功: {filename}, 提取到 {len(result.get('key_value_pairs', []))} 个键值对")
|
||
return result
|
||
|
||
logger.warning(f"无法从 AI 返回中解析 JSON: {filename}")
|
||
return {}
|
||
|
||
except json.JSONDecodeError as e:
|
||
logger.error(f"JSON 解析失败: {str(e)}, 文件: {filename}")
|
||
return {}
|
||
except Exception as e:
|
||
logger.error(f"AI 分析 TXT 失败: {str(e)}, 文件: {filename}", exc_info=True)
|
||
return {}
|
||
|
||
def _format_structured_for_context(self, structured_data: Dict[str, Any], filename: str) -> str:
|
||
"""
|
||
将结构化数据格式化为上下文文本
|
||
|
||
Args:
|
||
structured_data: AI 分析返回的结构化数据
|
||
filename: 文件名
|
||
|
||
Returns:
|
||
格式化的文本上下文
|
||
"""
|
||
parts = []
|
||
|
||
# 添加标题
|
||
parts.append(f"【文档: {filename} - AI 结构化分析结果】")
|
||
|
||
# 格式化键值对
|
||
key_value_pairs = structured_data.get("key_value_pairs", [])
|
||
if key_value_pairs:
|
||
parts.append("\n## 关键信息：")
|
||
for kv in key_value_pairs[:20]: # 最多 20 个
|
||
parts.append(f"- {kv.get('key', '')}: {kv.get('value', '')}")
|
||
|
||
# 格式化表格数据
|
||
tables = structured_data.get("tables", [])
|
||
if tables:
|
||
parts.append("\n## 表格数据：")
|
||
for i, table in enumerate(tables[:5]): # 最多 5 个表格
|
||
desc = table.get("description", f"表格{i+1}")
|
||
columns = table.get("columns", [])
|
||
rows = table.get("rows", [])
|
||
if columns and rows:
|
||
parts.append(f"\n### {desc}")
|
||
parts.append("| " + " | ".join(str(c) for c in columns) + " |")
|
||
parts.append("| " + " | ".join(["---"] * len(columns)) + " |")
|
||
for row in rows[:10]: # 每个表格最多 10 行
|
||
parts.append("| " + " | ".join(str(cell) for cell in row) + " |")
|
||
|
||
# 格式化数值数据
|
||
numeric_data = structured_data.get("numeric_data", [])
|
||
if numeric_data:
|
||
parts.append("\n## 数值数据：")
|
||
for num in numeric_data[:15]: # 最多 15 个
|
||
name = num.get("name", "")
|
||
value = num.get("value", "")
|
||
unit = num.get("unit", "")
|
||
parts.append(f"- {name}: {value} {unit}")
|
||
|
||
# 添加文本摘要
|
||
text_summary = structured_data.get("text_summary", "")
|
||
if text_summary:
|
||
parts.append(f"\n## 内容摘要：\n{text_summary}")
|
||
|
||
return "\n".join(parts)
|
||
|
||
async def get_template_fields_from_file(
|
||
self,
|
||
file_path: str,
|
||
file_type: str = "xlsx"
|
||
) -> List[TemplateField]:
|
||
"""
|
||
从模板文件提取字段定义
|
||
|
||
Args:
|
||
file_path: 模板文件路径
|
||
file_type: 文件类型 (xlsx/xls/docx)
|
||
|
||
Returns:
|
||
字段列表
|
||
"""
|
||
fields = []
|
||
|
||
try:
|
||
if file_type in ["xlsx", "xls"]:
|
||
fields = await self._get_template_fields_from_excel(file_path)
|
||
elif file_type == "docx":
|
||
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
|
||
|
||
# 尝试读取 Excel 文件
|
||
try:
|
||
# header=0 表示第一行是表头
|
||
df = pd.read_excel(file_path, header=0, nrows=5)
|
||
except Exception as e:
|
||
logger.warning(f"pandas 读取 Excel 表头失败，尝试无表头模式: {e}")
|
||
# 如果失败，尝试不使用表头模式
|
||
df = pd.read_excel(file_path, header=None, nrows=5)
