groundmark 0.1.0

PDF to grounded Markdown with bounding box annotations

groundmark

Grounded Markdown for PDFs

groundmark is a thin, batteries-included wrapper around anchorite. It provides concrete implementations of anchorite's provider protocols — Pydantic AI for LLM-based Markdown generation and pdfplumber for bounding box extraction — so you can go from PDF bytes to annotated Markdown in a single call. All the heavy lifting (Smith-Waterman alignment, annotation, stripping, quote resolution) lives in anchorite.

Give it a PDF and a model string, get back Markdown with embedded bounding box coordinates that trace every text span back to its location in the source PDF.

Architecture

The library processes documents in two streams that are then merged:

1. Semantic Stream: The PDF is sent to an LLM (via Pydantic AI) to produce clean Markdown with <!--page--> markers between pages.

2. Positional Stream: The PDF is parsed locally by pdfplumber to extract line-level text segments and their bounding boxes.

3. Alignment: Smith-Waterman alignment (via anchorite) maps each parsed line to its position in the Markdown, constrained by page boundaries.

4. Annotation: Bounding box coordinates are injected as HTML span attributes:

   <span data-bbox="120,45,180,890" data-page="3">The patient presented with</span>
   

Quick Start

import asyncio
import groundmark as gm

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

    config = gm.Config(model="bedrock:au.anthropic.claude-sonnet-4-6")

    # PDF -> annotated Markdown (one call)
    result = await gm.process(pdf_bytes, config)
    print(f"Coverage: {result.coverage_percent:.2%}")
    print(result.annotated_markdown[:500])

    # Strip for LLM consumption
    stripped = gm.strip(result.annotated_markdown)
    # stripped.plain_text: clean Markdown with spans removed
    # stripped.validation_map: list of (start, end, Anchor) ranges

    # Resolve verbatim quotes to PDF coordinates
    resolved = gm.resolve(result.annotated_markdown, ["the patient presented with"])
    # -> {"the patient presented with": [(page, BBox), ...]}

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

Debug Visualizer

The included visualizer overlays extracted bounding boxes onto the source PDF, useful for diagnosing alignment issues. Blue highlights show raw extracted boxes from pdfplumber; red highlights show aligned boxes from the annotated Markdown.

python -m groundmark.visualize input.pdf output.pdf --model "bedrock:au.anthropic.claude-sonnet-4-6"

# Or with cached Markdown:
python -m groundmark.visualize input.pdf output.pdf --markdown cached.md

Screenshot from Santoro et al., "Health outcomes and drug utilisation in children with Noonan syndrome: a European cohort study," Orphanet J Rare Dis 20:76 (2025). doi:10.1186/s13023-025-03594-7. CC-BY 4.0.

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:

Provider	Default	Environment Variable
Bedrock (boto3)	300s	AWS_READ_TIMEOUT
Anthropic (httpx)	600s	— (use ModelSettings(timeout=...))

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.