first-update
This commit is contained in:
86
easy-dataset-main/app/api/check-update/route.js
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86
easy-dataset-main/app/api/check-update/route.js
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@@ -0,0 +1,86 @@
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import { NextResponse } from 'next/server';
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import path from 'path';
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import fs from 'fs';
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// Get current version
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function getCurrentVersion() {
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try {
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const packageJsonPath = path.join(process.cwd(), 'package.json');
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const packageJson = JSON.parse(fs.readFileSync(packageJsonPath, 'utf8'));
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return packageJson.version;
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} catch (error) {
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console.error('Failed to read version from package.json:', String(error));
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return '1.0.0';
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}
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}
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// Get latest version from GitHub
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async function getLatestVersion() {
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try {
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const owner = 'ConardLi';
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const repo = 'easy-dataset';
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const response = await fetch(`https://api.github.com/repos/${owner}/${repo}/releases/latest`);
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if (!response.ok) {
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throw new Error(`GitHub API request failed: ${response.status}`);
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}
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const data = await response.json();
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return data.tag_name.replace('v', '');
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} catch (error) {
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console.error('Failed to fetch latest version:', String(error));
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return null;
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}
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}
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// Check for updates
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export async function GET() {
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try {
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const currentVersion = getCurrentVersion();
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const latestVersion = await getLatestVersion();
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if (!latestVersion) {
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return NextResponse.json({
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hasUpdate: false,
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currentVersion,
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latestVersion: null,
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error: 'Failed to fetch latest version'
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});
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}
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// Simple semver-like comparison
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const hasUpdate = compareVersions(latestVersion, currentVersion) > 0;
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return NextResponse.json({
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hasUpdate,
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currentVersion,
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latestVersion,
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releaseUrl: hasUpdate ? `https://github.com/ConardLi/easy-dataset/releases/tag/v${latestVersion}` : null
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});
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} catch (error) {
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console.error('Failed to check for updates:', String(error));
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return NextResponse.json(
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{
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hasUpdate: false,
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error: 'Failed to check for updates'
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},
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{ status: 500 }
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);
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}
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}
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// Simple version comparison
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function compareVersions(a, b) {
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const partsA = a.split('.').map(Number);
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const partsB = b.split('.').map(Number);
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for (let i = 0; i < Math.max(partsA.length, partsB.length); i++) {
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const numA = i < partsA.length ? partsA[i] : 0;
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const numB = i < partsB.length ? partsB[i] : 0;
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if (numA > numB) return 1;
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if (numA < numB) return -1;
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}
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return 0;
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}
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75
easy-dataset-main/app/api/llm/fetch-models/route.js
Normal file
75
easy-dataset-main/app/api/llm/fetch-models/route.js
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@@ -0,0 +1,75 @@
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import { NextResponse } from 'next/server';
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import axios from 'axios';
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// Fetch model list from provider
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export async function POST(request) {
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try {
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const { endpoint, providerId, apiKey } = await request.json();
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if (!endpoint) {
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return NextResponse.json({ error: 'Missing required parameter: endpoint' }, { status: 400 });
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}
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let url = endpoint.replace(/\/$/, ''); // Remove trailing slash
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// Handle Ollama endpoint
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if (providerId === 'ollama') {
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// Remove possible /v1 or other version suffix
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url = url.replace(/\/v\d+$/, '');
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// Append /api if missing
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if (!url.includes('/api')) {
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url += '/api';
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}
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url += '/tags';
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} else {
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url += '/models';
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}
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const headers = {};
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if (apiKey) {
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headers.Authorization = `Bearer ${apiKey}`;
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}
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const response = await axios.get(url, { headers });
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// Format response per provider
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let formattedModels = [];
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if (providerId === 'ollama') {
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// Ollama /api/tags format: { models: [{ name: 'model-name', ... }] }
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if (response.data.models && Array.isArray(response.data.models)) {
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formattedModels = response.data.models.map(item => ({
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modelId: item.name,
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modelName: item.name,
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providerId
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}));
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}
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} else {
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// Default handling (OpenAI-compatible)
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if (response.data.data && Array.isArray(response.data.data)) {
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formattedModels = response.data.data.map(item => ({
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modelId: item.id,
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modelName: item.id,
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providerId
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}));
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}
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}
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return NextResponse.json(formattedModels);
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} catch (error) {
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console.error('Failed to fetch model list:', String(error));
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// Handle known error shapes
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if (error.response) {
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if (error.response.status === 401) {
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return NextResponse.json({ error: 'Invalid API key' }, { status: 401 });
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}
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return NextResponse.json(
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{ error: `Failed to fetch model list: ${error.response.statusText}` },
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{ status: error.response.status }
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);
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}
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return NextResponse.json({ error: `Failed to fetch model list: ${error.message}` }, { status: 500 });
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}
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}
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39
easy-dataset-main/app/api/llm/model/route.js
Normal file
39
easy-dataset-main/app/api/llm/model/route.js
Normal file
@@ -0,0 +1,39 @@
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import { NextResponse } from 'next/server';
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import { getLlmModelsByProviderId } from '@/lib/db/llm-models';
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// Get LLM models
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export async function GET(request) {
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try {
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const searchParams = request.nextUrl.searchParams;
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let providerId = searchParams.get('providerId');
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if (!providerId) {
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return NextResponse.json({ error: 'Invalid parameters' }, { status: 400 });
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}
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const models = await getLlmModelsByProviderId(providerId);
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if (!models) {
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return NextResponse.json({ error: 'LLM provider not found' }, { status: 404 });
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}
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return NextResponse.json(models);
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} catch (error) {
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console.error('Database query error:', String(error));
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return NextResponse.json({ error: 'Database query failed' }, { status: 500 });
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}
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}
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// Sync latest model list
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export async function POST(request) {
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try {
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const { newModels, providerId } = await request.json();
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const models = await getLlmModelsByProviderId(providerId);
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const existingModelIds = models.map(model => model.modelId);
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const diffModels = newModels.filter(item => !existingModelIds.includes(item.modelId));
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if (diffModels.length > 0) {
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// return NextResponse.json(await createLlmModels(diffModels));
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return NextResponse.json({ message: 'No new models to insert' }, { status: 200 });
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} else {
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return NextResponse.json({ message: 'No new models to insert' }, { status: 200 });
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}
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} catch (error) {
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return NextResponse.json({ error: 'Database insert failed' }, { status: 500 });
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}
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}
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26
easy-dataset-main/app/api/llm/ollama/models/route.js
Normal file
26
easy-dataset-main/app/api/llm/ollama/models/route.js
Normal file
@@ -0,0 +1,26 @@
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import { NextResponse } from 'next/server';
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const OllamaClient = require('@/lib/llm/core/providers/ollama');
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// Force dynamic route to prevent static generation
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export const dynamic = 'force-dynamic';
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export async function GET(request) {
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try {
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// Read host and port from query params
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const { searchParams } = new URL(request.url);
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const host = searchParams.get('host') || '127.0.0.1';
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const port = searchParams.get('port') || '11434';
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// Create Ollama API client
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const ollama = new OllamaClient({
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endpoint: `http://${host}:${port}/api`
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});
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// Fetch model list
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const models = await ollama.getModels();
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return NextResponse.json(models);
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} catch (error) {
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// console.error('fetch Ollama models error:', error);
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return NextResponse.json({ error: 'fetch Models failed' }, { status: 500 });
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}
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}
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14
easy-dataset-main/app/api/llm/providers/route.js
Normal file
14
easy-dataset-main/app/api/llm/providers/route.js
Normal file
@@ -0,0 +1,14 @@
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import { NextResponse } from 'next/server';
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import { getLlmProviders } from '@/lib/db/llm-providers';
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import { sortProvidersByPriority } from '@/lib/util/providerLogo';
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// Get LLM provider data
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export async function GET() {
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try {
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const result = await getLlmProviders();
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return NextResponse.json(sortProvidersByPriority(result, item => item.id));
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} catch (error) {
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console.error('Database query error:', String(error));
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return NextResponse.json({ error: 'Database query failed' }, { status: 500 });
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}
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}
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107
easy-dataset-main/app/api/monitoring/logs/route.js
Normal file
107
easy-dataset-main/app/api/monitoring/logs/route.js
Normal file
@@ -0,0 +1,107 @@
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import { NextResponse } from 'next/server';
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import { db } from '@/lib/db';
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export const dynamic = 'force-dynamic';
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export async function GET(request) {
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try {
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const { searchParams } = new URL(request.url);
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const timeRange = searchParams.get('timeRange') || '7d';
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const projectId = searchParams.get('projectId');
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const provider = searchParams.get('provider');
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const status = searchParams.get('status');
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const page = parseInt(searchParams.get('page') || '1', 10);
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const pageSize = parseInt(searchParams.get('pageSize') || '10', 10);
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const searchTerm = searchParams.get('search') || '';
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let startDate = new Date();
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if (timeRange === '24h') {
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startDate.setHours(startDate.getHours() - 24);
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} else if (timeRange === '30d') {
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startDate.setDate(startDate.getDate() - 30);
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} else {
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startDate.setDate(startDate.getDate() - 7);
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}
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const where = {
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createAt: {
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gte: startDate
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}
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};
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if (projectId && projectId !== 'all') {
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where.projectId = projectId;
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}
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if (provider && provider !== 'all') {
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where.provider = provider;
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}
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if (status && status !== 'all') {
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where.status = status;
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}
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if (searchTerm) {
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where.OR = [{ model: { contains: searchTerm } }, { errorMessage: { contains: searchTerm } }];
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}
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const total = await db.llmUsageLogs.count({ where });
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const logs = await db.llmUsageLogs.findMany({
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where,
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select: {
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id: true,
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projectId: true,
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provider: true,
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model: true,
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inputTokens: true,
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outputTokens: true,
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totalTokens: true,
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latency: true,
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status: true,
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errorMessage: true,
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createAt: true
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},
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orderBy: {
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createAt: 'desc'
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},
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skip: (page - 1) * pageSize,
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take: pageSize
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||||
});
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const projectIds = [...new Set(logs.map(log => log.projectId))];
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const projects = await db.projects.findMany({
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where: { id: { in: projectIds } },
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select: { id: true, name: true }
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||||
});
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const projectMap = projects.reduce((acc, p) => {
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acc[p.id] = p.name;
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return acc;
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||||
}, {});
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||||
const details = logs.map(log => ({
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id: log.id,
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||||
projectId: log.projectId,
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projectName: projectMap[log.projectId] || 'Unknown Project',
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||||
provider: log.provider,
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model: log.model,
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||||
status: log.status,
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||||
failureReason: log.errorMessage,
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inputTokens: log.inputTokens,
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outputTokens: log.outputTokens,
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||||
totalTokens: log.totalTokens,
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||||
calls: 1, // Single record
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||||
avgLatency: log.status === 'SUCCESS' ? (log.latency / 1000).toFixed(2) + 's' : '-',
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||||
createAt: log.createAt
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||||
}));
|
||||
|
||||
return NextResponse.json({
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||||
details,
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||||
total,
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||||
page,
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||||
pageSize,
|
||||
totalPages: Math.ceil(total / pageSize)
|
||||
});
|
||||
} catch (error) {
|
||||
console.error('Failed to fetch monitoring logs:', error);
|
||||
return NextResponse.json({ error: error.message }, { status: 500 });
|
||||
}
|
||||
}
|
||||
188
easy-dataset-main/app/api/monitoring/stats/route.js
Normal file
188
easy-dataset-main/app/api/monitoring/stats/route.js
Normal file
@@ -0,0 +1,188 @@
|
||||
import { NextResponse } from 'next/server';
|
||||
import { db } from '@/lib/db';
|
||||
|
||||
export const dynamic = 'force-dynamic';
|
||||
|
||||
export async function GET(request) {
|
||||
try {
|
||||
const { searchParams } = new URL(request.url);
|
||||
const timeRange = searchParams.get('timeRange') || '7d'; // 24h, 7d, 30d
|
||||
const projectId = searchParams.get('projectId');
|
||||
const provider = searchParams.get('provider');
|
||||
const status = searchParams.get('status');
|
||||
|
||||
let startDate = new Date();
|
||||
|
||||
if (timeRange === '24h') {
|
||||
startDate.setHours(startDate.getHours() - 24);
|
||||
} else if (timeRange === '30d') {
|
||||
startDate.setDate(startDate.getDate() - 30);
|
||||
} else {
|
||||
startDate.setDate(startDate.getDate() - 7);
|
||||
}
|
||||
|
||||
const where = {
|
||||
createAt: {
|
||||
gte: startDate
|
||||
}
|
||||
};
|
||||
|
||||
if (projectId && projectId !== 'all') {
|
||||
where.projectId = projectId;
|
||||
}
|
||||
if (provider && provider !== 'all') {
|
||||
where.provider = provider;
|
||||
}
|
||||
if (status && status !== 'all') {
|
||||
where.status = status;
|
||||
}
|
||||
|
||||
// 1. Fetch data for aggregation
|
||||
// Note: Prisma aggregation can be slow on very large datasets. If needed, optimize with pre-aggregated tables.
|
||||
const logs = await db.llmUsageLogs.findMany({
|
||||
where,
|
||||
select: {
|
||||
id: true,
|
||||
projectId: true,
|
||||
provider: true,
|
||||
model: true,
|
||||
inputTokens: true,
|
||||
outputTokens: true,
|
||||
totalTokens: true,
|
||||
latency: true,
|
||||
status: true,
|
||||
errorMessage: true,
|
||||
createAt: true,
|
||||
dateString: true
|
||||
},
|
||||
orderBy: {
|
||||
createAt: 'desc'
|
||||
}
|
||||
});
|
||||
|
||||
// Build project name map
|
||||
const projects = await db.projects.findMany({
|
||||
select: { id: true, name: true }
|
||||
});
|
||||
const projectMap = projects.reduce((acc, p) => {
|
||||
acc[p.id] = p.name;
|
||||
return acc;
|
||||
}, {});
|
||||
|
||||
// 2. Process and aggregate
|
||||
const summary = {
|
||||
totalTokens: 0,
|
||||
inputTokens: 0,
|
||||
outputTokens: 0,
|
||||
totalCalls: logs.length,
|
||||
successCalls: 0,
|
||||
failedCalls: 0,
|
||||
totalLatency: 0,
|
||||
avgLatency: 0
|
||||
};
|
||||
|
||||
const trendMap = {};
|
||||
const modelStats = {};
|
||||
const detailedStatsMap = {}; // Key: projectId-model-status-errorMessage
|
||||
|
||||
logs.forEach(log => {
|
||||
// Summary
|
||||
summary.totalTokens += log.totalTokens;
|
||||
summary.inputTokens += log.inputTokens;
|
||||
summary.outputTokens += log.outputTokens;
|
||||
|
||||
if (log.status === 'SUCCESS') {
|
||||
summary.successCalls++;
|
||||
summary.totalLatency += log.latency;
|
||||
} else {
|
||||
summary.failedCalls++;
|
||||
}
|
||||
|
||||
// Trend (by day or hour)
|
||||
let timeKey;
|
||||
if (timeRange === '24h') {
|
||||
const date = new Date(log.createAt);
|
||||
timeKey = `${String(date.getHours()).padStart(2, '0')}:00`;
|
||||
} else {
|
||||
timeKey = log.dateString.slice(5); // MM-DD
|
||||
}
|
||||
|
||||
if (!trendMap[timeKey]) {
|
||||
trendMap[timeKey] = { name: timeKey, input: 0, output: 0 };
|
||||
}
|
||||
trendMap[timeKey].input += log.inputTokens;
|
||||
trendMap[timeKey].output += log.outputTokens;
|
||||
|
||||
// Model Distribution
|
||||
const modelKey = log.model;
|
||||
if (!modelStats[modelKey]) {
|
||||
modelStats[modelKey] = { name: modelKey, value: 0 };
|
||||
}
|
||||
modelStats[modelKey].value += log.totalTokens;
|
||||
|
||||
// Detailed Table Aggregation
|
||||
// Key: projectId + model + status + (errorMessage || '')
|
||||
const errorKey = log.errorMessage || '';
|
||||
const detailKey = `${log.projectId}|${log.model}|${log.status}|${errorKey}`;
|
||||
|
||||
if (!detailedStatsMap[detailKey]) {
|
||||
detailedStatsMap[detailKey] = {
|
||||
projectId: log.projectId,
|
||||
projectName: projectMap[log.projectId] || 'Unknown Project',
|
||||
provider: log.provider,
|
||||
model: log.model,
|
||||
status: log.status,
|
||||
failureReason: log.errorMessage,
|
||||
inputTokens: 0,
|
||||
outputTokens: 0,
|
||||
totalTokens: 0,
|
||||
calls: 0,
|
||||
totalLatency: 0
|
||||
};
|
||||
}
|
||||
const detailItem = detailedStatsMap[detailKey];
|
||||
detailItem.inputTokens += log.inputTokens;
|
||||
detailItem.outputTokens += log.outputTokens;
|
||||
detailItem.totalTokens += log.totalTokens;
|
||||
detailItem.calls += 1;
|
||||
if (log.status === 'SUCCESS') {
|
||||
detailItem.totalLatency += log.latency;
|
||||
}
|
||||
});
|
||||
|
||||
// Calculate averages
|
||||
if (summary.successCalls > 0) {
|
||||
summary.avgLatency = Math.round(summary.totalLatency / summary.successCalls);
|
||||
}
|
||||
summary.avgTokensPerCall = summary.totalCalls > 0 ? Math.round(summary.totalTokens / summary.totalCalls) : 0;
|
||||
summary.failureRate = summary.totalCalls > 0 ? summary.failedCalls / summary.totalCalls : 0;
|
||||
|
||||
// Format chart data
|
||||
const trend = Object.values(trendMap).sort((a, b) => {
|
||||
// Simple sorting; for production use, consider stricter time ordering.
|
||||
return a.name.localeCompare(b.name);
|
||||
});
|
||||
|
||||
const modelDistribution = Object.values(modelStats).sort((a, b) => b.value - a.value);
|
||||
|
||||
// Format detailed table data
|
||||
const details = Object.values(detailedStatsMap)
|
||||
.map(item => ({
|
||||
...item,
|
||||
avgLatency:
|
||||
item.status === 'SUCCESS' && item.calls > 0 ? (item.totalLatency / item.calls / 1000).toFixed(2) + 's' : '-'
|
||||
}))
|
||||
.sort((a, b) => b.totalTokens - a.totalTokens); // Default sorting by token usage
|
||||
|
||||
return NextResponse.json({
|
||||
summary,
|
||||
trend,
|
||||
modelDistribution,
|
||||
details,
|
||||
projects
|
||||
});
|
||||
} catch (error) {
|
||||
console.error('Failed to fetch monitoring stats:', error);
|
||||
return NextResponse.json({ error: error.message }, { status: 500 });
|
||||
}
|
||||
}
|
||||
132
easy-dataset-main/app/api/monitoring/summary/route.js
Normal file
132
easy-dataset-main/app/api/monitoring/summary/route.js
Normal file
@@ -0,0 +1,132 @@
|
||||
import { NextResponse } from 'next/server';
|
||||
import { db } from '@/lib/db';
|
||||
|
||||
export const dynamic = 'force-dynamic';
|
||||
|
||||
export async function GET(request) {
|
||||
try {
|
||||
const { searchParams } = new URL(request.url);
|
||||
const timeRange = searchParams.get('timeRange') || '7d';
|
||||
const projectId = searchParams.get('projectId');
|
||||
const provider = searchParams.get('provider');
|
||||
const status = searchParams.get('status');
|
||||
|
||||
let startDate = new Date();
|
||||
|
||||
if (timeRange === '24h') {
|
||||
startDate.setHours(startDate.getHours() - 24);
|
||||
} else if (timeRange === '30d') {
|
||||
startDate.setDate(startDate.getDate() - 30);
|
||||
} else {
|
||||
startDate.setDate(startDate.getDate() - 7);
|
||||
}
|
||||
|
||||
const where = {
|
||||
createAt: {
|
||||
gte: startDate
|
||||
}
|
||||
};
|
||||
|
||||
if (projectId && projectId !== 'all') {
|
||||
where.projectId = projectId;
|
||||
}
|
||||
if (provider && provider !== 'all') {
|
||||
where.provider = provider;
|
||||
}
|
||||
if (status && status !== 'all') {
|
||||
where.status = status;
|
||||
}
|
||||
|
||||
const logs = await db.llmUsageLogs.findMany({
|
||||
where,
|
||||
select: {
|
||||
inputTokens: true,
|
||||
outputTokens: true,
|
||||
totalTokens: true,
|
||||
latency: true,
|
||||
status: true,
|
||||
createAt: true,
|
||||
dateString: true,
|
||||
model: true
|
||||
}
|
||||
});
|
||||
|
||||
const summary = {
|
||||
totalTokens: 0,
|
||||
inputTokens: 0,
|
||||
outputTokens: 0,
|
||||
totalCalls: logs.length,
|
||||
successCalls: 0,
|
||||
failedCalls: 0,
|
||||
totalLatency: 0,
|
||||
avgLatency: 0
|
||||
};
|
||||
|
||||
const trendMap = {};
|
||||
const modelStats = {};
|
||||
|
||||
logs.forEach(log => {
|
||||
summary.totalTokens += log.totalTokens;
|
||||
summary.inputTokens += log.inputTokens;
|
||||
summary.outputTokens += log.outputTokens;
|
||||
|
||||
if (log.status === 'SUCCESS') {
|
||||
summary.successCalls++;
|
||||
summary.totalLatency += log.latency;
|
||||
} else {
|
||||
summary.failedCalls++;
|
||||
}
|
||||
|
||||
let timeKey;
|
||||
if (timeRange === '24h') {
|
||||
const date = new Date(log.createAt);
|
||||
timeKey = `${String(date.getHours()).padStart(2, '0')}:00`;
|
||||
} else {
|
||||
timeKey = log.dateString.slice(5);
|
||||
}
|
||||
|
||||
if (!trendMap[timeKey]) {
|
||||
trendMap[timeKey] = { name: timeKey, input: 0, output: 0 };
|
||||
}
|
||||
trendMap[timeKey].input += log.inputTokens;
|
||||
trendMap[timeKey].output += log.outputTokens;
|
||||
|
||||
const modelKey = log.model;
|
||||
if (!modelStats[modelKey]) {
|
||||
modelStats[modelKey] = { name: modelKey, value: 0 };
|
||||
}
|
||||
modelStats[modelKey].value += log.totalTokens;
|
||||
});
|
||||
|
||||
if (summary.successCalls > 0) {
|
||||
summary.avgLatency = Math.round(summary.totalLatency / summary.successCalls);
|
||||
}
|
||||
summary.avgTokensPerCall = summary.totalCalls > 0 ? Math.round(summary.totalTokens / summary.totalCalls) : 0;
|
||||
summary.failureRate = summary.totalCalls > 0 ? summary.failedCalls / summary.totalCalls : 0;
|
||||
|
||||
const trend = Object.values(trendMap).sort((a, b) => a.name.localeCompare(b.name));
|
||||
const modelDistribution = Object.values(modelStats).sort((a, b) => b.value - a.value);
|
||||
|
||||
const projects = await db.projects.findMany({
|
||||
select: { id: true, name: true },
|
||||
orderBy: { createAt: 'desc' }
|
||||
});
|
||||
|
||||
const allLogs = await db.llmUsageLogs.findMany({
|
||||
select: { provider: true },
|
||||
distinct: ['provider']
|
||||
});
|
||||
const providers = allLogs.map(log => log.provider).filter(Boolean);
|
||||
|
||||
return NextResponse.json({
|
||||
summary,
|
||||
trend,
|
||||
modelDistribution,
|
||||
projects,
|
||||
providers
|
||||
});
|
||||
} catch (error) {
|
||||
console.error('Failed to fetch monitoring summary:', error);
|
||||
return NextResponse.json({ error: error.message }, { status: 500 });
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,176 @@
|
||||
import { NextResponse } from 'next/server';
|
||||
import { getUploadFileInfoById } from '@/lib/db/upload-files';
|
||||
import { createGaPairs, getGaPairsByFileId } from '@/lib/db/ga-pairs';
|
||||
|
||||
/**
|
||||
* 批量手动添加 GA 对到多个文件
|
||||
*/
|
||||
export async function POST(request, { params }) {
|
||||
try {
|
||||
const { projectId } = params;
|
||||
const body = await request.json();
|
||||
|
||||
if (!projectId) {
|
||||
return NextResponse.json({ error: 'Project ID is required' }, { status: 400 });
|
||||
}
|
||||
|
||||
const { fileIds, gaPair, appendMode = false } = body;
|
||||
|
||||
if (!fileIds || !Array.isArray(fileIds) || fileIds.length === 0) {
|
||||
return NextResponse.json({ error: 'File IDs array is required' }, { status: 400 });
|
||||
}
|
||||
|
||||
if (!gaPair || !gaPair.genreTitle || !gaPair.audienceTitle) {
|
||||
return NextResponse.json({ error: 'GA pair with genreTitle and audienceTitle is required' }, { status: 400 });
|
||||
}
|
||||
|
||||
console.log('开始处理批量手动添加GA对请求');
|
||||
console.log('项目ID:', projectId);
|
||||
console.log('请求的文件IDs:', fileIds);
|
||||
console.log('GA对:', gaPair);
|
||||
|
||||
// 使用 getUploadFileInfoById 逐个验证文件
|
||||
const validFiles = [];
|
||||
const invalidFileIds = [];
|
||||
|
||||
for (const fileId of fileIds) {
|
||||
try {
|
||||
console.log(`正在验证文件: ${fileId}`);
|
||||
const fileInfo = await getUploadFileInfoById(fileId);
|
||||
|
||||
if (fileInfo && fileInfo.projectId === projectId) {
|
||||
console.log(`文件验证成功: ${fileInfo.fileName}`);
|
||||
validFiles.push(fileInfo);
|
||||
} else if (fileInfo) {
|
||||
console.log(`文件属于其他项目: ${fileInfo.projectId} != ${projectId}`);
|
||||
invalidFileIds.push(fileId);
|
||||
} else {
|
||||
console.log(`文件不存在: ${fileId}`);
|
||||
invalidFileIds.push(fileId);
|
||||
}
|
||||
} catch (error) {
|
||||
console.error(`验证文件 ${fileId} 时出错:`, String(error));
|
||||
invalidFileIds.push(fileId);
|
||||
}
|
||||
}
|
||||
|
||||
console.log(`文件验证完成: 有效${validFiles.length}个, 无效${invalidFileIds.length}个`);
|
||||
|
||||
if (validFiles.length === 0) {
|
||||
return NextResponse.json(
|
||||
{
|
||||
error: 'No valid files found',
|
||||
debug: {
|
||||
projectId,
|
||||
requestedIds: fileIds,
|
||||
invalidIds: invalidFileIds,
|
||||
message: 'None of the requested files belong to this project or exist in the database'
|
||||
}
|
||||
},
|
||||
{ status: 404 }
|
||||
);
|
||||
}
|
||||
|
||||
// 批量手动添加 GA 对
|
||||
console.log('开始批量手动添加GA对...');
|
||||
console.log('追加模式:', appendMode);
|
||||
const results = [];
|
||||
|
||||
for (const file of validFiles) {
|
||||
try {
|
||||
console.log(`处理文件: ${file.fileName}`);
|
||||
|
||||
// 检查是否已存在 GA 对
|
||||
const existingPairs = await getGaPairsByFileId(file.id);
|
||||
|
||||
let pairNumber = 1;
|
||||
if (appendMode && existingPairs && existingPairs.length > 0) {
|
||||
// 追加模式：在现有 GA 对后面添加
|
||||
pairNumber = existingPairs.length + 1;
|
||||
} else if (!appendMode && existingPairs && existingPairs.length > 0) {
|
||||
// 非追加模式：如果已存在 GA 对则跳过
|
||||
console.log(`文件 ${file.fileName} 已存在GA对，跳过`);
|
||||
results.push({
|
||||
fileId: file.id,
|
||||
fileName: file.fileName,
|
||||
success: true,
|
||||
skipped: true,
|
||||
message: 'GA pairs already exist'
|
||||
});
|
||||
continue;
|
||||
}
|
||||
|
||||
// 创建 GA 对数据
|
||||
const gaPairData = [
|
||||
{
|
||||
projectId,
|
||||
fileId: file.id,
|
||||
pairNumber,
|
||||
genreTitle: gaPair.genreTitle.trim(),
|
||||
genreDesc: gaPair.genreDesc?.trim() || '',
|
||||
audienceTitle: gaPair.audienceTitle.trim(),
|
||||
audienceDesc: gaPair.audienceDesc?.trim() || '',
|
||||
isActive: true
|
||||
}
|
||||
];
|
||||
|
||||
// 保存 GA 对
|
||||
if (appendMode) {
|
||||
// 追加模式：只创建新的 GA 对
|
||||
await createGaPairs(gaPairData);
|
||||
} else {
|
||||
// 非追加模式：使用 saveGaPairs 替换现有的
|
||||
const { saveGaPairs } = await import('@/lib/db/ga-pairs');
|
||||
await saveGaPairs(projectId, file.id, [
|
||||
{
|
||||
genre: { title: gaPair.genreTitle.trim(), description: gaPair.genreDesc?.trim() || '' },
|
||||
audience: { title: gaPair.audienceTitle.trim(), description: gaPair.audienceDesc?.trim() || '' }
|
||||
}
|
||||
]);
|
||||
}
|
||||
|
||||
results.push({
|
||||
fileId: file.id,
|
||||
fileName: file.fileName,
|
||||
success: true,
|
||||
skipped: false,
|
||||
message: 'GA pair added successfully'
|
||||
});
|
||||
|
||||
console.log(`成功为文件 ${file.fileName} 添加GA对`);
|
||||
} catch (error) {
|
||||
console.error(`为文件 ${file.fileName} 添加GA对失败:`, error);
|
||||
results.push({
|
||||
fileId: file.id,
|
||||
fileName: file.fileName,
|
||||
success: false,
|
||||
skipped: false,
|
||||
error: error.message,
|
||||
message: `Failed: ${error.message}`
|
||||
});
|
||||
}
|
||||
}
|
||||
|
||||
// 统计结果
|
||||
const successCount = results.filter(r => r.success).length;
|
||||
const failureCount = results.filter(r => !r.success).length;
|
||||
|
||||
console.log(`批量手动添加完成: 成功${successCount}个, 失败${failureCount}个`);
|
||||
|
||||
return NextResponse.json({
|
||||
success: true,
|
||||
data: results,
|
||||
summary: {
|
||||
total: results.length,
|
||||
success: successCount,
|
||||
failure: failureCount,
|
||||
processed: validFiles.length,
|
||||
skipped: invalidFileIds.length
|
||||
},
|
||||
message: `Added GA pairs to ${successCount} files, ${failureCount} failed, ${invalidFileIds.length} files not found`
|
||||
});
|
||||
} catch (error) {
|
||||
console.error('Error batch adding manual GA pairs:', String(error));
|
||||
return NextResponse.json({ error: String(error) || 'Failed to batch add manual GA pairs' }, { status: 500 });
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,196 @@
|
||||
import { NextResponse } from 'next/server';
|
||||
import { getUploadFileInfoById, delUploadFileInfoById } from '@/lib/db/upload-files';
|
||||
import { getProject } from '@/lib/db/projects';
|
||||
import { getProjectChunks, getProjectTocByName } from '@/lib/file/text-splitter';
|
||||
import { batchSaveTags } from '@/lib/db/tags';
|
||||
import { handleDomainTree } from '@/lib/util/domain-tree';
|
||||
import path from 'path';
|
||||
import { getProjectRoot } from '@/lib/db/base';
|
||||
import { promises as fs } from 'fs';
|
||||
|
||||
/**
|
||||
* 批量删除文件
|
||||
* 复用单个文件删除的完整逻辑，包括领域树修订
|
||||
*/
|
||||
export async function POST(request, { params }) {
|
||||
try {
|
||||
const { projectId } = params;
|
||||
const body = await request.json();
|
||||
|
||||
if (!projectId) {
|
||||
return NextResponse.json({ error: 'Project ID is required' }, { status: 400 });
|
||||
}
|
||||
|
||||
const { fileIds, domainTreeAction = 'keep', model, language = '中文' } = body;
|
||||
|
||||
if (!fileIds || !Array.isArray(fileIds) || fileIds.length === 0) {
|
||||
return NextResponse.json({ error: 'File IDs array is required' }, { status: 400 });
|
||||
}
|
||||
|
||||
console.log('开始处理批量删除文件请求');
|
||||
console.log('项目ID:', projectId);
|
||||
console.log('请求的文件IDs:', fileIds);
|
||||
console.log('领域树操作:', domainTreeAction);
|
||||
|
||||
// 获取项目信息
|
||||
const project = await getProject(projectId);
|
||||
if (!project) {
|
||||
return NextResponse.json({ error: 'The project does not exist' }, { status: 404 });
|
||||
}
|
||||
|
||||
// 验证文件并删除
|
||||
const results = [];
|
||||
const deletedTocs = [];
|
||||
let deletedCount = 0;
|
||||
let failedCount = 0;
|
||||
let totalStats = {
|
||||
deletedChunks: 0,
|
||||
deletedQuestions: 0,
|
||||
deletedDatasets: 0
|
||||
};
|
||||
|
||||
for (const fileId of fileIds) {
|
||||
try {
|
||||
console.log(`正在验证文件: ${fileId}`);
|
||||
const fileInfo = await getUploadFileInfoById(fileId);
|
||||
|
||||
if (!fileInfo) {
|
||||
console.log(`文件不存在: ${fileId}`);
|
||||
results.push({
|
||||
fileId,
|
||||
success: false,
|
||||
error: 'File not found'
|
||||
});
|
||||
failedCount++;
|
||||
continue;
|
||||
}
|
||||
|
||||
if (fileInfo.projectId !== projectId) {
|
||||
console.log(`文件属于其他项目: ${fileInfo.projectId} != ${projectId}`);
|
||||
results.push({
|
||||
fileId,
|
||||
success: false,
|
||||
error: 'File belongs to another project'
|
||||
});
|
||||
failedCount++;
|
||||
continue;
|
||||
}
|
||||
|
||||
// 删除文件及其相关的文本块、问题和数据集
|
||||
console.log(`删除文件: ${fileInfo.fileName}`);
|
||||
const { stats, fileName } = await delUploadFileInfoById(fileId);
|
||||
|
||||
// 累计统计信息
|
||||
totalStats.deletedChunks += stats.deletedChunks || 0;
|
||||
totalStats.deletedQuestions += stats.deletedQuestions || 0;
|
||||
totalStats.deletedDatasets += stats.deletedDatasets || 0;
|
||||
|
||||
// 获取并保存删除的 TOC 信息
|
||||
const deleteToc = await getProjectTocByName(projectId, fileName);
|
||||
if (deleteToc) {
|
||||
deletedTocs.push(deleteToc);
|
||||
}
|
||||
|
||||
// 删除 TOC 文件
|
||||
try {
|
||||
const projectRoot = await getProjectRoot();
|
||||
const projectPath = path.join(projectRoot, projectId);
|
||||
const tocDir = path.join(projectPath, 'toc');
|
||||
const baseName = path.basename(fileInfo.fileName, path.extname(fileInfo.fileName));
|
||||
const tocPath = path.join(tocDir, `${baseName}-toc.json`);
|
||||
await fs.unlink(tocPath);
|
||||
console.log(`成功删除 TOC 文件: ${tocPath}`);
|
||||
} catch (error) {
|
||||
console.error(`删除 TOC 文件失败:`, String(error));
|
||||
}
|
||||
|
||||
results.push({
|
||||
fileId,
|
||||
fileName: fileInfo.fileName,
|
||||
success: true,
|
||||
stats
|
||||
});
|
||||
deletedCount++;
|
||||
|
||||
console.log(`成功删除文件: ${fileInfo.fileName}`);
|
||||
} catch (error) {
|
||||
console.error(`删除文件 ${fileId} 时出错:`, error);
|
||||
results.push({
|
||||
fileId,
|
||||
success: false,
|
||||
error: error.message
|
||||
});
|
||||
failedCount++;
|
||||
}
|
||||
}
|
||||
|
||||
console.log(`批量删除完成: 成功${deletedCount}个, 失败${failedCount}个`);
|
||||
|
||||
// 如果选择了保持领域树不变，直接返回删除结果
|
||||
if (domainTreeAction === 'keep') {
|
||||
return NextResponse.json({
|
||||
success: true,
|
||||
deletedCount,
|
||||
failedCount,
|
||||
total: fileIds.length,
|
||||
results,
|
||||
stats: totalStats,
|
||||
domainTreeAction: 'keep',
|
||||
message: `Successfully deleted ${deletedCount} files, ${failedCount} failed`
|
||||
});
|
||||
}
|
||||
|
||||
// 处理领域树更新
|
||||
try {
|
||||
// 获取项目的所有文件
|
||||
const { chunks, toc } = await getProjectChunks(projectId);
|
||||
|
||||
// 如果不存在文本块，说明项目已经没有文件了
|
||||
if (!chunks || chunks.length === 0) {
|
||||
// 清空领域树
|
||||
await batchSaveTags(projectId, []);
|
||||
return NextResponse.json({
|
||||
success: true,
|
||||
deletedCount,
|
||||
failedCount,
|
||||
total: fileIds.length,
|
||||
results,
|
||||
stats: totalStats,
|
||||
domainTreeAction,
|
||||
message: `Successfully deleted ${deletedCount} files, domain tree cleared`,
|
||||
domainTreeCleared: true
|
||||
});
|
||||
}
|
||||
|
||||
// 调用领域树处理模块
|
||||
await handleDomainTree({
|
||||
projectId,
|
||||
action: domainTreeAction,
|
||||
allToc: toc,
|
||||
model: model,
|
||||
language,
|
||||
deleteToc: deletedTocs.length > 0 ? deletedTocs : undefined,
|
||||
project
|
||||
});
|
||||
|
||||
console.log('领域树更新成功');
|
||||
} catch (error) {
|
||||
console.error('Error updating domain tree after batch deletion:', String(error));
|
||||
// 即使领域树更新失败，也不影响文件删除的结果
|
||||
}
|
||||
|
||||
return NextResponse.json({
|
||||
success: true,
|
||||
deletedCount,
|
||||
failedCount,
|
||||
total: fileIds.length,
|
||||
results,
|
||||
stats: totalStats,
|
||||
domainTreeAction,
|
||||
message: `Successfully deleted ${deletedCount} files, ${failedCount} failed`
|
||||
});
|
||||
} catch (error) {
|
||||
console.error('Error batch deleting files:', String(error));
|
||||
return NextResponse.json({ error: String(error) || 'Failed to batch delete files' }, { status: 500 });
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,106 @@
|
||||
import { NextResponse } from 'next/server';
|
||||
import { batchGenerateGaPairs } from '@/lib/services/ga/ga-pairs';
|
||||
import { getUploadFileInfoById } from '@/lib/db/upload-files'; // 导入单个文件查询函数
|
||||
|
||||
/**
|
||||
* 批量生成多个文件的 GA 对
|
||||
*/
|
||||
export async function POST(request, { params }) {
|
||||
try {
|
||||
const { projectId } = params;
|
||||
const body = await request.json();
|
||||
|
||||
if (!projectId) {
|
||||
return NextResponse.json({ error: 'Project ID is required' }, { status: 400 });
|
||||
}
|
||||
|
||||
const { fileIds, modelConfigId, language = '中文', appendMode = false } = body;
|
||||
|
||||
if (!fileIds || !Array.isArray(fileIds) || fileIds.length === 0) {
|
||||
return NextResponse.json({ error: 'File IDs array is required' }, { status: 400 });
|
||||
}
|
||||
|
||||
if (!modelConfigId) {
|
||||
return NextResponse.json({ error: 'Model configuration ID is required' }, { status: 400 });
|
||||
}
|
||||
|
||||
console.log('开始处理批量生成GA对请求');
|
||||
console.log('项目ID:', projectId);
|
||||
console.log('请求的文件IDs:', fileIds);
|
||||
|
||||
// 使用 getUploadFileInfoById 逐个验证文件
|
||||
const validFiles = [];
|
||||
const invalidFileIds = [];
|
||||
|
||||
for (const fileId of fileIds) {
|
||||
try {
|
||||
console.log(`正在验证文件: ${fileId}`);
|
||||
const fileInfo = await getUploadFileInfoById(fileId);
|
||||
|
||||
if (fileInfo && fileInfo.projectId === projectId) {
|
||||
console.log(`文件验证成功: ${fileInfo.fileName}`);
|
||||
validFiles.push(fileInfo);
|
||||
} else if (fileInfo) {
|
||||
console.log(`文件属于其他项目: ${fileInfo.projectId} != ${projectId}`);
|
||||
invalidFileIds.push(fileId);
|
||||
} else {
|
||||
console.log(`文件不存在: ${fileId}`);
|
||||
invalidFileIds.push(fileId);
|
||||
}
|
||||
} catch (error) {
|
||||
console.error(`验证文件 ${fileId} 时出错:`, String(error));
|
||||
invalidFileIds.push(fileId);
|
||||
}
|
||||
}
|
||||
|
||||
console.log(`文件验证完成: 有效${validFiles.length}个, 无效${invalidFileIds.length}个`);
|
||||
|
||||
if (validFiles.length === 0) {
|
||||
return NextResponse.json(
|
||||
{
|
||||
error: 'No valid files found',
|
||||
debug: {
|
||||
projectId,
|
||||
requestedIds: fileIds,
|
||||
invalidIds: invalidFileIds,
|
||||
message: 'None of the requested files belong to this project or exist in the database'
|
||||
}
|
||||
},
|
||||
{ status: 404 }
|
||||
);
|
||||
}
|
||||
|
||||
// 批量生成 GA 对
|
||||
console.log('开始批量生成GA对...');
|
||||
console.log('追加模式:', appendMode);
|
||||
const results = await batchGenerateGaPairs(
|
||||
projectId,
|
||||
validFiles,
|
||||
modelConfigId,
|
||||
language,
|
||||
appendMode // 传递追加模式参数
|
||||
);
|
||||
|
||||
// 统计结果
|
||||
const successCount = results.filter(r => r.success).length;
|
||||
const failureCount = results.filter(r => !r.success).length;
|
||||
|
||||
console.log(`批量生成完成: 成功${successCount}个, 失败${failureCount}个`);
|
||||
|
||||
return NextResponse.json({
|
||||
success: true,
|
||||
data: results,
|
||||
summary: {
|
||||
total: results.length,
|
||||
success: successCount,
|
||||
failure: failureCount,
|
||||
processed: validFiles.length,
|
||||
skipped: invalidFileIds.length
|
||||
},
|
||||
message: `Generated GA pairs for ${successCount} files, ${failureCount} failed, ${invalidFileIds.length} files not found`
|
||||
});
|
||||
} catch (error) {
|
||||
console.error('Error batch generating GA pairs:', String(error));
|
||||
return NextResponse.json({ error: String(error) || 'Failed to batch generate GA pairs' }, { status: 500 });
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,161 @@
|
||||
import { NextResponse } from 'next/server';
|
||||
import { db } from '@/lib/db/index';
|
||||
import LLMClient from '@/lib/llm/core/index';
|
||||
import { getModelConfigById } from '@/lib/db/model-config';
|
||||
|
||||
/**
|
||||
* Get current question and generate answers from two models
|
||||
*/
|
||||
export async function GET(request, { params }) {
|
||||
try {
|
||||
const { projectId, taskId } = params;
|
||||
|
||||
const task = await db.task.findFirst({
|
||||
where: {
|
||||
id: taskId,
|
||||
projectId,
|
||||
taskType: 'blind-test'
|
||||
}
|
||||
});
|
||||
|
||||
if (!task) {
|
||||
return NextResponse.json({ code: 404, error: 'Task not found' }, { status: 404 });
|
||||
}
|
||||
|
||||
if (task.status !== 0) {
|
||||
return NextResponse.json({ code: 400, error: 'Task has ended' }, { status: 400 });
|
||||
}
|
||||
|
||||
// Parse task detail
|
||||
let detail = {};
|
||||
let modelInfo = {};
|
||||
try {
|
||||
detail = task.detail ? JSON.parse(task.detail) : {};
|
||||
modelInfo = task.modelInfo ? JSON.parse(task.modelInfo) : {};
|
||||
} catch (e) {
|
||||
console.error('Failed to parse task detail:', e);
|
||||
}
|
||||
|
||||
const questionIds = detail.questionIds || detail.evalDatasetIds || [];
|
||||
const currentIndex = detail.currentIndex || 0;
|
||||
|
||||
// Check if all questions are completed
|
||||
if (questionIds.length === 0 || currentIndex >= questionIds.length) {
|
||||
return NextResponse.json({
|
||||
code: 0,
|
||||
data: {
|
||||
completed: true,
|
||||
message: 'All questions completed'
|
||||
}
|
||||
});
|
||||
}
|
||||
|
||||
// Fetch current question
|
||||
const currentQuestionId = questionIds[currentIndex];
|
||||
const currentQuestion = await db.evalDatasets.findUnique({
|
||||
where: { id: currentQuestionId },
|
||||
select: {
|
||||
id: true,
|
||||
question: true,
|
||||
questionType: true,
|
||||
correctAnswer: true,
|
||||
tags: true
|
||||
}
|
||||
});
|
||||
|
||||
if (!currentQuestion) {
|
||||
return NextResponse.json({ code: 404, error: 'Question not found' }, { status: 404 });
|
||||
}
|
||||
|
||||
// Fetch both model configs
|
||||
const [modelConfigA, modelConfigB] = await Promise.all([
|
||||
getModelConfigById(modelInfo.modelA.providerId),
|
||||
getModelConfigById(modelInfo.modelB.providerId)
|
||||
]);
|
||||
|
||||
if (!modelConfigA || !modelConfigB) {
|
||||
return NextResponse.json({ code: 400, error: 'Model configuration not found' }, { status: 400 });
|
||||
}
|
||||
|
||||
// Build prompts
|
||||
const systemPrompt = "You are a helpful assistant. Provide detailed and accurate answers to the user's question.";
|
||||
const userPrompt = currentQuestion.question;
|
||||
|
||||
// Call both models in parallel
|
||||
const startTimeA = Date.now();
|
||||
const startTimeB = Date.now();
|
||||
|
||||
let answerA = '';
|
||||
let answerB = '';
|
||||
let errorA = null;
|
||||
let errorB = null;
|
||||
let durationA = 0;
|
||||
let durationB = 0;
|
||||
|
||||
try {
|
||||
// Call model A
|
||||
const clientA = new LLMClient(modelConfigA);
|
||||
|
||||
const resultA = await clientA.chat([
|
||||
{ role: 'system', content: systemPrompt },
|
||||
{ role: 'user', content: userPrompt }
|
||||
]);
|
||||
|
||||
answerA = resultA.text || '';
|
||||
durationA = Date.now() - startTimeA;
|
||||
} catch (err) {
|
||||
console.error('Model A call failed:', err);
|
||||
errorA = err.message;
|
||||
durationA = Date.now() - startTimeA;
|
||||
}
|
||||
|
||||
try {
|
||||
// Call model B
|
||||
const clientB = new LLMClient(modelConfigB);
|
||||
|
||||
const resultB = await clientB.chat([
|
||||
{ role: 'system', content: systemPrompt },
|
||||
{ role: 'user', content: userPrompt }
|
||||
]);
|
||||
|
||||
answerB = resultB.text || '';
|
||||
durationB = Date.now() - startTimeB;
|
||||
} catch (err) {
|
||||
console.error('Model B call failed:', err);
|
||||
errorB = err.message;
|
||||
durationB = Date.now() - startTimeB;
|
||||
}
|
||||
|
||||
// Randomly swap positions (core blind-test behavior)
|
||||
const isSwapped = Math.random() > 0.5;
|
||||
|
||||
return NextResponse.json({
|
||||
code: 0,
|
||||
data: {
|
||||
completed: false,
|
||||
currentIndex,
|
||||
totalCount: evalDatasetIds.length,
|
||||
question: currentQuestion,
|
||||
// Blind test: do not reveal which model is which
|
||||
leftAnswer: {
|
||||
content: isSwapped ? answerB : answerA,
|
||||
error: isSwapped ? errorB : errorA,
|
||||
duration: isSwapped ? durationB : durationA
|
||||
},
|
||||
rightAnswer: {
|
||||
content: isSwapped ? answerA : answerB,
|
||||
error: isSwapped ? errorA : errorB,
|
||||
duration: isSwapped ? durationA : durationB
|
||||
},
|
||||
// Server stores the actual mapping for scoring
|
||||
_swap: isSwapped
|
||||
}
|
||||
});
|
||||
} catch (error) {
|
||||
console.error('Failed to fetch current question:', error);
|
||||
return NextResponse.json(
|
||||
{ code: 500, error: 'Failed to fetch current question', message: error.message },
|
||||
{ status: 500 }
|
||||
);
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,64 @@
|
||||
import { NextResponse } from 'next/server';
|
||||
import { db } from '@/lib/db/index';
|
||||
|
||||
/**
|
||||
* Get current question info (including random swap info)
|
||||
*/
|
||||
export async function GET(request, { params }) {
|
||||
const { projectId, taskId } = params;
|
||||
|
||||
try {
|
||||
if (!projectId || !taskId) {
|
||||
return NextResponse.json({ error: 'Missing required parameters' }, { status: 400 });
|
||||
}
|
||||
|
||||
// Fetch task
|
||||
const task = await db.task.findUnique({
|
||||
where: { id: taskId }
|
||||
});
|
||||
|
||||
if (!task || task.taskType !== 'blind-test') {
|
||||
return NextResponse.json({ error: 'Task not found' }, { status: 404 });
|
||||
}
|
||||
|
||||
// Parse task detail
|
||||
const detail = JSON.parse(task.detail || '{}');
|
||||
// Support both evalDatasetIds and questionIds
|
||||
const questionIds = detail.questionIds || detail.evalDatasetIds || [];
|
||||
const currentIndex = detail.currentIndex || 0;
|
||||
|
||||
// Check if task is completed
|
||||
if (questionIds.length === 0 || currentIndex >= questionIds.length) {
|
||||
return NextResponse.json({
|
||||
completed: true,
|
||||
currentIndex,
|
||||
totalQuestions: questionIds.length
|
||||
});
|
||||
}
|
||||
|
||||
// Fetch current question
|
||||
const currentQuestionId = questionIds[currentIndex];
|
||||
const currentQuestion = await db.evalDatasets.findUnique({
|
||||
where: { id: currentQuestionId }
|
||||
});
|
||||
|
||||
if (!currentQuestion) {
|
||||
return NextResponse.json({ error: 'Question not found' }, { status: 404 });
|
||||
}
|
||||
|
||||
// Randomly decide whether to swap (core blind-test behavior)
|
||||
const isSwapped = Math.random() > 0.5;
|
||||
|
||||
return NextResponse.json({
|
||||
questionId: currentQuestion.id,
|
||||
question: currentQuestion.question,
|
||||
answer: currentQuestion.correctAnswer || '',
|
||||
questionIndex: currentIndex + 1,
|
||||
totalQuestions: questionIds.length,
|
||||
isSwapped
|
||||
});
|
||||
} catch (error) {
|
||||
console.error('Failed to fetch question info:', error);
|
||||
return NextResponse.json({ error: error.message }, { status: 500 });
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,190 @@
|
||||
import { NextResponse } from 'next/server';
|
||||
import { db } from '@/lib/db/index';
|
||||
|
||||
/**
|
||||
* Get blind-test task details
|
||||
* Results are fetched from EvalResults table
|
||||
*/
|
||||
export async function GET(request, { params }) {
|
||||
try {
|
||||
const { projectId, taskId } = params;
|
||||
|
||||
const task = await db.task.findFirst({
|
||||
where: {
|
||||
id: taskId,
|
||||
projectId,
|
||||
taskType: 'blind-test'
|
||||
}
|
||||
});
|
||||
|
||||
if (!task) {
|
||||
return NextResponse.json({ code: 404, error: 'Task not found' }, { status: 404 });
|
||||
}
|
||||
|
||||
let detail = {};
|
||||
let modelInfo = {};
|
||||
try {
|
||||
detail = task.detail ? JSON.parse(task.detail) : {};
|
||||
modelInfo = task.modelInfo ? JSON.parse(task.modelInfo) : {};
|
||||
} catch (e) {
|
||||
console.error('Failed to parse task detail:', e);
|
||||
}
|
||||
|
||||
// Fetch all related evaluation questions
|
||||
const evalDatasetIds = detail.evalDatasetIds || [];
|
||||
const evalDatasets = await db.evalDatasets.findMany({
|
||||
where: {
|
||||
id: { in: evalDatasetIds }
|
||||
},
|
||||
select: {
|
||||
id: true,
|
||||
question: true,
|
||||
questionType: true,
|
||||
correctAnswer: true,
|
||||
tags: true
|
||||
}
|
||||
});
|
||||
|
||||
// Sort by evalDatasetIds order
|
||||
const orderedDatasets = evalDatasetIds.map(id => evalDatasets.find(d => d.id === id)).filter(Boolean);
|
||||
|
||||
// Fetch results from EvalResults table
|
||||
const evalResults = await db.evalResults.findMany({
|
||||
where: { taskId },
|
||||
orderBy: { createAt: 'asc' }
|
||||
});
|
||||
|
||||
// Parse results into the format expected by frontend
|
||||
const results = evalResults.map(r => {
|
||||
let modelAnswer = {};
|
||||
let judgeData = {};
|
||||
try {
|
||||
modelAnswer = JSON.parse(r.modelAnswer || '{}');
|
||||
judgeData = JSON.parse(r.judgeResponse || '{}');
|
||||
} catch (e) {
|
||||
// Ignore parse errors
|
||||
}
|
||||
return {
|
||||
questionId: r.evalDatasetId,
|
||||
vote: judgeData.vote,
|
||||
isSwapped: judgeData.isSwapped,
|
||||
modelAScore: judgeData.modelAScore || 0,
|
||||
modelBScore: judgeData.modelBScore || 0,
|
||||
leftAnswer: modelAnswer.leftAnswer || '',
|
||||
rightAnswer: modelAnswer.rightAnswer || '',
|
||||
timestamp: r.createAt
|
||||
};
|
||||
});
|
||||
|
||||
return NextResponse.json({
|
||||
code: 0,
|
||||
data: {
|
||||
...task,
|
||||
detail: {
|
||||
...detail,
|
||||
results // Include results from EvalResults table
|
||||
},
|
||||
modelInfo,
|
||||
evalDatasets: orderedDatasets
|
||||
}
|
||||
});
|
||||
} catch (error) {
|
||||
console.error('Failed to fetch blind-test task details:', error);
|
||||
return NextResponse.json(
|
||||
{ code: 500, error: 'Failed to fetch blind-test task details', message: error.message },
|
||||
{ status: 500 }
|
||||
);
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Update blind-test task (interrupt/stop)
|
||||
*/
|
||||
export async function PUT(request, { params }) {
|
||||
try {
|
||||
const { projectId, taskId } = params;
|
||||
const { action } = await request.json();
|
||||
|
||||
const task = await db.task.findFirst({
|
||||
where: {
|
||||
id: taskId,
|
||||
projectId,
|
||||
taskType: 'blind-test'
|
||||
}
|
||||
});
|
||||
|
||||
if (!task) {
|
||||
return NextResponse.json({ code: 404, error: 'Task not found' }, { status: 404 });
|
||||
}
|
||||
|
||||
if (action === 'interrupt') {
|
||||
if (task.status !== 0) {
|
||||
return NextResponse.json({ code: 400, error: 'Only running tasks can be interrupted' }, { status: 400 });
|
||||
}
|
||||
|
||||
const updatedTask = await db.task.update({
|
||||
where: { id: taskId },
|
||||
data: {
|
||||
status: 3, // Interrupted
|
||||
endTime: new Date()
|
||||
}
|
||||
});
|
||||
|
||||
return NextResponse.json({
|
||||
code: 0,
|
||||
data: updatedTask,
|
||||
message: 'Task interrupted'
|
||||
});
|
||||
}
|
||||
|
||||
return NextResponse.json({ code: 400, error: 'Unknown action' }, { status: 400 });
|
||||
} catch (error) {
|
||||
console.error('Failed to update blind-test task:', error);
|
||||
return NextResponse.json(
|
||||
{ code: 500, error: 'Failed to update blind-test task', message: error.message },
|
||||
{ status: 500 }
|
||||
);
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Delete blind-test task and its results
|
||||
*/
|
||||
export async function DELETE(request, { params }) {
|
||||
try {
|
||||
const { projectId, taskId } = params;
|
||||
|
||||
const task = await db.task.findFirst({
|
||||
where: {
|
||||
id: taskId,
|
||||
projectId,
|
||||
taskType: 'blind-test'
|
||||
}
|
||||
});
|
||||
|
||||
if (!task) {
|
||||
return NextResponse.json({ code: 404, error: 'Task not found' }, { status: 404 });
|
||||
}
|
||||
|
||||
// Delete related EvalResults first
|
||||
await db.evalResults.deleteMany({
|
||||
where: { taskId }
|
||||
});
|
||||
|
||||
// Then delete the task
|
||||
await db.task.delete({
|
||||
where: { id: taskId }
|
||||
});
|
||||
|
||||
return NextResponse.json({
|
||||
code: 0,
|
||||
message: 'Task deleted'
|
||||
});
|
||||
} catch (error) {
|
||||
console.error('Failed to delete blind-test task:', error);
|
||||
return NextResponse.json(
|
||||
{ code: 500, error: 'Failed to delete blind-test task', message: error.message },
|
||||
{ status: 500 }
|
||||
);
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,92 @@
|
||||
import { NextResponse } from 'next/server';
|
||||
import { db } from '@/lib/db/index';
|
||||
import LLMClient from '@/lib/llm/core/index';
|
||||
import { getModelConfigById } from '@/lib/db/model-config';
|
||||
|
||||
/**
|
||||
* Stream answer for a specified model
|
||||
* Query param: model=A or model=B
|
||||
*/
|
||||
export async function GET(request, { params }) {
|
||||
const { projectId, taskId } = params;
|
||||
const { searchParams } = new URL(request.url);
|
||||
const modelType = searchParams.get('model'); // 'A' or 'B'
|
||||
|
||||
try {
|
||||
if (!projectId || !taskId) {
|
||||
return NextResponse.json({ error: 'Missing required parameters' }, { status: 400 });
|
||||
}
|
||||
|
||||
if (!modelType || !['A', 'B'].includes(modelType)) {
|
||||
return NextResponse.json({ error: 'Model type must be specified (A or B)' }, { status: 400 });
|
||||
}
|
||||
|
||||
// Fetch task
|
||||
const task = await db.task.findUnique({
|
||||
where: { id: taskId }
|
||||
});
|
||||
|
||||
if (!task || task.taskType !== 'blind-test') {
|
||||
return NextResponse.json({ error: 'Task not found' }, { status: 404 });
|
||||
}
|
||||
|
||||
// Parse task detail
|
||||
const detail = JSON.parse(task.detail || '{}');
|
||||
const modelInfo = JSON.parse(task.modelInfo || '{}');
|
||||
// Support both evalDatasetIds and questionIds
|
||||
const questionIds = detail.questionIds || detail.evalDatasetIds || [];
|
||||
const currentIndex = detail.currentIndex || 0;
|
||||
|
||||
// Check if task is completed
|
||||
if (questionIds.length === 0 || currentIndex >= questionIds.length) {
|
||||
return NextResponse.json({ completed: true });
|
||||
}
|
||||
|
||||
// Fetch current question
|
||||
const currentQuestionId = questionIds[currentIndex];
|
||||
const currentQuestion = await db.evalDatasets.findUnique({
|
||||
where: { id: currentQuestionId }
|
||||
});
|
||||
|
||||
if (!currentQuestion) {
|
||||
return NextResponse.json({ error: 'Question not found' }, { status: 404 });
|
||||
}
|
||||
|
||||
// Resolve model config based on modelType
|
||||
const modelConfigKey = modelType === 'A' ? 'modelA' : 'modelB';
|
||||
const modelConfig = await getModelConfigById(modelInfo[modelConfigKey].id);
|
||||
|
||||
if (!modelConfig) {
|
||||
return NextResponse.json({ error: 'Model configuration not found' }, { status: 400 });
|
||||
}
|
||||
|
||||
// Prepare messages
|
||||
const messages = [
|
||||
{
|
||||
role: 'system',
|
||||
content: "You are a helpful assistant. Provide detailed and accurate answers to the user's question."
