111 lines
5.0 KiB
Python
111 lines
5.0 KiB
Python
import asyncio
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import logging
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from sqlalchemy import select, update, func, and_, case # JAVÍTVA: and_ és case importálva
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from app.db.session import SessionLocal
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from app.models.vehicle_definitions import VehicleModelDefinition
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from app.services.ai_service import AIService
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# Logolás finomhangolása
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logging.basicConfig(level=logging.INFO, format='%(asctime)s [%(levelname)s] %(name)s: %(message)s')
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logger = logging.getLogger("Robot-Alchemist-v2.2")
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class AlchemistBot:
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def __init__(self):
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self.batch_size = 5 # GPU VRAM kímélése (Ollama párhuzamosítás mellett)
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self.delay_between_records = 12 # Quadro P4000 hűtési idő/késleltetés
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async def synthesize_vehicle(self, vehicle_id: int):
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"""AI dúsítás végrehajtása a begyűjtött kontextusból."""
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async with SessionLocal() as db:
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res = await db.execute(select(VehicleModelDefinition).where(VehicleModelDefinition.id == vehicle_id))
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v = res.scalar_one_or_none()
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if not v or not v.raw_search_context:
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logger.warning(f"⚠️ Nincs kontextus az ID:{vehicle_id} rekordhoz, átugrás.")
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return
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make, model = v.make, v.marketing_name
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logger.info(f"🧪 Arany dúsítás indul (AI Synthesis): {make} {model}")
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# Státusz zárolása a feldolgozás idejére
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await db.execute(
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update(VehicleModelDefinition)
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.where(VehicleModelDefinition.id == vehicle_id)
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.values(status='ai_synthesis_in_progress')
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)
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await db.commit()
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# AI hívás: Gold-Data kinyerése a "szemetesládából"
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gold_data = await AIService.get_gold_data_from_research(make, model, v.raw_search_context)
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async with SessionLocal() as db:
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if gold_data:
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# Értékek kinyerése és normalizálása
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ccm = gold_data.get("ccm")
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kw = gold_data.get("kw")
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m_name = gold_data.get("marketing_name", model)[:50]
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t_code = gold_data.get("technical_code")
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await db.execute(
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update(VehicleModelDefinition)
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.where(VehicleModelDefinition.id == vehicle_id)
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.values(
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marketing_name=m_name,
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technical_code=t_code or v.technical_code,
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engine_capacity=ccm,
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power_kw=kw,
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features_json=gold_data, # A teljes technikai JSON (olaj, gumi, stb.)
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status='gold_enriched',
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updated_at=func.now()
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)
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)
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logger.info(f"✨ GOLD ENRICHED: {make} {m_name} ({ccm} ccm, {kw} kW)")
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else:
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# Hiba esetén visszatesszük a sorba, növelve a kísérletek számát
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await db.execute(
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update(VehicleModelDefinition)
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.where(VehicleModelDefinition.id == vehicle_id)
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.values(
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status='awaiting_ai_synthesis',
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attempts=v.attempts + 1,
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last_error="AI extraction failed or returned empty"
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)
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)
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logger.warning(f"⚠️ Sikertelen dúsítás: {make} {model}")
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await db.commit()
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async def run(self):
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logger.info("🚀 Robot 2.2 (Alchemist) ONLINE - Prioritásos feldolgozás")
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while True:
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async with SessionLocal() as db:
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# --- PRIORITÁSI LOGIKA (Megegyezik a Researcher botéval) ---
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priorities = case(
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(and_(VehicleModelDefinition.vehicle_type == 'car',
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VehicleModelDefinition.make.in_(['SUZUKI', 'TOYOTA', 'SKODA', 'VOLKSWAGEN', 'OPEL'])), 1),
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(VehicleModelDefinition.vehicle_type == 'car', 2),
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(and_(VehicleModelDefinition.vehicle_type == 'motorcycle',
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VehicleModelDefinition.make.in_(['HONDA', 'YAMAHA', 'SUZUKI', 'KAWASAKI'])), 3),
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else_=4
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)
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# Lekérdezés prioritás szerint, majd a legrégebben frissített rekordok szerint
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stmt = select(VehicleModelDefinition.id).where(
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VehicleModelDefinition.status == 'awaiting_ai_synthesis'
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).order_by(priorities, VehicleModelDefinition.updated_at.asc()).limit(self.batch_size)
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res = await db.execute(stmt)
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ids = [r[0] for r in res.fetchall()]
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if not ids:
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# Ha üres a tartály, pihenünk és várunk a porszívóra
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await asyncio.sleep(20)
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continue
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for vid in ids:
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await self.synthesize_vehicle(vid)
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# Quadro P4000 hűtés és Ollama API tehermentesítés
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await asyncio.sleep(self.delay_between_records)
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if __name__ == "__main__":
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asyncio.run(AlchemistBot().run()) |