What is a Vector Embedding?

A vector embedding is a numerical representation of an object, like a word, image, or sound, that captures its meaning and context. It’s essentially a long list of numbers that acts as a unique numerical ID.

How it Works

1. Translation: An AI model, trained on massive amounts of data, translates the original object (e.g., the word “king”) into a vector. This vector’s numbers are not random; they are carefully chosen to represent the object’s features and relationships to other objects.
2. Multidimensional Space: You can think of these vectors as points in a multi-dimensional space. The key is that objects with similar meanings or characteristics are placed closer together in this space. For example, the vector for “queen” would be very close to “king,” but the vector for “apple” would be very far away.
3. Vector Arithmetic: This numerical representation allows computers to perform mathematical operations on non-numerical data. For instance, in some early models, the famous analogy “king” – “man” + “woman” = “queen” holds true in vector space.

Vector embeddings are fundamental to modern AI systems, enabling them to understand concepts, find semantic similarities, and power applications like semantic search, recommendation engines, and large language models (LLMs).