在我的情况下，我试图通过一个辅助函数将提示传递给实际的GPT3模型，在我的例子中是text-ada-001，然后最终将其应用于pandas列，使用以下代码。但我遇到了以下错误：

    def sentiment_prompt(text):    return """Is the sentiment Positive, Negative or Neutral for the following text:        "{}"    """.format(text)    def sentiment_text(text):        response = openai.Completion.create(           engine="text-ada-001",           prompt=sentiment_prompt(text),           max_tokens=1000,           temperature=0,           top_p=1,           frequency_penalty=0,           presence_penalty=0    )    sentiment = response.choices[0].text    return sentiment

然后最终应用到我的pandas列上：

    df['sentiment'] = df['text'].apply(lambda x :sentiment_text(x))

错误如下：

    RateLimitError: Rate limit reached for default-global-with-image-limits in organization org-XXXX on requests per min. Limit: 60 / min. Please try again in 1s. Contact [email protected] if you continue to have issues. Please add a payment method to your account to increase your rate limit. Visit https://platform.openai.com/account/billing to add a payment method.

为了克服这个错误，我查看了这个链接，发现tenacity可以帮助解决我的问题。但我不确定如何构建我的代码。目前我正在做以下操作

我如何使用链接中建议的代码来克服速率限制错误？

回答：

在代码开始时导入tenacity，然后在调用OpenAI库的create方法时添加其装饰器。这样你的代码看起来会像这样：

from tenacity import (    retry,    stop_after_attempt,    wait_random_exponential,) @retry(wait=wait_random_exponential(min=1, max=60), stop=stop_after_attempt(6))def sentiment_text(text):        your_prompt = """Is the sentiment Positive, Negative or Neutral for the                          following text:                             "{}"                      """.format(text)        response = openai.Completion.create(           engine="text-ada-001",           prompt=your_prompt ,           max_tokens=1000,           temperature=0,           top_p=1,           frequency_penalty=0,           presence_penalty=0        )        sentiment = response.choices[0].text        return sentiment