igny8/new-updated.md

IGNY8 AI & Configuration Settings Report

---

1. AI Mode Configuration

Architecture: Two-tier database-driven model configuration system

AIModelConfig Model (Primary)

Stores all AI model configurations in database, replacing legacy hardcoded constants.

Model Types:

• Text Generation ✅
• Image Generation ✅

Supported Providers:

• OpenAI ✅
• Runware ✅

Key Configuration Fields:

Category	Fields
Identity	model_id, display_name, model_type, provider
Text Pricing	input_token_rate, output_token_rate (per 1M tokens in USD)
Text Limits	max_input_tokens, max_output_tokens
Image Pricing	cost_per_image (fixed USD per image)
Image Config	available_sizes (JSON array of valid dimensions)
Status	is_active, is_default, sort_order
Metadata	notes, release_date, deprecation_date

Seeded Models:

• OpenAI Text: gpt-4o-mini, gpt-4o, gpt-5.1(default)
• OpenAI Image: dall-e-3 (default)
• Runware: runware:97@1, google:4@2

---

2. Global Integration Settings

Model: GlobalIntegrationSettings (Singleton - always pk=1)

Stores platform-wide API keys and default settings used by ALL accounts.

Provider	API Key Field	Default Model	Parameters
OpenAI	openai_api_key	gpt-5.1	temperature: 0.7, max_tokens: 8192
DALL-E	dalle_api_key	dall-e-3	size: 1024x1024
Runware	runware_api_key	runware:97@1 & google:4@2	—

Default Provider Settings:

• default_text_provider: 'openai'
• default_image_service: 'openai'

Universal Image Settings:

• image_quality: standard/hd
• image_style: photorealistic, illustration, etc.
• max_in_article_images: Default 2

Critical Security Rule: API keys exist ONLY in GlobalIntegrationSettings - never stored at account/user level.

---

4. User-Specific Record Creation (Save Mechanism)

Three-Tier Hierarchy:

Global (Platform) → Account (Tenant) → User (Personal)

Models Involved

Model	Scope	Unique Key
GlobalIntegrationSettings	Platform-wide	Singleton (pk=1)
AccountSettings	Per-tenant	account + key
IntegrationSettings	Per-tenant overrides	account + integration_type
UserSettings	Per-user preferences	user + account + key

Save Flow When User Changes Config

1. Frontend calls POST/PUT to /api/v1/system/settings/user/
2. Backend ViewSet extracts user and account from authenticated request
3. Check existing: Query for existing setting with same user + account + key
4. Create or Update:
   • If not exists → serializer.save(user=user, account=account) creates new record
   • If exists → Updates the value JSON field
5. Validation: Schema validation runs against SETTINGS_SCHEMAS before save
6. Response returns the saved setting object

Integration Override Pattern

For AI/integration settings specifically:

1. User changes model/temperature (NOT API keys)
2. System strips any API key fields from request (security)
3. IntegrationSettings.objects.get_or_create(account=account, integration_type=type)
4. Only allowed override fields saved in config JSON field
5. On read, system merges: Global defaults → Account overrides

---

6. Image Generation: Internal Cost vs Customer Credit Allocation

Internal Cost (What Platform Pays to Providers)

Model	Provider	Cost Per Image (USD)
dall-e-3	OpenAI	$0.05
runware:97@1	Runware - Hi Dream Full	~$0.013
google:4@2	Runware - Google Nano Banaan	~$0.15

Storage: AIModelConfig.cost_per_image field + legacy IMAGE_MODEL_RATES constants

!!! This need to be fixed rates tobe laoded and used form configured AI Models !! not from hard coded location

Customer Credit Cost (What Customer Pays)

Operation	Credits Charged	Price per Credit	Min Charge
Image Generation	5 credits	$0.02	$0.10
Image Prompt Extraction	2 credits	$0.01	$0.02

!!!morre robust image gneartion csoting and pricing mecahnishm required, withotu long chains or workarounds!!!

Configuration Model: CreditCostConfig

• tokens_per_credit: 50 (image gen uses fewer tokens per credit = higher cost)
• min_credits: 5
• price_per_credit_usd: $0.02

Margin Calculation

Metric	DALL-E 3 Example
Provider Cost	$0.040
Customer Charge	$0.10 (5 credits × $0.02)
Margin	$0.06 (60% of customer charge)
Markup	~150%

Flow:

1. Before AI call: Calculate required credits based on image count
2. Check balance: Verify account has sufficient credits
3. Deduct credits: Remove from balance, log transaction
4. Execute: Make AI provider API call
5. Track: CreditUsageLog stores both credits_used (customer) and cost_usd (actual)

Revenue Analytics queries CreditUsageLog to calculate:

• Total revenue = Σ(credits_used × credit_price)
• Total cost = Σ(cost_usd from provider)
• Margin = Revenue - Cost

---

Summary

The IGNY8 platform implements a sophisticated multi-tier configuration system:

• AI configuration is database-driven with fallback to legacy constants
• Global settings hold platform API keys; accounts only override model/parameters
• User settings create per-user records keyed by user + account + key combination
• Credit system charges customers a markup (~150%) over actual provider costs
• Several fields are deprecated including mobile_image_size, reference_id, and the get_model() method