feat(zopk): Skrypt pipeline do automatycznej ekstrakcji wiedzy

Uruchamia po kolei: scraping treści, ekstrakcję AI, generowanie embeddingów.
Do użycia w cron job co godzinę.

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

scripts/zopk_knowledge_pipeline.py

@@ -0,0 +1,148 @@
+#!/usr/bin/env python3
+"""
+ZOPK Knowledge Pipeline - Automatyczny pipeline ekstrakcji wiedzy.
+
+Uruchamia po kolei:
+1. Scraping treści artykułów (Google News URL decode + fetch)
+2. Ekstrakcja AI (chunks, fakty, encje)
+3. Generowanie embeddingów (pgvector)
+
+Usage:
+    python scripts/zopk_knowledge_pipeline.py
+
+Cron (co godzinę):
+    0 * * * * cd /var/www/nordabiznes && /var/www/nordabiznes/venv/bin/python3 scripts/zopk_knowledge_pipeline.py >> /var/log/nordabiznes/knowledge_pipeline.log 2>&1
+"""
+
+import os
+import sys
+import logging
+from datetime import datetime
+
+# Add parent directory to path for imports
+sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+
+from dotenv import load_dotenv
+load_dotenv()
+
+from sqlalchemy import create_engine
+from sqlalchemy.orm import sessionmaker
+
+# Configure logging
+logging.basicConfig(
+    level=logging.INFO,
+    format='%(asctime)s [%(levelname)s] %(message)s',
+    datefmt='%Y-%m-%d %H:%M:%S'
+)
+logger = logging.getLogger(__name__)
+
+
+def get_db_session():
+    """Create database session."""
+    DATABASE_URL = os.getenv('DATABASE_URL')
+    if not DATABASE_URL:
+        raise ValueError("DATABASE_URL not set in environment")
+
+    engine = create_engine(DATABASE_URL)
+    Session = sessionmaker(bind=engine)
+    return Session()
+
+
+def run_scraping(db, limit: int = 50) -> dict:
+    """Step 1: Scrape article content."""
+    logger.info("=" * 60)
+    logger.info("STEP 1: Scraping article content")
+    logger.info("=" * 60)
+
+    from zopk_content_scraper import ZOPKContentScraper
+
+    scraper = ZOPKContentScraper(db)
+    stats = scraper.batch_scrape(limit=limit)
+
+    logger.info(f"Scraping complete: {stats['scraped']} scraped, {stats['failed']} failed, {stats['skipped']} skipped")
+    return stats
+
+
+def run_extraction(db, limit: int = 50) -> dict:
+    """Step 2: Extract knowledge with AI."""
+    logger.info("=" * 60)
+    logger.info("STEP 2: AI Knowledge Extraction")
+    logger.info("=" * 60)
+
+    from zopk_knowledge_service import ZOPKKnowledgeService
+
+    service = ZOPKKnowledgeService(db)
+    stats = service.batch_extract(limit=limit)
+
+    logger.info(f"Extraction complete: {stats['processed']} processed, {stats['failed']} failed")
+    return stats
+
+
+def run_embeddings(db, limit: int = 100) -> dict:
+    """Step 3: Generate embeddings."""
+    logger.info("=" * 60)
+    logger.info("STEP 3: Generating Embeddings")
+    logger.info("=" * 60)
+
+    from zopk_knowledge_service import generate_chunk_embeddings
+
+    stats = generate_chunk_embeddings(db, limit=limit)
+
+    logger.info(f"Embeddings complete: {stats['generated']} generated, {stats['failed']} failed")
+    return stats
+
+
+def main():
+    """Run the full knowledge pipeline."""
+    start_time = datetime.now()
+    logger.info("=" * 60)
+    logger.info("ZOPK KNOWLEDGE PIPELINE STARTED")
+    logger.info(f"Time: {start_time.strftime('%Y-%m-%d %H:%M:%S')}")
+    logger.info("=" * 60)
+
+    try:
+        db = get_db_session()
+
+        # Step 1: Scraping
+        scrape_stats = run_scraping(db, limit=50)
+
+        # Step 2: AI Extraction (only if we have scraped content)
+        if scrape_stats['scraped'] > 0 or True:  # Always try - there might be pending articles
+            extract_stats = run_extraction(db, limit=50)
+        else:
+            extract_stats = {'processed': 0, 'failed': 0}
+            logger.info("Skipping extraction - no new scraped content")
+
+        # Step 3: Embeddings (only if we have new chunks)
+        if extract_stats.get('chunks_created', 0) > 0 or True:  # Always try
+            embed_stats = run_embeddings(db, limit=100)
+        else:
+            embed_stats = {'generated': 0, 'failed': 0}
+            logger.info("Skipping embeddings - no new chunks")
+
+        db.close()
+
+        # Summary
+        end_time = datetime.now()
+        duration = (end_time - start_time).total_seconds()
+
+        logger.info("=" * 60)
+        logger.info("PIPELINE SUMMARY")
+        logger.info("=" * 60)
+        logger.info(f"Scraping:   {scrape_stats['scraped']} success, {scrape_stats['failed']} failed")
+        logger.info(f"Extraction: {extract_stats.get('processed', 0)} success, {extract_stats.get('failed', 0)} failed")
+        logger.info(f"Embeddings: {embed_stats.get('generated', 0)} success, {embed_stats.get('failed', 0)} failed")
+        logger.info(f"Duration:   {duration:.1f} seconds")
+        logger.info("=" * 60)
+        logger.info("PIPELINE COMPLETED SUCCESSFULLY")
+        logger.info("=" * 60)
+
+        return 0
+
+    except Exception as e:
+        logger.error(f"Pipeline failed with error: {e}", exc_info=True)
+        return 1
+
+
+if __name__ == '__main__':
+    sys.exit(main())