239 lines
9.4 KiB
Python
239 lines
9.4 KiB
Python
import os, re, json, math, time, asyncio
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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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from dotenv import load_dotenv
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import httpx
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import psycopg
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from psycopg.rows import dict_row
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load_dotenv()
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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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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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VECTOR_SCHEMA = os.getenv("VECTOR_SCHEMA", "ai_bible")
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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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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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BOOK_CANON = {b:("OT" if b in BOOKS_OT else "NT") for b in BOOKS_OT + BOOKS_NT}
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@dataclass
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class Verse:
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testament: str
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book: str
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chapter: int
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verse: int
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text_raw: str
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text_norm: str
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def normalize_text(s: str) -> str:
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s = re.sub(r"\s+", " ", s.strip())
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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_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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for line in md_text.splitlines():
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line = line.rstrip()
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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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if not is_in_bible_content:
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continue
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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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# 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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# 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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# 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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async def embed_batch(client, inputs):
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payload = {"input": inputs}
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headers = {"api-key": AZ_API_KEY, "Content-Type": "application/json"}
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for attempt in range(6):
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try:
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r = await client.post(EMBED_URL, headers=headers, json=payload, timeout=60)
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if r.status_code == 200:
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data = r.json()
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ordered = sorted(data["data"], key=lambda x: x["index"])
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return [d["embedding"] for d in ordered]
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elif r.status_code in (429, 500, 503):
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backoff = 2 ** attempt + (0.1 * attempt)
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print(f"Rate limited, waiting {backoff:.1f}s...")
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await asyncio.sleep(backoff)
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else:
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raise RuntimeError(f"Embedding error {r.status_code}: {r.text}")
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except Exception as e:
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backoff = 2 ** attempt + (0.1 * attempt)
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print(f"Error on attempt {attempt + 1}: {e}, waiting {backoff:.1f}s...")
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await asyncio.sleep(backoff)
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raise RuntimeError("Failed to embed after retries")
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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 create_table_sql() -> 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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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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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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tsv tsvector,
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embedding vector({EMBED_DIMS}),
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created_at TIMESTAMPTZ DEFAULT now(),
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updated_at TIMESTAMPTZ DEFAULT now()
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);
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"""
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def create_indexes_sql() -> 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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"""
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def upsert_sql() -> 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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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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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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embedding=EXCLUDED.embedding,
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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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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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batch_size = 128
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# First create the schema + table structure for this language/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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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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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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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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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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# 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"""
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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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# 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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if __name__ == "__main__":
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asyncio.run(main())
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