qdrant

How to find out whether your index is actually returning the right answers — building ground truth, computing recall@k, and the four ways a benchmark quietly lies to you. The methodology part of the series, and the one the others depend on.

The index Qdrant actually uses, explained by building it up from one idea — link every vector to a few neighbours and walk downhill. Layers, m, ef_construct and ef, what each one costs, and why the search is allowed to be wrong.

Create a collection, put points in it, get answers out — and find out what the distance metric really decides. On unit vectors all three metrics return the identical ranking; the moment lengths mean nothing, two of them break.

What an embedding actually is, why similarity becomes arithmetic, and the exact corpus size at which exact search stops being an option — measured against a real Qdrant, including the two ways this part’s own lab was wrong while passing every check.