Enhance RAG system to support multiple vector databases and improve AI chat functionality

- 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>

app/api/chat/route.ts

@@ -170,8 +170,18 @@ export async function POST(request: Request) {
 
 async function generateBiblicalResponse(message: string, locale: string, history: any[]): Promise<string> {
   try {
-    // Search for relevant Bible verses using vector search with language filtering
-    const relevantVerses = await searchBibleHybrid(message, locale, 5)
+    // Temporarily bypass vector search to test Azure OpenAI
+    console.log('Chat API - Starting biblical response generation for:', message.substring(0, 50))
+    let relevantVerses: any[] = []
+
+    try {
+      // Search for relevant Bible verses using vector search with language filtering
+      relevantVerses = await searchBibleHybrid(message, locale, 5)
+      console.log('Chat API - Vector search successful, found', relevantVerses.length, 'verses')
+    } catch (vectorError) {
+      console.warn('Chat API - Vector search failed:', vectorError instanceof Error ? vectorError.message : String(vectorError))
+      // Continue without verses - test if Azure OpenAI works alone
+    }
 
     // Create context from relevant verses
     const versesContext = relevantVerses
@@ -221,6 +231,9 @@ Current question: ${message}`
     const systemPrompt = systemPrompts[locale as keyof typeof systemPrompts] || systemPrompts.en
 
     // Call Azure OpenAI
+    console.log('Chat API - Calling Azure OpenAI with endpoint:', process.env.AZURE_OPENAI_ENDPOINT)
+    console.log('Chat API - Using deployment:', process.env.AZURE_OPENAI_DEPLOYMENT)
+
     const response = await fetch(
       `${process.env.AZURE_OPENAI_ENDPOINT}/openai/deployments/${process.env.AZURE_OPENAI_DEPLOYMENT}/chat/completions?api-version=${process.env.AZURE_OPENAI_API_VERSION}`,
       {
@@ -247,12 +260,33 @@ Current question: ${message}`
       }
     )
 
+    console.log('Chat API - Azure OpenAI response status:', response.status)
+
     if (!response.ok) {
       throw new Error(`Azure OpenAI API error: ${response.status}`)
     }
 
     const data = await response.json()
-    return data.choices[0].message.content
+
+    // Handle content filtering or empty responses
+    if (!data.choices || data.choices.length === 0) {
+      throw new Error('No response choices returned from Azure OpenAI')
+    }
+
+    const choice = data.choices[0]
+
+    // Check for content filtering
+    if (choice.finish_reason === 'content_filter') {
+      console.warn('Content was filtered by Azure OpenAI:', choice.content_filter_results)
+      throw new Error('Content was filtered by Azure OpenAI content policy')
+    }
+
+    // Check if message content exists
+    if (!choice.message || !choice.message.content) {
+      throw new Error('Empty response content from Azure OpenAI')
+    }
+
+    return choice.message.content
 
   } catch (error) {
     console.error('Error calling Azure OpenAI:', error)

app/api/test-vector/route.ts

@@ -0,0 +1,81 @@
+import { NextResponse } from 'next/server'
+import { Pool } from 'pg'
+
+const pool = new Pool({
+  connectionString: process.env.DATABASE_URL,
+})
+
+export async function GET() {
+  try {
+    console.log('Test Vector - Starting database connection test')
+
+    const client = await pool.connect()
+    try {
+      // Test basic connection
+      const testQuery = await client.query('SELECT NOW() as current_time')
+      console.log('Test Vector - Database connection successful:', testQuery.rows[0])
+
+      // Check if ai_bible schema exists
+      const schemaCheck = await client.query(`
+        SELECT EXISTS (
+          SELECT 1 FROM information_schema.schemata
+          WHERE schema_name = 'ai_bible'
+        ) AS exists
+      `)
+      console.log('Test Vector - ai_bible schema exists:', schemaCheck.rows[0].exists)
+
+      // List all vector tables
+      const VECTOR_SCHEMA = process.env.VECTOR_SCHEMA || 'ai_bible'
+      const tables = await client.query(`
+        SELECT table_name FROM information_schema.tables
+        WHERE table_schema = $1 AND table_name LIKE 'bv_%'
+        ORDER BY table_name
+      `, [VECTOR_SCHEMA])
+
+      console.log('Test Vector - Found vector tables:', tables.rows.length)
+      tables.rows.forEach(row => console.log('- ' + row.table_name))
+
+      // Check for English tables specifically
+      const englishTables = await client.query(`
+        SELECT table_name FROM information_schema.tables
+        WHERE table_schema = $1 AND table_name LIKE 'bv_en_%'
+        ORDER BY table_name
+      `, [VECTOR_SCHEMA])
+
+      console.log('Test Vector - English tables found:', englishTables.rows.length)
+
+      // Check BibleVersion table for available versions
+      const versions = await client.query(`
+        SELECT language, abbreviation, "isDefault", name
+        FROM "BibleVersion"
+        WHERE language IN ('en', 'ro')
+        ORDER BY language, "isDefault" DESC, "createdAt" ASC
+      `)
+
+      console.log('Test Vector - Bible versions available:')
+      versions.rows.forEach(v => console.log(`- ${v.language}: ${v.abbreviation} (${v.name}) - default: ${v.isDefault}`))
+
+      return NextResponse.json({
+        success: true,
+        database_connected: true,
+        ai_bible_schema_exists: schemaCheck.rows[0].exists,
+        total_vector_tables: tables.rows.length,
+        english_vector_tables: englishTables.rows.length,
+        vector_tables: tables.rows.map(r => r.table_name),
+        english_tables: englishTables.rows.map(r => r.table_name),
+        bible_versions: versions.rows,
+        current_time: testQuery.rows[0].current_time
+      })
+
+    } finally {
+      client.release()
+    }
+  } catch (error) {
+    console.error('Test Vector - Database connection failed:', error)
+    return NextResponse.json({
+      success: false,
+      error: error instanceof Error ? error.message : String(error),
+      database_connected: false
+    }, { status: 500 })
+  }
+}