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Overview

The CONFSEC Python SDK provides developers with a convenient way to make secure and anonymous AI inference requests via CONFSEC. It can function as a drop-in replacement for existing OpenAI clients, or as an HTTP client for lower-level access to the CONFSEC API. Using the SDK, programs can make requests without the need to deploy and manager the CONFSEC proxy.

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Installation

pip install confsec

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Quickstart

Use our OpenAI wrapper as a drop-in replacement for existing OpenAI clients: 
# Use OpenAI wrapper
from confsec.openai import OpenAI
client = OpenAI()
Or, for lower-level access, use the CONFSEC-enabled HTTP client directly: 
# Use HTTP client
import os
from confsec import ConfsecClient

with ConfsecClient(api_key=os.environ["CONFSEC_API_KEY"]) as client:
    http = client.get_http_client()

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Configuration

We aim to make the SDK as config-free as possible. However, there are some parameters you can optionally configure to control how the client interacts with the CONFSEC backend:
  • concurrent_requests_target (int): Allows the client to specify the desired request parallelism. This primarily impacts the number of credits that the client will maintain cached and available to use immediately. Higher values for this parameter will increase the maximum request throughput that the client can achieve, but also increases the amount of credits that may be lost permanently if the client process terminates without properly closing the client.
  • default_node_tags (list[str]): Allows the client to specify default filters for CONFSEC compute nodes that will be applied to all requests. Users should not need to configure this in most cases, especially when using the OpenAI wrapper, since the model field of any request will be automatically mapped to the appropriate CONFSEC node tags.

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Usage

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OpenAI Wrapper

The OpenAI class can be initialized explicitly with an API key, by passing the api_key parameter to the constructor. Otherwise, it will attempt to load the API key from the CONFSEC_API_KEY environment variable. It is very important to call client.close() when you are done with the client to ensure that all resources are properly released. This can be done explicitly, or by using the OpenAI class as a context manager. Failure to do so may result in credits being lost. Currently, the following subset of APIs are supported:
  • Completions
  • Chat
import os
client = OpenAI()

stream = client.chat.completions.create(
    model="deepseek-r1:1.5b",
    messages=[
        {
            "role": "user",
            "content": "What is the meaning of life?",
        }
    ],
    stream=True,
)
for chunk in stream:
    print(chunk.choices[0].delta.content, end="")

client.close()

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HTTP Client

The ConfsecClient class can also be initialized explicitly for lower-level access to the CONFSEC API. It’s recommended to create an HTTP client using the get_http_client method of the ConfsecClient class which will use the client as the transport, instead of calling ConfsecClient’s methods directly. As with the OpenAI class, it is very important to call client.close() when you are done with the client to ensure that all resources are properly released. This can be done explicitly, or by using the ConfsecClient class as a context manager. Failure to do so may result in credits being lost. 
import os
from confsec import ConfsecClient

with ConfsecClient(api_key=os.environ["CONFSEC_API_KEY"]) as client:
    http = client.get_http_client()
    response = http.request(
        "POST",
        # Important: the base URL must be set to "https://confsec.invalid"
        "https://confsec.invalid/v1/chat/completions",
        json={
            "model": "deepseek-r1:1.5b",
            "messages": [
                {"role": "user", "content": "What is the meaning of life?"}
            ]
        }
    )
    print(response.json())