from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive from toolbox import CatchException, report_exception, 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' } try: session.proxies.update(proxies) except: report_exception(chatbot, history, a=f"获取代理失败 无代理状态下很可能无法访问OpenAI家族的模型及谷歌学术 建议：检查USE_PROXY选项是否修改。", b=f"尝试直接连接") yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 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_exception(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