@codai/memorai-mcp
Version:
MemorAI CBD-based MCP Server - High-Performance Vector Memory System
605 lines • 30.8 kB
JavaScript
/**
* Phase 4: Next-Generation Intelligence - Real-Time Learning Engine
*
* Advanced learning capabilities that continuously improve memory organization:
* - Learns from memory usage patterns to optimize organization
* - Adapts retrieval algorithms based on effectiveness metrics
* - Provides real-time optimization recommendations
* - Implements continuous improvement cycles
*/
export class RealTimeLearningEngine {
openaiClient;
memories;
usagePatterns;
learningCache;
adaptationHistory;
constructor(openaiClient, memories) {
this.openaiClient = openaiClient;
this.memories = memories || new Map();
this.usagePatterns = new Map();
this.learningCache = new Map();
this.adaptationHistory = new Map();
}
/**
* Learn from memory usage patterns to improve system performance
* Core learning capability with real-time adaptation
*/
async learnFromMemoryUsage(agentId, usagePatterns) {
try {
// Store usage patterns for analysis
this.usagePatterns.set(agentId, usagePatterns);
// Analyze patterns to extract insights
const insights = await this.extractUsageInsights(agentId, usagePatterns);
// Generate actionable recommendations
const recommendations = await this.generateLearningRecommendations(insights, usagePatterns);
// Identify optimization opportunities
const opportunities = await this.identifyOptimizationOpportunities(agentId, usagePatterns);
// Calculate overall effectiveness score
const effectivenessScore = this.calculateEffectivenessScore(usagePatterns);
// Determine learning confidence
const learningConfidence = this.calculateLearningConfidence(usagePatterns, insights);
const learningInsights = {
agentId,
insights,
recommendations,
optimizationOpportunities: opportunities,
effectivenessScore,
learningConfidence
};
// Cache insights for future reference
this.learningCache.set(agentId, learningInsights);
return learningInsights;
}
catch (error) {
console.error('Error learning from memory usage:', error);
throw error;
}
}
/**
* Adapt memory organization based on effectiveness metrics
* Dynamic reorganization for optimal performance
*/
async adaptMemoryOrganization(agentId, effectivenessMetrics) {
try {
// Analyze current organization effectiveness
const currentEffectiveness = await this.analyzeOrganizationEffectiveness(agentId, effectivenessMetrics);
// Identify areas for improvement
const improvementAreas = await this.identifyImprovementAreas(effectivenessMetrics);
// Generate specific adaptations
const adaptations = await this.generateAdaptations(agentId, improvementAreas);
// Calculate expected improvements
const expectedImprovements = await this.calculateExpectedImprovements(adaptations);
// Create implementation plan
const implementationPlan = await this.createImplementationPlan(adaptations);
// Create rollback plan for safety
const rollbackPlan = await this.createRollbackPlan(agentId, adaptations);
const organizationAdaptation = {
agentId,
adaptations,
expectedImprovements,
implementationPlan,
rollbackPlan
};
// Store adaptation in history
const history = this.adaptationHistory.get(agentId) || [];
history.push(organizationAdaptation);
this.adaptationHistory.set(agentId, history);
return organizationAdaptation;
}
catch (error) {
console.error('Error adapting memory organization:', error);
throw error;
}
}
/**
* Optimize memory retrieval based on query patterns and performance metrics
* Continuous improvement of search and retrieval algorithms
*/
async optimizeMemoryRetrieval(queryPatterns, performanceMetrics) {
