GPT-rjl-ad2 / crazy_functions /谷歌检索小助手.py
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from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
from toolbox import CatchException, report_execption, promote_file_to_downloadzone
from toolbox import update_ui, update_ui_lastest_msg, disable_auto_promotion, write_history_to_file
import logging
import requests
import time
import random
ENABLE_ALL_VERSION_SEARCH = True
def get_meta_information(url, chatbot, history):
import arxiv
import difflib
import re
from bs4 import BeautifulSoup
from toolbox import get_conf
from urllib.parse import urlparse
session = requests.session()
proxies, = get_conf('proxies')
headers = {
'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/116.0.0.0 Safari/537.36',
'Accept-Encoding': 'gzip, deflate, br',
'Accept-Language': 'en-US,en;q=0.9,zh-CN;q=0.8,zh;q=0.7',
'Cache-Control':'max-age=0',
'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,image/avif,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3;q=0.7',
'Connection': 'keep-alive'
}
session.proxies.update(proxies)
session.headers.update(headers)
response = session.get(url)
# 解析网页内容
soup = BeautifulSoup(response.text, "html.parser")
def string_similar(s1, s2):
return difflib.SequenceMatcher(None, s1, s2).quick_ratio()
if ENABLE_ALL_VERSION_SEARCH:
def search_all_version(url):
time.sleep(random.randint(1,5)) # 睡一会防止触发google反爬虫
response = session.get(url)
soup = BeautifulSoup(response.text, "html.parser")
for result in soup.select(".gs_ri"):
try:
url = result.select_one(".gs_rt").a['href']
except:
continue
arxiv_id = extract_arxiv_id(url)
if not arxiv_id:
continue
search = arxiv.Search(
id_list=[arxiv_id],
max_results=1,
sort_by=arxiv.SortCriterion.Relevance,
)
try: paper = next(search.results())
except: paper = None
return paper
return None
def extract_arxiv_id(url):
# 返回给定的url解析出的arxiv_id,如url未成功匹配返回None
pattern = r'arxiv.org/abs/([^/]+)'
match = re.search(pattern, url)
if match:
return match.group(1)
else:
return None
profile = []
# 获取所有文章的标题和作者
for result in soup.select(".gs_ri"):
title = result.a.text.replace('\n', ' ').replace(' ', ' ')
author = result.select_one(".gs_a").text
try:
citation = result.select_one(".gs_fl > a[href*='cites']").text # 引用次数是链接中的文本,直接取出来
except:
citation = 'cited by 0'
abstract = result.select_one(".gs_rs").text.strip() # 摘要在 .gs_rs 中的文本,需要清除首尾空格
# 首先在arxiv上搜索,获取文章摘要
search = arxiv.Search(
query = title,
max_results = 1,
sort_by = arxiv.SortCriterion.Relevance,
)
try: paper = next(search.results())
except: paper = None
is_match = paper is not None and string_similar(title, paper.title) > 0.90
# 如果在Arxiv上匹配失败,检索文章的历史版本的题目
if not is_match and ENABLE_ALL_VERSION_SEARCH:
other_versions_page_url = [tag['href'] for tag in result.select_one('.gs_flb').select('.gs_nph') if 'cluster' in tag['href']]
if len(other_versions_page_url) > 0:
other_versions_page_url = other_versions_page_url[0]
paper = search_all_version('http://' + urlparse(url).netloc + other_versions_page_url)
is_match = paper is not None and string_similar(title, paper.title) > 0.90
if is_match:
# same paper
abstract = paper.summary.replace('\n', ' ')
is_paper_in_arxiv = True
else:
# different paper
abstract = abstract
is_paper_in_arxiv = False
logging.info('[title]:' + title)
logging.info('[author]:' + author)
logging.info('[citation]:' + citation)
profile.append({
'title': title,
'author': author,
'citation': citation,
'abstract': abstract,
'is_paper_in_arxiv': is_paper_in_arxiv,
})
chatbot[-1] = [chatbot[-1][0], title + f'\n\n是否在arxiv中(不在arxiv中无法获取完整摘要):{is_paper_in_arxiv}\n\n' + abstract]
yield from update_ui(chatbot=chatbot, history=[]) # 刷新界面
return profile
@CatchException
def 谷歌检索小助手(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
disable_auto_promotion(chatbot=chatbot)
# 基本信息:功能、贡献者
chatbot.append([
"函数插件功能?",
"分析用户提供的谷歌学术(google scholar)搜索页面中,出现的所有文章: binary-husky,插件初始化中..."])
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
# 尝试导入依赖,如果缺少依赖,则给出安装建议
try:
import arxiv
import math
from bs4 import BeautifulSoup
except:
report_execption(chatbot, history,
a = f"解析项目: {txt}",
b = f"导入软件依赖失败。使用该模块需要额外依赖,安装方法```pip install --upgrade beautifulsoup4 arxiv```。")
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
return
# 清空历史,以免输入溢出
history = []
meta_paper_info_list = yield from get_meta_information(txt, chatbot, history)
if len(meta_paper_info_list) == 0:
yield from update_ui_lastest_msg(lastmsg='获取文献失败,可能触发了google反爬虫机制。',chatbot=chatbot, history=history, delay=0)
return
batchsize = 5
for batch in range(math.ceil(len(meta_paper_info_list)/batchsize)):
if len(meta_paper_info_list[:batchsize]) > 0:
i_say = "下面是一些学术文献的数据,提取出以下内容:" + \
"1、英文题目;2、中文题目翻译;3、作者;4、arxiv公开(is_paper_in_arxiv);4、引用数量(cite);5、中文摘要翻译。" + \
f"以下是信息源:{str(meta_paper_info_list[:batchsize])}"
inputs_show_user = f"请分析此页面中出现的所有文章:{txt},这是第{batch+1}批"
gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(
inputs=i_say, inputs_show_user=inputs_show_user,
llm_kwargs=llm_kwargs, chatbot=chatbot, history=[],
sys_prompt="你是一个学术翻译,请从数据中提取信息。你必须使用Markdown表格。你必须逐个文献进行处理。"
)
history.extend([ f"第{batch+1}批", gpt_say ])
meta_paper_info_list = meta_paper_info_list[batchsize:]
chatbot.append(["状态?",
"已经全部完成,您可以试试让AI写一个Related Works,例如您可以继续输入Write a \"Related Works\" section about \"你搜索的研究领域\" for me."])
msg = '正常'
yield from update_ui(chatbot=chatbot, history=history, msg=msg) # 刷新界面
path = write_history_to_file(history)
promote_file_to_downloadzone(path, chatbot=chatbot)
chatbot.append(("完成了吗?", path));
yield from update_ui(chatbot=chatbot, history=history, msg=msg) # 刷新界面