Langchain rate limit error while invoking OpenAI API

[smith-co]

System Info

Description

While using the Langchain application, I am frequently encountering an error that relates to rate limiting when invoking OpenAI's API. This tends to occur when I try to perform multiple translations consecutively or concurrently, causing a significant interruption to the user experience.

Who can help?

No response

Information

• The official example notebooks/scripts
• My own modified scripts

Related Components

• LLMs/Chat Models
• Embedding Models
• Prompts / Prompt Templates / Prompt Selectors
• Output Parsers
• Document Loaders
• Vector Stores / Retrievers
• Memory
• Agents / Agent Executors
• Tools / Toolkits
• Chains
• Callbacks/Tracing
• Async

Reproduction

Steps to Reproduce

1. Open the Langchain application.
2. Select a source language and enter some text for translation.
3. Choose the target language and submit for translation.
4. Repeat steps 2-3 multiple times in quick succession or concurrently.

Expected behavior

Expected Behavior

The application should be able to handle multiple translation requests without any disruptions, including but not limited to rate limit errors from OpenAI's API.

Actual Behavior

When submitting multiple translation requests quickly or at the same time, a rate limit error is produced and no translations are returned. The error message is as follows:

Error: OpenAI API rate limit exceeded

How to implement retry logic with LangChain?

[devstein]

Generated by 🤖

Answer

To implement retry logic in LangChain to handle rate limit errors when invoking the OpenAI API, you can use the _create_retry_decorator function provided in the context. This function creates a retry decorator using the tenacity library, which is already configured to handle rate limit errors (openai.error.RateLimitError). You can use this decorator to wrap any function that calls the OpenAI API.
Here's an example of how to use the _create_retry_decorator function with an API call:

# Assuming you have an instance of BaseOpenAI or OpenAIChat called `llm_instance`
retry_decorator = _create_retry_decorator(llm_instance)

@retry_decorator
def api_call_with_retry():
    # Your API call here, e.g.
    response = openai.generate(["Tell me a joke."])

# Call the API with retry logic
api_call_with_retry()

In this example, the api_call_with_retry function will be retried according to the retry logic defined in the _create_retry_decorator function if it encounters a rate limit error or any of the other specified exceptions

Sources

• langchain/llms/openai.py

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This response is meant to be helpful and save users time. It is not meant to be a precise solution.

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[dosubot]

Hi, @smith-co! I'm Dosu, and I'm helping the LangChain team manage their backlog. I wanted to let you know that we are marking this issue as stale.

Based on my understanding, the issue you raised is about rate limit errors when using OpenAI's API for translations. It seems that devstein has provided a helpful response suggesting the use of the _create_retry_decorator function provided in the context of LangChain. This function creates a retry decorator using the tenacity library to handle rate limit errors. The response has received positive reactions from multiple users.

Before we close this issue, we wanted to check if it is still relevant to the latest version of the LangChain repository. If it is, please let us know by commenting on the issue. Otherwise, feel free to close the issue yourself, or it will be automatically closed in 7 days.

Thank you for your contribution to the LangChain repository!