Vector Search & Nearest Neighbors Lab
Interactive 2D vector database search. Place points, configure a query vector, choose Cosine vs Euclidean distance, and retrieve top-K nearest neighbors.
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
Calculates nearest neighbors dynamically on an interactive coordinate plane and ranks matches by similarity score.
Frequently Asked Questions
Common questions about calculations, assumptions, and edge cases.
Yes, Vector Search & Nearest Neighbors Lab is 100% free with unlimited calculations and zero paywalls or subscriptions.
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