import { Logger } from '../../utils/logger';
import { ModelOrchestrator } from './model-orchestrator';

/**
 * @interface AgentEnhancementConfig
 * @description Configuration for Vertex AI-powered Agent Enhancement.
 */
export interface AgentEnhancementConfig {
  defaultReasoningModel: string; // e.g., 'gemini-pro'
  // Add configuration for context window, fine-tuning parameters, etc.
}

/**
 * @interface AgentEnhancementOperations
 * @description Defines the operations available for Vertex AI-powered Agent Enhancement.
 */
export interface AgentEnhancementOperations {
  reason(agentContext: any, prompt: string): Promise<any>;
  selectSpecializedModel(taskContext: any, availableModels: string[]): Promise<string>;
  optimizePrompt(initialPrompt: string, feedback: any): Promise<string>;
  adaptModelBehavior(modelId: string, performanceData: any): Promise<void>;
}

/**
 * @class AgentEnhancement
 * @description Provides Vertex AI-powered reasoning, decision making, and model optimization for agents.
 */
export class AgentEnhancement implements AgentEnhancementOperations {
  private config: AgentEnhancementConfig;
  private logger: Logger;
  private modelOrchestrator: ModelOrchestrator;

  constructor(config: AgentEnhancementConfig, modelOrchestrator: ModelOrchestrator) {
    this.config = config;
    this.logger = new Logger('AgentEnhancement');
    this.modelOrchestrator = modelOrchestrator;
    this.logger.info('Vertex AI Agent Enhancement initialized.');
  }

  /**
   * Provides Vertex AI-powered reasoning and decision making for an agent.
   * @param {any} agentContext The current context of the agent.
   * @param {string} prompt The prompt or question for the AI model.
   * @returns {Promise<any>} The AI-generated reasoning or decision.
   */
  public async reason(agentContext: any, prompt: string): Promise<any> {
    this.logger.info(`Agent reasoning with Vertex AI using model: ${this.config.defaultReasoningModel}`);
    const fullPrompt = `Agent Context: ${JSON.stringify(agentContext)}
Task: ${prompt}
Reasoning:`;
    const result = await this.modelOrchestrator.invokeModel(this.config.defaultReasoningModel, fullPrompt);
    this.logger.debug('AI reasoning result:', result);
    return result;
  }

  /**
   * Selects a specialized model based on task context and available models.
   * @param {any} taskContext The context of the specialized task.
   * @param {string[]} availableModels A list of available specialized model IDs.
   * @returns {Promise<string>} The ID of the selected specialized model.
   */
  public async selectSpecializedModel(taskContext: any, availableModels: string[]): Promise<string> {
    this.logger.info(`Selecting specialized model for task context:`, taskContext);
    // This would involve using an AI model to analyze taskContext and select the best fit
    // from availableModels. For now, a simple selection.
    if (availableModels.length > 0) {
      const selectedModel = availableModels[0]; // Just pick the first one for simulation
      this.logger.debug(`Selected specialized model: ${selectedModel}`);
      return selectedModel;
    }
    throw new Error('No specialized models available for selection.');
  }

  /**
   * Optimizes a prompt based on feedback or desired outcomes.
   * @param {string} initialPrompt The initial prompt.
   * @param {any} feedback Feedback on the prompt's performance or desired changes.
   * @returns {Promise<string>} The optimized prompt.
   */
  public async optimizePrompt(initialPrompt: string, feedback: any): Promise<string> {
    this.logger.info(`Optimizing prompt based on feedback:`, feedback);
    // This would involve using an AI model to refine the prompt.
    await new Promise(resolve => setTimeout(resolve, 100)); // Simulate AI optimization
    const optimizedPrompt = `${initialPrompt} (optimized based on ${JSON.stringify(feedback)})`;
    this.logger.debug('Optimized prompt:', optimizedPrompt);
    return optimizedPrompt;
  }

  /**
   * Adapts model behavior in real-time based on performance data (conceptual).
   * @param {string} modelId The ID of the model to adapt.
   * @param {any} performanceData Real-time performance metrics.
   * @returns {Promise<void>}
   */
  public async adaptModelBehavior(modelId: string, performanceData: any): Promise<void> {
    this.logger.info(`Adapting model ${modelId} behavior based on performance data (conceptual):`, performanceData);
    // This would involve:
    // - Triggering real-time fine-tuning of the model.
    // - Adjusting model parameters or routing based on latency, accuracy, or cost.
    // - Switching to a different model if performance degrades.
  }
}