Vector Search Explainer - Free Online Tool | PivaBox

Interactive visualization of vector semantic search — see how words map to 2D space and find nearest neighbors by cosine similarity

How to Use Vector Search Explainer

1. Type a word into the search box (e.g., "dog", "car", or "happy") and click Search
2. The 2D scatter plot highlights the query word as a star marker and draws lines to its 3 nearest neighbors by vector distance
3. Read the cosine similarity scores and the step-by-step explanation to understand how vector search ranks results by semantic similarity

Frequently Asked Questions

Is Vector Search Explainer free?

Yes, PivaBox Vector Search Explainer is completely free to use. All computation and visualization runs locally in your browser.

What is cosine similarity?

Cosine similarity measures the cosine of the angle between two vectors. It ranges from -1 (completely opposite) to 1 (identical direction). In vector search, it tells you how semantically similar two pieces of text are — higher cosine similarity means more similar meaning.

Are these real embeddings?

No — the word positions are pre-arranged for educational purposes to illustrate semantic clustering (animals near animals, vehicles near vehicles, etc.). Real embeddings would be hundreds or thousands of dimensions generated by a neural network model.