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.
value (::Google::Protobuf::Map{::String => ::String}) —
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.
Returns
(::Google::Protobuf::Map{::String => ::String}) —
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.
(::Boolean) — 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.
value (::Boolean) — 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.
Returns
(::Boolean) — 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.
#table
deftable()->::String
Returns
(::String) — BigQuery table resource for Vision AI Platform to ingest annotations to.
#table=
deftable=(value)->::String
Parameter
value (::String) — BigQuery table resource for Vision AI Platform to ingest annotations to.
Returns
(::String) — BigQuery table resource for Vision AI Platform to ingest annotations to.
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Hard to understand","hardToUnderstand","thumb-down"],["Incorrect information or sample code","incorrectInformationOrSampleCode","thumb-down"],["Missing the information/samples I need","missingTheInformationSamplesINeed","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2025-09-09 UTC."],[],[],null,["# Vision AI V1 API - Class Google::Cloud::VisionAI::V1::BigQueryConfig (v1.1.1)\n\nVersion latestkeyboard_arrow_down\n\n- [1.1.1 (latest)](/ruby/docs/reference/google-cloud-vision_ai-v1/latest/Google-Cloud-VisionAI-V1-BigQueryConfig)\n- [1.1.0](/ruby/docs/reference/google-cloud-vision_ai-v1/1.1.0/Google-Cloud-VisionAI-V1-BigQueryConfig)\n- [1.0.1](/ruby/docs/reference/google-cloud-vision_ai-v1/1.0.1/Google-Cloud-VisionAI-V1-BigQueryConfig)\n- [0.4.0](/ruby/docs/reference/google-cloud-vision_ai-v1/0.4.0/Google-Cloud-VisionAI-V1-BigQueryConfig)\n- [0.3.0](/ruby/docs/reference/google-cloud-vision_ai-v1/0.3.0/Google-Cloud-VisionAI-V1-BigQueryConfig)\n- [0.2.0](/ruby/docs/reference/google-cloud-vision_ai-v1/0.2.0/Google-Cloud-VisionAI-V1-BigQueryConfig)\n- [0.1.0](/ruby/docs/reference/google-cloud-vision_ai-v1/0.1.0/Google-Cloud-VisionAI-V1-BigQueryConfig) \nReference documentation and code samples for the Vision AI V1 API class Google::Cloud::VisionAI::V1::BigQueryConfig.\n\nMessage of configurations for BigQuery processor. \n\nInherits\n--------\n\n- Object \n\nExtended By\n-----------\n\n- Google::Protobuf::MessageExts::ClassMethods \n\nIncludes\n--------\n\n- Google::Protobuf::MessageExts\n\nMethods\n-------\n\n### #cloud_function_mapping\n\n def cloud_function_mapping() -\u003e ::Google::Protobuf::Map{::String =\u003e ::String}\n\n**Returns**\n\n- (::Google::Protobuf::Map{::String =\\\u003e ::String}) --- Data Schema\n By default, Vision AI Application will try to write annotations to the\n target BigQuery table using the following schema:\n\n ingestion_time: TIMESTAMP, the ingestion time of the original data.\n\n application: STRING, name of the application which produces the annotation.\n\n instance: STRING, Id of the instance which produces the annotation.\n\n node: STRING, name of the application graph node which produces the\n annotation.\n\n annotation: STRING or JSON, the actual annotation protobuf will be\n converted to json string with bytes field as 64 encoded string. It can be\n written to both String or Json type column.\n\n To forward annotation data to an existing BigQuery table, customer needs to\n make sure the compatibility of the schema.\n The map maps application node name to its corresponding cloud function\n endpoint to transform the annotations directly to the\n google.cloud.bigquery.storage.v1.AppendRowsRequest (only avro_rows or\n proto_rows should be set). If configured, annotations produced by\n corresponding application node will sent to the Cloud Function at first\n before be forwarded to BigQuery.\n\n If the default table schema doesn't fit, customer is able to transform the\n annotation output from Vision AI Application to arbitrary BigQuery table\n schema with CloudFunction.\n - The cloud function will receive AppPlatformCloudFunctionRequest where the annotations field will be the json format of Vision AI annotation.\n - The cloud function should return AppPlatformCloudFunctionResponse with AppendRowsRequest stored in the annotations field.\n - To drop the annotation, simply clear the annotations field in the returned AppPlatformCloudFunctionResponse.