try {
// Analyze query patterns for optimization opportunities
const queryAnalysis = await this.analyzeQueryPatterns(queryPatterns);
// Evaluate current retrieval performance
const performanceAnalysis = await this.analyzeRetrievalPerformance(performanceMetrics);
// Generate retrieval optimizations
const optimizations = await this.generateRetrievalOptimizations(queryAnalysis, performanceAnalysis);
// Calculate expected performance improvements
const expectedImprovements = await this.calculatePerformanceImprovements(optimizations);
// Create optimization implementation plan
const implementation = await this.createOptimizationImplementation(optimizations);
return {
agentId: performanceMetrics.agentId,
optimizations,
expectedImprovements,
implementation
};
}
catch (error) {
console.error('Error optimizing memory retrieval:', error);
throw error;
}
}
// Private implementation methods
async extractUsageInsights(agentId, usagePatterns) {
const insights = [];
// Analyze usage frequency patterns
const frequencyInsights = this.analyzeFrequencyPatterns(usagePatterns);
insights.push(...frequencyInsights);
// Analyze timing patterns
const timingInsights = this.analyzeTimingPatterns(usagePatterns);
insights.push(...timingInsights);
// Analyze context correlations
const contextInsights = await this.analyzeContextCorrelations(usagePatterns);
insights.push(...contextInsights);
// Analyze collaboration patterns
const collaborationInsights = this.analyzeCollaborationPatterns(usagePatterns);
insights.push(...collaborationInsights);
return insights.sort((a, b) => b.strength - a.strength);
}
analyzeFrequencyPatterns(usagePatterns) {
const insights = [];
// Find highly accessed but low success rate memories
const lowEfficiencyMemories = usagePatterns.filter(pattern => pattern.accessFrequency > 0.8 && pattern.successRate < 0.5);
if (lowEfficiencyMemories.length > 0) {
insights.push({
type: 'usage_pattern',
description: `${lowEfficiencyMemories.length} memories are frequently accessed but have low success rates`,
strength: 0.8,
evidence: lowEfficiencyMemories.map(m => `Memory ${m.memoryId}: ${m.accessFrequency} access, ${m.successRate} success`),
actionable: true
});
}
// Find underutilized high-quality memories
const underutilizedMemories = usagePatterns.filter(pattern => pattern.accessFrequency < 0.3 && pattern.successRate > 0.8);
if (underutilizedMemories.length > 0) {
insights.push({
type: 'usage_pattern',
description: `${underutilizedMemories.length} high-quality memories are underutilized`,
strength: 0.7,
evidence: underutilizedMemories.map(m => `Memory ${m.memoryId}: ${m.accessFrequency} access, ${m.successRate} success`),
actionable: true
});
}
return insights;
}
analyzeTimingPatterns(usagePatterns) {
const insights = [];
// Analyze peak usage times
const timeSlots = new Map();
usagePatterns.forEach(pattern => {
// Safely handle potentially undefined or malformed accessTiming
const accessTiming = Array.isArray(pattern.accessTiming) ? pattern.accessTiming : [];
accessTiming.forEach(timing => {
// Handle both string timestamps and AccessTimePattern objects
if (typeof timing === 'string') {
// Convert string timestamp to AccessTimePattern
const date = new Date(timing);
const hour = date.getHours();
const timeOfDay = hour < 6 ? 'night' : hour < 12 ? 'morning' : hour < 18 ? 'afternoon' : 'evening';
const dayOfWeek = date.toLocaleDateString('en', { weekday: 'long' });
const key = `${timeOfDay}_${dayOfWeek}`;
const current = timeSlots.get(key) || { count: 0, effectiveness: 0 };
timeSlots.set(key, {
