106 lines
4.6 KiB
Python
Executable File
106 lines
4.6 KiB
Python
Executable File
# /app/app/workers/vehicle/vehicle_robot_3_alchemist.py
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import asyncio
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import logging
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from sqlalchemy import select, update, func, and_, case
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from app.db.session import AsyncSessionLocal
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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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# MB 2.0 Naplózás
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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("Vehicle-Robot-3-Alchemist")
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class VehicleAlchemist:
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"""
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Vehicle Robot 3: AI Synthesizer (Alchemist)
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Feladata: A kutatási kontextusból strukturált "Gold Data" kinyerése AI segítségével.
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"""
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def __init__(self):
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self.batch_size = 5
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self.delay_between_records = 12 # P4000 GPU kímélő késleltetés
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async def synthesize_vehicle(self, vehicle_id: int):
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""" AI dúsítás végrehajtása az Ollama/AI segítségével. """
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async with AsyncSessionLocal() as db:
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# Szigorú sémakezelés és zárolás
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res = await db.execute(
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select(VehicleModelDefinition)
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.where(VehicleModelDefinition.id == vehicle_id)
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.with_for_update(skip_locked=True)
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)
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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 feldolgozható kontextus ID:{vehicle_id}")
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return
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make, model = v.make, v.marketing_name
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logger.info(f"🧪 Transzmutáció indul: {make} {model}")
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# Státusz váltás a feldolgozás idejére
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v.status = 'ai_synthesis_in_progress'
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await db.commit()
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# AI hívás a háttérben (Ollama konténer felé)
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# Itt történik a "mágia": a nyers szövegből JSON lesz
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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 AsyncSessionLocal() as db:
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if gold_data:
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# Strukturált adatok rögzítése a 'data' sémába
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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=gold_data.get("marketing_name", model)[:50],
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technical_code=gold_data.get("technical_code") or v.technical_code,
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engine_capacity=gold_data.get("ccm"),
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power_kw=gold_data.get("kw"),
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specifications=gold_data, # JSONB mező a teljes technikai laphoz
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status='gold_enriched', # MB 2.0: Ez a legmagasabb adatszint
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updated_at=func.now()
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)
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)
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logger.info(f"✨ ARANY ADAT GENERÁLVA: {make} {model}")
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else:
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# Ha az AI elbukott, visszatesszük várakozóba
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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='awaiting_ai_synthesis', attempts=v.attempts + 1)
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)
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logger.warning(f"⚠️ AI hiba, visszatéve a sorba: {make} {model}")
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await db.commit()
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async def run(self):
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logger.info("🚀 Vehicle Alchemist ONLINE - Adatpárolás indul...")
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while True:
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async with AsyncSessionLocal() as db:
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# MB 2.0 Prioritás: Legnépszerűbb márkák az élen
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top_makes = ['SUZUKI', 'TOYOTA', 'SKODA', 'VOLKSWAGEN', 'OPEL']
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priorities = case(
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(and_(VehicleModelDefinition.vehicle_type == 'car',
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VehicleModelDefinition.make.in_(top_makes)), 1),
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(VehicleModelDefinition.vehicle_type == 'car', 2),
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(VehicleModelDefinition.vehicle_type == 'motorcycle', 3),
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else_=4
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)
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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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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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await asyncio.sleep(self.delay_between_records)
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if __name__ == "__main__":
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asyncio.run(VehicleAlchemist().run()) |