Vector Search has risen to become a foundational tool in modern search and retrieval systems,

including the RAG pipelines that power many AI applications.

However, the demands on retrieval systems are growing more sophisticated,

which is revealing the limits of relying on a single-vector similarity score.

VESPA is a popular open-source search and data-serving engine.

Central to Vespa's architecture is tensor-based retrieval,

which is an approach that represents data as tensors rather than simple vectors.

Tensor-based retrieval enables richer mathematical operations and more flexible ranking functions

that can surmount the limitations of a single-vector similarity score.

Radu Jorges is a software engineer at Vespa, with the background spanning nearly 12 years of consulting

and training on Elasticsearch and Solar.

In this episode, Radu joined Sean Falconer to discuss why vector similarity alone falls short in production,

how tensor-based retrieval generalizes to support richer ranking functions, the tradeoffs in

chunking and multi-stage re-ranking architectures, and where AI search is headed next.

This episode is hosted by Sean Falconer. Check the show notes for more information on Sean's work

and where to the show.

Hi, thanks for having me.

Yeah, absolutely.

I'm glad you were able to be here.

I interviewed your founder, Vespa, and CEO probably a couple of years ago, so it's great

to catch up again on everything that's happening over at VESPA.

A lot has changed in the world of AI, and I'm sure in the world of VESPA over the last

...