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The Vertex Generative AI SDK helps developers use Google’s generative AI Gemini models and PaLM language models to build AI-powered features and applications. The SDKs support use cases like the following:
Generate text from texts, images and videos (multimodal generation)
Build stateful multi-turn conversations (chat)
Function calling
To install the google-cloud-aiplatform Python package, run the following command:
pip3 install --upgrade --user "google-cloud-aiplatform>=1.38"
For detailed instructions, see quickstart and Introduction to multimodal classes in the Vertex AI SDK.
Imports:from vertexai.generative_models import GenerativeModel, Image, Content, Part, Tool, FunctionDeclaration, GenerationConfig
from vertexai.generative_models import GenerativeModel
model = GenerativeModel("gemini-pro")
print(model.generate_content("Why is sky blue?"))
from vertexai.generative_models import GenerativeModel, Image
vision_model = GenerativeModel("gemini-pro-vision")
# Local image
image = Image.load_from_file("image.jpg")
print(vision_model.generate_content(["What is shown in this image?", image]))
# Image from Cloud Storage
image_part = generative_models.Part.from_uri("gs://download.tensorflow.org/example_images/320px-Felis_catus-cat_on_snow.jpg", mime_type="image/jpeg")
print(vision_model.generate_content([image_part, "Describe this image?"]))
# Text and video
video_part = Part.from_uri("gs://cloud-samples-data/video/animals.mp4", mime_type="video/mp4")
print(vision_model.generate_content(["What is in the video? ", video_part]))
from vertexai.generative_models import GenerativeModel, Image
vision_model = GenerativeModel("gemini-ultra-vision")
vision_chat = vision_model.start_chat()
image = Image.load_from_file("image.jpg")
print(vision_chat.send_message(["I like this image.", image]))
print(vision_chat.send_message("What things do I like?."))
from vertexai.generative_models import GenerativeModel
model = GenerativeModel(
"gemini-1.0-pro",
system_instruction=[
"Talk like a pirate.",
"Don't use rude words.",
],
)
print(model.generate_content("Why is sky blue?"))
# First, create tools that the model is can use to answer your questions.
# Describe a function by specifying it's schema (JsonSchema format)
get_current_weather_func = generative_models.FunctionDeclaration(
name="get_current_weather",
description="Get the current weather in a given location",
parameters={
"type": "object",
"properties": {
"location": {
"type": "string",
"description": "The city and state, e.g. San Francisco, CA"
},
"unit": {
"type": "string",
"enum": [
"celsius",
"fahrenheit",
]
}
},
"required": [
"location"
]
},
)
# Tool is a collection of related functions
weather_tool = generative_models.Tool(
function_declarations=[get_current_weather_func],
)
# Use tools in chat:
model = GenerativeModel(
"gemini-pro",
# You can specify tools when creating a model to avoid having to send them with every request.
tools=[weather_tool],
)
chat = model.start_chat()
# Send a message to the model. The model will respond with a function call.
print(chat.send_message("What is the weather like in Boston?"))
# Then send a function response to the model. The model will use it to answer.
print(chat.send_message(
Part.from_function_response(
name="get_current_weather",
response={
"content": {"weather": "super nice"},
}
),
))
from vertexai.preview.generative_models import GenerativeModel, Tool, FunctionDeclaration, AutomaticFunctionCallingResponder
# First, create functions that the model can use to answer your questions.
def get_current_weather(location: str, unit: str = "centigrade"):
"""Gets weather in the specified location.
Args:
location: The location for which to get the weather.
unit: Optional. Temperature unit. Can be Centigrade or Fahrenheit. Defaults to Centigrade.
"""
return dict(
location=location,
unit=unit,
weather="Super nice, but maybe a bit hot.",
)
# Infer function schema
get_current_weather_func = FunctionDeclaration.from_func(get_current_weather)
# Tool is a collection of related functions
weather_tool = Tool(
function_declarations=[get_current_weather_func],
)
# Use tools in chat:
model = GenerativeModel(
"gemini-pro",
# You can specify tools when creating a model to avoid having to send them with every request.
tools=[weather_tool],
)
# Activate automatic function calling:
afc_responder = AutomaticFunctionCallingResponder(
# Optional:
max_automatic_function_calls=5,
)
chat = model.start_chat(responder=afc_responder)
# Send a message to the model. The model will respond with a function call.
# The SDK will automatically call the requested function and respond to the model.
# The model will use the function call response to answer the original question.
print(chat.send_message("What is the weather like in Boston?"))
You can find complete documentation for the Vertex AI SDKs and the Gemini model in the Google Cloud documentation
ContributingSee Contributing for more information on contributing to the Vertex AI Python SDK.
LicenseThe contents of this repository are licensed under the Apache License, version 2.0.
Except as otherwise noted, the content of this page is licensed under the Creative Commons Attribution 4.0 License, and code samples are licensed under the Apache 2.0 License. For details, see the Google Developers Site Policies. Java is a registered trademark of Oracle and/or its affiliates.
Last updated 2025-08-07 UTC.
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Hard to understand","hardToUnderstand","thumb-down"],["Incorrect information or sample code","incorrectInformationOrSampleCode","thumb-down"],["Missing the information/samples I need","missingTheInformationSamplesINeed","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2025-08-07 UTC."],[],[]]
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