238 lines
6.8 KiB
TypeScript
238 lines
6.8 KiB
TypeScript
import { NextRequest, NextResponse } from 'next/server'
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import { prisma } from '@/lib/prisma'
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interface RouteParams {
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params: Promise<{ id: string }>
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}
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// POST /api/prompts/[id]/test - 运行 prompt 测试
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export async function POST(request: NextRequest, { params }: RouteParams) {
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const { id } = await params
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const body = await request.json()
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try {
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const {
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content,
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model = 'gpt-3.5-turbo',
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temperature = 0.7,
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maxTokens = 1000,
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userId
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} = body
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if (!userId) {
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return NextResponse.json({ error: 'User ID is required' }, { status: 401 })
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}
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if (!content) {
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return NextResponse.json(
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{ error: 'Prompt content is required' },
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{ status: 400 }
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)
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}
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// 验证 prompt 是否存在且属于用户
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const prompt = await prisma.prompt.findFirst({
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where: { id, userId }
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})
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if (!prompt) {
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return NextResponse.json({ error: 'Prompt not found' }, { status: 404 })
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}
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// 检查用户积分（这里简化处理，实际应该从用户表获取）
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const estimatedCost = calculateCost(model, content, maxTokens)
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// 模拟 AI API 调用
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const testResult = await runAITest({
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content,
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model,
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temperature,
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maxTokens
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})
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// 保存测试记录
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const testRun = await prisma.promptTestRun.create({
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data: {
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promptId: id,
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input: content,
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output: testResult.output,
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success: testResult.success,
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error: testResult.error,
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}
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})
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return NextResponse.json({
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testRun,
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result: testResult,
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cost: estimatedCost,
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model,
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timestamp: new Date().toISOString()
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})
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} catch (error) {
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console.error('Error running prompt test:', error)
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// 保存失败的测试记录
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try {
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await prisma.promptTestRun.create({
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data: {
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promptId: id,
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input: body.content || '',
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output: null,
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success: false,
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error: error instanceof Error ? error.message : 'Unknown error',
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}
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})
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} catch (dbError) {
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console.error('Error saving failed test run:', dbError)
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}
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return NextResponse.json(
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{ error: 'Failed to run prompt test' },
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{ status: 500 }
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)
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}
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}
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// 模拟 AI API 调用
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async function runAITest({
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content,
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model
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}: {
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content: string
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model: string
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temperature: number
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maxTokens: number
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}) {
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// 模拟 API 延迟
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await new Promise(resolve => setTimeout(resolve, 1000 + Math.random() * 2000))
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// 模拟不同的响应结果
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const scenarios = [
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{
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success: true,
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output: generateMockResponse(content, model),
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error: null
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},
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{
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success: false,
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output: null,
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error: 'Rate limit exceeded. Please try again later.'
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},
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{
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success: false,
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output: null,
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error: 'Invalid API key or insufficient credits.'
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}
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]
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// 90% 成功率
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const isSuccess = Math.random() > 0.1
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if (isSuccess) {
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return scenarios[0]
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} else {
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return scenarios[Math.floor(Math.random() * (scenarios.length - 1)) + 1]
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}
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}
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// 生成模拟响应
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function generateMockResponse(content: string, model: string): string {
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const responses = [
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`Based on your prompt: "${content.substring(0, 50)}${content.length > 50 ? '...' : ''}"
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Here's a comprehensive response generated by ${model}:
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✨ **Key Insights:**
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1. Your prompt is well-structured and clear
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2. The instructions provide good context
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3. The expected output format is defined
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📊 **Analysis:**
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- Prompt clarity: Excellent (95%)
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- Specificity: High (88%)
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- Context provided: Good (82%)
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- Output format: Well-defined (90%)
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🎯 **Suggestions for improvement:**
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- Consider adding more specific examples
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- Define the target audience more clearly
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- Specify the desired tone or style
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💡 **Generated Content:**
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[This would be the actual AI-generated content based on your prompt. The response would vary depending on the specific instructions and context provided in your prompt.]
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📈 **Performance Metrics:**
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- Response time: ${(Math.random() * 2 + 0.5).toFixed(2)}s
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- Tokens used: ${Math.floor(Math.random() * 500 + 100)}
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- Quality score: ${Math.floor(Math.random() * 20 + 80)}%`,
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`Response from ${model}:
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Your prompt has been processed successfully. Here are the results:
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🔍 **Prompt Analysis:**
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The input prompt demonstrates good structure with clear instructions. The model was able to understand the context and generate appropriate responses.
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📝 **Generated Output:**
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[Simulated AI response content would appear here. This represents what the actual AI model would generate based on your specific prompt instructions.]
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⚡ **Performance Data:**
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- Model: ${model}
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- Processing time: ${(Math.random() * 3 + 1).toFixed(2)} seconds
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- Input tokens: ${Math.floor(content.length / 4)}
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- Output tokens: ${Math.floor(Math.random() * 400 + 200)}
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- Total cost: $${(Math.random() * 0.05 + 0.01).toFixed(4)}
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✅ **Quality Indicators:**
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- Relevance: High
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- Coherence: Excellent
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- Completeness: Good
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- Accuracy: Very Good`,
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`${model} Response:
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Thank you for your prompt. I've analyzed your request and generated the following response:
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🎯 **Understanding:** Your prompt asks for specific information/action, and I've interpreted it as follows...
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📋 **Response:**
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[This section would contain the actual generated content based on your prompt. The AI model processes your instructions and provides relevant, contextual responses.]
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🔧 **Technical Details:**
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- Model used: ${model}
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- Temperature: ${Math.random().toFixed(2)}
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- Max tokens: ${Math.floor(Math.random() * 1000 + 500)}
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- Actual tokens: ${Math.floor(Math.random() * 800 + 200)}
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📊 **Evaluation:**
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- Prompt effectiveness: ${Math.floor(Math.random() * 15 + 85)}%
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- Response quality: ${Math.floor(Math.random() * 10 + 90)}%
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- Instruction following: ${Math.floor(Math.random() * 8 + 92)}%`
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]
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return responses[Math.floor(Math.random() * responses.length)]
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}
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// 计算估算成本
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function calculateCost(model: string, content: string, maxTokens: number): number {
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const inputTokens = Math.ceil(content.length / 4) // 粗略估算
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const outputTokens = Math.min(maxTokens, 500) // 估算输出 tokens
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// 不同模型的价格（每1000 tokens）
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const pricing: Record<string, { input: number; output: number }> = {
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'gpt-3.5-turbo': { input: 0.001, output: 0.002 },
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'gpt-4': { input: 0.03, output: 0.06 },
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'gpt-4-turbo': { input: 0.01, output: 0.03 },
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'claude-3-sonnet': { input: 0.003, output: 0.015 },
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'claude-3-haiku': { input: 0.00025, output: 0.00125 }
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}
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const modelPricing = pricing[model] || pricing['gpt-3.5-turbo']
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const inputCost = (inputTokens / 1000) * modelPricing.input
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const outputCost = (outputTokens / 1000) * modelPricing.output
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return Number((inputCost + outputCost).toFixed(6))
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}
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