Andrei
2d27eae756
- Update vector-search.ts to query all available vector tables per language instead of single table - Add getAllVectorTables() function to discover all language-specific vector tables - Enhance searchBibleHybrid() to query multiple tables and merge results by relevance score - Enhance searchBibleSemantic() to combine results from all available vector databases - Add comprehensive error handling and logging for vector search operations - Improve Azure OpenAI content filtering detection and error handling - Add test-vector API endpoint for database diagnostics and debugging - Fix environment configuration with complete Azure OpenAI settings - Enable multi-translation biblical context from diverse Bible versions simultaneously Tested: Romanian chat works excellently with rich biblical context and verse citations Issue: English requires vector table creation - 47 English Bible versions exist but no vector tables 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <noreply@anthropic.com>
81 lines
2.8 KiB
TypeScript
81 lines
2.8 KiB
TypeScript
import { NextResponse } from 'next/server'
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import { Pool } from 'pg'
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const pool = new Pool({
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connectionString: process.env.DATABASE_URL,
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})
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export async function GET() {
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try {
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console.log('Test Vector - Starting database connection test')
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const client = await pool.connect()
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try {
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// Test basic connection
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const testQuery = await client.query('SELECT NOW() as current_time')
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console.log('Test Vector - Database connection successful:', testQuery.rows[0])
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// Check if ai_bible schema exists
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const schemaCheck = await client.query(`
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SELECT EXISTS (
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SELECT 1 FROM information_schema.schemata
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WHERE schema_name = 'ai_bible'
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) AS exists
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`)
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console.log('Test Vector - ai_bible schema exists:', schemaCheck.rows[0].exists)
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// List all vector tables
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const VECTOR_SCHEMA = process.env.VECTOR_SCHEMA || 'ai_bible'
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const tables = await client.query(`
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SELECT table_name FROM information_schema.tables
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WHERE table_schema = $1 AND table_name LIKE 'bv_%'
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ORDER BY table_name
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`, [VECTOR_SCHEMA])
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console.log('Test Vector - Found vector tables:', tables.rows.length)
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tables.rows.forEach(row => console.log('- ' + row.table_name))
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// Check for English tables specifically
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const englishTables = await client.query(`
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SELECT table_name FROM information_schema.tables
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WHERE table_schema = $1 AND table_name LIKE 'bv_en_%'
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ORDER BY table_name
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`, [VECTOR_SCHEMA])
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console.log('Test Vector - English tables found:', englishTables.rows.length)
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// Check BibleVersion table for available versions
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const versions = await client.query(`
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SELECT language, abbreviation, "isDefault", name
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FROM "BibleVersion"
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WHERE language IN ('en', 'ro')
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ORDER BY language, "isDefault" DESC, "createdAt" ASC
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`)
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console.log('Test Vector - Bible versions available:')
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versions.rows.forEach(v => console.log(`- ${v.language}: ${v.abbreviation} (${v.name}) - default: ${v.isDefault}`))
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return NextResponse.json({
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success: true,
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database_connected: true,
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ai_bible_schema_exists: schemaCheck.rows[0].exists,
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total_vector_tables: tables.rows.length,
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english_vector_tables: englishTables.rows.length,
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vector_tables: tables.rows.map(r => r.table_name),
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english_tables: englishTables.rows.map(r => r.table_name),
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bible_versions: versions.rows,
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current_time: testQuery.rows[0].current_time
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})
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} finally {
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client.release()
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}
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} catch (error) {
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console.error('Test Vector - Database connection failed:', error)
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return NextResponse.json({
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success: false,
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error: error instanceof Error ? error.message : String(error),
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database_connected: false
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}, { status: 500 })
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}
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} |