8908c11c86
- Added error type handling in AIEngine for better error categorization during model configuration and execution. - Updated _handle_error method to accept and log error types. - Improved ResourceDebugOverlay to silently ignore 404 responses from the metrics endpoint, preventing unnecessary logging and retries. - Refactored authStore to utilize fetchAPI for automatic token handling and improved error logging without throwing exceptions.
471 lines
23 KiB
Python
471 lines
23 KiB
Python
"""
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AI Engine - Central orchestrator for all AI functions
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"""
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import logging
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from typing import Dict, Any, Optional
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from igny8_core.ai.base import BaseAIFunction
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from igny8_core.ai.tracker import StepTracker, ProgressTracker, CostTracker
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from igny8_core.ai.ai_core import AICore
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from igny8_core.ai.settings import get_model_config
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logger = logging.getLogger(__name__)
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class AIEngine:
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"""
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Central orchestrator for all AI functions.
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Manages lifecycle, progress, logging, retries, cost tracking.
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"""
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def __init__(self, celery_task=None, account=None):
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self.task = celery_task
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self.account = account
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self.tracker = ProgressTracker(celery_task)
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self.step_tracker = StepTracker('ai_engine') # For Celery progress callbacks
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self.cost_tracker = CostTracker()
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def _get_input_description(self, function_name: str, payload: dict, count: int) -> str:
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"""Get user-friendly input description"""
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if function_name == 'auto_cluster':
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return f"{count} keyword{'s' if count != 1 else ''}"
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elif function_name == 'generate_ideas':
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return f"{count} cluster{'s' if count != 1 else ''}"
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elif function_name == 'generate_content':
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return f"{count} task{'s' if count != 1 else ''}"
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elif function_name == 'generate_images':
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return f"{count} task{'s' if count != 1 else ''}"
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return f"{count} item{'s' if count != 1 else ''}"
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def _build_validation_message(self, function_name: str, payload: dict, count: int, input_description: str) -> str:
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"""Build validation message with item names for better UX"""
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if function_name == 'auto_cluster' and count > 0:
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try:
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from igny8_core.modules.planner.models import Keywords
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ids = payload.get('ids', [])
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keywords = Keywords.objects.filter(id__in=ids, account=self.account).values_list('keyword', flat=True)[:3]
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keyword_list = list(keywords)
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if len(keyword_list) > 0:
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remaining = count - len(keyword_list)
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if remaining > 0:
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keywords_text = ', '.join(keyword_list)
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return f"Validating {keywords_text} and {remaining} more keyword{'s' if remaining != 1 else ''}"
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else:
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keywords_text = ', '.join(keyword_list)
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return f"Validating {keywords_text}"
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except Exception as e:
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logger.warning(f"Failed to load keyword names for validation message: {e}")
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# Fallback to simple count message
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return f"Validating {input_description}"
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def _get_prep_message(self, function_name: str, count: int, data: Any) -> str:
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"""Get user-friendly prep message"""
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if function_name == 'auto_cluster':
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return f"Loading {count} keyword{'s' if count != 1 else ''}"
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elif function_name == 'generate_ideas':
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return f"Loading {count} cluster{'s' if count != 1 else ''}"
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elif function_name == 'generate_content':
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return f"Preparing {count} content idea{'s' if count != 1 else ''}"
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elif function_name == 'generate_images':
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return f"Extracting image prompts from {count} task{'s' if count != 1 else ''}"
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elif function_name == 'generate_image_prompts':
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# Extract max_images from data if available
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if isinstance(data, list) and len(data) > 0:
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max_images = data[0].get('max_images', 2)
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total_images = 1 + max_images # 1 featured + max_images in-article
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return f"Mapping Content for {total_images} Image Prompts"
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elif isinstance(data, dict) and 'max_images' in data:
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max_images = data.get('max_images', 2)
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total_images = 1 + max_images
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return f"Mapping Content for {total_images} Image Prompts"
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return f"Mapping Content for Image Prompts"
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return f"Preparing {count} item{'s' if count != 1 else ''}"
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def _get_ai_call_message(self, function_name: str, count: int) -> str:
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"""Get user-friendly AI call message"""
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if function_name == 'auto_cluster':
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return f"Grouping {count} keyword{'s' if count != 1 else ''} into clusters"
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elif function_name == 'generate_ideas':
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return f"Generating content ideas for {count} cluster{'s' if count != 1 else ''}"
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elif function_name == 'generate_content':
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return f"Writing article{'s' if count != 1 else ''} with AI"
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elif function_name == 'generate_images':
