pip3 install --upgrade --user "google-cloud-aiplatform>=1.114.0"

importvertexaifrom vertexai import types

client = vertexai.Client(project='my-project', location='us-central1')

import pandas as pdprompts_df = pd.DataFrame({    "prompt": [        "What is the capital of France?",        "Write a haiku about a cat.",        "Write a Python function to calculate the factorial of a number.",        "Translate 'How are you?' to French.",    ],})inference_results = client.evals.run_inference(    model="gemini-2.5-flash",        ("def factorial(n):\n"         "    if n < 0:\n"         "        return 'Factorial does not exist for negative numbers'\n"         "    elif n == 0:\n"         "        return 1\n"         "    else:\n"         "        fact = 1\n"         "        i = 1\n"         "        while i <= n:\n"         "            fact *= i\n"         "            i += 1\n"         "        return fact"),)inference_results.show()

eval_result = client.evals.evaluate(    dataset=inference_results,    metrics=[        types.RubricMetric.GENERAL_QUALITY,    ])eval_result.show()

def get_exchange_rate(    currency_from: str = "USD",    currency_to: str = "EUR",    currency_date: str = "latest",):    """Retrieves the exchange rate between two currencies on a specified date.    Uses the Frankfurter API (https://api.frankfurter.app/) to obtain    exchange rate data.    Returns:        dict: A dictionary containing the exchange rate information.            Example: {"amount": 1.0, "base": "USD", "date": "2023-11-24",                "rates": {"EUR": 0.95534}}    """    import requests    response = requests.get(        f"https://api.frankfurter.app/{currency_date}",        params={"from": currency_from, "to": currency_to},    )    return response.json()

from google.adk.agents import Agentfrom vertexai.agent_engines import AdkAppapp = AdkApp(agent=Agent(    model="gemini-2.0-flash",        # Required.    name='currency_exchange_agent',  # Required.    tools=[get_exchange_rate],       # Optional.))

async for event in app.async_stream_query(    user_id="user-id",    message="What is the exchange rate from US dollars to SEK today?",):    print(event)

remote_app = client.agent_engines.create(    agent=app,    config={        "requirements": ["google-cloud-aiplatform[agent_engines,adk]"],    },)

async for event in remote_app.async_stream_query(    user_id="user-id",    message="What is the exchange rate from US dollars to SEK today?",):    print(event)

prompt = "Generate system instructions for a question-answering assistant"response = client.prompt_optimizer.optimize_prompt(prompt=prompt)print(response.raw_text_response)if response.parsed_response:    print(response.parsed_response.suggested_prompt)

import loggingproject_number = PROJECT_NUMBER # replace with your project numberservice_account = f"{project_number}-compute@developer.gserviceaccount.com"vapo_config = types.PromptOptimizerVAPOConfig(    config_path="gs://your-bucket/config.json",    service_account_project_number=project_number,    wait_for_completion=False)# Set up logging to see the progress of the optimization joblogging.basicConfig(encoding='utf-8', level=logging.INFO, force=True)result = client.prompt_optimizer.optimize(method="vapo", config=vapo_config)

await client.aio.prompt_optimizer.optimize(method="vapo", config=vapo_config)

prompt = {    "prompt_data": {        "contents": [{"parts": [{"text": "Hello, {name}! How are you?"}]}],        "system_instruction": {"parts": [{"text": "Please answer in a short sentence."}]},        "variables": [            {"name": {"text": "Alice"}},        ],        "model": "gemini-2.5-flash",    },}prompt_resource = client.prompts.create(    prompt=prompt,)

import typesfrom google.genai import types as genai_typesprompt = types.Prompt(    prompt_data=types.PromptData(        contents=[genai_types.Content(parts=[genai_types.Part(text="Hello, {name}! How are you?")])],        system_instruction=genai_types.Content(parts=[genai_types.Part(text="Please answer in a short sentence.")]),        variables=[            {"name": genai_types.Part(text="Alice")},        ],        model="gemini-2.5-flash",    ),)

retrieved_prompt = client.prompts.get(prompt_id=prompt_resource.prompt_id)

from google import genaifrom google.genai import types as genai_types# Create a Client in the Gen AI SDKgenai_client = genai.Client(vertexai=True, project="your-project", location="your-location")# Call generate_content() with the promptresponse = genai_client.models.generate_content(    model=retrieved_prompt.prompt_data.model,    contents=retrieved_prompt.assemble_contents(),)

importvertexai

vertexai.init(project='my-project', location='us-central1')

from vertexai.generative_models import GenerativeModelmodel = GenerativeModel("gemini-pro")print(model.generate_content("Why is sky blue?"))

