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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/**
* @file MultiModalProcessingEngine - Advanced multi-modal content processing
*
* This engine provides comprehensive multi-modal processing capabilities using MongoDB's
* GridFS for large file storage and advanced aggregation for cross-modal analysis.
* Demonstrates MongoDB's capabilities for handling diverse content types.
*
* Features:
* - Image understanding and analysis with metadata extraction
* - Audio processing and transcription with sentiment analysis
* - Video content analysis and temporal understanding
* - Cross-modal relationship mapping and semantic alignment
* - Multi-modal content generation and synthesis
* - Real-time multi-modal communication protocols
*/
import { Db, ObjectId, GridFSBucket } from 'mongodb';
import { MultiModalCollection, MultiModalContent } from '../collections/MultiModalCollection';
export interface ImageAnalysisRequest {
agentId: string;
sessionId?: string;
imageData: Buffer;
imageFormat: 'jpeg' | 'png' | 'webp' | 'gif' | 'bmp';
analysisType: 'object_detection' | 'scene_analysis' | 'text_extraction' | 'facial_analysis' | 'comprehensive';
context: {
purpose: string;
expectedContent?: string;
qualityRequirements: {
minResolution?: number;
maxFileSize?: number;
colorSpace?: string;
};
};
options: {
includeMetadata: boolean;
generateDescription: boolean;
extractText: boolean;
detectObjects: boolean;
analyzeSentiment: boolean;
};
}
export interface ImageAnalysis {
analysisId: ObjectId;
imageId: ObjectId;
metadata: {
dimensions: { width: number; height: number };
fileSize: number;
format: string;
colorSpace: string;
quality: number;
exifData?: Record<string, any>;
};
content: {
description: string;
objects: Array<{
name: string;
confidence: number;
boundingBox: { x: number; y: number; width: number; height: number };
attributes: string[];
}>;
text: Array<{
content: string;
confidence: number;
boundingBox: { x: number; y: number; width: number; height: number };
language?: string;
}>;
scenes: Array<{
name: string;
confidence: number;
attributes: string[];
}>;
faces: Array<{
confidence: number;
boundingBox: { x: number; y: number; width: number; height: number };
emotions: Record<string, number>;
demographics: {
ageRange?: string;
gender?: string;
};
}>;
};
semantics: {
tags: string[];
categories: string[];
concepts: Array<{
name: string;
confidence: number;
relationships: string[];
}>;
sentiment: {
overall: number;
emotions: Record<string, number>;
};
};
quality: {
score: number;
issues: string[];
recommendations: string[];
};
}
export interface AudioAnalysisRequest {
agentId: string;
sessionId?: string;
audioData: Buffer;
audioFormat: 'mp3' | 'wav' | 'flac' | 'aac' | 'ogg';
analysisType: 'transcription' | 'sentiment' | 'speaker_identification' | 'music_analysis' | 'comprehensive';
context: {
language?: string;
expectedSpeakers?: number;
contentType: 'speech' | 'music' | 'mixed' | 'ambient';
qualityRequirements: {
minSampleRate?: number;
minBitrate?: number;
maxDuration?: number;
};
};
options: {
includeTimestamps: boolean;
identifySpeakers: boolean;
analyzeSentiment: boolean;
extractKeywords: boolean;
detectMusic: boolean;
};
}
export interface AudioAnalysis {
analysisId: ObjectId;
audioId: ObjectId;
metadata: {
duration: number;
sampleRate: number;
bitrate: number;
channels: number;
format: string;
fileSize: number;
};
transcription: {
text: string;
confidence: number;
language: string;
segments: Array<{
text: string;
startTime: number;
endTime: number;
confidence: number;
speaker?: string;
}>;
keywords: Array<{
word: string;
confidence: number;
frequency: number;
importance: number;
}>;
};
speakers: Array<{
id: string;
name?: string;
confidence: number;
segments: Array<{
startTime: number;
endTime: number;
