Using types

All API methods support Pydantic types and dictionaries, which you can access from google.genai.types. You can import the types module with the following:

from google.genai import types

Below is an example generate_content() call using types from the types module:

response = client.models.generate_content(
    model='gemini-2.5-flash',
    contents=types.Part.from_text(text='Why is the sky blue?'),
    config=types.GenerateContentConfig(
        temperature=0,
        top_p=0.95,
        top_k=20,
    ),
)

Alternatively, you can accomplish the same request using dictionaries instead of types:

response = client.models.generate_content(
    model='gemini-2.5-flash',
    contents={'text': 'Why is the sky blue?'},
    config={
        'temperature': 0,
        'top_p': 0.95,
        'top_k': 20,
    },
)

(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 or GOOGLE_API_KEY. 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 takes precedence.

export GEMINI_API_KEY='your-api-key'

Gemini API on Vertex AI: Set GOOGLE_GENAI_USE_VERTEXAI, GOOGLE_CLOUD_PROJECT and GOOGLE_CLOUD_LOCATION, 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 and avoid httpx "client has been closed" error like issues#1763.

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.

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 for the Gemini Developer API:

from google import genai
from google.genai import types

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]. In Gen AI SDK we configure trust_env=True to match with the default behavior of httpx. Additional args of aiohttp.ClientSession.request() (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 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 proxy, httpx supports socks5 proxy if you pass it via args to httpx.Client(). You may install httpx[socks] 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:

client = Client(
    vertexai=True,
    http_options=types.HttpOptionsDict(
        base_url='https://test-api-gateway-proxy.com',
        base_url_resource_scope=types.ResourceScope.COLLECTION,
    ),
)

response = client.models.generate_content(
    model='gemini-3-pro-preview', contents='Why is the sky blue?'
)

If base_url_resource_scope=types.ResourceScope.COLLECTION, the resource name will not include api version, project, or location.

Expected request url will be: https://test-api-gateway-proxy.com/publishers/google/models/gemini-3-pro-preview

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 module 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 argument for generate_content

The SDK always converts the inputs to the contents argument into list[types.Content]. The following shows some common ways to provide your inputs.

Provide a list[types.Content]

This is the canonical way to provide contents, SDK will not do any conversion.

Provide a types.Content 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 is a subclass of types.Content, it sets the role field to be user.

Provide a list of strings

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 is a subclass of types.Content, the role field in types.UserContent is fixed to be user.

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 role:

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

Where a types.ModelContent is a subclass of types.Content, the role field in types.ModelContent is fixed to be model.

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 a content with a model 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 is a subclass of types.Content, the role field in types.ModelContent is fixed to be model.

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 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 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. The SDK leaves items of types.Content as is, it groups consecutive non function call parts into a single types.UserContent, and it groups consecutive function call parts into a single types.ModelContent.

If you put a list within a list, the inner list can only contain types.PartUnion items. The SDK will convert the inner list into a single types.UserContent.

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 is essential for longer model responses. To make a model more deterministic, lowering the temperature 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-2.5-flash',
    contents='high',
    config=types.GenerateContentConfig(
        system_instruction='I say high, you say low',
        max_output_tokens=3,
        temperature=0.3,
    ),
)
print(response.text)

List Base Models

To retrieve tuned models, see list tuned models.

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, 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 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.text)

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_json_schema=CountryInfo.model_json_schema(),
    ),
)
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_json_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)

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 and Compute 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)

Async

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

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

from google.genai import types

response1 = client.models.generate_images(
    model='imagen-4.0-generate-001',
    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

response2 = client.models.upscale_image(
    model='imagen-4.0-upscale-preview',
    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-3.1-generate-preview',
    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-3.1-generate-preview',
    # 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 Gemini Developer API supports video extension on Veo 3.1 for previously generated videos. Vertex supports video extension on Veo 2.0.

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-3.1-generate-preview',
    # Prompt is optional if Video is provided
    prompt='Night sky',
    # Input video must be in GCS for Vertex or a URI for Gemini
    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 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.

!gcloud storage cp gs://cloud-samples-data/generative-ai/pdf/2312.11805v3.pdf .
!gcloud storage cp gs://cloud-samples-data/generative-ai/pdf/2403.05530.pdf .

Upload

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

print(file1)
print(file2)

Get

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

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

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)

Interactions (Preview)

Warning: The Interactions API is in Beta. This is a preview of an experimental feature. Features and schemas are subject to breaking changes.

The Interactions API is a unified interface for interacting with Gemini models and agents. It simplifies state management, tool orchestration, and long-running tasks.

See the documentation site for more details.

Basic Interaction

interaction = client.interactions.create(
    model='gemini-2.5-flash',
    input='Tell me a short joke about programming.'
)
print(interaction.outputs[-1].text)

Stateful Conversation

The Interactions API supports server-side state management. You can continue a conversation by referencing the previous_interaction_id.

