This is the second of two parts. Read also:
In Part 1, we explored the growing limitations of vector-only search systems, highlighting how flat embeddings fall short in scenarios requiring structured filtering, real-time updates, personalized ranking and multimodal understanding.
As AI applications evolve, it’s clear that semantic similarity alone isn’t enough. What’s needed is structure — a way to represent relationships within and across modalities in a form that’s both expressive and performant.
That’s where tensors come…