|
||||
},
|
||||
{ role: 'user', content: currentQuestion.question }
|
||||
];
|
||||
|
||||
// Create LLM client
|
||||
const client = new LLMClient({
|
||||
projectId,
|
||||
...modelConfig
|
||||
});
|
||||
|
||||
// Call streaming API and return response directly
|
||||
const response = await client.chatStreamAPI(messages);
|
||||
|
||||
return new Response(response.body, {
|
||||
headers: {
|
||||
'Content-Type': 'text/plain; charset=utf-8',
|
||||
'Cache-Control': 'no-cache',
|
||||
Connection: 'keep-alive'
|
||||
}
|
||||
});
|
||||
} catch (error) {
|
||||
console.error(`Model ${modelType} streaming call failed:`, error);
|
||||
return NextResponse.json({ error: error.message }, { status: 500 });
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,213 @@
|
||||
import { NextResponse } from 'next/server';
|
||||
import { db } from '@/lib/db/index';
|
||||
import LLMClient from '@/lib/llm/core/index';
|
||||
import { getModelConfigById } from '@/lib/db/model-config';
|
||||
|
||||
/**
|
||||
* Stream answers from two models for the current question
|
||||
*/
|
||||
export async function GET(request, { params }) {
|
||||
const { projectId, taskId } = params;
|
||||
|
||||
try {
|
||||
if (!projectId || !taskId) {
|
||||
return NextResponse.json({ error: 'Missing required parameters' }, { status: 400 });
|
||||
}
|
||||
|
||||
// Fetch task
|
||||
const task = await db.task.findUnique({
|
||||
where: { id: taskId }
|
||||
});
|
||||
|
||||
if (!task || task.taskType !== 'blind-test') {
|
||||
return NextResponse.json({ error: 'Task not found' }, { status: 404 });
|
||||
}
|
||||
|
||||
// Parse task detail
|
||||
const detail = JSON.parse(task.detail || '{}');
|
||||
const modelInfo = JSON.parse(task.modelInfo || '{}');
|
||||
const { questionIds = [], currentIndex = 0 } = detail;
|
||||
|
||||
// Check if task is completed
|
||||
if (currentIndex >= questionIds.length) {
|
||||
return NextResponse.json({ completed: true });
|
||||
}
|
||||
|
||||
// Fetch current question
|
||||
const currentQuestionId = questionIds[currentIndex];
|
||||
const currentQuestion = await db.evalDatasets.findUnique({
|
||||
where: { id: currentQuestionId }
|
||||
});
|
||||
|
||||
if (!currentQuestion) {
|
||||
return NextResponse.json({ error: 'Question not found' }, { status: 404 });
|
||||
}
|
||||
|
||||
// Fetch model configs
|
||||
const [modelConfigA, modelConfigB] = await Promise.all([
|
||||
getModelConfigById(modelInfo.modelA.providerId),
|
||||
getModelConfigById(modelInfo.modelB.providerId)
|
||||
]);
|
||||
|
||||
if (!modelConfigA || !modelConfigB) {
|
||||
return NextResponse.json({ error: 'Model configuration not found' }, { status: 400 });
|
||||
}
|
||||
|
||||
// Randomly swap positions (core blind-test behavior)
|
||||
const isSwapped = Math.random() > 0.5;
|
||||
|
||||
// Create streaming response
|
||||
const encoder = new TextEncoder();
|
||||
const stream = new ReadableStream({
|
||||
async start(controller) {
|
||||
try {
|
||||
// Send init message
|
||||
controller.enqueue(
|
||||
encoder.encode(
|
||||
JSON.stringify({
|
||||
type: 'init',
|
||||
question: currentQuestion.question,
|
||||
questionId: currentQuestion.id,
|
||||
questionIndex: currentIndex + 1,
|
||||
totalQuestions: questionIds.length,
|
||||
isSwapped
|
||||
}) + '\n'
|
||||
)
|
||||
);
|
||||
|
||||
// Prepare messages
|
||||
const messages = [
|
||||
{
|
||||
role: 'system',
|
||||
content: "You are a helpful assistant. Provide detailed and accurate answers to the user's question."
|
||||
},
|
||||
{ role: 'user', content: currentQuestion.question }
|
||||
];
|
||||
|
||||
// Create LLM clients
|
||||
const clientA = new LLMClient({
|
||||
projectId,
|
||||
...modelConfigA
|
||||
});
|
||||
|
||||
const clientB = new LLMClient({
|
||||
projectId,
|
||||
...modelConfigB
|
||||
});
|
||||
|
||||
let answerA = '';
|
||||
let answerB = '';
|
||||
const startTime = Date.now();
|
||||
|
||||
// Call both models in parallel (streaming)
|
||||
await Promise.all([
|
||||
(async () => {
|
||||
try {
|
||||
const response = await clientA.chatStreamAPI(messages);
|
||||
const reader = response.body.getReader();
|
||||
const decoder = new TextDecoder();
|
||||
|
||||
while (true) {
|
||||
const { done, value } = await reader.read();
|
||||
if (done) break;
|
||||
|
||||
const chunk = decoder.decode(value, { stream: true });
|
||||
answerA += chunk;
|
||||
|
||||
// Send chunk update
|
||||
controller.enqueue(
|
||||
encoder.encode(
|
||||
JSON.stringify({
|
||||
type: 'chunk',
|
||||
model: isSwapped ? 'B' : 'A',
|
||||
content: chunk
|
||||
}) + '\n'
|
||||
)
|
||||
);
|
||||
}
|
||||
} catch (err) {
|
||||
console.error('Model A call failed:', err);
|
||||
controller.enqueue(
|
||||
encoder.encode(
|
||||
JSON.stringify({
|
||||
type: 'error',
|
||||
model: isSwapped ? 'B' : 'A',
|
||||
error: err.message
|
||||
}) + '\n'
|
||||
)
|
||||
);
|
||||
}
|
||||
})(),
|
||||
(async () => {
|
||||
try {
|
||||
const response = await clientB.chatStreamAPI(messages);
|
||||
const reader = response.body.getReader();
|
||||
const decoder = new TextDecoder();
|
||||
|
||||
while (true) {
|
||||
const { done, value } = await reader.read();
|
||||
if (done) break;
|
||||
|
||||
const chunk = decoder.decode(value, { stream: true });
|
||||
answerB += chunk;
|
||||
|
||||
// Send chunk update
|
||||
controller.enqueue(
|
||||
encoder.encode(
|
||||
JSON.stringify({
|
||||
type: 'chunk',
|
||||
model: isSwapped ? 'A' : 'B',
|
||||
content: chunk
|
||||
}) + '\n'
|
||||
)
|
||||
);
|
||||
}
|
||||
} catch (err) {
|
||||
console.error('Model B call failed:', err);
|
||||
controller.enqueue(
|
||||
encoder.encode(
|
||||
JSON.stringify({
|
||||
type: 'error',
|
||||
model: isSwapped ? 'A' : 'B',
|
||||
error: err.message
|
||||
}) + '\n'
|
||||
)
|
||||
);
|
||||
}
|
||||
})()
|
||||
]);
|
||||
|
||||
const duration = Date.now() - startTime;
|
||||
|
||||
// Send done message
|
||||
controller.enqueue(
|
||||
encoder.encode(
|
||||
JSON.stringify({
|
||||
type: 'done',
|
||||
duration,
|
||||
answerA: isSwapped ? answerB : answerA,
|
||||
answerB: isSwapped ? answerA : answerB
|
||||
}) + '\n'
|
||||
)
|
||||
);
|
||||
|
||||
controller.close();
|
||||
} catch (error) {
|
||||
console.error('Streaming handler failed:', error);
|
||||
controller.error(error);
|
||||
}
|
||||
}
|
||||
});
|
||||
|
||||
return new Response(stream, {
|
||||
headers: {
|
||||
'Content-Type': 'text/plain; charset=utf-8',
|
||||
'Cache-Control': 'no-cache',
|
||||
Connection: 'keep-alive'
|
||||
}
|
||||
});
|
||||
} catch (error) {
|
||||
console.error('API error:', error);
|
||||
return NextResponse.json({ error: error.message }, { status: 500 });
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,154 @@
|
||||
import { NextResponse } from 'next/server';
|
||||
import { db } from '@/lib/db/index';
|
||||
|
||||
/**
|
||||
* Submit vote result
|
||||
* vote: 'left' | 'right' | 'both_good' | 'both_bad'
|
||||
* Results are stored in EvalResults table
|
||||
*/
|
||||
export async function POST(request, { params }) {
|
||||
try {
|
||||
const { projectId, taskId } = params;
|
||||
const { vote, questionId, isSwapped, leftAnswer, rightAnswer } = await request.json();
|
||||
|
||||
// Validate vote option
|
||||
const validVotes = ['left', 'right', 'both_good', 'both_bad'];
|
||||
if (!validVotes.includes(vote)) {
|
||||
return NextResponse.json({ code: 400, error: 'Invalid vote option' }, { status: 400 });
|
||||
}
|
||||
|
||||
if (!questionId) {
|
||||
return NextResponse.json({ code: 400, error: 'Question ID is required' }, { status: 400 });
|
||||
}
|
||||
|
||||
const task = await db.task.findFirst({
|
||||
where: {
|
||||
id: taskId,
|
||||
projectId,
|
||||
taskType: 'blind-test'
|
||||
}
|
||||
});
|
||||
|
||||
if (!task) {
|
||||
return NextResponse.json({ code: 404, error: 'Task not found' }, { status: 404 });
|
||||
}
|
||||
|
||||
if (task.status !== 0) {
|
||||
return NextResponse.json({ code: 400, error: 'Task has ended' }, { status: 400 });
|
||||
}
|
||||
|
||||
// Parse task details
|
||||
let detail = {};
|
||||
try {
|
||||
detail = task.detail ? JSON.parse(task.detail) : {};
|
||||
} catch (e) {
|
||||
console.error('Failed to parse task detail:', e);
|
||||
}
|
||||
|
||||
// Calculate scores
|
||||
// isSwapped: true means left is model B and right is model A
|
||||
// isSwapped: false means left is model A and right is model B
|
||||
let modelAScore = 0;
|
||||
let modelBScore = 0;
|
||||
|
||||
if (vote === 'left') {
|
||||
if (isSwapped) {
|
||||
modelBScore = 1; // Left is B
|
||||
} else {
|
||||
modelAScore = 1; // Left is A
|
||||
}
|
||||
} else if (vote === 'right') {
|
||||
if (isSwapped) {
|
||||
modelAScore = 1; // Right is A
|
||||
} else {
|
||||
modelBScore = 1; // Right is B
|
||||
}
|
||||
} else if (vote === 'both_good') {
|
||||
modelAScore = 0.5;
|
||||
modelBScore = 0.5;
|
||||
}
|
||||
// both_bad: both scores remain 0
|
||||
|
||||
// Store result in EvalResults table
|
||||
const evalResult = await db.evalResults.create({
|
||||
data: {
|
||||
projectId,
|
||||
taskId,
|
||||
evalDatasetId: questionId,
|
||||
modelAnswer: JSON.stringify({
|
||||
leftAnswer: leftAnswer || '',
|
||||
rightAnswer: rightAnswer || ''
|
||||
}),
|
||||
score: modelAScore, // Store modelA score for sorting/aggregation
|
||||
isCorrect: false, // Not applicable for blind-test
|
||||
judgeResponse: JSON.stringify({
|
||||
vote,
|
||||
isSwapped,
|
||||
modelAScore,
|
||||
modelBScore
|
||||
}),
|
||||
duration: 0,
|
||||
status: 0
|
||||
}
|
||||
});
|
||||
|
||||
// Update task progress
|
||||
const evalDatasetIds = detail.evalDatasetIds || [];
|
||||
const newCurrentIndex = (detail.currentIndex || 0) + 1;
|
||||
const isCompleted = newCurrentIndex >= evalDatasetIds.length;
|
||||
|
||||
const updatedDetail = {
|
||||
...detail,
|
||||
currentIndex: newCurrentIndex
|
||||
};
|
||||
|
||||
await db.task.update({
|
||||
where: { id: taskId },
|
||||
data: {
|
||||
detail: JSON.stringify(updatedDetail),
|
||||
completedCount: newCurrentIndex,
|
||||
status: isCompleted ? 1 : 0, // 1-completed, 0-running
|
||||
endTime: isCompleted ? new Date() : null
|
||||
}
|
||||
});
|
||||
|
||||
// Calculate current total scores from EvalResults
|
||||
const allResults = await db.evalResults.findMany({
|
||||
where: { taskId },
|
||||
select: { judgeResponse: true }
|
||||
});
|
||||
|
||||
let totalModelAScore = 0;
|
||||
let totalModelBScore = 0;
|
||||
for (const r of allResults) {
|
||||
try {
|
||||
const judge = JSON.parse(r.judgeResponse || '{}');
|
||||
totalModelAScore += judge.modelAScore || 0;
|
||||
totalModelBScore += judge.modelBScore || 0;
|
||||
} catch (e) {
|
||||
// Ignore parse errors
|
||||
}
|
||||
}
|
||||
|
||||
return NextResponse.json({
|
||||
code: 0,
|
||||
data: {
|
||||
success: true,
|
||||
isCompleted,
|
||||
currentIndex: newCurrentIndex,
|
||||
totalCount: evalDatasetIds.length,
|
||||
scores: {
|
||||
modelA: totalModelAScore,
|
||||
modelB: totalModelBScore
|
||||
}
|
||||
},
|
||||
message: isCompleted ? 'Blind-test task completed' : 'Vote recorded'
|
||||
});
|
||||
} catch (error) {
|
||||
console.error('Failed to submit vote result:', error);
|
||||
return NextResponse.json(
|
||||
{ code: 500, error: 'Failed to submit vote result', message: error.message },
|
||||
{ status: 500 }
|
||||
);
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,226 @@
|
||||
import { NextResponse } from 'next/server';
|
||||
import { db } from '@/lib/db/index';
|
||||
|
||||
/**
|
||||
* Get all blind-test tasks for a project
|
||||
*/
|
||||
export async function GET(request, { params }) {
|
||||
try {
|
||||
const { projectId } = params;
|
||||
const { searchParams } = new URL(request.url);
|
||||
const page = parseInt(searchParams.get('page') || '1');
|
||||
const pageSize = parseInt(searchParams.get('pageSize') || '20');
|
||||
|
||||
if (!projectId) {
|
||||
return NextResponse.json({ error: 'Project ID is required' }, { status: 400 });
|
||||
}
|
||||
|
||||
const skip = (page - 1) * pageSize;
|
||||
|
||||
// Fetch task list and total count
|
||||
const [tasks, total] = await Promise.all([
|
||||
db.task.findMany({
|
||||
where: {
|
||||
projectId,
|
||||
taskType: 'blind-test'
|
||||
},
|
||||
orderBy: { createAt: 'desc' },
|
||||
skip,
|
||||
take: pageSize
|
||||
}),
|
||||
db.task.count({
|
||||
where: {
|
||||
projectId,
|
||||
taskType: 'blind-test'
|
||||
}
|
||||
})
|
||||
]);
|
||||
|
||||
// Fetch evaluation results for all tasks to calculate scores
|
||||
const taskIds = tasks.map(t => t.id);
|
||||
const allEvalResults = await db.evalResults.findMany({
|
||||
where: { taskId: { in: taskIds } },
|
||||
select: {
|
||||
taskId: true,
|
||||
judgeResponse: true
|
||||
}
|
||||
});
|
||||
|
||||
// Group results by taskId and calculate scores
|
||||
const taskScores = {};
|
||||
for (const result of allEvalResults) {
|
||||
if (!taskScores[result.taskId]) {
|
||||
taskScores[result.taskId] = { modelAScore: 0, modelBScore: 0 };
|
||||
}
|
||||
try {
|
||||
const judge = JSON.parse(result.judgeResponse || '{}');
|
||||
taskScores[result.taskId].modelAScore += judge.modelAScore || 0;
|
||||
taskScores[result.taskId].modelBScore += judge.modelBScore || 0;
|
||||
} catch (e) {
|
||||
// Ignore parse errors
|
||||
}
|
||||
}
|
||||
|
||||
// Parse task detail fields and attach scores
|
||||
const tasksWithDetails = tasks.map(task => {
|
||||
let detail = {};
|
||||
let modelInfo = {};
|
||||
try {
|
||||
detail = task.detail ? JSON.parse(task.detail) : {};
|
||||
modelInfo = task.modelInfo ? JSON.parse(task.modelInfo) : {};
|
||||
} catch (e) {
|
||||
console.error('Failed to parse task detail:', e);
|
||||
}
|
||||
|
||||
// Attach calculated scores as results array
|
||||
const scores = taskScores[task.id] || { modelAScore: 0, modelBScore: 0 };
|
||||
const results = [
|
||||
{
|
||||
modelAScore: scores.modelAScore,
|
||||
modelBScore: scores.modelBScore
|
||||
}
|
||||
];
|
||||
|
||||
return {
|
||||
...task,
|
||||
detail: {
|
||||
...detail,
|
||||
results // Attach results for display in task card
|
||||
},
|
||||
modelInfo
|
||||
};
|
||||
});
|
||||
|
||||
return NextResponse.json({
|
||||
code: 0,
|
||||
data: {
|
||||
items: tasksWithDetails,
|
||||
total,
|
||||
page,
|
||||
pageSize,
|
||||
totalPages: Math.ceil(total / pageSize)
|
||||
}
|
||||
});
|
||||
} catch (error) {
|
||||
console.error('Failed to fetch blind-test task list:', error);
|
||||
return NextResponse.json(
|
||||
{ code: 500, error: 'Failed to fetch blind-test task list', message: error.message },
|
||||
{ status: 500 }
|
||||
);
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Create a blind-test task
|
||||
*/
|
||||
export async function POST(request, { params }) {
|
||||
try {
|
||||
const { projectId } = params;
|
||||
const data = await request.json();
|
||||
|
||||
const { modelA, modelB, evalDatasetIds, language = 'zh-CN' } = data;
|
||||
|
||||
if (!modelA || !modelA.modelId || !modelA.providerId) {
|
||||
return NextResponse.json({ code: 400, error: 'Please select model A' }, { status: 400 });
|
||||
}
|
||||
|
||||
if (!modelB || !modelB.modelId || !modelB.providerId) {
|
||||
return NextResponse.json({ code: 400, error: 'Please select model B' }, { status: 400 });
|
||||
}
|
||||
|
||||
if (modelA.modelId === modelB.modelId && modelA.providerId === modelB.providerId) {
|
||||
return NextResponse.json({ code: 400, error: 'The two models must be different' }, { status: 400 });
|
||||
}
|
||||
|
||||
if (!evalDatasetIds || evalDatasetIds.length === 0) {
|
||||
return NextResponse.json({ code: 400, error: 'Please select questions to evaluate' }, { status: 400 });
|
||||
}
|
||||
|
||||
const evalDatasets = await db.evalDatasets.findMany({
|
||||
where: {
|
||||
id: { in: evalDatasetIds },
|
||||
projectId
|
||||
},
|
||||
select: { id: true, questionType: true }
|
||||
});
|
||||
|
||||
const invalidQuestions = evalDatasets.filter(
|
||||
q => q.questionType !== 'short_answer' && q.questionType !== 'open_ended'
|
||||
);
|
||||
|
||||
if (invalidQuestions.length > 0) {
|
||||
return NextResponse.json(
|
||||
{
|
||||
code: 400,
|
||||
error: 'Blind-test tasks only support short-answer and open-ended questions'
|
||||
},
|
||||
{ status: 400 }
|
||||
);
|
||||
}
|
||||
|
||||
// Fetch model config info
|
||||
const [modelConfigA, modelConfigB] = await Promise.all([
|
||||
db.modelConfig.findFirst({
|
||||
where: { projectId, providerId: modelA.providerId, modelId: modelA.modelId }
|
||||
}),
|
||||
db.modelConfig.findFirst({
|
||||
where: { projectId, providerId: modelB.providerId, modelId: modelB.modelId }
|
||||
})
|
||||
]);
|
||||
|
||||
// Build model info (two models)
|
||||
const modelInfo = {
|
||||
modelA: {
|
||||
id: modelConfigA?.id,
|
||||
modelId: modelA.modelId,
|
||||
modelName: modelConfigA?.modelName || modelA.modelId,
|
||||
providerId: modelA.providerId,
|
||||
providerName: modelConfigA?.providerName || modelA.providerId
|
||||
},
|
||||
modelB: {
|
||||
id: modelConfigB?.id,
|
||||
modelId: modelB.modelId,
|
||||
modelName: modelConfigB?.modelName || modelB.modelId,
|
||||
providerId: modelB.providerId,
|
||||
providerName: modelConfigB?.providerName || modelB.providerId
|
||||
}
|
||||
};
|
||||
|
||||
// Build task detail (only store evalDatasetIds and currentIndex)
|
||||
const taskDetail = {
|
||||
evalDatasetIds,
|
||||
currentIndex: 0 // Current question index
|
||||
};
|
||||
|
||||
// Create task
|
||||
const newTask = await db.task.create({
|
||||
data: {
|
||||
projectId,
|
||||
taskType: 'blind-test',
|
||||
status: 0, // Running
|
||||
modelInfo: JSON.stringify(modelInfo),
|
||||
language,
|
||||
detail: JSON.stringify(taskDetail),
|
||||
totalCount: evalDatasetIds.length,
|
||||
completedCount: 0,
|
||||
note: ''
|
||||
}
|
||||
});
|
||||
|
||||
return NextResponse.json({
|
||||
code: 0,
|
||||
data: {
|
||||
...newTask,
|
||||
detail: taskDetail,
|
||||
modelInfo
|
||||
},
|
||||
message: 'Blind-test task created'
|
||||
});
|
||||
} catch (error) {
|
||||
console.error('Failed to create blind-test task:', error);
|
||||
return NextResponse.json(
|
||||
{ code: 500, error: 'Failed to create blind-test task', message: error.message },
|
||||
{ status: 500 }
|
||||
);
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,40 @@
|
||||
import { NextResponse } from 'next/server';
|
||||
import logger from '@/lib/util/logger';
|
||||
import cleanService from '@/lib/services/clean';
|
||||
|
||||
// 为指定文本块进行数据清洗
|
||||
export async function POST(request, { params }) {
|
||||
try {
|
||||
const { projectId, chunkId } = params;
|
||||
|
||||
// 验证项目ID和文本块ID
|
||||
if (!projectId || !chunkId) {
|
||||
return NextResponse.json({ error: 'Project ID or text block ID cannot be empty' }, { status: 400 });
|
||||
}
|
||||
|
||||
// 获取请求体
|
||||
const { model, language = '中文' } = await request.json();
|
||||
|
||||
if (!model) {
|
||||
return NextResponse.json({ error: 'Model cannot be empty' }, { status: 400 });
|
||||
}
|
||||
|
||||
// 使用数据清洗服务
|
||||
const result = await cleanService.cleanDataForChunk(projectId, chunkId, {
|
||||
model,
|
||||
language
|
||||
});
|
||||
|
||||
// 返回清洗结果
|
||||
return NextResponse.json({
|
||||
chunkId,
|
||||
originalLength: result.originalLength,
|
||||
cleanedLength: result.cleanedLength,
|
||||
success: result.success,
|
||||
message: '数据清洗完成'
|
||||
});
|
||||
} catch (error) {
|
||||
logger.error('Error cleaning data:', error);
|
||||
return NextResponse.json({ error: error.message || 'Error cleaning data' }, { status: 500 });
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,35 @@
|
||||
import { NextResponse } from 'next/server';
|
||||
import { generateEvalQuestionsForChunk } from '@/lib/services/eval';
|
||||
import logger from '@/lib/util/logger';
|
||||
|
||||
/**
|
||||
* 为指定文本块生成测评题目
|
||||
*/
|
||||
export async function POST(request, { params }) {
|
||||
try {
|
||||
const { projectId, chunkId } = params;
|
||||
|
||||
// 验证参数
|
||||
if (!projectId || !chunkId) {
|
||||
return NextResponse.json({ error: 'Project ID and Chunk ID are required' }, { status: 400 });
|
||||
}
|
||||
|
||||
// 获取请求体
|
||||
const { model, language = 'zh-CN' } = await request.json();
|
||||
|
||||
if (!model) {
|
||||
return NextResponse.json({ error: 'Model configuration is required' }, { status: 400 });
|
||||
}
|
||||
|
||||
// 调用服务层生成测评题目
|
||||
const result = await generateEvalQuestionsForChunk(projectId, chunkId, {
|
||||
model,
|
||||
language
|
||||
});
|
||||
|
||||
return NextResponse.json(result);
|
||||
} catch (error) {
|
||||
logger.error('Error generating eval questions:', error);
|
||||
return NextResponse.json({ error: error.message || 'Failed to generate eval questions' }, { status: 500 });
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,73 @@
|
||||
import { NextResponse } from 'next/server';
|
||||
import { getQuestionsForChunk } from '@/lib/db/questions';
|
||||
import logger from '@/lib/util/logger';
|
||||
import questionService from '@/lib/services/questions';
|
||||
|
||||
// 为指定文本块生成问题
|
||||
export async function POST(request, { params }) {
|
||||
try {
|
||||
const { projectId, chunkId } = params;
|
||||
|
||||
// 验证项目ID和文本块ID
|
||||
if (!projectId || !chunkId) {
|
||||
return NextResponse.json({ error: 'Project ID or text block ID cannot be empty' }, { status: 400 });
|
||||
} // 获取请求体
|
||||
const { model, language = '中文', number, enableGaExpansion = false } = await request.json();
|
||||
|
||||
if (!model) {
|
||||
return NextResponse.json({ error: 'Model cannot be empty' }, { status: 400 });
|
||||
}
|
||||
|
||||
// 后续会根据是否有GA对来选择是否启用GA扩展选择服务函数
|
||||
const serviceFunc = questionService.generateQuestionsForChunkWithGA;
|
||||
|
||||
// 使用问题生成服务
|
||||
const result = await serviceFunc(projectId, chunkId, {
|
||||
model,
|
||||
language,
|
||||
number,
|
||||
enableGaExpansion
|
||||
});
|
||||
|
||||
// 统一返回格式，确保包含GA扩展信息
|
||||
const response = {
|
||||
chunkId,
|
||||
questions: result.questions || result.labelQuestions || [],
|
||||
total: result.total || (result.questions || result.labelQuestions || []).length,
|
||||
gaExpansionUsed: result.gaExpansionUsed || false,
|
||||
gaPairsCount: result.gaPairsCount || 0,
|
||||
expectedTotal: result.expectedTotal || result.total
|
||||
};
|
||||
|
||||
// 返回生成的问题
|
||||
return NextResponse.json(response);
|
||||
} catch (error) {
|
||||
logger.error('Error generating questions:', error);
|
||||
return NextResponse.json({ error: error.message || 'Error generating questions' }, { status: 500 });
|
||||
}
|
||||
}
|
||||
|
||||
// 获取指定文本块的问题
|
||||
export async function GET(request, { params }) {
|
||||
try {
|
||||
const { projectId, chunkId } = params;
|
||||
|
||||
// 验证项目ID和文本块ID
|
||||
if (!projectId || !chunkId) {
|
||||
return NextResponse.json({ error: 'The item ID or text block ID cannot be empty' }, { status: 400 });
|
||||
}
|
||||
|
||||
// 获取文本块的问题
|
||||
const questions = await getQuestionsForChunk(projectId, chunkId);
|
||||
|
||||
// 返回问题列表
|
||||
return NextResponse.json({
|
||||
chunkId,
|
||||
questions,
|
||||
total: questions.length
|
||||
});
|
||||
} catch (error) {
|
||||
console.error('Error getting questions:', String(error));
|
||||
return NextResponse.json({ error: error.message || 'Error getting questions' }, { status: 500 });
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,73 @@
|
||||
import { NextResponse } from 'next/server';
|
||||
import { deleteChunkById, getChunkById, updateChunkById } from '@/lib/db/chunks';
|
||||
|
||||
// 获取文本块内容
|
||||
export async function GET(request, { params }) {
|
||||
try {
|
||||
const { projectId, chunkId } = params;
|
||||
// 验证参数
|
||||
if (!projectId) {
|
||||
return NextResponse.json({ error: 'Project ID cannot be empty' }, { status: 400 });
|
||||
}
|
||||
if (!chunkId) {
|
||||
return NextResponse.json({ error: 'Text block ID cannot be empty' }, { status: 400 });
|
||||
}
|
||||
// 获取文本块内容
|
||||
const chunk = await getChunkById(chunkId);
|
||||
|
||||
return NextResponse.json(chunk);
|
||||
} catch (error) {
|
||||
console.error('Failed to get text block content:', String(error));
|
||||
return NextResponse.json({ error: error.message || 'Failed to get text block content' }, { status: 500 });
|
||||
}
|
||||
}
|
||||
|
||||
// 删除文本块
|
||||
export async function DELETE(request, { params }) {
|
||||
try {
|
||||
const { projectId, chunkId } = params;
|
||||
// 验证参数
|
||||
if (!projectId) {
|
||||
return NextResponse.json({ error: 'Project ID cannot be empty' }, { status: 400 });
|
||||
}
|
||||
if (!chunkId) {
|
||||
return NextResponse.json({ error: 'Text block ID cannot be empty' }, { status: 400 });
|
||||
}
|
||||
await deleteChunkById(chunkId);
|
||||
|
||||
return NextResponse.json({ message: 'Text block deleted successfully' });
|
||||
} catch (error) {
|
||||
console.error('Failed to delete text block:', String(error));
|
||||
return NextResponse.json({ error: error.message || 'Failed to delete text block' }, { status: 500 });
|
||||
}
|
||||
}
|
||||
|
||||
// 编辑文本块内容
|
||||
export async function PATCH(request, { params }) {
|
||||
try {
|
||||
const { projectId, chunkId } = params;
|
||||
|
||||
// 验证参数
|
||||
if (!projectId) {
|
||||
return NextResponse.json({ error: '项目ID不能为空' }, { status: 400 });
|
||||
}
|
||||
|
||||
if (!chunkId) {
|
||||
return NextResponse.json({ error: '文本块ID不能为空' }, { status: 400 });
|
||||
}
|
||||
|
||||
// 解析请求体获取新内容
|
||||
const requestData = await request.json();
|
||||
const { content } = requestData;
|
||||
|
||||
if (!content) {
|
||||
return NextResponse.json({ error: '内容不能为空' }, { status: 400 });
|
||||
}
|
||||
|
||||
let res = await updateChunkById(chunkId, { content });
|
||||
return NextResponse.json(res);
|
||||
} catch (error) {
|
||||
console.error('编辑文本块失败:', String(error));
|
||||
return NextResponse.json({ error: error.message || '编辑文本块失败' }, { status: 500 });
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,20 @@
|
||||
import { getChunkContentsByNames } from '@/lib/db/chunks';
|
||||
import { NextResponse } from 'next/server';
|
||||
|
||||
export async function POST(request, { params }) {
|
||||
try {
|
||||
const { projectId } = params;
|
||||
const { chunkNames } = await request.json();
|
||||
|
||||
if (!chunkNames || !Array.isArray(chunkNames)) {
|
||||
return NextResponse.json({ error: 'chunkNames 参数必须是数组' }, { status: 400 });
|
||||
}
|
||||
|
||||
const chunkContentMap = await getChunkContentsByNames(projectId, chunkNames);
|
||||
|
||||
return NextResponse.json(chunkContentMap);
|
||||
} catch (error) {
|
||||
console.error('批量获取文本块内容失败:', error);
|
||||
return NextResponse.json({ error: '批量获取文本块内容失败' }, { status: 500 });
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,102 @@
|
||||
import { NextRequest, NextResponse } from 'next/server';
|
||||
import { PrismaClient } from '@prisma/client';
|
||||
|
||||
const prisma = new PrismaClient();
|
||||
|
||||
/**
|
||||
* 批量编辑文本块内容
|
||||
* POST /api/projects/[projectId]/chunks/batch-edit
|
||||
*/
|
||||
export async function POST(request, { params }) {
|
||||
try {
|
||||
const { projectId } = params;
|
||||
const body = await request.json();
|
||||
const { position, content, chunkIds } = body;
|
||||
|
||||
// 验证参数
|
||||
if (!position || !content || !chunkIds || !Array.isArray(chunkIds) || chunkIds.length === 0) {
|
||||
return NextResponse.json({ error: 'Missing required parameters: position, content, chunkIds' }, { status: 400 });
|
||||
}
|
||||
|
||||
if (!['start', 'end'].includes(position)) {
|
||||
return NextResponse.json({ error: 'Position must be "start" or "end"' }, { status: 400 });
|
||||
}
|
||||
|
||||
// 验证项目权限（获取要编辑的文本块）
|
||||
const chunksToUpdate = await prisma.chunks.findMany({
|
||||
where: {
|
||||
id: { in: chunkIds },
|
||||
projectId: projectId
|
||||
},
|
||||
select: {
|
||||
id: true,
|
||||
content: true,
|
||||
name: true
|
||||
}
|
||||
});
|
||||
|
||||
if (chunksToUpdate.length === 0) {
|
||||
return NextResponse.json({ error: 'Not found' }, { status: 404 });
|
||||
}
|
||||
|
||||
if (chunksToUpdate.length !== chunkIds.length) {
|
||||
return NextResponse.json({ error: 'Some chunks not found' }, { status: 400 });
|
||||
}
|
||||
|
||||
// 准备更新数据
|
||||
const updates = chunksToUpdate.map(chunk => {
|
||||
let newContent;
|
||||
|
||||
if (position === 'start') {
|
||||
// 在开头添加内容
|
||||
newContent = content + '\n\n' + chunk.content;
|
||||
} else {
|
||||
// 在结尾添加内容
|
||||
newContent = chunk.content + '\n\n' + content;
|
||||
}
|
||||
|
||||
return {
|
||||
where: { id: chunk.id },
|
||||
data: {
|
||||
content: newContent,
|
||||
size: newContent.length,
|
||||
updateAt: new Date()
|
||||
}
|
||||
};
|
||||
});
|
||||
|
||||
async function processBatches(items, batchSize, processFn) {
|
||||
const results = [];
|
||||
for (let i = 0; i < items.length; i += batchSize) {
|
||||
const batch = items.slice(i, i + batchSize);
|
||||
const batchResults = await Promise.all(batch.map(processFn));
|
||||
results.push(...batchResults);
|
||||
}
|
||||
return results;
|
||||
}
|
||||
|
||||
const BATCH_SIZE = 50; // 每批处理 50 个
|
||||
await processBatches(updates, BATCH_SIZE, update => prisma.chunks.update(update));
|
||||
|
||||
// 记录操作日志（可选）
|
||||
console.log(`Successfully updated ${chunksToUpdate.length} chunks`);
|
||||
|
||||
return NextResponse.json({
|
||||
success: true,
|
||||
updatedCount: chunksToUpdate.length,
|
||||
message: `Successfully updated ${chunksToUpdate.length} chunks`
|
||||
});
|
||||
} catch (error) {
|
||||
console.error('批量编辑文本块失败:', error);
|
||||
|
||||
return NextResponse.json(
|
||||
{
|
||||
error: 'Batch edit chunks failed',
|
||||
details: error.message
|
||||
},
|
||||
{ status: 500 }
|
||||
);
|
||||
} finally {
|
||||
await prisma.$disconnect();
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,35 @@
|
||||
import { NextResponse } from 'next/server';
|
||||
import { getChunkByName } from '@/lib/db/chunks';
|
||||
|
||||
/**
|
||||
* 根据文本块名称获取文本块
|
||||
* @param {Request} request 请求对象
|
||||
* @param {object} context 上下文，包含路径参数
|
||||
* @returns {Promise<NextResponse>} 响应对象
|
||||
*/
|
||||
export async function GET(request, { params }) {
|
||||
try {
|
||||
const { projectId } = params;
|
||||
|
||||
// 从查询参数中获取 chunkName
|
||||
const { searchParams } = new URL(request.url);
|
||||
const chunkName = searchParams.get('chunkName');
|
||||
|
||||
if (!chunkName) {
|
||||
return NextResponse.json({ error: '文本块名称不能为空' }, { status: 400 });
|
||||
}
|
||||
|
||||
// 根据名称和项目ID查询文本块
|
||||
const chunk = await getChunkByName(projectId, chunkName);
|
||||
|
||||
if (!chunk) {
|
||||
return NextResponse.json({ error: '未找到指定的文本块' }, { status: 404 });
|
||||
}
|
||||
|
||||
// 返回文本块信息
|
||||
return NextResponse.json(chunk);
|
||||
} catch (error) {
|
||||
console.error('根据名称获取文本块失败:', String(error));
|
||||
return NextResponse.json({ error: '获取文本块失败: ' + error.message }, { status: 500 });
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,21 @@
|
||||
import { NextResponse } from 'next/server';
|
||||
import { deleteChunkById, getChunkByFileIds, getChunkById, getChunksByFileIds, updateChunkById } from '@/lib/db/chunks';
|
||||
|
||||
// 获取文本块内容
|
||||
export async function POST(request, { params }) {
|
||||
try {
|
||||
const { projectId } = params;
|
||||
// 验证参数
|
||||
if (!projectId) {
|
||||
return NextResponse.json({ error: 'Project ID cannot be empty' }, { status: 400 });
|
||||
}
|
||||
const { array } = await request.json();
|
||||
// 获取文本块内容
|
||||
const chunk = await getChunksByFileIds(array);
|
||||
|
||||
return NextResponse.json(chunk);
|
||||
} catch (error) {
|
||||
console.error('Failed to get text block content:', String(error));
|
||||
return NextResponse.json({ error: String(error) || 'Failed to get text block content' }, { status: 500 });
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,36 @@
|
||||
import { NextResponse } from 'next/server';
|
||||
import { getProject, updateProject, getTaskConfig } from '@/lib/db/projects';
|
||||
|
||||
// 获取项目配置
|
||||
export async function GET(request, { params }) {
|
||||
try {
|
||||
const projectId = params.projectId;
|
||||
const config = await getProject(projectId);
|
||||
const taskConfig = await getTaskConfig(projectId);
|
||||
return NextResponse.json({ ...config, ...taskConfig });
|
||||
} catch (error) {
|
||||
console.error('获取项目配置失败:', String(error));
|
||||
return NextResponse.json({ error: error.message }, { status: 500 });
|
||||
}
|
||||
}
|
||||
|
||||
// 更新项目配置
|
||||
export async function PUT(request, { params }) {
|
||||
try {
|
||||
const projectId = params.projectId;
|
||||
const newConfig = await request.json();
|
||||
const currentConfig = await getProject(projectId);
|
||||
|
||||
// 只更新 prompts 部分
|
||||
const updatedConfig = {
|
||||
...currentConfig,
|
||||
...newConfig.prompts
|
||||
};
|
||||
|
||||
const config = await updateProject(projectId, updatedConfig);
|
||||
return NextResponse.json(config);
|
||||
} catch (error) {
|
||||
console.error('更新项目配置失败:', String(error));
|
||||
return NextResponse.json({ error: error.message }, { status: 500 });
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,105 @@
|
||||
import { NextResponse } from 'next/server';
|
||||
import {
|
||||
getCustomPrompts,
|
||||
getCustomPrompt,
|
||||
saveCustomPrompt,
|
||||
deleteCustomPrompt,
|
||||
batchSaveCustomPrompts,
|
||||
toggleCustomPrompt,
|
||||
getPromptTemplates
|
||||
} from '@/lib/db/custom-prompts';
|
||||
|
||||
// 获取项目的自定义提示词
|
||||
export async function GET(request, { params }) {
|
||||
try {
|
||||
const { projectId } = params;
|
||||
const { searchParams } = new URL(request.url);
|
||||
const promptType = searchParams.get('promptType');
|
||||
const language = searchParams.get('language');
|
||||
|
||||
if (!projectId) {
|
||||
return NextResponse.json({ error: 'Project ID is required' }, { status: 400 });
|
||||
}
|
||||
|
||||
const customPrompts = await getCustomPrompts(projectId, promptType, language);
|
||||
const templates = await getPromptTemplates();
|
||||
|
||||
return NextResponse.json({
|
||||
success: true,
|
||||
customPrompts,
|
||||
templates
|
||||
});
|
||||
} catch (error) {
|
||||
console.error('获取自定义提示词失败:', error);
|
||||
return NextResponse.json({ error: error.message }, { status: 500 });
|
||||
}
|
||||
}
|
||||
|
||||
// 保存自定义提示词
|
||||
export async function POST(request, { params }) {
|
||||
try {
|
||||
const { projectId } = params;
|
||||
const body = await request.json();
|
||||
|
||||
if (!projectId) {
|
||||
return NextResponse.json({ error: 'Project ID is required' }, { status: 400 });
|
||||
}
|
||||
|
||||
// 批量保存
|
||||
if (body.prompts && Array.isArray(body.prompts)) {
|
||||
const results = await batchSaveCustomPrompts(projectId, body.prompts);
|
||||
return NextResponse.json({
|
||||
success: true,
|
||||
results
|
||||
});
|
||||
}
|
||||
|
||||
// 单个保存
|
||||
const { promptType, promptKey, language, content } = body;
|
||||
if (!promptType || !promptKey || !language || content === undefined) {
|
||||
return NextResponse.json(
|
||||
{
|
||||
error: 'promptType, promptKey, language and content are required'
|
||||
},
|
||||
{ status: 400 }
|
||||
);
|
||||
}
|
||||
|
||||
const result = await saveCustomPrompt(projectId, promptType, promptKey, language, content);
|
||||
return NextResponse.json({
|
||||
success: true,
|
||||
result
|
||||
});
|
||||
} catch (error) {
|
||||
console.error('保存自定义提示词失败:', error);
|
||||
return NextResponse.json({ error: error.message }, { status: 500 });
|
||||
}
|
||||
}
|
||||
|
||||
// 删除自定义提示词
|
||||