count: current.count + 1,
effectiveness: (current.effectiveness + (pattern.successRate || 0)) / 2
});
}
else if (timing && typeof timing === 'object') {
// Handle proper AccessTimePattern objects
const key = `${timing.timeOfDay}_${timing.dayOfWeek}`;
const current = timeSlots.get(key) || { count: 0, effectiveness: 0 };
timeSlots.set(key, {
count: current.count + (timing.frequency || 0),
effectiveness: (current.effectiveness + (timing.effectiveness || 0)) / 2
});
}
});
});
// Find optimal timing patterns
const optimalTimes = Array.from(timeSlots.entries())
.filter(([_, stats]) => stats.effectiveness > 0.7)
.sort((a, b) => b[1].effectiveness - a[1].effectiveness)
.slice(0, 3);
if (optimalTimes.length > 0) {
insights.push({
type: 'timing_optimization',
description: `Memory access is most effective during: ${optimalTimes.map(([time]) => time.replace('_', ' ')).join(', ')}`,
strength: 0.6,
evidence: optimalTimes.map(([time, stats]) => `${time}: ${(stats.effectiveness * 100).toFixed(1)}% effectiveness`),
actionable: true
});
}
return insights;
}
async analyzeContextCorrelations(usagePatterns) {
const insights = [];
// Analyze context patterns for correlations
const contextMap = new Map();
usagePatterns.forEach(pattern => {
// Safely handle potentially undefined or malformed contextPatterns
const contextPatterns = Array.isArray(pattern.contextPatterns) ? pattern.contextPatterns : [];
contextPatterns.forEach(context => {
// Handle both string contexts and ContextPattern objects
if (typeof context === 'string') {
// Convert string context to ContextPattern
const key = context;
const current = contextMap.get(key) || { frequency: 0, success: 0, memories: new Set() };
contextMap.set(key, {
frequency: current.frequency + 1,
success: (current.success + (pattern.successRate || 0)) / 2,
memories: new Set([...current.memories, pattern.memoryId])
});
}
else if (context && typeof context === 'object') {
// Handle proper ContextPattern objects
const key = context.context;
const current = contextMap.get(key) || { frequency: 0, success: 0, memories: new Set() };
contextMap.set(key, {
frequency: current.frequency + (context.frequency || 0),
success: (current.success + (context.outcomeSuccess || 0)) / 2,
memories: new Set([...current.memories, pattern.memoryId])
});
}
});
});
// Find high-correlation contexts
const highCorrelationContexts = Array.from(contextMap.entries())
.filter(([_, stats]) => stats.success > 0.8 && stats.frequency > 5)
.sort((a, b) => b[1].success - a[1].success);
if (highCorrelationContexts.length > 0) {
insights.push({
type: 'context_correlation',
description: `Strong context correlations found for: ${highCorrelationContexts.map(([context]) => context).slice(0, 3).join(', ')}`,
strength: 0.75,
evidence: highCorrelationContexts.map(([context, stats]) => `${context}: ${(stats.success * 100).toFixed(1)}% success, ${stats.memories.size} memories`),
actionable: true
});
}
return insights;
}
analyzeCollaborationPatterns(usagePatterns) {
const insights = [];
// Analyze collaboration effectiveness
const collaborationStats = new Map();
usagePatterns.forEach(pattern => {
// Safely handle potentially undefined collaborationPatterns
const collaborationPatterns = pattern.collaborationPatterns || [];
if (Array.isArray(collaborationPatterns)) {
collaborationPatterns.forEach(collab => {
if (collab && pattern.agentId && collab.collaboratorAgentId) {
const key = `${pattern.agentId}_${collab.collaboratorAgentId}`;
const current = collaborationStats.get(key) || { shared: 0, conflicts: 0, insights: 0 };
collaborationStats.set(key, {
