Sprint 1-3 Completed (Backend + Frontend): Backend (Sprint 1-2): - Implement 5-layer Agent framework (Query->Planner->Executor->Tools->Reflection) - Create agent_schema with 6 tables (agent_definitions, stages, prompts, sessions, traces, reflexion_rules) - Create protocol_schema with 2 tables (protocol_contexts, protocol_generations) - Implement Protocol Agent core services (Orchestrator, ContextService, PromptBuilder) - Integrate LLM service adapter (DeepSeek/Qwen/GPT-5/Claude) - 6 API endpoints with full authentication - 10/10 API tests passed Frontend (Sprint 3): - Add Protocol Agent entry in AgentHub (indigo theme card) - Implement ProtocolAgentPage with 3-column layout - Collapsible sidebar (Gemini style, 48px <-> 280px) - StatePanel with 5 stage cards (scientific_question, pico, study_design, sample_size, endpoints) - ChatArea with sync button and action cards integration - 100% prototype design restoration (608 lines CSS) - Detailed endpoints structure: baseline, exposure, outcomes, confounders Features: - 5-stage dialogue flow for research protocol design - Conversation-driven interaction with sync-to-protocol button - Real-time context state management - One-click protocol generation button (UI ready, backend pending) Database: - agent_schema: 6 tables for reusable Agent framework - protocol_schema: 2 tables for Protocol Agent - Seed data: 1 agent + 5 stages + 9 prompts + 4 reflexion rules Code Stats: - Backend: 13 files, 4338 lines - Frontend: 14 files, 2071 lines - Total: 27 files, 6409 lines Status: MVP core functionality completed, pending frontend-backend integration testing Next: Sprint 4 - One-click protocol generation + Word export
102 lines
2.1 KiB
TypeScript
102 lines
2.1 KiB
TypeScript
import { PrismaClient } from '@prisma/client';
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const prisma = new PrismaClient();
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async function getTableColumns(schema: string, tableName: string): Promise<any[]> {
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return prisma.$queryRawUnsafe(`
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SELECT column_name, data_type, is_nullable, column_default
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FROM information_schema.columns
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WHERE table_schema = '${schema}' AND table_name = '${tableName}'
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ORDER BY ordinal_position
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`);
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}
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async function main() {
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console.log('🔍 DC 和 ASL 模块表结构对比\n');
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console.log('=' .repeat(70));
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// DC 模块的表
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const dcTables = [
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'dc_extraction_items',
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'dc_extraction_tasks',
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'dc_health_checks',
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'dc_templates',
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'dc_tool_c_ai_history',
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'dc_tool_c_sessions'
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];
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// ASL 模块的表
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const aslTables = [
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'fulltext_screening_results',
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'fulltext_screening_tasks',
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'literatures',
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'screening_projects',
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'screening_results',
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'screening_tasks'
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];
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console.log('\n📋 DC 模块 (dc_schema) 当前表结构:\n');
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for (const table of dcTables) {
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console.log(`\n--- dc_schema.${table} ---`);
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try {
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const cols = await getTableColumns('dc_schema', table);
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if (cols.length === 0) {
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console.log(' ❌ 表不存在');
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} else {
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cols.forEach((c: any) => {
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console.log(` ${c.column_name}: ${c.data_type} ${c.is_nullable === 'NO' ? 'NOT NULL' : ''}`);
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});
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}
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} catch (e) {
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console.log(' ❌ 查询失败');
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}
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}
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console.log('\n\n📋 ASL 模块 (asl_schema) 当前表结构:\n');
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for (const table of aslTables) {
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console.log(`\n--- asl_schema.${table} ---`);
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try {
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const cols = await getTableColumns('asl_schema', table);
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if (cols.length === 0) {
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console.log(' ❌ 表不存在');
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} else {
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cols.forEach((c: any) => {
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console.log(` ${c.column_name}: ${c.data_type} ${c.is_nullable === 'NO' ? 'NOT NULL' : ''}`);
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});
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}
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} catch (e) {
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console.log(' ❌ 查询失败');
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}
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}
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console.log('\n' + '=' .repeat(70));
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}
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main()
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.catch(console.error)
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.finally(() => prisma.$disconnect());
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