GT AI OS Community v2.0.33 - Add NVIDIA NIM and Nemotron agents
- Updated python_coding_microproject.csv to use NVIDIA NIM Kimi K2 - Updated kali_linux_shell_simulator.csv to use NVIDIA NIM Kimi K2 - Made more general-purpose (flexible targets, expanded tools) - Added nemotron-mini-agent.csv for fast local inference via Ollama - Added nemotron-agent.csv for advanced reasoning via Ollama - Added wiki page: Projects for NVIDIA NIMs and Nemotron
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HackWeasel
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apps/control-panel-backend/app/api/internal/optics.py
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apps/control-panel-backend/app/api/internal/optics.py
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"""
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Internal API for service-to-service Optics settings retrieval
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"""
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from fastapi import APIRouter, Depends, HTTPException, status, Header, Query
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from sqlalchemy.ext.asyncio import AsyncSession
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from sqlalchemy import select, text
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from typing import Optional
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from app.core.database import get_db
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from app.models.tenant import Tenant
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from app.core.config import settings
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router = APIRouter(prefix="/internal/optics", tags=["Internal Optics"])
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async def verify_service_auth(
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x_service_auth: str = Header(None),
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x_service_name: str = Header(None)
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) -> bool:
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"""Verify service-to-service authentication"""
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if not x_service_auth or not x_service_name:
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raise HTTPException(
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status_code=status.HTTP_401_UNAUTHORIZED,
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detail="Service authentication required"
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)
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# Verify service token (in production, use proper service mesh auth)
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expected_token = settings.SERVICE_AUTH_TOKEN or "internal-service-token"
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if x_service_auth != expected_token:
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raise HTTPException(
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status_code=status.HTTP_401_UNAUTHORIZED,
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detail="Invalid service authentication"
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)
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# Verify service is allowed
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allowed_services = ["resource-cluster", "tenant-backend"]
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if x_service_name not in allowed_services:
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raise HTTPException(
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status_code=status.HTTP_403_FORBIDDEN,
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detail=f"Service {x_service_name} not authorized"
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)
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return True
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@router.get("/tenant/{tenant_domain}/settings")
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async def get_tenant_optics_settings(
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tenant_domain: str,
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db: AsyncSession = Depends(get_db),
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authorized: bool = Depends(verify_service_auth)
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):
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"""
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Internal endpoint for tenant backend to get Optics settings.
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Returns:
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- enabled: Whether Optics is enabled for this tenant
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- storage_pricing: Storage cost rates per tier (in cents per MB per month)
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- budget: Budget limits and thresholds
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"""
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# Query tenant by domain
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result = await db.execute(
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select(Tenant).where(Tenant.domain == tenant_domain)
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)
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tenant = result.scalar_one_or_none()
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if not tenant:
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raise HTTPException(
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status_code=status.HTTP_404_NOT_FOUND,
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detail=f"Tenant not found: {tenant_domain}"
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)
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# Hot tier default: $0.15/GiB/month = ~0.0146 cents/MiB
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HOT_TIER_DEFAULT_CENTS_PER_MIB = 0.146484375 # $0.15/GiB = $0.15/1024 per MiB * 100 cents
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return {
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"enabled": tenant.optics_enabled or False,
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"storage_pricing": {
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"dataset_hot": float(tenant.storage_price_dataset_hot) if tenant.storage_price_dataset_hot else HOT_TIER_DEFAULT_CENTS_PER_MIB,
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"conversation_hot": float(tenant.storage_price_conversation_hot) if tenant.storage_price_conversation_hot else HOT_TIER_DEFAULT_CENTS_PER_MIB,
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},
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"cold_allocation": {
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"allocated_tibs": float(tenant.cold_storage_allocated_tibs) if tenant.cold_storage_allocated_tibs else None,
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"price_per_tib": float(tenant.cold_storage_price_per_tib) if tenant.cold_storage_price_per_tib else 10.00,
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},
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"budget": {
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"monthly_budget_cents": tenant.monthly_budget_cents,
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"warning_threshold": tenant.budget_warning_threshold or 80,
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"critical_threshold": tenant.budget_critical_threshold or 90,
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"enforcement_enabled": tenant.budget_enforcement_enabled or False
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},
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"tenant_id": tenant.id,
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"tenant_name": tenant.name
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}
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@router.get("/model-pricing")
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async def get_model_pricing(
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db: AsyncSession = Depends(get_db),
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authorized: bool = Depends(verify_service_auth)
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):
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"""
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Internal endpoint for tenant backend to get model pricing.
