Search with feature attributions — return WHY each match scored where it did. Use when debugging recall quality, auditing for bias, or explaining results to a user.
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查看安装方法安装前可以查看交给 AI 代理的指令内容。
Standard search returns scores; explainable recall returns features — which dimensions of the embedding (or which keywords in hybrid search) drove the match.
agentdb_explainable_recall(
query: <embedding | string>
k: 5
features: 'embedding-dims' | 'bm25-tokens' | 'hybrid-both' | 'metadata'
)
Returns: [
{
id, score,
explanation: {
topDims?: [{ dim: 12, contribution: 0.18 }, ...],
topTokens?: [{ token: "jwt", contribution: 0.31 }, ...],
metadataMatch?: { topic: 'auth', project: 'api' }
}
},
...
]
| Use | Features setting |
|---|---|
| Debug an unexpected high-score | embedding-dims — see which dims spiked |
| Verify keyword fall-back works | bm25-tokens — see if exact terms were the driver |
| Confirm metadata filters fired | metadata — see which filter values matched |
| Build user-facing UI | hybrid-both — show both text-level + dim-level signals |
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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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