Reasoning Engine API

Reasoning Engine API 为生成式 AI 应用中的自定义代理工作流提供托管式运行时。您可以使用 LangChain 等编排框架创建应用，并使用 Reasoning Engine 进行部署。此服务具有 Vertex AI 集成的所有安全性、隐私性、可观测性和可伸缩性优势。

如需详细了解 Reasoning Engine 的概念性信息，请参阅部署应用。

局限性

  • Reasoning Engine API 仅支持 Python 编排框架。
  • 仅 us-central1 区域支持 Reasoning Engine API。

示例语法

用于创建和注册推理引擎资源的语法。

Python

class SimpleAdditionApp:
    def query() -> str:
        """
           ...

        """

        return
...

reasoning_engine = reasoning_engines.ReasoningEngine.create(
    SimpleAdditionApp(),
    display_name="",
    description="",
    requirements=[...],
    extra_packages=[...],
)

参数列表

参数
display_name

必需：string

ReasoningEngine 的显示名称。
description

可选：string。

ReasoningEngine 的说明。
spec

必需：ReasoningEngineSpec

ReasoningEngine 的配置。
package_spec

必需：PackageSpec

用户提供的软件包规范，例如序列化的对象和软件包要求。
class_methods

可选：protobuf.Struct。

对象类方法的声明。

PackageSpec

PackageSpec 包含对存储 OpenAPI YAML 文件的 Cloud Storage URI 的引用。

参数
pickle_object_gcs_uri

可选：string。

序列化的 Python 对象的 Cloud Storage URI。
dependency_files_gcs_uri

可选：string。

扩展名为 tar.gz 的依赖项文件的 Cloud Storage URI。
requirements_gcs_uri

可选：string。

requirements.txt 文件的 Cloud Storage URI。
python_version

可选：string。

Python 版本。支持的版本包括 Python 3.8、3.9、3.10 和 3.11。 如果未指定，则默认值为 3.10。

QueryReasoningEngine

参数
input

protobuf.struct

input 中的参数应与创建步骤中定义的 query 类方法一致。

示例

部署基本应用配置

以下示例使用了一个将两个整数相加的应用和一个带有 Reasoning Engine 的远程应用：

Python

如需了解如何安装或更新 Vertex AI SDK for Python，请参阅安装 Vertex AI SDK for Python。 如需了解详情，请参阅 Python API 参考文档。 

import vertexai
from vertexai.preview import reasoning_engines

# TODO(developer): Update and un-comment below lines
# project_id = "PROJECT_ID"
# staging_bucket = "gs://YOUR_BUCKET_NAME"

vertexai.init(
    project=project_id, location="us-central1", staging_bucket=staging_bucket
)

class SimpleAdditionApp:
    def query(self, a: int, b: int) -> str:
        """Query the application.

        Args:
            a: The first input number
            b: The second input number

        Returns:
            int: The additional result.
        """

        return f"{int(a)} + {int(b)} is {int(a + b)}"

# Locally test
app = SimpleAdditionApp()
app.query(a=1, b=2)

# Create a remote app with reasoning engine.
# This may take 1-2 minutes to finish.
reasoning_engine = reasoning_engines.ReasoningEngine.create(
    SimpleAdditionApp(),
    display_name="Demo Addition App",
    description="A simple demo addition app",
    requirements=[],
    extra_packages=[],
)

部署高级应用配置

以下是一个高级示例，它使用了 LangChain 的链、提示模板和 Gemini API：

Python

如需了解如何安装或更新 Vertex AI SDK for Python，请参阅安装 Vertex AI SDK for Python。 如需了解详情，请参阅 Python API 参考文档。 


from typing import List

import vertexai
from vertexai.preview import reasoning_engines

# TODO(developer): Update and un-comment below lines
# project_id = "PROJECT_ID"
# location = "us-central1"
# staging_bucket = "gs://YOUR_BUCKET_NAME"

vertexai.init(project=project_id, location=location, staging_bucket=staging_bucket)

class LangchainApp:
    def __init__(self, project: str, location: str) -> None:
        self.project_id = project
        self.location = location

    def set_up(self) -> None:
        from langchain_core.prompts import ChatPromptTemplate
        from langchain_google_vertexai import ChatVertexAI

        system = (
            "You are a helpful assistant that answers questions "
            "about Google Cloud."
        )
        human = "{text}"
        prompt = ChatPromptTemplate.from_messages(
            [("system", system), ("human", human)]
        )
        chat = ChatVertexAI(project=self.project_id, location=self.location)
        self.chain = prompt | chat

    def query(self, question: str) -> Union[str, List[Union[str, Dict]]]:
        """Query the application.

        Args:
            question: The user prompt.

