free_ask_internet.py

# -*- coding: utf-8 -*-

import json
import os 
from pprint import pprint
import requests
import trafilatura
from trafilatura import bare_extraction
from concurrent.futures import ThreadPoolExecutor
import concurrent
import requests
import openai
import time 
from datetime import datetime
from urllib.parse import urlparse
import tldextract
import platform
import urllib.parse

 
def extract_url_content(url):
    downloaded = trafilatura.fetch_url(url)
    content =  trafilatura.extract(downloaded)
    
    return {"url":url, "content":content}


 

def search_web_ref(query:str, debug=False):
 
    content_list = []

    try:

        safe_string = urllib.parse.quote_plus(":all !general " + query)

        response = requests.get('http://searxng:8080?q=' + safe_string + '&format=json')
        response.raise_for_status()
        search_results = response.json()
 
        if debug:
            print("JSON Response:")
            pprint(search_results)
        pedding_urls = []

        conv_links = []

        if search_results.get('results'):
            for item in search_results.get('results')[0:9]:
                name = item.get('title')
                snippet = item.get('content')
                url = item.get('url')
                pedding_urls.append(url)

                if url:
                    url_parsed = urlparse(url)
                    domain = url_parsed.netloc
                    icon_url =  url_parsed.scheme + '://' + url_parsed.netloc + '/favicon.ico'
                    site_name = tldextract.extract(url).domain
 
                conv_links.append({
                    'site_name':site_name,
                    'icon_url':icon_url,
                    'title':name,
                    'url':url,
                    'snippet':snippet
                })

            results = []
            futures = []

            executor = ThreadPoolExecutor(max_workers=10) 
            for url in pedding_urls:
                futures.append(executor.submit(extract_url_content,url))
            try:
                for future in futures:
                    res = future.result(timeout=5)
                    results.append(res)
            except concurrent.futures.TimeoutError:
                print("任务执行超时")
                executor.shutdown(wait=False,cancel_futures=True)

            for content in results:
                if content and content.get('content'):
                    
                    item_dict = {
                        "url":content.get('url'),
                        "content": content.get('content'),
                        "length":len(content.get('content'))
                    }
                    content_list.append(item_dict)
                if debug:
                    print("URL: {}".format(url))
                    print("=================")
 
        return  conv_links,content_list
    except Exception as ex:
        raise ex


def gen_prompt(question,content_list, lang="zh-CN", context_length_limit=11000,debug=False):
    
    limit_len = (context_length_limit - 2000)
    if len(question) > limit_len:
        question = question[0:limit_len]
    
    ref_content = [ item.get("content") for item in content_list]
    
    answer_language = ' Simplified Chinese '
    if lang == "zh-CN":
        answer_language = ' Simplified Chinese '
    if lang == "zh-TW":
        answer_language = ' Traditional Chinese '
    if lang == "en-US":
        answer_language = ' English '


    if len(ref_content) > 0:
        
        if False:
            prompts = '''
            您是一位由 nash_su 开发的大型语言人工智能助手。您将被提供一个用户问题，并需要撰写一个清晰、简洁且准确的答案。提供了一组与问题相关的上下文，每个都以[[citation:x]]这样的编号开头，x代表一个数字。请在适当的情况下在句子末尾引用上下文。答案必须正确、精确，并以专家的中立和职业语气撰写。请将答案限制在2000个标记内。不要提供与问题无关的信息，也不要重复。如果给出的上下文信息不足，请在相关主题后写上“信息缺失：”。请按照引用编号[citation:x]的格式在答案中对应部分引用上下文。如果一句话源自多个上下文，请列出所有相关的引用编号，例如[citation:3][citation:5]，不要将引用集中在最后返回，而是在答案对应部分列出。除非是代码、特定的名称或引用编号，答案的语言应与问题相同。以下是上下文的内容集：
            '''  + "\n\n" + "```" 
            ref_index = 1

            for ref_text in ref_content:
                
                prompts = prompts + "\n\n" + " [citation:{}]  ".format(str(ref_index)) +  ref_text
                ref_index += 1

