HaHafeng
dfc0fe0b9a
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
33 lines
534 B
TypeScript
33 lines
534 B
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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const functions: any[] = await prisma.$queryRaw`
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SELECT routine_name, routine_type
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FROM information_schema.routines
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WHERE routine_schema = 'platform_schema'
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ORDER BY routine_name
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`;
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console.log('platform_schema 中的函数:');
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functions.forEach(f => console.log(` ✅ ${f.routine_name} (${f.routine_type})`));
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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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