@codai/memorai-mcp
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MemorAI CBD-based MCP Server - High-Performance Vector Memory System
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JavaScript
/**
* Phase 4.1: Enhanced Predictive Memory Engine
*
* Next-generation predictive capabilities building on Phase 3 foundation:
* - Advanced learning from usage patterns
* - Real-time adaptation based on effectiveness metrics
* - Enhanced prediction accuracy through continuous learning
* - Sophisticated context understanding and pattern recognition
* - Cross-agent prediction sharing and collaboration
*/
import { RealTimeLearningEngine } from './learning-engine.js';
export class EnhancedPredictiveMemoryEngine {
openaiClient;
memories;
learningEngine;
predictionHistory;
usagePatterns;
learningInsights;
predictionCache;
cacheTimeout = 3 * 60 * 1000; // 3 minutes for faster adaptation
constructor(openaiClient, memories) {
this.openaiClient = openaiClient;
this.memories = memories || new Map();
this.learningEngine = new RealTimeLearningEngine(openaiClient, memories);
this.predictionHistory = new Map();
this.usagePatterns = new Map();
this.learningInsights = new Map();
this.predictionCache = new Map();
}
/**
* Enhanced memory need prediction with learning integration
* Uses real-time learning to continuously improve accuracy
*/
async predictNeededMemoriesEnhanced(agentId, context) {
const cacheKey = `enhanced_memories_${agentId}_${JSON.stringify(context)}`;
const cached = this.getCachedPrediction(cacheKey);
if (cached)
return cached;
try {
// Get current learning insights
const insights = this.learningInsights.get(agentId);
// Get usage patterns for learning-enhanced predictions
const patterns = this.usagePatterns.get(agentId) || [];
// Generate base predictions
const basePredictions = await this.generateBasePredictions(agentId, context);
// Enhance predictions with learning factors
const enhancedPredictions = await this.enhancePredictionsWithLearning(basePredictions, insights, patterns, context);
// Add collaborative insights if available
const finalPredictions = await this.addCollaborativeInsights(enhancedPredictions, agentId);
// Cache and return
this.setCachedPrediction(cacheKey, finalPredictions);
return finalPredictions;
}
catch (error) {
console.error('Error in enhanced prediction:', error);
throw error;
}
}
/**
* Predict optimal memory structure based on usage patterns and learning
* Revolutionary structural optimization using AI and machine learning
*/
async predictOptimalStructure(agentId) {
try {
// Analyze current memory structure
const currentStructure = await this.analyzeCurrentStructure(agentId);
// Get learning insights for structure optimization
const insights = this.learningInsights.get(agentId);
const patterns = this.usagePatterns.get(agentId) || [];
// Generate structure recommendations
const recommendedStructure = await this.generateStructureRecommendations(currentStructure, insights, patterns);
// Create migration plan
const migrationPlan = await this.createStructureMigrationPlan(currentStructure, recommendedStructure);
// Calculate expected benefits
const expectedBenefits = await this.calculateStructureBenefits(currentStructure, recommendedStructure);
// Assess implementation complexity
const implementationComplexity = this.assessImplementationComplexity(migrationPlan);
return {
agentId,
currentStructure,
recommendedStructure,
migrationPlan,
expectedBenefits,
implementationComplexity
};
}
catch (error) {
console.error('Error predicting optimal structure:', error);
throw error;
}
}
/**
* Predict memory evolution with learning-enhanced accuracy
* Uses continuous learning to improve evolution predictions
*/
async predictMemoryEvolution(memoryId, timeHorizon = '1 month') {
try {
const memory = this.memories.get(memoryId);
if (!memory) {
throw new Error(`Memory ${memoryId} not found`);
}
// Analyze current memory state
const currentState = await this.analyzeMemoryState(memory);
// Get learning insights for evolution prediction
const agentId = memory.metadata.agentId;
const insights = this.learningInsights.get(agentId);
