镜像自地址
https://github.com/binary-husky/gpt_academic.git
已同步 2025-12-06 14:36:48 +00:00
311 行
13 KiB
Python
311 行
13 KiB
Python
import requests
|
||
import random
|
||
import time
|
||
import re
|
||
import json
|
||
from bs4 import BeautifulSoup
|
||
from functools import lru_cache
|
||
from itertools import zip_longest
|
||
from check_proxy import check_proxy
|
||
from toolbox import CatchException, update_ui, get_conf, update_ui_lastest_msg
|
||
from crazy_functions.crazy_utils import request_gpt_model_in_new_thread_with_ui_alive, input_clipping
|
||
from request_llms.bridge_all import model_info
|
||
from request_llms.bridge_all import predict_no_ui_long_connection
|
||
from crazy_functions.prompts.internet import SearchOptimizerPrompt, SearchAcademicOptimizerPrompt
|
||
|
||
def search_optimizer(
|
||
query,
|
||
proxies,
|
||
history,
|
||
llm_kwargs,
|
||
optimizer=1,
|
||
categories="general",
|
||
searxng_url=None,
|
||
engines=None,
|
||
):
|
||
# ------------- < 第1步：尝试进行搜索优化 > -------------
|
||
# * 增强优化，会尝试结合历史记录进行搜索优化
|
||
if optimizer == 2:
|
||
his = " "
|
||
if len(history) == 0:
|
||
pass
|
||
else:
|
||
for i, h in enumerate(history):
|
||
if i % 2 == 0:
|
||
his += f"Q: {h}\n"
|
||
else:
|
||
his += f"A: {h}\n"
|
||
if categories == "general":
|
||
sys_prompt = SearchOptimizerPrompt.format(query=query, history=his, num=4)
|
||
elif categories == "science":
|
||
sys_prompt = SearchAcademicOptimizerPrompt.format(query=query, history=his, num=4)
|
||
else:
|
||
his = " "
|
||
if categories == "general":
|
||
sys_prompt = SearchOptimizerPrompt.format(query=query, history=his, num=3)
|
||
elif categories == "science":
|
||
sys_prompt = SearchAcademicOptimizerPrompt.format(query=query, history=his, num=3)
|
||
|
||
mutable = ["", time.time(), ""]
|
||
llm_kwargs["temperature"] = 0.8
|
||
try:
|
||
querys_json = predict_no_ui_long_connection(
|
||
inputs=query,
|
||
llm_kwargs=llm_kwargs,
|
||
history=[],
|
||
sys_prompt=sys_prompt,
|
||
observe_window=mutable,
|
||
)
|
||
except Exception:
|
||
querys_json = "1234"
|
||
#* 尝试解码优化后的搜索结果
|
||
querys_json = re.sub(r"```json|```", "", querys_json)
|
||
try:
|
||
querys = json.loads(querys_json)
|
||
except Exception:
|
||
#* 如果解码失败,降低温度再试一次
|
||
try:
|
||
llm_kwargs["temperature"] = 0.4
|
||
querys_json = predict_no_ui_long_connection(
|
||
inputs=query,
|
||
llm_kwargs=llm_kwargs,
|
||
history=[],
|
||
sys_prompt=sys_prompt,
|
||
observe_window=mutable,
|
||
)
|
||
querys_json = re.sub(r"```json|```", "", querys_json)
|
||
querys = json.loads(querys_json)
|
||
except Exception:
|
||
#* 如果再次失败，直接返回原始问题
|
||
querys = [query]
|
||
links = []
|
||
success = 0
|
||
Exceptions = ""
|
||
for q in querys:
|
||
try:
|
||
link = searxng_request(q, proxies, categories, searxng_url, engines=engines)
|
||
if len(link) > 0:
|
||
links.append(link[:-5])
|
||
success += 1
|
||
except Exception:
|
||
Exceptions = Exception
|
||
pass
|
||
if success == 0:
|
||
raise ValueError(f"在线搜索失败！\n{Exceptions}")
|
||
# * 清洗搜索结果，依次放入每组第一，第二个搜索结果，并清洗重复的搜索结果
|
||
seen_links = set()
|
||
result = []
|
||
for tuple in zip_longest(*links, fillvalue=None):
|
||
for item in tuple:
|
||
if item is not None:
|
||
link = item["link"]
|
||
if link not in seen_links:
|
||
seen_links.add(link)
|
||
result.append(item)
|
||
return result
|
||
|
||
|
||
@lru_cache
|
||
def get_auth_ip():
|
||
