biblical-guide.com/AI_CHAT_FIX_PLAN.md

AI Chat Vector Search Implementation Plan

Biblical Guide - Vector Database Integration & Multi-Language Search

Document Version: 1.0 Date: January 2025 Status: Implementation Plan

---

Table of Contents

1. Executive Summary
2. Current State Analysis
3. Requirements & Specifications
4. Architecture Overview
5. Database Schema Analysis
6. Implementation Plan
7. Vector Search Logic
8. API Integration
9. Testing Strategy
10. Deployment & Monitoring

---

1. Executive Summary

1.1 Objective

Implement a robust vector search system for the AI chat that:

• ✅ Searches through all Bible versions in the user's language
• ✅ Returns comprehensive answers with Bible version citations
• ✅ Falls back to English versions when language-specific content is unavailable
• ✅ Maintains conversation context and accuracy
• ✅ Provides multilingual responses (English, Romanian, Spanish, Italian)

1.2 Current Status

• Completed: Vector embeddings for ALL Bible versions in dedicated vector database
• Pending: Integration of vector search into AI chat system
• Pending: Multi-language search logic implementation
• Pending: Fallback mechanism for incomplete translations

1.3 Success Criteria

1. Accuracy: AI chat returns biblically accurate answers with verse citations
2. Language Support: Searches user's language first, falls back to English when needed
3. Performance: Vector search completes in < 2 seconds
4. Coverage: All 4 languages supported (en, ro, es, it)
5. Citations: Always provides Bible version and verse references

---

2. Current State Analysis

2.1 Codebase Investigation Tasks

TASK 1: Identify Current AI Chat Implementation

• Locate AI chat API routes (/app/api/chat/)
• Find chat component files (/components/chat/)
• Identify current AI provider (OpenAI, Anthropic, etc.)
• Review conversation storage mechanism
• Check current embedding/vector search usage (if any)

TASK 2: Database Schema Review

• Analyze vector database structure
• Document all Bible version tables
• Verify embedding dimensions and metadata
• Check indexing configuration
• Review language-to-version mapping

TASK 3: API Connection Audit

• Verify AI API credentials (OpenAI, Anthropic, etc.)
• Check vector database connection (Supabase, Pinecone, pgvector, etc.)
• Test API rate limits and quotas
• Validate environment variables
• Test connection stability

TASK 4: Current Search Flow Analysis

• Document existing question → answer flow
• Identify where vector search should be integrated
• Review prompt engineering approach
• Check context window management
• Analyze response formatting

---

3. Requirements & Specifications

3.1 Functional Requirements

FR-1: Multi-Language Vector Search

Description: System must search Bible versions in user's language first

User Languages → Bible Versions Mapping:

English (en) → Bible versions:
  - KJV (King James Version)
  - NIV (New International Version)
  - ESV (English Standard Version)
  - NASB (New American Standard Bible)
  - [other English versions]

Romanian (ro) → Bible versions:
  - Cornilescu 1924
  - Cornilescu 2024
  - Dumitru Cornilescu
  - [other Romanian versions]

Spanish (es) → Bible versions:
  - Reina-Valera 1960
  - Nueva Versión Internacional (NVI)
  - Biblia de las Américas
  - [other Spanish versions]

Italian (it) → Bible versions:
  - Nuova Riveduta 2006
  - La Sacra Bibbia (Diodati)
  - Conferenza Episcopale Italiana
  - [other Italian versions]

Acceptance Criteria:

• System identifies user language from chat context
• Searches all versions available in that language
• Returns results from multiple versions when applicable
• Cites version name with each reference

FR-2: Fallback to English

Description: When no language-specific content found, search English versions

Fallback Conditions:

1. No Bible versions available in user's language
2. Bible incomplete in user's language (missing books/verses)
3. Vector search returns no results in user's language
4. Confidence score below threshold

Acceptance Criteria:

• Automatic fallback when conditions met
• User informed about language switch (transparent)
• Response translated to user's language
• English version citations included

FR-3: Citation Requirements

Description: All answers must include proper Bible citations

Citation Format:

[Version Abbreviation] [Book] [Chapter]:[Verse]

Examples:
- "According to KJV John 3:16..."
- "Cornilescu Genesis 1:1 states..."
- "As written in NIV Romans 8:28..."

Acceptance Criteria:

• Every Bible reference includes version name
• Book, chapter, verse always specified
• Multiple versions cited when providing comprehensive answer
• Citations are clickable links to Bible reader

FR-4: Comprehensive Answers

Description: Search multiple versions and synthesize comprehensive response

Answer Structure:

1. Direct answer to user's question
2. Supporting verses from multiple versions
3. Cross-references when relevant
4. Contextual explanation
5. Links to full passages in Bible reader

Acceptance Criteria:

• Uses 2-4 Bible versions in typical response
• Provides context around cited verses
• Highlights version differences when significant
• Offers to show full passage

3.2 Technical Requirements

TR-1: Vector Database Structure

Database Type: PostgreSQL with pgvector extension (or Supabase Vector)

Table Schema (per Bible version):

-- Example: bible_vectors_kjv (King James Version)
CREATE TABLE bible_vectors_kjv (
  id SERIAL PRIMARY KEY,
  book VARCHAR(50) NOT NULL,           -- e.g., "Genesis"
  chapter INTEGER NOT NULL,
  verse INTEGER NOT NULL,
  text TEXT NOT NULL,                  -- The verse text
  embedding vector(1536),              -- OpenAI ada-002 embeddings
  metadata JSONB,                      -- Additional data
  version VARCHAR(20) DEFAULT 'KJV',
  language VARCHAR(5) DEFAULT 'en',

  UNIQUE(book, chapter, verse)
);

-- Vector similarity index
CREATE INDEX ON bible_vectors_kjv
  USING ivfflat (embedding vector_cosine_ops);

Version Tables:

• bible_vectors_kjv (English)
• bible_vectors_niv (English)
• bible_vectors_esv (English)
• bible_vectors_cornilescu (Romanian)
• bible_vectors_rvr1960 (Spanish)
• bible_vectors_nvi_spanish (Spanish)
• bible_vectors_nuova_riveduta (Italian)
• [additional tables for each version]

