LARRUZ builds a PDF chat app in Streamlit that uploads files, chunks them, creates embeddings with OpenAI, stores them in a vector database, and answers questions about the PDF. The tutorial uses Python and VS Code, with basic Python knowledge enough to follow along.
You probably have seen these websites where you can upload your PDF files and then you can chat with these documents that you uploaded. Now, in today's video we are going to be doing exactly the same, but we'll be creating our own user interface. So here is how our app is going to look like. You will be able to simply drag a PDF file, or it can be any other file type, and then ask questions directly from that PDF file. Then you will get a response. So by the end of the video you will be able to replicate this. All you need is just some basic Python experience for the rest of the step. I will walk you through a step by step process. Okay, so here's the architecture that we're going to be looking at. So in this video we will be creating both the front end as well as the back end of our chat app. This is going to be an extremely detailed video where I'm going to be walking you through a step by step process as well as live coding to create this PDF chat. Now, the end product is going to be very similar to what you will see on something like Chat PDF. So for example, here we have the Chat PDF. In order to use this, you need to simply drag your PDF file. For example, I'm uploading the Constitution of the United States, right? And then it kind of creates a summary and then after that you can simply interact with it. So let's say how many states are in the USA? Sorry, okay. And then it's actually using ChatGPT at the back end. And based on ChatGPT's data as well as the data and you have in your PDF file, it comes up with an answer. So let's say it doesn't provide the information on the current number of state in the USA, but as of 2021, so there were 50 states. So this is like a way we are going to be designing a very similar chat app, right? And it's going to look something like this. So for the graphical user interface, we're going to be using Streamlit right at the back end in order to process your data, create embeddings and response from the large language model that is going to be coming from the OpenAI API. However, you can easily replace those with open source models. But just to show you the process, I'm going to be using OpenAI API. Later on I will walk you through different steps and show you how you can easily do this. I have a whole bunch of videos on that. So that's going to be pretty easy to do. Now for the front end. Again, it's going to be based on Streamlit. So the user will upload a PDF file just like we did here, and then it will accept prompts from the user as an input and generate responses based on the LLM and embeddings that we use and show those back to the user. Now that's just an overview of how the system is going to look like. In terms of background requirements, all you need is to have knowledge of Python. The rest I'm going to walk you through a step by step process. So essentially this is what we're going to be doing. We'll upload our PDF files divided into chunks, compute embeddings, then create a vector store that's going to become a knowledge base. And in terms of the query, so the user will ask a question, compute embeddings, then do a search on the knowledge base and then give us a result. Right. If these things do not make sense, that's absolutely fine. By the end of the video you will understand all these concepts. Okay, so I'm going to be coding everything in Visual Code Studio. Now, all the required packages are listed in this requirements txt file, but if you face any errors, you might have to install some other packages. Just do those. Right, so now I am in directory and there are just two files. One is app py where I'm going to be putting all my code.
