The Role of Vector Databases in AI Applications

As AI applications become more sophisticated, they need a way to efficiently search and retrieve information based on meaning and context, not just keywords. This is where vector databases come in. This article explains the crucial role that vector databases play in the modern AI stack. We will demystify the concept of vector embeddings—numerical representations of data like text or images—and explain why traditional databases are not equipped to handle them. We will explore key use cases, such as building powerful semantic search engines, creating intelligent recommendation systems, and enabling long-term memory for AI agents.