feat(analytics): add Python NeighborhoodScore service + NestJS HTTP proxy (TEC-2756)

- libs/ai-services: new POST /neighborhood/score router computing weighted
  6-axis livability score from per-category POI counts; algorithm versioned
  for future iteration (sigmoid curves, percentile thresholds).
- apps/api: HttpNeighborhoodScoreService proxies to Python first, falls back
  to PrismaNeighborhoodScoreService when AI service unavailable. Mirrors the
  HttpAVMService pattern. Existing GET /analytics/neighborhoods/:district/score
  endpoint and CQRS handler now flow through the proxy.
- AnalyticsModule binds Http variant by default, retains Prisma variant as
  injectable fallback.
- Tests: 5 pytest cases for Python heuristic, 4 vitest cases for HTTP proxy
  fallback behaviour.

Co-Authored-By: Paperclip <noreply@paperclip.ing>

libs/ai-services/app/models/neighborhood.py

@@ -0,0 +1,33 @@
+from pydantic import BaseModel, Field
+
+
+class NeighborhoodPOICounts(BaseModel):
+    education: int = Field(0, ge=0, description="SCHOOL + UNIVERSITY within 2km")
+    healthcare: int = Field(0, ge=0, description="HOSPITAL + CLINIC within 3km")
+    transport: int = Field(0, ge=0, description="METRO_STATION + BUS_STOP within 1km")
+    shopping: int = Field(0, ge=0, description="MALL + MARKET + SUPERMARKET within 2km")
+    greenery: int = Field(0, ge=0, description="PARK within 1km")
+    safety: int = Field(0, ge=0, description="POLICE_STATION + FIRE_STATION within 3km")
+
+
+class NeighborhoodScoreRequest(BaseModel):
+    district: str = Field(..., min_length=1, description="District name (e.g. Quận 1)")
+    city: str = Field(..., min_length=1, description="City name (e.g. Hồ Chí Minh)")
+    poi_counts: NeighborhoodPOICounts = Field(
+        ...,
+        description="Per-category POI counts already filtered by radius in NestJS",
+    )
+
+
+class NeighborhoodScoreResponse(BaseModel):
+    district: str
+    city: str
+    education_score: float = Field(..., ge=0, le=10)
+    healthcare_score: float = Field(..., ge=0, le=10)
+    transport_score: float = Field(..., ge=0, le=10)
+    shopping_score: float = Field(..., ge=0, le=10)
+    greenery_score: float = Field(..., ge=0, le=10)
+    safety_score: float = Field(..., ge=0, le=10)
+    total_score: float = Field(..., ge=0, le=100)
+    poi_counts: dict[str, int]
+    algorithm_version: str