1104 lines
46 KiB
Python
1104 lines
46 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"""
|
||
cell: str # 单元格位置，如 "A1"
|
||
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)
|
||
ai_structured_data: Optional[Dict[str, Any]] = None # AI 结构化分析结果缓存
|
||
|
||
|
||
@dataclass
|
||
class FillResult:
|
||
"""填写结果"""
|
||
field: str
|
||
values: List[Any] = None # 支持多个值
|
||
value: Any = "" # 保留兼容
|
||
source: str = "" # 来源文档
|
||
confidence: float = 1.0 # 置信度
|
||
|
||
def __post_init__(self):
|
||
if self.values is None:
|
||
self.values = []
|
||
|
||
|
||
class TemplateFillService:
|
||
"""表格填写服务"""
|
||
|
||
def __init__(self):
|
||
self.llm = llm_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]:
|
||
"""
|
||
填写表格模板
|
||
|
||
Args:
|
||
template_fields: 模板字段列表
|
||
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:
|
||
填写结果
|
||
"""
|
||
filled_data = {}
|
||
fill_details = []
|
||
|
||
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")
|
||
|
||
# 1. 加载源文档内容
|
||
source_docs = await self._load_source_documents(source_doc_ids, source_file_paths)
|
||
|
||
logger.info(f"加载了 {len(source_docs)} 个源文档")
|
||
for doc in source_docs:
|
||
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)}")
|
||
|
||
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 idx, field in enumerate(template_fields):
|
||
try:
|
||
logger.info(f"提取字段 [{idx+1}/{len(template_fields)}]: {field.name}")
|
||
# 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 - 使用 values 数组
|
||
filled_data[field.name] = result.values if result.values else [""]
|
||
fill_details.append({
|
||
"field": field.name,
|
||
"cell": field.cell,
|
||
"values": result.values,
|
||
"value": result.value,
|
||
"source": result.source,
|
||
"confidence": result.confidence
|
||
})
|
||
|
||
logger.info(f"字段 {field.name} 填写完成: {len(result.values)} 个值")
|
||
|
||
except Exception as e:
|
||
logger.error(f"填写字段 {field.name} 失败: {str(e)}", exc_info=True)
|
||
filled_data[field.name] = [f"[提取失败: {str(e)}]"]
|
||
fill_details.append({
|
||
"field": field.name,
|
||
"cell": field.cell,
|
||
"values": [f"[提取失败]"],
|
||
"value": f"[提取失败]",
|
||
"source": "error",
|
||
"confidence": 0.0
|
||
})
|
||
|
||
# 计算最大行数
|
||
max_rows = max(len(v) for v in filled_data.values()) if filled_data else 1
|
||
logger.info(f"填表完成: {len(filled_data)} 个字段, 最大行数: {max_rows}")
|
||
|
||
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]:
|
||
"""
|
||
加载源文档内容，并对 TXT 文件进行 AI 结构化分析
|
||
|
||
Args:
|
||
source_doc_ids: MongoDB 文档 ID 列表
|
||
source_file_paths: 源文档文件路径列表
|
||
|
||
Returns:
|
||
源文档列表
|
||
"""
|
||
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:
|
||
doc_type = doc.get("doc_type", "unknown")
|
||
content = doc.get("content", "")
|
||
|
||
# 对 TXT 文档进行 AI 结构化分析
|
||
ai_structured = None
|
||
if doc_type == "txt" and content:
|
||
logger.info(f"MongoDB TXT 文档需要 AI 分析: {doc_id}, 内容长度: {len(content)}")
|
||
ai_structured = await self._analyze_txt_once(content, doc.get("metadata", {}).get("original_filename", "unknown"))
|
||
logger.info(f"AI 分析结果: has_data={ai_structured is not None}")
|
||
|
||
source_docs.append(SourceDocument(
|
||
doc_id=doc_id,
|
||
filename=doc.get("metadata", {}).get("original_filename", "unknown"),
|
||
doc_type=doc_type,
|
||
content=content,
|
||
structured_data=doc.get("structured_data", {}),
|
||
ai_structured_data=ai_structured
|
||
))
|
||
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:
|
||
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:
|
||
logger.info(f" 加载文件: {file_path}")
|
||
parser = ParserFactory.get_parser(file_path)
|
||
result = parser.parse(file_path)
|
||
logger.info(f" 解析结果: success={result.success}, error={result.error}")
|
||
if result.success:
|
||
doc_data = result.data if result.data else {}
|
||
doc_content = doc_data.get("content", "") if isinstance(doc_data, dict) else ""
|
||
|
||
