/ sandbox / embeddings
Vector embeddings · semantic search
Pick a few public-domain novels, embed every paragraph into a 384-d vector with MiniLM-L6-v2 — entirely in your browser via Transformers.js — then search by meaning, not keyword.
semantic search
// ready to embed
Three steps. One browser.
- 1Load the model — ~25MB, cached forever in IndexedDB
- 2Index a few books — streams ~paragraph-sized chunks through MiniLM
- 3Search by meaning — cosine similarity against every chunk in RAM