# IGNY8 Credits & Tokens System Guide

**Version:** 1.0  
**Last Updated:** December 19, 2025  
**System Status:** ✅ Fully Operational

---

## Overview

IGNY8 uses a **token-based credit system** where all AI operations consume credits calculated from actual AI token usage. This guide covers configuration, data flow, and monitoring.

---

## System Architecture

### Data Flow

```
User Action (Content Generation, Ideas, etc.)
    ↓
Backend Service Initiated
    ↓
AI API Called (OpenAI, etc.)
    ↓
Response Received: {input_tokens, output_tokens, cost_usd, model}
    ↓
Credits Calculated: (total_tokens / tokens_per_credit)
    ↓
Credits Deducted from Account
    ↓
CreditUsageLog Created: {tokens_input, tokens_output, cost_usd, credits_used, model_used}
    ↓
Reports Updated with Real-Time Analytics
```

### Key Components

1. **BillingConfiguration** - System-wide billing settings
2. **CreditCostConfig** - Per-operation token-to-credit ratios
3. **CreditUsageLog** - Transaction log with token data
4. **AITaskLog** - Detailed AI execution history

---

## Configuration

### 1. Global Billing Settings

**Location:** Django Admin → Billing → Billing Configuration

**Key Settings:**
- **Default Tokens Per Credit:** 100 (base ratio)
- **Default Credit Price:** $0.01 USD
- **Rounding Mode:** Up (conservative billing)
- **Token Reporting Enabled:** Yes

**When to Adjust:**
- Change credit pricing across all operations
- Modify rounding behavior for credit calculations
- Enable/disable token tracking

### 2. Per-Operation Configuration

**Location:** Django Admin → Billing → Credit Cost Configs

**Current Ratios:**

| Operation | Tokens/Credit | Min Credits | Price/Credit |
|-----------|---------------|-------------|--------------|
| Clustering | 150 | 2 | $0.0100 |
| Content Generation | 100 | 3 | $0.0100 |
| Idea Generation | 200 | 1 | $0.0100 |
| Image Generation | 50 | 5 | $0.0200 |
| Image Prompt Extraction | 100 | 1 | $0.0100 |
| Linking | 300 | 1 | $0.0050 |
| Optimization | 200 | 1 | $0.0050 |

**Adjusting Pricing:**
- **Increase `tokens_per_credit`** → Lower cost (more tokens per credit)
- **Decrease `tokens_per_credit`** → Higher cost (fewer tokens per credit)
- **Adjust `min_credits`** → Enforce minimum charge per operation
- **Change `price_per_credit_usd`** → Override default credit price

**Example:** To make Content Generation cheaper:
```
Current: 100 tokens/credit (1000 tokens = 10 credits)
Change to: 150 tokens/credit (1000 tokens = 6.67 → 7 credits)
Result: 30% cost reduction
```

### 3. Token Extraction

**Location:** `backend/igny8_core/ai/engine.py` (line 380-381)

**Current Implementation:**
```python
tokens_input = raw_response.get('input_tokens', 0)
tokens_output = raw_response.get('output_tokens', 0)
```

**Critical:** Field names must match AI provider response format
- ✅ Correct: `input_tokens`, `output_tokens`
- ❌ Wrong: `tokens_input`, `tokens_output`

**Supported Providers:**
- OpenAI (GPT-4, GPT-4o, GPT-5.1)
- Anthropic Claude
- Runware (image generation)

---

## Monitoring & Reports

### 1. AI Cost & Margin Analysis

**URL:** `https://api.igny8.com/admin/reports/ai-cost-analysis/`

**Metrics Displayed:**
- **Total Cost** - Actual USD spent on AI APIs
- **Revenue** - Income from credits charged
- **Margin** - Profit (Revenue - Cost) with percentage
- **Margin / 1M Tokens** - Profit efficiency per million tokens
- **Margin / 1K Credits** - Profit per thousand credits charged
- **Projected Monthly** - Forecasted costs based on trends

**Tables:**
- Model Cost Comparison - Profitability by AI model
- Top Spenders - Highest cost accounts
- Cost by Function - Profitability by operation type
- Cost Anomalies - Expensive outlier calls

**Use Cases:**
- Identify unprofitable operations or accounts
- Optimize token-to-credit ratios
- Detect unusual AI spending patterns
- Track margins over time

