[Question]: RateLimitError: Error code: 429

[craigjurs]

Question Validation

• [X] I have searched both the documentation and discord for an answer.

Question

Trying to run the example code given in the docs: https://docs.llamaindex.ai/en/stable/examples/embeddings/OpenAI/

%pip install llama-index-embeddings-openai
!pip install llama-index
os.environ["OPENAI_API_KEY"] = "sk-xxx"

from llama_index.embeddings.openai import OpenAIEmbedding
from llama_index.core import Settings

embed_model = OpenAIEmbedding(embed_batch_size=10)
Settings.embed_model = embed_model

# get API key and create embeddings
from llama_index.embeddings.openai import OpenAIEmbedding

embed_model = OpenAIEmbedding(model="text-embedding-3-large")

embeddings = embed_model.get_text_embedding(
    "Open AI new Embeddings models is great."
)

WARNING:llama_index.embeddings.openai.utils:Retrying llama_index.embeddings.openai.base.get_embedding in 0.9939636916397955 seconds as it raised RateLimitError: Error code: 429 - {'error': {'message': 'You exceeded your current quota, please check your plan and billing details. For more information on this error, read the docs: https://platform.openai.com/docs/guides/error-codes/api-errors.', 'type': 'insufficient_quota', 'param': None, 'code': 'insufficient_quota'}}.

related to --> https://github.com/run-llama/llama_index/discussions/8362 ...???

Can anyone please help me with this one? Ive been struggling for a bit. ty!

[dosubot[bot]]

The RateLimitError you're encountering indicates that your requests to the OpenAI API exceed the rate limits for your account. Here are targeted strategies to address this:

1. Retry with Exponential Backoff: Implement a retry mechanism that waits longer between each attempt after a failure, reducing the likelihood of hitting the rate limit. The llama-index library supports this with a built-in retry decorator.

2. Caching: To avoid redundant requests, cache results of identical queries.

3. Request Throttling: Manually adjust the frequency of your requests to stay within OpenAI's rate limits.

4. Batch Size Adjustment: The embed_batch_size parameter in OpenAIEmbedding affects how many texts are processed per request. Reducing the batch size can help manage your rate of API calls.

5. API Key and Credentials: Ensure your API key is valid and correctly configured. The llama-index library provides functions to validate and resolve OpenAI credentials.

6. Local Models for Development: Use local models during development to minimize API calls, reserving OpenAI model usage for production or final testing.

For further insights, you might explore discussions in the LlamaIndex repository related to rate limit handling, such as issues #12104, #11593, and #333. These discussions offer additional context and solutions from the community.

Remember, OpenAI's rate limits vary by account type and usage, so if issues persist, consider reaching out to OpenAI support for more detailed assistance.

#### Details

**Sources** - [Implement Rate Limit Handling in TogetherEmbedding() Class.](https://github.com/run-llama/llama_index/issues/12104) - [[Question]: RAG CLI example gives openAI Rate Limit Error](https://github.com/run-llama/llama_index/issues/11593) - [How to solve limit rate during `load_data` ?](https://github.com/run-llama/llama_index/issues/333) - [llama-index-legacy/llama_index/legacy/embeddings/utils.py](https://github.com/run-llama/llama_index/blob/main/llama-index-legacy/llama_index/legacy/embeddings/utils.py) - [llama-index-integrations/embeddings/llama-index-embeddings-openai/llama_index/embeddings/openai/utils.py](https://github.com/run-llama/llama_index/blob/main/llama-index-integrations/embeddings/llama-index-embeddings-openai/llama_index/embeddings/openai/utils.py) - [llama-index-legacy/llama_index/legacy/embeddings/openai.py](https://github.com/run-llama/llama_index/blob/main/llama-index-legacy/llama_index/legacy/embeddings/openai.py) - [llama-index-integrations/embeddings/llama-index-embeddings-openai/llama_index/embeddings/openai/base.py](https://github.com/run-llama/llama_index/blob/main/llama-index-integrations/embeddings/llama-index-embeddings-openai/llama_index/embeddings/openai/base.py)
**About Dosu** 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.

