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:

1. Save the integration sample as a .json file on your system.
2. In the Google Cloud console, go to the Application Integration page.

   Go to Application Integration

3. In the navigation menu, click Integrations. The Integrations page appears.
4. Select an existing integration or create a new integration by clicking Create integration.

   If you are creating a new integration:

   1. Enter a name and description in the Create Integration dialog.
   2. Select a region for the integration.
      Note: The Regions dropdown only lists the regions provisioned in your Google Cloud project. To provision a new region, Click Enable Region. See Enable new region for more information.
   3. Select a service account for the integration. You can change or update the service account details of an integration any time from the Integration summary pane in the integration toolbar.
      Note: The option to select a service account is displayed only if you have enabled integration governance for the selected region.
   4. Click Create.

   This opens the integration in the integration editor.

5. In the integration editor, click Upload/download menu and then select Upload integration.
6. 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.

7. In the integration editor, click Test.
8. Click Test integration. This runs the integration and displays the execution result in the Test Integration pane.