Supress Rate Limit Warning

[Passive Book (passive-book)]

I am running the below

result = await asyncio.wait_for(qa_chain._acall({'query': query}), timeout=120)

I am getting the below warning

WARNING:langchain.llms.base:Retrying langchain.chat_models.openai.acompletion_with_retry.<locals>._completion_with_retry in 4.0 seconds as it raised RateLimitError: Rate limit reached for gpt-3.5-turbo-16k in organization org-xU4GSPRwToXPhQ1yeE4U0NbU on tokens per min. Limit: 180000 / min. Current: 176671 / min. Contact us through our help center at help.openai.com if you continue to have issues..

How do I suppress this warning?

[dosubot[bot]]

🤖

Hello,

To suppress the rate limit warning in LangChain when running the command result = await asyncio.wait_for(qa_chain._acall({'query': query}), timeout=120), you can use the suppress_langchain_deprecation_warning context manager provided in the LangChain API.

Here is how you can use it:

from libs.langchain.langchain._api.deprecation import suppress_langchain_deprecation_warning
import asyncio

with suppress_langchain_deprecation_warning():
    result = await asyncio.wait_for(qa_chain._acall({'query': query}), timeout=120)

This context manager suppresses the LangChainDeprecationWarning and LangChainPendingDeprecationWarning by using the warnings.catch_warnings() context manager and setting the filter to "ignore" for these warning types.

However, please note that suppressing warnings is generally not recommended as they provide useful information about potential issues in your code. The rate limit warning you're seeing is there to inform you that you're nearing the rate limit for your OpenAI API key. If you exceed this limit, your requests to the API will be throttled, which could cause your application to fail.

Instead of suppressing the warning, you might want to consider adjusting your usage of the API to stay within the rate limits. For example, you could adjust the batch size in the Language Learning Model (LLM) used to avoid rate limit errors. This can be done by setting the batch size on the LLM. For example:

llm = OpenAI(batch_size=5, temperature=0)

This code sets the batch size to 5 when using the OpenAI model. Adjusting this parameter to a suitable value should help avoid rate limit errors. You can find more information about this in the Langchain QA over large documents results in Rate limit errors issue.

I hope this helps! Let me know if you have any other questions.

Sources

• Langchain QA over large documents results in Rate limit errors
• libs/langchain/langchain/_api/deprecation.py
• libs/langchain/langchain/chains/graph_qa/sparql.py
• libs/langchain/langchain/chains/graph_qa/base.py
• libs/langchain/langchain/chains/graph_qa/arangodb.py

---

This response is meant to be useful and save you time. It is not meant to be a precise solution, but rather a starting point for your own research.

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If you want to continue the conversation, start your reply with Dosu Bot (@dosu-bot).

[Passive Book (passive-book)]

Dosu Bot (@dosu-bot) This is not an API Deprecation warning for us to use from libs.langchain.langchain._api.deprecation import suppress_langchain_deprecation_warning

This is the warning.
WARNING:langchain.llms.base:Retrying langchain.chat_models.openai.acompletion_with_retry.<locals>._completion_with_retry in 4.0 seconds as it raised RateLimitError: Rate limit reached for gpt-3.5-turbo-16k in organization on tokens per min. Limit: 180000 / min. Current: 176671 / min. Contact us through our help center at help.openai.com if you continue to have issues..

[dosubot[bot]]

🤖

Hello,

Thank you for reaching out. The rate limit warning you're encountering is not directly related to the LangChain framework, but rather it's a warning from the OpenAI API indicating that you've hit the rate limit for your current usage tier.

However, if you want to suppress this specific warning in your Python application, you can use Python's built-in warnings module. Here's an example of how you can do this:

import warnings
import langchain

# Ignore specific warning category from a specific module
warnings.filterwarnings("ignore", category=langchain.llms.base.RateLimitWarning)

Please replace langchain.llms.base.RateLimitWarning with the exact warning category that you're seeing in your application. You can usually find this in the warning message itself.

Please note that suppressing warnings can sometimes lead to overlooking important issues, so use this with caution. The rate limit warning is there to let you know that you're hitting the API usage limit, and ignoring it might lead to unexpected behavior if you're not handling rate limits in your application.

As for the similar issues you've found in the LangChain repository, they seem to be related to handling rate limits in the LangChain framework itself, not suppressing the warning message. If you're encountering issues with rate limits while using LangChain, I would recommend looking into these issues and their solutions.

I hope this helps! Let me know if you have any other questions.

Sources

• Langchain QA over large documents results in Rate limit errors
• libs/langchain/langchain/_api/deprecation.py

---

This response is meant to be useful and save you time. It is not meant to be a precise solution, but rather a starting point for your own research.

Help me be more useful! Please leave a 👍 if this is helpful and 👎 if it is irrelevant.

If you want to continue the conversation, start your reply with Dosu Bot (@dosu-bot).

[ANIRUDDHA ADAK (aniruddhaadak80)]

Passive Book (@passivebook)

Thanks for starting this discussion! When dealing with AI/LLM integrations, Vector DBs, or agent frameworks, quirks like this can usually be traced back to a few specific moving parts:

1. Environment & Dependency Versions: The AI tooling ecosystem evolves incredibly rapidly. Double-check your local environment (Node/Python versions) to ensure packages like langchain, transformers, or openai are on the latest stable releases. Breaking API changes upstream happen often.
2. Context Window & Embeddings: If you are seeing strange truncation, blank responses, or timeouts, make sure the payload you are sending fits well within the specific model???s maximum context window. Also verify your vector chunk sizes match the dimensionality of your chosen embedding model.
3. Rate Limits: If you are hitting external APIs, occasionally verify that your API key is correctly scoped and that you are not hitting concurrency or token rate limits which might silently fail or hang the process.

If you are still blocked, providing a minimal reproducible snippet or logging the raw request/response payload (scrubbed of secrets) usually helps pinpoint the exact failure layer much faster.

Hope this helps point you in the right direction. Let me know if you make any progress!