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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172
apps/tenant-backend/app/api/v1/models.py
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172
apps/tenant-backend/app/api/v1/models.py
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"""
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Tenant Models API - Interface to Resource Cluster Model Management
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Provides tenant-scoped access to available AI models from the Resource Cluster.
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"""
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from typing import Dict, Any, List, Optional
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from fastapi import APIRouter, HTTPException, status, Depends
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import httpx
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import logging
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from app.core.security import get_current_user
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from app.core.config import get_settings
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from app.core.cache import get_cache
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from app.services.resource_cluster_client import ResourceClusterClient
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logger = logging.getLogger(__name__)
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settings = get_settings()
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cache = get_cache()
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router = APIRouter(prefix="/api/v1/models", tags=["Models"])
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@router.get("/", summary="List available models for tenant")
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async def list_available_models(
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current_user: Dict = Depends(get_current_user)
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) -> Dict[str, Any]:
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"""Get list of AI models available to the current tenant"""
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try:
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# Get tenant domain from current user
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tenant_domain = current_user.get("tenant_domain", "default")
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# Check cache first (5-minute TTL)
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cache_key = f"models_list_{tenant_domain}"
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cached_models = cache.get(cache_key, ttl=300)
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if cached_models:
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logger.debug(f"Returning cached model list for tenant {tenant_domain}")
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return {**cached_models, "cached": True}
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# Call Resource Cluster models API - use Docker service name if in container
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import os
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if os.path.exists('/.dockerenv'):
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resource_cluster_url = "http://resource-cluster:8000"
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else:
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resource_cluster_url = settings.resource_cluster_url
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async with httpx.AsyncClient() as client:
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response = await client.get(
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f"{resource_cluster_url}/api/v1/models/",
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headers={
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"X-Tenant-Domain": tenant_domain
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},
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timeout=30.0
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)
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if response.status_code == 200:
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models_data = response.json()
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models = models_data.get("models", [])
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# Filter models by health and deployment status
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available_models = [
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{
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"value": model["id"], # model_id string for backwards compatibility
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"uuid": model.get("uuid"), # Database UUID for unique identification
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"label": model["name"],
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"description": model["description"],
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"provider": model["provider"],
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"model_type": model["model_type"],
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"max_tokens": model["performance"]["max_tokens"],
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"context_window": model["performance"]["context_window"],
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"cost_per_1k_tokens": model["performance"]["cost_per_1k_tokens"],
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"latency_p50_ms": model["performance"]["latency_p50_ms"],
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"health_status": model["status"]["health"],
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"deployment_status": model["status"]["deployment"]
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}
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for model in models
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if (model["status"]["deployment"] == "available" and
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model["status"]["health"] in ["healthy", "unknown"] and
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model["model_type"] != "embedding")
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]
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# Sort by provider preference (NVIDIA first, then Groq) and then by performance
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provider_order = {"nvidia": 0, "groq": 1}
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available_models.sort(key=lambda x: (
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provider_order.get(x["provider"], 99), # NVIDIA first, then Groq
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x["latency_p50_ms"] or 999 # Lower latency first
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))
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result = {
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"models": available_models,
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"total": len(available_models),
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"tenant_domain": tenant_domain,
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"last_updated": models_data.get("last_updated"),
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"cached": False
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}
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# Cache the result for 5 minutes
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cache.set(cache_key, result)
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logger.debug(f"Cached model list for tenant {tenant_domain}")
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return result
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else:
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# Resource Cluster unavailable - return empty list
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logger.warning(f"Resource Cluster unavailable (HTTP {response.status_code})")
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return {
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"models": [],
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"total": 0,
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"tenant_domain": tenant_domain,
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"message": "No models available - resource cluster unavailable"
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}
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except Exception as e:
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logger.error(f"Error fetching models from Resource Cluster: {e}")
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# Return empty list in case of error
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return {
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"models": [],
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"total": 0,
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"tenant_domain": current_user.get("tenant_domain", "default"),
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"message": "No models available - service error"
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}
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@router.get("/{model_id}", summary="Get model details")
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async def get_model_details(
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model_id: str,
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current_user: Dict = Depends(get_current_user)
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) -> Dict[str, Any]:
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"""Get detailed information about a specific model"""
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try:
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tenant_domain = current_user.get("tenant_domain", "default")
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# Call Resource Cluster for model details - use Docker service name if in container
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import os
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if os.path.exists('/.dockerenv'):
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resource_cluster_url = "http://resource-cluster:8000"
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else:
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resource_cluster_url = settings.resource_cluster_url
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async with httpx.AsyncClient() as client:
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response = await client.get(
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f"{resource_cluster_url}/api/v1/models/{model_id}",
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headers={
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"X-Tenant-Domain": tenant_domain
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},
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timeout=15.0
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)
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if response.status_code == 200:
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return response.json()
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elif response.status_code == 404:
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raise HTTPException(
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status_code=status.HTTP_404_NOT_FOUND,
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detail=f"Model {model_id} not found"
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)
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else:
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raise HTTPException(
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status_code=status.HTTP_503_SERVICE_UNAVAILABLE,
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detail="Resource Cluster unavailable"
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)
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except HTTPException:
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raise
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except Exception as e:
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logger.error(f"Error fetching model {model_id} details: {e}")
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raise HTTPException(
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status_code=status.HTTP_503_SERVICE_UNAVAILABLE,
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detail="Failed to get model details"
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)
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