layoutscribe 0.1.0a3

LLM-only, agentic layout & text extraction to Markdown/Text/Layout JSON

LayoutScribe

LLM-powered layout & text extraction for PDFs, slides, and Word docs

LLM-only, agentic parser that converts PDF / PPTX / DOCX into clean Markdown, plain text, and layout JSON (with normalized bounding boxes).
Built with LangGraph (agent orchestration), LiteLLM (provider-agnostic multimodal calls), and MLflow (tracing).

No OCR engines, no heuristic parsers. Rendering to images is allowed; all structure and text understanding is done by a multimodal LLM.

Features (0.1)

• Inputs: PDF, PPTX, DOCX (rendered pages/slides as images)
• Outputs:
  • Markdown (headings, lists, tables, captions)
  • Plain text
  • Layout JSON (blocks with type, bbox[0..1], text, conf)
• Agentic pipeline: planner → page_vision (async) → reviewer (validate/re-ask) → composer
• Robustness:
  • Re-ask on schema/geometry violations (IoU/coverage checks)
  • Fallback injection when LLM returns empty content so Markdown is never blank
• Provider-agnostic via LiteLLM (OpenAI, Azure OpenAI, Claude, Gemini)
• MLflow tracing for params, metrics, artifacts

Status

0.1 (alpha) released — see CHANGELOG.md and docs/ROADMAP.md.

Quick Links

• docs/ARCHITECTURE.md – modules & flow
• docs/PROMPTS_AND_SCHEMA.md – prompt rules and schema notes
• docs/schema/layout_page.schema.json – formal JSON Schema (Draft 2020-12)
• docs/CONFIGURATION.md – env vars, provider-specific setup, .env example
• docs/API_SPEC.md / docs/CLI_SPEC.md – contracts & examples
• docs/BENCHMARKS.md – datasets & metrics
• docs/TESTING_STRATEGY.md – testing plan & commands
• docs/PROVIDERS.md – model matrix & concurrency guidance
• docs/SECURITY.md – keys, artifacts, and vulnerability reporting
• docs/ROADMAP.md – milestones
• CONTRIBUTING.md – how to help
• CHANGELOG.md – notable changes

Installation

Requires Python 3.10+.

pip install layoutscribe

Optional extras:

# Office file support (PPTX/DOCX rendering via python-pptx / python-docx)
pip install "layoutscribe[office]"

# Development tools (ruff, black, pytest)
pip install "layoutscribe[dev]"

Runtime notes:

• PDF rendering: PyMuPDF (included)
• PPTX/DOCX support: python-pptx, python-docx (install with [office])

Getting Started

Set provider keys as environment variables (see CONFIGURATION.md). Example .env:

OPENAI_API_KEY=sk-...
LAYOUTSCRIBE_DPI=180

Quickstart

CLI

layoutscribe parse ./samples/report.pdf \
  --llm openai/gpt-4o \
  --outputs markdown text layout_json \
  --output-dir ./artifacts/report \
  --dpi 180 --parallel-pages 6 --budget-usd 0.50

Python API

import asyncio
from layoutscribe.api import parse as ls_parse


async def main() -> None:
  doc = await ls_parse(
    path="samples/report.pdf",
    outputs=["markdown", "text", "layout_json"],
    llm="openai/gpt-4o",
    dpi=180,
    parallel_pages=6,
    budget_usd=0.50,
    save_intermediate=True,
  )
  print(doc.metadata)
  print(doc.markdown[:1000])


if __name__ == "__main__":
  asyncio.run(main())

Outputs & Artifacts

./artifacts/report/
  document.md
  document.txt
  layout.json
  overlays/
    page-0001.png
    page-0002.png
  intermediate/
    page-0001.json

Configuration

See docs/CONFIGURATION.md for provider-specific env vars, defaults, and precedence. MLflow tracing is opt-in via --trace-mlflow.

LiteLLM provider setup

LiteLLM reads provider keys from environment variables. Set only those you need:

# OpenAI
OPENAI_API_KEY=sk-...

# Azure OpenAI
AZURE_OPENAI_API_KEY=...  
AZURE_OPENAI_ENDPOINT=https://<your-resource>.openai.azure.com/  
AZURE_OPENAI_API_VERSION=2024-02-15-preview

# Anthropic
ANTHROPIC_API_KEY=...

# Google (Gemini)
GOOGLE_API_KEY=...

Use --llm to pick a model via LiteLLM:

--llm openai/gpt-4o
--llm azure/<deployment_name>
--llm anthropic/claude-3.5-sonnet
--llm google/gemini-1.5-pro

Notes:

• For Azure, ensure the deployment name references a vision-capable model and that your endpoint/API version are set.
• Keep temperature low (0–0.2) for consistent JSON.
• Respect provider rate limits; we use retries with exponential backoff.

Limitations (0.1)

• No OCR engines; relies entirely on a multimodal LLM
• Basic tables only (CSV-like); no complex rowspan/colspan recovery
• No handwriting support; language translation out of scope
• Confidence scores (if present) are heuristic and not calibrated

Community & Support

• Open issues and discussions on GitHub
• For security concerns, follow SECURITY.md (use private advisories)

License

Apache-2.0 (see LICENSE).