You start every workflow by defining the structure of the incoming event. A Pydantic schema becomes the single source of truth for validation, documentation, and serialization.Documentation Index
Fetch the complete documentation index at: https://launchpad.datalumina.com/llms.txt
Use this file to discover all available pages before exploring further.
Why Schemas Matter
Schemas give you automatic type validation, inline documentation through field descriptions, and straightforward JSON serialization, so you can accept reliable inputs and publish clear API docs without extra boilerplate.Customer Care Event Schema
For our customer care use case, we’ve defined the following schema:Schema Components Explained
Automatic ID Generation
Theticket_id field uses default_factory=uuid4 to automatically generate a unique identifier for each ticket when created.
Timestamp Management
Thetimestamp field automatically captures the creation time in UTC, ensuring consistent time tracking across different timezones.
Required Fields
Fields marked with... (ellipsis) are required and must be provided when creating an event instance.
Field Descriptions
Each field includes a description that serves as inline documentation and helps with API documentation generation.File Location
Colocate each workflow’s schema in its own package (app/launchpad/workflows/<name>/schema.py) so the input model lives next to the workflow that validates against it. Truly cross-workflow schemas can go under app/launchpad/schemas/.