name: markitdown description: Expert guidance for converting files to Markdown using Microsoft's MarkItDown utility. Convert PDF, Word, PowerPoint, Excel, images, audio, HTML, CSV, JSON, XML, ZIP, and EPub files to LLM-friendly Markdown format. Use when processing documents for AI analysis, extracting content from files, or preparing data for language models.

MarkItDown File Conversion Expert

Expert guidance for converting diverse file formats to Markdown using Microsoft's MarkItDown utility. Transform documents, media, web content, and structured data into token-efficient Markdown optimized for LLM consumption.

Core Capabilities

1. Document Conversion - PDF, Word, PowerPoint, Excel
2. Media Processing - Images (OCR), Audio (transcription)
3. Web Content - HTML, YouTube URLs
4. Structured Data - CSV, JSON, XML
5. Archive Handling - ZIP files, EPub e-books
6. LLM Optimization - Token-efficient output for AI models

Quick Reference

Supported Formats

Key Features

• Structure Preservation - Maintains headings, lists, tables, links
• Token Efficiency - Optimized for LLM consumption
• OCR Support - Extract text from images
• Audio Transcription - Convert speech to text
• EXIF Metadata - Extract image metadata
• Azure Integration - Advanced PDF processing
• LLM Descriptions - Image descriptions via OpenAI

Instructions

Installation

Python 3.10+ Required

Full Installation (All Features):

pip install 'markitdown[all]'

Selective Installation:

# PDF support only
pip install 'markitdown[pdf]'

# Office documents
pip install 'markitdown[docx,pptx,xlsx]'

# Media files
pip install 'markitdown[audio,image]'

# Multiple features
pip install 'markitdown[pdf,docx,pptx,xlsx]'

Command-Line Usage

**Basic Con```bash

Output to stdout

markitdown document.pdf

Save to file

markitdown document.pdf > output.md

Specify output file

markitdown document.pdf -o output.md

**Batch Con```bash
# Convert multiple files
for file in *.pdf; do
  markitdown "$file" > "${file%.pdf}.md"
done

Python API Usage

**Basic Con```python from markitdown import MarkItDown

Initialize converter

md = MarkItDown()

Convert file

result = md.convert("document.pdf") print(result.text_content)

Save to file

with open("output.md", "w") as f: f.write(result.text_content)

**Convert from Stream:**
```python
with open("document.pdf", "rb") as f:
    result = md.convert_stream(f, file_extension=".pdf")
    print(result.text_content)

Batch Processing:

import os
from markitdown import MarkItDown

md = MarkItDown()
input_dir = "./documents"
output_dir = "./markdown"

for filename in os.listdir(input_dir):
    input_path = os.path.join(input_dir, filename)
    if os.path.isfile(input_path):
        result = md.convert(input_path)

        # Create output filename
        base_name = os.path.splitext(filename)[0]
        output_path = os.path.join(output_dir, f"{base_name}.md")

        with open(output_path, "w") as f:
            f.write(result.text_content)

        print(f"Converted: {filename}")

Advanced Features

Azure Document Intelligence

Enhanced PDF processing with Azure AI.

Setup:

pip install 'markitdown[azure-doc-intelligence]'

Configuration:

from markitdown import MarkItDown

md = MarkItDown(
    azure_doc_intelligence_endpoint="https://your-resource.cognitiveservices.azure.com",
    azure_doc_intelligence_key="your-key-here"
)

result = md.convert("complex-document.pdf")

Benefits:

• Better table extraction
• Improved layout understanding
• Enhanced text recognition
• Complex document handling

LLM-Powered Image Descriptions

Generate AI descriptions for images in documents.

Setup:

pip install 'markitdown[llm]'

Configuration:

from markitdown import MarkItDown

md = MarkItDown(
    llm_model="gpt-4o",  # or gpt-4o-mini
    llm_client=None       # Uses default Azure OpenAI or OpenAI
)

# Convert PowerPoint with image descriptions
result = md.convert("presentation.pptx")

Supported:

• PowerPoint slides with images
• Standalone image files
• Images in other document types

Docker Usage

Build Image:

docker build -t markitdown:latest .

**Run Con```bash docker run --rm -i markitdown:latest < document.pdf > output.md

**Volume Mounting:**
```bash
docker run --rm -v $(pwd):/data markitdown:latest /data/document.pdf > output.md

Common Workflows

Document Processing Pipeline

Workflow:

1. Receive document (PDF, Word, Excel, etc.)
2. Convert to Markdown with MarkItDown
3. Process Markdown with LLM
4. Extract insights or generate summaries
5. Present results

Python Implementation:

from markitdown import MarkItDown

def process_document(file_path):
    # Convert to Markdown
    md = MarkItDown()
    result = md.convert(file_path)
    markdown_content = result.text_content

    # Process with LLM (your code here)
    # insights = analyze_with_llm(markdown_content)

    return markdown_content

# Process multiple documents
documents = ["report.pdf", "data.xlsx", "slides.pptx"]
for doc in documents:
    content = process_document(doc)
    print(f"Processed: {doc}")

Batch Document Conversion

Script:

import os
from pathlib import Path
from markitdown import MarkItDown

def batch_convert(input_folder, output_folder):
    md = MarkItDown()

