# Full installation (recommended)
pip install 'markitdown[all]'

# Minimal with specific formats
pip install 'markitdown[pdf,docx,pptx]'

# Using uv
uv pip install 'markitdown[all]'

# Basic conversion
markitdown document.pdf > output.md

# Specify output file
markitdown document.pdf -o output.md

# Pipe input
cat document.pdf | markitdown > output.md

# With Azure Document Intelligence
markitdown document.pdf -o output.md -d -e "<endpoint>"

from markitdown import MarkItDown

# Basic conversion
md = MarkItDown()
result = md.convert("document.xlsx")
print(result.text_content)

# With LLM for image descriptions
from openai import OpenAI

client = OpenAI()
md = MarkItDown(
    llm_client=client,
    llm_model="gpt-4o",
    llm_prompt="Describe this image in detail"
)
result = md.convert("image.jpg")
print(result.text_content)

# With Azure Document Intelligence
md = MarkItDown(docintel_endpoint="<your-endpoint>")
result = md.convert("complex-document.pdf")
print(result.text_content)

from markitdown import MarkItDown
from pathlib import Path

md = MarkItDown()
input_dir = Path("./documents")
output_dir = Path("./markdown")
output_dir.mkdir(exist_ok=True)

for file in input_dir.glob("*"):
    if file.is_file():
        try:
            result = md.convert(str(file))
            output_file = output_dir / f"{file.stem}.md"
            output_file.write_text(result.text_content)
            print(f"Converted: {file.name}")
        except Exception as e:
            print(f"Failed: {file.name} - {e}")

from markitdown import MarkItDown

def prepare_for_llm(file_path: str) -> str:
    """Convert document to LLM-ready markdown."""
    md = MarkItDown()
    result = md.convert(file_path)

    # Add source reference
    content = f"# Source: {file_path}\n\n{result.text_content}"
    return content

# Use with your LLM
context = prepare_for_llm("report.pdf")

# CLI
markitdown "https://www.youtube.com/watch?v=VIDEO_ID" > transcript.md

# Python
from markitdown import MarkItDown

md = MarkItDown()
result = md.convert("https://www.youtube.com/watch?v=VIDEO_ID")
print(result.text_content)

from markitdown import MarkItDown
from openai import OpenAI

# Initialize with LLM support
client = OpenAI()
md = MarkItDown(
    llm_client=client,
    llm_model="gpt-4o"
)

# Convert image with AI description
result = md.convert("screenshot.png")
print(result.text_content)

from markitdown import MarkItDown

md = MarkItDown()
result = md.convert("analysis.ipynb")
print(result.text_content)  # Code cells, outputs, markdown

from markitdown import MarkItDown

md = MarkItDown()
result = md.convert("https://en.wikipedia.org/wiki/Python")
print(result.text_content)  # Main article content only

from markitdown import MarkItDown

md = MarkItDown()
result = md.convert("https://example.com/feed.xml")
print(result.text_content)  # Feed entries as markdown

# List installed plugins
markitdown --list-plugins

# Enable plugins during conversion
markitdown --use-plugins document.pdf

# Enable plugins in Python
md = MarkItDown(enable_plugins=True)
result = md.convert("document.pdf")

# Install MCP server
pip install markitdown-mcp

# Or from source
git clone https://github.com/microsoft/markitdown.git
cd markitdown/packages/markitdown-mcp
pip install -e .

# Build image
docker build -t markitdown:latest .

# Convert file
docker run --rm -i markitdown:latest < document.pdf > output.md

# ModuleNotFoundError for specific format
pip install 'markitdown[pdf]'  # Install missing dependency

# Azure authentication
export AZURE_DOCUMENT_INTELLIGENCE_ENDPOINT="<endpoint>"
export AZURE_DOCUMENT_INTELLIGENCE_KEY="<key>"

# Create virtual environment
python -m venv .venv
source .venv/bin/activate  # Linux/macOS
.venv\Scripts\activate     # Windows

# Install
pip install 'markitdown[all]'