|
||
# 如果没有表头，使用列索引作为列名
|
||
if df.shape[1] > 0:
|
||
# 检查第一行是否可以作为表头
|
||
first_row = df.iloc[0].tolist()
|
||
if all(pd.notna(v) and str(v).strip() != '' for v in first_row):
|
||
# 第一行有内容，作为表头
|
||
df.columns = [str(v) if pd.notna(v) else f"列{i}" for i, v in enumerate(first_row)]
|
||
df = df.iloc[1:] # 移除表头行
|
||
else:
|
||
# 第一行不是有效表头，使用默认列名
|
||
df.columns = [f"列{i}" for i in range(df.shape[1])]
|
||
|
||
logger.info(f"读取 Excel 表头: {df.shape}, 列: {list(df.columns)[:10]}")
|
||
|
||
# 如果 DataFrame 列为空或只有默认索引，尝试其他方式
|
||
if len(df.columns) == 0 or (len(df.columns) == 1 and df.columns[0] == 0):
|
||
logger.warning(f"表头解析结果异常，重新解析: {df.columns}")
|
||
# 尝试读取整个文件获取列信息
|
||
df_full = pd.read_excel(file_path, header=None)
|
||
if df_full.shape[1] > 0:
|
||
# 使用第一行作为列名
|
||
df = df_full
|
||
df.columns = [str(v) if pd.notna(v) and str(v).strip() else f"列{i}" for i, v in enumerate(df.iloc[0])]
|
||
df = df.iloc[1:]
|
||
|
||
for idx, col in enumerate(df.columns):
|
||
cell = self._column_to_cell(idx)
|
||
col_str = str(col)
|
||
if col_str == '0' or col_str.startswith('Unnamed'):
|
||
col_str = f"字段{idx+1}"
|
||
|
||
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=""
|
||
))
|
||
|
||
logger.info(f"从 Excel 提取到 {len(fields)} 个字段")
|
||
|
||
except Exception as e:
|
||
logger.error(f"从Excel提取字段失败: {str(e)}", exc_info=True)
|
||
|
||
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 = ""
|
||
while col_idx >= 0:
|
||
result = chr(65 + (col_idx % 26)) + result
|
||
col_idx = col_idx // 26 - 1
|
||
return result
|
||
|
||
def _extract_value_from_text(self, text: str, field_name: str) -> str:
|
||
"""
|
||
从非 JSON 文本中提取字段值（单值版本）
|
||
|
||
Args:
|
||
text: 原始文本
|
||
field_name: 字段名称
|
||
|
||
Returns:
|
||
提取的值
|
||
"""
|
||
values = self._extract_values_from_text(text, field_name)
|
||
return values[0] if values else ""
|
||
|
||
def _extract_values_from_structured_data(self, source_docs: List[SourceDocument], field_name: str) -> List[str]:
|
||
"""
|
||
从结构化数据（Excel rows）或 AI 结构化分析结果中直接提取指定列的值
|
||
|
||
适用于有 rows 结构的文档数据，无需 LLM 即可提取
|
||
|
||
Args:
|
||
source_docs: 源文档列表
|
||
field_name: 字段名称
|
||
|
||
Returns:
|
||
值列表，如果无法提取则返回空列表
|
||
"""
|
||
all_values = []
|
||
|
||
for doc in source_docs:
|
||
# 优先从 AI 结构化数据中提取（适用于 TXT 文件）
|
||
if doc.ai_structured_data:
|
||
ai_table = doc.ai_structured_data.get("table", {})
|
||
columns = ai_table.get("columns", [])
|
||
rows = ai_table.get("rows", [])
|
||
if columns and rows:
|
||
values = self._extract_column_values(rows, columns, field_name)
|
||
if values:
|
||
all_values.extend(values)
|
||
logger.info(f"从 TXT AI 结构化数据提取到 {len(values)} 个值: {doc.filename}")
|
||
break
|
||
|
||
# 尝试从 structured_data 中提取
|
||
structured = doc.structured_data
|
||
|
||
if not structured:
|
||
continue
|
||
|
||
# 处理多 sheet 格式: {sheets: {sheet_name: {columns, rows}}}
|
||
if structured.get("sheets"):
