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>
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apps/control-panel-backend/app/models/model_config.py
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apps/control-panel-backend/app/models/model_config.py
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"""
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Model Configuration Database Schema for GT 2.0 Admin Control Panel
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This model stores configurations for all AI models across the GT 2.0 platform.
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Configurations are synced to resource clusters via RabbitMQ messages.
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"""
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from sqlalchemy import Column, String, JSON, Boolean, DateTime, Float, Integer, Text, UniqueConstraint
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from sqlalchemy.dialects.postgresql import UUID
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from sqlalchemy.orm import relationship
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from sqlalchemy.sql import func
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import uuid
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from app.core.database import Base
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class ModelConfig(Base):
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"""Model configuration stored in PostgreSQL admin database"""
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__tablename__ = "model_configs"
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# Primary key - UUID
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id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
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# Business identifier - unique per provider (same model_id can exist for different providers)
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model_id = Column(String(255), nullable=False, index=True)
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name = Column(String(255), nullable=False)
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version = Column(String(50), default="1.0")
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# Provider information
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provider = Column(String(50), nullable=False) # groq, external, openai, anthropic, nvidia
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model_type = Column(String(50), nullable=False) # llm, embedding, audio, tts, vision
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# Endpoint configuration
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endpoint = Column(String(500), nullable=False)
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api_key_name = Column(String(100)) # Environment variable name for API key
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# Model specifications
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context_window = Column(Integer)
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max_tokens = Column(Integer)
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dimensions = Column(Integer) # For embedding models
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# Capabilities (JSON object)
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capabilities = Column(JSON, default={})
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# Cost information (per million tokens, as per Groq pricing)
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cost_per_million_input = Column(Float, default=0.0)
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cost_per_million_output = Column(Float, default=0.0)
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# Configuration and metadata
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description = Column(Text)
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config = Column(JSON, default={}) # Additional provider-specific config
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# Status and health
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is_active = Column(Boolean, default=True)
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health_status = Column(String(20), default="unknown") # healthy, unhealthy, unknown
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last_health_check = Column(DateTime)
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# Compound model flag (for pass-through pricing based on actual usage)
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is_compound = Column(Boolean, default=False)
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# Usage tracking (will be updated from resource clusters)
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request_count = Column(Integer, default=0)
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error_count = Column(Integer, default=0)
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success_rate = Column(Float, default=100.0)
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avg_latency_ms = Column(Float, default=0.0)
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# Tenant access control (JSON array)
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# Example: {"allowed_tenants": ["tenant1", "tenant2"], "blocked_tenants": [], "global_access": true}
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tenant_restrictions = Column(JSON, default=lambda: {"global_access": True})
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# Required capabilities to use this model (JSON array)
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# Example: ["llm:execute", "advanced:reasoning", "vision:analyze"]
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required_capabilities = Column(JSON, default=list)
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# Lifecycle timestamps
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created_at = Column(DateTime, default=func.now())
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updated_at = Column(DateTime, default=func.now(), onupdate=func.now())
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# Relationships
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tenant_configs = relationship("TenantModelConfig", back_populates="model_config", cascade="all, delete-orphan")
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# Unique constraint: same model_id can exist for different providers
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__table_args__ = (
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UniqueConstraint('model_id', 'provider', name='model_configs_model_id_provider_unique'),
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)
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def to_dict(self) -> dict:
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"""Convert model to dictionary for API responses"""
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return {
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"id": str(self.id) if self.id else None,
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"model_id": self.model_id,
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"name": self.name,
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"version": self.version,
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"provider": self.provider,
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"model_type": self.model_type,
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"endpoint": self.endpoint,
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"api_key_name": self.api_key_name,
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"specifications": {
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"context_window": self.context_window,
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"max_tokens": self.max_tokens,
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"dimensions": self.dimensions,
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},
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"capabilities": self.capabilities or {},
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"cost": {
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"per_million_input": self.cost_per_million_input,
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"per_million_output": self.cost_per_million_output,
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},
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"description": self.description,
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"config": self.config or {},
