[PHOTON1233]

Ive got some amazing articles saved on my computer! Will get back to you. Until then, its basically a csv with (head,relation,tail)—> converted to a KG (networkx)-> nodes embedded and vector stored ->queried similarity nodes on conversation-> n-neighbours connected to KG extracted and fed into llm relevant context.

[unraveller]

where does traditional RAG semantic text embedding fit into this Knowledge Graph scheme then? before and/or after the node embeddings are grabbed for prompt context? or not needed at all?

Anything that makes RAG more generalizable automagically in the background is welcome.