How would you build a hybrid search query that retrieves candidates from both a dense embedding and a BM25-style sparse vector, then fuses the results?

I would model the collection with a named dense vector and a named sparse vector, generate both query representations, retrieve a candidate set from each using prefetch, and then apply a fusion query such as Reciprocal Rank Fusion. RRF is a good starting point because dense and sparse scores do not necessarily share the same numerical scale. The important design decision is candidate depth: each branch should retrieve enough candidates that relevant documents are unlikely to disappear before fusion, while not making the first-stage search unnecessarily expensive. I would then measure recall, latency, and fusion quality offline before tuning limits. A common mistake is multiplying or directly adding raw dense and sparse scores without calibration. The exact Universal Query API and client syntax is version-sensitive, so production code should match the installed Qdrant SDK/server release.