feat: Robot ecosystem v1.2.6 - Google Search RAG & Master-Merge logic stabilized

.env

@@ -1,109 +1,44 @@
+# --- DOCKER PROJEKT ---
 COMPOSE_PROJECT_NAME=service_finder
 
-# --- ADATBÁZIS KAPCSOLAT (Központi) ---
+# --- ADATBÁZIS (Központi shared-postgres) ---
-# Itt a 'shared-postgres' nevet használjuk, ami a központi konténer neve
 APP_DB_HOST=shared-postgres
 APP_DB_PORT=5432
 APP_DB_NAME=service_finder
 APP_DB_USER=service_finder_app
-# IDE ÍRD AZT A JELSZÓT, amit a pgAdminban/ALTER USER parancsnál adtál a 'service_finder_app'-nak!
+APP_DB_PASSWORD=MiskociA74
-APP_DB_PASSWORD='MiskociA74'
 
-# Ez a legfontosabb sor, ezt használja a Python program:
+# Alembic és FastAPI számára (Asyncpg-t használsz, ez így jó)
 DATABASE_URL=postgresql+asyncpg://service_finder_app:MiskociA74@shared-postgres:5432/service_finder
-# Migrációhoz (néha szinkron driver kell):
 MIGRATION_DATABASE_URL=postgresql+asyncpg://service_finder_app:MiskociA74@shared-postgres:5432/service_finder
 
-# --- ALKALMAZÁS BEÁLLÍTÁSOK ---
+# --- REDIS ---
-ALLOWED_ORIGINS="https://app.profibot.hu,https://dev.profibot.hu,http://localhost:3000"
-PYTHONPATH=/app
-
-# --- MINIO (Fájltárolás) ---
-# Ez maradhat helyi konténer, vagy köthetjük a központihoz is, de most hagyjuk a projektben
-MINIO_ENDPOINT=minio:9000
-MINIO_ROOT_USER=kincses
-MINIO_ROOT_PASSWORD='MiskociA74'
-MINIO_ACCESS_KEY=kincses
-MINIO_SECRET_KEY='MiskociA74'
-
-# --- EGYÉB API KULCSOK ---
-SENDGRID_API_KEY=SG.SENDGRID_API_KEY=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
-FROM_EMAIL=info@profibot.hu
-
-# Biztonsági kulcs a tokenekhez (KÖTELEZŐ!)
-SECRET_KEY=2dca2ff3bf9b8184e14038d5d08e646b31bd4a5f5ffc7e19d28e294f3bb3760b
-_______________________________________________________________
-# ==============================================================================
-# 🛠️ INFRASTRUKTÚRA (Docker & Database)
-# ==============================================================================
-# Adatbázis alapok
-POSTGRES_USER=kincses
-POSTGRES_PASSWORD='MiskociA74'
-POSTGRES_DB=service_finder
-
-# Kapcsolati URL a Python számára (Központi shared-postgres)
-DATABASE_URL=postgresql+asyncpg://service_finder_app:MiskociA74@shared-postgres:5432/service_finder
-
-# Migrációhoz használt URL (Alembic számára)
-MIGRATION_DATABASE_URL=postgresql+asyncpg://service_finder_app:MiskociA74@shared-postgres:5432/service_finder
-
-# Redis elérés
 REDIS_URL=redis://service_finder_redis:6379/0
 
-# ==============================================================================
+# --- BIZTONSÁG ---
-# 🚀 ALKALMAZÁS BEÁLLÍTÁSOK (FastAPI)
+SECRET_KEY=2dca2ff3bf9b8184e14038d5d08e646b31bd4a5f5ffc7e19d28e294f3bb3760b
-# ==============================================================================
-ENV=development
-DEBUG=True
-PYTHONPATH=/app
-
-# Biztonsági kulcs a JWT tokenekhez (Generálj egy hosszú véletlen sort!)
-# Példa generáláshoz: openssl rand -hex 32
-SECRET_KEY='2dca2ff3bf9b8184e14038d5d08e646b31bd4a5f5ffc7e19d28e294f3bb3760b'
 ALGORITHM=HS256
+ALLOWED_ORIGINS="https://app.profibot.hu,https://dev.profibot.hu,http://localhost:3000"
 
-# CORS: Milyen címekről érhető el az API? (Vesszővel elválasztva)
+# --- MINIO & NAS ---
-CORS_ORIGINS=https://app.profibot.hu,https://dev.profibot.hu,http://localhost:3000,http://192.168.100.10:3000
-
-# Frontend címe a kiküldött linkekhez (Visszaigazolás, jelszó-visszaállítás)
-FRONTEND_BASE_URL=http://192.168.100.10:3000
-
-# ==============================================================================
-# 📧 EMAIL RENDSZER (SMTP / SendGrid)
-# ==============================================================================
-# EMAIL_PROVIDER lehet: 'smtp' vagy 'sendgrid' vagy 'disabled'
-EMAIL_PROVIDER=sendgrid
-EMAILS_FROM_EMAIL=info@profibot.hu
-EMAILS_FROM_NAME='Service Finder'
-
-# SendGrid beállítások
-SENDGRID_API_KEY=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
-
-# SMTP Fallback (Csak ha az EMAIL_PROVIDER=smtp)
-SMTP_HOST=smtp.gmail.com
-SMTP_PORT=587
-SMTP_USER=info@profibot.hu
-SMTP_PASSWORD='SAJÁT_APP_PASSWORD'
-
-# ==============================================================================
-# 📦 MINIO (Fájltárolás - NAS-ra kivezetve)
-# ==============================================================================
 MINIO_ENDPOINT=minio:9000
 MINIO_ROOT_USER=kincses
-MINIO_ROOT_PASSWORD='MiskociA74'
+MINIO_ROOT_PASSWORD=MiskociA74
 MINIO_ACCESS_KEY=kincses
-MINIO_SECRET_KEY='MiskociA74'
+MINIO_SECRET_KEY=MiskociA74
 
-# --- n8n CONFIG ---
+# --- EMAILS ---
+EMAIL_PROVIDER=sendgrid
+SENDGRID_API_KEY=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
+EMAILS_FROM_EMAIL=info@profibot.hu
+
+# --- n8n ---
 N8N_DB_PASSWORD=MiskociA74
-# Az n8n-en belül a központi DB elérése:
-# Host: shared-postgres
-# User: service_finder_app
 
-# --- Frontend ---
+# --- ROBOTOK API KULCSAI ---
-FRONTEND_BASE_URL=https://dev.profibot.hu/docs
+RDW_APP_TOKEN=kSMUn0tvnmoM6TMSegLpFvKI8
+GEMINI_API_KEY=AIzaSyAaCVNPwf8PCphu_pt6spjAa2OVu8Exug8
 
-
+# --- RENDSZER ---
-GOOGLE_API_KEY=AIzaSyB3-Uo6qFBNi83hK01uoaUARtYHxERbtXg
+PYTHONPATH=/app
-
+ENV=development
-RDW_APP_TOKEN=kSMUn0tvnmoM6TMSegLpFvKI8

.env_old

@@ -0,0 +1,112 @@
+COMPOSE_PROJECT_NAME=service_finder
+
+# --- ADATBÁZIS KAPCSOLAT (Központi) ---
+# Itt a 'shared-postgres' nevet használjuk, ami a központi konténer neve
+APP_DB_HOST=shared-postgres
+APP_DB_PORT=5432
+APP_DB_NAME=service_finder
+APP_DB_USER=service_finder_app
+# IDE ÍRD AZT A JELSZÓT, amit a pgAdminban/ALTER USER parancsnál adtál a 'service_finder_app'-nak!
+APP_DB_PASSWORD='MiskociA74'
+
+# Ez a legfontosabb sor, ezt használja a Python program:
+DATABASE_URL=postgresql+asyncpg://service_finder_app:MiskociA74@shared-postgres:5432/service_finder
+# Migrációhoz (néha szinkron driver kell):
+MIGRATION_DATABASE_URL=postgresql+asyncpg://service_finder_app:MiskociA74@shared-postgres:5432/service_finder
+
+# --- ALKALMAZÁS BEÁLLÍTÁSOK ---
+ALLOWED_ORIGINS="https://app.profibot.hu,https://dev.profibot.hu,http://localhost:3000"
+PYTHONPATH=/app
+
+# --- MINIO (Fájltárolás) ---
+# Ez maradhat helyi konténer, vagy köthetjük a központihoz is, de most hagyjuk a projektben
+MINIO_ENDPOINT=minio:9000
+MINIO_ROOT_USER=kincses
+MINIO_ROOT_PASSWORD='MiskociA74'
+MINIO_ACCESS_KEY=kincses
+MINIO_SECRET_KEY='MiskociA74'
+
+# --- EGYÉB API KULCSOK ---
+SENDGRID_API_KEY=SG.SENDGRID_API_KEY=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
+FROM_EMAIL=info@profibot.hu
+
+# Biztonsági kulcs a tokenekhez (KÖTELEZŐ!)
+SECRET_KEY=2dca2ff3bf9b8184e14038d5d08e646b31bd4a5f5ffc7e19d28e294f3bb3760b
+_______________________________________________________________
+# ==============================================================================
+# 🛠️ INFRASTRUKTÚRA (Docker & Database)
+# ==============================================================================
+# Adatbázis alapok
+POSTGRES_USER=kincses
+POSTGRES_PASSWORD='MiskociA74'
+POSTGRES_DB=service_finder
+
+# Kapcsolati URL a Python számára (Központi shared-postgres)
+DATABASE_URL=postgresql+asyncpg://service_finder_app:MiskociA74@shared-postgres:5432/service_finder
+
+# Migrációhoz használt URL (Alembic számára)
+MIGRATION_DATABASE_URL=postgresql+asyncpg://service_finder_app:MiskociA74@shared-postgres:5432/service_finder
+
+# Redis elérés
+REDIS_URL=redis://service_finder_redis:6379/0
+
+# ==============================================================================
+# 🚀 ALKALMAZÁS BEÁLLÍTÁSOK (FastAPI)
+# ==============================================================================
+ENV=development
+DEBUG=True
+PYTHONPATH=/app
+
+# Biztonsági kulcs a JWT tokenekhez (Generálj egy hosszú véletlen sort!)
+# Példa generáláshoz: openssl rand -hex 32
+SECRET_KEY='2dca2ff3bf9b8184e14038d5d08e646b31bd4a5f5ffc7e19d28e294f3bb3760b'
+ALGORITHM=HS256
+
+# CORS: Milyen címekről érhető el az API? (Vesszővel elválasztva)
+CORS_ORIGINS=https://app.profibot.hu,https://dev.profibot.hu,http://localhost:3000,http://192.168.100.10:3000
+
+# Frontend címe a kiküldött linkekhez (Visszaigazolás, jelszó-visszaállítás)
+FRONTEND_BASE_URL=http://192.168.100.10:3000
+
+# ==============================================================================
+# 📧 EMAIL RENDSZER (SMTP / SendGrid)
+# ==============================================================================
+# EMAIL_PROVIDER lehet: 'smtp' vagy 'sendgrid' vagy 'disabled'
+EMAIL_PROVIDER=sendgrid
+EMAILS_FROM_EMAIL=info@profibot.hu
+EMAILS_FROM_NAME='Service Finder'
+
+# SendGrid beállítások
+SENDGRID_API_KEY=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
+
+# SMTP Fallback (Csak ha az EMAIL_PROVIDER=smtp)
+SMTP_HOST=smtp.gmail.com
+SMTP_PORT=587
+SMTP_USER=info@profibot.hu
+SMTP_PASSWORD='SAJÁT_APP_PASSWORD'
+
+# ==============================================================================
+# 📦 MINIO (Fájltárolás - NAS-ra kivezetve)
+# ==============================================================================
+MINIO_ENDPOINT=minio:9000
+MINIO_ROOT_USER=kincses
+MINIO_ROOT_PASSWORD='MiskociA74'
+MINIO_ACCESS_KEY=kincses
+MINIO_SECRET_KEY='MiskociA74'
+
+# --- n8n CONFIG ---
+N8N_DB_PASSWORD=MiskociA74
+# Az n8n-en belül a központi DB elérése:
+# Host: shared-postgres
+# User: service_finder_app
+
+# --- Frontend ---
+FRONTEND_BASE_URL=https://dev.profibot.hu/docs
+
+
+
+# Holland autó adatbázis free token
+RDW_APP_TOKEN=kSMUn0tvnmoM6TMSegLpFvKI8
+
+# gemini service_finder_robot
+GEMINI_API_KEY=AIzaSyAaCVNPwf8PCphu_pt6spjAa2OVu8Exug8

backend/app/models/vehicle_definitions.py

@@ -1,4 +1,4 @@
-from sqlalchemy import Column, Integer, String, JSON, UniqueConstraint, text, Boolean, DateTime, ForeignKey, Numeric
+from sqlalchemy import Column, Integer, String, JSON, UniqueConstraint, text, Boolean, DateTime, ForeignKey, Numeric, Index
 from sqlalchemy.orm import relationship
 from sqlalchemy.sql import func
 from app.db.base_class import Base
@@ -41,10 +41,11 @@ class ModelFeatureMap(Base):
     value = Column(String(100))
 
 class VehicleModelDefinition(Base):
-    """MDM Master rekordok"""
+    """MDM Master rekordok - Kibővítve Deduplikációs és Évjárat mezőkkel (v1.2.5)"""
     __tablename__ = "vehicle_model_definitions"
     __table_args__ = (
         UniqueConstraint('make', 'technical_code', 'vehicle_type', name='uix_make_tech_type'),
+        Index('idx_vmd_lookup', 'make', 'technical_code'),
         {"schema": "data"}
     )
 
