Okay, so in this blog we are going to work in the same way we work in some blocks before we are going
to divide the screen into areas.
In one area, we are going to see the slides that you have attached, and in the other we are going
to, uh, take a look at the notebooks we have prepared for you.
And as you as you will see, they are connected.
Right.
So we have divided this block in two lessons.
The first lesson is more introductory.
The second lesson it goes more in detail, uh, around lambda index.
So.
In this one we have a little introduction.
So remember that in his first version, Lamar Index, uh, is a framework less generalistic than long
chain.
It aims to do fewer things, but do them better.
It specialises in generating possibilities for professional drag applications.
So the initial name of llama index.
Was GPT index.
So remember that in the context of LM applications, when we use the word index we are referring to
indexing.
And when you name your company GPT index what you are referring is okay I'm going to use GPT meaning
the new LM models.
As you know the the the top LM models are they come from the GPT family.
So chat, GPT and many other LM models.
They come from a base LM model called GPT.
So when you name your company GPT index, what you are referring to is okay, I'm going to use GPT.
So LM models, the new LM models in order to index.
So in order to search into data.
So you will see that in the next lesson.
When we go in detail around la mendes, this initial name makes a lot of sense.
So a layman.
This is still very young and a small company.
It still needs to find its strategic vision and business model.
It has a great dependency on the evolution of ChatGPT.
All these things are also applicable to landscape.
So these are both very similar companies in very similar stages.
They are they they are both a they have a raised a a similar amount of investment between 10 and $20
million.
They are both in Silicon Valley I and they come both from the same company.
So the two founders of Llama and Landscape, they used to work together in the same company.
So it's a very interesting, uh, history.
So a although the first version of Le Mendes was not very user friendly, they are now striving to make
a second improved version in that regard.
So it's very interesting to see how Llama index is trying to get more simple.
And at the same time, launching seems to be a being more complex.
This is very interesting in my in my impression as a, you know, a spectator looking at these two frameworks.
For me, this is interesting.
So in this initial lesson, we have focused our attention in the quick start guide that llama index
provides.
We will see where in the next lesson, but as you will see in the notebook that we attach, we have
started playing around with llama index.
The first thing we do is we load the dot m file with our secret credentials to access our OpenAI key,
because as long chain llama index is going to use mostly ChatGPT as the LM foundation models for the
LM applications built on llama index.
Okay, so we do this.
We load the file and then see how fast and how easy it is to load a private document, create a vector
database and ask questions to this private document.
In llama, index is just three lines is really amazing.
So see that the way we use is very similar to to line chain.
But instead of loading from line chain we are going to load from llama index.
See the hyphen here.
So this is the way we load a private document.
See that in this example we are loading a document.
In this case we are loading an article.
This is an article talking about startups and the concept of good and bad in startups etc. with an evil
with an evil.
But see that right now we are loading this document, this article which is a text.
TXT file, but we are loading it from a folder called 000 hyphen data.
This is not going to be the case with you because you are using a data folder.
Okay, so the article that we are using is a I think is is called good good dot txt I think it is in
your data folder in the sample data folder we have provided in GitHub.
But you can use whatever txt document you want.
So this is the way you load the document.
This is the way you create a vector database.
This vector database is loading the private document.
And this is the way we start, um asking questions with this uh query function.
Okay.
So super easy to start with Lamar index.
Here we have a couple of additional notes about, you know, what they are.
Teaching in the quick start C under the heart in my opinion.
Under the hood in my opinion, this is not relevant right now.
How to save the vector database okay, interesting.
And in my opinion these are, uh, interesting uh, points, uh, about what is coming in the next lesson.
So we start talking about the customization options that you can find in the mind.
This is the most powerful, uh, thing about Lambda Index, the customization and the optimization options
it provides.
So we will see more about that.
Uh, you will see that even when they talk about different use cases like agents, multi-modality,
blah, blah, the right now, the number one use case and the I would say 99% of the cases, all the
cases in the real world are, uh, a rack techniques.
The first two are techniques.
Multimodality is is is is getting a lot of attention around the lambda index community as it is around
the long chain community is still very early, like in the case with agents.
But the interesting thing for you to to understand is that the multimodal, uh, applications are initially
LM applications.
So it is an LM application that uh, apart from using text, like all the LM applications, it is going
to use images, videos, audio, etc., other kinds of data.
Right?
So this is the multimodality.
But the interesting thing is that the the base, the foundation of this multimodal application is an
LM application.
So you are in a very good position because you are learning the foundations of the LM applications and
the multimodal applications as well.
So, so, uh, this is the excuse me, this is the most interesting thing about the mind is, as we
will see in the next lesson, customization and optimizing other interesting things around Lambda Index.
They have a templates like launching very, uh, very early, uh, new uh functionality, as we will
see.
And in my opinion, by far the most interesting thing about Lambda Index is the SEQ Insights project.
So these are project that has been built by the Lambda index team.
And they have open source.
This project, meaning that this project is available for everybody to to look under the hood.
Uh, so we are going to use this project as a way to understand very, very, very in detail what an
LM application can do.
We have even, uh, extended the level of research, investigation and analysis of this project, uh,
because in our opinion, Lambda Index gives you the project, but the explanations and tutorial that
are, uh, going with the project are a little bit, uh, in my opinion, too light to, to, uh, to
short.
So we have a invested a lot of time studying this project.
And we are going to go into detail, uh, with you.
So in this initial lesson, we just wanted to give you an initial introduction about lambda index and
a way your interest, uh, about the next lesson, which is the lesson we are going to to focus on the
details, uh, around the, the, the lambda index framework.
Okay.
So let's go to the next lesson and see that.