markdown-chunker 0.1.3

A robust Markdown chunking library that preserves structure and context

Markdown Chunker

A robust Python library for intelligently chunking Markdown documents while preserving structural integrity and maintaining context.

📋 Overview

Markdown Chunker helps you divide large Markdown files into smaller, more manageable chunks while preserving the structure, meaning, and context of the original document. It's ideal for:

• Integrating long documents with AI models that have token limits
• Creating semantic chunks for vector databases
• Preparing content for efficient processing by NLP systems
• Splitting documents for parallel processing while maintaining integrity

✨ Features

• 🧠 Smart Chunking: Splits Markdown documents intelligently, preserving structure and meaning
• 🔍 Content-Aware: Handles various Markdown elements with specialized intelligence:
  • Headings (never split)
  • Tables (split with headers preserved)
  • Code blocks (kept intact)
  • Lists (split between items)
  • Blockquotes (split at paragraph boundaries)
  • Footnotes (kept with their references when possible)
  • YAML Front Matter (kept intact)
  • HTML (preserves tag structure)
• 🔄 Automatic Header/Footer Detection: Identifies and removes repeating headers and footers
• 🚫 Duplicate Prevention: Automatically detects and removes duplicate chunks
• ⚙️ Configurable Size Constraints: Customize minimum and maximum chunk sizes
• 🏗️ Structure Preservation: Maintains Markdown syntax and document structure
• 📝 Metadata Generation: Optionally adds metadata in each chunk as YAML front matter
• ⚡ Parallel Processing: Efficiently processes large documents using multiple cores

🔧 Installation

# Install from PyPI (recommended)
pip install markdown-chunker

# Install the development version directly from GitHub
pip install git+https://github.com/hadjebi/markdown_chunker.git

🚀 Quick Start

from markdown_chunker import MarkdownChunkingStrategy

# Create a chunking strategy with default configuration
strategy = MarkdownChunkingStrategy(add_metadata=True)

# Or customize the parameters
strategy = MarkdownChunkingStrategy(
    min_chunk_len=512,    # Minimum chunk size (default: 512)
    soft_max_len=1024,    # Preferred maximum chunk size (default: 1024)
    hard_max_len=2048,    # Absolute maximum chunk size (default: 2048)
    detect_headers_footers=True,  # Detect and remove repeating headers/footers
    remove_duplicates=True,       # Remove duplicate chunks
    add_metadata=True             # Add metadata in each chunk as YAML front matter
)

# Chunk a Markdown document
with open('document.md', 'r') as f:
    content = f.read()

chunks = strategy.chunk_markdown(content)

# Process the chunks
for i, chunk in enumerate(chunks):
    print(f"Chunk {i + 1}:")
    print(chunk)
    print("-" * 80)

🖥️ Command Line Interface

The package includes a powerful command-line interface for chunking markdown files:

# Use the markdown-chunker command after installing with pip
markdown-chunker --add-metadata examples/sample.md

# Specify a custom output directory
markdown-chunker --add-metadata examples/sample.md custom_output_dir

# Customize parameters
markdown-chunker --add-metadata --min-chunk-len=256 --soft-max-len=512 --hard-max-len=1024 examples/sample.md

# Enable parallel processing for large documents
markdown-chunker --add-metadata --parallel --max-workers=4 examples/large_document.md

Available Options:

• --min-chunk-len: Minimum chunk length in characters (default: 512)
• --soft-max-len: Soft maximum chunk length in characters (default: 1024)
• --hard-max-len: Hard maximum chunk length in characters (default: 2048)
• --no-headers-footers: Disable header and footer detection
• --no-duplicates: Disable duplicate detection
• --add-metadata: Add metadata in each chunk as YAML front matter
• --document-title: Specify a document title for metadata (auto-detected if not provided)
• --parallel: Enable parallel processing for large documents
• --max-workers: Maximum number of worker processes for parallel processing
• --verbose: Enable verbose output

🔬 Chunking Strategy

The library implements a sophisticated chunking strategy that follows these rules:

1. Structure Preservation

   • Headings are never split
   • Code blocks are kept intact
   • Tables are split only when necessary, with headers preserved
   • Lists are split between items to maintain structure
   • Blockquotes are split at paragraph boundaries
   • Footnotes are kept with their references when possible
   • HTML tags are preserved in their structure

2. Size Management

   • Chunks are kept between min_chunk_len and soft_max_len when possible
   • Content is never split beyond hard_max_len
   • Small chunks are merged when below min_chunk_len

