"What does the Text Classification component do?"

"It analyzes input text and assigns it to one or more categories you define, using an AI model for accurate, automated classification."

"Can I define my own categories?"

"Yes, you can set custom categories and descriptions to tailor classification for your specific workflow needs."

"Does it support context from previous messages?"

"Yes, you can enable chat history input to improve the accuracy of classification by considering prior conversation context."

"What models can be used for classification?"

"You can select from various language models, including small or large LLMs, depending on your accuracy and speed requirements."

"Is it possible to control how many categories are assigned?"

"Yes, you can specify whether the classification should return one, multiple, or no categories at all for each input."

Text Classification

Automatically sort and label input text into categories using AI, with customizable options for categories, models, and classification types.

Component description

How the Text Classification component works

The Classification component is designed to perform text classification, sorting incoming text into one or more user-defined categories. This is particularly useful in AI workflows where automatic categorization, routing, or labeling of incoming messages or documents is required.

What the Component Does

At its core, this component takes an input text and classifies it according to a set of categories and their optional descriptions. It supports integration with conversational history and allows customization of the classification logic using various configuration options. The component is model-agnostic and can leverage any compatible language model, including lightweight models, making it flexible and efficient for a variety of use cases.

Key Inputs

Input Name	Type	Required	Description
Input Text	Message	Yes	The main text to be classified.
Chat History	InMemoryChatMessageHistory	No	Previous conversation messages to provide context and improve classification accuracy.
LLM (Model)	BaseChatModel	No	Specifies which large language model to use for the classification.
Categories	Data (Nested Dict)	Yes	A dictionary of category names and optional descriptions to define what the text should be classified into.
Classification Type	Dropdown (str)	Yes	Choose how many categories can be selected: One or More, Zero or More, One Only, Zero or One.
Custom System Message	Message	No	An optional system prompt to further guide the classification model’s behavior.
Tool Description	str (multiline)	No	A description for the tool, helpful when used within agent frameworks.
Tool Name	str	No	Optional, for referencing this tool in agent-based workflows.
Verbose	bool	No	Option to enable verbose output for debugging or transparency.

Notable Features

Chat History Integration: By incorporating chat history, the component can classify text with greater context, increasing precision in multi-turn conversations.
Configurable Classification Type: Supports flexible classification logic, allowing single or multiple categories per input as per workflow needs.
Customizable Prompts: Advanced users can add system messages to tweak or fine-tune the classification prompt.
Model Flexibility: Works with a range of language models, including smaller/faster models.

Outputs

Output Name	Type	Description
Categories	Message	The resulting classification(s) for the input text.
Tool	Tool	The classification tool instance, for integration in agent workflows.

Example Use Cases

Customer Support: Automatically categorize incoming support tickets or chat messages to route them to the correct department.
Content Moderation: Classify user-generated content into safe, spam, or flagged categories.
Document Management: Organize documents or emails by topic or department.
Conversational AI: Provide context-aware responses by classifying user intent based on conversation history.

Why Use This Component?

This component streamlines the process of integrating robust text classification into your AI workflows. Its flexibility, context-awareness, and support for both basic and advanced configuration make it a valuable building block for automation, analytics, and conversational AI systems. Whether you need simple keyword-based categorization or nuanced, context-rich intent detection, this component can be tailored to your requirements.

Examples of flow templates using Text Classification component

To help you get started quickly, we have prepared several example flow templates that demonstrate how to use the Text Classification component effectively. These templates showcase different use cases and best practices, making it easier for you to understand and implement the component in your own projects.

Search Intent Classifier & Landing Page Generator

This AI-powered workflow classifies search queries by intent, researches top-ranking URLs, and generates a highly optimized landing page for PPC and SEO campaig...

Jun 6, 2025 4 min read

Frequently asked questions

What does the Text Classification component do?: It analyzes input text and assigns it to one or more categories you define, using an AI model for accurate, automated classification.
Can I define my own categories?: Yes, you can set custom categories and descriptions to tailor classification for your specific workflow needs.
Does it support context from previous messages?: Yes, you can enable chat history input to improve the accuracy of classification by considering prior conversation context.
What models can be used for classification?: You can select from various language models, including small or large LLMs, depending on your accuracy and speed requirements.
Is it possible to control how many categories are assigned?: Yes, you can specify whether the classification should return one, multiple, or no categories at all for each input.

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