And welcome to the next part of visualizing the data.
And just very quickly to show it to you in 3D.
My box managed to survive me restarting and getting rid of that massive plot.
I hope you didn't follow my track, but you did it more sensibly.
Anyways, now to visualize in 3D just again, to get that that sense of appreciation for what it means
to have a vector embedding of text.
This time I have stuck with a more reasonable 10,000, boringly and otherwise.
The code is just like we did it before when we looked at Rag, and we create the scatter plot using
the Plotly library again.
And this is what it looks like in the 3D visualization.
It's hard to stop it from from zooming in and out, but there we go.
And just as before, when we looked at the much smaller vector data space, it looks a little bit,
um, uh, strange from, from a distance like that.
But when you rotate it around and you interact with it, You absolutely start to see you get to appreciate
the 3D, and you get to see how there are clusters that represent related kinds of products.
And you can actually copy the code that we used before.
So you get it to print the text of each one if you wish.
It will use up more memory again, but you can do that.
And that's a pretty cool way to satisfy yourself that the data is being represented in this way, that
similar things are close to each other.
That's really the important takeaway here.
And you'll see when when purple dots have strayed away from the mainstream, you'll you'll get a sense
of why.
And it's really helpful to do that.
So this is again more more of an exercise to build intuition designed to help see that as we scale up
rag to this much bigger problem with much larger number of documents, that the same rules apply, and
that you can visualize and experiment with your data in much the same way.
Quite enough preamble on on vector data stores.
It's time for us to actually build the Rag pipeline to estimate product prices using similar products.
Let's get to it.