Speed up your OpenAI requests by balancing prompts to multiple API keys.
Project description
OpenAI-Manager
Speed up your OpenAI requests by balancing prompts to multiple API keys. Quite useful if you are playing with code-davinci-002
endpoint.
If you seldomly trigger rate limit errors, it is unnecessary to use this package.
Disclaimer
Before using this tool, you are required to read the EULA and ToS of OpenAI L.P. carefully. Actions that violate the OpenAI user agreement may result in the API Key and associated account being suspended. The author shall not be held liable for any consequential damages.
Design
TL;DR: this package helps you manage rate limit (both request-level and token-level) for each api_key for maximum number of requests to OpenAI API.
This is extremely helpful if you use CODEX
endpoint or you have a handful of free-trial accounts due to limited budget. Free-trial accounts apply strict rate limit.
Quickstart
-
Install openai-manager on PyPI.
pip install openai-manager
-
Prepare your OpenAI credentials in
- Environmental Varibles: any envvars beginning with
OPENAI_API_KEY
will be used to initialized the manager. Best practice to load your api keys is to prepare a.env
file like:
OPENAI_API_KEY_1=sk-Nxo****** OPENAI_API_KEY_2=sk-TG2****** OPENAI_API_KEY_3=sk-Kpt****** # You can set a global proxy for all api_keys OPENAI_API_PROXY=http://127.0.0.1:7890 # You can also append proxy to each api_key. # Make sure the indices match. OPENAI_API_PROXY_1=http://127.0.0.1:7890 OPENAI_API_PROXY_2=http://127.0.0.1:7890 OPENAI_API_PROXY_3=http://127.0.0.1:7890
Then load your environmental varibles before running any scripts:
export $(grep -v '^#' .env | xargs)
- YAML config file: you can add more fine-grained restrictions on each API key if you know the ratelimit for each key in advance. (WIP)
- Environmental Varibles: any envvars beginning with
-
Run this minimal running example to see how to boost your OpenAI completions. (more interfaces coming!)
import openai as official_openai import openai_manager @timeit def test_official_separate(): for i in range(10): prompt = "Once upon a time, " response = official_openai.Completion.create( model="code-davinci-002", prompt=prompt, max_tokens=20, ) print("Answer {}: {}".format(i, response["choices"][0]["text"])) @timeit def test_manager(): prompt = "Once upon a time, " prompts = [prompt] * 10 responses = openai_manager.Completion.create( model="code-davinci-002", prompt=prompts, max_tokens=20, ) assert len(responses) == 10 for i, response in enumerate(responses): print("Answer {}: {}".format(i, response["choices"][0]["text"]))
Performance Assessment
WIP
Frequently Asked Questions
-
Q: Why don't we just use official batching function?
prompt = "Once upon a time, " prompts = [prompt] * 10 response = openai.Completion.create( model="code-davinci-002", prompt=prompts, # official batching allows multiple prompts in one request max_tokens=20, ) assert len(response["choices"]) == 10 for i, answer in enumerate(response["choices"]): print("Answer {}: {}".format(i, answer["text"]))
A:
code-davinci-002
or other similar OpenAI endpoints apply strict token-level rate limit, even if you upgrade to pay-as-you-go user. Simple batching would not solve this.
Acknowledgement
TODO
- Support all functions in OpenAI Python API.
- Completions
- Embeddings
- Generations
- ChatCompletions
- Better back-off strategy for maximum throughput.
- Properly handling exceptions raised by OpenAI API.
- Automatic rotation of tons of OpenAI API Keys. (Removing invaild, adding new, etc.)
Donation
If this package helps your research, consider making a donation via GitHub!
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