WEBVTT 00:00.530 --> 00:04.220 And welcome to the next part of visualizing the data. 00:04.220 --> 00:06.500 And just very quickly to show it to you in 3D. 00:06.530 --> 00:12.350 My box managed to survive me restarting and getting rid of that massive plot. 00:12.380 --> 00:16.250 I hope you didn't follow my track, but you did it more sensibly. 00:16.280 --> 00:25.730 Anyways, now to visualize in 3D just again, to get that that sense of appreciation for what it means 00:25.730 --> 00:28.250 to have a vector embedding of text. 00:28.490 --> 00:34.070 This time I have stuck with a more reasonable 10,000, boringly and otherwise. 00:34.070 --> 00:41.450 The code is just like we did it before when we looked at Rag, and we create the scatter plot using 00:41.450 --> 00:43.250 the Plotly library again. 00:43.250 --> 00:47.660 And this is what it looks like in the 3D visualization. 00:47.690 --> 00:52.160 It's hard to stop it from from zooming in and out, but there we go. 00:52.280 --> 00:58.700 And just as before, when we looked at the much smaller vector data space, it looks a little bit, 00:58.700 --> 01:02.780 um, uh, strange from, from a distance like that. 01:02.780 --> 01:10.340 But when you rotate it around and you interact with it, You absolutely start to see you get to appreciate 01:10.340 --> 01:17.900 the 3D, and you get to see how there are clusters that represent related kinds of products. 01:17.930 --> 01:20.060 And you can actually copy the code that we used before. 01:20.060 --> 01:22.940 So you get it to print the text of each one if you wish. 01:22.940 --> 01:25.790 It will use up more memory again, but you can do that. 01:25.940 --> 01:31.760 And that's a pretty cool way to satisfy yourself that the data is being represented in this way, that 01:31.760 --> 01:33.440 similar things are close to each other. 01:33.440 --> 01:35.810 That's really the important takeaway here. 01:35.810 --> 01:41.510 And you'll see when when purple dots have strayed away from the mainstream, you'll you'll get a sense 01:41.510 --> 01:42.350 of why. 01:42.380 --> 01:44.150 And it's really helpful to do that. 01:44.150 --> 01:54.050 So this is again more more of an exercise to build intuition designed to help see that as we scale up 01:54.050 --> 02:00.170 rag to this much bigger problem with much larger number of documents, that the same rules apply, and 02:00.170 --> 02:03.860 that you can visualize and experiment with your data in much the same way. 02:04.160 --> 02:07.280 Quite enough preamble on on vector data stores. 02:07.280 --> 02:13.010 It's time for us to actually build the Rag pipeline to estimate product prices using similar products. 02:13.010 --> 02:14.240 Let's get to it.