apps/backend/src/services/embeddings/EmbeddingService.ts

import { logger } from '../../utils/logger.js';
import { LocalGPUEmbeddingsProvider } from './LocalGPUEmbeddings.js';

export interface EmbeddingProvider {
  name: string;
  dimensions: number;
  generateEmbedding(text: string): Promise<number[]>;
  generateEmbeddings(texts: string[]): Promise<number[][]>;
}

/**
 * HuggingFace Embeddings Provider
 * Uses HuggingFace Inference API
 */
class HuggingFaceEmbeddingsProvider implements EmbeddingProvider {
  name = 'huggingface';
  dimensions = 768;
  private apiKey: string;
  private model = 'sentence-transformers/all-MiniLM-L6-v2';

  constructor(apiKey?: string) {
    this.apiKey = apiKey || process.env.HUGGINGFACE_API_KEY || '';
  }

  async generateEmbedding(text: string): Promise<number[]> {
    if (!this.apiKey) {
      throw new Error('HuggingFace API key not configured');
    }

    const response = await fetch(
      `https://api-inference.huggingface.co/pipeline/feature-extraction/${this.model}`,
      {
        method: 'POST',
        headers: {
          Authorization: `Bearer ${this.apiKey}`,
          'Content-Type': 'application/json',
        },
        body: JSON.stringify({ inputs: text }),
      }
    );

    if (!response.ok) {
      throw new Error(`HuggingFace API error: ${response.statusText}`);
    }

    const embedding = await response.json();
    return embedding;
  }

  async generateEmbeddings(texts: string[]): Promise<number[][]> {
    const embeddings = await Promise.all(texts.map(t => this.generateEmbedding(t)));
    return embeddings;
  }
}

/**
 * OpenAI Embeddings Provider
 * Uses OpenAI Embeddings API
 */
class OpenAIEmbeddingsProvider implements EmbeddingProvider {
  name = 'openai';
  dimensions = 1536;
  private apiKey: string;
  private model = 'text-embedding-3-small';

  constructor(apiKey?: string) {
    this.apiKey = apiKey || process.env.OPENAI_API_KEY || '';
  }

  async generateEmbedding(text: string): Promise<number[]> {
    if (!this.apiKey) {
      throw new Error('OpenAI API key not configured');
    }

    const response = await fetch('https://api.openai.com/v1/embeddings', {
      method: 'POST',
      headers: {
        Authorization: `Bearer ${this.apiKey}`,
        'Content-Type': 'application/json',
      },
      body: JSON.stringify({
        model: this.model,
        input: text,
      }),
    });

    if (!response.ok) {
      throw new Error(`OpenAI API error: ${response.statusText}`);
    }

    const data = await response.json();
    return data.data[0].embedding;
  }

  async generateEmbeddings(texts: string[]): Promise<number[][]> {
    if (!this.apiKey) {
      throw new Error('OpenAI API key not configured');
    }

    const response = await fetch('https://api.openai.com/v1/embeddings', {
      method: 'POST',
      headers: {
        Authorization: `Bearer ${this.apiKey}`,
        'Content-Type': 'application/json',
      },
      body: JSON.stringify({
        model: this.model,
        input: texts,
      }),
    });

    if (!response.ok) {
      throw new Error(`OpenAI API error: ${response.statusText}`);
    }

    const data = await response.json();
    return data.data.map((item: any) => item.embedding);
  }
}

/**
 * Local Transformers.js Provider (Fallback)
 * Uses browser-compatible ML models
 */
class TransformersEmbeddingsProvider implements EmbeddingProvider {
  name = 'transformers';
  dimensions = 384;
  private isInitialized = false;
  private pipeline: any;

  async initialize(): Promise<void> {
    if (this.isInitialized) return;

