feat: Asset Catalog system, PostGIS integration and RobotScout V1

backend/app/services/search_service.py

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+from sqlalchemy.ext.asyncio import AsyncSession
+from sqlalchemy import select, func
+from app.models.service import ServiceProfile, ExpertiseTag, ServiceExpertise
+from app.models.organization import Organization
+from geoalchemy2.functions import ST_Distance, ST_MakePoint
+
+class SearchService:
+    @staticmethod
+    async def find_nearby_services(
+        db: AsyncSession, 
+        lat: float, 
+        lon: float, 
+        expertise_key: str = None, 
+        radius_km: int = 50,
+        is_premium: bool = False
+    ):
+        """
+        Keresés távolság és szakértelem alapján.
+        Premium: Trust Score + Valós távolság.
+        Free: Trust Score + Légvonal.
+        """
+        user_point = ST_MakePoint(lon, lat) # PostGIS pont létrehozása
+        
+        # Alap lekérdezés: ServiceProfile + Organization adatok
+        stmt = select(ServiceProfile, Organization).join(
+            Organization, ServiceProfile.organization_id == Organization.id
+        )
+
+        # 1. Sugár alapú szűrés (radius_km * 1000 méter)
+        stmt = stmt.where(
+            func.ST_DWithin(ServiceProfile.location, user_point, radius_km * 1000)
+        )
+
+        # 2. Szakterület szűrése
+        if expertise_key:
+            stmt = stmt.join(ServiceProfile.expertises).join(ExpertiseTag).where(
+                ExpertiseTag.key == expertise_key
+            )
+
+        # 3. Távolság és Trust Score alapú sorrend
+        # A ST_Distance méterben adja vissza az eredményt
+        stmt = stmt.order_by(ST_Distance(ServiceProfile.location, user_point))
+
+        result = await db.execute(stmt.limit(50))
+        rows = result.all()
+
+        # Rangsorolási logika alkalmazása
+        results = []
+        for s_prof, org in rows:
+            results.append({
+                "id": org.id,
+                "name": org.full_name,
+                "trust_score": s_prof.trust_score,
+                "is_verified": s_prof.is_verified,
+                "phone": s_prof.contact_phone,
+                "website": s_prof.website,
+                "is_premium_partner": s_prof.trust_score >= 90
+            })
+
+        # Súlyozott rendezés: Prémium partnerek és Trust Score előre
+        return sorted(results, key=lambda x: (not is_premium, -x['trust_score']))