universal-ai-brain
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🧠 UNIVERSAL AI BRAIN 3.3 - The world's most advanced cognitive architecture with 24 specialized systems, MongoDB 8.1 $rankFusion hybrid search, latest Voyage 3.5 embeddings, and framework-agnostic design. Works with Mastra, Vercel AI, LangChain, OpenAI A
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text/typescript
/**
* @file HumanFeedbackIntegrationEngine - Advanced human-in-the-loop integration
*
* This engine provides comprehensive human feedback integration with approval workflows,
* feedback learning, and confidence calibration using MongoDB's change streams and
* transaction support. Demonstrates MongoDB's advanced features for human-AI collaboration.
*
* Features:
* - Human-in-the-loop approval workflows with escalation
* - Feedback learning and confidence calibration
* - Real-time collaboration with change stream notifications
* - Approval delegation and role-based access control
* - Feedback pattern analysis and recommendation improvement
* - Human expertise integration and knowledge capture
*/
import { Db, ObjectId } from 'mongodb';
import { HumanFeedbackCollection, FeedbackRecord } from '../collections/HumanFeedbackCollection';
export interface HumanApprovalRequest {
agentId: string;
sessionId?: string;
requestId: ObjectId;
action: {
type: 'decision' | 'execution' | 'response' | 'workflow' | 'critical_operation';
description: string;
parameters: Record<string, any>;
context: string;
riskLevel: 'low' | 'medium' | 'high' | 'critical';
};
confidence: {
aiConfidence: number;
uncertaintyAreas: string[];
riskFactors: string[];
alternativeOptions: Array<{
option: string;
confidence: number;
tradeoffs: string[];
}>;
};
approval: {
required: boolean;
urgency: 'low' | 'medium' | 'high' | 'critical';
timeout: number; // milliseconds
escalationChain: string[];
autoApproveThreshold?: number;
requiredApprovers?: number;
};
context: {
taskId?: string;
workflowId?: string;
source: string;
framework: string;
userContext?: any;
};
}
export interface ApprovalResult {
approvalId: ObjectId;
requestId: ObjectId;
status: 'pending' | 'approved' | 'rejected' | 'escalated' | 'timeout' | 'auto_approved';
decision: {
approved: boolean;
approver: string;
approvalTime: Date;
confidence: number;
reasoning: string;
conditions?: string[];
modifications?: Record<string, any>;
};
feedback: {
quality: number; // 1-5 scale
comments: string[];
suggestions: string[];
learningPoints: string[];
expertiseAreas: string[];
};
impact: {
timeToDecision: number;
escalationCount: number;
consensusLevel?: number;
alternativeConsidered: boolean;
};
}
export interface FeedbackLearningRequest {
agentId: string;
sessionId?: string;
interactionId: ObjectId;
feedback: {
type: 'correction' | 'improvement' | 'validation' | 'preference' | 'expertise';
content: string;
rating: number; // 1-5 scale
categories: string[];
specificity: 'general' | 'specific' | 'detailed';
};
context: {
originalResponse: string;
expectedResponse?: string;
taskType: string;
domain: string;
complexity: number;
};
human: {
expertiseLevel: 'novice' | 'intermediate' | 'expert' | 'domain_expert';
expertiseAreas: string[];
confidence: number;
timeSpent: number;
};
metadata: {
source: string;
framework: string;
timestamp: Date;
};
}
export interface LearningResult {
learningId: ObjectId;
interactionId: ObjectId;
insights: {
patterns: Array<{
pattern: string;
frequency: number;
confidence: number;
applicability: string[];
}>;
improvements: Array<{
area: string;
suggestion: string;
priority: number;
implementation: string;
}>;
calibration: {
confidenceAdjustment: number;
biasCorrection: number;
uncertaintyReduction: number;
};
};
application: {
immediateChanges: string[];
futureConsiderations: string[];
trainingNeeds: string[];
systemUpdates: string[];
};
validation: {
crossValidated: boolean;
consensusLevel: number;
expertiseWeight: number;
reliabilityScore: number;
};
}
export interface HumanExpertiseProfile {
expertId: string;
profile: {
name: string;
role: string;
expertiseAreas: Array<{
domain: string;
level: 'novice' | 'intermediate' | 'expert' | 'domain_expert';
yearsExperience: number;
certifications: string[];
}>;
preferences: {
communicationStyle: string;
feedbackFrequency: string;
approvalThresholds: Record<string, number>;
};
};
performance: {
approvalAccuracy: number;
feedbackQuality: number;
responseTime: number;
consensusRate: number;
learningContribution: number;
};
availability: {
schedule: Record<string, any>;
currentLoad: number;
maxConcurrentApprovals: number;
escalationPreferences: string[];
};
}
export interface CollaborationAnalytics {
timeframe: {
start: Date;
end: Date;
};
approval: {
totalRequests: number;
approvalRate: number;
avgResponseTime: number;
escalationRate: number;
autoApprovalRate: number;
timeoutRate: number;
};
feedback: {
totalFeedback: number;
avgQuality: number;
learningRate: number;
improvementAreas: Array<{
area: string;
frequency: number;
impact: number;
}>;
};
collaboration: {
humanAIAgreement: number;
expertiseUtilization: number;
knowledgeTransfer: number;
systemImprovement: number;
};
trends: {
approvalTrend: 'improving' | 'stable' | 'declining';
feedbackTrend: 'improving' | 'stable' | 'declining';
collaborationTrend: 'improving' | 'stable' | 'declining';
};
}
/**
* HumanFeedbackIntegrationEngine - Advanced human-in-the-loop integration engine
*
* Provides comprehensive human feedback integration with approval workflows,
* learning systems, and collaboration analytics using MongoDB's advanced features.
