> ## Documentation Index
> Fetch the complete documentation index at: https://mintlify.com/Kismetkanceled/geniehelper/llms.txt
> Use this file to discover all available pages before exploring further.

# Taxonomy System

> 6-concept content classification system with 3,208 tags for organizing and analyzing adult creator content

The Taxonomy System is a proprietary 6-concept classification framework designed to organize adult content with semantic precision. It powers auto-tagging, content search, performance analytics, and cross-platform content strategy.

## Overview

**Total tags**: 3,208 classified across 6 super-concepts

**Purpose**: Enables creators to:

* Auto-tag uploaded media for fast organization
* Analyze which content themes perform best on each platform
* Search their library by mood, activity, aesthetic, etc.
* Generate platform-optimized captions based on tag patterns

**Implementation**: Tags are stored in `taxonomy_mapping` collection, linked to `taxonomy_dimensions` (the 6 concepts).

***

## The 6 Concepts

| Concept       | Description                     | Example Tags                                                                       |
| ------------- | ------------------------------- | ---------------------------------------------------------------------------------- |
| **Aesthetic** | Visual style and theme          | `glamour`, `luxury`, `vintage`, `gothic`, `minimalist`, `neon`, `cyberpunk`        |
| **Activity**  | What's happening in the content | `posing`, `dancing`, `workout`, `cooking`, `gaming`, `bathing`, `teasing`          |
| **Mood**      | Emotional tone and energy       | `confident`, `playful`, `seductive`, `shy`, `dominant`, `submissive`, `mysterious` |
| **Setting**   | Location and environment        | `indoor`, `outdoor`, `bedroom`, `bathroom`, `kitchen`, `gym`, `beach`, `car`       |
| **Attire**    | Clothing and accessories        | `lingerie`, `swimwear`, `latex`, `leather`, `cosplay`, `uniform`, `casual`, `nude` |
| **Body Type** | Physical attributes             | `athletic`, `curvy`, `petite`, `plus-size`, `tall`, `muscular`, `slim`             |

***

## How It Works

### 1. Tag Assignment

Tags are assigned to content via:

* **AI classification**: `scout-fast-tag:latest` model (custom SmolLM fine-tune)
* **Manual tagging**: Creators can add/remove tags in Media Library UI
* **ACTION flow**: `[ACTION:taxonomy-tag:{"media_id":"uuid"}]`

**AI Classification Pipeline**:

```mermaid theme={null}
graph LR
    A[Upload media] --> B[scout-fast-tag:latest]
    B --> C{Success?}
    C -->|Yes| D[Write to taxonomy_assignments]
    C -->|No| E[Fallback: phi-3.5:latest]
    E --> D
```

### 2. Storage Schema

**`taxonomy_dimensions`** (6 rows)

```json theme={null}
{
  "id": "uuid",
  "name": "aesthetic",
  "description": "Visual style and theme",
  "sort_order": 1
}
```

**`taxonomy_mapping`** (3,208 rows)

```json theme={null}
{
  "id": "uuid",
  "dimension_id": "uuid",  // FK to taxonomy_dimensions
  "tag": "glamour",
  "description": "Luxurious, high-end visual presentation",
  "parent_tag": null,  // For hierarchical tags
  "platform_weight": {
    "onlyfans": 1.2,
    "fansly": 1.0,
    "instagram": 0.8
  }
}
```

**`taxonomy_assignments`** (junction table)

```json theme={null}
{
  "id": "uuid",
  "media_id": "uuid",  // FK to scraped_media
  "tag_id": "uuid",    // FK to taxonomy_mapping
  "confidence": 0.92,  // AI confidence score
  "source": "ai",      // 'ai' | 'manual' | 'bulk'
  "created_at": "2026-03-04T12:00:00Z"
}
```

### 3. Tag Weighting

Each tag has platform-specific weights to account for performance differences:

**Example**: `glamour` tag

* OnlyFans: 1.2 (performs 20% better than baseline)
* Fansly: 1.0 (baseline performance)
* Instagram: 0.8 (underperforms due to algorithm)

Weights are used by:

* `post-create` ACTION flow (selects high-weight tags for captions)
* Analytics dashboards (filters low-performing content)
* Content strategy recommendations

***

## Tag Hierarchy

Some concepts support hierarchical tags:

```
aesthetic
  └─ luxury
      ├─ glamour
      ├─ high-fashion
      └─ designer
  └─ vintage
      ├─ retro
      ├─ 80s
      └─ pin-up
```

**Storage**: `parent_tag` field in `taxonomy_mapping` references another tag's ID

**Inference**: When a child tag is assigned, the parent is implicitly included in search/filters

***

## Use Cases

### Auto-Tagging Workflow

1. Creator uploads 50 photos to Media Library
2. Dashboard triggers `[ACTION:taxonomy-tag:{"media_id":"..."}]` for each
3. `scout-fast-tag` classifies all 50 in \~25 seconds
4. Tags written to `taxonomy_assignments` with confidence scores
5. Creator reviews low-confidence tags (less than 0.7) and corrects if needed

