deepset-ai--haystack/docs-website/docs/pipeline-components/preprocessors/markdownheadersplitter.mdx

---
title: "MarkdownHeaderSplitter"
id: markdownheadersplitter
slug: "/markdownheadersplitter"
description: "Split documents at ATX-style Markdown headers (#), with optional secondary splitting. Preserves header hierarchy as metadata."
---

# MarkdownHeaderSplitter

Split documents at ATX-style Markdown headers (`#`, `##`, and so on), with optional secondary splitting. Header hierarchy is preserved as metadata on each chunk.

<div className="key-value-table">

|  |  |
| --- | --- |
| **Most common position in a pipeline** | In indexing pipelines after [Converters](../converters.mdx) and [`DocumentCleaner`](documentcleaner.mdx) |
| **Mandatory run variables**            | `documents`: A list of text documents to split. |
| **Output variables**                   | `documents`: A list of documents split at headers (and optionally by secondary split). |
| **API reference**                      | [PreProcessors](/reference/preprocessors-api) |
| **GitHub link**                        | [https://github.com/deepset-ai/haystack/blob/main/haystack/components/preprocessors/markdown_header_splitter.py](https://github.com/deepset-ai/haystack/blob/main/haystack/components/preprocessors/markdown_header_splitter.py) |
| **Package name** | `haystack-ai` |

</div>

## Overview

The `MarkdownHeaderSplitter` processes text documents by:

- Splitting them into chunks at ATX-style Markdown headers (`#`, `##`, …, `######`), preserving header hierarchy as metadata.
- Optionally applying a secondary split (by word, passage, period, or line) to each chunk using Haystack's [`DocumentSplitter`](documentsplitter.mdx).
- Preserving and propagating metadata such as parent headers, page numbers, and split IDs.

Only ATX-style headers are recognized (e.g. `# Title`). Setext-style headers (`Underline with ===`) aren't supported.

Parameters you can set when initializing the component:

- `page_break_character`: Character used to identify page breaks. Defaults to form feed `\f`.
- `keep_headers`: If `True`, headers remain in the chunk content. If `False`, headers are moved to metadata only. Defaults to `True`.
- `secondary_split`: Optional secondary split after header splitting. Options: `None`, `"word"`, `"passage"`, `"period"`, `"line"`. Defaults to `None`.
- `split_length`: Maximum number of units per split when using secondary splitting. Defaults to `200`.
- `split_overlap`: Number of overlapping units between splits when using secondary splitting. Defaults to `0`.
- `split_threshold`: Minimum number of units per split when using secondary splitting. Defaults to `0`.
- `skip_empty_documents`: Whether to skip documents with empty content. Defaults to `True`.

Each output document's metadata includes:

- `source_id`: ID of the original document.
- `page_number`: Page number. Updated when `page_break_character` is found.
- `split_id`: Index of the chunk within its parent.
- `header`: The header text for this chunk.
- `parent_headers`: List of parent header texts in hierarchy order.

The component only works with text documents. Documents with `None` or non-string content raise a `ValueError`.

## Usage

### On its own

```python
from haystack import Document
from haystack.components.preprocessors import MarkdownHeaderSplitter

text = (
    "# Introduction\n"
    "This is the intro section.\n"
    "## Getting Started\n"
    "Here is how to start.\n"
    "## Advanced\n"
    "Advanced content here."
)
doc = Document(content=text)
splitter = MarkdownHeaderSplitter(keep_headers=True)
result = splitter.run(documents=[doc])

# result["documents"] contains one document per header section,
# with meta["header"], meta["parent_headers"], meta["source_id"], and so on
```

### With secondary splitting

When sections are long, you can add a secondary split, for example by word, so each chunk stays within a maximum size:

```python
from haystack import Document
from haystack.components.preprocessors import MarkdownHeaderSplitter

text = "# Section\n" + "Some long body text. " * 50
doc = Document(content=text)
splitter = MarkdownHeaderSplitter(
    keep_headers=True,
    secondary_split="word",
    split_length=20,
    split_overlap=2,
)
result = splitter.run(documents=[doc])
```

### In a pipeline

This pipeline converts Markdown files to documents, cleans them, splits by headers, and writes to an in-memory document store:

```python
from pathlib import Path

from haystack import Pipeline
from haystack.document_stores.in_memory import InMemoryDocumentStore
from haystack.components.converters.txt import TextFileToDocument
from haystack.components.preprocessors import MarkdownHeaderSplitter
from haystack.components.writers import DocumentWriter

document_store = InMemoryDocumentStore()

p = Pipeline()
p.add_component("text_file_converter", TextFileToDocument())
p.add_component("splitter", MarkdownHeaderSplitter(keep_headers=True))
p.add_component("writer", DocumentWriter(document_store=document_store))
p.connect("text_file_converter.documents", "splitter.documents")
p.connect("splitter.documents", "writer.documents")

path = "path/to/your/files"
files = list(Path(path).glob("*.md"))
p.run({"text_file_converter": {"sources": files}})
```