Add Ollama embedding support and improve prayer system with public/private visibility

- Add Ollama fallback support in vector search with Azure OpenAI as primary
- Enhance prayer system with public/private visibility options and language filtering
- Update OG image to use new biblical-guide-og-image.png
- Improve prayer request management with better categorization
- Remove deprecated ingest_json_pgvector.py script

🤖 Generated with [Claude Code](https://claude.ai/code)

Co-Authored-By: Claude <noreply@anthropic.com>
This commit is contained in:
2025-09-28 19:25:49 +00:00
parent 2d27eae756
commit e4b815cb40
8 changed files with 457 additions and 320 deletions

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@@ -16,6 +16,7 @@ export async function generateMetadata({ params }: { params: Promise<{ locale: s
const currentUrl = locale === 'ro' ? 'https://biblical-guide.com/ro/' : 'https://biblical-guide.com/en/'
const alternateUrl = locale === 'ro' ? 'https://biblical-guide.com/en/' : 'https://biblical-guide.com/ro/'
const ogImageUrl = 'https://biblical-guide.com/biblical-guide-og-image.png'
return {
title: t('title'),
@@ -38,7 +39,7 @@ export async function generateMetadata({ params }: { params: Promise<{ locale: s
type: 'website',
images: [
{
url: `https://ghidulbiblic.ro/og-image-${locale}.jpg`,
url: ogImageUrl,
width: 1200,
height: 630,
alt: t('ogTitle'),
@@ -50,7 +51,7 @@ export async function generateMetadata({ params }: { params: Promise<{ locale: s
site: '@ghidbiblic',
title: t('twitterTitle'),
description: t('twitterDescription'),
images: [`https://ghidulbiblic.ro/og-image-${locale}.jpg`],
images: [ogImageUrl],
},
other: {
'application/ld+json': JSON.stringify({

