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"file_path": "\/var\/www\/scripts\/pipeline\/backfill_knowledge_semantics.py",
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"content": " db.log(\"ERROR\", f\"Backfill: Failed to store knowledge semantics for entity {entity_id}: {e}\")\n return False\n\n\ndef main():\n \"\"\"Main entry point.\"\"\"\n parser = argparse.ArgumentParser(description=\"Backfill Knowledge Semantics\")\n parser.add_argument(\"--limit\", type=int, default=0, help=\"Max entities to process (0=all)\")\n parser.add_argument(\"--batch\", type=int, default=25, help=\"Batch size for progress output\")\n parser.add_argument(\"--model\", default=\"mistral\", help=\"Ollama model to use\")\n parser.add_argument(\"--dry-run\", action=\"store_true\", help=\"Just count, don't process\")\n\n args = parser.parse_args()\n\n db.connect()\n\n try:\n # Get pending entities\n entities = get_pending_entities(args.limit)\n total = len(entities)\n\n print(\"Knowledge Semantics Backfill\")\n print(\"=\" * 50)\n print(f\"Pending entities: {total}\")\n print(f\"Model: {args.model}\")\n print(f\"Batch size: {args.batch}\")\n\n if args.dry_run:\n print(\"\\nDry run - no processing\")\n return\n\n if total == 0:\n print(\"\\nNo pending entities - all done!\")\n return\n\n print(\"\\nStarting analysis...\")\n print(\"-\" * 50)\n\n success = 0\n errors = 0\n start_time = time.time()\n\n for i, entity in enumerate(entities, 1):\n # Analyze\n semantics = analyze_entity(entity, args.model)\n\n if semantics:\n if store_semantics(entity[\"id\"], semantics):\n success += 1\n else:",
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