Maximal Marginal Relevance rerank — get diverse top-k instead of redundant top-k. Use when standard search returns 5 near-duplicates, or when you want broader coverage of a topic.
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Diversity reranking on top of vector search. MMR balances relevance to the query with novelty among the picked results.
agentdb_diversity_rank(
query: <embedding | string>
candidates: <id list> // typically the top-50 from vector search
k: 5 // final size
lambda: 0.5 // 0=pure diversity, 1=pure relevance
)
MMR picks greedily:
score(i) = λ · sim(query, i) - (1-λ) · max sim(i, picked_j)
High λ → behaves like vanilla top-k (relevance-only). Low λ → emphasizes novelty among picks.
Default λ = 0.5 is a sensible balance for most uses.
agentdb_diversity_rank with k=5.This is a 50→5 reduction with much higher topic coverage than top-5 directly.
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