@@ -58,28 +59,46 @@ class VehicleModelDefinition(Base):
     vehicle_type_id = Column(Integer, ForeignKey("data.vehicle_types.id"))
     vehicle_class = Column(String(50))
     
-    # --- LOGISZTIKAI ÉS TECHNIKAI FIX OSZLOPOK (v1.9) ---
+    # --- ÚJ MEZŐK AZ INTELLIGENS ÖSSZEFÉSÜLÉSHEZ ---
+    # Ha ez a rekord egy duplikátum, itt tároljuk, melyik az eredeti (Master) rekord
+    parent_id = Column(Integer, ForeignKey("data.vehicle_model_definitions.id"), nullable=True)
+    
+    # Gyártási intervallum meghatározása
+    year_from = Column(Integer, nullable=True, index=True)
+    year_to = Column(Integer, nullable=True, index=True)
+    
+    # Alternatív elnevezések kereshetőséghez (pl. ["Tracer 9", "MT-09 Tracer"])
+    synonyms = Column(JSON, server_default=text("'[]'::jsonb"))
+    # -----------------------------------------------
+
+    # --- LOGISZTIKAI ÉS TECHNIKAI FIX OSZLOPOK ---
     engine_capacity = Column(Integer, index=True)
     power_kw = Column(Integer, index=True)
-    max_weight_kg = Column(Integer, index=True) # Össztömeg
+    max_weight_kg = Column(Integer, index=True)
     
-    axle_count = Column(Integer)                 # Tengelyek száma (Teher/Busz)
+    axle_count = Column(Integer)
-    payload_capacity_kg = Column(Integer)        # Teherbírás
+    payload_capacity_kg = Column(Integer)
-    cargo_volume_m3 = Column(Numeric(10, 2))     # Raktér térfogat
+    cargo_volume_m3 = Column(Numeric(10, 2))
-    cargo_length_mm = Column(Integer)            # Raktér méretek
+    cargo_length_mm = Column(Integer)
     cargo_width_mm = Column(Integer)
     cargo_height_mm = Column(Integer)
-    # --------------------------------------------------
+    # ----------------------------------------------
 
     specifications = Column(JSON, server_default=text("'{}'::jsonb"))
-    features_json = Column(JSON, server_default=text("'{}'::jsonb")) # Összesített gyorseléréshez
+    features_json = Column(JSON, server_default=text("'{}'::jsonb"))
     
-    status = Column(String(20), server_default="unverified")
+    status = Column(String(20), server_default="unverified") # unverified, ai_enriched, duplicate, manual_check
     is_master = Column(Boolean, default=False)
     source = Column(String(50))
     
     created_at = Column(DateTime(timezone=True), server_default=func.now())
     updated_at = Column(DateTime(timezone=True), onupdate=func.now())
 
+    # Kapcsolatok
     v_type_rel = relationship("VehicleType", back_populates="definitions")
+    
+    # Önmagára hivatkozó kapcsolat a duplikációk kezeléséhez
+    master_record = relationship("VehicleModelDefinition", remote_side=[id], backref="merged_variants")
+
+    # Meglévő kapcsolatok megtartása
     variants = relationship("AssetCatalog", back_populates="master_definition")

backend/app/services/ai_service.py

@@ -1,72 +1,111 @@
 import os
 import json
 import logging
-import google.generativeai as genai
+import asyncio
+import re
 from typing import Dict, Any, Optional
+from google import genai
+from google.genai import types
+from sqlalchemy import select
+from app.db.session import SessionLocal
+from app.models import SystemParameter
 
 logger = logging.getLogger("AI-Service")
 
 class AIService:
-    # Konfiguráció a .env-ből
+    """
+    AI Service v1.2.5 - Final Integrated Edition
+    - Robot 2: Technikai dúsítás (Search + Regex JSON parsing)
+    - Robot 3: OCR (Controlled JSON generation)
+    """
     api_key = os.getenv("GEMINI_API_KEY")
-    if api_key:
+    client = genai.Client(api_key=api_key) if api_key else None
-        genai.configure(api_key=api_key)
+    PRIMARY_MODEL = "gemini-2.0-flash"
-    
+
-    # 1.5 Flash a legjobb ár/érték/sebesség arányú multimodális modell
+    @classmethod
-    model = genai.GenerativeModel('gemini-1.5-flash')
+    async def get_config_delay(cls) -> float:
+        try:
+            async with SessionLocal() as db:
+                stmt = select(SystemParameter).where(SystemParameter.key == "AI_REQUEST_DELAY")
+                res = await db.execute(stmt)
+                param = res.scalar_one_or_none()
+                return float(param.value) if param else 1.0
+        except Exception: return 1.0
 
     @classmethod
     async def get_clean_vehicle_data(cls, make: str, raw_model: str, v_type: str) -> Optional[Dict[str, Any]]:
-        """Robot 2: Technikai dúsítás és névtisztítás (pl. Yamaha 4HN)."""
+        """Robot 2: Adatbányászat Google Search segítségével."""
+        if not cls.client: return None
+        await asyncio.sleep(await cls.get_config_delay())
+
+        search_tool = types.Tool(google_search=types.GoogleSearch())
+
         prompt = f"""
-        Rendszer: Technikai gépjárműszakértő vagy.
+        KERESS RÁ az interneten: {make} {raw_model} ({v_type}) pontos gyári modellkódja és technikai adatai.
-        Feladat: Tisztítsd meg a '{make} {raw_model}' ({v_type}) adatot.
+        Adj választ szigorúan csak egy JSON blokkban:
-        Kimenet: Kizárólag JSON, magyarázat nélkül.
-        Formátum: 
         {{
-            "marketing_name": "Tiszta modellnév",
+            "marketing_name": "tiszta név",
-            "technical_code": "Modellkód/Generáció",
+            "synonyms": ["név1", "név2"],
+            "technical_code": "gyári kód",
+            "year_from": int,
+            "year_to": int_vagy_null,
             "ccm": int,
             "kw": int,
-            "maintenance": {{
+            "maintenance": {{ "oil_type": "string", "oil_qty": float, "spark_plug": "string", "coolant": "string" }}
-                "oil_type": "pl. 10W-40",
-                "oil_qty": float,
-                "spark_plug": "típus",
-                "coolant": "típus"
-            }}
         }}
+        FONTOS: A 'technical_code' NEM lehet üres. Ha nem találod, adj 'N/A' értéket!
         """
+        
+        # Search tool használata esetén a response_mime_type tilos!
+        config = types.GenerateContentConfig(
+            system_instruction="Profi járműtechnikai adatbányász vagy. Csak tiszta JSON-t válaszolsz markdown kódblokk nélkül.",
+            tools=[search_tool],
+            temperature=0.1
+        )
+
         try:
-            response = cls.model.generate_content(prompt)
+            response = cls.client.models.generate_content(model=cls.PRIMARY_MODEL, contents=prompt, config=config)
-            # A Gemini néha ```json ... ``` blokkba teszi, ezt le kell tisztítani
+            text = response.text
-            json_text = response.text.replace("```json", "").replace("```", "").strip()
+            # Tisztítás: ha az AI mégis tenne bele markdown jeleket
-            return json.loads(json_text)
+            clean_json = re.sub(r'```json\s*|```', '', text).strip()
+            res_json = json.loads(clean_json)
+            if isinstance(res_json, list) and len(res_json) > 0: res_json = res_json[0]
+            return res_json if isinstance(res_json, dict) else None
         except Exception as e:
-            logger.error(f"❌ AI Dúsítás hiba: {e}")
+            logger.error(f"❌ AI hiba ({make} {raw_model}): {e}")
             return None
 
     @classmethod
     async def analyze_document_image(cls, image_data: bytes, doc_type: str) -> Optional[Dict[str, Any]]:
-        """Robot 3: AI OCR - Forgalmi, Személyi, Számla, KM-óra elemzés."""
+        """Robot 3: OCR funkció - Forgalmi, Személyi, Számla, Odometer."""
+        if not cls.client: return None
+        await asyncio.sleep(await cls.get_config_delay())
         
         prompts = {
-            "identity": "Olvasd le az okmányról: vezetéknév, keresztnév, okmányszám, lejárati idő, születési dátum.",
+            "identity": "Személyes okmány adatok (név, szám, lejárat).",
-            "vehicle_reg": "Olvasd le a forgalmiból: rendszám, alvázszám (VIN), gyártmány, típus, kw, ccm, együttes tömeg, műszaki érvényesség.",
+            "vehicle_reg": "Forgalmi adatok (rendszám, alvázszám, kW, ccm).",
-            "invoice": "Olvasd le a számláról: eladó neve/adószáma, vevő neve, bruttó összeg, dátum, tételek (alkatrész/munkadíj).",
+            "invoice": "Számla adatok (partner, végösszeg, dátum).",
-            "odometer": "Olvasd le a képen látható műszerfalról a kilométeróra vagy üzemóra állását. Csak a számot add vissza."
+            "odometer": "Csak a kilométeróra állása számként."
         }
-
+        
-        prompt = f"Rendszer: Profi OCR és dokumentum-elemző vagy. {prompts.get(doc_type, 'Elemezd a képet.')} Válaszolj tiszta JSON formátumban."
+        # Itt maradhat a response_mime_type, mert nem használunk Search-öt
-
+        config = types.GenerateContentConfig(
+            system_instruction="Profi OCR dokumentum-elemző vagy. Csak tiszta JSON-t válaszolsz.",
+            response_mime_type="application/json"
+        )
+        
         try:
-            # A Gemini közvetlenül tud fogadni bytes adatot (képként)
+            response = cls.client.models.generate_content(
-            contents = [
+                model=cls.PRIMARY_MODEL,
-                prompt,
+                contents=[
-                {"mime_type": "image/jpeg", "data": image_data}
+                    f"Elemezd ezt a képet ({doc_type}): {prompts.get(doc_type, 'OCR')}", 
-            ]
+                    types.Part.from_bytes(data=image_data, mime_type="image/jpeg")
-            response = cls.model.generate_content(contents)
+                ],
-            json_text = response.text.replace("```json", "").replace("```", "").strip()
+                config=config
-            return json.loads(json_text)
+            )
+            res_json = json.loads(response.text)
+            if isinstance(res_json, list) and len(res_json) > 0: res_json = res_json[0]
+            return res_json if isinstance(res_json, dict) else None
         except Exception as e:
-            logger.error(f"❌ AI OCR hiba ({doc_type}): {e}")
+            logger.error(f"❌ OCR hiba: {e}")
             return None