|
||
sheets = structured.get("sheets", {})
|
||
for sheet_name, sheet_data in sheets.items():
|
||
if isinstance(sheet_data, dict):
|
||
columns = sheet_data.get("columns", [])
|
||
rows = sheet_data.get("rows", [])
|
||
values = self._extract_column_values(rows, columns, field_name)
|
||
if values:
|
||
all_values.extend(values)
|
||
logger.info(f"从 sheet {sheet_name} 提取到 {len(values)} 个值")
|
||
break # 只用第一个匹配的 sheet
|
||
if all_values:
|
||
break
|
||
|
||
# 处理单 sheet 格式: {columns: [...], rows: [...]}
|
||
elif structured.get("rows"):
|
||
columns = structured.get("columns", [])
|
||
rows = structured.get("rows", [])
|
||
values = self._extract_column_values(rows, columns, field_name)
|
||
if values:
|
||
all_values.extend(values)
|
||
logger.info(f"从文档 {doc.filename} 提取到 {len(values)} 个值")
|
||
break
|
||
|
||
return all_values
|
||
|
||
def _extract_column_values(self, rows: List, columns: List, field_name: str) -> List[str]:
|
||
"""
|
||
从 rows 和 columns 中提取指定列的值
|
||
|
||
Args:
|
||
rows: 行数据列表
|
||
columns: 列名列表
|
||
field_name: 要提取的字段名
|
||
|
||
Returns:
|
||
值列表
|
||
"""
|
||
if not rows or not columns:
|
||
return []
|
||
|
||
# 查找匹配的列（模糊匹配）
|
||
target_col = None
|
||
for col in columns:
|
||
col_str = str(col)
|
||
if field_name.lower() in col_str.lower() or col_str.lower() in field_name.lower():
|
||
target_col = col
|
||
break
|
||
|
||
if not target_col:
|
||
logger.warning(f"未找到匹配列: {field_name}, 可用列: {columns}")
|
||
return []
|
||
|
||
values = []
|
||
for row in rows:
|
||
if isinstance(row, dict):
|
||
val = row.get(target_col, "")
|
||
elif isinstance(row, list) and target_col in columns:
|
||
val = row[columns.index(target_col)]
|
||
else:
|
||
val = ""
|
||
values.append(str(val) if val is not None else "")
|
||
|
||
return values
|
||
|
||
def _extract_values_from_json(self, result) -> List[str]:
|
||
"""
|
||
从解析后的 JSON 对象/数组中提取值数组
|
||
|
||
Args:
|
||
result: json.loads() 返回的对象
|
||
|
||
Returns:
|
||
值列表
|
||
"""
|
||
if isinstance(result, dict):
|
||
# 优先找 values 数组
|
||
if "values" in result and isinstance(result["values"], list):
|
||
vals = [str(v).strip() for v in result["values"] if v and str(v).strip()]
|
||
if vals:
|
||
return vals
|
||
# 尝试找 value 字段
|
||
if "value" in result:
|
||
val = str(result["value"]).strip()
|
||
if val:
|
||
return [val]
|
||
# 尝试找任何数组类型的键
|
||
for key in result.keys():
|
||
val = result[key]
|
||
if isinstance(val, list) and len(val) > 0:
|
||
if all(isinstance(v, (str, int, float, bool)) or v is None for v in val):
|
||
vals = [str(v).strip() for v in val if v is not None and str(v).strip()]
|
||
if vals:
|
||
return vals
|
||
elif isinstance(val, (str, int, float, bool)):
|
||
return [str(val).strip()]
|
||
elif isinstance(result, list):
|
||
vals = [str(v).strip() for v in result if v is not None and str(v).strip()]
|
||
if vals:
|
||
return vals
|
||
return []
|
