- googleapis/python-genai: Google Gen AI Python SDK provides an interface for developers to integrate Google's generative models into their Python applications.

Documentation: https://googleapis..io/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.

pip install google-genai

from google import genai
from google.genai import types

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 GOOGLE_API_KEY as shown below:

export GOOGLE_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()

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')
)

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

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

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

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-2.0-flash-001',
    contents=['Could you summarize this file?', file]
)
print(response.text)

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

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

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?')]
  )
]

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.

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.

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.

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 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.

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',
    )
  ])
]

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',
      )
    ]
  )
]

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.

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-2.0-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)

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)

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])

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])

from google.genai import types

response = client.models.generate_content(
    model='gemini-2.0-flash-001',
    contents='Say something bad.',
    config=types.GenerateContentConfig(
        safety_settings=[
            types.SafetySetting(
                category='HARM_CATEGORY_HATE_SPEECH',
                threshold='BLOCK_ONLY_HIGH',
            )
        ]
    ),
)
print(response.text)

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-2.0-flash-001',
    contents='What is the weather like in Boston?',
    config=types.GenerateContentConfig(tools=[get_current_weather]),
)

print(response.text)

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-2.0-flash-001',
  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

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=types.Schema(
        type='OBJECT',
        properties={
            'location': types.Schema(
                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-2.0-flash-001',
    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-2.0-flash-001',
    contents=[
        user_prompt_content,
        function_call_content,
        function_response_content,
    ],
    config=types.GenerateContentConfig(
        tools=[tool],
    ),
)

print(response.text)

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-2.0-flash-001",
    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-2.0-flash-001",
    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')
        ),
    ),
)

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.

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-2.0-flash-001',
    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-2.0-flash-001',
    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)

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

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

response = client.models.generate_content(
      model='gemini-2.0-flash-001',
      contents='What instrument plays multiple notes at once?',
      config={
          'response_mime_type': 'text/x.enum',
          'response_schema': InstrumentEnum,
      },
  )
print(response.text)

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-2.0-flash-001',
      contents='What instrument plays multiple notes at once?',
      config={
          'response_mime_type': 'application/json',
          'response_schema': InstrumentEnum,
      },
  )
print(response.text)

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

for chunk in client.models.generate_content_stream(
    model='gemini-2.0-flash-001', contents='Tell me a story in 300 words.'
):
    print(chunk.text, end='')

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-2.0-flash-001',
    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-2.0-flash-001',
    contents=[
        'What is this image about?',
        types.Part.from_bytes(data=image_bytes, mime_type=YOUR_IMAGE_MIME_TYPE),
    ],
):
    print(chunk.text, end='')

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-2.0-flash-001', contents='Tell me a story in 300 words.'
)

print(response.text)

async for chunk in await client.aio.models.generate_content_stream(
    model='gemini-2.0-flash-001', contents='Tell me a story in 300 words.'
):
    print(chunk.text, end='')

response = client.models.count_tokens(
    model='gemini-2.0-flash-001',
    contents='why is the sky blue?',
)
print(response)

Compute tokens is only supported in Vertex AI.

response = client.models.compute_tokens(
    model='gemini-2.0-flash-001',
    contents='why is the sky blue?',
)
print(response)

response = await client.aio.models.count_tokens(
    model='gemini-2.0-flash-001',
    contents='why is the sky blue?',
)
print(response)

response = client.models.embed_content(
    model='text-embedding-004',
    contents='why is the sky blue?',
)
print(response)

from google.genai import types

# multiple contents with config
response = client.models.embed_content(
    model='text-embedding-004',
    contents=['why is the sky blue?', 'What is your age?'],
    config=types.EmbedContentConfig(output_dimensionality=10),
)

print(response)

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 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-001',
    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 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()

Support for generate videos in Vertex and Gemini Developer API is behind an allowlist

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,
        fps=24,
        duration_seconds=5,
        enhance_prompt=True,
    ),
)

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

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

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.

chat = client.chats.create(model='gemini-2.0-flash-001')
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)

chat = client.chats.create(model='gemini-2.0-flash-001')
for chunk in chat.send_message_stream('tell me a story'):
    print(chunk.text)

chat = client.aio.chats.create(model='gemini-2.0-flash-001')
response = await chat.send_message('tell me a story')
print(response.text)

chat = client.aio.chats.create(model='gemini-2.0-flash-001')
async for chunk in await chat.send_message_stream('tell me a story'):
    print(chunk.text)

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 .

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

print(file1)
print(file2)

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

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

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

client.caches contains the control plane APIs for cached content. See the 'Create a client' section above to initialize a client.

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-2.0-flash-001',
    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',
    ),
)

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

from google.genai import types

response = client.models.generate_content(
    model='gemini-2.0-flash-001',
    contents='Summarize the pdfs',
    config=types.GenerateContentConfig(
        cached_content=cached_content.name,
    ),
)
print(response.text)

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

Vertex AI supports tuning from GCS source
Gemini Developer API supports tuning from inline examples

from google.genai import types

if client.vertexai:
    model = 'gemini-2.0-flash-001'
    training_dataset = types.TuningDataset(
        gcs_uri='gs://cloud-samples-data/ai-platform/generative_ai/gemini-1_5/text/sft_train_data.jsonl',
    )
else:
    model = 'models/gemini-2.0-flash-001'
    training_dataset = types.TuningDataset(
        examples=[
            types.TuningExample(
                text_input=f'Input text {i}',
                output=f'Output text {i}',
            )
            for i in range(5)
        ],
    )

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)

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

import time

running_states = set(
    [
        'JOB_STATE_PENDING',
        'JOB_STATE_RUNNING',
    ]
)

while tuning_job.state in running_states:
    print(tuning_job.state)
    tuning_job = client.tunings.get(name=tuning_job.name)
    time.sleep(10)

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

print(response.text)

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

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 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])

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)

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 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])

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

# Specify model and source file only, destination and job display name will be auto-populated
job = client.batches.create(
    model='gemini-2.0-flash-001',
    src='bq://my-project.my-dataset.my-table',
)

job

# 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

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

delete_job

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)

Name		Name	Last commit message	Last commit date
Latest commit History 484 Commits
.		.
docs		docs
google/genai		google/genai
.gitignore		.gitignore
CHANGELOG.md		CHANGELOG.md
CONTRIBUTING.md		CONTRIBUTING.md
LICENSE		LICENSE
MANIFEST.in		MANIFEST.in
README.md		README.md
pyproject.toml		pyproject.toml
requirements.txt		requirements.txt

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