TR-2: Vector Search Parameters

interface VectorSearchParams {
  query: string;              // User's question (embedded)
  languages: string[];        // e.g., ['ro', 'en']
  limit: number;              // Top K results (default: 10)
  similarityThreshold: number; // Minimum similarity (default: 0.7)
  versions?: string[];        // Specific versions to search
}

interface SearchResult {
  book: string;
  chapter: number;
  verse: number;
  text: string;
  version: string;
  language: string;
  similarity: number;         // Cosine similarity score
}

TR-3: AI Model Configuration

interface AIConfig {
  provider: 'openai' | 'anthropic';
  model: string;              // e.g., 'gpt-4-turbo' or 'claude-3-5-sonnet'
  temperature: number;        // 0.3 for factual responses
  maxTokens: number;          // 1500 for typical responses
  systemPrompt: string;       // Biblical AI assistant instructions
}

TR-4: Performance Requirements

• Vector search: < 2 seconds
• Full AI response: < 5 seconds
• Concurrent users: Support 100+
• Embedding cache: 24 hours
• Database connection pool: 10-20 connections

---

4. Architecture Overview

4.1 System Components

┌─────────────────────────────────────────────────────────────┐
│                        User Interface                        │
│                    (Chat Component)                          │
└────────────────────────┬────────────────────────────────────┘
                         │
                         │ POST /api/chat
                         │ { message, locale, conversationId }
                         ▼
┌─────────────────────────────────────────────────────────────┐
│                    Chat API Route                            │
│  1. Identify user language                                   │
│  2. Generate embedding for question                          │
│  3. Trigger multi-language vector search                     │
└────────────────────────┬────────────────────────────────────┘
                         │
                         ▼
┌─────────────────────────────────────────────────────────────┐
│                 Vector Search Service                        │
│  1. Search primary language versions                         │
│  2. Check result quality/completeness                        │
│  3. Fallback to English if needed                            │
│  4. Return top results with metadata                         │
└────────────────────────┬────────────────────────────────────┘
                         │
                         │ Query multiple tables
                         ▼
┌─────────────────────────────────────────────────────────────┐
│              Vector Database (PostgreSQL)                    │
│  ┌──────────────────┐  ┌──────────────────┐                │
│  │ bible_vectors_kjv│  │bible_vectors_niv │  [English]     │
│  └──────────────────┘  └──────────────────┘                │
│  ┌──────────────────┐  ┌──────────────────┐                │
│  │bible_vectors_    │  │bible_vectors_    │  [Romanian]    │
│  │  cornilescu      │  │  cornilescu2024  │                │
│  └──────────────────┘  └──────────────────┘                │
│  ┌──────────────────┐  ┌──────────────────┐                │
│  │bible_vectors_    │  │bible_vectors_nvi_│  [Spanish]     │
│  │  rvr1960         │  │  spanish         │                │
│  └──────────────────┘  └──────────────────┘                │
│  ┌──────────────────┐                                       │
│  │bible_vectors_    │                      [Italian]        │
│  │  nuova_riveduta  │                                       │
│  └──────────────────┘                                       │
└────────────────────────┬────────────────────────────────────┘
                         │ Return results
                         ▼
┌─────────────────────────────────────────────────────────────┐
│                  AI Context Builder                          │
│  1. Format search results into context                       │
│  2. Build system prompt with instructions                    │
│  3. Include conversation history                             │
│  4. Add citation requirements                                │
└────────────────────────┬────────────────────────────────────┘
                         │
                         ▼
┌─────────────────────────────────────────────────────────────┐
│                    AI Provider (OpenAI/Anthropic)           │
│  Generate comprehensive answer with citations                │
└────────────────────────┬────────────────────────────────────┘
                         │
                         ▼
┌─────────────────────────────────────────────────────────────┐
│                Response Formatter                            │
│  1. Parse citations                                          │
│  2. Create links to Bible reader                             │
│  3. Format markdown                                          │
│  4. Return to user                                           │
└────────────────────────┬────────────────────────────────────┘
                         │
                         ▼
                    User receives answer

4.2 Data Flow

Scenario 1: Romanian User Asks Question

User (ro): "Ce spune Biblia despre iubire?" (What does the Bible say about love?)
  ↓
1. Detect language: Romanian (ro)
2. Generate embedding of question
3. Search Romanian Bible versions:
   - bible_vectors_cornilescu
   - bible_vectors_cornilescu2024
4. Find top 10 results about love (1 Corinteni 13, Ioan 3:16, etc.)
5. Build AI prompt with Romanian verses
6. AI generates answer in Romanian
7. Response: "Biblia vorbește mult despre iubire. Cornilescu 1 Corinteni 13:4
   spune: 'Dragostea este îndelung răbdătoare...' Și Ioan 3:16..."

Scenario 2: Italian User - Incomplete Bible

User (it): "Chi era Giobbe?" (Who was Job?)
  ↓
1. Detect language: Italian (it)
2. Generate embedding
3. Search Italian Bible versions:
   - bible_vectors_nuova_riveduta
4. Results found: 3 verses (incomplete Book of Job)
5. Confidence score: LOW (0.5 < 0.7 threshold)
6. FALLBACK: Search English versions (KJV, NIV, ESV)
7. Find comprehensive results in English
8. AI translates context to Italian
9. Response: "Giobbe era un uomo giusto... (Job was a righteous man...)
   [Da KJV Job 1:1] 'There was a man in the land of Uz, whose name was Job...'"

---

5. Database Schema Analysis

5.1 Discovery Tasks

TASK 5: Enumerate All Vector Tables

-- Query to find all Bible vector tables
SELECT tablename
FROM pg_tables
WHERE tablename LIKE 'bible_vectors_%'
ORDER BY tablename;

Expected Output:

bible_vectors_kjv
bible_vectors_niv
bible_vectors_esv
bible_vectors_cornilescu
bible_vectors_rvr1960
bible_vectors_nvi_spanish
bible_vectors_nuova_riveduta
[... additional tables]

TASK 6: Analyze Table Structure

-- Check schema of a sample table
SELECT column_name, data_type, character_maximum_length
FROM information_schema.columns
WHERE table_name = 'bible_vectors_kjv';

-- Check indexing
SELECT indexname, indexdef
FROM pg_indexes
WHERE tablename = 'bible_vectors_kjv';

-- Check embedding dimensions
SELECT pg_column_size(embedding) / 4 as dimensions
FROM bible_vectors_kjv
LIMIT 1;

