优化OpenAI API：解决GPT应用中意外代码生成问题

import express from "express";
import * as dotenv from "dotenv";
import cors from 'cors';
import { Configuration, OpenAIApi } from "openai";

dotenv.config();

const configuration = new Configuration({
    apiKey: process.env.OPENAI_API_KEY,
});

const openai = new OpenAIApi(configuration);

const app = express();
app.use(cors());
app.use(express.json());

app.get("/", (req, res) => {
    res.status(200).send({
        message: "Welcome to OpenAI API",
    });
});

app.post('/', async (req, res) => {
    try {
        const prompt = req.body.prompt;

        const response = await openai.createCompletion({
            model: "text-davinci-003", // 问题模型
            prompt: `${prompt}`,
            temperature: 0,
            max_tokens: 4000,
            top_p: 1,
            frequency_penalty: 0.5,
            presence_penalty: 0,
        });
        res.status(200).send({
            bot: response.data.choices[0].text
        })
    }
    catch (error) {
        console.log(error);
        res.status(500).send({ error })
    }
})

app.listen(5000, () => console.log("Sever is running on port :- http://localhost:5000"))

package com.example.demo.controller;

import java.util.List;

import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.web.bind.annotation.*;

import com.example.demo.model.*;
import com.example.demo.*;

@RestController    // This means that this class is a Controller
@RequestMapping(path="/demo") // This means URL's start with /demo (after Application path)
public class MainController {
    @Autowired // This means to get the bean called userRepository
               // Which is auto-generated by Spring, we will use it to handle the data
    private UserRepository userRepository;

    @GetMapping(path="/add") // Map ONLY GET Requests
    public @ResponseBody String addNewUser (@RequestParam String name, @RequestParam String email) {
        // ... (此处省略大量Spring Boot代码) ...
        return userRepository

import express from "express";
import * as dotenv from "dotenv";
import cors from 'cors';
import { Configuration, OpenAIApi } from "openai"; // 保持不变

dotenv.config();

const configuration = new Configuration({
    apiKey: process.env.OPENAI_API_KEY,
});

const openai = new OpenAIApi(configuration);

const app = express();
app.use(cors());
app.use(express.json());

app.get("/", (req, res) => {
    res.status(200).send({
        message: "Welcome to OpenAI API",
    });
});

app.post('/', async (req, res) => {
    try {
        const userPrompt = req.body.prompt; // 用户输入的提示词

        // 构建messages数组，这是Chat Completion API的关键
        const messages = [
            // System角色：设定模型行为和整体指导
            {
                role: "system",
                content: "你是一个专业的JavaScript编程助手，专注于前端开发。请提供简洁、准确的JavaScript代码和解释，并且只在用户明确要求时才提供代码。在其他情况下，请用自然语言回答。"
            },
            // User角色：传递用户的当前请求
            {
                role: "user",
                content: userPrompt
            }
            // 可以在此处添加更多历史对话，以提供上下文
            // { role: "assistant", content: "这是之前的助手回复..." },
            // { role: "user", content: "这是之前的用户问题..." }
        ];

        const response = await openai.createChatCompletion({
            model: "gpt-3.5-turbo", // 更改为更适合对话和代码任务的模型
            messages: messages,     // 使用messages数组传递对话历史和当前提示
            temperature: 0.7,       // 适当调整温度，0表示更确定性，1表示更随机
            max_tokens: 1000,       // 限制最大生成长度，防止冗余或跑题
            top_p: 1,
            frequency_penalty: 0,
            presence_penalty: 0,
        });

        res.status(200).send({
            // Chat Completion API的响应结构不同，需要从choices[0].message.content获取
            bot: response.data.choices[0].message.content
        });
    }
    catch (error) {
        console.error("Error calling OpenAI API:", error.response ? error.response.data : error.message);
        res.status(500).send({ error: error.response ? error.response.data : error.message });
    }
});

app.listen(5000, () => console.log("Sever is running on port :- http://localhost:5000"))