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return f"Creating image{'s' if count != 1 else ''} with AI"
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return f"Processing with AI"
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def _get_parse_message(self, function_name: str) -> str:
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"""Get user-friendly parse message"""
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if function_name == 'auto_cluster':
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return "Organizing clusters"
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elif function_name == 'generate_ideas':
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return "Structuring outlines"
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elif function_name == 'generate_content':
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return "Formatting content"
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elif function_name == 'generate_images':
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return "Processing images"
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return "Processing results"
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def _get_parse_message_with_count(self, function_name: str, count: int) -> str:
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"""Get user-friendly parse message with count"""
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if function_name == 'auto_cluster':
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return f"{count} cluster{'s' if count != 1 else ''} created"
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elif function_name == 'generate_ideas':
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return f"{count} idea{'s' if count != 1 else ''} created"
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elif function_name == 'generate_content':
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return f"{count} article{'s' if count != 1 else ''} created"
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elif function_name == 'generate_images':
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return f"{count} image{'s' if count != 1 else ''} created"
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elif function_name == 'generate_image_prompts':
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# Count is total prompts, in-article is count - 1 (subtract featured)
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in_article_count = max(0, count - 1)
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if in_article_count > 0:
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return f"Writing {in_article_count} In‑article Image Prompts"
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return "Writing In‑article Image Prompts"
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return f"{count} item{'s' if count != 1 else ''} processed"
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def _get_save_message(self, function_name: str, count: int) -> str:
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"""Get user-friendly save message"""
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if function_name == 'auto_cluster':
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return f"Saving {count} cluster{'s' if count != 1 else ''}"
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elif function_name == 'generate_ideas':
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return f"Saving {count} idea{'s' if count != 1 else ''}"
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elif function_name == 'generate_content':
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return f"Saving {count} article{'s' if count != 1 else ''}"
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elif function_name == 'generate_images':
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return f"Saving {count} image{'s' if count != 1 else ''}"
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elif function_name == 'generate_image_prompts':
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# Count is total prompts created
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return f"Assigning {count} Prompts to Dedicated Slots"
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return f"Saving {count} item{'s' if count != 1 else ''}"
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def execute(self, fn: BaseAIFunction, payload: dict) -> dict:
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"""
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Unified execution pipeline for all AI functions.
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Phases with improved percentage mapping:
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- INIT (0-10%): Validation & preparation
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- PREP (10-25%): Data loading & prompt building
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- AI_CALL (25-70%): API call to provider (longest phase)
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- PARSE (70-85%): Response parsing
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- SAVE (85-98%): Database operations
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- DONE (98-100%): Finalization
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"""
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function_name = fn.get_name()
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self.step_tracker.function_name = function_name
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try:
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# Phase 1: INIT - Validation & Setup (0-10%)
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# Extract input data for user-friendly messages
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ids = payload.get('ids', [])
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input_count = len(ids) if ids else 0
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input_description = self._get_input_description(function_name, payload, input_count)
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validated = fn.validate(payload, self.account)
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if not validated['valid']:
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return self._handle_error(validated['error'], fn)
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# Build validation message with keyword names for auto_cluster
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validation_message = self._build_validation_message(function_name, payload, input_count, input_description)
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self.step_tracker.add_request_step("INIT", "success", validation_message)
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self.tracker.update("INIT", 10, validation_message, meta=self.step_tracker.get_meta())
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# Phase 2: PREP - Data Loading & Prompt Building (10-25%)
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data = fn.prepare(payload, self.account)
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if isinstance(data, (list, tuple)):
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data_count = len(data)
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elif isinstance(data, dict):
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# Check for cluster_data (for generate_ideas) or keywords (for auto_cluster)
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if 'cluster_data' in data:
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data_count = len(data['cluster_data'])
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elif 'keywords' in data:
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data_count = len(data['keywords'])
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else:
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data_count = data.get('count', input_count)
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else:
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data_count = input_count
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prep_message = self._get_prep_message(function_name, data_count, data)
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prompt = fn.build_prompt(data, self.account)
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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 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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error_msg = "Account is required for AI function execution"
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logger.error(f"[AIEngine] {error_msg}")
