Overview
The Ai Pipeline node is designed to manage and execute 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 want to automate AI processing pipelines, for example, initiating a sequence of AI tasks, processing data through various AI models, and managing task lifecycle within the pipeline.
Use Case Examples
- Starting an AI pipeline to process a batch of files through multiple AI tasks.
- Executing a specific AI task like converting a file to text within a pipeline.
- Ending a task after completion to update the pipeline status.
Properties
| Name | Meaning |
|---|---|
| Operation | Specifies the action to perform within the AI pipeline, such as starting the pipeline, starting a task, ending a task, or executing a task. |
| Task Type | Defines the type of task to execute when the operation is 'executeTask'. |
| Task ID | Identifier for the specific task to execute when the operation is 'executeTask'. |
Output
JSON
data- The output data from the executed operation, which varies depending on the operation performed.
Dependencies
- The node depends on external operation modules for each action: start.pipeline.operation, end.pipeline.operation, start.task.operation, end.task.operation, execute.task.operation.
Troubleshooting
- Common issues may include missing or incorrect task IDs when executing tasks, leading to errors in task execution.
- Errors may occur if the operation parameter is not set correctly or if the required external operation modules fail to execute.
- Ensure that all required credentials or API keys for the AI services used in the operations are properly configured.