在 Vertex AI Agent Engine 上开发和部署智能体

在 Vertex AI Agent Engine 上开发和部署智能体

本文介绍了如何使用以下智能体框架创建和部署一个智能体，以返回在指定日期两种货币之间的汇率：

• 智能体开发套件 (ADK)

• LangGraph

• LangChain

• AG2

• LlamaIndex 查询流水线（预览版）

准备工作

注意：如需启用 API，您需要拥有 serviceusage.services.enable 权限。如果您没有此权限，请让您的管理员为您授予 Service Usage Admin (roles/serviceusage.serviceUsageAdmin) 角色。

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 the resourcemanager.projects.create permission. Learn how to grant roles.

Note: If you don't plan to keep the resources that you create in this procedure, create a project instead of selecting an existing project. After you finish these steps, you can delete the project, removing all resources associated with the project.

Go to project selector

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 the serviceusage.services.enable permission. Learn how to grant roles.

Enable the APIs

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 the resourcemanager.projects.create permission. Learn how to grant roles.

Note: If you don't plan to keep the resources that you create in this procedure, create a project instead of selecting an existing project. After you finish these steps, you can delete the project, removing all resources associated with the project.

Go to project selector

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 the serviceusage.services.enable permission. Learn how to grant roles.

Enable the APIs

如需获得使用 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：

• Vertex AI User (roles/aiplatform.user)
• Storage Admin (roles/storage.admin)

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。

您也可以通过自定义角色或其他预定义角色来获取所需的权限。

安装并初始化 Python 版 Vertex AI SDK

1. 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]>=1.112

   LangGraph

   pip install --upgrade --quiet google-cloud-aiplatform[agent_engines,langchain]>=1.112

   LangChain

   pip install --upgrade --quiet google-cloud-aiplatform[agent_engines,langchain]>=1.112

   AG2

   pip install --upgrade --quiet google-cloud-aiplatform[agent_engines,ag2]>=1.112

   LlamaIndex

   预览版

   此功能 受服务专用条款的约束。 非正式版功能“按原样”提供，且可能仅提供有限支持。 如需了解详情，请参阅发布阶段说明。

   pip install --upgrade --quiet google-cloud-aiplatform[agent_engines,llama_index]>=1.112

2. 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

3. 运行以下代码以导入 Vertex AI Agent Engine 并初始化 SDK：

   import vertexai
   
   client = vertexai.Client(
       project="PROJECT_ID",               # Your project ID.
       location="LOCATION",                # Your cloud region.
   )
   

   其中：

   • PROJECT_ID 是您将在其中开发和部署代理的 Google Cloud 项目 ID。

   • LOCATION 是支持的区域之一。

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，实例化智能体：

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

预览版

此功能 受服务专用条款的约束。 非正式版功能“按原样”提供，且可能仅提供有限支持。 如需了解详情，请参阅发布阶段说明。

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，在本地测试智能体：

ADK

注意：在本地测试 ADK 智能体时，会使用内存中的会话。

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)

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

预览版

此功能 受服务专用条款的约束。 非正式版功能“按原样”提供，且可能仅提供有限支持。 如需了解详情，请参阅发布阶段说明。

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

部署代理

如需部署智能体，请执行以下操作：

ADK

注意：将 ADK 智能体部署到 Vertex AI Agent Engine 会自动生成受管理的会话资源。您可以在清理部分中删除受管理的会话资源以及智能体。

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

预览版

此功能 受服务专用条款的约束。 非正式版功能“按原样”提供，且可能仅提供有限支持。 如需了解详情，请参阅发布阶段说明。

from vertexai import agent_engines

remote_agent = agent_engines.create(
    agent,
    config={
        "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部署的智能体：

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)

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

预览版

此功能 受服务专用条款的约束。 非正式版功能“按原样”提供，且可能仅提供有限支持。 如需了解详情，请参阅发布阶段说明。

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)

后续步骤

• 探索笔记本。
• 设置环境。
• 开发代理。
• 部署代理。
• 使用代理。
• 管理代理。
• 获取支持。