Vector Search Database: News & 2026 Guide

This post updates the understanding of vector search databases by highlighting their integration into existing data management platforms, the trend of vectors as a first-class data type, the rise of hybrid search, and the importance of metadata filtering. It discusses the impact of RAG, semantic caching, and agent memory on driving demand. The post also provides updated evaluation criteria for 2026, focusing on hybrid search, filtered query performance, tail latency, and operational footprint, and positions Redis as a solution that maps to these criteria by offering vector search alongside its other capabilities.