Google Gen AI SDK

Google Gen AI SDK

Documentation: https://googleapis.github.io/python-genai/

https://github.com/googleapis/python-genai

Google Gen AI Python SDK provides an interface for developers to integrate Google's generative models into their Python applications. It supports the Gemini Developer API and Vertex AI APIs.

Installation

pip install google-genai

With `uv`:

uv pip install google-genai

Imports

from google import genai
from google.genai import types

Create a client

Please run one of the following code blocks to create a client for different services (Gemini Developer API or Vertex AI).

from google import genai

# Only run this block for Gemini Developer API
client = genai.Client(api_key='GEMINI_API_KEY')

from google import genai

# Only run this block for Vertex AI API
client = genai.Client(
    vertexai=True, project='your-project-id', location='us-central1'
)

(Optional) Using environment variables:

You can create a client by configuring the necessary environment variables. Configuration setup instructions depends on whether you're using the Gemini Developer API or the Gemini API in Vertex AI.

Gemini Developer API: Set the [GEMINI_API_KEY]{.title-ref} or [GOOGLE_API_KEY]{.title-ref}. It will automatically be picked up by the client. It's recommended that you set only one of those variables, but if both are set, [GOOGLE_API_KEY]{.title-ref} takes precedence.

export GEMINI_API_KEY='your-api-key'

Gemini API on Vertex AI: Set [GOOGLE_GENAI_USE_VERTEXAI]{.title-ref}, [GOOGLE_CLOUD_PROJECT]{.title-ref} and [GOOGLE_CLOUD_LOCATION]{.title-ref}, as shown below:

export GOOGLE_GENAI_USE_VERTEXAI=true
export GOOGLE_CLOUD_PROJECT='your-project-id'
export GOOGLE_CLOUD_LOCATION='us-central1'

from google import genai

client = genai.Client()

Close a client

Explicitly close the sync client to ensure that resources, such as the underlying HTTP connections, are properly cleaned up and closed.

from google.genai import Client

client = Client()
response_1 = client.models.generate_content(
    model=MODEL_ID,
    contents='Hello',
)
response_2 = client.models.generate_content(
    model=MODEL_ID,
    contents='Ask a question',
)
# Close the sync client to release resources.
client.close()

To explicitly close the async client:

from google.genai import Client

aclient = Client(
    vertexai=True, project='my-project-id', location='us-central1'
).aio
response_1 = await aclient.models.generate_content(
    model=MODEL_ID,
    contents='Hello',
)
response_2 = await aclient.models.generate_content(
    model=MODEL_ID,
    contents='Ask a question',
)
# Close the async client to release resources.
await aclient.aclose()

Client context managers

By using the sync client context manager, it will close the underlying sync client when exiting the with block.

from google.genai import Client

with Client() as client:
    response_1 = client.models.generate_content(
        model=MODEL_ID,
        contents='Hello',
    )
    response_2 = client.models.generate_content(
        model=MODEL_ID,
        contents='Ask a question',
    )

By using the async client context manager, it will close the underlying async client when exiting the with block.

from google.genai import Client

async with Client().aio as aclient:
    response_1 = await aclient.models.generate_content(
        model=MODEL_ID,
        contents='Hello',
    )
    response_2 = await aclient.models.generate_content(
        model=MODEL_ID,
        contents='Ask a question',
    )

API Selection

By default, the SDK uses the beta API endpoints provided by Google to support preview features in the APIs. The stable API endpoints can be selected by setting the API version to [v1]{.title-ref}.

To set the API version use http_options. For example, to set the API version to v1 for Vertex AI:

from google import genai
from google.genai import types

client = genai.Client(
    vertexai=True,
    project='your-project-id',
    location='us-central1',
    http_options=types.HttpOptions(api_version='v1')
)

To set the API version to [v1alpha]{.title-ref} for the Gemini Developer API:

from google import genai
from google.genai import types

# Only run this block for Gemini Developer API
client = genai.Client(
    api_key='GEMINI_API_KEY',
    http_options=types.HttpOptions(api_version='v1alpha')
)

