ai & tokens
This commit is contained in:
IGNY8 VPS (Salman)
@@ -251,3 +251,325 @@ def data_quality_report(request):
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context.update(admin_context)
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return render(request, 'admin/reports/data_quality.html', context)
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@staff_member_required
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def token_usage_report(request):
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"""Comprehensive token usage analytics with multi-dimensional insights"""
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from igny8_core.business.billing.models import CreditUsageLog
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from igny8_core.auth.models import Account
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from decimal import Decimal
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# Date filter setup
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days_filter = request.GET.get('days', '30')
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try:
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days = int(days_filter)
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except ValueError:
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days = 30
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start_date = timezone.now() - timedelta(days=days)
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# Base queryset - filter for records with token data
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logs = CreditUsageLog.objects.filter(
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created_at__gte=start_date,
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tokens_input__isnull=False,
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tokens_output__isnull=False
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)
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# Total statistics
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total_tokens_input = logs.aggregate(total=Sum('tokens_input'))['total'] or 0
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total_tokens_output = logs.aggregate(total=Sum('tokens_output'))['total'] or 0
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total_tokens = total_tokens_input + total_tokens_output
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total_calls = logs.count()
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avg_tokens_per_call = total_tokens / total_calls if total_calls > 0 else 0
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# Token usage by model
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token_by_model = logs.values('model_used').annotate(
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total_tokens_input=Sum('tokens_input'),
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total_tokens_output=Sum('tokens_output'),
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call_count=Count('id'),
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total_cost=Sum('cost_usd')
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).order_by('-total_tokens_input')[:10]
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# Add total_tokens to each model and sort by total
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for model in token_by_model:
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model['total_tokens'] = (model['total_tokens_input'] or 0) + (model['total_tokens_output'] or 0)
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model['avg_tokens'] = model['total_tokens'] / model['call_count'] if model['call_count'] > 0 else 0
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model['model'] = model['model_used'] # Add alias for template
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token_by_model = sorted(token_by_model, key=lambda x: x['total_tokens'], reverse=True)
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# Token usage by function/operation
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token_by_function = logs.values('operation_type').annotate(
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total_tokens_input=Sum('tokens_input'),
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total_tokens_output=Sum('tokens_output'),
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call_count=Count('id'),
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total_cost=Sum('cost_usd')
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).order_by('-total_tokens_input')[:10]
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# Add total_tokens to each function and sort by total
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for func in token_by_function:
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func['total_tokens'] = (func['total_tokens_input'] or 0) + (func['total_tokens_output'] or 0)
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func['avg_tokens'] = func['total_tokens'] / func['call_count'] if func['call_count'] > 0 else 0
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func['function'] = func['operation_type'] # Add alias for template
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token_by_function = sorted(token_by_function, key=lambda x: x['total_tokens'], reverse=True)
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# Token usage by account (top consumers)
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token_by_account = logs.values('account__name', 'account_id').annotate(
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total_tokens_input=Sum('tokens_input'),
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total_tokens_output=Sum('tokens_output'),
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call_count=Count('id'),
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total_cost=Sum('cost_usd')
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).order_by('-total_tokens_input')[:15]
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# Add total_tokens to each account and sort by total
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for account in token_by_account:
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account['total_tokens'] = (account['total_tokens_input'] or 0) + (account['total_tokens_output'] or 0)
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token_by_account = sorted(token_by_account, key=lambda x: x['total_tokens'], reverse=True)[:15]
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# Daily token trends (time series)
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daily_data = []
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daily_labels = []
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for i in range(days):
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day = timezone.now().date() - timedelta(days=days-i-1)
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day_logs = logs.filter(created_at__date=day)
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day_tokens_input = day_logs.aggregate(total=Sum('tokens_input'))['total'] or 0
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day_tokens_output = day_logs.aggregate(total=Sum('tokens_output'))['total'] or 0
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day_tokens = day_tokens_input + day_tokens_output
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daily_labels.append(day.strftime('%m/%d'))
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daily_data.append(int(day_tokens))
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# Token efficiency metrics (CreditUsageLog doesn't have error field, so assume all successful)
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success_rate = 100.0
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successful_tokens = total_tokens
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wasted_tokens = 0
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# Create tokens_by_status for template compatibility
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tokens_by_status = [{
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'error': None,
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'total_tokens': total_tokens,
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'call_count': total_calls,
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'avg_tokens': avg_tokens_per_call
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}]
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# Peak usage times (hour of day)
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hourly_usage = logs.extra(
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select={'hour': "EXTRACT(hour FROM created_at)"}
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).values('hour').annotate(
