import { Logger } from '../../utils/logger';

/**
 * @interface ModelOrchestratorConfig
 * @description Configuration for the Vertex AI Model Orchestrator.
 */
export interface ModelOrchestratorConfig {
  defaultModel: string; // e.g., 'gemini-pro'
  availableModels: { id: string; capabilities: string[]; costPerToken: number; latencyMs: number; }[];
  // Add configuration for auto-scaling, caching, etc.
}

/**
 * @interface ModelOrchestratorOperations
 * @description Defines the operations available for the Vertex AI Model Orchestrator.
 */
export interface ModelOrchestratorOperations {
  selectModel(taskRequirements: string[], complexity: 'low' | 'medium' | 'high'): Promise<string>;
  invokeModel(modelId: string, prompt: any, options?: any): Promise<any>;
  optimizeCost(modelId: string, usage: number): Promise<void>;
  monitorPerformance(modelId: string, metrics: any): Promise<void>;
}

/**
 * @class ModelOrchestrator
 * @description Manages dynamic model selection, invocation, and optimization for Vertex AI.
 */
export class ModelOrchestrator implements ModelOrchestratorOperations {
  private config: ModelOrchestratorConfig;
  private logger: Logger;
  // Placeholder for Vertex AI client
  // private vertexAiClient: any;

  constructor(config: ModelOrchestratorConfig) {
    this.config = config;
    this.logger = new Logger('ModelOrchestrator');
    this.logger.info('Vertex AI Model Orchestrator initialized.');
    // Initialize Vertex AI client here (conceptual)
  }

  /**
   * Selects the most appropriate model based on task requirements and complexity.
   * @param {string[]} taskRequirements Capabilities or features required by the task.
   * @param {'low' | 'medium' | 'high'} complexity The complexity of the task.
   * @returns {Promise<string>} The ID of the selected model.
   */
  public async selectModel(taskRequirements: string[], complexity: 'low' | 'medium' | 'high'): Promise<string> {
    this.logger.info(`Selecting model for task with requirements: ${taskRequirements.join(', ')} and complexity: ${complexity}`);

    // Simple selection logic: prioritize based on complexity and capabilities
    let bestModel = this.config.defaultModel;
    let bestScore = -1;

    for (const model of this.config.availableModels) {
      let score = 0;
      // Score based on matching capabilities
      taskRequirements.forEach(req => {
        if (model.capabilities.includes(req)) {
          score++;
        }
      });

      // Adjust score based on complexity
      if (complexity === 'high' && model.id.includes('ultra')) score += 2;
      if (complexity === 'medium' && model.id.includes('pro')) score += 1;
      if (complexity === 'low' && model.id.includes('flash')) score += 1;

      // Prioritize lower latency and cost (conceptual)
      score -= model.latencyMs / 1000; // Deduct for latency
      score -= model.costPerToken * 1000; // Deduct for cost

      if (score > bestScore) {
        bestScore = score;
        bestModel = model.id;
      }
    }

    this.logger.debug(`Selected model: ${bestModel}`);
    return bestModel;
  }

  /**
   * Invokes a specified Vertex AI model with the given prompt and options.
   * @param {string} modelId The ID of the model to invoke.
   * @param {any} prompt The input prompt for the model.
   * @param {any} [options] Optional parameters for the model invocation.
   * @returns {Promise<any>} The model's response.
   */
  public async invokeModel(modelId: string, prompt: any, options?: any): Promise<any> {
    this.logger.info(`Invoking Vertex AI model ${modelId} with prompt:`, prompt);
    // Placeholder for actual Vertex AI API call
    await new Promise(resolve => setTimeout(resolve, 150)); // Simulate API call
    const simulatedResponse = { model: modelId, output: `Simulated response for: ${JSON.stringify(prompt)}`, tokensUsed: 100 };
    this.logger.debug('Model invocation successful.', simulatedResponse);
    return simulatedResponse;
  }

  /**
   * Optimizes cost by intelligent model routing and caching (conceptual).
   * @param {string} modelId The ID of the model.
   * @param {number} usage The usage amount (e.g., tokens).
   * @returns {Promise<void>}
   */
  public async optimizeCost(modelId: string, usage: number): Promise<void> {
    this.logger.info(`Optimizing cost for model ${modelId} with usage ${usage} (conceptual)...`);
    // This would involve:
    // - Routing requests to cheaper models for simpler tasks.
    // - Implementing caching for frequently requested prompts.
    // - Batching requests to reduce API call overhead.
  }

  /**
   * Monitors model performance and supports A/B testing (conceptual).
   * @param {string} modelId The ID of the model.
   * @param {any} metrics Performance metrics collected.
   * @returns {Promise<void>}
   */
  public async monitorPerformance(modelId: string, metrics: any): Promise<void> {
    this.logger.info(`Monitoring performance for model ${modelId} (conceptual)...`, metrics);
    // This would involve:
    // - Collecting latency, throughput, and error rates.
    // - Running A/B tests for different model versions or configurations.
    // - Storing metrics in BigQuery for analysis.
  }
}