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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

• 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

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

• 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

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

Both components are essential for managing data lifecycle in modular AI workflows, giving you the flexibility to create and modify structured data as needed.