Custom client

Reuse your client

Pass a custom client when your application already manages provider transport, authentication, retries, or observability outside ViscribeAI. The custom client must expose the OpenAI-compatible Chat Completions shape that ViscribeAI calls internally.

Python
TypeScript

from openai import OpenAI
from viscribe import ViscribeAI

openai_client = OpenAI(api_key="sk-your-provider-key")

client = ViscribeAI(
    model_config={"model": "gpt-5-mini"},
    client=openai_client,
)

result = client.images.extract(
    image_path="examples/receipt.png",
    output_schema=[{"name": "total_amount", "type": "number"}],
)

import OpenAI from "openai";
import { ViscribeAI } from "viscribe";

const openaiClient = new OpenAI({
  apiKey: "sk-your-provider-key",
});

const client = new ViscribeAI({
  modelConfig: { model: "gpt-5-mini" },
  client: openaiClient,
});

const result = await client.images.extract({
  imagePath: "examples/receipt.png",
  outputSchema: [{ name: "total_amount", type: "number" }],
});

Expected shape

ViscribeAI calls chat.completions.create on the client. The response should match the OpenAI Chat Completions response shape closely enough for ViscribeAI to read choices, message content or parsed output, refusal data, finish reasons, and usage metadata.

Use model_config or modelConfig for model request options even when passing a custom client.

OpenAI-compatible Extract structured data

Getting started

Image extraction

Community

Reuse your client

Expected shape

​Reuse your client

​Expected shape

Reuse your client

Expected shape