In this lesson, we are going to talk about some consideration considerations to select the right orchestration
framework.
So as you remember, we have three main alternatives, uh, to, to use as orchestration frameworks.
We have long chain, we have lemma index and we have the API of OpenAI.
So let's talk a little bit about each of them.
And uh we will give you our own conclusions and recommendations.
So let's start talking about long chain in its first version launching what was a simple way to learn
the basic concepts of LM applications and develop a mature applications.
Long chain has many connectors and it is very interesting if ChatGPT is not hegemonic.
In its second version, what is called the long chain expression language.
Long chain is not so simple.
It seems they are turning towards a solution more prepared for professional applications.
Langton is still a very young and a small company.
It still needs to find its strategic vision and business model.
It is highly dependent on the evolution of ChatGPT.
So luncheon is the most popular orchestration framework.
It is a very interesting way to learn the basic concepts of Elm applications, and is a good way to
develop a mature application store.
Demos.
It was simple to use.
It was easy to learn.
In the second version they are preparing what they call long chain expression languages.
Things are not so simple anymore.
So we.
We'll tell you what we think about the evolution of land change later.
Let's first talk about llama index.
So llama index is the top alternative to a long chain right now.
In its first version, Llama Index, uh is a less generalist framework than long chain.
He wants to do fewer things but do them better.
It specialises in generating possibilities for professional rack applications, so launching does less.
Excuse me, llama index does less things than launching, but better.
This is very important.
So for professional LMDh applications, engineers are paying attention to Lambda index because in many
cases long chain is a so simple approach for them.
So they are seeing that lambda index is better prepared for more complex, more professional solutions.
So they go to long chain to learn a toy demos.
And after that they go to Lambda Index for professional solutions.
This is changing lately because long Chain has seen that and is improving their framework as well.
But this has been the trend we have observed.
These are personal opinion.
Llama index is also a very young and small company, and it also needs to find its strategic vision
and business model.
And right now, like Lange chain, it is highly dependent on the evolution of ChatGPT.
What do we mean when we say that they are highly dependent on the evolution of ChatGPT is that they
are moving after the chat GPT uh moves?
Why?
Because as I was telling you, during this first year of Elm applications, 2023 was the first year
in the life of applications.
ChatGPT was absolutely dominant in the market.
So these two orchestration frameworks were extremely dependent on the evolution of ChatGPT.
If you remember, Lang chain and lambda index are like universal languages to talk with different Elm
models.
So this is one of the main advantages of working with Lang chain and lambda index that you can use different
Elm models, you can use ChatGPT or you can use anthropic or Lambda two, etc..
And with this universal approach of lambda index and lang chain, you don't have to learn, you know,
the anthropic native language or the Lambda two native language, or the ChatGPT native language, because
you have this universal language which is lang chain or a lambda index.
But this was a theoretical approach.
In reality, in the first year of Elm applications, ChatGPT was so dominant that a lang chain and lambda
index had had to follow the path of ChatGPT.
This was the first year.
Let's see what happens in the.
In the following years, we will talk a little bit about that, uh, later in this in this lesson.
So what about OpenAI, the API of OpenAI.
So some people during the first year of Elm applications, they so they, they, they thought okay,
so.
ChatGPT is totally dominant.
Why should I use a middleman to work with the ChatGPT API?
Because at the end of the day, long chain or lambda index are just middlemen between the engineer and
the LM model API.
So since ChatGPT was so dominant during the first year, some engineers said, okay, you know what?
I'm going to learn directly?
The API of ChatGPT.
What we call OpenAI API.
You know, OpenAI is the company behind ChatGPT.
So.
In its first version, the OpenAI API was not very simple.
In the second version, a consequence of the changes presented at Dev Day in November 2023, this second
version aims to be simpler and more versatile.
OpenAI seems to have taken the path Apple took with the iMac and iPhone.
So it does not open its technology.
It reserves many areas that remain opaque to the developer.
It controls a closed up market and sometimes it cannibalizes it.
It happened with the first round of startups.
It focuses on generating revenue, especially for from large companies, but also from developers and
users, and it is conditioned by its alliance with Microsoft, favoring its products like Azure, Redis,
etc., and favoring also Microsoft's revenue streams.
So what we saw in November 2023 is that OpenAI.
Wants to be more simple and more versatile for developers.
And they have changed their API in order to, you know, talk directly to ChatGPT.
They have changed their API in order to make it simpler, more simple, and more versatile.
But for engineers, OpenAI has some problems, does not open its technology.
It controls a closed up market.
Sometimes it cannibalizes the startups in the in the closed app market.
It focuses on generating revenue and it is conditioned by its alliance with Microsoft.
All these are personal opinions.
This is what we are perceiving in the market.
So what are our conclusions as of today?
It is very important to highlight this work.
As of today, because things are changing very quickly.
So in one year from now or six months from now, we can have a totally different reality.
But as of today, what are we seeing in the market?
First, OpenAI made a very strong statement at the Dev Day in November 2023, implicitly indicating
that it wants to take market share from long chain and Lamar index.
It facilitates the creation of basic Elm applications while maintaining many opaque areas.
And it launches multimodality and many other new features.
So OpenAI.
Made a strong statement in November 2023.
