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Zhongwei Li
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skills/markitdown/assets/example_usage.md
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skills/markitdown/assets/example_usage.md
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# MarkItDown Example Usage
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This document provides practical examples of using MarkItDown in various scenarios.
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## Basic Examples
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### 1. Simple File Conversion
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```python
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from markitdown import MarkItDown
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md = MarkItDown()
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# Convert a PDF
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result = md.convert("research_paper.pdf")
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print(result.text_content)
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# Convert a Word document
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result = md.convert("manuscript.docx")
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print(result.text_content)
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# Convert a PowerPoint
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result = md.convert("presentation.pptx")
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print(result.text_content)
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```
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### 2. Save to File
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```python
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from markitdown import MarkItDown
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md = MarkItDown()
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result = md.convert("document.pdf")
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with open("output.md", "w", encoding="utf-8") as f:
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f.write(result.text_content)
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```
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### 3. Convert from Stream
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```python
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from markitdown import MarkItDown
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md = MarkItDown()
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with open("document.pdf", "rb") as f:
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result = md.convert_stream(f, file_extension=".pdf")
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print(result.text_content)
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```
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## Scientific Workflows
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### Convert Research Papers
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```python
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from markitdown import MarkItDown
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from pathlib import Path
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md = MarkItDown()
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# Convert all papers in a directory
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papers_dir = Path("research_papers/")
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output_dir = Path("markdown_papers/")
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output_dir.mkdir(exist_ok=True)
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for paper in papers_dir.glob("*.pdf"):
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result = md.convert(str(paper))
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# Save with original filename
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output_file = output_dir / f"{paper.stem}.md"
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output_file.write_text(result.text_content)
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print(f"Converted: {paper.name}")
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```
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### Extract Tables from Excel
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```python
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from markitdown import MarkItDown
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md = MarkItDown()
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# Convert Excel to Markdown tables
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result = md.convert("experimental_data.xlsx")
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# The result contains Markdown-formatted tables
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print(result.text_content)
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# Save for further processing
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with open("data_tables.md", "w") as f:
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f.write(result.text_content)
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```
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### Process Presentation Slides
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```python
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from markitdown import MarkItDown
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from openai import OpenAI
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# With AI descriptions for images
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client = OpenAI()
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md = MarkItDown(
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llm_client=client,
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llm_model="anthropic/claude-sonnet-4.5",
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llm_prompt="Describe this scientific slide, focusing on data and key findings"
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)
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result = md.convert("conference_talk.pptx")
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# Save with metadata
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output = f"""# Conference Talk
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{result.text_content}
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"""
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with open("talk_notes.md", "w") as f:
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f.write(output)
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```
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## AI-Enhanced Conversions
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### Detailed Image Descriptions
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```python
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from markitdown import MarkItDown
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from openai import OpenAI
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# Initialize OpenRouter client
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client = OpenAI(
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api_key="your-openrouter-api-key",
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base_url="https://openrouter.ai/api/v1"
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)
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# Scientific diagram analysis
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scientific_prompt = """
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Analyze this scientific figure. Describe:
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- Type of visualization (graph, microscopy, diagram, etc.)
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- Key data points and trends
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- Axes, labels, and legends
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- Scientific significance
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Be technical and precise.
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"""
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md = MarkItDown(
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llm_client=client,
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llm_model="anthropic/claude-sonnet-4.5", # recommended for scientific vision
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llm_prompt=scientific_prompt
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)
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# Convert paper with figures
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result = md.convert("paper_with_figures.pdf")
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print(result.text_content)
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```
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### Different Prompts for Different Files
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```python
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from markitdown import MarkItDown
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from openai import OpenAI
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# Initialize OpenRouter client
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client = OpenAI(
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api_key="your-openrouter-api-key",
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base_url="https://openrouter.ai/api/v1"
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)
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# Scientific papers - use Claude for technical analysis
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scientific_md = MarkItDown(
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llm_client=client,
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llm_model="anthropic/claude-sonnet-4.5",
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llm_prompt="Describe scientific figures with technical precision"
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)
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# Presentations - use GPT-4o for visual understanding
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presentation_md = MarkItDown(
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llm_client=client,
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llm_model="anthropic/claude-sonnet-4.5",
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llm_prompt="Summarize slide content and key visual elements"
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)
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# Use appropriate instance for each file
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paper_result = scientific_md.convert("research.pdf")
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slides_result = presentation_md.convert("talk.pptx")
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```
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## Batch Processing
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### Process Multiple Files
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```python
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from markitdown import MarkItDown
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from pathlib import Path
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md = MarkItDown()
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files_to_convert = [
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"paper1.pdf",
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"data.xlsx",
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"presentation.pptx",
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"notes.docx"
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]
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for file in files_to_convert:
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try:
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result = md.convert(file)
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output = Path(file).stem + ".md"
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with open(output, "w") as f:
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f.write(result.text_content)
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print(f"✓ {file} -> {output}")
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except Exception as e:
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print(f"✗ Error converting {file}: {e}")
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```
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### Parallel Processing
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```python
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from markitdown import MarkItDown
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from pathlib import Path
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from concurrent.futures import ThreadPoolExecutor
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def convert_file(filepath):
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md = MarkItDown()
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result = md.convert(filepath)
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output = Path(filepath).stem + ".md"
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with open(output, "w") as f:
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f.write(result.text_content)
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return filepath, output
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files = list(Path("documents/").glob("*.pdf"))
