Release v1.27.0: Enhance markdown-tools with Heavy Mode
Add multi-tool orchestration for best-quality document conversion: - Dual mode: Quick (fast) and Heavy (best quality, multi-tool merge) - New convert.py - main orchestrator with tool selection matrix - New merge_outputs.py - segment-level multi-tool output merger - New validate_output.py - quality validation with HTML reports - Enhanced extract_pdf_images.py - metadata (page, position, dimensions) - PyMuPDF4LLM integration for LLM-optimized PDF conversion - pandoc integration for DOCX/PPTX structure preservation - Quality metrics: text/table/image retention with pass/warn/fail - New references: heavy-mode-guide.md, tool-comparison.md Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
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markdown-tools/references/heavy-mode-guide.md
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# Heavy Mode Guide
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Detailed documentation for markdown-tools Heavy Mode conversion.
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## Overview
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Heavy Mode runs multiple conversion tools in parallel and intelligently merges their outputs to produce the highest quality markdown possible.
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## When to Use Heavy Mode
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Use Heavy Mode when:
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- Document has complex tables that need precise formatting
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- Images must be preserved with proper references
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- Structure hierarchy (headings, lists) must be accurate
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- Output quality is more important than conversion speed
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- Document will be used for LLM processing
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Use Quick Mode when:
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- Speed is priority
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- Document is simple (mostly text)
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- Output is for draft/review purposes
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## Tool Capabilities
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### PyMuPDF4LLM (Best for PDFs)
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**Strengths:**
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- Native table detection with multiple strategies
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- Image extraction with position metadata
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- LLM-optimized output format
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- Preserves reading order
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**Usage:**
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```python
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import pymupdf4llm
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md_text = pymupdf4llm.to_markdown(
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"document.pdf",
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write_images=True,
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table_strategy="lines_strict",
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image_path="./assets",
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dpi=150
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)
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```
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### markitdown (Universal Converter)
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**Strengths:**
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- Supports many formats (PDF, DOCX, PPTX, XLSX)
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- Good text extraction
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- Simple API
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**Limitations:**
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- May miss complex tables
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- No native image extraction
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### pandoc (Best for Office Docs)
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**Strengths:**
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- Excellent DOCX/PPTX structure preservation
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- Proper heading hierarchy
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- List formatting
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**Limitations:**
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- Requires system installation
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- PDF support limited
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## Merge Strategy
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### Segment-Level Selection
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Heavy Mode doesn't just pick one tool's output. It:
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1. Parses each output into segments
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2. Scores each segment independently
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3. Selects the best version of each segment
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### Segment Types
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| Type | Detection Pattern | Scoring Criteria |
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|------|-------------------|------------------|
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| Table | `\|.*\|` rows | Row count, column count, header separator |
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| Heading | `^#{1-6} ` | Proper level, reasonable length |
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| Image | `!\[.*\]\(.*\)` | Alt text present, local path |
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| List | `^[-*+\d.] ` | Item count, nesting depth |
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| Code | Triple backticks | Line count, language specified |
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| Paragraph | Default | Word count, completeness |
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### Scoring Example
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```
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Table from pymupdf4llm:
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- 10 rows × 5 columns = 5.0 points
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- Header separator present = 1.0 points
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- Total: 6.0 points
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Table from markitdown:
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- 8 rows × 5 columns = 4.0 points
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- No header separator = 0.0 points
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- Total: 4.0 points
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→ Select pymupdf4llm version
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```
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## Advanced Usage
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### Force Specific Tool
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```bash
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# Use only pandoc
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uv run scripts/convert.py document.docx -o output.md --tool pandoc
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```
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### Custom Assets Directory
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```bash
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# Heavy mode with custom image output
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uv run scripts/convert.py document.pdf -o output.md --heavy --assets-dir ./images
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```
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### Validate After Conversion
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```bash
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# Convert then validate
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uv run scripts/convert.py document.pdf -o output.md --heavy
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uv run scripts/validate_output.py document.pdf output.md --report quality.html
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```
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## Troubleshooting
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### Low Text Retention Score
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**Causes:**
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- PDF has scanned images (not searchable text)
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- Encoding issues in source document
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- Complex layouts confusing the parser
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**Solutions:**
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- Use OCR preprocessing for scanned PDFs
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- Try different tool with `--tool` flag
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- Manual cleanup may be needed
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### Missing Tables
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**Causes:**
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- Tables without visible borders
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- Tables spanning multiple pages
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- Merged cells
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**Solutions:**
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- Use Heavy Mode for better detection
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- Try pymupdf4llm with different table_strategy
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- Manual table reconstruction
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### Image References Broken
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**Causes:**
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- Assets directory not created
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- Relative path issues
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- Image extraction failed
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**Solutions:**
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- Ensure `--assets-dir` points to correct location
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- Check `images_metadata.json` for extraction status
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- Use `extract_pdf_images.py` separately
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markdown-tools/references/tool-comparison.md
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# Tool Comparison
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Comparison of document-to-markdown conversion tools.
