Knowledge Triples – Orbifold Consulting

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2025-01-20 10:30:05

Graph AI is a large part about creating triples and extracting them so that an LLM gets augmented in function of a particular question. In the past years (we are 2024) there has been a lot flux in this direction:

There is a general agreement that graphs lead to more accurate RAG (see Neo4j’s comparison for instance) but it’s equally clear that creating graphs is more demanding than simply dumping and querying vectors. It’s also unclear whether knowledge graphs mean triples (as in RDF) or property graphs. Frameworks typically output triples but store them in property graphs. Mamy people, on the other end, highlight the importance of ontologies and the virtues of RDF. The wisdom, like so often, is: it all depends on what you’re after and lots of details (budget, tech stack, vision…).

There are many examples out there and the essence of graph RAG is not complex. It’s only when you go a few steps further that it becomes challenging, for instance: