What are the three core building blocks of Qdrant's data model?

At the top level, a collection is a named group of points that share a vector configuration. A point is the individual record Qdrant searches and consists of an ID, one or more vectors, and optional payload.

Vectors are the numerical representations used for similarity search, while payload is structured JSON metadata attached to the point. In practice, the useful mental model is Collection -> Point -> vectors plus payload, with the collection defining how each vector space is indexed and compared.

This model is intentionally different from a relational schema. You do not normally create a row with arbitrary vector dimensions and then use a SQL-style index; instead, you define the vector configuration at the collection/vector-space level and store records as points.

A common mistake is treating a collection as just a table name. Its vector configuration is part of the search contract. With named vectors, one point can hold multiple vector spaces, each with its own dimensionality and distance configuration.