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## Feature Matrix
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| Feature | pymupdf4llm | markitdown | pandoc |
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|---------|-------------|------------|--------|
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| **PDF Support** | ✅ Excellent | ✅ Good | ⚠️ Limited |
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| **DOCX Support** | ❌ No | ✅ Good | ✅ Excellent |
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| **PPTX Support** | ❌ No | ✅ Good | ✅ Good |
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| **XLSX Support** | ❌ No | ✅ Good | ⚠️ Limited |
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| **Table Detection** | ✅ Multiple strategies | ⚠️ Basic | ✅ Good |
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| **Image Extraction** | ✅ With metadata | ❌ No | ✅ Yes |
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| **Heading Hierarchy** | ✅ Good | ⚠️ Variable | ✅ Excellent |
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| **List Formatting** | ✅ Good | ⚠️ Basic | ✅ Excellent |
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| **LLM Optimization** | ✅ Built-in | ❌ No | ❌ No |
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## Installation
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### pymupdf4llm
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```bash
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pip install pymupdf4llm
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# Or with uv
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uv pip install pymupdf4llm
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```
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**Dependencies:** None (pure Python with PyMuPDF)
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### markitdown
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```bash
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# With PDF support
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uv tool install "markitdown[pdf]"
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# Or
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pip install "markitdown[pdf]"
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```
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**Dependencies:** Various per format (pdfminer, python-docx, etc.)
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### pandoc
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```bash
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# macOS
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brew install pandoc
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# Ubuntu/Debian
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apt-get install pandoc
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# Windows
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choco install pandoc
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```
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**Dependencies:** System installation required
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## Performance Benchmarks
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### PDF Conversion (100-page document)
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| Tool | Time | Memory | Output Quality |
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|------|------|--------|----------------|
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| pymupdf4llm | ~15s | 150MB | Excellent |
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| markitdown | ~45s | 200MB | Good |
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| pandoc | ~60s | 100MB | Variable |
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### DOCX Conversion (50-page document)
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| Tool | Time | Memory | Output Quality |
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|------|------|--------|----------------|
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| pandoc | ~5s | 50MB | Excellent |
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| markitdown | ~10s | 80MB | Good |
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## Best Practices
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### For PDFs
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1. **First choice:** pymupdf4llm
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- Best table detection
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- Image extraction with metadata
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- LLM-optimized output
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2. **Fallback:** markitdown
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- When pymupdf4llm fails
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- Simpler documents
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### For DOCX/DOC
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1. **First choice:** pandoc
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- Best structure preservation
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- Proper heading hierarchy
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- List formatting
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2. **Fallback:** markitdown
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- When pandoc unavailable
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- Quick conversion needed
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### For PPTX
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1. **First choice:** markitdown
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- Good slide content extraction
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- Handles speaker notes
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2. **Fallback:** pandoc
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- Better structure preservation
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### For XLSX
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1. **Only option:** markitdown
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- Table to markdown conversion
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- Sheet handling
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## Common Issues by Tool
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### pymupdf4llm
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| Issue | Solution |
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|-------|----------|
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| "Cannot import fitz" | `pip install pymupdf` |
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| Tables not detected | Try different `table_strategy` |
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| Images not extracted | Enable `write_images=True` |
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### markitdown
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| Issue | Solution |
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|-------|----------|
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| PDF support missing | Install with `[pdf]` extra |
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| Slow conversion | Expected for large files |
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| Missing content | Try alternative tool |
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### pandoc
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| Issue | Solution |
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|-------|----------|
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| Command not found | Install via package manager |
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| PDF conversion fails | Use pymupdf4llm instead |
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| Images not extracted | Add `--extract-media` flag |
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## API Comparison
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### pymupdf4llm
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```python
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import pymupdf4llm
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md = pymupdf4llm.to_markdown(
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"doc.pdf",
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write_images=True,
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table_strategy="lines_strict",
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image_path="./assets"
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)
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```
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### markitdown
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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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print(result.text_content)
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```
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### pandoc
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```bash
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pandoc document.docx -t markdown --wrap=none --extract-media=./assets
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```
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```python
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import subprocess
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result = subprocess.run(
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["pandoc", "doc.docx", "-t", "markdown", "--wrap=none"],
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capture_output=True, text=True
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)
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print(result.stdout)
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```
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