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with ThreadPoolExecutor(max_workers=4) as executor:
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results = executor.map(convert_file, [str(f) for f in files])
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for input_file, output_file in results:
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print(f"Converted: {input_file} -> {output_file}")
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```
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## Integration Examples
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### Literature Review Pipeline
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```python
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from markitdown import MarkItDown
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from pathlib import Path
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import json
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md = MarkItDown()
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# Convert papers and create metadata
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papers_dir = Path("literature/")
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output_dir = Path("literature_markdown/")
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output_dir.mkdir(exist_ok=True)
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catalog = []
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for paper in papers_dir.glob("*.pdf"):
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result = md.convert(str(paper))
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# Save Markdown
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md_file = output_dir / f"{paper.stem}.md"
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md_file.write_text(result.text_content)
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# Store metadata
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catalog.append({
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"title": result.title or paper.stem,
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"source": paper.name,
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"markdown": str(md_file),
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"word_count": len(result.text_content.split())
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})
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# Save catalog
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with open(output_dir / "catalog.json", "w") as f:
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json.dump(catalog, f, indent=2)
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```
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### Data Extraction Pipeline
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```python
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from markitdown import MarkItDown
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import re
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md = MarkItDown()
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# Convert Excel data to Markdown
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result = md.convert("experimental_results.xlsx")
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# Extract tables (Markdown tables start with |)
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tables = []
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current_table = []
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in_table = False
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for line in result.text_content.split('\n'):
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if line.strip().startswith('|'):
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in_table = True
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current_table.append(line)
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elif in_table:
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if current_table:
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tables.append('\n'.join(current_table))
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current_table = []
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in_table = False
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# Process each table
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for i, table in enumerate(tables):
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print(f"Table {i+1}:")
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print(table)
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print("\n" + "="*50 + "\n")
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```
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### YouTube Transcript Analysis
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```python
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from markitdown import MarkItDown
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md = MarkItDown()
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# Get transcript
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video_url = "https://www.youtube.com/watch?v=VIDEO_ID"
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result = md.convert(video_url)
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# Save transcript
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with open("lecture_transcript.md", "w") as f:
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f.write(f"# Lecture Transcript\n\n")
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f.write(f"**Source**: {video_url}\n\n")
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f.write(result.text_content)
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```
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## Error Handling
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### Robust Conversion
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```python
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from markitdown import MarkItDown
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from pathlib import Path
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import logging
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logging.basicConfig(level=logging.INFO)
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logger = logging.getLogger(__name__)
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md = MarkItDown()
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def safe_convert(filepath):
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"""Convert file with error handling."""
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try:
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result = md.convert(filepath)
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output = Path(filepath).stem + ".md"
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with open(output, "w") as f:
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f.write(result.text_content)
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logger.info(f"Successfully converted {filepath}")
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return True
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except FileNotFoundError:
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logger.error(f"File not found: {filepath}")
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return False
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except ValueError as e:
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logger.error(f"Invalid file format for {filepath}: {e}")
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return False
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except Exception as e:
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logger.error(f"Unexpected error converting {filepath}: {e}")
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return False
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# Use it
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files = ["paper.pdf", "data.xlsx", "slides.pptx"]
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results = [safe_convert(f) for f in files]
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print(f"Successfully converted {sum(results)}/{len(files)} files")
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```
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## Advanced Use Cases
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### Custom Metadata Extraction
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```python
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from markitdown import MarkItDown
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import re
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from datetime import datetime
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md = MarkItDown()
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def convert_with_metadata(filepath):
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result = md.convert(filepath)
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# Extract metadata from content
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metadata = {
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"file": filepath,
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"title": result.title,
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"converted_at": datetime.now().isoformat(),
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"word_count": len(result.text_content.split()),
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"char_count": len(result.text_content)
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}
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# Try to find author
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author_match = re.search(r'(?:Author|By):\s*(.+?)(?:\n|$)', result.text_content)
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if author_match:
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metadata["author"] = author_match.group(1).strip()
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# Create formatted output
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output = f"""---
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title: {metadata['title']}
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author: {metadata.get('author', 'Unknown')}
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source: {metadata['file']}
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converted: {metadata['converted_at']}
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words: {metadata['word_count']}
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---
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{result.text_content}
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"""
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return output, metadata
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# Use it
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content, meta = convert_with_metadata("paper.pdf")
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print(meta)
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```
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### Format-Specific Processing
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```python
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from markitdown import MarkItDown
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from pathlib import Path
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md = MarkItDown()
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def process_by_format(filepath):
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path = Path(filepath)
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result = md.convert(filepath)
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if path.suffix == '.pdf':
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# Add PDF-specific metadata
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output = f"# PDF Document: {path.stem}\n\n"
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output += result.text_content
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elif path.suffix == '.xlsx':
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# Add table count
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table_count = result.text_content.count('|---')
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output = f"# Excel Data: {path.stem}\n\n"
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output += f"**Tables**: {table_count}\n\n"
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output += result.text_content
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elif path.suffix == '.pptx':
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# Add slide count
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slide_count = result.text_content.count('## Slide')
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output = f"# Presentation: {path.stem}\n\n"
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output += f"**Slides**: {slide_count}\n\n"
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output += result.text_content
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else:
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output = result.text_content
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return output
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# Use it
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content = process_by_format("presentation.pptx")
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print(content)
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```
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Reference in New Issue
Block a user