# 1. First turn
interaction1 = client.interactions.create(
    model='gemini-2.5-flash',
    input='Hi, my name is Amir.'
)
print(f"Model: {interaction1.outputs[-1].text}")

# 2. Second turn (passing previous_interaction_id)
interaction2 = client.interactions.create(
    model='gemini-2.5-flash',
    input='What is my name?',
    previous_interaction_id=interaction1.id
)
print(f"Model: {interaction2.outputs[-1].text}")

Agents (Deep Research)

You can use specialized agents like deep-research-pro-preview-12-2025 for complex tasks.

import time

# 1. Start the Deep Research Agent
initial_interaction = client.interactions.create(
    input='Research the history of the Google TPUs with a focus on 2025 and 2026.',
    agent='deep-research-pro-preview-12-2025',
    background=True
)
print(f"Research started. Interaction ID: {initial_interaction.id}")

# 2. Poll for results
while True:
    interaction = client.interactions.get(id=initial_interaction.id)
    print(f"Status: {interaction.status}")

    if interaction.status == "completed":
        print("\nFinal Report:\n", interaction.outputs[-1].text)
        break
    elif interaction.status in ["failed", "cancelled"]:
        print(f"Failed with status: {interaction.status}")
        break

    time.sleep(10)

Multimodal Input

You can provide multimodal data (text, images, audio, etc.) in the input list.

import base64

# Assuming you have an image loaded as bytes
# base64_image = ...

interaction = client.interactions.create(
    model='gemini-2.5-flash',
    input=[
        {'type': 'text', 'text': 'Describe the image.'},
        {'type': 'image', 'data': base64_image, 'mime_type': 'image/png'}
    ]
)
print(interaction.outputs[-1].text)

Function Calling

You can define custom functions for the model to use. The Interactions API handles the tool selection, and you provide the execution result back to the model.

# 1. Define the tool
def get_weather(location: str):
    """Gets the weather for a given location."""
    return f"The weather in {location} is sunny."

weather_tool = {
    'type': 'function',
    'name': 'get_weather',
    'description': 'Gets the weather for a given location.',
    'parameters': {
        'type': 'object',
        'properties': {
            'location': {'type': 'string', 'description': 'The city and state, e.g. San Francisco, CA'}
        },
        'required': ['location']
    }
}

# 2. Send the request with tools
interaction = client.interactions.create(
    model='gemini-2.5-flash',
    input='What is the weather in Mountain View, CA?',
    tools=[weather_tool]
)

# 3. Handle the tool call
for output in interaction.outputs:
    if output.type == 'function_call':
        print(f"Tool Call: {output.name}({output.arguments})")

        # Execute your actual function here
        result = get_weather(**output.arguments)

        # Send result back to the model
        interaction = client.interactions.create(
            model='gemini-2.5-flash',
            previous_interaction_id=interaction.id,
            input=[{
                'type': 'function_result',
                'name': output.name,
                'call_id': output.id,
                'result': result
            }]
        )
        print(f"Response: {interaction.outputs[-1].text}")

Built-in Tools

You can also use Google's built-in tools, such as Google Search or Code Execution.

Grounding with Google Search

interaction = client.interactions.create(
    model='gemini-2.5-flash',
    input='Who won the last Super Bowl?',
    tools=[{'type': 'google_search'}]
)

# Find the text output (not the GoogleSearchResultContent)
text_output = next((o for o in interaction.outputs if o.type == 'text'), None)
if text_output:
    print(text_output.text)

Code Execution

interaction = client.interactions.create(
    model='gemini-2.5-flash',
    input='Calculate the 50th Fibonacci number.',
    tools=[{'type': 'code_execution'}]
)
print(interaction.outputs[-1].text)

Multimodal Output

The Interactions API can generate multimodal outputs, such as images. You must specify the response_modalities.

import base64

interaction = client.interactions.create(
    model='gemini-3-pro-image-preview',
    input='Generate an image of a futuristic city.',
    response_modalities=['IMAGE']
)

for output in interaction.outputs:
    if output.type == 'image':
        print(f"Generated image with mime_type: {output.mime_type}")
        # Save the image
        with open("generated_city.png", "wb") as f:
            f.write(base64.b64decode(output.data))

Tunings

client.tunings contains tuning job APIs and supports supervised fine tuning through tune. Only supported in Vertex AI. See the 'Create a client' section above to initialize a client.

