Sviluppare ed eseguire il deployment di agenti su Vertex AI Agent Engine
Questa pagina mostra come creare ed eseguire il deployment di un agente che restituisce il tasso di cambio tra due valute in una data specifica utilizzando i seguenti framework dell'agente:
Prima di iniziare
- 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.
-
In the Google Cloud console, on the project selector page, select or create a Google Cloud project.
Roles required to select or create a project
- Select a project: Selecting a project doesn't require a specific IAM role—you can select any project that you've been granted a role on.
-
Create a project: To create a project, you need the Project Creator
(
roles/resourcemanager.projectCreator), which contains theresourcemanager.projects.createpermission. Learn how to grant roles.
-
Verify that billing is enabled for your Google Cloud project.
-
Enable the Vertex AI and Cloud Storage APIs.
Roles required to enable APIs
To enable APIs, you need the Service Usage Admin IAM role (
roles/serviceusage.serviceUsageAdmin), which contains theserviceusage.services.enablepermission. Learn how to grant roles. -
In the Google Cloud console, on the project selector page, select or create a Google Cloud project.
Roles required to select or create a project
- Select a project: Selecting a project doesn't require a specific IAM role—you can select any project that you've been granted a role on.
-
Create a project: To create a project, you need the Project Creator
(
roles/resourcemanager.projectCreator), which contains theresourcemanager.projects.createpermission. Learn how to grant roles.
-
Verify that billing is enabled for your Google Cloud project.
-
Enable the Vertex AI and Cloud Storage APIs.
Roles required to enable APIs
To enable APIs, you need the Service Usage Admin IAM role (
roles/serviceusage.serviceUsageAdmin), which contains theserviceusage.services.enablepermission. Learn how to grant roles. -
Utente Vertex AI (
roles/aiplatform.user) -
Amministratore spazio di archiviazione (
roles/storage.admin) 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]>=1.112LangGraph
pip install --upgrade --quiet google-cloud-aiplatform[agent_engines,langchain]>=1.112LangChain
pip install --upgrade --quiet google-cloud-aiplatform[agent_engines,langchain]>=1.112AG2
pip install --upgrade --quiet google-cloud-aiplatform[agent_engines,ag2]>=1.112LlamaIndex
pip install --upgrade --quiet google-cloud-aiplatform[agent_engines,llama_index]>=1.112Autenticarsi come utente
Colab
Esegui questo codice:
from google.colab import auth auth.authenticate_user(project_id="PROJECT_ID")Cloud Shell
Non occorre alcun intervento.
Local Shell
Esegui questo comando:
gcloud auth application-default loginEsegui il seguente codice per importare Vertex AI Agent Engine e inizializzare l'SDK:
import vertexai client = vertexai.Client( project="PROJECT_ID", # Your project ID. location="LOCATION", # Your cloud region. )Dove:
PROJECT_IDè l' Google Cloud ID progetto in cui sviluppi ed esegui il deployment degli agentiLOCATIONè una delle regioni supportate.
- USER_ID: scegli il tuo ID utente con un limite di 128 caratteri.
Ad esempio,
user-123.
Per ottenere le autorizzazioni necessarie per utilizzare Vertex AI Agent Engine, chiedi all'amministratore di concederti i seguenti ruoli IAM sul progetto:
Per ulteriori informazioni sulla concessione dei ruoli, consulta Gestisci l'accesso a progetti, cartelle e organizzazioni.
Potresti anche riuscire a ottenere le autorizzazioni richieste tramite i ruoli personalizzati o altri ruoli predefiniti.
Installare e inizializzare l'SDK Vertex AI Python
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()
Ora crea un'istanza di un agente:
ADK
from google.adk.agents import Agent
from vertexai import agent_engines
agent = Agent(
model="gemini-2.0-flash",
name='currency_exchange_agent',
tools=[get_exchange_rate],
)
app = agent_engines.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
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)
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
remote_agent = client.agent_engines.create(
agent=app,
config={
"requirements": ["google-cloud-aiplatform[agent_engines,adk]"],
}
)
LangGraph
remote_agent = client.agent_engines.create(
agent,
config={
"requirements": ["google-cloud-aiplatform[agent_engines,langchain]"],
},
)
LangChain
remote_agent = client.agent_engines.create(
agent,
config={
"requirements": ["google-cloud-aiplatform[agent_engines,langchain]"],
},
)
AG2
from vertexai import agent_engines
remote_agent = agent_engines.create(
agent,
config={
"requirements": ["google-cloud-aiplatform[agent_engines,ag2]"],
},
)
LlamaIndex
from vertexai import agent_engines
remote_agent = agent_engines.create(
agent,
config={
"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
async for event in remote_agent.async_stream_query(
user_id="USER_ID",
message="What is the exchange rate from US dollars to SEK today?",
):
print(event)
dove USER_ID è l'ID utente che hai definito durante il test dell'agente
in locale. Ad esempio, currency-exchange.
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)