--- 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 that we do not support password protected pdf files.