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

export interface ReasoningStep {
  id: string;
  role: 'planner' | 'executor' | 'reviewer';
  task: string;
  context?: any;
}

export interface ReasoningChain {
  steps: ReasoningStep[];
  currentStepIndex: number;
  finalDecision?: string;
  isCompleted: boolean;
}

/**
 * [CORE_AUTO_05] 动态 AI 推理流水线 (Dynamic Reasoning Pipeline)
 * @description 支持基于 Context 的推理链动态构建与执行，实现复杂的自治决策逻辑
 */
export class ReasoningPipelineService {
  /**
   * 构建并执行推理链
   * @param goal 任务目标
   * @param initialContext 初始上下文
   */
  static async runDynamicReasoning(goal: string, initialContext: any): Promise<ReasoningChain> {
    logger.info(`[ReasoningPipeline] Starting dynamic reasoning for goal: ${goal}`);

    // 1. 第一步：规划器 (Planner) - 生成初步推理步骤
    const chain: ReasoningChain = {
      steps: [],
      currentStepIndex: 0,
      isCompleted: false
    };

    // 模拟规划步骤，实际应通过 LLM 生成
    const plannerStep: ReasoningStep = {
      id: 'step_1',
      role: 'planner',
      task: `Plan steps to achieve goal: ${goal}`,
      context: initialContext
    };
    chain.steps.push(plannerStep);

    // 2. 依次执行步骤 (实际应结合 LLM 动态调整)
    // 这里简化实现：直接执行一个推理链路
    try {
      // 模拟执行一个三阶段推理：规划 -> 核心逻辑执行 -> 审核
      const executionStep: ReasoningStep = {
        id: 'step_2',
        role: 'executor',
        task: `Execute the planned steps for goal: ${goal}`,
        context: { ...initialContext, plan: 'Step 1: Analyze Market; Step 2: Propose Strategy' }
      };
      chain.steps.push(executionStep);

      const reviewStep: ReasoningStep = {
        id: 'step_3',
        role: 'reviewer',
        task: `Review the results of execution for goal: ${goal}`,
        context: { ...initialContext, result: 'Proposed Strategy: Target US Market with aggressive pricing' }
      };
      chain.steps.push(reviewStep);

      // 设置最终决策
      chain.finalDecision = 'Execute aggressive US pricing strategy with 15% margin buffer.';
      chain.isCompleted = true;
      chain.currentStepIndex = 2;

      logger.info(`[ReasoningPipeline] Reasoning chain completed for goal: ${goal}`);
    } catch (err) {
      logger.error(`[ReasoningPipeline] Reasoning chain failed: ${err}`);
      chain.isCompleted = false;
    }

    return chain;
  }

  /**
   * 结合 LLM 进行真正的动态规划
   */
  static async planNextStep(goal: string, currentSteps: ReasoningStep[], feedback?: string): Promise<ReasoningStep | null> {
    const context = {
      goal,
      history: currentSteps.map(s => ({ role: s.role, task: s.task, result: s.context?.result })),
      feedback
    };

    // 逻辑：基于当前链的状态和反馈，由 LLM 决定下一步
    // 使用 AIService 生成下一个推理步骤
    // 模拟 LLM 决策
    if (currentSteps.length === 0) {
      return { id: `step_${currentSteps.length + 1}`, role: 'planner', task: `Create initial plan for: ${goal}`, context: {} };
    }

    if (currentSteps.length < 3) {
      return { id: `step_${currentSteps.length + 1}`, role: 'executor', task: `Execute subtask based on plan`, context: {} };
    }

    if (currentSteps.length === 3) {
      return { id: `step_${currentSteps.length + 1}`, role: 'reviewer', task: `Final review of output`, context: {} };
    }

    return null; // 推理结束
  }

  /**
   * 自动闭环执行推理
   */
  static async executeAutoReasoning(goal: string, initialContext: any): Promise<ReasoningChain> {
    const chain: ReasoningChain = {
      steps: [],
      currentStepIndex: 0,
      isCompleted: false
    };

    let nextStep = await this.planNextStep(goal, chain.steps);
    while (nextStep && chain.steps.length < 5) { // 限制最大步骤防止死循环
      logger.info(`[ReasoningPipeline] Executing step: ${nextStep.role} - ${nextStep.task}`);
      
      // 模拟执行结果
      nextStep.context = { ...nextStep.context, result: `Success: Task "${nextStep.task}" executed.` };
      chain.steps.push(nextStep);
      chain.currentStepIndex = chain.steps.length - 1;

      nextStep = await this.planNextStep(goal, chain.steps);
    }

    chain.isCompleted = true;
    chain.finalDecision = `Goal "${goal}" achieved after ${chain.steps.length} steps.`;
    return chain;
  }
}