backend/app/services/ai_service.py.bak

@@ -0,0 +1,116 @@
+import os
+import json
+import logging
+import asyncio
+from typing import Dict, Any, Optional
+from google import genai
+from google.genai import types
+from sqlalchemy import select
+from app.db.session import SessionLocal
+from app.models import SystemParameter
+
+logger = logging.getLogger("AI-Service")
+
+class AIService:
+    """
+    AI Service v1.2.4 - Production Ready
+    - Robot 2 (Technical Enrichment) & Robot 3 (OCR)
+    - Fix: JSON response cleaning and array-to-dict transformation.
+    """
+    api_key = os.getenv("GEMINI_API_KEY")
+    client = genai.Client(api_key=api_key) if api_key else None
+    PRIMARY_MODEL = "gemini-2.0-flash"
+
+    @classmethod
+    async def get_config_delay(cls) -> float:
+        """Lekéri az adminisztrálható késleltetést az adatbázisból."""
+        try:
+            async with SessionLocal() as db:
+                stmt = select(SystemParameter).where(SystemParameter.key == "AI_REQUEST_DELAY")
+                res = await db.execute(stmt)
+                param = res.scalar_one_or_none()
+                return float(param.value) if param else 1.0
+        except Exception:
+            return 1.0
+
+    @classmethod
+    async def get_clean_vehicle_data(cls, make: str, raw_model: str, v_type: str) -> Optional[Dict[str, Any]]:
+        """Robot 2: Gépjármű technikai adatok dúsítása."""
+        if not cls.client:
+            return None
+        
+        await asyncio.sleep(await cls.get_config_delay())
+
+        prompt = f"""
+        Jármű: {make} {raw_model} ({v_type}). 
+        Adj technikai adatokat JSON formátumban. 
+        FONTOS: A 'technical_code' mező NEM lehet üres. Ha nem tudod a gyári kódot, adj 'N/A' értéket!
+        
+        Várt struktúra:
+        {{
+            "marketing_name": "tiszta marketing név",
+            "technical_code": "gyári kód vagy N/A",
+            "ccm": egész szám,
+            "kw": egész szám,
+            "maintenance": {{
+                "oil_type": "viszkozitás",
+                "oil_qty": tizedes tört literben,
+                "spark_plug": "gyertya típus",
+                "coolant": "hűtőfolyadék"
+            }}
+        }}
+        """
+        config = types.GenerateContentConfig(
+            system_instruction="Profi gépjárműtechnikus vagy. Kizárólag tiszta JSON-t válaszolsz.",
+            response_mime_type="application/json",
+            temperature=0.1
+        )
+
+        try:
+            response = cls.client.models.generate_content(model=cls.PRIMARY_MODEL, contents=prompt, config=config)
+            res_json = json.loads(response.text)
+            
+            if isinstance(res_json, list) and len(res_json) > 0:
+                res_json = res_json[0]
+            
+            return res_json if isinstance(res_json, dict) else None
+        except Exception as e:
+            logger.error(f"❌ AI hiba ({make} {raw_model}): {e}")
+            return None
+
+    @classmethod
+    async def analyze_document_image(cls, image_data: bytes, doc_type: str) -> Optional[Dict[str, Any]]:
+        """Robot 3: Multimodális OCR elemzés (Képbeolvasás)."""
+        if not cls.client:
+            return None
+            
+        await asyncio.sleep(await cls.get_config_delay())
+        
+        prompts = {
+            "identity": "Személyes okmány adatok.",
+            "vehicle_reg": "Rendszám, alvázszám, technikai adatok.",
+            "invoice": "Számla adatok, összegek, dátumok.",
+            "odometer": "Csak a kilométeróra állása számként."
+        }
+        
+        config = types.GenerateContentConfig(
+            system_instruction="Profi OCR dokumentum-elemző vagy. Csak tiszta JSON-t válaszolsz.",
+            response_mime_type="application/json"
+        )
+        
+        try:
+            response = cls.client.models.generate_content(
+                model=cls.PRIMARY_MODEL,
+                contents=[
+                    f"Elemezd ezt a képet ({doc_type}): {prompts.get(doc_type, '')}",
+                    types.Part.from_bytes(data=image_data, mime_type="image/jpeg")
+                ],
+                config=config
+            )
+            res_json = json.loads(response.text)
+            if isinstance(res_json, list) and len(res_json) > 0:
+                res_json = res_json[0]
+            return res_json if isinstance(res_json, dict) else None
+        except Exception as e:
+            logger.error(f"❌ AI OCR hiba ({doc_type}): {e}")
+            return None

backend/app/workers/technical_enricher.py

@@ -3,133 +3,113 @@ import httpx
 import logging
 import os
 import datetime
-import json
+from sqlalchemy import select, and_
-from sqlalchemy import text, select, update
+from sqlalchemy.exc import IntegrityError
 from app.db.session import SessionLocal
 from app.models.vehicle_definitions import VehicleModelDefinition
-from app.models.audit import ProcessLog
 from app.services.ai_service import AIService
-from app.services.email_manager import EmailManager # Feltételezve, hogy létezik
 
 logging.basicConfig(level=logging.INFO)
-logger = logging.getLogger("Robot-v1.1.0-Master-Enricher")
+logger = logging.getLogger("Robot-Bulk-Master")
 
 class TechEnricher:
-    """
-    Master Enricher v1.1.0 - Hybrid RDW & AI Clean Edition
-    - Cél: vehicle_model_definitions (Master) tábla tisztítása és dúsítása.
-    - Megtartja a v1.0.4 RDW logikát, de kiegészíti AI-al a zajos adatokhoz (pl. Yamaha 4HN).
-    """
-    
     API_URL = "https://opendata.rdw.nl/resource/kyri-nuah.json"
     RDW_TOKEN = os.getenv("RDW_APP_TOKEN")
     HEADERS = {"X-App-Token": RDW_TOKEN} if RDW_TOKEN else {}
 
-    @classmethod
-    def clean_num(cls, v):
-        try: return int(float(v)) if v else None
-        except: return None
-
     @classmethod
     async def fetch_rdw_tech_data(cls, make, model):
-        """A v1.0.4-es RDW kereső logika."""
+        params = {"merk": make.upper(), "handelsbenaming": str(model).strip().upper(), "$limit": 1}
-        clean_model = str(model).upper().replace(str(make).upper(), "").strip()
-        if len(clean_model) < 2: return None
-
-        params = {"merk": make.upper(), "handelsbenaming": clean_model, "$limit": 1}
         async with httpx.AsyncClient(headers=cls.HEADERS) as client:
             try:
-                await asyncio.sleep(1.1) # RDW Rate limit védelem
+                resp = await client.get(cls.API_URL, params=params, timeout=15)
-                resp = await client.get(cls.API_URL, params=params, timeout=20)
+                return resp.json()[0] if resp.status_code == 200 and resp.json() else None
-                if resp.status_code == 200:
+            except: return None
-                    data = resp.json()
-                    return data[0] if data else None
-                return None
-            except Exception as e:
-                logger.error(f"❌ RDW API Hiba: {e}")
-                return None
 
     @classmethod
     async def run(cls):
-        logger.info("🚀 Master Enricher v1.1.0 INDUL...")
+        logger.info("🚀 Master-Merge Robot FOLYAMATOS ÜZEMMÓD INDUL...")
-        start_time = datetime.datetime.now()
-        stats = {"processed": 0, "failed": 0, "cleaned": []}
-
-        async with SessionLocal() as db:
-            # Csak azokat a Master rekordokat nézzük, amik még nincsenek hitelesítve
-            stmt = select(VehicleModelDefinition).where(
-                VehicleModelDefinition.status == "unverified"
-            ).limit(30) # Kisebb batch a biztonság érdekében
-            
-            res = await db.execute(stmt)
-            masters = res.scalars().all()
-
-            if not masters:
-                logger.info("😴 Nincs dúsításra váró adat.")
-                return
-
-            for master in masters:
-                try:
-                    logger.info(f"🧪 Feldolgozás: {master.make} {master.marketing_name}")
-                    
-                    # 1. Lépés: RDW adatok lekérése (v1.0.4 logika)
-                    rdw_data = await cls.fetch_rdw_tech_data(master.make, master.marketing_name)
-                    
-                    # 2. Lépés: AI segítség kérése, ha az RDW nem elég vagy a név 'zajos' (pl. 4HN)
-                    # Ha a névben gyanús kódok vannak, az AI tisztítja meg
-                    if not rdw_data or "(" in master.marketing_name or len(master.marketing_name) < 5:
-                        ai_data = await AIService.get_clean_vehicle_data(
-                            master.make, master.marketing_name, master.vehicle_type
-                        )
-                        if ai_data:
-                            old_name = master.marketing_name
-                            master.marketing_name = ai_data.get("marketing_name", old_name)
-                            master.technical_code = ai_data.get("technical_code", master.technical_code)
-                            master.engine_capacity = ai_data.get("ccm", master.engine_capacity)
-                            master.power_kw = ai_data.get("kw", master.power_kw)
-                            master.specifications = ai_data.get("maintenance", {})
-                            stats["cleaned"].append(f"{old_name} -> {master.marketing_name}")
-
-                    # Ha volt RDW adatunk, de az AI nem írta felül, töltsük be az RDW-t
-                    if rdw_data and master.status == "unverified":
-                        master.power_kw = cls.clean_num(rdw_data.get("netto_maximum_vermogen_kw"))
-                        master.engine_capacity = cls.clean_num(rdw_data.get("cilinderinhoud"))
-                        master.axle_count = cls.clean_num(rdw_data.get("aantal_assen"))
-                        
-                    master.status = "ai_enriched"
-                    stats["processed"] += 1
-                    await db.commit()
-
-                except Exception as e:
-                    logger.error(f"❌ Hiba a(z) {master.id} rekordnál: {e}")
-                    stats["failed"] += 1
-                    await db.rollback()
-
-            # 3. JELENTÉS MENTÉSE ÉS EMAIL KÜLDÉS
-            end_time = datetime.datetime.now()
-            new_log = ProcessLog(
-                process_name="Master-Enricher",
-                start_time=start_time,
-                end_time=end_time,
-                items_processed=stats["processed"],
-                items_failed=stats["failed"],
-                details=stats
-            )
-            db.add(new_log)
-            await db.commit()
-
-            # Email küldés (Dummy hívás a meglévő EmailManager-hez)
-            await cls.send_report_email(stats)
-
-    @classmethod
-    async def send_report_email(cls, stats):
-        report_body = f"Reggeli Robot Jelentés - {datetime.date.today()}\n\n"
-        report_body += f"Sikeresen feldolgozva: {stats['processed']}\n"
-        report_body += f"Hibák: {stats['failed']}\n\n"
-        report_body += "Tisztított nevek:\n" + "\n".join(stats['cleaned'])
         
-        logger.info("📧 Email jelentés elküldve az adminnak.")
+        while True: # Folyamatos ciklus, amíg el nem fogy az adat
-        # EmailManager.send_admin_notification("Robot Report", report_body)
+            async with SessionLocal() as main_db:
+                stmt = select(VehicleModelDefinition.id).where(
+                    VehicleModelDefinition.status == "unverified"
+                ).limit(50) # Egyszerre 50 ID-t foglalunk le
+                res = await main_db.execute(stmt)
+                ids = res.scalars().all()
+
+            if not ids:
+                logger.info("🏁 Minden rekord feldolgozva. A robot megáll.")
+                break
+
+            logger.info(f"📦 Új csomag indítása: {len(ids)} rekord.")
+
+            for m_id in ids:
+                async with SessionLocal() as db:
+                    try:
+                        current = await db.get(VehicleModelDefinition, m_id)
+                        if not current: continue
+
+                        logger.info(f"🧪 Feldolgozás: {current.make} {current.marketing_name} (ID: {m_id})")
+                        
+                        rdw_data = await cls.fetch_rdw_tech_data(current.make, current.marketing_name)
+                        if rdw_data:
+                            current.engine_capacity = int(float(rdw_data.get("cilinderinhoud", 0))) or current.engine_capacity
+                            current.power_kw = int(float(rdw_data.get("netto_maximum_vermogen_kw", 0))) or current.power_kw
+
+                        ai_data = await AIService.get_clean_vehicle_data(current.make, current.marketing_name, current.vehicle_type)
+                        
+                        if ai_data:
+                            tech_code = ai_data.get("technical_code") or "N/A"
+                            new_ccm = ai_data.get("ccm") or current.engine_capacity
+
+                            master_record = None
+                            if tech_code and tech_code != "N/A":
+                                stmt_master = select(VehicleModelDefinition).where(and_(
+                                    VehicleModelDefinition.make == current.make,
+                                    VehicleModelDefinition.technical_code == tech_code,
+                                    VehicleModelDefinition.engine_capacity == new_ccm,
+                                    VehicleModelDefinition.status == 'ai_enriched',
+                                    VehicleModelDefinition.id != m_id
+                                ))
+                                master_record = (await db.execute(stmt_master)).scalar_one_or_none()
+
+                            if master_record:
+                                logger.info(f"🔗 Merge: ID:{m_id} -> Master ID:{master_record.id}")
+                                syns = set(master_record.synonyms or [])
+                                syns.update(ai_data.get("synonyms", []))
+                                syns.add(current.marketing_name)
+                                master_record.synonyms = list(syns)
+                                current.status = "duplicate"
+                                current.parent_id = master_record.id
+                            else:
+                                current.technical_code = tech_code if tech_code != "N/A" else f"N/A-{m_id}"
+                                current.marketing_name = ai_data.get("marketing_name", current.marketing_name)
+                                current.engine_capacity = new_ccm
+                                current.power_kw = ai_data.get("kw") or current.power_kw
+                                current.year_from = ai_data.get("year_from")
+                                current.year_to = ai_data.get("year_to")
+                                current.synonyms = ai_data.get("synonyms", [])
+                                
+                                if ai_data.get("maintenance"):
+                                    old_spec = current.specifications or {}
+                                    old_spec.update(ai_data.get("maintenance"))
+                                    current.specifications = old_spec
+                                
+                                current.status = "ai_enriched"
+                        else:
+                            if not current.technical_code:
+                                current.technical_code = f"UNKNOWN-{m_id}"
+
+                        current.updated_at = datetime.datetime.now()
+                        await db.commit()
+                        logger.info(f"✅ Mentve (ID: {m_id})")
+
+                    except Exception as e:
+                        await db.rollback()
+                        logger.error(f"❌ Hiba ID:{m_id}: {e}")
+                    finally:
+                        await db.close()
 
 if __name__ == "__main__":
     asyncio.run(TechEnricher.run())

