4a79aa6a93
* typo: Fix typos and rename functions across multiple files This commit addresses several minor issues: - Corrected spelling of function names (e.g., `update_ui_lastest_msg` to `update_ui_latest_msg`) - Fixed typos in comments and variable names - Corrected capitalization in some strings (e.g., "ArXiv" instead of "Arixv") - Renamed some variables for consistency - Corrected some console-related parameter names (e.g., `console_slience` to `console_silence`) The changes span multiple files across the project, including request LLM bridges, crazy functions, and utility modules. * fix: f-string expression part cannot include a backslash (#2139) * raise error when the uploaded tar contain hard/soft link (#2136) * minor bug fix * fine tune reasoning css * upgrade internet gpt plugin * Update README.md * fix GHSA-gqp5-wm97-qxcv * typo fix * update readme --------- Co-authored-by: binary-husky <96192199+binary-husky@users.noreply.github.com> Co-authored-by: binary-husky <qingxu.fu@outlook.com>
365 lines
15 KiB
Python
365 lines
15 KiB
Python
import requests
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import random
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import time
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import re
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import json
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from bs4 import BeautifulSoup
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from functools import lru_cache
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from itertools import zip_longest
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from check_proxy import check_proxy
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from toolbox import CatchException, update_ui, get_conf, update_ui_latest_msg
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from crazy_functions.crazy_utils import request_gpt_model_in_new_thread_with_ui_alive, input_clipping
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from request_llms.bridge_all import model_info
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from request_llms.bridge_all import predict_no_ui_long_connection
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from crazy_functions.prompts.internet import SearchOptimizerPrompt, SearchAcademicOptimizerPrompt
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def search_optimizer(
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query,
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proxies,
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history,
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llm_kwargs,
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optimizer=1,
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categories="general",
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searxng_url=None,
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engines=None,
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):
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# ------------- < 第1步：尝试进行搜索优化 > -------------
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# * 增强优化，会尝试结合历史记录进行搜索优化
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if optimizer == 2:
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his = " "
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if len(history) == 0:
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pass
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else:
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for i, h in enumerate(history):
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if i % 2 == 0:
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his += f"Q: {h}\n"
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else:
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his += f"A: {h}\n"
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if categories == "general":
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sys_prompt = SearchOptimizerPrompt.format(query=query, history=his, num=4)
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elif categories == "science":
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sys_prompt = SearchAcademicOptimizerPrompt.format(query=query, history=his, num=4)
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else:
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his = " "
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if categories == "general":
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sys_prompt = SearchOptimizerPrompt.format(query=query, history=his, num=3)
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elif categories == "science":
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sys_prompt = SearchAcademicOptimizerPrompt.format(query=query, history=his, num=3)
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mutable = ["", time.time(), ""]
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llm_kwargs["temperature"] = 0.8
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try:
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query_json = predict_no_ui_long_connection(
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inputs=query,
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llm_kwargs=llm_kwargs,
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history=[],
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sys_prompt=sys_prompt,
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observe_window=mutable,
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)
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except Exception:
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query_json = "null"
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#* 尝试解码优化后的搜索结果
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query_json = re.sub(r"```json|```", "", query_json)
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try:
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queries = json.loads(query_json)
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except Exception:
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#* 如果解码失败,降低温度再试一次
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try:
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llm_kwargs["temperature"] = 0.4
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query_json = predict_no_ui_long_connection(
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inputs=query,
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llm_kwargs=llm_kwargs,
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history=[],
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sys_prompt=sys_prompt,
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observe_window=mutable,
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)
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query_json = re.sub(r"```json|```", "", query_json)
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queries = json.loads(query_json)
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except Exception:
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#* 如果再次失败，直接返回原始问题
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queries = [query]
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links = []
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success = 0
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Exceptions = ""
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for q in queries:
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try:
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link = searxng_request(q, proxies, categories, searxng_url, engines=engines)
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if len(link) > 0:
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links.append(link[:-5])
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success += 1
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except Exception:
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Exceptions = Exception
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pass
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if success == 0:
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raise ValueError(f"在线搜索失败！\n{Exceptions}")
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# * 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
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seen_links = set()
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result = []
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for tuple in zip_longest(*links, fillvalue=None):
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for item in tuple:
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if item is not None:
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link = item["link"]
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if link not in seen_links:
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seen_links.add(link)
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result.append(item)
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return result
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@lru_cache
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def get_auth_ip():
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ip = check_proxy(None, return_ip=True)
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if ip is None:
