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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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