![]() Firefox and Chrome create PDFs that are slightly different.These ones just involve generating a PDF to work with! Rest assured you will find many of them when you attempt to work with PDFs. FootgunsĪ footgun is a thing that will shoot you in the foot. Let’s just print-to-PDF an entry from Wikipedia, in this case the “ Rabbit” article: Getting our PDFįirst, we need a sample file. I went with the first option since I didn’t want to shave too many yaks. Or something like this with similar tools. Convert the PDF to HTML using something like Pandoc, extracting images to a directory and then converting the HTML to text using Pandoc again.Screenshot every page of the PDF with ImageMagick and OCR it.Take a PDF file and use Jina Hub’s PDFSegmenter to extract the text and images into chunks.We may throw in a few others tools along the way for certain processing tasks, but the ones above are the big three. ![]() Instead we can just pull them from the cloud. Jina Hub - so we don’t have to build every single little processing unit.Jina - build a processing pipeline and neural search engine for our DocArray Documents, and scale up to serve on the cloud.This will wrap our PDF files, text chunks, image chunks, and any other inputs/outputs of our search engine. DocArray - a data structure for unstructured data.Since this task is a whole search pipeline that deals with different kinds of data, we’ll use some specialist tools to get this done: In future posts we’ll look at how to actually search through that data. So that’s what we’ll focus on in this post. If you want to follow along at home (and maybe fix a few of my bugs!), check the repo:Īs anyone who’s spent any time in data science knows, wrangling the data into a usable state is 90% of the job. This is just a rough and ready roadmap - so stay tuned to see how things really pan out. Finally we’ll look at some other useful tasks, like extracting metadata.Next we’ll look at how to search through that index using a client and Streamlit frontend.After extracting our PDF’s text and images, CLIP will generate a semantically-useful index that we can search by giving it an image or text as input (and it’ll understand the input semantically, not just match keywords or pixels). For the next post we’ll look at feeding these into CLIP, a deep learning model that “understands” text and images.In this post we’ll cover how to extract the images and text from PDFs, process them, and store them in a sane way.This will be part 1 of n posts that walk you through creating a PDF neural search engine using Python: I know several folks already building PDF search engines powered by AI, so I figured I’d give it a stab too. With neural search seeing rapid adoption, more people are looking at using it for indexing and searching through their unstructured data.
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