feat: implement AI chat with vector search and random loading messages
Major Features: - ✅ AI chat with Azure OpenAI GPT-4o integration - ✅ Vector search across Bible versions (ASV English, RVA 1909 Spanish) - ✅ Multi-language support with automatic English fallback - ✅ Bible version citations in responses [ASV] [RVA 1909] - ✅ Random Bible-themed loading messages (5 variants) - ✅ Safe build script with memory guardrails - ✅ 8GB swap memory for build safety - ✅ Stripe donation integration (multiple payment methods) AI Chat Improvements: - Implement vector search with 1536-dim embeddings (Azure text-embedding-ada-002) - Search all Bible versions in user's language, fallback to English - Cite Bible versions properly in AI responses - Add 5 random loading messages: "Searching the Scriptures...", etc. - Fix Ollama conflict (disabled to use Azure OpenAI exclusively) - Optimize hybrid search queries for actual table schema Build & Infrastructure: - Create safe-build.sh script with memory monitoring (prevents server crashes) - Add 8GB swap memory for emergency relief - Document build process in BUILD_GUIDE.md - Set Node.js memory limits (4GB max during builds) Database: - Clean up 115 old vector tables with wrong dimensions - Keep only 2 tables with correct 1536-dim embeddings - Add Stripe schema for donations and subscriptions Documentation: - AI_CHAT_FINAL_STATUS.md - Complete implementation status - AI_CHAT_IMPLEMENTATION_COMPLETE.md - Technical details - BUILD_GUIDE.md - Safe building guide with guardrails - CHAT_LOADING_MESSAGES.md - Loading messages implementation - STRIPE_IMPLEMENTATION_COMPLETE.md - Stripe integration docs - STRIPE_SETUP_GUIDE.md - Stripe configuration guide 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
This commit is contained in:
12
lib/stripe-server.ts
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12
lib/stripe-server.ts
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import Stripe from 'stripe'
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if (!process.env.STRIPE_SECRET_KEY) {
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throw new Error('STRIPE_SECRET_KEY is not defined in environment variables')
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}
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// Initialize Stripe on the server side ONLY
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// This file should NEVER be imported in client-side code
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export const stripe = new Stripe(process.env.STRIPE_SECRET_KEY, {
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apiVersion: '2025-09-30.clover',
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typescript: true,
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})
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35
lib/stripe.ts
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35
lib/stripe.ts
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@@ -0,0 +1,35 @@
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import { loadStripe, Stripe as StripeClient } from '@stripe/stripe-js'
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// Initialize Stripe on the client side
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let stripePromise: Promise<StripeClient | null>
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export const getStripe = () => {
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if (!stripePromise) {
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stripePromise = loadStripe(process.env.NEXT_PUBLIC_STRIPE_PUBLISHABLE_KEY!)
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}
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return stripePromise
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}
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// Donation amount presets (in USD)
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export const DONATION_PRESETS = [
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{ amount: 5, label: '$5' },
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{ amount: 10, label: '$10' },
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{ amount: 25, label: '$25' },
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{ amount: 50, label: '$50' },
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{ amount: 100, label: '$100' },
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{ amount: 250, label: '$250' },
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]
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// Helper function to format amount in cents to dollars
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export const formatAmount = (amountInCents: number, currency: string = 'usd'): string => {
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const formatter = new Intl.NumberFormat('en-US', {
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style: 'currency',
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currency: currency.toUpperCase(),
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minimumFractionDigits: 2,
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})
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return formatter.format(amountInCents / 100)
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}
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// Helper function to convert dollars to cents
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export const dollarsToCents = (dollars: number): number => {
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return Math.round(dollars * 100)
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}
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@@ -10,20 +10,54 @@ function safeIdent(s: string): string {
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return s.toLowerCase().replace(/[^a-z0-9_]+/g, '_').replace(/^_+|_+$/g, '')
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}
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// Get ALL vector tables for a given language
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// Get ALL vector tables for a given language that match the expected embedding dimensions
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async function getAllVectorTables(language: string): Promise<string[]> {
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const lang = safeIdent(language || 'ro')
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const expectedDims = parseInt(process.env.EMBED_DIMS || '1536', 10)
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// For now, use a hardcoded whitelist of tables we know have 1536 dimensions
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// This is much faster than querying each table
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const knownGoodTables: Record<string, string[]> = {
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'en': ['bv_en_eng_asv'],
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'es': ['bv_es_sparv1909'],
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// Add more as we create them
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}
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if (knownGoodTables[lang]) {
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return knownGoodTables[lang].map(table => `${VECTOR_SCHEMA}."${table}"`)
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}
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// Fallback: check dynamically (slower)
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const client = await pool.connect()
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try {
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// Get all vector tables for this language
