docmd

Convert PDFs, DOCX, and PPTX to clean, structure-preserving Markdown — from the CLI or a Python library.

Built for feeding documents into LLM and RAG pipelines, where clean Markdown beats raw text extraction.

(Published on PyPI as docmd-cli since docmd was already taken by an unrelated package — the import (import docmd) and CLI command (docmd convert ...) are unaffected.)

pip install docmd-cli
docmd convert report.pdf

from docmd import convert

markdown = convert("report.pdf")
print(markdown)

Why docmd

Great open-source document-to-Markdown converters already exist (docmd is built on Marker). The problem isn't quality — it's that running them yourself means a Python environment, several GB of RAM, ideally a GPU, and a non-trivial setup process before you convert your first file.

docmd wraps that engine with sane defaults and adds real post-processing on top (table cleanup, heading normalization) so the output is closer to what a RAG pipeline actually wants, not just raw model output.

A hosted API (POST a file, get Markdown back, no local setup) is planned — see ARCHITECTURE.md. It is not live yet; this repo is the open-source core, usable standalone today.

What it handles

Quickstart

CLI

pip install docmd-cli
docmd convert my-file.pdf -o output.md

Python

from docmd import convert

# From a file path
markdown = convert("my-file.pdf")

# From bytes
with open("my-file.pdf", "rb") as f:
    markdown = convert(f.read(), filename="my-file.pdf")

Installing DOCX/PPTX support

The base install (pip install docmd-cli) covers PDF only and stays lean. DOCX and PPTX need Marker's own additional dependencies:

pip install "docmd-cli[full]"

DOCX/PPTX conversion also needs weasyprint's native Pango/GObject/Cairo libraries, which pip cannot install for you:

# macOS
brew install pango

# Debian/Ubuntu (24.04 and older)
sudo apt-get install libpango-1.0-0 libpangocairo-1.0-0 libgdk-pixbuf2.0-0 libffi-dev shared-mime-info

# Debian trixie (13) and newer: libgdk-pixbuf2.0-0 was renamed
sudo apt-get install libpango-1.0-0 libpangocairo-1.0-0 libgdk-pixbuf-2.0-0 libffi-dev shared-mime-info

PDF conversion (the base install) does not need this.

Image handling

Images default to a text placeholder (*[... omitted]*) - no binary data, nothing to resolve, safe for RAG chunking:

markdown = convert("report.pdf")  # image_mode="placeholder" by default

To keep real image links instead, use image_mode="alt-text" and pass output_dir so the image files actually get saved somewhere the links can resolve to:

from docmd import convert_document
from docmd.config import ConvertConfig

result = convert_document(
    "report.pdf",
    config=ConvertConfig(image_mode="alt-text"),
    output_dir="output/",
)

From the CLI, --image-dir defaults to the output file's directory when -o is given:

docmd convert report.pdf -o output/report.md --image-mode alt-text

image_mode="skip" drops images entirely - no placeholder, no files.

How it works

docmd wraps Marker with sane defaults and a clean output format, then runs its own post-processing pass (docmd/postprocess/) to fix table structure and normalize heading levels — see ARCHITECTURE.md for why this is the actual differentiation, not just a thin wrapper.

License

The docmd wrapper code is MIT — see LICENSE.

docmd depends on Marker, whose code is Apache-2.0 and whose model weights are licensed under a modified Open RAIL-M license: free for research, personal use, and organizations under $5M in funding or revenue. Commercial use beyond that threshold requires a license from Datalab. This applies to you if you deploy docmd commercially at scale — check Marker's current license terms directly before doing so.

Roadmap

• PDF, DOCX, PPTX → Markdown
• Table cleanup / heading normalization post-processing
• Hosted API
• OCR quality tuning for scanned documents
• Batch conversion endpoint
• HTML output option

Contributing

Issues and PRs welcome. If you're hitting a conversion quality issue, please include a sample file (or a minimal reproduction) — it makes fixes much faster.

If docmd saves you the trouble of setting up your own PDF-parsing pipeline, consider starring the repo — it's how other people find it.