Hello guys!
So finally, I'm excited to implement the first end to end gen AI application that is nothing but a
Q&A chat bot.
Uh, I'll just give you a brief architecture.
What all things we are specifically going to do in this particular project.
And then from the next video we will start implementing it.
So over here, uh, some of the tools or some of the APIs that or some of the LLM models that we are
going to use is OpenAI LLM models.
Okay.
So we are going to use OpenAI LM models, uh, to name some of them are nothing but GPT four.
So this is uh this is a multi model okay.
GPT four.
Then we will also be using GPT four turbo.
Right.
We will create this entirely in a Streamlit web app so that you'll have multiple options to select the
model and probably continue it.
Okay.
So this is one of the LM models.
The second one that we are going to probably use is llama.
Now with the help of llama, what we are going to do is that whatever open source models are available
in llama.
Right.
Like uh, llama two.
Sorry.
Llama three, gamma two.
So here we are just going to write gamma two.
Along with that, if you are also familiar with Mistral.
So all these open source models also will try to use and create this Q&A chatbot okay.
So in our project, first of all, what we are going to do is that we are going to create the Streamlit
web app.
So this will be my Streamlit web app.
This Streamlit web app will be interacting.
I'll give an option wherein it will be interacting with the OpenAI APIs.
Okay, open AI, OpenAI API, and with the help of this OpenAI API, we will try to interact with various
LLM models like GPT four or GPT four turbo, or it can also be GPT.
GPT.
Let's say for over here.
Right.
Once we interact with this, then you'll be able to see that we will finally get our response.
Now we're not going to stop over here right?
Once this is entirely interacting with this LLM models, we'll take one step ahead and we will further
put all these logs in our Lange Smith platform.
Okay.
So Lange Smith will also be there.
So here you will be able to see that we will be having Lange Smith.
Now Lange Smith right is used.
It is specifically used for monitoring.
Then it is used for debugging, right?
To also see how much cost is basically getting involved.
So all this kind of information will be available in the blacksmith once you are able to track it.
So here we are just going to give our user query.
Then all this process is going to happen.
Okay I know this looks like a very simple project, but understand one thing is that as we go ahead,
we'll be building more complex project on top of it, right?
Like Rag applications.
Then we will be seeing Rag with SQL.
All this kind of applications will be trying to swing up.
So there are many projects that are probably going to come up.
But right now we will start with this basic project itself.
Okay.
The best idea about this particular project will be that, uh, first of all, we will go ahead in creating
our entire project.
Then second one is the will be setting up our environment variables.
Okay.
Environment variables.
So when I say project, I will also go ahead and set up our own environment.
Then environment variable.
Then we'll see that what all libraries we specifically want like requirements dot txt will be implemented.
Then we will go ahead and create our Streamlit web app.
Inside the Stream Live app, I'll show you how to probably call the OpenAI API key the Lang chain.
So these are like things that are happening in the industry.
And this is what I will try to cover in this particular project.
Right.
And finally, after doing all this things, we will also make sure that we will do the deployment right.
So all this steps will be specifically covered in this project.
So just to give you an idea what exactly the project is all about.
So here you can see this is what we are going to develop.
So on the left hand side you can see you can put your opening API key.
You can select different different models like GPT four turbo, GPT four uh or GPT four.
So if I select this I can also set up the temperature value and the maximum tokens that I need to get.
And let's say I will just go ahead and ask a question saying as high I'll be able to get some message.
How can I assist you?
Please explain what is generative AI.
So I should be able to get the answer over here.
Once I press enter, you'll be able to see that I will be able to get some kind of response.
Okay.
And, uh, based on the response, I will be able to see the entire data over here.
So this is what it will get executed.
And just to give you an idea over here, also, you can see, uh, the langsamt tracing is also happening.
We will go ahead and deep dive on all these tracings and all also okay.
So this will be taught in this particular project.
So let's go ahead and implement this end to end.
Thank you.