LLMs are only used to construct the graph, to navigate it we use an algorithmic approach. As of now, what we do is very similar to HippoRAG (https://github.com/OSU-NLP-Group/HippoRAG), their paper can give a good overview on how things are working under the hood!

That would be awesome, we have a discord you can join and we can talk there (link is in the github repo, message Antonio) or you can message antonio [at] circlemind.com

Thanks for sharing! These are all very helpful insights! We'll keep this in mind :)

We are building connectors for that, so it will soon :) At the moment we are using python-igraph (which does everything locally) as we wanted to offer something as ready to use as possible.

This is super interesting! Thanks for sharing. Here we are talking of graphs in the milions nodes/edges, so efficiency is not that big of a deal, since anyway things are gonna be parsed by a LLM to craft an asnwer which will always be the bottleneck. Indeed PageRank is the first step, but we would be happy to test more accurate alternatives. Importantly, we are using personalized pagerank here, meaning we give specific intial weights to a set (potentially quite large) of nodes, would TC support that (as well as giving weight to edges, since we are also looking into that)?