Rate Limit Error

[UncIeRick]

Hello everyone,

I'm currently running into a RateLimitError when making requests to OpenAI's API. The error message I receive is:
WARNING:langchain.llms.base:Retrying langchain.chat_models.openai.ChatOpenAI.completion_with_retry.._completion_with_retry in 4.0 seconds as it raised RateLimitError: Rate limit reached for 10KTPM-200RPM in organization org-0jOc6LNoCVKWBuIYQtJUll7B on tokens per min. Limit: 10000 / min. Please try again in 6ms. Contact us through our help center at help.openai.com if you continue to have issues..

To manage my input text size, I'm employing the RecursiveCharacterTextSplitter to split my content:

Text_Splitter = RecursiveCharacterTextSplitter.from_tiktoken_encoder(chunk_size=5210, chunk_overlap=0, add_start_index=True)

I used qa = ConversationalRetrievalChain.from_llm(llm,
combine_docs_chain_kwargs={
"prompt": ChatPromptTemplate.from_messages([
system_message_prompt,
human_message_prompt
]),
},
retriever=docsearch.as_retriever(),
return_source_documents=True,
verbose=True)

And gpt-4-0613

I'd appreciate if anyone can help me:

Understand the reason for hitting the rate limit despite splitting the text into chunks.
Any suggestions on how to optimize the chunk size or handling the API rate limits.
Thanks in advance!

[dosubot[bot]]

Answer generated by a 🤖

Answer

Hello,

The rate limit issue you're experiencing with OpenAI's API is likely not due to the text splitting logic in LangChain, but rather the frequency and volume of requests you're making to the API. OpenAI's API has a rate limit that restricts the number of requests you can make within a certain time period. If you exceed this limit, you'll start receiving rate limit errors.

However, if you're splitting your text into chunks and still hitting the rate limit, it's possible that your chunks are too large or you're making too many requests too quickly. You can optimize the chunk size by adjusting the chunk_size parameter when initializing the TextSplitter class. The default chunk_size is 4000, but you can reduce this if necessary. Keep in mind that reducing the chunk size will increase the number of API requests, which could also lead to hitting the rate limit.

Here's how you can adjust the chunk_size:

text_splitter = TextSplitter(chunk_size=2000)  # Reduce chunk size to 2000

To handle the API rate limits, you can implement a retry mechanism with exponential backoff. This means that if you hit the rate limit, you wait for a certain amount of time before retrying the request, and this wait time increases exponentially with each subsequent retry. This can help to spread out your requests and avoid hitting the rate limit.

Here's a simple example of how you might implement this:

import time
import random

def make_request_with_retry(api_call, max_retries=5):
    for i in range(max_retries):
        try:
            return api_call()
        except RateLimitError:
            wait_time = (2 ** i) + random.random()
            time.sleep(wait_time)
    raise Exception("Still hitting rate limit after max retries")

In this example, api_call is a function that makes a request to the OpenAI API, and RateLimitError is the exception that's raised when you hit the rate limit. If a RateLimitError is caught, the code waits for a certain amount of time before retrying the request. The wait time is calculated as (2 ** i) + random.random(), where i is the current retry count. This results in an exponential backoff with jitter, which helps to spread out the retries and reduce the chance of hitting the rate limit.

There are also similar issues that have been solved in the LangChain repository that might be helpful:

1. How to control the number of parallel jobs in the MapReduce chain?
2. RateLimit Exceeded using OpenAI
3. Rate limit error when using davinci model

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

Sources

• libs/langchain/langchain/text_splitter.py
• docs/docs_skeleton/docs/modules/data_connection/document_transformers/text_splitters/recursive_text_splitter.mdx
• libs/langchain/langchain/chat_models/openai.py

---

This response is meant to be useful, save you time, and share context. 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.

[UncIeRick]

Hello,

Thank you for the detailed explanation. However, I'd like to clarify that I encountered the RateLimitError from the very first query I made to OpenAI's API. Given that, it seems unlikely that the error was due to making too many requests in a short time frame.

Considering this, I'm leaning towards the possibility that either the chunk size or the content of the request might be the root cause. It would be helpful to delve deeper into understanding the specific conditions or configurations that could trigger this rate limit error even on the initial request.

Any insights or recommendations on this would be greatly appreciated.

Thanks again for your assistance!

[dosubot[bot]]

Answer generated by a 🤖

Answer

Hello,

Thank you for reaching out. The RateLimitError you're encountering is typically due to exceeding the number of requests allowed by the OpenAI API within a certain time frame. However, as you mentioned, this seems unlikely given that you're encountering the error on your very first request.

Based on similar issues in the LangChain repository, one possible cause could be insufficient credits in your OpenAI account. Please ensure that you have sufficient credits in your OpenAI account to avoid the RateLimitError. You can check this by visiting the OpenAI billing page at https://beta.openai.com/account/billing. If you're still encountering the error after confirming that you have sufficient credits, you might want to try deleting your old OpenAI key and creating a new one at https://beta.openai.com/account/api-keys.

Another possible cause could be that LangChain is making multiple requests to the OpenAI API in the backend, which could be hitting your rate limit. If this is the case, you might want to consider implementing a rate limiting mechanism in your application, such as adding delays between requests or limiting the number of requests made per unit of time. Here's an example of how you could implement a delay between requests:

import time

class RemoteLangChainRetriever(BaseRetriever):
    # ...

    def _get_relevant_documents(
        self, query: str, *, run_manager: CallbackManagerForRetrieverRun
    ) -> List[Document]:
        time.sleep(1)  # Add a delay between requests
        response = requests.post(
            self.url, json={self.input_key: query}, headers=self.headers
        )
        # ...

Please note that the actual delay needed might be different depending on the rate limit of the OpenAI API.

I hope this helps! If you're still encountering issues, please provide more information so we can better assist you.

Sources

• libs/langchain/langchain/retrievers/remote_retriever.py

---

This response is meant to be useful, save you time, and share context. 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.