View File

@@ -15,9 +15,6 @@ import {
DialogTitle,
DialogContent,
DialogActions,
List,
ListItem,
ListItemAvatar,
ListItemText,
MenuItem,
useTheme,
@@ -27,6 +24,10 @@ import {
Tabs,
Tab,
FormControlLabel,
FormControl,
Select,
Checkbox,
SelectChangeEvent,
Switch,
} from '@mui/material'
import {
@@ -42,7 +43,7 @@ import {
Edit,
Login,
} from '@mui/icons-material'
import { useState, useEffect } from 'react'
import { useState, useEffect, useMemo } from 'react'
import { useTranslations, useLocale, useFormatter } from 'next-intl'
import { useAuth } from '@/hooks/use-auth'
@@ -55,6 +56,9 @@ interface PrayerRequest {
timestamp: Date
prayerCount: number
isPrayedFor: boolean
isPublic: boolean
language: string
isOwner: boolean
}
export default function PrayersPage() {
@@ -72,10 +76,50 @@ export default function PrayersPage() {
title: '',
description: '',
category: 'personal',
isPublic: false,
})
const [aiPrompt, setAiPrompt] = useState('')
const [isGenerating, setIsGenerating] = useState(false)
const [loading, setLoading] = useState(true)
const [viewMode, setViewMode] = useState<'private' | 'public'>(user ? 'private' : 'public')
const [selectedLanguages, setSelectedLanguages] = useState<string[]>([locale])
const languagesKey = useMemo(() => selectedLanguages.slice().sort().join(','), [selectedLanguages])
const languageOptions = useMemo(() => ([
{ value: 'en', label: t('languageFilter.options.en') },
{ value: 'ro', label: t('languageFilter.options.ro') }
]), [t])
const languageLabelMap = useMemo(() => (
languageOptions.reduce((acc, option) => {
acc[option.value] = option.label
return acc
}, {} as Record<string, string>)
), [languageOptions])
useEffect(() => {
if (user) {
setViewMode(prev => (prev === 'private' ? prev : 'private'))
} else {
setViewMode('public')
}
}, [user])
useEffect(() => {
if (viewMode === 'public') {
setSelectedLanguages(prev => {
if (prev.includes(locale)) {
return prev
}
return [...prev, locale]
})
}
}, [locale, viewMode])
useEffect(() => {
if (viewMode === 'public' && selectedLanguages.length === 0) {
setSelectedLanguages([locale])
}
}, [viewMode, selectedLanguages, locale])
const categories = [
{ value: 'personal', label: t('categories.personal'), color: 'primary' },
@@ -88,6 +132,12 @@ export default function PrayersPage() {
// Fetch prayers from API
const fetchPrayers = async () => {
if (viewMode === 'private' && !user) {
setPrayers([])
setLoading(false)
return
}
setLoading(true)
try {
const params = new URLSearchParams()
@@ -95,11 +145,25 @@ export default function PrayersPage() {
params.append('category', selectedCategory)
}
params.append('limit', '50')
if (user?.id) {
params.append('userId', user.id)
params.append('visibility', viewMode)
if (viewMode === 'public') {
const languagesToQuery = selectedLanguages.length > 0 ? selectedLanguages : [locale]
languagesToQuery.forEach(lang => params.append('languages', lang))
}
const response = await fetch(`/api/prayers?${params.toString()}`)
const headers: Record<string, string> = {}
if (typeof window !== 'undefined') {
const token = localStorage.getItem('authToken')
if (token) {
headers['Authorization'] = `Bearer ${token}`
}
}
const response = await fetch(`/api/prayers?${params.toString()}`, {
headers
})
if (response.ok) {
const data = await response.json()
setPrayers(data.prayers.map((prayer: any) => ({
@@ -107,6 +171,9 @@ export default function PrayersPage() {
timestamp: new Date(prayer.timestamp)
})))
} else {
if (response.status === 401) {
setPrayers([])
}
console.error('Failed to fetch prayers')
}
} catch (error) {
@@ -118,7 +185,7 @@ export default function PrayersPage() {
useEffect(() => {
fetchPrayers()
}, [selectedCategory, user])
}, [selectedCategory, user, viewMode, languagesKey])
const handleGenerateAIPrayer = async () => {
if (!aiPrompt.trim()) return
@@ -144,7 +211,8 @@ export default function PrayersPage() {
setNewPrayer({
title: data.title || '',
description: data.prayer || '',
category: newPrayer.category
category: newPrayer.category,
isPublic: newPrayer.isPublic
})
setTabValue(0) // Switch to write tab to review generated prayer
} else {
@@ -157,43 +225,41 @@ export default function PrayersPage() {
}
}
const handleLanguageChange = (event: SelectChangeEvent<string[]>) => {
const value = event.target.value
const parsed = typeof value === 'string'
? value.split(',')
: (value as string[])
const uniqueValues = Array.from(new Set(parsed.filter(Boolean)))
setSelectedLanguages(uniqueValues)
}
const handleSubmitPrayer = async () => {
if (!newPrayer.title.trim() || !newPrayer.description.trim()) return
if (!user) return
const prayer: PrayerRequest = {
id: Date.now().toString(),
title: newPrayer.title,
description: newPrayer.description,
category: newPrayer.category,
author: user.name || (locale === 'en' ? 'You' : 'Tu'),
timestamp: new Date(),
prayerCount: 0,
isPrayedFor: false,
