> Llamaindex was used to add nodes into the graph store based on documents.
So it sounds like they are generating it based on LLM output rather then user-defined. I also wonder how often you need more than a single hop that graphdbs aim to speed up. In an agent system with self-checking and reranking, you're going to be performing multiple queries anyhow
There is also interesting research around embedding graphs that overlaps with these ideas.
I like your answer and a great example of the limitation of knowledge/semantic graphs. Personally, I'd put a knowledge graph on top of the responses to expose it to LLM as an authority & frame of reference. I think it should be an effective form of protection against hallucination and preventing outright incorrect / harmful outputs in contradiction with the facts known by the graph. At least in my experiments.
Topological relationships vs metric relationships. I suppose a great embedding could handle both, but a graph database might help in the tail, where the quality of the embeddings is weaker?
I agree that something that is more like an SQL query, where you have definitive inclusion, will be useful. The harder question is how do you build something like that? How much AI involvement is there in creating the more discrete relation knowledge base.
Go back to the original word2vec example, how would you put this in neo4j, in generalization?
king - man + woman = queen