So in this lesson, we are just going to see a graphical representation of the architecture of the professional
Elm application we just saw.
So this is the, uh, graphical representation of the architecture of the application.
We just saw the insights.ai.
So this is an application that has a back end framework based on fast API.
This is a framework based on Python language and the seq API.
The seq insights uh application is based on lambda index as an orchestration framework.
The vector database is uh, it uses is Postgres.
The back end server is render.
Com the front end framework is Next.js, which as you know is based on react.
And as you know, react is based on the JavaScript language.
And the front end server is very cellcom.
So these are the two main parts of our full stack application.
But in the back end we have a the application connected with ChatGPT as LLM foundation model.
For our application we have 3 or 4 external APIs uh connected as well.
And then we are storing in the PDFs and the private data we have in the PDFs.
In this application.
We are storing these PDFs in S3 from Amazon Web Services.
So this is the cloud storage okay.
So you will see that during the next lessons we are going to learn about all these different components.
Don't worry it looks like super complex, but it is going to be easier and more simple than you think.
You just need to have perseverance.
And remember the key is study and practice.
Okay?
So remember that you can play with the the application.
This LM application is open source by llama index.
So you can check it out in this URL.
And remember that this application loads a sexy documents.
These are financial documents and a it answers.
Questions about these documents.
It also provides source links and it is open source by Lamar Index.
As you know, Lamar Index is one of the orchestration frameworks we are going to use.
So the team at Lamar Index has created an open source, this application just to show you what you can
do using Lamar Index.
Remember that Lamar Index is the top alternative to Lang Chain, the other most popular orchestration
frameworks for NLM applications.
In the next lesson.
We are going to start talking about some of the.
Details of the advanced architecture of an LM application.
So we are not going to see these details in depth is going to be just a quick initial view of them.