server/modal-client.ts

/**
 * Modal API Client for Serverless Compute Integration
 */

interface ModalConfig {
  tokenId: string;
  tokenSecret: string;
  baseUrl: string;
}

interface DocumentProcessingTask {
  taskId: string;
  status: 'pending' | 'running' | 'completed' | 'failed';
  result?: any;
  error?: string;
}

interface VectorProcessingRequest {
  documents: Array<{
    id: string;
    content: string;
    metadata?: Record<string, any>;
  }>;
  modelName?: string;
  batchSize?: number;
}

class ModalClient {
  private config: ModalConfig;
  private authToken: string;

  constructor() {
    // Use environment variables - NEVER hardcode credentials
    const tokenId = process.env.MODAL_TOKEN_ID;
    const tokenSecret = process.env.MODAL_TOKEN_SECRET;
    
    if (!tokenId || !tokenSecret) {
      throw new Error('Modal credentials not configured. Please set MODAL_TOKEN_ID and MODAL_TOKEN_SECRET environment variables.');
    }
    
    this.config = {
      tokenId,
      tokenSecret,
      baseUrl: process.env.MODAL_BASE_URL || 'https://fazeelusmani18--knowledgebridge-main.modal.run'
    };
    
    // Create base64 encoded auth token
    this.authToken = Buffer.from(`${this.config.tokenId}:${this.config.tokenSecret}`).toString('base64');
  }

  private async makeRequest(endpoint: string, options: RequestInit = {}) {
    const url = `${this.config.baseUrl}${endpoint}`;
    
    const response = await fetch(url, {
      ...options,
      headers: {
        'Authorization': `Basic ${this.authToken}`,
        'Content-Type': 'application/json',
        ...options.headers,
      },
    });

    if (!response.ok) {
      throw new Error(`Modal API request failed: ${response.status} ${response.statusText}`);
    }

    return response.json();
  }

  /**
   * Process documents in batch using Modal's serverless compute
   */
  async batchProcessDocuments(request: VectorProcessingRequest): Promise<DocumentProcessingTask> {
    return this.makeRequest('/batch-process', {
      method: 'POST',
      body: JSON.stringify({
        documents: request.documents,
        config: {
          modelName: request.modelName || 'text-embedding-3-small',
          batchSize: request.batchSize || 50,
          computeResources: {
            cpu: 2,
            memory: '4Gi'
          }
        }
      })
    });
  }

  /**
   * Get status of a processing task
   */
  async getTaskStatus(taskId: string): Promise<DocumentProcessingTask> {
    return this.makeRequest(`/task/${taskId}`);
  }

  /**
   * Build FAISS index using Modal's distributed computing
   */
  async buildVectorIndex(documents: any[], indexConfig?: any): Promise<DocumentProcessingTask> {
    return this.makeRequest('/build-index', {
      method: 'POST',
      body: JSON.stringify({
        documents,
        indexConfig: {
          dimension: 1536,
          indexType: 'IVF',
          nlist: 100,
          ...indexConfig
        }
      })
    });
  }

  /**
   * Perform high-performance vector search
   */
  async vectorSearch(query: string, indexId: string, k: number = 10): Promise<any> {
    return this.makeRequest('/vector-search', {
      method: 'POST',
      body: JSON.stringify({
        query,
        indexId,
        k,
        includeMetadata: true
      })
    });
  }

  /**
   * Extract text from documents using OCR
   */
  async extractTextFromDocuments(documentUrls: string[]): Promise<DocumentProcessingTask> {
    return this.makeRequest('/ocr-extract', {
      method: 'POST',
      body: JSON.stringify({
        documentUrls,
        config: {
          languages: ['en'],
          outputFormat: 'structured'
        }
      })
    });
  }

  /**
   * Auto-categorize documents using distributed ML
   */
  async categorizeDocuments(documents: any[]): Promise<DocumentProcessingTask> {
    return this.makeRequest('/categorize', {
      method: 'POST',
      body: JSON.stringify({
        documents,
        categories: [
          'academic_paper',
          'technical_documentation',
          'research_report',
          'code_repository',
          'blog_post',
          'news_article'
        ]
      })
    });
  }
}

export const modalClient = new ModalClient();
export type { DocumentProcessingTask, VectorProcessingRequest };