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chore: import upstream snapshot with attribution
2026-07-13 12:32:26 +08:00

275 lines
10 KiB
Python

"""
PP-Structure-V3 文档解析器
使用 PP-Structure-V3 进行文档版面解析和内容提取
"""
import base64
import os
import time
from pathlib import Path
from typing import Any
import requests
from yuxi.knowledge.parser.base import BaseDocumentProcessor, DocumentParserException
from yuxi.utils import logger
class PPStructureV3Parser(BaseDocumentProcessor):
"""PP-Structure-V3 文档解析器 - 使用 PP-Structure-V3 进行版面解析"""
def __init__(self, server_url: str | None = None):
self.server_url = server_url or os.getenv("PADDLEX_URI") or "http://localhost:8080"
self.base_url = self.server_url.rstrip("/")
self.endpoint = f"{self.base_url}/layout-parsing"
def get_service_name(self) -> str:
return "pp_structure_v3_ocr"
def get_supported_extensions(self) -> list[str]:
"""PP-Structure-V3 支持 PDF 和多种图像格式"""
return [".pdf", ".jpg", ".jpeg", ".png", ".bmp", ".tiff", ".tif"]
def _encode_file_to_base64(self, file_path: str) -> str:
"""将文件编码为Base64"""
with open(file_path, "rb") as file:
encoded = base64.b64encode(file.read()).decode("utf-8")
return encoded
def _process_file_input(self, file_input: str) -> str:
"""处理文件输入:本地文件路径、URL或Base64内容"""
# 检查是否为本地文件路径
if os.path.exists(file_input):
logger.info(f"📁 检测到本地文件: {file_input}")
logger.info(f"📏 文件大小: {os.path.getsize(file_input) / 1024 / 1024:.2f} MB")
return self._encode_file_to_base64(file_input)
# 检查是否为URL
elif file_input.startswith(("http://", "https://")):
logger.info(f"🌐 检测到URL: {file_input}")
return file_input
# 否则假设为Base64编码内容
else:
logger.info(f"📝 假设为Base64编码内容,长度: {len(file_input)} 字符")
return file_input
def _call_layout_api(
self,
file_input: str,
file_type: int | None = None,
use_table_recognition: bool = True,
use_formula_recognition: bool = True,
use_seal_recognition: bool = False,
**kwargs,
) -> dict[str, Any]:
"""调用PP-Structure-V3版面解析API"""
# 处理文件输入
processed_file_input = self._process_file_input(file_input)
payload = {"file": processed_file_input}
# 添加核心参数
optional_params = {
"fileType": file_type,
"useTableRecognition": use_table_recognition,
"useFormulaRecognition": use_formula_recognition,
"useSealRecognition": use_seal_recognition,
}
# 添加非空参数
for key, value in optional_params.items():
if value is not None:
payload[key] = value
# 添加其他kwargs参数
for key, value in kwargs.items():
if value is not None:
payload[key] = value
response = requests.post(self.endpoint, json=payload, headers={"Content-Type": "application/json"}, timeout=300)
if response.status_code == 200:
return response.json()
else:
error_msg = f"PP-Structure-V3 API请求失败: {response.status_code}"
try:
error_result = response.json()
raise DocumentParserException(f"{error_msg}: {error_result}", self.get_service_name(), "api_error")
except Exception:
raise DocumentParserException(f"{error_msg}: {response.text}", self.get_service_name(), "api_error")
def _parse_api_result(self, api_result: dict[str, Any], file_path: str) -> dict[str, Any]:
"""解析API返回结果"""
# 基本信息
parsed_result = {
"success": True,
"file_path": file_path,
"file_name": os.path.basename(file_path),
"log_id": api_result.get("logId"),
"total_pages": 0,
"pages": [],
"full_text": "",
"summary": {},
}
result_data = api_result.get("result", {})
layout_results = result_data.get("layoutParsingResults", [])
# 数据信息
parsed_result["total_pages"] = len(layout_results)
# 统计信息
total_tables = 0
total_formulas = 0
all_text_content = []
# 解析每页结果
for page_result in layout_results:
# Markdown内容
if "markdown" in page_result:
markdown = page_result["markdown"]
if markdown.get("text"):