TASK 7: Create Language-to-Version Mapping

-- Create configuration table
CREATE TABLE IF NOT EXISTS bible_version_config (
  id SERIAL PRIMARY KEY,
  table_name VARCHAR(100) UNIQUE NOT NULL,
  version_name VARCHAR(100) NOT NULL,
  version_abbreviation VARCHAR(20) NOT NULL,
  language VARCHAR(5) NOT NULL,
  is_complete BOOLEAN DEFAULT true,
  books_count INTEGER,
  verses_count INTEGER,
  created_at TIMESTAMP DEFAULT NOW(),
  metadata JSONB
);

-- Insert mappings
INSERT INTO bible_version_config
  (table_name, version_name, version_abbreviation, language, is_complete)
VALUES
  ('bible_vectors_kjv', 'King James Version', 'KJV', 'en', true),
  ('bible_vectors_niv', 'New International Version', 'NIV', 'en', true),
  ('bible_vectors_esv', 'English Standard Version', 'ESV', 'en', true),
  ('bible_vectors_cornilescu', 'Dumitru Cornilescu 1924', 'Cornilescu', 'ro', true),
  ('bible_vectors_rvr1960', 'Reina-Valera 1960', 'RVR1960', 'es', true),
  ('bible_vectors_nvi_spanish', 'Nueva Versión Internacional', 'NVI', 'es', true),
  ('bible_vectors_nuova_riveduta', 'Nuova Riveduta 2006', 'NR2006', 'it', false);
  -- Add all your versions...

5.2 Version Statistics Query

-- Get statistics for each version
SELECT
  bvc.version_abbreviation,
  bvc.language,
  bvc.is_complete,
  COUNT(DISTINCT bv.book) as books_count,
  COUNT(*) as verses_count
FROM bible_version_config bvc
JOIN LATERAL (
  SELECT book FROM {table_name}  -- Dynamic table name
) bv ON true
GROUP BY bvc.version_abbreviation, bvc.language, bvc.is_complete
ORDER BY bvc.language, bvc.version_abbreviation;

---

6. Implementation Plan

Phase 1: Environment & Connection Verification (Day 1)

Step 1.1: Check AI API Credentials

File to Check: /root/biblical-guide/.env

# Required environment variables
OPENAI_API_KEY=sk-...                    # OpenAI API key
ANTHROPIC_API_KEY=sk-ant-...            # Anthropic API key (if using Claude)

# Vector Database Connection
VECTOR_DB_URL=postgresql://...          # Supabase or PostgreSQL connection
VECTOR_DB_PASSWORD=...                  # Database password

# Optional: Embedding API
EMBEDDING_API_KEY=...                   # If separate from main AI key

Verification Script:

// scripts/verify-ai-connection.ts
import OpenAI from 'openai';

async function verifyConnections() {
  console.log('🔍 Verifying AI API connections...\n');

  // 1. Check OpenAI
  try {
    const openai = new OpenAI({
      apiKey: process.env.OPENAI_API_KEY,
    });

    const response = await openai.chat.completions.create({
      model: 'gpt-3.5-turbo',
      messages: [{ role: 'user', content: 'Test' }],
      max_tokens: 5,
    });

    console.log('✅ OpenAI API: Connected');
    console.log(`   Model: ${response.model}`);
  } catch (error: any) {
    console.log('❌ OpenAI API: Failed');
    console.log(`   Error: ${error.message}`);
  }

  // 2. Check Vector Database
  try {
    const { Pool } = await import('pg');
    const pool = new Pool({
      connectionString: process.env.VECTOR_DB_URL,
    });

    const result = await pool.query('SELECT version()');
    console.log('✅ Vector Database: Connected');
    console.log(`   PostgreSQL: ${result.rows[0].version.split(' ')[1]}`);

    // Check for pgvector extension
    const extResult = await pool.query(
      "SELECT * FROM pg_extension WHERE extname = 'vector'"
    );

    if (extResult.rows.length > 0) {
      console.log('✅ pgvector extension: Installed');
    } else {
      console.log('⚠️  pgvector extension: Not found');
    }

    await pool.end();
  } catch (error: any) {
    console.log('❌ Vector Database: Failed');
    console.log(`   Error: ${error.message}`);
  }

  // 3. List all Bible vector tables
  try {
    const { Pool } = await import('pg');
    const pool = new Pool({
      connectionString: process.env.VECTOR_DB_URL,
    });

    const tables = await pool.query(`
      SELECT tablename
      FROM pg_tables
      WHERE tablename LIKE 'bible_vectors_%'
      ORDER BY tablename
    `);

    console.log(`\n📚 Found ${tables.rows.length} Bible version tables:`);
    tables.rows.forEach(row => {
      console.log(`   - ${row.tablename}`);
    });

    await pool.end();
  } catch (error: any) {
    console.log('❌ Table enumeration failed');
    console.log(`   Error: ${error.message}`);
  }
}

verifyConnections();

Run verification:

npx tsx scripts/verify-ai-connection.ts

Step 1.2: Identify Current Chat Implementation

Files to Locate:

• /app/api/chat/route.ts - Main chat API endpoint
• /components/chat/floating-chat.tsx - Chat UI component
• /lib/ai/ - AI utility functions (if exists)
• /lib/embeddings/ - Embedding functions (if exists)

Analysis Checklist:

• What AI provider is currently used? (OpenAI/Anthropic)
• Is vector search currently implemented?
• How are embeddings generated?
• Where is conversation history stored?
• How are responses formatted?

---

Phase 2: Vector Search Service Implementation (Day 2-3)

Step 2.1: Create Vector Database Connection

File: /lib/vector-db/index.ts

import { Pool } from 'pg';

// Singleton pool instance
let pool: Pool | null = null;

export function getVectorDbPool(): Pool {
  if (!pool) {
    pool = new Pool({
      connectionString: process.env.VECTOR_DB_URL,
      max: 20,
      idleTimeoutMillis: 30000,
      connectionTimeoutMillis: 2000,
    });
  }
  return pool;
}

export async function testConnection(): Promise<boolean> {
  try {
    const client = await getVectorDbPool().connect();
    await client.query('SELECT 1');
    client.release();
    return true;
  } catch (error) {
    console.error('Vector DB connection failed:', error);
    return false;
  }
}

Step 2.2: Create Bible Version Configuration Service

File: /lib/vector-db/version-config.ts

import { getVectorDbPool } from './index';

export interface BibleVersionConfig {
  tableName: string;
  versionName: string;
  versionAbbreviation: string;
  language: string;
  isComplete: boolean;
  booksCount?: number;
  versesCount?: number;
}

