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Equation 1 · Part 3 · Embeddings and the Geometry of Similarity

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sim⁡(u,v)=⟨u,v⟩∥u∥ ∥v∥.\operatorname{sim}(u, v) = \frac{\langle u, v \rangle}{\lVert u \rVert \, \lVert v \rVert}.
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What this part means

The expressions on both sides represent the same quantity under the stated assumptions.

Its job in the formula

The equals sign connects the complete expression on the left with the complete expression on the right. Both sides must have compatible units.

The passage around this formula

The conventional similarity metric is the cosine of the angle between two vectors: sim⁡(u,v)=⟨u,v⟩∥u∥ ∥v∥\operatorname{sim}(u, v) = \frac{\langle u, v \rangle}{\lVert u \rVert \, \lVert v \rVert}. Cosine became conventional for reasons that are mostly good. It is scale-invariant, which matters when vector norms correlate with nuisance properties like token frequency or document length. It reduces to an inner product on normalised vectors, which is cheap and which most approximate indexes support natively. And it is what several influential embedding models were explicitly trained to make meaningful: Sentence-BERT fine-tuned siamese networks precisely so that sentence embeddings could be compared with cosine similarity, cutting a pairwise-comparison workload from roughly 65 hours to…

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Learn the underlying idea

An equals sign says that the expression on its left and the expression on its right have the same value under the stated definitions and assumptions.

Open the illustrated equality: what the equals sign claims guide →

Sources cited in the surrounding passage

These citations provide research context; check each source for the exact claim it supports.