> interesting, so you think the issue with the above approach is the graph structure being too rigid / lossy (in terms of losing semantics)?

Yeah, exactly.

>And embeddings are also too lossy (in terms of losing context and structure)

Interestingly, it appears that the problem is not embeddings but rather retrieval. It appears that embeddings can contain a lot more information than we're currently able to pull out. Like, obviously they are lossy, but... less than maybe I thought before I started this project? Or at least can be made to be that way?

> But you guys are working on something less lossy for both semantics and context?

Yes! :) We're getting there! It's currently at the good-but-not-great like GPT-2ish kind of stage. It's a model-toddler - it can't get a job yet, but it's already doing pretty interesting stuff (i.e. it does much better than SOTA on some complex tasks). I feel pretty optimistic that we're going to be able to get it to work at a usable commercial level for at least some verticals — maybe at an alpha/design partner level — before the end of the year. We'll definitely launch the semantic part before the context part, so this probably means things like people search etc. first — and then the contextual chunking for big docs for legal etc... ideally sometime next year?