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Reciprocal rank fusion: why combining search results is harder than it looks
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You run a keyword search and get back a ranked list with Best Matching 25 (BM25) scores. You run a vector search over the same documents and get a second list with cosine similarities. You want to merge them into a single ranking that surfaces the mos...
Source：https://redis.io/blog/reciprocal-rank-fusion/PreviousVector embeddings & language: how models turn words into geometryNextInference latency: what it measures & why it varies
