iterationlayer 1.1.7

Official Python SDK for the Iteration Layer API

Iteration Layer Python SDK

Official Python SDK for the Iteration Layer API.

Installation

pip install iterationlayer

Usage

from iterationlayer import IterationLayer

client = IterationLayer(api_key="il_your_api_key")

Document Extraction

Extract structured data from documents using AI.

result = client.extract(
    files=[{"type": "url", "name": "invoice.pdf", "url": "https://example.com/invoice.pdf"}],
    schema={
        "fields": [
            {"type": "TEXT", "name": "company_name", "description": "The company name"},
            {"type": "CURRENCY_AMOUNT", "name": "total", "description": "The invoice total"},
        ]
    },
)

print(result["company_name"]["value"])       # "Acme Corp"
print(result["company_name"]["confidence"])  # 0.95

Image Transformation

Resize, crop, convert, and apply effects to images.

result = client.transform(
    file={"type": "url", "name": "photo.jpg", "url": "https://example.com/photo.jpg"},
    operations=[
        {"type": "resize", "width_in_px": 800, "height_in_px": 600, "fit": "cover"},
        {"type": "convert", "format": "webp", "quality": 85},
    ],
)

import base64
image_bytes = base64.b64decode(result["buffer"])

Image Generation

Compose images from layer definitions.

result = client.generate_image(
    dimensions={"width_in_px": 1200, "height_in_px": 630},
    layers=[
        {"type": "solid-color", "index": 0, "hex_color": "#1a1a2e"},
        {
            "type": "text",
            "index": 1,
            "text": "Hello World",
            "font_name": "Inter",
            "font_size_in_px": 48,
            "text_color": "#ffffff",
            "position": {"x_in_px": 50, "y_in_px": 50},
            "dimensions": {"width_in_px": 1100, "height_in_px": 530},
        },
    ],
    output_format="png",
)

import base64
image_bytes = base64.b64decode(result["buffer"])

Document Generation

Generate PDF, DOCX, EPUB, or PPTX from structured data.

result = client.generate_document(
    format="pdf",
    document={
        "metadata": {"title": "Invoice #123"},
        "page": {
            "size": {"preset": "A4"},
            "margins": {"top_in_pt": 36, "bottom_in_pt": 36, "left_in_pt": 36, "right_in_pt": 36},
        },
        "styles": {
            "text": {"font_family": "Helvetica", "font_size_in_pt": 12, "line_height": 1.5, "color": "#000000"},
            "headline": {"font_family": "Helvetica", "font_size_in_pt": 24, "color": "#000000", "spacing_before_in_pt": 12, "spacing_after_in_pt": 6},
            "link": {"color": "#0066cc"},
            "list": {"indent_in_pt": 18, "spacing_between_items_in_pt": 4},
            "table": {
                "header": {"background_color": "#f0f0f0", "font_family": "Helvetica", "font_size_in_pt": 12, "color": "#000000", "padding_in_pt": 6},
                "body": {"font_family": "Helvetica", "font_size_in_pt": 12, "color": "#000000", "padding_in_pt": 6},
            },
            "grid": {"gap_in_pt": 12},
            "separator": {"color": "#cccccc", "thickness_in_pt": 1, "margin_top_in_pt": 12, "margin_bottom_in_pt": 12},
            "image": {"alignment": "center", "margin_top_in_pt": 8, "margin_bottom_in_pt": 8},
        },
        "content": [
            {"type": "headline", "level": "h1", "text": "Invoice #123"},
            {"type": "paragraph", "markdown": "Thank you for your business."},
        ],
    },
)

import base64
pdf_bytes = base64.b64decode(result["buffer"])

Sheet Generation

Generate CSV, Markdown, or XLSX spreadsheets from structured data.

result = client.generate_sheet(
    format="xlsx",
    sheets=[
        {
            "name": "Invoices",
            "columns": [
                {"name": "Company", "width": 20},
                {"name": "Total", "width": 15},
            ],
            "rows": [
                [
                    {"value": "Acme Corp"},
                    {"value": 1500.50, "format": "currency", "currency_code": "EUR"},
                ],
            ],
        },
    ],
)

import base64
sheet_bytes = base64.b64decode(result["buffer"])

Webhooks (Async)

Use the *_async methods to receive results via webhook instead of waiting for the response.

result = client.extract_async(
    files=[{"type": "url", "name": "invoice.pdf", "url": "https://example.com/invoice.pdf"}],
    schema={
        "fields": [
            {"type": "CURRENCY_AMOUNT", "name": "total", "description": "The invoice total"},
        ]
    },
    webhook_url="https://your-app.com/webhooks/extraction",
)

Context Manager

The client can be used as a context manager to ensure the underlying HTTP connection is closed.

with IterationLayer(api_key="il_your_api_key") as client:
    result = client.extract(...)

Error Handling

from iterationlayer import IterationLayerError

try:
    result = client.extract(...)
except IterationLayerError as e:
    print(e.status_code)     # 422
    print(e.error_message)   # "Validation error: ..."

Documentation

Full documentation is available at https://iterationlayer.com/docs.

Issues & Feedback

Please report bugs and request features in the issues repository.