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Create Data
Create Data is a flow component designed to dynamically generate a new data record with a customizable number of fields. It serves as a foundational building block in AI workflows where the structured creation of data objects is required.
What Does the Component Do?
This component allows users to create a new data object, specifying exactly how many fields (between 1 and 15) should be included. The fields themselves can be used to store text or other information, making the component flexible for a variety of use cases such as initializing records, templates, or structured inputs for downstream processing.
Inputs
| Name | Type | Required | Description | Advanced |
|---|---|---|---|---|
| Number of Fields | Integer | No | Number of fields to add (min: 1, max: 15) | No |
| Text Key | String | No | Key to be used as text (for advanced use) | Yes |
- Number of Fields: Lets you define how many fields the new data record will have. This is useful when you know the structure of your data in advance.
- Text Key: An optional advanced setting to specify which key will be used for text data. This can help in cases where you want to label or organize your fields in a specific way.
Outputs
| Name | Type | Description |
|---|---|---|
| Data | Data | The created data record |
The output is a new data object that can be used elsewhere in your workflow—for example, as input for data processing, enrichment, or model inference steps.
Usefulness in AI Workflows
- Initialization: Use this component at the start of your workflow to generate structured data objects.
- Template Creation: Prepare blank or partially filled records to be updated or populated later.
- Modularity: Helps break down complex workflows by standardizing input data formats.
Update Data
Update Data is a flow component designed to modify existing data records by updating or replacing fields using key-value pairs, similar to how you would update a Python dictionary.
What Does the Component Do?
This component takes an existing data object and allows you to update specific fields by providing new key-value pairs. It is especially useful when you want to incrementally build up or modify a data record as it passes through different stages of your AI workflow.
Inputs
| Name | Type | Required | Description | Advanced |
|---|---|---|---|---|
| Data | Data | No | The record to update | No |
| Field to Replace | Dict / Message | No | Key-value pairs for updates | No |
- Data: The data object you want to update.
- Field to Replace: A dictionary or message specifying which field(s) to update and their new values.
Outputs
| Name | Type | Description |
|---|---|---|
| Data | Data | The updated data record |
The output is the modified data object, ready for further processing.
Usefulness in AI Workflows
- Incremental Data Enrichment: Add or update fields as new information becomes available.
- Dynamic Workflows: Adapt data records on the fly, based on the results of previous workflow steps.
- Integration: Seamlessly update data structures to be compatible with other components that require specific fields.
Summary Table
| Component | Main Function | Key Inputs | Output | Use Cases |
|---|---|---|---|---|
| Create Data | Create new data object | Number of Fields, Text Key | Data | Initialization, templating, structuring |
| Update Data | Update data object | Data, Field to Replace | Data | Data enrichment, dynamic updates |
Both components are essential for managing data lifecycle in modular AI workflows, giving you the flexibility to create and modify structured data as needed.
