Vector Search Is Reaching Its Limit. Here’s What Comes Next

This is the first of two parts.

Vector databases are a foundational component of many modern AI systems, powering fast and scalable retrieval through techniques like approximate nearest neighbor (ANN) search to surface information based on similarity. But as retrieval-augmented generation (RAG) applications evolve, they increasingly require richer data representations that capture relationships within and across modalities, like text, images and video.

With this growing complexity, the limitations of basic vector representations are becoming…