See the supported connectors for Application Integration.
Use the Vertex AI task to embed GenAI
This sample integration contains a flow to be used as a sub-integration for interacting with Google Cloud Vertex AI models. Before using the following code sample, ensure that all prerequisites are met.
Code sample
{
"triggerConfigs"
:
[
{
"label"
:
"API Trigger"
,
"startTasks"
:
[
{
"taskId"
:
"1"
}
],
"properties"
:
{
"Trigger name"
:
"vertex-ai-task_API_1"
},
"triggerType"
:
"API"
,
"triggerNumber"
:
"1"
,
"triggerId"
:
"api_trigger/vertex-ai-task_API_1"
,
"description"
:
"As inputs, we are only adding TextPrompt and ModelId. You can set Model ID for different Google models, such as text-bison, chat-bison, etc."
,
"position"
:
{
"x"
:
-210
}
}
],
"taskConfigs"
:
[
{
"task"
:
"Vertex AI - Predict"
,
"taskId"
:
"4"
,
"parameters"
:
{
"request"
:
{
"key"
:
"request"
,
"value"
:
{
"stringValue"
:
"$`Task_4_request`$"
}
},
"projectsId"
:
{
"key"
:
"projectsId"
,
"value"
:
{
"stringValue"
:
"$ProjectId$"
}
},
"endpoint"
:
{
"key"
:
"endpoint"
,
"value"
:
{
"stringValue"
:
"$endpoint$"
}
},
"locationsId"
:
{
"key"
:
"locationsId"
,
"value"
:
{
"stringValue"
:
"$Region$"
}
},
"response"
:
{
"key"
:
"response"
,
"value"
:
{
"stringArray"
:
{
"stringValues"
:
[
"$`Task_4_response`$"
]
}
}
},
"taskTemplateId"
:
{
"key"
:
"taskTemplateId"
,
"value"
:
{
"stringValue"
:
"2b5513a2-f3f4-4ac6-918e-8ea55b53cbb8"
}
}
},
"nextTasks"
:
[
{
"taskId"
:
"3"
}
],
"taskExecutionStrategy"
:
"WHEN_ALL_SUCCEED"
,
"displayName"
:
"Vertex AI - Predict (Preview)"
,
"description"
:
"This is the actual Vertex AI API call with the variables we\u0027ve previously setup. Notice that under authentication, you need to have a Service Account with Vertex AI Predict IAM permissions."
,
"taskTemplate"
:
"Vertex AI - Predict"
,
"externalTaskType"
:
"NORMAL_TASK"
,
"position"
:
{
"x"
:
-208
,
"y"
:
256
}
},
{
"task"
:
"FieldMappingTask"
,
"taskId"
:
"1"
,
"parameters"
:
{
"FieldMappingConfigTaskParameterKey"
:
{
"key"
:
"FieldMappingConfigTaskParameterKey"
,
"value"
:
{
"jsonValue"
:
"{\n \"@type\": \"type.googleapis.com/enterprise.crm.eventbus.proto.FieldMappingConfig\",\n \"mappedFields\": [{\n \"inputField\": {\n \"fieldType\": \"STRING_VALUE\",\n \"transformExpression\": {\n \"initialValue\": {\n \"baseFunction\": {\n \"functionType\": {\n \"baseFunction\": {\n \"functionName\": \"GET_PROJECT_ID\"\n }\n }\n }\n }\n }\n },\n \"outputField\": {\n \"referenceKey\": \"$ProjectId$\",\n \"fieldType\": \"STRING_VALUE\",\n \"cardinality\": \"OPTIONAL\"\n }\n }, {\n \"inputField\": {\n \"fieldType\": \"STRING_VALUE\",\n \"transformExpression\": {\n \"initialValue\": {\n \"baseFunction\": {\n \"functionType\": {\n \"baseFunction\": {\n \"functionName\": \"GET_REGION\"\n }\n }\n }\n }\n }\n },\n \"outputField\": {\n \"referenceKey\": \"$Region$\",\n \"fieldType\": \"STRING_VALUE\",\n \"cardinality\": \"OPTIONAL\"\n }\n }, {\n \"inputField\": {\n \"fieldType\": \"STRING_VALUE\",\n \"transformExpression\": {\n \"initialValue\": {\n \"referenceValue\": \"$endpoint$\"\n },\n \"transformationFunctions\": [{\n \"functionType\": {\n \"stringFunction\": {\n \"functionName\": \"CONCAT\"\n }\n },\n \"parameters\": [{\n \"initialValue\": {\n \"referenceValue\": \"$ModelId$\"\n }\n }]\n }]\n }\n },\n \"outputField\": {\n \"referenceKey\": \"$endpoint$\",\n \"fieldType\": \"STRING_VALUE\",\n \"cardinality\": \"OPTIONAL\"\n }\n }, {\n \"inputField\": {\n \"fieldType\": \"JSON_VALUE\",\n \"transformExpression\": {\n \"initialValue\": {\n \"referenceValue\": \"$PalmPromptRequest$\"\n },\n \"transformationFunctions\": [{\n \"functionType\": {\n \"jsonFunction\": {\n \"functionName\": \"RESOLVE_TEMPLATE\"\n }\n }\n }]\n }\n },\n \"outputField\": {\n \"referenceKey\": \"$`Task_4_request`$\",\n \"fieldType\": \"JSON_VALUE\",\n \"cardinality\": \"OPTIONAL\"\n }\n }]\n}"
}
}
},
"nextTasks"
:
[
{
"taskId"
:
"4"
}
],
"taskExecutionStrategy"
:
"WHEN_ALL_SUCCEED"
,
"displayName"
:
"Set Prompt Parameters"
,
"description"
:
"In here, we are setting the required variables for the Vertex AI task. The actual payload is set using the resolve_template function from a pre-defined Local Variable called PalmPromptRequest."
,
"externalTaskType"
:
"NORMAL_TASK"
,
"position"
:
{
"x"
:
-210
,
"y"
:
126
}
},
{
"task"
:
"FieldMappingTask"
,
"taskId"
:
"3"
,
"parameters"
:
{
"FieldMappingConfigTaskParameterKey"
:
{
"key"
:
"FieldMappingConfigTaskParameterKey"
,
"value"
:
{
"jsonValue"
:
"{\n \"@type\": \"type.googleapis.com/enterprise.crm.eventbus.proto.FieldMappingConfig\",\n \"mappedFields\": [{\n \"inputField\": {\n \"fieldType\": \"JSON_VALUE\",\n \"transformExpression\": {\n \"initialValue\": {\n \"referenceValue\": \"$`Task_4_response`.predictions$\"\n },\n \"transformationFunctions\": [{\n \"functionType\": {\n \"jsonFunction\": {\n \"functionName\": \"GET_ELEMENT\"\n }\n },\n \"parameters\": [{\n \"initialValue\": {\n \"literalValue\": {\n \"intValue\": \"0\"\n }\n }\n }]\n }, {\n \"functionType\": {\n \"jsonFunction\": {\n \"functionName\": \"GET_PROPERTY\"\n }\n },\n \"parameters\": [{\n \"initialValue\": {\n \"literalValue\": {\n \"stringValue\": \"content\"\n }\n }\n }]\n }]\n }\n },\n \"outputField\": {\n \"referenceKey\": \"$Content$\",\n \"fieldType\": \"STRING_VALUE\",\n \"cardinality\": \"OPTIONAL\"\n }\n }]\n}"
}
}
},
"taskExecutionStrategy"
:
"WHEN_ALL_SUCCEED"
,
"displayName"
:
"Map Prompt Response"
,
"description"
:
"Finally, we are mapping just the content of the Vertex AI task output as the final integration Output. "
,
"externalTaskType"
:
"NORMAL_TASK"
,
"position"
:
{
"x"
:
-210
,
"y"
:
378
}
}
],
"integrationParameters"
:
[
{
"key"
:
"TextPrompt"
,
"dataType"
:
"STRING_VALUE"
,
"displayName"
:
"TextPrompt"
,
"inputOutputType"
:
"IN"
},
{
"key"
:
"Region"
,
"dataType"
:
"STRING_VALUE"
,
"defaultValue"
:
{
"stringValue"
:
"us-central1"
},
"displayName"
:
"Region"
},
{
"key"
:
"ProjectId"
,
"dataType"
:
"STRING_VALUE"
,
"displayName"
:
"ProjectId"
},
{
"key"
:
"`Task_4_request`"
,
"dataType"
:
"JSON_VALUE"
,
"defaultValue"
:
{
"jsonValue"
:
"{\n}"
},
"displayName"
:
"`Task_4_request`"
,
"isTransient"
:
true
,
"producer"
:
"1_4"
,
"jsonSchema"
:
"{\n \"$schema\": \"http://json-schema.org/draft-07/schema#\",\n \"type\": \"object\",\n \"properties\": {\n \"instances\": {\n \"type\": \"array\"\n },\n \"parameters\": {\n \"type\": \"object\"\n }\n }\n}"
