Vector databases are changing the game for AI applications, but what exactly are they? How do they enable similarity search and what are the key concepts to understand when working with embeddings?
Introduction to Vector Databases
Vector databases are a new type of database that stores data as vectors, which are lists of numbers that represent the features of an object. This allows for efficient similarity search, which is the ability to find objects that are similar to a given object. To understand why this is useful, think of a music streaming service that wants to recommend songs to a user based on their listening history. A vector database can store the features of each song, such as the genre, tempo, and mood, and then find songs that are similar to the user's favorite songs.
// Import the required libraries
const faiss = require('faiss');