Faster async client option: Aiohttp

By default we use httpx for both sync and async client implementations. In order to have faster performance, you may install [google-genai[aiohttp]]{.title-ref}. In Gen AI SDK we configure [trust_env=True]{.title-ref} to match with the default behavior of httpx. Additional args of [aiohttp.ClientSession.request()]{.title-ref} (see _RequestOptions args) can be passed through the following way:

http_options = types.HttpOptions(
    async_client_args={'cookies': ..., 'ssl': ...},
)

client=Client(..., http_options=http_options)

Proxy

Both httpx and aiohttp libraries use [urllib.request.getproxies]{.title-ref} from environment variables. Before client initialization, you may set proxy (and optional SSL_CERT_FILE) by setting the environment variables:

export HTTPS_PROXY='http://username:password@proxy_uri:port'
export SSL_CERT_FILE='client.pem'

If you need [socks5]{.title-ref} proxy, httpx supports [socks5]{.title-ref} proxy if you pass it via args to httpx.Client(). You may install [httpx[socks]]{.title-ref} to use it. Then you can pass it through the following way:

http_options = types.HttpOptions(
    client_args={'proxy': 'socks5://user:pass@host:port'},
    async_client_args={'proxy': 'socks5://user:pass@host:port'},
)

client=Client(..., http_options=http_options)

Custom base url

In some cases you might need a custom base url (for example, API gateway proxy server) and bypass some authentication checks for project, location, or API key. You may pass the custom base url like this:

base_url = 'https://test-api-gateway-proxy.com'
client = Client(
    vertexai=True,  # Currently only vertexai=True is supported
    http_options={
        'base_url': base_url,
        'headers': {'Authorization': 'Bearer test_token'},
    },
)

Types

Parameter types can be specified as either dictionaries(TypedDict) or Pydantic Models. Pydantic model types are available in the types module.

Models

The client.models modules exposes model inferencing and model getters. See the 'Create a client' section above to initialize a client.

Generate Content

with text content input (text output)

response = client.models.generate_content(
    model='gemini-2.5-flash', contents='Why is the sky blue?'
)
print(response.text)

with text content input (image output)

from google.genai import types

response = client.models.generate_content(
    model='gemini-2.5-flash-image',
    contents='A cartoon infographic for flying sneakers',
    config=types.GenerateContentConfig(
        response_modalities=["IMAGE"],
        image_config=types.ImageConfig(
            aspect_ratio="9:16",
        ),
    ),
)

for part in response.parts:
    if part.inline_data:
        generated_image = part.as_image()
        generated_image.show()

with uploaded file (Gemini Developer API only)

download the file in console.

!wget -q https://storage.googleapis.com/generativeai-downloads/data/a11.txt

python code.

file = client.files.upload(file='a11.txt')
response = client.models.generate_content(
    model='gemini-2.5-flash',
    contents=['Could you summarize this file?', file]
)
print(response.text)

How to structure [contents]{.title-ref} argument for [generate_content]{.title-ref} ^^^^^^^^^^^^^^^^^^^^^^^^^^^ The SDK always converts the inputs to the [contents]{.title-ref} argument into [list[types.Content]]{.title-ref}. The following shows some common ways to provide your inputs.

Provide a [list[types.Content]]{.title-ref} """""""""""""""""""""""""""""" This is the canonical way to provide contents, SDK will not do any conversion.

Provide a [types.Content]{.title-ref} instance """"""""""""""""""""""""""""""

from google.genai import types

contents = types.Content(
    role='user',
    parts=[types.Part.from_text(text='Why is the sky blue?')]
)

SDK converts this to

[
    types.Content(
        role='user',
        parts=[types.Part.from_text(text='Why is the sky blue?')]
    )
]

Provide a string

contents='Why is the sky blue?'

The SDK will assume this is a text part, and it converts this into the following:

[
    types.UserContent(
        parts=[
            types.Part.from_text(text='Why is the sky blue?')
        ]
    )
]

Where a [types.UserContent]{.title-ref} is a subclass of [types.Content]{.title-ref}, it sets the [role]{.title-ref} field to be [user]{.title-ref}.

Provide a list of string

contents=['Why is the sky blue?', 'Why is the cloud white?']

