Major Features: - Created ekb_schema (13th schema) with 3 tables: KB/Document/Chunk - Implemented EmbeddingService (text-embedding-v4, 1024-dim vectors) - Implemented ChunkService (smart Markdown chunking) - Implemented VectorSearchService (multi-query + hybrid search) - Implemented RerankService (qwen3-rerank) - Integrated DeepSeek V3 QueryRewriter for cross-language search - Python service: Added pymupdf4llm for PDF-to-Markdown conversion - PKB: Dual-mode adapter (pgvector/dify/hybrid) Architecture: - Brain-Hand Model: Business layer (DeepSeek) + Engine layer (pgvector) - Cross-language support: Chinese query matches English documents - Small Embedding (1024) + Strong Reranker strategy Performance: - End-to-end latency: 2.5s - Cost per query: 0.0025 RMB - Accuracy improvement: +20.5% (cross-language) Tests: - test-embedding-service.ts: Vector embedding verified - test-rag-e2e.ts: Full pipeline tested - test-rerank.ts: Rerank quality validated - test-query-rewrite.ts: Cross-language search verified - test-pdf-ingest.ts: Real PDF document tested (Dongen 2003.pdf) Documentation: - Added 05-RAG-Engine-User-Guide.md - Added 02-Document-Processing-User-Guide.md - Updated system status documentation Status: Production ready
86 lines
3.1 KiB
TypeScript
86 lines
3.1 KiB
TypeScript
import { PrismaClient } from '@prisma/client';
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const prisma = new PrismaClient();
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async function main() {
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console.log('🔍 检查 IIT 和 ASL 模块的数据\n');
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console.log('=' .repeat(60));
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// IIT 模块
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console.log('\n📋 IIT 模块 (iit_schema):\n');
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const iitTables = [
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{ name: 'projects', query: 'SELECT COUNT(*) as count FROM iit_schema.projects' },
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{ name: 'audit_logs', query: 'SELECT COUNT(*) as count FROM iit_schema.audit_logs' },
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{ name: 'pending_actions', query: 'SELECT COUNT(*) as count FROM iit_schema.pending_actions' },
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{ name: 'task_runs', query: 'SELECT COUNT(*) as count FROM iit_schema.task_runs' },
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{ name: 'user_mappings', query: 'SELECT COUNT(*) as count FROM iit_schema.user_mappings' },
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];
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for (const t of iitTables) {
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const result: any = await prisma.$queryRawUnsafe(t.query);
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const count = Number(result[0].count);
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console.log(` ${t.name}: ${count} 条记录 ${count > 0 ? '✅' : '(空)'}`);
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}
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// 如果有数据,显示一些详情
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const iitProjects: any[] = await prisma.$queryRaw`SELECT id, name, status, created_at FROM iit_schema.projects LIMIT 5`;
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if (iitProjects.length > 0) {
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console.log('\n 最近的 IIT 项目:');
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iitProjects.forEach(p => console.log(` - ${p.name} (${p.status}) @ ${p.created_at}`));
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}
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// ASL 模块(智能文献筛选)
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console.log('\n\n📋 ASL 模块 - 智能文献筛选 (asl_schema):\n');
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const aslTables = [
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{ name: 'screening_projects', query: 'SELECT COUNT(*) as count FROM asl_schema.screening_projects' },
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{ name: 'literatures', query: 'SELECT COUNT(*) as count FROM asl_schema.literatures' },
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{ name: 'screening_tasks', query: 'SELECT COUNT(*) as count FROM asl_schema.screening_tasks' },
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{ name: 'screening_results', query: 'SELECT COUNT(*) as count FROM asl_schema.screening_results' },
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{ name: 'fulltext_screening_tasks', query: 'SELECT COUNT(*) as count FROM asl_schema.fulltext_screening_tasks' },
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{ name: 'fulltext_screening_results', query: 'SELECT COUNT(*) as count FROM asl_schema.fulltext_screening_results' },
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];
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for (const t of aslTables) {
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const result: any = await prisma.$queryRawUnsafe(t.query);
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const count = Number(result[0].count);
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console.log(` ${t.name}: ${count} 条记录 ${count > 0 ? '✅' : '(空)'}`);
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}
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// 如果有数据,显示一些详情
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const aslProjects: any[] = await prisma.$queryRaw`SELECT id, project_name, status, created_at FROM asl_schema.screening_projects LIMIT 5`;
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if (aslProjects.length > 0) {
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console.log('\n 最近的 ASL 项目:');
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aslProjects.forEach(p => console.log(` - ${p.project_name} (${p.status}) @ ${p.created_at}`));
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}
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const literatures: any[] = await prisma.$queryRaw`SELECT id, title, stage FROM asl_schema.literatures LIMIT 5`;
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if (literatures.length > 0) {
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console.log('\n 最近的文献:');
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literatures.forEach(l => console.log(` - ${l.title?.substring(0, 50)}... (${l.stage})`));
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}
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console.log('\n' + '=' .repeat(60));
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}
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main()
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.catch(console.error)
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.finally(() => prisma.$disconnect());
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