Files
service-finder/backend/app/services/search_service.py

61 lines
2.2 KiB
Python

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']))