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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_llms.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 = [] | |
if len(urls) == 0: | |
chatbot.append((f"结论:{txt}", | |
"[Local Message] 受到bing限制,无法从bing获取信息!")) | |
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 | |
return | |
# ------------- < 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 | |