### Performance Analysis

**Query**: "Which aesthetic performs best on OnlyFans?"

```sql theme={null}
SELECT 
  tm.tag,
  AVG(sm.likes) as avg_likes,
  COUNT(*) as post_count
FROM taxonomy_assignments ta
JOIN taxonomy_mapping tm ON ta.tag_id = tm.id
JOIN taxonomy_dimensions td ON tm.dimension_id = td.id
JOIN scraped_media sm ON ta.media_id = sm.id
WHERE td.name = 'aesthetic'
  AND sm.platform = 'onlyfans'
GROUP BY tm.tag
ORDER BY avg_likes DESC
LIMIT 10;
```

### Content Search

**Query**: "Show me all confident, glamour bedroom content"

```json theme={null}
{
  "filter": {
    "taxonomy_assignments": {
      "tag_id": {
        "_in": [
          "<confident_tag_uuid>",
          "<glamour_tag_uuid>",
          "<bedroom_tag_uuid>"
        ]
      }
    }
  }
}
```

### Caption Generation

**ACTION flow**: `post-create`

1. Fetch media tags from `taxonomy_assignments`
2. Filter to high-weight tags for target platform
3. Pass to `dolphin-mistral:7b` with prompt:
   ```
   Write a seductive OnlyFans caption for an image with these tags:
   - aesthetic: glamour, luxury
   - mood: confident
   - attire: lingerie
   - setting: bedroom
   ```
4. Model outputs platform-optimized caption

***

## Tag Sources

The 3,208 tags were compiled from:

* **OnlyFans top 1000 creators**: Scraped captions and hashtags
* **Adult content research**: Industry-standard categorization
* **Manual curation**: Deduplicated, normalized, weighted
* **User feedback**: Iteratively refined based on creator input

***

## AI Models Used

| Model                   | Role                | Speed         | Accuracy                        |
| ----------------------- | ------------------- | ------------- | ------------------------------- |
| `scout-fast-tag:latest` | Primary classifier  | \~500ms/image | 92% (on test set)               |
| `phi-3.5:latest`        | Fallback classifier | \~3-5s/image  | 89% (more robust on edge cases) |

**Training data**: 50,000 manually-tagged images from OnlyFans, Fansly, and ManyVids

**Fine-tuning**: Custom LoRA on SmolLM base model (1.2B parameters)

***

## Platform-Specific Optimizations

### OnlyFans

**High-performing tags**:

* `aesthetic`: glamour, luxury, intimate
* `mood`: seductive, confident, playful
* `attire`: lingerie, swimwear, latex

**Caption style**: Direct, teasing, heavy emoji use

### Fansly

**High-performing tags**:

* `aesthetic`: artistic, gothic, cosplay
* `mood`: mysterious, dominant, creative
* `attire`: cosplay, latex, alternative

**Caption style**: Narrative, character-driven, fan interaction

### Instagram (SFW subset)

**Allowed tags** (NSFW tags excluded):

* `aesthetic`: minimalist, vintage, luxury
* `mood`: confident, playful, inspiring
* `attire`: casual, swimwear, athletic

**Caption style**: Aspirational, behind-the-scenes, lifestyle

***

## API Access

### Read Tags for Media

```bash theme={null}
GET /api/directus/items/taxonomy_assignments
  ?filter[media_id][_eq]=<uuid>
  &fields=*,tag_id.tag,tag_id.dimension_id.name
```

### Assign Tag (Manual)

```bash theme={null}
POST /api/directus/items/taxonomy_assignments
{
  "media_id": "<uuid>",
  "tag_id": "<uuid>",
  "confidence": 1.0,
  "source": "manual"
}
```

### Trigger AI Tagging

```bash theme={null}
POST /api/llm/chat
{
  "message": "[ACTION:taxonomy-tag:{\"media_id\":\"<uuid>\"}]"
}
```

***

## Roadmap

### Planned Enhancements

* **Tag suggestions**: "Creators who tagged X also tagged Y"
* **Multi-language tags**: Spanish, French, German translations
* **Video tags**: Temporal tags ("first 10s: teasing, 10-30s: reveal")
* **Audio tags**: Music genre, spoken content, ASMR categories
* **Engagement prediction**: ML model predicts likes based on tag combination

### Known Limitations

* **Context blind**: Tags describe what's in frame, not why ("Is this teasing or artistic?")
* **Cultural bias**: Optimized for Western platforms (OnlyFans, Fansly)
* **No temporal tags**: Video content treated as single frame
* **Binary mood**: Can't capture mixed moods ("playful but dominant")

***

## Related

* [Ollama Models](/ai/ollama-models) — scout-fast-tag and phi-3.5 classifiers
* [Action Runner](/ai/action-runner) — `taxonomy-tag` flow definition
* **Collections**:
  * `taxonomy_dimensions` — 6 super-concepts
  * `taxonomy_mapping` — 3,208 tags
  * `taxonomy_assignments` — Media ↔ Tag junction table
* **Graph data**: `Nodes/Universe/taxonomy_graph.json` — 3,205-node graph (legacy format)