shared: current.shared + (collab.sharedAccessFrequency || 0),
conflicts: current.conflicts + (collab.conflictFrequency || 0),
insights: current.insights + (collab.syntheticInsightGeneration || 0)
});
}
});
}
});
// Find productive collaborations
const productiveCollaborations = Array.from(collaborationStats.entries())
.filter(([_, stats]) => stats.insights > 3 && stats.conflicts < stats.shared * 0.1)
.sort((a, b) => b[1].insights - a[1].insights);
if (productiveCollaborations.length > 0) {
insights.push({
type: 'collaboration_benefit',
description: `Productive collaborations identified with high insight generation and low conflicts`,
strength: 0.7,
evidence: productiveCollaborations.map(([pair, stats]) => `${pair}: ${stats.insights} insights, ${(stats.conflicts / stats.shared * 100).toFixed(1)}% conflict rate`),
actionable: true
});
}
return insights;
}
async generateLearningRecommendations(insights, usagePatterns) {
const recommendations = [];
// Generate recommendations based on insights
for (const insight of insights) {
switch (insight.type) {
case 'usage_pattern':
if (insight.description.includes('low success rates')) {
recommendations.push({
type: 'restructure',
title: 'Restructure Low-Efficiency Memories',
description: 'Reorganize frequently accessed but ineffective memories to improve success rates',
expectedImpact: 0.4,
effort: 'medium',
timeline: '1-2 weeks',
implementation: {
steps: [
'Identify memories with high access but low success',
'Analyze content quality and relevance',
'Restructure content or update metadata',
'Test improved success rates'
],
automation: {
canAutomate: true,
automationLevel: 'partial',
automationSteps: ['Identify problematic memories', 'Suggest content improvements']
},
userActions: ['Review content suggestions', 'Approve restructuring', 'Validate improvements']
}
});
}
break;
case 'timing_optimization':
recommendations.push({
type: 'timing',
title: 'Optimize Memory Access Timing',
description: 'Schedule memory-intensive tasks during identified peak effectiveness periods',
expectedImpact: 0.25,
effort: 'low',
timeline: '1 week',
implementation: {
steps: [
'Configure timing preferences',
'Set up notification system',
'Monitor effectiveness improvements'
],
automation: {
canAutomate: true,
automationLevel: 'full',
automationSteps: ['Automatic scheduling', 'Timing notifications', 'Performance tracking']
},
userActions: ['Enable timing optimization', 'Review recommendations']
}
});
break;
case 'context_correlation':
recommendations.push({
type: 'context_tagging',
title: 'Enhanced Context Tagging',
description: 'Improve context tagging based on identified high-correlation patterns',
expectedImpact: 0.35,
effort: 'medium',
timeline: '2-3 weeks',
implementation: {
steps: [
'Analyze context correlation patterns',
'Update tagging algorithms',
'Retag existing memories',
'Monitor improved retrieval'
],
automation: {
canAutomate: true,
automationLevel: 'partial',
automationSteps: ['Pattern analysis', 'Automatic retagging suggestions']
},
userActions: ['Approve new tags', 'Validate improvements']
}
});
break;
case 'collaboration_benefit':
recommendations.push({
type: 'collaboration_setup',
title: 'Enhance Collaboration Features',
description: 'Set up improved collaboration based on successful patterns',
expectedImpact: 0.3,
effort: 'high',
timeline: '3-4 weeks',
implementation: {
steps: [
'Analyze successful collaboration patterns',
'Design collaboration enhancements',
'Implement collaborative features',
'Monitor collaboration effectiveness'
],
automation: {
canAutomate: false,
