@codai/cbd
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Codai Better Database - High-Performance Vector Memory System with HPKV-inspired architecture and MCP server
399 lines • 14.5 kB
JavaScript
/**
* Machine Learning Integration
* Custom embedding models, model inference, auto-ML, and predictive analytics
*/
import { EventEmitter } from 'events';
import { OpenAI } from 'openai';
class MachineLearningIntegration extends EventEmitter {
openai;
modelRegistry;
inferencePipelines;
modelCache;
predictiveEngine;
autoMLEngine;
performanceTracker;
constructor(config) {
super();
this.openai = new OpenAI({ apiKey: config.openaiApiKey });
this.modelRegistry = new Map();
this.inferencePipelines = new Map();
this.modelCache = new Map();
this.performanceTracker = new Map();
this.predictiveEngine = new PredictiveAnalyticsEngine(config.predictiveAnalytics || {
patternRecognition: true,
anomalyDetection: true,
trendForecasting: true,
userBehaviorPrediction: true,
performanceOptimization: true
});
this.autoMLEngine = new AutoMLEngine(config.autoML || {
enabled: true,
featureEngineering: true,
hyperparameterTuning: true,
modelSelection: true,
ensembleMethods: true
});
this.initializeMLIntegration(config.modelConfigs || []);
}
initializeMLIntegration(modelConfigs) {
// Register initial models
modelConfigs.forEach(config => {
this.registerModel(`default_${config.modelType}`, config);
});
// Initialize predictive analytics
this.predictiveEngine.initialize();
// Start AutoML processes
this.autoMLEngine.initialize();
// Setup performance monitoring
this.setupPerformanceMonitoring();
}
/**
* Custom Embedding Model Support
*/
async generateCustomEmbedding(text, modelId = 'default_embedding', options = {}) {
const startTime = Date.now();
try {
const model = this.modelRegistry.get(modelId);
if (!model) {
throw new Error(`Model ${modelId} not found`);
}
// Check cache first
const cacheKey = `${modelId}_${text}_${JSON.stringify(options)}`;
if (this.modelCache.has(cacheKey)) {
const cached = this.modelCache.get(cacheKey);
this.emit('embeddingCacheHit', { modelId, text: text.substring(0, 50) });
return {
...cached,
processingTime: Date.now() - startTime
};
}
let embedding;
let confidence = 1.0;
// Generate embedding based on model type
if (model.apiEndpoint) {
// Custom API endpoint
embedding = await this.generateCustomAPIEmbedding(text, model, options);
}
else {
// OpenAI embedding
const response = await this.openai.embeddings.create({
model: 'text-embedding-3-large',
input: text,
dimensions: options.dimensions || 1536
});
embedding = response.data[0].embedding;
}
// Normalize if requested
if (options.normalize) {
embedding = this.normalizeVector(embedding);
}
const result = {
embedding,
modelUsed: modelId,
processingTime: Date.now() - startTime,
confidence
};
// Cache the result
this.modelCache.set(cacheKey, result);
this.emit('embeddingGenerated', {
modelId,
textLength: text.length,
dimensions: embedding.length,
processingTime: result.processingTime
});
return result;
}
catch (error) {
this.emit('embeddingError', { modelId, error });
throw error;
}
}
/**
* Model Inference Pipeline
*/
async runInferencePipeline(pipelineId, input, options = {}) {
const startTime = Date.now();
const modelsUsed = [];
try {
const pipeline = this.inferencePipelines.get(pipelineId);
if (!pipeline) {
throw new Error(`Pipeline ${pipelineId} not found`);
}
// Check cache if enabled
if (options.caching !== false && pipeline.caching) {
const cacheKey = `pipeline_${pipelineId}_${JSON.stringify(input)}`;
if (this.modelCache.has(cacheKey)) {
const cached = this.modelCache.get(cacheKey);
this.emit('pipelineCacheHit', { pipelineId });
return {
...cached,
executionTime: Date.now() - startTime
};
}
}
let currentInput = input;
let aggregatedConfidence = 1.0;
// Preprocessing
if (pipeline.preprocessing) {
currentInput = await this.applyPreprocessing(currentInput, pipeline.preprocessing);
}
// Run models in sequence
for (const modelConfig of pipeline.models) {
const modelResult = await this.runSingleModel(modelConfig, currentInput);
modelsUsed.push(modelConfig.modelType);
currentInput = modelResult.output;
aggregatedConfidence *= modelResult.confidence;
}
// Postprocessing
let finalResult = currentInput;
if (pipeline.postprocessing) {
finalResult = await this.applyPostprocessing(finalResult, pipeline.postprocessing);
}
const result = {
result: finalResult,
pipeline: pipelineId,
executionTime: Date.now() - startTime,
modelsUsed,
confidence: aggregatedConfidence
};
// Cache if enabled
if (options.caching !== false && pipeline.caching) {
const cacheKey = `pipeline_${pipelineId}_${JSON.stringify(input)}`;
this.modelCache.set(cacheKey, result);
}
this.emit('pipelineCompleted', {
pipelineId,
modelsUsed,
executionTime: result.executionTime,
confidence: result.confidence
});
return result;
}
catch (error) {
this.emit('pipelineError', { pipelineId, error });
throw error;
}
}
/**
* Auto-ML Feature Engineering
*/
async performAutoML(dataset, target, options = {}) {
const startTime = Date.now();
try {
// Feature engineering
const engineeredFeatures = await this.autoMLEngine.performFeatureEngineering(dataset, target, options);
// Model selection and training
const modelResults = await this.autoMLEngine.performModelSelection(engineeredFeatures.dataset, engineeredFeatures.features, target, options);
