google-genai 1.7.0

GenAI Python SDK

Google Gen AI SDK

---

Documentation: https://googleapis.github.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.

Installation

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).

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

# 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'

client = genai.Client()

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:

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:

client = genai.Client(
    api_key='GEMINI_API_KEY',
    http_options=types.HttpOptions(api_version='v1alpha')
)

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.

Generate Content

with text content

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

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-2.0-flash-001',
    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

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

Provide a function call part

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

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.

Provide a non function call part

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

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, the variability and length of the output can be influenced by the temperature and max_output_tokens respectively.

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.

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.

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

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

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)

Function Calling

Automatic Python function Support

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

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)

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:

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

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.

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.

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)

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:

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.

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')
        ),
    ),
)

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.

Pydantic Model Schema support

Schemas can be provided as Pydantic Models.

from pydantic import BaseModel


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)

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)

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.

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)

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

Streaming

Streaming for text content

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='')

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.

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.

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='')

Async

client.aio exposes all the analogous async methods that are available on client

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)

Streaming

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='')

Count Tokens and Compute Tokens

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

Async

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

Embed Content

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

# 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)

Imagen

Generate Images

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

# 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.

# 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

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.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

Generate Videos

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

# 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()

Chats

Create a chat session to start a multi-turn conversations with the model.

Send Message

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

Streaming

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

Async

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

Async Streaming

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

Files are only supported in Gemini Developer API.

!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

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

Create

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-1.5-pro-002',
    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

response = client.models.generate_content(
    model='gemini-1.5-pro-002',
    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 tuning through tune.

Tune

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

if client.vertexai:
    model = 'gemini-1.5-pro-002'
    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-1.0-pro-001'
    training_dataset = types.TuningDataset(
        examples=[
            types.TuningExample(
                text_input=f'Input text {i}',
                output=f'Output text {i}',
            )
            for i in range(5)
        ],
    )

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

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)

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

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.

Create

# Specify model and source file only, destination and job display name will be auto-populated
job = client.batches.create(
    model='gemini-1.5-flash-002',
    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

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)