Lord1Egypt Reading Assistant - 阅读助手
激活词: 读一下 / 总结 / 提炼 / 摘要
功能
- 4点结构化提炼：主旨 / 论据 / 行动 / 疑问
- 支持网页URL抓取和纯文本输入
- 自动保存阅读记录到知识库
Python 实现
import os, json, re
from datetime import datetime
class ReadingAssistant:
def __init__(self):
self.log_file = os.path.join(os.path.dirname(__file__), "reading_log.json")
def extract(self, text: str, source: str = "text") -> dict:
"""对输入文本做4点结构化提炼"""
# 分段落
paragraphs = [p.strip() for p in re.split(r'\n\n+', text) if p.strip()]
result = {
"source": source,
"timestamp": datetime.now().isoformat(),
"stats": {
"paragraphs": len(paragraphs),
"total_chars": len(text),
"total_words": len(text.split())
},
"extraction": {
"main_idea": self._extract_main_idea(paragraphs),
"key_arguments": self._extract_arguments(paragraphs),
"action_items": self._extract_actions(paragraphs),
"open_questions": self._extract_questions(text)
}
}
self._log(result)
return result
def _extract_main_idea(self, paragraphs: list) -> str:
"""提取主旨：取第一段或包含关键词的句子"""
candidates = [p for p in paragraphs if any(k in p.lower()
for k in ["this paper", "we propose", "in this", "本文", "我们提出"])]
if candidates:
return candidates[0][:300]
return paragraphs[0][:300] if paragraphs else ""
def _extract_arguments(self, paragraphs: list) -> list:
"""提取论据：含数据/引用的段落"""
args = []
for p in paragraphs:
if any(k in p for k in ["%", "图", "table", "实验", "accuracy", "结果表明", "实验表明"]):
args.append(p[:200])
return args[:5]
def _extract_actions(self, paragraphs: list) -> list:
"""提取行动项：含should/must/需要/应该的句子"""
actions = []
for p in paragraphs:
sentences = re.split(r'[。.!?]', p)
for s in sentences:
if any(k in s for k in ["should", "must", "need", "需要", "应该", "建议", "推荐"]):
actions.append(s.strip())
return actions[:5]
def _extract_questions(self, text: str) -> list:
"""提取开放问题：含问号的句子"""
questions = re.findall(r'[^。]*?[？?][^。]*', text)
return [q.strip() for q in questions][:5]
def _log(self, result: dict):
"""保存阅读记录"""
logs = []
if os.path.exists(self.log_file):
with open(self.log_file, encoding="utf-8") as f:
logs = json.load(f)
logs.append(result)
with open(self.log_file, "w", encoding="utf-8") as f:
json.dump(logs, f, ensure_ascii=False, indent=2)
# 使用示例
reader = ReadingAssistant()
# 从文本提炼
article = """
We propose FlashAttention, a novel attention algorithm that computes exact attention
with significantly fewer memory accesses. Our experiments show 2-4x speedup over
baseline implementations on GPT-2 and BERT. The key insight is to tile the attention
computation, avoiding materialization of the full N×N attention matrix.
We recommend using block sizes of 128×128 for optimal performance.
"""
result = reader.extract(article, source="AI论文: FlashAttention")
print(f"主旨: {result['extraction']['main_idea'][:80]}...")
print(f"论据: {len(result['extraction']['key_arguments'])} 条")
print(f"行动: {result['extraction']['action_items']}")
网页抓取模式
from urllib.request import urlopen
from urllib.parse import urlparse
def extract_url(url: str) -> dict:
"""从URL抓取并提炼"""
try:
resp = urlopen(url, timeout=10)
html = resp.read().decode("utf-8", errors="ignore")
# 简单去标签
text = re.sub(r'<[^>]+>', '', html)
text = re.sub(r'\s+', ' ', text).strip()
reader = ReadingAssistant()
return reader.extract(text, source=url)
except Exception as e:
return {"error": str(e)}
# 使用示例
# result = extract_url("https://arxiv.org/abs/2205.14135")
输出格式
📄 阅读摘要
━━━━━━━━━━━━━━━━━━━━
💡 主旨:
FlashAttention通过分块计算注意力，
避免完整N×N矩阵物化，实现2-4x加速
📊 论据:
1. GPT-2和BERT上验证2-4x加速
2. 推荐块大小128×128
✅ 行动:
- 建议在长序列任务中使用FlashAttention
❓ 疑问:
- 是否支持梯度检查点？
使用场景
- 论文快速浏览: 贴arXiv链接，30秒了解核心贡献
- 公众号文章: 太长不看？提炼4点够了
- 技术文档: API文档的快速导航
- 邮件处理: 长邮件自动摘要
依赖
- Python 3.8+
- urllib (标准库，可选URL抓取)