all_text_content.append(markdown["text"])
# 详细识别结果
if "prunedResult" in page_result:
pruned = page_result["prunedResult"]
# 表格识别
table_result = pruned.get("table_result", [])
total_tables += len(table_result)
# 公式识别
formula_result = pruned.get("formula_result", [])
total_formulas += len(formula_result)
# 汇总全文内容
parsed_result["full_text"] = "\n\n".join(all_text_content)
# 汇总统计信息
parsed_result["summary"] = {
"total_tables": total_tables,
"total_formulas": total_formulas,
"total_characters": len(parsed_result["full_text"]),
}
return parsed_result
def check_health(self) -> dict:
"""检查 PP-Structure-V3 服务健康状态"""
try:
response = requests.get(f"{self.base_url}/health", timeout=5)
if response.status_code == 200:
return {
"status": "healthy",
"message": "PP-Structure-V3 服务运行正常",
"details": {"server_url": self.server_url},
}
else:
return {
"status": "unhealthy",
"message": f"PP-Structure-V3 服务响应异常: {response.status_code}",
"details": {"server_url": self.server_url},
}
except requests.exceptions.ConnectionError:
return {
"status": "unavailable",
"message": "PP-Structure-V3 服务无法连接,请检查服务是否启动",
"details": {"server_url": self.server_url},
}
except requests.exceptions.Timeout:
return {
"status": "timeout",
"message": "PP-Structure-V3 服务连接超时",
"details": {"server_url": self.server_url},
}
except Exception as e:
return {
"status": "error",
"message": f"PP-Structure-V3 健康检查失败: {str(e)}",
"details": {"server_url": self.server_url, "error": str(e)},
}
def process_file(self, file_path: str, params: dict | None = None) -> str:
"""
使用 PP-Structure-V3 处理文档
Args:
file_path: 文件路径
params: 处理参数
- use_table_recognition: 启用表格识别 (默认: True)
- use_formula_recognition: 启用公式识别 (默认: True)
- use_seal_recognition: 启用印章识别 (默认: False)
Returns:
str: 提取的 Markdown 文本
"""
if not os.path.exists(file_path):
raise DocumentParserException(f"文件不存在: {file_path}", self.get_service_name(), "file_not_found")
file_ext = Path(file_path).suffix.lower()
if not self.supports_file_type(file_ext):
raise DocumentParserException(
f"不支持的文件类型: {file_ext}", self.get_service_name(), "unsupported_file_type"
)
# 先检查服务健康状态
health = self.check_health()
if health["status"] != "healthy":
raise DocumentParserException(
f"PP-Structure-V3 服务不可用: {health['message']}", self.get_service_name(), health["status"]
)
try:
start_time = time.time()
params = params or {}
# 判断文件类型
file_type = 0 if file_ext == ".pdf" else 1
logger.info(f"PP-Structure-V3 开始处理: {os.path.basename(file_path)}")
# 调用API
api_result = self._call_layout_api(
file_input=file_path,
file_type=file_type,
use_table_recognition=params.get("use_table_recognition", True),
use_formula_recognition=params.get("use_formula_recognition", True),
use_seal_recognition=params.get("use_seal_recognition", False),
)
# 检查API调用是否成功
if api_result.get("errorCode") != 0:
raise DocumentParserException(
f"PP-Structure-V3 API错误: {api_result.get('errorMsg', '未知错误')}",
self.get_service_name(),
"api_error",
)
# 解析结果
result = self._parse_api_result(api_result, file_path)
text = result.get("full_text", "")
processing_time = time.time() - start_time
logger.info(
f"PP-Structure-V3 处理成功: {os.path.basename(file_path)} - {len(text)} 字符 ({processing_time:.2f}s)"
)
# 记录统计信息
summary = result.get("summary", {})
if summary:
logger.info(f" 统计: {summary.get('total_tables', 0)} 表格, {summary.get('total_formulas', 0)} 公式")
return text
except DocumentParserException:
raise
except Exception as e:
processing_time = time.time() - start_time
error_msg = f"PP-Structure-V3 处理失败: {str(e)}"
logger.error(f"{error_msg} ({processing_time:.2f}s)")
raise DocumentParserException(error_msg, self.get_service_name(), "processing_failed")