ip = check_proxy(None, return_ip=True)
|
||
if ip is None:
|
||
return '114.114.114.' + str(random.randint(1, 10))
|
||
return ip
|
||
|
||
|
||
def searxng_request(query, proxies, categories='general', searxng_url=None, engines=None):
|
||
if searxng_url is None:
|
||
urls = get_conf("SEARXNG_URLS")
|
||
url = random.choice(urls)
|
||
else:
|
||
url = searxng_url
|
||
|
||
if engines == "Mixed":
|
||
engines = None
|
||
|
||
if categories == 'general':
|
||
params = {
|
||
'q': query, # 搜索查询
|
||
'format': 'json', # 输出格式为JSON
|
||
'language': 'zh', # 搜索语言
|
||
'engines': engines,
|
||
}
|
||
elif categories == 'science':
|
||
params = {
|
||
'q': query, # 搜索查询
|
||
'format': 'json', # 输出格式为JSON
|
||
'language': 'zh', # 搜索语言
|
||
'categories': 'science'
|
||
}
|
||
else:
|
||
raise ValueError('不支持的检索类型')
|
||
|
||
headers = {
|
||
'Accept-Language': 'zh-CN,zh;q=0.9',
|
||
'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/58.0.3029.110 Safari/537.36',
|
||
'X-Forwarded-For': get_auth_ip(),
|
||
'X-Real-IP': get_auth_ip()
|
||
}
|
||
results = []
|
||
response = requests.post(url, params=params, headers=headers, proxies=proxies, timeout=30)
|
||
if response.status_code == 200:
|
||
json_result = response.json()
|
||
for result in json_result['results']:
|
||
item = {
|
||
"title": result.get("title", ""),
|
||
"source": result.get("engines", "unknown"),
|
||
"content": result.get("content", ""),
|
||
"link": result["url"],
|
||
}
|
||
results.append(item)
|
||
return results
|
||
else:
|
||
if response.status_code == 429:
|
||
raise ValueError("Searxng（在线搜索服务）当前使用人数太多，请稍后。")
|
||
else:
|
||
raise ValueError("在线搜索失败，状态码: " + str(response.status_code) + '\t' + response.content.decode('utf-8'))
|
||
|
||
|
||
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 internet_search_with_analysis_prompt(prompt, analysis_prompt, llm_kwargs, chatbot):
|
||
from toolbox import get_conf
|
||
proxies = get_conf('proxies')
|
||
categories = 'general'
|
||
searxng_url = None # 使用默认的searxng_url
|
||
engines = None # 使用默认的搜索引擎
|
||
yield from update_ui_lastest_msg(lastmsg=f"检索中: {prompt} ...", chatbot=chatbot, history=[], delay=1)
|
||
urls = searxng_request(prompt, proxies, categories, searxng_url, engines=engines)
|
||
yield from update_ui_lastest_msg(lastmsg=f"依次访问搜索到的网站 ...", chatbot=chatbot, history=[], delay=1)
|
||
if len(urls) == 0:
|
||
return None
|
||
max_search_result = 5 # 最多收纳多少个网页的结果
|
||
history = []
|
||
for index, url in enumerate(urls[:max_search_result]):
|
||
yield from update_ui_lastest_msg(lastmsg=f"依次访问搜索到的网站: {url['link']} ...", chatbot=chatbot, history=[], delay=1)
|
||
res = scrape_text(url['link'], proxies)
|
||
prefix = f"第{index}份搜索结果 [源自{url['source'][0]}搜索] （{url['title'][:25]}）："
|
||
history.extend([prefix, res])
|
||
i_say = f"从以上搜索结果中抽取信息，然后回答问题：{prompt} {analysis_prompt}"
|
||
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=8192
|
||
)
|
||
gpt_say = predict_no_ui_long_connection(
|
||
inputs=i_say,
|
||
llm_kwargs=llm_kwargs,
|
||
history=history,
|
||
sys_prompt="请从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。",
|
||
console_slience=False,
|
||
)
|
||
return gpt_say
|
||
|
||
@CatchException
|
||
def 连接网络回答问题(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request):
|
||
optimizer_history = history[:-8]
|
||
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}", "检索中..."))
