https://github.com/shobrook/openlimit
Maximize your usage of OpenAI models without hitting rate limits
https://github.com/shobrook/openlimit
Last synced: 6 months ago
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Maximize your usage of OpenAI models without hitting rate limits
- Host: GitHub
- URL: https://github.com/shobrook/openlimit
- Owner: shobrook
- License: gpl-3.0
- Created: 2023-04-19T09:54:30.000Z (over 2 years ago)
- Default Branch: master
- Last Pushed: 2024-07-30T22:20:22.000Z (about 1 year ago)
- Last Synced: 2024-10-12T20:30:06.402Z (12 months ago)
- Language: Python
- Homepage:
- Size: 50.8 KB
- Stars: 138
- Watchers: 2
- Forks: 22
- Open Issues: 9
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# openlimit
A simple tool for maximizing usage of the OpenAI API without hitting the rate limit.
- Handles both _request_ and _token_ limits
- Precisely (to the millisecond) enforces rate limits with one line of code
- Handles _synchronous_ and _asynchronous_ requests
- Plugs into Redis to track limits across multiple threads, processes, or servers
Implements the [generic cell rate algorithm,](https://en.wikipedia.org/wiki/Generic_cell_rate_algorithm) a variant of the leaky bucket pattern.
## Installation
You can install `openlimit` with pip:
```bash
$ pip install openlimit
```
## Usage
### Define a rate limit
First, define your rate limits for the OpenAI model you're using. For example:
```python
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.
| Rate limiter | Supported models |
| ----------------------- | ---------------------------------------------------------------------------------- |
| `ChatRateLimiter` | gpt-4, gpt-4-0314, gpt-4-32k, gpt-4-32k-0314, gpt-3.5-turbo, gpt-3.5-turbo-0301 |
| `CompletionRateLimiter` | text-davinci-003, text-davinci-002, text-curie-001, text-babbage-001, text-ada-001 |
| `EmbeddingRateLimiter` | text-embedding-ada-002 |
### Apply the rate limit
To apply the rate limit, add a `with` statement to your API calls:
```python
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:
```python
@rate_limiter.is_limited()
def call_openai(**chat_params):
response = openai.ChatCompletion.create(**chat_params)
return response
```
### Asynchronous requests
Rate limits can be enforced for asynchronous requests too:
```python
chat_params = {
"model": "gpt-4",
"messages": [{"role": "user", "content": "Hello!"}]
}
async with rate_limiter.limit(**chat_params):
response = await openai.ChatCompletion.acreate(**chat_params)
```
### Distributed requests
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.
```python
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.
### Token counting
Aside from rate limiting, `openlimit` also provides methods for counting tokens consumed by requests.
#### Chat requests
To count the _maximum_ number of tokens that could be consumed by a chat request (e.g. `gpt-3.5-turbo`, `gpt-4`), pass the [request arguments](https://platform.openai.com/docs/api-reference/chat/create) into the following function:
```python
from openlimit.utilities import num_tokens_consumed_by_chat_request
request_args = {
"model": "gpt-3.5-turbo",
"messages": [{"role": "...", "content": "..."}, ...],
"max_tokens": 15,
"n": 1
}
num_tokens = num_tokens_consumed_by_chat_requests(**request_args)
```
#### Completion requests
Similar to chat requests, to count tokens for completion requests (e.g. `text-davinci-003`), pass the [request arguments](https://platform.openai.com/docs/api-reference/completions/create) into the following function:
```python
from openlimit.utilities import num_tokens_consumed_by_completion_request
request_args = {
"model": "text-davinci-003",
"prompt": "...",
"max_tokens": 15,
"n": 1
}
num_tokens = num_tokens_consumed_by_completion_request(**request_args)
```
#### Embedding requests
For embedding requests (e.g. `text-embedding-ada-002`), pass the [request arguments](https://platform.openai.com/docs/api-reference/embeddings/create) into the following function:
```python
from openlimit.utilities import num_tokens_consumed_by_embedding_request
request_args = {
"model": "text-embedding-ada-002",
"input": "..."
}
num_tokens = num_tokens_consumed_by_embedding_request(**request_args)
```
## Contributing
If you want to contribute to the library, get started with [Adrenaline.](https://useadrenaline.com/) Paste in a link to this repository to familiarize yourself.