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---
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>
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