Vector Embeddings Explained

Learn how vector embeddings convert words and images into multi-dimensional numerical coordinate arrays.

Abstract Algorithms

Jul 2, 2026·1 min read·Intermediate

⚡

Quick Take

Vector embeddings represent semantic meaning by converting text, images, or audio into arrays of floating-point numbers (coordinates in high-dimensional space). 📊 Semantic Distance Y-Axis (Pet Size)

Vector embeddings represent semantic meaning by converting text, images, or audio into arrays of floating-point numbers (coordinates in high-dimensional space).

📊 Semantic Distance

Y-Axis (Pet Size)
  ▲
  │   [Cat: 0.1, 0.9]           [Dog: 0.2, 0.8]
  │
  │
  │
  │                                [Elephant: 0.9, 0.1]
  │
  └───────────────────────────────────────────────────► X-Axis (Animal Size)

Vector Dimensions: Modern models project text into vectors with thousands of dimensions (e.g., OpenAI's text-embedding-3-small uses 1536).
Cosine Similarity: Measures the angle between two vectors. A smaller angle (closer to 1.0) means the concepts are semantically similar, even if they use different words (e.g., "king" and "queen").

AI-generated article quiz

Test your understanding

🧠

Ready to test what you just learned?

Generate four focused questions from this article. Answers include immediate explanations.

Reader feedback

Was this article useful?

Rate it if it helped, then continue with the next deep dive when you are ready.