},
{
"key"
:
"`Task_4_response`"
,
"dataType"
:
"JSON_VALUE"
,
"displayName"
:
"`Task_4_response`"
,
"isTransient"
:
true
,
"producer"
:
"1_4"
,
"jsonSchema"
:
"{\n \"$schema\": \"http://json-schema.org/draft-07/schema#\",\n \"type\": \"object\",\n \"properties\": {\n \"deployedModelId\": {\n \"type\": \"string\"\n },\n \"modelVersionId\": {\n \"type\": \"string\"\n },\n \"model\": {\n \"type\": \"string\"\n },\n \"predictions\": {\n \"type\": \"array\"\n },\n \"modelDisplayName\": {\n \"type\": \"string\"\n }\n }\n}"
},
{
"key"
:
"ModelId"
,
"dataType"
:
"STRING_VALUE"
,
"defaultValue"
:
{
"stringValue"
:
"text-bison@001"
},
"displayName"
:
"ModelId"
,
"inputOutputType"
:
"IN"
},
{
"key"
:
"endpoint"
,
"dataType"
:
"STRING_VALUE"
,
"defaultValue"
:
{
"stringValue"
:
"publishers/google/models/"
},
"displayName"
:
"endpoint"
},
{
"key"
:
"PalmPromptRequest"
,
"dataType"
:
"JSON_VALUE"
,
"defaultValue"
:
{
"jsonValue"
:
"{\n \"instances\": [{\n \"prompt\": \"$TextPrompt$\"\n }],\n \"parameters\": {\n \"temperature\": 0.2,\n \"maxOutputTokens\": 768.0,\n \"topP\": 0.8,\n \"topK\": 40.0\n }\n}"
},
"displayName"
:
"PalmPromptRequest"
,
"jsonSchema"
:
"{\n \"$schema\": \"http://json-schema.org/draft-04/schema#\",\n \"type\": \"object\",\n \"properties\": {\n \"instances\": {\n \"type\": \"array\",\n \"items\": {\n \"type\": \"object\",\n \"properties\": {\n \"prompt\": {\n \"type\": \"string\"\n }\n }\n }\n },\n \"parameters\": {\n \"type\": \"object\",\n \"properties\": {\n \"topK\": {\n \"type\": \"number\"\n },\n \"temperature\": {\n \"type\": \"number\"\n },\n \"maxOutputTokens\": {\n \"type\": \"number\"\n },\n \"topP\": {\n \"type\": \"number\"\n }\n }\n }\n }\n}"
},
{
"key"
:
"Content"
,
"dataType"
:
"STRING_VALUE"
,
"displayName"
:
"Content"
,
"inputOutputType"
:
"OUT"
}
]
}
Sample integration flow
The following image shows a sample layout of the integration editor for this integration code sample.
Upload and run the sample integration
To upload and run the sample integration, do the following steps:
- Save the integration sample as a .json file on your system.
- In the Google Cloud console, go to the Application Integration page.
- Click Integrations from the left navigation menu to open the Integrations page.
- Select an existing integration or create a new integration by clicking Create integration
.
If you are creating a new integration:
- Enter a name and description in the Create Integration dialog.
- Select a region for the integration.
- Select a service account for the integration. You can change or update the service account details of an integration any time from the info Integration summary pane in the integration toolbar.
- Click Create .
This opens the integration in the integration editor .
- In the integration editor , click publish Upload/download menu and then select Upload integration .
- In the file browser dialog, select the file that you saved in step 1, and then click Open
.
A new version of the integration is created using the uploaded file.
- In the integration editor , click Test .
- Click Test integration . This runs the integration and displays the execution result in the Test Integration pane.