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token_input=Sum('tokens_input'),
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token_output=Sum('tokens_output'),
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call_count=Count('id')
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).order_by('hour')
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# Add total token_count for each hour
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for hour_data in hourly_usage:
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hour_data['token_count'] = (hour_data['token_input'] or 0) + (hour_data['token_output'] or 0)
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# Cost efficiency
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total_cost = logs.aggregate(total=Sum('cost_usd'))['total'] or Decimal('0.00')
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cost_per_1k_tokens = (total_cost / (total_tokens / 1000)) if total_tokens > 0 else Decimal('0.00')
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context = {
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'title': 'Token Usage Report',
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'days_filter': days,
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'total_tokens': int(total_tokens),
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'total_calls': total_calls,
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'avg_tokens_per_call': round(avg_tokens_per_call, 2),
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'token_by_model': list(token_by_model),
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'token_by_function': list(token_by_function),
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'token_by_account': list(token_by_account),
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'daily_labels': json.dumps(daily_labels),
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'daily_data': json.dumps(daily_data),
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'tokens_by_status': list(tokens_by_status),
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'success_rate': round(success_rate, 2),
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'successful_tokens': int(successful_tokens),
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'wasted_tokens': int(wasted_tokens),
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'hourly_usage': list(hourly_usage),
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'total_cost': float(total_cost),
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'cost_per_1k_tokens': float(cost_per_1k_tokens),
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'current_app': '_reports', # For active menu state
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}
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# Merge with admin context
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from igny8_core.admin.site import admin_site
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admin_context = admin_site.each_context(request)
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context.update(admin_context)
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return render(request, 'admin/reports/token_usage.html', context)
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@staff_member_required
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def ai_cost_analysis(request):
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"""Multi-dimensional AI cost analysis with model pricing, trends, and predictions"""
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from igny8_core.business.billing.models import CreditUsageLog
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from igny8_core.auth.models import Account
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from decimal import Decimal
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# Date filter setup
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days_filter = request.GET.get('days', '30')
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try:
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days = int(days_filter)
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except ValueError:
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days = 30
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start_date = timezone.now() - timedelta(days=days)
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# Base queryset - filter for records with cost data
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logs = CreditUsageLog.objects.filter(
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created_at__gte=start_date,
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cost_usd__isnull=False
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)
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# Overall cost metrics
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total_cost = logs.aggregate(total=Sum('cost_usd'))['total'] or Decimal('0.00')
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total_calls = logs.count()
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avg_cost_per_call = logs.aggregate(avg=Avg('cost_usd'))['avg'] or Decimal('0.00')
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total_tokens_input = logs.aggregate(total=Sum('tokens_input'))['total'] or 0
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total_tokens_output = logs.aggregate(total=Sum('tokens_output'))['total'] or 0
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total_tokens = total_tokens_input + total_tokens_output
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# Cost by model with efficiency metrics
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cost_by_model = logs.values('model_used').annotate(
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total_cost=Sum('cost_usd'),
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call_count=Count('id'),
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avg_cost=Avg('cost_usd'),
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total_tokens_input=Sum('tokens_input'),
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total_tokens_output=Sum('tokens_output')
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).order_by('-total_cost')
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# Add cost efficiency (cost per 1K tokens) for each model
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for model in cost_by_model:
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model['total_tokens'] = (model['total_tokens_input'] or 0) + (model['total_tokens_output'] or 0)
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model['avg_tokens'] = model['total_tokens'] / model['call_count'] if model['call_count'] > 0 else 0
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model['model'] = model['model_used'] # Add alias for template
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if model['total_tokens'] and model['total_tokens'] > 0:
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model['cost_per_1k_tokens'] = float(model['total_cost']) / (model['total_tokens'] / 1000)
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else:
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model['cost_per_1k_tokens'] = 0
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# Cost by account (top spenders)
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cost_by_account = logs.values('account__name', 'account_id').annotate(
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total_cost=Sum('cost_usd'),
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call_count=Count('id'),
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total_tokens_input=Sum('tokens_input'),
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total_tokens_output=Sum('tokens_output'),
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avg_cost=Avg('cost_usd')
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).order_by('-total_cost')[:15]
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# Add total_tokens to each account
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for account in cost_by_account:
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account['total_tokens'] = (account['total_tokens_input'] or 0) + (account['total_tokens_output'] or 0)
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# Cost by function/operation
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cost_by_function = logs.values('operation_type').annotate(
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total_cost=Sum('cost_usd'),
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call_count=Count('id'),
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avg_cost=Avg('cost_usd'),
