snow-flow
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Snow-Flow v3.2.0: Complete ServiceNow Enterprise Suite with 180+ MCP Tools. ATF Testing, Knowledge Management, Service Catalog, Change Management with CAB scheduling, Virtual Agent chatbots with NLU, Performance Analytics KPIs, Flow Designer automation, A
456 lines • 17.6 kB
JavaScript
;
/**
* TensorFlow.js ML Service
* REAL machine learning implementation for ServiceNow predictions
* No more fake regex matching!
*/
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Object.defineProperty(exports, "__esModule", { value: true });
exports.tensorflowML = exports.TensorFlowMLService = void 0;
const tf = __importStar(require("@tensorflow/tfjs-node"));
const logger_js_1 = require("../utils/logger.js");
class TensorFlowMLService {
constructor() {
this.models = new Map();
this.vocabularies = new Map();
this.MODEL_DIR = '.snow-flow/ml-models';
this.logger = new logger_js_1.Logger('TensorFlowMLService');
this.initializeModels();
}
static getInstance() {
if (!TensorFlowMLService.instance) {
TensorFlowMLService.instance = new TensorFlowMLService();
}
return TensorFlowMLService.instance;
}
/**
* Initialize pre-trained models
*/
async initializeModels() {
try {
// Create model directory if doesn't exist
const fs = await Promise.resolve().then(() => __importStar(require('fs')));
const path = await Promise.resolve().then(() => __importStar(require('path')));
const modelPath = path.join(process.cwd(), this.MODEL_DIR);
if (!fs.existsSync(modelPath)) {
fs.mkdirSync(modelPath, { recursive: true });
}
// Initialize default models if not trained yet
await this.createDefaultModels();
this.logger.info('✅ TensorFlow.js ML models initialized (REAL ML!)');
}
catch (error) {
this.logger.error('Failed to initialize ML models:', error);
}
}
/**
* Create default neural network models
*/
async createDefaultModels() {
// Incident Classification Model
if (!this.models.has('incident_classifier')) {
const model = tf.sequential({
layers: [
tf.layers.dense({ inputShape: [100], units: 128, activation: 'relu' }),
tf.layers.dropout({ rate: 0.2 }),
tf.layers.dense({ units: 64, activation: 'relu' }),
tf.layers.dropout({ rate: 0.2 }),
tf.layers.dense({ units: 32, activation: 'relu' }),
tf.layers.dense({ units: 10, activation: 'softmax' }) // 10 categories
]
});
model.compile({
optimizer: tf.train.adam(0.001),
loss: 'categoricalCrossentropy',
metrics: ['accuracy']
});
this.models.set('incident_classifier', model);
this.logger.info('Created incident classification neural network');
}
// Change Risk Prediction Model
if (!this.models.has('change_risk')) {
const model = tf.sequential({
layers: [
tf.layers.dense({ inputShape: [20], units: 64, activation: 'relu' }),
tf.layers.dropout({ rate: 0.3 }),
tf.layers.dense({ units: 32, activation: 'relu' }),
tf.layers.dense({ units: 16, activation: 'relu' }),
tf.layers.dense({ units: 3, activation: 'softmax' }) // Low, Medium, High risk
]
});
model.compile({
optimizer: tf.train.adam(0.001),
loss: 'categoricalCrossentropy',
metrics: ['accuracy']
});
this.models.set('change_risk', model);
this.logger.info('Created change risk prediction neural network');
}
// Anomaly Detection Model (Autoencoder)
if (!this.models.has('anomaly_detector')) {
const encoder = tf.sequential({
layers: [
tf.layers.dense({ inputShape: [50], units: 32, activation: 'relu' }),
tf.layers.dense({ units: 16, activation: 'relu' }),
tf.layers.dense({ units: 8, activation: 'relu' }) // Bottleneck
]
});
const decoder = tf.sequential({
layers: [
tf.layers.dense({ inputShape: [8], units: 16, activation: 'relu' }),
tf.layers.dense({ units: 32, activation: 'relu' }),
tf.layers.dense({ units: 50, activation: 'sigmoid' })
]
});
// Combine encoder and decoder
const autoencoder = tf.sequential({
layers: [...encoder.layers, ...decoder.layers]
});
autoencoder.compile({
optimizer: tf.train.adam(0.001),
loss: 'meanSquaredError'
});
this.models.set('anomaly_detector', autoencoder);
this.logger.info('Created anomaly detection autoencoder');
}
}
/**
* Train incident classification model with real data
*/
async trainIncidentClassifier(incidents) {
this.logger.info(`Training incident classifier with ${incidents.length} samples`);
// Prepare training data
