How to Tune Vector Search Without Guessing

A five-part Qdrant study argues that vector-search tuning should be driven by measurable diagnostics rather than isolated parameter changes, using experiments across five public datasets ranging from 5,183 to 4.6 million documents. It identifies seven collection settings that can silently limit quality, including sparse-vector IDF handling and BM25 average document length, while emphasizing that too few labeled queries can make small improvements statistically unreliable. The research distinguishes retrieval failures from ranking failures, finding that expanding candidate depth could improve best-achievable scores substantially while changes to visible results remained small when ranking buried relevant candidates; HNSW ef had comparatively limited effect in the tests. For hybrid search, reciprocal rank fusion’s k value significantly changed top results, although Qdrant’s parameter-free DBSF method outperformed default RRF on three datasets. It also finds that rerankers may appear more valuable when fusion has not been tuned, and warns that quantization rescoring can cause major latency increases once original vectors no longer fit in memory, creating a tradeoff between speed and nearest-neighbor recall.

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