Create comprehensive bookmarks management page
Add complete bookmarks page with navigation functionality: Features: - Dedicated /bookmarks page for viewing all saved bookmarks - Support for both chapter and verse bookmarks in unified view - Statistics dashboard showing total, chapter, and verse bookmark counts - Tabbed filtering (All, Chapters, Verses) for easy organization - Direct navigation to Bible reading page with URL parameters - Delete functionality for individual bookmarks - Empty state with call-to-action to start reading Navigation Integration: - Add Bookmarks to main navigation menu (authenticated users only) - Add Bookmarks to user profile dropdown menu - Dynamic navigation based on authentication state Bible Page Enhancements: - URL parameter support for bookmark navigation (book, chapter, verse) - Verse highlighting when navigating from bookmarks - Auto-clear highlight after 3 seconds for better UX API Endpoints: - /api/bookmarks/all - Unified endpoint for all user bookmarks - Returns transformed data optimized for frontend consumption Multilingual Support: - Full Romanian and English translations - Consistent messaging across all bookmark interfaces 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <noreply@anthropic.com>
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andupetcu
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scripts/ingest_json_pgvector.py
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169
scripts/ingest_json_pgvector.py
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import os, json, re, asyncio
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from pathlib import Path
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from typing import List, Dict
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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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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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VECTOR_SCHEMA = os.getenv("VECTOR_SCHEMA", "ai_bible")
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LANG_CODE = os.getenv("LANG_CODE", "en")
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TRANSLATION = os.getenv("TRANSLATION_CODE", "WEB")
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JSON_DIR = os.getenv("JSON_DIR", f"data/en_bible/{TRANSLATION}")
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assert AZ_ENDPOINT and AZ_API_KEY and DB_URL and 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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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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def normalize(s: str) -> str:
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s = re.sub(r"\s+", " ", s.strip())
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return s
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async def embed_batch(client: httpx.AsyncClient, inputs: List[str]) -> List[List[float]]:
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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, 502, 503):
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backoff = 2 ** attempt + (0.25 * attempt)
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print(f"Rate/Server limited ({r.status_code}), 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 + (0.25 * 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 load_json() -> List[Dict]:
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ot = json.loads(Path(Path(JSON_DIR)/'old_testament.json').read_text('utf-8'))
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nt = json.loads(Path(Path(JSON_DIR)/'new_testament.json').read_text('utf-8'))
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verses = []
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for test in (ot, nt):
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testament = test.get('testament')
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for book in test.get('books', []):
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bname = book.get('name')
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for ch in book.get('chapters', []):
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cnum = int(ch.get('chapterNum'))
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for v in ch.get('verses', []):
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vnum = int(v.get('verseNum'))
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text = str(v.get('text') or '').strip()
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if text:
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verses.append({
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'testament': testament,
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'book': bname,
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'chapter': cnum,
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'verse': vnum,
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'text_raw': text,
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'text_norm': normalize(text),
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})
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return verses
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async def main():
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print("Starting JSON embedding ingestion...", JSON_DIR)
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verses = load_json()
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print("Verses loaded:", len(verses))
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batch_size = int(os.getenv('BATCH_SIZE', '128'))
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# Prepare schema/table
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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"Ensuring schema/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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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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embs = await embed_batch(client, inputs)
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rows = []
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ts_lang = 'english' if LANG_CODE.lower().startswith('en') else 'simple'
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for v, e in zip(batch, embs):
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rows.append({ **v, 'ts_lang': ts_lang, 'embedding': e })
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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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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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try:
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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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except Exception as e:
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print('IVFFLAT creation skipped (tune maintenance_work_mem):', e)
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print("✅ JSON embedding ingestion completed successfully!")
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if __name__ == '__main__':
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asyncio.run(main())
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