Dictionary
Cosine Similarity
The dot-product-of-unit-vectors metric that ranks embeddings.
1 min readupdated 2026-07-04
/ quick answer
Cosine similarity measures the angle between two vectors. It ignores magnitude, focusing on direction — the standard metric for comparing sentence embeddings. The dot-product-of-unit-vectors metric that ranks embeddings.
The dot-product-of-unit-vectors metric that ranks embeddings. Cosine similarity measures the angle between two vectors. It ignores magnitude, focusing on direction — the standard metric for comparing sentence embeddings. In practice: Two paraphrases of the same sentence yield a cosine similarity of ~0.95. This dictionary node is part of the Onexial knowledge graph and links to related concepts, workflows and tools below.
Definition
Cosine similarity measures the angle between two vectors. It ignores magnitude, focusing on direction — the standard metric for comparing sentence embeddings.
Example
Two paraphrases of the same sentence yield a cosine similarity of ~0.95.
Related Tool Stacks
/ frequently asked
What is Cosine Similarity?
Cosine similarity measures the angle between two vectors. It ignores magnitude, focusing on direction — the standard metric for comparing sentence embeddings.
What is an example of Cosine Similarity?
Two paraphrases of the same sentence yield a cosine similarity of ~0.95.
Why does Cosine Similarity matter for AI and automation?
The dot-product-of-unit-vectors metric that ranks embeddings. It connects to the workflows, prompts and tool stacks linked on this page, so you can move from definition to execution without leaving Onexial.
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