The SDK assumes these are 2 text parts, it converts this into a single content, like the following:

[
    types.UserContent(
        parts=[
            types.Part.from_text(text='Why is the sky blue?'),
            types.Part.from_text(text='Why is the cloud white?'),
        ]
    )
]

Where a [types.UserContent]{.title-ref} is a subclass of [types.Content]{.title-ref}, the [role]{.title-ref} field in [types.UserContent]{.title-ref} is fixed to be [user]{.title-ref}.

Provide a function call part

from google.genai import types

contents = types.Part.from_function_call(
    name='get_weather_by_location',
    args={'location': 'Boston'}
)

The SDK converts a function call part to a content with a [model]{.title-ref} role:

[
    types.ModelContent(
        parts=[
            types.Part.from_function_call(
                name='get_weather_by_location',
                args={'location': 'Boston'}
            )
        ]
    )
]

Where a [types.ModelContent]{.title-ref} is a subclass of [types.Content]{.title-ref}, the [role]{.title-ref} field in [types.ModelContent]{.title-ref} is fixed to be [model]{.title-ref}.

Provide a list of function call parts """"""""""""""""""""""""""""""

from google.genai import types

contents = [
    types.Part.from_function_call(
        name='get_weather_by_location',
        args={'location': 'Boston'}
    ),
    types.Part.from_function_call(
        name='get_weather_by_location',
        args={'location': 'New York'}
    ),
]

The SDK converts a list of function call parts to the a content with a [model]{.title-ref} role:

[
    types.ModelContent(
        parts=[
            types.Part.from_function_call(
                name='get_weather_by_location',
                args={'location': 'Boston'}
            ),
            types.Part.from_function_call(
                name='get_weather_by_location',
                args={'location': 'New York'}
            )
        ]
    )
]

Where a [types.ModelContent]{.title-ref} is a subclass of [types.Content]{.title-ref}, the [role]{.title-ref} field in [types.ModelContent]{.title-ref} is fixed to be [model]{.title-ref}.

Provide a non function call part """"""""""""""""""""""""

from google.genai import types

contents = types.Part.from_uri(
    file_uri: 'gs://generativeai-downloads/images/scones.jpg',
    mime_type: 'image/jpeg',
)

The SDK converts all non function call parts into a content with a [user]{.title-ref} role.

[
    types.UserContent(parts=[
        types.Part.from_uri(
            file_uri: 'gs://generativeai-downloads/images/scones.jpg',
            mime_type: 'image/jpeg',
        )
    ])
]

Provide a list of non function call parts """"""""""""""""""""

from google.genai import types

contents = [
    types.Part.from_text('What is this image about?'),
    types.Part.from_uri(
        file_uri: 'gs://generativeai-downloads/images/scones.jpg',
        mime_type: 'image/jpeg',
    )
]

The SDK will convert the list of parts into a content with a [user]{.title-ref} role

[
    types.UserContent(
        parts=[
            types.Part.from_text('What is this image about?'),
            types.Part.from_uri(
                file_uri: 'gs://generativeai-downloads/images/scones.jpg',
                mime_type: 'image/jpeg',
            )
        ]
    )
]

Mix types in contents

You can also provide a list of [types.ContentUnion]{.title-ref}. The SDK leaves items of [types.Content]{.title-ref} as is, it groups consecutive non function call parts into a single [types.UserContent]{.title-ref}, and it groups consecutive function call parts into a single [types.ModelContent]{.title-ref}.

If you put a list within a list, the inner list can only contain [types.PartUnion]{.title-ref} items. The SDK will convert the inner list into a single [types.UserContent]{.title-ref}.

System Instructions and Other Configs

The output of the model can be influenced by several optional settings available in generate_content's config parameter. For example, increasing [max_output_tokens]{.title-ref} is essential for longer model responses. To make a model more deterministic, lowering the [temperature]{.title-ref} parameter reduces randomness, with values near 0 minimizing variability. Capabilities and parameter defaults for each model is shown in the Vertex AI docs and Gemini API docs respectively.

from google.genai import types

response = client.models.generate_content(
    model='gemini-2.0-flash-001',
    contents='high',
    config=types.GenerateContentConfig(
        system_instruction='I say high, you say low',
        max_output_tokens=3,
        temperature=0.3,
    ),
)
print(response.text)

