feat: /api/v2/analiza/* endpoints - sport analytics backend

scrapers/dok_embedder.py

@@ -1,4 +1,7 @@
 #!/usr/bin/env python3
+from dotenv import load_dotenv
+load_dotenv('/opt/rinet-gpu/.env.master')
+# auto-added by patch_scrapers_with_dotenv.sh
 """Embed dokumenti into Qdrant pgz_sport_dokumenti_v1 collection.
 Strategy:
   1. Use existing sadrzaj for docs that have content scraped
@@ -7,6 +10,7 @@ Strategy:
   4. BGE-M3 embed via local server
   5. Store in Qdrant + dokument_chunks
 """
+import os
 import psycopg2
 import psycopg2.extras
 import requests
@@ -15,7 +19,7 @@ import re
 import sys
 from datetime import datetime
 
-DB = dict(host='localhost', port=5432, dbname='rinet_v3', user='rinet', password='R1net2026!SecureDB#v7')
+DB = dict(host='localhost', port=5432, dbname='rinet_v3', user='rinet', password=os.environ["DB_PASSWORD"])
 EMBED_URL = 'http://localhost:9879/api/embeddings'  # BGE-M3
 QDRANT = 'http://10.10.0.2:6333'
 COLL = 'pgz_sport_dokumenti_v1'