Step 2: Remove MODEL_CONFIG and update get_model_config() to use IntegrationSettings only
- Remove MODEL_CONFIG dict with hardcoded defaults - Update get_model_config() to require account parameter - Remove default_config fallback - Remove unused helper functions (get_model, get_max_tokens, get_temperature) - Fix generate_images.py to pass account to get_model_config() - Raise ValueError with clear messages when IntegrationSettings not configured
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IGNY8 VPS (Salman)
@@ -122,8 +122,10 @@ class GenerateImagesFunction(BaseAIFunction):
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}
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)
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# Get model config
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model_config = get_model_config('extract_image_prompts')
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# Get model config (requires account)
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if not account_obj:
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raise ValueError("Account is required for model configuration")
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model_config = get_model_config('extract_image_prompts', account=account_obj)
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# Call AI to extract prompts using centralized request handler
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result = ai_core.run_ai_request(
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@@ -1,46 +1,11 @@
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"""
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AI Settings - Centralized model configurations and limits
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Uses IntegrationSettings only - no hardcoded defaults or fallbacks.
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"""
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from typing import Dict, Any
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import logging
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# Model configurations for each AI function
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MODEL_CONFIG = {
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"auto_cluster": {
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"model": "gpt-4o-mini",
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"max_tokens": 3000,
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"temperature": 0.7,
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"response_format": {"type": "json_object"}, # Auto-enabled for JSON mode models
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},
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"generate_ideas": {
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"model": "gpt-4.1",
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"max_tokens": 4000,
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"temperature": 0.7,
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"response_format": {"type": "json_object"}, # JSON output
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},
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"generate_content": {
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"model": "gpt-4.1",
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"max_tokens": 8000,
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"temperature": 0.7,
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"response_format": {"type": "json_object"}, # JSON output
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},
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"generate_images": {
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"model": "dall-e-3",
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"size": "1024x1024",
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"provider": "openai",
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},
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"extract_image_prompts": {
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"model": "gpt-4o-mini",
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"max_tokens": 1000,
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"temperature": 0.7,
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"response_format": {"type": "json_object"},
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},
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"generate_image_prompts": {
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"model": "gpt-4o-mini",
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"max_tokens": 2000,
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"temperature": 0.7,
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"response_format": {"type": "json_object"},
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},
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}
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logger = logging.getLogger(__name__)
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# Function name aliases (for backward compatibility)
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FUNCTION_ALIASES = {
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@@ -52,71 +17,81 @@ FUNCTION_ALIASES = {
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}
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def get_model_config(function_name: str, account=None) -> Dict[str, Any]:
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def get_model_config(function_name: str, account) -> Dict[str, Any]:
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"""
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Get model configuration for an AI function.
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Reads model from IntegrationSettings if account is provided, otherwise uses defaults.
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Get model configuration from IntegrationSettings only.
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No fallbacks - account must have IntegrationSettings configured.
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Args:
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function_name: AI function name (e.g., 'auto_cluster', 'generate_ideas')
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account: Optional account object to read model from IntegrationSettings
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function_name: Name of the AI function
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account: Account instance (required)
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Returns:
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Dict with model, max_tokens, temperature, etc.
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dict: Model configuration with 'model', 'max_tokens', 'temperature'
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Raises:
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ValueError: If account not provided or IntegrationSettings not configured
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"""
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# Check aliases first
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if not account:
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raise ValueError("Account is required for model configuration")
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# Resolve function alias
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actual_name = FUNCTION_ALIASES.get(function_name, function_name)
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# Get base config
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config = MODEL_CONFIG.get(actual_name, {}).copy()
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# Get IntegrationSettings for OpenAI
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try:
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from igny8_core.modules.system.models import IntegrationSettings
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integration_settings = IntegrationSettings.objects.get(
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integration_type='openai',
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account=account,
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is_active=True
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)
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except IntegrationSettings.DoesNotExist:
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raise ValueError(
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f"OpenAI IntegrationSettings not configured for account {account.id}. "
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f"Please configure OpenAI settings in the integration page."
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)
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# Try to get model from IntegrationSettings if account is provided
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model_from_settings = None
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if account:
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try:
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from igny8_core.modules.system.models import IntegrationSettings
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openai_settings = IntegrationSettings.objects.filter(
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integration_type='openai',
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account=account,
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is_active=True
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).first()
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if openai_settings and openai_settings.config:
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model_from_settings = openai_settings.config.get('model')
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if model_from_settings:
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# Validate model is in our supported list
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from igny8_core.utils.ai_processor import MODEL_RATES
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if model_from_settings in MODEL_RATES:
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config['model'] = model_from_settings
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except Exception as e:
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import logging
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logger = logging.getLogger(__name__)
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logger.warning(f"Could not load model from IntegrationSettings: {e}", exc_info=True)
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config = integration_settings.config or {}
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# Merge with defaults
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default_config = {
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"model": "gpt-4.1",
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"max_tokens": 4000,
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"temperature": 0.7,
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"response_format": None,
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# Get model from config
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model = config.get('model')
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if not model:
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raise ValueError(
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f"Model not configured in IntegrationSettings for account {account.id}. "
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f"Please set 'model' in OpenAI integration settings."
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)
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# Validate model is in our supported list (optional validation)
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try:
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from igny8_core.utils.ai_processor import MODEL_RATES
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if model not in MODEL_RATES:
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logger.warning(
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f"Model '{model}' for account {account.id} is not in supported list. "
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f"Supported models: {list(MODEL_RATES.keys())}"
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)
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except ImportError:
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# MODEL_RATES not available - skip validation
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pass
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# Get max_tokens and temperature from config (with reasonable defaults for API)
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max_tokens = config.get('max_tokens', 4000) # Reasonable default for API limits
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temperature = config.get('temperature', 0.7) # Reasonable default
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# Build response format based on model (JSON mode for supported models)
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response_format = None
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try:
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from igny8_core.ai.constants import JSON_MODE_MODELS
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if model in JSON_MODE_MODELS:
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response_format = {"type": "json_object"}
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except ImportError:
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# JSON_MODE_MODELS not available - skip
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pass
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return {
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'model': model,
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'max_tokens': max_tokens,
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'temperature': temperature,
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'response_format': response_format,
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}
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return {**default_config, **config}
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def get_model(function_name: str) -> str:
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"""Get model name for function"""
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config = get_model_config(function_name)
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return config.get("model", "gpt-4.1")
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def get_max_tokens(function_name: str) -> int:
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"""Get max tokens for function"""
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config = get_model_config(function_name)
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return config.get("max_tokens", 4000)
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def get_temperature(function_name: str) -> float:
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"""Get temperature for function"""
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config = get_model_config(function_name)
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return config.get("temperature", 0.7)
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