dd5e1102eb
- Add pgvector support with bible_passages table for vector search - Create Python ingestion script for Azure OpenAI embed-3 embeddings - Implement hybrid search combining vector similarity and full-text search - Update AI chat to use vector search with Azure OpenAI gpt-4o - Add floating chat component with Material UI design - Import complete Romanian Bible (FIDELA) with 30K+ verses - Add vector search library for semantic Bible search - Create multi-language implementation plan for future expansion 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <noreply@anthropic.com>
231 lines
8.7 KiB
Python
231 lines
8.7 KiB
Python
import os, re, json, math, time, asyncio
|
|
from typing import List, Dict, Tuple, Iterable
|
|
from dataclasses import dataclass
|
|
from pathlib import Path
|
|
from dotenv import load_dotenv
|
|
import httpx
|
|
import psycopg
|
|
from psycopg.rows import dict_row
|
|
|
|
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")
|
|
BIBLE_MD_PATH = os.getenv("BIBLE_MD_PATH")
|
|
LANG_CODE = os.getenv("LANG_CODE", "ro")
|
|
TRANSLATION = os.getenv("TRANSLATION_CODE", "FIDELA")
|
|
|
|
assert AZ_ENDPOINT and AZ_API_KEY and DB_URL and BIBLE_MD_PATH, "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"
|
|
]
|
|
|
|
BOOK_CANON = {b:("OT" if b in BOOKS_OT else "NT") for b in BOOKS_OT + BOOKS_NT}
|
|
|
|
@dataclass
|
|
class Verse:
|
|
testament: str
|
|
book: str
|
|
chapter: int
|
|
verse: int
|
|
text_raw: str
|
|
text_norm: str
|
|
|
|
def normalize_text(s: str) -> str:
|
|
s = re.sub(r"\s+", " ", s.strip())
|
|
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_md(md_text: str):
|
|
cur_book, cur_ch = None, None
|
|
testament = None
|
|
is_in_bible_content = False
|
|
|
|
for line in md_text.splitlines():
|
|
line = line.rstrip()
|
|
|
|
# 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
|
|
|
|
if not is_in_bible_content:
|
|
continue
|
|
|
|
# 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
|
|
|
|
# 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
|
|
|
|
# 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()
|
|
|
|
# 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
|
|
}
|
|
|
|
async def embed_batch(client, inputs):
|
|
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, 503):
|
|
backoff = 2 ** attempt + (0.1 * attempt)
|
|
print(f"Rate limited, 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 + (0.1 * attempt)
|
|
print(f"Error on attempt {attempt + 1}: {e}, waiting {backoff:.1f}s...")
|
|
await asyncio.sleep(backoff)
|
|
raise RuntimeError("Failed to embed after retries")
|
|
|
|
# First, we need to create the table with proper SQL
|
|
CREATE_TABLE_SQL = """
|
|
CREATE TABLE IF NOT EXISTS bible_passages (
|
|
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,
|
|
lang TEXT NOT NULL DEFAULT 'ro',
|
|
translation TEXT NOT NULL DEFAULT 'FIDELA',
|
|
text_raw TEXT NOT NULL,
|
|
text_norm TEXT NOT NULL,
|
|
tsv tsvector,
|
|
embedding vector(1536),
|
|
created_at TIMESTAMPTZ DEFAULT now(),
|
|
updated_at TIMESTAMPTZ DEFAULT now()
|
|
);
|
|
"""
|
|
|
|
CREATE_INDEXES_SQL = """
|
|
-- Uniqueness by canonical reference within translation/language
|
|
CREATE UNIQUE INDEX IF NOT EXISTS ux_ref_lang ON bible_passages (translation, lang, book, chapter, verse);
|
|
|
|
-- Full-text index
|
|
CREATE INDEX IF NOT EXISTS idx_tsv ON bible_passages USING GIN (tsv);
|
|
|
|
-- Other indexes
|
|
CREATE INDEX IF NOT EXISTS idx_book_ch ON bible_passages (book, chapter);
|
|
CREATE INDEX IF NOT EXISTS idx_testament ON bible_passages (testament);
|
|
"""
|
|
|
|
UPSERT_SQL = """
|
|
INSERT INTO bible_passages (testament, book, chapter, verse, lang, translation, text_raw, text_norm, tsv, embedding)
|
|
VALUES (%(testament)s, %(book)s, %(chapter)s, %(verse)s, %(lang)s, %(translation)s, %(text_raw)s, %(text_norm)s,
|
|
to_tsvector(COALESCE(%(ts_lang)s,'simple')::regconfig, %(text_norm)s), %(embedding)s)
|
|
ON CONFLICT (translation, lang, 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();
|
|
"""
|
|
|
|
async def main():
|
|
print("Starting Bible embedding ingestion...")
|
|
|
|
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)}")
|
|
|
|
batch_size = 128
|
|
|
|
# First create the table structure
|
|
with psycopg.connect(DB_URL) as conn:
|
|
with conn.cursor() as cur:
|
|
print("Creating bible_passages table...")
|
|
cur.execute("CREATE EXTENSION IF NOT EXISTS vector;")
|
|
cur.execute(CREATE_TABLE_SQL)
|
|
cur.execute(CREATE_INDEXES_SQL)
|
|
conn.commit()
|
|
print("Table created successfully")
|
|
|
|
# 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]
|
|
|
|
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,
|
|
"lang": LANG_CODE,
|
|
"translation": TRANSLATION,
|
|
"ts_lang": "romanian",
|
|
"embedding": e
|
|
})
|
|
|
|
cur.executemany(UPSERT_SQL, 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("VACUUM ANALYZE bible_passages;")
|
|
cur.execute("""
|
|
CREATE INDEX IF NOT EXISTS idx_vec_ivfflat
|
|
ON bible_passages USING ivfflat (embedding vector_cosine_ops)
|
|
WITH (lists = 200);
|
|
""")
|
|
|
|
print("✅ Bible embedding ingestion completed successfully!")
|
|
|
|
if __name__ == "__main__":
|
|
asyncio.run(main()) |