Configure Dagster ops and jobs with a RunConfig schema to pass structured runtime parameters without hardcoding values in the op body

Steps

1. Define a Pydantic-based config class by subclassing dagster.Config and use it as the type annotation for the config parameter in the @op or @asset function signature
2. Access config values inside the op body via the config parameter; Dagster validates the provided YAML or dict against the Config class at launch time and raises an error for missing required fields
3. Pass config at runtime by providing RunConfig(ops={'op_name': {'config': {'field': 'value'}}}) when calling job.execute_in_process() or when submitting a run via the GraphQL API
4. Use dagster.Field with a default value and description to make config fields optional and self-documenting; these appear in the Dagster UI Launchpad with inline documentation
5. Define job-level config by using a separate Config class attached to the job and access it via context.op_config inside ops that are part of that job