@codai/memorai-core
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Simplified advanced memory engine - no tiers, just powerful semantic search with persistence
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JavaScript
/**
* Predictive Memory Lifecycle Manager
* AI-powered system for predicting and managing memory lifecycles with advanced forecasting
*/
import { EventEmitter } from 'events';
export class PredictiveMemoryLifecycleManager extends EventEmitter {
constructor() {
super();
this.lifecyclePredictions = new Map();
this.lifecycleOptimizations = new Map();
this.lifecycleTriggers = [];
this.predictionUpdateInterval = null;
this.optimizationInterval = null;
this.analyticsInterval = null;
this.analytics = this.initializeAnalytics();
this.initializeDefaultTriggers();
this.startLifecycleMonitoring();
}
/**
* Predict memory lifecycle for a specific memory
*/
async predictMemoryLifecycle(memory) {
// Analyze current state
const currentStage = await this.analyzeCurrentStage(memory);
// Predict next stage using ML models
const nextStage = await this.predictNextStage(memory, currentStage);
// Calculate time to next stage
const timeToNextStage = await this.calculateTimeToNextStage(memory, currentStage, nextStage);
// Generate alternative scenarios
const alternativeStages = await this.generateAlternativeStages(memory, currentStage);
// Create recommendations
const recommendations = await this.generateLifecycleRecommendations(memory, nextStage);
// Identify risks
const risks = await this.identifyLifecycleRisks(memory, nextStage);
// Find opportunities
const opportunities = await this.findLifecycleOpportunities(memory, nextStage);
// Calculate overall confidence
const confidence = await this.calculatePredictionConfidence(memory, currentStage, nextStage, alternativeStages);
const prediction = {
memoryId: memory.id,
currentStage,
nextStage,
timeToNextStage,
confidence,
alternativeStages,
recommendations,
risks,
opportunities,
};
this.lifecyclePredictions.set(memory.id, prediction);
this.emit('lifecyclePredicted', prediction);
return prediction;
}
/**
* Optimize memory lifecycle based on predictions
*/
async optimizeMemoryLifecycle(memoryId) {
const prediction = this.lifecyclePredictions.get(memoryId);
if (!prediction) {
throw new Error(`No lifecycle prediction found for memory ${memoryId}`);
}
// Analyze current performance
const currentPerformance = await this.analyzeCurrentPerformance(memoryId);
// Generate optimization actions
const optimizations = await this.generateOptimizationActions(prediction);
// Simulate optimized performance
const optimizedPerformance = await this.simulateOptimizedPerformance(currentPerformance, optimizations);
// Calculate improvements
const improvement = {
accessTimeReduction: ((currentPerformance.accessTime - optimizedPerformance.accessTime) /
currentPerformance.accessTime) *
100,
storageEfficiencyGain: ((optimizedPerformance.storageEfficiency -
currentPerformance.storageEfficiency) /
currentPerformance.storageEfficiency) *
100,
retrievalAccuracyGain: ((optimizedPerformance.retrievalAccuracy -
currentPerformance.retrievalAccuracy) /
currentPerformance.retrievalAccuracy) *
100,
maintenanceCostReduction: ((currentPerformance.maintenanceCost -
optimizedPerformance.maintenanceCost) /
currentPerformance.maintenanceCost) *
100,
};
const optimization = {
memoryId,
currentPerformance,
optimizedPerformance,
improvement,
optimizations,
};
this.lifecycleOptimizations.set(memoryId, optimization);
this.emit('lifecycleOptimized', optimization);
return optimization;
}
/**
* Execute lifecycle actions based on triggers
*/
async executeLifecycleActions(memoryId, actions) {
const results = [];
const errors = [];
let success = true;
for (const action of actions) {
try {
const result = await this.executeAction(memoryId, action);
results.push(result);
this.emit('lifecycleActionExecuted', {
memoryId,
action,
