pipeline-persistence

To save and reuse search workflows, you can store them as configurable YAML or JSON pipeline definitions. This enables repeatable, auditable queries by loading the saved configuration to run multi-source literature searches without repetitive setup.

You can schedule saved search pipelines across workspace and global scopes. By loading a stored YAML or JSON definition, the pipeline executes consistently for automated, repeatable multi-source literature searches at set intervals.

Pipeline persistence supports versioning for saved search workflows. It validates YAML or JSON schemas with built-in auto-correction rules to prevent errors, ensuring safe, consistent execution and reproducible query DAGs across versions.

The best way to ensure reproducible queries is defining search pipelines as YAML or JSON templates. Storing these structured DAGs with versioning and validation enables repeatable, auditable multi-source literature searches across workspace and global scopes.

Saved search pipelines fail validation when the YAML or JSON schema contains errors. The system applies auto-correction rules to fix issues and prevent errors, ensuring safe and consistent execution of the configured workflow DAG.

You need a YAML or JSON schema format to store and retrieve configurable search pipelines. Defining your workflow with these formats allows the system to apply versioning, validation, and auto-correction rules for safe execution.