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MrZilinXiao / openai-manager

Speed up your OpenAI requests by balancing prompts to multiple API keys.
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OpenAI-Manager

pypi versions Run Unittest

Speed up your OpenAI requests by balancing prompts to multiple API keys. Quite useful if you are playing with code-davinci-002 endpoint.

Update on 2023/03/24: OpenAI terminated all CODEX endpoint access today. An immediate migration to gpt-3.5-turbo or other endpoints is needed to ensure the stability of your service.

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.

Caution: do not deploy this tool directly in Mainland China, Hong Kong SAR or any other locations where OpenAI disallows API usage. Use OPENAI_API_PROXY environmental variable to set a proxy (e.g. Japan) for connectting OpenAI API. Failure to do so will bring quick termination of your account.

Design

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

  1. Install openai-manager on PyPI. Notice we need Python 3.8+ for maximum compatibility of asyncio and tiktoken.

    pip install -U openai-manager

  2. Prepare your OpenAI credentials in:

    Environment Variables 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: ```bash 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 ``` `openai-manager` will try to read the `.env` file in your current working directory. You can also load environmental varibles manually by: ```bash 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. See [example_config.yml](/example_config.yml) for details. ```python import openai_manager openai_manager.append_auth_from_config(config_path='example_config.yml') ```

  3. Two ways to use openai_manager:

    • Use it just like how you use official openai package. We implement exact the same call signature as official openai package.

      import openai as official_openai
import openai_manager
from openai_manager.utils import timeit

@timeit
def test_official_separate():
    for i in range(10):
        prompt = "Once upon a time, "
        response = official_openai.Completion.create(
            model="text-davinci-003",
            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="text-davinci-003",
        prompt=prompts,
        max_tokens=20,
    )
    assert len(responses) == 10
    for i, response in enumerate(responses):
        print("Answer {}: {}".format(i, response["choices"][0]["text"]))

    • Use it as a proxy server between you and OpenAI endpoint. First, run python -m openai_manager.serving --port 8000 --host localhost --api_key [your custom key]. Then set up the official python openai package:

      import openai
openai.api_base = "http://localhost:8000/v1"
openai.api_key = "[your custom key]"

# run like normal
prompt = ["Once upon a time, "] * 10
response = openai.Completion.create(
    model="text-davinci-003",
    prompt=prompt,
    max_tokens=20,
)
print(response["choices"][0]["text"])

Configuration

Most configurations are manupulated by environmental variables.

Rate limit triggers will be visible under logging.WARNING. Run export OPENAI_LOG_LEVEL=40 to ignore rate limit warnings if you believe current setting is stable enough.

Performance Assessment

After ChatCompletion release, the code-davinci-002 endpoint becomes slow. Using 10 API keys, running 100 completions with max_tokens=20 and other hyperparameters left default took 90 seconds on average. Using official API, it took 10 seconds per completion, thus 1000 in total.

Theroticallly, the throughput increases linearly with the number of API keys.

Frequently Asked Questions

  1. 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: Some OpenAI endpoints (like code-davinci-002) apply strict token-level rate limit, even if you upgrade to pay-as-you-go user. Simple batching would not solve this.

  2. Q: Why don't we just use server-less service (e.g. Cloudflare Workers, Tencent Cloud Functions) to do the same thing?

    A: First, I usually write in Python, and most cloud services do not support Python server-less function. Second, I am not sure server-less solutions are capable of handling rate limit controls given their status-less nature. Tracking usage of each API key would be difficult (practical but not elegant) for server-less solutions.

Acknowledgement

TODO

Features

Advance Functions

Donation

If this package helps your research, consider making a donation via GitHub!