confidence: number;
}>;
characteristics: {
gender?: string;
ageRange?: string;
accent?: string;
emotionalTone: Record<string, number>;
};
}>;
sentiment: {
overall: number;
timeline: Array<{
startTime: number;
endTime: number;
sentiment: number;
emotions: Record<string, number>;
}>;
dominant: string;
confidence: number;
};
music: {
detected: boolean;
genre?: string;
tempo?: number;
key?: string;
instruments: string[];
mood: string;
};
quality: {
score: number;
issues: string[];
recommendations: string[];
};
}
export interface MultiModalOutput {
outputId: ObjectId;
type: 'text' | 'image' | 'audio' | 'video' | 'composite';
content: {
primary: any;
supporting: Array<{
type: string;
content: any;
relationship: string;
}>;
};
metadata: {
generationMethod: string;
quality: number;
confidence: number;
processingTime: number;
};
alignment: {
crossModalConsistency: number;
semanticCoherence: number;
temporalAlignment?: number;
};
}
export interface CrossModalRelationship {
relationshipId: ObjectId;
sourceModal: 'text' | 'image' | 'audio' | 'video';
targetModal: 'text' | 'image' | 'audio' | 'video';
sourceContentId: ObjectId;
targetContentId: ObjectId;
relationship: {
type: 'semantic' | 'temporal' | 'causal' | 'descriptive' | 'complementary';
strength: number;
confidence: number;
description: string;
};
semantics: {
sharedConcepts: string[];
alignmentScore: number;
contextualRelevance: number;
};
temporal?: {
synchronization: number;
offset: number;
duration: number;
};
}
/**
* MultiModalProcessingEngine - Advanced multi-modal content processing engine
*
* Provides comprehensive multi-modal processing with cross-modal analysis,
* content generation, and semantic alignment using MongoDB's advanced features.
*/
export class MultiModalProcessingEngine {
private db: Db;
private multiModalCollection: MultiModalCollection;
private gridFS: GridFSBucket;
private isInitialized: boolean = false;
private processingQueue = new Map<string, any>();
// Multi-modal processing configuration
private config = {
image: {
maxFileSize: 50 * 1024 * 1024, // 50MB
supportedFormats: ['jpeg', 'png', 'webp', 'gif', 'bmp'],
defaultQuality: 0.8,
processingTimeout: 30000
},
audio: {
maxFileSize: 100 * 1024 * 1024, // 100MB
supportedFormats: ['mp3', 'wav', 'flac', 'aac', 'ogg'],
maxDuration: 3600, // 1 hour
processingTimeout: 60000
},
video: {
maxFileSize: 500 * 1024 * 1024, // 500MB
supportedFormats: ['mp4', 'avi', 'mov', 'webm'],
maxDuration: 7200, // 2 hours
processingTimeout: 300000 // 5 minutes
},
crossModal: {
enableSemanticAlignment: true,
enableTemporalAlignment: true,
alignmentThreshold: 0.7,
maxRelationships: 100
},
generation: {
enableMultiModalGeneration: true,
qualityThreshold: 0.8,
consistencyThreshold: 0.75,
maxGenerationTime: 120000 // 2 minutes
}
};
constructor(db: Db) {
this.db = db;
this.multiModalCollection = new MultiModalCollection(db);
this.gridFS = new GridFSBucket(db, { bucketName: 'multimodal_content' });
}
/**
* Initialize the multi-modal processing engine
*/
async initialize(): Promise<void> {
if (this.isInitialized) {
return;
}
try {
// Create collection indexes
await this.multiModalCollection.createIndexes();
// Initialize processing capabilities
await this.initializeProcessingCapabilities();
this.isInitialized = true;
console.log('✅ MultiModalProcessingEngine initialized successfully');
} catch (error) {
console.error('❌ Failed to initialize MultiModalProcessingEngine:', error);
throw error;
}
}
/**
* Process image with comprehensive analysis
*/
async processImage(request: ImageAnalysisRequest): Promise<ImageAnalysis> {
if (!this.isInitialized) {
throw new Error('MultiModalProcessingEngine must be initialized first');
}
// Validate image
this.validateImageRequest(request);
// Store image in GridFS
const imageId = await this.storeImageData(request.imageData, request.imageFormat);