// In-memory cache (refreshed every 24h)
let versionCache: BibleVersionConfig[] | null = null;
let cacheTimestamp: number = 0;
const CACHE_DURATION = 24 * 60 * 60 * 1000; // 24 hours

export async function getBibleVersionsByLanguage(
  language: string
): Promise<BibleVersionConfig[]> {
  const now = Date.now();

  // Return cached data if valid
  if (versionCache && (now - cacheTimestamp) < CACHE_DURATION) {
    return versionCache.filter(v => v.language === language);
  }

  // Fetch from database
  const pool = getVectorDbPool();
  const result = await pool.query<BibleVersionConfig>(`
    SELECT
      table_name as "tableName",
      version_name as "versionName",
      version_abbreviation as "versionAbbreviation",
      language,
      is_complete as "isComplete",
      books_count as "booksCount",
      verses_count as "versesCount"
    FROM bible_version_config
    ORDER BY language, version_abbreviation
  `);

  versionCache = result.rows;
  cacheTimestamp = now;

  return versionCache.filter(v => v.language === language);
}

export async function getAllVersions(): Promise<BibleVersionConfig[]> {
  const now = Date.now();

  if (versionCache && (now - cacheTimestamp) < CACHE_DURATION) {
    return versionCache;
  }

  const pool = getVectorDbPool();
  const result = await pool.query<BibleVersionConfig>(`
    SELECT
      table_name as "tableName",
      version_name as "versionName",
      version_abbreviation as "versionAbbreviation",
      language,
      is_complete as "isComplete",
      books_count as "booksCount",
      verses_count as "versesCount"
    FROM bible_version_config
    ORDER BY language, version_abbreviation
  `);

  versionCache = result.rows;
  cacheTimestamp = now;

  return versionCache;
}

export function clearCache(): void {
  versionCache = null;
  cacheTimestamp = 0;
}

Step 2.3: Create Embedding Service

File: /lib/ai/embeddings.ts

import OpenAI from 'openai';

const openai = new OpenAI({
  apiKey: process.env.OPENAI_API_KEY,
});

// Embedding cache (simple in-memory cache)
const embeddingCache = new Map<string, number[]>();
const CACHE_MAX_SIZE = 1000;

export async function generateEmbedding(text: string): Promise<number[]> {
  // Check cache first
  const cached = embeddingCache.get(text);
  if (cached) {
    return cached;
  }

  try {
    const response = await openai.embeddings.create({
      model: 'text-embedding-ada-002',
      input: text,
    });

    const embedding = response.data[0].embedding;

    // Add to cache (with size limit)
    if (embeddingCache.size >= CACHE_MAX_SIZE) {
      // Remove oldest entry
      const firstKey = embeddingCache.keys().next().value;
      embeddingCache.delete(firstKey);
    }
    embeddingCache.set(text, embedding);

    return embedding;
  } catch (error) {
    console.error('Embedding generation failed:', error);
    throw new Error('Failed to generate embedding');
  }
}

export function clearEmbeddingCache(): void {
  embeddingCache.clear();
}

Step 2.4: Create Multi-Language Vector Search Service

File: /lib/vector-db/search.ts

import { getVectorDbPool } from './index';
import { getBibleVersionsByLanguage } from './version-config';
import { generateEmbedding } from '../ai/embeddings';

export interface VectorSearchResult {
  book: string;
  chapter: number;
  verse: number;
  text: string;
  version: string;
  versionName: string;
  language: string;
  similarity: number;
}

export interface VectorSearchOptions {
  limit?: number;                    // Default: 10
  similarityThreshold?: number;      // Default: 0.7
  includeMetadata?: boolean;         // Default: true
}

/**
 * Search Bible verses across all versions in specified languages
 *
 * @param query - User's question
 * @param languages - Languages to search (e.g., ['ro', 'en'])
 * @param options - Search options
 * @returns Array of search results sorted by similarity
 */
export async function searchBibleVectors(
  query: string,
  languages: string[],
  options: VectorSearchOptions = {}
): Promise<VectorSearchResult[]> {
  const {
    limit = 10,
    similarityThreshold = 0.7,
  } = options;

  console.log(`🔍 Searching vectors for languages: ${languages.join(', ')}`);

  // 1. Generate embedding for query
  const queryEmbedding = await generateEmbedding(query);
  const embeddingString = `[${queryEmbedding.join(',')}]`;

  const pool = getVectorDbPool();
  const allResults: VectorSearchResult[] = [];

  // 2. Search each language
  for (const language of languages) {
    console.log(`   Searching ${language} versions...`);

    // Get all versions for this language
    const versions = await getBibleVersionsByLanguage(language);

    if (versions.length === 0) {
      console.log(`   ⚠️  No versions found for ${language}`);
      continue;
    }

    // 3. Search each version table
    for (const version of versions) {
      try {
        // Use parameterized query with UNION for multiple tables
        const query = `
          SELECT
            book,
            chapter,
            verse,
            text,
            version,
            1 - (embedding <=> $1::vector) as similarity
          FROM ${version.tableName}
          WHERE 1 - (embedding <=> $1::vector) > $2
          ORDER BY embedding <=> $1::vector
          LIMIT $3
        `;

        const result = await pool.query(query, [
          embeddingString,
          similarityThreshold,
          limit,
        ]);

        // Add results with version metadata
        const versionResults: VectorSearchResult[] = result.rows.map(row => ({
          book: row.book,
          chapter: row.chapter,
          verse: row.verse,
          text: row.text,
          version: version.versionAbbreviation,
          versionName: version.versionName,
          language: language,
          similarity: row.similarity,
        }));

        allResults.push(...versionResults);

        console.log(`   ✓ ${version.versionAbbreviation}: ${versionResults.length} results`);
      } catch (error: any) {
        console.error(`   ✗ Error searching ${version.tableName}:`, error.message);
      }
    }
  }

  // 4. Sort all results by similarity and limit
  allResults.sort((a, b) => b.similarity - a.similarity);
  const topResults = allResults.slice(0, limit);

  console.log(`✅ Total results: ${topResults.length}`);
  return topResults;
}

/**
 * Search with fallback logic:
 * 1. Search primary language
 * 2. If insufficient results, fallback to English
 *
 * @param query - User's question
 * @param primaryLanguage - User's language (e.g., 'ro')
 * @param options - Search options
 * @returns Search results with fallback indicator
 */
export async function searchWithFallback(
  query: string,
  primaryLanguage: string,
  options: VectorSearchOptions = {}
): Promise<{
  results: VectorSearchResult[];
  usedFallback: boolean;
  searchedLanguages: string[];
}> {
  const { limit = 10, similarityThreshold = 0.7 } = options;

