93 lines
2.9 KiB
JavaScript
93 lines
2.9 KiB
JavaScript
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import { NextResponse } from 'next/server';
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import { db } from '@/lib/db/index';
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import LLMClient from '@/lib/llm/core/index';
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import { getModelConfigById } from '@/lib/db/model-config';
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/**
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* Stream answer for a specified model
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* Query param: model=A or model=B
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*/
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export async function GET(request, { params }) {
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const { projectId, taskId } = params;
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const { searchParams } = new URL(request.url);
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const modelType = searchParams.get('model'); // 'A' or 'B'
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try {
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if (!projectId || !taskId) {
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return NextResponse.json({ error: 'Missing required parameters' }, { status: 400 });
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}
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if (!modelType || !['A', 'B'].includes(modelType)) {
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return NextResponse.json({ error: 'Model type must be specified (A or B)' }, { status: 400 });
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}
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// Fetch task
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const task = await db.task.findUnique({
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where: { id: taskId }
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});
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if (!task || task.taskType !== 'blind-test') {
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return NextResponse.json({ error: 'Task not found' }, { status: 404 });
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}
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// Parse task detail
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const detail = JSON.parse(task.detail || '{}');
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const modelInfo = JSON.parse(task.modelInfo || '{}');
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// Support both evalDatasetIds and questionIds
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const questionIds = detail.questionIds || detail.evalDatasetIds || [];
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const currentIndex = detail.currentIndex || 0;
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// Check if task is completed
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if (questionIds.length === 0 || currentIndex >= questionIds.length) {
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return NextResponse.json({ completed: true });
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}
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// Fetch current question
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const currentQuestionId = questionIds[currentIndex];
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const currentQuestion = await db.evalDatasets.findUnique({
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where: { id: currentQuestionId }
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});
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if (!currentQuestion) {
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return NextResponse.json({ error: 'Question not found' }, { status: 404 });
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}
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// Resolve model config based on modelType
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const modelConfigKey = modelType === 'A' ? 'modelA' : 'modelB';
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const modelConfig = await getModelConfigById(modelInfo[modelConfigKey].id);
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if (!modelConfig) {
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return NextResponse.json({ error: 'Model configuration not found' }, { status: 400 });
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}
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// Prepare messages
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const messages = [
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{
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role: 'system',
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content: "You are a helpful assistant. Provide detailed and accurate answers to the user's question."
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},
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{ role: 'user', content: currentQuestion.question }
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];
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// Create LLM client
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const client = new LLMClient({
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projectId,
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...modelConfig
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});
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// Call streaming API and return response directly
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const response = await client.chatStreamAPI(messages);
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return new Response(response.body, {
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headers: {
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'Content-Type': 'text/plain; charset=utf-8',
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'Cache-Control': 'no-cache',
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Connection: 'keep-alive'
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}
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});
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} catch (error) {
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console.error(`Model ${modelType} streaming call failed:`, error);
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return NextResponse.json({ error: error.message }, { status: 500 });
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}
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}
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