Phase 0: Update credit costs and CreditService, add credit checks to AI Engine
- Updated CREDIT_COSTS to match Phase 0 spec (flat structure) - Added get_credit_cost() method to CreditService - Updated check_credits() to accept operation_type and amount - Added deduct_credits_for_operation() convenience method - Updated AI Engine to check credits BEFORE AI call - Updated AI Engine to deduct credits AFTER successful execution - Added helper methods for operation type mapping and amount calculation
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IGNY8 VPS (Salman)
@@ -192,6 +192,31 @@ class AIEngine:
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self.step_tracker.add_request_step("PREP", "success", prep_message)
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self.tracker.update("PREP", 25, prep_message, meta=self.step_tracker.get_meta())
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# Phase 2.5: CREDIT CHECK - Check credits before AI call (25%)
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if self.account:
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try:
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from igny8_core.modules.billing.services import CreditService
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from igny8_core.modules.billing.exceptions import InsufficientCreditsError
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# Map function name to operation type
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operation_type = self._get_operation_type(function_name)
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# Calculate estimated cost
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estimated_amount = self._get_estimated_amount(function_name, data, payload)
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# Check credits BEFORE AI call
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CreditService.check_credits(self.account, operation_type, estimated_amount)
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logger.info(f"[AIEngine] Credit check passed: {operation_type}, estimated amount: {estimated_amount}")
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except InsufficientCreditsError as e:
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error_msg = str(e)
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error_type = 'InsufficientCreditsError'
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logger.error(f"[AIEngine] {error_msg}")
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return self._handle_error(error_msg, fn, error_type=error_type)
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except Exception as e:
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logger.warning(f"[AIEngine] Failed to check credits: {e}", exc_info=True)
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# Don't fail the operation if credit check fails (for backward compatibility)
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# Phase 3: AI_CALL - Provider API Call (25-70%)
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# Validate account exists before proceeding
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if not self.account:
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@@ -325,37 +350,45 @@ class AIEngine:
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# Store save_msg for use in DONE phase
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final_save_msg = save_msg
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# Track credit usage after successful save
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# Phase 5.5: DEDUCT CREDITS - Deduct credits after successful save
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if self.account and raw_response:
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try:
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from igny8_core.modules.billing.services import CreditService
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from igny8_core.modules.billing.models import CreditUsageLog
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from igny8_core.modules.billing.exceptions import InsufficientCreditsError
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# Calculate credits used (based on tokens or fixed cost)
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credits_used = self._calculate_credits_for_clustering(
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keyword_count=len(data.get('keywords', [])) if isinstance(data, dict) else len(data) if isinstance(data, list) else 1,
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tokens=raw_response.get('total_tokens', 0),
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cost=raw_response.get('cost', 0)
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)
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# Map function name to operation type
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operation_type = self._get_operation_type(function_name)
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# Log credit usage (don't deduct from account.credits, just log)
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CreditUsageLog.objects.create(
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# Calculate actual amount based on results
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actual_amount = self._get_actual_amount(function_name, save_result, parsed, data)
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# Deduct credits using the new convenience method
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CreditService.deduct_credits_for_operation(
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account=self.account,
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operation_type='clustering',
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credits_used=credits_used,
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operation_type=operation_type,
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amount=actual_amount,
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cost_usd=raw_response.get('cost'),
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model_used=raw_response.get('model', ''),
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tokens_input=raw_response.get('tokens_input', 0),
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tokens_output=raw_response.get('tokens_output', 0),
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related_object_type='cluster',
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related_object_type=self._get_related_object_type(function_name),
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related_object_id=save_result.get('id') or save_result.get('cluster_id') or save_result.get('task_id'),
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metadata={
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'function_name': function_name,
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'clusters_created': clusters_created,
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'keywords_updated': keywords_updated,
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'function_name': function_name
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'count': count,
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**save_result
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}
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)
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logger.info(f"[AIEngine] Credits deducted: {operation_type}, amount: {actual_amount}")
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except InsufficientCreditsError as e:
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# This shouldn't happen since we checked before, but log it
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logger.error(f"[AIEngine] Insufficient credits during deduction: {e}")
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except Exception as e:
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logger.warning(f"Failed to log credit usage: {e}", exc_info=True)