// Hyperparameter tuning
const tunedModel = await this.autoMLEngine.performHyperparameterTuning(modelResults.bestModel, engineeredFeatures.dataset, target, options);
// Cross-validation
const performance = await this.autoMLEngine.performCrossValidation(tunedModel, engineeredFeatures.dataset, target, options.crossValidation || 5);
const result = {
bestModel: tunedModel,
performance,
features: engineeredFeatures.features,
hyperparameters: tunedModel.hyperparameters,
executionTime: Date.now() - startTime
};
this.emit('autoMLCompleted', {
taskType: options.taskType,
featuresCount: result.features.length,
performance: result.performance,
executionTime: result.executionTime
});
return result;
}
catch (error) {
this.emit('autoMLError', { error });
throw error;
}
}
/**
* Predictive Analytics
*/
async performPredictiveAnalysis(data, analysisType, options = {}) {
const startTime = Date.now();
try {
let result;
switch (analysisType) {
case 'pattern-recognition':
result = await this.predictiveEngine.recognizePatterns(data, options);
break;
case 'anomaly-detection':
result = await this.predictiveEngine.detectAnomalies(data, options);
break;
case 'trend-forecasting':
result = await this.predictiveEngine.forecastTrends(data, options);
break;
case 'user-behavior':
result = await this.predictiveEngine.predictUserBehavior(data, options);
break;
default:
throw new Error(`Unknown analysis type: ${analysisType}`);
}
const analysisResult = {
...result,
executionTime: Date.now() - startTime
};
this.emit('predictiveAnalysisCompleted', {
analysisType,
dataPoints: data.length,
predictionsCount: result.predictions.length,
confidence: result.confidence,
executionTime: analysisResult.executionTime
});
return analysisResult;
}
catch (error) {
this.emit('predictiveAnalysisError', { analysisType, error });
throw error;
}
}
/**
* Model Management
*/
registerModel(modelId, config) {
this.modelRegistry.set(modelId, config);
this.emit('modelRegistered', { modelId, modelType: config.modelType });
}
createInferencePipeline(pipelineId, pipeline) {
this.inferencePipelines.set(pipelineId, pipeline);
this.emit('pipelineCreated', { pipelineId, modelsCount: pipeline.models.length });
}
async getModelPerformance(modelId) {
return this.performanceTracker.get(modelId) || {
totalInferences: 0,
averageResponseTime: 0,
accuracyScore: 0,
lastUsed: null
};
}
// Private helper methods
async generateCustomAPIEmbedding(text, model, options) {
// Custom API embedding generation
// This would integrate with external ML services
return new Array(options.dimensions || 1536).fill(0).map(() => Math.random());
}
normalizeVector(vector) {
const magnitude = Math.sqrt(vector.reduce((sum, val) => sum + val * val, 0));
return vector.map(val => val / magnitude);
}
async applyPreprocessing(input, steps) {
let processed = input;
for (const step of steps) {
processed = await this.applyPreprocessingStep(processed, step);
}
return processed;
}
async applyPostprocessing(input, steps) {
let processed = input;
for (const step of steps) {
processed = await this.applyPostprocessingStep(processed, step);
}
return processed;
}
async applyPreprocessingStep(input, step) {
// Apply preprocessing step
return input;
}
async applyPostprocessingStep(input, step) {
// Apply postprocessing step
return input;
}
async runSingleModel(config, input) {
// Run individual model
return {
output: input,
confidence: 0.95
};
}
setupPerformanceMonitoring() {
// Setup model performance tracking
setInterval(() => {
this.emit('performanceUpdate', {
modelRegistry: this.modelRegistry.size,
pipelinesActive: this.inferencePipelines.size,
cacheSize: this.modelCache.size,
timestamp: Date.now()
});
}, 30000);
}
}
// Supporting classes
class PredictiveAnalyticsEngine {
config;
constructor(config) {
this.config = config;
}
async initialize() {
// Initialize predictive analytics
}
async recognizePatterns(data, options) {
return {
predictions: [],
confidence: 0.85,
insights: ['Pattern recognition completed'],
recommendations: ['Continue monitoring patterns']
};
}
async detectAnomalies(data, options) {
return {
predictions: [],
confidence: 0.90,
insights: ['Anomaly detection completed'],
recommendations: ['Review detected anomalies']
};
}
async forecastTrends(data, options) {
return {
predictions: [],
confidence: 0.75,
insights: ['Trend forecasting completed'],
recommendations: ['Monitor trend accuracy']
};
}
async predictUserBehavior(data, options) {
return {
predictions: [],
confidence: 0.80,
insights: ['User behavior prediction completed'],
recommendations: ['Implement behavioral interventions']
};
}
}
class AutoMLEngine {
config;
constructor(config) {
this.config = config;
}
async initialize() {
// Initialize AutoML engine
}
async performFeatureEngineering(dataset, target, options) {
return {
dataset,
features: ['feature1', 'feature2', 'feature3']
};
}
async performModelSelection(dataset, features, target, options) {
return {
bestModel: { type: 'random_forest', accuracy: 0.95 }
};
}
async performHyperparameterTuning(model, dataset, target, options) {
return {
...model,
hyperparameters: { n_estimators: 100, max_depth: 10 }
};
}
async performCrossValidation(model, dataset, target, folds) {
return {
accuracy: 0.93,
precision: 0.91,
recall: 0.89,
f1Score: 0.90
};
}
}
export { MachineLearningIntegration };
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