# 检查并提取 structured_data
|
||
doc_structured = {}
|
||
if isinstance(doc_data, dict):
|
||
# Excel 多 sheet
|
||
if "sheets" in doc_data:
|
||
doc_structured = doc_data
|
||
# Excel 单 sheet 或有 rows 的格式
|
||
elif "rows" in doc_data:
|
||
doc_structured = doc_data
|
||
# Markdown 格式
|
||
elif "tables" in doc_data and doc_data["tables"]:
|
||
tables = doc_data["tables"]
|
||
first_table = tables[0]
|
||
doc_structured = {
|
||
"headers": first_table.get("headers", []),
|
||
"rows": first_table.get("rows", [])
|
||
}
|
||
elif "structured_data" in doc_data and isinstance(doc_data["structured_data"], dict):
|
||
tables = doc_data["structured_data"].get("tables", [])
|
||
if tables:
|
||
first_table = tables[0]
|
||
doc_structured = {
|
||
"headers": first_table.get("headers", []),
|
||
"rows": first_table.get("rows", [])
|
||
}
|
||
|
||
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)}")
|
||
|
||
# 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
|
||
table = None
|
||
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:
|
||
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 ==========
|
||
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
|
||
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 ==========
|
||
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...")
|
||
|
||
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}")
|
||
|
||
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:
|
||
extracted_values = self._extract_values_from_text(cleaned, field.name)
|
||
|
||
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:
|
||
doc_content = ""
|
||
row_count = 0
|
||
|
||
# Excel 多 sheet 格式
|
||
if doc.structured_data and doc.structured_data.get("sheets"):
|
||
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"
|
||
if field_name:
|
||
target_col = self._find_best_matching_column(columns, field_name)
|
||
if target_col:
|
||
doc_content += f"列名: {columns[target_col]}\n"
|
||
for row_idx, row in enumerate(rows):
|
||
if isinstance(row, dict):
|
||
val = row.get(columns[target_col], "")
|
||
elif isinstance(row, list) and target_col < len(row):
|
||
val = row[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
|
||
|
||
# Excel 单 sheet 格式
|
||
elif doc.structured_data and doc.structured_data.get("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 = self._find_best_matching_column(columns, field_name)
|
||
if target_col:
|
||
doc_content += f"列名: {columns[target_col]}\n"
|
||
for row_idx, row in enumerate(rows):
|
||
if isinstance(row, dict):
|
||
val = row.get(columns[target_col], "")
|
||
elif isinstance(row, list) and target_col < len(row):
|
||
val = row[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
|
||
|
||
# Markdown 表格格式
|
||
elif doc.structured_data and doc.structured_data.get("tables"):
|
||
tables = doc.structured_data.get("tables", [])
|
||
for table in tables:
|
||
if isinstance(table, dict):
|
||
headers = table.get("headers", [])
|
||
rows = table.get("rows", [])
|
||
if rows and headers:
|
||
doc_content += f"\n【文档: {doc.filename} - 表格】\n"
|
||
doc_content += " | ".join(str(h) for h in headers) + "\n"
|
||
for row in rows:
|
||
if 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:
|
||
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: 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:
|
||
target_col = self._find_best_matching_column(columns, field_name)
|
||
if target_col:
|
||
doc_content += f"列名: {columns[target_col]}\n"
|
||
for row_idx, row in enumerate(rows):
|
||
if isinstance(row, list) and target_col < len(row):
|
||
val = row[target_col]
|
||
else:
|
||
val = str(row.get(columns[target_col], "")) if isinstance(row, dict) else ""
|
||
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, 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:
|
||
doc_content = doc.content[:8000]
|
||
logger.warning(f"TXT AI 结果无表格，使用原始内容")
|
