perceptic-core-client 0.50.99

A client library for accessing perceptic-core-server API

perceptic-core-client

A client library for accessing perceptic-core-server API

Usage

First, create a client:

from perceptic_core_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 perceptic_core_client import AuthenticatedClient

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

Now call your endpoint and use your models:

from perceptic_core_client.models import MyDataModel
from perceptic_core_client.api.my_tag import get_my_data_model
from perceptic_core_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 perceptic_core_client.models import MyDataModel
from perceptic_core_client.api.my_tag import get_my_data_model
from perceptic_core_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 perceptic_core_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 perceptic_core_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 perceptic_core_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 uv 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: https://docs.astral.sh/uv/guides/integration/alternative-indexes/
3. Build a distribution with uv build, builds sdist and wheel by default.
4. Publish the client with uv publish, see documentation for publishing to private indexes.

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

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