65 lines
2.0 KiB
TypeScript
65 lines
2.0 KiB
TypeScript
// SSE 端点（P5：ai.StreamChat gRPC stream → BFF → 前端 EventSource）
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// 路由：GET /ai/chat/stream
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// 裁决依据：02-architecture-design.md §10
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import { Controller, Get, Query, Res, Req } from "@nestjs/common";
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import type { Request, Response } from "express";
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import { TeacherService } from "./teacher.service.js";
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import type { CallContext } from "../clients/types.js";
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import type { ChatMessage } from "../clients/ai/ai.types.js";
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@Controller("/ai/chat")
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export class AiChatSseController {
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constructor(private readonly teacherService: TeacherService) {}
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/**
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* SSE 流式聊天端点
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* 前端使用 EventSource 连接，BFF 透传 ai.StreamChat gRPC stream
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*/
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@Get("stream")
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async streamChat(
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@Req() req: Request,
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@Res() res: Response,
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@Query("prompt") prompt: string,
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): Promise<void> {
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// 设置 SSE 响应头
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res.setHeader("Content-Type", "text/event-stream");
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res.setHeader("Cache-Control", "no-cache");
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res.setHeader("Connection", "keep-alive");
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res.setHeader("X-Accel-Buffering", "no"); // 禁用 Nginx 缓冲
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// 构造 CallContext（从 req.headers 提取 x-user-id 和 x-request-id）
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const ctx: CallContext = {
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userId: (req.headers["x-user-id"] as string) ?? "dev-user",
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traceId: (req.headers["x-request-id"] as string) ?? "",
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};
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// 构造聊天消息
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const messages: ChatMessage[] = [{ role: "user", content: prompt }];
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try {
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const stream = await this.teacherService.streamChat(ctx, messages);
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for await (const chunk of stream) {
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const data = JSON.stringify({
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content: chunk.content,
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done: chunk.done,
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});
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res.write(`data: ${data}\n\n`);
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if (chunk.done) {
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break;
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}
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}
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res.write("event: done\ndata: {}\n\n");
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} catch (err) {
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const errorData = JSON.stringify({
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error: err instanceof Error ? err.message : "Unknown error",
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});
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res.write(`event: error\ndata: ${errorData}\n\n`);
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} finally {
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res.end();
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}
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}
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}
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