yuzhiran
317 lines
11 KiB
TypeScript
317 lines
11 KiB
TypeScript
import { Injectable, HttpException, HttpStatus } from '@nestjs/common'
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import { InjectModel } from '@nestjs/mongoose'
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import { Model } from 'mongoose'
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import { Interview, InterviewDocument } from './interview.schema'
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import { Progress, ProgressDocument } from '../schemas/progress.schema'
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import { AiService } from '../ai/ai.service'
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import { UserService } from '../user/user.service'
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import { QuotaService } from '../user/quota.service'
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import { TtsService } from '../tts/tts.service'
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import { analyzeSpeech } from '../../common/utils/filler-words'
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@Injectable()
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export class InterviewService {
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constructor(
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@InjectModel(Interview.name) private interviewModel: Model<InterviewDocument>,
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@InjectModel(Progress.name) private progressModel: Model<ProgressDocument>,
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private aiService: AiService,
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private userService: UserService,
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private quotaService: QuotaService,
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private ttsService: TtsService,
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) {}
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async create(userId: string, position: string) {
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await this.quotaService.checkAndDeductInterview(userId)
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const firstQuestion = await this.aiService.call({
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systemPrompt: `你是一位专业的${position}面试官。请针对校招该岗位提出第一个面试问题，要求具体且有针对性。直接输出问题，不要多余内容。`,
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userMessage: `请为${position}岗位的校招候选人提出第一个面试问题。`,
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temperature: 0.8,
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})
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const interview = await this.interviewModel.create({
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userId,
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position,
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messages: [{ role: 'ai', content: firstQuestion }],
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questionCount: 1,
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})
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return {
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id: interview._id.toString(),
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position: interview.position,
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messages: interview.messages,
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questionCount: interview.questionCount,
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}
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}
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async answer(interviewId: string, userId: string, answer: string) {
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const interview = await this.interviewModel.findOne({ _id: interviewId, userId }).exec()
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if (!interview) throw new HttpException('面试不存在', HttpStatus.NOT_FOUND)
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if (interview.status === 'completed') throw new HttpException('面试已结束', HttpStatus.BAD_REQUEST)
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// 检查轮次限制
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const user = await this.userService.getModel().findById(userId).exec()
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const maxRounds = user?.plan !== 'free' ? 10 : 5
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if (interview.questionCount >= maxRounds) {
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throw new HttpException(
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user?.plan !== 'free' ? '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',
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HttpStatus.FORBIDDEN
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)
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}
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// Save user's answer
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interview.messages.push({ role: 'user', content: answer })
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interview.questionCount += 1
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// Analyze filler words in user's answer
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const speechAnalysis = analyzeSpeech(answer)
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interview.fillerWords = speechAnalysis.fillerWords.map(f => ({ word: f.word, count: f.count }))
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interview.fillerScore = speechAnalysis.fillerScore
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interview.fillerDensity = speechAnalysis.fillerDensity
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// AI evaluates answer and generates next question
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const conversationHistory = interview.messages
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.slice(-6)
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.map(m => `${m.role === 'ai' ? '面试官' : '候选人'}: ${m.content}`)
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.join('\n')
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const aiResponse = await this.aiService.call({
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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。
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- 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
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- 评估要简短，然后立即问下一个问题
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- 使用「回答评价：...新的问题：...」的格式。`,
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userMessage: `岗位: ${interview.position}
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对话历史:
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${conversationHistory}
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候选人的回答: ${answer}
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请评估并问下一个问题。`,
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temperature: 0.7,
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maxTokens: 1024,
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})
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interview.messages.push({ role: 'ai', content: aiResponse })
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await interview.save()
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return {
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id: interview._id.toString(),
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messages: interview.messages.slice(-2),
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questionCount: interview.questionCount,
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}
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}
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async answerWithAvatar(interviewId: string, userId: string, answer: string) {
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const base = await this.answer(interviewId, userId, answer)
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const aiMsg = base.messages?.find(m => m.role === 'ai')
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if (aiMsg?.content) {
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try {
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const tts = await this.ttsService.synthesize(aiMsg.content)
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return { ...base, ttsHash: tts.hash, ttsDurationMs: tts.durationMs, ttsAmplitude: tts.amplitudeData }
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} catch {
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// TTS failure is non-critical, return without audio
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}
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}
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return base
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}
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async complete(interviewId: string, userId: string) {
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const interview = await this.interviewModel.findOne({ _id: interviewId, userId }).exec()
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if (!interview) throw new HttpException('面试不存在', HttpStatus.NOT_FOUND)
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if (interview.status === 'completed') throw new HttpException('面试已结束', HttpStatus.BAD_REQUEST)
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// Generate final summary with dimension scores
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const fullConversation = interview.messages
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.map(m => `${m.role === 'ai' ? '面试官' : '候选人'}: ${m.content}`)
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.join('\n')
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const summary = await this.aiService.call({
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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：
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{
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"总体评分": 85,
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"逻辑思维": 80,
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"表达能力": 85,
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"专业度": 90,
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"稳定性": 82,
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"优点": ["逻辑清晰", "举例充分"],
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"不足": ["回答过长", "缺少数据支撑"],
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"建议": ["使用STAR法则", "控制回答时间"]
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}`,
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userMessage: `岗位: ${interview.position}
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面试记录:
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${fullConversation}
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请生成评估报告。`,
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temperature: 0.5,
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maxTokens: 2048,
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})
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// Parse summary
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let totalScore = 0
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let dimensions = { logic: 0, expression: 0, professionalism: 0, stability: 0 }
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try {
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const parsed = JSON.parse(summary)
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totalScore = parsed.总体评分 || parsed.score || 0
