> ## 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 Collections

> 6-concept content classification system with 3,208 classified tags

## Overview

The taxonomy system classifies creator content using 6 super-concept dimensions with 3,208 pre-classified tags. Two collections power this: `taxonomy_dimensions` for the classification schema, and `taxonomy_mapping` for tag assignments.

***

## taxonomy\_dimensions

Defines the 6 super-concept dimensions used for content classification.

### Purpose

* Define classification schema for content tagging
* Store dimension names, descriptions, and allowed values
* Enable AI-powered content discovery and recommendations
* Support multi-dimensional content search

### The 6 Dimensions

| Dimension            | Description                   | Example Values                                             |
| -------------------- | ----------------------------- | ---------------------------------------------------------- |
| `body_type`          | Physical appearance and build | `athletic`, `curvy`, `petite`, `muscular`, `slim`          |
| `performance_style`  | Content tone and presentation | `sensual`, `playful`, `dominant`, `submissive`, `artistic` |
| `setting`            | Location and environment      | `bedroom`, `outdoor`, `bathroom`, `office`, `studio`       |
| `attire`             | Clothing and accessories      | `lingerie`, `casual`, `cosplay`, `fetish_wear`, `nude`     |
| `audience_appeal`    | Target audience and niche     | `mainstream`, `fetish`, `couples`, `solo`, `group`         |
| `production_quality` | Technical production level    | `professional`, `amateur`, `selfie`, `cinematic`           |

### Key Fields

| Field             | Type     | Description                                  |
| ----------------- | -------- | -------------------------------------------- |
| `id`              | UUID     | Primary key                                  |
| `dimension_name`  | String   | Unique dimension identifier (slug)           |
| `display_name`    | String   | Human-readable name                          |
| `description`     | Text     | Dimension purpose and usage                  |
| `allowed_values`  | JSON     | Array of valid tag values for this dimension |
| `is_multi_select` | Boolean  | Whether multiple values can be selected      |
| `sort_order`      | Integer  | Display order in UI                          |
| `created_at`      | DateTime | Dimension creation timestamp                 |

### Example Queries

#### List All Dimensions

<CodeGroup>
  ```javascript MCP Tool theme={null}
  await use_mcp_tool({
    server_name: "directus",
    tool_name: "read-items",
    arguments: {
      collection: "taxonomy_dimensions",
      fields: ["id", "dimension_name", "display_name", "description"],
      sort: ["sort_order"]
    }
  });
  ```

  ```bash REST API theme={null}
  curl -X GET "https://geniehelper.com/api/directus/items/taxonomy_dimensions" \
    -H "Authorization: Bearer YOUR_TOKEN" \
    -G \
    --data-urlencode 'sort=sort_order'
  ```
</CodeGroup>

#### Get Dimension with Values

<CodeGroup>
  ```javascript MCP Tool theme={null}
  await use_mcp_tool({
    server_name: "directus",
    tool_name: "read-item",
    arguments: {
      collection: "taxonomy_dimensions",
      id: "dimension-uuid",
      fields: ["dimension_name", "display_name", "allowed_values", "is_multi_select"]
    }
  });
  ```

  ```bash REST API theme={null}
  curl -X GET "https://geniehelper.com/api/directus/items/taxonomy_dimensions/DIMENSION_ID" \
    -H "Authorization: Bearer YOUR_TOKEN" \
    -G \
    --data-urlencode 'fields=dimension_name,display_name,allowed_values,is_multi_select'
  ```
</CodeGroup>

***

## taxonomy\_mapping

Stores 3,208 pre-classified tags mapped across the 6 dimensions.

### Purpose

* Map platform-specific tags to taxonomy dimensions
* Enable fast AI-powered content classification
* Support content search and discovery
* Provide training data for taxonomy classifiers

### Classification System

Each tag can have assignments across multiple dimensions:

```json theme={null}
// Example: "yoga pants" tag mapping
{
  "tag": "yoga pants",
  "platform_source": "onlyfans",
  "dimension_mappings": {
    "attire": ["activewear", "casual"],
    "body_type": ["athletic", "fit"],
    "setting": ["gym", "home"]
  },
  "confidence_score": 0.92
}
```

### Key Fields

| Field                | Type     | Description                                    |
| -------------------- | -------- | ---------------------------------------------- |
| `id`                 | UUID     | Primary key                                    |
| `tag`                | String   | Original tag text (normalized lowercase)       |
| `platform_source`    | String   | Platform where tag originated                  |
| `dimension_mappings` | JSON     | Object mapping dimension names to value arrays |
| `confidence_score`   | Decimal  | Classification confidence (0.0-1.0)            |
| `usage_count`        | Integer  | How many times this tag has been applied       |
| `created_at`         | DateTime | Mapping creation timestamp                     |
| `updated_at`         | DateTime | Last classification update                     |

### Example Queries

#### Search for Tag

<CodeGroup>
  ```javascript MCP Tool theme={null}
  await use_mcp_tool({
    server_name: "directus",
    tool_name: "search-items",
    arguments: {
      collection: "taxonomy_mapping",
      query: "lingerie",
      fields: ["tag", "dimension_mappings", "confidence_score"]
    }
  });
  ```

  ```bash REST API theme={null}
  curl -X GET "https://geniehelper.com/api/directus/items/taxonomy_mapping" \
    -H "Authorization: Bearer YOUR_TOKEN" \
    -G \
    --data-urlencode 'search=lingerie'
  ```
</CodeGroup>

