初始化：职引项目 v1.0

2026-06-08 16:28:00 +08:00
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+import { Injectable, HttpException, HttpStatus, forwardRef, Inject } from '@nestjs/common'
+import { InjectModel } from '@nestjs/mongoose'
+import { Model } from 'mongoose'
+import { Interview, InterviewDocument } from './interview.schema'
+import { Progress, ProgressDocument } from '../schemas/progress.schema'
+import { AiService } from '../ai/ai.service'
+import { UserService } from '../user/user.service'
+
+@Injectable()
+export class InterviewService {
+  constructor(
+    @InjectModel(Interview.name) private interviewModel: Model<InterviewDocument>,
+    @InjectModel(Progress.name) private progressModel: Model<ProgressDocument>,
+    private aiService: AiService,
+    private userService: UserService,
+  ) {}
+
+  async create(userId: string, position: string) {
+    // 扣减使用次数
+    await this.userService.deductRemaining(userId)
+
+    const firstQuestion = await this.aiService.call({
+      systemPrompt: `你是一位专业的${position}面试官。请针对校招该岗位提出第一个面试问题，要求具体且有针对性。直接输出问题，不要多余内容。`,
+      userMessage: `请为${position}岗位的校招候选人提出第一个面试问题。`,
+      temperature: 0.8,
+    })
+
+    const interview = await this.interviewModel.create({
+      userId,
+      position,
+      messages: [{ role: 'ai', content: firstQuestion }],
+      questionCount: 1,
+    })
+
+    return {
+      id: interview._id.toString(),
+      position: interview.position,
+      messages: interview.messages,
+      questionCount: interview.questionCount,
+    }
+  }
+
+  async answer(interviewId: string, userId: string, answer: string) {
+    const interview = await this.interviewModel.findOne({ _id: interviewId, userId }).exec()
+    if (!interview) throw new HttpException('面试不存在', HttpStatus.NOT_FOUND)
+    if (interview.status === 'completed') throw new HttpException('面试已结束', HttpStatus.BAD_REQUEST)
+
+    // 检查轮次限制
+    const user = await this.userService.getModel().findById(userId).exec()
+    const maxRounds = user?.plan === 'vip' ? 10 : 5
+    if (interview.questionCount >= maxRounds) {
+      throw new HttpException(
+        user?.plan === 'vip' ? '已达到每场面试最大轮次（10轮）' : '免费版每场最多5轮，升级会员可享10轮',
+        HttpStatus.FORBIDDEN
+      )
+    }
+
+    // Save user's answer
+    interview.messages.push({ role: 'user', content: answer })
+    interview.questionCount += 1
+
+    // AI evaluates answer and generates next question
+    const conversationHistory = interview.messages
+      .slice(-6)
+      .map(m => `${m.role === 'ai' ? '面试官' : '候选人'}: ${m.content}`)
+      .join('\n')
+
+    const aiResponse = await this.aiService.call({
+      systemPrompt: `你是一位专业的面试官。评估候选人的回答，然后提出下一个问题。
+- 如果这已经是第5-8个问题，给出总结性评价并建议结束面试
+- 评估要简短，然后立即问下一个问题
+- 使用「回答评价：...新的问题：...」的格式。`,
+      userMessage: `岗位: ${interview.position}
+对话历史:
+${conversationHistory}
+
+候选人的回答: ${answer}
+
+请评估并问下一个问题。`,
+      temperature: 0.7,
+      maxTokens: 1024,
+    })
+
+    interview.messages.push({ role: 'ai', content: aiResponse })
+    await interview.save()
+
+    return {
+      id: interview._id.toString(),
+      messages: interview.messages.slice(-2),
+      questionCount: interview.questionCount,
+    }
+  }
+
+  async complete(interviewId: string, userId: string) {
+    const interview = await this.interviewModel.findOne({ _id: interviewId, userId }).exec()
+    if (!interview) throw new HttpException('面试不存在', HttpStatus.NOT_FOUND)
+    if (interview.status === 'completed') throw new HttpException('面试已结束', HttpStatus.BAD_REQUEST)
+
+    // Generate final summary with dimension scores
+    const fullConversation = interview.messages
+      .map(m => `${m.role === 'ai' ? '面试官' : '候选人'}: ${m.content}`)
+      .join('\n')
+
+    const summary = await this.aiService.call({
+      systemPrompt: `你是一位专业的面试评估师。根据面试对话，生成评估报告。输出JSON格式：
+{
+  "总体评分": 85,
+  "逻辑思维": 80,
+  "表达能力": 85,
+  "专业度": 90,
+  "稳定性": 82,
+  "优点": ["逻辑清晰", "举例充分"],
+  "不足": ["回答过长", "缺少数据支撑"],
+  "建议": ["使用STAR法则", "控制回答时间"]
+}`,
+      userMessage: `岗位: ${interview.position}
+
+面试记录:
+${fullConversation}
+
+请生成评估报告。`,
+      temperature: 0.5,
+      maxTokens: 2048,
+    })
+
+    // Parse summary
+    let totalScore = 0
+    let dimensions = { logic: 0, expression: 0, professionalism: 0, stability: 0 }
+    try {
