I used a graph database in https://www.exploravention.com/products/askarch/ because software architects typically need to understand the dependencies of a complex software system before they can suitably lead that technology. A dependency graph is a good data structure to use when reasoning about dependencies and a graph database is a natural choice for capturing dependency graphs. See https://www.infoq.com/articles/architecting-rag-pipeline/ for more details on the architecture of this AI product. The graph database works very well for this use case.
If you are considering the use of a graph database for AI based search and you are not already familiar with graph database technology, then you should be advised that graph databases are not relational databases. If you cognitively model nodes = tables and edges = joins, then you will be in for some nasty surprises. You should consider some learning, and some unlearning, to do before proceeding with that choice.
I don't think the cognitive models are that distinct, just a different way in which relations are stored. In any case, not distinct enough to warrant 'unlearning' relational approaches. While I find graph based approaches more natural to some problems we can stretch the relational paradigm quite a bit.
If you are considering the use of a graph database for AI based search and you are not already familiar with graph database technology, then you should be advised that graph databases are not relational databases. If you cognitively model nodes = tables and edges = joins, then you will be in for some nasty surprises. You should consider some learning, and some unlearning, to do before proceeding with that choice.