I’m working with a ChatGPT integration that connects to my SQLite database through LangChain. When my OpenAI account hits the usage limit, the system keeps trying to connect multiple times before giving up.
The error message I see is: “Retrying langchain.llms.openai.completion_with_retry…_completion_with_retry in 4.0 seconds as it raised RateLimitError: You exceeded your current quota, please check your plan and billing details.”
The system tries 5 times by default, which means I have to wait around 30-40 seconds before it finally stops and shows the actual error. This makes it really hard to handle the error properly in my code since it only appears after all the retry attempts are done.
Is there a way to change how many times it retries? I’ve looked into using debug mode to trace where this retry logic comes from, but I couldn’t pinpoint the exact source. Maybe there’s a parameter I can set, or a way to customize the error handling, or some kind of callback function I can use to catch this sooner?
The retry mechanism lives in LangChain’s completion wrapper, not the OpenAI client. Hit this same issue last month during a production debug. Here’s what worked: I built a custom retry decorator that completely bypasses LangChain’s built-in retry logic. Just subclass the OpenAI LLM class and override the _call method with your own error handling. You’ll catch the RateLimitError immediately instead of waiting through multiple retry cycles. The trick is grabbing the error before it hits LangChain’s retry mechanism - it runs deeper than those parameters everyone suggests tweaking.
For OpenAI API timeouts, skip LangChain’s default settings and configure the client yourself. Pass a custom OpenAI client to your LangChain LLM with timeout=10 and max_retries=0. Then handle retries manually in your app logic. You get full control over retry behavior - exponential backoff, immediate errors, whatever fits your use case. Catch RateLimitError at the client level instead of letting LangChain handle it.
set request_timeout when u initialize your openai LLM - like OpenAI(request_timeout=5). it’ll fail faster instead of hanging forever. also check for a max_retries param in your langchain config - pretty sure the default retry logic comes from there, not the openai client.
I encountered a similar challenge with my OpenAI integration. The retry attempts are managed by the OpenAI client, but you can alter this by configuring your OpenAI LLM in LangChain. Setting max_retries=1 (or a number that suits your needs) significantly reduces the wait time. I found that limiting it to one retry improved my error handling process. Additionally, it’s beneficial to wrap your calls in a try-catch structure that specifically targets RateLimitError, allowing for quicker responses to quota issues instead of waiting for multiple retries to conclude.