---
title: "PDFMinerToDocument"
id: pdfminertodocument
slug: "/pdfminertodocument"
description: "A component that converts complex PDF files to documents using pdfminer arguments."
---

# PDFMinerToDocument

A component that converts complex PDF files to documents using pdfminer arguments.

<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/pdfminer.py  |

</div>

## Overview

The `PDFMinerToDocument` component converts PDF files into documents using [PDFMiner](https://pdfminersix.readthedocs.io/en/latest/) extraction tool arguments.

You can use it in an indexing pipeline to index the contents of a PDF file in 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 initializing the component, you can adjust several parameters to fit your PDF. See the full parameter list and descriptions in our [API reference](/reference/converters-api#pdfminertodocument).

## Usage

First, install `pdfminer` package to start using this converter:

```shell
pip install pdfminer.six
```

### On its own

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

converter = PDFMinerToDocument()
results = converter.run(sources=["sample.pdf"], meta={"date_added": datetime.now().isoformat()})
documents = results["documents"]

print(documents[0].content)

## 'This is a text from the PDF file.'
```

### In a pipeline

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