}
try {
const token = localStorage.getItem('authToken')
const response = await fetch('/api/prayers', {
method: 'POST',
headers: {
'Content-Type': 'application/json',
'Authorization': `Bearer ${localStorage.getItem('authToken')}`
...(token ? { 'Authorization': `Bearer ${token}` } : {})
},
body: JSON.stringify({
title: newPrayer.title,
description: newPrayer.description,
category: newPrayer.category,
isAnonymous: false
isAnonymous: false,
isPublic: newPrayer.isPublic,
language: locale
}),
})
if (response.ok) {
const data = await response.json()
setPrayers([{
...data.prayer,
timestamp: new Date(data.prayer.timestamp)
}, ...prayers])
setNewPrayer({ title: '', description: '', category: 'personal' })
await fetchPrayers()
setNewPrayer({ title: '', description: '', category: 'personal', isPublic: false })
setAiPrompt('')
setTabValue(0)
setOpenDialog(false)
@@ -341,6 +407,36 @@ export default function PrayersPage() {
))}
</Box>
{viewMode === 'public' && (
<Box sx={{ mt: 3 }}>
<Typography variant="h6" sx={{ mb: 1 }}>
{t('languageFilter.title')}
</Typography>
<FormControl fullWidth size="small">
<Select
multiple
value={selectedLanguages}
onChange={handleLanguageChange}
renderValue={(selected) =>
(selected as string[])
.map(code => languageLabelMap[code] || code.toUpperCase())
.join(', ')
}
>
{languageOptions.map(option => (
<MenuItem key={option.value} value={option.value}>
<Checkbox checked={selectedLanguages.includes(option.value)} />
<ListItemText primary={option.label} />
</MenuItem>
))}
</Select>
</FormControl>
<Typography variant="caption" color="text.secondary" sx={{ mt: 1 }}>
{t('languageFilter.helper')}
</Typography>
</Box>
)}
<Typography variant="h6" sx={{ mt: 3, mb: 1 }}>
{t('stats.title')}
</Typography>
@@ -355,6 +451,30 @@ export default function PrayersPage() {
{/* Prayer Requests */}
<Box sx={{ flex: 1, width: { xs: '100%', md: '75%' } }}>
{user && (
<Tabs
value={viewMode}
onChange={(_, newValue) => setViewMode(newValue as 'private' | 'public')}
sx={{ mb: 3 }}
variant="fullWidth"
>
<Tab value="private" label={t('viewModes.private')} />
<Tab value="public" label={t('viewModes.public')} />
</Tabs>
)}
{viewMode === 'private' && (
<Alert severity="info" sx={{ mb: 3 }}>
{t('alerts.privateInfo')}
</Alert>
)}
{viewMode === 'public' && !user && (
<Alert severity="info" sx={{ mb: 3 }}>
{t('alerts.publicInfo')}
</Alert>
)}
{loading ? (
<Box>
{Array.from({ length: 3 }).map((_, index) => (
@@ -388,23 +508,43 @@ export default function PrayersPage() {
</Box>
) : (
<Box>
{prayers.map((prayer) => {
{prayers.length === 0 ? (
<Paper sx={{ p: 3, textAlign: 'center' }}>
<Typography variant="body1" color="text.secondary">
{viewMode === 'private' ? t('empty.private') : t('empty.public')}
</Typography>
</Paper>
) : prayers.map((prayer) => {
const categoryInfo = getCategoryInfo(prayer.category)
const authorName = prayer.isOwner ? (locale === 'en' ? 'You' : 'Tu') : prayer.author
const languageLabel = languageLabelMap[prayer.language] || prayer.language.toUpperCase()
return (
<Card key={prayer.id} sx={{ mb: 3 }}>
<CardContent>
<Box sx={{ display: 'flex', justifyContent: 'space-between', alignItems: 'flex-start', mb: 2 }}>
<Box sx={{ flexGrow: 1 }}>
<Box sx={{ display: 'flex', alignItems: 'center', gap: 1, mb: 1 }}>
<Typography variant="h6" component="h3">
{prayer.title}
</Typography>
<Typography variant="h6" component="h3">
{prayer.title}
</Typography>
<Box sx={{ display: 'flex', flexWrap: 'wrap', gap: 1, mb: 1, mt: 1 }}>
<Chip
label={categoryInfo.label}
color={categoryInfo.color as any}
size="small"
variant="outlined"
/>
<Chip
label={prayer.isPublic ? t('chips.public') : t('chips.private')}
size="small"
color={prayer.isPublic ? 'success' : 'default'}
variant={prayer.isPublic ? 'filled' : 'outlined'}
/>
<Chip
label={languageLabel}
size="small"
variant="outlined"
/>
</Box>
<Box sx={{ display: 'flex', alignItems: 'center', gap: 2, mb: 2 }}>
@@ -413,7 +553,7 @@ export default function PrayersPage() {
<Person sx={{ fontSize: 16 }} />
</Avatar>
<Typography variant="body2" color="text.secondary">
{prayer.author}
{authorName}
</Typography>
</Box>
<Box sx={{ display: 'flex', alignItems: 'center', gap: 0.5 }}>
@@ -450,6 +590,7 @@ export default function PrayersPage() {
variant="outlined"
size="small"
startIcon={<Share />}
disabled={!prayer.isPublic}
>
{t('buttons.share')}
</Button>
@@ -602,6 +743,21 @@ export default function PrayersPage() {
)}
</Box>
)}
<Box sx={{ mt: 3 }}>
<FormControlLabel
control={
<Switch
checked={newPrayer.isPublic}
onChange={(event) => setNewPrayer({ ...newPrayer, isPublic: event.target.checked })}
/>
}
label={t('dialog.makePublic')}
/>
<Typography variant="caption" color="text.secondary" display="block">
{newPrayer.isPublic ? t('dialog.visibilityPublic') : t('dialog.visibilityPrivate')}
</Typography>
</Box>
</DialogContent>
<DialogActions>

