AI Filter

Overview:

The AI Filter block is designed to process input data by applying a specified natural language condition, facilitating effective data filtering and decision-making.

Inputs & Outputs

I/O	Feature	Type	Simple Explanation
input	`input`	string	The text to apply the filter on.
input	`condition`	string	The natural language condition that will dictate how the inputs are filtered.
input	`model`	selector	Select from a range of supported LLM models for carrying out inference based on provided inputs.
input	`cache_response`	boolean	When enabled, this feature saves AI calls by returning the same response if the inputs have not changed since the last processing instance.
output	`filtered_output`	string	- This represents the filtered result of your data evaluation. If your input satisfies the defined condition, it appears here unchanged; otherwise, it will be empty.

Use Cases

Consider how this block can enhance various processes:

Filtering Customer Inquiries: Efficiently identify and isolate customer inquiries that meet certain criteria expressed in everyday language, ensuring prompt responses.
Data Categorization: Automatically sort and organize textual information into distinct categories based on predefined conditions—useful for managing large datasets or incoming messages.
Automated Decision Making: Incorporate dynamic decision capabilities into automation workflows whereby textual content determines subsequent actions or outputs in a streamlined manner.

In essence, when there is a need to filter or make decisions based on textual data seamlessly, the AI Filter block emerges as an essential tool!

Getting Started

App Essentials

Blocks

Triggers

Overview:

Inputs & Outputs

Use Cases

​Overview:

​Inputs & Outputs

​Use Cases

Overview:

Inputs & Outputs

Use Cases