backend/app/workers/technical_enricher.py.bak

@@ -0,0 +1,102 @@
+import asyncio
+import httpx
+import logging
+import os  # <--- EZ HIÁNYZOTT!
+import datetime
+from sqlalchemy import select
+from sqlalchemy.exc import IntegrityError
+from app.db.session import SessionLocal
+from app.models.vehicle_definitions import VehicleModelDefinition
+from app.services.ai_service import AIService
+
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger("Robot-v1.2.4-Fixed")
+
+class TechEnricher:
+    API_URL = "https://opendata.rdw.nl/resource/kyri-nuah.json"
+    RDW_TOKEN = os.getenv("RDW_APP_TOKEN")
+    HEADERS = {"X-App-Token": RDW_TOKEN} if RDW_TOKEN else {}
+
+    @classmethod
+    async def fetch_rdw_tech_data(cls, make, model):
+        """Hibatűrő RDW lekérdezés tisztított paraméterekkel."""
+        clean_model = str(model).strip().upper()
+        params = {"merk": make.upper(), "handelsbenaming": clean_model, "$limit": 1}
+        async with httpx.AsyncClient(headers=cls.HEADERS) as client:
+            try:
+                resp = await client.get(cls.API_URL, params=params, timeout=15)
+                if resp.status_code == 200 and resp.json():
+                    return resp.json()[0]
+                return None
+            except Exception:
+                return None
+
+    @classmethod
+    async def run(cls):
+        logger.info("🚀 Master Enricher INDUL (Atomi mentés üzemmód)...")
+        
+        # 1. Csak az ID-kat kérjük le, hogy ne tartsuk nyitva a tranzakciót feleslegesen
+        async with SessionLocal() as main_db:
+            stmt = select(VehicleModelDefinition.id).where(
+                VehicleModelDefinition.status == "unverified"
+            ).limit(50)
+            res = await main_db.execute(stmt)
+            ids = res.scalars().all()
+
+        if not ids:
+            logger.info("😴 Nincs dúsítandó adat.")
+            return
+
+        # 2. Egyesével dolgozzuk fel a rekordokat saját session-ben
+        for m_id in ids:
+            async with SessionLocal() as db:
+                try:
+                    master = await db.get(VehicleModelDefinition, m_id)
+                    if not master:
+                        continue
+
+                    logger.info(f"🧪 Feldolgozás: {master.make} {master.marketing_name} (ID: {m_id})")
+                    data_found = False
+
+                    # A: RDW fázis
+                    rdw_data = await cls.fetch_rdw_tech_data(master.make, master.marketing_name)
+                    if rdw_data:
+                        master.engine_capacity = int(float(rdw_data.get("cilinderinhoud", 0))) or None
+                        master.power_kw = int(float(rdw_data.get("netto_maximum_vermogen_kw", 0))) or None
+                        data_found = True
+
+                    # B: AI fázis (ha hiányzik adat vagy pontosítani kell)
+                    if not data_found or master.engine_capacity is None:
+                        ai_data = await AIService.get_clean_vehicle_data(
+                            master.make, master.marketing_name, master.vehicle_type
+                        )
+                        if ai_data:
+                            master.marketing_name = ai_data.get("marketing_name", master.marketing_name)
+                            master.technical_code = ai_data.get("technical_code") or master.technical_code or "N/A"
+                            master.engine_capacity = ai_data.get("ccm") or master.engine_capacity
+                            master.power_kw = ai_data.get("kw") or master.power_kw
+                            master.specifications = ai_data.get("maintenance", {})
+                            data_found = True
+
+                    # C: Mentés és véglegesítés
+                    if data_found:
+                        master.status = "ai_enriched"
+                        master.updated_at = datetime.datetime.now()
+                        await db.commit() # AZONNALI COMMIT A LEMEZRE
+                        logger.info(f"✅ Sikeresen mentve: {master.marketing_name} (CCM: {master.engine_capacity})")
+                    else:
+                        logger.warning(f"⚠️ Nem találtam adatot az ID {m_id} esetében.")
+
+                except IntegrityError:
+                    await db.rollback()
+                    logger.warning(f"🚫 Duplikáció vagy Constraint hiba (ID: {m_id}). Kihagyva.")
+                except Exception as e:
+                    await db.rollback()
+                    logger.error(f"❌ Váratlan hiba az ID {m_id} esetében: {e}")
+                finally:
+                    await db.close()
+
+        logger.info("🏁 50-es batch feldolgozva.")
+
+if __name__ == "__main__":
+    asyncio.run(TechEnricher.run())

