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.
lambda: 'auto' and let it learn.还没有评价。欢迎分享你的使用感受。
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Master advanced AgentDB features including QUIC synchronization, multi-database management, custom distance metrics, hybrid search, and distributed systems integration. Use when building distributed AI systems, multi-agent coordination, or advanced vector search applications.
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Create and train AI learning plugins with AgentDB's 9 reinforcement learning algorithms. Includes Decision Transformer, Q-Learning, SARSA, Actor-Critic, and more. Use when building self-learning agents, implementing RL, or optimizing agent behavior through experience.
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Implement persistent memory patterns for AI agents using AgentDB. Includes session memory, long-term storage, pattern learning, and context management. Use when building stateful agents, chat systems, or intelligent assistants.
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Optimize AgentDB performance with quantization (4-32x memory reduction), HNSW indexing (150x faster search), caching, and batch operations. Use when optimizing memory usage, improving search speed, or scaling to millions of vectors.
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Implement semantic vector search with AgentDB for intelligent document retrieval, similarity matching, and context-aware querying. Use when building RAG systems, semantic search engines, or intelligent knowledge bases.
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Walk the causal graph in AgentDB to explain why two memories are connected, or trace a root cause. Use when the user asks "why did X happen", "what led to Y", or after an incident.
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