---
title: "TextFileToDocument"
id: textfiletodocument
slug: "/textfiletodocument"
description: "Converts text files to documents."
---

# TextFileToDocument

Converts text 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`: A list of paths to text files you want to convert                                   |
| **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/txt.py       |

</div>

## Overview

The `TextFileToDocument` component converts text files into documents. You can use it in an indexing pipeline to index the contents of text files 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 set the default encoding of the text files through the `encoding` parameter. If you don't provide any value, the component uses `"utf-8"` by default. Note that if the encoding is specified in the metadata of an input ByteStream, it will override this parameter's setting.

## Usage

### On its own

```python
from pathlib import Path
from haystack.components.converters import TextFileToDocument

converter = TextFileToDocument()

docs = converter.run(sources=[Path("my_file.txt")])
```

### In a pipeline

```python
from haystack import Pipeline
from haystack.document_stores.in_memory import InMemoryDocumentStore
from haystack.components.converters import TextFileToDocument
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", TextFileToDocument())
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)