Sviluppare ed eseguire il deployment di agenti su Vertex AI Agent Engine

Questa pagina mostra come creare e implementare un agente che restituisce il cambio tra due valute in una data specificata utilizzando i seguenti framework di agenti:

Prima di iniziare

  1. Sign in to your Google Cloud account. If you're new to Google Cloud, create an account to evaluate how our products perform in real-world scenarios. New customers also get $300 in free credits to run, test, and deploy workloads.

  2. In the Google Cloud console, on the project selector page, select or create a Google Cloud project.

    Go to project selector

  3. Make sure that billing is enabled for your Google Cloud project.

  4. Enable the Vertex AI and Cloud Storage APIs.

    Enable the APIs

    5.

  5. In the Google Cloud console, on the project selector page, select or create a Google Cloud project.

    Go to project selector

  6. Make sure that billing is enabled for your Google Cloud project.

  7. Enable the Vertex AI and Cloud Storage APIs.

    Enable the APIs

    8.

    Per ottenere le autorizzazioni necessarie per utilizzare Vertex AI Agent Engine, chiedi all'amministratore di concederti i seguenti ruoli IAM nel progetto:

    Per saperne di più sulla concessione dei ruoli, consulta Gestire l'accesso a progetti, cartelle e organizzazioni.

    Potresti anche riuscire a ottenere le autorizzazioni richieste tramite i ruoli personalizzati o altri ruoli predefiniti.

Installa e inizializza l'SDK Vertex AI per Python

  1. Esegui il seguente comando per installare l'SDK Vertex AI Python e altri pacchetti richiesti:

    ADK 

    pip install --upgrade --quiet google-cloud-aiplatform[agent_engines,adk]

    LangGraph 

    pip install --upgrade --quiet google-cloud-aiplatform[agent_engines,langchain]

    LangChain 

    pip install --upgrade --quiet google-cloud-aiplatform[agent_engines,langchain]

    AG2 

    pip install --upgrade --quiet google-cloud-aiplatform[agent_engines,ag2]

    LlamaIndex 

    pip install --upgrade --quiet google-cloud-aiplatform[agent_engines,llama_index]

  2. Autenticazione come utente

    Colab

    Esegui questo codice:

    from google.colab import auth

auth.authenticate_user(project_id="PROJECT_ID")

    Cloud Shell

    Non occorre alcun intervento.

    Shell locale

    Esegui questo comando:

    gcloud auth application-default login

  3. Esegui il seguente codice per importare Vertex AI Agent Engine e inizializzare l'SDK:

    import vertexai
from vertexai import agent_engines

vertexai.init(
    project="PROJECT_ID",               # Your project ID.
    location="LOCATION",                # Your cloud region.
    staging_bucket="gs://BUCKET_NAME",  # Your staging bucket.
)

Sviluppare un agente

Innanzitutto, sviluppa uno strumento:

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."""
    import requests

    response = requests.get(
        f"https://api.frankfurter.app/{currency_date}",
        params={"from": currency_from, "to": currency_to},
    )
    return response.json()

Poi, esegui l'inizializzazione di un agente:

ADK 

from google.adk.agents import Agent
from vertexai.preview.reasoning_engines import AdkApp

agent = Agent(
    model="gemini-2.0-flash",
    name='currency_exchange_agent',
    tools=[get_exchange_rate],
)

app = AdkApp(agent=agent)

LangGraph 

from vertexai import agent_engines

agent = agent_engines.LanggraphAgent(
    model="gemini-2.0-flash",
    tools=[get_exchange_rate],
    model_kwargs={
        "temperature": 0.28,
        "max_output_tokens": 1000,
        "top_p": 0.95,
    },
)

LangChain 

from vertexai import agent_engines

agent = agent_engines.LangchainAgent(
    model="gemini-2.0-flash",
    tools=[get_exchange_rate],
    model_kwargs={
        "temperature": 0.28,
        "max_output_tokens": 1000,
        "top_p": 0.95,
    },
)

AG2 

from vertexai import agent_engines

agent = agent_engines.AG2Agent(
    model="gemini-2.0-flash",
    runnable_name="Get Exchange Rate Agent",
    tools=[get_exchange_rate],
)

LlamaIndex 

from vertexai.preview import reasoning_engines

def runnable_with_tools_builder(model, runnable_kwargs=None, **kwargs):
    from llama_index.core.query_pipeline import QueryPipeline
    from llama_index.core.tools import FunctionTool
    from llama_index.core.agent import ReActAgent

    llama_index_tools = []
    for tool in runnable_kwargs.get("tools"):
        llama_index_tools.append(FunctionTool.from_defaults(tool))
    agent = ReActAgent.from_tools(llama_index_tools, llm=model, verbose=True)
    return QueryPipeline(modules = {"agent": agent})

agent = reasoning_engines.LlamaIndexQueryPipelineAgent(
    model="gemini-2.0-flash",
    runnable_kwargs={"tools": [get_exchange_rate]},
    runnable_builder=runnable_with_tools_builder,
)

Infine, testa l'agente localmente:

ADK 

for event in app.stream_query(
    user_id="USER_ID",
    message="What is the exchange rate from US dollars to SEK today?",
):
    print(event)

dove USER_ID è un ID definito dall'utente con un limite di 128 caratteri.

LangGraph 

agent.query(input={"messages": [
    ("user", "What is the exchange rate from US dollars to SEK today?"),
]})

LangChain 

agent.query(
    input="What is the exchange rate from US dollars to SEK today?"
)

AG2 

agent.query(
    input="What is the exchange rate from US dollars to SEK today?"
)

LlamaIndex 

agent.query(
    input="What is the exchange rate from US dollars to SEK today?"
)

Esegui il deployment di un agente

Per eseguire il deployment dell'agente:

ADK 

from vertexai import agent_engines

remote_agent = agent_engines.create(
    app,
    requirements=["google-cloud-aiplatform[agent_engines,adk]"],
)

LangGraph 

from vertexai import agent_engines

remote_agent = agent_engines.create(
    agent,
    requirements=["google-cloud-aiplatform[agent_engines,langchain]"],
)

LangChain 

from vertexai import agent_engines

remote_agent = agent_engines.create(
    agent,
    requirements=["google-cloud-aiplatform[agent_engines,langchain]"],
)

AG2 

from vertexai import agent_engines

remote_agent = agent_engines.create(
    agent,
    requirements=["google-cloud-aiplatform[agent_engines,ag2]"],
)

LlamaIndex 

from vertexai import agent_engines

remote_agent = agent_engines.create(
    agent,
    requirements=["google-cloud-aiplatform[agent_engines,llama_index]"],
)

Viene creata una risorsa reasoningEngine in Vertex AI.

Utilizzare un agente

Testa l'agente di cui è stato eseguito il deployment inviando una query:

ADK 

for event in remote_agent.stream_query(
    user_id="USER_ID",
    message="What is the exchange rate from US dollars to SEK today?",
):
    print(event)

LangGraph 

remote_agent.query(input={"messages": [
    ("user", "What is the exchange rate from US dollars to SEK today?"),
]})

LangChain 

remote_agent.query(
    input="What is the exchange rate from US dollars to SEK today?"
)

AG2 

remote_agent.query(
    input="What is the exchange rate from US dollars to SEK today?"
)

LlamaIndex 

remote_agent.query(
    input="What is the exchange rate from US dollars to SEK today?"
)

Esegui la pulizia

Per evitare che al tuo account Google Cloud vengano addebitati costi relativi alle risorse utilizzate in questa pagina, segui questi passaggi. 

remote_agent.delete(force=True)

Passaggi successivi