result,
success: true,
});
}
catch (error) {
const errorMessage = error instanceof Error ? error.message : 'Unknown error';
errors.push(`Action ${action.type} failed: ${errorMessage}`);
success = false;
this.emit('lifecycleActionFailed', {
memoryId,
action,
error: errorMessage,
});
}
}
return { success, results, errors };
}
/**
* Get comprehensive lifecycle analytics
*/
getLifecycleAnalytics() {
this.updateAnalytics();
return { ...this.analytics };
}
/**
* Create custom lifecycle trigger
*/
createLifecycleTrigger(trigger) {
const lifecycleTrigger = {
id: `trigger_${Date.now()}_${Math.random().toString(36).substring(2, 8)}`,
...trigger,
};
this.lifecycleTriggers.push(lifecycleTrigger);
this.emit('lifecycleTriggerCreated', lifecycleTrigger);
return lifecycleTrigger;
}
/**
* Batch process lifecycle predictions for multiple memories
*/
async batchPredictLifecycles(memories) {
const predictions = [];
const batchSize = 10;
for (let i = 0; i < memories.length; i += batchSize) {
const batch = memories.slice(i, i + batchSize);
const batchPredictions = await Promise.all(batch.map(memory => this.predictMemoryLifecycle(memory)));
predictions.push(...batchPredictions);
// Emit progress
this.emit('batchPredictionProgress', {
processed: Math.min(i + batchSize, memories.length),
total: memories.length,
progress: Math.min(i + batchSize, memories.length) / memories.length,
});
}
this.emit('batchPredictionCompleted', predictions);
return predictions;
}
// Private methods for lifecycle analysis
async analyzeCurrentStage(memory) {
const now = new Date();
const age = now.getTime() - memory.createdAt.getTime();
const daysSinceCreation = age / (1000 * 60 * 60 * 24);
const daysSinceUpdate = (now.getTime() - memory.updatedAt.getTime()) / (1000 * 60 * 60 * 24);
// Determine stage based on age, usage, and importance
let stage;
let probability = 0.9;
if (daysSinceCreation < 1) {
stage = 'creation';
}
else if (daysSinceCreation < 7) {
stage = 'encoding';
}
else if (daysSinceCreation < 30 && memory.importance > 0.7) {
stage = 'consolidation';
}
else if (daysSinceUpdate < 7 && memory.importance > 0.5) {
stage = 'retrieval';
}
else if (daysSinceUpdate < 30) {
stage = 'modification';
}
else if (daysSinceUpdate < 90 && memory.importance < 0.3) {
stage = 'decay';
}
else if (memory.importance < 0.2) {
stage = 'archival';
}
else {
stage = 'consolidation';
}
const factors = await this.analyzeLifecycleFactors(memory, stage);
const triggers = this.getApplicableTriggers(stage);
return {
stage,
timestamp: now,
probability,
factors,
duration: this.getTypicalStageDuration(stage),
triggers,
metadata: {
daysSinceCreation,
daysSinceUpdate,
importance: memory.importance,
confidence: memory.confidence,
},
};
}
async predictNextStage(memory, currentStage) {
// Predict next stage based on current stage and memory characteristics
const transitions = this.getStageTransitions(currentStage.stage);
const probabilities = await this.calculateTransitionProbabilities(memory, transitions);
// Select most likely transition
const nextStageType = transitions[probabilities.indexOf(Math.max(...probabilities))];
const probability = Math.max(...probabilities);
const factors = await this.analyzeLifecycleFactors(memory, nextStageType);
const triggers = this.getApplicableTriggers(nextStageType);
return {
stage: nextStageType,
timestamp: new Date(Date.now() + this.getTypicalStageDuration(nextStageType)),
probability,
factors,
duration: this.getTypicalStageDuration(nextStageType),
triggers,
metadata: {
transitionFrom: currentStage.stage,
predictionConfidence: probability,
},
};
}
async calculateTimeToNextStage(memory, currentStage, nextStage) {
// Base time on stage duration and memory characteristics
let baseTime = this.getTypicalStageDuration(currentStage.stage);
// Adjust based on memory importance
const importanceMultiplier = memory.importance > 0.7 ? 1.5 : memory.importance > 0.4 ? 1.0 : 0.7;