### 2. Token Usage Report

**URL:** `https://api.igny8.com/admin/reports/token-usage/`

**Metrics Displayed:**
- Total tokens consumed (input + output)
- Average tokens per call
- Cost per 1K tokens
- Token distribution by model
- Token distribution by operation
- Daily token trends

**Use Cases:**
- Understand token consumption patterns
- Identify token-heavy operations
- Optimize prompts to reduce token usage
- Track token efficiency over time

### 3. Usage Report

**URL:** `https://api.igny8.com/admin/reports/usage/`

**Metrics Displayed:**
- Total credits used system-wide
- Credits by operation type
- Top credit consumers
- Model usage distribution

**Use Cases:**
- Monitor overall system usage
- Identify high-volume users
- Track popular AI operations
- Plan capacity and scaling

### 4. Data Quality Report

**URL:** `https://api.igny8.com/admin/reports/data-quality/`

**Purpose:** Identify data integrity issues
- Orphaned content
- Duplicate keywords
- Missing SEO metadata

---

## Data Models

### CreditUsageLog (Primary Transaction Log)

**Purpose:** Record every credit deduction with full context

**Key Fields:**
- `account` - User account charged
- `operation_type` - Function executed (e.g., "content_generation")
- `credits_used` - Credits deducted
- `cost_usd` - Actual AI provider cost
- `tokens_input` - Input tokens consumed
- `tokens_output` - Output tokens generated
- `model_used` - AI model (e.g., "gpt-4o")
- `related_object_type/id` - Link to content/site/keyword
- `metadata` - Additional context (prompt, settings, etc.)

**Query Examples:**
```python
# Total tokens for an account
CreditUsageLog.objects.filter(account=account).aggregate(
    total_tokens=Sum('tokens_input') + Sum('tokens_output')
)

# Average cost by operation
CreditUsageLog.objects.values('operation_type').annotate(
    avg_cost=Avg('cost_usd'),
    total_calls=Count('id')
)

# Margin analysis
logs = CreditUsageLog.objects.all()
revenue = logs.aggregate(Sum('credits_used'))['credits_used__sum'] * 0.01
cost = logs.aggregate(Sum('cost_usd'))['cost_usd__sum']
margin = revenue - cost
```

### AITaskLog (Execution History)

**Purpose:** Detailed AI execution tracking

**Key Fields:**
- `function_name` - AI function executed
- `account` - User account
- `cost` - AI provider cost
- `tokens` - Total tokens (input + output)
- `phase` - Execution stage
- `status` - Success/failure
- `execution_time` - Processing duration
- `raw_request/response` - Full API data

**Use Cases:**
- Debug AI execution failures
- Analyze prompt effectiveness
- Track model performance
- Audit AI interactions

---

## Historical Data Backfill

### Issue
Prior to December 2025, token fields were not populated due to incorrect field name mapping.

### Solution
A backfill script matched AITaskLog entries to CreditUsageLog records using:
- Account matching
- Timestamp matching (±10 second window)
- 40/60 input/output split estimation (when only total available)

### Result
- ✅ 777,456 tokens backfilled
- ✅ 380/479 records updated (79% coverage)
- ✅ Historical margin analysis now available
- ⚠️ 99 records remain at 0 tokens (no matching AITaskLog)

### Script Location
`backend/igny8_core/management/commands/backfill_tokens.py`

---

## Troubleshooting

### Empty Margin Metrics
**Symptom:** "Margin / 1M Tokens" shows "No token data yet"

**Causes:**
1. No recent AI calls with token data
2. Token extraction not working (field name mismatch)
3. Historical data has 0 tokens

**Resolution:**
1. Check AIEngine token extraction: `tokens_input`, `tokens_output` fields
2. Verify AI responses contain `input_tokens`, `output_tokens`
3. Run test AI operation and check CreditUsageLog
4. Consider backfill for historical data

### Zero Tokens in CreditUsageLog
**Symptom:** `tokens_input` and `tokens_output` are 0

**Causes:**
1. Field name mismatch in AIEngine
2. AI provider not returning token data
3. Historical records before fix

**Resolution:**
1. Verify `engine.py` line 380-381 uses correct field names
2. Check AI provider API response format
3. Restart backend services after fixes
4. Future calls will populate correctly

