A vector in vector search represents a data item as a point in a high-dimensional mathematical space. This transformation allows complex data types like text, images, or audio to be analyzed numerically. For instance, consider a text sentence. A machine learning model, such as Word2Vec, might convert it into a 300-dimensional vector, with each dimension capturing specific linguistic or semantic features.
These vectors capture meaning through numerical relationships. Two vectors close to each other in this space represent similar data, while those farther apart indicate dissimilarity. For example, the words "king" and "queen" might have vectors close in proximity, reflecting their semantic similarity.
By representing data as vectors, we enable advanced search and retrieval systems to perform similarity-based operations efficiently. These systems rely on mathematical computations, such as calculating the Euclidean distance or cosine similarity, to determine how "close" or "related" one vector is to another.