agentdb-mmr

# MMR Rerank Diversity reranking on top of vector search. MMR balances relevance to the query with novelty among the picked results. ## When to use - Top-k search returns 5 essentially-identical hits - Need broader topic coverage (e.g., for synthesis prompts) - RAG pipelines where redundancy wastes tokens ## API ``` 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 ) ``` ## How it works 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. ## Pattern 1. Run vector search with k=50. 2. Pass results to `agentdb_diversity_rank` with k=5. 3. Use the diversified 5 in your prompt. This is a 50→5 reduction with much higher topic coverage than top-5 directly. ## Don't - Don't MMR on already-diverse search outputs. If your candidates span the topic well, MMR adds latency without lifting quality. - Don't tune λ per-query manually. The bandit can pick it; expose `lambda: 'auto'` and let it learn.