Typed Config

All API methods support Pydantic types for parameters as well as dictionaries. You can get the type from google.genai.types.

from google.genai import types

response = client.models.generate_content(
    model='gemini-2.0-flash-001',
    contents=types.Part.from_text(text='Why is the sky blue?'),
    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,
    ),
)

print(response.text)

List Base Models

To retrieve tuned models, see: List Tuned Models{.interpreted-text role="ref"}

for model in client.models.list():
    print(model)

pager = client.models.list(config={'page_size': 10})
print(pager.page_size)
print(pager[0])
pager.next_page()
print(pager[0])

List Base Models (Asynchronous)

async for job in await client.aio.models.list():
    print(job)

async_pager = await client.aio.models.list(config={'page_size': 10})
print(async_pager.page_size)
print(async_pager[0])
await async_pager.next_page()
print(async_pager[0])

Safety Settings

from google.genai import types

response = client.models.generate_content(
    model='gemini-2.5-flash',
    contents='Say something bad.',
    config=types.GenerateContentConfig(
        safety_settings=[
            types.SafetySetting(
                category='HARM_CATEGORY_HATE_SPEECH',
                threshold='BLOCK_ONLY_HIGH',
            )
        ]
    ),
)
print(response.text)

Function Calling

Automatic Python function Support:

You can pass a Python function directly and it will be automatically called and responded by default.

from google.genai import types

def get_current_weather(location: str) -> str:
    """Returns the current weather.

    Args:
        location: The city and state, e.g. San Francisco, CA
    """
    return 'sunny'


response = client.models.generate_content(
    model='gemini-2.5-flash',
    contents='What is the weather like in Boston?',
    config=types.GenerateContentConfig(
        tools=[get_current_weather],
    ),
)

print(response.text)

Disabling automatic function calling

If you pass in a python function as a tool directly, and do not want automatic function calling, you can disable automatic function calling as follows:

from google.genai import types

response = client.models.generate_content(
    model='gemini-2.5-flash',
    contents='What is the weather like in Boston?',
    config=types.GenerateContentConfig(
        tools=[get_current_weather],
        automatic_function_calling=types.AutomaticFunctionCallingConfig(
            disable=True
        ),
    ),
)

With automatic function calling disabled, you will get a list of function call parts in the response:

function_calls: Optional[List[types.FunctionCall]] = response.function_calls

Manually declare and invoke a function for function calling ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

If you don't want to use the automatic function support, you can manually declare the function and invoke it.

The following example shows how to declare a function and pass it as a tool. Then you will receive a function call part in the response.

from google.genai import types

function = types.FunctionDeclaration(
    name='get_current_weather',
    description='Get the current weather in a given location',
    parameters_json_schema={
        'type': 'object',
        'properties': {
            'location': {
                'type': 'string',
                'description': 'The city and state, e.g. San Francisco, CA',
            }
        },
        'required': ['location'],
    },
)

tool = types.Tool(function_declarations=[function])

response = client.models.generate_content(
    model='gemini-2.5-flash',
    contents='What is the weather like in Boston?',
    config=types.GenerateContentConfig(
        tools=[tool],
    ),
)
print(response.function_calls[0])

After you receive the function call part from the model, you can invoke the function and get the function response. And then you can pass the function response to the model. The following example shows how to do it for a simple function invocation.

from google.genai import types

user_prompt_content = types.Content(
    role='user',
    parts=[types.Part.from_text(text='What is the weather like in Boston?')],
)
function_call_part = response.function_calls[0]
function_call_content = response.candidates[0].content


try:
    function_result = get_current_weather(
        **function_call_part.function_call.args
    )
    function_response = {'result': function_result}
except (
    Exception
) as e:  # instead of raising the exception, you can let the model handle it
    function_response = {'error': str(e)}


function_response_part = types.Part.from_function_response(
    name=function_call_part.name,
    response=function_response,
)
function_response_content = types.Content(
    role='tool', parts=[function_response_part]
)

response = client.models.generate_content(
    model='gemini-2.5-flash',
    contents=[
        user_prompt_content,
        function_call_content,
        function_response_content,
    ],
    config=types.GenerateContentConfig(
        tools=[tool],
    ),
)

print(response.text)

Function calling with ANY tools config mode

If you configure function calling mode to be [ANY]{.title-ref}, then the model will always return function call parts. If you also pass a python function as a tool, by default the SDK will perform automatic function calling until the remote calls exceed the maximum remote call for automatic function calling (default to 10 times).

