Okay, so in this lesson we are going to present briefly a couple of other interesting level three LM
applications.
So as always you will find the the best information in the notebooks.
Uh, we are going to to work with two notebooks and also to a presentations with the summaries.
But the notebooks are always going to be the best source for you.
So first we are going to review the Create Llama project and the way to develop full stack starters
with it.
So as you know, the Create Llama is a tool developed by Llama Index to streamline the creation of rack
full stack application templates.
Do you remember that we saw uh, we talk about that when we review, uh, llama index.
So right now and I highlight this right now because I think it is going to improve.
But right now Create llama has a few limitations.
It does not include a database to host private documents.
So right now, private documents are manually included in in an app directory.
And therefore it does not include Crud functionality to manage private documents.
So it's a very simple approach.
It currently allows the creation of three types of full stack applications, or more or less pseudo
full stack applications.
In some cases, the option one is front end and back end with Next.js serverless.
So this is not a true full stack.
The option two is front end with Next.js and back end with fast API, which is something we are familiar
with.
And the option three is front end with Next.js and back end with express, which is based on JavaScript.
So some comments about these options.
The first option front end and back end with Next.js serverless.
This is the simplest option.
It only requires deployment on Vercel and in our opinion, it has the limitation that it will fall short
when we try to scale the application.
How does Lamar Index use it?
A you will see that the rag logic is in this file, and you will immediately see that this option one
is written in TypeScript.
So JavaScript.
The second option is a the frontend with Next.js and the backend with fast API.
The Laima index tutorial suggests that deploying both frontend and backend a a.
The suggestion is to deploy both front end and back end on render com, which we don't think is a good
idea, as it will limit us when scaling the front end of the application.
So it seems more appropriate to deploy the front end over cell and the back end on render.
Com as we did with the Todo application, right.
So this one is interesting, but it has also some limitations.
Here you in this file of the starter template you will find the rug logic of the of llama index.
And interesting thing this option two uses Python okay.
The third option is the front end with Next.js and the back end with express.
And as you know express is based on JavaScript.
So considering that llama index as well as long chain and the ChatGPT API are natively developing Python,
our understanding is that it is most advisable to specialize in back end with fast API instead of express.
Why?
Because there are so many artificial intelligence engineers used to work with Python.
That is going to be very difficult to, uh, move them to JavaScript.
So let's see what happens in the future.
But I don't think a Python is going anywhere in artificial intelligence because the main community of
users are Python users.
So I don't think this option three has a lot of appeal for, uh, artificial intelligence engineers.
So for this reason, we will not cover this third option in the course, although it may be a good alternative
for developers with a background in JavaScript of or working in teams specialized in JavaScript.
So let's see what happens a in our opinion.
Create the llama right now is not the perfect solution, but it can be.
So we expect that especially this second option is going to improve and become a much better option
for us.
Here you have a detailed notes in the notebook regarding the second, uh, the second level three LM
application we wanted to highlight here.
The project.
Chat Llama index this is a starter template for creating a rack application for chatting with your private
documents, which is very interesting.
It is similar to the Create Llama starter template, but with this is Next.js.
So it is very similar to the first option of create lemma.
Okay.
The one that was using Next.js serverless.
But in this case the application allows file uploads through the user interface instead of having to
do that manually in the application file, as we do with create lemma.
Okay.
Comments on this option.
First one, it uses the TypeScript version of llama index, not the Python version.
Baths.
And second, it falls a bit short for professional applications as it does not use backend API or back
end database.
So we think a that this option and this option.
The second option of create Llama, we have the feeling that they are going to evolve in a positive,
uh, direction.
The current status of both of them is not the ideal one, but we think a llama index and I would bet
also long chain are going to provide more professional starters or starters for better prepared for
professional applications.
So my recommendation for you is explore these ones experiment a little bit.
And the most important, pay attention to how they evolve.
Because I think, uh, options like these ones can be super interesting for us.
Artificial intelligence engineer.
So with this we are finishing this blog.
I think this is a very important blog.
Uh, and it is very important for you to be comfortable and familiar with the different, uh, lessons
and subsections of this blog.
So please go back if you are not, it is very important for you this this this one, this blog is very,
very important for you.
So please spend the necessary time to consolidate, uh, your, your, uh, lessons, uh, here.
Okay.
And once you are ready, we will go to the next, uh, blog where we are going to start talking about
a very important advanced concepts regarding LM applications.