||
elif doc.doc_type == "txt":
|
||
doc_content = doc.content[:8000]
|
||
logger.info(f"使用 TXT 原始内容: {doc.filename}, 长度: {len(doc_content)}")
|
||
else:
|
||
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)
|
||
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
|
||
|
||
def _find_best_matching_column(self, headers: List, field_name: str) -> Optional[int]:
|
||
"""查找最佳匹配的列索引"""
|
||
field_lower = field_name.lower().strip()
|
||
field_keywords = set(field_lower.replace(" ", "").split())
|
||
|
||
best_match_idx = None
|
||
best_match_score = 0
|
||
|
||
for idx, header in enumerate(headers):
|
||
header_str = str(header).strip()
|
||
header_lower = header_str.lower()
|
||
|
||
# 精确匹配
|
||
if header_lower == field_lower:
|
||
return idx
|
||
|
||
# 子字符串匹配
|
||
if field_lower in header_lower or header_lower in field_lower:
|
||
score = max(len(field_lower), len(header_lower)) / min(len(field_lower) + 1, len(header_lower) + 1)
|
||
if score > best_match_score:
|
||
best_match_score = score
|
||
best_match_idx = idx
|
||
continue
|
||
|
||
# 关键词重叠匹配
|
||
header_keywords = set(header_lower.replace(" ", "").split())
|
||
overlap = field_keywords & header_keywords
|
||
if overlap and len(overlap) > 0:
|
||
score = len(overlap) / max(len(field_keywords), len(header_keywords), 1)
|
||
if score > best_match_score:
|
||
best_match_score = score
|
||
best_match_idx = idx
|
||
|
||
if best_match_score >= 0.3:
|
||
return best_match_idx
|
||
|
||
return None
|
||
|
||
def _extract_values_from_structured_data(self, source_docs: List[SourceDocument], field_name: str) -> List[str]:
|
||
"""从结构化数据或 AI 结构化分析结果中直接提取指定列的值"""
|
||
all_values = []
|
||
logger.info(f"[_extract_values_from_structured_data] 开始提取字段: {field_name}, 文档数: {len(source_docs)}")
|
||
|
||
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:
|
||
target_idx = self._find_best_matching_column(columns, field_name)
|
||
if target_idx is not None:
|
||
values = []
|
||
for row in rows:
|
||
if isinstance(row, list) and target_idx < len(row):
|
||
val = row[target_idx]
|
||
elif isinstance(row, dict):
|
||
val = row.get(columns[target_idx], "")
|
||
else:
|
||
val = ""
|
||
if val:
|
||
values.append(str(val).strip())
|
||
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
|
||
|
||
# 从 structured_data 中提取
|
||
structured = doc.structured_data
|
||
if not structured:
|
||
continue
|
||
|
||
# 多 sheet 格式
|
||
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", [])
|
||
if rows and columns:
|
||
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")
|
||
return all_values
|
||
|
||
# Markdown 表格格式
|
||
elif structured.get("headers") and structured.get("rows"):
|
||
headers = structured.get("headers", [])
|
||
rows = structured.get("rows", [])
|
||
values = self._extract_column_values(rows, headers, field_name)
|
||
if values:
|
||
all_values.extend(values)
|
||
logger.info(f"从 Markdown 文档提取到 {len(values)} 个值")
|
||
return all_values
|
||
|
||
# 单 sheet 格式
|
||
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")
|
||
return all_values
|
||
|
||
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"""
|
||
if not rows or not columns:
|
||
return []
|
||
|
||
target_idx = self._find_best_matching_column(columns, field_name)
|
||
if target_idx is None:
|
||
logger.warning(f"未找到匹配列: {field_name}, 可用列: {columns}")
|
||
return []
|
||
|
||
target_col = columns[target_idx]
|
||
logger.info(f"列匹配成功: {field_name} -> {target_col} (索引: {target_idx})")
|
||
|
||
values = []
|
||
for row in rows:
|
||
if isinstance(row, dict):
|
||
val = row.get(target_col, "")
|
||
elif isinstance(row, list) and target_idx < len(row):
|
||
val = row[target_idx]
|
||
else:
|
||
val = ""
|
||
if val is not None and str(val).strip():
|
||
values.append(str(val).strip())
|
||
|
||
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"""
|
||
if isinstance(result, dict):
|
||