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logger.warning(f"[AIEngine] Failed to deduct credits: {e}", exc_info=True)
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# Don't fail the operation if credit deduction fails (for backward compatibility)
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# Phase 6: DONE - Finalization (98-100%)
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success_msg = f"Task completed: {final_save_msg}" if 'final_save_msg' in locals() else "Task completed successfully"
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@@ -453,18 +486,74 @@ class AIEngine:
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# Don't fail the task if logging fails
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logger.warning(f"Failed to log to database: {e}")
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def _calculate_credits_for_clustering(self, keyword_count, tokens, cost):
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"""Calculate credits used for clustering operation"""
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# Use plan's cost per request if available, otherwise calculate from tokens
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if self.account and hasattr(self.account, 'plan') and self.account.plan:
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plan = self.account.plan
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# Check if plan has ai_cost_per_request config
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if hasattr(plan, 'ai_cost_per_request') and plan.ai_cost_per_request:
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cluster_cost = plan.ai_cost_per_request.get('cluster', 0)
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if cluster_cost:
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return int(cluster_cost)
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# Fallback: 1 credit per 30 keywords (minimum 1)
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credits = max(1, int(keyword_count / 30))
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return credits
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def _get_operation_type(self, function_name):
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"""Map function name to operation type for credit system"""
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mapping = {
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'auto_cluster': 'clustering',
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'generate_ideas': 'idea_generation',
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'generate_content': 'content_generation',
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'generate_image_prompts': 'image_prompt_extraction',
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'generate_images': 'image_generation',
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}
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return mapping.get(function_name, function_name)
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def _get_estimated_amount(self, function_name, data, payload):
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"""Get estimated amount for credit calculation (before operation)"""
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if function_name == 'generate_content':
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# Estimate word count from task or default
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if isinstance(data, dict):
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return data.get('estimated_word_count', 1000)
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return 1000 # Default estimate
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elif function_name == 'generate_images':
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# Count images to generate
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if isinstance(payload, dict):
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image_ids = payload.get('image_ids', [])
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return len(image_ids) if image_ids else 1
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return 1
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elif function_name == 'generate_ideas':
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# Count clusters
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if isinstance(data, dict) and 'cluster_data' in data:
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return len(data['cluster_data'])
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return 1
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# For fixed cost operations (clustering, image_prompt_extraction), return None
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return None
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def _get_actual_amount(self, function_name, save_result, parsed, data):
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"""Get actual amount for credit calculation (after operation)"""
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if function_name == 'generate_content':
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# Get actual word count from saved content
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if isinstance(save_result, dict):
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word_count = save_result.get('word_count')
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if word_count:
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return word_count
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# Fallback: estimate from parsed content
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if isinstance(parsed, dict) and 'content' in parsed:
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content = parsed['content']
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return len(content.split()) if isinstance(content, str) else 1000
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return 1000
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elif function_name == 'generate_images':
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# Count successfully generated images
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count = save_result.get('count', 0)
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if count > 0:
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return count
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return 1
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elif function_name == 'generate_ideas':
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# Count ideas generated
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count = save_result.get('count', 0)
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if count > 0:
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return count
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return 1
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# For fixed cost operations, return None
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return None
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def _get_related_object_type(self, function_name):
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"""Get related object type for credit logging"""
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mapping = {
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'auto_cluster': 'cluster',
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'generate_ideas': 'content_idea',
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'generate_content': 'content',
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'generate_image_prompts': 'image',
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'generate_images': 'image',
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}
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return mapping.get(function_name, 'unknown')
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