const { features, labels } = await this.prepareIncidentData(incidents);
// Get or create model
let model = this.models.get('incident_classifier');
if (!model) {
await this.createDefaultModels();
model = this.models.get('incident_classifier');
}
// Train the model
const history = await model.fit(features, labels, {
epochs: 50,
batchSize: 32,
validationSplit: 0.2,
callbacks: {
onEpochEnd: (epoch, logs) => {
if (epoch % 10 === 0) {
this.logger.debug(`Epoch ${epoch}: loss=${logs?.loss?.toFixed(4)}, accuracy=${logs?.acc?.toFixed(4)}`);
}
}
}
});
// Clean up tensors
features.dispose();
labels.dispose();
const finalAccuracy = history.history.acc[history.history.acc.length - 1];
const finalLoss = history.history.loss[history.history.loss.length - 1];
this.logger.info(`✅ Training complete: accuracy=${(finalAccuracy * 100).toFixed(2)}%, loss=${finalLoss.toFixed(4)}`);
// Save the model
await this.saveModel('incident_classifier', model);
return {
accuracy: finalAccuracy,
loss: finalLoss,
epochs: 50
};
}
/**
* Classify an incident using the neural network
*/
async classifyIncident(incident) {
const model = this.models.get('incident_classifier');
if (!model) {
throw new Error('Incident classifier model not loaded. Please train the model first.');
}
// Convert incident to features
const features = await this.incidentToFeatures(incident);
// Make prediction
const prediction = model.predict(features);
const probabilities = await prediction.array();
// Get classification categories
const categories = [
'Hardware', 'Software', 'Network', 'Database', 'Application',
'Security', 'Access', 'Performance', 'Data', 'Other'
];
// Find highest probability
const probs = probabilities[0];
let maxProb = 0;
let maxIndex = 0;
const probabilityMap = {};
for (let i = 0; i < probs.length; i++) {
probabilityMap[categories[i]] = probs[i];
if (probs[i] > maxProb) {
maxProb = probs[i];
maxIndex = i;
}
}
// Clean up tensors
features.dispose();
prediction.dispose();
return {
classification: categories[maxIndex],
confidence: maxProb,
probabilities: probabilityMap,
modelVersion: '1.0.0',
isRealML: true
};
}
/**
* Predict change risk using neural network
*/
async predictChangeRisk(changeData) {
const model = this.models.get('change_risk');
if (!model) {
throw new Error('Change risk model not loaded. Please train the model first.');
}
// Convert change data to features
const features = this.changeToFeatures(changeData);
// Make prediction
const prediction = model.predict(features);
const probabilities = await prediction.array();
const riskLevels = ['Low', 'Medium', 'High'];
const probs = probabilities[0];
let maxProb = 0;
let maxIndex = 0;
const probabilityMap = {};
for (let i = 0; i < probs.length; i++) {
probabilityMap[riskLevels[i]] = probs[i];
if (probs[i] > maxProb) {
maxProb = probs[i];
maxIndex = i;
}
}
// Clean up tensors
features.dispose();
prediction.dispose();
return {
classification: riskLevels[maxIndex],
confidence: maxProb,
probabilities: probabilityMap,
modelVersion: '1.0.0',
isRealML: true
};
}
/**
* Detect anomalies using autoencoder
*/
async detectAnomaly(data) {
const model = this.models.get('anomaly_detector');
if (!model) {
throw new Error('Anomaly detector not loaded');
}
// Normalize input data
const input = tf.tensor2d([data]);
// Get reconstruction
const reconstruction = model.predict(input);
// Calculate reconstruction error
const error = tf.losses.meanSquaredError(input, reconstruction);
const errorValue = await error.array();
// Clean up tensors
input.dispose();
reconstruction.dispose();
error.dispose();
// Dynamic threshold based on training data
const threshold = 0.1; // This should be calculated from training data
return {
isAnomaly: errorValue > threshold,
reconstructionError: errorValue,
threshold
};
}
/**
* Prepare incident data for training
*/
async prepareIncidentData(incidents) {
// Build vocabulary if not exists
if (!this.vocabularies.has('incident')) {
this.buildVocabulary(incidents);
}
const featureArrays = [];
const labelArrays = [];
for (const incident of incidents) {
// Convert to feature vector
const features = await this.incidentToFeatureArray(incident);
featureArrays.push(features);
// One-hot encode category
const categories = [
'Hardware', 'Software', 'Network', 'Database', 'Application',
