Combining Prefetch Results

The outer query fuses ranked candidate lists from all parallel prefetches into one ranked list of results. Fusion methods differ in whether they use rank, score or directly vector representations of candidates (their similarity to the outer query) and whether final score incorporates payload metadata. All methods support flat (one fusion step) and nested (multi-stage) prefetch structures.

Tune in this order, cheapest first: confirm fusion beats each prefetch alone on your labels, compare Qdrant's default RRF vs DBSF, if tuning RRF: settle k, then sweep weights at that k, and validate on held-out queries. A weight pair is only valid for the k it was tuned with Tune in this order

Scores Are Not Comparable Across Prefetches & You Want Some Easy Baseline

Use when: searches produce scores on different scales, like BM25 and cosine on dense embeddings.

RRF

• RRF (Reciprocal Rank Fusion): rank-based, ignores scores magnitude.
• Start with RRF at Qdrant's defaults, k=2 and equal weights.
• Tune k to control rank sensitivity in RRF fusion, choosing the value from your labels Use labels to choose a k range.
• Add per-prefetch weights when one search should dominate, using Weighted RRF. Weights should be customized per collection and retrievers' score distributions. Tune weights last.

DBSF

• DBSF (Distribution-Based Score Fusion): normalizes score distributions per prefetch before fusing them, for that, instead of min-max, uses mean +- 3 deviations on prefetched list of scores. Avoid relying on resulting absolute scores, as scores in DBSF are normalized per prefetch (aka per a retrieved list of search results), and might be uncomparable across queries.
• DBSF has no parameters to tune, so compare it before hand-tuning RRF k and weights. Confirm on your own labels Compare RRF and DBSF on your labels