141 lines
5.4 KiB
Python
Executable File
141 lines
5.4 KiB
Python
Executable File
# /opt/docker/dev/service_finder/backend/app/services/search_service.py
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import logging
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from typing import List, Optional, Dict, Any
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from uuid import UUID
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from sqlalchemy.ext.asyncio import AsyncSession
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from sqlalchemy import select, func, and_
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from geoalchemy2.functions import ST_Distance, ST_MakePoint, ST_DWithin
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from app.models.marketplace.service import ServiceProfile, ExpertiseTag
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from app.models.marketplace.organization import Organization
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from app.models.identity import User
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from app.services.config_service import config
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logger = logging.getLogger("Search-Service-2.4-Agnostic")
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class SearchService:
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"""
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Sentinel Master Search Service 2.4.
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Csomag-agnosztikus rangsoroló motor.
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Minden üzleti logika (súlyozás, prioritás, láthatóság) az adatbázisból jön.
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"""
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@staticmethod
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async def find_nearby_services(
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db: AsyncSession,
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lat: float,
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lon: float,
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current_user: User,
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expertise_key: str = None
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):
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try:
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# 1. HIERARCHIKUS RANGSOROLÁSI SZABÁLYOK LEKÉRÉSE
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# A config_service automatikusan a legspecifikusabbat adja vissza:
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# 1. User-specifikus (Céges egyedi beállítás)
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# 2. Package-specifikus (pl. 'free', 'premium', 'ultra_gold')
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# 3. Global (Alapértelmezett)
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user_tier = current_user.tier_name
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if current_user.role in [UserRole.superadmin, UserRole.admin]:
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user_tier = "vip"
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ranking_rules = await config.get_setting(
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db,
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"RANKING_RULES",
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user_id=current_user.id, # Ez a belső config_service-ben kezeli a hierarchiát
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package_slug=user_tier,
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default={
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"ad_weight": 5000,
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"partner_weight": 1000,
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"trust_weight": 10,
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"dist_penalty": 20,
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"pref_weight": 0,
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"can_use_prefs": False,
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"search_radius_km": 30
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}
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)
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# 2. PREFERENCIÁK (Ha a szabályrendszer engedi)
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user_prefs = {"networks": [], "ids": []}
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if ranking_rules.get("can_use_prefs", False):
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user_prefs = await config.get_setting(
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db, "USER_SEARCH_PREFERENCES",
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scope_level="user", scope_id=str(current_user.id),
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default={"networks": [], "ids": []}
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)
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# 3. TÉRBELI LEKÉRDEZÉS
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user_point = ST_MakePoint(lon, lat)
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radius_m = ranking_rules.get("search_radius_km", 30) * 1000
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distance_col = ST_Distance(ServiceProfile.location, user_point).label("distance_meters")
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stmt = (
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select(ServiceProfile, Organization, distance_col)
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.join(Organization, ServiceProfile.organization_id == Organization.id)
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.where(
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and_(
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ST_DWithin(ServiceProfile.location, user_point, radius_m),
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ServiceProfile.is_active == True
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)
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)
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)
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if expertise_key:
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stmt = stmt.join(ServiceProfile.expertises).join(ExpertiseTag).where(ExpertiseTag.key == expertise_key)
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result = await db.execute(stmt)
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rows = result.all()
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# 4. UNIVERZÁLIS PONTOZÁS (Súlyozott mátrix alapján)
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final_results = []
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r = ranking_rules # Rövidítés a számításhoz
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for s_prof, org, dist_m in rows:
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dist_km = dist_m / 1000.0
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score = 0
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# --- PONTOZÁSI LOGIKA (Nincsenek fix csomagnevek!) ---
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# A. Hirdetési súly
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if s_prof.is_advertiser:
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score += r.get("ad_weight", 0)
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# B. Partner (Minősített) súly
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if s_prof.is_verified_partner:
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score += r.get("partner_weight", 0)
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# C. Minőség (Trust Score) súly
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score += (s_prof.trust_score * r.get("trust_weight", 0))
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# D. Egyéni/Céges preferencia súly (Csak ha engedélyezett)
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if r.get("can_use_prefs"):
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if s_prof.network_slug in user_prefs.get("networks", []):
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score += r.get("pref_weight", 0)
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if str(org.id) in user_prefs.get("ids", []):
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score += r.get("pref_weight", 0) * 1.2
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# E. Távolság büntetés
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score -= (dist_km * r.get("dist_penalty", 0))
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final_results.append({
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"id": org.id,
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"name": org.full_name,
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"trust_score": s_prof.trust_score,
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"distance_km": round(dist_km, 2),
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"rank_score": round(score, 2),
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"flags": {
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"is_ad": s_prof.is_advertiser,
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"is_partner": s_prof.is_verified_partner,
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"is_favorite": str(org.id) in user_prefs.get("ids", [])
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}
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})
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# 5. RENDEZÉS
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return sorted(final_results, key=lambda x: x['rank_score'], reverse=True)
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except Exception as e:
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logger.error(f"Search Service 2.4 Critical Error: {e}")
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raise e |