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const result = 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 $2
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ORDER BY table_name`,
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ORDER BY table_name
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LIMIT 10`,
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[VECTOR_SCHEMA, `bv_${lang}_%`]
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)
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return result.rows.map(row => `${VECTOR_SCHEMA}."${row.table_name}"`)
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// Quick check: just try the first table and see if it works
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if (result.rows.length > 0) {
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const firstTable = `${VECTOR_SCHEMA}."${result.rows[0].table_name}"`
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try {
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const dimCheck = await client.query(
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`SELECT pg_column_size(embedding) as size FROM ${firstTable} WHERE embedding IS NOT NULL LIMIT 1`
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)
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if (dimCheck.rows.length > 0) {
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const actualDims = Math.round(dimCheck.rows[0].size / 4)
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if (Math.abs(actualDims - expectedDims) <= 5) {
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// If first table matches, assume all do (they should be consistent)
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return result.rows.map(row => `${VECTOR_SCHEMA}."${row.table_name}"`)
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}
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}
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} catch (error) {
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console.warn(`Dimension check failed for ${lang}:`, error)
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}
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}
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return []
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} finally {
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client.release()
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}
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@@ -104,54 +138,77 @@ export async function getEmbedding(text: string): Promise<number[]> {
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export async function searchBibleSemantic(
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query: string,
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language: string = 'ro',
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limit: number = 10
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limit: number = 10,
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fallbackToEnglish: boolean = true
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): Promise<BibleVerse[]> {
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try {
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const tables = await getAllVectorTables(language)
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console.log(`🔍 Searching Bible: language="${language}", query="${query.substring(0, 50)}..."`)
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let tables = await getAllVectorTables(language)
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console.log(` Found ${tables.length} table(s) for language "${language}":`, tables.map(t => t.split('.')[1]))
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const queryEmbedding = await getEmbedding(query)
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const client = await pool.connect()
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try {
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if (tables.length === 0) {
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// Fallback to legacy bible_passages table
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const sql = `SELECT ref, book, chapter, verse, text_raw,
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1 - (embedding <=> $1) AS similarity
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FROM bible_passages
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WHERE embedding IS NOT NULL AND lang = $3
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ORDER BY embedding <=> $1
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LIMIT $2`
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const result = await client.query(sql, [JSON.stringify(queryEmbedding), limit, language])
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return result.rows
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try {
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let allResults: BibleVerse[] = []
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// Search in primary language tables
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if (tables.length > 0) {
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const limitPerTable = Math.max(5, Math.ceil(limit * 1.5 / tables.length))
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for (const table of tables) {
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try {
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const sql = `SELECT ref, book, chapter, verse, text_raw,
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1 - (embedding <=> $1) AS similarity,
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'${table}' as source_table
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FROM ${table}
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WHERE embedding IS NOT NULL
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ORDER BY embedding <=> $1
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LIMIT $2`
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const result = await client.query(sql, [JSON.stringify(queryEmbedding), limitPerTable])
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console.log(` ✓ ${table.split('.')[1]}: found ${result.rows.length} verses`)
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allResults.push(...result.rows)
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} catch (tableError) {
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console.warn(` ✗ Error querying ${table}:`, tableError)
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}
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}
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}
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// Query all vector tables and combine results
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const allResults: BibleVerse[] = []
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const limitPerTable = Math.max(1, Math.ceil(limit * 2 / tables.length))
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// Fallback to English if no results and fallback enabled
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if (allResults.length === 0 && fallbackToEnglish && language !== 'en') {
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console.log(` ⚠️ No results in "${language}", falling back to English...`)
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const englishTables = await getAllVectorTables('en')
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console.log(` Found ${englishTables.length} English table(s)`)
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for (const table of tables) {
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try {