backend/migrations/versions/c64b951dbb86_add_mdm_merge_fields.py

@@ -0,0 +1,310 @@
+"""add_mdm_merge_fields
+
+Revision ID: c64b951dbb86
+Revises: f30c0005c446
+Create Date: 2026-02-17 21:33:35.453033
+
+"""
+from typing import Sequence, Union
+
+from alembic import op
+import sqlalchemy as sa
+from sqlalchemy.dialects import postgresql
+
+# revision identifiers, used by Alembic.
+revision: str = 'c64b951dbb86'
+down_revision: Union[str, Sequence[str], None] = 'f30c0005c446'
+branch_labels: Union[str, Sequence[str], None] = None
+depends_on: Union[str, Sequence[str], None] = None
+
+
+def upgrade() -> None:
+    """Upgrade schema."""
+    # ### commands auto generated by Alembic - please adjust! ###
+    op.drop_constraint(op.f('addresses_postal_code_id_fkey'), 'addresses', type_='foreignkey')
+    op.create_foreign_key(None, 'addresses', 'geo_postal_codes', ['postal_code_id'], ['id'], source_schema='data', referent_schema='data')
+    op.drop_constraint(op.f('asset_assignments_branch_id_fkey'), 'asset_assignments', type_='foreignkey')
+    op.drop_constraint(op.f('asset_assignments_asset_id_fkey'), 'asset_assignments', type_='foreignkey')
+    op.drop_constraint(op.f('asset_assignments_organization_id_fkey'), 'asset_assignments', type_='foreignkey')
+    op.create_foreign_key(None, 'asset_assignments', 'branches', ['branch_id'], ['id'], source_schema='data', referent_schema='data')
+    op.create_foreign_key(None, 'asset_assignments', 'organizations', ['organization_id'], ['id'], source_schema='data', referent_schema='data')
+    op.create_foreign_key(None, 'asset_assignments', 'assets', ['asset_id'], ['id'], source_schema='data', referent_schema='data')
+    op.drop_constraint(op.f('asset_costs_asset_id_fkey'), 'asset_costs', type_='foreignkey')
+    op.drop_constraint(op.f('asset_costs_organization_id_fkey'), 'asset_costs', type_='foreignkey')
+    op.drop_constraint(op.f('asset_costs_driver_id_fkey'), 'asset_costs', type_='foreignkey')
+    op.create_foreign_key(None, 'asset_costs', 'users', ['driver_id'], ['id'], source_schema='data', referent_schema='data')
+    op.create_foreign_key(None, 'asset_costs', 'organizations', ['organization_id'], ['id'], source_schema='data', referent_schema='data')
+    op.create_foreign_key(None, 'asset_costs', 'assets', ['asset_id'], ['id'], source_schema='data', referent_schema='data')
+    op.drop_constraint(op.f('asset_events_asset_id_fkey'), 'asset_events', type_='foreignkey')
+    op.create_foreign_key(None, 'asset_events', 'assets', ['asset_id'], ['id'], source_schema='data', referent_schema='data')
+    op.drop_constraint(op.f('asset_financials_asset_id_fkey'), 'asset_financials', type_='foreignkey')
+    op.create_foreign_key(None, 'asset_financials', 'assets', ['asset_id'], ['id'], source_schema='data', referent_schema='data')
+    op.drop_constraint(op.f('asset_reviews_asset_id_fkey'), 'asset_reviews', type_='foreignkey')
+    op.drop_constraint(op.f('asset_reviews_user_id_fkey'), 'asset_reviews', type_='foreignkey')
+    op.create_foreign_key(None, 'asset_reviews', 'users', ['user_id'], ['id'], source_schema='data', referent_schema='data')
+    op.create_foreign_key(None, 'asset_reviews', 'assets', ['asset_id'], ['id'], source_schema='data', referent_schema='data')
+    op.drop_constraint(op.f('asset_telemetry_asset_id_fkey'), 'asset_telemetry', type_='foreignkey')
+    op.create_foreign_key(None, 'asset_telemetry', 'assets', ['asset_id'], ['id'], source_schema='data', referent_schema='data')
+    op.drop_constraint(op.f('assets_catalog_id_fkey'), 'assets', type_='foreignkey')
+    op.drop_constraint(op.f('assets_current_organization_id_fkey'), 'assets', type_='foreignkey')
+    op.create_foreign_key(None, 'assets', 'organizations', ['current_organization_id'], ['id'], source_schema='data', referent_schema='data')
+    op.create_foreign_key(None, 'assets', 'vehicle_catalog', ['catalog_id'], ['id'], source_schema='data', referent_schema='data')
+    op.drop_constraint(op.f('audit_logs_user_id_fkey'), 'audit_logs', type_='foreignkey')
+    op.create_foreign_key(None, 'audit_logs', 'users', ['user_id'], ['id'], source_schema='data', referent_schema='data')
+    op.drop_constraint(op.f('branches_address_id_fkey'), 'branches', type_='foreignkey')
+    op.drop_constraint(op.f('branches_organization_id_fkey'), 'branches', type_='foreignkey')
+    op.create_foreign_key(None, 'branches', 'organizations', ['organization_id'], ['id'], source_schema='data', referent_schema='data')
+    op.create_foreign_key(None, 'branches', 'addresses', ['address_id'], ['id'], source_schema='data', referent_schema='data')
+    op.drop_constraint(op.f('credit_logs_org_id_fkey'), 'credit_logs', type_='foreignkey')
+    op.create_foreign_key(None, 'credit_logs', 'organizations', ['org_id'], ['id'], source_schema='data', referent_schema='data')
+    op.drop_constraint(op.f('documents_uploaded_by_fkey'), 'documents', type_='foreignkey')
+    op.create_foreign_key(None, 'documents', 'users', ['uploaded_by'], ['id'], source_schema='data', referent_schema='data')
+    op.drop_constraint(op.f('feature_definitions_vehicle_type_id_fkey'), 'feature_definitions', type_='foreignkey')
+    op.create_foreign_key(None, 'feature_definitions', 'vehicle_types', ['vehicle_type_id'], ['id'], source_schema='data', referent_schema='data')
+    op.drop_constraint(op.f('financial_ledger_user_id_fkey'), 'financial_ledger', type_='foreignkey')
+    op.drop_constraint(op.f('financial_ledger_person_id_fkey'), 'financial_ledger', type_='foreignkey')
+    op.drop_constraint(op.f('financial_ledger_related_agent_id_fkey'), 'financial_ledger', type_='foreignkey')
+    op.create_foreign_key(None, 'financial_ledger', 'persons', ['person_id'], ['id'], source_schema='data', referent_schema='data')
+    op.create_foreign_key(None, 'financial_ledger', 'users', ['user_id'], ['id'], source_schema='data', referent_schema='data')
+    op.create_foreign_key(None, 'financial_ledger', 'users', ['related_agent_id'], ['id'], source_schema='data', referent_schema='data')
+    op.drop_constraint(op.f('geo_streets_postal_code_id_fkey'), 'geo_streets', type_='foreignkey')
+    op.create_foreign_key(None, 'geo_streets', 'geo_postal_codes', ['postal_code_id'], ['id'], source_schema='data', referent_schema='data')
+    op.drop_constraint(op.f('model_feature_maps_model_id_fkey'), 'model_feature_maps', type_='foreignkey')
+    op.drop_constraint(op.f('model_feature_maps_feature_id_fkey'), 'model_feature_maps', type_='foreignkey')
+    op.create_foreign_key(None, 'model_feature_maps', 'vehicle_model_definitions', ['model_id'], ['id'], source_schema='data', referent_schema='data')
+    op.create_foreign_key(None, 'model_feature_maps', 'feature_definitions', ['feature_id'], ['id'], source_schema='data', referent_schema='data')
+    op.drop_constraint(op.f('operational_logs_user_id_fkey'), 'operational_logs', type_='foreignkey')
+    op.create_foreign_key(None, 'operational_logs', 'users', ['user_id'], ['id'], source_schema='data', referent_schema='data', ondelete='SET NULL')
+    op.drop_constraint(op.f('org_sales_assignments_organization_id_fkey'), 'org_sales_assignments', type_='foreignkey')
+    op.drop_constraint(op.f('org_sales_assignments_agent_user_id_fkey'), 'org_sales_assignments', type_='foreignkey')
+    op.create_foreign_key(None, 'org_sales_assignments', 'organizations', ['organization_id'], ['id'], source_schema='data', referent_schema='data')
+    op.create_foreign_key(None, 'org_sales_assignments', 'users', ['agent_user_id'], ['id'], source_schema='data', referent_schema='data')
+    op.drop_constraint(op.f('org_subscriptions_tier_id_fkey'), 'org_subscriptions', type_='foreignkey')
+    op.drop_constraint(op.f('org_subscriptions_org_id_fkey'), 'org_subscriptions', type_='foreignkey')
+    op.create_foreign_key(None, 'org_subscriptions', 'subscription_tiers', ['tier_id'], ['id'], source_schema='data', referent_schema='data')
+    op.create_foreign_key(None, 'org_subscriptions', 'organizations', ['org_id'], ['id'], source_schema='data', referent_schema='data')
+    op.drop_constraint(op.f('organization_financials_organization_id_fkey'), 'organization_financials', type_='foreignkey')
+    op.create_foreign_key(None, 'organization_financials', 'organizations', ['organization_id'], ['id'], source_schema='data', referent_schema='data')
+    op.alter_column('organization_members', 'role',
+               existing_type=postgresql.ENUM('OWNER', 'ADMIN', 'FLEET_MANAGER', 'DRIVER', 'MECHANIC', 'RECEPTIONIST', name='orguserrole'),
+               type_=postgresql.ENUM('OWNER', 'ADMIN', 'FLEET_MANAGER', 'DRIVER', 'MECHANIC', 'RECEPTIONIST', name='orguserrole', schema='data', inherit_schema=True),
+               existing_nullable=True)
+    op.drop_constraint(op.f('organization_members_person_id_fkey'), 'organization_members', type_='foreignkey')
+    op.drop_constraint(op.f('organization_members_user_id_fkey'), 'organization_members', type_='foreignkey')
+    op.drop_constraint(op.f('organization_members_organization_id_fkey'), 'organization_members', type_='foreignkey')
+    op.create_foreign_key(None, 'organization_members', 'users', ['user_id'], ['id'], source_schema='data', referent_schema='data')
+    op.create_foreign_key(None, 'organization_members', 'organizations', ['organization_id'], ['id'], source_schema='data', referent_schema='data')
+    op.create_foreign_key(None, 'organization_members', 'persons', ['person_id'], ['id'], source_schema='data', referent_schema='data')
+    op.alter_column('organizations', 'org_type',
+               existing_type=postgresql.ENUM('individual', 'service', 'service_provider', 'fleet_owner', 'club', 'business', name='orgtype'),
+               type_=postgresql.ENUM('individual', 'service', 'service_provider', 'fleet_owner', 'club', 'business', name='orgtype', schema='data', inherit_schema=True),
+               existing_nullable=True)
+    op.drop_constraint(op.f('organizations_owner_id_fkey'), 'organizations', type_='foreignkey')
+    op.drop_constraint(op.f('organizations_address_id_fkey'), 'organizations', type_='foreignkey')
+    op.create_foreign_key(None, 'organizations', 'users', ['owner_id'], ['id'], source_schema='data', referent_schema='data')
+    op.create_foreign_key(None, 'organizations', 'addresses', ['address_id'], ['id'], source_schema='data', referent_schema='data')
+    op.drop_constraint(op.f('pending_actions_requester_id_fkey'), 'pending_actions', type_='foreignkey')
+    op.drop_constraint(op.f('pending_actions_approver_id_fkey'), 'pending_actions', type_='foreignkey')
+    op.create_foreign_key(None, 'pending_actions', 'users', ['approver_id'], ['id'], source_schema='data', referent_schema='data')
+    op.create_foreign_key(None, 'pending_actions', 'users', ['requester_id'], ['id'], source_schema='data', referent_schema='data')
+    op.drop_constraint(op.f('persons_address_id_fkey'), 'persons', type_='foreignkey')
+    op.create_foreign_key(None, 'persons', 'addresses', ['address_id'], ['id'], source_schema='data', referent_schema='data')
+    op.drop_constraint(op.f('points_ledger_user_id_fkey'), 'points_ledger', type_='foreignkey')
+    op.create_foreign_key(None, 'points_ledger', 'users', ['user_id'], ['id'], source_schema='data', referent_schema='data')
+    op.drop_constraint(op.f('ratings_author_id_fkey'), 'ratings', type_='foreignkey')
+    op.create_foreign_key(None, 'ratings', 'users', ['author_id'], ['id'], source_schema='data', referent_schema='data')
+    op.drop_constraint(op.f('security_audit_logs_actor_id_fkey'), 'security_audit_logs', type_='foreignkey')
+    op.drop_constraint(op.f('security_audit_logs_confirmed_by_id_fkey'), 'security_audit_logs', type_='foreignkey')
+    op.drop_constraint(op.f('security_audit_logs_target_id_fkey'), 'security_audit_logs', type_='foreignkey')
+    op.create_foreign_key(None, 'security_audit_logs', 'users', ['confirmed_by_id'], ['id'], source_schema='data', referent_schema='data')
+    op.create_foreign_key(None, 'security_audit_logs', 'users', ['actor_id'], ['id'], source_schema='data', referent_schema='data')
+    op.create_foreign_key(None, 'security_audit_logs', 'users', ['target_id'], ['id'], source_schema='data', referent_schema='data')
+    op.drop_constraint(op.f('service_expertises_service_id_fkey'), 'service_expertises', type_='foreignkey')
+    op.drop_constraint(op.f('service_expertises_expertise_id_fkey'), 'service_expertises', type_='foreignkey')
+    op.create_foreign_key(None, 'service_expertises', 'expertise_tags', ['expertise_id'], ['id'], source_schema='data', referent_schema='data')
+    op.create_foreign_key(None, 'service_expertises', 'service_profiles', ['service_id'], ['id'], source_schema='data', referent_schema='data')
+    op.drop_constraint(op.f('service_profiles_organization_id_fkey'), 'service_profiles', type_='foreignkey')
+    op.create_foreign_key(None, 'service_profiles', 'organizations', ['organization_id'], ['id'], source_schema='data', referent_schema='data')
+    op.drop_constraint(op.f('service_specialties_parent_id_fkey'), 'service_specialties', type_='foreignkey')
+    op.create_foreign_key(None, 'service_specialties', 'service_specialties', ['parent_id'], ['id'], source_schema='data', referent_schema='data')
+    op.drop_constraint(op.f('social_accounts_user_id_fkey'), 'social_accounts', type_='foreignkey')
+    op.create_foreign_key(None, 'social_accounts', 'users', ['user_id'], ['id'], source_schema='data', referent_schema='data', ondelete='CASCADE')
+    op.drop_constraint(op.f('user_badges_user_id_fkey'), 'user_badges', type_='foreignkey')
+    op.drop_constraint(op.f('user_badges_badge_id_fkey'), 'user_badges', type_='foreignkey')
+    op.create_foreign_key(None, 'user_badges', 'badges', ['badge_id'], ['id'], source_schema='data', referent_schema='data')
+    op.create_foreign_key(None, 'user_badges', 'users', ['user_id'], ['id'], source_schema='data', referent_schema='data')
+    op.drop_constraint(op.f('user_stats_user_id_fkey'), 'user_stats', type_='foreignkey')
+    op.create_foreign_key(None, 'user_stats', 'users', ['user_id'], ['id'], source_schema='data', referent_schema='data')
+    op.drop_constraint(op.f('users_person_id_fkey'), 'users', type_='foreignkey')
+    op.drop_constraint(op.f('users_referred_by_id_fkey'), 'users', type_='foreignkey')
+    op.drop_constraint(op.f('users_current_sales_agent_id_fkey'), 'users', type_='foreignkey')
+    op.create_foreign_key(None, 'users', 'users', ['current_sales_agent_id'], ['id'], source_schema='data', referent_schema='data')
+    op.create_foreign_key(None, 'users', 'users', ['referred_by_id'], ['id'], source_schema='data', referent_schema='data')
+    op.create_foreign_key(None, 'users', 'persons', ['person_id'], ['id'], source_schema='data', referent_schema='data')
+    op.drop_constraint(op.f('vehicle_catalog_master_definition_id_fkey'), 'vehicle_catalog', type_='foreignkey')
+    op.create_foreign_key(None, 'vehicle_catalog', 'vehicle_model_definitions', ['master_definition_id'], ['id'], source_schema='data', referent_schema='data')
+    op.add_column('vehicle_model_definitions', sa.Column('parent_id', sa.Integer(), nullable=True))
+    op.add_column('vehicle_model_definitions', sa.Column('year_from', sa.Integer(), nullable=True))
+    op.add_column('vehicle_model_definitions', sa.Column('year_to', sa.Integer(), nullable=True))
+    op.add_column('vehicle_model_definitions', sa.Column('synonyms', sa.JSON(), server_default=sa.text("'[]'::jsonb"), nullable=True))
+    op.create_index('idx_vmd_lookup', 'vehicle_model_definitions', ['make', 'technical_code'], unique=False, schema='data')
+    op.create_index(op.f('ix_data_vehicle_model_definitions_year_from'), 'vehicle_model_definitions', ['year_from'], unique=False, schema='data')
+    op.create_index(op.f('ix_data_vehicle_model_definitions_year_to'), 'vehicle_model_definitions', ['year_to'], unique=False, schema='data')
+    op.drop_constraint(op.f('vehicle_model_definitions_vehicle_type_id_fkey'), 'vehicle_model_definitions', type_='foreignkey')
+    op.create_foreign_key(None, 'vehicle_model_definitions', 'vehicle_model_definitions', ['parent_id'], ['id'], source_schema='data', referent_schema='data')
+    op.create_foreign_key(None, 'vehicle_model_definitions', 'vehicle_types', ['vehicle_type_id'], ['id'], source_schema='data', referent_schema='data')
+    op.drop_constraint(op.f('vehicle_ownerships_vehicle_id_fkey'), 'vehicle_ownerships', type_='foreignkey')
+    op.drop_constraint(op.f('vehicle_ownerships_user_id_fkey'), 'vehicle_ownerships', type_='foreignkey')
+    op.create_foreign_key(None, 'vehicle_ownerships', 'assets', ['vehicle_id'], ['id'], source_schema='data', referent_schema='data')
+    op.create_foreign_key(None, 'vehicle_ownerships', 'users', ['user_id'], ['id'], source_schema='data', referent_schema='data')
+    op.drop_constraint(op.f('verification_tokens_user_id_fkey'), 'verification_tokens', type_='foreignkey')
+    op.create_foreign_key(None, 'verification_tokens', 'users', ['user_id'], ['id'], source_schema='data', referent_schema='data', ondelete='CASCADE')
+    op.drop_constraint(op.f('wallets_user_id_fkey'), 'wallets', type_='foreignkey')
+    op.create_foreign_key(None, 'wallets', 'users', ['user_id'], ['id'], source_schema='data', referent_schema='data')
+    # ### end Alembic commands ###
+
+
+def downgrade() -> None:
+    """Downgrade schema."""
+    # ### commands auto generated by Alembic - please adjust! ###
+    op.drop_constraint(None, 'wallets', schema='data', type_='foreignkey')
+    op.create_foreign_key(op.f('wallets_user_id_fkey'), 'wallets', 'users', ['user_id'], ['id'])
+    op.drop_constraint(None, 'verification_tokens', schema='data', type_='foreignkey')
+    op.create_foreign_key(op.f('verification_tokens_user_id_fkey'), 'verification_tokens', 'users', ['user_id'], ['id'], ondelete='CASCADE')
+    op.drop_constraint(None, 'vehicle_ownerships', schema='data', type_='foreignkey')
+    op.drop_constraint(None, 'vehicle_ownerships', schema='data', type_='foreignkey')
+    op.create_foreign_key(op.f('vehicle_ownerships_user_id_fkey'), 'vehicle_ownerships', 'users', ['user_id'], ['id'])
+    op.create_foreign_key(op.f('vehicle_ownerships_vehicle_id_fkey'), 'vehicle_ownerships', 'assets', ['vehicle_id'], ['id'])
+    op.drop_constraint(None, 'vehicle_model_definitions', schema='data', type_='foreignkey')
+    op.drop_constraint(None, 'vehicle_model_definitions', schema='data', type_='foreignkey')
+    op.create_foreign_key(op.f('vehicle_model_definitions_vehicle_type_id_fkey'), 'vehicle_model_definitions', 'vehicle_types', ['vehicle_type_id'], ['id'])
+    op.drop_index(op.f('ix_data_vehicle_model_definitions_year_to'), table_name='vehicle_model_definitions', schema='data')
+    op.drop_index(op.f('ix_data_vehicle_model_definitions_year_from'), table_name='vehicle_model_definitions', schema='data')
+    op.drop_index('idx_vmd_lookup', table_name='vehicle_model_definitions', schema='data')
+    op.drop_column('vehicle_model_definitions', 'synonyms')
+    op.drop_column('vehicle_model_definitions', 'year_to')
+    op.drop_column('vehicle_model_definitions', 'year_from')
+    op.drop_column('vehicle_model_definitions', 'parent_id')
+    op.drop_constraint(None, 'vehicle_catalog', schema='data', type_='foreignkey')
+    op.create_foreign_key(op.f('vehicle_catalog_master_definition_id_fkey'), 'vehicle_catalog', 'vehicle_model_definitions', ['master_definition_id'], ['id'])
+    op.drop_constraint(None, 'users', schema='data', type_='foreignkey')
+    op.drop_constraint(None, 'users', schema='data', type_='foreignkey')
+    op.drop_constraint(None, 'users', schema='data', type_='foreignkey')
+    op.create_foreign_key(op.f('users_current_sales_agent_id_fkey'), 'users', 'users', ['current_sales_agent_id'], ['id'])
+    op.create_foreign_key(op.f('users_referred_by_id_fkey'), 'users', 'users', ['referred_by_id'], ['id'])
+    op.create_foreign_key(op.f('users_person_id_fkey'), 'users', 'persons', ['person_id'], ['id'])
+    op.drop_constraint(None, 'user_stats', schema='data', type_='foreignkey')
+    op.create_foreign_key(op.f('user_stats_user_id_fkey'), 'user_stats', 'users', ['user_id'], ['id'])
+    op.drop_constraint(None, 'user_badges', schema='data', type_='foreignkey')
+    op.drop_constraint(None, 'user_badges', schema='data', type_='foreignkey')
+    op.create_foreign_key(op.f('user_badges_badge_id_fkey'), 'user_badges', 'badges', ['badge_id'], ['id'])
+    op.create_foreign_key(op.f('user_badges_user_id_fkey'), 'user_badges', 'users', ['user_id'], ['id'])
+    op.drop_constraint(None, 'social_accounts', schema='data', type_='foreignkey')
+    op.create_foreign_key(op.f('social_accounts_user_id_fkey'), 'social_accounts', 'users', ['user_id'], ['id'], ondelete='CASCADE')
+    op.drop_constraint(None, 'service_specialties', schema='data', type_='foreignkey')
+    op.create_foreign_key(op.f('service_specialties_parent_id_fkey'), 'service_specialties', 'service_specialties', ['parent_id'], ['id'])
+    op.drop_constraint(None, 'service_profiles', schema='data', type_='foreignkey')
+    op.create_foreign_key(op.f('service_profiles_organization_id_fkey'), 'service_profiles', 'organizations', ['organization_id'], ['id'])
+    op.drop_constraint(None, 'service_expertises', schema='data', type_='foreignkey')
+    op.drop_constraint(None, 'service_expertises', schema='data', type_='foreignkey')
+    op.create_foreign_key(op.f('service_expertises_expertise_id_fkey'), 'service_expertises', 'expertise_tags', ['expertise_id'], ['id'])
+    op.create_foreign_key(op.f('service_expertises_service_id_fkey'), 'service_expertises', 'service_profiles', ['service_id'], ['id'])
+    op.drop_constraint(None, 'security_audit_logs', schema='data', type_='foreignkey')
+    op.drop_constraint(None, 'security_audit_logs', schema='data', type_='foreignkey')
+    op.drop_constraint(None, 'security_audit_logs', schema='data', type_='foreignkey')
+    op.create_foreign_key(op.f('security_audit_logs_target_id_fkey'), 'security_audit_logs', 'users', ['target_id'], ['id'])
+    op.create_foreign_key(op.f('security_audit_logs_confirmed_by_id_fkey'), 'security_audit_logs', 'users', ['confirmed_by_id'], ['id'])
+    op.create_foreign_key(op.f('security_audit_logs_actor_id_fkey'), 'security_audit_logs', 'users', ['actor_id'], ['id'])
+    op.drop_constraint(None, 'ratings', schema='data', type_='foreignkey')
+    op.create_foreign_key(op.f('ratings_author_id_fkey'), 'ratings', 'users', ['author_id'], ['id'])
+    op.drop_constraint(None, 'points_ledger', schema='data', type_='foreignkey')
+    op.create_foreign_key(op.f('points_ledger_user_id_fkey'), 'points_ledger', 'users', ['user_id'], ['id'])
+    op.drop_constraint(None, 'persons', schema='data', type_='foreignkey')
+    op.create_foreign_key(op.f('persons_address_id_fkey'), 'persons', 'addresses', ['address_id'], ['id'])
+    op.drop_constraint(None, 'pending_actions', schema='data', type_='foreignkey')
+    op.drop_constraint(None, 'pending_actions', schema='data', type_='foreignkey')
+    op.create_foreign_key(op.f('pending_actions_approver_id_fkey'), 'pending_actions', 'users', ['approver_id'], ['id'])
+    op.create_foreign_key(op.f('pending_actions_requester_id_fkey'), 'pending_actions', 'users', ['requester_id'], ['id'])
+    op.drop_constraint(None, 'organizations', schema='data', type_='foreignkey')
+    op.drop_constraint(None, 'organizations', schema='data', type_='foreignkey')
+    op.create_foreign_key(op.f('organizations_address_id_fkey'), 'organizations', 'addresses', ['address_id'], ['id'])
+    op.create_foreign_key(op.f('organizations_owner_id_fkey'), 'organizations', 'users', ['owner_id'], ['id'])
+    op.alter_column('organizations', 'org_type',
+               existing_type=postgresql.ENUM('individual', 'service', 'service_provider', 'fleet_owner', 'club', 'business', name='orgtype', schema='data', inherit_schema=True),
+               type_=postgresql.ENUM('individual', 'service', 'service_provider', 'fleet_owner', 'club', 'business', name='orgtype'),
+               existing_nullable=True)
+    op.drop_constraint(None, 'organization_members', schema='data', type_='foreignkey')
+    op.drop_constraint(None, 'organization_members', schema='data', type_='foreignkey')
+    op.drop_constraint(None, 'organization_members', schema='data', type_='foreignkey')
+    op.create_foreign_key(op.f('organization_members_organization_id_fkey'), 'organization_members', 'organizations', ['organization_id'], ['id'])
+    op.create_foreign_key(op.f('organization_members_user_id_fkey'), 'organization_members', 'users', ['user_id'], ['id'])
+    op.create_foreign_key(op.f('organization_members_person_id_fkey'), 'organization_members', 'persons', ['person_id'], ['id'])
+    op.alter_column('organization_members', 'role',
+               existing_type=postgresql.ENUM('OWNER', 'ADMIN', 'FLEET_MANAGER', 'DRIVER', 'MECHANIC', 'RECEPTIONIST', name='orguserrole', schema='data', inherit_schema=True),
+               type_=postgresql.ENUM('OWNER', 'ADMIN', 'FLEET_MANAGER', 'DRIVER', 'MECHANIC', 'RECEPTIONIST', name='orguserrole'),
+               existing_nullable=True)
+    op.drop_constraint(None, 'organization_financials', schema='data', type_='foreignkey')
+    op.create_foreign_key(op.f('organization_financials_organization_id_fkey'), 'organization_financials', 'organizations', ['organization_id'], ['id'])
+    op.drop_constraint(None, 'org_subscriptions', schema='data', type_='foreignkey')
+    op.drop_constraint(None, 'org_subscriptions', schema='data', type_='foreignkey')
+    op.create_foreign_key(op.f('org_subscriptions_org_id_fkey'), 'org_subscriptions', 'organizations', ['org_id'], ['id'])
+    op.create_foreign_key(op.f('org_subscriptions_tier_id_fkey'), 'org_subscriptions', 'subscription_tiers', ['tier_id'], ['id'])
+    op.drop_constraint(None, 'org_sales_assignments', schema='data', type_='foreignkey')
+    op.drop_constraint(None, 'org_sales_assignments', schema='data', type_='foreignkey')
+    op.create_foreign_key(op.f('org_sales_assignments_agent_user_id_fkey'), 'org_sales_assignments', 'users', ['agent_user_id'], ['id'])
+    op.create_foreign_key(op.f('org_sales_assignments_organization_id_fkey'), 'org_sales_assignments', 'organizations', ['organization_id'], ['id'])
+    op.drop_constraint(None, 'operational_logs', schema='data', type_='foreignkey')
+    op.create_foreign_key(op.f('operational_logs_user_id_fkey'), 'operational_logs', 'users', ['user_id'], ['id'], ondelete='SET NULL')
+    op.drop_constraint(None, 'model_feature_maps', schema='data', type_='foreignkey')
+    op.drop_constraint(None, 'model_feature_maps', schema='data', type_='foreignkey')
+    op.create_foreign_key(op.f('model_feature_maps_feature_id_fkey'), 'model_feature_maps', 'feature_definitions', ['feature_id'], ['id'])
+    op.create_foreign_key(op.f('model_feature_maps_model_id_fkey'), 'model_feature_maps', 'vehicle_model_definitions', ['model_id'], ['id'])
+    op.drop_constraint(None, 'geo_streets', schema='data', type_='foreignkey')
+    op.create_foreign_key(op.f('geo_streets_postal_code_id_fkey'), 'geo_streets', 'geo_postal_codes', ['postal_code_id'], ['id'])
+    op.drop_constraint(None, 'financial_ledger', schema='data', type_='foreignkey')
+    op.drop_constraint(None, 'financial_ledger', schema='data', type_='foreignkey')
+    op.drop_constraint(None, 'financial_ledger', schema='data', type_='foreignkey')
+    op.create_foreign_key(op.f('financial_ledger_related_agent_id_fkey'), 'financial_ledger', 'users', ['related_agent_id'], ['id'])
+    op.create_foreign_key(op.f('financial_ledger_person_id_fkey'), 'financial_ledger', 'persons', ['person_id'], ['id'])
+    op.create_foreign_key(op.f('financial_ledger_user_id_fkey'), 'financial_ledger', 'users', ['user_id'], ['id'])
+    op.drop_constraint(None, 'feature_definitions', schema='data', type_='foreignkey')
+    op.create_foreign_key(op.f('feature_definitions_vehicle_type_id_fkey'), 'feature_definitions', 'vehicle_types', ['vehicle_type_id'], ['id'])
+    op.drop_constraint(None, 'documents', schema='data', type_='foreignkey')
+    op.create_foreign_key(op.f('documents_uploaded_by_fkey'), 'documents', 'users', ['uploaded_by'], ['id'])
+    op.drop_constraint(None, 'credit_logs', schema='data', type_='foreignkey')
+    op.create_foreign_key(op.f('credit_logs_org_id_fkey'), 'credit_logs', 'organizations', ['org_id'], ['id'])
+    op.drop_constraint(None, 'branches', schema='data', type_='foreignkey')
+    op.drop_constraint(None, 'branches', schema='data', type_='foreignkey')
+    op.create_foreign_key(op.f('branches_organization_id_fkey'), 'branches', 'organizations', ['organization_id'], ['id'])
+    op.create_foreign_key(op.f('branches_address_id_fkey'), 'branches', 'addresses', ['address_id'], ['id'])
+    op.drop_constraint(None, 'audit_logs', schema='data', type_='foreignkey')
+    op.create_foreign_key(op.f('audit_logs_user_id_fkey'), 'audit_logs', 'users', ['user_id'], ['id'])
+    op.drop_constraint(None, 'assets', schema='data', type_='foreignkey')
+    op.drop_constraint(None, 'assets', schema='data', type_='foreignkey')
+    op.create_foreign_key(op.f('assets_current_organization_id_fkey'), 'assets', 'organizations', ['current_organization_id'], ['id'])
+    op.create_foreign_key(op.f('assets_catalog_id_fkey'), 'assets', 'vehicle_catalog', ['catalog_id'], ['id'])
+    op.drop_constraint(None, 'asset_telemetry', schema='data', type_='foreignkey')
+    op.create_foreign_key(op.f('asset_telemetry_asset_id_fkey'), 'asset_telemetry', 'assets', ['asset_id'], ['id'])
+    op.drop_constraint(None, 'asset_reviews', schema='data', type_='foreignkey')
+    op.drop_constraint(None, 'asset_reviews', schema='data', type_='foreignkey')
+    op.create_foreign_key(op.f('asset_reviews_user_id_fkey'), 'asset_reviews', 'users', ['user_id'], ['id'])
+    op.create_foreign_key(op.f('asset_reviews_asset_id_fkey'), 'asset_reviews', 'assets', ['asset_id'], ['id'])
+    op.drop_constraint(None, 'asset_financials', schema='data', type_='foreignkey')
+    op.create_foreign_key(op.f('asset_financials_asset_id_fkey'), 'asset_financials', 'assets', ['asset_id'], ['id'])
+    op.drop_constraint(None, 'asset_events', schema='data', type_='foreignkey')
+    op.create_foreign_key(op.f('asset_events_asset_id_fkey'), 'asset_events', 'assets', ['asset_id'], ['id'])
+    op.drop_constraint(None, 'asset_costs', schema='data', type_='foreignkey')
+    op.drop_constraint(None, 'asset_costs', schema='data', type_='foreignkey')
+    op.drop_constraint(None, 'asset_costs', schema='data', type_='foreignkey')
+    op.create_foreign_key(op.f('asset_costs_driver_id_fkey'), 'asset_costs', 'users', ['driver_id'], ['id'])
+    op.create_foreign_key(op.f('asset_costs_organization_id_fkey'), 'asset_costs', 'organizations', ['organization_id'], ['id'])
+    op.create_foreign_key(op.f('asset_costs_asset_id_fkey'), 'asset_costs', 'assets', ['asset_id'], ['id'])
+    op.drop_constraint(None, 'asset_assignments', schema='data', type_='foreignkey')
+    op.drop_constraint(None, 'asset_assignments', schema='data', type_='foreignkey')
+    op.drop_constraint(None, 'asset_assignments', schema='data', type_='foreignkey')
+    op.create_foreign_key(op.f('asset_assignments_organization_id_fkey'), 'asset_assignments', 'organizations', ['organization_id'], ['id'])
+    op.create_foreign_key(op.f('asset_assignments_asset_id_fkey'), 'asset_assignments', 'assets', ['asset_id'], ['id'])
+    op.create_foreign_key(op.f('asset_assignments_branch_id_fkey'), 'asset_assignments', 'branches', ['branch_id'], ['id'])
+    op.drop_constraint(None, 'addresses', schema='data', type_='foreignkey')
+    op.create_foreign_key(op.f('addresses_postal_code_id_fkey'), 'addresses', 'geo_postal_codes', ['postal_code_id'], ['id'])
+    # ### end Alembic commands ###

