Files
AIclinicalresearch/backend/check_queue_table.ts
HaHafeng dfc0fe0b9a feat(pkb): Integrate pgvector and create Dify replacement plan
Summary:
- Migrate PostgreSQL to pgvector/pgvector:pg15 Docker image
- Successfully install and verify pgvector 0.8.1 extension
- Create comprehensive Dify-to-pgvector migration plan
- Update PKB module documentation with pgvector status
- Update system documentation with pgvector integration

Key changes:
- docker-compose.yml: Switch to pgvector/pgvector:pg15 image
- Add EkbDocument and EkbChunk data model design
- Design R-C-R-G hybrid retrieval architecture
- Add clinical data JSONB fields (pico, studyDesign, regimen, safety, criteria, endpoints)
- Create detailed 10-day implementation roadmap

Documentation updates:
- PKB module status: pgvector RAG infrastructure ready
- System status: pgvector 0.8.1 integrated
- New: Dify replacement development plan (01-Dify替换为pgvector开发计划.md)
- New: Enterprise medical knowledge base solution V2

Tested: PostgreSQL with pgvector verified, frontend and backend functionality confirmed
2026-01-20 00:00:58 +08:00

46 lines
1023 B
TypeScript

import { PrismaClient } from '@prisma/client';
const prisma = new PrismaClient();
async function main() {
const cols: any[] = await prisma.$queryRaw`
SELECT column_name, data_type, is_nullable
FROM information_schema.columns
WHERE table_schema = 'platform_schema' AND table_name = 'queue'
ORDER BY ordinal_position
`;
console.log('platform_schema.queue 表的列:');
cols.forEach(c => console.log(` ${c.column_name}: ${c.data_type} ${c.is_nullable === 'NO' ? 'NOT NULL' : ''}`));
// 检查必要的列是否存在
const requiredCols = ['table_name', 'partition', 'retention_seconds', 'warning_queued'];
const existingCols = cols.map(c => c.column_name);
console.log('\n检查 create_queue 函数需要的列:');
for (const col of requiredCols) {
if (existingCols.includes(col)) {
console.log(`${col} 存在`);
} else {
console.log(`${col} 缺失!`);
}
}
}
main()
.catch(console.error)
.finally(() => prisma.$disconnect());