| name | agentdb-mmr |
| description | 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. |
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
- Run vector search with k=50.
- Pass results to
agentdb_diversity_rank with k=5.
- 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.