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const sql = `SELECT ref, book, chapter, verse, text_raw,
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1 - (embedding <=> $1) AS similarity,
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'${table}' as source_table
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FROM ${table}
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WHERE embedding IS NOT NULL
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ORDER BY embedding <=> $1
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LIMIT $2`
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for (const table of englishTables) {
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try {
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const sql = `SELECT ref, book, chapter, verse, text_raw,
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1 - (embedding <=> $1) AS similarity,
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'${table}' as source_table
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FROM ${table}
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WHERE embedding IS NOT NULL
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ORDER BY embedding <=> $1
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LIMIT $2`
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const result = await client.query(sql, [JSON.stringify(queryEmbedding), limitPerTable])
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allResults.push(...result.rows)
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} catch (tableError) {
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console.warn(`Error querying table ${table}:`, tableError)
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// Continue with other tables
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const result = await client.query(sql, [JSON.stringify(queryEmbedding), limit])
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console.log(` ✓ ${table.split('.')[1]} (EN fallback): found ${result.rows.length} verses`)
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allResults.push(...result.rows)
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} catch (tableError) {
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console.warn(` ✗ Error querying ${table}:`, tableError)
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}
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}
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}
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// Sort all results by similarity and return top results
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return allResults
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const topResults = allResults
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.sort((a, b) => (b.similarity || 0) - (a.similarity || 0))
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.slice(0, limit)
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console.log(` ✅ Returning ${topResults.length} total verses`)
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return topResults
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} finally {
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client.release()
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}
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@@ -164,85 +221,84 @@ export async function searchBibleSemantic(
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export async function searchBibleHybrid(
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query: string,
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language: string = 'ro',
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limit: number = 10
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limit: number = 10,
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fallbackToEnglish: boolean = true
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): Promise<BibleVerse[]> {
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try {
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const tables = await getAllVectorTables(language)
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console.log(`🔍 Hybrid Search: language="${language}", query="${query.substring(0, 50)}..."`)
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let tables = await getAllVectorTables(language)
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console.log(` Found ${tables.length} table(s) for language "${language}"`)
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const queryEmbedding = await getEmbedding(query)
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// Use appropriate text search configuration based on language
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const textConfig = language === 'ro' ? 'romanian' : 'english'
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const textConfig = language === 'ro' ? 'romanian' : language === 'es' ? 'spanish' : 'english'
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const client = await pool.connect()
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try {
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if (tables.length === 0) {
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// Fallback to legacy bible_passages table
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const sql = `WITH vector_search AS (
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SELECT id, 1 - (embedding <=> $1) AS vector_sim
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FROM bible_passages
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WHERE embedding IS NOT NULL AND lang = $4
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ORDER BY embedding <=> $1
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LIMIT 100
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),
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text_search AS (
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SELECT id, ts_rank(tsv, plainto_tsquery($5, $3)) AS text_rank
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FROM bible_passages
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WHERE tsv @@ plainto_tsquery($5, $3) AND lang = $4
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)
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SELECT bp.ref, bp.book, bp.chapter, bp.verse, bp.text_raw,
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COALESCE(vs.vector_sim, 0) * 0.7 + COALESCE(ts.text_rank, 0) * 0.3 AS combined_score
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FROM bible_passages bp
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LEFT JOIN vector_search vs ON vs.id = bp.id
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LEFT JOIN text_search ts ON ts.id = bp.id
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WHERE (vs.id IS NOT NULL OR ts.id IS NOT NULL) AND bp.lang = $4
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ORDER BY combined_score DESC
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LIMIT $2`
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const result = await client.query(sql, [JSON.stringify(queryEmbedding), limit, query, language, textConfig])
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return result.rows
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try {
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let allResults: BibleVerse[] = []
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// Search in primary language tables
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if (tables.length > 0) {
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const limitPerTable = Math.max(5, Math.ceil(limit * 1.5 / tables.length))
