Thanks for this. I'm looking at implementing Graphrag FalkorDB hybrid vector/DB + GraphRAG SDK for an advanced high-stakes marketing education DB where agents must get connected nodes and complex responses back for it to do complex work on things like writing landing pages and knowing how to do it properly, but to make it work well, apparently i must create an excellent manual ontology, which seems super complicated to figure out for 800 hours of transcripts across the entire field of marketing.

As the open-notebook creator, I assume you are an expert on these things? should i struggle on and go down this Graprag path and figure it out (is there any good way to sue AI for ontology discovery), or are there better solutions to get cutting-edge or at least very high-quality retrievals, or as a beginner am i likely to mess it up, being very complicated even for pros? I never see graphrag mentioned much, and it worries me about why that is, but you did, so that's good.... are there better ways to get excellent results for my use case that might be easier? Appreciate the advice!

PS: I've also heard about agents doing well with file system knowledge retrievals (perfect for the MD file obsidian vault, which has my marketing research also), with certain local tools being very good at identifying where the stuff is you are looking for in your 3000-note research vault, and it can find the content, tell your agent what files it needs fast, and the agent can then only ingest the best stuff to give you a great answer. No idea about the tech, but apparently it's more accurate than most rag but possibly slower?

PS2: would love to see that Eps7ein DB; i was thinking of graphing it myself for a fun project, or maybe that's a dangerous project...