  // Search primary language first
  const primaryResults = await searchBibleVectors(
    query,
    [primaryLanguage],
    { limit, similarityThreshold }
  );

  // Check if we need fallback
  const needsFallback = (
    primaryResults.length === 0 ||
    primaryResults.length < 3 ||
    (primaryResults[0]?.similarity || 0) < 0.75
  );

  if (!needsFallback || primaryLanguage === 'en') {
    return {
      results: primaryResults,
      usedFallback: false,
      searchedLanguages: [primaryLanguage],
    };
  }

  // Fallback to English
  console.log('⚠️  Insufficient results, falling back to English...');
  const englishResults = await searchBibleVectors(
    query,
    ['en'],
    { limit, similarityThreshold }
  );

  // Combine results (prioritize primary language)
  const combinedResults = [...primaryResults, ...englishResults]
    .sort((a, b) => b.similarity - a.similarity)
    .slice(0, limit);

  return {
    results: combinedResults,
    usedFallback: true,
    searchedLanguages: [primaryLanguage, 'en'],
  };
}

---

Phase 3: AI Chat Integration (Day 4-5)

Step 3.1: Create AI Response Generator

File: /lib/ai/chat.ts

import OpenAI from 'openai';
import { VectorSearchResult } from '../vector-db/search';

const openai = new OpenAI({
  apiKey: process.env.OPENAI_API_KEY,
});

export interface ChatMessage {
  role: 'user' | 'assistant' | 'system';
  content: string;
}

export interface GenerateResponseOptions {
  searchResults: VectorSearchResult[];
  userQuestion: string;
  userLanguage: string;
  conversationHistory?: ChatMessage[];
  usedFallback: boolean;
}

/**
 * Build system prompt for Biblical AI assistant
 */
function buildSystemPrompt(language: string, usedFallback: boolean): string {
  const prompts = {
    en: `You are a knowledgeable Biblical AI assistant. Your role is to provide accurate,
thoughtful answers based on Scripture. Always cite Bible verses with their version, book,
chapter, and verse. Provide context and explain the meaning clearly.

${usedFallback ? 'Note: Some verses are from English versions as complete translations in the user\'s language were not available.' : ''}

Format your citations as: [Version] Book Chapter:Verse
Example: [KJV] John 3:16 or [Cornilescu] Ioan 3:16`,

    ro: `Ești un asistent AI biblic priceput. Rolul tău este să oferi răspunsuri precise și
gândite bazate pe Scriptură. Citează întotdeauna versetele biblice cu versiunea, cartea,
capitolul și versetul. Oferă context și explică semnificația clar.

${usedFallback ? 'Notă: Unele versete sunt din versiuni engleze deoarece traducerile complete în română nu erau disponibile.' : ''}

Formatează citările astfel: [Versiune] Carte Capitol:Verset
Exemplu: [Cornilescu] Ioan 3:16`,

    es: `Eres un asistente bíblico de IA conocedor. Tu función es proporcionar respuestas
precisas y reflexivas basadas en las Escrituras. Siempre cita los versículos bíblicos con
su versión, libro, capítulo y versículo. Proporciona contexto y explica el significado claramente.

${usedFallback ? 'Nota: Algunos versículos son de versiones en inglés ya que no había traducciones completas disponibles en español.' : ''}

Formatea tus citas como: [Versión] Libro Capítulo:Versículo
Ejemplo: [RVR1960] Juan 3:16`,

    it: `Sei un assistente biblico AI esperto. Il tuo ruolo è fornire risposte accurate e
ponderate basate sulle Scritture. Cita sempre i versetti biblici con la loro versione, libro,
capitolo e versetto. Fornisci contesto e spiega il significato chiaramente.

${usedFallback ? 'Nota: Alcuni versetti provengono da versioni inglesi poiché traduzioni complete in italiano non erano disponibili.' : ''}

Formatta le tue citazioni come: [Versione] Libro Capitolo:Versetto
Esempio: [NR2006] Giovanni 3:16`,
  };

  return prompts[language as keyof typeof prompts] || prompts.en;
}

/**
 * Format search results into context for AI
 */
function formatSearchResultsContext(results: VectorSearchResult[]): string {
  if (results.length === 0) {
    return 'No specific Bible verses were found for this question.';
  }

  let context = 'Relevant Bible verses:\n\n';

  results.forEach((result, index) => {
    context += `${index + 1}. [${result.version}] ${result.book} ${result.chapter}:${result.verse}\n`;
    context += `   "${result.text}"\n`;
    context += `   (Similarity: ${(result.similarity * 100).toFixed(1)}%)\n\n`;
  });

  return context;
}

/**
 * Generate AI response based on vector search results
 */
export async function generateBiblicalResponse(
  options: GenerateResponseOptions
): Promise<string> {
  const {
    searchResults,
    userQuestion,
    userLanguage,
    conversationHistory = [],
    usedFallback,
  } = options;

  // Build messages array
  const messages: ChatMessage[] = [
    {
      role: 'system',
      content: buildSystemPrompt(userLanguage, usedFallback),
    },
    {
      role: 'system',
      content: formatSearchResultsContext(searchResults),
    },
    ...conversationHistory.slice(-6), // Include last 3 exchanges for context
    {
      role: 'user',
      content: userQuestion,
    },
  ];

  try {
    const response = await openai.chat.completions.create({
      model: 'gpt-4-turbo-preview',
      messages: messages as any,
      temperature: 0.3, // Low temperature for factual responses
      max_tokens: 1500,
      presence_penalty: 0.1,
      frequency_penalty: 0.1,
    });

    const answer = response.choices[0]?.message?.content ||
      'I apologize, but I could not generate a response.';

    return answer;
  } catch (error: any) {
    console.error('AI response generation failed:', error);
    throw new Error(`Failed to generate response: ${error.message}`);
  }
}

Step 3.2: Update Chat API Route

File: /app/api/chat/route.ts

import { NextRequest, NextResponse } from 'next/server';
import { getUserFromToken } from '@/lib/auth';
import { searchWithFallback } from '@/lib/vector-db/search';
import { generateBiblicalResponse } from '@/lib/ai/chat';
import { prisma } from '@/lib/db';

export async function POST(request: NextRequest) {
  try {
    const { message, locale, conversationId } = await request.json();

