How to specify the config schema for a graph-backed-asset?

To specify the config schema for a graph-backed asset in Dagster, you can define the configuration directly on the asset's underlying op. When you define an op with a config schema, this schema is used to configure the asset that the op computes. Here's an example from the documentation that illustrates how to define a config schema for an asset:

python from dagster import asset, Config, materialize  @asset(config_schema={"interest_rate": float}) def asset_with_config(context):     return some_financial_calculation(rate=context.op_config["interest_rate"])  materialize(     [asset_with_config],     run_config={"ops": {"asset_with_config": {"config": {"interest_rate": 0.05}}}} )

In this example, the

asset_with_config

asset has a config schema defined with a single parameter

interest_rate

of type

float

. When materializing the asset, you can provide the configuration value for

interest_rate

in the

run_config

. For graph-backed assets, you can apply the same principle. Define the config schema on the ops that make up the graph, and then when you materialize the graph-backed asset, provide the configuration values for those ops within the

run_config

. If you have a graph that outputs multiple assets and you want to define a config schema for the entire graph, you can use the

@graph_asset

decorator and provide the config schema as part of the decorator's arguments. However, as of my last update, there are some limitations and ongoing discussions about enhancing the support for config schemas at the graph level for graph-backed assets. For the most current information on this topic, please refer to the latest Dagster documentation and GitHub discussions.