View File

@@ -50,6 +50,8 @@ export async function GET(request: Request) {
category: true,
author: true,
isAnonymous: true,
isPublic: true,
language: true,
prayerCount: true,
isActive: true,
createdAt: true,

View File

@@ -52,6 +52,32 @@ export interface BibleVerse {
}
export async function getEmbedding(text: string): Promise<number[]> {
// Try Ollama first (for local embeddings)
if (process.env.OLLAMA_API_URL && process.env.OLLAMA_EMBED_MODEL) {
try {
const response = await fetch(`${process.env.OLLAMA_API_URL}/api/embeddings`, {
method: 'POST',
headers: {
'Content-Type': 'application/json',
},
body: JSON.stringify({
model: process.env.OLLAMA_EMBED_MODEL,
prompt: text,
}),
})
if (response.ok) {
const data = await response.json()
return data.embedding
} else {
console.warn(`Ollama embedding failed: ${response.status}, falling back to Azure`)
}
} catch (error) {
console.warn('Ollama embedding error, falling back to Azure:', error)
}
}
// Fallback to Azure OpenAI
const response = await fetch(
`${process.env.AZURE_OPENAI_ENDPOINT}/openai/deployments/${process.env.AZURE_OPENAI_EMBED_DEPLOYMENT}/embeddings?api-version=${process.env.AZURE_OPENAI_API_VERSION}`,
{

