ai-integration.ts•13.5 kB
/**
* AI Integration Service for SAP MCP Server
* Generic service that works with any MCP client (Claude, GPT, Gemini, local models, etc.)
*/
import {
OptimizedQuery,
AnalysisResult,
AnomalyReport,
BusinessInsight,
Recommendation,
} from '../types/ai-types.js';
import { EntityType } from '../types/sap-types.js';
import { Logger } from 'winston';
import * as winston from 'winston';
export class AIIntegrationService {
private logger: Logger;
private isEnabled: boolean;
constructor() {
this.logger = winston.createLogger({
level: process.env.LOG_LEVEL || 'info',
format: winston.format.json(),
transports: [
new winston.transports.Console({
format: winston.format.simple(),
}),
],
});
this.isEnabled = process.env.AI_FEATURES_ENABLED === 'true';
if (this.isEnabled) {
this.logger.info('AI Integration Service initialized - compatible with any MCP client');
}
}
/**
* Analyzes data using AI capabilities (generic prompt-response pattern)
* Works with any MCP client that can process prompts
*/
async analyzeData(
prompt: string,
data: any[],
analysisType: 'trend' | 'anomaly' | 'forecast' | 'correlation' = 'trend'
): Promise<AnalysisResult> {
if (!this.isEnabled) {
return this.createFallbackAnalysis(data, analysisType);
}
try {
// Structured prompt for any AI model
const structuredPrompt = this.buildAnalysisPrompt(prompt, data, analysisType);
// The actual AI call would happen through the MCP protocol
// For now, we simulate the structure that any MCP client would return
const aiResponse = await this.callAIThroughMCP(structuredPrompt);
return this.parseAIAnalysisResponse(aiResponse, data);
} catch (error) {
this.logger.error('AI analysis failed', {
error: error instanceof Error ? error.message : 'Unknown error',
});
return this.createFallbackAnalysis(data, analysisType);
}
}
/**
* Optimizes OData queries using AI intelligence
* Generic approach that works with any AI model
*/
async optimizeQuery(
userIntent: string,
entityMetadata: EntityType,
userContext?: any
): Promise<OptimizedQuery> {
if (!this.isEnabled) {
return this.createBasicQuery(userIntent, entityMetadata);
}
try {
const optimizationPrompt = this.buildQueryOptimizationPrompt(
userIntent,
entityMetadata,
userContext
);
const aiResponse = await this.callAIThroughMCP(optimizationPrompt);
return this.parseQueryOptimizationResponse(aiResponse, entityMetadata);
} catch (error) {
this.logger.error('Query optimization failed', {
error: error instanceof Error ? error.message : 'Unknown error',
});
return this.createBasicQuery(userIntent, entityMetadata);
}
}
/**
* Detects anomalies in SAP data using AI analysis
* Generic pattern that works with any AI model
*/
async detectAnomalies(
currentData: any[],
historicalData: any[],
entitySet: string
): Promise<AnomalyReport[]> {
if (!this.isEnabled) {
return this.createBasicAnomalyDetection(currentData, historicalData, entitySet);
}
try {
const anomalyPrompt = this.buildAnomalyDetectionPrompt(
currentData,
historicalData,
entitySet
);
const aiResponse = await this.callAIThroughMCP(anomalyPrompt);
return this.parseAnomalyDetectionResponse(aiResponse, entitySet);
} catch (error) {
this.logger.error('Anomaly detection failed', {
error: error instanceof Error ? error.message : 'Unknown error',
});
return this.createBasicAnomalyDetection(currentData, historicalData, entitySet);
}
}
/**
* Generates business insights from SAP data
* Works with any AI model that can understand business context
*/
async generateBusinessInsights(
data: any[],
entitySet: string,
businessContext?: string
): Promise<BusinessInsight[]> {
if (!this.isEnabled) {
return this.createBasicInsights(data, entitySet);
}
try {
const insightsPrompt = this.buildBusinessInsightsPrompt(data, entitySet, businessContext);
const aiResponse = await this.callAIThroughMCP(insightsPrompt);
return this.parseBusinessInsightsResponse(aiResponse);
} catch (error) {
this.logger.error('Business insights generation failed', {
error: error instanceof Error ? error.message : 'Unknown error',
});
return this.createBasicInsights(data, entitySet);
}
}
// Private methods for building structured prompts
private buildAnalysisPrompt(userPrompt: string, data: any[], analysisType: string): string {
return `
ANALYSIS REQUEST:
Type: ${analysisType}
User Request: ${userPrompt}
DATA SAMPLE (first 10 records):
${JSON.stringify(data.slice(0, 10), null, 2)}
TOTAL RECORDS: ${data.length}
REQUIRED OUTPUT FORMAT:
{
"insights": [
{
"type": "trend|anomaly|correlation|forecast|risk",
"title": "Brief title",
"description": "Detailed description",
"impact": "high|medium|low",
"confidence": 0.0-1.0,
"supportingData": []
}
],
"recommendations": [
{
"title": "Actionable recommendation",
"description": "What to do",
"priority": "critical|high|medium|low",
"estimatedImpact": "Expected outcome"
}
]
}
Please analyze the data and provide structured insights.
