automation and ai and some planning and fixes adn docs reorg
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IGNY8 VPS (Salman)
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docs/plans/flexible-model-configuration-plan.md
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311
docs/plans/flexible-model-configuration-plan.md
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# Flexible Model Configuration System Plan
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## Overview
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This plan outlines how to implement a flexible model configuration system that allows:
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- Adding/removing/activating models dynamically
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- Configuring rates for each model
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- Supporting multiple providers (OpenAI, Anthropic, Runware)
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- Per-account model overrides
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## Current State
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### Model Rates (hardcoded in `ai/constants.py`)
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```python
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MODEL_RATES = {
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'gpt-4.1': {'input': 2.00, 'output': 8.00}, # per 1M tokens
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'gpt-4o-mini': {'input': 0.15, 'output': 0.60},
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'gpt-4o': {'input': 2.50, 'output': 10.00},
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'gpt-5.1': {'input': 1.25, 'output': 10.00},
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'gpt-5.2': {'input': 1.75, 'output': 14.00},
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}
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IMAGE_MODEL_RATES = {
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'dall-e-3': 0.040, # per image
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'dall-e-2': 0.020,
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'gpt-image-1': 0.042,
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'gpt-image-1-mini': 0.011,
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}
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```
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### Current Settings Architecture
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- `GlobalIntegrationSettings` (singleton) - Platform-wide API keys and defaults
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- `IntegrationSettings` (per-account) - Model/parameter overrides
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- `GlobalAIPrompt` - Platform-wide prompt templates
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- `AIPrompt` (per-account) - Custom prompt overrides
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## Proposed Changes
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### Phase 1: Database Model for AI Models
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Create a new model `AIModel` to store model configurations:
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```python
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# backend/igny8_core/modules/system/global_settings_models.py
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class AIModel(models.Model):
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"""
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Dynamic AI model configuration.
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Replaces hardcoded MODEL_RATES and IMAGE_MODEL_RATES.
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"""
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PROVIDER_CHOICES = [
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('openai', 'OpenAI'),
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('anthropic', 'Anthropic'),
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('runware', 'Runware'),
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('google', 'Google AI'),
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]
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MODEL_TYPE_CHOICES = [
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('text', 'Text Generation'),
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('image', 'Image Generation'),
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('embedding', 'Embedding'),
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]
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# Identification
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model_id = models.CharField(
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max_length=100,
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unique=True,
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help_text="Model identifier (e.g., 'gpt-4o-mini', 'claude-3-sonnet')"
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)
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display_name = models.CharField(
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max_length=200,
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help_text="User-friendly name (e.g., 'GPT-4o Mini')"
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)
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provider = models.CharField(max_length=50, choices=PROVIDER_CHOICES)
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model_type = models.CharField(max_length=20, choices=MODEL_TYPE_CHOICES)
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# Pricing (per 1M tokens for text, per image for image models)
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input_rate = models.DecimalField(
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max_digits=10,
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decimal_places=4,
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default=0,
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help_text="Cost per 1M input tokens (text) or per request (image)"
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)
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output_rate = models.DecimalField(
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max_digits=10,
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decimal_places=4,
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default=0,
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help_text="Cost per 1M output tokens (text only)"
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)
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# Capabilities
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max_tokens = models.IntegerField(
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default=8192,
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help_text="Maximum tokens for this model"
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)
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supports_json_mode = models.BooleanField(
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default=True,
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help_text="Whether model supports JSON response format"
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)
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supports_vision = models.BooleanField(
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default=False,
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help_text="Whether model supports image input"
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)
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# Status
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is_active = models.BooleanField(default=True)
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is_default = models.BooleanField(
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default=False,
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help_text="Use as default when no specific model is configured"
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)
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sort_order = models.IntegerField(default=0)
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# Metadata
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created_at = models.DateTimeField(auto_now_add=True)
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updated_at = models.DateTimeField(auto_now=True)
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class Meta:
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db_table = 'igny8_ai_models'
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ordering = ['sort_order', 'display_name']
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def __str__(self):
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return f"{self.display_name} ({self.model_id})"
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```
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### Phase 2: Model Registry Service
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Create a service layer to manage models:
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```python
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# backend/igny8_core/ai/model_registry.py
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class ModelRegistry:
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"""
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Central registry for AI model configurations.
