MarkItDown - File to Markdown Conversion
Overview
MarkItDown is a Python tool developed by Microsoft for converting various file formats to Markdown. It's particularly useful for converting documents into LLM-friendly text format, as Markdown is token-efficient and well-understood by modern language models.
Key Benefits:
- Convert documents to clean, structured Markdown
- Token-efficient format for LLM processing
- Supports 15+ file formats
- Optional AI-enhanced image descriptions
- OCR for images and scanned documents
- Speech transcription for audio files
Visual Enhancement with Scientific Schematics
When creating documents with this skill, always consider adding scientific diagrams and schematics to enhance visual communication.
If your document does not already contain schematics or diagrams:
- Use the scientific-schematics skill to generate AI-powered publication-quality diagrams
- Simply describe your desired diagram in natural language
- Nano Banana Pro will automatically generate, review, and refine the schematic
For new documents: Scientific schematics should be generated by default to visually represent key concepts, workflows, architectures, or relationships described in the text.
How to generate schematics:
python scripts/generate_schematic.py "your diagram description" -o figures/output.png
The AI will automatically:
- Create publication-quality images with proper formatting
- Review and refine through multiple iterations
- Ensure accessibility (colorblind-friendly, high contrast)
- Save outputs in the figures/ directory
When to add schematics:
- Document conversion workflow diagrams
- File format architecture illustrations
- OCR processing pipeline diagrams
- Integration workflow visualizations
- System architecture diagrams
- Data flow diagrams
- Any complex concept that benefits from visualization
For detailed guidance on creating schematics, refer to the scientific-schematics skill documentation.
Supported Formats
| Format | Description | Notes |
|---|
| PDF | Portable Document Format | Full text extraction |
| DOCX | Microsoft Word | Tables, formatting preserved |
| PPTX | PowerPoint | Slides with notes |
| XLSX | Excel spreadsheets | Tables and data |
| Images | JPEG, PNG, GIF, WebP | EXIF metadata + OCR |
| Audio | WAV, MP3 | Metadata + transcription |
| HTML | Web pages | Clean conversion |
| CSV | Comma-separated values | Table format |
| JSON | JSON data | Structured representation |
| XML | XML documents | Structured format |
| ZIP | Archive files | Iterates contents |
| EPUB | E-books | Full text extraction |
| YouTube | Video URLs | Fetch transcriptions |
Quick Start
Installation
# Install with all features
pip install 'markitdown[all]'
# Or from source
git clone https://github.com/microsoft/markitdown.git
cd markitdown
pip install -e 'packages/markitdown[all]'
Command-Line Usage
# Basic conversion
markitdown document.pdf > output.md
# Specify output file
markitdown document.pdf -o output.md
# Pipe content
cat document.pdf | markitdown > output.md
# Enable plugins
markitdown --list-plugins # List available plugins
markitdown --use-plugins document.pdf -o output.md
Python API
from markitdown import MarkItDown
# Basic usage
md = MarkItDown()
result = md.convert("document.pdf")
print(result.text_content)
# Convert from stream
with open("document.pdf", "rb") as f:
result = md.convert_stream(f, file_extension=".pdf")
print(result.text_content)
Advanced Features
1. AI-Enhanced Image Descriptions
Use LLMs via OpenRouter to generate detailed image descriptions (for PPTX and image files):
from markitdown import MarkItDown
from openai import OpenAI
# Initialize OpenRouter client (OpenAI-compatible API)
client = OpenAI(
api_key="your-openrouter-api-key",
base_url="https://openrouter.ai/api/v1"
)
md = MarkItDown(
llm_client=client,
llm_model="anthropic/claude-opus-4.5", # recommended for scientific vision
llm_prompt="Describe this image in detail for scientific documentation"
)
result = md.convert("presentation.pptx")
print(result.text_content)
2. Azure Document Intelligence
For enhanced PDF conversion with Microsoft Document Intelligence:
# Command line
markitdown document.pdf -o output.md -d -e "<document_intelligence_endpoint>"
# Python API
from markitdown import MarkItDown
md = MarkItDown(docintel_endpoint="<document_intelligence_endpoint>")
result = md.convert("complex_document.pdf")
print(result.text_content)
3. Plugin System
MarkItDown supports 3rd-party plugins for extending functionality:
# List installed plugins
markitdown --list-plugins
# Enable plugins
markitdown --use-plugins file.pdf -o output.md
Find plugins on GitHub with hashtag: #markitdown-plugin
Optional Dependencies
Control which file formats you support:
# Install specific formats
pip install 'markitdown[pdf, docx, pptx]'
# All available options:
# [all] - All optional dependencies
# [pptx] - PowerPoint files
# [docx] - Word documents
# [xlsx] - Excel spreadsheets
# [xls] - Older Excel files
# [pdf] - PDF documents
# [outlook] - Outlook messages
# [az-doc-intel] - Azure Document Intelligence
# [audio-transcription] - WAV and MP3 transcription
# [youtube-transcription] - YouTube video transcription
Common Use Cases
1. Convert Scientific Papers to Markdown
from markitdown import MarkItDown
md = MarkItDown()
# Convert PDF paper
result = md.convert("research_paper.pdf")