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return '114.114.114.' + str(random.randint(1, 10))
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return ip
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def searxng_request(query, proxies, categories='general', searxng_url=None, engines=None):
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if searxng_url is None:
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urls = get_conf("SEARXNG_URLS")
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url = random.choice(urls)
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else:
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url = searxng_url
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if engines == "Mixed":
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engines = None
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if categories == 'general':
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params = {
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'q': query, # 搜索查询
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'format': 'json', # 输出格式为JSON
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'language': 'zh', # 搜索语言
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'engines': engines,
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}
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elif categories == 'science':
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params = {
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'q': query, # 搜索查询
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'format': 'json', # 输出格式为JSON
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'language': 'zh', # 搜索语言
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'categories': 'science'
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}
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else:
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raise ValueError('不支持的检索类型')
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headers = {
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'Accept-Language': 'zh-CN,zh;q=0.9',
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'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',
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'X-Forwarded-For': get_auth_ip(),
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'X-Real-IP': get_auth_ip()
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}
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results = []
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response = requests.post(url, params=params, headers=headers, proxies=proxies, timeout=30)
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if response.status_code == 200:
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json_result = response.json()
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for result in json_result['results']:
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item = {
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"title": result.get("title", ""),
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"source": result.get("engines", "unknown"),
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"content": result.get("content", ""),
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"link": result["url"],
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}
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results.append(item)
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return results
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else:
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if response.status_code == 429:
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raise ValueError("Searxng（在线搜索服务）当前使用人数太多，请稍后。")
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else:
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raise ValueError("在线搜索失败，状态码: " + str(response.status_code) + '\t' + response.content.decode('utf-8'))
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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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from loguru import logger
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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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# 首先采用Jina进行文本提取
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if get_conf("JINA_API_KEY"):
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try: return jina_scrape_text(url)
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except: logger.debug("Jina API 请求失败，回到旧方法")
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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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def jina_scrape_text(url) -> str:
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"jina_39727421c8fa4e4fa9bd698e5211feaaDyGeVFESNrRaepWiLT0wmHYJSh-d"
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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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"X-Retain-Images": "none",
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"Authorization": f'Bearer {get_conf("JINA_API_KEY")}'
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}
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response = requests.get("https://r.jina.ai/" + url, headers=headers, proxies=None, timeout=8)
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if response.status_code != 200:
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raise ValueError("Jina API 请求失败，开始尝试旧方法！" + response.text)
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if response.encoding == "ISO-8859-1": response.encoding = response.apparent_encoding
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result = response.text
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result = result.replace("\\[", "[").replace("\\]", "]").replace("\\(", "(").replace("\\)", ")")
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return response.text
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def internet_search_with_analysis_prompt(prompt, analysis_prompt, llm_kwargs, chatbot):
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from toolbox import get_conf
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proxies = get_conf('proxies')
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categories = 'general'
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searxng_url = None # 使用默认的searxng_url
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engines = None # 使用默认的搜索引擎
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yield from update_ui_latest_msg(lastmsg=f"检索中: {prompt} ...", chatbot=chatbot, history=[], delay=1)
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urls = searxng_request(prompt, proxies, categories, searxng_url, engines=engines)
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yield from update_ui_latest_msg(lastmsg=f"依次访问搜索到的网站 ...", chatbot=chatbot, history=[], delay=1)
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if len(urls) == 0:
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return None
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max_search_result = 5 # 最多收纳多少个网页的结果
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history = []
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for index, url in enumerate(urls[:max_search_result]):
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yield from update_ui_latest_msg(lastmsg=f"依次访问搜索到的网站: {url['link']} ...", chatbot=chatbot, history=[], delay=1)
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res = scrape_text(url['link'], proxies)
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prefix = f"第{index}份搜索结果 [源自{url['source'][0]}搜索] （{url['title'][:25]}）："
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history.extend([prefix, res])
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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：{prompt} {analysis_prompt}"
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i_say, history = input_clipping( # 裁剪输入，从最长的条目开始裁剪，防止爆token
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inputs=i_say,
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history=history,
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max_token_limit=8192
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)
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gpt_say = predict_no_ui_long_connection(
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inputs=i_say,
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llm_kwargs=llm_kwargs,
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history=history,
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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。",
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console_silence=False,
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)
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return gpt_say
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@CatchException
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def 连接网络回答问题(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request):
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optimizer_history = history[:-8]
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history = [] # 清空历史，以免输入溢出
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chatbot.append((f"请结合互联网信息回答以下问题：{txt}", "检索中..."))
