I've been looking like this for a long time. It would be perfect for a knowledge base of documents. You could have contracts sorted by subject matter, torts, procedural rules, what have you. Recalls wandering in the stacks to find books whose title and author you didn't know. How did you make it?

I've been collecting quotes for a long time (about twenty years now) and have recently been thinking of doing something interesting with them. For example, I recently added a flashcard "game" to my quote page:

https://www.oranlooney.com/quotes/

I've did something similar to your 3D viewer once, but for all possible solutions to the Soma cube:

https://www.oranlooney.com/demos/soma-forest/

The way that works is it uses t-SNE to embed the solutions in a 2D manifold based on similarity. This is completely different than John Conway's SOMAP solution.

In theory I could do something similar for quotes, passing each through an embedding model, computing the n^2 semantic distances, and using t-SNE to flatten that to 3D manifold, and using the resulting point to select the row, book, and shelf in a library.

Are you planning to make your 3D library code open source?