`;
}
private buildQueryOptimizationPrompt(
userIntent: string,
entityMetadata: EntityType,
userContext?: any
): string {
return `
QUERY OPTIMIZATION REQUEST:
User Intent: "${userIntent}"
Entity Metadata:
- Name: ${entityMetadata.name}
- Available Fields: ${entityMetadata.properties.map(p => `${p.name} (${p.type})`).join(', ')}
- Key Fields: ${entityMetadata.keys.join(', ')}
User Context: ${userContext ? JSON.stringify(userContext) : 'Not provided'}
REQUIRED OUTPUT FORMAT:
{
"select": ["field1", "field2"],
"filter": "OData filter expression",
"orderby": "field1 desc, field2 asc",
"top": 100,
"explanation": "Why this optimization was chosen",
"confidence": 0.0-1.0
}
Generate the most efficient OData query for this intent.
`;
}
private buildAnomalyDetectionPrompt(
currentData: any[],
historicalData: any[],
entitySet: string
): string {
return `
ANOMALY DETECTION REQUEST:
Entity Set: ${entitySet}
Current Data (${currentData.length} records):
${JSON.stringify(currentData.slice(0, 5), null, 2)}
Historical Baseline (${historicalData.length} records):
Statistical summary: ${this.calculateBasicStats(historicalData)}
REQUIRED OUTPUT FORMAT:
{
"anomalies": [
{
"type": "volume|value|pattern|process",
"severity": "critical|high|medium|low",
"title": "Brief title",
"description": "What is anomalous",
"confidence": 0.0-1.0,
"businessImpact": "Potential business impact",
"recommendedActions": ["action1", "action2"]
}
]
}
Identify anomalies compared to historical patterns.
`;
}
private buildBusinessInsightsPrompt(
data: any[],
entitySet: string,
businessContext?: string
): string {
return `
BUSINESS INSIGHTS REQUEST:
Entity Set: ${entitySet}
Business Context: ${businessContext || 'General business analysis'}
Data Sample:
${JSON.stringify(data.slice(0, 10), null, 2)}
Total Records: ${data.length}
REQUIRED OUTPUT FORMAT:
{
"insights": [
{
"type": "trend|performance|risk|opportunity",
"title": "Key insight",
"description": "Detailed explanation",
"impact": "high|medium|low",
"confidence": 0.0-1.0,
"actionable": true|false
}
]
}
Generate business-relevant insights from this SAP data.
`;
}
// Simulate calling AI through MCP protocol
private async callAIThroughMCP(prompt: string): Promise<any> {
// In a real implementation, this would use the MCP SDK to call
// the connected AI client (Claude, GPT, etc.)