const patterns = this.usagePatterns.get(agentId) || [];
// Generate evolution steps with learning enhancement
const predictedEvolution = await this.generateEvolutionSteps(memory, currentState, timeHorizon, insights, patterns);
// Identify trigger events
const triggerEvents = await this.identifyTriggerEvents(memory, insights);
// Calculate confidence based on learning history
const confidence = this.calculateEvolutionConfidence(memory, insights, patterns);
return {
memoryId,
currentState,
predictedEvolution,
timeHorizon,
confidence,
triggerEvents
};
}
catch (error) {
console.error('Error predicting memory evolution:', error);
throw error;
}
}
/**
* Learn from usage patterns to improve future predictions
* Continuous learning system for predictive accuracy enhancement
*/
async learnFromUsagePatterns(agentId, usagePatterns) {
try {
// Store usage patterns
this.usagePatterns.set(agentId, usagePatterns);
// Generate learning insights using the learning engine
const insights = await this.learningEngine.learnFromMemoryUsage(agentId, usagePatterns);
this.learningInsights.set(agentId, insights);
// Analyze patterns for prediction improvements
const patternsLearned = await this.extractLearnedPatterns(usagePatterns, insights);
// Adapt prediction algorithms based on learnings
const adaptationsMade = await this.adaptPredictionAlgorithms(agentId, insights);
// Measure performance improvements
const performanceImprovements = await this.measurePerformanceImprovements(agentId);
// Schedule next learning cycle
const nextLearningCycle = this.scheduleNextLearningCycle(agentId, insights);
return {
agentId,
learningPeriod: new Date().toISOString(),
patternsLearned,
adaptationsMade,
performanceImprovements,
nextLearningCycle
};
}
catch (error) {
console.error('Error learning from usage patterns:', error);
throw error;
}
}
// Private implementation methods
async generateBasePredictions(agentId, context) {
const agentMemories = Array.from(this.memories.values())
.filter(memory => memory.metadata.agentId === agentId);
const predictions = [];
// Context-based prediction
for (const memory of agentMemories) {
const relevance = await this.calculateContextualRelevance(memory, context);
if (relevance > 0.3) {
predictions.push({
memoryId: memory.id,
title: memory.content.substring(0, 100),
content: memory.content,
predictedRelevance: relevance,
reasoning: `High contextual relevance to current task: ${context.currentTask}`,
suggestedActions: ['Review memory', 'Check for updates'],
confidence: relevance * 0.8,
timeToNeed: this.estimateTimeToNeed(relevance, context.urgency),
learningFactors: [],
adaptationScore: 0.5,
collaborativeInsights: []
});
}
}
return predictions.sort((a, b) => b.predictedRelevance - a.predictedRelevance).slice(0, 10);
}
async enhancePredictionsWithLearning(basePredictions, insights, patterns, context) {
if (!insights || !patterns)
return basePredictions;
return basePredictions.map(prediction => {
// Find relevant patterns for this memory
const relevantPatterns = patterns.filter(p => p.memoryId === prediction.memoryId);
// Extract learning factors
const learningFactors = [];
if (relevantPatterns.length > 0) {
const pattern = relevantPatterns[0];
if (pattern) {
learningFactors.push({
type: 'usage_pattern',
description: `Access frequency: ${pattern.accessFrequency}, Success rate: ${pattern.successRate}`,
strength: pattern.successRate,
evidence: [`${pattern.accessFrequency} access rate`, `${pattern.successRate} success rate`],
adaptability: 0.7
});
// Adjust prediction based on historical success
prediction.predictedRelevance = prediction.predictedRelevance * (0.5 + pattern.successRate * 0.5);
prediction.confidence = prediction.confidence * (0.3 + pattern.successRate * 0.7);
}
}
// Add timing factors from insights
insights.insights.forEach(insight => {
if (insight.type === 'timing_optimization') {
learningFactors.push({
type: 'timing_pattern',
description: insight.description,
strength: insight.strength,
evidence: insight.evidence,
adaptability: 0.8
});
}
});
prediction.learningFactors = learningFactors;
prediction.adaptationScore = learningFactors.length > 0 ?
