OpenAIEmbeddingProvider.ts•2.47 kB
// OpenAI Embedding Provider
// Users should implement this provider to use OpenAI embeddings
import { BaseEmbeddingProvider } from '../BaseEmbeddingProvider.js';
import { PROVIDER_CONFIG, PROVIDER_MODEL_DIMENSIONS } from '../../../core/config.js';
/**
 * OpenAI Embedding Provider Implementation
 * Uses OpenAI's text-embedding models
 * 
 * To implement:
 * 1. Install OpenAI SDK: npm install openai
 * 2. Set OPENAI_API_KEY in environment
 * 3. Implement generateEmbeddingsInternal method
 */
export class OpenAIEmbeddingProvider extends BaseEmbeddingProvider {
  protected getProviderType(): string {
    return 'openai';
  }
  
  protected getDefaultModel(): string {
    return PROVIDER_CONFIG.OPENAI_MODEL || 'text-embedding-3-large';
  }
  
  protected getDefaultMaxBatchSize(): number {
    return 100;
  }
  
  protected isLocalProvider(): boolean {
    return false;
  }
  
  async isAvailable(): Promise<boolean> {
    // Check if OpenAI API key is configured
    if (!PROVIDER_CONFIG.OPENAI_API_KEY) {
      console.warn('OpenAI API key not configured');
      return false;
    }
    
    // TODO: Implement actual availability check
    // Example: Try to create a simple embedding
    return true;
  }
  
  protected async generateEmbeddingsInternal(texts: string[]): Promise<number[][]> {
    // TODO: Implement OpenAI embedding generation
    // Example implementation:
    /*
    const { OpenAI } = await import('openai');
    const client = new OpenAI({
      apiKey: PROVIDER_CONFIG.OPENAI_API_KEY,
      baseURL: PROVIDER_CONFIG.OPENAI_BASE_URL
    });
    
    const response = await client.embeddings.create({
      input: texts,
      model: this.getModel(),
      dimensions: this.getDimensions() // For text-embedding-3-* models
    });
    
    return response.data.map(item => item.embedding);
    */
    
    throw new Error('OpenAI provider not implemented. Please implement generateEmbeddingsInternal method.');
  }
  
  estimateCost(tokenCount: number): number {
    // OpenAI pricing (approximate)
    const model = this.getModel();
    if (model.includes('text-embedding-3-small')) {
      return tokenCount * 0.00002 / 1000; // $0.02 per 1M tokens
    } else if (model.includes('text-embedding-3-large')) {
      return tokenCount * 0.00013 / 1000; // $0.13 per 1M tokens
    } else {
      return tokenCount * 0.0001 / 1000; // Ada v2: $0.10 per 1M tokens
    }
  }
}
console.log('✅ OpenAI embedding provider template loaded');