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# ask-ai
s
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s
The error message you're encountering indicates that there is an attempt to index into an 
InvokedNodeOutputHandle
, which is not the correct way to handle outputs from ops within a Dagster job or graph. In Dagster, when an op returns multiple outputs, each output is accessed by its name rather than by indexing. If you need to pass different parts of an op's output to different subsequent ops, you should define multiple outputs for the op and then reference those outputs by name. Here's a brief explanation of how you might encounter this error and how to resolve it: 1. Encountering the Error: This error can occur if you have an op that is expected to return multiple outputs, but instead of yielding or returning a dictionary mapping output names to values, you return a list or tuple and then try to index into the result as if it were a list or tuple. 2. Resolving the Error: To resolve this, you should define multiple outputs for your op using the 
Out
class and then yield or return a dictionary where the keys correspond to the names of the outputs. Each subsequent op should then reference the specific output it depends on by name. Here's an example of how you might define an op with multiple outputs:
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python from dagster import Out, op  @op(out={"output1": Out(), "output2": Out()}) def my_op():     # Perform some computation     result1 = ...     result2 = ...     return {"output1": result1, "output2": result2}
And then you would use these outputs in a graph or job:
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python from dagster import graph  @graph def my_graph():     outputs = my_op()     op_that_uses_output1(outputs.output1)     op_that_uses_output2(outputs.output2)
By following this pattern, you can avoid the error and correctly pass different parts of an op's output to different ops.
To modify your code to support multiple asset check results, you'll need to make a few changes. First, ensure that you're using the 
@asset
decorator instead of 
@op
since we prefer to use assets in Dagster. Then, you'll need to collect all 
AssetCheckResult
instances into a list and yield them together as a single output. Here's how you can modify your code:
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python from dagster import asset, AssetMaterialization, AssetObservation  @asset def execute_shell_command_with_pipes(context) -> Any:     session_results = context.resources.pipes_session.get_results()     asset_checks = []     for single_result in session_results:         if isinstance(single_result, MaterializeResult):             materialize_metadata = single_result.metadata             # Yield an AssetMaterialization to record metadata about the asset.             yield AssetMaterialization(description="Materialization with metadata", metadata=materialize_metadata)         elif isinstance(single_result, AssetCheckResult):             # Collect AssetCheckResults to yield later.             asset_checks.append(single_result)         else:             # Handle other types of results or raise an error if unexpected result type.             pass      # Yield AssetObservations for custom messages.     custom_messages = context.resources.pipes_session.get_custom_messages()     for custom_message in custom_messages:         yield AssetObservation(asset_key="custom_message", metadata={"message": custom_message})      # If there are no reported asset checks, you can yield a default value or handle it as needed.     if not asset_checks:         # Handle the case where there are no asset checks.         pass     else:         # Yield all collected AssetCheckResults.         for check in asset_checks:             yield check
A few important notes about this code: 1. I've replaced 
@op
with 
@asset
to align with Dagster's asset-based APIs. 2. Instead of using 
Output
, I'm using 
AssetMaterialization
and 
AssetObservation
to record metadata and observations about the asset. 3. I'm collecting all 
AssetCheckResult
instances into the 
asset_checks
list and yielding them individually at the end. 4. I've removed the 
out
parameter since it's not necessary when using 
@asset
. The asset will automatically yield materializations, observations, and check results. 5. I've added 
context
as a parameter to the function, which is used to access resources like 
pipes_session
. Please adjust the code as needed to fit the rest of your Dagster job or graph, and ensure that the resources and types used are correctly defined elsewhere in your codebase.
