ai fucntiosn adn otehr atuoamtion fixes
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IGNY8 VPS (Salman)
@@ -432,7 +432,8 @@ class AIEngine:
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final_save_msg = save_msg
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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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logger.info(f"[AIEngine] Credit deduction check: account={self.account is not None}, raw_response={raw_response is not None}")
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if self.account and raw_response is not None:
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try:
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from igny8_core.business.billing.services.credit_service import CreditService
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from igny8_core.business.billing.exceptions import InsufficientCreditsError
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@@ -444,6 +445,12 @@ class AIEngine:
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tokens_input = raw_response.get('input_tokens', 0)
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tokens_output = raw_response.get('output_tokens', 0)
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logger.info(
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f"[AIEngine] Deducting credits: operation={operation_type}, "
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f"tokens_in={tokens_input}, tokens_out={tokens_output}, "
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f"model={raw_response.get('model', 'unknown')}"
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)
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# Extract site_id from save_result (could be from content, cluster, or task)
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site_id = save_result.get('site_id') or save_result.get('site')
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@@ -468,15 +475,17 @@ class AIEngine:
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)
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logger.info(
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f"[AIEngine] Credits deducted: {operation_type}, "
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f"tokens: {tokens_input + tokens_output} ({tokens_input} in, {tokens_output} out)"
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f"[AIEngine] Credits deducted successfully: {operation_type}, "
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f"total tokens: {tokens_input + tokens_output} ({tokens_input} in, {tokens_output} out)"
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)
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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"[AIEngine] Failed to deduct credits: {e}", exc_info=True)
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logger.error(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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else:
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logger.warning(f"[AIEngine] Skipping credit deduction: account={self.account is not None}, raw_response={raw_response is not None}")
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# Phase 6: DONE - Finalization (98-100%)
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done_msg = self._get_done_message(function_name, save_result)
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@@ -68,7 +68,22 @@ class AutoClusterFunction(BaseAIFunction):
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f"[AutoCluster] Validation passed: {min_validation['count']} keywords available (min: {min_validation['required']})"
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)
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# Removed plan limits check
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# Validate single sector - keywords must all belong to the same sector
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keywords = Keywords.objects.filter(id__in=ids)
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if account:
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keywords = keywords.filter(account=account)
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sector_ids = set(keywords.values_list('sector_id', flat=True))
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# Remove None values
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sector_ids.discard(None)
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if len(sector_ids) > 1:
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logger.warning(f"[AutoCluster] Validation failed: keywords span {len(sector_ids)} sectors")
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return {
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'valid': False,
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'error': f'Keywords must be from a single sector. Selected keywords span {len(sector_ids)} different sectors. Please filter by sector first.',
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'sector_count': len(sector_ids)
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}
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return {'valid': True}
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@@ -216,23 +231,23 @@ class AutoClusterFunction(BaseAIFunction):
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if not keywords:
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raise ValueError("No keywords available for saving")
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# Get context from first keyword (account/site/sector already validated at page level)
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# Get context from first keyword (account/site already validated at page level)
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first_keyword = keywords[0]
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account = account or first_keyword.account
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site = first_keyword.site
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# Get sector if needed
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from igny8_core.auth.models import Sector
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sector = first_keyword.sector
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if not sector and sector_id:
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try:
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sector = Sector.objects.get(id=sector_id)
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except Sector.DoesNotExist:
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sector = None
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if not account:
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raise ValueError("Account is required for cluster creation")
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# Build a lookup of keyword text -> keyword object for matching
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# Keywords may span multiple sectors, so don't filter by sector here
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keyword_by_text = {
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kw_obj.keyword.strip().lower(): kw_obj
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for kw_obj in keywords
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}
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logger.info(f"[save_output] Processing {len(parsed)} clusters for {len(keywords)} keywords")
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clusters_created = 0
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keywords_updated = 0
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@@ -253,74 +268,88 @@ class AutoClusterFunction(BaseAIFunction):
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cluster_keywords = cluster_data.get('keywords', [])
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if not cluster_name or not cluster_keywords:
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logger.warning(f"[save_output] Skipping cluster with empty name or keywords: {cluster_data}")
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continue
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# Get or create cluster
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if sector:
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cluster, created = Clusters.objects.get_or_create(
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name=cluster_name,
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# Match keywords from AI response to actual keyword objects
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matched_keyword_objects = []
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for kw_text in cluster_keywords:
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kw_normalized = kw_text.strip().lower()
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if kw_normalized in keyword_by_text:
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matched_keyword_objects.append(keyword_by_text[kw_normalized])
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if not matched_keyword_objects:
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logger.warning(f"[save_output] No keywords matched for cluster '{cluster_name}': {cluster_keywords}")
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continue
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# Determine sector for cluster from the matched keywords
