sference-sdk 0.1.0

Python SDK for the sference batch API

sference Python SDK

Installable package: sference-sdk (import: sference_sdk). Used by the sference CLI and your own automation.

Install

uv add sference-sdk

Fallback:

pip install sference-sdk

From a clone of this repo:

uv sync --package sference-sdk

Usage

Set SFERENCE_API_KEY, or pass api_key= to the client.

./workload.jsonl

Batch APIs take a JSONL file: one JSON object per line. OpenAI-compatible lines include custom_id, method, url, and body; content-only lines are {"content": "..."} (then pass model= on submit).

Example workload.jsonl:

{"custom_id":"example-1","method":"POST","url":"/v1/chat/completions","body":{"model":"Qwen/Qwen3.6-35B-A3B","messages":[{"role":"user","content":"Say hello in exactly one word."}]}}
{"custom_id":"example-2","method":"POST","url":"/v1/chat/completions","body":{"model":"Qwen/Qwen3.6-35B-A3B","messages":[{"role":"system","content":"You reply with one short sentence only."},{"role":"user","content":"What is 2+2?"}]}}

Batches (sync)

Best for a fixed JSONL workload: one submit, poll until terminal, then fetch structured results or download JSONL via the API.

from sference_sdk import SferenceClient

client = SferenceClient(api_key="sk_...")

batch = client.submit_batch(
    input_file="./workload.jsonl",
    model="Qwen/Qwen3.6-35B-A3B",
    window="24h",
)
done = client.wait_for_completion(batch.id, poll_interval=2.0, timeout=3600.0)
results = client.get_results(done.id)
print(results.status, results.output_url)

Use a model supported by your sference deployment.

OpenAI-compatible responses (sync)

Standalone or stream-associated jobs via POST /v1/responses. Keys need responses:read and responses:write (default on newly issued keys).

from sference_sdk import SferenceClient

client = SferenceClient(api_key="sk_...")

created = client.create_response(
    model="Qwen/Qwen3.6-35B-A3B",
    input=[{"role": "user", "content": "Hello"}],
    metadata={"completion_window": "24h"},
)
row = client.get_response(created.id)

For a stream, add stream_id inside metadata next to completion_window.

OpenAI Python SDK (openai package)

If you already use the official OpenAI client, point it at sference’s /v1 endpoint and the same API key (with responses:read and responses:write).

pip install openai

import asyncio
import os

from openai import AsyncOpenAI


async def main() -> None:
    client = AsyncOpenAI(
        base_url="https://api.sference.com/v1",
        api_key=os.environ["SFERENCE_API_KEY"],
    )

    response = await client.responses.create(
        model="Qwen/Qwen3.6-35B-A3B",
        input=[{"role": "user", "content": "Hello, world!"}],
        background=True,
    )
    # Poll GET /v1/responses/{id} until terminal; your openai version may expose
    # something like await client.responses.retrieve(response.id), or use
    # AsyncSferenceClient.get_response(response.id) with the same API key.


asyncio.run(main())

Metadata: to set completion_window or stream_id like the native SDK, pass them in the request body your openai version supports (for example metadata= on create, or extra_body={"metadata": {...}} if the helper does not list those fields yet).

Async client — batches

AsyncSferenceClient uses httpx.AsyncClient so batch polling can run alongside other async I/O without blocking threads.

Use case: You already know the full set of prompts (for example a JSONL file) and want one scheduled unit of work with a clear terminal state and bulk results.

Benefits: Simple lifecycle (submit → wait → fetch results), fits large static workloads and JSONL-heavy pipelines.

import asyncio

from sference_sdk import AsyncSferenceClient


async def main() -> None:
    async with AsyncSferenceClient(api_key="sk_...") as client:
        batch = await client.submit_batch(
            input_file="./workload.jsonl",
            model="Qwen/Qwen3.6-35B-A3B",
            window="24h",
        )
        done = await client.wait_for_completion(batch.id, poll_interval=2.0, timeout=3600.0)
        results = await client.get_results(done.id)
        print(results.status, results.output_url)


asyncio.run(main())

Async client — streams

Stream-associated jobs use create_response(..., metadata={"stream_id": ..., "completion_window": "24h"}). Consume completions with list_responses_events / iter_responses_events (optional stream_id, wait_ms long-poll; optional checkpoints align with CLI sference responses tail).

Use case: Work arrives over time, or you want one id to group many responses and observe completions as they land.

Benefits: Independent submits with aggregated progress, stream-level status in the API/UI, and efficient event tailing.

import asyncio

from sference_sdk import AsyncSferenceClient


async def main() -> None:
    async with AsyncSferenceClient(api_key="sk_...") as client:
        stream = await client.create_stream(name="sdk-demo", window="24h")
        await client.create_response(
            model="Qwen/Qwen3.6-35B-A3B",
            input=[{"role": "user", "content": "Hello"}],
            metadata={"stream_id": stream.id, "completion_window": "24h"},
        )
        async for ev in client.iter_responses_events(stream_id=stream.id, checkpoint=False):
            print(ev.completion_id, ev.status)


asyncio.run(main())

CLI

For sference batch … and sference stream … commands, see the CLI README.