p
@Scout no, it should be an op
s
I apologize for the confusion. To support multiple asset check results in an op, you need to yield multiple outputs for each type of result. Since you want to yield multiple asset check results, you'll need to collect them and yield them as a list or another collection type that suits your needs. Here's how you can modify your code to use an 
@op
with multiple outputs:
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python from dagster import Out, op, Output  @op(out={"result": Out(), "asset_checks": Out(is_required=False)}) def execute_shell_command_with_pipes():     session_results = pipes_session.get_results()     asset_checks = []     for single_result in session_results:         if isinstance(single_result, MaterializeResult):             materialize_metadata = single_result.metadata             yield Output(value=materialize_metadata, output_name="result", metadata=materialize_metadata)         elif isinstance(single_result, AssetCheckResult):             asset_checks.append(single_result)          custom_messages = pipes_session.get_custom_messages()     if custom_messages:         custom_message = custom_messages[0]         yield Output(custom_message, output_name="result", metadata={"custom_message": custom_message})          if asset_checks:         yield Output(asset_checks, output_name="asset_checks")     else:         # If there are no asset checks, you can yield a default value or None.         yield Output(None, output_name="asset_checks")
In this modified code: 1. I've defined two outputs for the `@op`: 
result
and 
asset_checks
. The 
asset_checks
output is marked as 
is_required=False
to indicate that it's optional. 2. I'm collecting all 
AssetCheckResult
instances into the 
asset_checks
list. 3. I'm yielding 
materialize_metadata
and 
custom_message
with the output name 
result
. 4. I'm yielding the list of 
asset_checks
with the output name 
asset_checks
. If there are no asset checks, I'm yielding 
None
for the 
asset_checks
output. Please ensure that the rest of your Dagster job or graph is set up to handle these multiple outputs correctly, and that the types 
MaterializeResult
and 
AssetCheckResult
are defined and imported as needed.
The error you're encountering suggests that you're trying to index into an 
InvokedNodeOutputHandle
object as if it were a list or tuple, which is not allowed in Dagster. When you have an op that yields multiple outputs, each output is accessed by its name, not by indexing. To access the outputs from an op that yields multiple outputs, you should reference the outputs by name in the downstream ops or assets. Here's how you can do it: Assuming you have an op like this:
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python @op(out={"result": Out(), "asset_checks": Out(is_required=False)}) def my_op():     # ... your logic here ...     yield Output(value=result, output_name="result")     yield Output(value=asset_checks, output_name="asset_checks")
You can access these outputs in a downstream asset like this:
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python @asset def my_asset(context, result, asset_checks):     # Now you can use 'result' and 'asset_checks' directly without indexing     # 'result' contains the value yielded with output_name="result"     # 'asset_checks' contains the value yielded with output_name="asset_checks"      # If you need to access individual asset checks, you can do so assuming 'asset_checks' is a list     asset_check1 = asset_checks[0] if asset_checks else None     asset_check2 = asset_checks[1] if asset_checks and len(asset_checks) > 1 else None      # Your logic here...
In the above example, 
result
and 
asset_checks
are provided as inputs to the asset, and you can use them directly without trying to index into them. If 
asset_checks
is a list, you can index into it within the body of the asset function, but not when trying to access the 
InvokedNodeOutputHandle
. Make sure that the downstream assets or ops are set up to receive these outputs correctly, and that the dependencies in your job or graph are correctly wired to pass these named outputs to the downstream nodes.
The error message you're seeing indicates that you're trying to index into an 
InvokedNodeOutputHandle
directly, which is not allowed. Instead, you should access the entire output and then perform any indexing or further processing within the body of the downstream op or asset. Here's how you can correctly handle the multiple outputs from your op: 1. Ensure that your op is yielding outputs with distinct names, as you've done in your provided code snippet. 2. In the downstream op or asset, you will receive these outputs as separate arguments, not as an indexable object. Here's an example of how you might define a downstream op that receives these outputs:
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python @op def downstream_op(result, asset_checks):     # You can now work with 'result' and 'asset_checks' directly.     # If 'asset_checks' is a list of AssetCheckResult objects, you can index into it here.     asset_check1 = asset_checks[0] if asset_checks and len(asset_checks) > 0 else None     asset_check2 = asset_checks[1] if asset_checks and len(asset_checks) > 1 else None      # Now you can use asset_check1 and asset_check2 as needed.     # ...
And here's how you would wire this up in a job:
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python @job def my_job():     result, asset_checks = my_op()     downstream_op(result, asset_checks)
In this setup, 
downstream_op
receives 
result
and 
asset_checks
as separate inputs. Within the body of 
downstream_op
, you can then index into 
asset_checks
if it's a list. Make sure that when you define the job, you're correctly connecting the outputs of 
my_op
to the inputs of 
downstream_op
. The 
result
and 
asset_checks
should be passed as arguments to 
downstream_op
based on their output names, not by trying to index into an 
InvokedNodeOutputHandle
.
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