Google Vertex AI Setup Guide

How it works

This connector invokes a Google Vertex AI model with your custom prompt and mapped Tealium data, then sends the response as a JSON event to Tealium Collect for real-time enrichment.

The Google Vertex AI connector should be used for targeted, high-value interactions rather than high-volume events. Excessive usage may result in rate limits or increased API costs in your Google Vertex AI account and add to your inbound Tealium event volume.

Prompts

The prompt sends context data to the model, asks a specific question, and defines the expected response.

Context data
The model must have context data to evaluate your request. Configure the connector action to use event data or visitor data, then reference this data object in your prompt:

• Event data: {{event_payload}}
• Visitor data: {{visitor_profile}}

For example, include this as the first line of your prompt:

You receive a JSON object describing a customer event:
{{event_payload}}

Question
You must ask the model to evaluate the context data and generate a value. Ask for one or more specific values and be clear what you’re asking for.

For example, this prompt asks for one of three values:

Based on the provided product review event, classify the customer's sentiment as one of: "dissatisfied", "neutral", or "satisfied".

In addition, you must instruct the model how to respond in a specific JSON format, so it can be sent to your account as a valid event.

For example, this part of the prompt specifies the exact JSON format and references Tealium variables using curly braces:

Return only a single JSON object on one line with the following structure: 
{
  "tealium_account": "{{tealium_account}}",
  "tealium_profile": "{{tealium_profile}}",
  "tealium_visitor_id": "{{tealium_visitor_id}}",
  "tealium_event":  "vertexai_response",
  "vertexai_review_sentiment": "<customer review sentiment>"
}

Set a value for tealium_event that’s unique to this connector and name the response attribute specific to this prompt. This makes it easy to create event feeds or rules that match these events.

Response event

This connector parses the response from the Google Vertex AI model. If it’s a valid JSON object with the required Tealium parameters, the connector sends it back to your account as an incoming event.

To capture these events, create enrichment rules or event feeds that match the events generated by the Google Vertex AI responses. For example, this rule captures sentiment events:

Example: Event trigger

In this example, the customer has just submitted a product review and the prompt asks the model to evaluate the sentiment.

Example prompt:

You receive a JSON object describing a customer's product review:
{{event_payload}}

Based on the provided product review event, classify the customer's sentiment as one of: "dissatisfied", "neutral", or "satisfied".

Return only a single JSON object on one line with the following structure: 
{
  "tealium_account": "{{tealium_account}}",
  "tealium_profile": "{{tealium_profile}}",
  "tealium_visitor_id": "{{tealium_visitor_id}}",
  "tealium_event":  "vertexai_response",
  "vertexai_review_sentiment": "<customer review sentiment>"
}

Example response event:

{
  "tealium_account": "acme",
  "tealium_profile": "main",
  "tealium_visitor_id": "383...05d",
  "tealium_event": "vertexai_response",
  "vertexai_review_sentiment": "satisfied"
}

Example: Audience trigger

In this example, the customer joined an audience named “Frequent Browser, No Purchases” and the prompt asks the model to evaluate the customer’s intent.

Example prompt:

You receive a JSON object describing a retail visitor:
{{visitor_profile}}

Based on the customer data provided, classify their likely intent as: "bargain hunting", "product comparison", "researching for later", or "not interested". If data is missing, make a best effort guess.

Return only a single JSON object on one line with the following structure: 
{
  "tealium_account": "{{tealium_account}}",
  "tealium_profile": "{{tealium_profile}}",
  "tealium_visitor_id": "{{tealium_visitor_id}}",
  "tealium_event":  "vertexai_response",
  "vertexai_intent": "<customer intent>"
}

Example response event:

{
  "tealium_account": "acme",
  "tealium_profile": "main",
  "tealium_visitor_id": "383...05d",
  "tealium_event": "vertexai_response",
  "vertexai_intent": "bargain hunting"
}

Testing

Before activating the Google Vertex AI connector in production, enable Debug Mode in the connector mappings and test your setup with trace to validate your configuration and prompt behavior. In debug mode, the connector executes the Vertex AI request, but logs the result without sending the response event back to your account. In the trace tool, inspect the full request and response in real time, verify the generated output, and ensure that the trigger conditions and attribute mappings are working as expected. This helps catch errors and optimize prompts before incurring production costs or affecting live data.

Usage and cost considerations

Before activating the Google Vertex AI connector, review your Google Cloud quotas and limits. The connector can generate a high volume of requests depending on your event and audience triggers, which may result in unexpected usage or overage costs.

For more information, see Google: Vertex AI quotas and limits.

Key considerations

• Usage tier and rate limits
  Google Vertex AI enforces rate limits based on your account tier (requests per minute and tokens per minute). High‑frequency events or large audiences can quickly reach these limits, causing throttling or failed requests.
• Pricing and token consumption
  Google Vertex AI charges based on the number of input and output tokens processed by the model. Longer prompts, larger payloads, and higher‑capacity models increase per‑request cost. Review pricing for the specific models you plan to use.
• Monthly spend and budget controls
  Set usage caps or alerts in your Google Cloud account to prevent unplanned spend. Without limits in place, automated workflows can accumulate significant costs.
• Trigger volume Avoid attaching the connector to high‑volume, low‑value events (such as page views). Use events or audiences that represent meaningful customer actions and occur at manageable frequency.

Best practices

To get the most value from this connector, follow these guidelines to build effective solutions:

• High-value triggers: Choose event feed or audience triggers that contain rich context or meaningful customer input. Triggering this connector for a high-volume use case may incur additional costs in your Google Cloud account or lead to failed requests.
• Be specific: Include details about what the model should evaluate and what values you expect. List the exact values you expect.
• JSON format: Include a valid JSON response template that can be sent as a Tealium event.
• Response values: Reference the response values to capture. For example, if your prompt asks to evaluate the purchase intent, reference <customer purchase intent> in the prompt where you want that value to appear in the event JSON.
• Tealium data: Reference Tealium data and mapped parameters using double-curly braces. For example, to reference the mapped value for tealium_account, write {{tealium_account}} in your prompt.

API information

This connector uses the following vendor API:

• API Name: Vertex API
• API Version: v1
• API Endpoint: https://aiplatform.googleapis.com/v1
• Documentation: Google API

Configuration

Go to the Connector Marketplace and add a new connector. For general instructions on how to add a connector, see About Connectors.

After adding the connector, configure the following settings:

• Google Cloud Platform Project ID: (Required) Your Google Cloud project ID with Vertex AI enabled.
• Private key JSON file: (Required) Paste the content of the JSON key generated for your Service Account. Grant the service account the Vertex AI User role (roles/aiplatform.user) in the project. This role allows the connector to both list available models and invoke the selected model through the Vertex AI Generative APIs.
• Location: Select the Vertex AI location where your model runs. Use global for Google publisher Gemini models. Choose a regional location if required for data residency or if your Vertex resources are regional.

Actions

Enter a name for the action and select the action type.

The following section describes how to set up parameters and options for each action.

Send Prompt to Vertex AI

This action invokes a Vertex AI model with your custom prompt and mapped Tealium data. If the model responds with a valid JSON event object, this event is sent back to your account where you can capture the generated value in a real-time enrichment.

Parameters

Prompt Parameters

Advanced Model Settings