速率限制

from openai import OpenAI
client = OpenAI()

from tenacity import (
    retry,
    stop_after_attempt,
    wait_random_exponential,
)  # for exponential backoff
 
@retry(wait=wait_random_exponential(min=1, max=60), stop=stop_after_attempt(6))
def completion_with_backoff(**kwargs):
    return client.completions.create(**kwargs)
 
completion_with_backoff(model="gpt-3.5-turbo-instruct", prompt="Once upon a time,")

import backoff 
import openai
from openai import OpenAI
client = OpenAI()

@backoff.on_exception(backoff.expo, openai.RateLimitError)
def completions_with_backoff(**kwargs):
    return client.completions.create(**kwargs)
 
completions_with_backoff(model="gpt-3.5-turbo-instruct", prompt="Once upon a time,")

# imports
import random
import time
 
import openai
from openai import OpenAI
client = OpenAI()
 
# 定义一个重试装饰器
def retry_with_exponential_backoff(
    func,
    initial_delay: float = 1,
    exponential_base: float = 2,
    jitter: bool = True,
    max_retries: int = 10,
    errors: tuple = (openai.RateLimitError,),
):
    """使用指数退避重试一个函数。"""
 
    def wrapper(*args, **kwargs):
        # 初始化变量
        num_retries = 0
        delay = initial_delay
 
        # 循环直到成功响应、达到最大重试次数或引发异常
        while True:
            try:
                return func(*args, **kwargs)
 
            # 在特定错误上重试
            except errors as e:
                # 增加重试次数
                num_retries += 1
 
                # 检查是否达到最大重试次数
                if num_retries > max_retries:
                    raise Exception(
                        f"超过最大重试次数 ({max_retries})."
                    )
 
                # 增加延迟
                delay *= exponential_base * (1 + jitter * random.random())
 
                # 休眠
                time.sleep(delay)
 
            # 对于未指定的任何错误，引发异常
            except Exception as e:
                raise e
 
    return wrapper
    
@retry_with_exponential_backoff
def completions_with_backoff(**kwargs):
    return client.completions.create(**kwargs)

from openai import OpenAI
client = OpenAI()
 
num_stories = 10
prompt = "Once upon a time,"
 
# serial example, with one story completion per request
for _ in range(num_stories):
    response = client.completions.create(
        model="curie",
        prompt=prompt,
        max_tokens=20,
    )
    # print story
    print(prompt + response.choices[0].text)

from openai import OpenAI
client = OpenAI()
 
num_stories = 10
prompts = ["Once upon a time,"] * num_stories
 
# batched example, with 10 story completions per request
response = client.completions.create(
    model="curie",
    prompt=prompts,
    max_tokens=20,
)
 
# match completions to prompts by index
stories = [""] * len(prompts)
for choice in response.choices:
    stories[choice.index] = prompts[choice.index] + choice.text
 
# print stories
for story in stories:
    print(story)