3. Header/Footer Handling

   • Automatically detects repeating headers and footers
   • Removes redundant elements while preserving unique content
   • Uses pattern matching to identify common elements

4. Duplicate Prevention

   • Detects and removes duplicate chunks
   • Preserves the first occurrence of duplicate content
   • Uses MD5 hashing for efficient comparison

5. Adds Metadata

   • Optionally adds metadata in each chunk as YAML front matter
   • Includes document information (title, source)
   • Provides chunk details (id, position, next/previous chunks)
   • Maintains heading hierarchy information
   • Identifies content types (tables, lists, code blocks, etc.)
   • Preserves and merges with existing YAML front matter

📝 Examples

Basic Usage

from markdown_chunker import MarkdownChunkingStrategy

strategy = MarkdownChunkingStrategy(add_metadata=True)

# Simple document with various elements
content = """
# Main Title

## Section 1

This is a paragraph with some content.

```python
def example():
    return "Hello, World!"

1. First item
2. Second item
   • Subitem
   • Another subitem

Important quote spanning multiple lines """

chunks = strategy.chunk_markdown(content)

### Custom Configuration

```python
from markdown_chunker import MarkdownChunkingStrategy

# Create a strategy with custom parameters
strategy = MarkdownChunkingStrategy(
    min_chunk_len=100,
    soft_max_len=200,
    hard_max_len=300,
    detect_headers_footers=False,  # Disable header/footer detection
    add_metadata=True              # Enable metadata adding
)

chunks = strategy.chunk_markdown(content)

Added Metadata

from markdown_chunker import MarkdownChunkingStrategy

# Create a strategy with added metadata
strategy = MarkdownChunkingStrategy(
    add_metadata=True,
    document_title="My Document",
    source_document="document.md"
)

with open('document.md', 'r') as f:
    content = f.read()

chunks = strategy.chunk_markdown(content)

# Each chunk will include YAML front matter like:
'''
---
chunk:
  id: 1
  total: 10
  previous: null
  next: 2
  length: 1024
  position: 10%
document:
  title: My Document
  source: document.md
content:
  types:
  - heading
  - paragraph
  word_count: 180
  characters: 1024
headings:
  main: Section 1
  all:
  - Main Title
  - Section 1
---

# Section 1

This is the beginning of section 1...
'''

Processing Large Documents

from markdown_chunker import MarkdownChunkingStrategy
import os

# Create a strategy with parallel processing for large documents
strategy = MarkdownChunkingStrategy(
    parallel_processing=True,
    max_workers=4,  # Number of worker processes
    add_metadata=True  # Include metadata in each chunk
)

# Process a large document
with open('large_document.md', 'r') as f:
    content = f.read()

chunks = strategy.chunk_markdown(content)

# Save chunks to files
os.makedirs('output', exist_ok=True)
for i, chunk in enumerate(chunks):
    with open(f'output/chunk_{i+1:03d}.md', 'w') as f:
        f.write(chunk)

📂 Sample Output

Example output directories are included in the repository:

• examples/outputs/basic_example/: Basic chunking with metadata
• examples/outputs/metadata_example/: Chunking with enhanced metadata
• examples/outputs/custom_params_example/: Chunking with custom size parameters and metadata
• examples/outputs/bmw_example/: Chunking of a large document (BMW Annual Report) with parallel processing and metadata

🔍 Advanced Usage

Parallel Processing

For large documents, you can enable parallel processing to significantly improve performance:

strategy = MarkdownChunkingStrategy(
    parallel_processing=True,
    max_workers=4  # Number of worker processes
)

Custom Content Handlers

The library is designed to be extensible. You can create custom content handlers for specialized Markdown elements:

from markdown_chunker import ContentHandler
from markdown_chunker.utils import is_special_element

class CustomElementHandler(ContentHandler):
    def can_handle(self, content):
        return is_special_element(content)
        
    def split(self, content, max_length):
        # Custom splitting logic
        return split_parts

# Add to strategy
strategy = MarkdownChunkingStrategy(add_metadata=True)
strategy.content_handlers.append(CustomElementHandler())

🤝 Contributing

Contributions are welcome! Here's how you can help:

1. Fork the repository
2. Create a feature branch: git checkout -b new-feature
3. Make your changes and commit: git commit -m 'Add new feature'
4. Push to your branch: git push origin new-feature
5. Create a pull request

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

📚 Documentation

For complete documentation, see the docs directory.

🙏 Acknowledgements

• Inspired by the needs of AI developers working with large documents
• Built upon the shoulders of the Python Markdown ecosystem