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Reciprocal Rank Fusion for Hybrid Retrieval is a medium coding interview question covering vector search, nlp, machine learning, sorting — the kind asked at FAANG (Facebook/Meta, Apple, Amazon, Netflix, Google) and AI-first companies like Anthropic, OpenAI, and Nvidia.
A hybrid search system runs a vector search and a keyword (BM25) search in parallel and must merge the two ranked lists into one. Implement reciprocal rank fusion: score each document as the sum of 1 / (k + rank) over every list it appears in, with rank counted from 1 and k defaulting to 60, then return the documents sorted by score. Handle any number of input rankings.
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