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for (const table of tables) {
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try {
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// Use simple semantic search (no text search - TSV column doesn't exist)
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const sql = `SELECT book || ' ' || chapter || ':' || verse as ref,
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book, chapter, verse, text_raw,
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1 - (embedding <=> $1) AS similarity,
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1 - (embedding <=> $1) AS combined_score,
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'${table}' as source_table
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FROM ${table}
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WHERE embedding IS NOT NULL
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ORDER BY embedding <=> $1
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LIMIT $2`
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const result = await client.query(sql, [JSON.stringify(queryEmbedding), limitPerTable])
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console.log(` ✓ ${table.split('.')[1]}: found ${result.rows.length} verses`)
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allResults.push(...result.rows)
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} catch (tableError) {
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console.warn(` ✗ Error querying ${table}:`, tableError)
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}
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}
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}
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// Query all vector tables and combine results
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const allResults: BibleVerse[] = []
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const limitPerTable = Math.max(1, Math.ceil(limit * 2 / tables.length)) // Get more results per table to ensure good diversity
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// Fallback to English if no results and fallback enabled
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if (allResults.length === 0 && fallbackToEnglish && language !== 'en') {
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console.log(` ⚠️ No results in "${language}", falling back to English...`)
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const englishTables = await getAllVectorTables('en')
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console.log(` Found ${englishTables.length} English table(s)`)
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for (const table of tables) {
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try {
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const sql = `WITH vector_search AS (
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SELECT id, 1 - (embedding <=> $1) AS vector_sim
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FROM ${table}
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WHERE embedding IS NOT NULL
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ORDER BY embedding <=> $1
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LIMIT 100
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),
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text_search AS (
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SELECT id, ts_rank(tsv, plainto_tsquery($4, $3)) AS text_rank
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FROM ${table}
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WHERE tsv @@ plainto_tsquery($4, $3)
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)
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SELECT bp.ref, bp.book, bp.chapter, bp.verse, bp.text_raw,
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COALESCE(vs.vector_sim, 0) * 0.7 + COALESCE(ts.text_rank, 0) * 0.3 AS combined_score,
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'${table}' as source_table
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FROM ${table} bp
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LEFT JOIN vector_search vs ON vs.id = bp.id
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LEFT JOIN text_search ts ON ts.id = bp.id
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WHERE (vs.id IS NOT NULL OR ts.id IS NOT NULL)
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ORDER BY combined_score DESC
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LIMIT $2`
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for (const table of englishTables) {
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try {
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// Use simple semantic search (no text search - TSV column doesn't exist)
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const sql = `SELECT book || ' ' || chapter || ':' || verse as ref,
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book, chapter, verse, text_raw,
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1 - (embedding <=> $1) AS similarity,
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1 - (embedding <=> $1) AS combined_score,
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'${table}' as source_table
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FROM ${table}
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WHERE embedding IS NOT NULL
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ORDER BY embedding <=> $1
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LIMIT $2`
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const result = await client.query(sql, [JSON.stringify(queryEmbedding), limitPerTable, query, textConfig])
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allResults.push(...result.rows)
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} catch (tableError) {
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console.warn(`Error querying table ${table}:`, tableError)
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// Continue with other tables
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const result = await client.query(sql, [JSON.stringify(queryEmbedding), limit])
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console.log(` ✓ ${table.split('.')[1]} (EN fallback): found ${result.rows.length} verses`)
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allResults.push(...result.rows)
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} catch (tableError) {
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console.warn(` ✗ Error querying ${table}:`, tableError)
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}
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}
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}
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// Sort all results by combined score and return top results
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return allResults
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const topResults = allResults
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.sort((a, b) => (b.combined_score || 0) - (a.combined_score || 0))
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.slice(0, limit)
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console.log(` ✅ Returning ${topResults.length} total verses`)
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return topResults
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} finally {
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client.release()
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}
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Reference in New Issue
Block a user