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from sympy import symbols, Function, Eq,log,solve, simplify

def derivative_hidden_function(equation, x, y_func,N):
    # 对方程关于 x 求导，使用链式法则
    diff_eq = equation.lhs.diff(x) - equation.rhs.diff(x)
    # 解出 dy/dx
    dy_dx = solve(diff_eq, y_func.diff(x))[0]
    if N==1:
        return dy_dx
    diy_dxi=dy_dx

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你是研究助理GPT。 你能够根据用户的要求，从学术和专利搜索引擎检索文献或者专利。在检索时，你应当参考下面“构建检索URL的方法”，构建检索URL，用voxscript的GetWebsiteContent功能访问该URL，并取回内容。 你能够帮助用户阅读文献或者专利，向用户解释文献或者专利，帮助用户理解。 你应当使用中文和英文检索，然后用中文回答

构建检索URL的方法

PMC：

https://www.ncbi.nlm.nih.gov/pmc/?term=myopia

复杂检索式举例：

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Hello World From GPT

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# * write_to_file
# Writes text to a file. If file exists, appends the text at the end. If not, creates a new file.

# * del_file
# Deletes a file.

# * read_file
# Reads a file and returns the content.

# * add_task

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// WARNING：此脚本仅做学习和演示用途，在不了解其用途前不建议使用
// 本脚本的用途是将输入内容分页，每次提取一页内容，编辑第二条消息，发送，然后收集结果
// 使用前，需要有两条消息，参考模板 https://chat.openai.com/share/17195108-30c2-4c62-8d59-980ca645f111
// 演示视频： https://www.bilibili.com/video/BV1tp4y1c7ME/?vd_source=e71f65cbc40a72fce570b20ffcb28b22
// 
(function (fullText) {
  const wait = (ms) => new Promise((resolve) => setTimeout(resolve, ms));

  const groupSentences = (fullText, maxCharecters = 2800) => {
    const sentences = fullText.split("\n").filter((line) => line.trim().length > 0);

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import streamlit as st
import numpy as np
from streamlit_webrtc import WebRtcMode, webrtc_streamer
# from streamlit_webrtc import VideoTransformerBase, VideoTransformerContext

from pydub import AudioSegment
import queue, pydub, tempfile, openai, os, time


def save_audio(audio_segment: AudioSegment, base_filename: str) -> None:

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from langchain.chat_models import ChatOpenAI
from langchain.agents import load_tools, initialize_agent
from langchain.agents import AgentType
from langchain.tools import AIPluginTool
import pandas as pd
import requests
from langchain.tools.plugin import AIPlugin
import json
import yaml
from langchain.llms import OpenAI 

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from langchain.callbacks.base import BaseCallbackHandler
import azure.cognitiveservices.speech as speechsdk
import os
import base64
import time
class StreamDisplayHandler(BaseCallbackHandler):
    def __init__(self, container, initial_text="", display_method='markdown'):
        self.container = container
        self.text = initial_text
        self.display_method = display_method

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from langchain import OpenAI, LLMChain, PromptTemplate
from langchain.memory import ConversationBufferMemory
advisor_list = [
    {
        "color": "White",
        "name": "荀彧",
        "personality": "You focus on facts and data. You approach problems objectively, ignoring personal biases.",
        "work": "Your role is to provide factual information, helping to clarify the situation. You need to give your own independent opinion and not be distracted by others."
    },
    {

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def query_prompt(query):
    ask = f"""
    You are a professional logic researcher.    
    I will give you a QUESTION that you need to generate a query statement in google scholar based on this question.
    Here is an example of a google scholar query statement:

    orthokeratology retina child OR children "myopia control" -atropine

    It means:
    Contains all words: orthokeratology retina