'Security', 'Access', 'Performance', 'Data', 'Other'
];
const categoryIndex = categories.indexOf(incident.category) || 9; // Default to 'Other'
const oneHot = new Array(10).fill(0);
oneHot[categoryIndex] = 1;
labelArrays.push(oneHot);
}
return {
features: tf.tensor2d(featureArrays),
labels: tf.tensor2d(labelArrays)
};
}
/**
* Convert incident to feature tensor
*/
async incidentToFeatures(incident) {
const features = await this.incidentToFeatureArray(incident);
return tf.tensor2d([features]);
}
/**
* Convert incident to feature array
*/
async incidentToFeatureArray(incident) {
const features = [];
// Numerical features
features.push(incident.priority / 5); // Normalize 1-5 to 0-1
features.push(incident.urgency / 3); // Normalize 1-3 to 0-1
features.push(incident.impact / 3); // Normalize 1-3 to 0-1
// Text features - use simple bag of words for now
const vocab = this.vocabularies.get('incident') || new Map();
const words = (incident.shortDescription + ' ' + incident.description)
.toLowerCase()
.split(/\s+/)
.slice(0, 97); // Use first 97 words to fit in 100 features
// Create feature vector
const textFeatures = new Array(97).fill(0);
for (let i = 0; i < words.length && i < 97; i++) {
const wordIndex = vocab.get(words[i]);
if (wordIndex !== undefined && wordIndex < 97) {
textFeatures[wordIndex] = 1;
}
}
features.push(...textFeatures);
// Ensure we have exactly 100 features
while (features.length < 100)
features.push(0);
if (features.length > 100)
features.length = 100;
return features;
}
/**
* Convert change data to features
*/
changeToFeatures(changeData) {
const features = [];
// Simple feature extraction
features.push(changeData.type === 'Emergency' ? 1 : 0);
features.push(changeData.type === 'Normal' ? 1 : 0);
features.push(changeData.type === 'Standard' ? 1 : 0);
// Text length features
features.push(Math.min(changeData.implementation_plan?.length || 0, 1000) / 1000);
features.push(Math.min(changeData.backout_plan?.length || 0, 1000) / 1000);
features.push(Math.min(changeData.test_plan?.length || 0, 1000) / 1000);
// Risk keywords
const riskKeywords = ['critical', 'major', 'production', 'outage', 'downtime'];
const text = (changeData.risk_assessment || '').toLowerCase();
for (const keyword of riskKeywords) {
features.push(text.includes(keyword) ? 1 : 0);
}
// Pad to 20 features
while (features.length < 20)
features.push(0);
if (features.length > 20)
features.length = 20;
return tf.tensor2d([features]);
}
/**
* Build vocabulary from training data
*/
buildVocabulary(incidents) {
const vocab = new Map();
const wordCounts = new Map();
// Count word frequencies
for (const incident of incidents) {
const words = (incident.shortDescription + ' ' + incident.description)
.toLowerCase()
.split(/\s+/);
for (const word of words) {
wordCounts.set(word, (wordCounts.get(word) || 0) + 1);
}
}
// Take top 97 most frequent words
const sortedWords = Array.from(wordCounts.entries())
.sort((a, b) => b[1] - a[1])
.slice(0, 97);
sortedWords.forEach(([word], index) => {
vocab.set(word, index);
});
this.vocabularies.set('incident', vocab);
this.logger.info(`Built vocabulary with ${vocab.size} words`);
}
/**
* Save model to disk
*/
async saveModel(name, model) {
try {
const path = `file://${this.MODEL_DIR}/${name}`;
await model.save(path);
this.logger.info(`Saved model ${name} to ${path}`);
}
catch (error) {
this.logger.error(`Failed to save model ${name}:`, error);
}
}
/**
* Load model from disk
*/
async loadModel(name) {
try {
const path = `file://${this.MODEL_DIR}/${name}/model.json`;
const model = await tf.loadLayersModel(path);
this.models.set(name, model);
this.logger.info(`Loaded model ${name} from ${path}`);
}
catch (error) {
this.logger.warn(`Could not load model ${name}, using default:`, error.message);
}
}
/**
* Get model summary
*/
getModelSummary(modelName) {
const model = this.models.get(modelName);
if (!model) {
return `Model ${modelName} not found`;
}
let summary = '';
model.summary(undefined, undefined, (line) => {
summary += line + '\n';
});
return summary;
}
}
exports.TensorFlowMLService = TensorFlowMLService;
// Export singleton instance
exports.tensorflowML = TensorFlowMLService.getInstance();
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