Taxonomy System

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Overview

• 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

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The 6 Concepts

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How It Works

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1. Tag Assignment

• 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"}]

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2. Storage Schema

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

{
  "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
  }
}

{
  "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"
}

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3. Tag Weighting

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

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

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Tag Hierarchy

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

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Use Cases

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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

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Performance Analysis

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;

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Content Search

{
  "filter": {
    "taxonomy_assignments": {
      "tag_id": {
        "_in": [
          "<confident_tag_uuid>",
          "<glamour_tag_uuid>",
          "<bedroom_tag_uuid>"
        ]
      }
    }
  }
}

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Caption Generation

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

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Tag Sources

• 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

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AI Models Used

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Platform-Specific Optimizations

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OnlyFans

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

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Fansly

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

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Instagram (SFW subset)

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

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API Access

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Read Tags for Media

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

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Assign Tag (Manual)

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

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Trigger AI Tagging

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

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Roadmap

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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

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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”)

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Related

• Ollama Models — scout-fast-tag and phi-3.5 classifiers
• 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)