Topic

Cosine Similarity & Vector Distance Metrics

6 posts on cosine similarity & vector distance metrics — part of glossary on the n4n AI blog.

Vector distance metrics compared: cosine, dot product, L2

Compare cosine similarity, dot product, and L2 distance for vector search — when each metric works, fails, and how to choose for your retrieval system.

July 28, 20266 min read

GlossaryHow-to

How cosine similarity measures semantic closeness

Learn how cosine similarity measures semantic closeness between embeddings with step-by-step code examples and production considerations.

July 28, 20264 min read

GlossaryComparison

Dot product vs cosine similarity for embeddings

Dot product vs cosine similarity for embeddings — when to use each, how normalization changes the math, and practical tradeoffs for retrieval and ranking.

July 28, 20266 min read

GlossaryComparison

Cosine similarity vs Euclidean distance: which to use

A practical comparison of cosine similarity and Euclidean distance for vector embeddings, with code examples and clear guidance on when to use each metric.

July 28, 20266 min read

GlossaryDefinition

Cosine similarity explained with a simple example

A practical guide to cosine similarity with code examples, covering the math, intuition, and common pitfalls engineers encounter when working with embeddings.

July 28, 20265 min read

GlossaryTutorial

Computing cosine similarity in Python step by step

Learn to compute cosine similarity in Python with NumPy, scikit-learn, and pure Python implementations, plus practical tips for production use.

July 28, 20264 min read