If you'd like to disable automatic function calling in [ANY]{.title-ref} mode:

from google.genai import types

def get_current_weather(location: str) -> str:
    """Returns the current weather.

    Args:
        location: The city and state, e.g. San Francisco, CA
    """
    return "sunny"

response = client.models.generate_content(
    model="gemini-2.5-flash",
    contents="What is the weather like in Boston?",
    config=types.GenerateContentConfig(
        tools=[get_current_weather],
        automatic_function_calling=types.AutomaticFunctionCallingConfig(
            disable=True
        ),
        tool_config=types.ToolConfig(
            function_calling_config=types.FunctionCallingConfig(mode='ANY')
        ),
    ),
)

If you'd like to set x number of automatic function call turns, you can configure the maximum remote calls to be x + 1. Assuming you prefer 1 turn for automatic function calling:

from google.genai import types

def get_current_weather(location: str) -> str:
    """Returns the current weather.

    Args:
        location: The city and state, e.g. San Francisco, CA
    """
    return "sunny"

response = client.models.generate_content(
    model="gemini-2.5-flash",
    contents="What is the weather like in Boston?",
    config=types.GenerateContentConfig(
        tools=[get_current_weather],
        automatic_function_calling=types.AutomaticFunctionCallingConfig(
            maximum_remote_calls=2
        ),
        tool_config=types.ToolConfig(
            function_calling_config=types.FunctionCallingConfig(mode='ANY')
        ),
    ),
)

Model Context Protocol (MCP) support (experimental)

Built-in MCP support is an experimental feature. You can pass a local MCP server as a tool directly.

import os
import asyncio
from datetime import datetime
from mcp import ClientSession, StdioServerParameters
from mcp.client.stdio import stdio_client
from google import genai

client = genai.Client()

# Create server parameters for stdio connection
server_params = StdioServerParameters(
    command="npx",  # Executable
    args=["-y", "@philschmid/weather-mcp"],  # MCP Server
    env=None,  # Optional environment variables
)

async def run():
    async with stdio_client(server_params) as (read, write):
        async with ClientSession(read, write) as session:
            # Prompt to get the weather for the current day in London.
            prompt = f"What is the weather in London in {datetime.now().strftime('%Y-%m-%d')}?"

            # Initialize the connection between client and server
            await session.initialize()

            # Send request to the model with MCP function declarations
            response = await client.aio.models.generate_content(
                model="gemini-2.5-flash",
                contents=prompt,
                config=genai.types.GenerateContentConfig(
                    temperature=0,
                    tools=[session],  # uses the session, will automatically call the tool using automatic function calling
                ),
            )
            print(response.text)

# Start the asyncio event loop and run the main function
asyncio.run(run())

JSON Response Schema

However you define your schema, don't duplicate it in your input prompt, including by giving examples of expected JSON output. If you do, the generated output might be lower in quality.

JSON Schema support

Schemas can be provided as standard JSON schema.

user_profile = {
    'properties': {
        'age': {
            'anyOf': [
                {'maximum': 20, 'minimum': 0, 'type': 'integer'},
                {'type': 'null'},
            ],
            'title': 'Age',
        },
        'username': {
            'description': "User's unique name",
            'title': 'Username',
            'type': 'string',
        },
    },
    'required': ['username', 'age'],
    'title': 'User Schema',
    'type': 'object',
}

response = client.models.generate_content(
    model='gemini-2.5-flash',
    contents='Give me a random user profile.',
    config={
        'response_mime_type': 'application/json',
        'response_json_schema': user_profile
    },
)
print(response.parsed)

Pydantic Model Schema support

Schemas can be provided as Pydantic Models.