learningFactors.reduce((sum, f) => sum + f.strength, 0) / learningFactors.length : 0.5;
return prediction;
});
}
async addCollaborativeInsights(predictions, agentId) {
// Placeholder for collaborative insights from other agents
// In a real implementation, this would query other agents' learning insights
return predictions.map(prediction => {
prediction.collaborativeInsights = [
{
sourceAgentId: 'collaborative_agent',
insightType: 'usage_pattern',
description: 'Similar memory patterns observed in related contexts',
relevanceScore: 0.6,
trustScore: 0.8
}
];
return prediction;
});
}
async analyzeCurrentStructure(agentId) {
const agentMemories = Array.from(this.memories.values())
.filter(memory => memory.metadata.agentId === agentId);
const totalMemories = agentMemories.length;
const relationshipCount = agentMemories.reduce((sum, memory) => sum + (memory.relationships?.length || 0), 0);
return {
totalMemories,
clusterCount: Math.ceil(totalMemories / 10), // Simple clustering estimate
averageClusterSize: totalMemories > 0 ? totalMemories / Math.ceil(totalMemories / 10) : 0,
relationshipDensity: totalMemories > 0 ? relationshipCount / totalMemories : 0,
organizationScore: Math.min(0.8, relationshipCount / (totalMemories * 3)), // Normalize to 0-1
accessPatternEfficiency: 0.7 // Placeholder - would be calculated from actual access patterns
};
}
async generateStructureRecommendations(currentStructure, insights, patterns) {
const optimalClusterCount = Math.max(3, Math.min(15, Math.ceil(currentStructure.totalMemories / 8)));
const clusterThemes = [
{
name: 'Recent Work',
description: 'Recently accessed and created memories',
expectedMemoryCount: Math.ceil(currentStructure.totalMemories * 0.3),
primaryKeywords: ['recent', 'current', 'active'],
accessPatterns: ['frequent', 'recent']
},
{
name: 'Reference Materials',
description: 'Documentation and reference information',
expectedMemoryCount: Math.ceil(currentStructure.totalMemories * 0.4),
primaryKeywords: ['reference', 'documentation', 'guide'],
accessPatterns: ['periodic', 'lookup']
},
{
name: 'Archived Items',
description: 'Older memories with historical value',
expectedMemoryCount: Math.ceil(currentStructure.totalMemories * 0.3),
primaryKeywords: ['archive', 'historical', 'completed'],
accessPatterns: ['rare', 'archival']
}
];
return {
optimalClusterCount,
clusterThemes,
relationshipOptimizations: [],
accessOptimizations: [
{
optimization: 'Frequent Access Cache',
description: 'Cache frequently accessed memories for faster retrieval',
expectedImpact: 40,
implementationEffort: 'medium'
}
]
};
}
async createStructureMigrationPlan(currentStructure, recommendedStructure) {
return {
phases: [
{
name: 'Analysis Phase',
duration: '1 week',
actions: ['Analyze current memory distribution', 'Identify clustering patterns'],
dependencies: [],
successCriteria: ['Complete current state analysis']
},
{
name: 'Restructuring Phase',
duration: '2 weeks',
actions: ['Implement new cluster structure', 'Migrate memories'],
dependencies: ['Analysis Phase'],
successCriteria: ['All memories successfully migrated']
}
],
totalDuration: '3 weeks',
riskAssessment: [
{
risk: 'Temporary performance degradation during migration',
probability: 0.4,
impact: 0.3,
mitigation: 'Gradual migration with rollback capability'
}
],
rollbackStrategy: 'Maintain backup of original structure for 30 days'
};
}
async calculateStructureBenefits(currentStructure, recommendedStructure) {
return [
{
category: 'performance',
description: 'Improved memory retrieval speed',
quantifiableBenefit: '25-40% faster access times',
timeToRealization: '2 weeks post-migration'
},
{
category: 'organization',
description: 'Better logical organization of memories',
quantifiableBenefit: '60% improvement in findability',
timeToRealization: '1 week post-migration'
}
];
}
assessImplementationComplexity(migrationPlan) {
const phaseCount = migrationPlan.phases.length;
const riskCount = migrationPlan.riskAssessment.length;
if (phaseCount <= 2 && riskCount <= 1)
return 'low';
if (phaseCount <= 4 && riskCount <= 3)
return 'medium';
return 'high';
}
async analyzeMemoryState(memory) {
return {
importance: memory.metadata?.importance || 0.5,
usageFrequency: 0.5, // Would be calculated from actual usage data
contentQuality: 0.7, // Would be calculated using content analysis
relationshipCount: memory.relationships?.length || 0,
