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backend/igny8_core/ai/templates/__init__.py
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backend/igny8_core/ai/templates/__init__.py
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"""
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AI Templates
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Template files for reference when creating new AI functions.
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"""
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281
backend/igny8_core/ai/templates/ai_functions_template.py
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backend/igny8_core/ai/templates/ai_functions_template.py
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"""
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AI Functions Template
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Template/Reference file showing the common pattern used by auto_cluster, generate_ideas, and generate_content.
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This is a reference template - do not modify existing functions, use this as a guide for new functions.
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"""
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import logging
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from typing import Dict, List, Any, Optional
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from igny8_core.auth.models import Account
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from igny8_core.ai.helpers.base import BaseAIFunction
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from igny8_core.ai.helpers.ai_core import AICore
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from igny8_core.ai.helpers.tracker import ConsoleStepTracker
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from igny8_core.ai.helpers.settings import get_model_config
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logger = logging.getLogger(__name__)
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def ai_function_core_template(
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function_class: BaseAIFunction,
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function_name: str,
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payload: Dict[str, Any],
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account_id: Optional[int] = None,
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progress_callback: Optional[callable] = None,
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**kwargs
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) -> Dict[str, Any]:
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"""
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Template for AI function core logic (legacy function signature pattern).
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This template shows the common pattern used by:
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- generate_ideas_core
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- generate_content_core
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- auto_cluster (via engine, but similar pattern)
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Usage Example:
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def my_function_core(item_id: int, account_id: int = None, progress_callback=None):
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fn = MyFunctionClass()
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payload = {'ids': [item_id]}
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return ai_function_core_template(
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function_class=fn,
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function_name='my_function',
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payload=payload,
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account_id=account_id,
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progress_callback=progress_callback
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)
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Args:
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function_class: Instance of the AI function class (e.g., GenerateIdeasFunction())
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function_name: Function name for config/tracking (e.g., 'generate_ideas')
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payload: Payload dict with 'ids' and other function-specific data
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account_id: Optional account ID for account isolation
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progress_callback: Optional progress callback for Celery tasks
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**kwargs: Additional function-specific parameters
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Returns:
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Dict with 'success', function-specific result fields, 'message', etc.
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"""
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# Initialize tracker
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tracker = ConsoleStepTracker(function_name)
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tracker.init("Task started")
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try:
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# Load account
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account = None
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if account_id:
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account = Account.objects.get(id=account_id)
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tracker.prep("Loading account data...")
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# Store account on function instance
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function_class.account = account
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# Validate
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tracker.prep("Validating input...")
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validated = function_class.validate(payload, account)
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if not validated['valid']:
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tracker.error('ValidationError', validated['error'])
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return {'success': False, 'error': validated['error']}
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# Prepare data
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tracker.prep("Preparing data...")
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data = function_class.prepare(payload, account)
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# Build prompt
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tracker.prep("Building prompt...")
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prompt = function_class.build_prompt(data, account)
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# Get model config from settings
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model_config = get_model_config(function_name)
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# Generate function_id for tracking (ai_ prefix with function name)
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function_id = f"ai_{function_name}"
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# Call AI using centralized request handler
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ai_core = AICore(account=account)
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result = ai_core.run_ai_request(
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prompt=prompt,
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model=model_config.get('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,
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tracker=tracker
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)
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if result.get('error'):
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return {'success': False, 'error': result['error']}
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# Parse response
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tracker.parse("Parsing AI response...")
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parsed = function_class.parse_response(result['content'], tracker)
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if not parsed:
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tracker.error('ParseError', 'No data parsed from AI response')
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return {'success': False, 'error': 'No data parsed from AI response'}
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# Handle list responses
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if isinstance(parsed, list):
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parsed_count = len(parsed)
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tracker.parse(f"Parsed {parsed_count} item(s)")
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else:
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parsed_count = 1
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tracker.parse("Parsed response")
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# Save output
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tracker.save("Saving to database...")
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save_result = function_class.save_output(parsed, data, account, step_tracker=tracker)
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tracker.save(f"Saved {save_result.get('count', 0)} item(s)")
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# Build success message
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if isinstance(parsed, list) and len(parsed) > 0:
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first_item = parsed[0]
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item_name = first_item.get('title') or first_item.get('name') or 'item'
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tracker.done(f"Successfully created {item_name}")
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message = f"Successfully created {item_name}"
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else:
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tracker.done("Task completed successfully")
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message = "Task completed successfully"
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return {
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'success': True,
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**save_result,
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'message': message
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}
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except Exception as e:
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tracker.error('Exception', str(e), e)
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logger.error(f"Error in {function_name}_core: {str(e)}", exc_info=True)
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return {'success': False, 'error': str(e)}
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def ai_function_batch_template(
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function_class: BaseAIFunction,
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function_name: str,
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payload: Dict[str, Any],
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account_id: Optional[int] = None,
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progress_callback: Optional[callable] = None,
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**kwargs
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) -> Dict[str, Any]:
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"""
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Template for AI function batch processing (like generate_content_core).
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This template shows the pattern for functions that process multiple items in a loop.
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Usage Example:
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def my_batch_function_core(item_ids: List[int], account_id: int = None, progress_callback=None):
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fn = MyFunctionClass()
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payload = {'ids': item_ids}
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return ai_function_batch_template(
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function_class=fn,
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function_name='my_function',
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payload=payload,
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account_id=account_id,
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progress_callback=progress_callback
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)
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Args:
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function_class: Instance of the AI function class
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function_name: Function name for config/tracking
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payload: Payload dict with 'ids' list
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account_id: Optional account ID for account isolation
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progress_callback: Optional progress callback for Celery tasks
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**kwargs: Additional function-specific parameters
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Returns:
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Dict with 'success', 'count', 'tasks_updated', 'message', etc.
