How to beginner · 3 min read

How to chunk markdown documents

Quick answer

To chunk markdown documents, parse the content by headings or paragraphs and split it into smaller text blocks using Python. Use libraries like markdown or simple regex to identify sections, then process each chunk separately for AI tasks.

PREREQUISITES

Python 3.8+
pip install markdown
Basic knowledge of Python string handling

Setup

Install the markdown package to parse markdown content easily. Use pip install markdown in your environment.

bash

pip install markdown

Step by step

This example shows how to chunk a markdown document by top-level headings (# Heading) and paragraphs. Each chunk includes the heading and its following paragraphs until the next heading.

python

import re

markdown_text = '''
# Introduction
This is the introduction paragraph.

# Usage
Usage details go here.
More usage info.

# Conclusion
Final thoughts.
'''

def chunk_markdown(md_text):
    # Split by top-level headings
    pattern = r'(?:^|\n)(# .+?)(?=\n# |\Z)'
    chunks = []
    matches = list(re.finditer(pattern, md_text, re.DOTALL))
    for i, match in enumerate(matches):
        start = match.start(1)
        end = matches[i + 1].start(1) if i + 1 < len(matches) else len(md_text)
        chunk = md_text[start:end].strip()
        chunks.append(chunk)
    return chunks

chunks = chunk_markdown(markdown_text)
for i, chunk in enumerate(chunks, 1):
    print(f"Chunk {i}:\n{chunk}\n")

output

Chunk 1:
# Introduction
This is the introduction paragraph.

Chunk 2:
# Usage
Usage details go here.
More usage info.

Chunk 3:
# Conclusion
Final thoughts.

Common variations

You can chunk markdown by different heading levels (e.g., ## or ###) by adjusting the regex pattern. Alternatively, use a markdown parser like mistune or markdown-it-py to build an AST and chunk by nodes.

For large documents, consider chunking by token count or character length to fit AI model context limits.

python

import mistune

markdown_text = '''# Title\nParagraph 1.\n## Subtitle\nParagraph 2.\n'''

class ChunkExtractor(mistune.HTMLRenderer):
    def __init__(self):
        super().__init__()
        self.chunks = []
        self.current_chunk = []

    def heading(self, text, level):
        if self.current_chunk:
            self.chunks.append('\n'.join(self.current_chunk))
            self.current_chunk = []
        self.current_chunk.append('#' * level + ' ' + text)

    def paragraph(self, text):
        self.current_chunk.append(text)

    def finalize(self):
        if self.current_chunk:
            self.chunks.append('\n'.join(self.current_chunk))
        return self.chunks

renderer = ChunkExtractor()
parser = mistune.create_markdown(renderer=renderer)
parser(markdown_text)
chunks = renderer.finalize()
for i, chunk in enumerate(chunks, 1):
    print(f"Chunk {i}:\n{chunk}\n")

output

Chunk 1:
# Title
Paragraph 1.

Chunk 2:
## Subtitle
Paragraph 2.

Troubleshooting

If chunks are empty or missing content, verify your regex pattern matches the markdown structure correctly.
For inconsistent markdown formatting, use a robust parser like mistune instead of regex.
When chunking for AI input, ensure chunks do not exceed model token limits by counting tokens or characters.

✅

Key Takeaways

Chunk markdown by headings or paragraphs to create manageable text blocks for AI processing.
Use regex for simple splitting or markdown parsers like mistune for robust chunking.
Always ensure chunks fit within your AI model's context length limits.

Verified 2026-04

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