from pydantic import BaseModel
from google.genai import types


class CountryInfo(BaseModel):
    name: str
    population: int
    capital: str
    continent: str
    gdp: int
    official_language: str
    total_area_sq_mi: int


response = client.models.generate_content(
    model='gemini-2.5-flash',
    contents='Give me information for the United States.',
    config=types.GenerateContentConfig(
        response_mime_type='application/json',
        response_schema=CountryInfo,
    ),
)
print(response.text)

from google.genai import types

response = client.models.generate_content(
    model='gemini-2.5-flash',
    contents='Give me information for the United States.',
    config=types.GenerateContentConfig(
        response_mime_type='application/json',
        response_schema={
            'required': [
                'name',
                'population',
                'capital',
                'continent',
                'gdp',
                'official_language',
                'total_area_sq_mi',
            ],
            'properties': {
                'name': {'type': 'STRING'},
                'population': {'type': 'INTEGER'},
                'capital': {'type': 'STRING'},
                'continent': {'type': 'STRING'},
                'gdp': {'type': 'INTEGER'},
                'official_language': {'type': 'STRING'},
                'total_area_sq_mi': {'type': 'INTEGER'},
            },
            'type': 'OBJECT',
        },
    ),
)
print(response.text)

Enum Response Schema

Text Response

You can set response_mime_type to 'text/x.enum' to return one of those enum values as the response.

from enum import Enum

class InstrumentEnum(Enum):
    PERCUSSION = 'Percussion'
    STRING = 'String'
    WOODWIND = 'Woodwind'
    BRASS = 'Brass'
    KEYBOARD = 'Keyboard'

response = client.models.generate_content(
    model='gemini-2.5-flash',
    contents='What instrument plays multiple notes at once?',
    config={
        'response_mime_type': 'text/x.enum',
        'response_schema': InstrumentEnum,
    },
)
print(response.text)

JSON Response

You can also set response_mime_type to 'application/json', the response will be identical but in quotes.

from enum import Enum

class InstrumentEnum(Enum):
    PERCUSSION = 'Percussion'
    STRING = 'String'
    WOODWIND = 'Woodwind'
    BRASS = 'Brass'
    KEYBOARD = 'Keyboard'

response = client.models.generate_content(
    model='gemini-2.5-flash',
    contents='What instrument plays multiple notes at once?',
    config={
        'response_mime_type': 'application/json',
        'response_schema': InstrumentEnum,
    },
)
print(response.text)

Generate Content (Synchronous Streaming)

Generate content in a streaming format so that the model outputs streams back to you, rather than being returned as one chunk.

Streaming for text content

for chunk in client.models.generate_content_stream(
    model='gemini-2.5-flash', contents='Tell me a story in 300 words.'
):
    print(chunk.text, end='')

Streaming for image content

If your image is stored in Google Cloud Storage, you can use the from_uri class method to create a Part object.

from google.genai import types

for chunk in client.models.generate_content_stream(
    model='gemini-2.5-flash',
    contents=[
        'What is this image about?',
        types.Part.from_uri(
            file_uri='gs://generativeai-downloads/images/scones.jpg',
            mime_type='image/jpeg',
        ),
    ],
):
    print(chunk.text, end='')

If your image is stored in your local file system, you can read it in as bytes data and use the from_bytes class method to create a Part object.

from google.genai import types

YOUR_IMAGE_PATH = 'your_image_path'
YOUR_IMAGE_MIME_TYPE = 'your_image_mime_type'
with open(YOUR_IMAGE_PATH, 'rb') as f:
    image_bytes = f.read()

for chunk in client.models.generate_content_stream(
    model='gemini-2.5-flash',
    contents=[
        'What is this image about?',
        types.Part.from_bytes(data=image_bytes, mime_type=YOUR_IMAGE_MIME_TYPE),
    ],
):
    print(chunk.text, end='')

Generate Content (Asynchronous Non Streaming)

client.aio exposes all the analogous async methods that are available on client. Note that it applies to all the modules.