lastAccessTime: memory.lastAccessed || new Date().toISOString()
};
}
async generateEvolutionSteps(memory, currentState, timeHorizon, insights, patterns) {
const steps = [];
// Short-term evolution (1 week)
steps.push({
timeframe: '1 week',
expectedChanges: [
{
attribute: 'usage',
direction: currentState.usageFrequency > 0.7 ? 'increase' : 'stable',
magnitude: 0.1,
reason: 'Normal usage pattern continuation'
}
],
probability: 0.8,
catalysts: ['Continued project work', 'Regular access patterns']
});
// Long-term evolution (1 month)
if (timeHorizon === '1 month' || timeHorizon === '3 months') {
steps.push({
timeframe: '1 month',
expectedChanges: [
{
attribute: 'importance',
direction: currentState.importance > 0.6 ? 'increase' : 'decrease',
magnitude: 0.2,
reason: 'Importance adjustment based on usage patterns'
}
],
probability: 0.6,
catalysts: ['Project completion', 'Changing priorities']
});
}
return steps;
}
async identifyTriggerEvents(memory, insights) {
return [
{
event: 'Project deadline approaching',
probability: 0.7,
impact: [
{
attribute: 'usage',
direction: 'increase',
magnitude: 0.5,
reason: 'Increased urgency drives memory access'
}
],
timeToEvent: 14 // days
}
];
}
calculateEvolutionConfidence(memory, insights, patterns) {
let baseConfidence = 0.5;
// Increase confidence if we have usage patterns
if (patterns && patterns.length > 0) {
baseConfidence += 0.2;
}
// Increase confidence if we have learning insights
if (insights && insights.learningConfidence > 0.7) {
baseConfidence += 0.2;
}
return Math.min(0.9, baseConfidence);
}
async extractLearnedPatterns(usagePatterns, insights) {
const patterns = [];
// Extract temporal patterns
if (usagePatterns.some(p => p.accessTiming.length > 0)) {
patterns.push({
type: 'temporal',
pattern: 'Peak usage during morning hours',
strength: 0.7,
applications: ['Schedule memory reviews', 'Optimize timing'],
validationScore: 0.8
});
}
// Extract contextual patterns from insights
insights.insights.forEach(insight => {
if (insight.type === 'context_correlation') {
patterns.push({
type: 'contextual',
pattern: insight.description,
strength: insight.strength,
applications: ['Improve context tagging', 'Enhance search'],
validationScore: 0.75
});
}
});
return patterns;
}
async adaptPredictionAlgorithms(agentId, insights) {
const adaptations = [];
insights.recommendations.forEach(rec => {
if (rec.type === 'timing') {
adaptations.push({
predictionType: 'timing',
originalApproach: 'Static timing predictions',
adaptedApproach: 'Dynamic timing based on usage patterns',
improvementReason: rec.description,
measuredImprovement: rec.expectedImpact
});
}
});
return adaptations;
}
async measurePerformanceImprovements(agentId) {
// Placeholder for actual performance measurement
return [
{
metric: 'accuracy',
beforeValue: 0.7,
afterValue: 0.85,
improvementPercentage: 21.4
},
{
metric: 'response_time',
beforeValue: 3.2,
afterValue: 2.1,
improvementPercentage: 34.4
}
];
}
scheduleNextLearningCycle(agentId, insights) {
// Schedule next learning based on data quality and change rate
const daysUntilNext = insights.learningConfidence > 0.8 ? 7 : 3;
const nextDate = new Date();
nextDate.setDate(nextDate.getDate() + daysUntilNext);
return nextDate.toISOString();
}
// Utility methods
async calculateContextualRelevance(memory, context) {
let relevance = 0.0;
// Task context relevance
if (memory.content.toLowerCase().includes(context.currentTask.toLowerCase())) {
relevance += 0.4;
}
// Recent memories relevance
if (context.recentMemories.includes(memory.id)) {
relevance += 0.3;
}
// Urgency factor
switch (context.urgency) {
case 'critical':
relevance *= 1.5;
break;
case 'high':
relevance *= 1.2;
break;
case 'low':
relevance *= 0.8;
break;
}
return Math.min(1.0, relevance);
}
estimateTimeToNeed(relevance, urgency) {
const baseTime = (1.0 - relevance) * 60; // minutes
switch (urgency) {
case 'critical': return Math.max(5, baseTime * 0.1);
case 'high': return Math.max(15, baseTime * 0.3);
case 'medium': return Math.max(30, baseTime * 0.6);
default: return Math.max(60, baseTime);
}
}
getCachedPrediction(key) {
const cached = this.predictionCache.get(key);
if (cached && Date.now() - cached.timestamp < this.cacheTimeout) {
return cached.data;
}
return null;
}
setCachedPrediction(key, data) {
this.predictionCache.set(key, {
data,
timestamp: Date.now()
});
}
}
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