python 如何调用GPT系列的api接口,实现想要的功能

目录

问题描述:

问题解决:


问题描述:

随着各种LLMs (Large Language Models)的出现,如何调用各种LLMs的api成为了经常会遇见的问题。

问题解决:

下面仅以生成给定sentence的复述句为例,说明如何应用。

data: 存放在.txt文件中,按行存放,每一行是一个句子

⚠️: 只能在本地运行,不可以在服务器上运行,并且,🪜要打开。

代码实现:

python 复制代码
import openai
import time
import os
import random 

from tqdm import tqdm
# openai.api_key = "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"
# openai.api_key = "sk-huUlpXTcDnvNevXgB1399aCa9dB247F599D81737B7CfD927" # 实验室共有


openai.api_key = "YOUR API KEY" # GPT4.0
openai.api_base = 'https://api.ngapi.top/v1'

def get_response(prompt, temperature=0.5, max_tokens=2048):
  completion = openai.ChatCompletion.create(
    # model="gpt-3.5-turbo",
    model="gpt-4",
    temperature=0,
    top_p=0,
    # max_tokens=max_tokens,
    messages=[
      {"role": "user", "content": f"{prompt}"}
    ]
  )
  return completion
  
python 复制代码
def generate_prompt(all_sentences): # all_asps是一个list

    prompts = []
    for cur_sent in all_sentences: # all_compared_pair是一个list:[asp,sub,obj]
        # prompt = "Please generate just one synonymous sentence for the given sentence. The current given sentence is : '{}'".format(cur_sent)
        prompt = f"Generate a paraphrased version of the following sentence: {cur_sent}"
        prompts.append(prompt)
    return prompts
python 复制代码
import re

def process_sentence(sentence):
    # 定义要添加空格的特殊字符, 比如's
    special_chars = [',', '.', '\'', ''', '"', '"', '(', ')', '[', ']', '{', '}', ':', ';', '?', '!'] # '-', 因为sub,obj中存在很多以'-'为连字符的sub,obj,所以原始句子中,这部分不可以加空格
    # 在特殊字符前添加空格
    for char in special_chars:
        if char == '(': # 特别的,左括号是在后面加空格
            sentence = sentence = re.sub(rf'([{char}])', r'\1 ', sentence)
        else:
            sentence = re.sub(rf'([{char}])', r' \1', sentence)
    return sentence

def get_res(all_sentences, wirte_path):

    if os.path.exists(wirte_path):
        res_file = open(wirte_path)
        begin = len(res_file.readlines()) + 1 # 断点续传
        res_file.close()
    else:
        begin = 0
    
    with open(wirte_path, 'a', buffering=1) as res_file:
        count = 0
        for p in tqdm(generate_prompt(all_sentences), total=len(all_sentences)):
            print(p)
            count += 1
            if count < begin:
                continue
            try:
                res = get_response(p)['choices'][0]['message']['content']
                # print("res", res)
                processed_res = process_sentence(res) # 处理成以单词,标点符号等为分隔符,空格
                # print("processed_res", processed_res)
                if "The server is overloaded or not ready yet" in processed_res:
                    time.sleep(30)
                    res_file.write("####" + '\n')
                    
                else:
                    res_file.write(processed_res + '\n')
            except Exception as e:
                if isinstance(e, openai.error.RateLimitError):
                    time.sleep(30)      # 处理拥挤的函数,就只是睡着而已
                    res = get_response(p)['choices'][0]['message']['content']
                    processed_res = process_sentence(res) # 
                    res_file.write(processed_res + '\n')
                else:
                    print(e)
                    exit(-1)
        # print("the number of prompt is :", count)
python 复制代码
from pdb import set_trace as stop


dataset_list = ['14lap','14res', '15res', '16res']
name_list = ['train', 'test', 'dev']

# name_list = ['dev']
# dataset_list = ['14lap']

for dataset in dataset_list:
    for name in name_list:
        # print("dataste", dataset)
        # print("name", name)
        sentence_path = f"/Users/qtxu/Workplace/Chatgpt/ATE_Task/{dataset}/{name}_sentence.txt"
        write_path = f'/Users/qtxu/Workplace/Chatgpt/ATE_Task/{dataset}/{name}_paraphrase_sentence.txt'

        with open(sentence_path,"r") as fr:
            lines = fr.readlines()
            all_sentences = []
            for line in lines:
                all_sentences.append(line)
        # print("all_sentences", all_sentences)
        get_res(all_sentences, write_path)
相关推荐
databook14 小时前
Manim实现闪光轨迹特效
后端·python·动效
Juchecar15 小时前
解惑:NumPy 中 ndarray.ndim 到底是什么?
python
用户83562907805116 小时前
Python 删除 Excel 工作表中的空白行列
后端·python
Json_16 小时前
使用python-fastApi框架开发一个学校宿舍管理系统-前后端分离项目
后端·python·fastapi
数据智能老司机1 天前
精通 Python 设计模式——分布式系统模式
python·设计模式·架构
数据智能老司机1 天前
精通 Python 设计模式——并发与异步模式
python·设计模式·编程语言
数据智能老司机1 天前
精通 Python 设计模式——测试模式
python·设计模式·架构
数据智能老司机1 天前
精通 Python 设计模式——性能模式
python·设计模式·架构
c8i1 天前
drf初步梳理
python·django
每日AI新事件1 天前
python的异步函数
python