OpenAI-Python-Client

A client library for accessing OpenAI API

Usage

First, create a client:

from openai_python_client import Client

client = Client(base_url="https://api.example.com")

If the endpoints you're going to hit require authentication, use AuthenticatedClient instead:

from openai_python_client import AuthenticatedClient

client = AuthenticatedClient(base_url="https://api.example.com", token="SuperSecretToken")

Now call your endpoint and use your models:

from openai_python_client.models import MyDataModel
from openai_python_client.api.my_tag import get_my_data_model
from openai_python_client.types import Response

with client as client:
    my_data: MyDataModel = get_my_data_model.sync(client=client)
    # or if you need more info (e.g. status_code)
    response: Response[MyDataModel] = get_my_data_model.sync_detailed(client=client)

Or do the same thing with an async version:

from openai_python_client.models import MyDataModel
from openai_python_client.api.my_tag import get_my_data_model
from openai_python_client.types import Response

async with client as client:
    my_data: MyDataModel = await get_my_data_model.asyncio(client=client)
    response: Response[MyDataModel] = await get_my_data_model.asyncio_detailed(client=client)

By default, when you're calling an HTTPS API it will attempt to verify that SSL is working correctly. Using certificate verification is highly recommended most of the time, but sometimes you may need to authenticate to a server (especially an internal server) using a custom certificate bundle.

client = AuthenticatedClient(
    base_url="https://internal_api.example.com", 
    token="SuperSecretToken",
    verify_ssl="/path/to/certificate_bundle.pem",
)

You can also disable certificate validation altogether, but beware that this is a security risk.

client = AuthenticatedClient(
    base_url="https://internal_api.example.com", 
    token="SuperSecretToken", 
    verify_ssl=False
)

Things to know:

1. Every path/method combo becomes a Python module with four functions:

   1. sync: Blocking request that returns parsed data (if successful) or None
   2. sync_detailed: Blocking request that always returns a Request, optionally with parsed set if the request was successful.
   3. asyncio: Like sync but async instead of blocking
   4. asyncio_detailed: Like sync_detailed but async instead of blocking

2. All path/query params, and bodies become method arguments.

3. If your endpoint had any tags on it, the first tag will be used as a module name for the function (my_tag above)

4. Any endpoint which did not have a tag will be in openai_python_client.api.default

Advanced customizations

There are more settings on the generated Client class which let you control more runtime behavior, check out the docstring on that class for more info. You can also customize the underlying httpx.Client or httpx.AsyncClient (depending on your use-case):

from openai_python_client import Client

def log_request(request):
    print(f"Request event hook: {request.method} {request.url} - Waiting for response")

def log_response(response):
    request = response.request
    print(f"Response event hook: {request.method} {request.url} - Status {response.status_code}")

client = Client(
    base_url="https://api.example.com",
    httpx_args={"event_hooks": {"request": [log_request], "response": [log_response]}},
)

# Or get the underlying httpx client to modify directly with client.get_httpx_client() or client.get_async_httpx_client()

You can even set the httpx client directly, but beware that this will override any existing settings (e.g., base_url):

import httpx
from openai_python_client import Client

client = Client(
    base_url="https://api.example.com",
)
# Note that base_url needs to be re-set, as would any shared cookies, headers, etc.
client.set_httpx_client(httpx.Client(base_url="https://api.example.com", proxies="http://localhost:8030"))

Building / publishing this package

This project uses Poetry to manage dependencies and packaging. Here are the basics:

1. Update the metadata in pyproject.toml (e.g. authors, version)
2. If you're using a private repository, configure it with Poetry
   1. poetry config repositories.<your-repository-name> <url-to-your-repository>
   2. poetry config http-basic.<your-repository-name> <username> <password>
3. Publish the client with poetry publish --build -r <your-repository-name> or, if for public PyPI, just poetry publish --build

If you want to install this client into another project without publishing it (e.g. for development) then:

1. If that project is using Poetry, you can simply do poetry add <path-to-this-client> from that project
2. If that project is not using Poetry:
   1. Build a wheel with poetry build -f wheel
   2. Install that wheel from the other project pip install <path-to-wheel>