Simple and efficient rate limiter for the OpenAI API. It can:

• Handle both request and token limits
• Precisely enforce rate limits with one line of code
• Limit synchronous and asynchronous requests
• Use Redis to track limits across multiple threads or processes

Implements the generic cell rate algorithm, a variant of the leaky bucket pattern.

You can install openlimit with pip:

$ pip install openlimit

First, define your rate limits for the OpenAI model you're using. For example:

from openlimit import ChatRateLimiter

rate_limiter = ChatRateLimiter(request_limit=200, token_limit=40000)

This sets a rate limit for a chat completion model (e.g. gpt-4, gpt-3.5-turbo). openlimit offers different rate limiter objects for different OpenAI models, all with the same parameters: request_limit and token_limit. Both limits are measured per-minute and may vary depending on the user.

To apply the rate limit, add a with statement to your API calls:

chat_params = { 
    "model": "gpt-4", 
    "messages": [{"role": "user", "content": "Hello!"}]
}

with rate_limiter.limit(**chat_params):
    response = openai.ChatCompletion.create(**chat_params)

Ensure that rate_limiter.limit receives the same parameters as the actual API call. This is important for calculating expected token usage.

Alternatively, you can decorate functions that make API calls, as long as the decorated function receives the same parameters as the API call:

@rate_limiter.is_limited()
def call_openai(**chat_params):
    response = openai.ChatCompletion.create(**chat_params)
    return response

Rate limits can be enforced for asynchronous requests too:

async def call_openai():
    chat_params = { 
        "model": "gpt-4", 
        "messages": [{"role": "user", "content": "Hello!"}]
    }

    async with rate_limiter.limit(**chat_params):
        response = await openai.ChatCompletion.acreate(**chat_params)

By default, openlimit uses an in-memory store to track rate limits. But if your application is distributed, you can easily plug in a Redis store to manage limits across multiple threads or processes.

from openlimit import ChatRateLimiterWithRedis

rate_limiter = ChatRateLimiterWithRedis(
    request_limit=200,
    token_limit=40000,
    redis_url="redis://localhost:5050"
)

# Use `rate_limiter` like you would normally ...

All RateLimiter objects have RateLimiterWithRedis counterparts.

If you want to contribute to the library, get started with Adrenaline. Simply paste in a link to this repository to familiarize yourself.