markdocx 5.0.0

Convert AI-generated Markdown textbooks to polished DOCX with native math equations and syntax-highlighted code

markdocx

A Markdown-to-Word converter built for AI-generated textbooks

Convert Markdown files — complete with LaTeX math, syntax-highlighted code, tables, and images — into polished .docx documents in one command.

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Why This Exists

Large language models (ChatGPT, Claude, Gemini, …) produce great Markdown, but the journey from .md to a well-formatted Word document is painful:

• LaTeX formulas become plain text or broken images
• Code blocks lose their highlighting
• Tables, lists, and blockquotes need manual reformatting

markdocx bridges that gap. Feed it a Markdown file that follows a few simple rules and get a publication-ready .docx — math rendered as native Word OMML equations, code with VS Code–style colors, diagrams rendered as images, and everything else properly formatted.

Features

Category	What you get
Math	Inline ($...$) and display ($$...$$) LaTeX → native OMML equations in Word
Code	30+ languages with Pygments syntax highlighting, VS Code light theme, language labels
Tables	Auto-formatted Table Grid — bold header row, left/center/right alignment, inline math in cells
Lists	Bullet (•◦▪) and numbered lists, up to 6 nesting levels
Matrix	\``matrix` blocks → visual matrix diagrams with brackets, labels, and captions
Charts	\``chart` blocks → bar, line, pie, and scatter charts via matplotlib
Graphs	\``graph` blocks → network/graph diagrams with weighted edges via networkx
Workflows	\``workflow` blocks → flowcharts with decision diamonds, process boxes, and arrows
Other	Blockquotes, horizontal rules, clickable hyperlinks, local images, footnotes

Quick Start

Installation

# Using uv (recommended)
uv add markdocx

# Or using pip
pip install markdocx

Install from source (for development)

git clone https://github.com/shynerri-source/markdocx.git
cd markdocx
uv sync        # or: pip install -e .

Usage

CLI

# Convert a single file
markdocx input.md
markdocx input.md -o output.docx

# Convert an entire directory
markdocx ./chapters/ -o ./output/

# Recursively search subdirectories
markdocx ./chapters/ -o ./output/ -r

# Verbose logging
markdocx input.md -v

Python API

from markdocx import convert_file, convert_directory

# Single file
convert_file("input.md", "output.docx")

# Entire directory
results = convert_directory("./chapters/", output_dir="./output/", recursive=True)

CLI Options

Flag	Description
input	Markdown file or directory to convert
-o, --output	Output file or directory path
-r, --recursive	Recursively find .md files in subdirectories
-v, --verbose	Show detailed processing logs

How It Works

Markdown file
     │
     ▼
 md_parser.py        ─── markdown-it-py tokenizer
     │
     ▼
 docx_builder.py     ─── walks the token stream, builds Word elements
     ├── math_renderer.py      ─── LaTeX → MathML → OMML (native Word equations)
     ├── code_renderer.py      ─── Pygments lexer → colored Word runs
     ├── diagram_renderer.py   ─── matrix / chart / graph / workflow → PNG images
     └── styles.py             ─── fonts, colors, spacing presets
     │
     ▼
  .docx file          ─── python-docx output

Math Pipeline

LaTeX is converted to native OMML (Office Math Markup Language), not images. This means formulas are editable, scale perfectly, and look like they were typed in Word's equation editor.

LaTeX string → latex2mathml → MathML → XSLT → OMML → Word paragraph

Code Pipeline

Source code → Pygments lexer + VS Code theme → colored Word runs inside a shaded table cell

Diagram Pipeline

Fenced code blocks with special language identifiers (matrix, chart, graph, workflow) are rendered as PNG images via matplotlib/networkx and embedded in the document.

```matrix
name: A
1 2 3
4 5 6
7 8 9
caption: Matrix A (3×3)
```

```chart
type: bar
title: Algorithm Performance
labels: Bubble Sort, Merge Sort, Quick Sort
Time (ms): 450, 38, 35
caption: Figure 1: Sorting comparison
```

```graph
directed: true
title: Shortest Path
A -> B: 5
B -> C: 3
A -> C: 7
caption: Figure 2: Weighted directed graph
```

```workflow
title: Login Process
[Start]
<User Input>
(Validate Credentials)
{Valid?}
(Grant Access)
[End]
caption: Figure 3: Authentication workflow
```

Block type	Formats	Rendered via
matrix	Simple text or JSON	matplotlib
chart	Simple key-value or JSON — bar, line, pie, scatter	matplotlib
graph	Edge list (A -> B: 5) or JSON — directed/undirected	matplotlib + networkx
workflow	Step notation ([Start], (Process), {Decision}, <I/O>) or JSON	matplotlib

Project Structure

markdocx/
├── main.py                  # Convenience entry point
├── pyproject.toml           # Project metadata & dependencies
├── src/
│   └── markdocx/            # Installable package
│       ├── __init__.py      # Public API (convert_file, convert_directory)
│       ├── cli.py           # CLI entry point (markdocx command)
│       ├── core.py          # Top-level orchestrator
│       ├── md_parser.py     # Markdown → token stream
│       ├── math_renderer.py    # LaTeX → OMML (native Word math)
│       ├── code_renderer.py    # Code → syntax-highlighted Word runs
│       ├── diagram_renderer.py # Matrix / Chart / Graph / Workflow → PNG
│       ├── docx_builder.py     # Token stream → DOCX elements
│       └── styles.py           # Fonts, colors, and layout presets
└── rule/
    ├── ai_gen_doc_rule.md      # AI writing rules (Vietnamese)
    └── ai_gen_doc_rule_en.md   # AI writing rules (English)

Dependencies

Package	Version	Role
python-docx	1.2.0	DOCX generation
markdown-it-py	4.0.0	Markdown parsing
mdit-py-plugins	0.5.0	Math & footnote plugins
latex2mathml	3.78.1	LaTeX → MathML conversion
lxml	6.0.2	XML/XSLT processing
Pygments	2.19.2	Syntax highlighting
matplotlib	3.10.8	Chart, matrix, workflow rendering
Pillow	12.1.0	Image processing
networkx	3.4+	Graph/network diagram layouts

AI Writing Rules

The rule/ directory contains detailed guidelines for prompting AI models to produce Markdown that converts cleanly:

File	Language	Description
rule/ai_gen_doc_rule.md	Vietnamese	Full rule set — heading structure, LaTeX constraints, code block format, tables, etc.
rule/ai_gen_doc_rule_en.md	English	Same rules, English version

How to use: Paste the contents of the appropriate rule file into your AI system prompt (or at the start of the conversation) before asking it to write textbook content.

Contributing

Contributions are welcome. Please open an issue first to discuss what you'd like to change.

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

License

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