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"status": {
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"is_active": self.is_active,
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"is_compound": self.is_compound,
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"health_status": self.health_status,
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"last_health_check": self.last_health_check.isoformat() if self.last_health_check else None,
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},
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"usage": {
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"request_count": self.request_count,
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"error_count": self.error_count,
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"success_rate": self.success_rate,
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"avg_latency_ms": self.avg_latency_ms,
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},
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"access_control": {
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"tenant_restrictions": self.tenant_restrictions or {},
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"required_capabilities": self.required_capabilities or [],
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},
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"timestamps": {
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"created_at": self.created_at.isoformat(),
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"updated_at": self.updated_at.isoformat(),
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}
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}
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@classmethod
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def from_dict(cls, data: dict) -> 'ModelConfig':
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"""Create ModelConfig from dictionary"""
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# Handle both nested and flat data formats
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specifications = data.get("specifications", {})
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cost = data.get("cost", {})
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status = data.get("status", {})
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access_control = data.get("access_control", {})
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return cls(
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model_id=data.get("model_id"),
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name=data.get("name"),
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version=data.get("version", "1.0"),
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provider=data.get("provider"),
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model_type=data.get("model_type"),
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endpoint=data.get("endpoint"),
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api_key_name=data.get("api_key_name"),
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# Handle both nested and flat context_window/max_tokens with type conversion
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context_window=int(specifications.get("context_window") or data.get("context_window", 0)) if (specifications.get("context_window") or data.get("context_window")) else None,
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max_tokens=int(specifications.get("max_tokens") or data.get("max_tokens", 0)) if (specifications.get("max_tokens") or data.get("max_tokens")) else None,
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dimensions=int(specifications.get("dimensions") or data.get("dimensions", 0)) if (specifications.get("dimensions") or data.get("dimensions")) else None,
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capabilities=data.get("capabilities", {}),
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# Handle both nested and flat cost fields with type conversion
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cost_per_million_input=float(cost.get("per_million_input") or data.get("cost_per_million_input", 0.0)),
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cost_per_million_output=float(cost.get("per_million_output") or data.get("cost_per_million_output", 0.0)),
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description=data.get("description"),
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config=data.get("config", {}),
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# Handle both nested and flat is_active
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is_active=status.get("is_active") if status.get("is_active") is not None else data.get("is_active", True),
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# Handle both nested and flat is_compound
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is_compound=status.get("is_compound") if status.get("is_compound") is not None else data.get("is_compound", False),
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tenant_restrictions=access_control.get("tenant_restrictions", data.get("tenant_restrictions", {"global_access": True})),
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required_capabilities=access_control.get("required_capabilities", data.get("required_capabilities", [])),
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)
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class ModelUsageLog(Base):
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"""Log of model usage events from resource clusters"""
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__tablename__ = "model_usage_logs"
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id = Column(Integer, primary_key=True, autoincrement=True)
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model_id = Column(String(255), nullable=False, index=True)
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tenant_id = Column(String(100), nullable=False, index=True)
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user_id = Column(String(100), nullable=False)
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# Usage metrics
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tokens_input = Column(Integer, default=0)
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tokens_output = Column(Integer, default=0)
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tokens_total = Column(Integer, default=0)
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cost = Column(Float, default=0.0)
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latency_ms = Column(Float)
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# Request metadata
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success = Column(Boolean, default=True)
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error_message = Column(Text)
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request_id = Column(String(100))
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# Timestamp
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timestamp = Column(DateTime, default=func.now())
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def to_dict(self) -> dict:
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"""Convert to dictionary"""
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return {
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"id": self.id,
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"model_id": self.model_id,
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"tenant_id": self.tenant_id,
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"user_id": self.user_id,
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"tokens": {
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"input": self.tokens_input,
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"output": self.tokens_output,
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"total": self.tokens_total,
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},
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"cost": self.cost,
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"latency_ms": self.latency_ms,
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"success": self.success,
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"error_message": self.error_message,
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"request_id": self.request_id,
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"timestamp": self.timestamp.isoformat(),
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}
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