Client Info scoping (#184)

* predicting for only the users with traffic in the past 72h - purchase propensity

* running inference only for users events in the past 72h

* including 72h users for all models predictions

* considering null values in TabWorkflow models

* deleting unused pipfile

* upgrading lib versions

* implementing reporting preprocessing as a new pipeline

* adding more code documentation

* adding important information on the main README.md and DEVELOPMENT.md

* adding schedule run name and more code documentation

* implementing a new scheduler using the vertex ai sdk & adding user_id to procedures for consistency

* adding more code documentation

* adding code doc to the python custom component

* adding more code documentation

* fixing aggregated predictions query

* removing unnecessary resources from deployment

* Writing MDS guide

* adding the MDS developer and troubleshooting documentation

* fixing deployment for activation pipelines and gemini dataset

* Update README.md

* Update README.md

* Update README.md

* Update README.md

* removing deprecated api

* fixing purchase propensity pipelines names

* adding extra condition for when there is not enough data for the window interval to be applied on backfill procedures

* adding more instructions for post deployment and fixing issues when GA4 export was configured for less than 10 days

* removing unnecessary comments

* adding the number of past days to process in the variables files

* adding comment about combining data from different ga4 export datasets to data store

* fixing small issues with feature engineering and ml pipelines

* fixing hyper parameter tuning for kmeans modeling

* fixing optuna parameters

* adding cloud shell image

* fixing the list of all possible users in the propensity training preparation tables

* additional guardrails for when there is not enough data

* adding more documentation

* adding more doc to feature store

* add feature store documentation

* adding ml pipelines docs

* adding ml pipelines docs

* adding more documentation

* adding user agent client info

* fixing scope of client info

* fix

---------

Co-authored-by: Carlos Timoteo <[email protected]>

python/pipelines/components/vertex/component.py

@@ -563,6 +563,19 @@ def get_tabular_model_explanation(
     import re
     from google.cloud import aiplatform
 
+    from google.api_core.gapic_v1.client_info import ClientInfo
+
+    USER_AGENT_FEATURES = 'cloud-solutions/marketing-analytics-jumpstart-features-v1'
+    USER_AGENT_PROPENSITY_TRAINING = 'cloud-solutions/marketing-analytics-jumpstart-propensity-training-v1'
+    USER_AGENT_PROPENSITY_PREDICTION= 'cloud-solutions/marketing-analytics-jumpstart-propensity-prediction-v1'
+    USER_AGENT_REGRESSION_TRAINING = 'cloud-solutions/marketing-analytics-jumpstart-regression-training-v1'
+    USER_AGENT_REGRESSION_PREDICTION = 'cloud-solutions/marketing-analytics-jumpstart-regression-prediction-v1'
+    USER_AGENT_SEGMENTATION_TRAINING = 'cloud-solutions/marketing-analytics-jumpstart-segmentation-training-v1'
+    USER_AGENT_SEGMENTATION_PREDICTION = 'cloud-solutions/marketing-analytics-jumpstart-segmentation-prediction-v1'
+    USER_AGENT_VBB_TRAINING = 'cloud-solutions/marketing-analytics-jumpstart-vbb-training-v1'
+    USER_AGENT_VBB_EXPLANATION = 'cloud-solutions/marketing-analytics-jumpstart-vbb-explanation-v1'
+
+
     api_endpoint = "us-central1-aiplatform.googleapis.com"
     # The AI Platform services require regional API endpoints.
     client_options = {"api_endpoint": api_endpoint}