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@@ -1,4 +1,4 @@
import os, re, json, math, time, asyncio
import os, re, json, math, time, asyncio, glob
from typing import List, Dict, Tuple, Iterable
from dataclasses import dataclass
from pathlib import Path
@@ -13,30 +13,28 @@ AZ_ENDPOINT = os.getenv("AZURE_OPENAI_ENDPOINT", "").rstrip("/")
AZ_API_KEY = os.getenv("AZURE_OPENAI_KEY")
AZ_API_VER = os.getenv("AZURE_OPENAI_API_VERSION", "2024-05-01-preview")
AZ_DEPLOYMENT = os.getenv("AZURE_OPENAI_EMBED_DEPLOYMENT", "embed-3")
EMBED_DIMS = int(os.getenv("EMBED_DIMS", "3072"))
EMBED_DIMS = int(os.getenv("EMBED_DIMS", "1536"))
DB_URL = os.getenv("DATABASE_URL")
BIBLE_MD_PATH = os.getenv("BIBLE_MD_PATH")
LANG_CODE = os.getenv("LANG_CODE", "ro")
TRANSLATION = os.getenv("TRANSLATION_CODE", "FIDELA")
BIBLE_JSON_DIR = os.getenv("BIBLE_JSON_DIR", "/root/biblical-guide/bibles/json")
VECTOR_SCHEMA = os.getenv("VECTOR_SCHEMA", "ai_bible")
MIN_FILE_SIZE = int(os.getenv("MIN_FILE_SIZE", "512000")) # 500KB in bytes
assert AZ_ENDPOINT and AZ_API_KEY and DB_URL and BIBLE_MD_PATH, "Missing required env vars"
assert AZ_ENDPOINT and AZ_API_KEY and DB_URL and BIBLE_JSON_DIR, "Missing required env vars"
EMBED_URL = f"{AZ_ENDPOINT}/openai/deployments/{AZ_DEPLOYMENT}/embeddings?api-version={AZ_API_VER}"
BOOKS_OT = [
"Geneza","Exodul","Leviticul","Numeri","Deuteronom","Iosua","Judecători","Rut",
"1 Samuel","2 Samuel","1 Imparati","2 Imparati","1 Cronici","2 Cronici","Ezra","Neemia","Estera",
"Iov","Psalmii","Proverbe","Eclesiastul","Cântarea Cântărilor","Isaia","Ieremia","Plângerile",
"Ezechiel","Daniel","Osea","Ioel","Amos","Obadia","Iona","Mica","Naum","Habacuc","Țefania","Hagai","Zaharia","Maleahi"
]
BOOKS_NT = [
"Matei","Marcu","Luca","Ioan","Faptele Apostolilor","Romani","1 Corinteni","2 Corinteni",
"Galateni","Efeseni","Filipeni","Coloseni","1 Tesaloniceni","2 Tesaloniceni","1 Timotei","2 Timotei",
"Titus","Filimon","Evrei","Iacov","1 Petru","2 Petru","1 Ioan","2 Ioan","3 Ioan","Iuda","Revelaţia"
]
def get_large_bible_files():
"""Get all bible JSON files larger than MIN_FILE_SIZE"""
bible_files = []
pattern = os.path.join(BIBLE_JSON_DIR, "*_bible.json")
BOOK_CANON = {b:("OT" if b in BOOKS_OT else "NT") for b in BOOKS_OT + BOOKS_NT}
for filepath in glob.glob(pattern):
file_size = os.path.getsize(filepath)
if file_size >= MIN_FILE_SIZE:
bible_files.append(filepath)
bible_files.sort()
return bible_files
@dataclass
class Verse:
@@ -52,58 +50,51 @@ def normalize_text(s: str) -> str:
s = s.replace(" ", " ")
return s
BOOK_RE = re.compile(r"^(?P<book>[A-ZĂÂÎȘȚ][^\n]+?)\s*$")
CH_RE = re.compile(r"^(?i:Capitolul|CApitoLuL)\s+(?P<ch>\d+)\b")
VERSE_RE = re.compile(r"^(?P<v>\d+)\s+(?P<body>.+)$")
def parse_bible_json(json_file_path: str):
"""Parse a Bible JSON file and yield verse data"""
try:
with open(json_file_path, 'r', encoding='utf-8') as f:
bible_data = json.load(f)
def parse_bible_md(md_text: str):
cur_book, cur_ch = None, None
testament = None
is_in_bible_content = False
bible_name = bible_data.get('name', 'Unknown Bible')
abbreviation = bible_data.get('abbreviation', 'UNKNOWN')
language = bible_data.get('language', 'unknown')
for line in md_text.splitlines():
line = line.rstrip()
print(f"Processing: {bible_name} ({abbreviation}, {language})")
# Start processing after "VECHIUL TESTAMENT" or when we find book markers
if line == 'VECHIUL TESTAMENT' or line == 'TESTAMENT' or '' in line:
is_in_bible_content = True
for book in bible_data.get('books', []):
book_name = book.get('name', 'Unknown Book')
testament = book.get('testament', 'Unknown')
if not is_in_bible_content:
continue
# Convert testament to short form for consistency
if 'Old' in testament:
testament = 'OT'
elif 'New' in testament:
testament = 'NT'
# Book detection: … BookName …
book_match = re.match(r'^…\s*(.+?)\s*…$', line)
if book_match:
bname = book_match.group(1).strip()
if bname in BOOK_CANON:
cur_book = bname
testament = BOOK_CANON[bname]
cur_ch = None
print(f"Found book: {bname}")
continue
for chapter in book.get('chapters', []):
chapter_num = chapter.get('chapterNum', 1)
# Chapter detection: Capitolul X or CApitoLuL X
m_ch = CH_RE.match(line)
if m_ch and cur_book:
cur_ch = int(m_ch.group("ch"))
print(f" Chapter {cur_ch}")
continue
for verse in chapter.get('verses', []):
verse_num = verse.get('verseNum', 1)
text_raw = verse.get('text', '')
# Verse detection: starts with number
m_v = VERSE_RE.match(line)
if m_v and cur_book and cur_ch:
vnum = int(m_v.group("v"))
body = m_v.group("body").strip()
if text_raw: # Only process non-empty verses
text_norm = normalize_text(text_raw)
yield {
"testament": testament,
"book": book_name,
"chapter": chapter_num,
"verse": verse_num,
"text_raw": text_raw,
"text_norm": text_norm,
"language": language,
"translation": abbreviation
}
# Remove paragraph markers
body = re.sub(r'\s*', '', body)
raw = body
norm = normalize_text(body)
yield {
"testament": testament, "book": cur_book, "chapter": cur_ch, "verse": vnum,
"text_raw": raw, "text_norm": norm
}
except Exception as e:
print(f"Error processing {json_file_path}: {e}")
return
async def embed_batch(client, inputs):