For example, client.aio.models.generate_content is the async version of client.models.generate_content

response = await client.aio.models.generate_content(
    model='gemini-2.5-flash', contents='Tell me a story in 300 words.'
)

print(response.text)

Generate Content (Asynchronous Streaming)

async for chunk in await client.aio.models.generate_content_stream(
    model='gemini-2.5-flash', contents='Tell me a story in 300 words.'
):
    print(chunk.text, end='')

Count Tokens

response = client.models.count_tokens(
    model='gemini-2.5-flash',
    contents='why is the sky blue?',
)
print(response)

Compute Tokens

Compute tokens is only supported in Vertex AI.

response = client.models.compute_tokens(
    model='gemini-2.5-flash',
    contents='why is the sky blue?',
)
print(response)

Count Tokens (Asynchronous)

response = await client.aio.models.count_tokens(
    model='gemini-2.5-flash',
    contents='why is the sky blue?',
)
print(response)

Local Count Tokens

tokenizer = genai.LocalTokenizer(model_name='gemini-2.5-flash')
result = tokenizer.count_tokens("What is your name?")

Local Compute Tokens

tokenizer = genai.LocalTokenizer(model_name='gemini-2.5-flash')
result = tokenizer.compute_tokens("What is your name?")

Embed Content

response = client.models.embed_content(
    model='gemini-embedding-001',
    contents='why is the sky blue?',
)
print(response)

from google.genai import types

# multiple contents with config
response = client.models.embed_content(
    model='gemini-embedding-001',
    contents=['why is the sky blue?', 'What is your age?'],
    config=types.EmbedContentConfig(output_dimensionality=10),
)

print(response)

Imagen

Generate Images

Support for generate images in Gemini Developer API is behind an allowlist

from google.genai import types

# Generate Image
response1 = client.models.generate_images(
    model='imagen-3.0-generate-002',
    prompt='An umbrella in the foreground, and a rainy night sky in the background',
    config=types.GenerateImagesConfig(
        number_of_images=1,
        include_rai_reason=True,
        output_mime_type='image/jpeg',
    ),
)
response1.generated_images[0].image.show()

Upscale Image

Upscale image is only supported in Vertex AI.

from google.genai import types

# Upscale the generated image from above
response2 = client.models.upscale_image(
    model='imagen-3.0-generate-002',
    image=response1.generated_images[0].image,
    upscale_factor='x2',
    config=types.UpscaleImageConfig(
        include_rai_reason=True,
        output_mime_type='image/jpeg',
    ),
)
response2.generated_images[0].image.show()

Edit Image

Edit image uses a separate model from generate and upscale.

Edit image is only supported in Vertex AI.

# Edit the generated image from above
from google.genai import types
from google.genai.types import RawReferenceImage, MaskReferenceImage

raw_ref_image = RawReferenceImage(
    reference_id=1,
    reference_image=response1.generated_images[0].image,
)

# Model computes a mask of the background
mask_ref_image = MaskReferenceImage(
    reference_id=2,
    config=types.MaskReferenceConfig(
        mask_mode='MASK_MODE_BACKGROUND',
        mask_dilation=0,
    ),
)

response3 = client.models.edit_image(
    model='imagen-3.0-capability-001',
    prompt='Sunlight and clear sky',
    reference_images=[raw_ref_image, mask_ref_image],
    config=types.EditImageConfig(
        edit_mode='EDIT_MODE_INPAINT_INSERTION',
        number_of_images=1,
        include_rai_reason=True,
        output_mime_type='image/jpeg',
    ),
)
response3.generated_images[0].image.show()

Veo

Support for generating videos is considered public preview

Generate Videos (Text to Video)

from google.genai import types

# Create operation
operation = client.models.generate_videos(
    model='veo-2.0-generate-001',
    prompt='A neon hologram of a cat driving at top speed',
    config=types.GenerateVideosConfig(
        number_of_videos=1,
        duration_seconds=5,
        enhance_prompt=True,
    ),
)

# Poll operation
while not operation.done:
    time.sleep(20)
    operation = client.operations.get(operation)

video = operation.response.generated_videos[0].video
video.show()

Generate Videos (Image to Video)

from google.genai import types

# Read local image (uses mimetypes.guess_type to infer mime type)
image = types.Image.from_file("local/path/file.png")

# Create operation
operation = client.models.generate_videos(
    model='veo-2.0-generate-001',
    # Prompt is optional if image is provided
    prompt='Night sky',
    image=image,
    config=types.GenerateVideosConfig(
        number_of_videos=1,
        duration_seconds=5,
        enhance_prompt=True,
        # Can also pass an Image into last_frame for frame interpolation
    ),
)