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Provides caching and fallback logic.
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"""
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_cache = {}
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_cache_ttl = 300 # 5 minutes
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@classmethod
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def get_model(cls, model_id: str) -> Optional[dict]:
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"""Get model configuration by ID"""
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# Check cache first
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# Fallback to database
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# Return dict with rates, capabilities, etc.
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pass
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@classmethod
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def get_models_by_type(cls, model_type: str) -> List[dict]:
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"""Get all active models of a type"""
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pass
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@classmethod
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def get_default_model(cls, model_type: str = 'text') -> dict:
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"""Get default model for a type"""
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pass
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@classmethod
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def calculate_cost(
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cls,
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model_id: str,
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input_tokens: int = 0,
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output_tokens: int = 0,
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image_count: int = 0
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) -> float:
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"""Calculate cost for an operation"""
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pass
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@classmethod
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def is_model_supported(cls, model_id: str) -> bool:
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"""Check if a model is configured and active"""
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pass
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```
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### Phase 3: Update AICore to Use Registry
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Modify `ai_core.py` to use the model registry:
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```python
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# In run_ai_request()
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from igny8_core.ai.model_registry import ModelRegistry
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# Replace hardcoded MODEL_RATES check
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if not ModelRegistry.is_model_supported(model):
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supported = ModelRegistry.get_models_by_type('text')
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error_msg = f"Model '{model}' is not supported. Available models: {[m['model_id'] for m in supported]}"
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# ...
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# Replace hardcoded cost calculation
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model_info = ModelRegistry.get_model(model)
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if model_info:
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cost = ModelRegistry.calculate_cost(
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model_id=model,
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input_tokens=input_tokens,
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output_tokens=output_tokens
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)
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```
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### Phase 4: Admin Interface
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Add Django admin for managing models:
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```python
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# backend/igny8_core/modules/system/admin.py
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@admin.register(AIModel)
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class AIModelAdmin(admin.ModelAdmin):
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list_display = ['model_id', 'display_name', 'provider', 'model_type', 'input_rate', 'output_rate', 'is_active', 'is_default']
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list_filter = ['provider', 'model_type', 'is_active', 'is_default']
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search_fields = ['model_id', 'display_name']
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ordering = ['sort_order', 'display_name']
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fieldsets = (
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('Identification', {
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'fields': ('model_id', 'display_name', 'provider', 'model_type')
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}),
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('Pricing', {
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'fields': ('input_rate', 'output_rate')
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}),
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('Capabilities', {
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'fields': ('max_tokens', 'supports_json_mode', 'supports_vision')
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}),
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('Status', {
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'fields': ('is_active', 'is_default', 'sort_order')
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}),
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)
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```
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### Phase 5: Data Migration
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Create a migration to seed initial models:
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```python
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# Migration file
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def seed_initial_models(apps, schema_editor):
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AIModel = apps.get_model('system', 'AIModel')
