What is a vector database used for?#
Vector databases enable "similarity search," which finds the most relevant information based on context and meaning. This unlocks powerful capabilities like more accurate product recommendations, smarter internal document search, and more effective fraud detection patterns.
How does a vector database work?#
First, an AI model converts your data—such as text, images, or audio—into a list of numbers called a "vector." This numerical representation captures the core meaning and context of the original item. For example, the vectors for "business suit" and "formal blazer" would be mathematically very similar, while the vector for "beach towel" would be very different.
The vector database stores and indexes these numerical vectors. When you perform a search, your query (e.g., "dark blue professional jacket") is also converted into a vector. The database then rapidly finds and returns the items whose vectors are mathematically closest to your query's vector, giving you results that are contextually similar, even if they don't contain the exact search words.
How is a vector database different from a traditional database?#
A vector database is built to find data based on conceptual similarity. In contrast, a traditional database (like the one used for your accounting or CRM system) is designed to find exact matches within structured information, such as retrieving a customer's record using their specific account number. You use a traditional database for precise, factual lookups and a vector database for exploration and contextual discovery.