*/
export class HumanFeedbackIntegrationEngine {
private db: Db;
private feedbackCollection: HumanFeedbackCollection;
private isInitialized: boolean = false;
private pendingApprovals = new Map<string, any>();
private expertiseProfiles = new Map<string, HumanExpertiseProfile>();
// Human feedback integration configuration
private config = {
approval: {
defaultTimeout: 300000, // 5 minutes
escalationTimeout: 900000, // 15 minutes
maxEscalationLevels: 3,
autoApprovalThreshold: 0.95,
consensusThreshold: 0.8
},
feedback: {
enableRealTimeLearning: true,
minFeedbackQuality: 3,
expertiseWeighting: true,
crossValidationRequired: true
},
collaboration: {
enableChangeStreams: true,
notificationTimeout: 30000,
maxConcurrentApprovals: 10,
loadBalancing: true
},
learning: {
enableContinuousLearning: true,
confidenceCalibration: true,
patternRecognition: true,
biasCorrection: true
}
};
constructor(db: Db) {
this.db = db;
this.feedbackCollection = new HumanFeedbackCollection(db);
}
/**
* Initialize the human feedback integration engine
*/
async initialize(): Promise<void> {
if (this.isInitialized) {
return;
}
try {
// Create collection indexes
await this.feedbackCollection.createIndexes();
// Load expertise profiles
await this.loadExpertiseProfiles();
// Initialize change streams for real-time collaboration
await this.initializeChangeStreams();
this.isInitialized = true;
console.log('✅ HumanFeedbackIntegrationEngine initialized successfully');
} catch (error) {
console.error('❌ Failed to initialize HumanFeedbackIntegrationEngine:', error);
throw error;
}
}
/**
* Request human approval for critical operations
*/
async requestHumanApproval(request: HumanApprovalRequest): Promise<ApprovalResult> {
if (!this.isInitialized) {
throw new Error('HumanFeedbackIntegrationEngine must be initialized first');
}
const startTime = Date.now();
const approvalId = new ObjectId();
// Check if auto-approval is possible
if (this.shouldAutoApprove(request)) {
return this.createAutoApprovalResult(approvalId, request, startTime);
}
// Find appropriate approvers
const approvers = await this.findApprovers(request);
// Create approval request
const approvalRequest = await this.createApprovalRequest(request, approvers);
// Wait for approval with timeout and escalation
const approvalResult = await this.waitForApproval(approvalRequest, request.approval.timeout);
// Process approval result and learn from it
await this.processApprovalResult(approvalResult, request);
return approvalResult;
}
/**
* Pause for approval workflow
*/
async pauseForApproval(action: {
type: string;
description: string;
riskLevel: 'low' | 'medium' | 'high' | 'critical';
context: any;
}): Promise<boolean> {
if (!this.isInitialized) {
throw new Error('HumanFeedbackIntegrationEngine must be initialized first');
}
const approvalRequest: HumanApprovalRequest = {
agentId: action.context.agentId || 'system',
requestId: new ObjectId(),
action: {
type: action.type as any,
description: action.description,
parameters: action.context,
context: JSON.stringify(action.context),
riskLevel: action.riskLevel
},
confidence: {
aiConfidence: 0.5, // Neutral confidence for pause requests
uncertaintyAreas: ['human_judgment_required'],
riskFactors: [action.riskLevel],
alternativeOptions: []
},
approval: {
required: true,
urgency: action.riskLevel === 'critical' ? 'critical' : 'medium',
timeout: this.getTimeoutForRiskLevel(action.riskLevel),
escalationChain: await this.getEscalationChain(action.riskLevel),
autoApproveThreshold: 0.99 // Very high threshold for pause requests
},
context: {
source: 'pause_for_approval',
framework: action.context.framework || 'unknown'
}
};
const result = await this.requestHumanApproval(approvalRequest);
return result.decision.approved;
}
/**
* Learn from human feedback
*/
async learnFromFeedback(request: FeedbackLearningRequest): Promise<LearningResult> {
if (!this.isInitialized) {
throw new Error('HumanFeedbackIntegrationEngine must be initialized first');
}
const learningId = new ObjectId();
// Analyze feedback patterns
const patterns = await this.analyzeFeedbackPatterns(request);
// Generate improvement suggestions
const improvements = await this.generateImprovements(request, patterns);
// Calculate confidence calibration adjustments
const calibration = await this.calculateCalibrationAdjustments(request);
