[adsharma]

For starters, LLMs themselves are a graph database with probabilistic edge traversal.

Some apps want it to be deterministic.

I'm surprised this question comes up so often.

It's mainly from the vector embedding camp, who rightfully observe that vector + keyword search gets you to 70-80% on evals. What is all this hype about graphs for the last 20-30%?

[Tsarp]

"LLMs themselves are a graph database with probabilistic edge traversal" whaat?

Do you have any good demos to showcase where graph DBs clearly have an advantage? Its mostly just toy made demos.

vector embeddings on the other hand no matter how limited clearly have proven themselves useful beyond youtube/linkedin thought leader demos.

[adsharma]

It comes from people who develop LLMs. Anthropic and Google. References below.

My other favorite quote: transformers are GNNs which won the hardware lottery.

Longer form at blog.ladybugmem.ai

You want to believe that everything probabilistic has more value and determinism doesn't? Or that the world is made up of tabular data? You have a lot of company.

The other side of the argument I believe has a lot of money.

https://www.anthropic.com/research/mapping-mind-language-mod...

https://research.google/blog/patchscopes-a-unifying-framewor...

[Tsarp]

Not sure how that was the take away from both the posts above.

I read the blog post and your website but unfortunately didnt help change my perspective.

Thanks for the share