Vector Databases Explained: The Engine Behind AI That Can Search Like Google

Author(s): MahendraMedapati

Originally published on Towards AI.

How specialized databases make large-scale AI applications possible — and why your next AI project probably needs one

Imagine you’re Netflix, and you want to build an AI system to help users find movies. You have 15,000 movies and TV shows, each with multiple descriptions, reviews, and metadata. Millions of user queries hit your servers daily, and users expect results in under 100 milliseconds — the same speed they get from Google.

In this article, the author explains how vector databases revolutionize AI by enabling faster and more efficient similarity searches compared to traditional databases. It covers the limitations of conventional databases when dealing with large datasets and the advantages of vector databases in handling approximate similarity searches, showcasing their real-world applications, including Netflix’s recommendation system and Google’s search enhancements. Additionally, the article discusses various vector databases, their characteristics, and guiding considerations for selecting the right database for different AI projects.

Read the full blog for free on Medium.

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Published via Towards AI