What is Qdrant's recommend query mode, and how is it different from plain similarity search?

A recommend query is useful when the desired item is better described by examples than by a single explicit query vector. You provide positive examples and optionally negative examples, and Qdrant derives a recommendation direction from those points.

That differs from ordinary similarity search, where the query is a vector supplied by the caller and Qdrant ranks points by distance to that vector. Recommendation queries let the retrieval intent be expressed as 'find items like these, but unlike those' without the application necessarily constructing the embedding itself.

The trade-off is expressiveness versus predictability. A recommendation can be useful for personalization and 'more like this' behavior, while direct vector search is simpler when the application already has a well-defined query embedding.

A common misconception is that recommend is a completely separate similarity algorithm. It still relies on vector-space similarity; the distinction is how the query representation is constructed from positive and negative examples. Supported recommendation semantics can evolve with Qdrant releases.