so uh yeah uh I'm going to tell you a bit about data structuring with u with na10 AI notes. So, this is something that basically o uh also covered in in his uh short demo um but uh yeah let's dive into it and I will actually use the open AI here. So uh maybe it will be something uh that you'll find interesting. So yeah uh basically I I would try to cover it with the um like a use case. In this example, we are going to parse uh resume documents. Uh so here I have like the example document from this John do which is who is the software developer. And we are going to extract uh some of those data which is education, employment history, and so on uh to the structured um structured form. And then we are going to process it in um in a workflow. So what we are going to uh build actually here is like a very small and easy uh chatbot um that will cover in telegram. That will be our interface here. Uh we'll use um open AI for actually parsing structuring the

data um. We will use, of course, n10 to connect to connect it everything together and to process this data. And at the last point, we will actually generate a PDF. And for this purpose, I'm going to use uh Gutenberg. Uh, if you're not familiar with Gutenberg, it's a really cool software. I I also have on my YouTube channel a small tutorial about this, so you're very welcome to um dive into it. Uh, but what we are going to do is actually have this um telegram chatbot uh to upload this uh resume. And in the output, we also will receive the PDF. But um, what is the difference between the one that we uploaded and the second one that we actually received? The um PDF that we received is uh fully made on this structur data. So, in this case, it doesn't look quite impressive because it's like just a plain HTML converted into PDF. But uh, we can like select the data that we want to put into this document and um, and actually convert it. So, this is one of the things that we can

do we can, of course, uh, add those, add this data into some kind of database with our workflow, but I focus only here on um converting it into actually the other PDF, which may seem to be a bit useless, but uh, it's only for the purpose of this presentation. So, if you receive resumes in very different formats, right, and it all brings it back to one format, yeah, so yeah, this is, that was actually, you know, the idea, because as far as I know, there are some HR, like, agencies, companies that struggle with this EXA, so yeah, I, I, I find it, uh, actually, that was the inspiration here. All right, so here we have like this workflow that is behind it. It may seem a bit, a bit complex at first, but actually it's quite easy. The very first part, uh, on the left, which are two conditional if noes, um, this is actually the authentication uh, for the chatbot. Uh, here I just want to uh, mention that yeah, it's the ch is only available for for the users with specific uh, chat ID, so this is like the

very simple version of authentication. Then we are going to extract the data with Native um node in na10, which is the extract from PDF um. It's worth mentioning that this node extracts the data from readable PDF. When it comes to the like things like scans, REITs, and so on, uh, we need some kind of OCR here to connect. But uh, for purpose of this presentation, I think it's uh totally okay to use it um. Then we'll have like this heart of this workflow, which is the chain um with um open AI uh model and with parsing uh parsing subnodes. I will go to this a bit a bit later in the next slide uh. Finally, we have a bunch of code notes which actually only are responsible for formatting the document and making the whole structure and finally the uh generation rating of this PDF. So when we uh run this uh run this workflow um, we will have an pared output here out of this uh chain and finally it should be like generated uh in Gutenberg and delivered to the to the user so this is

what is going behind here, and let's focus right now on this chain. So first, we are extracting the text from the rume. So as you can see here, we have the full text that we have from our document. The thing is that this text is actually quite a mess right now, so we need to make a structure of it. So for this purpose, we use this OpenAI chart model, and this one has very specific settings. So at first, I give it a prompt to extract the data, and what is quite important here, I also mentioned it in bold text. We want to receive the Unified JSON format. This is because we, as a response mode, in options, in this subnode, we want to use JSON. So this model will do its best to return to JSON, and as far as I know, it's available for now only in GPT-4 Turbo preview models, with, and of course, we set the temperature to zero because we don't want the model to be too

creative at this point. Um, so when we executed this chain, we should receive the structure of the document proposed by the model. So, uh, it also doesn't look good right now because this is totally stringified output. And what we need to do right now is simply put it through, uh, go through the schema, JSON schema. This is also something that Ol mentioned in, uh, in his demo. So first, here's an example. We have, uh, example the name, yes, of, uh, of the person of this resume. And um, we need to create a JSON schema for, uh, for this specific parameter here, right? So, of course, I'm not going to do it by, uh, by myself here. So I simply copy the proposed, um, proposed by the model JSON and ask ChatGPT to create it for me because this is simply, uh, easier and faster. And I can simply, um, operate on already on code that is ready and edit it by myself. I don't need to write it from scratch. So as you can see, this is

quite um extensive here. Uh, we can limit edit it however we want, uh, to have the schema that fits our requirements. In this, for this purpose, I'm going to copy it. And the last step actually here is to put the um structured uh output parser, uh, which by default has the schema. And I'm going to replace it with my schema. So when I execute this workflow right now, I should receive in the output not stringified version of this Json, but very well pared uh, pared data. So as you can see right now, we have very clear table with the data that we can do whatever we want with it. We can um, pass it to the database, process it, uh, make a PDF, and send back to the user. The possibilities are, are here actually um, very broad. So um, yes, this is uh, how this small project looks like. I will try to uh, prepare this template for a public uh, view. So uh, thank you very much and highly invite you to subscribe to my channel on my YouTube uh, I will try to upload.

How to Structure Data with n8n Nodes - Oskar

How to Structure Data with n8n Nodes - Oskar