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models = [
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# OpenAI Text Models
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{'model_id': 'gpt-4o-mini', 'display_name': 'GPT-4o Mini', 'provider': 'openai', 'model_type': 'text', 'input_rate': 0.15, 'output_rate': 0.60, 'is_default': True},
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{'model_id': 'gpt-4o', 'display_name': 'GPT-4o', 'provider': 'openai', 'model_type': 'text', 'input_rate': 2.50, 'output_rate': 10.00},
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{'model_id': 'gpt-4.1', 'display_name': 'GPT-4.1', 'provider': 'openai', 'model_type': 'text', 'input_rate': 2.00, 'output_rate': 8.00},
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{'model_id': 'gpt-5.1', 'display_name': 'GPT-5.1', 'provider': 'openai', 'model_type': 'text', 'input_rate': 1.25, 'output_rate': 10.00, 'max_tokens': 16000},
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{'model_id': 'gpt-5.2', 'display_name': 'GPT-5.2', 'provider': 'openai', 'model_type': 'text', 'input_rate': 1.75, 'output_rate': 14.00, 'max_tokens': 16000},
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# Anthropic Text Models
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{'model_id': 'claude-3-sonnet', 'display_name': 'Claude 3 Sonnet', 'provider': 'anthropic', 'model_type': 'text', 'input_rate': 3.00, 'output_rate': 15.00},
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{'model_id': 'claude-3-opus', 'display_name': 'Claude 3 Opus', 'provider': 'anthropic', 'model_type': 'text', 'input_rate': 15.00, 'output_rate': 75.00},
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{'model_id': 'claude-3-haiku', 'display_name': 'Claude 3 Haiku', 'provider': 'anthropic', 'model_type': 'text', 'input_rate': 0.25, 'output_rate': 1.25},
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# OpenAI Image Models
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{'model_id': 'dall-e-3', 'display_name': 'DALL-E 3', 'provider': 'openai', 'model_type': 'image', 'input_rate': 0.040, 'output_rate': 0},
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{'model_id': 'dall-e-2', 'display_name': 'DALL-E 2', 'provider': 'openai', 'model_type': 'image', 'input_rate': 0.020, 'output_rate': 0},
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{'model_id': 'gpt-image-1', 'display_name': 'GPT Image 1', 'provider': 'openai', 'model_type': 'image', 'input_rate': 0.042, 'output_rate': 0},
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# Runware Image Models
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{'model_id': 'runware:97@1', 'display_name': 'Runware 97@1', 'provider': 'runware', 'model_type': 'image', 'input_rate': 0.009, 'output_rate': 0},
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]
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for i, model in enumerate(models):
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AIModel.objects.create(sort_order=i, **model)
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```
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### Phase 6: API Endpoints for Model Management
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Add REST endpoints for managing models:
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```python
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# GET /api/v1/admin/ai-models/ - List all models
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# POST /api/v1/admin/ai-models/ - Create new model
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# PUT /api/v1/admin/ai-models/{id}/ - Update model
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# DELETE /api/v1/admin/ai-models/{id}/ - Delete model
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# POST /api/v1/admin/ai-models/{id}/toggle-active/ - Toggle active status
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# POST /api/v1/admin/ai-models/{id}/set-default/ - Set as default
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```
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### Phase 7: Frontend Admin UI
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Create admin UI for model management:
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- List view with filtering/sorting
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- Create/Edit form with validation
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- Quick toggle for active/default status
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- Price calculator preview
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## Implementation Order
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1. **Week 1**: Create `AIModel` model and migration
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2. **Week 1**: Create `ModelRegistry` service
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3. **Week 2**: Update `ai_core.py` to use registry
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4. **Week 2**: Update `constants.py` to load from database
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5. **Week 3**: Add Django admin interface
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6. **Week 3**: Add API endpoints
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7. **Week 4**: Create frontend admin UI
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8. **Week 4**: Testing and documentation
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## Backward Compatibility
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- Keep `constants.py` as fallback if database is empty
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- `ModelRegistry.get_model()` checks DB first, falls back to constants
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- No changes to existing `GlobalIntegrationSettings` or `IntegrationSettings`
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- Existing API calls continue to work unchanged
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## Benefits
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1. **No Code Changes for New Models**: Add models via admin UI
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2. **Easy Price Updates**: Update rates without deployment
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3. **Provider Flexibility**: Support any provider by adding models
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4. **Per-Provider Settings**: Configure different capabilities per provider
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5. **Audit Trail**: Track when models were added/modified
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6. **A/B Testing**: Easily enable/disable models for testing
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