Vector Cosine Similarity & Distance Engine

Calculate Cosine Similarity, Dot Product, Euclidean (L2), and Manhattan (L1) metrics for AI embeddings.

Presets:

Vector A (Embedding Array):

Vector B (Embedding Array):

Cosine Similarity (cos θ)

0.999258

Scale: +1.0 (Identical) · 0.0 (Unrelated) · -1.0 (Opposite)

Dot Product:2.1664

Euclidean (L2):0.066332

Manhattan (L1):0.16

Cosine Dist (1-cos):0.000742