#### Get Tags by Dimension

<CodeGroup>
  ```javascript MCP Tool theme={null}
  // Find all tags with "athletic" in body_type dimension
  await use_mcp_tool({
    server_name: "directus",
    tool_name: "read-items",
    arguments: {
      collection: "taxonomy_mapping",
      fields: ["tag", "dimension_mappings", "usage_count"],
      filter: {
        dimension_mappings: { _contains: "athletic" }
      },
      sort: ["-usage_count"],
      limit: 50
    }
  });
  ```

  ```bash REST API theme={null}
  curl -X GET "https://geniehelper.com/api/directus/items/taxonomy_mapping" \
    -H "Authorization: Bearer YOUR_TOKEN" \
    -G \
    --data-urlencode 'filter[dimension_mappings][_contains]=athletic' \
    --data-urlencode 'sort=-usage_count' \
    --data-urlencode 'limit=50'
  ```
</CodeGroup>

#### Create New Mapping

<CodeGroup>
  ```javascript MCP Tool theme={null}
  await use_mcp_tool({
    server_name: "directus",
    tool_name: "create-item",
    arguments: {
      collection: "taxonomy_mapping",
      data: {
        tag: "beach photoshoot",
        platform_source: "onlyfans",
        dimension_mappings: {
          setting: ["outdoor", "beach"],
          production_quality: ["professional"],
          performance_style: ["artistic"]
        },
        confidence_score: 0.88
      }
    }
  });
  ```

  ```bash REST API theme={null}
  curl -X POST "https://geniehelper.com/api/directus/items/taxonomy_mapping" \
    -H "Authorization: Bearer YOUR_TOKEN" \
    -H "Content-Type: application/json" \
    -d '{
      "tag": "beach photoshoot",
      "platform_source": "onlyfans",
      "dimension_mappings": {
        "setting": ["outdoor", "beach"],
        "production_quality": ["professional"],
        "performance_style": ["artistic"]
      },
      "confidence_score": 0.88
    }'
  ```
</CodeGroup>

## AI Classification

The taxonomy system powers AI-powered content classification:

### Action Flow

Use the `taxonomy-tag` action flow to auto-classify content:

```javascript theme={null}
// Agent emits action tag in chat response
"[ACTION:taxonomy-tag:{\"media_id\":\"uuid-here\"}]"

// Action runner executes flow:
// 1. Fetch media item caption + metadata
// 2. Call ollama MCP with scout-fast-tag model
// 3. Match returned tags against taxonomy_mapping
// 4. Apply dimension_mappings to media.taxonomy_tags
// 5. Update scraped_media record
```

### Custom Taxonomy Model

The `scout-fast-tag:latest` Ollama model is fine-tuned on the 3,208 taxonomy mappings:

```javascript theme={null}
// Using ollama MCP to classify content
await use_mcp_tool({
  server_name: "ollama",
  tool_name: "generate",
  arguments: {
    model: "scout-fast-tag:latest",
    prompt: `Classify this content caption into taxonomy tags:\n\n"${caption}"`,
    stream: false
  }
});
```

## Related Collections

* **`scraped_media`** - Content items tagged with taxonomy classifications
* **`action_flows`** - The `taxonomy-tag` flow that applies classifications
* **`agent_audits`** - Logs of taxonomy classification executions

## Workflow Integration

### Content Classification Flow

1. User uploads or scrapes new media with caption
2. AI chat interface or dashboard triggers classification
3. Agent emits `[ACTION:taxonomy-tag:{"media_id":"..."}]`
4. Action runner fetches media caption + existing tags
5. Calls Ollama `scout-fast-tag` model with caption
6. Model returns relevant tags from 3,208 mapping corpus
7. System looks up `taxonomy_mapping` for each tag
8. Aggregates `dimension_mappings` into final classification
9. Updates `scraped_media.taxonomy_tags` with result
10. Content now discoverable via dimension-based search

### Search and Discovery Flow

1. User searches for "athletic outdoor content"
2. System queries `taxonomy_mapping` for matching tags
3. Finds tags with `body_type: ["athletic"]` AND `setting: ["outdoor"]`
4. Queries `scraped_media` where `taxonomy_tags` contains those values
5. Returns ranked results by engagement metrics

## Best Practices

1. **Normalize tags before lookup** - Convert to lowercase, trim whitespace
2. **Use confidence thresholds** - Only apply mappings with `confidence_score >= 0.7`
3. **Track usage** - Increment `usage_count` when applying classifications
4. **Handle multi-select** - Check `taxonomy_dimensions.is_multi_select` before applying
5. **Batch classify** - Process multiple media items in single taxonomy flow execution
6. **Update existing classifications** - Re-run classification when captions are edited

## Data Sources

The 3,208 taxonomy mappings were sourced from:

* OnlyFans tag corpus (2,400+ tags)
* Fansly category system (600+ tags)
* Manual classification of top 200 industry terms
* Community-submitted classifications

## Model Training

The `scout-fast-tag` model was fine-tuned using:

* **Base model**: SmolLM 135M parameters
* **Training data**: `Nodes/Universe/taxonomy_graph.json` (3,205 nodes)
* **Training script**: `Nodes/Universe/apply_taxonomy_assignments.py`
* **Fine-tuning method**: HITL (Human-in-the-loop) corrections
* **Accuracy**: 87% on validation set

## See Also

* [Media Collections](/api/collections/media) - Content that receives classifications
* [Ollama MCP Tools](/api/mcp/ollama) - AI model inference for classification
* [Action Runner](/api/action-runner) - Flow execution system