            if len(prompts) >= limit_len:
                prompts = prompts[0:limit_len]        
            prompts = prompts + '''
    ```
    记住，不要一字不差的重复上下文内容. 回答必须使用简体中文，如果回答很长，请尽量结构化、分段落总结。请按照引用编号[citation:x]的格式在答案中对应部分引用上下文。如果一句话源自多个上下文，请列出所有相关的引用编号，例如[citation:3][citation:5]，不要将引用集中在最后返回，而是在答案对应部分列出。下面是用户问题：
    ''' + question  
        else:
            prompts = '''
            You are a large language AI assistant develop by nash_su. You are given a user question, and please write clean, concise and accurate answer to the question. You will be given a set of related contexts to the question, each starting with a reference number like [[citation:x]], where x is a number. Please use the context and cite the context at the end of each sentence if applicable.
            Your answer must be correct, accurate and written by an expert using an unbiased and professional tone. Please limit to 1024 tokens. Do not give any information that is not related to the question, and do not repeat. Say "information is missing on" followed by the related topic, if the given context do not provide sufficient information.

            Please cite the contexts with the reference numbers, in the format [citation:x]. If a sentence comes from multiple contexts, please list all applicable citations, like [citation:3][citation:5]. Other than code and specific names and citations, your answer must be written in the same language as the question.
            Here are the set of contexts:
            '''  + "\n\n" + "```" 
            ref_index = 1

            for ref_text in ref_content:
                
                prompts = prompts + "\n\n" + " [citation:{}]  ".format(str(ref_index)) +  ref_text
                ref_index += 1

            if len(prompts) >= limit_len:
                prompts = prompts[0:limit_len]        
            prompts = prompts + '''
            ```
            Above is the reference contexts. Remember, don't repeat the context word for word. Answer in ''' + answer_language + '''. If the response is lengthy, structure it in paragraphs and summarize where possible. Cite the context using the format [citation:x] where x is the reference number. If a sentence originates from multiple contexts, list all relevant citation numbers, like [citation:3][citation:5]. Don't cluster the citations at the end but include them in the answer where they correspond.
            Remember, don't blindly repeat the contexts verbatim. And here is the user question:
            ''' + question  
 
     
    else:
        prompts = question

    if debug:
        print(prompts)
        print("总长度："+ str(len(prompts)))
    return prompts


def chat(prompt, model:str,llm_auth_token:str,llm_base_url:str,using_custom_llm=False,stream=True, debug=False):
    openai.base_url = "http://127.0.0.1:3040/v1/"

    if model == "gpt3.5":
        openai.base_url = "http://llm-freegpt35:3040/v1/"
    
    if model == "kimi":
        openai.base_url = "http://llm-kimi:8000/v1/"
    if model == "glm4":
        openai.base_url = "http://llm-glm4:8000/v1/"
    if model == "qwen":
        openai.base_url = "http://llm-qwen:8000/v1/"
    

    if llm_auth_token == '':
        llm_auth_token = "CUSTOM"
        
    openai.api_key = llm_auth_token

    if using_custom_llm:
        openai.base_url = llm_base_url
        openai.api_key = llm_auth_token


    total_content = ""
    for chunk in openai.chat.completions.create(
        model=model,
        messages=[{
            "role": "user",
            "content": prompt
        }],
        stream=True,
        max_tokens=1024,temperature=0.2
    ):
        stream_resp = chunk.dict()
        token = stream_resp["choices"][0]["delta"].get("content", "")
        if token:
            
            total_content += token
            yield token
    if debug:
        print(total_content)
 

 
    
def ask_internet(query:str,  debug=False):
  
    content_list = search_web_ref(query,debug=debug)
    if debug:
        print(content_list)
    prompt = gen_prompt(query,content_list,context_length_limit=6000,debug=debug)
    total_token =  ""
 
    for token in chat(prompt=prompt):
    # for token in daxianggpt.chat(prompt=prompt):
        if token:
            total_token += token
            yield token
    yield "\n\n"
    # 是否返回参考资料
    if True:
        yield "---"
        yield "\n"
        yield "参考资料:\n"
        count = 1
        for url_content in content_list:
            url = url_content.get('url')
            yield "*[{}. {}]({})*".format(str(count),url,url )  
            yield "\n"
            count += 1