高并发异步与 AI 流式实战
在大语言模型（LLM）与智能体（Agent）开发中，流式传输 JSON Lines、Server-Sent Events (SSE)、WebSocket 双向长连接以及底层异步调度是构建现代 AI 微服务的核心。
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FastAPI 异步并发执行机制深度剖析
async def（异步函数）：运行在主线程的asyncio事件循环中，必须配合非阻塞库（如httpx、aiofiles、asyncio.sleep）。遇到await时自动让出 CPU 控制权支撑万级并发。def（同步函数）：FastAPI 自动将其派遣到外部线程池（ThreadPoolExecutor）执行，避免阻塞主事件循环。
避免在 async def 中调用同步阻塞代码
切勿在 async def 中调用同步阻塞代码（如 time.sleep(5) 或 requests.get），否则会导致整个主线程的事件循环被完全卡死！
流式传输 JSON Lines (Stream JSON Lines)
FastAPI 官方文档展示了通过 StreamingResponse 逐行推送 JSON Lines (.jsonl) 数据流的模式：
import asyncio
import json
from typing import AsyncGenerator
from fastapi import FastAPI
from fastapi.responses import StreamingResponse
app = FastAPI()
async def json_lines_generator() -> AsyncGenerator[str, None]:
for i in range(1, 6):
record = {"event_id": i, "status": "processing", "progress": i * 20}
# 每行输出一个独立的 JSON 字符串并以 \n 换行
yield json.dumps(record, ensure_ascii=False) + "\n"
await asyncio.sleep(0.5)
@app.get("/stream/jsonl")
async def stream_json_lines():
return StreamingResponse(
json_lines_generator(),
media_type="application/x-ndjson"
)Server-Sent Events (SSE) 服务端推送
import asyncio
import json
from typing import AsyncGenerator
from fastapi import FastAPI
from fastapi.responses import StreamingResponse
app = FastAPI()
async def sse_event_generator() -> AsyncGenerator[str, None]:
for i in range(1, 6):
event_data = {"step": i, "message": f"思考步骤 {i}..."}
# SSE 标准数据行以 data: 开头，以 \n\n 结尾
yield f"data: {json.dumps(event_data, ensure_ascii=False)}\n\n"
await asyncio.sleep(0.5)
yield "data: [DONE]\n\n"
@app.get("/api/v1/sse/events")
async def stream_sse():
return StreamingResponse(
sse_event_generator(),
media_type="text/event-stream",
headers={"Cache-Control": "no-cache", "Connection": "keep-alive"}
)大模型 (LLM) 真实流式输出实战
from typing import AsyncGenerator
from fastapi import FastAPI
from fastapi.responses import StreamingResponse
from pydantic import BaseModel
from openai import AsyncOpenAI
import os
app = FastAPI()
client = AsyncOpenAI(
api_key=os.getenv("OPENAI_API_KEY", "your-key"),
base_url=os.getenv("OPENAI_BASE_URL", "https://api.openai.com/v1")
)
class ChatRequest(BaseModel):
prompt: str
model: str = "gpt-4o"
async def llm_stream_generator(prompt: str, model: str) -> AsyncGenerator[str, None]:
stream = await client.chat.completions.create(
model=model,
messages=[{"role": "user", "content": prompt}],
stream=True
)
async for chunk in stream:
content = chunk.choices[0].delta.content
if content:
yield f"data: {content}\n\n"
yield "data: [DONE]\n\n"
@app.post("/api/v1/chat/stream")
async def chat_stream(request: ChatRequest):
return StreamingResponse(
llm_stream_generator(request.prompt, request.model),
media_type="text/event-stream",
headers={"Cache-Control": "no-cache", "Connection": "keep-alive"}
)WebSocket 全双工长连接实战
from typing import list
from fastapi import FastAPI, WebSocket, WebSocketDisconnect
app = FastAPI()
class ConnectionManager:
def __init__(self):
self.active_connections: list[WebSocket] = []
async def connect(self, websocket: WebSocket):
await websocket.accept()
self.active_connections.append(websocket)
def disconnect(self, websocket: WebSocket):
self.active_connections.remove(websocket)
async def broadcast(self, message: str):
for connection in self.active_connections:
await connection.send_text(message)
manager = ConnectionManager()
@app.websocket("/ws/chat/{client_id}")
async def websocket_chat(websocket: WebSocket, client_id: str):
await manager.connect(websocket)
await manager.broadcast(f"用户 [{client_id}] 进入了房间")
try:
while True:
data = await websocket.receive_text()
await manager.broadcast(f"[{client_id}]: {data}")
except WebSocketDisconnect:
manager.disconnect(websocket)
await manager.broadcast(f"用户 [{client_id}] 离开了房间")最后更新于