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by LeGenAI
check-embeddings.ts2.12 kB
#!/usr/bin/env tsx import { createClient } from '@supabase/supabase-js'; import dotenv from 'dotenv'; dotenv.config(); async function checkEmbeddings() { const supabase = createClient( process.env.SUPABASE_URL!, process.env.SUPABASE_KEY! ); try { // 데이터베이스 스키마 확인 console.log('🔍 Checking database connection...'); // magma_documents 테이블 확인 console.log('\n🔍 Checking magma_documents table...'); const { data: docs, error: docsError } = await supabase .from('magma_documents') .select('id, content, embedding') .limit(1); if (docsError) { console.error('Error fetching documents:', docsError); return; } if (docs && docs.length > 0) { console.log('📄 Sample document:', { id: docs[0].id, content_length: docs[0].content?.length || 0, embedding_dimension: docs[0].embedding?.length || 0 }); } // 총 문서 수 확인 const { count, error: countError } = await supabase .from('magma_documents') .select('*', { count: 'exact', head: true }); if (!countError) { console.log(`📊 Total documents: ${count}`); } // 임베딩 차원 통계 console.log('\n🔍 Checking embedding dimensions...'); const { data: embeddingStats, error: statsError } = await supabase .rpc('get_embedding_dimensions'); if (statsError) { console.log('No embedding dimension function, checking manually...'); // 수동으로 몇 개 문서의 임베딩 차원 확인 const { data: sampleDocs, error: sampleError } = await supabase .from('magma_documents') .select('embedding') .limit(5); if (!sampleError && sampleDocs) { sampleDocs.forEach((doc, idx) => { console.log(`Document ${idx + 1} embedding dimension: ${doc.embedding?.length || 0}`); }); } } else { console.log('Embedding dimension stats:', embeddingStats); } } catch (error) { console.error('Error:', error); } } checkEmbeddings().catch(console.error);

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