export async function DELETE(request, { params }) {
|
||||
try {
|
||||
const { projectId } = params;
|
||||
const { searchParams } = new URL(request.url);
|
||||
const promptType = searchParams.get('promptType');
|
||||
const promptKey = searchParams.get('promptKey');
|
||||
const language = searchParams.get('language');
|
||||
|
||||
if (!projectId || !promptType || !promptKey || !language) {
|
||||
return NextResponse.json(
|
||||
{
|
||||
error: 'projectId, promptType, promptKey and language are required'
|
||||
},
|
||||
{ status: 400 }
|
||||
);
|
||||
}
|
||||
|
||||
const success = await deleteCustomPrompt(projectId, promptType, promptKey, language);
|
||||
return NextResponse.json({
|
||||
success
|
||||
});
|
||||
} catch (error) {
|
||||
console.error('删除自定义提示词失败:', error);
|
||||
return NextResponse.json({ error: error.message }, { status: 500 });
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,116 @@
|
||||
import { NextResponse } from 'next/server';
|
||||
import { saveChunks, deleteChunksByFileId } from '@/lib/db/chunks';
|
||||
import path from 'path';
|
||||
import fs from 'fs/promises';
|
||||
import { getProjectRoot } from '@/lib/db/base';
|
||||
|
||||
/**
|
||||
* 处理自定义分块请求
|
||||
* @param {Request} request - 请求对象
|
||||
* @param {Object} params - 路由参数
|
||||
* @returns {Promise<Response>} - 响应对象
|
||||
*/
|
||||
export async function POST(request, { params }) {
|
||||
try {
|
||||
const { projectId } = params;
|
||||
const { fileId, fileName, content, splitPoints } = await request.json();
|
||||
|
||||
// 参数验证
|
||||
if (!projectId || !fileId || !fileName || !content || !splitPoints) {
|
||||
return NextResponse.json({ error: 'Missing required parameters' }, { status: 400 });
|
||||
}
|
||||
|
||||
// 获取项目根目录
|
||||
const projectRoot = await getProjectRoot();
|
||||
const projectPath = path.join(projectRoot, projectId);
|
||||
|
||||
// 检查项目是否存在
|
||||
try {
|
||||
await fs.access(projectPath);
|
||||
} catch (error) {
|
||||
return NextResponse.json({ error: 'Project does not exist' }, { status: 404 });
|
||||
}
|
||||
|
||||
// 先删除该文件已有的文本块
|
||||
await deleteChunksByFileId(projectId, fileId);
|
||||
|
||||
// 根据分块点将文件内容分割成多个块
|
||||
const customChunks = generateCustomChunks(projectId, fileId, fileName, content, splitPoints);
|
||||
|
||||
// 保存新的文本块
|
||||
await saveChunks(customChunks);
|
||||
|
||||
return NextResponse.json({
|
||||
success: true,
|
||||
message: 'Custom chunks saved successfully',
|
||||
totalChunks: customChunks.length
|
||||
});
|
||||
} catch (error) {
|
||||
console.error('自定义分块处理出错:', String(error));
|
||||
return NextResponse.json({ error: error.message || 'Failed to process custom split request' }, { status: 500 });
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* 根据分块点生成自定义文本块
|
||||
* @param {string} projectId - 项目ID
|
||||
* @param {string} fileId - 文件ID
|
||||
* @param {string} fileName - 文件名
|
||||
* @param {string} content - 文件内容
|
||||
* @param {Array} splitPoints - 分块点数组
|
||||
* @returns {Array} - 生成的文本块数组
|
||||
*/
|
||||
function generateCustomChunks(projectId, fileId, fileName, content, splitPoints) {
|
||||
// 按位置排序分块点
|
||||
const sortedPoints = [...splitPoints].sort((a, b) => a.position - b.position);
|
||||
|
||||
// 创建分块
|
||||
const chunks = [];
|
||||
let startPos = 0;
|
||||
|
||||
// 处理每个分块点
|
||||
for (let i = 0; i < sortedPoints.length; i++) {
|
||||
const endPos = sortedPoints[i].position;
|
||||
|
||||
// 提取当前分块内容
|
||||
const chunkContent = content.substring(startPos, endPos);
|
||||
|
||||
// 跳过空白分块
|
||||
if (chunkContent.trim().length === 0) {
|
||||
startPos = endPos;
|
||||
continue;
|
||||
}
|
||||
|
||||
// 创建分块对象
|
||||
const chunk = {
|
||||
projectId,
|
||||
name: `${path.basename(fileName, path.extname(fileName))}-part-${i + 1}`,
|
||||
fileId,
|
||||
fileName,
|
||||
content: chunkContent,
|
||||
summary: `${fileName} 自定义分块 ${i + 1}/${sortedPoints.length + 1}`,
|
||||
size: chunkContent.length
|
||||
};
|
||||
|
||||
chunks.push(chunk);
|
||||
startPos = endPos;
|
||||
}
|
||||
|
||||
// 添加最后一个分块（如果有内容）
|
||||
const lastChunkContent = content.substring(startPos);
|
||||
if (lastChunkContent.trim().length > 0) {
|
||||
const lastChunk = {
|
||||
projectId,
|
||||
name: `${path.basename(fileName, path.extname(fileName))}-part-${sortedPoints.length + 1}`,
|
||||
fileId,
|
||||
fileName,
|
||||
content: lastChunkContent,
|
||||
summary: `${fileName} 自定义分块 ${sortedPoints.length + 1}/${sortedPoints.length + 1}`,
|
||||
size: lastChunkContent.length
|
||||
};
|
||||
|
||||
chunks.push(lastChunk);
|
||||
}
|
||||
|
||||
return chunks;
|
||||
}
|
||||
@@ -0,0 +1,183 @@
|
||||
/**
|
||||
* 单个多轮对话数据集操作API
|
||||
*/
|
||||
|
||||
import { NextResponse } from 'next/server';
|
||||
import {
|
||||
getDatasetConversationById,
|
||||
updateDatasetConversation,
|
||||
deleteDatasetConversation,
|
||||
getConversationNavigationItems
|
||||
} from '@/lib/db/dataset-conversations';
|
||||
|
||||
/**
|
||||
* 获取单个多轮对话数据集详情
|
||||
*/
|
||||
export async function GET(request, { params }) {
|
||||
try {
|
||||
const { projectId, conversationId } = params;
|
||||
const { searchParams } = new URL(request.url);
|
||||
const operateType = searchParams.get('operateType');
|
||||
|
||||
// 如果是导航操作，返回导航项
|
||||
if (operateType !== null) {
|
||||
const data = await getConversationNavigationItems(projectId, conversationId, operateType);
|
||||
return NextResponse.json(data);
|
||||
}
|
||||
|
||||
const conversation = await getDatasetConversationById(conversationId);
|
||||
|
||||
if (!conversation) {
|
||||
return NextResponse.json(
|
||||
{
|
||||
success: false,
|
||||
message: '对话数据集不存在'
|
||||
},
|
||||
{ status: 404 }
|
||||
);
|
||||
}
|
||||
|
||||
if (conversation.projectId !== projectId) {
|
||||
return NextResponse.json(
|
||||
{
|
||||
success: false,
|
||||
message: '对话数据集不属于指定项目'
|
||||
},
|
||||
{ status: 403 }
|
||||
);
|
||||
}
|
||||
|
||||
return NextResponse.json(conversation);
|
||||
} catch (error) {
|
||||
console.error('获取多轮对话数据集详情失败:', error);
|
||||
return NextResponse.json(
|
||||
{
|
||||
success: false,
|
||||
message: error.message
|
||||
},
|
||||
{ status: 500 }
|
||||
);
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* 更新多轮对话数据集
|
||||
*/
|
||||
export async function PUT(request, { params }) {
|
||||
try {
|
||||
const { projectId, conversationId } = params;
|
||||
const body = await request.json();
|
||||
|
||||
// 验证对话数据集是否存在且属于项目
|
||||
const conversation = await getDatasetConversationById(conversationId);
|
||||
|
||||
if (!conversation) {
|
||||
return NextResponse.json(
|
||||
{
|
||||
success: false,
|
||||
message: '对话数据集不存在'
|
||||
},
|
||||
{ status: 404 }
|
||||
);
|
||||
}
|
||||
|
||||
if (conversation.projectId !== projectId) {
|
||||
return NextResponse.json(
|
||||
{
|
||||
success: false,
|
||||
message: '对话数据集不属于指定项目'
|
||||
},
|
||||
{ status: 403 }
|
||||
);
|
||||
}
|
||||
|
||||
// 只允许更新特定字段
|
||||
const allowedFields = ['score', 'tags', 'note', 'confirmed', 'aiEvaluation', 'messages'];
|
||||
const updateData = {};
|
||||
|
||||
allowedFields.forEach(field => {
|
||||
if (body.hasOwnProperty(field)) {
|
||||
if (field === 'messages') {
|
||||
// 将messages数组转换为rawMessages字符串存储
|
||||
updateData['rawMessages'] = JSON.stringify(body[field]);
|
||||
} else {
|
||||
updateData[field] = body[field];
|
||||
}
|
||||
}
|
||||
});
|
||||
|
||||
if (Object.keys(updateData).length === 0) {
|
||||
return NextResponse.json(
|
||||
{
|
||||
success: false,
|
||||
message: '没有有效的更新字段'
|
||||
},
|
||||
{ status: 400 }
|
||||
);
|
||||
}
|
||||
|
||||
const updatedConversation = await updateDatasetConversation(conversationId, updateData);
|
||||
|
||||
return NextResponse.json({
|
||||
success: true,
|
||||
data: updatedConversation
|
||||
});
|
||||
} catch (error) {
|
||||
console.error('更新多轮对话数据集失败:', error);
|
||||
return NextResponse.json(
|
||||
{
|
||||
success: false,
|
||||
message: error.message
|
||||
},
|
||||
{ status: 500 }
|
||||
);
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* 删除多轮对话数据集
|
||||
*/
|
||||
export async function DELETE(request, { params }) {
|
||||
try {
|
||||
const { projectId, conversationId } = params;
|
||||
|
||||
// 验证对话数据集是否存在且属于项目
|
||||
const conversation = await getDatasetConversationById(conversationId);
|
||||
|
||||
if (!conversation) {
|
||||
return NextResponse.json(
|
||||
{
|
||||
success: false,
|
||||
message: '对话数据集不存在'
|
||||
},
|
||||
{ status: 404 }
|
||||
);
|
||||
}
|
||||
|
||||
if (conversation.projectId !== projectId) {
|
||||
return NextResponse.json(
|
||||
{
|
||||
success: false,
|
||||
message: '对话数据集不属于指定项目'
|
||||
},
|
||||
{ status: 403 }
|
||||
);
|
||||
}
|
||||
|
||||
await deleteDatasetConversation(conversationId);
|
||||
|
||||
return NextResponse.json({
|
||||
success: true,
|
||||
message: '删除成功'
|
||||
});
|
||||
} catch (error) {
|
||||
console.error('删除多轮对话数据集失败:', error);
|
||||
return NextResponse.json(
|
||||
{
|
||||
success: false,
|
||||
message: error.message
|
||||
},
|
||||
{ status: 500 }
|
||||
);
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,68 @@
|
||||
/**
|
||||
* 多轮对话数据集导出API
|
||||
* 直接导出原始的 ShareGPT 格式数据集
|
||||
*/
|
||||
|
||||
import { NextResponse } from 'next/server';
|
||||
import { getAllDatasetConversations } from '@/lib/db/dataset-conversations';
|
||||
|
||||
/**
|
||||
* 导出多轮对话数据集
|
||||
*/
|
||||
export async function GET(request, { params }) {
|
||||
try {
|
||||
const { projectId } = params;
|
||||
const { searchParams } = new URL(request.url);
|
||||
|
||||
// 筛选条件
|
||||
const filters = {
|
||||
confirmed: searchParams.get('confirmed')
|
||||
};
|
||||
|
||||
// 清除空值
|
||||
Object.keys(filters).forEach(key => {
|
||||
if (!filters[key]) delete filters[key];
|
||||
});
|
||||
|
||||
// 获取所有对话数据集
|
||||
const conversations = await getAllDatasetConversations(projectId, filters);
|
||||
|
||||
if (conversations.length === 0) {
|
||||
return NextResponse.json([]);
|
||||
}
|
||||
|
||||
// 转换为 ShareGPT 格式数组
|
||||
const shareGptData = [];
|
||||
|
||||
for (const conversation of conversations) {
|
||||
try {
|
||||
// 解析 rawMessages
|
||||
const messages = JSON.parse(conversation.rawMessages || '[]');
|
||||
|
||||
if (messages.length > 0) {
|
||||
// 构建 ShareGPT 格式对象
|
||||
const shareGptItem = {
|
||||
messages: messages
|
||||
};
|
||||
|
||||
shareGptData.push(shareGptItem);
|
||||
}
|
||||
} catch (error) {
|
||||
console.error(`解析对话消息失败 ${conversation.id}:`, error);
|
||||
// 跳过解析失败的对话，继续处理其他对话
|
||||
continue;
|
||||
}
|
||||
}
|
||||
|
||||
return NextResponse.json(shareGptData);
|
||||
} catch (error) {
|
||||
console.error('导出多轮对话数据集失败:', error);
|
||||
return NextResponse.json(
|
||||
{
|
||||
success: false,
|
||||
message: error.message
|
||||
},
|
||||
{ status: 500 }
|
||||
);
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,135 @@
|
||||
/**
|
||||
* 多轮对话数据集管理API
|
||||
*/
|
||||
|
||||
import { NextResponse } from 'next/server';
|
||||
import {
|
||||
getDatasetConversationsByPagination,
|
||||
getAllDatasetConversationIds,
|
||||
createDatasetConversation
|
||||
} from '@/lib/db/dataset-conversations';
|
||||
import { generateMultiTurnConversation } from '@/lib/services/multi-turn/index';
|
||||
|
||||
/**
|
||||
* 获取多轮对话数据集列表（支持分页和筛选）
|
||||
*/
|
||||
export async function GET(request, { params }) {
|
||||
try {
|
||||
const { projectId } = params;
|
||||
const { searchParams } = new URL(request.url);
|
||||
|
||||
const getAllIds = searchParams.get('getAllIds') === 'true'; // 新增：获取所有对话ID的标志
|
||||
|
||||
// 筛选条件
|
||||
const filters = {
|
||||
keyword: searchParams.get('keyword'),
|
||||
roleA: searchParams.get('roleA'),
|
||||
roleB: searchParams.get('roleB'),
|
||||
scenario: searchParams.get('scenario'),
|
||||
scoreMin: searchParams.get('scoreMin'),
|
||||
scoreMax: searchParams.get('scoreMax'),
|
||||
confirmed: searchParams.get('confirmed')
|
||||
};
|
||||
|
||||
// 清除空值
|
||||
Object.keys(filters).forEach(key => {
|
||||
if (!filters[key]) delete filters[key];
|
||||
});
|
||||
|
||||
// 如果请求获取所有ID
|
||||
if (getAllIds) {
|
||||
const allConversationIds = await getAllDatasetConversationIds(projectId, filters);
|
||||
return NextResponse.json({ allConversationIds });
|
||||
}
|
||||
|
||||
// 正常分页查询
|
||||
const page = parseInt(searchParams.get('page') || '1');
|
||||
const pageSize = parseInt(searchParams.get('pageSize') || '20');
|
||||
|
||||
const result = await getDatasetConversationsByPagination(projectId, page, pageSize, filters);
|
||||
|
||||
return NextResponse.json({
|
||||
success: true,
|
||||
...result
|
||||
});
|
||||
} catch (error) {
|
||||
console.error('获取多轮对话数据集失败:', error);
|
||||
return NextResponse.json(
|
||||
{
|
||||
success: false,
|
||||
message: error.message
|
||||
},
|
||||
{ status: 500 }
|
||||
);
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* 创建多轮对话数据集
|
||||
*/
|
||||
export async function POST(request, { params }) {
|
||||
try {
|
||||
const { projectId } = params;
|
||||
const body = await request.json();
|
||||
|
||||
const { questionId, systemPrompt, scenario, rounds, roleA, roleB, model, language = '中文' } = body;
|
||||
|
||||
if (!questionId) {
|
||||
return NextResponse.json(
|
||||
{
|
||||
success: false,
|
||||
message: '问题ID不能为空'
|
||||
},
|
||||
{ status: 400 }
|
||||
);
|
||||
}
|
||||
|
||||
if (!model || !model.modelId) {
|
||||
return NextResponse.json(
|
||||
{
|
||||
success: false,
|
||||
message: '模型配置不能为空'
|
||||
},
|
||||
{ status: 400 }
|
||||
);
|
||||
}
|
||||
|
||||
// 构建配置
|
||||
const config = {
|
||||
systemPrompt: systemPrompt || '',
|
||||
scenario: scenario || '',
|
||||
rounds: rounds || 3,
|
||||
roleA: roleA || '用户',
|
||||
roleB: roleB || '助手',
|
||||
model,
|
||||
language
|
||||
};
|
||||
|
||||
// 生成多轮对话
|
||||
const result = await generateMultiTurnConversation(projectId, questionId, config);
|
||||
|
||||
if (!result.success) {
|
||||
return NextResponse.json(
|
||||
{
|
||||
success: false,
|
||||
message: result.error
|
||||
},
|
||||
{ status: 500 }
|
||||
);
|
||||
}
|
||||
|
||||
return NextResponse.json({
|
||||
success: true,
|
||||
data: result.data
|
||||
});
|
||||
} catch (error) {
|
||||
console.error('创建多轮对话数据集失败:', error);
|
||||
return NextResponse.json(
|
||||
{
|
||||
success: false,
|
||||
message: error.message
|
||||
},
|
||||
{ status: 500 }
|
||||
);
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,42 @@
|
||||
import { NextResponse } from 'next/server';
|
||||
import { getAllDatasetConversations } from '@/lib/db/dataset-conversations';
|
||||
|
||||
/**
|
||||
* 获取项目中多轮对话数据集的所有标签
|
||||
*/
|
||||
export async function GET(request, { params }) {
|
||||
try {
|
||||
const { projectId } = params;
|
||||
|
||||
if (!projectId) {
|
||||
return NextResponse.json({ error: '项目ID不能为空' }, { status: 400 });
|
||||
}
|
||||
|
||||
// 获取项目所有对话数据集
|
||||
const conversations = await getAllDatasetConversations(projectId);
|
||||
|
||||
// 提取所有标签
|
||||
const allTags = new Set();
|
||||
|
||||
conversations.forEach(conversation => {
|
||||
if (conversation.tags && typeof conversation.tags === 'string') {
|
||||
const tags = conversation.tags.split(/\s+/).filter(tag => tag.trim().length > 0);
|
||||
tags.forEach(tag => allTags.add(tag.trim()));
|
||||
}
|
||||
});
|
||||
|
||||
return NextResponse.json({
|
||||
success: true,
|
||||
tags: Array.from(allTags).sort()
|
||||
});
|
||||
} catch (error) {
|
||||
console.error('获取对话标签失败:', error);
|
||||
return NextResponse.json(
|
||||
{
|
||||
success: false,
|
||||
message: error.message
|
||||
},
|
||||
{ status: 500 }
|
||||
);
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,77 @@
|
||||
import { NextResponse } from 'next/server';
|
||||
import { db } from '@/lib/db';
|
||||
|
||||
export async function POST(req, { params }) {
|
||||
try {
|
||||
const { projectId, datasetId } = params;
|
||||
|
||||
// 1. 获取数据集详情
|
||||
const dataset = await db.datasets.findUnique({
|
||||
where: { id: datasetId, projectId }
|
||||
});
|
||||
|
||||
if (!dataset) {
|
||||
return NextResponse.json({ error: 'Dataset not found' }, { status: 404 });
|
||||
}
|
||||
|
||||
// 2. 尝试通过 questionId 查找关联的 chunkId
|
||||
let chunkId = null;
|
||||
if (dataset.questionId) {
|
||||
const question = await db.questions.findUnique({
|
||||
where: { id: dataset.questionId }
|
||||
});
|
||||
if (question) {
|
||||
chunkId = question.chunkId;
|
||||
}
|
||||
}
|
||||
|
||||
// 3. 创建评估数据集记录
|
||||
// 默认使用 open_ended 类型，因为通常数据集是问答对，适合作为评估
|
||||
let evalTags = [];
|
||||
try {
|
||||
evalTags = JSON.parse(dataset.tags || '[]');
|
||||
if (!Array.isArray(evalTags)) evalTags = [];
|
||||
} catch (e) {
|
||||
evalTags = [];
|
||||
}
|
||||
|
||||
// 排除 'Eval' 标签，并将数组转为逗号分隔的字符串
|
||||
const evalTagsString = evalTags.filter(tag => tag !== 'Eval').join(',');
|
||||
|
||||
const evalDataset = await db.evalDatasets.create({
|
||||
data: {
|
||||
projectId,
|
||||
question: dataset.question,
|
||||
questionType: 'open_ended',
|
||||
correctAnswer: dataset.answer,
|
||||
tags: evalTagsString,
|
||||
note: dataset.note,
|
||||
chunkId: chunkId,
|
||||
options: '' // 开放题不需要选项
|
||||
}
|
||||
});
|
||||
|
||||
// 4. 更新原数据集，添加 'Eval' 标签
|
||||
let currentTags = [];
|
||||
try {
|
||||
currentTags = JSON.parse(dataset.tags || '[]');
|
||||
} catch (e) {
|
||||
// ignore error
|
||||
}
|
||||
|
||||
if (!currentTags.includes('Eval')) {
|
||||
currentTags.push('Eval');
|
||||
await db.datasets.update({
|
||||
where: { id: datasetId },
|
||||
data: {
|
||||
tags: JSON.stringify(currentTags)
|
||||
}
|
||||
});
|
||||
}
|
||||
|
||||
return NextResponse.json({ success: true, evalDataset });
|
||||
} catch (error) {
|
||||
console.error('Failed to copy dataset to eval:', error);
|
||||
return NextResponse.json({ error: 'Internal Server Error' }, { status: 500 });
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,36 @@
|
||||
import { NextResponse } from 'next/server';
|
||||
import { evaluateDataset } from '@/lib/services/datasets/evaluation';
|
||||
|
||||
/**
|
||||
* 评估单个数据集的质量
|
||||
*/
|
||||
export async function POST(request, { params }) {
|
||||
try {
|
||||
const { projectId, datasetId } = params;
|
||||
const { model, language = 'zh-CN' } = await request.json();
|
||||
|
||||
if (!projectId || !datasetId) {
|
||||
return NextResponse.json({ success: false, message: '项目ID和数据集ID不能为空' }, { status: 400 });
|
||||
}
|
||||
|
||||
if (!model) {
|
||||
return NextResponse.json({ success: false, message: '模型配置不能为空' }, { status: 400 });
|
||||
}
|
||||
|
||||
// 使用评估服务进行数据集评估
|
||||
const result = await evaluateDataset(projectId, datasetId, model, language);
|
||||
|
||||
if (!result.success) {
|
||||
return NextResponse.json({ success: false, message: result.error }, { status: 500 });
|
||||
}
|
||||
|
||||
return NextResponse.json({
|
||||
success: true,
|
||||
message: '数据集评估完成',
|
||||
data: result.data
|
||||
});
|
||||
} catch (error) {
|
||||
console.error('数据集评估失败:', error);
|
||||
return NextResponse.json({ success: false, message: `评估失败: ${error.message}` }, { status: 500 });
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,82 @@
|
||||
import { NextResponse } from 'next/server';
|
||||
import { getDatasetsById, getDatasetsCounts, getNavigationItems, updateDatasetMetadata } from '@/lib/db/datasets';
|
||||
|
||||
/**
|
||||
* 获取项目的所有数据集
|
||||
*/
|
||||
export async function GET(request, { params }) {
|
||||
try {
|
||||
const { projectId, datasetId } = params;
|
||||
// 验证项目ID
|
||||
if (!projectId) {
|
||||
return NextResponse.json({ error: '项目ID不能为空' }, { status: 400 });
|
||||
}
|
||||
if (!datasetId) {
|
||||
return NextResponse.json({ error: '数据集ID不能为空' }, { status: 400 });
|
||||
}
|
||||
const { searchParams } = new URL(request.url);
|
||||
const operateType = searchParams.get('operateType');
|
||||
if (operateType !== null) {
|
||||
const data = await getNavigationItems(projectId, datasetId, operateType);
|
||||
return NextResponse.json(data);
|
||||
}
|
||||
const datasets = await getDatasetsById(datasetId);
|
||||
let counts = await getDatasetsCounts(projectId);
|
||||
|
||||
return NextResponse.json({ datasets, ...counts });
|
||||
} catch (error) {
|
||||
console.error('获取数据集详情失败:', String(error));
|
||||
return NextResponse.json(
|
||||
{
|
||||
error: error.message || '获取数据集详情失败'
|
||||
},
|
||||
{ status: 500 }
|
||||
);
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* 更新数据集元数据（评分、标签、备注）
|
||||
*/
|
||||
export async function PATCH(request, { params }) {
|
||||
try {
|
||||
const { projectId, datasetId } = params;
|
||||
|
||||
// 验证参数
|
||||
if (!projectId) {
|
||||
return NextResponse.json({ error: '项目ID不能为空' }, { status: 400 });
|
||||
}
|
||||
if (!datasetId) {
|
||||
return NextResponse.json({ error: '数据集ID不能为空' }, { status: 400 });
|
||||
}
|
||||
|
||||
const body = await request.json();
|
||||
const { score, tags, note } = body;
|
||||
|
||||
// 验证评分范围
|
||||
if (score !== undefined && (score < 0 || score > 5)) {
|
||||
return NextResponse.json({ error: '评分必须在0-5之间' }, { status: 400 });
|
||||
}
|
||||
|
||||
// 验证标签格式
|
||||
if (tags !== undefined && !Array.isArray(tags)) {
|
||||
return NextResponse.json({ error: '标签必须是数组格式' }, { status: 400 });
|
||||
}
|
||||
|
||||
// 更新数据集元数据
|
||||
const updatedDataset = await updateDatasetMetadata(datasetId, { score, tags, note });
|
||||
|
||||
return NextResponse.json({
|
||||
success: true,
|
||||
dataset: updatedDataset
|
||||
});
|
||||
} catch (error) {
|
||||
console.error('更新数据集元数据失败:', String(error));
|
||||
return NextResponse.json(
|
||||
{
|
||||
error: error.message || '更新数据集元数据失败'
|
||||
},
|
||||
{ status: 500 }
|
||||
);
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,52 @@
|
||||
import { NextResponse } from 'next/server';
|
||||
import { getDatasetsById } from '@/lib/db/datasets';
|
||||
import { getEncoding } from '@langchain/core/utils/tiktoken';
|
||||
|
||||
/**
|
||||
* 异步计算数据集文本的Token数量
|
||||
*/
|
||||
export async function GET(request, { params }) {
|
||||
try {
|
||||
const { projectId, datasetId } = params;
|
||||
|
||||
if (!datasetId) {
|
||||
return NextResponse.json({ error: '数据集ID不能为空' }, { status: 400 });
|
||||
}
|
||||
|
||||
const datasets = await getDatasetsById(datasetId);
|
||||
const tokenCounts = {
|
||||
answerTokens: 0,
|
||||
cotTokens: 0
|
||||
};
|
||||
|
||||
try {
|
||||
if (datasets.answer || datasets.cot) {
|
||||
// 使用 cl100k_base 编码，适用于 gpt-3.5-turbo 和 gpt-4
|
||||
const encoding = await getEncoding('cl100k_base');
|
||||
|
||||
if (datasets.answer) {
|
||||
const tokens = encoding.encode(datasets.answer);
|
||||
tokenCounts.answerTokens = tokens.length;
|
||||
}
|
||||
|
||||
if (datasets.cot) {
|
||||
const tokens = encoding.encode(datasets.cot);
|
||||
tokenCounts.cotTokens = tokens.length;
|
||||
}
|
||||
}
|
||||
} catch (error) {
|
||||
console.error('计算Token数量失败:', String(error));
|
||||
return NextResponse.json({ error: '计算Token数量失败' }, { status: 500 });
|
||||
}
|
||||
|
||||
return NextResponse.json(tokenCounts);
|
||||
} catch (error) {
|
||||
console.error('获取Token计数失败:', String(error));
|
||||
return NextResponse.json(
|
||||
{
|
||||
error: error.message || '获取Token计数失败'
|
||||
},
|
||||
{ status: 500 }
|
||||
);
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,55 @@
|
||||
/**
|
||||
* 批量数据集评估任务API
|
||||
* 创建批量评估数据集质量的异步任务
|
||||
*/
|
||||
|
||||
import { NextResponse } from 'next/server';
|
||||
import { db } from '@/lib/db/index';
|
||||
import { processTask } from '@/lib/services/tasks/index';
|
||||
|
||||
/**
|
||||
* 创建批量数据集评估任务
|
||||
*/
|
||||
export async function POST(request, { params }) {
|
||||
try {
|
||||
const { projectId } = params;
|
||||
const { model, language = 'zh-CN' } = await request.json();
|
||||
|
||||
if (!projectId) {
|
||||
return NextResponse.json({ success: false, message: '项目ID不能为空' }, { status: 400 });
|
||||
}
|
||||
|
||||
if (!model || !model.modelId) {
|
||||
return NextResponse.json({ success: false, message: '模型配置不能为空' }, { status: 400 });
|
||||
}
|
||||
|
||||
// 创建批量评估任务
|
||||
const newTask = await db.task.create({
|
||||
data: {
|
||||
projectId,
|
||||
taskType: 'dataset-evaluation',
|
||||
status: 0, // 初始状态: 处理中
|
||||
modelInfo: JSON.stringify(model),
|
||||
language: language || 'zh-CN',
|
||||
detail: '',
|
||||
totalCount: 0,
|
||||
note: '准备开始批量评估数据集质量...',
|
||||
completedCount: 0
|
||||
}
|
||||
});
|
||||
|
||||
// 异步处理任务
|
||||
processTask(newTask.id).catch(err => {
|
||||
console.error(`批量评估任务启动失败: ${newTask.id}`, String(err));
|
||||
});
|
||||
|
||||
return NextResponse.json({
|
||||
success: true,
|
||||
message: '批量评估任务已创建',
|
||||
data: { taskId: newTask.id }
|
||||
});
|
||||
} catch (error) {
|
||||
console.error('创建批量评估任务失败:', error);
|
||||
return NextResponse.json({ success: false, message: `创建任务失败: ${error.message}` }, { status: 500 });
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,128 @@
|
||||
import { NextResponse } from 'next/server';
|
||||
import {
|
||||
getDatasets,
|
||||
getBalancedDatasetsByTags,
|
||||
getTagsWithDatasetCounts,
|
||||
getDatasetsBatch,
|
||||
getBalancedDatasetsByTagsBatch,
|
||||
getDatasetsByIds,
|
||||
getDatasetsByIdsBatch
|
||||
} from '@/lib/db/datasets';
|
||||
|
||||
/**
|
||||
* 获取导出数据集
|
||||
*/
|
||||
export async function GET(request, { params }) {
|
||||
try {
|
||||
const { projectId } = params;
|
||||
const { searchParams } = new URL(request.url);
|
||||
|
||||
// 验证项目ID
|
||||
if (!projectId) {
|
||||
return NextResponse.json({ error: 'Project ID cannot be empty' }, { status: 400 });
|
||||
}
|
||||
|
||||
const confirmedParam = searchParams.get('confirmed');
|
||||
const confirmed = confirmedParam === null ? undefined : confirmedParam === 'true';
|
||||
|
||||
// 获取标签统计信息
|
||||
const tagStats = await getTagsWithDatasetCounts(projectId, confirmed);
|
||||
return NextResponse.json(tagStats);
|
||||
} catch (error) {
|
||||
console.error('Failed to get tag statistics:', String(error));
|
||||
return NextResponse.json(
|
||||
{
|
||||
error: error.message || 'Failed to get tag statistics'
|
||||
},
|
||||
{ status: 500 }
|
||||
);
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* 获取标签统计信息
|
||||
*/
|
||||
export async function POST(request, { params }) {
|
||||
try {
|
||||
const { projectId } = params;
|
||||
const body = await request.json();
|
||||
|
||||
// 验证项目ID
|
||||
if (!projectId) {
|
||||
return NextResponse.json({ error: 'Project ID cannot be empty' }, { status: 400 });
|
||||
}
|
||||
|
||||
let status = body.status;
|
||||
let confirmed = undefined;
|
||||
if (status === 'confirmed') confirmed = true;
|
||||
if (status === 'unconfirmed') confirmed = false;
|
||||
|
||||
// 检查是否是分批导出模式
|
||||
const batchMode = body.batchMode ? 'true' : 'false';
|
||||
const offset = body.offset ?? 0;
|
||||
const batchSize = body.batchSize ?? 1000;
|
||||
|
||||
// 检查是否是平衡导出
|
||||
const balanceMode = body.balanceMode ? 'true' : 'false';
|
||||
const balanceConfig = body.balanceConfig;
|
||||
|
||||
// 检查是否有选中的数据集 ID
|
||||
const selectedIds = Array.isArray(body.selectedIds) ? body.selectedIds : null;
|
||||
|
||||
if (batchMode === 'true') {
|
||||
// 分批导出模式
|
||||
if (selectedIds && selectedIds.length > 0) {
|
||||
// 按选中 ID 分批导出
|
||||
const datasets = await getDatasetsByIdsBatch(projectId, selectedIds, offset, batchSize);
|
||||
const hasMore = datasets.length === batchSize;
|
||||
return NextResponse.json({
|
||||
data: datasets,
|
||||
hasMore,
|
||||
offset: offset + datasets.length
|
||||
});
|
||||
} else if (balanceMode === 'true' && balanceConfig) {
|
||||
// 平衡分批导出
|
||||
const parsedConfig = typeof balanceConfig === 'string' ? JSON.parse(balanceConfig) : balanceConfig;
|
||||
const result = await getBalancedDatasetsByTagsBatch(projectId, parsedConfig, confirmed, offset, batchSize);
|
||||
return NextResponse.json({
|
||||
data: result.data,
|
||||
hasMore: result.hasMore,
|
||||
offset: offset + result.data.length
|
||||
});
|
||||
} else {
|
||||
// 常规分批导出
|
||||
const datasets = await getDatasetsBatch(projectId, confirmed, offset, batchSize);
|
||||
const hasMore = datasets.length === batchSize;
|
||||
return NextResponse.json({
|
||||
data: datasets,
|
||||
hasMore,
|
||||
offset: offset + datasets.length
|
||||
});
|
||||
}
|
||||
} else {
|
||||
// 传统一次性导出模式（保持向后兼容）
|
||||
if (selectedIds && selectedIds.length > 0) {
|
||||
// 按选中 ID 导出
|
||||
const datasets = await getDatasetsByIds(projectId, selectedIds);
|
||||
return NextResponse.json(datasets);
|
||||
} else if (balanceMode === 'true' && balanceConfig) {
|
||||
// 平衡导出模式
|
||||
const parsedConfig = typeof balanceConfig === 'string' ? JSON.parse(balanceConfig) : balanceConfig;
|
||||
const datasets = await getBalancedDatasetsByTags(projectId, parsedConfig, confirmed);
|
||||
return NextResponse.json(datasets);
|
||||
} else {
|
||||
// 常规导出模式
|
||||
const datasets = await getDatasets(projectId, confirmed);
|
||||
return NextResponse.json(datasets);
|
||||
}
|
||||
}
|
||||
} catch (error) {
|
||||
console.error('Failed to get datasets:', String(error));
|
||||
return NextResponse.json(
|
||||
{
|
||||
error: error.message || 'Failed to get datasets'
|
||||
},
|
||||
{ status: 500 }
|
||||
);
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,44 @@
|
||||
import { NextResponse } from 'next/server';
|
||||
import { getDatasetsById } from '@/lib/db/datasets';
|
||||
import LLMClient from '@/lib/llm/core/index';
|
||||
import { getEvalQuestionPrompt } from '@/lib/llm/prompts/evalQuestion';
|
||||
import { extractJsonFromLLMOutput } from '@/lib/llm/common/util';
|
||||
|
||||
export async function POST(request, { params }) {
|
||||
try {
|
||||
const { projectId } = params;
|
||||
const { datasetId, model, language, questionType = 'open_ended', count = 1 } = await request.json();
|
||||
|
||||
if (!datasetId || !model) {
|
||||
return NextResponse.json({ error: 'Missing required parameters' }, { status: 400 });
|
||||
}
|
||||
|
||||
// 1. 获取原数据集
|
||||
const dataset = await getDatasetsById(datasetId);
|
||||
if (!dataset) {
|
||||
return NextResponse.json({ error: 'Dataset not found' }, { status: 404 });
|
||||
}
|
||||
|
||||
// 2. 构建提示词
|
||||
// 将原问题和答案合并作为上下文文本
|
||||
const text = `Question: ${dataset.question}\nAnswer: ${dataset.answer}`;
|
||||
|
||||
const prompt = await getEvalQuestionPrompt(language || 'zh-CN', questionType, { text, number: count }, projectId);
|
||||
|
||||
// 3. 调用 LLM
|
||||
const client = new LLMClient(model);
|
||||
|
||||
const response = await client.getResponse(prompt);
|
||||
const result = extractJsonFromLLMOutput(response);
|
||||
|
||||
// 结果应该是一个数组
|
||||
if (!result || !Array.isArray(result)) {
|
||||
throw new Error('Failed to parse LLM output or output is not an array');
|
||||
}
|
||||
|
||||
return NextResponse.json({ success: true, data: result });
|
||||
} catch (error) {
|
||||
console.error('Generate eval variant failed:', error);
|
||||
return NextResponse.json({ error: error.message || 'Internal Server Error' }, { status: 500 });
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,109 @@
|
||||
import { NextResponse } from 'next/server';
|
||||
import { createDataset } from '@/lib/db/datasets';
|
||||
import { nanoid } from 'nanoid';
|
||||
|
||||
export async function POST(request, { params }) {
|
||||
try {
|
||||
const { projectId } = params;
|
||||
const { datasets, sourceInfo } = await request.json();
|
||||
|
||||
if (!datasets || !Array.isArray(datasets)) {
|
||||
return NextResponse.json({ error: 'Invalid datasets data' }, { status: 400 });
|
||||
}
|
||||
|
||||
const results = [];
|
||||
const errors = [];
|
||||
let successCount = 0;
|
||||
let skippedCount = 0;
|
||||
|
||||
for (let i = 0; i < datasets.length; i++) {
|
||||
try {
|
||||
const dataset = datasets[i];
|
||||
|
||||
// 安全获取与清洗字段
|
||||
const q = typeof dataset?.question === 'string' ? dataset.question.trim() : '';
|
||||
const a = typeof dataset?.answer === 'string' ? dataset.answer.trim() : '';
|
||||
|
||||
// 验证必填字段：缺失则跳过
|
||||
if (!q || !a) {
|
||||
errors.push(`第 ${i + 1} 条记录缺少必填字段(question/answer)，已跳过`);
|
||||
skippedCount++;
|
||||
continue;
|
||||
}
|
||||
|
||||
// 规范化可选字段
|
||||
const chunkName = dataset?.chunkName || 'Imported Data';
|
||||
const chunkContent = dataset?.chunkContent || 'Imported from external source';
|
||||
const model = dataset?.model || 'imported';
|
||||
const questionLabel = dataset?.questionLabel || '';
|
||||
const cot = typeof dataset?.cot === 'string' ? dataset.cot : '';
|
||||
const confirmed = typeof dataset?.confirmed === 'boolean' ? dataset.confirmed : false;
|
||||
const score = typeof dataset?.score === 'number' ? dataset.score : 0;
|
||||
// tags: 支持数组/字符串/对象
|
||||
let tags = '[]';
|
||||
if (Array.isArray(dataset?.tags)) {
|
||||
try {
|
||||
tags = JSON.stringify(dataset.tags);
|
||||
} catch {
|
||||
tags = '[]';
|
||||
}
|
||||
} else if (typeof dataset?.tags === 'string') {
|
||||
tags = dataset.tags;
|
||||
} else if (dataset?.tags && typeof dataset.tags === 'object') {
|
||||
try {
|
||||
tags = JSON.stringify(dataset.tags);
|
||||
} catch {
|
||||
tags = '[]';
|
||||
}
|
||||
}
|
||||
// other: 对象或字符串
|
||||
let other = '{}';
|
||||
if (typeof dataset?.other === 'string') {
|
||||
other = dataset.other;
|
||||
} else if (dataset?.other && typeof dataset.other === 'object') {
|
||||
try {
|
||||
other = JSON.stringify(dataset.other);
|
||||
} catch {
|
||||
other = '{}';
|
||||
}
|
||||
}
|
||||
const note = typeof dataset?.note === 'string' ? dataset.note : '';
|
||||
|
||||
// 创建数据集记录
|
||||
const newDataset = await createDataset({
|
||||
projectId,
|
||||
questionId: nanoid(), // 生成唯一的问题ID
|
||||
question: q,
|
||||
answer: a,
|
||||
chunkName,
|
||||
chunkContent,
|
||||
model,
|
||||
questionLabel,
|
||||
cot,
|
||||
confirmed,
|
||||
score,
|
||||
tags,
|
||||
note,
|
||||
other
|
||||
});
|
||||
|
||||
results.push(newDataset);
|
||||
successCount++;
|
||||
} catch (error) {
|
||||
errors.push(`第 ${i + 1} 条记录: ${error.message}`);
|
||||
}
|
||||
}
|
||||
|
||||
return NextResponse.json({
|
||||
success: successCount,
|
||||
total: datasets.length,
|
||||
failed: errors.length,
|
||||
skipped: skippedCount,
|
||||
errors,
|
||||
sourceInfo
|
||||
});
|
||||
} catch (error) {
|
||||
console.error('Import datasets error:', error);
|
||||
return NextResponse.json({ error: error.message }, { status: 500 });
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,89 @@
|
||||
import { NextResponse } from 'next/server';
|
||||
import { getDatasetsById, updateDataset } from '@/lib/db/datasets';
|
||||
import { getQuestionById } from '@/lib/db/questions';
|
||||
import { getChunkById } from '@/lib/db/chunks';
|
||||
import LLMClient from '@/lib/llm/core/index';
|
||||
import { getNewAnswerPrompt } from '@/lib/llm/prompts/newAnswer';
|
||||
import { extractJsonFromLLMOutput } from '@/lib/llm/common/util';
|
||||
|
||||
// 优化数据集答案
|
||||
export async function POST(request, { params }) {
|
||||
try {
|
||||
const { projectId } = params;
|
||||
|
||||
// 验证项目ID
|
||||
if (!projectId) {
|
||||
return NextResponse.json({ error: 'Project ID cannot be empty' }, { status: 400 });
|
||||
}
|
||||
|
||||
// 获取请求体
|
||||
const { datasetId, model, advice, language } = await request.json();
|
||||
|
||||
if (!datasetId) {
|
||||
return NextResponse.json({ error: 'Dataset ID cannot be empty' }, { status: 400 });
|
||||
}
|
||||
|
||||
if (!model) {
|
||||
return NextResponse.json({ error: 'Model cannot be empty' }, { status: 400 });
|
||||
}
|
||||
|
||||
if (!advice) {
|
||||
return NextResponse.json({ error: 'Please provide optimization suggestions' }, { status: 400 });
