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105 lines
3.0 KiB
TypeScript
105 lines
3.0 KiB
TypeScript
"use server";
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import {
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OptimizeCodeInput,
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OptimizeCodeOutput,
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OptimizeCodeOutputSchema,
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} from "@/types/ai-improve";
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import { openai } from "@/lib/ai";
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import { CoreMessage, generateText } from "ai";
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import { PrismaClient } from '@/generated/client';
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const prisma = new PrismaClient();
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/**
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* 调用AI优化代码
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* @param input 包含代码、错误信息、题目ID的输入
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* @returns 优化后的代码和说明
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*/
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export const optimizeCode = async (
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input: OptimizeCodeInput
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): Promise<OptimizeCodeOutput> => {
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const model = openai("gpt-4o-mini");
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// 获取题目详情（如果提供了problemId）
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let problemDetails = "";
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if (input.problemId) {
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try {
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const problem = await prisma.problem.findUnique({
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where: { problemId: input.problemId },
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});
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if (problem) {
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problemDetails = `
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Problem Requirements:
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-------------------
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Description: ${problem.description}
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Input: ${problem.inputSpec}
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Output: ${problem.outputSpec}
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Test Cases: ${JSON.stringify(problem.testCases)}
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`;
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}
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} catch (error) {
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console.error("Failed to fetch problem details:", error);
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}
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}
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// 构建AI提示词
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const prompt = `
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Analyze the following programming code for potential errors, inefficiencies or code style issues.
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Provide an optimized version of the code with explanations. Focus on:
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1. Fixing any syntax errors
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2. Improving performance
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3. Enhancing code readability
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4. Following best practices
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Original code:
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\`\`\`
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${input.code}
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\`\`\`
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Error message (if any): ${input.error || "No error message provided"}
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${problemDetails}
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Respond ONLY with the JSON object containing the optimized code and explanations.
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Format:
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{
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"optimizedCode": "optimized code here",
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"explanation": "explanation of changes made",
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"issuesFixed": ["list of issues fixed"]
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}
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`;
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// 发送请求给OpenAI
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const messages: CoreMessage[] = [{ role: "user", content: prompt }];
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let text;
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try {
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const response = await generateText({
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model: model,
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messages: messages,
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});
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text = response.text;
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} catch (error) {
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console.error("Error generating text with OpenAI:", error);
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throw new Error("Failed to generate response from OpenAI");
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}
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// 解析LLM响应
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let llmResponseJson;
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try {
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const cleanedText = text.trim();
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llmResponseJson = JSON.parse(cleanedText);
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} catch (error) {
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console.error("Failed to parse LLM response as JSON:", error);
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console.error("LLM raw output:", text);
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throw new Error("Invalid JSON response from LLM");
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}
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// 验证响应格式
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const validationResult = OptimizeCodeOutputSchema.safeParse(llmResponseJson);
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if (!validationResult.success) {
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console.error("Zod validation failed:", validationResult.error.format());
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throw new Error("Response validation failed");
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}
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return validationResult.data;
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}; |