Prompt Template 周报生成——用 NestJS + LangChain 搭建第一个 AI Demo

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@Public()
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@Controller('demos')
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export class DemosController {
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  @Post(':id/run')
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  async runDemo(@Param('id') id: string, @Body() body: unknown) {
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    return await this.demosService.runDemo(id, body)
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  }
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  @Post(':id/stream')
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  @SkipApiResponse()
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  async streamDemo(@Param('id') id: string, @Body() body: unknown, @Res() res: Response) {
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    prepareSseResponse(res)
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    writeSseEvent(res, { event: 'meta', data: { demoId: id, status: 'started' } })
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    for await (const text of this.demosService.streamDemo(id, body)) {
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      writeSseEvent(res, { event: 'token', data: { text } })
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    }
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    writeSseEvent(res, { event: 'done', data: { status: 'success' } })
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  }
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}

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import { z } from 'zod'
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export const REPORT_ROLES = ['部门Leader', '技术研发', '老板'] as const
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export const REPORT_TYPES = ['日报', '周报', '月报', '季度总结', '半年总结', '年度总结'] as const
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export const promptTemplateWeeklyReportInputSchema = z.object({
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  role: z.enum(REPORT_ROLES),
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  reportType: z.enum(REPORT_TYPES),
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  dateRange: z.string().min(1, '汇报时间不能为空'),
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  companyName: z.string().optional().default(''),
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  teamName: z.string().optional().default(''),
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  managerName: z.string().optional().default(''),
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  teamGoal: z.string().optional().default(''),
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  devActivities: z.string().min(15, '主要内容至少 15 个字'),
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  reportTemplate: z.string().optional().default(''),
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  customPrompt: z.string().optional().default(''),
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})

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export const REPORT_PROMPT = `
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你是一名{role}，需要根据以下数据生成一份专业的 Markdown 文档。
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【角色视角】{rolePerspective}
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报告类型：{reportType}
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{authorName}
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{companyName}{teamName}{managerName}时间范围：{dateRange}
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{teamGoal}
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活动数据：
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{devActivities}
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{fewShotExample}
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请生成一份格式规范的【{reportType}】，要求：
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- 开头有简短的整体 summary（两三句话）
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- {reportTypeGuide}
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- 语气和视角贴合 {role} 的身份定位
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- 适合作为给老板和团队传阅的专业文档
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`

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export const ROLE_PERSPECTIVE: Record<string, string> = {
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  部门Leader: '关注团队整体交付、风险管控和资源协调，语气专业稳重',
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  技术研发: '关注技术实现细节、性能优化和技术债，语气务实具体',
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  老板: '关注业务价值、ROI 和战略方向，语气简洁高度概括',
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}

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export const REPORT_TYPE_GUIDE: Record<string, string> = {
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  日报: '按【今日完成 / 明日计划 / 风险与阻塞】三部分组织',
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  周报: '有按模块/项目拆分的小结，用 Markdown 表格列出关键指标',
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  月报: '突出月度关键指标变化趋势，有下月重点规划',
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  季度总结: '按维度（业务/技术/团队）拆分复盘',
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  // ...
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}

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@Injectable()
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export class PromptTemplateWeeklyReportService implements DemoRunner {
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  readonly demoId = 'prompt-template-weekly-report'
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  async run(body: unknown): Promise<string> {
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    const request = this.parseRunRequest(body)                           // ① Zod 校验
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    const model = this.aiService.createStableModel({ temperature: 0.3 }) // ② 创建模型
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    const prompt = await this.formatPrompt(request)                      // ③ 填充模板
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    const response = await model.invoke(prompt)                          // ④ 调用 AI
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    return stringifyAiContent(response.content)
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  }
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  private async formatPrompt(request: PromptTemplateWeeklyReportInput): Promise<string> {
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    const templateSource = request.customPrompt || REPORT_PROMPT
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    const promptTemplate = PromptTemplate.fromTemplate(templateSource)
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    const fewShotExample = request.reportTemplate
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      ? `\n参考示例（请参照此风格和结构）：\n${request.reportTemplate}\n`
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      : ''
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    return promptTemplate.format({
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      ...request,
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      reportTypeGuide: REPORT_TYPE_GUIDE[request.reportType] ?? '结构清晰，重点突出',
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      rolePerspective: ROLE_PERSPECTIVE[request.role] ?? '',
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      fewShotExample,
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    })
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  }
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}

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async *stream(body: unknown): AsyncGenerator<string> {
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  const request = this.parseRunRequest(body)
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  const model = this.aiService.createStreamingModel({ temperature: 0.3 })
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  const prompt = await this.formatPrompt(request)
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  const stream = await model.stream(prompt)  // ← LangChain stream
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  for await (const chunk of stream) {
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    const text = stringifyAiContent(chunk.content)
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    if (text.length > 0) yield text          // ← 逐 token 产出
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  }
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}

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event: meta
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data: {"demoId":"prompt-template-weekly-report","status":"started"}
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event: token
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data: {"text":"这"}
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event: token
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data: {"text":"是"}
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event: token
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data: {"text":"一份"}
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event: done
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data: {"status":"success"}

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const response = await fetch(`${apiBase}/demos/${id}/stream`, {
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  method: 'POST',
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  headers: { 'Content-Type': 'application/json' },
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  body: JSON.stringify({ input: { ...form } }),
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})
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const reader = response.body.getReader()
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const decoder = new TextDecoder()
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let buffer = ''
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while (true) {
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  const { value, done } = await reader.read()
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  if (done) break
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  buffer += decoder.decode(value, { stream: true })
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  // 解析 SSE frame → 增量更新 UI
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}