from vertexai.generative_models import GenerativeModel, Imagevision_model = GenerativeModel("gemini-pro-vision")# Local imageimage = Image.load_from_file("image.jpg")print(vision_model.generate_content(["What is shown in this image?", image]))# Image from Cloud Storageimage_part = generative_models.Part.from_uri("gs://download.tensorflow.org/example_images/320px-Felis_catus-cat_on_snow.jpg", mime_type="image/jpeg")print(vision_model.generate_content([image_part, "Describe this image?"]))# Text and videovideo_part = Part.from_uri("gs://cloud-samples-data/video/animals.mp4", mime_type="video/mp4")print(vision_model.generate_content(["What is in the video? ", video_part]))

from vertexai.generative_models import GenerativeModel, Imagevision_model = GenerativeModel("gemini-ultra-vision")vision_chat = vision_model.start_chat()image = Image.load_from_file("image.jpg")print(vision_chat.send_message(["I like this image.", image]))print(vision_chat.send_message("What things do I like?."))

from vertexai.generative_models import GenerativeModelmodel = GenerativeModel(    "gemini-1.0-pro",    system_instruction=[        "Talk like a pirate.",        "Don't use rude words.",    ],)print(model.generate_content("Why is sky blue?"))

# First, create tools that the model is can use to answer your questions.# Describe a function by specifying it's schema (JsonSchema format)get_current_weather_func = generative_models.FunctionDeclaration(    name="get_current_weather",    description="Get the current weather in a given location",    parameters={        "type": "object",        "properties": {            "location": {                "type": "string",                "description": "The city and state, e.g. San Francisco, CA"            },            "unit": {                "type": "string",                "enum": [                    "celsius",                    "fahrenheit",                ]            }        },        "required": [            "location"        ]    },)# Tool is a collection of related functionsweather_tool = generative_models.Tool(    function_declarations=[get_current_weather_func],)# Use tools in chat:model = GenerativeModel(    "gemini-pro",    # You can specify tools when creating a model to avoid having to send them with every request.    tools=[weather_tool],)chat = model.start_chat()# Send a message to the model. The model will respond with a function call.print(chat.send_message("What is the weather like in Boston?"))# Then send a function response to the model. The model will use it to answer.print(chat.send_message(    Part.from_function_response(        name="get_current_weather",        response={            "content": {"weather": "super nice"},        }    ),))

from vertexai.preview.generative_models import GenerativeModel, Tool, FunctionDeclaration, AutomaticFunctionCallingResponder# First, create functions that the model can use to answer your questions.def get_current_weather(location: str, unit: str = "centigrade"):    """Gets weather in the specified location.    Args:        location: The location for which to get the weather.        unit: Optional. Temperature unit. Can be Centigrade or Fahrenheit. Defaults to Centigrade.    """    return dict(        location=location,        unit=unit,        weather="Super nice, but maybe a bit hot.",    )# Infer function schemaget_current_weather_func = FunctionDeclaration.from_func(get_current_weather)# Tool is a collection of related functionsweather_tool = Tool(    function_declarations=[get_current_weather_func],)# Use tools in chat:model = GenerativeModel(    "gemini-pro",    # You can specify tools when creating a model to avoid having to send them with every request.    tools=[weather_tool],)# Activate automatic function calling:afc_responder = AutomaticFunctionCallingResponder(    # Optional:    max_automatic_function_calls=5,)chat = model.start_chat(responder=afc_responder)# Send a message to the model. The model will respond with a function call.# The SDK will automatically call the requested function and respond to the model.# The model will use the function call response to answer the original question.print(chat.send_message("What is the weather like in Boston?"))