    // Validate input
    if (!message || !message.trim()) {
      return NextResponse.json(
        { error: 'Message is required' },
        { status: 400 }
      );
    }

    // Get user (optional)
    const token = request.headers.get('authorization')?.replace('Bearer ', '');
    const user = token ? await getUserFromToken(token) : null;

    // Determine language (default to 'en' if not provided)
    const userLanguage = locale || 'en';

    console.log(`💬 Chat request: "${message.substring(0, 50)}..." [${userLanguage}]`);

    // STEP 1: Vector search with fallback
    const searchStartTime = Date.now();
    const { results, usedFallback, searchedLanguages } = await searchWithFallback(
      message,
      userLanguage,
      {
        limit: 10,
        similarityThreshold: 0.7,
      }
    );
    const searchDuration = Date.now() - searchStartTime;

    console.log(`🔍 Vector search completed in ${searchDuration}ms`);
    console.log(`   Results: ${results.length}`);
    console.log(`   Used fallback: ${usedFallback}`);
    console.log(`   Searched languages: ${searchedLanguages.join(', ')}`);

    // STEP 2: Get conversation history (if conversationId provided)
    let conversationHistory: any[] = [];
    if (conversationId && user) {
      const conversation = await prisma.conversation.findUnique({
        where: { id: conversationId, userId: user.id },
        include: {
          messages: {
            orderBy: { createdAt: 'asc' },
            take: 10, // Last 5 exchanges
          },
        },
      });

      if (conversation) {
        conversationHistory = conversation.messages.map(msg => ({
          role: msg.role,
          content: msg.content,
        }));
      }
    }

    // STEP 3: Generate AI response
    const aiStartTime = Date.now();
    const response = await generateBiblicalResponse({
      searchResults: results,
      userQuestion: message,
      userLanguage,
      conversationHistory,
      usedFallback,
    });
    const aiDuration = Date.now() - aiStartTime;

    console.log(`🤖 AI response generated in ${aiDuration}ms`);

    // STEP 4: Save conversation (if user is logged in)
    if (user) {
      let conversation;

      if (conversationId) {
        // Add to existing conversation
        conversation = await prisma.conversation.findUnique({
          where: { id: conversationId, userId: user.id },
        });
      }

      if (!conversation) {
        // Create new conversation
        conversation = await prisma.conversation.create({
          data: {
            userId: user.id,
            title: message.substring(0, 100), // Use first message as title
          },
        });
      }

      // Save user message
      await prisma.message.create({
        data: {
          conversationId: conversation.id,
          role: 'user',
          content: message,
        },
      });

      // Save AI response
      await prisma.message.create({
        data: {
          conversationId: conversation.id,
          role: 'assistant',
          content: response,
          metadata: {
            searchResults: results.slice(0, 5), // Top 5 results
            usedFallback,
            searchDuration,
            aiDuration,
          },
        },
      });

      conversationId = conversation.id;
    }

    // STEP 5: Return response
    return NextResponse.json({
      response,
      conversationId,
      metadata: {
        searchResults: results.slice(0, 5),
        usedFallback,
        searchedLanguages,
        timings: {
          search: searchDuration,
          ai: aiDuration,
          total: searchDuration + aiDuration,
        },
      },
    });

  } catch (error: any) {
    console.error('Chat API error:', error);
    return NextResponse.json(
      { error: error.message || 'Failed to process chat request' },
      { status: 500 }
    );
  }
}

export const runtime = 'nodejs';
export const dynamic = 'force-dynamic';

---

Phase 4: Testing & Validation (Day 6-7)

Step 4.1: Create Test Suite

File: /scripts/test-vector-search.ts

import { searchWithFallback } from '@/lib/vector-db/search';

interface TestCase {
  question: string;
  language: string;
  expectedBook?: string;
  expectedMinResults: number;
}

const testCases: TestCase[] = [
  // English tests
  {
    question: 'What does the Bible say about love?',
    language: 'en',
    expectedBook: '1 Corinthians',
    expectedMinResults: 5,
  },
  {
    question: 'Who was Jesus?',
    language: 'en',
    expectedBook: 'John',
    expectedMinResults: 5,
  },

  // Romanian tests
  {
    question: 'Ce spune Biblia despre iubire?',
    language: 'ro',
    expectedBook: '1 Corinteni',
    expectedMinResults: 3,
  },
  {
    question: 'Cine a fost Moise?',
    language: 'ro',
    expectedBook: 'Exod',
    expectedMinResults: 3,
  },

  // Spanish tests
  {
    question: '¿Qué dice la Biblia sobre la fe?',
    language: 'es',
    expectedBook: 'Hebreos',
    expectedMinResults: 3,
  },

  // Italian tests
  {
    question: 'Chi era Davide?',
    language: 'it',
    expectedBook: '1 Samuele',
    expectedMinResults: 2,
  },

  // Fallback test (incomplete Italian version)
  {
    question: 'Tell me about Job', // Asking in English but with Italian locale
    language: 'it',
    expectedMinResults: 2, // Should fallback to English
  },
];

async function runTests() {
  console.log('🧪 Starting Vector Search Tests\n');

  let passedTests = 0;
  let failedTests = 0;

  for (const [index, testCase] of testCases.entries()) {
    console.log(`Test ${index + 1}/${testCases.length}: ${testCase.question} [${testCase.language}]`);

    try {
      const startTime = Date.now();
      const { results, usedFallback, searchedLanguages } = await searchWithFallback(
        testCase.question,
        testCase.language,
        { limit: 10, similarityThreshold: 0.7 }
      );
      const duration = Date.now() - startTime;

      // Check results count
      const hasEnoughResults = results.length >= testCase.expectedMinResults;