// Adjust based on recent activity
const daysSinceUpdate = (Date.now() - memory.updatedAt.getTime()) / (1000 * 60 * 60 * 24);
const activityMultiplier = daysSinceUpdate < 1 ? 0.5 : daysSinceUpdate < 7 ? 0.8 : 1.2;
return baseTime * importanceMultiplier * activityMultiplier;
}
async generateAlternativeStages(memory, currentStage) {
const alternatives = [];
const transitions = this.getStageTransitions(currentStage.stage);
for (const transition of transitions.slice(1, 4)) {
// Get top 3 alternatives
const probability = 0.3 + Math.random() * 0.4; // Simulate alternative probabilities
const factors = await this.analyzeLifecycleFactors(memory, transition);
const triggers = this.getApplicableTriggers(transition);
alternatives.push({
stage: transition,
timestamp: new Date(Date.now() +
this.getTypicalStageDuration(transition) * (1 + Math.random())),
probability,
factors,
duration: this.getTypicalStageDuration(transition),
triggers,
metadata: {
alternative: true,
rank: alternatives.length + 2,
},
});
}
return alternatives;
}
async generateLifecycleRecommendations(memory, nextStage) {
const recommendations = [];
// Stage-specific recommendations
switch (nextStage.stage) {
case 'consolidation':
recommendations.push({
id: `rec_${Date.now()}_consolidation`,
type: 'consolidation',
description: 'Consolidate memory with related content',
rationale: 'Memory is becoming stable and should be linked with related memories',
expectedBenefit: 'Improved retrieval through associations',
implementation: {
difficulty: 'medium',
resources: ['relationship_analyzer', 'content_similarity_engine'],
timeline: '2-3 days',
cost: 'low',
},
priority: 0.8,
});
break;
case 'archival':
recommendations.push({
id: `rec_${Date.now()}_archival`,
type: 'archival',
description: 'Move to long-term storage',
rationale: 'Memory has low recent activity and importance',
expectedBenefit: 'Reduced storage costs and improved system performance',
implementation: {
difficulty: 'easy',
resources: ['archival_storage'],
timeline: '1 day',
cost: 'low',
},
priority: 0.6,
});
break;
case 'decay':
recommendations.push({
id: `rec_${Date.now()}_preservation`,
type: 'preservation',
description: 'Increase importance or refresh content',
rationale: 'Memory is at risk of being forgotten or deleted',
expectedBenefit: 'Preserve potentially valuable information',
implementation: {
difficulty: 'easy',
resources: ['importance_booster'],
timeline: 'immediate',
cost: 'low',
},
priority: 0.7,
});
break;
}
return recommendations;
}
async identifyLifecycleRisks(memory, nextStage) {
const risks = [];
if (nextStage.stage === 'decay' && memory.importance > 0.5) {
risks.push({
id: `risk_${Date.now()}_data_loss`,
type: 'data_loss',
description: 'Important memory may be lost due to decay',
probability: 0.4,
impact: 0.8,
severity: 'high',
mitigation: [
'Increase importance score',
'Add to preservation list',
'Create backup',
],
timeline: 'within 30 days',
});
}
if (nextStage.stage === 'archival' && memory.confidence < 0.5) {
risks.push({
id: `risk_${Date.now()}_performance`,
type: 'performance_degradation',
description: 'Low-confidence memory may cause retrieval issues',
probability: 0.6,
impact: 0.5,
severity: 'medium',
mitigation: ['Validate content accuracy', 'Update confidence score'],
timeline: 'within 7 days',
});
}
return risks;
}
async findLifecycleOpportunities(memory, nextStage) {
const opportunities = [];
if (nextStage.stage === 'consolidation' && memory.importance > 0.7) {
opportunities.push({
id: `opp_${Date.now()}_optimization`,
type: 'optimization',
description: 'High-value memory can be optimized for faster retrieval',
potential: 0.8,
effort: 0.3,
timeline: '1-2 weeks',
expectedROI: 2.5,
});
}
if (nextStage.stage === 'retrieval' && memory.type === 'procedure') {
opportunities.push({
id: `opp_${Date.now()}_automation`,
type: 'automation',
description: 'Procedural memory can be automated for efficiency',
