[navar]

I created a small app that shows the difference between embedding-based ("semantic") and bm25 search:

http://search-sensei.s3-website-us-east-1.amazonaws.com/

(warning! It will download ~50MB of data for the model weights and onnx runtime on first load, but should otherwise run smoothly even on a phone)

It runs a small embedding model in the browser and returns search results in "real time".

It has a few illustrative examples where semantic search returns the intended results. For example bm25 does not understand that "j lo" or "jlo" refer to Jennifer Lopez. Similarly embedding based methods can better deal with things like typos.

EDIT: search is performed over 1000 news articles randomly sampled from 2016 to 2024