    // Skip transformers in Docker/production - ONNX runtime has architecture issues on Alpine
    const isDocker = process.env.NODE_ENV === 'production' || process.cwd().startsWith('/app');
    if (isDocker) {
      throw new Error('Transformers.js disabled in Docker mode (ONNX incompatibility)');
    }

    try {
      // Dynamic import to avoid bundling issues
      const { pipeline } = await import('@xenova/transformers');
      this.pipeline = await pipeline('feature-extraction', 'Xenova/all-MiniLM-L6-v2');
      this.isInitialized = true;
      logger.info('✅ Local Transformers.js embeddings initialized');
    } catch (error: any) {
      logger.warn('⚠️ Transformers.js not available:', error.message);
      throw error;
    }
  }

  async generateEmbedding(text: string): Promise<number[]> {
    if (!this.isInitialized) {
      await this.initialize();
    }

    const output = await this.pipeline(text, { pooling: 'mean', normalize: true });
    return Array.from(output.data);
  }

  async generateEmbeddings(texts: string[]): Promise<number[][]> {
    const embeddings = await Promise.all(texts.map(t => this.generateEmbedding(t)));
    return embeddings;
  }
}

/**
 * Unified Embedding Service
 * Auto-selects best available provider
 */
export class EmbeddingService {
  private provider: EmbeddingProvider | null = null;
  private preferredProvider: string;

  constructor(preferredProvider?: string) {
    this.preferredProvider = preferredProvider || process.env.EMBEDDING_PROVIDER || 'auto';
  }

  async initialize(): Promise<void> {
    if (this.provider) return;

    // Try providers in order of preference
    const providers: Array<{ name: string; factory: () => EmbeddingProvider }> = [
      { name: 'local-gpu', factory: () => new LocalGPUEmbeddingsProvider() },
      { name: 'openai', factory: () => new OpenAIEmbeddingsProvider() },
      { name: 'huggingface', factory: () => new HuggingFaceEmbeddingsProvider() },
      { name: 'transformers', factory: () => new TransformersEmbeddingsProvider() },
    ];

    // Check if GPU is explicitly enabled in environment (Docker/HF Spaces)
    const useGpu = process.env.USE_GPU === 'true';

    // If specific provider requested, try it first
    if (this.preferredProvider !== 'auto') {
      const preferred = providers.find(p => p.name === this.preferredProvider);
      if (preferred) {
        providers.unshift(preferred);
      }
    } else if (useGpu) {
       // Prioritize GPU if environment says so
       const gpuProvider = providers.find(p => p.name === 'local-gpu');
       if (gpuProvider) {
           providers.unshift(gpuProvider);
       }
    }

    for (const { name, factory } of providers) {
      try {
        // Skip GPU provider if not explicitly enabled to avoid spawning python processes locally unnecessarily
        if (name === 'local-gpu' && !useGpu && this.preferredProvider !== 'local-gpu') {
            continue;
        }

        const provider = factory();

        // Initialize provider
        if (provider instanceof TransformersEmbeddingsProvider) {
          await provider.initialize();
        } else if (provider instanceof LocalGPUEmbeddingsProvider) {
            await provider.initialize();
        } else {
          // Quick test with small text
          await provider.generateEmbedding('test');
        }

        this.provider = provider;
        logger.info(`🧠 Embedding provider initialized: ${name} (${provider.dimensions}D)`);
        return;
      } catch (error: any) {
        logger.warn(`⚠️ ${name} embeddings not available: ${error.message}`);
      }
    }

    throw new Error(
      'No embedding provider available. Please configure API keys or install @xenova/transformers.'
    );
  }

  async generateEmbedding(text: string): Promise<number[]> {
    if (!this.provider) {
      await this.initialize();
    }
    return this.provider!.generateEmbedding(text);
  }

  async generateEmbeddings(texts: string[]): Promise<number[][]> {
    if (!this.provider) {
      await this.initialize();
    }
    return this.provider!.generateEmbeddings(texts);
  }

  getDimensions(): number {
    if (!this.provider) {
      throw new Error('Embedding service not initialized');
    }
    return this.provider.dimensions;
  }

  getProviderName(): string {
    if (!this.provider) {
      throw new Error('Embedding service not initialized');
    }
    return this.provider.name;
  }
}

// Singleton instance
let embeddingServiceInstance: EmbeddingService | null = null;

export function getEmbeddingService(): EmbeddingService {
  if (!embeddingServiceInstance) {
    embeddingServiceInstance = new EmbeddingService();
  }
  return embeddingServiceInstance;
}