|
||||
}
|
||||
|
||||
// 获取数据集内容
|
||||
const dataset = await getDatasetsById(datasetId);
|
||||
if (!dataset) {
|
||||
return NextResponse.json({ error: 'Dataset does not exist' }, { status: 404 });
|
||||
}
|
||||
|
||||
// 创建LLM客户端
|
||||
const llmClient = new LLMClient(model);
|
||||
|
||||
const { question, answer, cot, chunkContent: storedChunkContent, questionId } = dataset;
|
||||
|
||||
let chunkContent = storedChunkContent || '';
|
||||
|
||||
if (!chunkContent && questionId) {
|
||||
try {
|
||||
const questionRecord = await getQuestionById(questionId);
|
||||
if (questionRecord?.chunkId) {
|
||||
const chunkRecord = await getChunkById(questionRecord.chunkId);
|
||||
chunkContent = chunkRecord?.content || '';
|
||||
}
|
||||
} catch (error) {
|
||||
console.error('Failed to load chunk content by questionId:', error);
|
||||
}
|
||||
}
|
||||
|
||||
// 生成优化后的答案和思维链
|
||||
const prompt = await getNewAnswerPrompt(language, { question, answer, cot, advice, chunkContent }, projectId);
|
||||
|
||||
const response = await llmClient.getResponse(prompt);
|
||||
|
||||
// 从LLM输出中提取JSON格式的优化结果
|
||||
const optimizedResult = extractJsonFromLLMOutput(response);
|
||||
|
||||
if (!optimizedResult || !optimizedResult.answer) {
|
||||
return NextResponse.json({ error: 'Failed to optimize answer, please try again' }, { status: 500 });
|
||||
}
|
||||
|
||||
// 更新数据集
|
||||
const updatedDataset = {
|
||||
...dataset,
|
||||
answer: optimizedResult.answer,
|
||||
cot: cot ? optimizedResult.cot || cot : '' // 如果没有提供思考过程，则不更新
|
||||
};
|
||||
|
||||
await updateDataset(updatedDataset);
|
||||
|
||||
// 返回优化后的数据集
|
||||
return NextResponse.json({
|
||||
success: true,
|
||||
dataset: updatedDataset
|
||||
});
|
||||
} catch (error) {
|
||||
console.error('Failed to optimize answer:', String(error));
|
||||
return NextResponse.json({ error: error.message || 'Failed to optimize answer' }, { status: 500 });
|
||||
}
|
||||
}
|
||||
193
easy-dataset-main/app/api/projects/[projectId]/datasets/route.js
Normal file
193
easy-dataset-main/app/api/projects/[projectId]/datasets/route.js
Normal file
@@ -0,0 +1,193 @@
|
||||
import { NextResponse } from 'next/server';
|
||||
import {
|
||||
deleteDataset,
|
||||
getDatasetsByPagination,
|
||||
getDatasetsIds,
|
||||
getDatasetsById,
|
||||
updateDataset
|
||||
} from '@/lib/db/datasets';
|
||||
import datasetService from '@/lib/services/datasets';
|
||||
|
||||
// 优化思维链函数已移至服务层
|
||||
|
||||
/**
|
||||
* 生成数据集（为单个问题生成答案）
|
||||
*/
|
||||
export async function POST(request, { params }) {
|
||||
try {
|
||||
const { projectId } = params;
|
||||
const { questionId, model, language } = await request.json();
|
||||
|
||||
// 使用数据集生成服务
|
||||
const result = await datasetService.generateDatasetForQuestion(projectId, questionId, {
|
||||
model,
|
||||
language
|
||||
});
|
||||
|
||||
return NextResponse.json(result);
|
||||
} catch (error) {
|
||||
console.error('Failed to generate dataset:', String(error));
|
||||
return NextResponse.json(
|
||||
{
|
||||
error: error.message || 'Failed to generate dataset'
|
||||
},
|
||||
{ status: 500 }
|
||||
);
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* 获取项目的所有数据集
|
||||
*/
|
||||
export async function GET(request, { params }) {
|
||||
try {
|
||||
const { projectId } = params;
|
||||
const { searchParams } = new URL(request.url);
|
||||
// 验证项目ID
|
||||
if (!projectId) {
|
||||
return NextResponse.json({ error: '项目ID不能为空' }, { status: 400 });
|
||||
}
|
||||
const page = parseInt(searchParams.get('page')) || 1;
|
||||
const size = parseInt(searchParams.get('size')) || 10;
|
||||
const input = searchParams.get('input');
|
||||
const field = searchParams.get('field') || 'question';
|
||||
const status = searchParams.get('status');
|
||||
const hasCot = searchParams.get('hasCot');
|
||||
const isDistill = searchParams.get('isDistill');
|
||||
const scoreRange = searchParams.get('scoreRange');
|
||||
const customTag = searchParams.get('customTag');
|
||||
const noteKeyword = searchParams.get('noteKeyword');
|
||||
const chunkName = searchParams.get('chunkName');
|
||||
let confirmed = undefined;
|
||||
if (status === 'confirmed') confirmed = true;
|
||||
if (status === 'unconfirmed') confirmed = false;
|
||||
|
||||
let selectedAll = searchParams.get('selectedAll');
|
||||
|
||||
if (selectedAll) {
|
||||
let data = await getDatasetsIds(
|
||||
projectId,
|
||||
confirmed,
|
||||
input,
|
||||
field,
|
||||
hasCot,
|
||||
isDistill,
|
||||
scoreRange,
|
||||
customTag,
|
||||
noteKeyword,
|
||||
chunkName
|
||||
);
|
||||
return NextResponse.json(data);
|
||||
}
|
||||
|
||||
// 获取数据集
|
||||
const datasets = await getDatasetsByPagination(
|
||||
projectId,
|
||||
page,
|
||||
size,
|
||||
confirmed,
|
||||
input,
|
||||
field, // 传递搜索字段参数
|
||||
hasCot, // 传递思维链筛选参数
|
||||
isDistill, // 传递蒸馏数据集筛选参数
|
||||
scoreRange, // 传递评分范围筛选参数
|
||||
customTag, // 传递自定义标签筛选参数
|
||||
noteKeyword, // 传递备注关键字筛选参数
|
||||
chunkName // 传递文本块名称筛选参数
|
||||
);
|
||||
|
||||
return NextResponse.json(datasets);
|
||||
} catch (error) {
|
||||
console.error('获取数据集失败:', String(error));
|
||||
return NextResponse.json(
|
||||
{
|
||||
error: error.message || '获取数据集失败'
|
||||
},
|
||||
{ status: 500 }
|
||||
);
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* 删除数据集
|
||||
*/
|
||||
export async function DELETE(request) {
|
||||
try {
|
||||
const { searchParams } = new URL(request.url);
|
||||
const datasetId = searchParams.get('id');
|
||||
if (!datasetId) {
|
||||
return NextResponse.json(
|
||||
{
|
||||
error: 'Dataset ID cannot be empty'
|
||||
},
|
||||
{ status: 400 }
|
||||
);
|
||||
}
|
||||
|
||||
await deleteDataset(datasetId);
|
||||
|
||||
return NextResponse.json({
|
||||
success: true,
|
||||
message: 'Dataset deleted successfully'
|
||||
});
|
||||
} catch (error) {
|
||||
console.error('Failed to delete dataset:', error);
|
||||
return NextResponse.json(
|
||||
{
|
||||
error: error.message || 'Failed to delete dataset'
|
||||
},
|
||||
{ status: 500 }
|
||||
);
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* 编辑数据集
|
||||
*/
|
||||
export async function PATCH(request) {
|
||||
try {
|
||||
const { searchParams } = new URL(request.url);
|
||||
const datasetId = searchParams.get('id');
|
||||
const { answer, cot, question, confirmed } = await request.json();
|
||||
if (!datasetId) {
|
||||
return NextResponse.json(
|
||||
{
|
||||
error: 'Dataset ID cannot be empty'
|
||||
},
|
||||
{ status: 400 }
|
||||
);
|
||||
}
|
||||
// 获取所有数据集
|
||||
let dataset = await getDatasetsById(datasetId);
|
||||
if (!dataset) {
|
||||
return NextResponse.json(
|
||||
{
|
||||
error: 'Dataset does not exist'
|
||||
},
|
||||
{ status: 404 }
|
||||
);
|
||||
}
|
||||
let data = { id: datasetId };
|
||||
if (confirmed !== undefined) data.confirmed = confirmed;
|
||||
if (answer) data.answer = answer;
|
||||
if (cot) data.cot = cot;
|
||||
if (question) data.question = question;
|
||||
|
||||
// 保存更新后的数据集列表
|
||||
await updateDataset(data);
|
||||
|
||||
return NextResponse.json({
|
||||
success: true,
|
||||
message: 'Dataset updated successfully',
|
||||
dataset: dataset
|
||||
});
|
||||
} catch (error) {
|
||||
console.error('Failed to update dataset:', String(error));
|
||||
return NextResponse.json(
|
||||
{
|
||||
error: error.message || 'Failed to update dataset'
|
||||
},
|
||||
{ status: 500 }
|
||||
);
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,28 @@
|
||||
import { NextResponse } from 'next/server';
|
||||
import { getUsedCustomTags } from '@/lib/db/datasets';
|
||||
|
||||
/**
|
||||
* 获取项目中使用过的自定义标签
|
||||
*/
|
||||
export async function GET(request, { params }) {
|
||||
try {
|
||||
const { projectId } = params;
|
||||
|
||||
// 验证项目ID
|
||||
if (!projectId) {
|
||||
return NextResponse.json({ error: '项目ID不能为空' }, { status: 400 });
|
||||
}
|
||||
|
||||
const tags = await getUsedCustomTags(projectId);
|
||||
|
||||
return NextResponse.json({ tags });
|
||||
} catch (error) {
|
||||
console.error('获取自定义标签失败:', String(error));
|
||||
return NextResponse.json(
|
||||
{
|
||||
error: error.message || '获取自定义标签失败'
|
||||
},
|
||||
{ status: 500 }
|
||||
);
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,38 @@
|
||||
import { NextResponse } from 'next/server';
|
||||
|
||||
// 获取默认提示词内容
|
||||
export async function GET(request, { params }) {
|
||||
try {
|
||||
const { searchParams } = new URL(request.url);
|
||||
const promptType = searchParams.get('promptType');
|
||||
const promptKey = searchParams.get('promptKey');
|
||||
|
||||
if (!promptType || !promptKey) {
|
||||
return NextResponse.json({ error: 'promptType and promptKey are required' }, { status: 400 });
|
||||
}
|
||||
|
||||
// 动态导入对应的提示词模块
|
||||
let promptModule;
|
||||
try {
|
||||
promptModule = await import(`@/lib/llm/prompts/${promptType}`);
|
||||
} catch (error) {
|
||||
return NextResponse.json({ error: `Prompt module ${promptType} not found` }, { status: 404 });
|
||||
}
|
||||
|
||||
// 获取指定的提示词常量
|
||||
const promptContent = promptModule[promptKey];
|
||||
if (!promptContent) {
|
||||
return NextResponse.json({ error: `Prompt key ${promptKey} not found in module ${promptType}` }, { status: 404 });
|
||||
}
|
||||
|
||||
return NextResponse.json({
|
||||
success: true,
|
||||
content: promptContent,
|
||||
promptType,
|
||||
promptKey
|
||||
});
|
||||
} catch (error) {
|
||||
console.error('获取默认提示词失败:', error);
|
||||
return NextResponse.json({ error: error.message }, { status: 500 });
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,67 @@
|
||||
import { NextResponse } from 'next/server';
|
||||
import { db } from '@/lib/db';
|
||||
|
||||
/**
|
||||
* 根据标签ID获取问题列表
|
||||
*/
|
||||
export async function GET(request, { params }) {
|
||||
try {
|
||||
const { projectId } = params;
|
||||
const { searchParams } = new URL(request.url);
|
||||
const tagId = searchParams.get('tagId');
|
||||
|
||||
// 验证参数
|
||||
if (!projectId) {
|
||||
return NextResponse.json({ error: '项目ID不能为空' }, { status: 400 });
|
||||
}
|
||||
|
||||
if (!tagId) {
|
||||
return NextResponse.json({ error: '标签ID不能为空' }, { status: 400 });
|
||||
}
|
||||
|
||||
// 获取标签信息
|
||||
const tag = await db.tags.findUnique({
|
||||
where: { id: tagId }
|
||||
});
|
||||
|
||||
if (!tag) {
|
||||
return NextResponse.json({ error: '标签不存在' }, { status: 404 });
|
||||
}
|
||||
|
||||
// 获取或创建蒸馏文本块
|
||||
let distillChunk = await db.chunks.findFirst({
|
||||
where: {
|
||||
projectId,
|
||||
name: 'Distilled Content'
|
||||
}
|
||||
});
|
||||
|
||||
if (!distillChunk) {
|
||||
// 创建一个特殊的蒸馏文本块
|
||||
distillChunk = await db.chunks.create({
|
||||
data: {
|
||||
name: 'Distilled Content',
|
||||
projectId,
|
||||
fileId: 'distilled',
|
||||
fileName: 'distilled.md',
|
||||
content:
|
||||
'This text block is used to store questions generated through data distillation and is not related to actual literature.',
|
||||
summary: 'Questions generated through data distillation',
|
||||
size: 0
|
||||
}
|
||||
});
|
||||
}
|
||||
const questions = await db.questions.findMany({
|
||||
where: {
|
||||
projectId,
|
||||
label: tag.label,
|
||||
chunkId: distillChunk.id
|
||||
}
|
||||
});
|
||||
|
||||
return NextResponse.json(questions);
|
||||
} catch (error) {
|
||||
console.error('[distill/questions/by-tag] 获取问题失败:', String(error));
|
||||
return NextResponse.json({ error: error.message || '获取问题失败' }, { status: 500 });
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,101 @@
|
||||
import { NextResponse } from 'next/server';
|
||||
import { distillQuestionsPrompt } from '@/lib/llm/prompts/distillQuestions';
|
||||
import { db } from '@/lib/db';
|
||||
|
||||
const LLMClient = require('@/lib/llm/core');
|
||||
|
||||
/**
|
||||
* 生成问题接口：根据某个标签链路构造指定数量的问题
|
||||
*/
|
||||
export async function POST(request, { params }) {
|
||||
try {
|
||||
const { projectId } = params;
|
||||
|
||||
// 验证项目ID
|
||||
if (!projectId) {
|
||||
return NextResponse.json({ error: '项目ID不能为空' }, { status: 400 });
|
||||
}
|
||||
|
||||
const { tagPath, currentTag, tagId, count = 5, model, language = 'zh' } = await request.json();
|
||||
|
||||
if (!currentTag || !tagPath) {
|
||||
const errorMsg = language === 'en' ? 'Tag information cannot be empty' : '标签信息不能为空';
|
||||
return NextResponse.json({ error: errorMsg }, { status: 400 });
|
||||
}
|
||||
|
||||
// 首先获取或创建蒸馏文本块
|
||||
let distillChunk = await db.chunks.findFirst({
|
||||
where: {
|
||||
projectId,
|
||||
name: 'Distilled Content'
|
||||
}
|
||||
});
|
||||
|
||||
if (!distillChunk) {
|
||||
// 创建一个特殊的蒸馏文本块
|
||||
distillChunk = await db.chunks.create({
|
||||
data: {
|
||||
name: 'Distilled Content',
|
||||
projectId,
|
||||
fileId: 'distilled',
|
||||
fileName: 'distilled.md',
|
||||
content:
|
||||
'This text block is used to store questions generated through data distillation and is not related to actual literature.',
|
||||
summary: 'Questions generated through data distillation',
|
||||
size: 0
|
||||
}
|
||||
});
|
||||
}
|
||||
|
||||
// 获取已有的问题，避免重复
|
||||
const existingQuestions = await db.questions.findMany({
|
||||
where: {
|
||||
projectId,
|
||||
label: currentTag,
|
||||
chunkId: distillChunk.id // 使用蒸馏文本块的 ID
|
||||
},
|
||||
select: { question: true }
|
||||
});
|
||||
|
||||
const existingQuestionTexts = existingQuestions.map(q => q.question);
|
||||
|
||||
const llmClient = new LLMClient(model);
|
||||
const prompt = await distillQuestionsPrompt(
|
||||
language,
|
||||
{ tagPath, currentTag, count, existingQuestionTexts },
|
||||
projectId
|
||||
);
|
||||
const { answer } = await llmClient.getResponseWithCOT(prompt);
|
||||
|
||||
let questions = [];
|
||||
try {
|
||||
questions = JSON.parse(answer);
|
||||
} catch (error) {
|
||||
console.error('解析问题JSON失败:', String(error));
|
||||
// 尝试使用正则表达式提取问题
|
||||
const matches = answer.match(/"([^"]+)"/g);
|
||||
if (matches) {
|
||||
questions = matches.map(match => match.replace(/"/g, ''));
|
||||
}
|
||||
}
|
||||
|
||||
// 保存问题到数据库
|
||||
const savedQuestions = [];
|
||||
for (const questionText of questions) {
|
||||
const question = await db.questions.create({
|
||||
data: {
|
||||
question: questionText,
|
||||
projectId,
|
||||
label: currentTag,
|
||||
chunkId: distillChunk.id
|
||||
}
|
||||
});
|
||||
savedQuestions.push(question);
|
||||
}
|
||||
|
||||
return NextResponse.json(savedQuestions);
|
||||
} catch (error) {
|
||||
console.error('生成问题失败:', String(error));
|
||||
return NextResponse.json({ error: error.message || '生成问题失败' }, { status: 500 });
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,61 @@
|
||||
import { NextResponse } from 'next/server';
|
||||
import { db } from '@/lib/db';
|
||||
|
||||
/**
|
||||
* 更新标签接口
|
||||
*/
|
||||
export async function PUT(request, { params }) {
|
||||
try {
|
||||
const { projectId, tagId } = params;
|
||||
|
||||
// 验证参数
|
||||
if (!projectId || !tagId) {
|
||||
return NextResponse.json({ error: '项目ID和标签ID不能为空' }, { status: 400 });
|
||||
}
|
||||
|
||||
const { label } = await request.json();
|
||||
|
||||
if (!label || !label.trim()) {
|
||||
return NextResponse.json({ error: '标签名称不能为空' }, { status: 400 });
|
||||
}
|
||||
|
||||
// 检查标签是否存在
|
||||
const existingTag = await db.tags.findUnique({
|
||||
where: { id: tagId }
|
||||
});
|
||||
|
||||
if (!existingTag) {
|
||||
return NextResponse.json({ error: '标签不存在' }, { status: 404 });
|
||||
}
|
||||
|
||||
// 检查项目ID是否匹配
|
||||
if (existingTag.projectId !== projectId) {
|
||||
return NextResponse.json({ error: '无权限编辑此标签' }, { status: 403 });
|
||||
}
|
||||
|
||||
// 检查新标签名称是否已存在（同级标签）
|
||||
const duplicateTag = await db.tags.findFirst({
|
||||
where: {
|
||||
projectId,
|
||||
label: label.trim(),
|
||||
parentId: existingTag.parentId,
|
||||
id: { not: tagId }
|
||||
}
|
||||
});
|
||||
|
||||
if (duplicateTag) {
|
||||
return NextResponse.json({ error: '同级标签名称已存在' }, { status: 400 });
|
||||
}
|
||||
|
||||
// 更新标签
|
||||
const updatedTag = await db.tags.update({
|
||||
where: { id: tagId },
|
||||
data: { label: label.trim() }
|
||||
});
|
||||
|
||||
return NextResponse.json(updatedTag);
|
||||
} catch (error) {
|
||||
console.error('[标签编辑] 更新标签失败:', String(error));
|
||||
return NextResponse.json({ error: error.message || '更新标签失败' }, { status: 500 });
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,31 @@
|
||||
import { NextResponse } from 'next/server';
|
||||
import { db } from '@/lib/db';
|
||||
|
||||
/**
|
||||
* 获取项目的所有蒸馏标签
|
||||
*/
|
||||
export async function GET(request, { params }) {
|
||||
try {
|
||||
const { projectId } = params;
|
||||
|
||||
// 验证项目ID
|
||||
if (!projectId) {
|
||||
return NextResponse.json({ error: '项目ID不能为空' }, { status: 400 });
|
||||
}
|
||||
|
||||
// 获取所有标签
|
||||
const tags = await db.tags.findMany({
|
||||
where: {
|
||||
projectId
|
||||
},
|
||||
orderBy: {
|
||||
label: 'asc'
|
||||
}
|
||||
});
|
||||
|
||||
return NextResponse.json(tags);
|
||||
} catch (error) {
|
||||
console.error('获取蒸馏标签失败:', String(error));
|
||||
return NextResponse.json({ error: error.message || '获取蒸馏标签失败' }, { status: 500 });
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,88 @@
|
||||
import { NextResponse } from 'next/server';
|
||||
import { distillTagsPrompt } from '@/lib/llm/prompts/distillTags';
|
||||
import { db } from '@/lib/db';
|
||||
import { getProject } from '@/lib/db/projects';
|
||||
|
||||
const LLMClient = require('@/lib/llm/core');
|
||||
|
||||
/**
|
||||
* 生成标签接口：根据顶级主题、某级标签构造指定数量的子标签
|
||||
*/
|
||||
export async function POST(request, { params }) {
|
||||
try {
|
||||
const { projectId } = params;
|
||||
|
||||
// 验证项目ID
|
||||
if (!projectId) {
|
||||
return NextResponse.json({ error: '项目ID不能为空' }, { status: 400 });
|
||||
}
|
||||
|
||||
const { parentTag, parentTagId, tagPath, count = 10, model, language = 'zh' } = await request.json();
|
||||
|
||||
if (!parentTag) {
|
||||
const errorMsg = language === 'en' ? 'Topic tag name cannot be empty' : '主题标签名称不能为空';
|
||||
return NextResponse.json({ error: errorMsg }, { status: 400 });
|
||||
}
|
||||
|
||||
// 查询现有标签
|
||||
const existingTags = await db.tags.findMany({
|
||||
where: {
|
||||
projectId,
|
||||
parentId: parentTagId || null
|
||||
}
|
||||
});
|
||||
|
||||
const existingTagNames = existingTags.map(tag => tag.label);
|
||||
|
||||
// 创建LLM客户端
|
||||
const llmClient = new LLMClient(model);
|
||||
|
||||
// 生成提示词
|
||||
const prompt = await distillTagsPrompt(
|
||||
language,
|
||||
{ tagPath, parentTag, existingTags: existingTagNames, count },
|
||||
projectId
|
||||
);
|
||||
|
||||
// 调用大模型生成标签
|
||||
const { answer } = await llmClient.getResponseWithCOT(prompt);
|
||||
|
||||
// 解析返回的标签
|
||||
let tags = [];
|
||||
|
||||
try {
|
||||
tags = JSON.parse(answer);
|
||||
} catch (error) {
|
||||
console.error('解析标签JSON失败:', String(error));
|
||||
// 尝试使用正则表达式提取标签
|
||||
const matches = answer.match(/"([^"]+)"/g);
|
||||
if (matches) {
|
||||
tags = matches.map(match => match.replace(/"/g, ''));
|
||||
}
|
||||
}
|
||||
|
||||
// 保存标签到数据库
|
||||
const savedTags = [];
|
||||
for (let i = 0; i < tags.length; i++) {
|
||||
const tagName = tags[i];
|
||||
try {
|
||||
const tag = await db.tags.create({
|
||||
data: {
|
||||
label: tagName,
|
||||
projectId,
|
||||
parentId: parentTagId || null
|
||||
}
|
||||
});
|
||||
savedTags.push(tag);
|
||||
} catch (error) {
|
||||
console.error(`[标签生成] 保存标签 ${tagName} 失败:`, String(error));
|
||||
throw error;
|
||||
}
|
||||
}
|
||||
return NextResponse.json(savedTags);
|
||||
} catch (error) {
|
||||
console.error('[标签生成] 生成标签失败:', String(error));
|
||||
console.error('[标签生成] 错误堆栈:', error.stack);
|
||||
return NextResponse.json({ error: error.message || '生成标签失败' }, { status: 500 });
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,108 @@
|
||||
import { NextResponse } from 'next/server';
|
||||
import { getEvalQuestionById, updateEvalQuestion, deleteEvalQuestion } from '@/lib/db/evalDatasets';
|
||||
import { db } from '@/lib/db/index';
|
||||
|
||||
/**
|
||||
* Get evaluation dataset details by ID
|
||||
* Supports operateType=prev|next to navigate neighbors
|
||||
*/
|
||||
export async function GET(request, { params }) {
|
||||
try {
|
||||
const { projectId, evalId } = params;
|
||||
const { searchParams } = new URL(request.url);
|
||||
const operateType = searchParams.get('operateType');
|
||||
|
||||
// Navigation request (prev/next)
|
||||
if (operateType) {
|
||||
const current = await db.evalDatasets.findUnique({
|
||||
where: { id: evalId },
|
||||
select: { createAt: true }
|
||||
});
|
||||
|
||||
if (!current) {
|
||||
return NextResponse.json(null);
|
||||
}
|
||||
|
||||
let neighbor = null;
|
||||
|
||||
if (operateType === 'prev') {
|
||||
// Get previous item (newer createAt when list is sorted desc)
|
||||
neighbor = await db.evalDatasets.findFirst({
|
||||
where: {
|
||||
projectId,
|
||||
createAt: { gt: current.createAt }
|
||||
},
|
||||
orderBy: { createAt: 'asc' },
|
||||
select: { id: true }
|
||||
});
|
||||
} else if (operateType === 'next') {
|
||||
// Get next item (older createAt)
|
||||
neighbor = await db.evalDatasets.findFirst({
|
||||
where: {
|
||||
projectId,
|
||||
createAt: { lt: current.createAt }
|
||||
},
|
||||
orderBy: { createAt: 'desc' },
|
||||
select: { id: true }
|
||||
});
|
||||
}
|
||||
|
||||
return NextResponse.json(neighbor || null);
|
||||
}
|
||||
|
||||
// Regular detail request
|
||||
const evalQuestion = await getEvalQuestionById(evalId);
|
||||
|
||||
if (!evalQuestion) {
|
||||
return NextResponse.json({ error: 'Eval question not found' }, { status: 404 });
|
||||
}
|
||||
|
||||
return NextResponse.json(evalQuestion);
|
||||
} catch (error) {
|
||||
console.error('Failed to get eval question:', error);
|
||||
return NextResponse.json({ error: error.message || 'Failed to get eval question' }, { status: 500 });
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Update evaluation dataset
|
||||
*/
|
||||
export async function PUT(request, { params }) {
|
||||
try {
|
||||
const { evalId } = params;
|
||||
const data = await request.json();
|
||||
|
||||
// Only allow specific fields
|
||||
const allowedFields = ['question', 'options', 'correctAnswer', 'tags', 'note'];
|
||||
const updateData = {};
|
||||
|
||||
for (const field of allowedFields) {
|
||||
if (data[field] !== undefined) {
|
||||
updateData[field] = data[field];
|
||||
}
|
||||
}
|
||||
|
||||
const updated = await updateEvalQuestion(evalId, updateData);
|
||||
|
||||
return NextResponse.json(updated);
|
||||
} catch (error) {
|
||||
console.error('Failed to update eval question:', error);
|
||||
return NextResponse.json({ error: error.message || 'Failed to update eval question' }, { status: 500 });
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Delete evaluation dataset
|
||||
*/
|
||||
export async function DELETE(request, { params }) {
|
||||
try {
|
||||
const { evalId } = params;
|
||||
|
||||
await deleteEvalQuestion(evalId);
|
||||
|
||||
return NextResponse.json({ success: true });
|
||||
} catch (error) {
|
||||
console.error('Failed to delete eval question:', error);
|
||||
return NextResponse.json({ error: error.message || 'Failed to delete eval question' }, { status: 500 });
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,63 @@
|
||||
import { NextResponse } from 'next/server';
|
||||
import { db } from '@/lib/db';
|
||||
import { buildEvalQuestionWhere } from '@/lib/db/evalDatasets';
|
||||
|
||||
export async function GET(request, { params }) {
|
||||
try {
|
||||
const { projectId } = params;
|
||||
const { searchParams } = new URL(request.url);
|
||||
|
||||
const questionType = searchParams.get('questionType') || '';
|
||||
const keyword = searchParams.get('keyword') || '';
|
||||
const chunkId = searchParams.get('chunkId') || '';
|
||||
|
||||
const questionTypes = searchParams.getAll('questionTypes') || [];
|
||||
|
||||
const tags =
|
||||
searchParams.getAll('tags').length > 0
|
||||
? searchParams.getAll('tags')
|
||||
: searchParams.get('tag')
|
||||
? searchParams.get('tag').split(',')
|
||||
: [];
|
||||
|
||||
const where = buildEvalQuestionWhere(projectId, {
|
||||
questionType: questionType || undefined,
|
||||
questionTypes: questionTypes.length > 0 ? questionTypes : undefined,
|
||||
keyword: keyword || undefined,
|
||||
chunkId: chunkId || undefined,
|
||||
tags: tags.length > 0 ? tags : undefined
|
||||
});
|
||||
|
||||
const [total, byTypeRaw] = await Promise.all([
|
||||
db.evalDatasets.count({ where }),
|
||||
db.evalDatasets.groupBy({
|
||||
by: ['questionType'],
|
||||
where,
|
||||
_count: { id: true }
|
||||
})
|
||||
]);
|
||||
|
||||
const byType = {};
|
||||
byTypeRaw.forEach(item => {
|
||||
byType[item.questionType] = item._count.id;
|
||||
});
|
||||
|
||||
const hasShortAnswer = (byType.short_answer || 0) > 0;
|
||||
const hasOpenEnded = (byType.open_ended || 0) > 0;
|
||||
const hasSubjective = hasShortAnswer || hasOpenEnded;
|
||||
|
||||
return NextResponse.json(
|
||||
{
|
||||
code: 0,
|
||||
data: { total, byType, hasSubjective, hasShortAnswer, hasOpenEnded }
|
||||
},
|
||||
{ status: 200 }
|
||||
);
|
||||
} catch (error) {
|
||||
console.error('Failed to count eval datasets:', error);
|
||||
return NextResponse.json(
|
||||
{ code: 500, error: 'Failed to count eval datasets', message: error.message },
|
||||
{ status: 500 }
|
||||
);
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,231 @@
|
||||
import { NextResponse } from 'next/server';
|
||||
import { db } from '@/lib/db/index';
|
||||
import { buildEvalQuestionWhere } from '@/lib/db/evalDatasets';
|
||||
|
||||
const BATCH_SIZE = 500;
|
||||
|
||||
/**
|
||||
* Convert an evaluation item to a CSV row
|
||||
*/
|
||||
function convertToCSVRow(item, isHeader = false) {
|
||||
if (isHeader) {
|
||||
return ['questionType', 'question', 'options', 'correctAnswer', 'tags'].join(',');
|
||||
}
|
||||
|
||||
const escapeCSV = str => {
|
||||
if (str === null || str === undefined) return '';
|
||||
const strValue = String(str);
|
||||
if (strValue.includes(',') || strValue.includes('"') || strValue.includes('\n')) {
|
||||
return `"${strValue.replace(/"/g, '""')}"`;
|
||||
}
|
||||
return strValue;
|
||||
};
|
||||
|
||||
return [
|
||||
escapeCSV(item.questionType),
|
||||
escapeCSV(item.question),
|
||||
escapeCSV(item.options),
|
||||
escapeCSV(item.correctAnswer),
|
||||
escapeCSV(item.tags)
|
||||
].join(',');
|
||||
}
|
||||
|
||||
/**
|
||||
* Convert an evaluation item to export format
|
||||
*/
|
||||
function formatExportItem(item) {
|
||||
return {
|
||||
questionType: item.questionType,
|
||||
question: item.question,
|
||||
options: item.options,
|
||||
correctAnswer: item.correctAnswer,
|
||||
tags: item.tags
|
||||
};
|
||||
}
|
||||
|
||||
/**
|
||||
* Export evaluation datasets
|
||||
* Supports JSON, JSONL, and CSV
|
||||
* Uses batched streaming for large datasets
|
||||
*/
|
||||
export async function POST(request, { params }) {
|
||||
try {
|
||||
const { projectId } = params;
|
||||
const body = await request.json();
|
||||
|
||||
const {
|
||||
format = 'json', // json | jsonl | csv
|
||||
questionTypes = [],
|
||||
tags = [],
|
||||
keyword = ''
|
||||
} = body;
|
||||
|
||||
// Validate format
|
||||
if (!['json', 'jsonl', 'csv'].includes(format)) {
|
||||
return NextResponse.json({ code: 400, error: 'Unsupported export format' }, { status: 400 });
|
||||
}
|
||||
|
||||
// Build query conditions
|
||||
const where = buildEvalQuestionWhere(projectId, {
|
||||
questionTypes: questionTypes.length > 0 ? questionTypes : undefined,
|
||||
tags: tags.length > 0 ? tags : undefined,
|
||||
keyword: keyword || undefined
|
||||
});
|
||||
|
||||
// Fetch total count
|
||||
const total = await db.evalDatasets.count({ where });
|
||||
|
||||
if (total === 0) {
|
||||
return NextResponse.json({ code: 400, error: 'No data matches the criteria' }, { status: 400 });
|
||||
}
|
||||
|
||||
// Return directly for small datasets
|
||||
if (total <= 1000) {
|
||||
const items = await db.evalDatasets.findMany({
|
||||
where,
|
||||
orderBy: { createAt: 'desc' }
|
||||
});
|
||||
|
||||
const formattedItems = items.map(formatExportItem);
|
||||
|
||||
if (format === 'json') {
|
||||
return new Response(JSON.stringify(formattedItems, null, 2), {
|
||||
headers: {
|
||||
'Content-Type': 'application/json',
|
||||
'Content-Disposition': `attachment; filename="eval-datasets-${Date.now()}.json"`
|
||||
}
|
||||
});
|
||||
}
|
||||
|
||||
if (format === 'jsonl') {
|
||||
const jsonlContent = formattedItems.map(item => JSON.stringify(item)).join('\n');
|
||||
return new Response(jsonlContent, {
|
||||
headers: {
|
||||
'Content-Type': 'application/x-ndjson',
|
||||
'Content-Disposition': `attachment; filename="eval-datasets-${Date.now()}.jsonl"`
|
||||
}
|
||||
});
|
||||
}
|
||||
|
||||
if (format === 'csv') {
|
||||
const csvContent = [convertToCSVRow(null, true), ...items.map(item => convertToCSVRow(item))].join('\n');
|
||||
return new Response('\uFEFF' + csvContent, {
|
||||
headers: {
|
||||
'Content-Type': 'text/csv; charset=utf-8',
|
||||
'Content-Disposition': `attachment; filename="eval-datasets-${Date.now()}.csv"`
|
||||
}
|
||||
});
|
||||
}
|
||||
}
|
||||
|
||||
// Stream export for large datasets
|
||||
const stream = new ReadableStream({
|
||||
async start(controller) {
|
||||
const encoder = new TextEncoder();
|
||||
let isFirst = true;
|
||||
|
||||
// CSV outputs header row first
|
||||
if (format === 'csv') {
|
||||
controller.enqueue(encoder.encode('\uFEFF' + convertToCSVRow(null, true) + '\n'));
|
||||
}
|
||||
|
||||
// JSON outputs opening bracket
|
||||
if (format === 'json') {
|
||||
controller.enqueue(encoder.encode('[\n'));
|
||||
}
|
||||
|
||||
// Fetch data in batches
|
||||
const totalBatches = Math.ceil(total / BATCH_SIZE);
|
||||
|
||||
for (let batch = 0; batch < totalBatches; batch++) {
|
||||
const items = await db.evalDatasets.findMany({
|
||||
where,
|
||||
orderBy: { createAt: 'desc' },
|
||||
skip: batch * BATCH_SIZE,
|
||||
take: BATCH_SIZE
|
||||
});
|
||||
|
||||
for (const item of items) {
|
||||
const formattedItem = formatExportItem(item);
|
||||
|
||||
if (format === 'json') {
|
||||
const prefix = isFirst ? '' : ',\n';
|
||||
controller.enqueue(encoder.encode(prefix + JSON.stringify(formattedItem)));
|
||||
isFirst = false;
|
||||
} else if (format === 'jsonl') {
|
||||
controller.enqueue(encoder.encode(JSON.stringify(formattedItem) + '\n'));
|
||||
} else if (format === 'csv') {
|
||||
controller.enqueue(encoder.encode(convertToCSVRow(item) + '\n'));
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// JSON outputs closing bracket
|
||||
if (format === 'json') {
|
||||
controller.enqueue(encoder.encode('\n]'));
|
||||
}
|
||||
|
||||
controller.close();
|
||||
}
|
||||
});
|
||||
|
||||
const contentTypes = {
|
||||
json: 'application/json',
|
||||
jsonl: 'application/x-ndjson',
|
||||
csv: 'text/csv; charset=utf-8'
|
||||
};
|
||||
|
||||
const extensions = {
|
||||
json: 'json',
|
||||
jsonl: 'jsonl',
|
||||
csv: 'csv'
|
||||
};
|
||||
|
||||
return new Response(stream, {
|
||||
headers: {
|
||||
'Content-Type': contentTypes[format],
|
||||
'Content-Disposition': `attachment; filename="eval-datasets-${Date.now()}.${extensions[format]}"`,
|
||||
'Transfer-Encoding': 'chunked'
|
||||
}
|
||||
});
|
||||
} catch (error) {
|
||||
console.error('Failed to export eval datasets:', error);
|
||||
return NextResponse.json({ code: 500, error: error.message || 'Export failed' }, { status: 500 });
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Get export preview (count only)
|
||||
*/
|
||||
export async function GET(request, { params }) {
|
||||
try {
|
||||
const { projectId } = params;
|
||||
const { searchParams } = new URL(request.url);
|
||||
|
||||
// Parse query params
|
||||
const questionTypes = searchParams.getAll('questionTypes');
|
||||
const tags = searchParams.getAll('tags');
|
||||
const keyword = searchParams.get('keyword') || '';
|
||||
|
||||
// Build query conditions
|
||||
const where = buildEvalQuestionWhere(projectId, {
|
||||
questionTypes: questionTypes.length > 0 ? questionTypes : undefined,
|
||||
tags: tags.length > 0 ? tags : undefined,
|
||||
keyword: keyword || undefined
|
||||
});
|
||||
|
||||
// Count rows
|
||||
const total = await db.evalDatasets.count({ where });
|
||||
|
||||
return NextResponse.json({
|
||||
code: 0,
|
||||
data: {
|
||||
total,
|
||||
isLargeDataset: total > 1000
|
||||
}
|
||||
});
|
||||
} catch (error) {
|
||||
console.error('Failed to get export preview:', error);
|
||||
return NextResponse.json({ code: 500, error: error.message || 'Failed to get export preview' }, { status: 500 });
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,380 @@
|
||||
import { NextResponse } from 'next/server';
|
||||
import { db } from '@/lib/db/index';
|
||||
import { nanoid } from 'nanoid';
|
||||
import * as XLSX from 'xlsx';
|
||||
|
||||
/**
|
||||
* Validate true/false item schema
|
||||
*/
|
||||
function validateTrueFalse(item, index) {
|
||||
const errors = [];
|
||||
if (!item.question || typeof item.question !== 'string') {
|
||||
errors.push(`Item ${index + 1}: missing or invalid "question"`);
|
||||
}
|
||||
if (!item.correctAnswer || (item.correctAnswer !== '✅' && item.correctAnswer !== '❌')) {
|
||||
errors.push(`Item ${index + 1}: "correctAnswer" must be "✅" or "❌"`);
|
||||
}
|
||||
return errors;
|
||||
}
|
||||
|
||||
/**
|
||||
* Validate single-choice item schema
|
||||
*/
|
||||
function validateSingleChoice(item, index) {
|
||||
const errors = [];
|
||||
if (!item.question || typeof item.question !== 'string') {
|
||||
errors.push(`Item ${index + 1}: missing or invalid "question"`);
|
||||
}
|
||||
|
||||
// Normalize options
|
||||
let options = item.options;
|
||||
if (typeof options === 'string') {
|
||||
try {
|
||||
options = JSON.parse(options);
|
||||
} catch (e) {
|
||||
errors.push(`Item ${index + 1}: invalid "options" format; unable to parse`);
|
||||
return errors;
|
||||
}
|
||||
}
|
||||
|
||||
if (!options || !Array.isArray(options) || options.length < 2) {
|
||||
errors.push(`Item ${index + 1}: "options" must be an array with at least 2 items`);
|
||||
}
|
||||
if (!item.correctAnswer || !/^[A-Z]$/.test(item.correctAnswer)) {
|
||||
errors.push(`Item ${index + 1}: "correctAnswer" must be a single uppercase letter (A-Z)`);
|
||||
}
|
||||
return errors;
|
||||
}
|
||||
|
||||
/**
|
||||
* Validate multiple-choice item schema
|
||||
*/
|
||||
function validateMultipleChoice(item, index) {
|
||||
const errors = [];
|
||||
if (!item.question || typeof item.question !== 'string') {
|
||||
errors.push(`Item ${index + 1}: missing or invalid "question"`);
|
||||
}
|
||||
|
||||
// Normalize options
|
||||
let options = item.options;
|
||||
if (typeof options === 'string') {
|
||||
try {
|
||||
options = JSON.parse(options);
|
||||
} catch (e) {
|
||||
errors.push(`Item ${index + 1}: invalid "options" format; unable to parse`);
|
||||
return errors;
|
||||
}
|
||||
}
|
||||
|
||||
if (!options || !Array.isArray(options) || options.length < 2) {
|
||||
errors.push(`Item ${index + 1}: "options" must be an array with at least 2 items`);
|
||||
}
|
||||
|
||||
// Normalize correctAnswer
|
||||
let correctAnswer = item.correctAnswer;
|
||||
if (typeof correctAnswer === 'string') {
|
||||
try {
|
||||
correctAnswer = JSON.parse(correctAnswer);
|
||||
} catch (e) {
|
||||
errors.push(`Item ${index + 1}: invalid "correctAnswer" format; unable to parse`);
|
||||
return errors;
|
||||
}
|
||||
}
|
||||
|
||||