|
||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||
|
||
# ------------- < 第1步：爬取搜索引擎的结果 > -------------
|
||
from toolbox import get_conf
|
||
proxies = get_conf('proxies')
|
||
categories = plugin_kwargs.get('categories', 'general')
|
||
searxng_url = plugin_kwargs.get('searxng_url', None)
|
||
engines = plugin_kwargs.get('engine', None)
|
||
optimizer = plugin_kwargs.get('optimizer', "关闭")
|
||
if optimizer == "关闭":
|
||
urls = searxng_request(txt, proxies, categories, searxng_url, engines=engines)
|
||
else:
|
||
urls = search_optimizer(txt, proxies, optimizer_history, llm_kwargs, optimizer, categories, searxng_url, engines)
|
||
history = []
|
||
if len(urls) == 0:
|
||
chatbot.append((f"结论：{txt}",
|
||
"[Local Message] 受到限制，无法从searxng获取信息！请尝试更换搜索引擎。"))
|
||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||
return
|
||
|
||
# ------------- < 第2步：依次访问网页 > -------------
|
||
max_search_result = 5 # 最多收纳多少个网页的结果
|
||
if optimizer == "开启(增强)":
|
||
max_search_result = 8
|
||
chatbot.append(["联网检索中 ...", None])
|
||
for index, url in enumerate(urls[:max_search_result]):
|
||
res = scrape_text(url['link'], proxies)
|
||
prefix = f"第{index}份搜索结果 [源自{url['source'][0]}搜索] （{url['title'][:25]}）："
|
||
history.extend([prefix, res])
|
||
res_squeeze = res.replace('\n', '...')
|
||
chatbot[-1] = [prefix + "\n\n" + res_squeeze[:500] + "......", None]
|
||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||
|
||
# ------------- < 第3步：ChatGPT综合 > -------------
|
||
if (optimizer != "开启(增强)"):
|
||
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=min(model_info[llm_kwargs['llm_model']]['max_token']*3//4, 8192)
|
||
)
|
||
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
|
||
|
||
#* 或者使用搜索优化器，这样可以保证后续问答能读取到有效的历史记录
|
||
else:
|
||
i_say = f"从以上搜索结果中抽取与问题：{txt} 相关的信息:"
|
||
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=min(model_info[llm_kwargs['llm_model']]['max_token']*3//4, 8192)
|
||
)
|
||
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，对最相关的三个搜索结果进行总结"
|
||
)
|
||
chatbot[-1] = (i_say, gpt_say)
|
||
history = []
|
||
history.append(i_say);history.append(gpt_say)
|
||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 界面更新
|
||
|
||
# ------------- < 第4步：根据综合回答问题 > -------------
|
||
i_say = f"请根据以上搜索结果回答问题：{txt}"
|
||
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="请根据给定的若干条搜索结果回答问题"
|
||
)
|
||
chatbot[-1] = (i_say, gpt_say)
|
||
history.append(i_say);history.append(gpt_say)
|
||
yield from update_ui(chatbot=chatbot, history=history) |