tasq/node_modules/agentdb/dist/simulation/scenarios/latent-space/self-organizing-hnsw.d.ts

52 lines
1.7 KiB
TypeScript

/**
* Self-Organizing HNSW Analysis
*
* Based on: hnsw-self-organizing.md
* Simulates autonomous graph restructuring, adaptive parameter tuning,
* dynamic topology evolution, and self-healing mechanisms in HNSW indexes.
*
* Research Foundation:
* - Autonomous graph restructuring (MPC-based control)
* - Adaptive parameter tuning (online learning)
* - Dynamic topology evolution
* - Self-healing mechanisms for deletion artifacts
*/
import type { SimulationScenario } from '../../types';
export interface SelfOrganizingMetrics {
degradationPrevention: number;
adaptationSpeed: number;
autonomyScore: number;
optimalMFound: number;
optimalEfConstructionFound: number;
parameterStability: number;
initialLatencyP95Ms: number;
day30LatencyP95Ms: number;
latencyImprovement: number;
fragmentationRate: number;
healingTimeMs: number;
postHealingRecall: number;
memoryOverhead: number;
cpuOverheadPercent: number;
energyEfficiency: number;
}
export interface AdaptationStrategy {
name: 'static' | 'mpc' | 'online-learning' | 'evolutionary' | 'hybrid';
parameters: {
horizon?: number;
learningRate?: number;
mutationRate?: number;
};
}
/**
* Self-Organizing HNSW Scenario
*
* This simulation:
* 1. Tests autonomous graph restructuring under workload shifts
* 2. Compares static vs self-organizing HNSW performance
* 3. Analyzes adaptive parameter tuning effectiveness
* 4. Measures self-healing from deletion artifacts
* 5. Evaluates long-term stability and efficiency
*/
export declare const selfOrganizingHNSWScenario: SimulationScenario;
export default selfOrganizingHNSWScenario;
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