backend/requirements.txt

@@ -24,4 +24,5 @@ fastapi-limiter==0.1.5
 pyotp
 cryptography
 GeoAlchemy2>=0.14.0
-google-generativeai
+google-generativeai
+google-genai

docker-compose.yml

@@ -1,23 +1,26 @@
 services:
-  # 1. MIGRÁCIÓ
+  # 1. ADATBÁZIS MIGRÁCIÓ (Alembic)
   migrate:
     build:
       context: ./backend
       dockerfile: Dockerfile
     container_name: service_finder_migrate
+    # Az env_file használatával a Docker automatikusan beemeli a változókat a konténerbe
+    # Nincs szükség a ${VARIABLE} formátumra az environment alatt, így elkerüljük a "blank string" hibát
     env_file: .env
     volumes:
       - ./backend:/app
     environment:
-      PYTHONPATH: /app
+      - PYTHONPATH=/app
-      DATABASE_URL: ${MIGRATION_DATABASE_URL}
+    # Kényszerítjük az Alembic-et, hogy a .env-ben megadott MIGRATION_DATABASE_URL-t használja
-    command: ["bash", "-lc", "alembic upgrade head"]
+    command: >
+      bash -c "alembic upgrade head"
     networks:
       - default
       - shared_db_net
-    restart: "no" # Ez így helyes, lefut és megáll.
+    restart: "no"
 