// For now, we simulate a basic response structure
this.logger.debug('Calling AI through MCP protocol', {
promptLength: prompt.length,
});
// Simulate AI processing delay
await new Promise(resolve => setTimeout(resolve, 100));
// Return structured response that any AI model could provide
return {
response: 'AI analysis would be returned here',
confidence: 0.8,
processingTime: 150,
};
}
// Response parsing methods
private parseAIAnalysisResponse(aiResponse: any, originalData: any[]): AnalysisResult {
// Parse and validate AI response into structured format
return {
insights: this.createBasicInsights(originalData, 'unknown'),
recommendations: [],
confidence: aiResponse.confidence || 0.5,
visualizations: [],
alerts: [],
metadata: {
analyzedAt: new Date(),
dataPoints: originalData.length,
processingTime: aiResponse.processingTime || 0,
},
};
}
private parseQueryOptimizationResponse(
aiResponse: any,
entityMetadata: EntityType
): OptimizedQuery {
// Parse AI response into OData query
return {
url: `${entityMetadata.entitySet}?$select=${entityMetadata.properties
.slice(0, 5)
.map(p => p.name)
.join(',')}`,
explanation: 'Basic query generated (AI parsing not implemented yet)',
cacheStrategy: 'medium',
estimatedRows: 100,
confidence: aiResponse.confidence || 0.5,
};
}
private parseAnomalyDetectionResponse(aiResponse: any, entitySet: string): AnomalyReport[] {
// Parse AI response into anomaly reports
return [];
}
private parseBusinessInsightsResponse(aiResponse: any): BusinessInsight[] {
// Parse AI response into business insights
return [];
}
// Fallback methods when AI is disabled or fails
private createFallbackAnalysis(data: any[], analysisType: string): AnalysisResult {
return {
insights: this.createBasicInsights(data, 'fallback'),
recommendations: this.createBasicRecommendations(),
confidence: 0.3, // Low confidence for basic analysis
visualizations: [],
alerts: [],
metadata: {
analyzedAt: new Date(),
dataPoints: data.length,
processingTime: 0,
},
};
}
private createBasicQuery(userIntent: string, entityMetadata: EntityType): OptimizedQuery {
// Create a basic OData query without AI optimization
return {
url: `${entityMetadata.entitySet}?$top=100`,
explanation: 'Basic query without AI optimization',
cacheStrategy: 'short',
estimatedRows: 100,
confidence: 0.3,
};
}
private createBasicAnomalyDetection(
currentData: any[],
historicalData: any[],
entitySet: string
): AnomalyReport[] {
// Basic statistical anomaly detection without AI
if (currentData.length === 0) return [];
const volumeAnomaly =
Math.abs(currentData.length - historicalData.length) > historicalData.length * 0.5;
if (volumeAnomaly) {
return [
{
id: `anomaly_${Date.now()}`,
entitySet,
anomalyType: 'volume',
severity: 'medium',
title: 'Volume Change Detected',
description: `Significant change in record count: ${currentData.length} vs historical average`,
detectedAt: new Date(),
dataPoints: currentData,
confidence: 0.6,
businessImpact: 'Potential process or system change',
recommendedActions: ['Investigate data source', 'Check system status'],
correlatedAnomalies: [],
},
];
}
return [];
}
private createBasicInsights(data: any[], entitySet: string): BusinessInsight[] {
if (data.length === 0) return [];
return [
{
type: 'trend',
title: `${entitySet} Data Overview`,
description: `Found ${data.length} records in ${entitySet}`,
impact: 'low',
confidence: 0.5,
supportingData: data.slice(0, 3),
},
];
}
private createBasicRecommendations(): Recommendation[] {
return [
{
id: 'basic_rec_1',
title: 'Enable AI Features',
description: 'Set AI_FEATURES_ENABLED=true to get intelligent insights',
priority: 'medium',
category: 'configuration',
estimatedImpact: 'Enhanced analytics and automation',
actionable: true,
requiredActions: ['Update environment variables', 'Restart service'],
},
];
}
// Utility methods
private calculateBasicStats(data: any[]): string {
if (data.length === 0) return 'No historical data';
return `Count: ${data.length}, Sample: ${JSON.stringify(data[0] || {})}`;
}
// Configuration methods
isAIEnabled(): boolean {
return this.isEnabled;
}
getCapabilities(): string[] {
const capabilities = ['basic_analysis', 'query_building', 'anomaly_detection'];
if (this.isEnabled) {
capabilities.push('ai_enhanced_analysis', 'intelligent_optimization', 'predictive_insights');
}
return capabilities;
}
async healthCheck(): Promise<{ status: string; capabilities: string[]; aiEnabled: boolean }> {
return {
status: 'operational',
capabilities: this.getCapabilities(),
aiEnabled: this.isEnabled,
};
}
}
// Singleton instance
export const aiIntegration = new AIIntegrationService();