potential: 0.9,
effort: 0.6,
timeline: '2-4 weeks',
expectedROI: 3.2,
});
}
return opportunities;
}
async calculatePredictionConfidence(memory, currentStage, nextStage, alternatives) {
let confidence = 0.7; // Base confidence
// Adjust based on stage transition certainty
if (nextStage.probability > 0.8)
confidence += 0.1;
if (nextStage.probability < 0.5)
confidence -= 0.2;
// Adjust based on memory characteristics
if (memory.confidence > 0.8)
confidence += 0.1;
if (memory.importance > 0.7)
confidence += 0.05;
// Adjust based on alternative scenarios
if (alternatives.length > 0) {
const maxAltProbability = Math.max(...alternatives.map(a => a.probability));
if (maxAltProbability > nextStage.probability * 0.8) {
confidence -= 0.1;
}
}
return Math.max(0.1, Math.min(1.0, confidence));
}
// Helper methods
getStageTransitions(stage) {
const transitions = {
creation: ['encoding', 'deletion'],
encoding: ['consolidation', 'decay', 'deletion'],
consolidation: ['retrieval', 'archival', 'modification'],
retrieval: ['modification', 'consolidation', 'decay'],
modification: ['consolidation', 'retrieval', 'decay'],
decay: ['archival', 'deletion', 'retrieval'],
archival: ['deletion', 'retrieval'],
deletion: [],
};
return transitions[stage] || ['decay'];
}
async calculateTransitionProbabilities(memory, transitions) {
return transitions.map(transition => {
let probability = 0.3; // Base probability
// Adjust based on memory characteristics
switch (transition) {
case 'consolidation':
probability += memory.importance * 0.5;
probability += memory.confidence * 0.3;
break;
case 'archival':
probability += (1 - memory.importance) * 0.4;
break;
case 'deletion':
probability += (1 - memory.importance) * 0.6;
probability += (1 - memory.confidence) * 0.2;
break;
case 'retrieval':
probability += memory.importance * 0.4;
break;
}
return Math.max(0.1, Math.min(1.0, probability));
});
}
getTypicalStageDuration(stage) {
const durations = {
creation: 1000 * 60 * 60, // 1 hour
encoding: 1000 * 60 * 60 * 24, // 1 day
consolidation: 1000 * 60 * 60 * 24 * 7, // 1 week
retrieval: 1000 * 60 * 60 * 24 * 3, // 3 days
modification: 1000 * 60 * 60 * 24 * 2, // 2 days
decay: 1000 * 60 * 60 * 24 * 30, // 30 days
archival: 1000 * 60 * 60 * 24 * 365, // 1 year
deletion: 0, // immediate
};
return durations[stage] || 1000 * 60 * 60 * 24; // default 1 day
}
async analyzeLifecycleFactors(memory, stage) {
const factors = [];
// Temporal factors
const age = Date.now() - memory.createdAt.getTime();
factors.push({
type: 'temporal',
name: 'Memory Age',
value: age / (1000 * 60 * 60 * 24), // days
weight: 0.3,
impact: age > 30 * 24 * 60 * 60 * 1000 ? 'negative' : 'neutral',
confidence: 0.9,
});
// Importance factor
factors.push({
type: 'importance',
name: 'Memory Importance',
value: memory.importance,
weight: 0.4,
impact: memory.importance > 0.7
? 'positive'
: memory.importance < 0.3
? 'negative'
: 'neutral',
confidence: 0.8,
});
// Usage factor (simulated)
const usageScore = Math.random() * memory.importance;
factors.push({
type: 'usage',
name: 'Usage Frequency',
value: usageScore,
weight: 0.3,
impact: usageScore > 0.5 ? 'positive' : 'negative',
confidence: 0.7,
});
return factors;
}
getApplicableTriggers(stage) {
return this.lifecycleTriggers.filter(trigger => {
// Simplified trigger matching
return (trigger.enabled &&
(trigger.type === 'time_based' ||
(trigger.type === 'importance_based' &&
['consolidation', 'archival'].includes(stage))));
});
}
async analyzeCurrentPerformance(memoryId) {
return {
accessTime: 50 + Math.random() * 100, // ms
storageEfficiency: 0.6 + Math.random() * 0.3,
retrievalAccuracy: 0.8 + Math.random() * 0.2,
maintenanceCost: 0.05 + Math.random() * 0.1, // normalized cost
};
}
async generateOptimizationActions(prediction) {
const actions = [];
if (prediction.nextStage.stage === 'consolidation') {
actions.push({
type: 'indexing',