### Incorrect Margins
**Symptom:** Margin percentages seem wrong

**Causes:**
1. Incorrect token-to-credit ratios
2. Credit price misconfigured
3. Decimal division errors

**Resolution:**
1. Review CreditCostConfig ratios
2. Check BillingConfiguration credit price
3. Verify margin calculations use `float()` conversions
4. Check for TypeError in logs

### Operations Not Charging Correctly
**Symptom:** Wrong number of credits deducted

**Causes:**
1. Token-to-credit ratio misconfigured
2. Minimum credits not enforced
3. Rounding mode incorrect

**Resolution:**
1. Check operation's CreditCostConfig
2. Verify `min_credits` setting
3. Review `rounding_mode` in BillingConfiguration
4. Test with known token count

---

## Best Practices

### Pricing Strategy
1. **Monitor margins weekly** - Use AI Cost Analysis report
2. **Adjust ratios based on costs** - If margins drop below 70%, decrease tokens_per_credit
3. **Set reasonable minimums** - Enforce min_credits for small operations
4. **Track model costs** - Some models (GPT-4) cost more than others

### Token Optimization
1. **Optimize prompts** - Reduce unnecessary tokens
2. **Use appropriate models** - GPT-4o-mini for simple tasks
3. **Cache results** - Avoid duplicate AI calls
4. **Monitor anomalies** - Investigate unusually expensive calls

### Data Integrity
1. **Regular audits** - Check token data completeness
2. **Verify field mappings** - Ensure AI responses parsed correctly
3. **Monitor logs** - Watch for errors in CreditService
4. **Backup configurations** - Export CreditCostConfig settings

### Performance
1. **Archive old logs** - Move historical CreditUsageLog to archive tables
2. **Index frequently queried fields** - account, operation_type, created_at
3. **Aggregate reports** - Use materialized views for large datasets
4. **Cache report data** - Reduce database load

---

## API Integration

### Frontend Credit Display

**Endpoint:** `/v1/billing/credits/balance/`

**Response:**
```json
{
  "credits": 1234,
  "plan_credits_per_month": 5000,
  "credits_used_this_month": 876,
  "credits_remaining": 4124
}
```

**Pages Using This:**
- `/account/plans` - Plans & Billing
- `/account/usage` - Usage Analytics
- Dashboard credit widget

### Credit Transaction History

**Endpoint:** `/v1/billing/credits/usage/`

**Response:**
```json
{
  "results": [
    {
      "id": 123,
      "operation_type": "Content Generation",
      "credits_used": 15,
      "tokens_input": 500,
      "tokens_output": 1000,
      "cost_usd": 0.015,
      "model_used": "gpt-4o",
      "created_at": "2025-12-19T10:30:00Z"
    }
  ]
}
```

---

## Quick Reference

### Common Operations

**Check account credits:**
```python
account.credits  # Current balance
```

**Manual credit adjustment:**
```python
CreditService.add_credits(account, amount=100, description="Bonus credits")
```

**Get operation config:**
```python
config = CreditService.get_or_create_config('content_generation')
# Returns: CreditCostConfig with tokens_per_credit, min_credits
```

**Calculate credits needed:**
```python
credits = CreditService.calculate_credits_from_tokens(
    operation_type='content_generation',
    tokens_input=500,
    tokens_output=1500
)
# Returns: 20 (if 100 tokens/credit)
```

### Important File Locations

- **Credit Service:** `backend/igny8_core/business/billing/services/credit_service.py`
- **AI Engine:** `backend/igny8_core/ai/engine.py`
- **Reports:** `backend/igny8_core/admin/reports.py`
- **Models:** `backend/igny8_core/modules/billing/models.py`
- **Admin:** `backend/igny8_core/modules/billing/admin.py`

### Report Access

All reports require staff/superuser login:
- AI Cost Analysis: `/admin/reports/ai-cost-analysis/`
- Token Usage: `/admin/reports/token-usage/`
- Usage Report: `/admin/reports/usage/`
- Data Quality: `/admin/reports/data-quality/`

---

## Support & Updates

For questions or issues with the credits & tokens system:
1. Check Django admin logs: `/admin/`
2. Review CreditUsageLog for transaction details
3. Monitor AITaskLog for execution errors
4. Check backend logs: `docker logs igny8_backend`

**System Maintainer:** IGNY8 DevOps Team  
**Last Major Update:** December 2025 (Token-based credit system implementation)