A and it seemed that it wants to take some market share from long chain and Laminex it is saying, you
know what people is using ChatGPT ah as foundation model for their LM application.
So I don't want to have middlemen to talk with engineers.
I want the engineers to talk directly with my API.
So I'm going to make it more simple.
I'm going to make it more powerful.
But since OpenAI is a private company, and since it is deciding to close some of its, uh, you know,
uh, uh, coding operations and some operations with ChatGPT with the OpenAI API are opaque.
We engineers do not have control.
Do not have the same degree of control that we have with other open source alternatives like long chain
and lambda index.
And this is a huge problem for professional engineers.
So for very amateur people.
Air for students.
For rookies, the API of OpenAI.
The API of API of ChatGPT may be a good alternative to start experimenting, but.
But the moment you want to build a professional application, you don't like the fact that many doors
in the OpenAI API and the OpenAI way of doing things are closed for you, you don't have control a around
many features of the OpenAI API.
Does the a perception we got after the death day in November 2023, and after listening to the community
of artificial intelligence engineers about that.
So OpenAI.
Second line chain.
So for us, line chain is a recommendable platform to start and familiarize oneself with the basic concepts
of LM applications.
Good platform to learn.
But.
In our opinion.
Luncheon runs the risk of losing direction and being left in no man's land with the second version.
What is called long chain expression language because it loses the advantage of simplicity.
So launching it was a very good way to learn.
Very good way to start, you know, trying things and experiment.
But with the second version, right now, you can use the first and the second version of Land Chain.
And you will see that in this program, we will use the first version for you to learn the basics.
And then we will use the second version.
But eh, we still don't know if they are going to keep the first one and the second one, or they are
going to change courses.
I don't know, it's too early and it's too young.
The company to to to see what they are going to to do.
But in our opinion this evolution of land chain can be risky.
And it reminds us what happened, for example with Angular and React.
So angular at some point was was the leader of the market in the front end frameworks of uh, of uh,
internet applications.
But at some point angular decided to go for the enterprise level applications.
So it became extremely complex and it lost most of their market share and react, uh, which was the
second, uh, in the competition, uh, react took the whole market by storm, by keeping their framework
very simple, you know, and easy to use.
So react right now is used in simple mid and also complex application because it is de facto a standard
in the front end, uh, community, front end developers community.
So we are afraid about long chain following the path of angular getting more complex, losing traction,
losing users, and losing the simplicity that made it so popular at the at the beginning.
So we are waiting and see what is happening there.
But we have a, uh, this feeling, you know, this perception.
Finally, llama index is, in our opinion, a recommendable platform to delve into the possibilities
of configuring rack application applications.
Although the first version of llama index was not very user friendly, they are now striving to make
the next version better.
So llama index.
Is following the opposite path of long chain.
Long chain is going to from simple to complex, and lambda index is going from complex to simple.
Long chain was a generalist framework.
They wanted to do everything.
Lambda index was much more focused on drag functionality, so they do less things, but better.
Both companies are similar in size and similar also in the investment amount they have raised.
They are both in Silicon Valley.
They are both a created by.
Aim founders that used to work together in the same previous company.
This is interesting.
So we don't know what is going to happen.
But our perception, our opinion is that right now, Lamar Index is following a better track than Lange
chain.
Lange chain is still the leader.
It has a huge community and we are seeing that Lange Chain is also reacting, trying to learn from the
lessons that Lamar Index is showing.
So we are seeing that the Lange chain is focusing more on rack solutions, and we will see what happens
with the second version of Lange chain.
But if in in our opinion, if Lange chain makes itself a more complex, it can lose their leadership
in the in the community.
But let's see what happens right now.
As you can see, we have three players.
Lantagne is the leader.
Lama index is a very interesting follower there.
And then we have OpenAI, who this year, at the end of the 2023 year, made a strong statement like,
hey, I don't want to be out of this market.
My LM model is the favorite a among the engineers.
So I don't want to see middle mans there.
I want to go directly with the engineers.
I don't want lang chain or lama index in the middle.
But in our opinion, the approach of OpenAI making some areas opaque not available for the engineers
is a bad choice if they want to work with professional engineers, if they want to work with amateurs,
uh, you know, developers that are looking for toy demos, it's okay.
But if you want to work with a professional engineers, you need to give them the versatility that,
uh, open source frameworks like Lang Chain or Lama Index are giving them.
So I just wanted to give you our honest opinion.
The conclusion we have is that we are going to use in this program long chain to start learning about
LLM applications.
We will use the first version of long chain in our projects, then the second version.
Then we will use long chain for you to understand how long chain works.
And we will also use the OpenAI API.
We will see these three alternatives.
You will see that our focus in this edition of the program is going to be more into long chain, but
we also will look into Lambda Index and OpenAI.
So during the 2024 year, let's see what happens with these three players and also with the alternatives
we have in the in the market.
As you know, there are some alternatives right now.
They are very minoritarian.
But let's see what happens in the following month.
So in this lesson we have seen some considerations to select the right orchestration framework.
In the next we are going to uh, to talk about the usage of programming languages in LM app development.
Super interesting lesson for everybody, even if you are familiar with programming languages, but especially
if you are new to programming languages.