A vector database stores information in a way that captures meaning, so you can search by ideas rather than just matching exact words.

Instead of saving data as plain text, it turns each item—like a document or image—into a list of numbers called a vector. These numbers represent the item's meaning or features. When you search, the database compares meanings to find the most relevant matches, even if the wording is different.

Think of it as a database that “understands” context and relationships, making it useful for tasks like semantic search, recommendations, and AI applications.