This seems to be coming from the “we must make ElasticSearch AI-compatible” department more than anything.
> This seems to be coming from the “we must make ElasticSearch AI-compatible” department more than anything.
I don't see the problem in that. It'd be great to have agentic capabilities embedded into Kibana and ES as long as it's not user hostile.
Saying, “just use SQLite” completely dismisses the idea that this is a _shared_ memory across teams. The ability to easily connect to the remote service and have everything “just work” pays dividends when you have dozens or hundreds of users.
Maintaining Elasticsearch isn't free, but picking an underpowered db and having to port to the right one is also quite time consuming.
also, I've run ES on an old laptop and it worked really well, so the cost of it can be pretty low if you're still in development
It seems like a cool approach. Don't know if it's novel but it's much smarter than "shove markdown files into directories".
"Good" is subjective.
I think the challenge is to teach how ranking works to people more effectively so that they can build it for themselves and host them on their own.
Like the other day someone who has worked in search explained to me why you would care about using learning-to-rank(LTR) technique to train your own feature vector weights on your data. My understanding is that weighted features work better(retreival wise) on textual data than plain BM-25 and vector embedding db indexing of text chunks of your data with minimal preprocessing. So if you have lots of conversations you can create a ton of features(like attributes of a conversation) from it and ones that matter more will rank higher. And you can use a regularization(like L1) to kill unimportant ones.
[EDIT]: IIUC, I think LTR is important because you likely want different features to matter more for different parts of your documents, e.g. what matters for codebase documentation is different from your personal journal.
It took me a while to wrap my head around the two terms since they seem similar -- but Accuracy is basically "did i get mostly good results" and Recall is "did I get most of the good results" and they're subtly different. :)
Those two terms, though, will unlock as deep a rabbit-hole as you'd like on the subject.
The point about memory is sometimes you remember great detail, sometimes you only remember that the memory exists, so having a good tool loop to attempt to recall and try permutations is good.