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return self._handle_error(error_msg, fn)
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ai_core = AICore(account=self.account)
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function_name = fn.get_name()
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# Generate function_id for tracking (ai-{function_name}-01)
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# Normalize underscores to hyphens to match frontend tracking IDs
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function_id_base = function_name.replace('_', '-')
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function_id = f"ai-{function_id_base}-01-desktop"
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# Get model config from settings (requires account)
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# This will raise ValueError if IntegrationSettings not configured
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try:
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model_config = get_model_config(function_name, account=self.account)
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model = model_config.get('model')
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except ValueError as e:
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# IntegrationSettings not configured or model missing
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error_msg = str(e)
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error_type = 'ConfigurationError'
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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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# Other unexpected errors
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error_msg = f"Failed to get model configuration: {str(e)}"
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error_type = type(e).__name__
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logger.error(f"[AIEngine] {error_msg}", exc_info=True)
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return self._handle_error(error_msg, fn, error_type=error_type)
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# Debug logging: Show model configuration (console only, not in step tracker)
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logger.info(f"[AIEngine] Model Configuration for {function_name}:")
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logger.info(f" - Model from get_model_config: {model}")
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logger.info(f" - Full model_config: {model_config}")
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# Track AI call start with user-friendly message
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ai_call_message = self._get_ai_call_message(function_name, data_count)
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self.step_tracker.add_response_step("AI_CALL", "success", ai_call_message)
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self.tracker.update("AI_CALL", 50, ai_call_message, meta=self.step_tracker.get_meta())
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try:
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# Use centralized run_ai_request()
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raw_response = ai_core.run_ai_request(
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prompt=prompt,
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model=model,
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max_tokens=model_config.get('max_tokens'),
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temperature=model_config.get('temperature'),
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response_format=model_config.get('response_format'),
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function_name=function_name,
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function_id=function_id # Pass function_id for tracking
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)
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except Exception as e:
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error_msg = f"AI call failed: {str(e)}"
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logger.error(f"Exception during AI call: {error_msg}", exc_info=True)
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return self._handle_error(error_msg, fn)
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if raw_response.get('error'):
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error_msg = raw_response.get('error', 'Unknown AI error')
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logger.error(f"AI call returned error: {error_msg}")
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return self._handle_error(error_msg, fn)
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if not raw_response.get('content'):
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error_msg = "AI call returned no content"
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logger.error(error_msg)
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return self._handle_error(error_msg, fn)
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# Track cost
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self.cost_tracker.record(
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function_name=function_name,
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cost=raw_response.get('cost', 0),
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tokens=raw_response.get('total_tokens', 0),
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model=raw_response.get('model')
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)
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# Update AI_CALL step with results
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self.step_tracker.response_steps[-1] = {
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**self.step_tracker.response_steps[-1],
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'message': f"Received {raw_response.get('total_tokens', 0)} tokens, Cost: ${raw_response.get('cost', 0):.6f}",
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'duration': raw_response.get('duration')
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}
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self.tracker.update("AI_CALL", 70, f"AI response received ({raw_response.get('total_tokens', 0)} tokens)", meta=self.step_tracker.get_meta())
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# Phase 4: PARSE - Response Parsing (70-85%)
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try:
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parse_message = self._get_parse_message(function_name)
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response_content = raw_response.get('content', '')
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parsed = fn.parse_response(response_content, self.step_tracker)
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if isinstance(parsed, (list, tuple)):
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parsed_count = len(parsed)
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elif isinstance(parsed, dict):
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# Check if it's a content dict (has 'content' field) or a result dict (has 'count')
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if 'content' in parsed:
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parsed_count = 1 # Single content item
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else:
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parsed_count = parsed.get('count', 1)
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else:
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parsed_count = 1
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# Update parse message with count for better UX
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parse_message = self._get_parse_message_with_count(function_name, parsed_count)
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self.step_tracker.add_response_step("PARSE", "success", parse_message)
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self.tracker.update("PARSE", 85, parse_message, meta=self.step_tracker.get_meta())
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except Exception as parse_error:
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error_msg = f"Failed to parse AI response: {str(parse_error)}"
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logger.error(f"AIEngine: {error_msg}", exc_info=True)
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logger.error(f"AIEngine: Response content was: {response_content[:500] if response_content else 'None'}...")
