---
title: "PyPDFToDocument"
id: pypdftodocument
slug: "/pypdftodocument"
description: "A component that converts PDF files to Documents."
---

# PyPDFToDocument

A component that converts PDF 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`: PDF 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/pypdf.py      |

</div>

## Overview

The `PyPDFToDocument` component converts PDF files into documents. You can use it in an indexing pipeline to index the contents of a PDF 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.

## Usage

You need to install `pypdf` package to use the `PyPDFToDocument` converter:

```shell
pip install pypdf
```

### On its own

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

converter = PyPDFToDocument()

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

### In a pipeline

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

🧑‍🍳 Cookbook: [PDF-Based Question Answering with Amazon Bedrock and Haystack](https://haystack.deepset.ai/cookbook/amazon_bedrock_for_documentation_qa)

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