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
|
||
if "value" in result:
|
||
val = str(result["value"]).strip()
|
||
if val:
|
||
return [val]
|
||
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 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"""
|
||
import re
|
||
|
||
if 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:
|
||
return json_text[:end_pos]
|
||
|
||
fixed = re.sub(r',\s*([}\]])', r'\1', json_text)
|
||
fixed = fixed.strip()
|
||
if fixed and not fixed.endswith('}') and not fixed.endswith(']'):
|
||
if fixed.startswith('{') and not fixed.endswith('}'):
|
||
fixed = fixed + '}'
|
||
elif fixed.startswith('[') and not fixed.endswith(']'):
|
||
fixed = fixed + ']'
|
||
return fixed
|
||
|
||
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:
|
||
return json_text[:end_pos]
|
||
|
||
return ""
|
||
|
||
def _extract_values_by_regex(self, text: str) -> List[str]:
|
||
"""使用正则从文本中提取 values 数组"""
|
||
import re
|
||
|
||
values_start = re.search(r'"values"\s*:\s*\[', text)
|
||
if values_start:
|
||
start_pos = values_start.end()
|
||
remaining = text[start_pos:]
|
||
values = re.findall(r'"([^"]+)"', remaining)
|
||
if values:
|
||
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")
|
||
return filtered
|
||
|
||
return []
|
||
|
||
def _extract_values_from_text(self, text: str, field_name: str) -> List[str]:
|
||
"""从非 JSON 文本中提取字段值"""
|
||
import re
|
||
import json
|
||
|
||
cleaned_text = text.strip().replace('```json', '').replace('```', '').strip()
|
||
|
||
try:
|
||
parsed = json.loads(cleaned_text)
|
||
if isinstance(parsed, dict):
|
||
if "values" in parsed and isinstance(parsed["values"], list):
|
||
return [str(v).strip() for v in parsed["values"] if v and str(v).strip()]
|
||
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
|
||
|
||
# 尝试用分号分割
|
||
if '；' in text or ';' in text:
|
||
separator = '；' if '；' in text else ';'
|
||
parts = [p.strip() for p in text.split(separator) if p.strip() and len(p.strip()) < 500]
|
||
if parts:
|
||
return parts
|
||
|
||
# 尝试正则匹配
|
||
patterns = [
|
||
rf'{re.escape(field_name)}[：:]\s*(.+?)(?:\n|$)',
|
||
rf'"value"\s*:\s*"([^"]+)"',
|
||
]
|
||
|
||
for pattern in patterns:
|
||
match = re.search(pattern, text, re.DOTALL)
|
||
if match:
|
||
value = match.group(1).strip()
|
||
if value and len(value) < 1000:
|
||
return [value]
|
||
|
||
content = text.strip()[:500] if text.strip() else ""
|
||
return [content] if content else []
|
||
|
||
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"""
|
||
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
|
||
|
||
try:
|
||
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: {e}")
|
||
df = pd.read_excel(file_path, header=None, nrows=5)
|
||
if df.shape[1] > 0:
|
||
first_row = df.iloc[0].tolist()
|
||
if all(pd.notna(v) and str(v).strip() != '' for v in first_row):
|
||
df.columns = [str(v) if pd.notna(v) else f"列{i}" for i, v in enumerate(first_row)]
|
||
df = df.iloc[1:]
|
||
else:
|
||
df.columns = [f"列{i}" for i in range(df.shape[1])]
|
||
|
||
if len(df.columns) == 0 or (len(df.columns) == 1 and df.columns[0] == 0):
|
||
df_full = pd.read_excel(file_path, header=None)
|
||
if df_full.shape[1] > 0:
|
||
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]
|
||
|
||
if cells and cells[0]:
|
||
field_name = cells[0]
|
||
hint = cells[1] if len(cells) > 1 else ""
|
||
|
||
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"""
|
||
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"]
|
||
hint_lower = hint.lower()
|
||
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)
|
||
|
||
import re
|
||
if re.search(r'\d{4}[年/-]\d{1,2}[月/-]\d{1,2}', value_str):
|
||
return "date"
|
||
|
||
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
|
||
|
||
|
||
# ==================== 全局单例 ====================
|
||
|
||
template_fill_service = TemplateFillService()
|