||
|
||
def _fix_json(self, json_text: str) -> str:
|
||
"""
|
||
尝试修复损坏的 JSON 字符串
|
||
|
||
Args:
|
||
json_text: 原始 JSON 文本
|
||
|
||
Returns:
|
||
修复后的 JSON 文本，如果无法修复则返回空字符串
|
||
"""
|
||
import re
|
||
|
||
# 如果以 { 开头，尝试找到配对的 }
|
||
if json_text.startswith('{'):
|
||
# 统计括号深度
|
||
depth = 0
|
||
end_pos = -1
|
||
for i, c in enumerate(json_text):
|
||
if c == '{':
|
||
depth += 1
|
||
elif c == '}':
|
||
depth -= 1
|
||
if depth == 0:
|
||
end_pos = i + 1
|
||
break
|
||
|
||
if end_pos > 0:
|
||
fixed = json_text[:end_pos]
|
||
logger.info(f"修复 JSON (配对括号): {fixed[:200]}")
|
||
return fixed
|
||
|
||
# 如果找不到配对，尝试移除 trailing comma 和其他问题
|
||
# 移除末尾多余的逗号
|
||
fixed = re.sub(r',\s*([}\]])', r'\1', json_text)
|
||
# 确保以 } 结尾
|
||
fixed = fixed.strip()
|
||
if fixed and not fixed.endswith('}') and not fixed.endswith(']'):
|
||
# 尝试补全
|
||
if fixed.startswith('{') and not fixed.endswith('}'):
|
||
fixed = fixed + '}'
|
||
elif fixed.startswith('[') and not fixed.endswith(']'):
|
||
fixed = fixed + ']'
|
||
logger.info(f"修复 JSON (正则): {fixed[:200]}")
|
||
return fixed
|
||
|
||
# 如果以 [ 开头
|
||
elif json_text.startswith('['):
|
||
depth = 0
|
||
end_pos = -1
|
||
for i, c in enumerate(json_text):
|
||
if c == '[':
|
||
depth += 1
|
||
elif c == ']':
|
||
depth -= 1
|
||
if depth == 0:
|
||
end_pos = i + 1
|
||
break
|
||
|
||
if end_pos > 0:
|
||
fixed = json_text[:end_pos]
|
||
logger.info(f"修复 JSON (数组配对): {fixed[:200]}")
|
||
return fixed
|
||
|
||
return ""
|
||
|
||
def _extract_values_by_regex(self, text: str) -> List[str]:
|
||
"""
|
||
使用正则从损坏/不完整的 JSON 文本中提取 values 数组
|
||
|
||
即使 JSON 被截断，只要能看到 "values": [...] 就能提取
|
||
|
||
Args:
|
||
text: 原始文本
|
||
|
||
Returns:
|
||
值列表
|
||
"""
|
||
import re
|
||
|
||
# 方法1: 查找 "values": [ 开始的位置
|
||
values_start = re.search(r'"values"\s*:\s*\[', text)
|
||
if values_start:
|
||
# 从 [ 之后开始提取内容
|
||
start_pos = values_start.end()
|
||
remaining = text[start_pos:]
|
||
|
||
# 提取所有被双引号包裹的字符串值
|
||
# 使用简单正则：匹配 "..." 捕获引号内的内容
|
||
values = re.findall(r'"([^"]+)"', remaining)
|
||
|
||
if values:
|
||
# 过滤掉空字符串和很短的（可能是键名）
|
||
filtered = [v.strip() for v in values if v.strip() and len(v) > 1]
|
||
if filtered:
|
||
logger.info(f"正则提取到 {len(filtered)} 个值: {filtered[:3]}")
|
||
return filtered
|
||
|
||
# 方法2: 备选 - 直接查找所有 : "value" 格式的值
|
||
all_strings = re.findall(r':\s*"([^"]{1,200})"', text)
|
||
if all_strings:
|
||
filtered = [s for s in all_strings if s and len(s) < 500]
|
||
if filtered:
|
||
logger.info(f"备选正则提取到 {len(filtered)} 个值: {filtered[:3]}")
|
||
return filtered
|
||
|
||
return []
|
||
|
||
def _extract_values_from_text(self, text: str, field_name: str) -> List[str]:
|
||
"""
|
||
从非 JSON 文本中提取多个字段值
|
||
|
||
Args:
|
||
text: 原始文本
|
||
field_name: 字段名称
|
||
|
||
Returns:
|
||
提取的值列表
|
||
"""
|
||
import re
|
||
import json
|
||
|
||
# 先尝试解析整个文本为 JSON，检查是否包含嵌套的 values 数组
|
||
