viscribe 1.2.0

Extract structured data from images using AI models.

ViscribeAI Python

Extract structured data from images using AI models.

Define the output schema, pass the image, pick the AI model, and get parsed structured output back instead of free-form text.

📦 Installation

pip install viscribe

🚀 Features

• 🖼️ One schema-driven extract helper for image workflows
• 🔄 Sync and async helpers: extract and aextract
• 📊 Pydantic models, JSON Schema dictionaries, and simple field definitions
• 📁 Local image paths, base64 images, and remote image URLs
• ⚙️ Reusable ViscribeAI().images.extract client namespace
• 🧩 OpenAI-compatible model configuration

🎯 Quick Start

from pydantic import BaseModel, Field
from viscribe.images import extract


class Receipt(BaseModel):
    merchant_name: str | None = Field(description="Store or business name")
    total_amount: float | None = Field(description="Final total on the receipt")
    date: str | None = Field(description="Receipt date if visible")
    line_items: list[str] = Field(description="Visible purchased items")


result = extract(
    image_path="examples/receipt.png",
    output_schema=Receipt,
    instruction="Extract the receipt fields visible in the image.",
    model_config={
        "model": "gpt-5-mini",
        "api_key": "sk-...",
        "temperature": 1,
    },
)

print(result.data.model_dump())

🧱 Schema Options

output_schema accepts:

• simple field definitions
• JSON Schema dictionaries
• Pydantic model classes

Simple field types are text, number, array_text, and array_number.

Strict schemas are checked against the OpenAI Structured Outputs subset before the request is sent. Unsupported schemas raise StructuredOutputSchemaError with the schema path and a suggested fix.

⚡ Async

from viscribe.images import aextract

result = await aextract(
    image_url="https://example.com/receipt.png",
    output_schema=[{"name": "total_amount", "type": "number"}],
    instruction="Extract the visible receipt total.",
)

♻️ Reusable Client

from viscribe import ViscribeAI

client = ViscribeAI(model_config={"model": "gpt-5-mini", "temperature": 1})

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

💬 Support & Feedback

• 📧 Email: [e-mail]
• 💻 GitHub Issues: Create an issue
• 💬 Discord: Join the community

🤝 Contributing

Please see the contributing guidelines.

🛠️ Development

uv sync
uv run ruff check .
uv run python -m pytest
uv build