if (!correctAnswer || !Array.isArray(correctAnswer) || correctAnswer.length < 1) {
|
||||
errors.push(`Item ${index + 1}: "correctAnswer" must be an array with at least 1 item`);
|
||||
}
|
||||
// Validate each answer token
|
||||
if (Array.isArray(correctAnswer)) {
|
||||
for (const ans of correctAnswer) {
|
||||
if (!/^[A-Z]$/.test(ans)) {
|
||||
errors.push(`Item ${index + 1}: "${ans}" is not a valid option letter in "correctAnswer"`);
|
||||
}
|
||||
}
|
||||
}
|
||||
return errors;
|
||||
}
|
||||
|
||||
/**
|
||||
* Validate QA item schema (short_answer and open_ended)
|
||||
*/
|
||||
function validateQA(item, index) {
|
||||
const errors = [];
|
||||
if (!item.question || typeof item.question !== 'string') {
|
||||
errors.push(`Item ${index + 1}: missing or invalid "question"`);
|
||||
}
|
||||
if (!item.correctAnswer || typeof item.correctAnswer !== 'string') {
|
||||
errors.push(`Item ${index + 1}: missing or invalid "correctAnswer"`);
|
||||
}
|
||||
return errors;
|
||||
}
|
||||
|
||||
/**
|
||||
* Validate data by question type
|
||||
*/
|
||||
function validateData(data, questionType) {
|
||||
const allErrors = [];
|
||||
|
||||
for (let i = 0; i < data.length; i++) {
|
||||
let errors = [];
|
||||
switch (questionType) {
|
||||
case 'true_false':
|
||||
errors = validateTrueFalse(data[i], i);
|
||||
break;
|
||||
case 'single_choice':
|
||||
errors = validateSingleChoice(data[i], i);
|
||||
break;
|
||||
case 'multiple_choice':
|
||||
errors = validateMultipleChoice(data[i], i);
|
||||
break;
|
||||
case 'short_answer':
|
||||
case 'open_ended':
|
||||
errors = validateQA(data[i], i);
|
||||
break;
|
||||
default:
|
||||
errors = [`Unsupported question type: ${questionType}`];
|
||||
}
|
||||
allErrors.push(...errors);
|
||||
}
|
||||
|
||||
return allErrors;
|
||||
}
|
||||
|
||||
/**
|
||||
* Parse an Excel file
|
||||
*/
|
||||
function parseExcel(buffer, questionType) {
|
||||
const excelHeaders = {
|
||||
question: '\u9898\u76ee',
|
||||
correctAnswer: '\u6b63\u786e\u7b54\u6848',
|
||||
answer: '\u7b54\u6848',
|
||||
options: '\u9009\u9879'
|
||||
};
|
||||
|
||||
const workbook = XLSX.read(buffer, { type: 'buffer' });
|
||||
const sheetName = workbook.SheetNames[0];
|
||||
const sheet = workbook.Sheets[sheetName];
|
||||
const rawData = XLSX.utils.sheet_to_json(sheet, { defval: '' });
|
||||
|
||||
// Convert to normalized schema
|
||||
const data = rawData.map(row => {
|
||||
const item = {
|
||||
question: row.question || row[excelHeaders.question] || '',
|
||||
correctAnswer: row.correctAnswer || row[excelHeaders.correctAnswer] || row[excelHeaders.answer] || ''
|
||||
};
|
||||
|
||||
// Handle options (choice questions)
|
||||
if (questionType === 'single_choice' || questionType === 'multiple_choice') {
|
||||
// Try to parse from options column
|
||||
if (row.options || row[excelHeaders.options]) {
|
||||
let optionsStr = (row.options || row[excelHeaders.options]).trim();
|
||||
|
||||
// Replace single quotes so it becomes valid JSON
|
||||
if (optionsStr.startsWith('[') && optionsStr.includes("'")) {
|
||||
optionsStr = optionsStr.replace(/'/g, '"');
|
||||
}
|
||||
|
||||
try {
|
||||
// Try JSON parsing
|
||||
item.options = JSON.parse(optionsStr);
|
||||
} catch {
|
||||
// Fallback: split by separators
|
||||
item.options = optionsStr
|
||||
.split(/[,;|，；]/)
|
||||
.map(o => o.trim())
|
||||
.filter(Boolean);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// Handle multiple-choice correctAnswer
|
||||
if (questionType === 'multiple_choice') {
|
||||
if (typeof item.correctAnswer === 'string') {
|
||||
let answerStr = item.correctAnswer.trim();
|
||||
|
||||
// Replace single quotes so it becomes valid JSON
|
||||
if (answerStr.startsWith('[') && answerStr.includes("'")) {
|
||||
answerStr = answerStr.replace(/'/g, '"');
|
||||
}
|
||||
|
||||
// Try JSON parsing
|
||||
try {
|
||||
item.correctAnswer = JSON.parse(answerStr);
|
||||
} catch {
|
||||
// Split string such as "A,B,C" or "ABC"
|
||||
if (answerStr.includes(',') || answerStr.includes('，')) {
|
||||
item.correctAnswer = answerStr.split(/[,，]/).map(a => a.trim().toUpperCase());
|
||||
} else {
|
||||
// Split characters such as "ABC" -> ["A", "B", "C"]
|
||||
item.correctAnswer = answerStr
|
||||
.toUpperCase()
|
||||
.split('')
|
||||
.filter(c => /[A-Z]/.test(c));
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
return item;
|
||||
});
|
||||
|
||||
return data;
|
||||
}
|
||||
|
||||
/**
|
||||
* Parse a JSON file
|
||||
*/
|
||||
function parseJSON(content) {
|
||||
return JSON.parse(content);
|
||||
}
|
||||
|
||||
/**
|
||||
* POST - Import evaluation datasets
|
||||
*/
|
||||
export async function POST(request, { params }) {
|
||||
try {
|
||||
const { projectId } = params;
|
||||
const formData = await request.formData();
|
||||
|
||||
const file = formData.get('file');
|
||||
const questionType = formData.get('questionType');
|
||||
const tags = formData.get('tags') || '';
|
||||
|
||||
console.log(`[Import] Start processing. Project: ${projectId}, questionType: ${questionType}, tags: ${tags}`);
|
||||
|
||||
if (!file) {
|
||||
return NextResponse.json({ code: 400, error: 'Please upload a file' }, { status: 400 });
|
||||
}
|
||||
|
||||
if (!questionType) {
|
||||
return NextResponse.json({ code: 400, error: 'Please select a question type' }, { status: 400 });
|
||||
}
|
||||
|
||||
// Validate question type
|
||||
const validTypes = ['true_false', 'single_choice', 'multiple_choice', 'short_answer', 'open_ended'];
|
||||
if (!validTypes.includes(questionType)) {
|
||||
return NextResponse.json({ code: 400, error: `Unsupported question type: ${questionType}` }, { status: 400 });
|
||||
}
|
||||
|
||||
// Get file extension
|
||||
const fileName = file.name;
|
||||
const fileExt = fileName.split('.').pop().toLowerCase();
|
||||
console.log(`[Import] File name: ${fileName}, extension: ${fileExt}`);
|
||||
|
||||
// Validate file type
|
||||
if (!['json', 'xls', 'xlsx'].includes(fileExt)) {
|
||||
return NextResponse.json(
|
||||
{ code: 400, error: 'Unsupported file format. Please upload a json, xls, or xlsx file' },
|
||||
{ status: 400 }
|
||||
);
|
||||
}
|
||||
|
||||
// Read file content
|
||||
const buffer = await file.arrayBuffer();
|
||||
let data = [];
|
||||
|
||||
// Parse file
|
||||
console.log('[Import] Parsing file...');
|
||||
if (fileExt === 'json') {
|
||||
const content = new TextDecoder().decode(buffer);
|
||||
data = parseJSON(content);
|
||||
} else {
|
||||
data = parseExcel(Buffer.from(buffer), questionType);
|
||||
}
|
||||
|
||||
console.log(`[Import] Parsing completed. Total items: ${data.length}`);
|
||||
|
||||
if (!Array.isArray(data) || data.length === 0) {
|
||||
return NextResponse.json({ code: 400, error: 'File is empty or has an invalid format' }, { status: 400 });
|
||||
}
|
||||
|
||||
// Validate data
|
||||
console.log('[Import] Validating data...');
|
||||
const errors = validateData(data, questionType);
|
||||
if (errors.length > 0) {
|
||||
console.log(`[Import] Validation failed. Error count: ${errors.length}`);
|
||||
return NextResponse.json(
|
||||
{
|
||||
code: 400,
|
||||
error: 'Data validation failed',
|
||||
details: errors.slice(0, 10),
|
||||
totalErrors: errors.length
|
||||
},
|
||||
{ status: 400 }
|
||||
);
|
||||
}
|
||||
|
||||
console.log('[Import] Validation passed. Writing to database...');
|
||||
|
||||
// Prepare data
|
||||
const now = new Date();
|
||||
const evalDatasets = data.map(item => {
|
||||
// Normalize options
|
||||
let options = item.options;
|
||||
if (typeof options === 'string') {
|
||||
try {
|
||||
options = JSON.parse(options);
|
||||
} catch (e) {
|
||||
// Keep original on parse failure
|
||||
}
|
||||
}
|
||||
|
||||
// Normalize correctAnswer
|
||||
let correctAnswer = item.correctAnswer;
|
||||
if (typeof correctAnswer === 'string' && questionType === 'multiple_choice') {
|
||||
try {
|
||||
correctAnswer = JSON.parse(correctAnswer);
|
||||
} catch (e) {
|
||||
// Keep original on parse failure
|
||||
}
|
||||
}
|
||||
|
||||
return {
|
||||
id: nanoid(),
|
||||
projectId,
|
||||
question: item.question,
|
||||
questionType,
|
||||
options: options ? JSON.stringify(options) : '',
|
||||
// For multiple_choice, store correctAnswer as JSON array string
|
||||
correctAnswer: Array.isArray(correctAnswer) ? JSON.stringify(correctAnswer) : correctAnswer,
|
||||
tags: tags || '',
|
||||
note: '',
|
||||
createAt: now,
|
||||
updateAt: now
|
||||
};
|
||||
});
|
||||
|
||||
// Batch insert
|
||||
const batchSize = 100;
|
||||
let insertedCount = 0;
|
||||
|
||||
for (let i = 0; i < evalDatasets.length; i += batchSize) {
|
||||
const batch = evalDatasets.slice(i, i + batchSize);
|
||||
await db.evalDatasets.createMany({ data: batch });
|
||||
insertedCount += batch.length;
|
||||
console.log(`[Import] Inserted ${insertedCount}/${evalDatasets.length} items`);
|
||||
}
|
||||
|
||||
console.log(`[Import] Import completed. Total inserted: ${insertedCount}`);
|
||||
|
||||
return NextResponse.json({
|
||||
code: 0,
|
||||
data: {
|
||||
total: insertedCount,
|
||||
questionType,
|
||||
tags
|
||||
},
|
||||
message: `Successfully imported ${insertedCount} evaluation items`
|
||||
});
|
||||
} catch (error) {
|
||||
console.error('[Import] Import failed:', error);
|
||||
return NextResponse.json(
|
||||
{
|
||||
code: 500,
|
||||
error: 'Import failed',
|
||||
message: error.message
|
||||
},
|
||||
{ status: 500 }
|
||||
);
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,164 @@
|
||||
import { NextResponse } from 'next/server';
|
||||
import { getEvalQuestionsWithPagination, getEvalQuestionsStats, deleteEvalQuestion } from '@/lib/db/evalDatasets';
|
||||
|
||||
/**
|
||||
* Get project's evaluation dataset list (paginated)
|
||||
*/
|
||||
export async function GET(request, { params }) {
|
||||
try {
|
||||
const { projectId } = params;
|
||||
const { searchParams } = new URL(request.url);
|
||||
|
||||
// Parse query params
|
||||
const page = parseInt(searchParams.get('page') || '1', 10);
|
||||
const pageSize = parseInt(searchParams.get('pageSize') || '20', 10);
|
||||
const questionType = searchParams.get('questionType') || '';
|
||||
const questionTypes = searchParams.getAll('questionTypes');
|
||||
const keyword = searchParams.get('keyword') || '';
|
||||
const chunkId = searchParams.get('chunkId') || '';
|
||||
// Support multiple tags params or comma-separated tag
|
||||
const tags =
|
||||
searchParams.getAll('tags').length > 0
|
||||
? searchParams.getAll('tags')
|
||||
: searchParams.get('tag')
|
||||
? searchParams.get('tag').split(',')
|
||||
: [];
|
||||
|
||||
const includeStats = searchParams.get('includeStats') === 'true';
|
||||
|
||||
const queryOptions = {
|
||||
page,
|
||||
pageSize,
|
||||
questionType: questionType || undefined,
|
||||
questionTypes: questionTypes.length > 0 ? questionTypes : undefined,
|
||||
keyword: keyword || undefined,
|
||||
chunkId: chunkId || undefined,
|
||||
tags: tags.length > 0 ? tags : undefined
|
||||
};
|
||||
|
||||
if (includeStats) {
|
||||
const [result, stats] = await Promise.all([
|
||||
getEvalQuestionsWithPagination(projectId, queryOptions),
|
||||
getEvalQuestionsStats(projectId)
|
||||
]);
|
||||
result.stats = stats;
|
||||
return NextResponse.json(result);
|
||||
}
|
||||
|
||||
const result = await getEvalQuestionsWithPagination(projectId, queryOptions);
|
||||
return NextResponse.json(result);
|
||||
} catch (error) {
|
||||
console.error('Failed to get eval datasets:', error);
|
||||
return NextResponse.json({ error: error.message || 'Failed to get eval datasets' }, { status: 500 });
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Batch delete evaluation datasets
|
||||
*/
|
||||
export async function DELETE(request, { params }) {
|
||||
try {
|
||||
const { ids } = await request.json();
|
||||
|
||||
if (!ids || !Array.isArray(ids) || ids.length === 0) {
|
||||
return NextResponse.json({ error: 'Invalid request: ids array is required' }, { status: 400 });
|
||||
}
|
||||
|
||||
const results = await Promise.all(ids.map(id => deleteEvalQuestion(id).catch(err => ({ error: err.message, id }))));
|
||||
const deleted = results.filter(r => !r.error).length;
|
||||
const failed = results.filter(r => r.error).length;
|
||||
|
||||
return NextResponse.json({
|
||||
success: true,
|
||||
deleted,
|
||||
failed,
|
||||
message: `Successfully deleted ${deleted} items${failed > 0 ? `, ${failed} failed` : ''}`
|
||||
});
|
||||
} catch (error) {
|
||||
console.error('Failed to delete eval datasets:', error);
|
||||
return NextResponse.json({ error: error.message || 'Failed to delete eval datasets' }, { status: 500 });
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Create a new evaluation dataset (or batch create)
|
||||
*/
|
||||
export async function POST(request, { params }) {
|
||||
try {
|
||||
const { projectId } = params;
|
||||
const body = await request.json();
|
||||
|
||||
const { createEvalQuestion, createManyEvalQuestions } = require('@/lib/db/evalDatasets');
|
||||
|
||||
// Handle batch creation
|
||||
if (Array.isArray(body) || (body.items && Array.isArray(body.items))) {
|
||||
const items = Array.isArray(body) ? body : body.items;
|
||||
|
||||
if (items.length === 0) {
|
||||
return NextResponse.json({ success: true, count: 0 });
|
||||
}
|
||||
|
||||
// Validate items
|
||||
const validItems = items
|
||||
.map(item => {
|
||||
// 确保标签格式正确: 数组转为逗号分隔字符串
|
||||
let tagsStr = item.tags || '';
|
||||
if (Array.isArray(tagsStr)) {
|
||||
tagsStr = tagsStr.join(',');
|
||||
}
|
||||
return {
|
||||
projectId,
|
||||
question: item.question,
|
||||
questionType: item.questionType || 'open_ended',
|
||||
correctAnswer:
|
||||
typeof item.correctAnswer === 'object' ? JSON.stringify(item.correctAnswer) : item.correctAnswer,
|
||||
tags: tagsStr,
|
||||
note: item.note || '',
|
||||
chunkId: item.chunkId || null,
|
||||
options: item.options
|
||||
? typeof item.options === 'object'
|
||||
? JSON.stringify(item.options)
|
||||
: item.options
|
||||
: ''
|
||||
};
|
||||
})
|
||||
.filter(item => item.question && item.correctAnswer);
|
||||
|
||||
if (validItems.length === 0) {
|
||||
return NextResponse.json({ error: 'No valid items to create' }, { status: 400 });
|
||||
}
|
||||
|
||||
const result = await createManyEvalQuestions(validItems);
|
||||
return NextResponse.json({ success: true, count: result.count });
|
||||
}
|
||||
|
||||
// Handle single creation
|
||||
const { question, correctAnswer, questionType = 'open_ended', tags, note, chunkId, options } = body;
|
||||
|
||||
if (!question || !correctAnswer) {
|
||||
return NextResponse.json({ error: 'Question and Correct Answer are required' }, { status: 400 });
|
||||
}
|
||||
|
||||
// 确保标签格式正确: 数组转为逗号分隔字符串
|
||||
let tagsStr = tags || '';
|
||||
if (Array.isArray(tagsStr)) {
|
||||
tagsStr = tagsStr.join(',');
|
||||
}
|
||||
|
||||
const evalDataset = await createEvalQuestion({
|
||||
projectId,
|
||||
question,
|
||||
questionType,
|
||||
correctAnswer: typeof correctAnswer === 'object' ? JSON.stringify(correctAnswer) : correctAnswer,
|
||||
tags: tagsStr,
|
||||
note: note || '',
|
||||
chunkId: chunkId || null,
|
||||
options: options ? (typeof options === 'object' ? JSON.stringify(options) : options) : ''
|
||||
});
|
||||
|
||||
return NextResponse.json({ success: true, evalDataset });
|
||||
} catch (error) {
|
||||
console.error('Failed to create eval dataset:', error);
|
||||
return NextResponse.json({ error: error.message || 'Failed to create eval dataset' }, { status: 500 });
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,124 @@
|
||||
import { NextResponse } from 'next/server';
|
||||
import { db } from '@/lib/db';
|
||||
import { buildEvalQuestionWhere } from '@/lib/db/evalDatasets';
|
||||
|
||||
const SMALL_TOTAL_THRESHOLD = 5000;
|
||||
const HARD_LIMIT = 50000;
|
||||
|
||||
function shuffleArray(arr) {
|
||||
const result = [...arr];
|
||||
for (let i = result.length - 1; i > 0; i -= 1) {
|
||||
const j = Math.floor(Math.random() * (i + 1));
|
||||
[result[i], result[j]] = [result[j], result[i]];
|
||||
}
|
||||
return result;
|
||||
}
|
||||
|
||||
export async function POST(request, { params }) {
|
||||
try {
|
||||
const { projectId } = params;
|
||||
const body = await request.json();
|
||||
|
||||
const {
|
||||
questionType = '',
|
||||
questionTypes = [],
|
||||
keyword = '',
|
||||
chunkId = '',
|
||||
tags = [],
|
||||
limit = 0,
|
||||
strategy = 'random'
|
||||
} = body || {};
|
||||
|
||||
const where = buildEvalQuestionWhere(projectId, {
|
||||
questionType: questionType || undefined,
|
||||
questionTypes: Array.isArray(questionTypes) && questionTypes.length > 0 ? questionTypes : undefined,
|
||||
keyword: keyword || undefined,
|
||||
chunkId: chunkId || undefined,
|
||||
tags: Array.isArray(tags) && tags.length > 0 ? tags : undefined
|
||||
});
|
||||
|
||||
const total = await db.evalDatasets.count({ where });
|
||||
|
||||
if (total === 0) {
|
||||
return NextResponse.json(
|
||||
{
|
||||
code: 0,
|
||||
data: {
|
||||
total: 0,
|
||||
selectedCount: 0,
|
||||
ids: [],
|
||||
strategyUsed: strategy
|
||||
}
|
||||
},
|
||||
{ status: 200 }
|
||||
);
|
||||
}
|
||||
|
||||
let normalizedLimit = typeof limit === 'number' && limit > 0 ? Math.min(limit, HARD_LIMIT) : HARD_LIMIT;
|
||||
|
||||
if (normalizedLimit >= total) {
|
||||
const items = await db.evalDatasets.findMany({
|
||||
where,
|
||||
select: { id: true },
|
||||
orderBy: { createAt: 'desc' }
|
||||
});
|
||||
|
||||
const ids = items.map(item => item.id);
|
||||
|
||||
return NextResponse.json(
|
||||
{
|
||||
code: 0,
|
||||
data: {
|
||||
total,
|
||||
selectedCount: ids.length,
|
||||
ids,
|
||||
strategyUsed: total > HARD_LIMIT ? 'top' : strategy
|
||||
}
|
||||
},
|
||||
{ status: 200 }
|
||||
);
|
||||
}
|
||||
|
||||
let ids = [];
|
||||
let strategyUsed = strategy;
|
||||
|
||||
if (total <= SMALL_TOTAL_THRESHOLD) {
|
||||
const items = await db.evalDatasets.findMany({
|
||||
where,
|
||||
select: { id: true },
|
||||
orderBy: { createAt: 'desc' }
|
||||
});
|
||||
const shuffled = shuffleArray(items);
|
||||
ids = shuffled.slice(0, normalizedLimit).map(item => item.id);
|
||||
strategyUsed = 'random-small';
|
||||
} else {
|
||||
const items = await db.evalDatasets.findMany({
|
||||
where,
|
||||
select: { id: true },
|
||||
orderBy: { createAt: 'desc' },
|
||||
take: normalizedLimit
|
||||
});
|
||||
ids = items.map(item => item.id);
|
||||
strategyUsed = 'top-latest';
|
||||
}
|
||||
|
||||
return NextResponse.json(
|
||||
{
|
||||
code: 0,
|
||||
data: {
|
||||
total,
|
||||
selectedCount: ids.length,
|
||||
ids,
|
||||
strategyUsed
|
||||
}
|
||||
},
|
||||
{ status: 200 }
|
||||
);
|
||||
} catch (error) {
|
||||
console.error('Failed to sample eval datasets:', error);
|
||||
return NextResponse.json(
|
||||
{ code: 500, error: 'Failed to sample eval datasets', message: error.message },
|
||||
{ status: 500 }
|
||||
);
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,35 @@
|
||||
import { NextResponse } from 'next/server';
|
||||
import { db } from '@/lib/db/index';
|
||||
|
||||
/**
|
||||
* Get all evaluation dataset tags in the project
|
||||
*/
|
||||
export async function GET(request, { params }) {
|
||||
try {
|
||||
const { projectId } = params;
|
||||
|
||||
// Fetch tags for all datasets in the project
|
||||
const datasets = await db.evalDatasets.findMany({
|
||||
where: { projectId },
|
||||
select: { tags: true }
|
||||
});
|
||||
|
||||
// Extract and de-duplicate tags
|
||||
const tagsSet = new Set();
|
||||
datasets.forEach(dataset => {
|
||||
if (dataset.tags) {
|
||||
// Support both English and Chinese commas
|
||||
const tags = dataset.tags
|
||||
.split(/[,，]/)
|
||||
.map(t => t.trim())
|
||||
.filter(Boolean);
|
||||
tags.forEach(tag => tagsSet.add(tag));
|
||||
}
|
||||
});
|
||||
|
||||
return NextResponse.json({ tags: Array.from(tagsSet).sort() });
|
||||
} catch (error) {
|
||||
console.error('Failed to get tags:', error);
|
||||
return NextResponse.json({ error: error.message || 'Failed to get tags' }, { status: 500 });
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,176 @@
|
||||
import { NextResponse } from 'next/server';
|
||||
import { db } from '@/lib/db/index';
|
||||
import { getEvalResultsByTaskId, getEvalResultsStats } from '@/lib/db/evalResults';
|
||||
|
||||
/**
|
||||
* Get evaluation task details and results
|
||||
*/
|
||||
export async function GET(request, { params }) {
|
||||
try {
|
||||
const { projectId, taskId } = params;
|
||||
|
||||
if (!projectId || !taskId) {
|
||||
return NextResponse.json({ error: 'Project ID and Task ID are required' }, { status: 400 });
|
||||
}
|
||||
|
||||
// Fetch task details
|
||||
const task = await db.task.findUnique({
|
||||
where: { id: taskId }
|
||||
});
|
||||
|
||||
if (!task) {
|
||||
return NextResponse.json({ error: 'Task not found' }, { status: 404 });
|
||||
}
|
||||
|
||||
if (task.projectId !== projectId) {
|
||||
return NextResponse.json({ error: 'Task does not belong to this project' }, { status: 403 });
|
||||
}
|
||||
|
||||
// Parse task detail fields
|
||||
let detail = {};
|
||||
let modelInfo = {};
|
||||
try {
|
||||
detail = task.detail ? JSON.parse(task.detail) : {};
|
||||
modelInfo = task.modelInfo ? JSON.parse(task.modelInfo) : {};
|
||||
} catch (e) {
|
||||
console.error('Failed to parse task detail:', e);
|
||||
}
|
||||
|
||||
// Parse query params
|
||||
const { searchParams } = new URL(request.url);
|
||||
const page = parseInt(searchParams.get('page') || '1');
|
||||
const pageSize = parseInt(searchParams.get('pageSize') || '10');
|
||||
const type = searchParams.get('type') || null;
|
||||
const isCorrectStr = searchParams.get('isCorrect');
|
||||
const isCorrect = isCorrectStr === 'true' ? true : isCorrectStr === 'false' ? false : null;
|
||||
|
||||
// Fetch results (supports pagination and filters)
|
||||
const { items: results, total } = await getEvalResultsByTaskId(taskId, {
|
||||
page,
|
||||
pageSize,
|
||||
type,
|
||||
isCorrect
|
||||
});
|
||||
|
||||
// Fetch stats
|
||||
const stats = await getEvalResultsStats(taskId);
|
||||
|
||||
return NextResponse.json({
|
||||
code: 0,
|
||||
data: {
|
||||
task: {
|
||||
...task,
|
||||
detail,
|
||||
modelInfo
|
||||
},
|
||||
results,
|
||||
total,
|
||||
page,
|
||||
pageSize,
|
||||
stats
|
||||
}
|
||||
});
|
||||
} catch (error) {
|
||||
console.error('Failed to fetch evaluation task details:', error);
|
||||
return NextResponse.json(
|
||||
{ code: 500, error: 'Failed to fetch evaluation task details', message: error.message },
|
||||
{ status: 500 }
|
||||
);
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Delete evaluation task
|
||||
*/
|
||||
export async function DELETE(request, { params }) {
|
||||
try {
|
||||
const { projectId, taskId } = params;
|
||||
|
||||
if (!projectId || !taskId) {
|
||||
return NextResponse.json({ error: 'Project ID and Task ID are required' }, { status: 400 });
|
||||
}
|
||||
|
||||
// Validate task exists and belongs to this project
|
||||
const task = await db.task.findUnique({
|
||||
where: { id: taskId }
|
||||
});
|
||||
|
||||
if (!task) {
|
||||
return NextResponse.json({ error: 'Task not found' }, { status: 404 });
|
||||
}
|
||||
|
||||
if (task.projectId !== projectId) {
|
||||
return NextResponse.json({ error: 'Task does not belong to this project' }, { status: 403 });
|
||||
}
|
||||
|
||||
// Delete evaluation results
|
||||
await db.evalResults.deleteMany({
|
||||
where: { taskId }
|
||||
});
|
||||
|
||||
// Delete task
|
||||
await db.task.delete({
|
||||
where: { id: taskId }
|
||||
});
|
||||
|
||||
return NextResponse.json({
|
||||
code: 0,
|
||||
message: 'Deleted'
|
||||
});
|
||||
} catch (error) {
|
||||
console.error('Failed to delete evaluation task:', error);
|
||||
return NextResponse.json(
|
||||
{ code: 500, error: 'Failed to delete evaluation task', message: error.message },
|
||||
{ status: 500 }
|
||||
);
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Interrupt evaluation task
|
||||
*/
|
||||
export async function PUT(request, { params }) {
|
||||
try {
|
||||
const { projectId, taskId } = params;
|
||||
const data = await request.json();
|
||||
const { action } = data;
|
||||
|
||||
if (!projectId || !taskId) {
|
||||
return NextResponse.json({ error: 'Project ID and Task ID are required' }, { status: 400 });
|
||||
}
|
||||
|
||||
// Validate task exists and belongs to this project
|
||||
const task = await db.task.findUnique({
|
||||
where: { id: taskId }
|
||||
});
|
||||
|
||||
if (!task) {
|
||||
return NextResponse.json({ error: 'Task not found' }, { status: 404 });
|
||||
}
|
||||
|
||||
if (task.projectId !== projectId) {
|
||||
return NextResponse.json({ error: 'Task does not belong to this project' }, { status: 403 });
|
||||
}
|
||||
|
||||
if (action === 'interrupt') {
|
||||
// Interrupt task
|
||||
await db.task.update({
|
||||
where: { id: taskId },
|
||||
data: {
|
||||
status: 3, // Interrupted
|
||||
endTime: new Date()
|
||||
}
|
||||
});
|
||||
|
||||
return NextResponse.json({
|
||||
code: 0,
|
||||
message: 'Task interrupted'
|
||||
});
|
||||
}
|
||||
|
||||
return NextResponse.json({ error: 'Unknown action' }, { status: 400 });
|
||||
} catch (error) {
|
||||
console.error('Failed to operate evaluation task:', error);
|
||||
return NextResponse.json({ code: 500, error: 'Operation failed', message: error.message }, { status: 500 });
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,207 @@
|
||||
import { NextResponse } from 'next/server';
|
||||
import { db } from '@/lib/db/index';
|
||||
import { processTask } from '@/lib/services/tasks';
|
||||
|
||||
/**
|
||||
* Get all evaluation tasks for a project
|
||||
*/
|
||||
export async function GET(request, { params }) {
|
||||
try {
|
||||
const { projectId } = params;
|
||||
const { searchParams } = new URL(request.url);
|
||||
const page = parseInt(searchParams.get('page') || '1');
|
||||
const pageSize = parseInt(searchParams.get('pageSize') || '20');
|
||||
|
||||
if (!projectId) {
|
||||
return NextResponse.json({ error: 'Project ID is required' }, { status: 400 });
|
||||
}
|
||||
|
||||
const skip = (page - 1) * pageSize;
|
||||
|
||||
// Fetch task list and total count
|
||||
const [tasks, total] = await Promise.all([
|
||||
db.task.findMany({
|
||||
where: {
|
||||
projectId,
|
||||
taskType: 'model-evaluation'
|
||||
},
|
||||
orderBy: { createAt: 'desc' },
|
||||
skip,
|
||||
take: pageSize
|
||||
}),
|
||||
db.task.count({
|
||||
where: {
|
||||
projectId,
|
||||
taskType: 'model-evaluation'
|
||||
}
|
||||
})
|
||||
]);
|
||||
|
||||
// Parse task detail fields
|
||||
const tasksWithDetails = tasks.map(task => {
|
||||
let detail = {};
|
||||
let modelInfo = {};
|
||||
try {
|
||||
detail = task.detail ? JSON.parse(task.detail) : {};
|
||||
modelInfo = task.modelInfo ? JSON.parse(task.modelInfo) : {};
|
||||
} catch (e) {
|
||||
console.error('Failed to parse task detail:', e);
|
||||
}
|
||||
return {
|
||||
...task,
|
||||
detail,
|
||||
modelInfo
|
||||
};
|
||||
});
|
||||
|
||||
return NextResponse.json({
|
||||
code: 0,
|
||||
data: {
|
||||
items: tasksWithDetails,
|
||||
total,
|
||||
page,
|
||||
pageSize,
|
||||
totalPages: Math.ceil(total / pageSize)
|
||||
}
|
||||
});
|
||||
} catch (error) {
|
||||
console.error('Failed to fetch evaluation task list:', error);
|
||||
return NextResponse.json(
|
||||
{ code: 500, error: 'Failed to fetch evaluation task list', message: error.message },
|
||||
{ status: 500 }
|
||||
);
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Create evaluation tasks
|
||||
* Supports selecting multiple models and creating one task per model
|
||||
*/
|
||||
export async function POST(request, { params }) {
|
||||
try {
|
||||
const { projectId } = params;
|
||||
const data = await request.json();
|
||||
|
||||
const {
|
||||
models, // Models to evaluate: [{ modelId, providerId }]
|
||||
evalDatasetIds, // Evaluation question IDs
|
||||
judgeModelId, // Judge model ID (for subjective grading)
|
||||
judgeProviderId, // Judge provider ID
|
||||
language = 'zh-CN',
|
||||
filterOptions = {}, // Filter options (for display)
|
||||
customScoreAnchors = null // Custom score anchors for subjective grading
|
||||
} = data;
|
||||
|
||||
// Validate required fields
|
||||
if (!models || models.length === 0) {
|
||||
return NextResponse.json({ code: 400, error: 'Please select at least one model to evaluate' }, { status: 400 });
|
||||
}
|
||||
|
||||
if (!evalDatasetIds || evalDatasetIds.length === 0) {
|
||||
return NextResponse.json({ code: 400, error: 'Please select questions to evaluate' }, { status: 400 });
|
||||
}
|
||||
|
||||
// Check for subjective questions
|
||||
const evalDatasets = await db.evalDatasets.findMany({
|
||||
where: {
|
||||
id: { in: evalDatasetIds },
|
||||
projectId
|
||||
},
|
||||
select: { questionType: true }
|
||||
});
|
||||
|
||||
const hasSubjectiveQuestions = evalDatasets.some(
|
||||
q => q.questionType === 'short_answer' || q.questionType === 'open_ended'
|
||||
);
|
||||
|
||||
// If there are subjective questions, a judge model is required
|
||||
if (hasSubjectiveQuestions && (!judgeModelId || !judgeProviderId)) {
|
||||
return NextResponse.json(
|
||||
{ code: 400, error: 'Short-answer or open-ended questions found. Please select a judge model for grading' },
|
||||
{ status: 400 }
|
||||
);
|
||||
}
|
||||
|
||||
// Judge model must not be the same as any test model
|
||||
if (judgeModelId && judgeProviderId) {
|
||||
const judgeModel = { modelId: judgeModelId, providerId: judgeProviderId };
|
||||
const isJudgeInTestModels = models.some(
|
||||
m => m.modelId === judgeModel.modelId && m.providerId === judgeModel.providerId
|
||||
);
|
||||
if (isJudgeInTestModels) {
|
||||
return NextResponse.json(
|
||||
{ code: 400, error: 'Judge model cannot be the same as a test model' },
|
||||
{ status: 400 }
|
||||
);
|
||||
}
|
||||
}
|
||||
|
||||
// Create one task per model
|
||||
const createdTasks = [];
|
||||
|
||||
for (const model of models) {
|
||||
const { modelId, providerId } = model;
|
||||
|
||||
// Fetch full model config
|
||||
const modelConfig = await db.modelConfig.findFirst({
|
||||
where: {
|
||||
projectId,
|
||||
providerId,
|
||||
modelId
|
||||
}
|
||||
});
|
||||
|
||||
// Keep providerId for lookup, add providerName for display
|
||||
const modelInfo = {
|
||||
modelId,
|
||||
modelName: modelConfig?.modelName || modelId,
|
||||
providerId: providerId, // Provider ID (DB ID)
|
||||
providerName: modelConfig?.providerName || providerId // Provider display name
|
||||
};
|
||||
|
||||
// Build task detail
|
||||
const taskDetail = {
|
||||
evalDatasetIds,
|
||||
judgeModelId: judgeModelId || null,
|
||||
judgeProviderId: judgeProviderId || null,
|
||||
filterOptions,
|
||||
hasSubjectiveQuestions,
|
||||
customScoreAnchors: customScoreAnchors || null // Store custom score anchors
|
||||
};
|
||||
|
||||
// Create task
|
||||
const newTask = await db.task.create({
|
||||
data: {
|
||||
projectId,
|
||||
taskType: 'model-evaluation',
|
||||
status: 0, // Processing
|
||||
modelInfo: JSON.stringify(modelInfo),
|
||||
language,
|
||||
detail: JSON.stringify(taskDetail),
|
||||
totalCount: evalDatasetIds.length,
|
||||
completedCount: 0,
|
||||
note: ''
|
||||
}
|
||||
});
|
||||
|
||||
createdTasks.push(newTask);
|
||||
|
||||
// Start task processing asynchronously
|
||||
processTask(newTask.id).catch(err => {
|
||||
console.error(`Failed to start evaluation task: ${newTask.id}`, err);
|
||||
});
|
||||
}
|
||||
|
||||
return NextResponse.json({
|
||||
code: 0,
|
||||
data: createdTasks,
|
||||
message: `Successfully created ${createdTasks.length} evaluation tasks`
|
||||
});
|
||||
} catch (error) {
|
||||
console.error('Failed to create evaluation task:', error);
|
||||
return NextResponse.json(
|
||||
{ code: 500, error: 'Failed to create evaluation task', message: error.message },
|
||||
{ status: 500 }
|
||||
);
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,313 @@
|
||||
import { NextResponse } from 'next/server';
|
||||
import { getGaPairsByFileId, toggleGaPairActive, saveGaPairs, createGaPairs } from '@/lib/db/ga-pairs';
|
||||
import { getUploadFileInfoById } from '@/lib/db/upload-files';
|
||||
import { generateGaPairs } from '@/lib/services/ga/ga-generation';
|
||||
import logger from '@/lib/util/logger';
|
||||
import { db } from '@/lib/db/index';
|
||||
|
||||
/**
|
||||
* 生成文件的 GA 对
|
||||
*/
|
||||
export async function POST(request, { params }) {
|
||||
try {
|
||||
const { projectId, fileId } = params;
|
||||
const { regenerate = false, appendMode = false, language = '中文' } = await request.json();
|
||||
|
||||
// 验证参数
|
||||
if (!projectId || !fileId) {
|
||||
return NextResponse.json({ error: 'Project ID and File ID are required' }, { status: 400 });
|
||||
}
|
||||
|
||||
logger.info(`Starting GA pairs generation for project: ${projectId}, file: ${fileId}, appendMode: ${appendMode}`);
|
||||
|
||||
// 检查文件是否存在
|
||||
const file = await getUploadFileInfoById(fileId);
|
||||
if (!file || file.projectId !== projectId) {
|
||||
return NextResponse.json({ error: 'File not found or does not belong to the project' }, { status: 404 });
|
||||
}
|
||||
|
||||
// 获取现有的GA对
|
||||
const existingGaPairs = await getGaPairsByFileId(fileId);
|
||||
|
||||
// 如果是追加模式且已有GA对，或者不是重新生成且已存在GA对
|
||||
if (!regenerate && !appendMode && existingGaPairs.length > 0) {
|
||||
return NextResponse.json({
|
||||
success: true,
|
||||
message: 'GA pairs already exist for this file',
|
||||
data: existingGaPairs
|
||||
});
|
||||
}
|
||||
|
||||
// 读取文件内容
|
||||
const fileContent = await getFileContent(projectId, file.fileName);
|
||||
if (!fileContent) {
|
||||
return NextResponse.json({ error: 'Failed to read file content' }, { status: 500 });
|
||||
}
|
||||
|
||||
logger.info(`File content loaded successfully, length: ${fileContent.length}`);
|
||||
|
||||
// 检查模型配置
|
||||
try {
|
||||
const { getActiveModel } = await import('@/lib/services/models');
|
||||
const activeModel = await getActiveModel(projectId);
|
||||
|
||||
if (!activeModel) {
|
||||
logger.error('No active model configuration found');
|
||||
return NextResponse.json(
|
||||
{ error: 'No active AI model configured. Please configure a model in settings first.' },
|
||||
{ status: 400 }
|
||||
);
|
||||
}
|
||||
|
||||
logger.info(`Using active model: ${activeModel.provider} - ${activeModel.model}`);
|
||||
} catch (modelError) {
|
||||
logger.error('Error checking model configuration:', modelError);
|
||||
return NextResponse.json(
|
||||
{ error: 'Failed to load model configuration. Please check your AI model settings.' },
|
||||
{ status: 500 }
|
||||
);
|
||||
}
|
||||
|
||||
// 调用 LLM 生成 GA 对
|
||||
logger.info(`Generating GA pairs for file: ${file.fileName}`);
|
||||
let generatedGaPairs;
|
||||
|
||||
try {
|
||||
generatedGaPairs = await generateGaPairs(fileContent, projectId, language);
|
||||
|
||||
if (!generatedGaPairs || generatedGaPairs.length === 0) {
|
||||
logger.warn('No GA pairs generated from LLM');
|
||||
return NextResponse.json(
|
||||
{
|
||||
error:
|
||||
'No GA pairs could be generated from the file content. The content might be too short or not suitable for GA pair generation.'
|
||||
},
|
||||
{ status: 400 }
|
||||
);
|
||||
}
|
||||
|
||||
logger.info(`Successfully generated ${generatedGaPairs.length} GA pairs from LLM`);
|
||||
} catch (generationError) {
|
||||
logger.error('GA pairs generation failed:', generationError);
|
||||
|
||||
// 现有的错误处理逻辑...
