核心能力

技术支撑

实现优势

高性能 Web 服务

Starlette v0.37+

轻量级 ASGI 框架，支持异步请求处理，性能比肩 Node.js

数据验证与序列化

Pydantic v2+

基于 Rust 重构的验证引擎，速度提升 5-10 倍，支持复杂类型校验

异步并发

Python 3.10+ 异步语法

原生支持async/await，避免 IO 阻塞导致的资源浪费

标准文档生成

OpenAPI 3.0 + Swagger UI

从类型提示自动生成交互式文档，零额外开发成本

类型安全

Python 类型提示

静态检查工具可提前发现错误，降低线上故障风险

框架

平均响应时间

吞吐量（QPS）

资源占用率

FastAPI（异步）

82ms

12195

CPU 68% 内存 420MB

Flask（同步 + Gunicorn）

317ms

3152

CPU 89% 内存 510MB

Django（同步）

423ms

2364

CPU 92% 内存 680MB

from fastapi import FastAPI

import asyncpg  # 异步数据库驱动

import asyncio

app = FastAPI()

# 异步数据库连接池（依赖注入）

async def get_db_pool():

    pool = await asyncpg.create_pool(

        user="admin", password="123456", database="ai_data", host="localhost"

    )

    yield pool

    await pool.close()

# 异步API接口

@app.get("/api/ai/history/{user_id}")

async def get_ai_history(user_id: int, pool=Depends(get_db_pool)):

    # 非阻塞数据库查询

    async with pool.acquire() as conn:

        records = await conn.fetch(

            "SELECT content, create_time FROM ai_chat WHERE user_id = $1", user_id

        )

    # 异步处理数据（避免阻塞事件循环）

    history = await asyncio.to_thread(format_history, records)

    return {"user_id": user_id, "history": history}

# CPU密集型任务用线程池隔离

def format_history(records):

    return [{"content": r["content"], "time": r["create_time"].isoformat()} for r in records]

from pydantic import BaseModel, Field, HttpUrl, EmailStr, validator

from typing import List, Optional

from datetime import date

# AI服务请求模型

class AIServiceRequest(BaseModel):

    model_name: str = Field(..., pattern=r"^gpt-\d+|llama-\d+$", description="仅支持GPT与Llama系列模型")

    prompt: str = Field(..., min_length=5, max_length=2000, description="提示词长度5-2000字")

    temperature: float = Field(0.7, ge=0.0, le=1.0, description="温度0.0-1.0")

    web_search: bool = False

    reference_urls: Optional[List[HttpUrl]] = None

    contact_email: EmailStr  # 自动验证邮箱格式

    # 自定义校验规则：开启网页搜索时必须提供参考链接

    @validator("reference_urls")

    def check_urls(cls, v, values):

        if values.get("web_search") and not v:

            raise ValueError("开启网页搜索时必须提供reference_urls")

        return v

# 自动生成请求示例与验证逻辑

@app.post("/api/ai/generate")

async def generate_content(req: AIServiceRequest):

    return {"status": "success", "data": {"task_id": "task_123456"}}

from fastapi import FastAPI

import torch

import asyncio

app = FastAPI()

# 加载模型（启动时加载，避免重复初始化）

model = torch.load("text_classifier.pt").eval()

@app.post("/api/ai/classify")

async def classify_text(text: str):

    # 用线程池运行同步推理代码

    result = await asyncio.to_thread(run_inference, text)

    return {"category": result}

def run_inference(text):

    with torch.no_grad():

        return model.predict([text])[0]

FROM python:3.11-slim

WORKDIR /app

COPY requirements.txt .

RUN pip install --no-cache-dir -r requirements.txt

COPY main.py .

EXPOSE 8000

# 用Uvicorn启动，开启多进程

CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--workers", "4"]

需求点

技术选型

选型理由

高并发处理

FastAPI + Uvicorn

异步架构支撑万级 QPS

请求验证

Pydantic v2

高性能复杂规则校验

模型部署

LangChain + OpenAI API

简化 LLM 集成流程

缓存优化

Redis + fastapi-cache2

降低重复请求成本

监控告警

Prometheus + Grafana

实时追踪服务状态

pip install fastapi uvicorn pydantic langchain openai redis fastapi-cache2

from fastapi import FastAPI, Depends, HTTPException

from fastapi_cache2 import CacheModule, cache

from fastapi_cache2.backends.redis import RedisBackend

from pydantic import BaseModel, Field

from langchain.llms import OpenAI

from redis import asyncio as aioredis

from datetime import timedelta

# 1. 初始化应用与依赖

app = FastAPI(title="企业AI文本生成API", version="1.0")

llm = OpenAI(api_key="sk-xxx", temperature=0.7)

# 2. 初始化Redis缓存

@app.on_event("startup")

async def startup():

    redis = await aioredis.from_url("redis://localhost:6379")

    CacheModule.init(RedisBackend(redis), expire=timedelta(minutes=10))

# 3. 定义请求模型

class GenerateRequest(BaseModel):

    task_type: str = Field(..., pattern=r"summary|translation|creation", description="任务类型")

    content: str = Field(..., min_length=10, max_length=5000)

    target_language: str = Field("zh", description="目标语言")

# 4. 定义认证依赖

def verify_api_key(api_key: str = Depends(OAuth2PasswordBearer(tokenUrl="token"))):

    if api_key != "valid_api_key":

        raise HTTPException(status_code=401, detail="无效API密钥")

    return api_key

# 5. 实现生成接口（带缓存）

@app.post("/api/ai/generate", dependencies=[Depends(verify_api_key)])

@cache()  # 自动缓存相同请求结果

async def generate(req: GenerateRequest):

    # 构建提示词

    prompts = {

        "summary": f"总结以下内容（{req.target_language}）：{req.content}",

        "translation": f"翻译为{req.target_language}：{req.content}",

        "creation": f"基于以下内容创作{req.target_language}文案：{req.content}"

    }

    # 调用LLM生成结果

    result = llm.predict(prompts[req.task_type])

    return {"task_type": req.task_type, "result": result}

uvicorn main:app --host 0.0.0.0 --port 8000 --workers 4