Filter Data
The Filter Data component lets you filter incoming data based on text-based key and value pairs, helping you extract only the data you need for the next steps in your workflow.
Component description
How the Filter Data component works
Filter Data Component
The Filter Data component is designed to help you filter data records based on specific text-based key/value pairs, much like filtering entries in a Python dictionary. This component is useful in AI workflows where you need to extract or isolate records that meet certain criteria from a larger dataset.
What does this component do?
This component examines the input data and selects only those records that match a given key and value. For example, if you have a dataset of user profiles and you want to find all profiles where "status": "active", you can use this component to filter for those records.
Inputs
| Input Name | Type | Description | Required | Example/Info |
|---|---|---|---|---|
| Input Data | Data | The record(s) to filter | No | The dataset you want to filter |
| Filter Key | Message | Key to filter by | No | e.g., “status” |
| Filter Value | Message | Value to match for the key | No | e.g., “active” |
- Input Data: This is the dataset you want to filter. It can be any data structure that supports key/value access, such as a dictionary or a list of dictionaries.
- Filter Key: The name of the key you want to filter by (e.g., “status”).
- Filter Value: The value that the key should match for a record to be included in the output (e.g., “active”).
Outputs
| Output Name | Type | Description |
|---|---|---|
| Filtered Data | Data | Data records matching filter |
- Filtered Data: The output will include only those data records where the key matches the specified value.
Why use the Filter Data component?
- Data Selection: Easily extract subsets of data relevant to your task, reducing noise and improving downstream processing.
- Automation: Automate common filtering tasks in AI pipelines without writing custom code.
- Flexibility: Works with any data record that supports key/value access, making it broadly applicable.
Typical Use Cases
- Selecting all user logs from a specific date or with a certain status.
- Filtering AI model outputs based on a tag or label.
- Preprocessing datasets to include only entries relevant for training, evaluation, or reporting.
Summary Table
| Feature | Details |
|---|---|
| Component Name | Filter Data |
| Description | Filters data using key/value pairs |
| Input Types | Data, Message (for key/value) |
| Output Types | Data (filtered) |
| Common Use | Data selection/filtering in workflows |
This component is an essential building block for managing and processing data in any AI workflow where filtering by attribute is required.
Frequently asked questions
- What does the Filter Data component do?
The Filter Data component extracts specific pieces of data from your input by matching text-based keys and values, similar to how you would filter items in a Python dictionary.
- When should I use the Filter Data component?
Use this component whenever you need to isolate or select certain records from a larger dataset as part of an automated workflow.
- Can I use Filter Data with any type of data?
It works with data structured in key-value pairs, making it ideal for messages, records, or other dictionary-like data formats.
- How do I set the filter key and value?
Simply provide the key to look for and the value you wish to match in the component settings—no coding required.
- What happens to data that doesn’t match the filter?
Only the data matching the specified key and value is passed along; non-matching data is excluded from the output.
Try Filter Data in FlowHunt
Start refining your data and power up your workflows with the Filter Data component.