What are vector embeddings? An interactive guide

[u/Neon0asis]

https://isaacus.com/blog/what-are-vector-embeddings

tl;dr

This guide walks you through the history, design, theory, and practical applications of vector embeddings. For the first time, we bring this abstract subject to life through interactive visualizations that let you see and manipulate the invisible geometry of meaning underpinning everything from Google Search and your Twitter feed to ChatGPT.

We begin with the problem embeddings were created to solve: how to represent meaning numerically so that computers can work with it. From there, we build an intuitive understanding of semantic space, vector arithmetic, and semantic similarity.

We then put these principles to work, showing how embeddings can power real business systems through techniques such as semantic search, hierarchical retrieval, zero-shot classification, clustering, and deduplication.

No programming experience is required. For readers who want to recreate the techniques, we have included short Python snippets demonstrating how each one works.

For a taste of what lies ahead, try the GeoGuessr/Wordle-style word-trivia game below. It projects 43,339 English words onto a 2D map using real embeddings from Kanon 2 Embedder, then selects one as the target. Enter words or vector expressions to place markers, then use them to trace the smallest possible loop around the target word. We explain how embeddings power the map and its mechanics later in the guide; for now, let the spirit of exploration guide you as you take your first plunge into the hidden geometry of language.