Hello guys!
In this video and in the upcoming series of video, we are going to develop this amazing end to end
j'en AI project that is nothing but conversational Q&A chat bot where we are specifically going to chat
with a PDF here.
We are also going to use our conversation history, right?
So there will be a lot many things that we really need to discuss over here.
It is a complete end to end project, and already in our previous module we have discussed about conversational
Q&A chatbot.
Okay, so first of all, what I will do is that, uh, in this video I will show you the demo.
So let me just go ahead and click and press my, uh, grok API key.
Once I click, uh, uh, you know, enter my grok API key, there will be a default session that will
be created.
Okay.
And this is just for me as a user, right?
Uh, if I probably go ahead and implement this in any web application, I will make sure to track the
session ID and based on the people using it, that session ID will be different.
Okay.
Now let me just go ahead and drag and drop one of the files.
So here I have my attention dot pdf.
Now once I do this, this entire PDF text will get converted into vectors and it will get stored in
a vector stored database.
Okay.
Now here I will just go ahead and ask one question.
Okay.
Tell me about Transformers.
Something like that okay I'll just search transformer.
Let's see whether I'll be able to get the response or not.
So here you can see that I'm getting the response.
The transformer, um, generalizes well to English constituency parsing, achieves F1 score of 91.3
which is trained on a set.
And this is semi-supervised.
Right.
So these are some of the information that I am able to get from the PDF.
Then I will say attention is all you need if I go ahead and search for this okay.
Let's see what kind of information I'll be able to get from here.
Right.
So here it says the attention mechanism is used in transform architecture to weigh the importance of
different input elements when computing the output.
Okay.
Uh, then I will just go and ask uh, uh, which topic were we discussing about?
Okay.
I'll just go and search like this.
Okay.
So here when we search this switch topic we'll be discussing about here you'll be able to see, um,
we were discussing about a transformer model and its architecture.
Okay.
So this is the information.
And this is able to get it from the previous context.
Okay.
Tell me about a detailed detail.
Summary of attention is all you need okay I'll ask this particular question.
Let's see whether I'll be able to get the answer or not.
So once I press enter and here I'm able to get something.
Okay.
It says, uh, tell me.
Okay, I don't know.
Okay, fine.
I think, uh, in the research paper I just have put two pages.
So that is the reason it is not able to find out.
Okay.
Tell me more about transformer and s architecture.
Okay.
I'll just go ahead and ask this question.
Let's see whether it should be able to retrieve the information and give me some kind of response.
So here is my detailed.
The transformer is the type of neural network architecture designed by so and so right.
Self-attention mechanism and all.
So I'll go ahead and ask what is self-attention mechanism now.
So now let me see what is self attention mechanism okay.
I'll go ahead and search for it.
Um so here I should be able to get some kind of response.
And it is very fast.
Okay.
Uh, it says I don't know.
Let me just go ahead and enter.
Once again, I should be very much precise while I'm writing the output.
So.
So here you can see the self-attention mechanism because there I did not write this particular word
properly okay.
A self-attention mechanism is a key component of transformer architecture and is used to so and so all
the information.
And here you can see the entire chat history is also basically getting considered right now if I go
ahead and ask like what is the previous message?
I asked previous topic that I asked that I ask I should be able to get the answer.
So this is my history and this is all history.
You'll also be able to see it.
Okay.
Uh, you asked about transformer, then followed up with question about the self-attention mechanism
and transformer architecture.
This is amazing, right?
Uh, provide me a detailed summary of the conversation we had.
I'm just going to ask this.
Okay.
Isn't it amazing?
Use case.
Think over it.
Okay.
So here I'm just saying.
Provide me a detailed summary of the conversation.
We had it.
And here you can.
You initially asked about transformer model architecture.
See, based on the chat history everything is available over here.
I provided a brief summary of the transformer model and its architecture, highlighting its use of self-attention
mechanism and parallelization to process the input sequence.
You asked me to provide more information about this, this, this, all this things has been visible,
right?
And this is my chat history.
And this is based on my session ID, the default session ID, what all conversation we had.
So yes, this was it.
Now in the next video we will go ahead and implement this entire end to end solution.
And I hope uh, please make sure that you follow this because already I have discussed about conversational
bot in the previous video.
So as this was it for my side, I will see you in the next video.
Thank you.