with open("paper.md", "w") as f:
f.write(result.text_content)
2. Extract Data from Excel for Analysis
from markitdown import MarkItDown
md = MarkItDown()
result = md.convert("data.xlsx")
# Result will be in Markdown table format
print(result.text_content)
3. Process Multiple Documents
from markitdown import MarkItDown
import os
from pathlib import Path
md = MarkItDown()
# Process all PDFs in a directory
pdf_dir = Path("papers/")
output_dir = Path("markdown_output/")
output_dir.mkdir(exist_ok=True)
for pdf_file in pdf_dir.glob("*.pdf"):
result = md.convert(str(pdf_file))
output_file = output_dir / f"{pdf_file.stem}.md"
output_file.write_text(result.text_content)
print(f"Converted: {pdf_file.name}")
4. Convert PowerPoint with AI Descriptions
from markitdown import MarkItDown
from openai import OpenAI
# Use OpenRouter for access to multiple AI models
client = OpenAI(
api_key="your-openrouter-api-key",
base_url="https://openrouter.ai/api/v1"
)
md = MarkItDown(
llm_client=client,
llm_model="anthropic/claude-opus-4.5", # recommended for presentations
llm_prompt="Describe this slide image in detail, focusing on key visual elements and data"
)
result = md.convert("presentation.pptx")
with open("presentation.md", "w") as f:
f.write(result.text_content)
5. Batch Convert with Different Formats
from markitdown import MarkItDown
from pathlib import Path
md = MarkItDown()
# Files to convert
files = [
"document.pdf",
"spreadsheet.xlsx",
"presentation.pptx",
"notes.docx"
]
for file in files:
try:
result = md.convert(file)
output = Path(file).stem + ".md"
with open(output, "w") as f:
f.write(result.text_content)
print(f"✓ Converted {file}")
except Exception as e:
print(f"✗ Error converting {file}: {e}")
6. Extract YouTube Video Transcription
from markitdown import MarkItDown
md = MarkItDown()
# Convert YouTube video to transcript
result = md.convert("https://www.youtube.com/watch?v=VIDEO_ID")
print(result.text_content)
Docker Usage
# Build image
docker build -t markitdown:latest .
# Run conversion
docker run --rm -i markitdown:latest < ~/document.pdf > output.md
Best Practices
1. Choose the Right Conversion Method
- Simple documents: Use basic
MarkItDown()
- Complex PDFs: Use Azure Document Intelligence
- Visual content: Enable AI image descriptions
- Scanned documents: Ensure OCR dependencies are installed
2. Handle Errors Gracefully
from markitdown import MarkItDown
md = MarkItDown()
try:
result = md.convert("document.pdf")
print(result.text_content)
except FileNotFoundError:
print("File not found")
except Exception as e:
print(f"Conversion error: {e}")
3. Process Large Files Efficiently
from markitdown import MarkItDown
md = MarkItDown()
# For large files, use streaming
with open("large_file.pdf", "rb") as f:
result = md.convert_stream(f, file_extension=".pdf")
# Process in chunks or save directly
with open("output.md", "w") as out:
out.write(result.text_content)
4. Optimize for Token Efficiency
Markdown output is already token-efficient, but you can:
- Remove excessive whitespace
- Consolidate similar sections
- Strip metadata if not needed
from markitdown import MarkItDown
import re
md = MarkItDown()
result = md.convert("document.pdf")
# Clean up extra whitespace
clean_text = re.sub(r'\n{3,}', '\n\n', result.text_content)
clean_text = clean_text.strip()
print(clean_text)
Integration with Scientific Workflows
Convert Literature for Review
from markitdown import MarkItDown
from pathlib import Path
md = MarkItDown()
# Convert all papers in literature folder
papers_dir = Path("literature/pdfs")
output_dir = Path("literature/markdown")
output_dir.mkdir(exist_ok=True)
for paper in papers_dir.glob("*.pdf"):
result = md.convert(str(paper))
# Save with metadata
output_file = output_dir / f"{paper.stem}.md"
content = f"# {paper.stem}\n\n"
content += f"**Source**: {paper.name}\n\n"
content += "---\n\n"
content += result.text_content
output_file.write_text(content)
# For AI-enhanced conversion with figures
from openai import OpenAI
client = OpenAI(
api_key="your-openrouter-api-key",
base_url="https://openrouter.ai/api/v1"
)
md_ai = MarkItDown(
llm_client=client,
llm_model="anthropic/claude-opus-4.5",
llm_prompt="Describe scientific figures with technical precision"
)
Extract Tables for Analysis
from markitdown import MarkItDown
import re
md = MarkItDown()
result = md.convert("data_tables.xlsx")
# Markdown tables can be parsed or used directly
print(result.text_content)
Troubleshooting
Common Issues
-
Missing dependencies: Install feature-specific packages
pip install 'markitdown[pdf]' # For PDF support
-
Binary file errors: Ensure files are opened in binary mode
with open("file.pdf", "rb") as f: # Note the "rb"
result = md.convert_stream(f, file_extension=".pdf")
-
OCR not working: Install tesseract
# macOS
brew install tesseract
# Ubuntu
sudo apt-get install tesseract-ocr
Performance Considerations
- PDF files: Large PDFs may take time; consider page ranges if supported
- Image OCR: OCR processing is CPU-intensive
- Audio transcription: Requires additional compute resources
- AI image descriptions: Requires API calls (costs may apply)
Next Steps
- See
references/api_reference.md for complete API documentation
- Check
references/file_formats.md for format-specific details
- Review
scripts/batch_convert.py for automation examples
- Explore
scripts/convert_with_ai.py for AI-enhanced conversions
Resources