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Copied a tutorial's embedding call. No idea if it's any good.
What you'll have at the end
A script that shows two sentences about the same topic scoring higher than two unrelated ones.
You need
A working call, whether to a hosted API or a locally loaded model, that already returns some kind of vector for a piece of text you send it.
Not covered
Deciding whether cosine similarity or a dot product is the right metric for your own vectors, and picking which embedding model to standardize on; this only proves the call you already have is producing vectors that actually carry meaning.
Leans on
Choose cosine similarity or dot product for your vectors
You want the deeper comparison between cosine similarity and a plain dot product instead of taking cosine as the default.
Add semantic search to your own notes
Your embedding call already checks out and you want to put it to work searching a real set of your own documents.
Checked 15 Aug 2026
Part of the Embeddings cookbook