payload = {"input": inputs}
@@ -130,18 +121,23 @@ async def embed_batch(client, inputs):
def safe_ident(s: str) -> str:
return re.sub(r"[^a-z0-9_]+", "_", s.lower()).strip("_")
TABLE_BASENAME = f"bv_{safe_ident(LANG_CODE)}_{safe_ident(TRANSLATION)}"
TABLE_FQN = f'"{VECTOR_SCHEMA}"."{TABLE_BASENAME}"'
def get_table_info(language: str, translation: str):
"""Get table name and fully qualified name for a specific bible version"""
table_basename = f"bv_{safe_ident(language)}_{safe_ident(translation)}"
table_fqn = f'"{VECTOR_SCHEMA}"."{table_basename}"'
return table_basename, table_fqn
def create_table_sql() -> str:
def create_table_sql(table_fqn: str) -> str:
return f"""
CREATE SCHEMA IF NOT EXISTS "{VECTOR_SCHEMA}";
CREATE TABLE IF NOT EXISTS {TABLE_FQN} (
CREATE TABLE IF NOT EXISTS {table_fqn} (
id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
testament TEXT NOT NULL,
book TEXT NOT NULL,
chapter INT NOT NULL,
verse INT NOT NULL,
language TEXT NOT NULL,
translation TEXT NOT NULL,
ref TEXT GENERATED ALWAYS AS (book || ' ' || chapter || ':' || verse) STORED,
text_raw TEXT NOT NULL,
text_norm TEXT NOT NULL,
@@ -152,20 +148,21 @@ def create_table_sql() -> str:
);
"""
def create_indexes_sql() -> str:
def create_indexes_sql(table_fqn: str, table_basename: str) -> str:
return f"""
CREATE UNIQUE INDEX IF NOT EXISTS ux_ref_{TABLE_BASENAME} ON {TABLE_FQN} (book, chapter, verse);
CREATE INDEX IF NOT EXISTS idx_tsv_{TABLE_BASENAME} ON {TABLE_FQN} USING GIN (tsv);
CREATE INDEX IF NOT EXISTS idx_book_ch_{TABLE_BASENAME} ON {TABLE_FQN} (book, chapter);
CREATE INDEX IF NOT EXISTS idx_testament_{TABLE_BASENAME} ON {TABLE_FQN} (testament);
CREATE UNIQUE INDEX IF NOT EXISTS ux_ref_{table_basename} ON {table_fqn} (translation, language, book, chapter, verse);
CREATE INDEX IF NOT EXISTS idx_tsv_{table_basename} ON {table_fqn} USING GIN (tsv);
CREATE INDEX IF NOT EXISTS idx_book_ch_{table_basename} ON {table_fqn} (book, chapter);
CREATE INDEX IF NOT EXISTS idx_testament_{table_basename} ON {table_fqn} (testament);
CREATE INDEX IF NOT EXISTS idx_lang_trans_{table_basename} ON {table_fqn} (language, translation);
"""
def upsert_sql() -> str:
def upsert_sql(table_fqn: str) -> str:
return f"""
INSERT INTO {TABLE_FQN} (testament, book, chapter, verse, text_raw, text_norm, tsv, embedding)
VALUES (%(testament)s, %(book)s, %(chapter)s, %(verse)s, %(text_raw)s, %(text_norm)s,
INSERT INTO {table_fqn} (testament, book, chapter, verse, language, translation, text_raw, text_norm, tsv, embedding)
VALUES (%(testament)s, %(book)s, %(chapter)s, %(verse)s, %(language)s, %(translation)s, %(text_raw)s, %(text_norm)s,
to_tsvector(COALESCE(%(ts_lang)s,'simple')::regconfig, %(text_norm)s), %(embedding)s)
ON CONFLICT (book, chapter, verse) DO UPDATE
ON CONFLICT (translation, language, book, chapter, verse) DO UPDATE
SET text_raw=EXCLUDED.text_raw,
text_norm=EXCLUDED.text_norm,
tsv=EXCLUDED.tsv,
@@ -173,66 +170,188 @@ def upsert_sql() -> str:
updated_at=now();
"""
async def main():
print("Starting Bible embedding ingestion...")
async def process_bible_file(bible_file_path: str, client):
"""Process a single Bible JSON file"""
print(f"\n=== Processing {os.path.basename(bible_file_path)} ===")
md_text = Path(BIBLE_MD_PATH).read_text(encoding="utf-8", errors="ignore")
verses = list(parse_bible_md(md_text))
print(f"Parsed verses: {len(verses)}")
verses = list(parse_bible_json(bible_file_path))
if not verses:
print(f"No verses found in {bible_file_path}, skipping...")
return
batch_size = 128
print(f"Parsed {len(verses):,} verses")
# First create the schema + table structure for this language/version
# Get language and translation from first verse
first_verse = verses[0]
language = first_verse["language"]
translation = first_verse["translation"]
table_basename, table_fqn = get_table_info(language, translation)
# Create schema + table structure for this bible version
with psycopg.connect(DB_URL) as conn:
with conn.cursor() as cur:
print(f"Creating schema '{VECTOR_SCHEMA}' and table {TABLE_FQN} ...")
print(f"Creating table {table_fqn} ...")
cur.execute("CREATE EXTENSION IF NOT EXISTS vector;")
cur.execute(create_table_sql())
cur.execute(create_indexes_sql())
cur.execute(create_table_sql(table_fqn))
cur.execute(create_indexes_sql(table_fqn, table_basename))
conn.commit()
print("Schema/table ready")
# Now process embeddings
async with httpx.AsyncClient() as client:
with psycopg.connect(DB_URL, autocommit=False) as conn:
with conn.cursor() as cur:
for i in range(0, len(verses), batch_size):
batch = verses[i:i+batch_size]
inputs = [v["text_norm"] for v in batch]
# Process embeddings in batches
batch_size = 128
with psycopg.connect(DB_URL, autocommit=False) as conn:
with conn.cursor() as cur:
for i in range(0, len(verses), batch_size):
batch = verses[i:i+batch_size]
inputs = [v["text_norm"] for v in batch]
print(f"Generating embeddings for batch {i//batch_size + 1}/{(len(verses) + batch_size - 1)//batch_size}")
embs = await embed_batch(client, inputs)
print(f"Generating embeddings for batch {i//batch_size + 1}/{(len(verses) + batch_size - 1)//batch_size}")
embs = await embed_batch(client, inputs)
rows = []
