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from typing import List, Dict
from src.core.html_processor import HTMLProcessor
from src.api.llm_api import LLMInterface
import logging
from concurrent.futures import ThreadPoolExecutor, as_completed
class DocumentProcessor:
def __init__(self, llm: LLMInterface):
self.html_processor = HTMLProcessor()
self.llm = llm
# 添加缓存
self.cache = {}
def _clean_text(self, text: str) -> str:
"""清理文本内容"""
import re
# 移除多余空白
text = re.sub(r'\s+', ' ', text)
# 移除特殊字符
text = re.sub(r'[^\w\s\u4e00-\u9fff。，！？、]', '', text)
return text.strip()
def process_documents(self, search_results: List[Dict]) -> List[Dict]:
processed_docs = []
batch_size = 5 # 批处理大小
# 并行处理文档
with ThreadPoolExecutor(max_workers=5) as executor:
futures = []
for result in search_results:
if result['url'] in self.cache:
processed_docs.append(self.cache[result['url']])
continue
futures.append(
executor.submit(self._process_single_doc, result)
)
for future in as_completed(futures):
try:
doc = future.result()
if doc:
self.cache[doc['url']] = doc
processed_docs.append(doc)
except Exception as e:
logging.error(f"处理文档失败: {str(e)}")
return processed_docs[:5] # 限制返回数量
def _process_single_doc(self, result: Dict) -> Dict:
try:
# 提取HTML内容
html = self.html_processor.fetch_html(result['url'])
if not html:
return None
# 提取主要内容
content = self.html_processor.extract_main_content(html)
content = self._clean_text(content)
if len(content) < 100: # 内容太短
return None
# 生成更有针对性的总结
summary = self.llm.summarize_document(
content=content,
title=result.get('title', ''),
url=result['url']
)
if summary:
return {
'passage': summary,
'title': result.get('title', ''),
'url': result['url']
}
except Exception as e:
logging.error(f"处理文档失败 ({result['url']}): {str(e)}")
return None |