Configuring Regional Endpoints for Vertex AI Gemini Models Based on API Call Location

[rg97]

Hello! I'm new to vertexai and I'm trying to understand how to configure location endpoints of gemini models accessed through vertexai.

I am using gemini models through vertexai with something like this --

import vertexai

vertexai.generative_models.GenerativeModel(
        model_name=model_name,
        generation_config=generation_config,
        safety_settings=safety_config,
)

 Now say that this script is run with some api call -- location.api.company.ai 

I would want that if location in the api call is "us", then the "us" endpoint of gemini-pro should be queried and if the location is "eu", then the enpoints should be that of EU region.

I read somewhere that one way to configure location endpoints is by initializing vertexai before calling generative_models by doing something like 

import vertexai
import os

vertexai.init(location=os.env("DEPLOYMENT_REGION")

vertexai.generative_models.GenerativeModel(
        model_name=model_name,
        generation_config=generation_config,
        safety_settings=safety_config,

My question is --

• Is the initialization required? Or would it automatically access the location based on the location of servers?
• Is this the right way of doing it or would you recommend any other way?

I'm new to vertexai so sorry if I am missing something. Please do let me know if you need any more information from my end.

Thanks!

[ruthseki · Accepted solution]

Hi @rg97,

Welcome to Google Cloud Community!

Vertex AI requires explicit location configuration. The Vertex AI client library does not automatically determine the location based on the server where your code is running. You must explicitly tell it which region to use. It’s important to ensure your data processing complies with regional regulations, reducing latency by accessing models closer to your application. Some features might only be available in specific regions.

In addition, vertexai.init() is essential. The vertexai.init() function sets the global configuration for your Vertex AI client. This configuration includes the project, location, and credentials. Without it, the client doesn't know where to send API requests.

Also, the example you provided using vertexai.init() is the recommended and correct way to specify the location. 

Here are some key improvements that you may apply:

1. Project ID: The vertexai.init() function also requires your Google Cloud project ID. Make sure you replace "your-gcp-project-id" with your actual project.
2. Function for Reusability: Wrap the initialization and model creation logic into a function, get_gemini_model. This makes it easy to create model instances for different regions without repeating code.
3. Error Handling: Include a try...except block to catch potential initialization errors. This is good practice for robust code.
4. Explicit Location: Pass the location parameter to vertexai.init() and used to configure the Vertex AI client. This is the most important part. Make sure you use valid region codes (e.g., "us-central1", "europe-west4", "asia-east1"). 
5. Configuration: Add placeholder generation_config and safety_config dictionaries. You should customize these to fit your specific requirements.

If you're deploying your application, using environment variables for the location and project ID is a good practice. 

Also, double-check the list of available regions for Vertex AI and Gemini models in the official Google Cloud documentation. Pricing may vary by region. Test the latency from different regions to determine the best location for your application. Ensure your application has the necessary permissions (e.g., the roles/aiplatform.user role) to access the Vertex AI API in the chosen region. The service account used by your application needs to have the appropriate IAM roles. When you create other Vertex AI resources (like datasets or models), you'll often need to specify their location as well.

In summary, you must initialize Vertex AI with vertexai.init() to specify the location. The provided function approach allows you to create separate model instances for different regions. Use environment variables for flexibility in deployments and always verify your IAM permissions and valid region codes. Remember to replace placeholders with your project ID and desired configurations.

Was this helpful? If so, please accept this answer as “Solution”. If you need additional assistance, reply here within 2 business days and I’ll be happy to help.

[rg97]

Thank you, I appreciate your help!

[jhanjhan]

Hi