Overview
The Ai Pipeline node is designed to manage AI-related workflows by supporting operations such as starting a pipeline, starting a task, ending a task, and executing a task. It is useful in scenarios where users need to automate and orchestrate AI processing steps, for example, initiating an AI data processing pipeline, managing individual AI tasks within that pipeline, and executing specific AI tasks like file-to-text or text-to-text conversions.
Use Case Examples
- Starting an AI pipeline to process data streams.
- Starting a specific AI task within a pipeline, such as converting a file to text.
- Ending a task after completion to update the workflow status.
- Executing a task that transforms text data.
Properties
| Name | Meaning |
|---|---|
| Operation | Specifies the action to perform within the AI pipeline, such as starting or ending a pipeline or task, or executing a task. |
| Task Type | Defines the type of task to execute when the operation is 'Execute Task'. |
| Task ID | Identifier for the specific task to be executed when the operation is 'Execute Task'. |
Output
JSON
result- The output data or status resulting from the executed operation, such as pipeline or task details.
Dependencies
- The node relies on internal operation modules for starting and ending pipelines and tasks, and executing tasks. It may require API credentials or tokens depending on the implementation of these operations.
Troubleshooting
- Ensure the correct operation is selected; unsupported operations will return input data unchanged.
- When executing a task, provide a valid Task ID; missing or invalid IDs may cause errors.
- Check that required credentials or API keys are configured if the operations interact with external AI services.