Reciprocal Rank Fusion (RRF) is an algorithmic scoring method that merges and ranks search results from multiple independent retrieval pipelines.
Reciprocal Rank Fusion (RRF) is an algorithm that combines the ranked results of multiple search algorithms (such as vector and keyword search) into a single unified ranking based on their reciprocal positions.
Because vector similarity scores (0.0 to 1.0) and keyword scores (BM25) use incompatible scales, simple numerical addition fails. RRF scores documents based strictly on their ranking position: $RRF(d) = \sum \frac{1}{k + r(d)}$.
Documents that appear near the top of both the semantic vector list and the exact keyword list receive exponentially higher composite scores.
SiteMind’s hybrid search pipeline uses RRF with a standard constant ($k=60$) to fuse dense vector matches, full-text tsvector hits, and trigram candidates into an optimal context ranking.
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Related Technical Concepts
Hybrid Search (Dense + Sparse Retrieval)
Hybrid Search merges dense vector search with sparse keyword matching to deliver superior accuracy across both conceptual and exact-term queries.
Full-Text tsvector Indexing
tsvector is PostgreSQL’s native text search type that parses, stems, and indexes words for lightning-fast lexical keyword searching.
Trigram Fuzzy Matching (pg_trgm)
Trigram matching breaks text into 3-character slices, allowing search engines to match queries even when visitors make typos or misspellings.
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