Problem Statement: Currently, the Text Classifier AI node requires predefined categories for classification. This limitation requires manual updates and configuration adjustments whenever new categories are needed, which is inefficient and not scalable, especially in environments with diverse and evolving classification needs.
Proposed Solution: Introduce a feature that allows dynamic categorization in the Text Classifier AI node. This feature would enable the classifier to accept categories as input at runtime, rather than requiring them to be hardcoded into the node’s configuration. The AI could then adapt to different sets of categories provided by the users or inferred from the dataset.
Use Case: Imagine a scenario where a company has numerous clients, each needing to classify texts into unique, client-specific categories. With dynamic categorization, the company could automate this process by feeding the AI with categories specified by each client at runtime. This would eliminate the need for deploying separate classifiers for each client or manually updating the category list for every change in classification requirements.
Benefits:
- Flexibility: Users can tailor the classifier to varied and specific needs without waiting for code updates or deployments.
- Scalability: Supports a growing number of users or cases without additional development overhead.
- Efficiency: Reduces the time and resources spent on manual updates and maintenance of the classifier’s configuration.
This enhancement would significantly streamline operations for users who manage multiple classification models across diverse client requirements, thereby improving both user satisfaction and operational efficiency.