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Returns all model pricing from model_configs table.
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"""
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from app.models.model_config import ModelConfig
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result = await db.execute(
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select(ModelConfig).where(ModelConfig.is_active == True)
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)
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models = result.scalars().all()
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pricing = {}
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for model in models:
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pricing[model.model_id] = {
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"name": model.name,
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"provider": model.provider,
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"cost_per_million_input": model.cost_per_million_input or 0.0,
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"cost_per_million_output": model.cost_per_million_output or 0.0
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}
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return {
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"models": pricing,
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"default_pricing": {
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"cost_per_million_input": 0.10,
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"cost_per_million_output": 0.10
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}
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}
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@router.get("/tenant/{tenant_domain}/embedding-usage")
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async def get_tenant_embedding_usage(
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tenant_domain: str,
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start_date: str = Query(..., description="Start date (YYYY-MM-DD)"),
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end_date: str = Query(..., description="End date (YYYY-MM-DD)"),
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db: AsyncSession = Depends(get_db),
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authorized: bool = Depends(verify_service_auth)
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):
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"""
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Internal endpoint for tenant backend to get embedding usage for billing.
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Queries the embedding_usage_logs table for a tenant within a date range.
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This enables Issue #241 - Embedding Model Pricing.
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Args:
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tenant_domain: Tenant domain (e.g., 'test-company')
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start_date: Start date in YYYY-MM-DD format
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end_date: End date in YYYY-MM-DD format
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Returns:
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{
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"total_tokens": int,
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"total_cost_cents": float,
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"embedding_count": int,
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"by_model": [{"model": str, "tokens": int, "cost_cents": float, "count": int}]
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}
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"""
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from datetime import datetime, timedelta
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try:
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# Parse string dates to datetime objects for asyncpg
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start_dt = datetime.strptime(start_date, "%Y-%m-%d")
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end_dt = datetime.strptime(end_date, "%Y-%m-%d") + timedelta(days=1) # Include full end day
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# Query embedding usage aggregated by model
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query = text("""
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SELECT
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model,
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COALESCE(SUM(tokens_used), 0) as total_tokens,
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COALESCE(SUM(cost_cents), 0) as total_cost_cents,
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COALESCE(SUM(embedding_count), 0) as embedding_count,
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COUNT(*) as request_count
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FROM public.embedding_usage_logs
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WHERE tenant_id = :tenant_domain
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AND timestamp >= :start_dt
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AND timestamp <= :end_dt
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GROUP BY model
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ORDER BY total_cost_cents DESC
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""")
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result = await db.execute(
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query,
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{
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"tenant_domain": tenant_domain,
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"start_dt": start_dt,
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"end_dt": end_dt
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}
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)
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rows = result.fetchall()
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# Aggregate results
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total_tokens = 0
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total_cost_cents = 0.0
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total_embedding_count = 0
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by_model = []
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for row in rows:
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model_data = {
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"model": row.model or "unknown",
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"tokens": int(row.total_tokens),
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"cost_cents": float(row.total_cost_cents),
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"count": int(row.embedding_count),
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"requests": int(row.request_count)
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}
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by_model.append(model_data)
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total_tokens += model_data["tokens"]
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total_cost_cents += model_data["cost_cents"]
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total_embedding_count += model_data["count"]
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return {
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"total_tokens": total_tokens,
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"total_cost_cents": round(total_cost_cents, 4),
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"embedding_count": total_embedding_count,
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"by_model": by_model
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}
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except Exception as e:
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# Log but return empty response on error (don't block billing)
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import logging
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logger = logging.getLogger(__name__)
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logger.error(f"Error fetching embedding usage for {tenant_domain}: {e}")
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return {
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"total_tokens": 0,
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"total_cost_cents": 0.0,
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"embedding_count": 0,
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"by_model": []
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}
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