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