7sedam7

i don't know, seems pretty reasonable choice, especially with harder problem for requests that look like bots

I built a search engine over the full Epstein document dump (1.4M files) as a live demo for LaSearch, a search engine I've been working on. It's full-text with semantic understanding and fuzzy matching — no embeddings, no vectors, no ML models. Everything runs in the browser, results appear as you type with no noticeable lag on a 1.4M document corpus. The interesting technical constraint was making semantic-like behavior (finding conceptually related results, not just exact keyword matches) without the overhead of vector search. Happy to go into the approach if there's interest. Demo: https://epstein.lasearch.app LaSearch (the underlying engine, also available as a desktop app for your own files): https://lasearch.app

Cool, i have something similar that works locally on your machine: https://lasearch.app

(uses custom algorithm instead of embeddings. Not as powerful in terms of contextual understanding, but enough for search and a LOT faster, plus minimal resource usage)

very cool touch, i was personally not patient enough, but likely my users won't be too, so this seem like a useful product