Yeah. So that's a good question and the question is once you get the themes so all the line algorithm will do is assign an ID to these different groups right and then what you do after that is sort of your choice and your question is are you just sort of taking from the data and just putting that there or are you making some sort of inference afterward to label the different groups. Here we're doing the former. We're just taking what's in the data and we're just showing it to you. Um, and the advantage of that is every time I run it, it'll be the same as long as the data stays the same. If the data changes, it will change to reflect the data. So, it's very stable. The disadvantage to it would be if your links in your documents um, and the titles and things that are being scooped up, because this is really only looking basically at document metadata and link metadata. um if those things aren't already wellleeled, this view might not be super informative right off the bat. That's why you have, you know, with like the the graph rag methods that Microsoft came up with, they do a lot of heavy entity extraction because then what that will give is this sort of and they'll do um hierarchical level summaries, right? So after they get the light in community, which is the same algorithm that they use, they'll do a summary that'll be LLM driven on top of each theme. Um, so that's possible to do. Um, but then obviously it costs more money, it's slower, and the you if you run it twice, it might not return the same thing. So there's trade-offs between uh each way of doing it. I'm showing you the sort of lighter way of doing it and the easier way. Um, just because it's faster and if you're just getting started with this, it might be