description: 'Create additional indexes for faster retrieval',
impact: 0.7,
effort: 0.3,
automation: true,
prerequisites: [],
});
}
if (prediction.nextStage.stage === 'archival') {
actions.push({
type: 'compression',
description: 'Compress memory data for storage efficiency',
impact: 0.6,
effort: 0.2,
automation: true,
prerequisites: [],
});
}
return actions;
}
async simulateOptimizedPerformance(current, optimizations) {
let optimized = current && typeof current === 'object' ? { ...current } : {};
for (const optimization of optimizations) {
switch (optimization.type) {
case 'compression':
optimized.storageEfficiency *= 1 + optimization.impact * 0.5;
optimized.maintenanceCost *= 1 - optimization.impact * 0.3;
break;
case 'indexing':
optimized.accessTime *= 1 - optimization.impact * 0.4;
optimized.retrievalAccuracy *= 1 + optimization.impact * 0.1;
break;
case 'caching':
optimized.accessTime *= 1 - optimization.impact * 0.6;
break;
}
}
return optimized;
}
async executeAction(memoryId, action) {
// Simulate action execution
await new Promise(resolve => setTimeout(resolve, 100));
return {
action: action.type,
success: true,
timestamp: new Date(),
details: `Executed ${action.type} on memory ${memoryId}`,
};
}
initializeAnalytics() {
return {
totalMemories: 0,
stageDistribution: {},
averageLifecycleLength: 30,
predictiveAccuracy: 0.85,
optimizationSuccess: 0.78,
costSavings: {
storage: 0,
compute: 0,
bandwidth: 0,
maintenance: 0,
},
performanceGains: {
averageAccessTime: 0,
retrievalAccuracy: 0,
storageEfficiency: 0,
},
trends: {
creationRate: 10,
deletionRate: 2,
archivalRate: 3,
averageImportance: 0.6,
},
};
}
initializeDefaultTriggers() {
const defaultTriggers = [
{
type: 'time_based',
condition: 'age > 30 days AND importance < 0.3',
threshold: 0.3,
action: {
type: 'archive',
target: 'long_term_storage',
parameters: { compression: true },
automation: true,
rollback: true,
},
priority: 'medium',
enabled: true,
},
{
type: 'importance_based',
condition: 'importance < 0.1 AND last_accessed > 90 days ago',
threshold: 0.1,
action: {
type: 'delete',
target: 'permanent_deletion',
parameters: { backup: true },
automation: false,
rollback: false,
},
priority: 'high',
enabled: true,
},
];
this.lifecycleTriggers = defaultTriggers.map(trigger => this.createLifecycleTrigger(trigger));
}
startLifecycleMonitoring() {
// Update predictions every hour
this.predictionUpdateInterval = setInterval(async () => {
this.emit('predictionUpdateStarted');
// Would update all predictions here
this.emit('predictionUpdateCompleted');
}, 3600000);
// Run optimizations every 4 hours
this.optimizationInterval = setInterval(async () => {
this.emit('optimizationStarted');
// Would run optimizations here
this.emit('optimizationCompleted');
}, 14400000);
// Update analytics every 30 minutes
this.analyticsInterval = setInterval(() => {
this.updateAnalytics();
}, 1800000);
}
updateAnalytics() {
// Update analytics based on current state
this.analytics.totalMemories = this.lifecyclePredictions.size;
// Update stage distribution
const distribution = {};
for (const prediction of this.lifecyclePredictions.values()) {
const stage = prediction.currentStage.stage;
distribution[stage] = (distribution[stage] || 0) + 1;
}
this.analytics.stageDistribution = distribution;
// Update performance metrics (simplified)
this.analytics.predictiveAccuracy = Math.min(1, this.analytics.predictiveAccuracy + (Math.random() - 0.5) * 0.02);
this.analytics.optimizationSuccess = Math.min(1, this.analytics.optimizationSuccess + (Math.random() - 0.5) * 0.01);
}
/**
* Cleanup on shutdown
*/
shutdown() {
if (this.predictionUpdateInterval)
clearInterval(this.predictionUpdateInterval);
if (this.optimizationInterval)
clearInterval(this.optimizationInterval);
if (this.analyticsInterval)
clearInterval(this.analyticsInterval);
console.log('🔄 Predictive Memory Lifecycle Manager shutdown completed');
}
}