Vector Database

A vector database stores and indexes vector embeddings so that teams can quickly access large-scale data. These databases are most often used for similarity searches, metadata filtering, and horizontal scaling.

A vector database works by storing vector embeddings for the content being indexed. Then, when the vector embedding gets added to the vector database, only slight references are needed to connect the dots to the original content. When an application then issues a query to the database, new embeddings are made for that query and used to search for similar embeddings across the entire database.

Creating Vector Embeddings for a Vector Database

The power of a vector database relies on vector embeddings. Vector embeddings a numerical representation of data. The reason they’re so potent, especially for natural language processing, image searches, and more, is because these numerical representations can be attached to a variety of mediums including text, graphics, and more.

Machine learning algorithms require numerical data and values. Vector embeddings are what connect other forms of data to those numerical representations to allow the machine learning algorithms to work effectively.

But vector embeddings go beyond basic attachment of numbers to other forms of data. In using vector embeddings, machine learning is able to attach semantic similarities and representations, allowing these algorithms to discern much like the human brain would discern between data points.

How Do Vector Databases Work?

Traditional databases store data for quick access in a spreadsheet type format. When a query is made, the algorithm looks at data in specific rows or columns to determine the output.

A vector database works a little differently, because vector embeddings go beyond rows and columns. Instead, similarity metrics are used to find vectors that are most alike to the query. This requires a combinatory approach known as Approximate Nearest Neighbor (ANN) which looks for hashing, quantization, and graph-based queries. In some cases, the vector database will requery a result to compare with other similarity measures.

Why Do You Need a Vector Database?

Working with vector data alone limits teams. Because vector data is complex and large in scale, extracting core insights for fast analysis and outputs is challenging at best. By having vector embeddings inside of a database that can use similarity searches to determine outputs, performance, flexibility, and scalability all increase allowing you to get more out of the data already at your fingertips.

In addition, vector databases allow teams to add more advanced features to their generative artificial intelligence (AI) systems, allowing for more semantic searches and retrievals.