Integrating Vertex AI AgentBuilder with Google Cloud Functions

Introduction

In this tutorial, we will explore how to integrate Vertex AI AgentBuilder with Google Cloud Functions. This integration allows you to enhance the capabilities of your AI agent by leveraging additional skills and tools available through Google Cloud Functions. By combining semantic understanding with syntactic processing, this integration opens up new possibilities for building powerful AI applications.

Step 1: Set Up Your Google Cloud Environment

To begin, ensure that you have a Google Cloud account and that you have enabled the necessary APIs.

1. Create a Google Cloud Project

   • Go to the Google Cloud Console.
   • Click on "Select a project" at the top and then "New Project."
   • Fill in the required details and click "Create."

2. Enable APIs

   • Navigate to the "API & Services" section.
   • Enable the following APIs:
     • Vertex AI API
     • Cloud Functions API
     • Any other relevant APIs for your project.

3. Set Up Billing

   • Ensure that billing is enabled for your Google Cloud project to avoid interruptions.

Step 2: Create Your Agent Using Vertex AI

Next, you will create an agent using Vertex AI AgentBuilder.

1. Access Vertex AI

   • In the Google Cloud Console, navigate to "Vertex AI."
   • Click on "Agent Builder."

2. Create a New Agent

   • Click on "Create Agent."
   • Fill in the necessary information such as the agent's name, description, and language.
   • Choose the appropriate model and click "Create."

3. Define Skills and Tools

   • Within the agent configuration, define the skills and tools you want your agent to have.
   • This may include intents, entities, and any other relevant parameters.

Step 3: Develop a Google Cloud Function

Create a Google Cloud Function that will interact with your Vertex AI agent.

1. Navigate to Cloud Functions

   • In the Google Cloud Console, go to "Cloud Functions."
   • Click on "Create Function."

2. Configure Your Function

   • Set the name and region for your function.
   • Choose the trigger type (HTTP trigger is common for this integration).

3. Write Your Function Code

   • In the code editor, write the necessary code to connect with your Vertex AI agent. You can start with a simple function that fetches celebrity information as shown in the provided code repository.

   Example code snippet:

   from flask import Flask, request
   import requests
   
   app = Flask(__name__)
   
   @app.route('/celebrity_info', methods=['GET'])
   def celebrity_info():
       # Logic to retrieve celebrity information
       return "Celebrity Info"
   

4. Deploy Your Function

   • Click "Deploy" to publish your function.

Step 4: Connect Cloud Function with Vertex AI Agent

Integrate your Cloud Function with the Vertex AI agent you created.

1. Get the URL of the Cloud Function

   • After deployment, find the URL of your Cloud Function in the Cloud Functions dashboard.

2. Update Agent Configuration

   • Go back to your Vertex AI agent settings.
   • Add the Cloud Function URL as an endpoint for the agent to call.

3. Test the Integration

   • Use the testing tools in Vertex AI to send requests to your agent and verify that it responds correctly using the Cloud Function.

Conclusion

In this tutorial, we successfully integrated Vertex AI AgentBuilder with Google Cloud Functions, allowing the agent to utilize additional skills and tools. By following these steps, you can enhance your AI applications, making them more versatile and powerful.

For further exploration, consider experimenting with different types of functions and integrating more complex workflows. You can also visit the code repository for examples and additional resources.