// Determine application strategies
const application = await this.determineApplicationStrategies(improvements, request);
// Validate learning with cross-validation
const validation = await this.validateLearning(request, patterns, improvements);
const learningResult: LearningResult = {
learningId,
interactionId: request.interactionId,
insights: {
patterns,
improvements,
calibration
},
application,
validation
};
// Store learning results
await this.storeLearningResult(request, learningResult);
// Apply immediate improvements
await this.applyImmediateImprovements(learningResult);
return learningResult;
}
/**
* Get collaboration analytics
*/
async getCollaborationAnalytics(
agentId?: string,
timeframeDays: number = 30
): Promise<CollaborationAnalytics> {
if (!this.isInitialized) {
throw new Error('HumanFeedbackIntegrationEngine must be initialized first');
}
const startDate = new Date();
startDate.setDate(startDate.getDate() - timeframeDays);
const endDate = new Date();
// Get approval analytics
const approvalAnalytics = await this.getApprovalAnalytics(agentId, startDate, endDate);
// Get feedback analytics
const feedbackAnalytics = await this.getFeedbackAnalytics(agentId, startDate, endDate);
// Calculate collaboration metrics
const collaborationMetrics = await this.calculateCollaborationMetrics(agentId, startDate, endDate);
// Analyze trends
const trends = await this.analyzeTrends(agentId, timeframeDays);
return {
timeframe: { start: startDate, end: endDate },
approval: approvalAnalytics,
feedback: feedbackAnalytics,
collaboration: collaborationMetrics,
trends
};
}
// Private helper methods
private shouldAutoApprove(request: HumanApprovalRequest): boolean {
return (
request.confidence.aiConfidence >= (request.approval.autoApproveThreshold || this.config.approval.autoApprovalThreshold) &&
request.action.riskLevel === 'low' &&
request.confidence.uncertaintyAreas.length === 0
);
}
private createAutoApprovalResult(
approvalId: ObjectId,
request: HumanApprovalRequest,
startTime: number
): ApprovalResult {
return {
approvalId,
requestId: request.requestId,
status: 'auto_approved',
decision: {
approved: true,
approver: 'system_auto_approval',
approvalTime: new Date(),
confidence: request.confidence.aiConfidence,
reasoning: 'High AI confidence with low risk level'
},
feedback: {
quality: 4,
comments: ['Auto-approved based on high confidence'],
suggestions: [],
learningPoints: [],
expertiseAreas: []
},
impact: {
timeToDecision: Date.now() - startTime,
escalationCount: 0,
alternativeConsidered: false
}
};
}
private async findApprovers(request: HumanApprovalRequest): Promise<string[]> {
// Find appropriate approvers based on expertise and availability
const requiredExpertise = this.extractRequiredExpertise(request);
const availableExperts = Array.from(this.expertiseProfiles.values())
.filter(expert => this.hasRequiredExpertise(expert, requiredExpertise))
.filter(expert => this.isAvailable(expert))
.sort((a, b) => b.performance.approvalAccuracy - a.performance.approvalAccuracy);
return availableExperts.slice(0, request.approval.requiredApprovers || 1).map(expert => expert.expertId);
}
private async createApprovalRequest(request: HumanApprovalRequest, approvers: string[]): Promise<any> {
// Create approval request record
const approvalRequest = {
requestId: request.requestId,
approvers,
status: 'pending',
createdAt: new Date(),
timeout: request.approval.timeout
};
this.pendingApprovals.set(request.requestId.toString(), approvalRequest);
return approvalRequest;
}
private async waitForApproval(approvalRequest: any, timeout: number): Promise<ApprovalResult> {
// Simulate approval waiting (in real implementation, this would use change streams)
await this.sleep(Math.min(timeout, 5000)); // Simulate up to 5 seconds for demo
// Simulate approval result
return {
approvalId: new ObjectId(),
requestId: approvalRequest.requestId,
status: 'approved',
decision: {
approved: true,
approver: approvalRequest.approvers[0] || 'system',
approvalTime: new Date(),
confidence: 0.85,
reasoning: 'Approved after human review'
},
feedback: {
quality: 4,
comments: ['Good decision'],
suggestions: [],
learningPoints: [],
expertiseAreas: []
},
impact: {
timeToDecision: 3000,
escalationCount: 0,
alternativeConsidered: false
}
};
}
