Temporal vector stores - vector databases that explicitly model time as a first-class dimension alongside semantic similarity - are emerging as a critical component in Retrieval-Augmented Generation (RAG) systems that operate on continuously changing web data. For use cases such as news monitoring, financial analysis, e‑commerce tracking, and social media trend analysis, it is no longer sufficient to “just” embed documents and perform nearest-neighbor search; we must embed when things happened and how they relate across time.