deepset-ai--haystack/docs-website/docs/pipeline-components/converters/markdowntodocument.mdx

---
title: "MarkdownToDocument"
id: markdowntodocument
slug: "/markdowntodocument"
description: "A component that converts Markdown files to documents."
---

# MarkdownToDocument

A component that converts Markdown files to documents.

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

|  |  |
| --- | --- |
| **Most common position in a pipeline** | Before [PreProcessors](../preprocessors.mdx) , or right at the beginning of an indexing pipeline |
| **Mandatory run variables**            | `sources`: Markdown file paths or [`ByteStream`](../../concepts/data-classes.mdx#bytestream)  objects        |
| **Output variables**                   | `documents`: A list of documents                                                                |
| **API reference**                      | [Converters](/reference/converters-api)                                                                |
| **GitHub link**                        | https://github.com/deepset-ai/haystack/blob/main/haystack/components/converters/markdown.py   |
| **Package name** | `haystack-ai` |

</div>

## Overview

The `MarkdownToDocument` component converts Markdown files into documents. You can use it in an indexing pipeline to index the contents of a Markdown file into a Document Store. It takes a list of file paths or [ByteStream](../../concepts/data-classes.mdx#bytestream) objects as input and outputs the converted result as a list of documents. Optionally, you can attach metadata to the documents through the `meta` input parameter.

When you initialize the component, you can optionally turn off progress bars by setting `progress_bar` to `False`. If you want to convert the contents of tables into a single line, you can enable that through the `table_to_single_line` parameter.

If your Markdown files start with YAML frontmatter, set `extract_frontmatter=True` to move that data into `Document.meta` and remove it from the converted document content. Metadata passed through the `meta` input takes precedence over frontmatter keys.

## Usage

You need to install `markdown-it-py` and `mdit_plain packages` to use the `MarkdownToDocument` component:

```shell
pip install markdown-it-py mdit_plain
```

### On its own

```python
from haystack.components.converters import MarkdownToDocument

converter = MarkdownToDocument()

docs = converter.run(sources=Path("my_file.md"))
```

### With YAML frontmatter

Given `equity_note.md`:

```markdown
---
ticker: AAPL
source: earnings_call
date: 2026-06-12
---

# Thesis
Revenue guidance improved.
```

```python
from haystack.components.converters import MarkdownToDocument

converter = MarkdownToDocument(extract_frontmatter=True)

docs = converter.run(sources=["equity_note.md"])["documents"]
print(docs[0].meta["ticker"])
print(docs[0].content)
```

### In a pipeline

```python
from haystack import Pipeline
from haystack.document_stores.in_memory import InMemoryDocumentStore
from haystack.components.converters import MarkdownToDocument
from haystack.components.preprocessors import DocumentCleaner
from haystack.components.preprocessors import DocumentSplitter
from haystack.components.writers import DocumentWriter

document_store = InMemoryDocumentStore()

pipeline = Pipeline()
pipeline.add_component("converter", MarkdownToDocument())
pipeline.add_component("cleaner", DocumentCleaner())
pipeline.add_component(
    "splitter",
    DocumentSplitter(split_by="sentence", split_length=5),
)
pipeline.add_component("writer", DocumentWriter(document_store=document_store))
pipeline.connect("converter", "cleaner")
pipeline.connect("cleaner", "splitter")
pipeline.connect("splitter", "writer")

pipeline.run({"converter": {"sources": file_names}})
```

## Additional References

:notebook: Tutorial: [Preprocessing Different File Types](https://haystack.deepset.ai/tutorials/30_file_type_preprocessing_index_pipeline)