What is vector embeddings?

Vector embeddings are numerical representations of data points (e.g., words, images, documents, users) in a multi-dimensional space. The position and orientation of a vector within this space encodes semantic or contextual information about the data it represents. They are commonly used for tasks such as similarity search, recommendation systems, and machine learning feature engineering because they allow algorithms to quantify relationships between data points.