Understanding Vector Databases

A vector database is a type of database specifically designed to store and manage vector data using arbitrary but related coordinates to related data. Unlike traditional databases that handle scalar data (like numbers, strings, or dates), vector databases are optimized for high-dimensional data points. But first, we have to talk about vector embeddings.

Vector embeddings are a method used in natural language processing (NLP) to represent words as vectors in a lower-dimensional space. This technique simplifies complex data for processing by models like Word2Vec, GloVe, or BERT. These real-world embeddings are highly complex, often with hundreds of dimensions, capturing nuanced attributes of words.