在 Vertex AI Agent Engine 上开发和部署代理
本页介绍了如何使用以下代理框架创建和部署代理，以返回指定日期两种货币之间的汇率：
准备工作
- 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.
-
Make sure that billing is enabled for your Google Cloud project.
-
Enable the Vertex AI and Cloud Storage APIs.
-
In the Google Cloud console, on the project selector page, select or create a Google Cloud project.
-
Make sure that billing is enabled for your Google Cloud project.
-
Enable the Vertex AI and Cloud Storage APIs.
-
Vertex AI User (
roles/aiplatform.user) -
Storage Admin (
roles/storage.admin)
如需获取使用 Vertex AI Agent Engine 所需的权限，请让您的管理员为您授予项目的以下 IAM 角色：
如需详细了解如何授予角色，请参阅管理对项目、文件夹和组织的访问权限。
安装并初始化 Python 版 Vertex AI SDK
运行以下命令以安装 Vertex AI SDK for Python 和其他所需的软件包：
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]以用户身份进行身份验证
Colab
运行以下代码：
from google.colab import auth auth.authenticate_user(project_id="PROJECT_ID")Cloud Shell
您无需执行任何操作。
本地 Shell
运行以下命令：
gcloud auth application-default login运行以下代码以导入 Vertex AI Agent Engine 并初始化 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. )
开发代理
首先，开发一个工具：
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()
接下来，实例化代理：
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,
)
最后，在本地测试代理：
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)
其中 USER_ID 是用户定义的 ID，字符数限制为 128。
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?"
)
部署代理
如需部署代理，请执行以下操作：
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]"],
)
这会在 Vertex AI 中创建一个 reasoningEngine 资源。
使用代理
通过发送查询来测试已部署的代理：
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?"
)
清理
为避免因本页中使用的资源导致您的 Google Cloud 账号产生费用，请按照以下步骤操作。
remote_agent.delete(force=True)