---
title: "HTMLToDocument"
id: htmltodocument
slug: "/htmltodocument"
description: "A component that converts HTML files to documents."
---

# HTMLToDocument

A component that converts HTML 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 HTML 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/html.py       |

</div>

## Overview

The `HTMLToDocument` component converts HTML files into documents. It can be used in an indexing pipeline to index the contents of an HTML file into a Document Store or even in a querying pipeline after the [`LinkContentFetcher`](../fetchers/linkcontentfetcher.mdx).  The `HTMLToDocument` component takes a list of HTML file paths or [`ByteStream`](../../concepts/data-classes.mdx#bytestream) objects as input and converts the files to 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  `extraction_kwargs`,  a dictionary containing keyword arguments to customize the extraction process. These are passed to the underlying Trafilatura `extract` function. For the full list of available arguments, see the [Trafilatura documentation](https://trafilatura.readthedocs.io/en/latest/corefunctions.html#extract).

## Usage

### On its own

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

converter = HTMLToDocument()

docs = converter.run(sources=[Path("saved_page.html")])
```

### In a pipeline

Here's an example of an indexing pipeline that writes the contents of an HTML file into an `InMemoryDocumentStore`:

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