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