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| from toolbox import CatchException, update_ui | |
| from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive, input_clipping | |
| import requests | |
| from bs4 import BeautifulSoup | |
| from request_llm.bridge_all import model_info | |
| def bing_search(query, proxies=None): | |
| query = query | |
| url = f"https://cn.bing.com/search?q={query}" | |
| 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'} | |
| response = requests.get(url, headers=headers, proxies=proxies) | |
| soup = BeautifulSoup(response.content, 'html.parser') | |
| results = [] | |
| for g in soup.find_all('li', class_='b_algo'): | |
| anchors = g.find_all('a') | |
| if anchors: | |
| link = anchors[0]['href'] | |
| if not link.startswith('http'): | |
| continue | |
| title = g.find('h2').text | |
| item = {'title': title, 'link': link} | |
| results.append(item) | |
| for r in results: | |
| print(r['link']) | |
| return results | |
| def scrape_text(url, proxies) -> str: | |
| """Scrape text from a webpage | |
| Args: | |
| url (str): The URL to scrape text from | |
| Returns: | |
| str: The scraped text | |
| """ | |
| 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', | |
| 'Content-Type': 'text/plain', | |
| } | |
| try: | |
| response = requests.get(url, headers=headers, proxies=proxies, timeout=8) | |
| if response.encoding == "ISO-8859-1": response.encoding = response.apparent_encoding | |
| except: | |
| return "无法连接到该网页" | |
| soup = BeautifulSoup(response.text, "html.parser") | |
| for script in soup(["script", "style"]): | |
| script.extract() | |
| text = soup.get_text() | |
| lines = (line.strip() for line in text.splitlines()) | |
| chunks = (phrase.strip() for line in lines for phrase in line.split(" ")) | |
| text = "\n".join(chunk for chunk in chunks if chunk) | |
| return text | |
| def 连接bing搜索回答问题(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port): | |
| """ | |
| 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 | |
| llm_kwargs gpt模型参数，如温度和top_p等，一般原样传递下去就行 | |
| plugin_kwargs 插件模型的参数，暂时没有用武之地 | |
| chatbot 聊天显示框的句柄，用于显示给用户 | |
| history 聊天历史，前情提要 | |
| system_prompt 给gpt的静默提醒 | |
| web_port 当前软件运行的端口号 | |
| """ | |
| history = [] # 清空历史，以免输入溢出 | |
| chatbot.append((f"请结合互联网信息回答以下问题：{txt}", | |
| "[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！")) | |
| yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 由于请求gpt需要一段时间，我们先及时地做一次界面更新 | |
| # ------------- < 第1步：爬取搜索引擎的结果 > ------------- | |
| from toolbox import get_conf | |
| proxies, = get_conf('proxies') | |
| urls = bing_search(txt, proxies) | |
| history = [] | |
| # ------------- < 第2步：依次访问网页 > ------------- | |
| max_search_result = 8 # 最多收纳多少个网页的结果 | |
| for index, url in enumerate(urls[:max_search_result]): | |
| res = scrape_text(url['link'], proxies) | |
| history.extend([f"第{index}份搜索结果：", res]) | |
| chatbot.append([f"第{index}份搜索结果：", res[:500]+"......"]) | |
| yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 由于请求gpt需要一段时间，我们先及时地做一次界面更新 | |
| # ------------- < 第3步：ChatGPT综合 > ------------- | |
| i_say = f"从以上搜索结果中抽取信息，然后回答问题：{txt}" | |
| i_say, history = input_clipping( # 裁剪输入，从最长的条目开始裁剪，防止爆token | |
| inputs=i_say, | |
| history=history, | |
| max_token_limit=model_info[llm_kwargs['llm_model']]['max_token']*3//4 | |
| ) | |
| gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive( | |
| inputs=i_say, inputs_show_user=i_say, | |
| llm_kwargs=llm_kwargs, chatbot=chatbot, history=history, | |
| 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。" | |
| ) | |
| chatbot[-1] = (i_say, gpt_say) | |
| history.append(i_say);history.append(gpt_say) | |
| yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 界面更新 | |