To continue the conversation, mention @dosu.

[craigjurs]

@dosu I think the rate limit thing is a red herring. Im trying to embed 1 sentence here using the example code from the documentation. I have a fully paid plan. There is no way Im hitting the rate limit.

[dosubot[bot]]

If you're encountering a RateLimitError with a single sentence embedding and you're on a fully paid plan, it's worth exploring a few targeted solutions:

1. Review and Adjust the Retry Strategy: The LlamaIndex library's retry mechanism might need tweaking. Consider increasing the max_retries, stop_after_delay_seconds, and adjusting the min_seconds and max_seconds for the wait strategy. This approach spaces out retries more effectively, potentially avoiding hitting rate limits unexpectedly.

2. Examine API Usage Patterns: Double-check your API usage to ensure there aren't any unintended spikes in requests. Even on paid plans, certain operations might have specific rate limits.

3. Leverage Debugging Tools: Utilize the LlamaIndex library's debugging features to trace and identify the root cause of the rate limit error. This can provide insights into whether the issue is with the API calls or the way the library handles these calls.

4. Reach Out to OpenAI Support: If after adjusting your strategy and reviewing your usage you still face issues, contacting OpenAI support could clarify any account-specific limitations or operational quirks not immediately apparent.

These steps are designed to help you navigate and potentially resolve the RateLimitError without hitting your quota, especially when your usage should be within the limits of your plan.

#### Details

**Sources** - [[Bug]: Why do I upgrade llama_index to the newest version and get RateLimitError?](https://github.com/run-llama/llama_index/issues/6125) - [rate limit error ](https://github.com/run-llama/llama_index/issues/670) - [llama-index-integrations/embeddings/llama-index-embeddings-openai/llama_index/embeddings/openai/utils.py](https://github.com/run-llama/llama_index/blob/main/llama-index-integrations/embeddings/llama-index-embeddings-openai/llama_index/embeddings/openai/utils.py) - [docs/docs/understanding/tracing_and_debugging/tracing_and_debugging.md](https://github.com/run-llama/llama_index/blob/main/docs/docs/understanding/tracing_and_debugging/tracing_and_debugging.md) - [llama-index-core/llama_index/core/callbacks/llama_debug.py](https://github.com/run-llama/llama_index/blob/main/llama-index-core/llama_index/core/callbacks/llama_debug.py) - [llama-index-integrations/llms/llama-index-llms-openai/llama_index/llms/openai/utils.py](https://github.com/run-llama/llama_index/blob/main/llama-index-integrations/llms/llama-index-llms-openai/llama_index/llms/openai/utils.py) - [llama-index-integrations/llms/llama-index-llms-openai/tests/test_openai.py](https://github.com/run-llama/llama_index/blob/main/llama-index-integrations/llms/llama-index-llms-openai/tests/test_openai.py)
**About Dosu** 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.

To continue the conversation, mention @dosu.

[RussellLuo]

Hi @craigjurs, to ensure that OpenAI is functioning properly, you can first try a cURL command:

curl https://api.openai.com/v1/embeddings \
  -H "Authorization: Bearer $OPENAI_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "input": "Open AI new Embeddings models is great",
    "model": "text-embedding-3-large"
  }'

[craigjurs]

Hi @RussellLuo - Im getting the same issue

So I guess this means its on my side? :(

Update: After some investigation Ive found that indeed its my lack of attention about the refresh on the openai account which caused the api key I was using to be inactive. Ive learned there is an automatic recharge setting that allows this not to happen again. Apologies to waste anyone's time.

[craigjurs]

user error w openai account

[tiemnd99]

I fixed it by change time retry at llama_index/embeddings/openai/base.py, line 19-25: embedding_retry_decorator = create_retry_decorator( max_retries=10, # Increase retries random_exponential=True, stop_after_delay_seconds=1200, # Increase total wait time min_seconds=120, # Increase minimum wait time max_seconds=1200, # Increase maximum wait time )