What are named vectors in Qdrant, and when would one point need multiple vectors?

Named vectors let one Qdrant point contain multiple independently configured vector spaces. For example, a product point can have a text embedding and an image embedding, each with its own dimensionality and distance metric.

This is useful when the vectors represent different modalities, fields, or retrieval models but should still belong to the same logical entity. Qdrant's current model also distinguishes named vectors from multivectors: a named vector is a separate vector field, while a multivector represents a collection or matrix of vectors used by models such as late-interaction retrieval.

The trade-off is simplicity versus flexibility. Separate collections can isolate workloads and schemas more strongly, but they require application-level joins between results. Named vectors keep the logical entity together, at the cost of storing and maintaining more vector data per point.

A common misconception is that 'multiple vectors' automatically means late interaction. They are different concepts. Named vectors are separate vector spaces; multivectors are a specific vector representation supported by Qdrant.