tabby/experimental/eval/tabby-python-client

tabby-python-client

A client library for accessing Tabby Server

Usage

First, create a client:

from tabby_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 tabby_python_client import AuthenticatedClient

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

Now call your endpoint and use your models:

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

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

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
)

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.

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 tabby_python_client.api.default

Building / publishing this Client

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>