|
||||
let errorMessage = 'Failed to generate GA pairs';
|
||||
if (generationError.message.includes('No active model')) {
|
||||
errorMessage = 'No active AI model available. Please configure and activate a model in settings.';
|
||||
} else if (generationError.message.includes('API key')) {
|
||||
errorMessage = 'Invalid API key or model configuration. Please check your AI model settings.';
|
||||
} else if (generationError.message.includes('rate limit')) {
|
||||
errorMessage = 'API rate limit exceeded. Please try again later.';
|
||||
} else {
|
||||
errorMessage = `AI model error: ${generationError.message}`;
|
||||
}
|
||||
|
||||
return NextResponse.json({ error: errorMessage }, { status: 500 });
|
||||
}
|
||||
|
||||
// 保存到数据库
|
||||
try {
|
||||
if (appendMode && existingGaPairs.length > 0) {
|
||||
// 追加模式：只保存新生成的GA对，不删除现有的
|
||||
logger.info(`Appending ${generatedGaPairs.length} new GA pairs to existing ${existingGaPairs.length} pairs`);
|
||||
|
||||
// 为新GA对设置正确的pairNumber
|
||||
const startPairNumber = existingGaPairs.length + 1;
|
||||
const newGaPairData = generatedGaPairs.map((pair, index) => ({
|
||||
projectId,
|
||||
fileId,
|
||||
pairNumber: startPairNumber + index,
|
||||
genreTitle: pair.genre?.title || pair.genreTitle || '',
|
||||
genreDesc: pair.genre?.description || pair.genreDesc || '',
|
||||
audienceTitle: pair.audience?.title || pair.audienceTitle || '',
|
||||
audienceDesc: pair.audience?.description || pair.audienceDesc || '',
|
||||
isActive: true
|
||||
}));
|
||||
|
||||
// 只创建新的GA对，不删除现有的
|
||||
await createGaPairs(newGaPairData);
|
||||
logger.info('New GA pairs appended to database successfully');
|
||||
} else {
|
||||
// 覆盖模式：删除现有的，保存新的
|
||||
await saveGaPairs(projectId, fileId, generatedGaPairs);
|
||||
logger.info('GA pairs saved to database successfully');
|
||||
}
|
||||
} catch (saveError) {
|
||||
logger.error('Failed to save GA pairs to database:', saveError);
|
||||
return NextResponse.json(
|
||||
{ error: 'Generated GA pairs successfully but failed to save to database' },
|
||||
{ status: 500 }
|
||||
);
|
||||
}
|
||||
|
||||
// 获取保存后的所有GA对
|
||||
const allGaPairs = await getGaPairsByFileId(fileId);
|
||||
|
||||
if (appendMode && existingGaPairs.length > 0) {
|
||||
// 追加模式：只返回新生成的GA对
|
||||
const newGaPairs = allGaPairs.slice(existingGaPairs.length);
|
||||
logger.info(`Successfully appended ${newGaPairs.length} GA pairs. Total pairs: ${allGaPairs.length}`);
|
||||
|
||||
return NextResponse.json({
|
||||
success: true,
|
||||
message: `${newGaPairs.length} new GA pairs appended successfully`,
|
||||
data: newGaPairs,
|
||||
total: allGaPairs.length
|
||||
});
|
||||
} else {
|
||||
// 覆盖模式：返回所有GA对
|
||||
logger.info(`Successfully generated and saved ${allGaPairs.length} GA pairs for file: ${file.fileName}`);
|
||||
|
||||
return NextResponse.json({
|
||||
success: true,
|
||||
message: 'GA pairs generated successfully',
|
||||
data: allGaPairs
|
||||
});
|
||||
}
|
||||
} catch (error) {
|
||||
logger.error('Unexpected error in GA pairs generation:', error);
|
||||
return NextResponse.json(
|
||||
{ error: error.message || 'Unexpected error occurred during GA pairs generation' },
|
||||
{ status: 500 }
|
||||
);
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* 获取文件的 GA 对
|
||||
*/
|
||||
export async function GET(request, { params }) {
|
||||
try {
|
||||
const { projectId, fileId } = params;
|
||||
|
||||
if (!projectId || !fileId) {
|
||||
return NextResponse.json({ error: 'Project ID and File ID are required' }, { status: 400 });
|
||||
}
|
||||
|
||||
const gaPairs = await getGaPairsByFileId(fileId);
|
||||
|
||||
return NextResponse.json({
|
||||
success: true,
|
||||
data: gaPairs
|
||||
});
|
||||
} catch (error) {
|
||||
console.error('Error getting GA pairs:', String(error));
|
||||
return NextResponse.json({ error: 'Failed to get GA pairs' }, { status: 500 });
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* 更新/替换文件的所有 GA 对
|
||||
*/
|
||||
export async function PUT(request, { params }) {
|
||||
try {
|
||||
const { projectId, fileId } = params;
|
||||
const body = await request.json();
|
||||
|
||||
if (!projectId || !fileId) {
|
||||
return NextResponse.json({ error: 'Project ID and File ID are required' }, { status: 400 });
|
||||
}
|
||||
|
||||
const { updates } = body;
|
||||
|
||||
if (!updates || !Array.isArray(updates)) {
|
||||
return NextResponse.json({ error: 'Updates array is required' }, { status: 400 });
|
||||
}
|
||||
|
||||
logger.info(`Replacing all GA pairs for file ${fileId} with ${updates.length} pairs`);
|
||||
|
||||
// 使用数据库事务确保原子性操作
|
||||
const results = await db.$transaction(async tx => {
|
||||
// 1. 先删除所有现有的GA对
|
||||
await tx.gaPairs.deleteMany({
|
||||
where: { fileId }
|
||||
});
|
||||
|
||||
// 2. 然后创建新的GA对
|
||||
if (updates.length > 0) {
|
||||
const gaPairData = updates.map((pair, index) => ({
|
||||
projectId,
|
||||
fileId,
|
||||
pairNumber: index + 1,
|
||||
genreTitle: pair.genreTitle || pair.genre?.title || pair.genre || '',
|
||||
genreDesc: pair.genreDesc || pair.genre?.description || '',
|
||||
audienceTitle: pair.audienceTitle || pair.audience?.title || pair.audience || '',
|
||||
audienceDesc: pair.audienceDesc || pair.audience?.description || '',
|
||||
isActive: pair.isActive !== undefined ? pair.isActive : true
|
||||
}));
|
||||
|
||||
// 验证数据
|
||||
for (const data of gaPairData) {
|
||||
if (!data.genreTitle || !data.audienceTitle) {
|
||||
throw new Error(`Invalid GA pair data: missing genre or audience title`);
|
||||
}
|
||||
}
|
||||
|
||||
await tx.gaPairs.createMany({ data: gaPairData });
|
||||
}
|
||||
|
||||
// 3. 返回新创建的GA对
|
||||
return await tx.gaPairs.findMany({
|
||||
where: { fileId },
|
||||
orderBy: { pairNumber: 'asc' }
|
||||
});
|
||||
});
|
||||
|
||||
logger.info(`Successfully replaced GA pairs, new count: ${results.length}`);
|
||||
|
||||
return NextResponse.json({
|
||||
success: true,
|
||||
data: results
|
||||
});
|
||||
} catch (error) {
|
||||
logger.error('Error updating GA pairs:', error);
|
||||
return NextResponse.json({ error: error.message || 'Failed to update GA pairs' }, { status: 500 });
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* 切换 GA 对激活状态
|
||||
*/
|
||||
export async function PATCH(request, { params }) {
|
||||
try {
|
||||
const { projectId, fileId } = params;
|
||||
const body = await request.json();
|
||||
|
||||
if (!projectId || !fileId) {
|
||||
return NextResponse.json({ error: 'Project ID and File ID are required' }, { status: 400 });
|
||||
}
|
||||
|
||||
const { gaPairId, isActive } = body;
|
||||
|
||||
if (!gaPairId || typeof isActive !== 'boolean') {
|
||||
return NextResponse.json({ error: 'GA pair ID and active status are required' }, { status: 400 });
|
||||
}
|
||||
|
||||
const updatedPair = await toggleGaPairActive(gaPairId, isActive);
|
||||
|
||||
return NextResponse.json({
|
||||
success: true,
|
||||
data: updatedPair
|
||||
});
|
||||
} catch (error) {
|
||||
console.error('Error toggling GA pair active status:', String(error));
|
||||
return NextResponse.json({ error: 'Failed to toggle GA pair active status' }, { status: 500 });
|
||||
}
|
||||
}
|
||||
|
||||
// Helper function to read file content
|
||||
async function getFileContent(projectId, fileName) {
|
||||
try {
|
||||
const { getProjectRoot } = await import('@/lib/db/base');
|
||||
const path = await import('path');
|
||||
const fs = await import('fs');
|
||||
|
||||
const projectRoot = await getProjectRoot();
|
||||
const filePath = path.join(projectRoot, projectId, 'files', fileName.replace('.pdf', '.md'));
|
||||
|
||||
return await fs.promises.readFile(filePath, 'utf8');
|
||||
} catch (error) {
|
||||
logger.error('Failed to read file content:', error);
|
||||
return null;
|
||||
}
|
||||
}
|
||||
243
easy-dataset-main/app/api/projects/[projectId]/files/route.js
Normal file
243
easy-dataset-main/app/api/projects/[projectId]/files/route.js
Normal file
@@ -0,0 +1,243 @@
|
||||
import { NextResponse } from 'next/server';
|
||||
import { getProject } from '@/lib/db/projects';
|
||||
import path from 'path';
|
||||
import { getProjectRoot, ensureDir } from '@/lib/db/base';
|
||||
import { promises as fs } from 'fs';
|
||||
import {
|
||||
checkUploadFileInfoByMD5,
|
||||
createUploadFileInfo,
|
||||
delUploadFileInfoById,
|
||||
getUploadFilesPagination
|
||||
} from '@/lib/db/upload-files';
|
||||
import { getFileMD5 } from '@/lib/util/file';
|
||||
import { batchSaveTags } from '@/lib/db/tags';
|
||||
import { getProjectChunks, getProjectTocByName } from '@/lib/file/text-splitter';
|
||||
import { handleDomainTree } from '@/lib/util/domain-tree';
|
||||
|
||||
// Replace the deprecated config export with the new export syntax
|
||||
export const dynamic = 'force-dynamic';
|
||||
// This tells Next.js not to parse the request body automatically
|
||||
export const bodyParser = false;
|
||||
|
||||
// 获取项目文件列表
|
||||
export async function GET(request, { params }) {
|
||||
try {
|
||||
const { projectId } = params;
|
||||
|
||||
// 验证项目ID
|
||||
if (!projectId) {
|
||||
return NextResponse.json({ error: 'The project ID cannot be empty' }, { status: 400 });
|
||||
}
|
||||
const { searchParams } = new URL(request.url);
|
||||
const page = parseInt(searchParams.get('page')) || 1;
|
||||
const pageSize = parseInt(searchParams.get('pageSize')) || 10; // 每页10个文件，支持分页
|
||||
const fileName = searchParams.get('fileName') || '';
|
||||
const getAllIds = searchParams.get('getAllIds') === 'true'; // 新增：获取所有文件ID的标志
|
||||
|
||||
// 如果请求所有文件ID，直接返回ID列表
|
||||
if (getAllIds) {
|
||||
const allFiles = await getUploadFilesPagination(projectId, 1, 9999, fileName); // 获取所有文件
|
||||
const allFileIds = allFiles.data?.map(file => String(file.id)) || [];
|
||||
return NextResponse.json({ allFileIds });
|
||||
}
|
||||
// 获取文件列表
|
||||
const files = await getUploadFilesPagination(projectId, page, pageSize, fileName);
|
||||
|
||||
return NextResponse.json(files);
|
||||
} catch (error) {
|
||||
console.error('Error obtaining file list:', String(error));
|
||||
return NextResponse.json({ error: error.message || 'Error obtaining file list' }, { status: 500 });
|
||||
}
|
||||
}
|
||||
|
||||
// 删除文件
|
||||
export async function DELETE(request, { params }) {
|
||||
try {
|
||||
const { projectId } = params;
|
||||
const { searchParams } = new URL(request.url);
|
||||
const fileId = searchParams.get('fileId');
|
||||
const domainTreeAction = searchParams.get('domainTreeAction') || 'keep';
|
||||
|
||||
// 从请求体中获取模型信息和语言环境
|
||||
const requestData = await request.json();
|
||||
const model = requestData.model;
|
||||
const language = requestData.language || 'en';
|
||||
|
||||
// 验证项目ID和文件名
|
||||
if (!projectId) {
|
||||
return NextResponse.json({ error: 'The project ID cannot be empty' }, { status: 400 });
|
||||
}
|
||||
|
||||
if (!fileId) {
|
||||
return NextResponse.json({ error: 'The file name cannot be empty' }, { status: 400 });
|
||||
}
|
||||
|
||||
// 获取项目信息
|
||||
const project = await getProject(projectId);
|
||||
if (!project) {
|
||||
return NextResponse.json({ error: 'The project does not exist' }, { status: 404 });
|
||||
}
|
||||
|
||||
// 删除文件及其相关的文本块、问题和数据集
|
||||
const { stats, fileName, fileInfo } = await delUploadFileInfoById(fileId);
|
||||
const deleteToc = await getProjectTocByName(projectId, fileName);
|
||||
try {
|
||||
const projectRoot = await getProjectRoot();
|
||||
const projectPath = path.join(projectRoot, projectId);
|
||||
const tocDir = path.join(projectPath, 'toc');
|
||||
const baseName = path.basename(fileInfo.fileName, path.extname(fileInfo.fileName));
|
||||
const tocPath = path.join(tocDir, `${baseName}-toc.json`);
|
||||
|
||||
// 检查文件是否存在再删除
|
||||
await fs.unlink(tocPath);
|
||||
console.log(`成功删除 TOC 文件: ${tocPath}`);
|
||||
} catch (error) {
|
||||
console.error(`删除 TOC 文件失败:`, String(error));
|
||||
// 即使 TOC 文件删除失败，不影响整体结果
|
||||
}
|
||||
|
||||
// 如果选择了保持领域树不变，直接返回删除结果
|
||||
if (domainTreeAction === 'keep') {
|
||||
return NextResponse.json({
|
||||
message: '文件删除成功',
|
||||
stats: stats,
|
||||
domainTreeAction: 'keep',
|
||||
cascadeDelete: true
|
||||
});
|
||||
}
|
||||
|
||||
// 处理领域树更新
|
||||
try {
|
||||
// 获取项目的所有文件
|
||||
const { chunks, toc } = await getProjectChunks(projectId);
|
||||
|
||||
// 如果不存在文本块，说明项目已经没有文件了
|
||||
if (!chunks || chunks.length === 0) {
|
||||
// 清空领域树
|
||||
await batchSaveTags(projectId, []);
|
||||
return NextResponse.json({
|
||||
message: '文件删除成功，领域树已清空',
|
||||
stats: stats,
|
||||
domainTreeAction,
|
||||
cascadeDelete: true
|
||||
});
|
||||
}
|
||||
|
||||
// 调用领域树处理模块
|
||||
await handleDomainTree({
|
||||
projectId,
|
||||
action: domainTreeAction,
|
||||
allToc: toc,
|
||||
model,
|
||||
language,
|
||||
deleteToc,
|
||||
project
|
||||
});
|
||||
} catch (error) {
|
||||
console.error('Error updating domain tree after file deletion:', String(error));
|
||||
// 即使领域树更新失败，也不影响文件删除的结果
|
||||
}
|
||||
|
||||
return NextResponse.json({
|
||||
message: '文件删除成功',
|
||||
stats: stats,
|
||||
domainTreeAction,
|
||||
cascadeDelete: true
|
||||
});
|
||||
} catch (error) {
|
||||
console.error('Error deleting file:', String(error));
|
||||
return NextResponse.json({ error: error.message || 'Error deleting file' }, { status: 500 });
|
||||
}
|
||||
}
|
||||
|
||||
// 上传文件
|
||||
export async function POST(request, { params }) {
|
||||
console.log('File upload request processing, parameters:', params);
|
||||
const { projectId } = params;
|
||||
|
||||
// 验证项目ID
|
||||
if (!projectId) {
|
||||
console.log('The project ID cannot be empty, returning 400 error');
|
||||
return NextResponse.json({ error: 'The project ID cannot be empty' }, { status: 400 });
|
||||
}
|
||||
|
||||
// 获取项目信息
|
||||
const project = await getProject(projectId);
|
||||
if (!project) {
|
||||
console.log('The project does not exist, returning 404 error');
|
||||
return NextResponse.json({ error: 'The project does not exist' }, { status: 404 });
|
||||
}
|
||||
console.log('Project information retrieved successfully:', project.name || project.id);
|
||||
|
||||
try {
|
||||
console.log('Try using alternate methods for file upload...');
|
||||
|
||||
// 检查请求头中是否包含文件名
|
||||
const encodedFileName = request.headers.get('x-file-name');
|
||||
const fileName = encodedFileName ? decodeURIComponent(encodedFileName) : null;
|
||||
console.log('Get file name from request header:', fileName);
|
||||
|
||||
if (!fileName) {
|
||||
console.log('The request header does not contain a file name');
|
||||
return NextResponse.json(
|
||||
{ error: 'The request header does not contain a file name (x-file-name)' },
|
||||
{ status: 400 }
|
||||
);
|
||||
}
|
||||
|
||||
// 检查文件类型
|
||||
if (!fileName.endsWith('.md') && !fileName.endsWith('.pdf')) {
|
||||
return NextResponse.json({ error: 'Only Markdown files are supported' }, { status: 400 });
|
||||
}
|
||||
|
||||
// 直接从请求体中读取二进制数据
|
||||
const fileBuffer = Buffer.from(await request.arrayBuffer());
|
||||
|
||||
// 保存文件
|
||||
const projectRoot = await getProjectRoot();
|
||||
const projectPath = path.join(projectRoot, projectId);
|
||||
const filesDir = path.join(projectPath, 'files');
|
||||
|
||||
await ensureDir(filesDir);
|
||||
|
||||
const filePath = path.join(filesDir, fileName);
|
||||
await fs.writeFile(filePath, fileBuffer);
|
||||
//获取文件大小
|
||||
const stats = await fs.stat(filePath);
|
||||
//获取文件md5
|
||||
const md5 = await getFileMD5(filePath);
|
||||
//获取文件扩展名
|
||||
const ext = path.extname(filePath);
|
||||
|
||||
// let res = await checkUploadFileInfoByMD5(projectId, md5);
|
||||
// if (res) {
|
||||
// return NextResponse.json({ error: `【${fileName}】该文件已在此项目中存在` }, { status: 400 });
|
||||
// }
|
||||
|
||||
let fileInfo = await createUploadFileInfo({
|
||||
projectId,
|
||||
fileName,
|
||||
size: stats.size,
|
||||
md5,
|
||||
fileExt: ext,
|
||||
path: filesDir
|
||||
});
|
||||
|
||||
console.log('The file upload process is complete, and a successful response is returned');
|
||||
return NextResponse.json({
|
||||
message: 'File uploaded successfully',
|
||||
fileName,
|
||||
filePath,
|
||||
fileId: fileInfo.id
|
||||
});
|
||||
} catch (error) {
|
||||
console.error('Error processing file upload:', String(error));
|
||||
console.error('Error stack:', error.stack);
|
||||
return NextResponse.json(
|
||||
{
|
||||
error: 'File upload failed: ' + (error.message || 'Unknown error')
|
||||
},
|
||||
{ status: 500 }
|
||||
);
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,126 @@
|
||||
import { NextResponse } from 'next/server';
|
||||
import { getProjectChunks } from '@/lib/file/text-splitter';
|
||||
import { getTaskConfig } from '@/lib/db/projects';
|
||||
import { getChunkById } from '@/lib/db/chunks';
|
||||
import { generateQuestionsForChunk, generateQuestionsForChunkWithGA } from '@/lib/services/questions';
|
||||
|
||||
// 批量生成问题
|
||||
export async function POST(request, { params }) {
|
||||
try {
|
||||
const { projectId } = params;
|
||||
|
||||
// 验证项目ID
|
||||
if (!projectId) {
|
||||
return NextResponse.json({ error: 'The project ID cannot be empty' }, { status: 400 });
|
||||
}
|
||||
|
||||
// 获取请求体
|
||||
const { model, chunkIds, language = '中文', enableGaExpansion = false } = await request.json();
|
||||
|
||||
if (!model) {
|
||||
return NextResponse.json({ error: 'The model cannot be empty' }, { status: 400 });
|
||||
}
|
||||
|
||||
// 如果没有指定文本块ID，则获取所有文本块
|
||||
let chunks = [];
|
||||
if (!chunkIds || chunkIds.length === 0) {
|
||||
const result = await getProjectChunks(projectId);
|
||||
chunks = result.chunks || [];
|
||||
} else {
|
||||
// 获取指定的文本块
|
||||
chunks = await Promise.all(
|
||||
chunkIds.map(async chunkId => {
|
||||
const chunk = await getChunkById(chunkId);
|
||||
if (chunk) {
|
||||
return {
|
||||
id: chunk.id,
|
||||
content: chunk.content,
|
||||
length: chunk.content.length
|
||||
};
|
||||
}
|
||||
return null;
|
||||
})
|
||||
);
|
||||
chunks = chunks.filter(Boolean); // 过滤掉不存在的文本块
|
||||
}
|
||||
if (chunks.length === 0) {
|
||||
return NextResponse.json({ error: 'No valid text blocks found' }, { status: 404 });
|
||||
}
|
||||
|
||||
const results = [];
|
||||
const errors = [];
|
||||
|
||||
// 获取项目 task-config 信息
|
||||
const taskConfig = await getTaskConfig(projectId);
|
||||
const { questionGenerationLength } = taskConfig;
|
||||
for (const chunk of chunks) {
|
||||
try {
|
||||
// 根据文本长度自动计算问题数量
|
||||
const questionNumber = Math.floor(chunk.length / questionGenerationLength);
|
||||
|
||||
let result;
|
||||
if (enableGaExpansion) {
|
||||
// 使用GA增强的问题生成
|
||||
result = await generateQuestionsForChunkWithGA(projectId, chunk.id, {
|
||||
model,
|
||||
language,
|
||||
number: questionNumber
|
||||
});
|
||||
} else {
|
||||
// 使用标准问题生成
|
||||
result = await generateQuestionsForChunk(projectId, chunk.id, {
|
||||
model,
|
||||
language,
|
||||
number: questionNumber
|
||||
});
|
||||
}
|
||||
|
||||
// 统一处理返回结果格式
|
||||
if (result && result.questions && Array.isArray(result.questions)) {
|
||||
// GA增强模式的结果格式
|
||||
results.push({
|
||||
chunkId: chunk.id,
|
||||
success: true,
|
||||
questions: result.questions,
|
||||
total: result.total,
|
||||
gaExpansionUsed: result.gaExpansionUsed,
|
||||
gaPairsCount: result.gaPairsCount
|
||||
});
|
||||
} else if (result && result.labelQuestions && Array.isArray(result.labelQuestions)) {
|
||||
// 标准模式的结果格式
|
||||
results.push({
|
||||
chunkId: chunk.id,
|
||||
success: true,
|
||||
questions: result.labelQuestions,
|
||||
total: result.total,
|
||||
gaExpansionUsed: false,
|
||||
gaPairsCount: 0
|
||||
});
|
||||
} else {
|
||||
errors.push({
|
||||
chunkId: chunk.id,
|
||||
error: 'Failed to parse questions'
|
||||
});
|
||||
}
|
||||
} catch (error) {
|
||||
console.error(`Failed to generate questions for text block ${chunk.id}:`, String(error));
|
||||
errors.push({
|
||||
chunkId: chunk.id,
|
||||
error: error.message || 'Failed to generate questions'
|
||||
});
|
||||
}
|
||||
}
|
||||
|
||||
// 返回生成结果
|
||||
return NextResponse.json({
|
||||
results,
|
||||
errors,
|
||||
totalSuccess: results.length,
|
||||
totalErrors: errors.length,
|
||||
totalChunks: chunks.length
|
||||
});
|
||||
} catch (error) {
|
||||
console.error('Failed to generate questions:', String(error));
|
||||
return NextResponse.json({ error: error.message || 'Failed to generate questions' }, { status: 500 });
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,310 @@
|
||||
import { NextResponse } from 'next/server';
|
||||
import { getProject } from '@/lib/db/projects';
|
||||
import { getDatasets } from '@/lib/db/datasets';
|
||||
import fs from 'fs';
|
||||
import path from 'path';
|
||||
import os from 'os';
|
||||
import { uploadFiles, createRepo, checkRepoAccess } from '@huggingface/hub';
|
||||
|
||||
// 上传数据集到 HuggingFace
|
||||
export async function POST(request, { params }) {
|
||||
try {
|
||||
const projectId = params.projectId;
|
||||
const {
|
||||
token,
|
||||
datasetName,
|
||||
isPrivate,
|
||||
formatType,
|
||||
systemPrompt,
|
||||
confirmedOnly,
|
||||
includeCOT,
|
||||
fileFormat,
|
||||
customFields,
|
||||
reasoningLanguage
|
||||
} = await request.json();
|
||||
|
||||
// 获取项目信息
|
||||
const project = await getProject(projectId);
|
||||
if (!project) {
|
||||
return NextResponse.json({ error: '项目不存在' }, { status: 404 });
|
||||
}
|
||||
|
||||
// 获取数据集问题
|
||||
const questions = await getDatasets(projectId, confirmedOnly);
|
||||
if (!questions || questions.length === 0) {
|
||||
return NextResponse.json({ error: '没有可用的数据集问题' }, { status: 400 });
|
||||
}
|
||||
|
||||
// 格式化数据集
|
||||
const formattedData = formatDataset(questions, formatType, systemPrompt, includeCOT, customFields);
|
||||
|
||||
// 创建临时目录
|
||||
const tempDir = path.join(os.tmpdir(), `hf-upload-${projectId}-${Date.now()}`);
|
||||
fs.mkdirSync(tempDir, { recursive: true });
|
||||
|
||||
// 创建数据集文件
|
||||
const datasetFilePath = path.join(tempDir, `dataset.${fileFormat}`);
|
||||
if (fileFormat === 'json') {
|
||||
fs.writeFileSync(datasetFilePath, JSON.stringify(formattedData, null, 2));
|
||||
} else if (fileFormat === 'jsonl') {
|
||||
const jsonlContent = formattedData.map(item => JSON.stringify(item)).join('\n');
|
||||
fs.writeFileSync(datasetFilePath, jsonlContent);
|
||||
} else if (fileFormat === 'csv') {
|
||||
const csvContent = convertToCSV(formattedData);
|
||||
fs.writeFileSync(datasetFilePath, csvContent);
|
||||
}
|
||||
|
||||
// 创建 README.md 文件
|
||||
const readmePath = path.join(tempDir, 'README.md');
|
||||
const readmeContent = generateReadme(project.name, project.description, formatType);
|
||||
fs.writeFileSync(readmePath, readmeContent);
|
||||
|
||||
// 使用 Hugging Face REST API 上传数据集
|
||||
const visibility = isPrivate ? 'private' : 'public';
|
||||
|
||||
try {
|
||||
// 准备仓库配置
|
||||
const repo = { type: 'dataset', name: datasetName };
|
||||
|
||||
// 检查仓库是否存在
|
||||
let repoExists = true;
|
||||
try {
|
||||
await checkRepoAccess({ repo, accessToken: token });
|
||||
console.log(`Repository ${datasetName} exists, continuing to upload files`);
|
||||
} catch (error) {
|
||||
// If error code is 404, the repository does not exist
|
||||
if (error.statusCode === 404) {
|
||||
repoExists = false;
|
||||
console.log(`Repository ${datasetName} does not exist, preparing to create`);
|
||||
} else {
|
||||
// Other errors (e.g., permission errors)
|
||||
throw new Error(`Failed to check repository access: ${error.message}`);
|
||||
}
|
||||
}
|
||||
|
||||
// If the repository does not exist, create a new one
|
||||
if (!repoExists) {
|
||||
try {
|
||||
await createRepo({
|
||||
repo,
|
||||
accessToken: token,
|
||||
private: isPrivate,
|
||||
license: 'mit',
|
||||
description: project.description || 'Dataset created with Easy Dataset'
|
||||
});
|
||||
console.log(`Successfully created dataset repository: ${datasetName}`);
|
||||
} catch (error) {
|
||||
throw new Error(`Failed to create dataset repository: ${error.message}`);
|
||||
}
|
||||
}
|
||||
|
||||
// 2. 上传数据集文件
|
||||
await uploadFile(token, datasetName, datasetFilePath, `dataset.${fileFormat}`);
|
||||
|
||||
// 3. 上传 README.md
|
||||
await uploadFile(token, datasetName, readmePath, 'README.md');
|
||||
} catch (error) {
|
||||
console.error('Upload to HuggingFace Failed:', String(error));
|
||||
return NextResponse.json({ error: `Upload Error: ${error.message}` }, { status: 500 });
|
||||
}
|
||||
|
||||
// 清理临时目录
|
||||
fs.rmSync(tempDir, { recursive: true, force: true });
|
||||
|
||||
// 返回成功信息
|
||||
const datasetUrl = `https://huggingface.co/datasets/${datasetName}`;
|
||||
return NextResponse.json({
|
||||
success: true,
|
||||
message: 'Upload successfully HuggingFace',
|
||||
url: datasetUrl
|
||||
});
|
||||
} catch (error) {
|
||||
console.error('Upload Faile:', String(error));
|
||||
return NextResponse.json({ error: error.message }, { status: 500 });
|
||||
}
|
||||
}
|
||||
|
||||
// 格式化数据集
|
||||
function formatDataset(questions, formatType, systemPrompt, includeCOT, customFields) {
|
||||
if (formatType === 'alpaca') {
|
||||
return questions.map(q => {
|
||||
const item = {
|
||||
instruction: q.question,
|
||||
input: '',
|
||||
output: includeCOT && q.cot ? `${q.cot}\n\n${q.answer}` : q.answer
|
||||
};
|
||||
|
||||
if (systemPrompt) {
|
||||
item.system = systemPrompt;
|
||||
}
|
||||
|
||||
return item;
|
||||
});
|
||||
} else if (formatType === 'sharegpt') {
|
||||
return questions.map(q => {
|
||||
const messages = [];
|
||||
|
||||
if (systemPrompt) {
|
||||
messages.push({
|
||||
role: 'system',
|
||||
content: systemPrompt
|
||||
});
|
||||
}
|
||||
|
||||
messages.push({
|
||||
role: 'user',
|
||||
content: q.question
|
||||
});
|
||||
|
||||
messages.push({
|
||||
role: 'assistant',
|
||||
content: includeCOT && q.cot ? `${q.cot}\n\n${q.answer}` : q.answer
|
||||
});
|
||||
|
||||
return { messages };
|
||||
});
|
||||
} else if (formatType === 'multilingualthinking') {
|
||||
return questions.map(q => {
|
||||
const messages = [];
|
||||
|
||||
// Main message block
|
||||
const mainMsg = {
|
||||
reasoning_language: reasoningLanguage ? reasoningLanguage : 'English',
|
||||
user: q.question,
|
||||
analysis: includeCOT && q.cot ? `${q.cot}` : null,
|
||||
final: q.answer
|
||||
};
|
||||
if (systemPrompt) {
|
||||
mainMsg.developer = systemPrompt;
|
||||
}
|
||||
messages.push(mainMsg);
|
||||
|
||||
// Optional system prompt
|
||||
if (systemPrompt) {
|
||||
messages.push({
|
||||
role: 'system',
|
||||
content: systemPrompt,
|
||||
thinking: null
|
||||
});
|
||||
}
|
||||
|
||||
// User message
|
||||
messages.push({
|
||||
role: 'user',
|
||||
content: q.question,
|
||||
thinking: null
|
||||
});
|
||||
|
||||
// Assistant message
|
||||
messages.push({
|
||||
role: 'assistant',
|
||||
content: q.answer,
|
||||
thinking: includeCOT && q.cot ? `${q.cot}` : null
|
||||
});
|
||||
|
||||
return { messages };
|
||||
});
|
||||
} else if (formatType === 'custom' && customFields) {
|
||||
return questions.map(q => {
|
||||
const item = {
|
||||
[customFields.questionField]: q.question,
|
||||
[customFields.answerField]: q.answer
|
||||
};
|
||||
|
||||
if (includeCOT && q.cot) {
|
||||
item[customFields.cotField] = q.cot;
|
||||
}
|
||||
|
||||
if (customFields.includeLabels && q.labels) {
|
||||
item.labels = q.labels;
|
||||
}
|
||||
|
||||
if (customFields.includeChunk && q.chunkId) {
|
||||
item.chunkId = q.chunkId;
|
||||
}
|
||||
|
||||
return item;
|
||||
});
|
||||
}
|
||||
|
||||
// 默认返回 alpaca 格式
|
||||
return questions.map(q => ({
|
||||
instruction: q.question,
|
||||
output: includeCOT && q.cot ? `${q.cot}\n\n${q.answer}` : q.answer
|
||||
}));
|
||||
}
|
||||
|
||||
// 将数据转换为 CSV 格式
|
||||
function convertToCSV(data) {
|
||||
if (!data || data.length === 0) return '';
|
||||
|
||||
const headers = Object.keys(data[0]);
|
||||
const headerRow = headers.join(',');
|
||||
|
||||
const rows = data.map(item => {
|
||||
return headers
|
||||
.map(header => {
|
||||
const value = item[header];
|
||||
if (typeof value === 'string') {
|
||||
// 处理字符串中的逗号和引号
|
||||
return `"${value.replace(/"/g, '""')}"`;
|
||||
} else if (Array.isArray(value)) {
|
||||
return `"${JSON.stringify(value).replace(/"/g, '""')}"`;
|
||||
} else if (typeof value === 'object' && value !== null) {
|
||||
return `"${JSON.stringify(value).replace(/"/g, '""')}"`;
|
||||
}
|
||||
return value;
|
||||
})
|
||||
.join(',');
|
||||
});
|
||||
|
||||
return [headerRow, ...rows].join('\n');
|
||||
}
|
||||
|
||||
// 使用 @huggingface/hub 包上传文件到 HuggingFace
|
||||
async function uploadFile(token, datasetName, filePath, destFileName) {
|
||||
try {
|
||||
// 准备仓库配置
|
||||
const repo = { type: 'dataset', name: datasetName };
|
||||
|
||||
// 创建文件 URL
|
||||
const fileUrl = new URL(`file://${filePath}`);
|
||||
|
||||
// 使用 @huggingface/hub 包上传文件
|
||||
await uploadFiles({
|
||||
repo,
|
||||
accessToken: token,
|
||||
files: [
|
||||
{
|
||||
path: destFileName,
|
||||
content: fileUrl
|
||||
}
|
||||
],
|
||||
commitTitle: `Upload ${destFileName}`,
|
||||
commitDescription: `Files uploaded using Easy Dataset`
|
||||
});
|
||||
|
||||
return { success: true };
|
||||
} catch (error) {
|
||||
console.error(`File ${destFileName} Upload Error:`, String(error));
|
||||
throw error;
|
||||
}
|
||||
}
|
||||
|
||||
// Generate README.md file
|
||||
function generateReadme(projectName, projectDescription, formatType) {
|
||||
return `# ${projectName}
|
||||
|
||||
## Description
|
||||
${projectDescription || 'This dataset was created using the Easy Dataset tool.'}
|
||||
|
||||
## Format
|
||||
This dataset is in ${formatType} format.
|
||||
|
||||
## Creation Method
|
||||
This dataset was created using the [Easy Dataset](https://github.com/ConardLi/easy-dataset) tool.
|
||||
|
||||
> Easy Dataset is a specialized application designed to streamline the creation of fine-tuning datasets for Large Language Models (LLMs). It offers an intuitive interface for uploading domain-specific files, intelligently splitting content, generating questions, and producing high-quality training data for model fine-tuning.
|
||||
|
||||
`;
|
||||
}
|
||||
@@ -0,0 +1,109 @@
|
||||
import { NextResponse } from 'next/server';
|
||||
import { getImageDatasetById, updateImageDataset, deleteImageDataset } from '@/lib/db/imageDatasets';
|
||||
import { getProjectPath } from '@/lib/db/base';
|
||||
import fs from 'fs/promises';
|
||||
import path from 'path';
|
||||
|
||||
// 获取单个数据集详情
|
||||
export async function GET(request, { params }) {
|
||||
try {
|
||||
const { projectId, datasetId } = params;
|
||||
|
||||
const dataset = await getImageDatasetById(datasetId);
|
||||
|
||||
if (!dataset || dataset.projectId !== projectId) {
|
||||
return NextResponse.json({ error: 'Dataset not found' }, { status: 404 });
|
||||
}
|
||||
|
||||
// 获取项目路径
|
||||
const projectPath = await getProjectPath(projectId);
|
||||
|
||||
// 读取图片 base64
|
||||
let base64 = null;
|
||||
try {
|
||||
const imagePath = path.join(projectPath, 'images', dataset.imageName);
|
||||
const imageBuffer = await fs.readFile(imagePath);
|
||||
const base64Data = imageBuffer.toString('base64');
|
||||
const ext = path.extname(dataset.imageName).toLowerCase();
|
||||
const mimeType = ext === '.png' ? 'image/png' : ext === '.gif' ? 'image/gif' : 'image/jpeg';
|
||||
base64 = `data:${mimeType};base64,${base64Data}`;
|
||||
} catch (error) {
|
||||
console.error(`Failed to read image ${dataset.imageName}:`, error);
|
||||
}
|
||||
|
||||
// 添加图片 base64
|
||||
const datasetWithImage = {
|
||||
...dataset,
|
||||
base64
|
||||
};
|
||||
|
||||
return NextResponse.json(datasetWithImage);
|
||||
} catch (error) {
|
||||
console.error('Failed to get dataset detail:', error);
|
||||
return NextResponse.json({ error: error.message || 'Failed to get dataset detail' }, { status: 500 });
|
||||
}
|
||||
}
|
||||
|
||||
// 更新数据集
|
||||
export async function PUT(request, { params }) {
|
||||
try {
|
||||
const { projectId, datasetId } = params;
|
||||
const updates = await request.json();
|
||||
|
||||
// 验证数据集存在且属于该项目
|
||||
const dataset = await getImageDatasetById(datasetId);
|
||||
if (!dataset || dataset.projectId !== projectId) {
|
||||
return NextResponse.json({ error: 'Dataset not found' }, { status: 404 });
|
||||
}
|
||||
|
||||
// 更新数据集
|
||||
const updated = await updateImageDataset(datasetId, updates);
|
||||
|
||||
// 获取项目路径
|
||||
const projectPath = await getProjectPath(projectId);
|
||||
|
||||
// 读取图片 base64
|
||||
let base64 = null;
|
||||
try {
|
||||
const imagePath = path.join(projectPath, 'images', updated.imageName);
|
||||
const imageBuffer = await fs.readFile(imagePath);
|
||||
const base64Data = imageBuffer.toString('base64');
|
||||
const ext = path.extname(updated.imageName).toLowerCase();
|
||||
const mimeType = ext === '.png' ? 'image/png' : ext === '.gif' ? 'image/gif' : 'image/jpeg';
|
||||
base64 = `data:${mimeType};base64,${base64Data}`;
|
||||
} catch (error) {
|
||||
console.error(`Failed to read image ${updated.imageName}:`, error);
|
||||
}
|
||||
|
||||
// 添加图片 base64
|
||||
const updatedWithImage = {
|
||||
...updated,
|
||||
base64
|
||||
};
|
||||
|
||||
return NextResponse.json(updatedWithImage);
|
||||
} catch (error) {
|
||||
console.error('Failed to update dataset:', error);
|
||||
return NextResponse.json({ error: error.message || 'Failed to update dataset' }, { status: 500 });
|
||||
}
|
||||
}
|
||||
|
||||
// 删除数据集
|
||||
export async function DELETE(request, { params }) {
|
||||
try {
|
||||
const { projectId, datasetId } = params;
|
||||
|
||||
// 验证数据集存在且属于该项目
|
||||
const dataset = await getImageDatasetById(datasetId);
|
||||
if (!dataset || dataset.projectId !== projectId) {
|
||||
return NextResponse.json({ error: 'Dataset not found' }, { status: 404 });
|
||||
}
|
||||
|
||||
await deleteImageDataset(datasetId);
|
||||
|
||||
return NextResponse.json({ success: true });
|
||||
} catch (error) {
|
||||
console.error('Failed to delete dataset:', error);
|
||||
return NextResponse.json({ error: error.message || 'Failed to delete dataset' }, { status: 500 });
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,85 @@
|
||||
import { NextResponse } from 'next/server';
|
||||
import { getImageDatasetsForExport } from '@/lib/db/imageDatasets';
|
||||
import archiver from 'archiver';
|
||||
import { getProjectPath } from '@/lib/db/base';
|
||||
import path from 'path';
|
||||
import fs from 'fs';
|
||||
|
||||
/**
|
||||
* 导出图片文件压缩包
|
||||
*/
|
||||
export async function GET(request, { params }) {
|
||||
try {
|
||||
const { projectId } = params;
|
||||
const { searchParams } = new URL(request.url);
|
||||
const confirmedOnly = searchParams.get('confirmedOnly') === 'true';
|
||||
|
||||
// 验证项目ID
|
||||
if (!projectId) {
|
||||
return NextResponse.json({ error: 'Project ID cannot be empty' }, { status: 400 });
|
||||
}
|
||||
|
||||
// 获取数据集（用于确定需要哪些图片）
|
||||
const datasets = await getImageDatasetsForExport(projectId, confirmedOnly);
|
||||
|
||||
if (!datasets || datasets.length === 0) {
|
||||
return NextResponse.json({ error: 'No data to export' }, { status: 404 });
|
||||
}
|
||||
|
||||
// 获取所有需要的图片名称
|
||||
const imageNames = new Set(datasets.map(d => d.imageName).filter(Boolean));
|
||||
|
||||
if (imageNames.size === 0) {
|
||||
return NextResponse.json({ error: 'No images to export' }, { status: 404 });
|
||||
}
|
||||
|
||||
// 创建压缩包
|
||||
const archive = archiver('zip', {
|
||||
zlib: { level: 9 }
|
||||
});
|
||||
|
||||
// 设置响应头
|
||||
const dateStr = new Date().toISOString().slice(0, 10);
|
||||
const filename = `images-${projectId}-${dateStr}.zip`;
|
||||
|
||||
// 添加图片文件到压缩包
|
||||
const projectPath = await getProjectPath(projectId);
|
||||
const imageDir = path.join(projectPath, 'images');
|
||||
|
||||
if (!fs.existsSync(imageDir)) {
|
||||
return NextResponse.json({ error: 'Image directory not found' }, { status: 404 });
|
||||
}
|
||||
|
||||
let addedCount = 0;
|
||||
for (const imageName of imageNames) {
|
||||
const imagePath = path.join(imageDir, imageName);
|
||||
if (fs.existsSync(imagePath)) {
|
||||
archive.file(imagePath, { name: imageName });
|
||||
addedCount++;
|
||||
}
|
||||
}
|
||||
|
||||
if (addedCount === 0) {
|
||||
return NextResponse.json({ error: 'No image files found' }, { status: 404 });
|
||||
}
|
||||
|
||||
// 完成压缩
|
||||
archive.finalize();
|
||||
|
||||
// 返回流式响应
|
||||
return new NextResponse(archive, {
|
||||
headers: {
|
||||
'Content-Type': 'application/zip',
|
||||
'Content-Disposition': `attachment; filename="${filename}"`
|
||||
}
|
||||
});
|
||||
} catch (error) {
|
||||
console.error('Failed to export images:', String(error));
|
||||
return NextResponse.json(
|
||||
{
|
||||
error: error.message || 'Failed to export images'
|
||||
},
|
||||
{ status: 500 }
|
||||
);
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,32 @@
|
||||
import { NextResponse } from 'next/server';
|
||||
import { getImageDatasetsForExport } from '@/lib/db/imageDatasets';
|
||||
|
||||
/**
|
||||
* 导出图像数据集
|
||||
*/
|
||||
export async function POST(request, { params }) {
|
||||
try {
|
||||
const { projectId } = params;
|
||||
const body = await request.json();
|
||||
|
||||
// 验证项目ID
|
||||
if (!projectId) {
|
||||
return NextResponse.json({ error: 'Project ID cannot be empty' }, { status: 400 });
|
||||
}
|
||||
|
||||
const confirmedOnly = body.confirmedOnly || false;
|
||||
|
||||
// 获取数据集
|
||||
const datasets = await getImageDatasetsForExport(projectId, confirmedOnly);
|
||||
|
||||
return NextResponse.json(datasets);
|
||||
} catch (error) {
|
||||
console.error('Failed to export image datasets:', String(error));
|
||||
return NextResponse.json(
|
||||
{
|
||||
error: error.message || 'Failed to export image datasets'
|
||||
},
|
||||
{ status: 500 }
|
||||
);
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,72 @@
|
||||
import { NextResponse } from 'next/server';
|
||||
import { getImageDatasetsByProject } from '@/lib/db/imageDatasets';
|
||||
import { getProjectPath } from '@/lib/db/base';
|
||||
import fs from 'fs/promises';
|
||||
import path from 'path';
|
||||
|
||||
// 获取图片数据集列表
|
||||
export async function GET(request, { params }) {
|
||||
try {
|
||||
const { projectId } = params;
|
||||
const { searchParams } = new URL(request.url);
|
||||
|
||||
const page = parseInt(searchParams.get('page')) || 1;
|
||||
const pageSize = parseInt(searchParams.get('pageSize')) || 20;
|
||||
const search = searchParams.get('search') || '';
|
||||
const confirmed = searchParams.get('confirmed');
|
||||
const minScore = searchParams.get('minScore');
|
||||
const maxScore = searchParams.get('maxScore');
|
||||
|
||||
// 构建筛选条件
|
||||
const filters = {};
|
||||
if (search) {
|
||||
filters.search = search;
|
||||
}
|
||||
if (confirmed !== null && confirmed !== undefined) {
|
||||
filters.confirmed = confirmed === 'true';
|
||||
}
|
||||
if (minScore) {
|
||||
filters.minScore = parseInt(minScore);
|
||||
}
|
||||
if (maxScore) {
|
||||
filters.maxScore = parseInt(maxScore);
|
||||
}
|
||||
|
||||
const result = await getImageDatasetsByProject(projectId, page, pageSize, filters);
|
||||
|
||||
// 获取项目路径
|
||||
const projectPath = await getProjectPath(projectId);
|
||||
|
||||
// 为每个数据集添加图片 base64
|
||||
const datasetsWithImages = await Promise.all(
|
||||
result.data.map(async dataset => {
|
||||
try {
|
||||
const imagePath = path.join(projectPath, 'images', dataset.imageName);
|
||||
const imageBuffer = await fs.readFile(imagePath);
|
||||
const base64 = imageBuffer.toString('base64');
|
||||
const ext = path.extname(dataset.imageName).toLowerCase();
|
||||
const mimeType = ext === '.png' ? 'image/png' : ext === '.gif' ? 'image/gif' : 'image/jpeg';
|
||||
|
||||
return {
|
||||
...dataset,
|
||||
base64: `data:${mimeType};base64,${base64}`
|
||||
};
|
||||
} catch (error) {
|
||||
console.error(`Failed to read image ${dataset.imageName}:`, error);
|
||||
return {
|
||||
...dataset,
|
||||
base64: null
|
||||
};
|
||||
}
|
||||
})
|
||||
);
|
||||
|
||||
return NextResponse.json({
|
||||
data: datasetsWithImages,
|
||||
total: result.total
|
||||
});
|
||||
} catch (error) {
|
||||
console.error('Failed to get image datasets:', error);
|
||||
return NextResponse.json({ error: error.message || 'Failed to get image datasets' }, { status: 500 });
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,37 @@
|
||||
import { NextResponse } from 'next/server';
|
||||
import { getImageDatasetsTagsByProject } from '@/lib/db/imageDatasets';
|
||||
|
||||
// 获取项目中所有已使用的标签
|
||||
export async function GET(request, { params }) {
|
||||
try {
|
||||
const { projectId } = params;
|
||||
|
||||
// 获取项目的所有数据集
|
||||
const datasets = await getImageDatasetsTagsByProject(projectId);
|
||||
|
||||
console.log('datasets', datasets);
|
||||
|
||||
// 提取所有标签
|
||||
const tagsSet = new Set();
|
||||
datasets.forEach(dataset => {
|
||||
if (dataset.tags) {
|
||||
try {
|
||||
const tags = JSON.parse(dataset.tags);
|
||||
if (Array.isArray(tags)) {
|
||||
tags.forEach(tag => tagsSet.add(tag));
|
||||
}
|
||||
} catch (e) {
|
||||
// 忽略解析错误
|
||||
}
|
||||
}
|
||||
});
|
||||
|
||||
// 转换为数组并排序
|
||||
const tags = Array.from(tagsSet).sort();
|
||||
|
||||
return NextResponse.json({ tags });
|
||||
} catch (error) {
|
||||
console.error('Failed to get tags:', error);
|
||||
return NextResponse.json({ error: error.message || 'Failed to get tags' }, { status: 500 });
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,31 @@
|
||||
import { NextResponse } from 'next/server';
|
||||
import { getImageDetailWithQuestions } from '@/lib/services/images';
|
||||
|
||||
// 根据图片ID获取图片详情，包含问题列表和已标注数据
|
||||
export async function GET(request, { params }) {
|
||||
try {
|
||||
const { projectId, imageId } = params;
|
||||
|
||||
// 调用服务层获取图片详情
|
||||
const imageData = await getImageDetailWithQuestions(projectId, imageId);
|
||||
|
||||
return NextResponse.json({
|
||||
success: true,
|
||||
data: imageData
|
||||
});
|
||||
} catch (error) {
|
||||
console.error('Failed to get image details:', error);
|
||||
|
||||
// 根据错误类型返回不同的状态码
|
||||
let statusCode = 500;
|
||||
if (error.message === '缺少图片ID') {
|
||||
statusCode = 400;
|
||||
} else if (error.message === '图片不存在') {
|
||||
statusCode = 404;
|
||||
} else if (error.message === '图片不属于指定项目') {
|
||||
statusCode = 403;
|
||||
}
|
||||
|
||||
return NextResponse.json({ error: error.message || 'Failed to get image details' }, { status: statusCode });
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,89 @@
|
||||
import { NextResponse } from 'next/server';
|
||||
import { PrismaClient } from '@prisma/client';
|
||||
import { getImageById, getImageChunk } from '@/lib/db/images';
|
||||
import { createImageDataset } from '@/lib/db/imageDatasets';
|
||||
|
||||
const prisma = new PrismaClient();
|
||||
|
||||
// 创建标注
|
||||
export async function POST(request, { params }) {
|
||||
try {
|
||||
const { projectId } = params;
|
||||
const { imageId, questionId, question, answerType, answer, note } = await request.json();
|
||||
|
||||
// 验证必填字段
|
||||
if (!imageId || !question || !answerType || answer === undefined || answer === null) {
|
||||
return NextResponse.json({ error: '缺少必要参数：imageId, question, answerType, answer' }, { status: 400 });
|
||||
}
|
||||
|
||||
// 验证图片存在
|
||||
const image = await getImageById(imageId);
|
||||
if (!image || image.projectId !== projectId) {
|
||||
return NextResponse.json({ error: '图片不存在' }, { status: 404 });
|
||||
}
|
||||
|
||||
// 验证答案类型
|
||||
if (!['text', 'label', 'custom_format'].includes(answerType)) {
|
||||
return NextResponse.json({ error: '无效的答案类型' }, { status: 400 });
|
||||
}
|
||||
|
||||
// 验证答案内容
|
||||
if (answerType === 'text' && typeof answer !== 'string') {
|
||||
return NextResponse.json({ error: '文本类型答案必须是字符串' }, { status: 400 });
|
||||
}
|
||||
if (answerType === 'label' && !Array.isArray(answer)) {
|
||||
return NextResponse.json({ error: '标签类型答案必须是数组' }, { status: 400 });
|
||||
}
|
||||
|
||||
// 序列化答案
|
||||
let answerString = answer;
|
||||
if (answerType !== 'text' && typeof answerString !== 'string') {
|
||||
answerString = JSON.stringify(answer, null, 2);
|
||||
}
|
||||
|
||||
// 1. 获取问题记录（前端传递的 questionId 指向已有的问题）
|
||||
if (!questionId) {
|
||||
return NextResponse.json({ error: '缺少必要参数：questionId' }, { status: 400 });
|
||||
}
|
||||
|
||||
const questionRecord = await prisma.questions.findUnique({
|
||||
where: { id: questionId }
|
||||
});
|
||||
|
||||
if (!questionRecord) {
|
||||
return NextResponse.json({ error: '问题不存在' }, { status: 404 });
|
||||
}
|
||||
|
||||
// 验证问题属于该图片
|
||||
if (questionRecord.imageId !== imageId) {
|
||||
return NextResponse.json({ error: '问题不属于该图片' }, { status: 400 });
|
||||
}
|
||||
|
||||
// 2. 更新问题为已回答
|
||||
await prisma.questions.update({
|
||||
where: { id: questionRecord.id },
|
||||
data: { answered: true }
|
||||
});
|
||||
|
||||
// 3. 创建 ImageDataset 记录
|
||||
const dataset = await createImageDataset(projectId, {
|
||||
imageId: image.id,
|
||||
imageName: image.imageName,
|
||||
questionId: questionRecord.id,
|
||||
question,
|
||||
answer: answerString,
|
||||
answerType,
|
||||
model: 'manual',
|
||||
note: note || ''
|
||||
});
|
||||
|
||||
return NextResponse.json({
|
||||
success: true,
|
||||
dataset,
|
||||
questionId: questionRecord.id
|
||||
});
|
||||
} catch (error) {
|
||||
console.error('Failed to create annotation:', error);
|
||||
return NextResponse.json({ error: error.message || 'Failed to create annotation' }, { status: 500 });
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,41 @@
|
||||
import { NextResponse } from 'next/server';
|
||||
import { getImageByName } from '@/lib/db/images';
|
||||
import imageService from '@/lib/services/images';
|
||||
|
||||
// 生成图像数据集
|
||||
export async function POST(request, { params }) {
|
||||
try {
|
||||
const { projectId } = params;
|
||||
const { imageName, question, model, language = 'zh', previewOnly = false } = await request.json();
|
||||
|
||||
if (!imageName || !question) {
|
||||
return NextResponse.json({ error: '缺少必要参数' }, { status: 400 });
|
||||
}
|
||||
|
||||
if (!model) {
|
||||
return NextResponse.json({ error: '请选择一个视觉模型' }, { status: 400 });
|
||||
}
|
||||
|
||||
// 获取图片信息
|
||||
const image = await getImageByName(projectId, imageName);
|
||||
if (!image) {
|
||||
return NextResponse.json({ error: '图片不存在' }, { status: 404 });
|
||||
}
|
||||
|
||||
// 调用图片数据集生成服务
|
||||
const result = await imageService.generateDatasetForImage(projectId, image.id, question, {
|
||||
model,
|
||||
language,
|
||||
previewOnly
|
||||
});
|
||||
|
||||
return NextResponse.json({
|
||||
success: true,
|
||||
answer: result.answer,
|
||||
dataset: result.dataset
|
||||
});
|
||||
} catch (error) {
|
||||
console.error('Failed to generate image dataset:', error);
|
||||
return NextResponse.json({ error: error.message || 'Failed to generate dataset' }, { status: 500 });
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,41 @@
|
||||
import { NextResponse } from 'next/server';
|
||||
import { PrismaClient } from '@prisma/client';
|
||||
import { getImageDetailWithQuestions } from '@/lib/services/images';
|
||||
|
||||
const prisma = new PrismaClient();
|
||||
|
||||
// 获取下一个有未标注问题的图片
|
||||
export async function GET(request, { params }) {
|
||||
try {
|
||||
const { projectId } = params;
|
||||
|
||||
// 查找第一个有未标注问题的图片
|
||||
const unansweredQuestion = await prisma.questions.findFirst({
|
||||
where: {
|
||||
projectId,
|
||||
imageId: {
|
||||
not: null
|
||||
},
|
||||
answered: false
|
||||
}
|
||||
});
|
||||
|
||||
if (!unansweredQuestion) {
|
||||
return NextResponse.json({
|
||||
success: true,
|
||||
data: null
|
||||
});
|
||||
}
|
||||
|
||||
// 调用服务层获取图片详情
|
||||
const imageData = await getImageDetailWithQuestions(projectId, unansweredQuestion.imageId);
|
||||
|
||||
return NextResponse.json({
|
||||
success: true,
|
||||
data: imageData
|
||||
});
|
||||
} catch (error) {
|
||||
console.error('Failed to get next unanswered image:', error);
|
||||
return NextResponse.json({ error: error.message || 'Failed to get next unanswered image' }, { status: 500 });
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,98 @@
|
||||
import { NextResponse } from 'next/server';
|
||||
import { getProjectPath } from '@/lib/db/base';
|
||||
import { importImagesFromDirectories } from '@/lib/services/images';
|
||||
import fs from 'fs/promises';
|
||||
import path from 'path';
|
||||
import { savePdfAsImages } from '@/lib/util/file';
|
||||
|
||||
// PDF 转图片并导入
|
||||
export async function POST(request, { params }) {
|
||||
let tempPdfPath = null;
|
||||
let tempImagesDir = null;
|
||||
|
||||
try {
|
||||
const { projectId } = params;
|
||||
const formData = await request.formData();
|
||||
const pdfFile = formData.get('file');
|
||||
|
||||
if (!pdfFile) {
|
||||
return NextResponse.json({ error: '请选择 PDF 文件' }, { status: 400 });
|
||||
}
|
||||
|
||||
if (!pdfFile.name.toLowerCase().endsWith('.pdf')) {
|
||||
return NextResponse.json({ error: '只支持 PDF 文件' }, { status: 400 });
|
||||
}
|
||||
|
||||
const projectPath = await getProjectPath(projectId);
|
||||
const tempDir = path.join(projectPath, 'temp');
|
||||
await fs.mkdir(tempDir, { recursive: true });
|
||||
|
||||
// 1. 保存 PDF 到临时目录
|
||||
tempPdfPath = path.join(tempDir, `temp_${Date.now()}_${pdfFile.name}`);
|
||||
const pdfBuffer = Buffer.from(await pdfFile.arrayBuffer());
|
||||
await fs.writeFile(tempPdfPath, pdfBuffer);
|
||||
|
||||
// 2. 创建临时图片目录
|
||||
tempImagesDir = path.join(tempDir, `pdf_images_${Date.now()}`);
|
||||
await fs.mkdir(tempImagesDir, { recursive: true });
|
||||
|
||||
// 3. 调用 pdf2md-js 转换 PDF 为图片
|
||||
console.log('开始转换 PDF 为图片...');
|
||||
const imagePaths = await savePdfAsImages(tempPdfPath, tempImagesDir, 3);
|
||||
console.log('PDF 转换完成，生成图片数量:', imagePaths.length);
|
||||
|
||||
if (!imagePaths || imagePaths.length === 0) {
|
||||
throw new Error('PDF 转换失败，未生成图片');
|
||||
}
|
||||
|
||||
// 4. 直接调用服务层导入图片
|
||||
const importResult = await importImagesFromDirectories(projectId, [tempImagesDir]);
|
||||
|
||||
// 5. 清理临时文件
|
||||
try {
|
||||
if (tempPdfPath) {
|
||||
await fs.unlink(tempPdfPath);
|
||||
}
|
||||
if (tempImagesDir) {
|
||||
const tempImages = await fs.readdir(tempImagesDir);
|
||||
for (const img of tempImages) {
|
||||
await fs.unlink(path.join(tempImagesDir, img));
|
||||
}
|
||||
await fs.rmdir(tempImagesDir);
|
||||
}
|
||||
const tempDirContents = await fs.readdir(tempDir);
|
||||
if (tempDirContents.length === 0) {
|
||||
await fs.rmdir(tempDir);
|
||||
}
|
||||
} catch (cleanupErr) {
|
||||
console.warn('清理临时文件失败:', cleanupErr);
|
||||
}
|
||||
|
||||
return NextResponse.json({
|
||||
success: true,
|
||||
count: importResult.count,
|
||||
images: importResult.images,
|
||||
pdfName: pdfFile.name
|
||||
});
|
||||
} catch (error) {
|
||||
console.error('Failed to convert PDF:', error);
|
||||
|
||||
// 清理临时文件
|
||||
try {
|
||||
if (tempPdfPath) {
|
||||
await fs.unlink(tempPdfPath).catch(() => {});
|
||||
}
|
||||
if (tempImagesDir) {
|
||||
const tempImages = await fs.readdir(tempImagesDir).catch(() => []);
|
||||
for (const img of tempImages) {
|
||||
await fs.unlink(path.join(tempImagesDir, img)).catch(() => {});
|
||||
}
|
||||
await fs.rmdir(tempImagesDir).catch(() => {});
|
||||
}
|
||||
} catch (cleanupErr) {
|
||||
console.warn('清理临时文件失败:', cleanupErr);
|
||||
}
|
||||
|
||||
return NextResponse.json({ error: error.message || 'Failed to convert PDF' }, { status: 500 });
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,40 @@
|
||||
import { NextResponse } from 'next/server';
|
||||
import { getImageByName } from '@/lib/db/images';
|
||||
import imageService from '@/lib/services/images';
|
||||
|
||||
// 生成图片问题
|
||||
export async function POST(request, { params }) {
|
||||
try {
|
||||
const { projectId } = params;
|
||||
const { imageName, count = 3, model, language = 'zh' } = await request.json();
|
||||
|
||||
if (!imageName) {
|
||||
return NextResponse.json({ error: '缺少图片名称' }, { status: 400 });
|
||||
}
|
||||
|
||||
if (!model) {
|
||||
return NextResponse.json({ error: '请选择一个视觉模型' }, { status: 400 });
|
||||
}
|
||||
|
||||
// 获取图片信息
|
||||
const image = await getImageByName(projectId, imageName);
|
||||
if (!image) {
|
||||
return NextResponse.json({ error: '图片不存在' }, { status: 404 });
|
||||
}
|
||||
|
||||
// 调用图片问题生成服务
|
||||
const result = await imageService.generateQuestionsForImage(projectId, image.id, {
|
||||
model,
|
||||
language,
|
||||
count
|
||||
});
|
||||
|
||||
return NextResponse.json({
|
||||
success: true,
|
||||
questions: result.questions
|
||||
});
|
||||
} catch (error) {
|
||||
console.error('Failed to generate image questions:', error);
|
||||
return NextResponse.json({ error: error.message || 'Failed to generate questions' }, { status: 500 });
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,92 @@
|
||||
import { NextResponse } from 'next/server';
|
||||
import { getImages, deleteImage, getImageDetail } from '@/lib/db/images';
|
||||
import { getProjectPath } from '@/lib/db/base';
|
||||
import { db } from '@/lib/db/index';
|
||||
import { importImagesFromDirectories } from '@/lib/services/images';
|
||||
import fs from 'fs/promises';
|
||||
import path from 'path';
|
||||
|
||||
// 获取图片列表
|
||||
export async function GET(request, { params }) {
|
||||
try {
|
||||
const { projectId } = params;
|
||||
const { searchParams } = new URL(request.url);
|
||||
|
||||
const page = parseInt(searchParams.get('page')) || 1;
|
||||
const pageSize = parseInt(searchParams.get('pageSize')) || 20;
|
||||
const imageName = searchParams.get('imageName') || '';
|
||||
const hasQuestions = searchParams.get('hasQuestions');
|
||||
const hasDatasets = searchParams.get('hasDatasets');
|
||||
const simple = searchParams.get('simple');
|
||||
|
||||
const result = await getImages(projectId, page, pageSize, imageName, hasQuestions, hasDatasets, simple);
|
||||
|
||||
return NextResponse.json(result);
|
||||
} catch (error) {
|
||||
console.error('Failed to get images:', error);
|
||||
return NextResponse.json({ error: error.message || 'Failed to get images' }, { status: 500 });
|
||||
}
|
||||
}
|
||||
|
||||
// 导入图片
|
||||
export async function POST(request, { params }) {
|
||||
try {
|
||||
const { projectId } = params;
|
||||
const { directories } = await request.json();
|
||||
|
||||
// 调用服务层处理图片导入
|
||||
const result = await importImagesFromDirectories(projectId, directories);
|
||||
|
||||
return NextResponse.json(result);
|
||||
} catch (error) {
|
||||
console.error('Failed to import images:', error);
|
||||
return NextResponse.json({ error: error.message || 'Failed to import images' }, { status: 500 });
|
||||
}
|
||||
}
|
||||
|
||||
// 删除图片
|
||||
export async function DELETE(request, { params }) {
|
||||
try {
|
||||
const { projectId } = params;
|
||||
const { searchParams } = new URL(request.url);
|
||||
const imageId = searchParams.get('imageId');
|
||||
|
||||
if (!imageId) {
|
||||
return NextResponse.json({ error: '缺少图片ID' }, { status: 400 });
|
||||
}
|
||||
|
||||
// 获取图片信息
|
||||
const image = await getImageDetail(imageId);
|
||||
|
||||
if (!image) {
|
||||
return NextResponse.json({ error: '图片不存在' }, { status: 404 });
|
||||
}
|
||||
|
||||
// 删除关联的数据集
|
||||
await db.imageDatasets.deleteMany({
|
||||
where: { imageId }
|
||||
});
|
||||
|
||||
// 删除关联的问题
|
||||
await db.questions.deleteMany({
|
||||
where: { imageId }
|
||||
});
|
||||
|
||||
// 删除文件
|
||||
const projectPath = await getProjectPath(projectId);
|
||||
const filePath = path.join(projectPath, 'images', image.imageName);
|
||||
try {
|
||||
await fs.unlink(filePath);
|
||||
} catch (err) {
|
||||
console.warn('删除文件失败:', err);
|
||||
}
|
||||
|
||||
// 删除数据库记录
|
||||
await deleteImage(imageId);
|
||||
|
||||
return NextResponse.json({ success: true });
|
||||
} catch (error) {
|
||||
console.error('Failed to delete image:', error);
|
||||
return NextResponse.json({ error: error.message || 'Failed to delete image' }, { status: 500 });
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,127 @@
|
||||
import { NextResponse } from 'next/server';
|
||||
import { getProjectPath } from '@/lib/db/base';
|
||||
import { importImagesFromDirectories } from '@/lib/services/images';
|
||||
import fs from 'fs/promises';
|
||||
import path from 'path';
|
||||
import AdmZip from 'adm-zip';
|
||||
|
||||
// 压缩包解压并导入图片
|
||||
export async function POST(request, { params }) {
|
||||
let tempZipPath = null;
|
||||
let tempExtractDir = null;
|
||||
|
||||
try {
|
||||
const { projectId } = params;
|
||||
const formData = await request.formData();
|
||||
const zipFile = formData.get('file');
|
||||
|
||||
if (!zipFile) {
|
||||
return NextResponse.json({ error: '请选择压缩包文件' }, { status: 400 });
|
||||
}
|
||||
|
||||
if (!zipFile.name.toLowerCase().endsWith('.zip')) {
|
||||
return NextResponse.json({ error: '只支持 ZIP 格式的压缩包' }, { status: 400 });
|
||||
}
|
||||
|
||||
const projectPath = await getProjectPath(projectId);
|
||||
const tempDir = path.join(projectPath, 'temp');
|
||||
await fs.mkdir(tempDir, { recursive: true });
|
||||
|
||||
// 1. 保存压缩包到临时目录
|
||||
tempZipPath = path.join(tempDir, `temp_${Date.now()}_${zipFile.name}`);
|
||||
const zipBuffer = Buffer.from(await zipFile.arrayBuffer());
|
||||
await fs.writeFile(tempZipPath, zipBuffer);
|
||||
|
||||
// 2. 创建临时解压目录
|
||||
tempExtractDir = path.join(tempDir, `zip_extract_${Date.now()}`);
|
||||
await fs.mkdir(tempExtractDir, { recursive: true });
|
||||
|
||||
// 3. 使用 adm-zip 解压文件
|
||||
console.log('开始解压压缩包...');
|
||||
const zip = new AdmZip(tempZipPath);
|
||||
const zipEntries = zip.getEntries();
|
||||
|
||||
// 支持的图片扩展名
|
||||
const imageExtensions = ['.jpg', '.jpeg', '.png', '.gif', '.bmp', '.webp', '.svg'];
|
||||
let extractedCount = 0;
|
||||
|
||||
// 遍历压缩包中的所有文件
|
||||
for (const entry of zipEntries) {
|
||||
// 跳过目录和隐藏文件
|
||||
if (
|
||||
entry.isDirectory ||
|
||||
entry.entryName.startsWith('__MACOSX') ||
|
||||
path.basename(entry.entryName).startsWith('.')