# Poll operation
while not operation.done:
    time.sleep(20)
    operation = client.operations.get(operation)

video = operation.response.generated_videos[0].video
video.show()

Generate Videos (Video to Video)

Currently, only Vertex AI supports Video to Video generation (Video extension).

from google.genai import types

# Read local video (uses mimetypes.guess_type to infer mime type)
video = types.Video.from_file("local/path/video.mp4")

# Create operation
operation = client.models.generate_videos(
    model='veo-2.0-generate-001',
    # Prompt is optional if Video is provided
    prompt='Night sky',
    # Input video must be in GCS
    video=types.Video(
        uri="gs://bucket-name/inputs/videos/cat_driving.mp4",
    ),
    config=types.GenerateVideosConfig(
        number_of_videos=1,
        duration_seconds=5,
        enhance_prompt=True,
    ),
)

# Poll operation
while not operation.done:
    time.sleep(20)
    operation = client.operations.get(operation)

video = operation.response.generated_videos[0].video
video.show()

Chats

Create a chat session to start a multi-turn conversations with the model. Then, use [chat.send_message]{.title-ref} function multiple times within the same chat session so that it can reflect on its previous responses (i.e., engage in an ongoing conversation). See the 'Create a client' section above to initialize a client.

Send Message (Synchronous Non-Streaming)

chat = client.chats.create(model='gemini-2.5-flash')
response = chat.send_message('tell me a story')
print(response.text)
response = chat.send_message('summarize the story you told me in 1 sentence')
print(response.text)

Send Message (Synchronous Streaming)

chat = client.chats.create(model='gemini-2.5-flash')
for chunk in chat.send_message_stream('tell me a story'):
    print(chunk.text)

Send Message (Asynchronous Non-Streaming)

chat = client.aio.chats.create(model='gemini-2.5-flash')
response = await chat.send_message('tell me a story')
print(response.text)

Send Message (Asynchronous Streaming)

chat = client.aio.chats.create(model='gemini-2.5-flash')
async for chunk in await chat.send_message_stream('tell me a story'):
    print(chunk.text)

Files

Files are only supported in Gemini Developer API. See the 'Create a client' section above to initialize a client.

gsutil cp gs://cloud-samples-data/generative-ai/pdf/2312.11805v3.pdf .
gsutil cp gs://cloud-samples-data/generative-ai/pdf/2403.05530.pdf .

Upload File

file1 = client.files.upload(file='2312.11805v3.pdf')
file2 = client.files.upload(file='2403.05530.pdf')

print(file1)
print(file2)

Get File

file1 = client.files.upload(file='2312.11805v3.pdf')
file_info = client.files.get(name=file1.name)

Delete

file3 = client.files.upload(file='2312.11805v3.pdf')

client.files.delete(name=file3.name)

Caches

client.caches contains the control plane APIs for cached content.

: See the 'Create a client' section above to initialize a client.

Create Cache

from google.genai import types

if client.vertexai:
    file_uris = [
        'gs://cloud-samples-data/generative-ai/pdf/2312.11805v3.pdf',
        'gs://cloud-samples-data/generative-ai/pdf/2403.05530.pdf',
    ]
else:
    file_uris = [file1.uri, file2.uri]

cached_content = client.caches.create(
    model='gemini-2.5-flash',
    config=types.CreateCachedContentConfig(
        contents=[
            types.Content(
                role='user',
                parts=[
                    types.Part.from_uri(
                        file_uri=file_uris[0], mime_type='application/pdf'
                    ),
                    types.Part.from_uri(
                        file_uri=file_uris[1],
                        mime_type='application/pdf',
                    ),
                ],
            )
        ],
        system_instruction='What is the sum of the two pdfs?',
        display_name='test cache',
        ttl='3600s',
    ),
)

Get Cache

cached_content = client.caches.get(name=cached_content.name)

Generate Content with Caches

from google.genai import types

response = client.models.generate_content(
    model='gemini-2.5-flash',
    contents='Summarize the pdfs',
    config=types.GenerateContentConfig(
        cached_content=cached_content.name,
    ),
)
print(response.text)

Tunings

client.tunings contains tuning job APIs and supports supervised fine

…(truncated)