This is such a basic thing nowadays, and ElasticSearch is massive overkill for it. Something like SQLite or LanceDB or basically any vector database is much more appropriate.
This seems to be coming from the “we must make ElasticSearch AI-compatible” department more than anything.
Nah, "Any other vector DB" starts to fall apart once you need stuff like scripted scoring like OP uses. Then it starts to be a question of, "do you need ANN for performance?" since SQLite only does brute-force vector scoring. And granted, brute-force is performant for far more vectors than most people give it credit for, but it definitely hits a wall well below 1 million if you want it to have webpage-type latency.
Maintaining Elasticsearch isn't free, but picking an underpowered db and having to port to the right one is also quite time consuming.
ElasitcSearch is fine. If your dataset isn't too big you aren't going to hit shard and memory limits and if you do chances are you are already in a large enough organisation that you'll have the manpower to do the required maintenance. It's not rocket science.
> 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.
If you already have Elasticsearch, it makes sense to continue utilizing it.
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.
Especially for indie users/devs and smaller teams. I built a part of this(the retriever) in < 4 hours https://github.com/itissid/wiki for replacing deepwiki.
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.
This seems to be coming from the “we must make ElasticSearch AI-compatible” department more than anything.
Maintaining Elasticsearch isn't free, but picking an underpowered db and having to port to the right one is also quite time consuming.
> 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.
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.