|
||||
) {
|
||||
continue;
|
||||
}
|
||||
|
||||
const ext = path.extname(entry.entryName).toLowerCase();
|
||||
if (imageExtensions.includes(ext)) {
|
||||
// 提取文件名（不包含路径）
|
||||
const fileName = path.basename(entry.entryName);
|
||||
const targetPath = path.join(tempExtractDir, fileName);
|
||||
|
||||
// 解压文件
|
||||
zip.extractEntryTo(entry, tempExtractDir, false, true, false, fileName);
|
||||
extractedCount++;
|
||||
}
|
||||
}
|
||||
|
||||
console.log(`压缩包解压完成，提取图片数量: ${extractedCount}`);
|
||||
|
||||
if (extractedCount === 0) {
|
||||
throw new Error('压缩包中没有找到支持的图片文件');
|
||||
}
|
||||
|
||||
// 4. 调用服务层导入图片
|
||||
const importResult = await importImagesFromDirectories(projectId, [tempExtractDir]);
|
||||
|
||||
// 5. 清理临时文件
|
||||
try {
|
||||
if (tempZipPath) {
|
||||
await fs.unlink(tempZipPath);
|
||||
}
|
||||
if (tempExtractDir) {
|
||||
const tempImages = await fs.readdir(tempExtractDir);
|
||||
for (const img of tempImages) {
|
||||
await fs.unlink(path.join(tempExtractDir, img));
|
||||
}
|
||||
await fs.rmdir(tempExtractDir);
|
||||
}
|
||||
const tempDirContents = await fs.readdir(tempDir);
|
||||
if (tempDirContents.length === 0) {
|
||||
await fs.rmdir(tempDir);
|
||||
}
|
||||
} catch (cleanupErr) {
|
||||
console.warn('清理临时文件失败:', cleanupErr);
|
||||
}
|
||||
|
||||
return NextResponse.json({
|
||||
success: true,
|
||||
count: importResult.count,
|
||||
images: importResult.images,
|
||||
zipName: zipFile.name
|
||||
});
|
||||
} catch (error) {
|
||||
console.error('Failed to import ZIP:', error);
|
||||
|
||||
// 清理临时文件
|
||||
try {
|
||||
if (tempZipPath) {
|
||||
await fs.unlink(tempZipPath).catch(() => {});
|
||||
}
|
||||
if (tempExtractDir) {
|
||||
const tempImages = await fs.readdir(tempExtractDir).catch(() => []);
|
||||
for (const img of tempImages) {
|
||||
await fs.unlink(path.join(tempExtractDir, img)).catch(() => {});
|
||||
}
|
||||
await fs.rmdir(tempExtractDir).catch(() => {});
|
||||
}
|
||||
} catch (cleanupErr) {
|
||||
console.warn('清理临时文件失败:', cleanupErr);
|
||||
}
|
||||
|
||||
return NextResponse.json({ error: error.message || 'Failed to import ZIP' }, { status: 500 });
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,27 @@
|
||||
import { NextResponse } from 'next/server';
|
||||
import path from 'path';
|
||||
import fs from 'fs';
|
||||
import { getProjectRoot } from '@/lib/db/base';
|
||||
|
||||
export async function GET(request, { params }) {
|
||||
try {
|
||||
const { projectId } = params;
|
||||
if (!projectId) {
|
||||
return NextResponse.json({ error: 'The project ID cannot be empty' }, { status: 400 });
|
||||
}
|
||||
|
||||
const projectRoot = await getProjectRoot();
|
||||
const projectPath = path.join(projectRoot, projectId);
|
||||
const configPath = path.join(projectPath, 'dataset_info.json');
|
||||
|
||||
const exists = fs.existsSync(configPath);
|
||||
|
||||
return NextResponse.json({
|
||||
exists,
|
||||
configPath: exists ? configPath : null
|
||||
});
|
||||
} catch (error) {
|
||||
console.error('Error checking Llama Factory config:', String(error));
|
||||
return NextResponse.json({ error: error.message }, { status: 500 });
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,141 @@
|
||||
import { NextResponse } from 'next/server';
|
||||
import path from 'path';
|
||||
import fs from 'fs';
|
||||
import { getProjectRoot } from '@/lib/db/base';
|
||||
import { getDatasets } from '@/lib/db/datasets';
|
||||
|
||||
export async function POST(request, { params }) {
|
||||
try {
|
||||
const { projectId } = params;
|
||||
const { formatType, systemPrompt, confirmedOnly, includeCOT, reasoningLanguage } = await request.json();
|
||||
|
||||
if (!projectId) {
|
||||
return NextResponse.json({ error: 'The project ID cannot be empty' }, { status: 400 });
|
||||
}
|
||||
|
||||
// 获取项目根目录
|
||||
const projectRoot = await getProjectRoot();
|
||||
const projectPath = path.join(projectRoot, projectId);
|
||||
const configPath = path.join(projectPath, 'dataset_info.json');
|
||||
const alpacaPath = path.join(projectPath, 'alpaca.json');
|
||||
const sharegptPath = path.join(projectPath, 'sharegpt.json');
|
||||
const multilingualThinkingPath = path.join(projectPath, 'multilingual-thinking.json');
|
||||
|
||||
// 获取数据集
|
||||
let datasets = await getDatasets(projectId, !!confirmedOnly);
|
||||
|
||||
// 创建 dataset_info.json 配置
|
||||
const config = {
|
||||
[`[Easy Dataset] [${projectId}] Alpaca`]: {
|
||||
file_name: 'alpaca.json',
|
||||
columns: {
|
||||
prompt: 'instruction',
|
||||
query: 'input',
|
||||
response: 'output',
|
||||
system: 'system'
|
||||
}
|
||||
},
|
||||
[`[Easy Dataset] [${projectId}] ShareGPT`]: {
|
||||
file_name: 'sharegpt.json',
|
||||
formatting: 'sharegpt',
|
||||
columns: {
|
||||
messages: 'messages'
|
||||
},
|
||||
tags: {
|
||||
role_tag: 'role',
|
||||
content_tag: 'content',
|
||||
user_tag: 'user',
|
||||
assistant_tag: 'assistant',
|
||||
system_tag: 'system'
|
||||
}
|
||||
},
|
||||
[`[Easy Dataset] [${projectId}] multilingual-thinking`]: {
|
||||
file_name: 'multilingual-thinking.json',
|
||||
formatting: 'multilingual-thinking',
|
||||
columns: {
|
||||
messages: 'messages'
|
||||
},
|
||||
tags: {
|
||||
role_tag: 'role',
|
||||
content_tag: 'content',
|
||||
user_tag: 'user',
|
||||
assistant_tag: 'assistant',
|
||||
system_tag: 'system'
|
||||
}
|
||||
}
|
||||
};
|
||||
|
||||
// 生成数据文件
|
||||
const alpacaData = datasets.map(({ question, answer, cot }) => ({
|
||||
instruction: question,
|
||||
input: '',
|
||||
output: cot && includeCOT ? `<think>${cot}</think>\n${answer}` : answer,
|
||||
system: systemPrompt || ''
|
||||
}));
|
||||
|
||||
const sharegptData = datasets.map(({ question, answer, cot }) => {
|
||||
const messages = [];
|
||||
if (systemPrompt) {
|
||||
messages.push({
|
||||
role: 'system',
|
||||
content: systemPrompt
|
||||
});
|
||||
}
|
||||
messages.push({
|
||||
role: 'user',
|
||||
content: question
|
||||
});
|
||||
messages.push({
|
||||
role: 'assistant',
|
||||
content: cot && includeCOT ? `<think>${cot}</think>\n${answer}` : answer
|
||||
});
|
||||
return { messages };
|
||||
});
|
||||
const multilingualThinkingData = datasets.map(({ question, answer, cot }) => ({
|
||||
reasoning_language: reasoningLanguage ? reasoningLanguage : 'English',
|
||||
developer: systemPrompt ? systemPrompt : '', // system prompt (may be empty)
|
||||
user: question,
|
||||
analysis: includeCOT && cot ? cot : null, // null if no COT
|
||||
final: answer,
|
||||
messages: [
|
||||
{
|
||||
content: systemPrompt ? systemPrompt : '',
|
||||
role: 'system',
|
||||
thinking: null
|
||||
},
|
||||
{
|
||||
content: question,
|
||||
role: 'user',
|
||||
thinking: null
|
||||
},
|
||||
{
|
||||
content: answer,
|
||||
role: 'assistant',
|
||||
thinking: includeCOT && cot ? cot : null
|
||||
}
|
||||
]
|
||||
}));
|
||||
|
||||
const multilingualThinkingLines = multilingualThinkingData.map(item => JSON.stringify(item, null, 2)).join('\n');
|
||||
|
||||
await fs.promises.writeFile(multilingualThinkingPath, multilingualThinkingLines, 'utf8');
|
||||
|
||||
// 写入文件
|
||||
await fs.promises.writeFile(configPath, JSON.stringify(config, null, 2));
|
||||
await fs.promises.writeFile(alpacaPath, JSON.stringify(alpacaData, null, 2));
|
||||
await fs.promises.writeFile(sharegptPath, JSON.stringify(sharegptData, null, 2));
|
||||
|
||||
return NextResponse.json({
|
||||
success: true,
|
||||
configPath,
|
||||
files: [
|
||||
{ path: alpacaPath, format: 'alpaca' },
|
||||
{ path: sharegptPath, format: 'sharegpt' },
|
||||
{ path: multilingualThinkingPath, format: 'multilingual-thinking' }
|
||||
]
|
||||
});
|
||||
} catch (error) {
|
||||
console.error('Error generating Llama Factory config:', String(error));
|
||||
return NextResponse.json({ error: error.message }, { status: 500 });
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,18 @@
|
||||
import { NextResponse } from 'next/server';
|
||||
import { deleteModelConfigById } from '@/lib/db/model-config';
|
||||
|
||||
// 删除模型配置
|
||||
export async function DELETE(request, { params }) {
|
||||
try {
|
||||
const { projectId, modelConfigId } = params;
|
||||
// 验证项目 ID
|
||||
if (!projectId) {
|
||||
return NextResponse.json({ error: 'The project ID cannot be empty' }, { status: 400 });
|
||||
}
|
||||
await deleteModelConfigById(modelConfigId);
|
||||
return NextResponse.json(true);
|
||||
} catch (error) {
|
||||
console.error('Error obtaining model configuration:', String(error));
|
||||
return NextResponse.json({ error: 'Failed to obtain model configuration' }, { status: 500 });
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,103 @@
|
||||
import { NextResponse } from 'next/server';
|
||||
import { createInitModelConfig, getModelConfigByProjectId, saveModelConfig } from '@/lib/db/model-config';
|
||||
import { DEFAULT_MODEL_SETTINGS, MODEL_PROVIDERS } from '@/constant/model';
|
||||
import { getProject } from '@/lib/db/projects';
|
||||
import { sortProvidersByPriority } from '@/lib/util/providerLogo';
|
||||
|
||||
function normalizeModelEndpoint(endpoint = '') {
|
||||
let normalizedEndpoint = String(endpoint).trim();
|
||||
if (!normalizedEndpoint) {
|
||||
return '';
|
||||
}
|
||||
if (normalizedEndpoint.includes('/chat/completions')) {
|
||||
normalizedEndpoint = normalizedEndpoint.replace('/chat/completions', '');
|
||||
}
|
||||
return normalizedEndpoint;
|
||||
}
|
||||
|
||||
// 获取模型配置列表
|
||||
export async function GET(request, { params }) {
|
||||
try {
|
||||
const { projectId } = params;
|
||||
// 验证项目 ID
|
||||
if (!projectId) {
|
||||
return NextResponse.json({ error: 'The project ID cannot be empty' }, { status: 400 });
|
||||
}
|
||||
let modelConfigList = await getModelConfigByProjectId(projectId);
|
||||
if (!modelConfigList || modelConfigList.length === 0) {
|
||||
let insertModelConfigList = [];
|
||||
const sortedProviders = sortProvidersByPriority(MODEL_PROVIDERS, item => item.id);
|
||||
sortedProviders.forEach(item => {
|
||||
let data = {
|
||||
projectId: projectId,
|
||||
providerId: item.id,
|
||||
providerName: item.name,
|
||||
endpoint: item.defaultEndpoint,
|
||||
apiKey: '',
|
||||
modelId: '',
|
||||
modelName: '',
|
||||
type: 'text',
|
||||
temperature: DEFAULT_MODEL_SETTINGS.temperature,
|
||||
maxTokens: DEFAULT_MODEL_SETTINGS.maxTokens,
|
||||
topK: 0,
|
||||
topP: DEFAULT_MODEL_SETTINGS.topP,
|
||||
status: 1
|
||||
};
|
||||
insertModelConfigList.push(data);
|
||||
});
|
||||
modelConfigList = await createInitModelConfig(insertModelConfigList);
|
||||
}
|
||||
modelConfigList = sortProvidersByPriority(modelConfigList, item => item.providerId);
|
||||
let project = await getProject(projectId);
|
||||
return NextResponse.json({ data: modelConfigList, defaultModelConfigId: project.defaultModelConfigId });
|
||||
} catch (error) {
|
||||
console.error('Error obtaining model configuration:', String(error));
|
||||
return NextResponse.json({ error: 'Failed to obtain model configuration' }, { status: 500 });
|
||||
}
|
||||
}
|
||||
|
||||
// 保存模型配置
|
||||
export async function POST(request, { params }) {
|
||||
try {
|
||||
const { projectId } = params;
|
||||
|
||||
// 验证项目 ID
|
||||
if (!projectId) {
|
||||
return NextResponse.json({ error: 'The project ID cannot be empty' }, { status: 400 });
|
||||
}
|
||||
// 获取请求体
|
||||
const modelConfig = await request.json();
|
||||
|
||||
// 验证请求体
|
||||
if (!modelConfig) {
|
||||
return NextResponse.json({ error: 'The model configuration cannot be empty ' }, { status: 400 });
|
||||
}
|
||||
modelConfig.projectId = projectId;
|
||||
modelConfig.endpoint = normalizeModelEndpoint(modelConfig.endpoint);
|
||||
// 如果没有 modelId，使用 modelName 补齐（兼容旧逻辑）
|
||||
if (!modelConfig.modelId && modelConfig.modelName) {
|
||||
modelConfig.modelId = modelConfig.modelName;
|
||||
}
|
||||
// 如果没有 modelName，使用 modelId 补齐
|
||||
if (!modelConfig.modelName && modelConfig.modelId) {
|
||||
modelConfig.modelName = modelConfig.modelId;
|
||||
}
|
||||
if (!modelConfig.topK) {
|
||||
modelConfig.topK = 0;
|
||||
}
|
||||
if (!modelConfig.status) {
|
||||
modelConfig.status = 1;
|
||||
}
|
||||
const parsedMaxTokens = Number(modelConfig.maxTokens ?? DEFAULT_MODEL_SETTINGS.maxTokens);
|
||||
if (!Number.isInteger(parsedMaxTokens) || parsedMaxTokens < 1) {
|
||||
return NextResponse.json({ error: 'maxTokens must be a positive integer' }, { status: 400 });
|
||||
}
|
||||
modelConfig.maxTokens = parsedMaxTokens;
|
||||
const res = await saveModelConfig(modelConfig);
|
||||
|
||||
return NextResponse.json(res);
|
||||
} catch (error) {
|
||||
console.error('Error updating model configuration:', String(error));
|
||||
return NextResponse.json({ error: 'Failed to update model configuration' }, { status: 500 });
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,173 @@
|
||||
import { NextResponse } from 'next/server';
|
||||
import { getProjectRoot } from '@/lib/db/base';
|
||||
import path from 'path';
|
||||
import fs from 'fs/promises';
|
||||
|
||||
export async function GET(request, { params }) {
|
||||
try {
|
||||
const { projectId, modelId } = params;
|
||||
|
||||
// 验证项目ID和模型ID
|
||||
if (!projectId || !modelId) {
|
||||
return NextResponse.json({ error: 'The project ID and model ID cannot be empty' }, { status: 400 });
|
||||
}
|
||||
|
||||
// 获取项目根目录
|
||||
const projectRoot = await getProjectRoot();
|
||||
const projectPath = path.join(projectRoot, projectId);
|
||||
|
||||
// 检查项目是否存在
|
||||
try {
|
||||
await fs.access(projectPath);
|
||||
} catch (error) {
|
||||
return NextResponse.json({ error: 'The project does not exist' }, { status: 404 });
|
||||
}
|
||||
|
||||
// 获取模型配置文件路径
|
||||
const modelConfigPath = path.join(projectPath, 'model-config.json');
|
||||
|
||||
// 检查模型配置文件是否存在
|
||||
try {
|
||||
await fs.access(modelConfigPath);
|
||||
} catch (error) {
|
||||
return NextResponse.json({ error: 'The model configuration does not exist' }, { status: 404 });
|
||||
}
|
||||
|
||||
// 读取模型配置文件
|
||||
const modelConfigData = await fs.readFile(modelConfigPath, 'utf-8');
|
||||
const modelConfig = JSON.parse(modelConfigData);
|
||||
|
||||
// 查找指定ID的模型
|
||||
const model = modelConfig.find(model => model.id === modelId);
|
||||
|
||||
if (!model) {
|
||||
return NextResponse.json({ error: 'The model does not exist' }, { status: 404 });
|
||||
}
|
||||
|
||||
return NextResponse.json(model);
|
||||
} catch (error) {
|
||||
console.error('Error getting model:', String(error));
|
||||
return NextResponse.json({ error: 'Failed to get model' }, { status: 500 });
|
||||
}
|
||||
}
|
||||
|
||||
export async function PUT(request, { params }) {
|
||||
try {
|
||||
const { projectId, modelId } = params;
|
||||
|
||||
// 验证项目ID和模型ID
|
||||
if (!projectId || !modelId) {
|
||||
return NextResponse.json({ error: 'The project ID and model ID cannot be empty' }, { status: 400 });
|
||||
}
|
||||
|
||||
// 获取请求体
|
||||
const modelData = await request.json();
|
||||
|
||||
// 验证请求体
|
||||
if (!modelData || !modelData.provider || !modelData.name) {
|
||||
return NextResponse.json({ error: 'The model data is incomplete' }, { status: 400 });
|
||||
}
|
||||
|
||||
// 获取项目根目录
|
||||
const projectRoot = await getProjectRoot();
|
||||
const projectPath = path.join(projectRoot, projectId);
|
||||
|
||||
// 检查项目是否存在
|
||||
try {
|
||||
await fs.access(projectPath);
|
||||
} catch (error) {
|
||||
return NextResponse.json({ error: 'The project does not exist' }, { status: 404 });
|
||||
}
|
||||
|
||||
// 获取模型配置文件路径
|
||||
const modelConfigPath = path.join(projectPath, 'model-config.json');
|
||||
|
||||
// 读取模型配置文件
|
||||
let modelConfig = [];
|
||||
try {
|
||||
const modelConfigData = await fs.readFile(modelConfigPath, 'utf-8');
|
||||
modelConfig = JSON.parse(modelConfigData);
|
||||
} catch (error) {
|
||||
// 如果文件不存在，创建一个空数组
|
||||
}
|
||||
|
||||
// 更新模型数据
|
||||
const modelIndex = modelConfig.findIndex(model => model.id === modelId);
|
||||
|
||||
if (modelIndex >= 0) {
|
||||
// 更新现有模型
|
||||
modelConfig[modelIndex] = {
|
||||
...modelConfig[modelIndex],
|
||||
...modelData,
|
||||
id: modelId // 确保ID不变
|
||||
};
|
||||
} else {
|
||||
// 添加新模型
|
||||
modelConfig.push({
|
||||
...modelData,
|
||||
id: modelId
|
||||
});
|
||||
}
|
||||
|
||||
// 写入模型配置文件
|
||||
await fs.writeFile(modelConfigPath, JSON.stringify(modelConfig, null, 2), 'utf-8');
|
||||
|
||||
return NextResponse.json({ message: 'Model configuration updated successfully' });
|
||||
} catch (error) {
|
||||
console.error('Error updating model configuration:', String(error));
|
||||
return NextResponse.json({ error: 'Failed to update model configuration' }, { status: 500 });
|
||||
}
|
||||
}
|
||||
|
||||
export async function DELETE(request, { params }) {
|
||||
try {
|
||||
const { projectId, modelId } = params;
|
||||
|
||||
// 验证项目ID和模型ID
|
||||
if (!projectId || !modelId) {
|
||||
return NextResponse.json({ error: 'The project ID and model ID cannot be empty' }, { status: 400 });
|
||||
}
|
||||
|
||||
// 获取项目根目录
|
||||
const projectRoot = await getProjectRoot();
|
||||
const projectPath = path.join(projectRoot, projectId);
|
||||
|
||||
// 检查项目是否存在
|
||||
try {
|
||||
await fs.access(projectPath);
|
||||
} catch (error) {
|
||||
return NextResponse.json({ error: 'The project does not exist' }, { status: 404 });
|
||||
}
|
||||
|
||||
// 获取模型配置文件路径
|
||||
const modelConfigPath = path.join(projectPath, 'model-config.json');
|
||||
|
||||
// 检查模型配置文件是否存在
|
||||
try {
|
||||
await fs.access(modelConfigPath);
|
||||
} catch (error) {
|
||||
return NextResponse.json({ error: 'The model configuration does not exist' }, { status: 404 });
|
||||
}
|
||||
|
||||
// 读取模型配置文件
|
||||
const modelConfigData = await fs.readFile(modelConfigPath, 'utf-8');
|
||||
let modelConfig = JSON.parse(modelConfigData);
|
||||
|
||||
// 过滤掉要删除的模型
|
||||
const initialLength = modelConfig.length;
|
||||
modelConfig = modelConfig.filter(model => model.id !== modelId);
|
||||
|
||||
// 检查是否找到并删除了模型
|
||||
if (modelConfig.length === initialLength) {
|
||||
return NextResponse.json({ error: 'The model does not exist' }, { status: 404 });
|
||||
}
|
||||
|
||||
// 写入模型配置文件
|
||||
await fs.writeFile(modelConfigPath, JSON.stringify(modelConfig, null, 2), 'utf-8');
|
||||
|
||||
return NextResponse.json({ message: 'Model deleted successfully' });
|
||||
} catch (error) {
|
||||
console.error('Error deleting model:', String(error));
|
||||
return NextResponse.json({ error: 'Failed to delete model' }, { status: 500 });
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,89 @@
|
||||
import { NextResponse } from 'next/server';
|
||||
import path from 'path';
|
||||
import fs from 'fs/promises';
|
||||
import { getProjectRoot } from '@/lib/db/base';
|
||||
|
||||
// 获取模型配置
|
||||
export async function GET(request, { params }) {
|
||||
try {
|
||||
const { projectId } = params;
|
||||
|
||||
// 验证项目 ID
|
||||
if (!projectId) {
|
||||
return NextResponse.json({ error: 'The project ID cannot be empty' }, { status: 400 });
|
||||
}
|
||||
|
||||
// 获取项目根目录
|
||||
const projectRoot = await getProjectRoot();
|
||||
const projectPath = path.join(projectRoot, projectId);
|
||||
|
||||
// 检查项目是否存在
|
||||
try {
|
||||
await fs.access(projectPath);
|
||||
} catch (error) {
|
||||
return NextResponse.json({ error: 'The project does not exist' }, { status: 404 });
|
||||
}
|
||||
|
||||
// 获取模型配置文件路径
|
||||
const modelConfigPath = path.join(projectPath, 'model-config.json');
|
||||
|
||||
// 检查模型配置文件是否存在
|
||||
try {
|
||||
await fs.access(modelConfigPath);
|
||||
} catch (error) {
|
||||
// 如果配置文件不存在，返回默认配置
|
||||
return NextResponse.json([]);
|
||||
}
|
||||
|
||||
// 读取模型配置文件
|
||||
const modelConfigData = await fs.readFile(modelConfigPath, 'utf-8');
|
||||
const modelConfig = JSON.parse(modelConfigData);
|
||||
|
||||
return NextResponse.json(modelConfig);
|
||||
} catch (error) {
|
||||
console.error('Error obtaining model configuration:', String(error));
|
||||
return NextResponse.json({ error: 'Failed to obtain model configuration' }, { status: 500 });
|
||||
}
|
||||
}
|
||||
|
||||
// 更新模型配置
|
||||
export async function PUT(request, { params }) {
|
||||
try {
|
||||
const { projectId } = params;
|
||||
|
||||
// 验证项目 ID
|
||||
if (!projectId) {
|
||||
return NextResponse.json({ error: 'The project ID cannot be empty' }, { status: 400 });
|
||||
}
|
||||
|
||||
// 获取请求体
|
||||
const modelConfig = await request.json();
|
||||
|
||||
// 验证请求体
|
||||
if (!modelConfig || !Array.isArray(modelConfig)) {
|
||||
return NextResponse.json({ error: 'The model configuration must be an array' }, { status: 400 });
|
||||
}
|
||||
|
||||
// 获取项目根目录
|
||||
const projectRoot = await getProjectRoot();
|
||||
const projectPath = path.join(projectRoot, projectId);
|
||||
|
||||
// 检查项目是否存在
|
||||
try {
|
||||
await fs.access(projectPath);
|
||||
} catch (error) {
|
||||
return NextResponse.json({ error: 'The project does not exist' }, { status: 404 });
|
||||
}
|
||||
|
||||
// 获取模型配置文件路径
|
||||
const modelConfigPath = path.join(projectPath, 'model-config.json');
|
||||
|
||||
// 写入模型配置文件
|
||||
await fs.writeFile(modelConfigPath, JSON.stringify(modelConfig, null, 2), 'utf-8');
|
||||
|
||||
return NextResponse.json({ message: 'Model configuration updated successfully' });
|
||||
} catch (error) {
|
||||
console.error('Error updating model configuration:', String(error));
|
||||
return NextResponse.json({ error: 'Failed to update model configuration' }, { status: 500 });
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,99 @@
|
||||
import { NextResponse } from 'next/server';
|
||||
import LLMClient from '@/lib/llm/core/index';
|
||||
import { getModelConfigById } from '@/lib/db/model-config';
|
||||
|
||||
async function resolveLatestModelConfig(projectId, incomingModel = {}) {
|
||||
const modelId = incomingModel?.id;
|
||||
if (!modelId) {
|
||||
return incomingModel;
|
||||
}
|
||||
|
||||
try {
|
||||
const latestModelConfig = await getModelConfigById(modelId);
|
||||
if (!latestModelConfig) {
|
||||
return incomingModel;
|
||||
}
|
||||
if (String(latestModelConfig.projectId) !== String(projectId)) {
|
||||
return incomingModel;
|
||||
}
|
||||
|
||||
// Keep transient client-only fields, but force endpoint/auth/model fields to latest DB values.
|
||||
return {
|
||||
...incomingModel,
|
||||
...latestModelConfig
|
||||
};
|
||||
} catch (error) {
|
||||
console.error('Failed to resolve latest model config:', String(error));
|
||||
return incomingModel;
|
||||
}
|
||||
}
|
||||
|
||||
export async function POST(request, { params }) {
|
||||
try {
|
||||
const { projectId } = params;
|
||||
|
||||
// Validate project ID.
|
||||
if (!projectId) {
|
||||
return NextResponse.json({ error: 'The project ID cannot be empty' }, { status: 400 });
|
||||
}
|
||||
|
||||
// Read request payload.
|
||||
const { model, messages } = await request.json();
|
||||
const resolvedModel = await resolveLatestModelConfig(projectId, model);
|
||||
|
||||
// Validate request parameters.
|
||||
if (!resolvedModel) {
|
||||
return NextResponse.json({ error: 'The model parameters cannot be empty' }, { status: 400 });
|
||||
}
|
||||
|
||||
if (!Array.isArray(messages) || messages.length === 0) {
|
||||
return NextResponse.json({ error: 'The message list cannot be empty' }, { status: 400 });
|
||||
}
|
||||
|
||||
// Use custom LLM client.
|
||||
const llmClient = new LLMClient(resolvedModel);
|
||||
|
||||
// Normalize message payload for text + vision models.
|
||||
const formattedMessages = messages.map(msg => {
|
||||
// Plain text message.
|
||||
if (typeof msg.content === 'string') {
|
||||
return {
|
||||
role: msg.role,
|
||||
content: msg.content
|
||||
};
|
||||
}
|
||||
// Multimodal message (e.g. image parts).
|
||||
if (Array.isArray(msg.content)) {
|
||||
return {
|
||||
role: msg.role,
|
||||
content: msg.content
|
||||
};
|
||||
}
|
||||
// Fallback.
|
||||
return {
|
||||
role: msg.role,
|
||||
content: msg.content
|
||||
};
|
||||
});
|
||||
|
||||
// Call LLM API.
|
||||
let response = '';
|
||||
try {
|
||||
const { answer, cot } = await llmClient.getResponseWithCOT(formattedMessages.filter(f => f.role !== 'error'));
|
||||
response = `<think>${cot}</think>${answer}`;
|
||||
} catch (error) {
|
||||
console.error('Failed to call LLM API:', String(error));
|
||||
return NextResponse.json(
|
||||
{
|
||||
error: `Failed to call ${resolvedModel.modelId || resolvedModel.modelName || 'unknown'} model: ${error.message}`
|
||||
},
|
||||
{ status: 500 }
|
||||
);
|
||||
}
|
||||
|
||||
return NextResponse.json({ response });
|
||||
} catch (error) {
|
||||
console.error('Failed to process chat request:', String(error));
|
||||
return NextResponse.json({ error: `Failed to process chat request: ${error.message}` }, { status: 500 });
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,89 @@
|
||||
import { NextResponse } from 'next/server';
|
||||
import LLMClient from '@/lib/llm/core/index';
|
||||
import { getModelConfigById } from '@/lib/db/model-config';
|
||||
|
||||
async function resolveLatestModelConfig(projectId, incomingModel = {}) {
|
||||
const modelId = incomingModel?.id;
|
||||
if (!modelId) {
|
||||
return incomingModel;
|
||||
}
|
||||
|
||||
try {
|
||||
const latestModelConfig = await getModelConfigById(modelId);
|
||||
if (!latestModelConfig) {
|
||||
return incomingModel;
|
||||
}
|
||||
if (String(latestModelConfig.projectId) !== String(projectId)) {
|
||||
return incomingModel;
|
||||
}
|
||||
|
||||
return {
|
||||
...incomingModel,
|
||||
...latestModelConfig
|
||||
};
|
||||
} catch (error) {
|
||||
console.error('Failed to resolve latest model config:', String(error));
|
||||
return incomingModel;
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Streaming chat endpoint.
|
||||
*/
|
||||
export async function POST(request, { params }) {
|
||||
const { projectId } = params;
|
||||
|
||||
try {
|
||||
const body = await request.json();
|
||||
const { model, messages } = body;
|
||||
const resolvedModel = await resolveLatestModelConfig(projectId, model);
|
||||
|
||||
if (!resolvedModel || !messages) {
|
||||
return NextResponse.json({ error: 'Missing necessary parameters' }, { status: 400 });
|
||||
}
|
||||
|
||||
// Use custom LLM client.
|
||||
const llmClient = new LLMClient(resolvedModel);
|
||||
|
||||
// Normalize message payload for text + vision models.
|
||||
const formattedMessages = messages.map(msg => {
|
||||
// Plain text message.
|
||||
if (typeof msg.content === 'string') {
|
||||
return {
|
||||
role: msg.role,
|
||||
content: msg.content
|
||||
};
|
||||
}
|
||||
// Multimodal message (e.g. image parts).
|
||||
if (Array.isArray(msg.content)) {
|
||||
return {
|
||||
role: msg.role,
|
||||
content: msg.content
|
||||
};
|
||||
}
|
||||
// Fallback.
|
||||
return {
|
||||
role: msg.role,
|
||||
content: msg.content
|
||||
};
|
||||
});
|
||||
|
||||
try {
|
||||
// Stream response from provider.
|
||||
const response = await llmClient.chatStreamAPI(formattedMessages.filter(f => f.role !== 'error'));
|
||||
// Return native streaming response.