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return self._handle_error(error_msg, fn)
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# Phase 5: SAVE - Database Operations (85-98%)
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save_result = fn.save_output(parsed, data, self.account, self.tracker, step_tracker=self.step_tracker)
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clusters_created = save_result.get('clusters_created', 0)
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keywords_updated = save_result.get('keywords_updated', 0)
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count = save_result.get('count', 0)
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# Use user-friendly save message based on function type
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if clusters_created:
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save_msg = f"Saving {clusters_created} cluster{'s' if clusters_created != 1 else ''}"
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elif count:
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save_msg = self._get_save_message(function_name, count)
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else:
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save_msg = self._get_save_message(function_name, data_count)
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self.step_tracker.add_request_step("SAVE", "success", save_msg)
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self.tracker.update("SAVE", 98, save_msg, meta=self.step_tracker.get_meta())
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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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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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# 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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# Log credit usage (don't deduct from account.credits, just log)
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CreditUsageLog.objects.create(
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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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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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metadata={
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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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}
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)
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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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# 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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self.step_tracker.add_request_step("DONE", "success", "Task completed successfully")
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self.tracker.update("DONE", 100, "Task complete!", meta=self.step_tracker.get_meta())
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# Log to database
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self._log_to_database(fn, payload, parsed, save_result)
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return {
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'success': True,
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**save_result,
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'request_steps': self.step_tracker.request_steps,
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'response_steps': self.step_tracker.response_steps,
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'cost': self.cost_tracker.get_total(),
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'tokens': self.cost_tracker.get_total_tokens()
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}
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except Exception as e:
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error_msg = str(e)
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error_type = type(e).__name__
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logger.error(f"Error in AIEngine.execute for {function_name}: {error_msg}", exc_info=True)
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return self._handle_error(error_msg, fn, exc_info=True, error_type=error_type)
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def _handle_error(self, error: str, fn: BaseAIFunction = None, exc_info=False, error_type: str = None):
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"""Centralized error handling"""
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function_name = fn.get_name() if fn else 'unknown'
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# Determine error type
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if error_type:
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final_error_type = error_type
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elif isinstance(error, Exception):
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final_error_type = type(error).__name__
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else:
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final_error_type = 'Error'
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self.step_tracker.add_request_step("Error", "error", error, error=error)
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error_meta = {
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'error': error,
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'error_type': final_error_type,
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**self.step_tracker.get_meta()
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}
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self.tracker.error(error, meta=error_meta)
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if exc_info:
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logger.error(f"Error in {function_name}: {error}", exc_info=True)
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else:
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logger.error(f"Error in {function_name}: {error}")
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self._log_to_database(fn, None, None, None, error=error)
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return {
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'success': False,
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'error': error,
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'error_type': final_error_type,
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'request_steps': self.step_tracker.request_steps,
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'response_steps': self.step_tracker.response_steps
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}
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def _log_to_database(
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self,
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fn: BaseAIFunction = None,
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payload: dict = None,
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parsed: Any = None,
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save_result: dict = None,
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error: str = None
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):
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"""Log to unified ai_task_logs table"""
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try:
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from igny8_core.ai.models import AITaskLog
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|
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# Only log if account exists (AITaskLog requires account)
|
||
if not self.account:
|
||
logger.warning("Cannot log AI task - no account available")
|
||
return
|
||
|
||
AITaskLog.objects.create(
|
||
task_id=self.task.request.id if self.task else None,
|
||
function_name=fn.get_name() if fn else None,
|
||
account=self.account,
|
||
phase=self.tracker.current_phase,
|
||
message=self.tracker.current_message,
|
||
status='error' if error else 'success',
|
||
duration=self.tracker.get_duration(),
|
||
cost=self.cost_tracker.get_total(),
|
||
tokens=self.cost_tracker.get_total_tokens(),
|
||
request_steps=self.step_tracker.request_steps,
|
||
response_steps=self.step_tracker.response_steps,
|
||
error=error,
|
||
payload=payload,
|
||
result=save_result
|
||
)
|
||
except Exception as e:
|
||
# Don't fail the task if logging fails
|
||
logger.warning(f"Failed to log to database: {e}")
|
||
|
||
def _calculate_credits_for_clustering(self, keyword_count, tokens, cost):
|
||
"""Calculate credits used for clustering operation"""
|
||
# Use plan's cost per request if available, otherwise calculate from tokens
|
||
if self.account and hasattr(self.account, 'plan') and self.account.plan:
|
||
plan = self.account.plan
|
||
# Check if plan has ai_cost_per_request config
|
||
if hasattr(plan, 'ai_cost_per_request') and plan.ai_cost_per_request:
|
||
cluster_cost = plan.ai_cost_per_request.get('cluster', 0)
|
||
if cluster_cost:
|
||
return int(cluster_cost)
|
||
|
||
# Fallback: 1 credit per 30 keywords (minimum 1)
|
||
credits = max(1, int(keyword_count / 30))
|
||
return credits
|
||
|