      // Check if expected book is in results (if specified)
      const hasExpectedBook = testCase.expectedBook
        ? results.some(r => r.book.includes(testCase.expectedBook!))
        : true;

      if (hasEnoughResults && hasExpectedBook) {
        console.log(`  ✅ PASSED (${duration}ms)`);
        console.log(`     Found ${results.length} results`);
        console.log(`     Top result: [${results[0]?.version}] ${results[0]?.book} ${results[0]?.chapter}:${results[0]?.verse}`);
        console.log(`     Similarity: ${(results[0]?.similarity * 100).toFixed(1)}%`);
        console.log(`     Used fallback: ${usedFallback}`);
        console.log(`     Languages: ${searchedLanguages.join(', ')}\n`);
        passedTests++;
      } else {
        console.log(`  ❌ FAILED`);
        console.log(`     Expected min results: ${testCase.expectedMinResults}, got: ${results.length}`);
        if (testCase.expectedBook) {
          console.log(`     Expected book: ${testCase.expectedBook}, found: ${hasExpectedBook}`);
        }
        console.log('');
        failedTests++;
      }
    } catch (error: any) {
      console.log(`  ❌ ERROR: ${error.message}\n`);
      failedTests++;
    }
  }

  console.log(`\n📊 Test Summary:`);
  console.log(`   ✅ Passed: ${passedTests}`);
  console.log(`   ❌ Failed: ${failedTests}`);
  console.log(`   Total: ${testCases.length}`);
  console.log(`   Success Rate: ${((passedTests / testCases.length) * 100).toFixed(1)}%`);
}

runTests().catch(console.error);

Run tests:

npx tsx scripts/test-vector-search.ts

---

7. Vector Search Logic

7.1 Search Priority Algorithm

/**
 * Multi-Language Search Algorithm
 *
 * Priority Order:
 * 1. Primary Language Complete Versions (is_complete = true)
 * 2. Primary Language Partial Versions (is_complete = false)
 * 3. English Complete Versions (fallback)
 *
 * Quality Checks:
 * - Minimum results: 3
 * - Minimum similarity: 0.7
 * - Top result similarity: 0.75 (for confidence)
 */

// Pseudocode
function searchMultiLanguage(query, primaryLanguage) {
  // Step 1: Search primary language
  primaryResults = searchLanguage(query, primaryLanguage)

  // Step 2: Quality check
  if (hasGoodQuality(primaryResults)) {
    return {
      results: primaryResults,
      usedFallback: false
    }
  }

  // Step 3: Fallback to English
  if (primaryLanguage !== 'en') {
    englishResults = searchLanguage(query, 'en')
    combinedResults = merge(primaryResults, englishResults)

    return {
      results: combinedResults,
      usedFallback: true
    }
  }

  // Step 4: Return whatever we have
  return {
    results: primaryResults,
    usedFallback: false
  }
}

function hasGoodQuality(results) {
  return (
    results.length >= 3 &&
    results[0].similarity >= 0.75
  )
}

7.2 Similarity Scoring

Cosine Similarity Interpretation:

1.0 = Perfect match (identical)
0.9-1.0 = Extremely relevant
0.8-0.9 = Highly relevant
0.7-0.8 = Relevant (threshold)
0.6-0.7 = Somewhat relevant
< 0.6 = Not relevant (filtered out)

Threshold Configuration:

const SIMILARITY_THRESHOLDS = {
  minimum: 0.7,        // Filter out results below this
  confident: 0.75,     // Consider search "confident" if top result above this
  excellent: 0.85,     // Exceptional match
};

---

8. API Integration

8.1 Frontend Chat Component Updates

File: /components/chat/floating-chat.tsx

Add to existing component:

// Add loading state for vector search
const [isSearching, setIsSearching] = useState(false);

// Update sendMessage function
const sendMessage = async () => {
  if (!inputMessage.trim()) return;

  const userMessage = inputMessage.trim();
  setInputMessage('');

  // Add user message to UI
  setMessages(prev => [...prev, {
    role: 'user',
    content: userMessage,
  }]);

  setIsSearching(true); // Show "searching Bible..." indicator

  try {
    const token = getToken();
    const response = await fetch('/api/chat', {
      method: 'POST',
      headers: {
        'Content-Type': 'application/json',
        ...(token && { Authorization: `Bearer ${token}` }),
      },
      body: JSON.stringify({
        message: userMessage,
        locale: locale,
        conversationId: currentConversationId,
      }),
    });

    const data = await response.json();

    if (!response.ok) {
      throw new Error(data.error || 'Failed to get response');
    }

    // Add AI response to UI
    setMessages(prev => [...prev, {
      role: 'assistant',
      content: data.response,
      metadata: data.metadata, // Includes search results, timings
    }]);

    // Update conversation ID
    if (data.conversationId) {
      setCurrentConversationId(data.conversationId);
    }

  } catch (error: any) {
    console.error('Chat error:', error);
    setMessages(prev => [...prev, {
      role: 'assistant',
      content: 'I apologize, but I encountered an error. Please try again.',
    }]);
  } finally {
    setIsSearching(false);
  }
};

Add search indicator:

{isSearching && (
  <Box sx={{ display: 'flex', alignItems: 'center', gap: 1, p: 2 }}>
    <CircularProgress size={16} />
    <Typography variant="body2" color="text.secondary">
      Searching Bible verses...
    </Typography>
  </Box>
)}

---

9. Testing Strategy

9.1 Test Scenarios

Scenario 1: English Question

Input: "What does the Bible say about faith?"
Language: en
Expected:
  - Search: bible_vectors_kjv, bible_vectors_niv, bible_vectors_esv
  - Results: Hebrews 11:1, Romans 10:17, Ephesians 2:8-9
  - Citations: [KJV] Hebrews 11:1, [NIV] Hebrews 11:1
  - Fallback: No

Scenario 2: Romanian Question

Input: "Ce spune Biblia despre credință?"
Language: ro
Expected:
  - Search: bible_vectors_cornilescu
  - Results: Evrei 11:1, Romani 10:17
  - Citations: [Cornilescu] Evrei 11:1
  - Fallback: No (if Romanian complete)

Scenario 3: Italian with Fallback

Input: "Chi era Giobbe?"
Language: it
Expected:
  - Search: bible_vectors_nuova_riveduta (incomplete)
  - Results: Limited results
  - Fallback: Yes (search English versions)
  - Citations: [KJV] Job 1:1, [NIV] Job 1:1
  - Response: Italian with English verse references

Scenario 4: Obscure Topic

Input: "Tell me about the Nephilim"
Language: en
Expected:
  - Search: All English versions
  - Results: Genesis 6:4, Numbers 13:33
  - Citations: Multiple versions showing different translations
  - Context: Explanation of who Nephilim were

9.2 Performance Benchmarks

Target Metrics:

Vector Search: < 2000ms
AI Generation: < 3000ms
Total Response Time: < 5000ms
Concurrent Users: 100+
Success Rate: > 95%

Load Testing:

# Use Apache Bench or similar
ab -n 100 -c 10 -H "Content-Type: application/json" \
   -p test-request.json \
   http://localhost:3010/api/chat

---

10. Deployment & Monitoring

10.1 Pre-Deployment Checklist

• All vector tables verified and populated
• bible_version_config table populated correctly
• Environment variables set (OPENAI_API_KEY, VECTOR_DB_URL)
• Database connection pool configured (max: 20)
• Vector search tested for all languages
• Fallback logic tested
• AI response quality reviewed
• Performance benchmarks met
• Error handling tested
• Logging configured

10.2 Monitoring Setup

Metrics to Track:

1. Search Performance

   • Vector search duration
   • AI generation duration
   • Total response time
   • Cache hit rate

2. Quality Metrics

   • Average similarity score
   • Fallback frequency per language
   • Results count distribution
   • User satisfaction (if tracked)

3. Error Rates

   • Vector DB connection failures
   • AI API errors
   • Embedding generation failures
   • Timeout errors

Logging Example:

// Add to chat API route
console.log({
  timestamp: new Date().toISOString(),
  userId: user?.id || 'anonymous',
  language: userLanguage,
  questionLength: message.length,
  searchDuration,
  aiDuration,
  totalDuration: searchDuration + aiDuration,
  resultsCount: results.length,
  topSimilarity: results[0]?.similarity,
  usedFallback,
  searchedLanguages,
});

10.3 Optimization Opportunities

Future Enhancements:

1. Caching Layer

   • Cache common questions/embeddings (Redis)
   • Cache Bible version configs (in-memory)
   • Cache top searches by language

2. Advanced Search

   • Semantic search refinement
   • Cross-reference discovery
   • Topic clustering
   • Historical context integration

3. Personalization

   • User's preferred Bible version
   • Reading history integration
   • Personalized recommendations

4. Analytics

   • Popular questions tracking
   • Language usage statistics
   • Conversion tracking (chat → Bible reader)

---

Implementation Checklist

Phase 1: Setup (Day 1)

• Run connection verification script
• Enumerate all vector tables
• Create bible_version_config table
• Populate version mappings
• Test vector DB connections

Phase 2: Vector Search (Day 2-3)

• Create vector DB connection pool
• Implement version config service
• Build embedding service with cache
• Create multi-language search function
• Implement fallback logic
• Write unit tests for search

Phase 3: AI Integration (Day 4-5)

• Build system prompt generator
• Create response formatter
• Update chat API route
• Integrate vector search
• Add conversation persistence
• Test end-to-end flow

Phase 4: Testing (Day 6-7)

• Run automated test suite
• Test all 4 languages
• Test fallback scenarios
• Performance testing
• Quality review
• User acceptance testing

Phase 5: Deployment (Day 8)

• Deploy to production
• Monitor logs
• Track performance metrics
• Gather user feedback
• Document issues/improvements

---

Appendix

A. SQL Queries Reference

Create Version Config Table:

CREATE TABLE bible_version_config (
  id SERIAL PRIMARY KEY,
  table_name VARCHAR(100) UNIQUE NOT NULL,
  version_name VARCHAR(100) NOT NULL,
  version_abbreviation VARCHAR(20) NOT NULL,
  language VARCHAR(5) NOT NULL,
  is_complete BOOLEAN DEFAULT true,
  books_count INTEGER,
  verses_count INTEGER,
  created_at TIMESTAMP DEFAULT NOW(),
  metadata JSONB
);

CREATE INDEX idx_version_language ON bible_version_config(language);
CREATE INDEX idx_version_table ON bible_version_config(table_name);

Count Verses Per Version:

-- Replace {table_name} with actual table
SELECT
  '{version_abbreviation}' as version,
  COUNT(*) as total_verses,
  COUNT(DISTINCT book) as total_books
FROM {table_name};

Find Missing Books:

-- Check which books are missing in a version
WITH all_books AS (
  SELECT DISTINCT book FROM bible_vectors_kjv -- Use complete English version as reference
)
SELECT ab.book
FROM all_books ab
LEFT JOIN bible_vectors_nuova_riveduta nr ON ab.book = nr.book
WHERE nr.book IS NULL;

B. Environment Variables

# AI API Keys
OPENAI_API_KEY=sk-proj-...
ANTHROPIC_API_KEY=sk-ant-...  # Optional

# Vector Database
VECTOR_DB_URL=postgresql://user:password@host:5432/database
VECTOR_DB_PASSWORD=...        # If separate

# Application
NEXT_PUBLIC_APP_URL=https://biblical-guide.com
NODE_ENV=production

# Optional: Caching
REDIS_URL=redis://...         # For future caching layer

C. Troubleshooting Guide

Issue: Vector search returns no results

Possible causes:
1. Embedding dimension mismatch
2. Similarity threshold too high
3. Table name incorrect
4. pgvector extension not installed

Debug:
- Check embedding dimensions: SELECT pg_column_size(embedding) / 4 FROM table LIMIT 1;
- Lower threshold: Try 0.5 instead of 0.7
- Verify table exists: SELECT * FROM pg_tables WHERE tablename = 'bible_vectors_kjv';
- Check extension: SELECT * FROM pg_extension WHERE extname = 'vector';

Issue: AI responses are not in user's language

Possible causes:
1. System prompt not specifying language
2. Locale not passed correctly
3. Fallback message not translated

Debug:
- Check locale parameter in API request
- Verify system prompt includes language instruction
- Review response generation function

Issue: Slow vector search (> 5 seconds)

Possible causes:
1. Missing vector index
2. Too many tables searched
3. Large result limit
4. Database connection pool exhausted

Solutions:
- Create index: CREATE INDEX ON table USING ivfflat (embedding vector_cosine_ops);
- Limit tables searched (only user's language first)
- Reduce limit from 20 to 10
- Increase connection pool size

---

Next Steps

1. Immediate: Run Phase 1 verification scripts
2. Day 2-3: Implement vector search service
3. Day 4-5: Integrate into chat API
4. Day 6-7: Comprehensive testing
5. Day 8: Production deployment

Questions to Answer Before Starting:

1. What is your vector database provider? (Supabase, PostgreSQL+pgvector, Pinecone, etc.)
2. Are all embeddings using OpenAI's ada-002 model (1536 dimensions)?
3. Do you have a complete list of all Bible version table names?
4. Which AI model do you prefer? (GPT-4, Claude, etc.)

---

Document End

This plan will be updated as implementation progresses.