|
||||
return response;
|
||||
} catch (error) {
|
||||
console.error('Failed to call LLM API:', error);
|
||||
return NextResponse.json(
|
||||
{
|
||||
error: `Failed to call ${resolvedModel.modelId || resolvedModel.modelName || 'unknown'} model: ${error.message}`
|
||||
},
|
||||
{ status: 500 }
|
||||
);
|
||||
}
|
||||
} catch (error) {
|
||||
console.error('Failed to process stream chat request:', String(error));
|
||||
return NextResponse.json({ error: `Failed to process stream chat request: ${error.message}` }, { status: 500 });
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,42 @@
|
||||
import { NextResponse } from 'next/server';
|
||||
import fs from 'fs';
|
||||
import path from 'path';
|
||||
import { getProjectRoot } from '@/lib/db/base';
|
||||
import { getUploadFileInfoById } from '@/lib/db/upload-files';
|
||||
|
||||
// 获取文件内容
|
||||
export async function GET(request, { params }) {
|
||||
try {
|
||||
const { projectId, fileId } = params;
|
||||
|
||||
// 验证参数
|
||||
if (!projectId) {
|
||||
return NextResponse.json({ error: 'Project ID cannot be empty' }, { status: 400 });
|
||||
}
|
||||
|
||||
// 获取项目根目录
|
||||
let fileInfo = await getUploadFileInfoById(fileId);
|
||||
if (!fileInfo) {
|
||||
return NextResponse.json({ error: 'file does not exist' }, { status: 400 });
|
||||
}
|
||||
|
||||
// 获取文件路径
|
||||
let filePath = path.join(fileInfo.path, fileInfo.fileName);
|
||||
if (fileInfo.fileExt !== '.md') {
|
||||
filePath = path.join(fileInfo.path, fileInfo.fileName.replace(/\.[^/.]+$/, '.md'));
|
||||
}
|
||||
//获取文件
|
||||
const buffer = fs.readFileSync(filePath);
|
||||
|
||||
const text = buffer.toString('utf-8');
|
||||
|
||||
return NextResponse.json({
|
||||
fileId: fileId,
|
||||
fileName: fileInfo.fileName,
|
||||
content: text
|
||||
});
|
||||
} catch (error) {
|
||||
console.error('Failed to get text block content:', String(error));
|
||||
return NextResponse.json({ error: error.message || 'Failed to get text block content' }, { status: 500 });
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,26 @@
|
||||
import { NextResponse } from 'next/server';
|
||||
import { deleteQuestion } from '@/lib/db/questions';
|
||||
|
||||
// 删除单个问题
|
||||
export async function DELETE(request, { params }) {
|
||||
try {
|
||||
const { projectId, questionId } = params;
|
||||
|
||||
// 验证参数
|
||||
if (!projectId) {
|
||||
return NextResponse.json({ error: 'Project ID is required' }, { status: 400 });
|
||||
}
|
||||
|
||||
if (!questionId) {
|
||||
return NextResponse.json({ error: 'Question ID is required' }, { status: 400 });
|
||||
}
|
||||
|
||||
// 删除问题
|
||||
await deleteQuestion(questionId);
|
||||
|
||||
return NextResponse.json({ success: true, message: 'Delete successful' });
|
||||
} catch (error) {
|
||||
console.error('Delete failed:', String(error));
|
||||
return NextResponse.json({ error: error.message || 'Delete failed' }, { status: 500 });
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,23 @@
|
||||
import { NextResponse } from 'next/server';
|
||||
import { batchDeleteQuestions } from '@/lib/db/questions';
|
||||
|
||||
// 批量删除问题
|
||||
export async function DELETE(request) {
|
||||
try {
|
||||
const body = await request.json();
|
||||
const { questionIds } = body;
|
||||
|
||||
// 验证参数
|
||||
if (questionIds.length === 0) {
|
||||
return NextResponse.json({ error: 'Question ID is required' }, { status: 400 });
|
||||
}
|
||||
|
||||
// 删除问题
|
||||
await batchDeleteQuestions(questionIds);
|
||||
|
||||
return NextResponse.json({ success: true, message: 'Delete successful' });
|
||||
} catch (error) {
|
||||
console.error('Delete failed:', String(error));
|
||||
return NextResponse.json({ error: error.message || 'Delete failed' }, { status: 500 });
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,100 @@
|
||||
import { NextResponse } from 'next/server';
|
||||
|
||||
export async function POST(request, { params }) {
|
||||
try {
|
||||
const { projectId } = params;
|
||||
const body = await request.json();
|
||||
const { format, selectedIds, filters } = body;
|
||||
|
||||
let questions;
|
||||
|
||||
// 如果有选中的问题 ID，按 ID 获取
|
||||
if (selectedIds && selectedIds.length > 0) {
|
||||
questions = await getQuestionsByIds(projectId, selectedIds);
|
||||
} else {
|
||||
// 否则获取全部问题（不限分页）
|
||||
questions = await getAllQuestions(
|
||||
projectId,
|
||||
filters?.searchTerm || '',
|
||||
filters?.chunkName || '',
|
||||
filters?.sourceType || 'all'
|
||||
);
|
||||
}
|
||||
|
||||
// 固定导出字段：问题内容、文本块名称、问题标签
|
||||
const filteredQuestions = questions.map(q => ({
|
||||
question: q.question,
|
||||
chunkName: q.chunk?.name || q.chunkName || '',
|
||||
questionLabel: q.questionLabel || ''
|
||||
}));
|
||||
|
||||
return NextResponse.json(filteredQuestions);
|
||||
} catch (error) {
|
||||
console.error('Failed to export questions:', error);
|
||||
return NextResponse.json({ error: error.message }, { status: 500 });
|
||||
}
|
||||
}
|
||||
|
||||
// 获取全部问题（不限分页）
|
||||
async function getAllQuestions(projectId, searchTerm = '', chunkName = '', sourceType = 'all') {
|
||||
const { db } = await import('@/lib/db/index');
|
||||
|
||||
const whereClause = {
|
||||
projectId
|
||||
};
|
||||
|
||||
// 搜索条件
|
||||
if (searchTerm) {
|
||||
whereClause.OR = [{ question: { contains: searchTerm } }, { questionLabel: { contains: searchTerm } }];
|
||||
}
|
||||
|
||||
// 文本块名称筛选
|
||||
if (chunkName) {
|
||||
whereClause.chunk = {
|
||||
name: { contains: chunkName }
|
||||
};
|
||||
}
|
||||
|
||||
// 数据源类型筛选
|
||||
if (sourceType === 'text') {
|
||||
whereClause.imageName = null;
|
||||
} else if (sourceType === 'image') {
|
||||
whereClause.imageName = { not: null };
|
||||
}
|
||||
|
||||
return await db.questions.findMany({
|
||||
where: whereClause,
|
||||
include: {
|
||||
chunk: {
|
||||
select: {
|
||||
name: true
|
||||
}
|
||||
}
|
||||
},
|
||||
orderBy: {
|
||||
createAt: 'desc'
|
||||
}
|
||||
});
|
||||
}
|
||||
|
||||
// 根据 ID 列表获取问题
|
||||
async function getQuestionsByIds(projectId, questionIds) {
|
||||
const { db } = await import('@/lib/db/index');
|
||||
|
||||
return await db.questions.findMany({
|
||||
where: {
|
||||
projectId,
|
||||
id: { in: questionIds }
|
||||
},
|
||||
include: {
|
||||
chunk: {
|
||||
select: {
|
||||
name: true
|
||||
}
|
||||
}
|
||||
},
|
||||
orderBy: {
|
||||
createAt: 'desc'
|
||||
}
|
||||
});
|
||||
}
|
||||
@@ -0,0 +1,110 @@
|
||||
import { NextResponse } from 'next/server';
|
||||
import {
|
||||
getAllQuestionsByProjectId,
|
||||
getQuestions,
|
||||
getQuestionsIds,
|
||||
saveQuestions,
|
||||
updateQuestion
|
||||
} from '@/lib/db/questions';
|
||||
import { getImageById, getImageChunk } from '@/lib/db/images';
|
||||
|
||||
// 获取项目的所有问题
|
||||
export async function GET(request, { params }) {
|
||||
try {
|
||||
const { projectId } = params;
|
||||
// 验证项目ID
|
||||
if (!projectId) {
|
||||
return NextResponse.json({ error: 'Missing project ID' }, { status: 400 });
|
||||
}
|
||||
const { searchParams } = new URL(request.url);
|
||||
let status = searchParams.get('status');
|
||||
let answered = undefined;
|
||||
if (status === 'answered') answered = true;
|
||||
if (status === 'unanswered') answered = false;
|
||||
const chunkName = searchParams.get('chunkName');
|
||||
const sourceType = searchParams.get('sourceType') || 'all'; // 'all', 'text', 'image'
|
||||
const searchMatchMode = searchParams.get('searchMatchMode') || 'match'; // 'match', 'notMatch'
|
||||
let selectedAll = searchParams.get('selectedAll');
|
||||
if (selectedAll) {
|
||||
let data = await getQuestionsIds(
|
||||
projectId,
|
||||
answered,
|
||||
searchParams.get('input'),
|
||||
chunkName,
|
||||
sourceType,
|
||||
searchMatchMode
|
||||
);
|
||||
return NextResponse.json(data);
|
||||
}
|
||||
let all = searchParams.get('all');
|
||||
if (all) {
|
||||
let data = await getAllQuestionsByProjectId(projectId);
|
||||
return NextResponse.json(data);
|
||||
}
|
||||
// 获取问题列表
|
||||
const questions = await getQuestions(
|
||||
projectId,
|
||||
parseInt(searchParams.get('page')),
|
||||
parseInt(searchParams.get('size')),
|
||||
answered,
|
||||
searchParams.get('input'),
|
||||
chunkName,
|
||||
sourceType,
|
||||
searchMatchMode
|
||||
);
|
||||
|
||||
return NextResponse.json(questions);
|
||||
} catch (error) {
|
||||
console.error('Failed to get questions:', String(error));
|
||||
return NextResponse.json({ error: error.message || 'Failed to get questions' }, { status: 500 });
|
||||
}
|
||||
}
|
||||
|
||||
// 新增问题
|
||||
export async function POST(request, { params }) {
|
||||
try {
|
||||
const { projectId } = params;
|
||||
const body = await request.json();
|
||||
const { question, chunkId, label } = body;
|
||||
|
||||
// 验证必要参数
|
||||
if (!projectId || !question) {
|
||||
return NextResponse.json({ error: 'Missing necessary parameters' }, { status: 400 });
|
||||
}
|
||||
|
||||
if (!body.chunkId && body.imageId) {
|
||||
const chunk = await getImageChunk(projectId);
|
||||
body.chunkId = chunk.id;
|
||||
body.label = 'image';
|
||||
}
|
||||
|
||||
// 添加新问题
|
||||
let questions = [body];
|
||||
// 保存更新后的数据
|
||||
let data = await saveQuestions(projectId, questions);
|
||||
|
||||
// 返回成功响应
|
||||
return NextResponse.json(data);
|
||||
} catch (error) {
|
||||
console.error('Failed to create question:', String(error));
|
||||
return NextResponse.json({ error: error.message || 'Failed to create question' }, { status: 500 });
|
||||
}
|
||||
}
|
||||
|
||||
// 更新问题
|
||||
export async function PUT(request) {
|
||||
try {
|
||||
const body = await request.json();
|
||||
// 保存更新后的数据
|
||||
const { imageId } = body;
|
||||
if (imageId) {
|
||||
body.imageName = (await getImageById(imageId))?.imageName;
|
||||
}
|
||||
let data = await updateQuestion(body);
|
||||
// 返回更新后的问题数据
|
||||
return NextResponse.json(data);
|
||||
} catch (error) {
|
||||
console.error('更新问题失败:', String(error));
|
||||
return NextResponse.json({ error: error.message || '更新问题失败' }, { status: 500 });
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,110 @@
|
||||
import { NextResponse } from 'next/server';
|
||||
import templateDb from '@/lib/db/questionTemplates';
|
||||
import { generateQuestionsFromTemplateEdit } from '@/lib/services/questions/template';
|
||||
|
||||
// 获取单个模板
|
||||
export async function GET(request, { params }) {
|
||||
try {
|
||||
const { templateId } = params;
|
||||
|
||||
const template = await templateDb.getTemplateById(templateId);
|
||||
|
||||
if (!template) {
|
||||
return NextResponse.json({ error: '模板不存在' }, { status: 404 });
|
||||
}
|
||||
|
||||
// 获取使用统计
|
||||
const usageCount = await templateDb.getTemplateUsageCount(templateId);
|
||||
|
||||
return NextResponse.json({
|
||||
success: true,
|
||||
template: {
|
||||
...template,
|
||||
usageCount
|
||||
}
|
||||
});
|
||||
} catch (error) {
|
||||
console.error('Failed to get template:', error);
|
||||
return NextResponse.json({ error: error.message || 'Failed to get template' }, { status: 500 });
|
||||
}
|
||||
}
|
||||
|
||||
// 更新问题模板
|
||||
export async function PUT(request, { params }) {
|
||||
try {
|
||||
const { projectId, templateId } = params;
|
||||
const data = await request.json();
|
||||
|
||||
const { question, sourceType, answerType, description, labels, customFormat, order, autoGenerate } = data;
|
||||
|
||||
// 验证数据源类型
|
||||
if (sourceType && !['image', 'text'].includes(sourceType)) {
|
||||
return NextResponse.json({ error: '无效的数据源类型' }, { status: 400 });
|
||||
}
|
||||
|
||||
// 验证答案类型
|
||||
if (answerType && !['text', 'label', 'custom_format'].includes(answerType)) {
|
||||
return NextResponse.json({ error: '无效的答案类型' }, { status: 400 });
|
||||
}
|
||||
|
||||
const updateData = {};
|
||||
if (question !== undefined) updateData.question = question;
|
||||
if (sourceType !== undefined) updateData.sourceType = sourceType;
|
||||
if (answerType !== undefined) updateData.answerType = answerType;
|
||||
if (description !== undefined) updateData.description = description;
|
||||
if (labels !== undefined) updateData.labels = labels;
|
||||
if (customFormat !== undefined) updateData.customFormat = customFormat;
|
||||
if (order !== undefined) updateData.order = order;
|
||||
|
||||
const template = await templateDb.updateTemplate(templateId, updateData);
|
||||
|
||||
let generationResult = null;
|
||||
|
||||
// 如果启用自动生成，则为还未创建此模板问题的数据源创建问题
|
||||
if (autoGenerate) {
|
||||
try {
|
||||
generationResult = await generateQuestionsFromTemplateEdit(projectId, template);
|
||||
} catch (error) {
|
||||
console.error('编辑模式自动生成问题失败:', error);
|
||||
generationResult = {
|
||||
success: false,
|
||||
successCount: 0,
|
||||
failCount: 0,
|
||||
message: '自动生成问题时发生错误'
|
||||
};
|
||||
}
|
||||
}
|
||||
|
||||
return NextResponse.json({
|
||||
success: true,
|
||||
template,
|
||||
generation: generationResult
|
||||
});
|
||||
} catch (error) {
|
||||
console.error('Failed to update template:', error);
|
||||
return NextResponse.json({ error: error.message || 'Failed to update template' }, { status: 500 });
|
||||
}
|
||||
}
|
||||
|
||||
// 删除问题模板
|
||||
export async function DELETE(request, { params }) {
|
||||
try {
|
||||
const { templateId } = params;
|
||||
|
||||
// 检查是否有关联的问题
|
||||
const usageCount = await templateDb.getTemplateUsageCount(templateId);
|
||||
if (usageCount > 0) {
|
||||
return NextResponse.json({ error: `此模板已被 ${usageCount} 个问题使用，无法删除` }, { status: 400 });
|
||||
}
|
||||
|
||||
await templateDb.deleteTemplate(templateId);
|
||||
|
||||
return NextResponse.json({
|
||||
success: true,
|
||||
message: '模板删除成功'
|
||||
});
|
||||
} catch (error) {
|
||||
console.error('Failed to delete template:', error);
|
||||
return NextResponse.json({ error: error.message || 'Failed to delete template' }, { status: 500 });
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,116 @@
|
||||
import { NextResponse } from 'next/server';
|
||||
import templateDb from '@/lib/db/questionTemplates';
|
||||
import { generateQuestionsFromTemplate, checkTemplateGenerationAvailability } from '@/lib/services/questions/template';
|
||||
|
||||
// 获取问题模板列表
|
||||
export async function GET(request, { params }) {
|
||||
try {
|
||||
const { projectId } = params;
|
||||
const { searchParams } = new URL(request.url);
|
||||
const sourceType = searchParams.get('sourceType');
|
||||
const search = searchParams.get('search');
|
||||
|
||||
const templates = await templateDb.getTemplates(projectId, { sourceType, search });
|
||||
|
||||
// 获取使用统计
|
||||
const templateIds = templates.map(t => t.id);
|
||||
const usageCounts = await templateDb.getTemplatesUsageCount(templateIds);
|
||||
|
||||
// 添加使用统计到模板数据
|
||||
const templatesWithUsage = templates.map(template => ({
|
||||
...template,
|
||||
usageCount: usageCounts[template.id] || 0
|
||||
}));
|
||||
|
||||
return NextResponse.json({
|
||||
success: true,
|
||||
templates: templatesWithUsage
|
||||
});
|
||||
} catch (error) {
|
||||
console.error('Failed to get templates:', error);
|
||||
return NextResponse.json({ error: error.message || 'Failed to get templates' }, { status: 500 });
|
||||
}
|
||||
}
|
||||
|
||||
// 创建问题模板
|
||||
export async function POST(request, { params }) {
|
||||
try {
|
||||
const { projectId } = params;
|
||||
const data = await request.json();
|
||||
|
||||
const { question, sourceType, answerType, description, labels, customFormat, order, autoGenerate } = data;
|
||||
|
||||
// 验证必填字段
|
||||
if (!question || !sourceType || !answerType) {
|
||||
return NextResponse.json({ error: '缺少必要参数：question, sourceType, answerType' }, { status: 400 });
|
||||
}
|
||||
|
||||
// 验证数据源类型
|
||||
if (!['image', 'text'].includes(sourceType)) {
|
||||
return NextResponse.json({ error: '无效的数据源类型' }, { status: 400 });
|
||||
}
|
||||
|
||||
// 验证答案类型
|
||||
if (!['text', 'label', 'custom_format'].includes(answerType)) {
|
||||
return NextResponse.json({ error: '无效的答案类型' }, { status: 400 });
|
||||
}
|
||||
|
||||
// 如果是标签类型，验证 labels
|
||||
if (answerType === 'label' && (!labels || !Array.isArray(labels) || labels.length === 0)) {
|
||||
return NextResponse.json({ error: '标签类型问题必须提供标签列表' }, { status: 400 });
|
||||
}
|
||||
|
||||
// 如果是自定义格式，验证 customFormat
|
||||
if (answerType === 'custom_format' && !customFormat) {
|
||||
return NextResponse.json({ error: '自定义格式问题必须提供格式定义' }, { status: 400 });
|
||||
}
|
||||
|
||||
const template = await templateDb.createTemplate(projectId, {
|
||||
question,
|
||||
sourceType,
|
||||
answerType,
|
||||
description,
|
||||
labels: answerType === 'label' ? labels : [],
|
||||
customFormat: answerType === 'custom_format' ? customFormat : null,
|
||||
order: order || 0
|
||||
});
|
||||
|
||||
let generationResult = null;
|
||||
|
||||
// 如果启用自动生成，则为所有相关数据源创建问题
|
||||
if (autoGenerate) {
|
||||
try {
|
||||
// 先检查是否有可用的数据源
|
||||
const availability = await checkTemplateGenerationAvailability(projectId, sourceType);
|
||||
|
||||
if (availability.available) {
|
||||
generationResult = await generateQuestionsFromTemplate(projectId, template);
|
||||
} else {
|
||||
generationResult = {
|
||||
success: false,
|
||||
successCount: 0,
|
||||
failCount: 0,
|
||||
message: availability.message
|
||||
};
|
||||
}
|
||||
} catch (error) {
|
||||
console.error('自动生成问题失败:', error);
|
||||
generationResult = {
|
||||
success: false,
|
||||
successCount: 0,
|
||||
failCount: 0,
|
||||
message: '自动生成问题时发生错误'
|
||||
};
|
||||
}
|
||||
}
|
||||
|
||||
return NextResponse.json({
|
||||
success: true,
|
||||
template,
|
||||
generation: generationResult
|
||||
});
|
||||
} catch (error) {
|
||||
console.error('Failed to create template:', error);
|
||||
return NextResponse.json({ error: error.message || 'Failed to create template' }, { status: 500 });
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,44 @@
|
||||
import { NextResponse } from 'next/server';
|
||||
import { getQuestionsForTree, getQuestionsByTag } from '@/lib/db/questions';
|
||||
|
||||
/**
|
||||
* 获取项目的问题树形视图数据
|
||||
* @param {Request} request - 请求对象
|
||||
* @param {Object} params - 路由参数
|
||||
* @returns {Promise<Response>} - 包含问题数据的响应
|
||||
*/
|
||||
export async function GET(request, { params }) {
|
||||
try {
|
||||
const { projectId } = params;
|
||||
|
||||
// 验证项目ID
|
||||
if (!projectId) {
|
||||
return NextResponse.json({ error: '项目ID不能为空' }, { status: 400 });
|
||||
}
|
||||
|
||||
const { searchParams } = new URL(request.url);
|
||||
const tag = searchParams.get('tag');
|
||||
const input = searchParams.get('input');
|
||||
const tagsOnly = searchParams.get('tagsOnly') === 'true';
|
||||
const isDistill = searchParams.get('isDistill') === 'true';
|
||||
// 默认排除图片问题（label='image'），可通过 excludeImage=false 参数改变
|
||||
const excludeImage = searchParams.get('excludeImage') !== 'false';
|
||||
|
||||
if (tag) {
|
||||
// 获取指定标签的问题数据（包含完整字段）
|
||||
const questions = await getQuestionsByTag(projectId, tag, input, isDistill, excludeImage);
|
||||
return NextResponse.json(questions);
|
||||
} else if (tagsOnly) {
|
||||
// 只获取标签信息（仅包含 id 和 label 字段）
|
||||
const treeData = await getQuestionsForTree(projectId, input, isDistill, excludeImage);
|
||||
return NextResponse.json(treeData);
|
||||
} else {
|
||||
// 兼容原有请求，获取树形视图数据（仅包含 id 和 label 字段）
|
||||
const treeData = await getQuestionsForTree(projectId, null, isDistill, excludeImage);
|
||||
return NextResponse.json(treeData);
|
||||
}
|
||||
} catch (error) {
|
||||
console.error('获取问题树形数据失败:', String(error));
|
||||
return NextResponse.json({ error: error.message || '获取问题树形数据失败' }, { status: 500 });
|
||||
}
|
||||
}
|
||||
65
easy-dataset-main/app/api/projects/[projectId]/route.js
Normal file
65
easy-dataset-main/app/api/projects/[projectId]/route.js
Normal file
@@ -0,0 +1,65 @@
|
||||
// 获取项目详情
|
||||
import { deleteProject, getProject, updateProject, getTaskConfig } from '@/lib/db/projects';
|
||||
|
||||
export async function GET(request, { params }) {
|
||||
try {
|
||||
const { projectId } = params;
|
||||
const project = await getProject(projectId);
|
||||
const taskConfig = await getTaskConfig(projectId);
|
||||
if (!project) {
|
||||
return Response.json({ error: '项目不存在' }, { status: 404 });
|
||||
}
|
||||
return Response.json({ ...project, taskConfig });
|
||||
} catch (error) {
|
||||
console.error('获取项目详情出错:', String(error));
|
||||
return Response.json({ error: String(error) }, { status: 500 });
|
||||
}
|
||||
}
|
||||
|
||||
// 更新项目
|
||||
export async function PUT(request, { params }) {
|
||||
try {
|
||||
const { projectId } = params;
|
||||
const projectData = await request.json();
|
||||
|
||||
const hasNameField = Object.prototype.hasOwnProperty.call(projectData, 'name');
|
||||
const hasDefaultModelField = Object.prototype.hasOwnProperty.call(projectData, 'defaultModelConfigId');
|
||||
|
||||
// 至少允许更新名称或默认模型（defaultModelConfigId 可显式为 null）
|
||||
if (!hasNameField && !hasDefaultModelField) {
|
||||
return Response.json({ error: '项目名称不能为空' }, { status: 400 });
|
||||
}
|
||||
|
||||
if (hasNameField && !projectData.name && !hasDefaultModelField) {
|
||||
return Response.json({ error: '项目名称不能为空' }, { status: 400 });
|
||||
}
|
||||
|
||||
const updatedProject = await updateProject(projectId, projectData);
|
||||
|
||||
if (!updatedProject) {
|
||||
return Response.json({ error: '项目不存在' }, { status: 404 });
|
||||
}
|
||||
|
||||
return Response.json(updatedProject);
|
||||
} catch (error) {
|
||||
console.error('更新项目出错:', String(error));
|
||||
return Response.json({ error: String(error) }, { status: 500 });
|
||||
}
|
||||
}
|
||||
|
||||
// 删除项目
|
||||
export async function DELETE(request, { params }) {
|
||||
try {
|
||||
const { projectId } = params;
|
||||
const success = await deleteProject(projectId);
|
||||
|
||||
if (!success) {
|
||||
return Response.json({ error: '项目不存在' }, { status: 404 });
|
||||
}
|
||||
|
||||
return Response.json({ success: true });
|
||||
} catch (error) {
|
||||
console.error('删除项目出错:', error);
|
||||
return Response.json({ error: error.message }, { status: 500 });
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,88 @@
|
||||
import { NextResponse } from 'next/server';
|
||||
import { splitProjectFile, getProjectChunks } from '@/lib/file/text-splitter';
|
||||
import { getProject, updateProject } from '@/lib/db/projects';
|
||||
import { getTags } from '@/lib/db/tags';
|
||||
import { handleDomainTree } from '@/lib/util/domain-tree';
|
||||
|
||||
// 处理文本分割请求
|
||||
export async function POST(request, { params }) {
|
||||
try {
|
||||
const { projectId } = params;
|
||||
|
||||
// 获取请求体
|
||||
const { fileNames, model, language, domainTreeAction = 'rebuild' } = await request.json();
|
||||
|
||||
if (!model) {
|
||||
return NextResponse.json({ error: 'Please Select Model' }, { status: 400 });
|
||||
}
|
||||
|
||||
const project = await getProject(projectId);
|
||||
|
||||
let result = {
|
||||
totalChunks: 0,
|
||||
chunks: [],
|
||||
toc: ''
|
||||
};
|
||||
for (let i = 0; i < fileNames.length; i++) {
|
||||
const fileName = fileNames[i];
|
||||
// 分割文本
|
||||
const { toc, chunks, totalChunks } = await splitProjectFile(projectId, fileName);
|
||||
result.toc += toc;
|
||||
result.chunks.push(...chunks);
|
||||
result.totalChunks += totalChunks;
|
||||
console.log(projectId, fileName, `Text split completed, ${domainTreeAction} domain tree`);
|
||||
}
|
||||
|
||||
// 调用领域树处理模块
|
||||
const tags = await handleDomainTree({
|
||||
projectId,
|
||||
action: domainTreeAction,
|
||||
newToc: result.toc,
|
||||
model,
|
||||
language,
|
||||
fileNames,
|
||||
project
|
||||
});
|
||||
|
||||
if (!tags && domainTreeAction !== 'keep') {
|
||||
await updateProject(projectId, { ...project });
|
||||
return NextResponse.json(
|
||||
{ error: 'AI analysis failed, please check model configuration, delete file and retry!' },
|
||||
{ status: 400 }
|
||||
);
|
||||
}
|
||||
|
||||
return NextResponse.json({ ...result, tags });
|
||||
} catch (error) {
|
||||
console.error('Text split error:', String(error));
|
||||
return NextResponse.json({ error: error.message || 'Text split failed' }, { status: 500 });
|
||||
}
|
||||
}
|
||||
|
||||
// 获取项目中的所有文本块
|
||||
export async function GET(request, { params }) {
|
||||
try {
|
||||
const { projectId } = params;
|
||||
const { searchParams } = new URL(request.url);
|
||||
const filter = searchParams.get('filter');
|
||||
// 验证项目ID
|
||||
if (!projectId) {
|
||||
return NextResponse.json({ error: 'The project ID cannot be empty' }, { status: 400 });
|
||||
}
|
||||
|
||||
// 获取文本块详细信息
|
||||
const result = await getProjectChunks(projectId, filter);
|
||||
|
||||
const tags = await getTags(projectId);
|
||||
|
||||
// 返回详细的文本块信息和文件结果（单个文件）
|
||||
return NextResponse.json({
|
||||
chunks: result.chunks,
|
||||
...result.fileResult, // 单个文件结果，而不是数组
|
||||
tags
|
||||
});
|
||||
} catch (error) {
|
||||
console.error('Failed to get text chunks:', String(error));
|
||||
return NextResponse.json({ error: error.message || 'Failed to get text chunks' }, { status: 500 });
|
||||
}
|
||||
}
|
||||
102
easy-dataset-main/app/api/projects/[projectId]/tags/route.js
Normal file
102
easy-dataset-main/app/api/projects/[projectId]/tags/route.js
Normal file
@@ -0,0 +1,102 @@
|
||||
import { NextResponse } from 'next/server';
|
||||
import { getTags, createTag, updateTag, deleteTag } from '@/lib/db/tags';
|
||||
import { getQuestionsByTagName } from '@/lib/db/questions';
|
||||
|
||||
// 获取项目的标签树
|
||||
export async function GET(request, { params }) {
|
||||
try {
|
||||
const { projectId } = params;
|
||||
|
||||
// 验证项目ID
|
||||
if (!projectId) {
|
||||
return NextResponse.json({ error: 'Project ID is required' }, { status: 400 });
|
||||
}
|
||||
|
||||
// 获取标签树
|
||||
const tags = await getTags(projectId);
|
||||
|
||||
return NextResponse.json({ tags });
|
||||
} catch (error) {
|
||||
console.error('Failed to obtain the label tree:', String(error));
|
||||
return NextResponse.json({ error: error.message || 'Failed to obtain the label tree' }, { status: 500 });
|
||||
}
|
||||
}
|
||||
|
||||
// 更新项目的标签树
|
||||
export async function PUT(request, { params }) {
|
||||
try {
|
||||
const { projectId } = params;
|
||||
|
||||
// 验证项目ID
|
||||
if (!projectId) {
|
||||
return NextResponse.json({ error: 'Project ID is required' }, { status: 400 });
|
||||
}
|
||||
|
||||
// 获取请求体
|
||||
const { tags } = await request.json();
|
||||
if (tags.id === undefined || tags.id === null || tags.id === '') {
|
||||
console.log('createTag', tags);
|
||||
let res = await createTag(projectId, tags.label, tags.parentId);
|
||||
return NextResponse.json({ tags: res });
|
||||
} else {
|
||||
let res = await updateTag(tags.label, tags.id);
|
||||
return NextResponse.json({ tags: res });
|
||||
}
|
||||
} catch (error) {
|
||||
console.error('Failed to update tags:', String(error));
|
||||
return NextResponse.json({ error: error.message || 'Failed to update tags' }, { status: 500 });
|
||||
}
|
||||
}
|
||||
|
||||
export async function POST(request, { params }) {
|
||||
try {
|
||||
const { projectId } = params;
|
||||
|
||||
// 验证项目ID
|
||||
if (!projectId) {
|
||||
return NextResponse.json({ error: 'Project ID is required' }, { status: 400 });
|
||||
}
|
||||
const { tagName } = await request.json();
|
||||
console.log('tagName', tagName);
|
||||
let data = await getQuestionsByTagName(projectId, tagName);
|
||||
|
||||
return NextResponse.json(data);
|
||||
} catch (error) {
|
||||
console.error('Failed to obtain the label tree:', String(error));
|
||||
return NextResponse.json({ error: error.message || 'Failed to obtain the label tree' }, { status: 500 });
|
||||
}
|
||||
}
|
||||
|
||||
export async function DELETE(request, { params }) {
|
||||
try {
|
||||
const { projectId } = params;
|
||||
|
||||
// 验证项目ID
|
||||
if (!projectId) {
|
||||
return NextResponse.json({ error: 'Project ID is required' }, { status: 400 });
|
||||
}
|
||||
|
||||
// 获取要删除的标签ID
|
||||
const { searchParams } = new URL(request.url);
|
||||
const id = searchParams.get('id');
|
||||
|
||||
if (!id) {
|
||||
return NextResponse.json({ error: '标签 ID 是必需的' }, { status: 400 });
|
||||
}
|
||||
|
||||
console.log(`正在删除标签: ${id}`);
|
||||
const result = await deleteTag(id);
|
||||
console.log(`删除标签成功: ${id}`);
|
||||
|
||||
return NextResponse.json({ success: true, message: '删除标签成功', data: result });
|
||||
} catch (error) {
|
||||
console.error('删除标签失败:', String(error));
|
||||
return NextResponse.json(
|
||||
{
|
||||
error: error.message || '删除标签失败',
|
||||
success: false
|
||||
},
|
||||
{ status: 500 }
|
||||
);
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,171 @@
|
||||
import { NextResponse } from 'next/server';
|
||||
import { PrismaClient } from '@prisma/client';
|
||||
|
||||
const prisma = new PrismaClient();
|
||||
|
||||
// 获取任务详情
|
||||
export async function GET(request, { params }) {
|
||||
try {
|
||||
const { projectId, taskId } = params;
|
||||
|
||||
// 验证必填参数
|
||||
if (!projectId || !taskId) {
|
||||
return NextResponse.json(
|
||||
{
|
||||
code: 400,
|
||||
error: '缺少必要参数'
|
||||
},
|
||||
{ status: 400 }
|
||||
);
|
||||
}
|
||||
|
||||
// 查询任务详情
|
||||
const task = await prisma.task.findUnique({
|
||||
where: {
|
||||
id: taskId,
|
||||
projectId
|
||||
}
|
||||
});
|
||||
|
||||
if (!task) {
|
||||
return NextResponse.json(
|
||||
{
|
||||
code: 404,
|
||||
error: '任务不存在'
|
||||
},
|
||||
{ status: 404 }
|
||||
);
|
||||
}
|
||||
|
||||
return NextResponse.json({
|
||||
code: 0,
|
||||
data: task,
|
||||
message: '获取任务详情成功'
|
||||
});
|
||||
} catch (error) {
|
||||
console.error('获取任务详情失败:', String(error));
|
||||
return NextResponse.json(
|
||||
{
|
||||
code: 500,
|
||||
error: '获取任务详情失败',
|
||||
message: error.message
|
||||
},
|
||||
{ status: 500 }
|
||||
);
|
||||
}
|
||||
}
|
||||
|
||||
// 更新任务状态
|
||||
export async function PATCH(request, { params }) {
|
||||
try {
|
||||
const { projectId, taskId } = params;
|
||||
const data = await request.json();
|
||||
|
||||
// 验证必填参数
|
||||
if (!projectId || !taskId) {
|
||||
return NextResponse.json(
|
||||
{
|
||||
code: 400,
|
||||
error: '缺少必要参数'
|
||||
},
|
||||
{ status: 400 }
|
||||
);
|
||||
}
|
||||
|
||||
// 获取要更新的字段
|
||||
const { status, completedCount, totalCount, detail, note, endTime } = data;
|
||||
|
||||
// 构建更新数据
|
||||
const updateData = {};
|
||||
|
||||
if (status !== undefined) {
|
||||
updateData.status = status;
|
||||
}
|
||||
|
||||
if (completedCount !== undefined) {
|
||||
updateData.completedCount = completedCount;
|
||||
}
|
||||
|
||||
if (totalCount !== undefined) {
|
||||
updateData.totalCount = totalCount;
|
||||
}
|
||||
|
||||
if (detail !== undefined) {
|
||||
updateData.detail = detail;
|
||||
}
|
||||
|
||||
if (note !== undefined) {
|
||||
updateData.note = note;
|
||||
}
|
||||
|
||||
// 如果状态变为已完成、失败或已中断，自动添加结束时间
|
||||
if (status === 1 || status === 2 || status === 3) {
|
||||
updateData.endTime = endTime || new Date();
|
||||
}
|
||||
|
||||
// 更新任务
|
||||
const updatedTask = await prisma.task.update({
|
||||
where: {
|
||||
id: taskId
|
||||
},
|
||||
data: updateData
|
||||
});
|
||||
|
||||
return NextResponse.json({
|
||||
code: 0,
|
||||
data: updatedTask,
|
||||
message: '更新任务状态成功'
|
||||
});
|
||||
} catch (error) {
|
||||
console.error('更新任务状态失败:', String(error));
|
||||
return NextResponse.json(
|
||||
{
|
||||
code: 500,
|
||||
error: '更新任务状态失败',
|
||||
message: error.message
|
||||
},
|
||||
{ status: 500 }
|
||||
);
|
||||
}
|
||||
}
|
||||
|
||||
// 删除任务
|
||||
export async function DELETE(request, { params }) {
|
||||
try {
|
||||
const { projectId, taskId } = params;
|
||||
|
||||
// 验证必填参数
|
||||
if (!projectId || !taskId) {
|
||||
return NextResponse.json(
|
||||
{
|
||||
code: 400,
|
||||
error: '缺少必要参数'
|
||||
},
|
||||
{ status: 400 }
|
||||
);
|
||||
}
|
||||
|
||||
// 删除任务
|
||||
await prisma.task.delete({
|
||||
where: {
|
||||
id: taskId,
|
||||
projectId
|
||||
}
|
||||
});
|
||||
|
||||
return NextResponse.json({
|
||||
code: 0,
|
||||
message: '删除任务成功'
|
||||
});
|
||||
} catch (error) {
|
||||
console.error('删除任务失败:', String(error));
|
||||
return NextResponse.json(
|
||||
{
|
||||
code: 500,
|
||||
error: '删除任务失败',
|
||||
message: error.message
|
||||
},
|
||||
{ status: 500 }
|
||||
);
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,63 @@
|
||||
import { NextResponse } from 'next/server';
|
||||
import { PrismaClient } from '@prisma/client';
|
||||
|
||||
const prisma = new PrismaClient();
|
||||
|
||||
// 获取项目的所有任务列表
|
||||
export async function GET(request, { params }) {
|
||||
try {
|
||||
const { projectId } = params;
|
||||
const { searchParams } = new URL(request.url);
|
||||
|
||||
// 可选参数: 任务类型和任务状态
|
||||
const taskType = searchParams.get('taskType');
|
||||
const statusStr = searchParams.get('status');
|
||||
|
||||
// 分页参数
|
||||
const page = parseInt(searchParams.get('page') || '0');
|
||||
const limit = parseInt(searchParams.get('limit') || '10');
|
||||
|
||||
// 构建查询条件
|
||||
const where = { projectId };
|
||||
|
||||
if (taskType) {
|
||||
where.taskType = taskType;
|
||||
}
|
||||
|
||||
if (statusStr && !isNaN(parseInt(statusStr))) {
|
||||
where.status = parseInt(statusStr);
|
||||
}
|
||||
|
||||
// 获取任务总数
|
||||
const total = await prisma.task.count({ where });
|
||||
|
||||
// 获取任务列表，按创建时间降序排序，并应用分页
|
||||
const tasks = await prisma.task.findMany({
|
||||
where,
|
||||
orderBy: {
|
||||
createAt: 'desc'
|
||||
},
|
||||
skip: page * limit,
|
||||
take: limit
|
||||
});
|
||||
|
||||
return NextResponse.json({
|
||||
code: 0,
|
||||
data: tasks,
|
||||
total,
|
||||
page,
|
||||
limit,
|
||||
message: '任务列表获取成功'
|
||||
});
|
||||
} catch (error) {
|
||||
console.error('获取任务列表失败:', String(error));
|
||||
return NextResponse.json(
|
||||
{
|
||||
code: 500,
|
||||
error: '获取任务列表失败',
|
||||
message: error.message
|
||||
},
|
||||
{ status: 500 }
|
||||
);
|
||||
}
|
||||
}
|
||||
164
easy-dataset-main/app/api/projects/[projectId]/tasks/route.js
Normal file
164
easy-dataset-main/app/api/projects/[projectId]/tasks/route.js
Normal file
@@ -0,0 +1,164 @@
|
||||
import { NextResponse } from 'next/server';
|
||||
import path from 'path';
|
||||
import fs from 'fs/promises';
|
||||
import { getProjectRoot } from '@/lib/db/base';
|
||||
import { getTaskConfig } from '@/lib/db/projects';
|
||||
import { processTask } from '@/lib/services/tasks';
|
||||
import { db } from '@/lib/db/index';
|
||||
|
||||
function normalizeModelEndpoint(endpoint = '') {
|
||||
let normalizedEndpoint = String(endpoint).trim();
|
||||
if (!normalizedEndpoint) {
|
||||
return '';
|
||||
}
|
||||
if (normalizedEndpoint.includes('/chat/completions')) {
|
||||
normalizedEndpoint = normalizedEndpoint.replace('/chat/completions', '');
|
||||
}
|
||||
return normalizedEndpoint;
|
||||
}
|
||||
|
||||
function normalizeTaskModelInfo(modelInfo) {
|
||||
if (!modelInfo) {
|
||||
return {};
|
||||
}
|
||||
let parsedModelInfo = modelInfo;
|
||||
if (typeof modelInfo === 'string') {
|
||||
try {
|
||||
parsedModelInfo = JSON.parse(modelInfo);
|
||||
} catch (error) {
|
||||
return {};
|
||||
}
|
||||
}
|
||||
if (parsedModelInfo && typeof parsedModelInfo === 'object' && parsedModelInfo.endpoint) {
|
||||
parsedModelInfo.endpoint = normalizeModelEndpoint(parsedModelInfo.endpoint);
|
||||
}
|
||||
return parsedModelInfo;
|
||||
}
|
||||
|
||||
// 获取任务配置
|
||||
export async function GET(request, { params }) {
|
||||
try {
|
||||
const { projectId } = params;
|
||||
|
||||
// 验证项目 ID
|
||||
if (!projectId) {
|
||||
return NextResponse.json({ error: 'Project ID is required' }, { status: 400 });
|
||||
}
|
||||
|
||||
// 获取项目根目录
|
||||
const projectRoot = await getProjectRoot();
|
||||
const projectPath = path.join(projectRoot, projectId);
|
||||
|
||||
// 检查项目是否存在
|
||||
try {
|
||||
await fs.access(projectPath);
|
||||
} catch (error) {
|
||||
return NextResponse.json({ error: 'Project does not exist' + projectPath }, { status: 404 });
|
||||
}
|
||||
|
||||
const taskConfig = await getTaskConfig(projectId);
|
||||
return NextResponse.json(taskConfig);
|
||||
} catch (error) {
|
||||
console.error('Failed to obtain task configuration:', String(error));
|
||||
return NextResponse.json({ error: 'Failed to obtain task configuration' }, { status: 500 });
|
||||
}
|
||||
}
|
||||
|
||||
// 更新任务配置
|
||||
export async function PUT(request, { params }) {
|
||||
try {
|
||||
const { projectId } = params;
|
||||
|
||||
// 验证项目 ID
|
||||
if (!projectId) {
|
||||
return NextResponse.json({ error: 'Project ID is required' }, { status: 400 });
|
||||
}
|
||||
|
||||
// 获取请求体
|
||||
const taskConfig = await request.json();
|
||||
|
||||
// 验证请求体
|
||||
if (!taskConfig) {
|
||||
return NextResponse.json({ error: 'Task configuration cannot be empty' }, { status: 400 });
|
||||
}
|
||||
|
||||
// 获取项目根目录
|
||||
const projectRoot = await getProjectRoot();
|
||||
const projectPath = path.join(projectRoot, projectId);
|
||||
|
||||
// 检查项目是否存在
|
||||
try {
|
||||
await fs.access(projectPath);
|
||||
} catch (error) {
|
||||
return NextResponse.json({ error: 'Project does not exist' }, { status: 404 });
|
||||
}
|
||||
|
||||
// 获取任务配置文件路径
|
||||
const taskConfigPath = path.join(projectPath, 'task-config.json');
|
||||
|
||||
// 写入任务配置文件
|
||||
await fs.writeFile(taskConfigPath, JSON.stringify(taskConfig, null, 2), 'utf-8');
|
||||
|
||||
return NextResponse.json({ message: 'Task configuration updated successfully' });
|
||||
} catch (error) {
|
||||
console.error('Failed to update task configuration:', String(error));
|
||||
return NextResponse.json({ error: 'Failed to update task configuration' }, { status: 500 });
|
||||
}
|
||||
}
|
||||
|
||||
// 创建新任务
|
||||
export async function POST(request, { params }) {
|
||||
try {
|
||||
const { projectId } = params;
|
||||
const data = await request.json();
|
||||
|
||||
// 验证必填字段
|
||||
const { taskType, modelInfo, language, detail = '', totalCount = 0, note } = data;
|
||||
|
||||
if (!taskType) {
|
||||
return NextResponse.json(
|
||||
{
|
||||
code: 400,
|
||||
error: 'Missing required parameter: taskType'
|
||||
},
|
||||
{ status: 400 }
|
||||
);
|
||||
}
|
||||
|
||||
// 创建新任务
|
||||
const newTask = await db.task.create({
|
||||
data: {
|
||||
projectId,
|
||||
taskType,
|
||||
status: 0, // 初始状态: 处理中
|
||||
modelInfo: JSON.stringify(normalizeTaskModelInfo(modelInfo)),
|
||||
language: language || 'zh-CN',
|
||||
detail: detail || '',
|
||||
totalCount,
|
||||
note: note ? JSON.stringify(note) : '',
|
||||
completedCount: 0
|
||||
}
|
||||
});
|
||||
|
||||
// 异步启动任务处理
|
||||
processTask(newTask.id).catch(err => {
|
||||
console.error(`Task startup failed: ${newTask.id}`, String(err));
|
||||
});
|
||||
|
||||
return NextResponse.json({
|
||||
code: 0,
|
||||
data: newTask,
|
||||
message: 'Task created successfully'
|
||||
});
|
||||
} catch (error) {
|
||||
console.error('Failed to create task:', String(error));
|
||||
return NextResponse.json(
|
||||
{
|
||||
code: 500,
|
||||
error: 'Failed to create task',
|
||||
message: error.message
|
||||
},
|
||||
{ status: 500 }
|
||||
);
|
||||
}
|
||||
}
|
||||
59
easy-dataset-main/app/api/projects/delete-directory/route.js
Normal file
59
easy-dataset-main/app/api/projects/delete-directory/route.js
Normal file
@@ -0,0 +1,59 @@
|
||||
import { getProjectRoot } from '@/lib/db/base';
|
||||
import { NextResponse } from 'next/server';
|
||||
import path from 'path';
|
||||
import fs from 'fs';
|
||||
import { promisify } from 'util';
|
||||
|
||||
const rmdir = promisify(fs.rm);
|
||||
|
||||
/**
|
||||
* Delete project directory
|
||||
* @returns {Promise<Response>} Operation result response
|
||||
*/
|
||||
export async function POST(request) {
|
||||
try {
|
||||
const { projectId } = await request.json();
|
||||
|
||||
if (!projectId) {
|
||||
return NextResponse.json(
|
||||
{
|
||||
success: false,
|
||||
error: 'Project ID is required'
|
||||
},
|
||||
{ status: 400 }
|
||||
);
|
||||
}
|
||||
|
||||
// Get project root directory
|
||||
const projectRoot = await getProjectRoot();
|
||||
const projectPath = path.join(projectRoot, projectId);
|
||||
|
||||
// Check if directory exists
|
||||
if (!fs.existsSync(projectPath)) {
|
||||
return NextResponse.json(
|
||||
{
|
||||
success: false,
|
||||
error: 'Project directory not found'
|
||||
},
|
||||
{ status: 404 }
|
||||
);
|
||||
}
|
||||
|
||||
// Recursively remove directory
|
||||
await rmdir(projectPath, { recursive: true, force: true });
|
||||
|
||||
return NextResponse.json({
|
||||
success: true,
|
||||
message: 'Project directory deleted'
|
||||
});
|
||||
} catch (error) {
|
||||
console.error('Failed to delete project directory:', String(error));
|
||||
return NextResponse.json(
|
||||
{
|
||||
success: false,
|
||||
error: error.message
|
||||
},
|
||||
{ status: 500 }
|
||||
);
|
||||
}
|
||||
}
|
||||
169
easy-dataset-main/app/api/projects/migrate/route.js
Normal file
169
easy-dataset-main/app/api/projects/migrate/route.js
Normal file
@@ -0,0 +1,169 @@
|
||||
import { NextResponse } from 'next/server';
|
||||
import { main } from '@/lib/db/fileToDb';
|
||||
|
||||
// Store migration task states
|
||||
const migrationTasks = new Map();
|
||||
|
||||
/**
|
||||
* Start a migration task
|
||||
*/
|
||||
export async function POST() {
|
||||
try {
|
||||
// Generate a unique task ID
|
||||
const taskId = Date.now().toString();
|
||||
|
||||
// Initialize task state
|
||||
migrationTasks.set(taskId, {
|
||||
status: 'running',
|
||||
progress: 0,
|
||||
total: 0,
|
||||
completed: 0,
|
||||
error: null,
|
||||
startTime: Date.now()
|
||||
});
|
||||
|
||||
// Execute migration asynchronously
|
||||
executeMigration(taskId);
|
||||
|
||||
// Return task ID
|
||||
return NextResponse.json({
|
||||
success: true,
|
||||
taskId
|
||||
});
|
||||
} catch (error) {
|
||||
console.error('Failed to start migration task:', String(error));
|
||||
return NextResponse.json(
|
||||
{
|
||||
success: false,
|
||||
error: error.message
|
||||
},
|
||||
{ status: 500 }
|
||||
);
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Get migration task status
|
||||
*/
|
||||
export async function GET(request) {
|
||||
try {
|
||||
// Get task ID from URL
|
||||
const { searchParams } = new URL(request.url);
|
||||
const taskId = searchParams.get('taskId');
|
||||
|
||||
if (!taskId) {
|
||||
return NextResponse.json(
|
||||
{
|
||||
success: false,
|
||||
error: 'Missing taskId'
|
||||
},
|
||||
{ status: 400 }
|
||||
);
|
||||
}
|
||||
|
||||
// Read task state
|
||||
const task = migrationTasks.get(taskId);
|
||||
|
||||
if (!task) {
|
||||
return NextResponse.json(
|
||||
{
|
||||
success: false,
|
||||
error: 'Task not found'
|
||||
},
|
||||
{ status: 404 }
|
||||
);
|
||||
}
|
||||
|
||||
// Return task state
|
||||
return NextResponse.json({
|
||||
success: true,
|
||||
task
|
||||
});
|
||||
} catch (error) {
|
||||
console.error('Failed to get migration task status:', String(error));
|
||||
return NextResponse.json(
|
||||
{
|
||||
success: false,
|
||||
error: error.message
|
||||
},
|
||||
{ status: 500 }
|
||||
);
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Execute migration task asynchronously
|
||||
* @param {string} taskId Task ID
|
||||
*/
|
||||
async function executeMigration(taskId) {
|
||||
try {
|
||||
// Read task state
|
||||
const task = migrationTasks.get(taskId);
|
||||
|
||||
if (!task) {
|
||||
console.error(`Task not found: ${taskId}`);
|
||||
return;
|
||||
}
|
||||
|
||||
// Reset task state to running
|
||||
task.status = 'running';
|
||||
task.progress = 0;
|
||||
task.completed = 0;
|
||||
task.total = 0;
|
||||
task.startTime = Date.now();
|
||||
|
||||
// Persist task state once per second so clients can poll progress
|
||||
const statusUpdateInterval = setInterval(() => {
|
||||
// Only update while still running
|
||||
if (task.status === 'running') {
|
||||
migrationTasks.set(taskId, { ...task });
|
||||
console.log(
|
||||
`Migration task status updated: ${taskId}, progress: ${task.progress}%, completed: ${task.completed}/${task.total}`
|
||||
);
|
||||
} else {
|
||||
// Stop updating when task ends
|
||||
clearInterval(statusUpdateInterval);
|
||||
}
|
||||
}, 1000);
|
||||
|
||||
// Run migration and let main(task) mutate progress fields
|
||||
const count = await main(task);
|
||||
|
||||
// Clear status update timer
|
||||
clearInterval(statusUpdateInterval);
|
||||
|
||||
// Mark as completed
|
||||
task.status = 'completed';
|
||||
task.progress = 100;
|
||||
task.completed = count;
|
||||
if (task.total === 0) task.total = count;
|
||||
task.endTime = Date.now();
|
||||
|
||||
// Persist final task state
|
||||
migrationTasks.set(taskId, { ...task });
|
||||
|
||||
// Clean up task state after 30 minutes
|
||||
setTimeout(
|
||||
() => {
|
||||
migrationTasks.delete(taskId);
|
||||
console.log(`Migration task state cleaned up: ${taskId}`);
|
||||
},
|
||||
30 * 60 * 1000
|
||||
);
|
||||
} catch (error) {
|
||||
console.error(`Failed to execute migration task: ${taskId}`, String(error));
|
||||
|
||||
// Read task state
|
||||
const task = migrationTasks.get(taskId);
|
||||
|
||||
if (task) {
|
||||
// Mark as failed
|
||||
task.status = 'failed';
|
||||
task.error = error.message;
|
||||
task.endTime = Date.now();
|
||||
|
||||
// Persist task state
|
||||
migrationTasks.set(taskId, task);
|
||||
}
|
||||
}
|
||||
}
|
||||
Some files were not shown because too many files have changed in this diff Show More
Reference in New Issue
Block a user