[laminarflow027]

This is an excellent article that asks some much-needed questions on the literature that exists connecting LLMs and RAGs on unstructured data, with knowledge graphs in between. We've seen plenty of articles that speculate on how one can build a simple retrieval system on top of a KG, but there are two challenges: a) constructing a high quality KG isn't easy, and b) keyword or phrase embedding on metadata for pre-filtering on relevant sections of the graph is required.

As some others here have pointed out, information extraction and searching with relevant context are the hardest parts of any search system, and it's clear that simply chunking vectors up and throwing them into a vector DB has limitations, no matter what the vector DB vendors tell you. Just like this article says, I hope that 2024 is the year where we actually get some papers that perform more rigorous evaluations of systems that use vector DBs, graph DBs, or a combination of them for building RAGs.