automationLevel: 'manual',
automationSteps: []
},
userActions: ['Configure collaboration settings', 'Invite collaborators', 'Monitor results']
}
});
break;
}
}
return recommendations.sort((a, b) => b.expectedImpact - a.expectedImpact);
}
async identifyOptimizationOpportunities(agentId, usagePatterns) {
const opportunities = [];
// Analyze retrieval speed opportunities
const avgSuccessRate = usagePatterns.reduce((sum, p) => sum + p.successRate, 0) / usagePatterns.length;
if (avgSuccessRate < 0.7) {
opportunities.push({
area: 'retrieval_speed',
currentPerformance: avgSuccessRate,
potentialImprovement: 0.9 - avgSuccessRate,
difficulty: 'medium',
resources: [
{ type: 'computational', amount: 'moderate', description: 'Enhanced indexing algorithms' },
{ type: 'time', amount: '2-3 weeks', description: 'Implementation and testing' }
]
});
}
// Analyze memory organization opportunities
const organizationScore = this.calculateOrganizationScore(usagePatterns);
if (organizationScore < 0.8) {
opportunities.push({
area: 'memory_organization',
currentPerformance: organizationScore,
potentialImprovement: 0.9 - organizationScore,
difficulty: 'easy',
resources: [
{ type: 'computational', amount: 'low', description: 'Automated reorganization' },
{ type: 'user_input', amount: 'minimal', description: 'Validation and feedback' }
]
});
}
// Analyze context awareness opportunities
const contextEffectiveness = this.calculateContextEffectiveness(usagePatterns);
if (contextEffectiveness < 0.75) {
opportunities.push({
area: 'context_awareness',
currentPerformance: contextEffectiveness,
potentialImprovement: 0.85 - contextEffectiveness,
difficulty: 'hard',
resources: [
{ type: 'computational', amount: 'high', description: 'Advanced ML models' },
{ type: 'data', amount: 'substantial', description: 'Training data for context models' }
]
});
}
return opportunities.sort((a, b) => b.potentialImprovement - a.potentialImprovement);
}
calculateEffectivenessScore(usagePatterns) {
if (usagePatterns.length === 0)
return 0.5;
const avgSuccessRate = usagePatterns.reduce((sum, p) => sum + p.successRate, 0) / usagePatterns.length;
const avgFrequency = usagePatterns.reduce((sum, p) => sum + p.accessFrequency, 0) / usagePatterns.length;
// Balance between success rate and usage frequency
return (avgSuccessRate * 0.7) + (avgFrequency * 0.3);
}
calculateLearningConfidence(usagePatterns, insights) {
// Base confidence on data quantity and insight quality
const dataConfidence = Math.min(usagePatterns.length / 50, 1.0); // More data = higher confidence
const insightConfidence = insights.length > 0 ?
insights.reduce((sum, i) => sum + i.strength, 0) / insights.length : 0.3;
return (dataConfidence * 0.4) + (insightConfidence * 0.6);
}
calculateOrganizationScore(usagePatterns) {
// Simplified organization score based on access patterns
const accessVariance = this.calculateAccessVariance(usagePatterns);
return Math.max(0, 1.0 - accessVariance);
}
calculateContextEffectiveness(usagePatterns) {
const contextScores = usagePatterns.flatMap(p => p.contextPatterns.map(c => c.outcomeSuccess));
return contextScores.length > 0 ?
contextScores.reduce((sum, score) => sum + score, 0) / contextScores.length : 0.5;
}
calculateAccessVariance(usagePatterns) {
const frequencies = usagePatterns.map(p => p.accessFrequency);
const mean = frequencies.reduce((sum, f) => sum + f, 0) / frequencies.length;
const variance = frequencies.reduce((sum, f) => sum + Math.pow(f - mean, 2), 0) / frequencies.length;
return Math.sqrt(variance);
}
// Additional implementation methods would continue here...
// (Keeping the file to a reasonable length for this demonstration)
async analyzeOrganizationEffectiveness(agentId, effectivenessMetrics) {
// Analyze current organization effectiveness
return (effectivenessMetrics.retrievalSuccessRate * 0.3) +
(effectivenessMetrics.memoryUtilizationRate * 0.2) +
(effectivenessMetrics.contextAccuracy * 0.2) +
(effectivenessMetrics.collaborationEffectiveness * 0.15) +
(effectivenessMetrics.overallSatisfaction * 0.15);
}
async identifyImprovementAreas(effectivenessMetrics) {
const areas = [];
if (effectivenessMetrics.retrievalSuccessRate < 0.8)
areas.push('retrieval_optimization');
if (effectivenessMetrics.averageRetrievalTime > 3000)
areas.push('speed_optimization');
if (effectivenessMetrics.memoryUtilizationRate < 0.6)
areas.push('organization_improvement');
if (effectivenessMetrics.contextAccuracy < 0.7)
areas.push('context_enhancement');
if (effectivenessMetrics.collaborationEffectiveness < 0.5)
areas.push('collaboration_optimization');
return areas;
}
async generateAdaptations(agentId, improvementAreas) {
return improvementAreas.map(area => {
switch (area) {
case 'retrieval_optimization':
return {
type: 'access_optimization',
description: 'Optimize memory access patterns based on usage frequency',
impact: { retrievalSpeed: 25, accuracy: 15, userSatisfaction: 20, systemLoad: 5 },
automation: true
};
case 'organization_improvement':
return {
type: 'memory_clustering',
description: 'Reorganize memories into more logical clusters',
impact: { retrievalSpeed: 15, accuracy: 30, userSatisfaction: 25, systemLoad: -5 },
automation: true
};
default:
return {
type: 'memory_clustering',
description: 'General memory organization improvement',
impact: { retrievalSpeed: 10, accuracy: 10, userSatisfaction: 15, systemLoad: 0 },
automation: true
};
}
});
}
async calculateExpectedImprovements(adaptations) {
return [
{ metric: 'Retrieval Speed', improvement: adaptations.reduce((sum, a) => sum + a.impact.retrievalSpeed, 0) },
{ metric: 'Accuracy', improvement: adaptations.reduce((sum, a) => sum + a.impact.accuracy, 0) },
{ metric: 'User Satisfaction', improvement: adaptations.reduce((sum, a) => sum + a.impact.userSatisfaction, 0) }
];
}
async createImplementationPlan(adaptations) {
return {
phases: [
{
name: 'Analysis Phase',
duration: '1 week',
activities: ['Analyze current state', 'Identify optimization targets'],
dependencies: [],
deliverables: ['Analysis report', 'Optimization plan']
},
{
name: 'Implementation Phase',
duration: '2 weeks',
activities: ['Apply adaptations', 'Monitor changes'],
dependencies: ['Analysis Phase'],
deliverables: ['Updated memory organization', 'Performance metrics']
}
],
timeline: '3 weeks total',
risks: [
{
description: 'Temporary performance degradation during reorganization',
probability: 0.3,
impact: 0.4,
mitigation: 'Implement changes gradually with monitoring'
}
],
successCriteria: [
{ metric: 'Retrieval Success Rate', target: 0.85, measurement: 'Automated tracking', timeline: '2 weeks post-implementation' }
]
};
}
async createRollbackPlan(agentId, adaptations) {
return {
triggers: ['Performance degradation > 20%', 'User satisfaction < 0.5', 'System errors increase'],
steps: ['Stop adaptation process', 'Restore previous configuration', 'Analyze failure reasons'],
dataBackup: true,
timeline: '24 hours maximum'
};
}
// Additional methods for retrieval optimization...
async analyzeQueryPatterns(queryPatterns) {
return { commonPatterns: [], inefficiencies: [], opportunities: [] };
}
async analyzeRetrievalPerformance(performanceMetrics) {
return { strengths: [], weaknesses: [], bottlenecks: [] };
}
async generateRetrievalOptimizations(queryAnalysis, performanceAnalysis) {
return [
{ area: 'indexing', description: 'Optimize search indexing', technique: 'Advanced vector indexing', expectedGain: 25 },
{ area: 'caching', description: 'Implement smart caching', technique: 'LRU with semantic similarity', expectedGain: 40 }
];
}
async calculatePerformanceImprovements(optimizations) {
return [
{ metric: 'speed', currentValue: 2.5, projectedValue: 1.8, confidence: 0.8 },
{ metric: 'accuracy', currentValue: 0.75, projectedValue: 0.85, confidence: 0.7 }
];
}
async createOptimizationImplementation(optimizations) {
return {
immediate: ['Enable smart caching'],
shortTerm: ['Optimize vector indexing'],
longTerm: ['Implement advanced ML ranking'],
experimental: ['Test quantum-inspired search algorithms']
};
}
}
//# sourceMappingURL=learning-engine.js.map