Unverified50% confidenceFactExact time
RRF uses only the rank in each list rather than the raw scores, so it is immune to differences in score scale between BM25 and vector similarity.
1
Sources
50%
Confidence
Long-term
Relevance
10/19/2026
First Seen
Sources
Related Entities
Related Claims
VerifiedHybrid retrieval combines BM25 sparse retrieval with dense vector retrieval, merging ranking results via RRF or linear weighting.70% similarUnverifiedRRF is a tuning-free rank fusion algorithm that uses only rank positions, accumulating scores as 1/(k+rank), with the constant k typically set to 60.66% similarVerifiedHybrid retrieval, which fuses BM25 keyword scores with vector similarity scores through weighted combination (typically using the RRF reciprocal rank fusion algorithm), has become the mainstream paradigm for RAG systems.65% similarUnverifiedA common approach to relevance ranking is a TF-IDF/BM25-like weighted model that also considers field weights; MiniSearch allows configuring weights for different fields and supports applying different score coefficients to prefix-match and fuzzy-match results.61% similarUnverifiedModern search systems usually combine lexical ranking with other signals rather than replacing BM25, and BM25's reliability on exact matches is still difficult for vector retrieval to fully replace.61% similar
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