import pandas as pdfrom vertexai.evaluation import EvalTask, MetricPromptTemplateExampleseval_dataset = pd.DataFrame({        "prompt"  : [...],        "reference": [...],        "response" : [...],        "baseline_model_response": [...],})eval_task = EvalTask(    dataset=eval_dataset,    metrics=[            "bleu",            "rouge_l_sum",            MetricPromptTemplateExamples.Pointwise.FLUENCY,            MetricPromptTemplateExamples.Pairwise.SAFETY    ],    experiment="my-experiment",)eval_result = eval_task.evaluate(experiment_run_name="eval-experiment-run")

from vertexai.evaluation import EvalTaskfrom vertexai.generative_models import GenerativeModeleval_dataset = pd.DataFrame({    "reference": [...],    "prompt"  : [...],})result = EvalTask(    dataset=eval_dataset,    metrics=["exact_match", "bleu", "rouge_1", "rouge_l_sum"],    experiment="my-experiment",).evaluate(    model=GenerativeModel("gemini-1.5-pro"),    experiment_run_name="gemini-eval-run")

import pandas as pdfrom vertexai.evaluation import EvalTask, MetricPromptTemplateExamplesfrom vertexai.generative_models import GenerativeModeleval_dataset = pd.DataFrame({    "context"    : [...],    "instruction": [...],})result = EvalTask(    dataset=eval_dataset,    metrics=[MetricPromptTemplateExamples.Pointwise.SUMMARIZATION_QUALITY],).evaluate(    model=GenerativeModel("gemini-1.5-pro"),    prompt_template="{instruction}. Article: {context}. Summary:",)

from openai import OpenAIfrom vertexai.evaluation import EvalTask, MetricPromptTemplateExamplesclient = OpenAI()def custom_model_fn(input: str) -> str:    response = client.chat.completions.create(        model="gpt-3.5-turbo",        messages=[        {"role": "user", "content": input}        ]    )    return response.choices[0].message.contenteval_dataset = pd.DataFrame({    "prompt"  : [...],    "reference": [...],})result = EvalTask(    dataset=eval_dataset,    metrics=[MetricPromptTemplateExamples.Pointwise.SAFETY],    experiment="my-experiment",).evaluate(    model=custom_model_fn,    experiment_run_name="gpt-eval-run")

import pandas as pdfrom vertexai.evaluation import EvalTask, MetricPromptTemplateExamples, PairwiseMetricfrom vertexai.generative_models import GenerativeModelbaseline_model = GenerativeModel("gemini-1.0-pro")candidate_model = GenerativeModel("gemini-1.5-pro")pairwise_groundedness = PairwiseMetric(    metric_prompt_template=MetricPromptTemplateExamples.get_prompt_template(        "pairwise_groundedness"    ),    baseline_model=baseline_model,)eval_dataset = pd.DataFrame({    "prompt"  : [...],})result = EvalTask(    dataset=eval_dataset,    metrics=[pairwise_groundedness],    experiment="my-pairwise-experiment",).evaluate(    model=candidate_model,    experiment_run_name="gemini-pairwise-eval-run",)

pip3 install --upgrade --user "google-cloud-aiplatform[agent_engines,adk]>=1.111"

def get_exchange_rate(    currency_from: str = "USD",    currency_to: str = "EUR",    currency_date: str = "latest",):    """Retrieves the exchange rate between two currencies on a specified date.    Uses the Frankfurter API (https://api.frankfurter.app/) to obtain    exchange rate data.    Returns:        dict: A dictionary containing the exchange rate information.            Example: {"amount": 1.0, "base": "USD", "date": "2023-11-24",                "rates": {"EUR": 0.95534}}    """    import requests    response = requests.get(        f"https://api.frankfurter.app/{currency_date}",        params={"from": currency_from, "to": currency_to},    )    return response.json()

from google.adk.agents import Agentfrom vertexai.agent_engines import AdkAppapp = AdkApp(agent=Agent(    model="gemini-2.0-flash",        # Required.    name='currency_exchange_agent',  # Required.    tools=[get_exchange_rate],       # Optional.))

async for event in app.async_stream_query(    user_id="user-id",    message="What is the exchange rate from US dollars to SEK today?",):    print(event)

vertexai.init(    project='my-project',    location='us-central1',    staging_bucket="gs://my-staging-bucket",)remote_app = vertexai.agent_engines.create(    app,    requirements=["google-cloud-aiplatform[agent_engines,adk]"],)

async for event in remote_app.async_stream_query(    user_id="user-id",    message="What is the exchange rate from US dollars to SEK today?",):    print(event)