Ragflow Create Categorize Node From JSON

RagFlow Create/Update Categorize Node from JSON

This skill automates the creation and synchronization of complex Categorize nodes from a JSON file. It handles both fresh node creation and updating existing nodes by name, ensuring descriptions and examples are perfectly in sync.

JSON Format Requirement

The input JSON should be a list of objects with category (or name), description (optional), and examples (string or list):

[
  {
    "category": "my_category",
    "description": "Specific guidance for this category",
    "examples": "Example 1\nExample 2"
  }
]

Workflow

1. Extract Agent DSL: Use the ragflow-agent-repair skill.

   • python3 /root/.gemini/skills/ragflow-agent-repair/scripts/repair.py <agent_id> get > agent.hex

2. Generate/Update DSL: Run the bundled script via a Python wrapper (to avoid shell limits on DSL size).

   import sys
   sys.path.append("/root/skills/ragflow-create-categorize-node-from-json/scripts")
   from create_node import create_categorize_node
   
   with open('agent.hex', 'r') as f: hex_data = f.read().strip()
   updated_dsl = create_categorize_node(hex_data, "/path/to/data.json", "Node Name")
   with open('updated.json', 'w') as f: f.write(updated_dsl)
   

3. Apply Update: Use the ragflow-agent-repair skill.

   • python3 /root/.gemini/skills/ragflow-agent-repair/scripts/repair.py <agent_id> update updated.json

4. Clear Cache: Flush Redis and hard refresh the browser.

   • docker exec ragflow-redis-1 redis-cli -a <password> FLUSHALL

Key Improvements

• Update Mode: If a node with the same name exists, it reuses the ID and visual position, updating only the content and parameters.
• Explicit Descriptions: Uses the description field from JSON if available, improving classification accuracy.
• Connection Preservation: Preserves existing upstream/downstream connections when updating.