This document explains the Categorizer node, which uses AI to classify text into custom categories.

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Node Inputs

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Required Fields

  • Input: Text to categorize
  • Categories: Define your classification groups:
    • Category Name: Label for the category
    • Category Description: Explain what belongs in this category

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Optional Fields

  • Include Justification: Get AI’s reasoning for selections
  • Additional Context: Extra guidance for categorization
  • Temperature: Controls AI decision-making (0-1)
    • 0: More focused, consistent
    • 1: More creative, varied
  • Cache Response: Save responses for reuse

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Show As Input

The node allows you to configure certain parameters as dynamic inputs. You can enable these in the “Configure Inputs” section:

  • include_justification: Boolean

    • true/false to include explanation for category assignment

  • Additional Context: String

    • Extra information to guide the categorization process
    • Example: “These items are different types of software bugs”

  • model_preference: String

    • Name of the AI model to use
    • Accepted values: “Claude 3.5 Sonnet”, “Claude 3 Haiku”, “GPT-4o”, “GPT-4o Mini”, etc.

  • Cache Response: Boolean

    • true/false to enable/disable response caching
    • Helps reduce API calls for identical inputs

  • Temperature: Number

    • Value between 0 and 1
    • Controls categorization consistency

When enabled as inputs, these parameters can be dynamically set by previous nodes in your workflow. If not enabled, the values set in the node configuration will be used.

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Node Output

  • Selected Category: Chosen category name
  • Justification: AI’s reasoning (if enabled)

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Node Functionality

The Categorizer node:

  • Analyzes input text
  • Matches to best category
  • Provides reasoning (optional)
  • Handles batch processing
  • Supports custom categories

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Available AI Models

  • Claude 3.5 Sonnet
  • Claude 3 Haiku
  • OpenAI o1
  • OpenAI o1 mini
  • GPT-4o
  • GPT-4o Mini
  • DeepSeek V3
  • DeepSeek R1
  • Gemini 1.5 Pro/Flash
  • And more

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Example Use Cases

  1. Sentiment Analysis:
Categories:
- Positive: "Expresses satisfaction or approval"
- Negative: "Shows dissatisfaction or criticism"
- Neutral: "States facts without emotion"
  1. Support Tickets:
Categories:
- Bug Report: "Technical issues or errors"
- Feature Request: "New functionality suggestions"
- Account Issue: "Login or access problems"
  1. Content Classification:
Categories:
- News: "Current events and reporting"
- Opinion: "Personal views and analysis"
- Tutorial: "How-to guides and instructions"

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Loop Mode

Input: List of customer feedback
Process: Categorize each item
Output: Category per item + justifications

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Important Considerations

  1. Expert models (OpenAI o1) cost 30 credits, advanced models (GPT-4o & Claude 3.5) cost 20 credits, and standard models cost 2 credits per run
  2. You can drop the credit cost to 1 by providing your own API key under the credentials page
  3. Write clear category descriptions for accurate outputs
  4. Enable justification for important decisions
  5. Use additional context for complex rules

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Additional Information

Video Tutorial

In summary, the Categorizer node helps organize text into meaningful groups using AI, with optional explanations for each decision.

SummarizerScorer