open-xtract 0.1.2

Open-source framework that extracts structured data from unstructured data.

OpenXtract

Turn documents into structured data

Open-source toolkit for extracting clean, structured data from text, images, and PDFs.

• GitHub
• PyPI

Installation

pip install open-xtract
# or
uv add open-xtract

Usage

The model string should look like: <provider>:<model_string>

Ex. "openai:gpt-5-nano", "xai:grok-4"

from pydantic import BaseModel
from open_xtract import OpenXtract

class InvoiceData(BaseModel):
    invoice_number: str
    date: str
    total_amount: float
    vendor: str

ox = OpenXtract(model="openai:gpt-5-nano")  # or any model

# Extract from text (str)
result = ox.extract("Total: $123.45 on 2025-03-01 from ACME", InvoiceData)
print(result)

# Extract from image (bytes)
with open("/path/to/receipt.png", "rb") as f:
    img_bytes = f.read()
result = ox.extract(img_bytes, InvoiceData)
print(result)

# Extract from PDF (bytes) — each page is rendered to an image internally
with open("/path/to/invoice.pdf", "rb") as f:
    pdf_bytes = f.read()
result = ox.extract(pdf_bytes, InvoiceData)
print(result)

Advanced Features

Model Configuration

# Use any OpenAI-compatible model
ox = OpenXtract(model="openrouter:qwen/qwen3-max")
ox = OpenXtract(model="xai:grok-4")

Features

• Extract structured data from text
• Model-agnostic (works with any OpenAI-compatible API)
• Simple, clean API

Contributing

See CONTRIBUTING.md for contribution guidelines.

License

MIT - see LICENSE.

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