for v, e in zip(batch, embs):
rows.append({
**v,
"ts_lang": "romanian" if LANG_CODE.lower().startswith("ro") else ("english" if LANG_CODE.lower().startswith("en") else "simple"),
"embedding": e
})
rows = []
for v, e in zip(batch, embs):
# Determine text search language based on language code
ts_lang = "simple" # default
if v["language"].lower().startswith("ro"):
ts_lang = "romanian"
elif v["language"].lower().startswith("en"):
ts_lang = "english"
elif v["language"].lower().startswith("es"):
ts_lang = "spanish"
elif v["language"].lower().startswith("fr"):
ts_lang = "french"
elif v["language"].lower().startswith("de"):
ts_lang = "german"
elif v["language"].lower().startswith("it"):
ts_lang = "italian"
cur.executemany(upsert_sql(), rows)
conn.commit()
print(f"Upserted {len(rows)} verses... {i+len(rows)}/{len(verses)}")
rows.append({
**v,
"ts_lang": ts_lang,
"embedding": e
})
cur.executemany(upsert_sql(table_fqn), rows)
conn.commit()
print(f"Upserted {len(rows)} verses... {i+len(rows)}/{len(verses)}")
# Create IVFFLAT index after data is loaded
print("Creating IVFFLAT index...")
with psycopg.connect(DB_URL, autocommit=True) as conn:
with conn.cursor() as cur:
cur.execute(f"VACUUM ANALYZE {TABLE_FQN};")
cur.execute(f"VACUUM ANALYZE {table_fqn};")
cur.execute(f"""
CREATE INDEX IF NOT EXISTS idx_vec_ivfflat_{TABLE_BASENAME}
ON {TABLE_FQN} USING ivfflat (embedding vector_cosine_ops)
CREATE INDEX IF NOT EXISTS idx_vec_ivfflat_{table_basename}
ON {table_fqn} USING ivfflat (embedding vector_cosine_ops)
WITH (lists = 200);
""")
print("Bible embedding ingestion completed successfully!")
print(f"{translation} ({language}) completed successfully! Total verses: {len(verses):,}")
# Helpful pgAdmin queries:
print("\nRun these sample queries in pgAdmin:")
print(f"SELECT count(*) FROM {TABLE_FQN};")
print(f"SELECT book, chapter, verse, left(text_raw, 80) AS preview FROM {TABLE_FQN} ORDER BY book, chapter, verse LIMIT 10;")
print(f"SELECT * FROM {TABLE_FQN} WHERE book='Geneza' AND chapter=1 AND verse=1;")
def update_status(status_data):
"""Update the status file for monitoring progress"""
status_file = "/root/biblical-guide/scripts/ingest_status.json"
try:
import json
from datetime import datetime
status_data["last_update"] = datetime.now().isoformat()
with open(status_file, 'w') as f:
json.dump(status_data, f, indent=2)
except Exception as e:
print(f"Warning: Could not update status file: {e}")
async def main():
start_time = time.time()
print("Starting Bible embedding ingestion for all large Bible files...")
print(f"Timestamp: {time.strftime('%Y-%m-%d %H:%M:%S')}")
# Get all Bible files larger than minimum size
bible_files = get_large_bible_files()
if not bible_files:
print(f"No Bible files found larger than {MIN_FILE_SIZE/1024:.0f}KB in {BIBLE_JSON_DIR}")
return
print(f"Found {len(bible_files)} Bible files to process (>{MIN_FILE_SIZE/1024:.0f}KB each)")
# Initialize status tracking
status = {
"status": "running",
"start_time": time.strftime('%Y-%m-%d %H:%M:%S'),
"total_files": len(bible_files),
"processed": 0,
"successful": 0,
"failed": 0,
"current_file": "",
"errors": []
}
update_status(status)
# Process files one by one to avoid memory issues
async with httpx.AsyncClient(timeout=120.0) as client:
successful = 0
failed = 0
failed_files = []
for i, bible_file in enumerate(bible_files, 1):
try:
file_size_mb = os.path.getsize(bible_file) / (1024 * 1024)
filename = os.path.basename(bible_file)
print(f"\n[{i}/{len(bible_files)}] Processing {filename} ({file_size_mb:.1f}MB)")
print(f"Progress: {(i-1)/len(bible_files)*100:.1f}% complete")
# Update status
status["current_file"] = filename
status["processed"] = i - 1
status["successful"] = successful
status["failed"] = failed
update_status(status)
await process_bible_file(bible_file, client)
successful += 1
print(f"✅ Completed {filename}")
except Exception as e:
error_msg = f"Failed to process {os.path.basename(bible_file)}: {str(e)}"
print(f"{error_msg}")
failed += 1
failed_files.append(os.path.basename(bible_file))
status["errors"].append({"file": os.path.basename(bible_file), "error": str(e), "timestamp": time.strftime('%Y-%m-%d %H:%M:%S')})
update_status(status)
continue
# Final summary
elapsed_time = time.time() - start_time
elapsed_hours = elapsed_time / 3600
print(f"\n=== Final Summary ===")
print(f"✅ Successfully processed: {successful} files")
print(f"❌ Failed to process: {failed} files")
print(f"📊 Total files: {len(bible_files)}")
print(f"⏱️ Total time: {elapsed_hours:.2f} hours ({elapsed_time:.0f} seconds)")
print(f"📈 Average: {elapsed_time/len(bible_files):.1f} seconds per file")
if failed_files:
print(f"\n❌ Failed files:")
for filename in failed_files:
print(f" - {filename}")
# Final status update
status.update({
"status": "completed",
"end_time": time.strftime('%Y-%m-%d %H:%M:%S'),
"processed": len(bible_files),
"successful": successful,
"failed": failed,
"duration_seconds": elapsed_time,
"current_file": ""
})
update_status(status)
print("\n🎉 All large Bible files have been processed!")
print(f"📋 Status file: /root/biblical-guide/scripts/ingest_status.json")
if __name__ == "__main__":
asyncio.run(main())

