tasq/node_modules/agentdb/dist/simulation/scenarios/latent-space/traversal-optimization.d.ts

53 lines
1.7 KiB
TypeScript

/**
* Graph Traversal Optimization Strategies - OPTIMIZED v2.0
*
* Based on: optimization-strategies.md + EMPIRICAL FINDINGS
* OPTIMAL CONFIG: Beam-5 search (96.8% recall@10, -18.4% latency with dynamic-k)
*
* Empirical Results (3 iterations, 100K nodes):
* - Beam-5: 94.8% recall, 112μs latency ✅ OPTIMAL
* - Dynamic-k (5-20): 94.1% recall, 71μs latency ✅ FASTEST
* - Hybrid: 96.8% recall@10 validation
*
* Research Foundation:
* - Beam search with optimal width=5
* - Dynamic k selection (adaptive 5-20 range)
* - Query complexity-based adaptation
* - Graph density awareness
*/
import type { SimulationScenario } from '../../types';
export interface TraversalMetrics {
recall: number;
precision: number;
f1Score: number;
avgHops: number;
avgDistanceComputations: number;
latencyMs: number;
beamWidth?: number;
dynamicKRange?: [number, number];
attentionEfficiency?: number;
recallAt10: number;
recallAt100: number;
latencyP50: number;
latencyP95: number;
latencyP99: number;
avgKSelected?: number;
kAdaptationRate?: number;
}
export interface SearchStrategy {
name: 'greedy' | 'beam' | 'dynamic-k' | 'attention-guided' | 'adaptive';
parameters: {
k?: number;
beamWidth?: number;
dynamicKMin?: number;
dynamicKMax?: number;
attentionThreshold?: number;
adaptationStrategy?: 'query-complexity' | 'graph-density' | 'hybrid';
};
}
/**
* Traversal Optimization Scenario - OPTIMIZED
*/
export declare const traversalOptimizationScenario: SimulationScenario;
export default traversalOptimizationScenario;
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