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NLP (on-device)
Semantic Similarity
Compare two texts by meaning, not words, with the embeddings that power search and RAG.
Production proof: The core operation behind semantic search and trustworthy RAG, running on-device with the same model I use for offline clinical search. Read the case study →
How it’s engineered
- A ~23 MB sentence-transformer (all-MiniLM-L6-v2) turns each text into a 384-dimension vector in your browser; similarity is the cosine angle between them.
- Two sentences that mean the same thing score high even with no shared words. That is exactly why embeddings beat keyword matching for search and retrieval.
- This is the retrieval primitive under RAG: rank passages by meaning, then ground the answer in the closest ones.