    # Create output folder
    Path(output_folder).mkdir(parents=True, exist_ok=True)

    # Get all supported files
    supported_extensions = [
        '.pdf', '.docx', '.pptx', '.xlsx', '.xls',
        '.html', '.csv', '.json', '.xml', '.zip', '.epub',
        '.png', '.jpg', '.jpeg', '.gif',
        '.wav', '.mp3'
    ]

    files = [
        f for f in os.listdir(input_folder)
        if any(f.lower().endswith(ext) for ext in supported_extensions)
    ]

    for filename in files:
        input_path = os.path.join(input_folder, filename)
        output_filename = Path(filename).stem + ".md"
        output_path = os.path.join(output_folder, output_filename)

        try:
            result = md.convert(input_path)
            with open(output_path, "w", encoding="utf-8") as f:
                f.write(result.text_content)
            print(f"✓ Converted: {filename}")
        except Exception as e:
            print(f"✗ Failed: {filename} - {e}")

# Usage
batch_convert("./input_docs", "./output_markdown")

Audio Transcription Pipeline

Workflow:

from markitdown import MarkItDown

def transcribe_audio(audio_file):
    md = MarkItDown()
    result = md.convert(audio_file)

    # result.text_content contains transcription
    return result.text_content

# Process audio file
transcript = transcribe_audio("meeting.wav")

# Save transcript
with open("meeting_transcript.md", "w") as f:
    f.write(f"# Meeting Transcript\n\n{transcript}")

Image OCR Extraction

Workflow:

from markitdown import MarkItDown

def extract_text_from_image(image_file):
    md = MarkItDown()
    result = md.convert(image_file)

    # Includes EXIF metadata and OCR text
    return result.text_content

# Extract text from scanned document
text = extract_text_from_image("scanned_page.png")
print(text)

Best Practices

File Processing

1. Check File Size - Large files take longer to process
2. Validate Format - Ensure file extension matches content
3. Handle Errors - Implement try-catch for conversion failures
4. Test Output - Verify Markdown quality for critical documents
5. Use Streams - For large files, use convert_stream() for memory efficiency

LLM Integration

1. Token Limits - Check Markdown length before sending to LLM
2. Chunking - Split long documents for processing
3. Preserve Structure - Markdown headers help LLMs understand context
4. Clean Output - Remove unnecessary formatting if needed
5. Metadata - Include file name and type in LLM context

Performance Optimization

1. Batch Processing - Convert multiple files in parallel
2. Cache Results - Store converted Markdown to avoid re-processing
3. Selective Features - Only install needed dependencies
4. Stream Processing - Use streams for memory efficiency
5. Azure Services - Use Azure Document Intelligence for complex PDFs

Supported Format Details

PDF

• Text extraction
• Table preservation
• Link extraction
• Structure maintenance
• Enhanced: Azure Document Intelligence for complex layouts

Word (DOCX)

• Headings hierarchy
• Lists (ordered/unordered)
• Tables
• Images (with optional descriptions)
• Links

PowerPoint (PPTX)

• Slide titles
• Content structure
• Tables
• Images (with optional LLM descriptions)
• Notes

Excel (XLSX/XLS)

• Sheet names
• Tables with headers
• Data preservation
• Multiple sheets

Images

• EXIF metadata extraction
• OCR text extraction
• Optional LLM descriptions (with OpenAI)
• Supported: PNG, JPG, JPEG, GIF

Audio

• Transcription to text
• Speaker detection
• Timestamp support
• Formats: WAV, MP3

HTML

• Content extraction
• Link preservation
• Table conversion
• Header hierarchy

CSV/JSON/XML

• Structure-aware conversion
• Data table formatting
• Key-value preservation

YouTube

• Video metadata
• Transcript extraction
• Description and comments

ZIP/EPub

• Archive extraction
• Recursive processing
• Content aggregation

Troubleshooting

Installation Issues

Problem: Dependency conflicts

Solution:

# Use virtual environment
python -m venv venv
source venv/bin/activate  # Linux/Mac
venv\Scripts\activate     # Windows

pip install 'markitdown[all]'

Conversion Failures

Problem: "Unsupported file format"

Solutions:

• Verify file extension matches content
• Check file isn't corrupted
• Ensure required dependencies installed
• Try opening file in native application first

Problem: "Memory error" on large files

Solutions:

• Use stream processing: convert_stream()
• Process file in chunks
• Increase available memory
• Use Azure Document Intelligence for PDFs

Output Quality

Problem: Poor OCR results

Solutions:

• Ensure image is high resolution
• Check image is not rotated
• Use Azure Document Intelligence
• Pre-process image (contrast, brightness)

Problem: Missing tables or structure

Solutions:

• Use Azure Document Intelligence for PDFs
• Verify source document is well-formatted
• Check PDF isn't scanned image (needs OCR)

When to Use This Skill

• Converting documents for LLM analysis
• Extracting text from PDFs and images
• Processing Office documents programmatically
• Transcribing audio to text
• Building document processing pipelines
• Preparing data for AI/ML workflows
• Creating searchable archives from documents
• Extracting structured data from files

Keywords

markitdown, file conversion, markdown, pdf to markdown, document processing, ocr, audio transcription, llm preprocessing, document extraction, text extraction, file parsing, microsoft, autogen, python utility, batch conversion