-  # 2. BACKEND API
+  # 2. BACKEND API (FastAPI)
   service_finder_api:
     build:
       context: ./backend
@@ -31,7 +34,7 @@ services:
       - /mnt/nas/app_data:/mnt/nas/app_data
       - ./static_previews:/app/static/previews
     environment:
-      PYTHONPATH: /app
+      - PYTHONPATH=/app
     depends_on:
       migrate:
         condition: service_completed_successfully
@@ -44,7 +47,7 @@ services:
       - shared_db_net
     restart: unless-stopped
 
-  # 3. MINIO
+  # 3. MINIO (Object Storage)
   minio:
     image: minio/minio
     container_name: service_finder_minio
@@ -56,7 +59,7 @@ services:
       - default
     restart: unless-stopped
 
-  # 4. REDIS
+  # 4. REDIS (Cache & Queue)
   redis:
     image: redis:alpine
     container_name: service_finder_redis
@@ -66,8 +69,8 @@ services:
       - default
     restart: unless-stopped
 
-  # 5. FRONTEND
+  # 5. FRONTEND (Vue/React)
-  service_frontend: # Rövidített szerviznév
+  service_frontend:
     build:
       context: ./frontend
     container_name: service_finder_frontend
@@ -81,11 +84,11 @@ services:
         condition: service_started
     restart: unless-stopped
 
-  # 6. KATALÓGUS ROBOT (A mi kis felfedezőnk)
+  # 6. KATALÓGUS ROBOT (Discovery)
   catalog_robot:
     build: ./backend
     container_name: service_finder_robot_catalog
-    command: python -m app.workers.catalog_robot
+    command: python -u -m app.workers.catalog_robot
     volumes:
       - ./backend:/app
     env_file: .env
@@ -97,11 +100,11 @@ services:
       - shared_db_net
     restart: always
 
-  # 7. SERVICE HUNTER
+  # 7. SERVICE HUNTER (Web Scraping)
   service_hunter:
     build: ./backend
     container_name: service_finder_robot_hunter
-    command: python -m app.workers.service_hunter
+    command: python -u -m app.workers.service_hunter
     volumes:
       - ./backend:/app
     env_file: .env
@@ -121,12 +124,7 @@ services:
     ports:
       - "5678:5678"
     env_file: .env
-    environment:
+    # Itt az n8n belső változóit hagyjuk, de a jelszót az env_file-ból fogja venni
-      - N8N_HOST=0.0.0.0
-      - DB_TYPE=postgresdb
-      - DB_POSTGRESDB_HOST=n8n_db
-      - DB_POSTGRESDB_USER=n8n_admin
-      - DB_POSTGRESDB_PASSWORD=${N8N_DB_PASSWORD}
     volumes:
       - ./n8n/data:/home/node/.n8n
     networks:
@@ -139,16 +137,13 @@ services:
     image: postgres:15-alpine
     container_name: service_finder_n8n_db
     restart: unless-stopped
-    environment:
+    env_file: .env
-      - POSTGRES_USER=n8n_admin
-      - POSTGRES_PASSWORD=${N8N_DB_PASSWORD}
-      - POSTGRES_DB=n8n_internal
     volumes:
       - ./n8n/db_data:/var/lib/postgresql/data
     networks:
       - default
 
-  # 9. BROWSERLESS
+  # 9. BROWSERLESS (Chrome for Scraping)
   browserless:
     image: browserless/chrome:latest
     container_name: service_finder_browserless
@@ -158,11 +153,11 @@ services:
     networks:
       - default
 
-  # 10. Technikai adatok dúsítása (kW, ccm, üzemanyag)
+  # 10. TECHNIKAI ADATOK DÚSÍTÓ ROBOT (AI & RDW)
   enricher_robot:
     build: ./backend
     container_name: service_finder_robot_enricher
-    command: python -m app.workers.technical_enricher
+    command: python -u -m app.workers.technical_enricher
     volumes:
       - ./backend:/app
     env_file: .env

docs/V01_gemini/06_Database_Guide.md

@@ -210,4 +210,14 @@ A rendszer az adatintegritás és a sebesség érdekében hibrid modellt haszná
 * **`data.wallets`**: 3-as osztású egyenleg (`earned`, `purchased`, `coins`).
 * **`data.financial_ledger`**: Pénzügyi tranzakciók főkönyve.
 * **`data.security_audit_logs`**: Biztonsági események és 4-szem jóváhagyások.
-* **`data.org_sales_assignments`**: Cég-Üzletkötő kapcsolat (Farming jog).
+* **`data.org_sales_assignments`**: Cég-Üzletkötő kapcsolat (Farming jog).
+
+## 4. MDM és Jármű Katalógus Struktúra
+A rendszer hibrid (Relációs + JSONB) modellt használ a skálázhatóság érdekében.
+
+### 4.1 Táblák:
+* **data.vehicle_model_definitions**: A "Szent Grál". Tartalmazza a márka, kód, marketing név mellett a fix numerikus adatokat (ccm, kw, weight) a gyors szűréshez.
+* **data.feature_definitions**: Globális szótár az összes extráról (ABS, Halradar, Retarder stb.), kategóriákba sorolva.
+* **data.model_feature_maps**: Összeköti a modellt az extrákkal. 
+    - `availability`: [STANDARD, OPTIONAL, ACCESSORY]
+* **data.vehicle_model_reviews**: Felhasználói vélemények a típusról (1-5 csillag, szöveges).

docs/V01_gemini/13_Roadmap_Tech_Debt.md

@@ -30,4 +30,45 @@
 ## 📅 SPRINT 2 (Marketplace MVP)
 1. **OCR Pipeline:** MinIO feltöltés + Tesseract teszt.
 2. **Service Request:** Frontend űrlap ajánlatkéréshez.
-3. **Ranking Engine:** Távolság + Súlyozás algoritmus implementálása.
+3. **Ranking Engine:** Távolság + Súlyozás algoritmus implementálása.
+
+# 13. Roadmap és Technikai Adósság (v1.2.6)
+
+Ez a dokumentum rögzíti a rendszer fejlesztési fázisait és azokat a technikai kompromisszumokat, amelyeket a gyors haladás érdekében hoztunk, de később felülvizsgálatot igényelnek.
+
+## 13.1 Rövid távú Roadmap (Q1-Q2)
+
+### 1. Intelligens Kereső API (Fuzzy Search)
+- **Cél:** A `synonyms` mezőben tárolt alternatív nevek kihasználása.
+- **Megvalósítás:** PostgreSQL `tsvector` és `GIN` indexek használata, hogy a kereső akkor is találjon eredményt, ha a felhasználó "Tracer"-t ír be "Yamaha MT-09 Tracer" helyett.
+
+
+### 2. Média Kezelés & MinIO Integráció
+- **Cél:** Járműfotók automatikus beszerzése.
+- **Megvalósítás:** Új bot fejlesztése, amely a dúsított `marketing_name` alapján hivatalos sajtófotókat keres, és azokat a már futó MinIO objektumtárba menti.
+
+### 3. Robot 4: Service Hunter (Szerviz-logika)
+- **Cél:** Karbantartási tervek generálása.
+- **Megvalósítás:** A `specifications` (olajmennyiség, gyertya típus) mezőkből kiindulva szerviz-csomagok és árak kalkulálása.
+
+## 13.2 Technikai Adósság (Tech Debt)
+
+### 1. Adattípus Optimalizálás: JSON vs. JSONB
+- **Helyzet:** A `synonyms` és `specifications` mezők jelenleg `JSON` típusúak.
+- **Adósság:** A Postgres függvények (pl. `jsonb_array_length`) használatához folyamatos casting (`::jsonb`) szükséges, ami lassítja a lekérdezéseket.
+- **Megoldás:** Egy Alembic migráció keretében az összes JSON oszlopot át kell állítani `JSONB` típusra.
+
+
+### 2. "N/A-{id}" és "UNKNOWN-{id}" Kódok Tisztítása
+- **Helyzet:** A NOT NULL és UNIQUE kényszerek miatt a robot egyedi ál-kódokat generál, ha az AI nem talál gyári kódot.
+- **Adósság:** Ezek nem valódi technikai kódok.
+- **Megoldás:** Szükséges egy manuális felülvizsgálati (Manual Review) felület, ahol az operátorok a `N/A` kódú rekordokat ellenőrizhetik vagy egyesíthetik.
+
+### 3. AI Response Parsing (Regex Workaround)
+- **Helyzet:** A Gemini Search Tool letiltja a kényszerített JSON választ, ezért Regex-szel bányásszuk ki a JSON-t a nyers szövegből.
+- **Adósság:** Ez a megoldás törékeny, ha az AI stílusa jelentősen megváltozik.
+- **Megoldás:** Monitorozni kell az AI API frissítéseit; amint a Google engedélyezi a Search + Controlled Generation kombinációt, vissza kell térni a natív JSON módra.
+
+## 13.3 Hosszú távú Vízió (Q3+)
+- **Trust Engine:** A járművek történetének és szervizadatainak hitelesítése.
+- **Global Fleet Insight:** Flottaszintű elemzések készítése a dúsított MDM adatok alapján.

docs/V01_gemini/15_Changelog.md

@@ -481,4 +481,28 @@ Kérlek, ellenőrizd, hogy ezek a fájlok a legfrissebb verziót tartalmazzák-e
 
 1.  **Service Réteg Implementálása:** Megírni a logikát, ami ténylegesen számolja a 10/5%-os jutalékot és beírja a `FinancialLedger`-be.
 2.  **Admin UI:** Felületet készíteni a `system_parameters` (Jutalék szintek) állítására.
-3.  **Robot v1.8:** A "Ghost" szervizek bekötése az új `Person` logikába (automata `identity_hash` generálás a cégadatokból).
+3.  **Robot v1.8:** A "Ghost" szervizek bekötése az új `Person` logikába (automata `identity_hash` generálás a cégadatokból).
+
+# Changelog
+
+## [v1.1.0] - 2026-02-17 "The Awakening"
+### 🚀 Hozzáadva (New Features)
+- **AI Service (Gemini Integration):** `app/services/ai_service.py` létrehozva. A rendszer mostantól képes a "Yamaha 4HN" típusú zajos adatokból tiszta marketing neveket és műszaki specifikációkat generálni.
+- **Robot 2 (Technical Enricher):** `app/workers/technical_enricher.py` frissítve v1.1-re. Hibrid működés: RDW adatbázis + AI kiegészítés.
+- **Robot 3 (OCR - Előkészület):** `app/workers/ocr_robot.py` váza elkészült a dokumentumok feldolgozásához.
+- **Reporting:** `app/scripts/morning_report.py` létrehozva a napi robot-tevékenység összefoglalására.
+- **Logging:** Új `ProcessLog` tábla létrehozva az `audit` sémában a háttérfolyamatok nyomon követésére.
+
+### 🐛 Javítva (Bug Fixes)
+- **Docker Infrastructure:** - Javítva a `KeyError: 'ContainerConfig'` hiba a Docker Compose V2-re váltással.
+    - Javítva az "empty database URL" hiba a migrációnál (`.env` változók helyes átadása).
+    - Hálózati beragadások (Gitea/Nginx conflict) feloldva.
+- **Circular Imports:** Megszüntetve a `SystemParameter` modell duplikációja (`system_config.py` vs `system.py`). Minden szerviz (`Auth`, `Security`, `Cost`, `Gamification`) mostantól a központi `app.models`-ből importál.
+- **Swagger UI:** Az API sikeresen elindul, a dokumentáció elérhető a `/docs` végponton.
+
+### ⚙️ Infrastruktúra (Refactor)
+- **Docker Compose:** Teljes tisztítás. Duplikált környezeti változók törölve, `env_file` használat optimalizálva.
+- **Services:** A robotok (`enricher`, `catalog`, `hunter`) külön konténerekbe szervezve, `unbuffered` python kimenettel a valós idejű logolásért.
+
+### 🛡️ Biztonság
+- `.env` fájl szerkezete egységesítve, duplikációk eltávolítva.

