langchain: Issue: openai.error.RateLimitError: Rate limit reached for default-text-embedding-ada-002 in organization

Answer generated by a 🤖

Answer

I understand that you’re encountering a rate limit error when trying to process a large file of 5,000,000 characters with the OpenAI API. This is likely because the number of tokens in your file exceeds the rate limit of 150,000 tokens per minute for your organization.

To address this issue, you could introduce a rate limiter in your code to ensure you don’t surpass the API’s rate limit. This can be achieved by using Python’s built-in libraries like time to introduce delays in your code, or by using more advanced libraries like ratelimiter.

Here’s a simple way to add a delay in your ingest method:

import time

def ingest(self, file_path) -> None:
    loader = TextLoader(file_path, encoding="utf-8")
    documents = loader.load()
    splitted_documents = self.text_splitter.split_documents(documents)

    if self.db is None:
        self.db = FAISS.from_documents(splitted_documents, self.embeddings)
        self.chain = ConversationalRetrievalChain.from_llm(self.llm, self.db.as_retriever())
        self.chat_history = []
    else:
        for doc in splitted_documents:
            self.db.add_documents([doc])
            time.sleep(2)  # Add delay here

In this example, a delay of 2 seconds is introduced after each document is processed. You might need to adjust this delay based on your rate limit and the size of your documents.

Please note that this is a basic solution and might not be the most efficient if you’re dealing with large volumes of data. For a more efficient solution, you might want to explore more advanced rate limiting strategies or libraries.

It’s also worth noting that your code already includes a retry mechanism for handling OpenAI API errors, including rate limit errors. However, this mechanism only retries the request when it fails and doesn’t prevent the rate limit from being exceeded in the first place. Therefore, implementing a rate limiter as suggested above could still be beneficial.

If you find this solution effective and believe it could benefit other users experiencing similar issues, feel free to make a pull request. If this solution doesn’t align with your experience, I would recommend upgrading to the latest version of the software.

Sources

• langchain/embeddings/openai.py
• langchain/llms/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.

Answer generated by a 🤖

Answer

I understand that you’re encountering a RateLimitError when trying to process a large file of 5,000,000 characters using the OpenAI API. This error is likely due to exceeding the API’s rate limit, which is 150,000 tokens per minute.

To address this issue, one potential solution could be to introduce a delay in your code to ensure you’re not exceeding the rate limit. For instance, if you’re processing 150,000 tokens, you could wait at least one minute before making another request. Here’s an example of how you could implement this:

import time

def ingest(self, file_path) -> None:
    loader = TextLoader(file_path, encoding="utf-8")
    documents = loader.load()
    splitted_documents = self.text_splitter.split_documents(documents)

    for document in splitted_documents:
        if self.db is None:
            self.db = FAISS.from_documents([document], self.embeddings)
            self.chain = ConversationalRetrievalChain.from_llm(self.llm, self.db.as_retriever())
            self.chat_history = []
        else:
            self.db.add_documents([document])
        time.sleep(60)  # wait for 60 seconds before processing the next document

This code introduces a 60-second delay after processing each document, which should help avoid exceeding the rate limit. Please note that this is a simple example and the actual delay may need to be adjusted based on the size of your documents and your specific rate limit.

Another potential solution could be to increase your rate limit by adding a payment method to your OpenAI account, as suggested in the error message. You can do this by visiting https://platform.openai.com/account/billing.

Please note that these are potential solutions and may not completely resolve your issue. If you find that these solutions work for you and you believe this is a bug that could impact other users, feel free to make a pull request with your changes. If these solutions don’t align with your experience, I would recommend upgrading to the latest version of the software, as this issue may have been addressed in a more recent update.

Sources

• langchain/embeddings/openai.py
• langchain/embeddings/mosaicml.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.

I wonder if this is an actual bug, or if this is just OpenAI’s way of applying additional rate limits when they’re overloaded, and instead of providing a new/separate error message/code, they’re just reusing the existing rate limit errors?

That’s the only thing that’d make sense to me, as the error messages themselves are always reporting the “current” value being much, much lower than the “maximum” value, which makes no sense.

What worked for me was to include a more appropiate delimiter for the TextSplitter In my case: text_splitter = CharacterTextSplitter(chunk_size=800, chunk_overlap=50, separator="\n")

Yes, please: I am also (occasionally) seeing all my embedding API calls fail with “Rate limit reached”, but reporting: “Current: 1 / min” or “Current: 0 / min”, which makes no sense. (Update: apologies, just realized this is the langchain issue tracker! Had assumed it was OpenAI’s! Still, if anyone knows what this (seemingly bogus) rate limit error means, do tell!)

Same here. They make it difficult for businesses to process their documents. It already takes a long time to prepare and get things cleaned and ready for indexing. if you stop the process while you’re getting these retry messages, you’re still billed. We are moving to a local embedding LLM because of this. And it helps me justify a Tesla H100 80GB GPU on the company’s dime. 😉

I still have this error on a different data set. Large files are not causing it - all files are <150KB in size. The suggested solution with the text_splitter above didn’t work for me. I have about 100 files and it is impossible so far to find the problematic one, if it is indeed just one.

[Edit: I realised it was loading a subfolder recursively and deleted that. This stopped the issue. Presumably file or files in the subfolder were causing the problem. Because I was under time pressure I could not investigate further but I will get around to it. I understand the team is mega busy but this really hurts when it happens and would love someone to take a look so that my blood pressure can get back to normal @hwchase17]

Also facing this issue. It was caused by a large file. The suggested answer by the bot would not be suitable. I consider this to be fundamental functionality that needs to be fixed.

@navicstein could you try making a PR for your fix?

What does Current: 0 / min mean in the error posted by OP?