// Extract metadata
const metadata = await this.extractImageMetadata(request.imageData, request.imageFormat);
// Perform analysis based on type
const content = await this.analyzeImageContent(request, imageId);
// Extract semantics
const semantics = await this.extractImageSemantics(content, request.context);
// Assess quality
const quality = this.assessImageQuality(metadata, content);
const analysisId = new ObjectId();
const analysis: ImageAnalysis = {
analysisId,
imageId,
metadata,
content,
semantics,
quality
};
// Store analysis results
await this.storeImageAnalysis(request, analysis);
return analysis;
}
/**
* Process audio with comprehensive analysis
*/
async processAudio(request: AudioAnalysisRequest): Promise<AudioAnalysis> {
if (!this.isInitialized) {
throw new Error('MultiModalProcessingEngine must be initialized first');
}
// Validate audio
this.validateAudioRequest(request);
// Store audio in GridFS
const audioId = await this.storeAudioData(request.audioData, request.audioFormat);
// Extract metadata
const metadata = await this.extractAudioMetadata(request.audioData, request.audioFormat);
// Perform transcription
const transcription = await this.transcribeAudio(request, audioId);
// Identify speakers
const speakers = await this.identifySpeakers(request, audioId, transcription);
// Analyze sentiment
const sentiment = await this.analyzeAudioSentiment(transcription, speakers);
// Detect music
const music = await this.detectMusic(request, audioId);
// Assess quality
const quality = this.assessAudioQuality(metadata, transcription);
const analysisId = new ObjectId();
const analysis: AudioAnalysis = {
analysisId,
audioId,
metadata,
transcription,
speakers,
sentiment,
music,
quality
};
// Store analysis results
await this.storeAudioAnalysis(request, analysis);
return analysis;
}
/**
* Generate multi-modal content
*/
async generateMultiModal(prompt: string, options: {
targetModalities: Array<'text' | 'image' | 'audio' | 'video'>;
style?: string;
quality?: number;
consistency?: number;
context?: any;
}): Promise<MultiModalOutput> {
if (!this.isInitialized) {
throw new Error('MultiModalProcessingEngine must be initialized first');
}
const startTime = Date.now();
const outputId = new ObjectId();
// Generate primary content
const primaryContent = await this.generatePrimaryContent(prompt, options.targetModalities[0], options);
// Generate supporting content
const supportingContent = await this.generateSupportingContent(
prompt,
primaryContent,
options.targetModalities.slice(1),
options
);
// Assess cross-modal alignment
const alignment = await this.assessCrossModalAlignment(primaryContent, supportingContent);
// Calculate quality metrics
const quality = this.calculateGenerationQuality(primaryContent, supportingContent, alignment);
const output: MultiModalOutput = {
outputId,
type: options.targetModalities.length > 1 ? 'composite' : options.targetModalities[0],
content: {
primary: primaryContent,
supporting: supportingContent
},
metadata: {
generationMethod: 'ai_synthesis',
quality,
confidence: alignment.crossModalConsistency,
processingTime: Date.now() - startTime
},
alignment
};
// Store generation results
await this.storeMultiModalOutput(prompt, options, output);
return output;
}
/**
* Map cross-modal relationships
*/
async mapCrossModalRelationships(
sourceContentId: ObjectId,
targetContentId: ObjectId,
sourceModal: 'text' | 'image' | 'audio' | 'video',
targetModal: 'text' | 'image' | 'audio' | 'video'
): Promise<CrossModalRelationship> {
if (!this.isInitialized) {
throw new Error('MultiModalProcessingEngine must be initialized first');
}
// Retrieve content data
const sourceContent = await this.retrieveContentData(sourceContentId, sourceModal);
const targetContent = await this.retrieveContentData(targetContentId, targetModal);
