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Issue: openai.error.RateLimitError: Rate limit reached for default-text-embedding-ada-002 in organization
Issue you'd like to raise.
I want to download a large file, 5,000,000 characters, and I get an error: openai.error.RateLimitError: Rate limit reached for default-text-embedding-ada-002 in organization org-DOqIVFPozlLEOcvlTbpvpcKt on tokens per min. Limit: 150000 / min. Current: 0 / min. Contact us through our help center at help.openai.com if you continue to have issues. Please add a payment method to your account to increase your rate limit. Visit https://platform.openai.com/account/billing to add a payment method.
My code: `class Agent: def init(self, openai_api_key: str | None = None) -> None:
self.key = openai_api_key
self.embeddings = OpenAIEmbeddings(openai_api_key=openai_api_key, request_timeout=120, max_retries=10)
self.text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
self.llm = ChatOpenAI(temperature=0, openai_api_key=openai_api_key, max_tokens=500, model_name="gpt-3.5-turbo-16k")
self.chat_history = None
self.chain = None
self.db = None
def ask(self, question: str) -> str:
response = self.chain({"question": question, "chat_history": self.chat_history})
response = response["answer"].strip()
self.chat_history.append((question, response))
return response
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:
self.db.add_documents(splitted_documents)
def forget(self) -> None:
self.db = None
self.chain = None
self.chat_history = None`
Is there a solution to this problem?
Suggestion:
No response
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
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I have an error here in this line: self.db = FAISS.from_documents(splitted_documents, self.embeddings)
File "G:\Proects\Project 2023\sergeypod9\chatpdf\agent.py", line 36, in ingest self.db = FAISS.from_documents(splitted_documents, self.embeddings) File "G:\Proects\Project 2023\sergeypod9\chatpdf\venv\lib\site-packages\langchain\vectorstores\base.py", line 332, in from_documents return cls.from_texts(texts, embedding, metadatas=metadatas, **kwargs) File "G:\Proects\Project 2023\sergeypod9\chatpdf\venv\lib\site-packages\langchain\vectorstores\faiss.py", line 517, in from_texts embeddings = embedding.embed_documents(texts) File "G:\Proects\Project 2023\sergeypod9\chatpdf\venv\lib\site-packages\langchain\embeddings\openai.py", line 430, in embed_documents return self.get_len_safe_embeddings(texts, engine=self.deployment) File "G:\Proects\Project 2023\sergeypod9\chatpdf\venv\lib\site-packages\langchain\embeddings\openai.py", line 286, in get_len_safe_embeddings response = embed_with_retry( File "G:\Proects\Project 2023\sergeypod9\chatpdf\venv\lib\site-packages\langchain\embeddings\openai.py", line 97, in embed_with_retry return embed_with_retry(**kwargs) File "G:\Proects\Project 2023\sergeypod9\chatpdf\venv\lib\site-packages\tenacity_init.py", line 289, in wrapped_f return self(f, *args, **kw) File "G:\Proects\Project 2023\sergeypod9\chatpdf\venv\lib\site-packages\tenacity_init.py", line 379, in call do = self.iter(retry_state=retry_state) File "G:\Proects\Project 2023\sergeypod9\chatpdf\venv\lib\site-packages\tenacity_init.py", line 325, in iter raise retry_exc.reraise() File "G:\Proects\Project 2023\sergeypod9\chatpdf\venv\lib\site-packages\tenacity_init_.py", line 158, in reraise raise self.last_attempt.result() File "C:\Users\sosat\AppData\Local\Programs\Python\Python310\lib\concurrent\futures_base.py", line 451, in result return self.__get_result() File "C:\Users\sosat\AppData\Local\Programs\Python\Python310\lib\concurrent\futures_base.py", line 403, in __get_result raise self.exception File "G:\Proects\Project 2023\sergeypod9\chatpdf\venv\lib\site-packages\tenacity_init.py", line 382, in call result = fn(*args, **kwargs) File "G:\Proects\Project 2023\sergeypod9\chatpdf\venv\lib\site-packages\langchain\embeddings\openai.py", line 95, in _embed_with_retry return embeddings.client.create(**kwargs) File "G:\Proects\Project 2023\sergeypod9\chatpdf\venv\lib\site-packages\openai\api_resources\embedding.py", line 33, in create response = super().create(*args, **kwargs) File "G:\Proects\Project 2023\sergeypod9\chatpdf\venv\lib\site-packages\openai\api_resources\abstract\engine_api_resource.py", line 153, in create response, _, api_key = requestor.request( File "G:\Proects\Project 2023\sergeypod9\chatpdf\venv\lib\site-packages\openai\api_requestor.py", line 298, in request resp, got_stream = self._interpret_response(result, True) File "G:\Proects\Project 2023\sergeypod9\chatpdf\venv\lib\site-packages\openai\api_requestor.py", line 700, in _interpret_response self._interpret_response_line( File "G:\Proects\Project 2023\sergeypod9\chatpdf\venv\lib\site-packages\openai\api_requestor.py", line 763, in _interpret_response_line raise self.handle_error_response( openai.error.RateLimitError: Rate limit reached for default-text-embedding-ada-002 in organization org-DOqIVFPozlLEOcvlTbpvpcKt on tokens per min. Limit: 150000 / min. Current: 0 / min. Contact us through our help center at help.openai.com if you continue to have issues. Please add a payment method to your account to increase your rate limit. Visit https://platform.openai.com/account/billing to add a payment method.
Process finished with exit code 1
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
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.
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!)
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.
I'm having this issue, for large documents & batched texts, the embedder needs to wait to overcome rate limits in openAI before handling the next batch of documents, I didn't find an easy way to this within the library so I forked it and added a sleeper and it works
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.
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")
I am getting this error
Retrying langchain.embeddings.openai.embed_with_retry.
What does this mean and how can I solve it ?
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")
This worked for me as well. Thanks!
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]
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. ;-)
Retrying langchain.embeddings.openai.embed_with_retry.
i found this solution on openai site but unable to change it into the code. please suggest something If you encounter a RateLimitError, please try the following steps:
-
Wait until your rate limit resets (one minute) and retry your request. The error message should give you a sense of your usage rate and permitted usage.
-
Send fewer tokens or requests or slow down. You may need to reduce the frequency or volume of your requests, batch your tokens, or implement exponential backoff. You can read our rate limit guidance here.
-
You can also check your usage statistics from your account dashboard.
@pandeyManish1230 It's an easy fix, just wait 1 minute per document, if they are large, or you can just count each embedding and before you hit the limit put a 1 minute pause.
LarryStewart2022
from langchain.document_loaders import PyPDFLoader from langchain.vectorstores import FAISS from langchain.chat_models import ChatOpenAI from langchain.embeddings.openai import OpenAIEmbeddings from langchain.text_splitter import RecursiveCharacterTextSplitter from langchain.chains import RetrievalQA, ConversationalRetrievalChain import os import openai
os.environ['OPENAI_API_KEY'] = " " openai.api_key = os.environ['OPENAI_API_KEY']
with open('jaimatadi.txt', 'r') as file: text_file = file.read()
text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
texts = text_splitter.create_documents([text_file])
directory = "index_stores"
vector_index = FAISS.from_documents(texts, OpenAIEmbeddings())
vector_index.save_local(directory)
please help me with this code .
Had one large json file in the repo that was causing this error for me. Ignored that file and now the script works.
Hi, @Imperator8,
I'm helping the LangChain team manage their backlog and am marking this issue as stale. The issue involves encountering a rate limit error when attempting to download a large file using the OpenAI API. Several potential solutions have been discussed, including introducing delays in the code, adjusting the text splitter, and waiting for the rate limit to reset. The issue remains unresolved.
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 for your understanding and contribution to LangChain!
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!)
I am getting the same 0 / min rate limit error via llamaindex (different library entirely) but using their OpenAI module. So I don't think this is a langchain issue, but OpenAI's. And feel like @Dids is right, for my use case it's always < 1000 characters.
Also something to keep in mind when using a library on top of OpenAI is that they might perform several requests per each 1 of your requests to the lib. With the free tier of OpenAI using their node client I'm having to introduce artificial delays to avoid rate limiting, they give you very little to sandbox with.