Tune

• Vertex AI supports tuning from GCS source or from a Vertex AI Multimodal Dataset

from google.genai import types

model = 'gemini-2.5-flash'
training_dataset = types.TuningDataset(
    # or gcs_uri=my_vertex_multimodal_dataset
    gcs_uri='gs://your-gcs-bucket/your-tuning-data.jsonl',
)

from google.genai import types

tuning_job = client.tunings.tune(
    base_model=model,
    training_dataset=training_dataset,
    config=types.CreateTuningJobConfig(
        epoch_count=1, tuned_model_display_name='test_dataset_examples model'
    ),
)
print(tuning_job)

Get Tuning Job

tuning_job = client.tunings.get(name=tuning_job.name)
print(tuning_job)

import time

completed_states = set(
    [
        'JOB_STATE_SUCCEEDED',
        'JOB_STATE_FAILED',
        'JOB_STATE_CANCELLED',
    ]
)

while tuning_job.state not in completed_states:
    print(tuning_job.state)
    tuning_job = client.tunings.get(name=tuning_job.name)
    time.sleep(10)

Use Tuned Model

response = client.models.generate_content(
    model=tuning_job.tuned_model.endpoint,
    contents='why is the sky blue?',
)

print(response.text)

Get Tuned Model

tuned_model = client.models.get(model=tuning_job.tuned_model.model)
print(tuned_model)

List Tuned Models

To retrieve base models, see list base models.

for model in client.models.list(config={'page_size': 10, 'query_base': False}):
    print(model)

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

Async

async for job in await client.aio.models.list(config={'page_size': 10, 'query_base': False}):
    print(job)

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

Update Tuned Model

from google.genai import types

model = pager[0]

model = client.models.update(
    model=model.name,
    config=types.UpdateModelConfig(
        display_name='my tuned model', description='my tuned model description'
    ),
)

print(model)

List Tuning Jobs

for job in client.tunings.list(config={'page_size': 10}):
    print(job)

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

Async

async for job in await client.aio.tunings.list(config={'page_size': 10}):
    print(job)

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

Batch Prediction

Only supported in Vertex AI. See the 'Create a client' section above to initialize a client.

Create

Vertex AI:

# Specify model and source file only, destination and job display name will be auto-populated
job = client.batches.create(
    model='gemini-2.5-flash',
    src='bq://my-project.my-dataset.my-table',  # or "gs://path/to/input/data"
)

print(job)

Gemini Developer API:

# Create a batch job with inlined requests
batch_job = client.batches.create(
    model="gemini-2.5-flash",
    src=[{
        "contents": [{
            "parts": [{
                "text": "Hello!",
            }],
            "role": "user",
        }],
        "config": {"response_modalities": ["text"]},
    }],
)

job

In order to create a batch job with file name. Need to upload a json file. For example myrequests.json:

{"key":"request_1", "request": {"contents": [{"parts": [{"text":
 "Explain how AI works in a few words"}]}], "generation_config": {"response_modalities": ["TEXT"]}}}
{"key":"request_2", "request": {"contents": [{"parts": [{"text": "Explain how Crypto works in a few words"}]}]}}

Then upload the file.

# Upload the file
file = client.files.upload(
    file='myrequests.json',
    config=types.UploadFileConfig(display_name='test-json')
)

# Create a batch job with file name
batch_job = client.batches.create(
    model="gemini-2.5-flash",
    src="https://raw.githubusercontent.com/googleapis/python-genai/main/files/test-json",
)

# Get a job by name
job = client.batches.get(name=job.name)

job.state

completed_states = set(
    [
        'JOB_STATE_SUCCEEDED',
        'JOB_STATE_FAILED',
        'JOB_STATE_CANCELLED',
        'JOB_STATE_PAUSED',
    ]
)

while job.state not in completed_states:
    print(job.state)
    job = client.batches.get(name=job.name)
    time.sleep(30)

job

List

for job in client.batches.list(config=types.ListBatchJobsConfig(page_size=10)):
    print(job)

pager = client.batches.list(config=types.ListBatchJobsConfig(page_size=10))
print(pager.page_size)
print(pager[0])
pager.next_page()
print(pager[0])

Async

async for job in await client.aio.batches.list(
    config=types.ListBatchJobsConfig(page_size=10)
):
    print(job)

async_pager = await client.aio.batches.list(
    config=types.ListBatchJobsConfig(page_size=10)
)
print(async_pager.page_size)
print(async_pager[0])
await async_pager.next_page()
print(async_pager[0])

Delete

# Delete the job resource
delete_job = client.batches.delete(name=job.name)

delete_job

Error Handling

To handle errors raised by the model service, the SDK provides this APIError class.

from google.genai import errors

try:
    client.models.generate_content(
        model="invalid-model-name",
        contents="What is your name?",
    )
except errors.APIError as e:
    print(e.code) # 404
    print(e.message)

Extra Request Body

The extra_body field in HttpOptions accepts a dictionary of additional JSON properties to include in the request body. This can be used to access new or experimental backend features that are not yet formally supported in the SDK. The structure of the dictionary must match the backend API's request structure.

• Vertex AI backend API docs: https://cloud.google.com/vertex-ai/docs/reference/rest
• Gemini API backend API docs: https://ai.google.dev/api/rest

response = client.models.generate_content(
    model="gemini-2.5-pro",
    contents="What is the weather in Boston? and how about Sunnyvale?",
    config=types.GenerateContentConfig(
        tools=[get_current_weather],
        http_options=types.HttpOptions(extra_body={'tool_config': {'function_calling_config': {'mode': 'COMPOSITIONAL'}}}),
    ),
)