GT AI OS Community Edition v2.0.33

Security hardening release addressing CodeQL and Dependabot alerts:

- Fix stack trace exposure in error responses
- Add SSRF protection with DNS resolution checking
- Implement proper URL hostname validation (replaces substring matching)
- Add centralized path sanitization to prevent path traversal
- Fix ReDoS vulnerability in email validation regex
- Improve HTML sanitization in validation utilities
- Fix capability wildcard matching in auth utilities
- Update glob dependency to address CVE
- Add CodeQL suppression comments for verified false positives

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

apps/control-panel-backend/app/models/usage.py

@@ -0,0 +1,70 @@
+"""
+Usage tracking database model
+"""
+from datetime import datetime
+from typing import Dict, Any
+from sqlalchemy import Column, Integer, String, DateTime, ForeignKey, JSON
+from sqlalchemy.dialects.postgresql import JSONB
+from sqlalchemy.orm import relationship
+from sqlalchemy.sql import func
+
+from app.core.database import Base
+
+
+class UsageRecord(Base):
+    """Usage tracking for billing and monitoring"""
+    
+    __tablename__ = "usage_records"
+    
+    id = Column(Integer, primary_key=True, index=True)
+    tenant_id = Column(Integer, ForeignKey("tenants.id", ondelete="CASCADE"), nullable=False, index=True)
+    resource_id = Column(Integer, ForeignKey("ai_resources.id", ondelete="CASCADE"), nullable=False, index=True)
+    user_email = Column(String(255), nullable=False, index=True)
+    request_type = Column(String(50), nullable=False, index=True)  # chat, embedding, image_generation, etc.
+    tokens_used = Column(Integer, nullable=False, default=0)
+    cost_cents = Column(Integer, nullable=False, default=0)
+    request_metadata = Column(JSON, nullable=False, default=dict)
+    
+    # Timestamp
+    created_at = Column(DateTime(timezone=True), server_default=func.now(), nullable=False, index=True)
+    
+    # Relationships
+    tenant = relationship("Tenant", back_populates="usage_records")
+    ai_resource = relationship("AIResource", back_populates="usage_records")
+    
+    def __repr__(self):
+        return f"<UsageRecord(id={self.id}, tenant_id={self.tenant_id}, tokens={self.tokens_used})>"
+    
+    def to_dict(self) -> Dict[str, Any]:
+        """Convert usage record to dictionary"""
+        return {
+            "id": self.id,
+            "tenant_id": self.tenant_id,
+            "resource_id": self.resource_id,
+            "user_email": self.user_email,
+            "request_type": self.request_type,
+            "tokens_used": self.tokens_used,
+            "cost_cents": self.cost_cents,
+            "request_metadata": self.request_metadata,
+            "created_at": self.created_at.isoformat() if self.created_at else None
+        }
+    
+    @property
+    def cost_dollars(self) -> float:
+        """Get cost in dollars"""
+        return self.cost_cents / 100.0
+    
+    @classmethod
+    def calculate_cost(cls, tokens_used: int, resource_type: str, provider: str) -> int:
+        """Calculate cost in cents based on usage"""
+        # Cost calculation logic (example rates)
+        if provider == "groq":
+            if resource_type == "llm":
+                # Groq LLM pricing: ~$0.0001 per 1K tokens
+                return max(1, int((tokens_used / 1000) * 0.01 * 100))  # Convert to cents
+            elif resource_type == "embedding":
+                # Embedding pricing: ~$0.00002 per 1K tokens
+                return max(1, int((tokens_used / 1000) * 0.002 * 100))  # Convert to cents
+        
+        # Default fallback cost
+        return max(1, int((tokens_used / 1000) * 0.001 * 100))  # 0.1 cents per 1K tokens