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from toolbox import CatchException, update_ui |
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from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive, input_clipping |
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import requests |
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from bs4 import BeautifulSoup |
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from request_llms.bridge_all import model_info |
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def bing_search(query, proxies=None): |
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query = query |
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url = f"https://cn.bing.com/search?q={query}" |
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headers = {'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/94.0.4606.61 Safari/537.36'} |
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response = requests.get(url, headers=headers, proxies=proxies) |
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soup = BeautifulSoup(response.content, 'html.parser') |
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results = [] |
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for g in soup.find_all('li', class_='b_algo'): |
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anchors = g.find_all('a') |
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if anchors: |
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link = anchors[0]['href'] |
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if not link.startswith('http'): |
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continue |
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title = g.find('h2').text |
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item = {'title': title, 'link': link} |
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results.append(item) |
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for r in results: |
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print(r['link']) |
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return results |
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def scrape_text(url, proxies) -> str: |
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"""Scrape text from a webpage |
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Args: |
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url (str): The URL to scrape text from |
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Returns: |
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str: The scraped text |
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""" |
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headers = { |
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'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/94.0.4606.61 Safari/537.36', |
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'Content-Type': 'text/plain', |
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} |
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try: |
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response = requests.get(url, headers=headers, proxies=proxies, timeout=8) |
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if response.encoding == "ISO-8859-1": response.encoding = response.apparent_encoding |
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except: |
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return "无法连接到该网页" |
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soup = BeautifulSoup(response.text, "html.parser") |
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for script in soup(["script", "style"]): |
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script.extract() |
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text = soup.get_text() |
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lines = (line.strip() for line in text.splitlines()) |
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chunks = (phrase.strip() for line in lines for phrase in line.split(" ")) |
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text = "\n".join(chunk for chunk in chunks if chunk) |
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return text |
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@CatchException |
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def 连接bing搜索回答问题(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request): |
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""" |
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txt 输入栏用户输入的文本，例如需要翻译的一段话，再例如一个包含了待处理文件的路径 |
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llm_kwargs gpt模型参数，如温度和top_p等，一般原样传递下去就行 |
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plugin_kwargs 插件模型的参数，暂时没有用武之地 |
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chatbot 聊天显示框的句柄，用于显示给用户 |
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history 聊天历史，前情提要 |
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system_prompt 给gpt的静默提醒 |
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user_request 当前用户的请求信息（IP地址等） |
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""" |
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history = [] |
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chatbot.append((f"请结合互联网信息回答以下问题：{txt}", |
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"[Local Message] 请注意，您正在调用一个[函数插件]的模板，该模板可以实现ChatGPT联网信息综合。该函数面向希望实现更多有趣功能的开发者，它可以作为创建新功能函数的模板。您若希望分享新的功能模组，请不吝PR！")) |
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yield from update_ui(chatbot=chatbot, history=history) |
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from toolbox import get_conf |
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proxies = get_conf('proxies') |
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urls = bing_search(txt, proxies) |
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history = [] |
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if len(urls) == 0: |
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chatbot.append((f"结论：{txt}", |
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"[Local Message] 受到bing限制，无法从bing获取信息！")) |
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yield from update_ui(chatbot=chatbot, history=history) |
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return |
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max_search_result = 8 |
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for index, url in enumerate(urls[:max_search_result]): |
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res = scrape_text(url['link'], proxies) |
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history.extend([f"第{index}份搜索结果：", res]) |
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chatbot.append([f"第{index}份搜索结果：", res[:500]+"......"]) |
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yield from update_ui(chatbot=chatbot, history=history) |
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i_say = f"从以上搜索结果中抽取信息，然后回答问题：{txt}" |
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i_say, history = input_clipping( |
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inputs=i_say, |
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history=history, |
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max_token_limit=model_info[llm_kwargs['llm_model']]['max_token']*3//4 |
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) |
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gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive( |
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inputs=i_say, inputs_show_user=i_say, |
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llm_kwargs=llm_kwargs, chatbot=chatbot, history=history, |
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sys_prompt="请从给定的若干条搜索结果中抽取信息，对最相关的两个搜索结果进行总结，然后回答问题。" |
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) |
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chatbot[-1] = (i_say, gpt_say) |
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history.append(i_say);history.append(gpt_say) |
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yield from update_ui(chatbot=chatbot, history=history) |
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