327 lines
7.2 KiB
Markdown
327 lines
7.2 KiB
Markdown
# 流式输出（SSE）接入
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> 使用 Server-Sent Events 实现 AI 流式响应，提升用户体验。
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## 🌊 什么是流式输出？
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流式输出（Streaming）是一种数据传输方式，服务器将数据「分块」发送给客户端，而不是等待全部完成后再返回。
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### 对比
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| 方式 | 等待时间 | 体验 | 适用场景 |
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|------|----------|------|----------|
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| 普通请求 | 等待完整响应 | 一般 | 简单、快速的请求 |
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| 流式输出 | 实时看到生成过程 | ⭐ 优秀 | AI 生成、长文本 |
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## 🚀 开始使用
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### 前端：接收流式响应
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```typescript
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import { WebsopyClient } from '@websopy/sdk'
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const client = new WebsopyClient({
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apiKey: process.env.WEBSOPY_API_KEY
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})
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async function streamChat() {
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const stream = await client.ai.chatStream({
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messages: [
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{ role: 'user', content: '写一篇关于 AI 的文章' }
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]
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})
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// 方法 1：遍历数据块
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for await (const chunk of stream) {
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if (chunk.type === 'content') {
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process.stdout.write(chunk.content)
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}
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}
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// 或方法 2：事件监听
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// stream.on('data', (chunk) => { ... })
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// stream.on('end', () => { ... })
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}
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```
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### 响应数据结构
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```typescript
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interface StreamChunk {
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type: 'content' | 'tool_call' | 'done' | 'error'
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content?: string
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tool?: {
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name: string
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arguments: string
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}
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usage?: {
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promptTokens: number
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completionTokens: number
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}
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}
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// 示例数据块
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{ type: 'content', content: 'AI' }
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{ type: 'content', content: ' 正在' }
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{ type: 'content', content: '改变' }
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{ type: 'content', content: '世界...' }
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{ type: 'done', usage: { promptTokens: 20, completionTokens: 150 } }
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```
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## 🌐 原生 SSE 实现
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如果不用 SDK，直接使用 Fetch API：
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### GET 请求
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```typescript
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async function nativeStream() {
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const response = await fetch(
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'https://api.websopy.com/v1/ai/chat?message=Hello',
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{
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headers: {
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'Authorization': `Bearer ${apiKey}`,
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'Accept': 'text/event-stream'
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}
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}
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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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while (true) {
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const { done, value } = await reader.read()
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if (done) break
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const chunk = decoder.decode(value)
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// 解析 SSE 数据
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const lines = chunk.split('\n')
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for (const line of lines) {
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if (line.startsWith('data: ')) {
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const data = JSON.parse(line.slice(6))
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console.log('收到:', data)
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}
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}
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}
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}
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```
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### POST 请求
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```typescript
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async function streamPost() {
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const response = await fetch(
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'https://api.websopy.com/v1/ai/chat/stream',
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{
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method: 'POST',
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headers: {
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'Authorization': `Bearer ${apiKey}`,
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'Content-Type': 'application/json',
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'Accept': 'text/event-stream'
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},
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body: JSON.stringify({
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messages: [
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{ role: 'user', content: '解释量子计算' }
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],
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model: 'gpt-4',
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stream: true
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})
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}
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)
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// 处理流...
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}
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```
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## 🎨 前端组件示例
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### React Hook
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```typescript
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import { useState } from 'react'
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function useStreamingChat() {
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const [messages, setMessages] = useState([])
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const [streaming, setStreaming] = useState(false)
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const [currentContent, setCurrentContent] = useState('')
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const sendMessage = async (content) => {
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setStreaming(true)
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setCurrentContent('')
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// 添加用户消息
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setMessages(prev => [...prev, { role: 'user', content }])
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const stream = await client.ai.chatStream({
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messages: [...messages, { role: 'user', content }]
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})
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for await (const chunk of stream) {
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if (chunk.type === 'content') {
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setCurrentContent(prev => prev + chunk.content)
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}
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}
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// 保存完整消息
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setMessages(prev => [...prev, { role: 'assistant', content: currentContent }])
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setStreaming(false)
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}
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return { messages, streaming, currentContent, sendMessage }
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}
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```
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### Vue Composition API
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```typescript
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import { ref } from 'vue'
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export function useStreamingChat() {
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const messages = ref([])
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const streaming = ref(false)
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const currentContent = ref('')
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const sendMessage = async (content) => {
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streaming.value = true
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currentContent.value = ''
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messages.value.push({ role: 'user', content })
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const stream = await client.ai.chatStream({
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messages: messages.value
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})
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for await (const chunk of stream) {
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if (chunk.type === 'content') {
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currentContent.value += chunk.content
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}
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}
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messages.value.push({ role: 'assistant', content: currentContent.value })
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streaming.value = false
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}
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return { messages, streaming, currentContent, sendMessage }
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}
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```
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## ⚙️ 服务端 SSE 配置
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### Node.js Express
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```typescript
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import express from 'express'
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app.post('/api/chat/stream', async (req, res) => {
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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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// 保持连接
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res.flushHeaders()
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try {
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const stream = await client.ai.chatStream(req.body)
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for await (const chunk of stream) {
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res.write(`data: ${JSON.stringify(chunk)}\n\n`)
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}
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res.write('data: [DONE]\n\n')
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} catch (error) {
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res.write(`data: ${JSON.stringify({ error: error.message })}\n\n`)
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}
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res.end()
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})
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```
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### Next.js API Route
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```typescript
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// app/api/chat/route.ts
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export async function POST(req: Request) {
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const encoder = new TextEncoder()
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const stream = new ReadableStream({
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async start(controller) {
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try {
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const stream = await client.ai.chatStream(await req.json())
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for await (const chunk of stream) {
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controller.enqueue(
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encoder.encode(`data: ${JSON.stringify(chunk)}\n\n`)
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)
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}
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controller.enqueue(encoder.encode('data: [DONE]\n\n'))
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} catch (error) {
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controller.enqueue(
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encoder.encode(`data: ${JSON.stringify({ error: error.message })}\n\n`)
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)
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}
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controller.close()
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}
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})
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return new Response(stream, {
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headers: {
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'Content-Type': 'text/event-stream',
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'Cache-Control': 'no-cache',
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'Connection': 'keep-alive'
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}
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})
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}
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```
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## ❓ 常见问题
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### Q: 流式中断怎么办？
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```typescript
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async function streamWithReconnect() {
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const maxRetries = 3
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let retries = 0
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while (retries < maxRetries) {
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try {
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const stream = await client.ai.chatStream({...})
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return stream
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} catch (error) {
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retries++
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await sleep(1000 * retries) // 指数退避
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}
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}
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}
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```
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### Q: 如何在流式过程中取消请求？
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```typescript
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const controller = new AbortController()
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// 发送请求
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const stream = await client.ai.chatStream({
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messages: [...],
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signal: controller.signal
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})
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// 取消请求
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controller.abort()
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```
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### Q: SSE 和 WebSocket 区别？
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| 特性 | SSE | WebSocket |
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|------|-----|-----------|
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| 方向 | 单向（服务端→客户端） | 双向 |
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| 协议 | HTTP | ws:// |
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| 重连 | 自动 | 需手动处理 |
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| 复杂度 | 简单 | 复杂 |
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| 适用 | AI 流式输出 | 实时聊天 |
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---
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**上一步：** [API Key 创建与管理](./apikey.md)
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**下一步：** [Webhook 事件接入](./webhook.md)
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