docs/components/data-sources/pdf-file.mdx

---
title: '📰 PDF'
---

You can load any pdf file from your local file system or through a URL.

## Usage

### Load from a local file

```python
from embedchain import App
app = App()
app.add('/path/to/file.pdf', data_type='pdf_file')
```

### Load from URL

```python
from embedchain import App
app = App()
app.add('https://arxiv.org/pdf/1706.03762.pdf', data_type='pdf_file')
app.query("What is the paper 'attention is all you need' about?", citations=True)
# Answer: The paper "Attention Is All You Need" proposes a new network architecture called the Transformer, which is based solely on attention mechanisms. It suggests that complex recurrent or convolutional neural networks can be replaced with a simpler architecture that connects the encoder and decoder through attention. The paper discusses how this approach can improve sequence transduction models, such as neural machine translation.
# Contexts:
# [
#     (
#         'Provided proper attribution is ...',
#         {
#             'page': 0,
#             'url': 'https://arxiv.org/pdf/1706.03762.pdf',
#             'score': 0.3676220203221626,
#             ...
#         }
#     ),
# ]
```

We also store the page number under the key `page` with each chunk that helps understand where the answer is coming from. You can fetch the `page` key while during retrieval (refer to the example given above).

<Note>
Note that we do not support password protected pdf files.
</Note>