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# Use the sector from the first matched keyword (all should ideally be same sector)
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cluster_sector = matched_keyword_objects[0].sector
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# Try to find existing cluster by name (case-insensitive) in same site/sector
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# This allows reusing clusters even if AI generates slightly different casing
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existing_cluster = None
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if cluster_sector:
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existing_cluster = Clusters.objects.filter(
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account=account,
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site=site,
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sector=sector,
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defaults={
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'description': cluster_data.get('description', ''),
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'status': 'new', # FIXED: Changed from 'active' to 'new'
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}
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)
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sector=cluster_sector,
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name__iexact=cluster_name,
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deleted_at__isnull=True # Exclude soft-deleted clusters
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).first()
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else:
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cluster, created = Clusters.objects.get_or_create(
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name=cluster_name,
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existing_cluster = Clusters.objects.filter(
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account=account,
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site=site,
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sector__isnull=True,
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defaults={
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'description': cluster_data.get('description', ''),
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'status': 'new', # FIXED: Changed from 'active' to 'new'
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'sector': None,
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}
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name__iexact=cluster_name,
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deleted_at__isnull=True
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).first()
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if existing_cluster:
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cluster = existing_cluster
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created = False
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logger.info(f"[save_output] Found existing cluster '{cluster.name}' (id={cluster.id})")
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else:
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# Create new cluster
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cluster = Clusters.objects.create(
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name=cluster_name,
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account=account,
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site=site,
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sector=cluster_sector,
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description=cluster_data.get('description', ''),
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status='new',
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)
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created = True
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if created:
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clusters_created += 1
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logger.info(f"[save_output] Created cluster '{cluster_name}' (id={cluster.id}) in sector {cluster_sector.id if cluster_sector else 'None'}")
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# Match and assign keywords (case-insensitive)
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cluster_keywords_normalized = {kw.strip().lower(): kw.strip() for kw in cluster_keywords}
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available_keywords_normalized = {
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kw_obj.keyword.strip().lower(): kw_obj
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for kw_obj in keywords
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}
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matched_keyword_objects = []
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for cluster_kw_normalized, cluster_kw_original in cluster_keywords_normalized.items():
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if cluster_kw_normalized in available_keywords_normalized:
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matched_keyword_objects.append(available_keywords_normalized[cluster_kw_normalized])
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# Update matched keywords
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if matched_keyword_objects:
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matched_ids = [kw.id for kw in matched_keyword_objects]
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keyword_filter = Keywords.objects.filter(
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id__in=matched_ids,
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account=account
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)
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if sector:
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keyword_filter = keyword_filter.filter(sector=sector)
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else:
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keyword_filter = keyword_filter.filter(sector__isnull=True)
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# FIXED: Ensure keywords status updates from 'new' to 'mapped'
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updated_count = keyword_filter.update(
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cluster=cluster,
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status='mapped' # Status changes from 'new' to 'mapped'
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)
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keywords_updated += updated_count
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# Update matched keywords - directly by their IDs, no sector filtering needed
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# since we already matched them from the input keywords list
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matched_ids = [kw.id for kw in matched_keyword_objects]
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updated_count = Keywords.objects.filter(
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id__in=matched_ids,
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account=account
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).update(
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cluster=cluster,
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status='mapped'
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)
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keywords_updated += updated_count
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logger.info(f"[save_output] Cluster '{cluster_name}': matched {len(matched_keyword_objects)} keywords, updated {updated_count}")
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# Recalculate cluster metrics
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# Recalculate cluster metrics for all clusters in this site
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from django.db.models import Sum, Case, When, F, IntegerField
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cluster_filter = Clusters.objects.filter(account=account)
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if sector:
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cluster_filter = cluster_filter.filter(sector=sector)
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else:
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cluster_filter = cluster_filter.filter(sector__isnull=True)
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# Get all cluster IDs that were created/updated in this batch
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updated_cluster_ids = set()
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for kw in keywords:
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if kw.cluster_id:
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updated_cluster_ids.add(kw.cluster_id)
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# Also include newly created clusters
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cluster_filter = Clusters.objects.filter(account=account, site=site)
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for cluster in cluster_filter:
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cluster.keywords_count = Keywords.objects.filter(cluster=cluster).count()
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