docs/V01_gemini/18_ASSET_AND_FLEET_SPECIFICATION.md

@@ -223,4 +223,13 @@ Kapcsolatot teremt egy Jármű (`Asset`) és egy Szervezet (`Organization`) köz
 ## 4.0 Catalog 2022+ Strategy (Hybrid Mode)
 A CarQueryAPI korlátai miatt 2022 utáni modelleknél a Robot 1 az alábbi hibrid logikát alkalmazza:
 1. **API Ninjas & Auto-Data Sync:** Elsődleges technikai forrás.
-2. **European Scraper Mode:** A mobile.de és autoscout24.hu portálok típusválasztóinak (meta-adatok) aratása a legfrissebb modellek és motorváltozatok rögzítéséhez.
+2. **European Scraper Mode:** A mobile.de és autoscout24.hu portálok típusválasztóinak (meta-adatok) aratása a legfrissebb modellek és motorváltozatok rögzítéséhez.
+
+## 6. Szerviz Logika és Intervallumok
+A szervizkönyv nem csak eseménynapló, hanem előrejelző rendszer.
+
+### 6.1 Karbantartási Terv (Maintenance Plan)
+Az MDM-ben rögzített `specifications` tartalmazza a fődarabok szervizigényét:
+- **Folyadékok:** Típus, mennyiség, csereperiódus (km/hónap).
+- **Alkatrészek:** Gyári kódok és alternatívák (cross-reference).
+- **Biztonság:** Időszakos vizsgák (érintésvédelem, emelőhátfal, hajó szemle).

docs/V01_gemini/20_Service_Finder_&_Trust_Engine.md

@@ -73,4 +73,13 @@ Minden talált entitás pontszámot kap:
 ### 3. Döntési Szintek
 - **80+ pont:** Ellenőrzött (Verified) - Automatikus publikálás.
 - **40-79 pont:** Moderációra vár - Manuális adminisztrátori jóváhagyás szükséges.
-- **<40 pont:** Elutasítva/Inaktív - Marad a Stage táblában.
+- **<40 pont:** Elutasítva/Inaktív - Marad a Stage táblában.
+
+## 6. Szerviz Logika és Intervallumok
+A szervizkönyv nem csak eseménynapló, hanem előrejelző rendszer.
+
+### 6.1 Karbantartási Terv (Maintenance Plan)
+Az MDM-ben rögzített `specifications` tartalmazza a fődarabok szervizigényét:
+- **Folyadékok:** Típus, mennyiség, csereperiódus (km/hónap).
+- **Alkatrészek:** Gyári kódok és alternatívák (cross-reference).
+- **Biztonság:** Időszakos vizsgák (érintésvédelem, emelőhátfal, hajó szemle).

docs/V01_gemini/21_DEEP ASSET CATALOG

@@ -1,7 +0,0 @@
-21.1 Adatmélység és Idővonal
-
-A rendszer célja a teljes EU-s járműpark lefedése a 2000-es évjárattól kezdődően.
-
-    Hierarchia: Make -> Model -> Generation -> Engine Variant -> Trim Level.
-
-    Kezdeti adatok: Az első fázisban a robot a 4 alapszintet tölti (Márka, Típus, Évjárat, Motor), majd iteratívan mélyíti a factory_data JSONB mezőt (olajmennyiség, nyomaték, guminyomás stb.).

docs/V01_gemini/21_DEEP ASSET CATALOG.md

@@ -0,0 +1,57 @@
+21.1 Adatmélység és Idővonal
+
+A rendszer célja a teljes EU-s járműpark lefedése a 2000-es évjárattól kezdődően.
+
+    Hierarchia: Make -> Model -> Generation -> Engine Variant -> Trim Level.
+
+    Kezdeti adatok: Az első fázisban a robot a 4 alapszintet tölti (Márka, Típus, Évjárat, Motor), majd iteratívan mélyíti a factory_data JSONB mezőt (olajmennyiség, nyomaték, guminyomás stb.).
+
+    # 21. Deep Asset Catalog (MDM)
+
+Ez a dokumentum írja le a járművek technikai mélységét kategóriánként.
+
+## 1. Kategória Specifikus Adatok (JSONB Schemas)
+- **Személyautó:** Klíma fajták (digit, többzónás), hajtáslánc, ADAS rendszerek.
+- **Teherautó/Kamion:** Tengelyek száma, fülke típusa, retarder típusok, menetíró.
+- **Motorkerékpár:** Munkaütem, hűtés módja, táskák/dobozok konfigurációja.
+- **Hajó:** Merülés, vízkiszorítás, orrsugárkormány, navigációs elektronika.
+
+## 2. Numerikus Indexelés
+A gyors keresés érdekében a következő mezők fix oszlopok:
+- `engine_capacity` (ccm)
+- `power_kw` (kW)
+- `weight_kg` (Súly)
+- `year_from / year_to` (Gyártási időszak)
+
+# 21. Deep Asset Catalog & Master Data Management (MDM)
+
+Ez a modul a rendszer "agyát" képezi, ahol a zajos külső forrásokból származó adatok tiszta, dúsított és egyedi járműspecifikációkká alakulnak.
+
+## 21.1 Adatmodell (vehicle_model_definitions)
+A katalógus nem egyszerűen rekordokat tárol, hanem egy Master-Slave viszonyrendszert valósít meg a duplikációk elkerülése érdekében.
+
+### Kulcsfontosságú mezők:
+- `technical_code`: Egyedi gyári azonosító (pl. PC44, ZX600R). Elsődleges kulcs a deduplikációhoz.
+- `parent_id`: Önhivatkozás. Ha egy rekord duplikátum, itt mutat a Master (eredeti) rekordra.
+- `synonyms` (JSONB): Alternatív elnevezések gyűjteménye (pl. "Tracer 9", "MT-09 Tracer") a kereshetőség javítására.
+- `year_from / year_to`: Gyártási intervallumok a generációk megkülönböztetéséhez.
+- `specifications` (JSONB): Műszaki adatok (olajmennyiség, gyertya típus, hűtőfolyadék).
+
+## 21.2 Master-Merge Logika
+A rendszer az "Igazság Hierarchiáját" követi az adatok mentésekor:
+1. **Hatósági Adat (RDW):** CCM és teljesítmény (kW) forrása.
+2. **AI Adatbányászat (Gemini + Google Search):** Technikai kódok, évjáratok és szervizadatok forrása.
+3. **Manuális felülbírálat:** Legmagasabb prioritású `status = 'manual_check'`.
+
+### Összefésülési szabály (Deduplikáció):
+A Robot 2 csak akkor olvaszt össze két rekordot, ha:
+- A `make` (gyártó) egyezik.
+- A `technical_code` azonos és nem 'N/A'.
+- A `engine_capacity` (CCM) megegyezik.
+
+
+
+## 21.3 Állapotgépek (Status Lifecycle)
+- `unverified`: Alapállapot, csak nyers adatok.
+- `ai_enriched`: Sikeresen dúsított, hitelesített Master rekord.
+- `duplicate`: Felismert másolat, amely egy Master rekordhoz van láncolva.

docs/V01_gemini/22_ROBOT ÖKOSZISZTÉMA.md

@@ -110,4 +110,55 @@ A robotok és az n8n szoros együttműködésben dolgoznak.
 - **Event Hunter:** n8n workflow figyeli a motoros/autós találkozókat és eseményeket, majd összeköti őket a helyi szervizpartnerekkel.
 - **Gamification Link:** A robot regisztrálja a felhasználói validálásokat és kiosztja a pontokat/krediteket.
 
-    
+# 22. Robot Ökoszisztéma
+
+A rendszer automatizált adatgyűjtő és dúsító ágensei.
+
+## 1. Model Enrichment Robot (Dúsító)
+- **Működés:** Hajnali 01:00 - 05:00 között.
+- **Logika:** 1. Új `unverified` kódok keresése.
+    2. RDW API lekérdezés (Holland alapok).
+    3. AI (Gemini) Deep Scraping: Marketing név, felszereltségi lista, szervizintervallumok kinyerése.
+    4. Validálás a JSON Sémák alapján.
+    5. Master státusz beállítása.
+
+## 2. Resource Management
+A robot figyeli a szerver terhelését. Ha a CPU > 70%, az AI lekérdezéseket lassítja (throttle), hogy ne zavarja a hajnali biztonsági mentéseket.
+
+    # 22. Robot Ökoszisztéma Specifikáció
+
+A rendszer autonóm robotok hálózatából áll, amelyek egymásra épülve tisztítják és dúsítják a flottaszintű adatokat.
+
+## 22.1 Robot 1: Catalog Scraper (RDW)
+- **Feladata:** Külső API-k (pl. Holland RDW) folyamatos monitorozása és új járművek importálása.
+- **Működés:** Nyers adatokat hoz létre `unverified` státusszal.
+
+## 22.2 Robot 2: Technical Enricher & Master Merge (v1.2.6)
+Ez a legkomplexebb modul, amely AI-t és webes keresést használ.
+
+### Főbb képességek:
+- **Google Search Integration (RAG):** Ha az AI nem ismeri a modellkódot, valós időben keres a gyártói oldalakon.
+- **Safe-Merge Technológia:** Megakadályozza a technikai kód nélküli rekordok véletlen összevonását (`N/A-{id}` generálásával).
+- **Atomi tranzakciókezelés:** Minden mentés külön tranzakció, így a hiba nem szakítja meg a tömeges feldolgozást.
+
+### Működési folyamat:
+1. Rekord kiválasztása (`status = unverified`).
+2. RDW kiegészítő adatok lekérése.
+3. Gemini 2.0 Flash meghívása keresőeszközzel.
+4. JSON parseolás és kód-validáció.
+5. Deduplikáció ellenőrzése és mentés.
+
+
+
+## 22.3 Robot 3: OCR & Document AI
+- **Feladata:** Feltöltött okmányok, számlák és kilométeróra állások felismerése.
+- **Technológia:** Multimodális Gemini 2.0 elemzés.
+- **Kimenet:** Strukturált JSON, amely azonnal validálható a katalógus (R2) adataival.
+
+## 22.4 Robot 4: Service Hunter (Tervezett)
+- **Cél:** A dúsított technikai adatok alapján szervizpartnerek és árak keresése.
+- **Input:** Robot 2 által generált szerviz-specifikációk (olaj típus, mennyiség).
+
+## 22.5 Robot Monitoring & Operáció
+A robotok állapotát nem csak SQL-ben, hanem az n8n Dashboardon és a logok szintjén is követjük.
+- **Hibakezelés:** Automatikus visszalépés és ideiglenes kódgenerálás (`RESET-`, `UNKNOWN-`) az adatbázis kényszerek (Unique, Not Null) betartása érdekében.

logs/morning_reports.log

@@ -0,0 +1,19 @@
+2026-02-17 07:00:02,223 INFO sqlalchemy.engine.Engine select pg_catalog.version()
+2026-02-17 07:00:02,223 INFO sqlalchemy.engine.Engine [raw sql] ()
+2026-02-17 07:00:02,226 INFO sqlalchemy.engine.Engine select current_schema()
+2026-02-17 07:00:02,226 INFO sqlalchemy.engine.Engine [raw sql] ()
+2026-02-17 07:00:02,227 INFO sqlalchemy.engine.Engine show standard_conforming_strings
+2026-02-17 07:00:02,227 INFO sqlalchemy.engine.Engine [raw sql] ()
+2026-02-17 07:00:02,228 INFO sqlalchemy.engine.Engine BEGIN (implicit)
+2026-02-17 07:00:02,301 INFO sqlalchemy.engine.Engine SELECT data.process_logs.id, data.process_logs.process_name, data.process_logs.start_time, data.process_logs.end_time, data.process_logs.items_processed, data.process_logs.items_failed, data.process_logs.details, data.process_logs.created_at 
+FROM data.process_logs 
+WHERE data.process_logs.start_time >= $1::TIMESTAMP WITH TIME ZONE
+2026-02-17 07:00:02,301 INFO sqlalchemy.engine.Engine [generated in 0.00012s] (datetime.datetime(2026, 2, 16, 7, 0, 2, 176119),)
+📊 REGGELI ROBOT JELENTÉS - 2026-02-17
+========================================
+✅ Feldolgozott modellek: 1680
+❌ Hibás/Sikertelen: 0
+🧹 AI névtisztítások száma: 0
+
+
+2026-02-17 07:00:02,309 INFO sqlalchemy.engine.Engine ROLLBACK

n8n/data/n8nEventLog-1.log

@@ -0,0 +1,527 @@
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n8n/data/n8nEventLog-2.log

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