+      const parsed = JSON.parse(summary)
+      totalScore = parsed.总体评分 || parsed.score || 0
+      dimensions = {
+        logic: parsed.逻辑思维 || 0,
+        expression: parsed.表达能力 || 0,
+        professionalism: parsed.专业度 || 0,
+        stability: parsed.稳定性 || 0,
+      }
+    } catch {
+      const match = summary.match(/(\d{1,3})(?=\s*分)/)
+      totalScore = match ? parseInt(match[1]) : 0
+    }
+
+    interview.status = 'completed'
+    interview.totalScore = totalScore
+    interview.summary = summary
+    await interview.save()
+
+    // === PROGRESS TRACKING ===
+    await this.trackProgress(userId, interview._id.toString(), interview.position, totalScore, dimensions)
+
+    return {
+      id: interview._id.toString(),
+      totalScore,
+      summary,
+      dimensions,
+      questionCount: interview.questionCount,
+      position: interview.position,
+      contributionPrompt: '分享你的面试经验，帮助更多同学！是否愿意贡献面经？（选填公司名称 + 遇到的面试题）',
+    }
+  }
+
+  private async trackProgress(
+    userId: string,
+    interviewId: string,
+    position: string,
+    totalScore: number,
+    dimensions: { logic: number; expression: number; professionalism: number; stability: number },
+  ) {
+    let progress = await this.progressModel.findOne({ userId }).exec()
+    if (!progress) {
+      progress = await this.progressModel.create({
+        userId,
+        totalInterviews: 0,
+        completedInterviews: 0,
+        recentScores: [],
+        streakHistory: [],
+      })
+    }
+
+    progress.totalInterviews += 1
+    progress.completedInterviews += 1
+
+    // Update rolling averages
+    const n = progress.completedInterviews
+    progress.avgLogic = Math.round(((progress.avgLogic * (n - 1)) + dimensions.logic) / n)
+    progress.avgExpression = Math.round(((progress.avgExpression * (n - 1)) + dimensions.expression) / n)
+    progress.avgProfessionalism = Math.round(((progress.avgProfessionalism * (n - 1)) + dimensions.professionalism) / n)
+    progress.avgStability = Math.round(((progress.avgStability * (n - 1)) + dimensions.stability) / n)
+
+    // Add to recent scores
+    progress.recentScores.push({
+      interviewId,
+      date: new Date(),
+      position,
+      totalScore,
+      dimensions,
+    })
+    // Keep only last 20
+    if (progress.recentScores.length > 20) {
+      progress.recentScores = progress.recentScores.slice(-20)
+    }
+
+    // === STREAK TRACKING ===
+    const today = new Date()
+    today.setHours(0, 0, 0, 0)
+
+    if (progress.lastInterviewDate) {
+      const lastDate = new Date(progress.lastInterviewDate)
+      lastDate.setHours(0, 0, 0, 0)
+      const diffDays = Math.floor((today.getTime() - lastDate.getTime()) / (1000 * 60 * 60 * 24))
+
+      if (diffDays === 1) {
+        // Consecutive day
+        progress.streak += 1
+      } else if (diffDays > 1) {
+        // Streak broken
+        progress.streak = 1
+      }
+      // same day = no change
+    } else {
+      progress.streak = 1
+    }
+
+    progress.lastInterviewDate = today
+    progress.streakHistory.push(today)
+
+    await progress.save()
+  }
+
+  async getDetail(interviewId: string, userId: string) {
+    const interview = await this.interviewModel.findOne({ _id: interviewId, userId }).exec()
+    if (!interview) throw new HttpException('面试不存在', HttpStatus.NOT_FOUND)
+    return interview
+  }
+
+  async getList(userId: string) {
+    const interviews = await this.interviewModel
+      .find({ userId })
+      .sort({ createdAt: -1 })
+      .select('position status totalScore questionCount createdAt')
+      .exec()
+
+    return interviews.map(i => ({
+      id: i._id.toString(),
+      position: i.position,
+      status: i.status,
+      totalScore: i.totalScore,
+      questionCount: i.questionCount,
+      time: (i as any).createdAt,
+    }))
+  }
+
+  async getStats(userId: string) {
+    const interviews = await this.interviewModel.find({ userId }).exec()
+    const completed = interviews.filter(i => i.status === 'completed')
+    const totalScore = completed.reduce((s, i) => s + i.totalScore, 0)
+
+    return {
+      interviewCount: interviews.length,
+      completedCount: completed.length,
+      avgScore: completed.length > 0 ? Math.round(totalScore / completed.length) : 0,
+    }
+  }
+}