Vector Search
Finding the most relevant content by comparing meaning-carrying vectors, ranking by nearest neighbour rather than keyword overlap.
In the world of modern search, finding relevant information is no longer just about matching keywords. Instead, we use vector search, a method that compares the actual meaning of words. It does this by using vector embeddings, which are dense numeric representations of meaning. When two passages mean the same thing, they sit close together in a mathematical vector space, even if they do not share a single word.
To determine which search results are the most relevant, we score them using a similarity measure, like cosine similarity. This process forms the crucial retrieval step behind retrieval-augmented generation, the technology that powers grounded AI answers.
Of course, speed and efficiency matter just as much as accuracy. Recent benchmarks show how practical these trade-offs can be. By using Matryoshka learning to shorten embeddings from over one thousand dimensions down to just two hundred and fifty-six, we can cut processing time roughly in half. The best part is that accuracy remains incredibly high, at over ninety-nine percent.
Ultimately, efficient vector search and the encoding choices behind it are the very foundation of modern relevance engineering.
Vector search finds the most relevant content by comparing vector embeddings — dense numeric representations of meaning — and returning the nearest neighbours, rather than matching keywords. Two passages that mean the same thing sit close together in vector space even if they share no words.
Relevance is scored with a similarity measure, typically cosine similarity or, equivalently for normalised vectors, a dot product. This is the "retrieve" step behind retrieval-augmented generation and grounded AI answers.
Our own benchmarking showed how practical the trade-offs are: truncating 1,024-dimensional embeddings to 256 via Matryoshka learning roughly halved processing time while holding accuracy at 99.5%. Efficient vector search — and the encoding choices behind it — is a foundation of modern relevance engineering.