Word Embeddings
Enter words to see how they are represented as vectors in 3D space
Control reference words
ON: Stable positions, relative PCA positioning based on reference words
OFF: Dynamic positioning based on PCA dimenationality reducing for all words
Reference words:
Added Words (0)
Dimentionality Reduction to 3D
Word Arithmetic
Process Overview:
Input: "king - man + woman"
Parse: [king] [-] [man] [+] [woman]
Vectors: Get 1536D embeddings for each word
Matrix Arithmetic: king[1536] - man[1536] + woman[1536] = result[1536]
Search: cosine similarity search
How Word Embeddings Work
What are Word Embeddings?
Word embeddings are dense vector representations of words where similar words have similar vectors. Each word is represented as a point in high-dimensional space.
This demo uses OpenAI's text-embedding-3-small model to convert each word to a 1536-dimensional vector, then reduces it to 3D using PCA for visualisation.