Data Schema
By default, Vision AI Application will try to write annotations to the
target BigQuery table using the following schema:
ingestion_time: TIMESTAMP, the ingestion time of the original data.
application: STRING, name of the application which produces the annotation.
instance: STRING, Id of the instance which produces the annotation.
node: STRING, name of the application graph node which produces the
annotation.
annotation: STRING or JSON, the actual annotation protobuf will be
converted to json string with bytes field as 64 encoded string. It can be
written to both String or Json type column.
To forward annotation data to an existing BigQuery table, customer needs to
make sure the compatibility of the schema.
The map maps application node name to its corresponding cloud function
endpoint to transform the annotations directly to the
google.cloud.bigquery.storage.v1.AppendRowsRequest (only avro_rows or
proto_rows should be set). If configured, annotations produced by
corresponding application node will sent to the Cloud Function at first
before be forwarded to BigQuery.
If the default table schema doesn't fit, customer is able to transform the
annotation output from Vision AI Application to arbitrary BigQuery table
schema with CloudFunction.
The cloud function will receive AppPlatformCloudFunctionRequest where
the annotations field will be the json format of Vision AI annotation.
The cloud function should return AppPlatformCloudFunctionResponse with
AppendRowsRequest stored in the annotations field.
To drop the annotation, simply clear the annotations field in the
returned AppPlatformCloudFunctionResponse.
Data Schema
By default, Vision AI Application will try to write annotations to the
target BigQuery table using the following schema:
ingestion_time: TIMESTAMP, the ingestion time of the original data.
application: STRING, name of the application which produces the annotation.
instance: STRING, Id of the instance which produces the annotation.
node: STRING, name of the application graph node which produces the
annotation.
annotation: STRING or JSON, the actual annotation protobuf will be
converted to json string with bytes field as 64 encoded string. It can be
written to both String or Json type column.
To forward annotation data to an existing BigQuery table, customer needs to
make sure the compatibility of the schema.
The map maps application node name to its corresponding cloud function
endpoint to transform the annotations directly to the
google.cloud.bigquery.storage.v1.AppendRowsRequest (only avro_rows or
proto_rows should be set). If configured, annotations produced by
corresponding application node will sent to the Cloud Function at first
before be forwarded to BigQuery.
If the default table schema doesn't fit, customer is able to transform the
annotation output from Vision AI Application to arbitrary BigQuery table
schema with CloudFunction.
The cloud function will receive AppPlatformCloudFunctionRequest where
the annotations field will be the json format of Vision AI annotation.
The cloud function should return AppPlatformCloudFunctionResponse with
AppendRowsRequest stored in the annotations field.
To drop the annotation, simply clear the annotations field in the
returned AppPlatformCloudFunctionResponse.
Data Schema
By default, Vision AI Application will try to write annotations to the
target BigQuery table using the following schema:
ingestion_time: TIMESTAMP, the ingestion time of the original data.
application: STRING, name of the application which produces the annotation.
instance: STRING, Id of the instance which produces the annotation.
node: STRING, name of the application graph node which produces the
annotation.
annotation: STRING or JSON, the actual annotation protobuf will be
converted to json string with bytes field as 64 encoded string. It can be
written to both String or Json type column.
To forward annotation data to an existing BigQuery table, customer needs to
make sure the compatibility of the schema.
The map maps application node name to its corresponding cloud function
endpoint to transform the annotations directly to the
google.cloud.bigquery.storage.v1.AppendRowsRequest (only avro_rows or
proto_rows should be set). If configured, annotations produced by
corresponding application node will sent to the Cloud Function at first
before be forwarded to BigQuery.
If the default table schema doesn't fit, customer is able to transform the
annotation output from Vision AI Application to arbitrary BigQuery table
schema with CloudFunction.
The cloud function will receive AppPlatformCloudFunctionRequest where
the annotations field will be the json format of Vision AI annotation.
The cloud function should return AppPlatformCloudFunctionResponse with
AppendRowsRequest stored in the annotations field.
To drop the annotation, simply clear the annotations field in the
returned AppPlatformCloudFunctionResponse.
Data Schema
By default, Vision AI Application will try to write annotations to the
target BigQuery table using the following schema:
ingestion_time: TIMESTAMP, the ingestion time of the original data.
application: STRING, name of the application which produces the annotation.
instance: STRING, Id of the instance which produces the annotation.
node: STRING, name of the application graph node which produces the
annotation.
annotation: STRING or JSON, the actual annotation protobuf will be
converted to json string with bytes field as 64 encoded string. It can be
written to both String or Json type column.
To forward annotation data to an existing BigQuery table, customer needs to
make sure the compatibility of the schema.
The map maps application node name to its corresponding cloud function
endpoint to transform the annotations directly to the
google.cloud.bigquery.storage.v1.AppendRowsRequest (only avro_rows or
proto_rows should be set). If configured, annotations produced by
corresponding application node will sent to the Cloud Function at first
before be forwarded to BigQuery.
If the default table schema doesn't fit, customer is able to transform the
annotation output from Vision AI Application to arbitrary BigQuery table
schema with CloudFunction.
The cloud function will receive AppPlatformCloudFunctionRequest where
the annotations field will be the json format of Vision AI annotation.
The cloud function should return AppPlatformCloudFunctionResponse with
AppendRowsRequest stored in the annotations field.
To drop the annotation, simply clear the annotations field in the
returned AppPlatformCloudFunctionResponse.
Data Schema
By default, Vision AI Application will try to write annotations to the
target BigQuery table using the following schema:
ingestion_time: TIMESTAMP, the ingestion time of the original data.
application: STRING, name of the application which produces the annotation.
instance: STRING, Id of the instance which produces the annotation.
node: STRING, name of the application graph node which produces the
annotation.
annotation: STRING or JSON, the actual annotation protobuf will be
converted to json string with bytes field as 64 encoded string. It can be
written to both String or Json type column.
To forward annotation data to an existing BigQuery table, customer needs to
make sure the compatibility of the schema.
The map maps application node name to its corresponding cloud function
endpoint to transform the annotations directly to the
google.cloud.bigquery.storage.v1.AppendRowsRequest (only avro_rows or
proto_rows should be set). If configured, annotations produced by
corresponding application node will sent to the Cloud Function at first
before be forwarded to BigQuery.
If the default table schema doesn't fit, customer is able to transform the
annotation output from Vision AI Application to arbitrary BigQuery table
schema with CloudFunction.
The cloud function will receive AppPlatformCloudFunctionRequest where
the annotations field will be the json format of Vision AI annotation.
The cloud function should return AppPlatformCloudFunctionResponse with
AppendRowsRequest stored in the annotations field.
To drop the annotation, simply clear the annotations field in the
returned AppPlatformCloudFunctionResponse.
If true, App Platform will create the BigQuery DataSet and the
BigQuery Table with default schema if the specified table doesn't exist.
This doesn't work if any cloud function customized schema is specified
since the system doesn't know your desired schema.
JSON column will be used in the default table created by App Platform.
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Missing the information I need","missingTheInformationINeed","thumb-down"],["Too complicated / too many steps","tooComplicatedTooManySteps","thumb-down"],["Out of date","outOfDate","thumb-down"],["Samples / code issue","samplesCodeIssue","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2025-08-07 UTC."],[],[]]
