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Okay, so here you have the notebook.

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This is the main document for you to to read.

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And the slides which include a summary of the contents you see in the notebook.

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You will also be able to download the code of this exercise from GitHub.

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But see here in the notebook the the main steps we are taking in order to to build this Rag application.

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So we are going to use the PDF full stack application.

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Uh, we build in the in a in a previous lesson, the second application we have built in this block.

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And we are going to include some changes to this, uh, to this file.

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So remember that you will need to add the OpenAI key in the dot env file in the back end.

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Remember you don't want to share this, uh, OpenAI key with anyone.

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So remember to update your dot.

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Gitignore file in order to avoid you to share the contents of this file.

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When you load the content of the project into your GitHub repository.

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Okay, so this is important.

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Second thing you will need to add the imports, uh, for launching an OpenAI in the back end routers

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PDFs.

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Uh py file okay.

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So remember that, uh, in your case you will have to use a different way of, uh, importing because

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you will have to include the changes, uh, of the uh, version 010 from launching.

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You know, about that.

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We are going to include a reminder video, uh, in this, in this blog as well.

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But we have included the reminder several times.

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You have a reminder in the in the level one applications block, and you also have the reminder in the

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level two applications block.

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So remember that the the main change of the uh, version 010 is that instead of importing, for example,

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OpenAI in this way from long chain import OpenAI, now you need to use a new package called called long

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chain OpenAI to do from long chain OpenAI import OpenAI.

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So you remember the changes.

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So so please, uh, modify this, uh, code with the new code you need then after you have done it.

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You will have to change the code in the in the functionality.

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Right.

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So this is the simple functionality we, uh, started using in the previous app.

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You remember that we initially did a very simple thing and then went to the poem writing.

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So this is the simple, uh, and the simple functionality just for you to try it with this, with this

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app as well.

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You know how to try it.

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It's the same way we we did it in the, in the previous exercise.

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And now is where uh, here you have the A code you need to enter in the same back end routers pdfs.py

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in the same file.

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This is the code you need to enter in order to, uh, make a rack technique work in this, uh, in this

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application.

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Okay.

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And in order for this to work, you will need to add all these, uh, imports in the in on the top of

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the file.

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Okay.

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So you will be able to check this new functionality with a, with, uh, your terminal.

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Okay.

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So this is a way to ask a question to your, uh, PDF, a document.

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Okay.

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But we are not going to include the front end, uh, changes here.

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So this is going to be your first to do, uh, task.

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Okay.

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So this is going to be your homework.

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So we are including a first version of the rack code.

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And it will be for you to, uh, update the front end with the knowledge you already have and to fine

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tune the, the back end, uh, in that way.

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Okay.

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So, uh, what we want is that the user selects one PDF file, and then he can enter a question about

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it in the in an input box below the main PDF list, uh box.

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And after the question is submitted, the answer is displayed below.

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Okay.

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So take a look at this and try to prepare the front end for uh, this application.

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Okay.

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So let's go to the next lesson where we are going to deep, uh, to dive deeper into the most advanced

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LM application of the, uh, program.

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So let's go there in the next video.