LLM 前端集成方案This topic has been discussed many times in the community, but with each version update, many conclusions need revising. This article revisits the topic based on the latest version.

Getting Started

The key is to understand the core logic:

import { openai } from '@ai-sdk/openai'
import { streamText } from 'ai'

export async function POST(req) {
  const { messages } = await req.json()
  const result = await streamText({
    model: openai('gpt-4o'),
    messages,
    system: '你是一个专业的前端开发助手。',
    maxTokens: 2000
  })
  return result.toDataStreamResponse()
}

Performance optimization should be tailored to specific scenarios; not every situation requires aggressive optimization.

Source Code Analysis

We can improve this in the following ways:

'use client'
import { useChat } from 'ai/react'

export function AIChat() {
  const { messages, input, handleInputChange, handleSubmit, isLoading } = useChat({
    api: '/api/chat'
  })
  return (
    <div className="chat-container">
      {messages.map(m => (
        <div key={m.id} className={`message ${m.role}`}>
          <p>{m.content}</p>
        </div>
      ))}
      <form onSubmit={handleSubmit}>
        <input value={input} onChange={handleInputChange} />
        <button type="submit" disabled={isLoading}>发送</button>
      </form>
    </div>
  )
}

This solution has been running stably in production for over six months and has been validated in practice.

Real-world Application

Let's start with the basic implementation:

import { openai } from '@ai-sdk/openai'
import { streamText } from 'ai'

export async function POST(req) {
  const { messages } = await req.json()
  const result = await streamText({
    model: openai('gpt-4o'),
    messages,
    system: '你是一个专业的前端开发助手。',
    maxTokens: 2000
  })
  return result.toDataStreamResponse()
}

This code demonstrates the basic usage. In real projects, you'll also need to consider error handling and edge cases.

Optimization Tips

Building on this, we can further optimize:

'use client'
import { useChat } from 'ai/react'

export function AIChat() {
  const { messages, input, handleInputChange, handleSubmit, isLoading } = useChat({
    api: '/api/chat'
  })
  return (
    <div className="chat-container">
      {messages.map(m => (
        <div key={m.id} className={`message ${m.role}`}>
          <p>{m.content}</p>
        </div>
      ))}
      <form onSubmit={handleSubmit}>
        <input value={input} onChange={handleInputChange} />
        <button type="submit" disabled={isLoading}>发送</button>
      </form>
    </div>
  )
}

This pattern is very practical in large projects and can significantly reduce maintenance costs.

Summary

• In team collaboration, conventions and documentation matter more than the technology itself
• Keep an eye on community trends; technical approaches need continuous iteration
• Don't adopt new technology just for the sake of it
• Code samples are for reference only and need adjusting to your business scenario