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dimensions = {
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logic: parsed.逻辑思维 || 0,
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expression: parsed.表达能力 || 0,
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professionalism: parsed.专业度 || 0,
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stability: parsed.稳定性 || 0,
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}
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} catch {
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const match = summary.match(/(\d{1,3})(?=\s*分)/)
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totalScore = match ? parseInt(match[1]) : 0
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}
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interview.status = 'completed'
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interview.totalScore = totalScore
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interview.summary = summary
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await interview.save()
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// === PROGRESS TRACKING ===
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await this.trackProgress(userId, interview._id.toString(), interview.position, totalScore, dimensions)
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return {
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id: interview._id.toString(),
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totalScore,
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summary,
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dimensions,
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questionCount: interview.questionCount,
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position: interview.position,
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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）',
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}
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}
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private async trackProgress(
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userId: string,
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interviewId: string,
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position: string,
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totalScore: number,
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dimensions: { logic: number; expression: number; professionalism: number; stability: number },
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) {
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let progress = await this.progressModel.findOne({ userId }).exec()
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if (!progress) {
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progress = await this.progressModel.create({
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userId,
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totalInterviews: 0,
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completedInterviews: 0,
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recentScores: [],
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streakHistory: [],
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})
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}
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progress.totalInterviews += 1
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progress.completedInterviews += 1
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// Update rolling averages
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const n = progress.completedInterviews
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progress.avgLogic = Math.round(((progress.avgLogic * (n - 1)) + dimensions.logic) / n)
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progress.avgExpression = Math.round(((progress.avgExpression * (n - 1)) + dimensions.expression) / n)
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progress.avgProfessionalism = Math.round(((progress.avgProfessionalism * (n - 1)) + dimensions.professionalism) / n)
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progress.avgStability = Math.round(((progress.avgStability * (n - 1)) + dimensions.stability) / n)
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// Add to recent scores
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progress.recentScores.push({
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interviewId,
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date: new Date(),
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position,
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totalScore,
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dimensions,
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})
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// Keep only last 20
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if (progress.recentScores.length > 20) {
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progress.recentScores = progress.recentScores.slice(-20)
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}
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// === STREAK TRACKING ===
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const today = new Date()
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today.setHours(0, 0, 0, 0)
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if (progress.lastInterviewDate) {
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const lastDate = new Date(progress.lastInterviewDate)
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lastDate.setHours(0, 0, 0, 0)
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const diffDays = Math.floor((today.getTime() - lastDate.getTime()) / (1000 * 60 * 60 * 24))
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if (diffDays === 1) {
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// Consecutive day
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progress.streak += 1
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} else if (diffDays > 1) {
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// Streak broken
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progress.streak = 1
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}
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// same day = no change
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} else {
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progress.streak = 1
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}
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progress.lastInterviewDate = today
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progress.streakHistory.push(today)
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await progress.save()
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}
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async getDetail(interviewId: string, userId: string) {
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const interview = await this.interviewModel.findOne({ _id: interviewId, userId }).exec()
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if (!interview) throw new HttpException('面试不存在', HttpStatus.NOT_FOUND)
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// Compute aggregate speech analysis from user messages
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const userAnswers = interview.messages
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.filter(m => m.role === 'user')
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.map(m => m.content)
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.join('\n')
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const speechAnalysis = userAnswers ? analyzeSpeech(userAnswers) : null
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// Parse dimensions from summary JSON
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let dimensions: Record<string, number> | null = null
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if (interview.summary) {
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try {
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const parsed = JSON.parse(interview.summary)
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dimensions = {
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logic: parsed['逻辑思维'] || 0,
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expression: parsed['表达能力'] || 0,
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professionalism: parsed['专业度'] || 0,
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stability: parsed['稳定性'] || 0,
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}
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} catch {}
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}
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return {
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...interview.toObject(),
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fillerWords: speechAnalysis?.fillerWords || interview.fillerWords,
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fillerScore: speechAnalysis?.fillerScore || interview.fillerScore,
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fillerDensity: speechAnalysis?.fillerDensity || interview.fillerDensity,
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speechAnalysis,
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dimensions,
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}
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}
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async getList(userId: string) {
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const interviews = await this.interviewModel
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.find({ userId })
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.sort({ createdAt: -1 })
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.select('position status totalScore questionCount createdAt')
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.exec()
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return interviews.map(i => ({
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id: i._id.toString(),
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position: i.position,
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status: i.status,
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totalScore: i.totalScore,
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questionCount: i.questionCount,
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time: i.createdAt,
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}))
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}
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async getStats(userId: string) {
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const interviews = await this.interviewModel.find({ userId }).exec()
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const completed = interviews.filter(i => i.status === 'completed')
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const totalScore = completed.reduce((s, i) => s + i.totalScore, 0)
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return {
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interviewCount: interviews.length,
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completedCount: completed.length,
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avgScore: completed.length > 0 ? Math.round(totalScore / completed.length) : 0,
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}
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}
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} |