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To see an example of getting started with Chat with the Gemini Pro model, run the "Getting Started with Chat with the Gemini Pro model" Jupyter notebook in one of the following environments:

Open in Colab | Open in Colab Enterprise | Open in Vertex AI Workbench user-managed notebooks | View on GitHub

This page shows you how to send chat prompts to a Gemini model by using the Google Cloud console, REST API, and supported SDKs.

To learn how to add images and other media to your request, see Image understanding.

For a list of languages supported by Gemini, see Language support.

To explore the generative AI models and APIs that are available on Vertex AI, go to Model Garden in the Google Cloud console.

Go to Model Garden

If you're looking for a way to use Gemini directly from your mobile and web apps, see the Vertex AI in Firebase SDKs for Android, Swift, web, and Flutter apps.

Generate text

For testing and iterating on chat prompts, we recommend using the Google Cloud console. To send prompts programmatically to the model, you can use the REST API, Google Gen AI SDK, Vertex AI SDK for Python, or one of the other supported libraries and SDKs.

You can use system instructions to steer the behavior of the model based on a specific need or use case. For example, you can define a persona or role for a chatbot that responds to customer service requests. For more information, see the system instructions code samples.

You can use the Google Gen AI SDK to send requests if you're using Gemini 2.0 Flash.

Streaming and non-streaming responses

You can choose whether the model generates streaming responses or non-streaming responses. For streaming responses, you receive each response as soon as its output token is generated. For non-streaming responses, you receive all responses after all of the output tokens are generated.

Streaming

Gen AI SDK for Python More

Install

pip install --upgrade google-genai

To learn more, see the SDK reference documentation.

Set environment variables to use the Gen AI SDK with Vertex AI:

# Replace the `GOOGLE_CLOUD_PROJECT` and `GOOGLE_CLOUD_LOCATION` values
# with appropriate values for your project.
export GOOGLE_CLOUD_PROJECT=GOOGLE_CLOUD_PROJECT
export GOOGLE_CLOUD_LOCATION=global
export GOOGLE_GENAI_USE_VERTEXAI=True

from google import genai
from google.genai.types import HttpOptions

client = genai.Client(http_options=HttpOptions(api_version="v1"))
chat_session = client.chats.create(model="gemini-2.0-flash-001")
response_text = ""

for chunk in chat_session.send_message_stream("Why is the sky blue?"):
 print(chunk.text, end="")
 response_text += chunk.text
# Example response:
# The
# sky appears blue due to a phenomenon called **Rayleigh scattering**. Here's
# a breakdown of why:
# ...

Non-Streaming

Gen AI SDK for Python More

Install

pip install --upgrade google-genai

To learn more, see the SDK reference documentation.

Set environment variables to use the Gen AI SDK with Vertex AI:

# Replace the `GOOGLE_CLOUD_PROJECT` and `GOOGLE_CLOUD_LOCATION` values
# with appropriate values for your project.
export GOOGLE_CLOUD_PROJECT=GOOGLE_CLOUD_PROJECT
export GOOGLE_CLOUD_LOCATION=global
export GOOGLE_GENAI_USE_VERTEXAI=True

from google import genai
from google.genai.types import HttpOptions, ModelContent, Part, UserContent

client = genai.Client(http_options=HttpOptions(api_version="v1"))
chat_session = client.chats.create(
 model="gemini-2.0-flash-001",
 history=[
 UserContent(parts=[Part(text="Hello")]),
 ModelContent(
 parts=[Part(text="Great to meet you. What would you like to know?")],
 ),
 ],
)
response = chat_session.send_message("Tell me a story.")
print(response.text)
# Example response:
# Okay, here's a story for you:
# ...

What's next

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