Issue with Microsoft Teams Webhook: Images Not Displaying

GX Core Support
1

I’ve been using the MicrosoftTeamsNotificationAction to notify my team about critical issues, and almost everything works perfectly. However, the images included in the webhook messages are not showing up in Teams.

I checked the webhook logs and noticed the following image URL was being sent:

                        "type": "ColumnSet",
                        "columns": [
                            {
                                "type": "Column",
                                "items": [
                                    {
                                        "type": "Image",
                                        "url": "https://www.greatexpectations.io/image/gx-logo-mark-400",
                                        "altText": "my_checkpoint",
                                        "size": "small"
                                    }
                                ],
                                "width": "auto"
                            },

https://greatexpectations.io/image/gx-logo-mark-400

When trying to access the image directly, I encountered an error:

image
image1163×727 30.5 KB

In the git repo you will find this setting in great_expectations/great_expectations/render/renderer/microsoft_teams_renderer.py at develop · great-expectations/great_expectations · GitHub

To troubleshoot this, I ran the following notebook code in Databricks.

%pip install great-expectations

# Import great_expectations and request a Data Context.
import great_expectations as gx

context = gx.get_context(mode="file")

# Optional. Request a File Data Context from a specific folder.
context = gx.get_context(mode="file", project_root_dir="./new_context_folder")

# Optional. Review the configuration of the returned File Data Context.
print(context)

context = gx.get_context(mode="file", project_root_dir="./new_context_folder")

# Define the Data Source name
data_source_name = "my_data_source"

# Add the Data Source to the Data Context
data_source = context.data_sources.add_pandas(name=data_source_name)

# Retrieve the Data Source
data_source_name = "my_data_source"
data_source = context.data_sources.get(data_source_name)

# Define the Data Asset name
data_asset_name = "my_dataframe_data_asset"

# Add a Data Asset to the Data Source
data_asset = data_source.add_dataframe_asset(name=data_asset_name)

# Retrieve the Data Asset
data_source_name = "my_data_source"
data_asset_name = "my_dataframe_data_asset"
data_asset = context.data_sources.get(data_source_name).get_asset(data_asset_name)

# Define the Batch Definition name
batch_definition_name = "my_batch_definition"

# Add a Batch Definition to the Data Asset
batch_definition = data_asset.add_batch_definition_whole_dataframe(
    batch_definition_name
)

# Create an Expectation Suite
suite_name = "my_expectation_suite"
suite = gx.ExpectationSuite(name=suite_name)

# Add the Expectation Suite to the Data Context
suite = context.suites.add(suite)

# Create an Expectation to put into an Expectation Suite
expectation = gx.expectations.ExpectColumnValuesToNotBeNull(column="passenger_count")

# Add the previously created Expectation to the Expectation Suite
suite.add_expectation(expectation)

# Add another Expectation to the Expectation Suite.
suite.add_expectation(
    gx.expectations.ExpectColumnValuesToNotBeNull(column="pickup_datetime")
)

# Update the configuration of an Expectation, then push the changes to the Expectation Suite
expectation.column = "pickup_location_id"
expectation.save()

# Retrieve an Expectation Suite from the Data Context
existing_suite_name = (
    "my_expectation_suite"  # replace this with the name of your Expectation Suite
)
suite = context.suites.get(name=existing_suite_name)

context = gx.get_context(mode="file", project_root_dir="./new_context_folder")

# Retrieve an Expectation Suite
expectation_suite_name = "my_expectation_suite"
expectation_suite = context.suites.get(name=expectation_suite_name)

# Retrieve a Batch Definition
data_source_name = "my_data_source"
data_asset_name = "my_dataframe_data_asset"
batch_definition_name = "my_batch_definition"
batch_definition = (
    context.data_sources.get(data_source_name)
    .get_asset(data_asset_name)
    .get_batch_definition(batch_definition_name)
)

# Create a Validation Definition
definition_name = "my_validation_definition"
validation_definition = gx.ValidationDefinition(
    data=batch_definition, suite=expectation_suite, name=definition_name
)

# Add the Validation Definition to the Data Context
validation_definition = context.validation_definitions.add(validation_definition)


import pandas as pd

# Retrieve the Validation Definition
validation_definition_name = "my_validation_definition"
validation_definition = context.validation_definitions.get(validation_definition_name)

# Define Batch parameters
df = pd.read_csv(
    "https://raw.githubusercontent.com/great-expectations/gx_tutorials/main/data/yellow_tripdata_sample_2019-01.csv"
)

# Accepted keys are determined by the BatchDefinition used to instantiate this ValidationDefinition.
batch_parameters_dataframe = {"dataframe": df}
#batch_parameters_daily = {"year": "2020", "month": "1", "day": "17"}
#batch_parameters_yearly = {"year": "2019"}

# Run the Validation Definition
validation_results = validation_definition.run(batch_parameters=batch_parameters_dataframe)


# Review the Validation Results
print(validation_results)


import great_expectations as gx
from great_expectations.checkpoint import (
    MicrosoftTeamsNotificationAction,
)


# Create a list of one or more Validation Definitions for the Checkpoint to run
validation_definitions = [
    context.validation_definitions.get("my_validation_definition")
]

# Create a list of Actions for the Checkpoint to perform
action_list = [
    # This Action sends a Slack Notification if an Expectation fails.
    MicrosoftTeamsNotificationAction(
        name="send_teams_notification_on_all_expectations",
        teams_webhook="${webhook_url}",
        notify_on="all",
#        show_failed_expectations=True,
    ),
    # This Action updates the Data Docs static website with the Validation
    #   Results after the Checkpoint is run.
    # UpdateDataDocsAction(
    #     name="update_all_data_docs",
    # ),
]

# Create the Checkpoint
checkpoint_name = "my_checkpoint"
checkpoint = gx.Checkpoint(
    name=checkpoint_name,
    validation_definitions=validation_definitions,
    actions=action_list,
    result_format={"result_format": "COMPLETE"},
)

# Save the Checkpoint to the Data Context
context.checkpoints.add(checkpoint)

# Retrieve the Checkpoint later
checkpoint_name = "my_checkpoint"
checkpoint = context.checkpoints.get(checkpoint_name)


import os

os.environ["webhook_url"] = "https://your-webhook-url"


checkpoint = context.checkpoints.get("my_checkpoint")

batch_parameters_dataframe = {"dataframe": df}

expectation_parameters = {
    "expect_fare_max_to_be_above": 5.00,
    "expect_fare_max_to_be_below": 1000.00,
}

validation_results = checkpoint.run(
    batch_parameters=batch_parameters_dataframe, expectation_parameters=expectation_parameters
)