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Okay.

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So here you have a demo of the application we are going to build in this lesson.

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A as you can see this is a full stack application.

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And that A uses ChatGPT.

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So the important thing uh, for this project is that, that you see how to open the door to orchestration

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frameworks and LMS in your full stack application.

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This is the most important thing we want you to take from this lesson.

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So the functionality here is very, very simple.

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We are using the a basic application.

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We created a at the beginning of this blog.

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The the you know the to do the to do a crud we created.

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And we have added one functionality that makes use of a orchestration orchestration frameworks in this

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case is long chain and a one lm model in this case is ChatGPT from OpenAI.

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So the functionality we have added is that once we enter a new task, we will be able to click here

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in this button, and this button will generate a poem.

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A based on the task we are entering.

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Okay.

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So for example, if we enter here lunch is our next two thing.

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This task is created.

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And now if we click on this button.

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The application is going to generate a poem or something like a poem, you know about that.

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So this is just a way to show, to demonstrate that this application is right now making use of orchestration

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frameworks and LMS.

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So the important thing, as I was saying, the important thing of this exercise is for you to learn

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how to integrate orchestration frameworks and LMS in your full stack applications.

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Once you learn that this is how to open the door after learning how to open the door, you can do every

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kind of things, as we will see in the next project with with a rack.

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Uh, exercise.

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Okay.

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And now let's see the changes in the code and the changes in the, in the basic application we will

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need to do so.

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Let's see that in the.

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Well, we can stay in this in this same video.

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So let's go.

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Let's go to the slides.

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And here you have the A.

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The notebook.

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As always, the notebook is the document with more accurate and and detailed information for you.

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Okay, so let's take a look at the notes in the in the slides.

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So what you are, uh, you remember what we are going to do is a is a is we are going to reproduce the

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basic application we created in the first lesson of this blog, the to do application.

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And a we are going to, uh, integrate an orchestration framework long chain in this case and an LLM,

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a foundation, LLM in this case ChatGPT from OpenAI.

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So our recommendation is that you make a copy of the todo app on your computer so as not to write over

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the original todo app.

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This is when you start practicing by yourself.

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The first thing, as always, that we recommend is that you download the code from GitHub.

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You play around with it and then you try to reproduce it.

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You know as many times as you need in order to feel comfortable with the with the exercise.

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So you will see that we are going to make a few changes to the basic application.

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And here you have the changes in detail.

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The first thing is that A you need to add the OpenAI key in the a dot m file of the back end.

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Okay.

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The back end, remember.

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And when you do this a as, as we said in the previous lesson, you will have to remind not to share

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this dot m file with anyone.

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So remember to configure the git ignore file dot.

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Gitignore file properly.

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In order to avoid that this dot m file goes to your GitHub repository.

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Okay.

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Because we you don't want to share your OpenAI key.

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The second thing, and that you will need to do is to add imports in the A to DOS router.

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So in the back end slash routers slash to DOS dot pi you are going to include imports here.

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Remember is where you are going to include the imports according to the modifications of the version

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010 that you already know from long chain.

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So remember that instead of doing from long chain import open AI, you will have to do from long chain

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a hyphen OpenAI import OpenAI.

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You remember that, right?

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So please review the video.

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We have included the video several times.

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Uh, I'm going to include the video also after this one or before this one for you to have that a close

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to this, uh, chapter.

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So remember that the imports are going to be a little bit different in your case, why this is working

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for us?

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Because we have this app included in a virtual environment that is configured with a, with an old version

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of long chain.

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Why?

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Because we we did this.

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We built this before the, the the version 010.

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But the changes you will have to do are very simple okay?

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So don't worry about it.

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Just remember what you have already learned.

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And in case of doubt, you know where to find answers.

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So we are going to import OpenAI uh prompt template and also LM chain.

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We are going to do, as you know, a very simple, uh, thing with uh, long chain and OpenAI in this

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application.

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So a uh, first import, then we are going to add a basic long chain code.

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Okay.

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So in the same file in the back end routers todos.py you can include this code in order to check this

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very simple functionality okay.

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This is where you are going to try the functionality that you added here.

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And in order to check uh that this is working, you can start the back end server in your terminal and

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check how this works.

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Okay.

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And now this is a very simple long chain.

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Excuse me.

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Very simple launching functionality.

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And after that you are going to include the a little bit more advanced functionality that we have in

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the demo, which is the the one that writes a poem a after the task included.

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Okay.

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So this is the functionality you will have to add in the back end routers to do dot pi.

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And in the front end you will have to make a few small changes.

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Remember that we are including the new button there and the functionality associated with it.

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So the you have some changes in the to do component.

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Here you have the changes for you to review and and include it.

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And once you have that in your front end, you are ready to you already know how to a how to start the

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full stack application in your computer.

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And if you wish, you also know how to deploy this application into render and Cellcom.

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Just follow the same the same procedure.

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Okay, so now we have seen one step further.

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We started this blog with a simple level three application.

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Then we went a little bit farther with a medium level three application.

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And now we have a basic level three LM application.

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We have integrated orchestration frameworks and also an LM.

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So in the next lesson.

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We are going to go one step further and we are going to use the medium app we created, the one that

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is a loading PDF files in S3 in Amazon S3, and then we are going to use to include the lag functionality

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into that app.

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Okay.

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So we will we are going to do that in the next video.