适配器模式是什么？适合什么场景？

import OpenAI from 'openai'
import Anthropic from '@anthropic-ai/sdk'

// 统一接口：chat({ system, messages }) → { text, usage: { input, output } }
function createOpenAIAdapter({ apiKey, model }) {
  const client = new OpenAI({ apiKey })
  return {
    async chat({ system, messages }) {
      const res = await client.chat.completions.create({
        model,
        messages: system ? [{ role: 'system', content: system }, ...messages] : messages,
      })
      return {
        text: res.choices[0].message.content,
        usage: { input: res.usage.prompt_tokens, output: res.usage.completion_tokens },
      }
    },
  }
}

function createAnthropicAdapter({ apiKey, model, maxTokens = 4096 }) {
  const client = new Anthropic({ apiKey })
  return {
    async chat({ system, messages }) {
      const res = await client.messages.create({ model, system, max_tokens: maxTokens, messages })
      return {
        // content 是内容块数组，只取其中的文本块
        text: res.content.filter((block) => block.type === 'text').map((block) => block.text).join(''),
        usage: { input: res.usage.input_tokens, output: res.usage.output_tokens },
      }
    },
  }
}

// 业务代码只依赖统一接口；厂商、模型名和密钥都来自服务端的配置
const llm = process.env.LLM_PROVIDER === 'anthropic'
  ? createAnthropicAdapter({ apiKey: process.env.ANTHROPIC_API_KEY, model: process.env.LLM_MODEL })
  : createOpenAIAdapter({ apiKey: process.env.OPENAI_API_KEY, model: process.env.LLM_MODEL })

const { text } = await llm.chat({
  system: '你是一名技术面试官',
  messages: [{ role: 'user', content: '请出一道 Node.js 的面试题' }],
})

// 旧接口返回：{ user_name: 'Tom', avatar_url: null, reg_time: 1700000000 }
function adaptUser(raw) {
  return {
    name: raw.user_name,
    avatar: raw.avatar_url ?? '/default-avatar.png', // 补上缺失的值
    registeredAt: new Date(raw.reg_time * 1000),      // 秒级时间戳转成 Date
  }
}

export async function fetchUser(id) {
  const res = await fetch(`/api/user/${id}`)
  return adaptUser(await res.json())
}