groundmark

PDF to Markdown conversion and quote-to-bbox resolution.

What it does

1. Convert: Send PDF pages to a vision-capable LLM (via Pydantic AI) to produce clean Markdown with <!--page--> markers between pages.
2. Resolve: Given verbatim quote strings, locate them in the source PDF and return bounding box coordinates. Uses pypdfium2 for per-character bbox extraction and seq-smith for Smith-Waterman alignment.

Quick Start

import asyncio
from groundmark import DocumentIndex
from groundmark.convert import Config, ModelConfig, convert

async def main():
    pdf_bytes = open("document.pdf", "rb").read()

    # PDF -> Markdown (requires pydantic-ai, install with e.g. groundmark[bedrock])
    model = ModelConfig(name="bedrock:au.anthropic.claude-sonnet-4-6")
    result = await convert(pdf_bytes, Config(model=model))
    print(result.markdown[:500])

    # Resolve verbatim quotes to PDF bounding boxes
    doc = DocumentIndex(pdf_bytes)
    resolved = doc.resolve(["the patient presented with"])
    # -> {"the patient presented with": [(page, BBox(top, left, bottom, right)), ...]}

    # The DocumentIndex can be reused for multiple resolve calls against the same PDF
    more = doc.resolve(["another quote from the same paper"])

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

Installation

# Resolve only (no LLM dependencies)
uv add groundmark

# With LLM provider extra(s) for conversion
uv add groundmark --extra anthropic,bedrock,google,openai

Configuration

Timeouts

The LLM call for PDF-to-Markdown conversion can take several minutes for large documents, especially with Opus on Bedrock. Timeout defaults by provider:

For Bedrock with Opus, 300s may not be enough. Set a higher timeout:

export AWS_READ_TIMEOUT=600

License

This project is licensed under the MIT License - see the LICENSE file for details.