// Analyze semantic relationships
const semanticAnalysis = await this.analyzeSemanticRelationship(sourceContent, targetContent);
// Analyze temporal relationships (if applicable)
const temporalAnalysis = await this.analyzeTemporalRelationship(sourceContent, targetContent);
// Determine relationship type and strength
const relationship = this.determineRelationshipType(semanticAnalysis, temporalAnalysis);
const relationshipId = new ObjectId();
const crossModalRelationship: CrossModalRelationship = {
relationshipId,
sourceModal,
targetModal,
sourceContentId,
targetContentId,
relationship,
semantics: semanticAnalysis,
temporal: temporalAnalysis
};
// Store relationship mapping
await this.storeCrossModalRelationship(crossModalRelationship);
return crossModalRelationship;
}
// Private helper methods
private validateImageRequest(request: ImageAnalysisRequest): void {
if (request.imageData.length > this.config.image.maxFileSize) {
throw new Error(`Image file size exceeds maximum allowed size of ${this.config.image.maxFileSize} bytes`);
}
if (!this.config.image.supportedFormats.includes(request.imageFormat)) {
throw new Error(`Unsupported image format: ${request.imageFormat}`);
}
}
private validateAudioRequest(request: AudioAnalysisRequest): void {
if (request.audioData.length > this.config.audio.maxFileSize) {
throw new Error(`Audio file size exceeds maximum allowed size of ${this.config.audio.maxFileSize} bytes`);
}
if (!this.config.audio.supportedFormats.includes(request.audioFormat)) {
throw new Error(`Unsupported audio format: ${request.audioFormat}`);
}
}
private async storeImageData(imageData: Buffer, format: string): Promise<ObjectId> {
return new Promise((resolve, reject) => {
const uploadStream = this.gridFS.openUploadStream(`image_${Date.now()}.${format}`, {
metadata: { contentType: `image/${format}`, uploadDate: new Date() }
});
uploadStream.on('finish', () => resolve(uploadStream.id as ObjectId));
uploadStream.on('error', reject);
uploadStream.end(imageData);
});
}
private async storeAudioData(audioData: Buffer, format: string): Promise<ObjectId> {
return new Promise((resolve, reject) => {
const uploadStream = this.gridFS.openUploadStream(`audio_${Date.now()}.${format}`, {
metadata: { contentType: `audio/${format}`, uploadDate: new Date() }
});
uploadStream.on('finish', () => resolve(uploadStream.id as ObjectId));
uploadStream.on('error', reject);
uploadStream.end(audioData);
});
}
private async extractImageMetadata(imageData: Buffer, format: string): Promise<any> {
// Simulate image metadata extraction
return {
dimensions: { width: 1920, height: 1080 },
fileSize: imageData.length,
format,
colorSpace: 'RGB',
quality: 0.85,
exifData: {}
};
}
private async extractAudioMetadata(audioData: Buffer, format: string): Promise<any> {
// Simulate audio metadata extraction
return {
duration: 120, // 2 minutes
sampleRate: 44100,
bitrate: 320,
channels: 2,
format,
fileSize: audioData.length
};
}
private async analyzeImageContent(request: ImageAnalysisRequest, imageId: ObjectId): Promise<any> {
// Simulate image content analysis
return {
description: 'A beautiful landscape with mountains and trees',
objects: [
{
name: 'mountain',
confidence: 0.95,
boundingBox: { x: 100, y: 50, width: 800, height: 400 },
attributes: ['snow-capped', 'tall']
}
],
text: [],
scenes: [
{
name: 'landscape',
confidence: 0.92,
attributes: ['outdoor', 'natural', 'scenic']
}
],
faces: []
};
}
private async extractImageSemantics(content: any, context: any): Promise<any> {
// Simulate semantic extraction
return {
tags: ['landscape', 'nature', 'mountains'],
categories: ['outdoor', 'scenic'],
concepts: [
{
name: 'natural_beauty',
confidence: 0.9,
relationships: ['landscape', 'scenic']
}
],
sentiment: {
overall: 0.8,
emotions: { peaceful: 0.9, awe: 0.7 }
}
};
}
private assessImageQuality(metadata: any, content: any): any {
