In this lesson, we are going to preview a professional LMS application that we will see in detail later.
So the llama index team has open source the project.
Seek insights.
Right now this is one of the most advanced and sophisticated production ready LM apps available.
It will be worthy to study it in detail.
We will see the code in detail.
We will see how to build it in bit in detail, and we will see that this application is a chat application.
We will see now how it works.
It uses a technique that we are going to master, which is the rack technique and a it answers questions
about.
Sick 10-K and 10-q documents, so it answers questions about a financial documents.
It is production ready.
It is using a full stack a.
Quote a it is ready for you to fork and use.
And all the setup.
All the setup is open source and it is easy to deploy on vercel and render.
Com.
So you will see that a this application is a QA chat grounded in source of truth.
Seek documents.
It has a PDF viewer.
It has a token level streaming of chat responses.
It will stream of reasoning steps like subquestions.
It has citation of source data and it makes use of API based tools in addition to semantic search.
The architecture of this application that we will know in detail.
Uses a render Dccom as backend server.
Fast API as backend framework.
Postgres as vector database.
Next.js as frontend framework, Vercel as frontend server, and then it uses several external APIs and
it stores a private documents in Amazon Amazon S3.
So we will know the details of this application later.
Right now I only want to show you a quick demo of the application you can use.
You can play around with this application in Insights.ai, but.
A very quickly.
This is an application that we can use to make questions about different, uh, financial documents.
For example, we can select documents from Apple and documents from Amazon from for the same year.
And after we, we can add until, uh, eight, uh, documents.
Let's try with these two.
So these two are financial documents for Amazon company and for Apple company in the 2020 a year.
And this is a kind of a financial type of document.
So what we are doing is we are providing the LM application with private documents.
So we try to do this.
For example with ChatGPT.
We cannot do it because ChatGPT ChatGPT doesn't have this knowledge, this private data.
But in our LM application in this and this application is built on top of ChatGPT, we can use private
documents like this one.
So once we have loaded our documents and these documents come come from an external API.
We can start having a conversation about these documents.
And as you see in the following screen, here we have the the PDFs we are using.
And in the left side of the screen we have a chat bot where we can make questions about these documents
we have loaded.
For example, we can say um.
Compare.
Both documents.
Um.
Make.
Make recommend.
Make investment recommendations.
Commendations.
About.
Them.
Citing.
Sources.
Okay, so if I click enter, you will see that this application is going to start preparing the response.
And while it is preparing the response is going to allow to see.
It's going to allow us to see how it is progressing.
So right now, as you see it, is reviewing, uh, documents in order to prepare the response.
And every time it is reviewing documents, it is going to show us the link to the source.
It is used to prepare the response.
So for us right now, it is not important.
The response we have here probably is a good and very interesting response.
But what I wanted to show you at this point is the way a professional LLM application works.
So far from the typical toy demo you can find in many courses with or without a toy user interface,
this is a real application.
This is a professional application ready to ready for you to start using it.
And as you see, we have front end elements.
We have back end elements, we have.
This is a way to be able to confirm that the response that is provided by the application is an accurate
response.
Do you remember that we said that sometimes applications can, uh, make hallucinations, fake responses.
So it is very important for you to have a way to check if the response is based on, uh, true data.
Right.
So this is what this application is doing.
And you can see, you know, the front end elements you have here because they are actionable items
as well here.
But it is very interesting because if you click here, you can go exactly to the uh, paragraph or the
page that it is using in any case.
Right.
So I just wanted to show you very quickly what a professional LMS application, uh, looks like.
Right.
So you will see that this is what we are going to learn to do.
This is very far from the toy demos.
This is very far from the level one application and level two applications.
This is what we call a level three application, which is a professional application ready to use in
the real world.
Okay.
So we are going to study this application further in the next lessons and uh, other applications as
well.