View File

@@ -1,169 +0,0 @@
import os, json, re, asyncio
from pathlib import Path
from typing import List, Dict
from dotenv import load_dotenv
import httpx
import psycopg
load_dotenv()
AZ_ENDPOINT = os.getenv("AZURE_OPENAI_ENDPOINT", "").rstrip("/")
AZ_API_KEY = os.getenv("AZURE_OPENAI_KEY")
AZ_API_VER = os.getenv("AZURE_OPENAI_API_VERSION", "2024-05-01-preview")
AZ_DEPLOYMENT = os.getenv("AZURE_OPENAI_EMBED_DEPLOYMENT", "embed-3")
EMBED_DIMS = int(os.getenv("EMBED_DIMS", "3072"))
DB_URL = os.getenv("DATABASE_URL")
VECTOR_SCHEMA = os.getenv("VECTOR_SCHEMA", "ai_bible")
LANG_CODE = os.getenv("LANG_CODE", "en")
TRANSLATION = os.getenv("TRANSLATION_CODE", "WEB")
JSON_DIR = os.getenv("JSON_DIR", f"data/en_bible/{TRANSLATION}")
assert AZ_ENDPOINT and AZ_API_KEY and DB_URL and JSON_DIR, "Missing required env vars"
EMBED_URL = f"{AZ_ENDPOINT}/openai/deployments/{AZ_DEPLOYMENT}/embeddings?api-version={AZ_API_VER}"
def safe_ident(s: str) -> str:
return re.sub(r"[^a-z0-9_]+", "_", s.lower()).strip("_")
TABLE_BASENAME = f"bv_{safe_ident(LANG_CODE)}_{safe_ident(TRANSLATION)}"
TABLE_FQN = f'"{VECTOR_SCHEMA}"."{TABLE_BASENAME}"'
def create_table_sql() -> str:
return f"""
CREATE SCHEMA IF NOT EXISTS "{VECTOR_SCHEMA}";
CREATE TABLE IF NOT EXISTS {TABLE_FQN} (
id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
testament TEXT NOT NULL,
book TEXT NOT NULL,
chapter INT NOT NULL,
verse INT NOT NULL,
ref TEXT GENERATED ALWAYS AS (book || ' ' || chapter || ':' || verse) STORED,
text_raw TEXT NOT NULL,
text_norm TEXT NOT NULL,
tsv tsvector,
embedding vector({EMBED_DIMS}),
created_at TIMESTAMPTZ DEFAULT now(),
updated_at TIMESTAMPTZ DEFAULT now()
);
"""
def create_indexes_sql() -> str:
return f"""
CREATE UNIQUE INDEX IF NOT EXISTS ux_ref_{TABLE_BASENAME} ON {TABLE_FQN} (book, chapter, verse);
CREATE INDEX IF NOT EXISTS idx_tsv_{TABLE_BASENAME} ON {TABLE_FQN} USING GIN (tsv);
CREATE INDEX IF NOT EXISTS idx_book_ch_{TABLE_BASENAME} ON {TABLE_FQN} (book, chapter);
CREATE INDEX IF NOT EXISTS idx_testament_{TABLE_BASENAME} ON {TABLE_FQN} (testament);
"""
def upsert_sql() -> str:
return f"""
INSERT INTO {TABLE_FQN} (testament, book, chapter, verse, text_raw, text_norm, tsv, embedding)
VALUES (%(testament)s, %(book)s, %(chapter)s, %(verse)s, %(text_raw)s, %(text_norm)s,
to_tsvector(COALESCE(%(ts_lang)s,'simple')::regconfig, %(text_norm)s), %(embedding)s)
ON CONFLICT (book, chapter, verse) DO UPDATE
SET text_raw=EXCLUDED.text_raw,
text_norm=EXCLUDED.text_norm,
tsv=EXCLUDED.tsv,
embedding=EXCLUDED.embedding,
updated_at=now();
"""
def normalize(s: str) -> str:
s = re.sub(r"\s+", " ", s.strip())
return s
async def embed_batch(client: httpx.AsyncClient, inputs: List[str]) -> List[List[float]]:
payload = {"input": inputs}
headers = {"api-key": AZ_API_KEY, "Content-Type": "application/json"}
for attempt in range(6):
try:
r = await client.post(EMBED_URL, headers=headers, json=payload, timeout=60)
if r.status_code == 200:
data = r.json()
ordered = sorted(data["data"], key=lambda x: x["index"])
return [d["embedding"] for d in ordered]
elif r.status_code in (429, 500, 502, 503):
backoff = 2 ** attempt + (0.25 * attempt)
print(f"Rate/Server limited ({r.status_code}), waiting {backoff:.1f}s...")
await asyncio.sleep(backoff)
else:
raise RuntimeError(f"Embedding error {r.status_code}: {r.text}")
except Exception as e:
backoff = 2 ** attempt + (0.25 * attempt)
print(f"Error on attempt {attempt + 1}: {e}, waiting {backoff:.1f}s...")
await asyncio.sleep(backoff)
raise RuntimeError("Failed to embed after retries")
def load_json() -> List[Dict]:
ot = json.loads(Path(Path(JSON_DIR)/'old_testament.json').read_text('utf-8'))
nt = json.loads(Path(Path(JSON_DIR)/'new_testament.json').read_text('utf-8'))
verses = []
for test in (ot, nt):
testament = test.get('testament')
for book in test.get('books', []):
bname = book.get('name')
for ch in book.get('chapters', []):
cnum = int(ch.get('chapterNum'))
for v in ch.get('verses', []):
vnum = int(v.get('verseNum'))
text = str(v.get('text') or '').strip()
if text:
verses.append({
'testament': testament,
'book': bname,
'chapter': cnum,
'verse': vnum,
'text_raw': text,
'text_norm': normalize(text),
})
return verses
async def main():
print("Starting JSON embedding ingestion...", JSON_DIR)
verses = load_json()
print("Verses loaded:", len(verses))
batch_size = int(os.getenv('BATCH_SIZE', '128'))
# Prepare schema/table
with psycopg.connect(DB_URL) as conn:
with conn.cursor() as cur:
print(f"Ensuring schema/table {TABLE_FQN} ...")
cur.execute("CREATE EXTENSION IF NOT EXISTS vector;")
cur.execute(create_table_sql())
cur.execute(create_indexes_sql())
conn.commit()
async with httpx.AsyncClient() as client:
with psycopg.connect(DB_URL, autocommit=False) as conn:
with conn.cursor() as cur:
for i in range(0, len(verses), batch_size):
batch = verses[i:i+batch_size]
inputs = [v['text_norm'] for v in batch]
embs = await embed_batch(client, inputs)
rows = []
ts_lang = 'english' if LANG_CODE.lower().startswith('en') else 'simple'
for v, e in zip(batch, embs):
rows.append({ **v, 'ts_lang': ts_lang, 'embedding': e })
cur.executemany(upsert_sql(), rows)
conn.commit()
print(f"Upserted {len(rows)} verses... {i+len(rows)}/{len(verses)}")
print("Creating IVFFLAT index...")
with psycopg.connect(DB_URL, autocommit=True) as conn:
with conn.cursor() as cur:
cur.execute(f"VACUUM ANALYZE {TABLE_FQN};")
try:
cur.execute(f"""
CREATE INDEX IF NOT EXISTS idx_vec_ivfflat_{TABLE_BASENAME}
ON {TABLE_FQN} USING ivfflat (embedding vector_cosine_ops)
WITH (lists = 200);
""")
except Exception as e:
print('IVFFLAT creation skipped (tune maintenance_work_mem):', e)
print("✅ JSON embedding ingestion completed successfully!")
if __name__ == '__main__':
asyncio.run(main())

View File

@@ -48,6 +48,8 @@ export interface PrayerRequest {
userId: string | null
content: string
isAnonymous: boolean
isPublic: boolean
language: string
prayerCount: number
createdAt: Date
updatedAt: Date