Vector Similarity Calculator for Embeddings

Calculate cosine similarity, euclidean distance, and dot product between vector embeddings. Critical tool for ML engineers testing similarity thresholds, optimizing retrieval systems, and validating embedding quality in production environments.

Vector A

Vector B

Similarity Results

Cosine Similarity

Range: -1 to 1 (1 = identical)

Euclidean Distance

Range: 0 to ∞ (0 = identical)

Dot Product

Raw similarity measure

Vector Info

Dimensions: -

Vector A Magnitude: -

Vector B Magnitude: -

Working with embeddings?

Calculate Costs → RAG Chunking → Format JSON → Python Compiler →