Tutorial: Vertex AI API in express mode

Vertex AI in express mode lets you quickly try out core generative AI features that are available on Vertex AI. This tutorial shows you how to perform the following tasks by using the Vertex AI API in express mode:

  • Install and initialize the Google Gen AI SDK for express mode.
  • Send a request to the Gemini for Google Cloud API, including the following:
    • Non-streaming request
    • Streaming request
    • Function calling request

Install and initialize the Google Gen AI SDK for express mode

The Google Gen AI SDK lets you use Google generative AI models and features to build AI-powered applications. When using Vertex AI in express mode, install and initialize the google-genai package to authenticate using your generated API key.

Install

To install the Google Gen AI SDK for express mode, run the following commands:

# Developer TODO: If you're using Colab, uncomment the following lines:
# from google.colab import auth
# auth.authenticate_user()

!pip install google-genai

!pip install --force-reinstall -qq "numpy<2.0"

If you're using Colab, ignore any dependency conflicts and restart the runtime after installation.

Initialize

Configure the API key for express mode and environment variables. For details on getting an API key, see Vertex AI in express mode overview.

from google import genai
from google.genai import types

# Developer TODO: Replace YOUR_API_KEY with your API key.
API_KEY = "YOUR_API_KEY"

client = genai.Client(
    vertexai=True, api_key=API_KEY
)

Send a request to the Gemini for Google Cloud API

You can send either streaming or non-streaming requests to the Gemini for Google Cloud API. Streaming requests return the response in chunks as the request is being processed. To a human user, streamed responses reduce the perception of latency. Non-streaming requests return the response in one chunk after the request is processed.

Streaming request

To send a streaming request, set stream=True and print the response in chunks.

from google import genai
from google.genai import types

def generate():
  client = genai.Client(vertexai=True, api_key=YOUR_API_KEY)

  config=types.GenerateContentConfig(
      temperature=0,
      top_p=0.95,
      top_k=20,
      candidate_count=1,
      seed=5,
      max_output_tokens=100,
      stop_sequences=["STOP!"],
      presence_penalty=0.0,
      frequency_penalty=0.0,
      safety_settings=[
          types.SafetySetting(
              category="HARM_CATEGORY_HATE_SPEECH",
              threshold="BLOCK_ONLY_HIGH",
          )
      ],
  )
  for chunk in client.models.generate_content_stream(
    model="gemini-2.5-flash-lite",
    contents="Explain bubble sort to me",
    config=config,
  ):
    print(chunk.text)

generate()

Non-streaming request

The following code sample defines a function that sends a non-streaming request to the gemini-2.5-flash-lite. It shows you how to configure basic request parameters and safety settings.

from google import genai
from google.genai import types

def generate():
  client = genai.Client(vertexai=True, api_key=YOUR_API_KEY)

  config=types.GenerateContentConfig(
      temperature=0,
      top_p=0.95,
      top_k=20,
      candidate_count=1,
      seed=5,
      max_output_tokens=100,
      stop_sequences=["STOP!"],
      presence_penalty=0.0,
      frequency_penalty=0.0,
      safety_settings=[
          types.SafetySetting(
              category="HARM_CATEGORY_HATE_SPEECH",
              threshold="BLOCK_ONLY_HIGH",
          )
      ],
  )
  response = client.models.generate_content(
    model="gemini-2.5-flash-lite",
    contents="Explain bubble sort to me",
    config=config,
  )
  print(response.text)

generate()

Function calling request

The following code sample declares a function and passes it as a tool, and then receives a function call part in the response. After you receive the function call part from the model, you can invoke the function and get the response, and then pass the response to the model.


function_response_parts = [
    {
        'function_response': {
            'name': 'get_current_weather',
            'response': {
                'name': 'get_current_weather',
                'content': {'weather': 'super nice'},
            },
        },
    },
]
manual_function_calling_contents = [
    {'role': 'user', 'parts': [{'text': 'What is the weather in Boston?'}]},
    {
        'role': 'model',
        'parts': [{
            'function_call': {
                'name': 'get_current_weather',
                'args': {'location': 'Boston'},
            }
        }],
    },
    {'role': 'user', 'parts': function_response_parts},
]
function_declarations = [{
    'name': 'get_current_weather',
    'description': 'Get the current weather in a city',
    'parameters': {
        'type': 'OBJECT',
        'properties': {
            'location': {
                'type': 'STRING',
                'description': 'The location to get the weather for',
            },
            'unit': {
                'type': 'STRING',
                'enum': ['C', 'F'],
            },
        },
    },
}]

response = client.models.generate_content(
    model="gemini-2.0-flash-001",
    contents=manual_function_calling_contents,
    config=dict(tools=[{'function_declarations': function_declarations}]),
)
print(response.text)

Clean up

This tutorial does not create any Google Cloud resources, so no clean up is needed to avoid charges.

What's next