What distance metrics does Qdrant support?

Qdrant supports four distance metrics: cosine similarity, dot product, Euclidean distance, and Manhattan distance. Cosine and dot product are the most common choices for text embeddings, with dot product being faster when vectors are pre-normalized since it skips the normalization step. The metric is set per vector configuration at collection creation. Qdrant also supports named vectors, letting a single collection store multiple vectors per point with different dimensions and metrics, useful for multimodal data combining text and image embeddings.