DevOps AI Toolkit

/** * Embedding Service * * Optional semantic search enhancement for pattern matching. * Gracefully falls back to keyword-only search when embedding providers are not available. */ import { createAmazonBedrock } from '@ai-sdk/amazon-bedrock'; import { google } from '@ai-sdk/google'; import { createOpenAI } from '@ai-sdk/openai'; import { embed } from 'ai'; import { withAITracing } from './tracing'; /** * Supported embedding providers - single source of truth */ export const EMBEDDING_PROVIDERS = ['openai', 'google', 'amazon_bedrock'] as const; export type EmbeddingProviderType = typeof EMBEDDING_PROVIDERS[number]; export interface EmbeddingConfig { provider?: EmbeddingProviderType; apiKey?: string; model?: string; dimensions?: number; } export interface EmbeddingProvider { generateEmbedding(text: string): Promise<number[]>; generateEmbeddings(texts: string[]): Promise<number[][]>; isAvailable(): boolean; getDimensions(): number; getModel(): string; } /** * Unified Vercel AI SDK Embedding Provider * Supports OpenAI, Google, and Amazon Bedrock through Vercel AI SDK */ export class VercelEmbeddingProvider implements EmbeddingProvider { private providerType: EmbeddingProviderType; private apiKey: string; private model: string; private dimensions: number; private available: boolean; private modelInstance: any; constructor(config: EmbeddingConfig & { provider: EmbeddingProviderType }) { this.providerType = config.provider; this.available = false; // Get API key based on provider switch (this.providerType) { case 'openai': this.apiKey = config.apiKey || process.env.CUSTOM_EMBEDDINGS_API_KEY || process.env.OPENAI_API_KEY || ''; this.model = config.model || process.env.EMBEDDINGS_MODEL || 'text-embedding-3-small'; this.dimensions = config.dimensions || (process.env.EMBEDDINGS_DIMENSIONS ? parseInt(process.env.EMBEDDINGS_DIMENSIONS, 10) : 1536); break; case 'google': this.apiKey = config.apiKey || process.env.GOOGLE_API_KEY || ''; this.model = config.model || process.env.EMBEDDINGS_MODEL || 'gemini-embedding-001'; this.dimensions = config.dimensions || 768; break; case 'amazon_bedrock': // AWS SDK handles credentials automatically - no API key needed this.apiKey = 'bedrock-uses-aws-credentials'; this.model = config.model || process.env.EMBEDDINGS_MODEL || 'amazon.titan-embed-text-v2:0'; this.dimensions = config.dimensions || 1024; // Titan v2 default break; } if (!this.apiKey) { this.available = false; return; } try { // Initialize model instance based on provider switch (this.providerType) { case 'openai': { const baseURL = process.env.CUSTOM_EMBEDDINGS_BASE_URL; const openai = createOpenAI({ apiKey: this.apiKey, ...(baseURL && { baseURL }) }); this.modelInstance = openai.textEmbedding(this.model); break; } case 'google': // Set environment variable that Google SDK expects process.env.GOOGLE_GENERATIVE_AI_API_KEY = this.apiKey; this.modelInstance = google.textEmbedding(this.model); break; case 'amazon_bedrock': { // AWS SDK automatically uses credential chain: // 1. Environment variables (AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY, AWS_REGION) // 2. ~/.aws/credentials file // 3. IAM roles (EC2 instance profiles, ECS roles, EKS service accounts) const bedrock = createAmazonBedrock({ region: process.env.AWS_REGION || 'us-east-1', }); this.modelInstance = bedrock.textEmbeddingModel(this.model); break; } } this.available = true; } catch (error) { this.available = false; } } async generateEmbedding(text: string): Promise<number[]> { if (!this.isAvailable()) { throw new Error(`${this.providerType} embedding provider not available`); } if (!text || text.trim().length === 0) { throw new Error('Text cannot be empty for embedding generation'); } // Wrap embed() call with OpenTelemetry tracing return await withAITracing( { provider: this.providerType, model: this.model, operation: 'embeddings' }, async () => { try { const embedOptions: any = { model: this.modelInstance, value: text.trim() }; // Add Google-specific options if (this.providerType === 'google') { embedOptions.providerOptions = { google: { outputDimensionality: this.dimensions, taskType: 'SEMANTIC_SIMILARITY' } }; } const result = await embed(embedOptions); return result.embedding; } catch (error) { if (error instanceof Error) { throw new Error(`${this.providerType} embedding failed: ${error.message}`); } throw new Error(`${this.providerType} embedding failed: ${String(error)}`); } }, (embedding) => ({ embeddingCount: 1, embeddingDimensions: embedding.length }) ); } async generateEmbeddings(texts: string[]): Promise<number[][]> { if (!this.isAvailable()) { throw new Error(`${this.providerType} embedding provider not available`); } if (!texts || texts.length === 0) { return []; } const validTexts = texts .map(t => t?.trim()) .filter(t => t && t.length > 0); if (validTexts.length === 0) { return []; } // Wrap batch embed calls with OpenTelemetry tracing return await withAITracing( { provider: this.providerType, model: this.model, operation: 'embeddings' }, async () => { try { const results = await Promise.all( validTexts.map(text => { const embedOptions: any = { model: this.modelInstance, value: text }; // Add Google-specific options if (this.providerType === 'google') { embedOptions.providerOptions = { google: { outputDimensionality: this.dimensions, taskType: 'SEMANTIC_SIMILARITY' } }; } return embed(embedOptions); }) ); return results.map(result => result.embedding); } catch (error) { if (error instanceof Error) { throw new Error(`${this.providerType} batch embedding failed: ${error.message}`); } throw new Error(`${this.providerType} batch embedding failed: ${String(error)}`); } }, (embeddings) => ({ embeddingCount: embeddings.length, embeddingDimensions: embeddings[0]?.length || this.dimensions }) ); } isAvailable(): boolean { return this.available; } getDimensions(): number { return this.dimensions; } getModel(): string { return this.model; } getProviderType(): string { return this.providerType; } } /** * Factory function to create embedding provider based on configuration */ function createEmbeddingProvider(config: EmbeddingConfig = {}): EmbeddingProvider | null { const providerType = (config.provider || process.env.EMBEDDINGS_PROVIDER || 'openai').toLowerCase(); // Validate provider type using centralized list if (!EMBEDDING_PROVIDERS.includes(providerType as EmbeddingProviderType)) { console.warn(`Unknown embedding provider: ${providerType}, falling back to openai`); return createEmbeddingProvider({ ...config, provider: 'openai' }); } try { const provider = new VercelEmbeddingProvider({ ...config, provider: providerType as EmbeddingProviderType }); return provider.isAvailable() ? provider : null; } catch (error) { console.error(`Failed to create ${providerType} embedding provider:`, error); return null; } } /** * Main Embedding Service * Provides optional semantic search capabilities with graceful degradation */ export class EmbeddingService { private provider: EmbeddingProvider | null; constructor(config: EmbeddingConfig = {}) { // Use factory to initialize appropriate provider this.provider = createEmbeddingProvider(config); } /** * Generate embedding for text * Throws error if embeddings not available or generation fails */ async generateEmbedding(text: string): Promise<number[]> { if (!this.isAvailable()) { throw new Error('Embedding service not available'); } try { return await this.provider!.generateEmbedding(text); } catch (error) { // Throw error immediately - no silent fallback throw new Error(`Embedding generation failed: ${error instanceof Error ? error.message : String(error)}`); } } /** * Generate embeddings for multiple texts (optional enhancement) * Returns empty array if embeddings not available */ async generateEmbeddings(texts: string[]): Promise<number[][]> { if (!this.isAvailable()) { return []; } try { return await this.provider!.generateEmbeddings(texts); } catch (error) { // Log error but don't throw - allow graceful fallback console.warn('Batch embedding generation failed, falling back to keyword search:', error); return []; } } /** * Check if semantic search is available */ isAvailable(): boolean { return this.provider !== null && this.provider.isAvailable(); } /** * Get embedding dimensions (if available) */ getDimensions(): number { return this.provider?.getDimensions() || 1536; } /** * Get status information for debugging/logging */ getStatus(): { available: boolean; provider: string | null; model?: string; dimensions?: number; reason?: string; } { if (this.isAvailable()) { // Get provider type from VercelEmbeddingProvider const providerName = (this.provider as VercelEmbeddingProvider).getProviderType?.() || 'unknown'; return { available: true, provider: providerName, model: this.provider!.getModel(), dimensions: this.provider!.getDimensions() }; } const requestedProvider = process.env.EMBEDDINGS_PROVIDER || 'openai'; const keyMap = { 'openai': 'OPENAI_API_KEY', 'google': 'GOOGLE_API_KEY', 'amazon_bedrock': 'AWS credentials (AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY, AWS_REGION)' }; const requiredKey = keyMap[requestedProvider as keyof typeof keyMap] || 'OPENAI_API_KEY'; return { available: false, provider: null, reason: `${requiredKey} not set - vector operations will fail` }; } /** * Create searchable text from pattern data */ createPatternSearchText(pattern: { description: string; triggers: string[]; suggestedResources: string[]; rationale: string; }): string { return [ pattern.description, ...pattern.triggers, pattern.rationale, // Include resource types for better semantic matching ...pattern.suggestedResources.map(r => `kubernetes ${r.toLowerCase()}`) ].join(' ').trim(); } }