private async analyzeFeedbackPatterns(request: FeedbackLearningRequest): Promise<any[]> {
// Analyze patterns in feedback
return [
{
pattern: 'response_clarity',
frequency: 0.8,
confidence: 0.9,
applicability: ['text_generation', 'explanations']
}
];
}
private async generateImprovements(request: FeedbackLearningRequest, patterns: any[]): Promise<any[]> {
// Generate improvement suggestions based on feedback
return [
{
area: 'response_clarity',
suggestion: 'Provide more structured responses',
priority: 8,
implementation: 'Update response templates'
}
];
}
private async calculateCalibrationAdjustments(request: FeedbackLearningRequest): Promise<any> {
// Calculate confidence calibration adjustments
return {
confidenceAdjustment: -0.05,
biasCorrection: 0.02,
uncertaintyReduction: 0.1
};
}
private async determineApplicationStrategies(improvements: any[], request: FeedbackLearningRequest): Promise<any> {
return {
immediateChanges: ['Update response formatting'],
futureConsiderations: ['Implement structured response templates'],
trainingNeeds: ['Clarity training'],
systemUpdates: ['Response validation']
};
}
private async validateLearning(request: FeedbackLearningRequest, patterns: any[], improvements: any[]): Promise<any> {
return {
crossValidated: true,
consensusLevel: 0.85,
expertiseWeight: this.getExpertiseWeight(request.human.expertiseLevel),
reliabilityScore: 0.9
};
}
private getExpertiseWeight(level: string): number {
const weights = {
'novice': 0.3,
'intermediate': 0.6,
'expert': 0.9,
'domain_expert': 1.0
};
return weights[level] || 0.5;
}
private getTimeoutForRiskLevel(riskLevel: string): number {
const timeouts = {
'low': 300000, // 5 minutes
'medium': 600000, // 10 minutes
'high': 900000, // 15 minutes
'critical': 1800000 // 30 minutes
};
return timeouts[riskLevel] || this.config.approval.defaultTimeout;
}
private async getEscalationChain(riskLevel: string): Promise<string[]> {
// Return escalation chain based on risk level
return ['supervisor', 'manager', 'director'];
}
private extractRequiredExpertise(request: HumanApprovalRequest): string[] {
// Extract required expertise from request
return [request.action.type, request.context.framework || 'general'];
}
private hasRequiredExpertise(expert: HumanExpertiseProfile, required: string[]): boolean {
const expertAreas = expert.profile.expertiseAreas.map(area => area.domain);
return required.some(req => expertAreas.includes(req));
}
private isAvailable(expert: HumanExpertiseProfile): boolean {
return expert.availability.currentLoad < expert.availability.maxConcurrentApprovals;
}
private async sleep(ms: number): Promise<void> {
return new Promise(resolve => setTimeout(resolve, ms));
}
// Database operations and analytics methods
private async loadExpertiseProfiles(): Promise<void> {
// Load expertise profiles from database
}
private async initializeChangeStreams(): Promise<void> {
// Initialize MongoDB change streams for real-time collaboration
}
private async processApprovalResult(result: ApprovalResult, request: HumanApprovalRequest): Promise<void> {
// Process and learn from approval result
}
private async storeLearningResult(request: FeedbackLearningRequest, result: LearningResult): Promise<void> {
// Store learning result in database
}
private async applyImmediateImprovements(result: LearningResult): Promise<void> {
// Apply immediate improvements to the system
}
private async getApprovalAnalytics(agentId?: string, startDate?: Date, endDate?: Date): Promise<any> {
return {
totalRequests: 100,
approvalRate: 0.85,
avgResponseTime: 180000,
escalationRate: 0.1,
autoApprovalRate: 0.3,
timeoutRate: 0.05
};
}
private async getFeedbackAnalytics(agentId?: string, startDate?: Date, endDate?: Date): Promise<any> {
return {
totalFeedback: 50,
avgQuality: 4.2,
learningRate: 0.8,
improvementAreas: [
{ area: 'clarity', frequency: 15, impact: 0.7 },
{ area: 'accuracy', frequency: 10, impact: 0.9 }
]
};
}
private async calculateCollaborationMetrics(agentId?: string, startDate?: Date, endDate?: Date): Promise<any> {
return {
humanAIAgreement: 0.82,
expertiseUtilization: 0.75,
knowledgeTransfer: 0.68,
systemImprovement: 0.71
};
}
private async analyzeTrends(agentId?: string, timeframeDays?: number): Promise<any> {
return {
approvalTrend: 'improving' as const,
feedbackTrend: 'stable' as const,
collaborationTrend: 'improving' as const
};
}
}