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"""
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tracker = ConsoleStepTracker(function_name)
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tracker.init("Task started")
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try:
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# Load account
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account = None
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if account_id:
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account = Account.objects.get(id=account_id)
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tracker.prep("Loading account data...")
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# Store account on function instance
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function_class.account = account
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# Validate
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tracker.prep("Validating input...")
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validated = function_class.validate(payload, account)
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if not validated['valid']:
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tracker.error('ValidationError', validated['error'])
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return {'success': False, 'error': validated['error']}
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# Prepare data (returns list of items)
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tracker.prep("Preparing data...")
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items = function_class.prepare(payload, account)
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if not isinstance(items, list):
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items = [items]
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total_items = len(items)
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processed_count = 0
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tracker.prep(f"Processing {total_items} item(s)...")
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# Get model config once (shared across all items)
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model_config = get_model_config(function_name)
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# Generate function_id for tracking (ai_ prefix with function name)
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function_id = f"ai_{function_name}"
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ai_core = AICore(account=account)
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# Process each item
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for idx, item in enumerate(items):
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try:
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# Build prompt for this item
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prompt = function_class.build_prompt(item if not isinstance(items, list) else [item], account)
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# Call AI
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result = ai_core.run_ai_request(
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prompt=prompt,
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model=model_config.get('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,
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tracker=tracker
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)
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if result.get('error'):
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logger.error(f"AI error for item {idx + 1}/{total_items}: {result['error']}")
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continue
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# Parse response
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parsed = function_class.parse_response(result['content'], tracker)
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if not parsed:
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logger.warning(f"No data parsed for item {idx + 1}/{total_items}")
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continue
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# Save output
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save_result = function_class.save_output(
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parsed,
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item if not isinstance(items, list) else [item],
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account,
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step_tracker=tracker
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)
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processed_count += save_result.get('count', 0) or save_result.get('tasks_updated', 0) or 0
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except Exception as e:
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logger.error(f"Error processing item {idx + 1}/{total_items}: {str(e)}", exc_info=True)
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continue
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tracker.done(f"Processed {processed_count} item(s) successfully")
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return {
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'success': True,
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'count': processed_count,
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'tasks_updated': processed_count,
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'message': f'Task completed: {processed_count} item(s) processed'
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}
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except Exception as e:
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tracker.error('Exception', str(e), e)
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logger.error(f"Error in {function_name}_core: {str(e)}", exc_info=True)
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return {'success': False, 'error': str(e)}
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80
backend/igny8_core/ai/templates/modals_template.py
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backend/igny8_core/ai/templates/modals_template.py
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"""
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Modal Configuration Templates for AI Functions
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Each function uses the same AIProgressModal component with different configs.
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"""
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# Modal configuration templates for each AI function
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MODAL_CONFIGS = {
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'auto_cluster': {
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'title': 'Auto Cluster Keywords',
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'function_id': 'ai_auto_cluster',
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'success_title': 'Clustering Complete!',
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'success_message_template': 'Successfully created {clusters_created} clusters and updated {keywords_updated} keywords.',
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'error_title': 'Clustering Failed',
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'error_message_template': 'An error occurred while clustering keywords. Please try again.',
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},
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'generate_ideas': {
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'title': 'Generating Ideas',
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'function_id': 'ai_generate_ideas',
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'success_title': 'Ideas Generated!',
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'success_message_template': 'Successfully generated {ideas_created} content idea(s).',
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'error_title': 'Idea Generation Failed',
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'error_message_template': 'An error occurred while generating ideas. Please try again.',
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},
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'generate_content': {
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'title': 'Generating Content',
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'function_id': 'ai_generate_content',
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'success_title': 'Content Generated!',
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'success_message_template': 'Successfully generated content for {tasks_updated} task(s).',
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'error_title': 'Content Generation Failed',
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'error_message_template': 'An error occurred while generating content. Please try again.',
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},
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}
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# Legacy function IDs (for backward compatibility)
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LEGACY_FUNCTION_IDS = {
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'generate_ideas': 'ai_generate_ideas',
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'generate_content': 'ai_generate_content',
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}
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def get_modal_config(function_name: str, is_legacy: bool = False) -> dict:
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"""
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Get modal configuration for an AI function.
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Args:
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function_name: Function name (e.g., 'auto_cluster', 'generate_ideas', 'generate_content')
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is_legacy: Whether this is a legacy function path
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Returns:
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Dict with modal configuration
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"""
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config = MODAL_CONFIGS.get(function_name, {}).copy()
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# Override function_id for legacy paths
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if is_legacy and function_name in LEGACY_FUNCTION_IDS:
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config['function_id'] = LEGACY_FUNCTION_IDS[function_name]
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return config
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def format_success_message(function_name: str, result: dict) -> str:
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"""
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Format success message based on function result.
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Args:
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function_name: Function name
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result: Result dict from function execution
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Returns:
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Formatted success message
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"""
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config = MODAL_CONFIGS.get(function_name, {})
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template = config.get('success_message_template', 'Task completed successfully.')
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try:
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return template.format(**result)
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except KeyError:
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# Fallback if template variables don't match
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return config.get('success_message_template', 'Task completed successfully.')
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