做进口零食批发网站大连seo优化

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：

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

def generate_prompt(all_sentences): # all_asps是一个listprompts = []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

import redef process_sentence(sentence):# 定义要添加空格的特殊字符, 比如’sspecial_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 sentencedef 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 = 0with open(wirte_path, 'a', buffering=1) as res_file:count = 0for p in tqdm(generate_prompt(all_sentences), total=len(all_sentences)):print(p)count += 1if count < begin:continuetry: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)

from pdb import set_trace as stopdataset_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)