Closed lefta closed 7 months ago
🤖
Hello,
Thank you for bringing this to our attention. I understand that you're experiencing issues with the OpenAI API rate limit and the retry behavior of LangChain.
Currently, when the OpenAI API rate limit is reached, LangChain is designed to retry the request. This is handled by the _create_retry_decorator
function, which creates a retry decorator that will be used to retry the API call when certain types of errors occur, including openai.error.RateLimitError
. The maximum number of retries is determined by the max_retries
attribute of the llm
object, which defaults to 6
.
Here is the relevant code:
def _create_retry_decorator(
llm: Union[BaseOpenAI, OpenAIChat],
run_manager: Optional[
Union[AsyncCallbackManagerForLLMRun, CallbackManagerForLLMRun]
] = None,
) -> Callable[[Any], Any]:
import openai
errors = [
openai.error.Timeout,
openai.error.APIError,
openai.error.APIConnectionError,
openai.error.RateLimitError,
openai.error.ServiceUnavailableError,
]
return create_base_retry_decorator(
error_types=errors, max_retries=llm.max_retries, run_manager=run_manager
)
You can find this in the libs/langchain/langchain/llms/openai.py file.
However, I understand that you would prefer for an error to be raised immediately when the rate limit is reached, rather than retrying the request. This is a valid concern, and I believe it would be beneficial to have this as a configurable option in LangChain.
I will raise this issue with the LangChain development team for consideration in a future update. In the meantime, you could potentially modify the _create_retry_decorator
function in your local copy of LangChain to remove openai.error.RateLimitError
from the list of errors that trigger a retry, if this suits your needs.
I hope this helps, and please let me know if you have any further questions or concerns.
Best, Dosu
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Hi, @lefta,
I'm helping the LangChain team manage their backlog and am marking this issue as stale. From what I understand, you raised the issue regarding adding support for OpenAI quotas in the Langchain Python library, specifically to handle retries and errors appropriately. I acknowledged the concern and suggested a potential workaround by modifying the _create_retry_decorator
function locally. The issue is currently unresolved, and it seems like it needs further consideration from the LangChain development team for a future update.
Could you please confirm if this issue is still relevant to the latest version of the LangChain repository? If it is, please let the LangChain team 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!
System Info
Python 3.11 Lanchain 315
Who can help?
@hwchase17 @agola11
Information
Related Components
Reproduction
import openai import os from langchain.llms import OpenAI llm = OpenAI(temperature=0.3) print(llm.predict("What is the capital of India?"))
Expected behavior
When OpenAI quotas are reached (or no payment method is defined), requests should not be retried but should raise an appropriate error.
Error in console :
Equivalent TS issue : https://github.com/langchain-ai/langchainjs/issues/1929 Fix in TS land : https://github.com/langchain-ai/langchainjs/pull/1934