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total_tokens_input=Sum('tokens_input'),
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total_tokens_output=Sum('tokens_output')
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).order_by('-total_cost')[:10]
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# Add total_tokens and function alias
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for func in cost_by_function:
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func['total_tokens'] = (func['total_tokens_input'] or 0) + (func['total_tokens_output'] or 0)
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func['function'] = func['operation_type'] # Add alias for template
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# Daily cost trends (time series)
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daily_cost_data = []
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daily_cost_labels = []
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daily_call_data = []
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for i in range(days):
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day = timezone.now().date() - timedelta(days=days-i-1)
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day_logs = logs.filter(created_at__date=day)
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day_cost = day_logs.aggregate(total=Sum('cost_usd'))['total'] or Decimal('0.00')
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day_calls = day_logs.count()
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daily_cost_labels.append(day.strftime('%m/%d'))
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daily_cost_data.append(float(day_cost))
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daily_call_data.append(day_calls)
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# Cost prediction (simple linear extrapolation)
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if len(daily_cost_data) > 7:
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recent_avg_daily = sum(daily_cost_data[-7:]) / 7
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projected_monthly = recent_avg_daily * 30
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else:
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projected_monthly = 0
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# Failed requests cost (CreditUsageLog doesn't track errors, so no failed cost)
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failed_cost = Decimal('0.00')
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# Cost anomalies (calls costing > 3x average)
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if avg_cost_per_call > 0:
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anomaly_threshold = float(avg_cost_per_call) * 3
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anomalies = logs.filter(cost_usd__gt=anomaly_threshold).values(
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'model_used', 'operation_type', 'account__name', 'cost_usd', 'tokens_input', 'tokens_output', 'created_at'
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).order_by('-cost_usd')[:10]
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# Add aliases and calculate total tokens for each anomaly
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for anomaly in anomalies:
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anomaly['model'] = anomaly['model_used']
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anomaly['function'] = anomaly['operation_type']
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anomaly['cost'] = anomaly['cost_usd']
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anomaly['tokens'] = (anomaly['tokens_input'] or 0) + (anomaly['tokens_output'] or 0)
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else:
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anomalies = []
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# Model comparison matrix
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model_comparison = []
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for model_data in cost_by_model:
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model_name = model_data['model']
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model_comparison.append({
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'model': model_name,
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'total_cost': float(model_data['total_cost']),
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'calls': model_data['call_count'],
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'avg_cost': float(model_data['avg_cost']),
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'total_tokens': model_data['total_tokens'],
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'cost_per_1k': model_data['cost_per_1k_tokens'],
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})
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# Cost distribution percentages
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if total_cost > 0:
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for item in cost_by_model:
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item['cost_percentage'] = float((item['total_cost'] / total_cost) * 100)
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# Peak cost hours
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hourly_cost = logs.extra(
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select={'hour': "EXTRACT(hour FROM created_at)"}
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).values('hour').annotate(
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total_cost=Sum('cost_usd'),
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call_count=Count('id')
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).order_by('hour')
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# Cost efficiency score (CreditUsageLog doesn't track errors, assume all successful)
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successful_cost = total_cost
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efficiency_score = 100.0
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context = {
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'title': 'AI Cost Analysis',
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'days_filter': days,
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'total_cost': float(total_cost),
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'total_calls': total_calls,
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'avg_cost_per_call': float(avg_cost_per_call),
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'total_tokens': int(total_tokens),
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'cost_by_model': list(cost_by_model),
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'cost_by_account': list(cost_by_account),
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'cost_by_function': list(cost_by_function),
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'daily_cost_labels': json.dumps(daily_cost_labels),
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'daily_cost_data': json.dumps(daily_cost_data),
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'daily_call_data': json.dumps(daily_call_data),
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'projected_monthly': round(projected_monthly, 2),
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'failed_cost': float(failed_cost),
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'wasted_percentage': float((failed_cost / total_cost * 100) if total_cost > 0 else 0),
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'anomalies': list(anomalies),
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'model_comparison': model_comparison,
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'hourly_cost': list(hourly_cost),
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'efficiency_score': round(efficiency_score, 2),
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'successful_cost': float(successful_cost),
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'current_app': '_reports', # For active menu state
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}
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# Merge with admin context
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from igny8_core.admin.site import admin_site
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admin_context = admin_site.each_context(request)
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context.update(admin_context)
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return render(request, 'admin/reports/ai_cost_analysis.html', context)
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