---
title: "DocumentPreprocessor"
id: documentpreprocessor
slug: "/documentpreprocessor"
description: "Divides a list of text documents into a list of shorter text documents and then makes them more readable by cleaning."
---

# DocumentPreprocessor

Divides a list of text documents into a list of shorter text documents and then makes them more readable by cleaning.

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

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

</div>

## Overview

`DocumentPreprocessor` first splits and then cleans documents.

It is a SuperComponent that combines a `DocumentSplitter` and a `DocumentCleaner` into a single component.

### Parameters

The `DocumentPreprocessor` exposes all initialization parameters of the underlying `DocumentSplitter` and `DocumentCleaner`, and they are all optional. A detailed description of their parameters is in the respective documentation pages:

- [DocumentSplitter](documentsplitter.mdx)
- [DocumentCleaner](documentcleaner.mdx)

## Usage

### On its own

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

doc = Document(content="I love pizza!")
preprocessor = DocumentPreprocessor()

result = preprocessor.run(documents=[doc])
print(result["documents"])
```

### In a pipeline

You can use the `DocumentPreprocessor` in your indexing pipeline. The example below requires installing additional dependencies for the `MultiFileConverter`:

```shell
pip install pypdf markdown-it-py  mdit_plain trafilatura python-pptx python-docx jq openpyxl tabulate pandas
```

```python
from haystack import Pipeline
from haystack.components.converters import MultiFileConverter
from haystack.components.preprocessors import DocumentPreprocessor
from haystack.components.writers import DocumentWriter
from haystack.document_stores.in_memory import InMemoryDocumentStore

document_store = InMemoryDocumentStore()

pipeline = Pipeline()
pipeline.add_component("converter", MultiFileConverter())
pipeline.add_component("preprocessor", DocumentPreprocessor())
pipeline.add_component("writer", DocumentWriter(document_store=document_store))
pipeline.connect("converter", "preprocessor")
pipeline.connect("preprocessor", "writer")

result = pipeline.run(data={"sources": ["test.txt", "test.pdf"]})
print(result)
# {'writer': {'documents_written': 3}}
```
