Related documents aside, technical documentation benefits from really great search.
Embeddings are a _very_ useful tool for building better search - they can handle "fuzzy" matches, where a user can say things like "that feature that lets me run a function against every column of data" because they can't remember the name of the feature.
With embeddings you can implement a hybrid approach, where you mix both keyword search (still necessary because embeddings can miss things that use jargon they weren't trained on) and vector similarity search.
In-site search is super important. I suspect that many docs maintainers don't realize how heavily it's used. Many docs sites don't even track in-site search queries!
One of the things I love about Sphinx is that it has a decent, client-side, JS-powered offline search. I recently hacked together a workflow for making it search-as-you-type [1]. jasonjmcghee's comment [2] has got me pondering whether we can augment it with transformer.js embeddings.
Embeddings are a _very_ useful tool for building better search - they can handle "fuzzy" matches, where a user can say things like "that feature that lets me run a function against every column of data" because they can't remember the name of the feature.
With embeddings you can implement a hybrid approach, where you mix both keyword search (still necessary because embeddings can miss things that use jargon they weren't trained on) and vector similarity search.
I wish I had good examples to point to for this!