2D Vector Embedding Visualizer
Visualize semantic embeddings in 2D vector space. Compare Cosine Similarity, Euclidean Distance, and semantic clustering.
Concept Breakdown: High-Dimensional Semantic Vector Spaces
Computers turn words into coordinates on a multi-dimensional map. Concepts with similar meanings (like "dog" and "puppy") float near each other, while unrelated ideas are far away.
When you search "cute pet", an AI doesn't just match keywords. It finds the coordinate for "cute pet" and retrieves whatever word sits closest on the map (like "kitten").
Click anywhere on the 2D grid below to move the green Query pin. Watch the rankings update in real time to show which words the AI considers most semantically related!
Semantic Vector Arithmetic: King - Man + Woman = ?
In high-dimensional embedding spaces (such as Word2Vec or OpenAI text-embedding-3), semantic concepts form directional vectors:
Practice Challenge: Nearest Neighbor Search
Click on the 2D plane near the bottom-left quadrant to align your query point with "kitten" until Cosine Similarity exceeds 0.92.
Quick Reference & Instructions
Simple steps, pro tips, and execution details
Provide Inputs
Type, paste, or select your values in the form fields below.
Instant Live Analysis
Calculations and formatting happen automatically with zero delay as you type.
Copy or Use Output
Copy results or apply the clean output directly to your projects.
How It Works
Projects high-dimensional semantic word vectors onto an interactive 2D coordinate canvas with live query distance measurement.
Formula & Logic
Frequently Asked Questions
Common questions about calculations, assumptions, and edge cases.
Yes, 2D Vector Embedding Visualizer is 100% free with unlimited calculations and zero paywalls or subscriptions.
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