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yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
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# ------------- < 第1步：爬取搜索引擎的结果 > -------------
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from toolbox import get_conf
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proxies = get_conf('proxies')
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categories = plugin_kwargs.get('categories', 'general')
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searxng_url = plugin_kwargs.get('searxng_url', None)
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engines = plugin_kwargs.get('engine', None)
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optimizer = plugin_kwargs.get('optimizer', "关闭")
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if optimizer == "关闭":
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urls = searxng_request(txt, proxies, categories, searxng_url, engines=engines)
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else:
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urls = search_optimizer(txt, proxies, optimizer_history, llm_kwargs, optimizer, categories, searxng_url, engines)
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history = []
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if len(urls) == 0:
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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。"))
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yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
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return
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# ------------- < 第2步：依次访问网页 > -------------
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from concurrent.futures import ThreadPoolExecutor
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from textwrap import dedent
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max_search_result = 5 # 最多收纳多少个网页的结果
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if optimizer == "开启(增强)":
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max_search_result = 8
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template = dedent("""
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<details>
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<summary>{TITLE}</summary>
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<div class="search_result">{URL}</div>
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<div class="search_result">{CONTENT}</div>
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</details>
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""")
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buffer = ""
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# 创建线程池
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with ThreadPoolExecutor(max_workers=5) as executor:
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# 提交任务到线程池
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futures = []
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for index, url in enumerate(urls[:max_search_result]):
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future = executor.submit(scrape_text, url['link'], proxies)
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futures.append((index, future, url))
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# 处理完成的任务
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for index, future, url in futures:
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# 开始
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prefix = f"正在加载 第{index+1}份搜索结果 [源自{url['source'][0]}搜索] （{url['title'][:25]}）："
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string_structure = template.format(TITLE=prefix, URL=url['link'], CONTENT="正在加载，请稍后 ......")
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yield from update_ui_latest_msg(lastmsg=(buffer + string_structure), chatbot=chatbot, history=history, delay=0.1) # 刷新界面
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# 获取结果
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res = future.result()
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# 显示结果
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prefix = f"第{index+1}份搜索结果 [源自{url['source'][0]}搜索] （{url['title'][:25]}）："
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string_structure = template.format(TITLE=prefix, URL=url['link'], CONTENT=res[:1000] + "......")
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buffer += string_structure
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# 更新历史
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history.extend([prefix, res])
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yield from update_ui_latest_msg(lastmsg=buffer, chatbot=chatbot, history=history, delay=0.1) # 刷新界面
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# ------------- < 第3步：ChatGPT综合 > -------------
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if (optimizer != "开启(增强)"):
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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( # 裁剪输入，从最长的条目开始裁剪，防止爆token
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inputs=i_say,
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history=history,
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max_token_limit=min(model_info[llm_kwargs['llm_model']]['max_token']*3//4, 8192)
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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) # 刷新界面 # 界面更新
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#* 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
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else:
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i_say = f"从以上搜索结果中抽取与问题：{txt} 相关的信息:"
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i_say, history = input_clipping( # 裁剪输入，从最长的条目开始裁剪，防止爆token
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inputs=i_say,
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history=history,
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max_token_limit=min(model_info[llm_kwargs['llm_model']]['max_token']*3//4, 8192)
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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，对最相关的三个搜索结果进行总结"
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)
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chatbot[-1] = (i_say, gpt_say)
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history = []
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history.append(i_say);history.append(gpt_say)
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yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 界面更新
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# ------------- < 第4步：根据综合回答问题 > -------------
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i_say = f"请根据以上搜索结果回答问题：{txt}"
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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="请根据给定的若干条搜索结果回答问题"
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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) |