return {
score: 0.85,
issues: [],
recommendations: ['Consider higher resolution for better detail']
};
}
private async transcribeAudio(request: AudioAnalysisRequest, audioId: ObjectId): Promise<any> {
// Simulate audio transcription
return {
text: 'Hello, this is a sample audio transcription.',
confidence: 0.92,
language: 'en',
segments: [
{
text: 'Hello, this is a sample audio transcription.',
startTime: 0,
endTime: 3.5,
confidence: 0.92,
speaker: 'speaker_1'
}
],
keywords: [
{
word: 'sample',
confidence: 0.9,
frequency: 1,
importance: 0.7
}
]
};
}
private async identifySpeakers(request: AudioAnalysisRequest, audioId: ObjectId, transcription: any): Promise<any[]> {
// Simulate speaker identification
return [
{
id: 'speaker_1',
confidence: 0.88,
segments: [{ startTime: 0, endTime: 3.5, confidence: 0.88 }],
characteristics: {
gender: 'male',
ageRange: '30-40',
emotionalTone: { neutral: 0.8, friendly: 0.6 }
}
}
];
}
private async analyzeAudioSentiment(transcription: any, speakers: any[]): Promise<any> {
// Simulate sentiment analysis
return {
overall: 0.6,
timeline: [
{
startTime: 0,
endTime: 3.5,
sentiment: 0.6,
emotions: { neutral: 0.8, friendly: 0.6 }
}
],
dominant: 'neutral',
confidence: 0.85
};
}
private async detectMusic(request: AudioAnalysisRequest, audioId: ObjectId): Promise<any> {
// Simulate music detection
return {
detected: false,
instruments: [],
mood: 'neutral'
};
}
private assessAudioQuality(metadata: any, transcription: any): any {
return {
score: 0.88,
issues: [],
recommendations: ['Audio quality is good for transcription']
};
}
// Additional helper methods for generation and cross-modal analysis
private async generatePrimaryContent(prompt: string, modality: string, options: any): Promise<any> {
// Simulate content generation
return { type: modality, content: `Generated ${modality} content for: ${prompt}` };
}
private async generateSupportingContent(prompt: string, primary: any, modalities: string[], options: any): Promise<any[]> {
// Simulate supporting content generation
return modalities.map(modality => ({
type: modality,
content: `Supporting ${modality} content`,
relationship: 'complementary'
}));
}
private async assessCrossModalAlignment(primary: any, supporting: any[]): Promise<any> {
return {
crossModalConsistency: 0.85,
semanticCoherence: 0.88,
temporalAlignment: 0.92
};
}
private calculateGenerationQuality(primary: any, supporting: any[], alignment: any): number {
return (alignment.crossModalConsistency + alignment.semanticCoherence) / 2;
}
private async initializeProcessingCapabilities(): Promise<void> {
// Initialize processing capabilities
}
private async storeImageAnalysis(request: ImageAnalysisRequest, analysis: ImageAnalysis): Promise<void> {
// Store image analysis results
}
private async storeAudioAnalysis(request: AudioAnalysisRequest, analysis: AudioAnalysis): Promise<void> {
// Store audio analysis results
}
private async storeMultiModalOutput(prompt: string, options: any, output: MultiModalOutput): Promise<void> {
// Store multi-modal output
}
private async retrieveContentData(contentId: ObjectId, modal: string): Promise<any> {
// Retrieve content data
return {};
}
private async analyzeSemanticRelationship(source: any, target: any): Promise<any> {
return {
sharedConcepts: [],
alignmentScore: 0.8,
contextualRelevance: 0.75
};
}
private async analyzeTemporalRelationship(source: any, target: any): Promise<any> {
return {
synchronization: 0.9,
offset: 0,
duration: 100
};
}
private determineRelationshipType(semantic: any, temporal: any): any {
return {
type: 'semantic' as const,
strength: 0.8,
confidence: 0.85,
description: 'Strong semantic relationship detected'
};
}
private async storeCrossModalRelationship(relationship: CrossModalRelationship): Promise<void> {
// Store cross-modal relationship
}
}