feat(ai-services): add Python FastAPI AI/ML services container

Create libs/ai-services/ with FastAPI app providing:
- POST /avm/predict — XGBoost-backed property price prediction (heuristic fallback)
- POST /avm/extract-features — Vietnamese NLP feature extraction from listing text
- POST /moderation/check — content moderation with rule-based flagging
- GET /health — health check endpoint

Includes Dockerfile (Python 3.12), docker-compose integration, Pydantic models,
and 9 passing tests covering all endpoints.

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

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

@@ -0,0 +1,20 @@
+from pydantic import BaseModel, Field
+
+
+class ModerationRequest(BaseModel):
+    text: str = Field(..., min_length=1, description="Text content to moderate")
+    context: str = Field("listing", description="Context: listing, comment, profile")
+
+
+class ModerationFlag(BaseModel):
+    category: str
+    severity: str = Field(..., description="low, medium, high")
+    matched_text: str
+    reason: str
+
+
+class ModerationResponse(BaseModel):
+    is_flagged: bool
+    score: float = Field(..., ge=0, le=1, description="Overall risk score")
+    flags: list[ModerationFlag] = Field(default_factory=list)
+    cleaned_text: str | None = Field(None, description="Text with flagged content redacted")