\n\n### #cloud_function_mapping=\n\n def cloud_function_mapping=(value) -\u003e ::Google::Protobuf::Map{::String =\u003e ::String}\n\n**Parameter**\n\n- **value** (::Google::Protobuf::Map{::String =\\\u003e ::String}) ---\n\n Data Schema\n By default, Vision AI Application will try to write annotations to the\n target BigQuery table using the following schema:\n\n ingestion_time: TIMESTAMP, the ingestion time of the original data.\n\n application: STRING, name of the application which produces the annotation.\n\n instance: STRING, Id of the instance which produces the annotation.\n\n node: STRING, name of the application graph node which produces the\n annotation.\n\n annotation: STRING or JSON, the actual annotation protobuf will be\n converted to json string with bytes field as 64 encoded string. It can be\n written to both String or Json type column.\n\n To forward annotation data to an existing BigQuery table, customer needs to\n make sure the compatibility of the schema.\n The map maps application node name to its corresponding cloud function\n endpoint to transform the annotations directly to the\n google.cloud.bigquery.storage.v1.AppendRowsRequest (only avro_rows or\n proto_rows should be set). If configured, annotations produced by\n corresponding application node will sent to the Cloud Function at first\n before be forwarded to BigQuery.\n\n If the default table schema doesn't fit, customer is able to transform the\n annotation output from Vision AI Application to arbitrary BigQuery table\n schema with CloudFunction.\n - The cloud function will receive AppPlatformCloudFunctionRequest where the annotations field will be the json format of Vision AI annotation.\n - The cloud function should return AppPlatformCloudFunctionResponse with AppendRowsRequest stored in the annotations field.\n- To drop the annotation, simply clear the annotations field in the returned AppPlatformCloudFunctionResponse. \n**Returns**\n\n- (::Google::Protobuf::Map{::String =\\\u003e ::String}) --- Data Schema\n By default, Vision AI Application will try to write annotations to the\n target BigQuery table using the following schema:\n\n ingestion_time: TIMESTAMP, the ingestion time of the original data.\n\n application: STRING, name of the application which produces the annotation.\n\n instance: STRING, Id of the instance which produces the annotation.\n\n node: STRING, name of the application graph node which produces the\n annotation.\n\n annotation: STRING or JSON, the actual annotation protobuf will be\n converted to json string with bytes field as 64 encoded string. It can be\n written to both String or Json type column.\n\n To forward annotation data to an existing BigQuery table, customer needs to\n make sure the compatibility of the schema.\n The map maps application node name to its corresponding cloud function\n endpoint to transform the annotations directly to the\n google.cloud.bigquery.storage.v1.AppendRowsRequest (only avro_rows or\n proto_rows should be set). If configured, annotations produced by\n corresponding application node will sent to the Cloud Function at first\n before be forwarded to BigQuery.\n\n If the default table schema doesn't fit, customer is able to transform the\n annotation output from Vision AI Application to arbitrary BigQuery table\n schema with CloudFunction.\n - The cloud function will receive AppPlatformCloudFunctionRequest where the annotations field will be the json format of Vision AI annotation.\n - The cloud function should return AppPlatformCloudFunctionResponse with AppendRowsRequest stored in the annotations field.\n - To drop the annotation, simply clear the annotations field in the returned AppPlatformCloudFunctionResponse.\n\n### #create_default_table_if_not_exists\n\n def create_default_table_if_not_exists() -\u003e ::Boolean\n\n**Returns**\n\n- (::Boolean) --- 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.\n\n### #create_default_table_if_not_exists=\n\n def create_default_table_if_not_exists=(value) -\u003e ::Boolean\n\n**Parameter**\n\n- **value** (::Boolean) --- 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. \n**Returns**\n\n- (::Boolean) --- 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.\n\n### #table\n\n def table() -\u003e ::String\n\n**Returns**\n\n- (::String) --- BigQuery table resource for Vision AI Platform to ingest annotations to.\n\n### #table=\n\n def table=(value) -\u003e ::String\n\n**Parameter**\n\n- **value** (::String) --- BigQuery table resource for Vision AI Platform to ingest annotations to. \n**Returns**\n\n- (::String) --- BigQuery table resource for Vision AI Platform to ingest annotations to."]]