cleaned_text = text.strip()
|
||
# 移除可能的 markdown 代码块标记
|
||
cleaned_text = cleaned_text.replace('```json', '').replace('```', '').strip()
|
||
|
||
try:
|
||
# 尝试解析整个文本为 JSON
|
||
parsed = json.loads(cleaned_text)
|
||
if isinstance(parsed, dict):
|
||
# 如果是 {"values": [...]} 格式，提取 values
|
||
if "values" in parsed and isinstance(parsed["values"], list):
|
||
return [str(v).strip() for v in parsed["values"] if v and str(v).strip()]
|
||
# 如果是其他 dict 格式，尝试找 values 键
|
||
for key in ["values", "value", "data", "result"]:
|
||
if key in parsed and isinstance(parsed[key], list):
|
||
return [str(v).strip() for v in parsed[key] if v and str(v).strip()]
|
||
elif key in parsed:
|
||
return [str(parsed[key]).strip()]
|
||
elif isinstance(parsed, list):
|
||
return [str(v).strip() for v in parsed if v and str(v).strip()]
|
||
except (json.JSONDecodeError, TypeError):
|
||
pass
|
||
|
||
# 尝试匹配 JSON 数组格式
|
||
array_match = re.search(r'\[[\s\S]*?\]', text)
|
||
if array_match:
|
||
try:
|
||
arr = json.loads(array_match.group())
|
||
if isinstance(arr, list):
|
||
# 检查数组元素是否是 {"values": [...]} 结构
|
||
if arr and isinstance(arr[0], dict) and "values" in arr[0]:
|
||
# 提取嵌套的 values
|
||
result = []
|
||
for item in arr:
|
||
if isinstance(item, dict) and "values" in item and isinstance(item["values"], list):
|
||
result.extend([str(v).strip() for v in item["values"] if v and str(v).strip()])
|
||
elif isinstance(item, dict):
|
||
result.append(str(item))
|
||
else:
|
||
result.append(str(item))
|
||
if result:
|
||
return result
|
||
return [str(v).strip() for v in arr if v and str(v).strip()]
|
||
except:
|
||
pass
|
||
|
||
# 尝试用分号分割（如果文本中有分号分隔的多个值）
|
||
if '；' in text or ';' in text:
|
||
separator = '；' if '；' in text else ';'
|
||
parts = text.split(separator)
|
||
values = []
|
||
for part in parts:
|
||
part = part.strip()
|
||
if part and len(part) < 500:
|
||
# 清理 Markdown 格式
|
||
part = re.sub(r'^\*\*|\*\*$', '', part)
|
||
part = re.sub(r'^\*|\*$', '', part)
|
||
values.append(part.strip())
|
||
if values:
|
||
return values
|
||
|
||
# 尝试多种模式匹配
|
||
patterns = [
|
||
# "字段名: 值" 或 "字段名：值" 格式
|
||
rf'{re.escape(field_name)}[：:]\s*(.+?)(?:\n|$)',
|
||
# "值" 在引号中
|
||
rf'"value"\s*:\s*"([^"]+)"',
|
||
# "值" 在单引号中
|
||
rf"['\"]?value['\"]?\s*:\s*['\"]([^'\"]+)['\"]",
|
||
]
|
||
|
||
for pattern in patterns:
|
||
match = re.search(pattern, text, re.DOTALL)
|
||
if match:
|
||
value = match.group(1).strip()
|
||
# 清理 Markdown 格式
|
||
value = re.sub(r'^\*\*|\*\*$', '', value)
|
||
value = re.sub(r'^\*|\*$', '', value)
|
||
value = value.strip()
|
||
if value and len(value) < 1000:
|
||
return [value]
|
||
|
||
# 如果无法匹配，返回原始内容
|
||
content = text.strip()[:500] if text.strip() else ""
|
||
return [content] if content else []
|
||
|
||
|
||
# ==================== 全局单例 ====================
|
||
|
||
template_fill_service = TemplateFillService()
|