        Returns:
            str: The LLM response.
        """
        return self.chain.invoke({"text": question}).content

# Locally test
app = LangchainApp(project=project_id, location=location)
app.set_up()
print(app.query("What is Vertex AI?"))

# Create a remote app with reasoning engine
# This may take 1-2 minutes to finish because it builds a container and turn up HTTP servers.
reasoning_engine = reasoning_engines.ReasoningEngine.create(
    LangchainApp(project=project_id, location=location),
    requirements=[
        "google-cloud-aiplatform==1.50.0",
        "langchain-google-vertexai",
        "langchain-core",
    ],
    display_name="Demo LangChain App",
    description="This is a simple LangChain app.",
    # sys_version="3.10",  # Optional
    extra_packages=[],
)

查询 Reasoning Engine

查询推理引擎。

此示例使用的是部署基本应用配置示例中的 SimpleAdditionApp 类。

REST

在使用任何请求数据之前，请先进行以下替换：

  • PROJECT_ID：您的项目 ID。
  • LOCATION：处理请求的区域。必须为 us-central1。
  • REASONING_ENGINE_ID：推理引擎的 ID。
  • INPUT：protobuf.struct: input 中的参数应与部署基本应用配置期间定义的 def query(self, question: str) 方法内的参数相匹配。

HTTP 方法和网址： 

POST https://LOCATION-aiplatform.googleapis.com/v1beta1/projects/PROJECT_ID/locations/LOCATION/reasoningEngines/REASONING_ENGINE_ID:query

请求 JSON 正文： 

{
  "input": {
    INPUT
  }
}

如需发送请求，请选择以下方式之一：

curl

将请求正文保存在名为 request.json 的文件中，然后执行以下命令： 

curl -X POST \
     -H "Authorization: Bearer $(gcloud auth print-access-token)" \
     -H "Content-Type: application/json; charset=utf-8" \
     -d @request.json \
     "https://LOCATION-aiplatform.googleapis.com/v1beta1/projects/PROJECT_ID/locations/LOCATION/reasoningEngines/REASONING_ENGINE_ID:query"

PowerShell

将请求正文保存在名为 request.json 的文件中，然后执行以下命令： 

$cred = gcloud auth print-access-token
$headers = @{ "Authorization" = "Bearer $cred" }

Invoke-WebRequest `
    -Method POST `
    -Headers $headers `
    -ContentType: "application/json; charset=utf-8" `
    -InFile request.json `
    -Uri "https://LOCATION-aiplatform.googleapis.com/v1beta1/projects/PROJECT_ID/locations/LOCATION/reasoningEngines/REASONING_ENGINE_ID:query" | Select-Object -Expand Content

Python

如需了解如何安装或更新 Vertex AI SDK for Python，请参阅安装 Vertex AI SDK for Python。 如需了解详情，请参阅 Python API 参考文档。 

import vertexai
from vertexai.preview import reasoning_engines

# TODO(developer): Update and un-comment below lines
# project_id = "PROJECT_ID"
# reasoning_engine_id = "REASONING_ENGINE_ID"

vertexai.init(project=project_id, location="us-central1")
reasoning_engine = reasoning_engines.ReasoningEngine(reasoning_engine_id)

# Replace with kwargs for `.query()` method.
response = reasoning_engine.query(a=1, b=2)
print(response)

列出 Reasoning Engine

列出项目中的推理引擎。

REST

在使用任何请求数据之前，请先进行以下替换：

  • PROJECT_ID：您的项目 ID。
  • PROJECT_ID：您的项目 ID。
  • LOCATION：处理请求的区域。必须为 us-central1。

HTTP 方法和网址： 

GET https://LOCATION-aiplatform.googleapis.com/v1beta1/projects/PROJECT_ID/locations/LOCATION/reasoningEngines

如需发送请求，请选择以下方式之一：

curl

执行以下命令：

curl -X GET \
     -H "Authorization: Bearer $(gcloud auth print-access-token)" \
     "https://LOCATION-aiplatform.googleapis.com/v1beta1/projects/PROJECT_ID/locations/LOCATION/reasoningEngines"

PowerShell

执行以下命令： 

$cred = gcloud auth print-access-token
$headers = @{ "Authorization" = "Bearer $cred" }

Invoke-WebRequest `
    -Method GET `
    -Headers $headers `
    -Uri "https://LOCATION-aiplatform.googleapis.com/v1beta1/projects/PROJECT_ID/locations/LOCATION/reasoningEngines" | Select-Object -Expand Content

Python

如需了解如何安装或更新 Vertex AI SDK for Python，请参阅安装 Vertex AI SDK for Python。 如需了解详情，请参阅 Python API 参考文档。 

import vertexai
from vertexai.preview import reasoning_engines

# TODO(developer): Update and un-comment below lines
# project_id = "PROJECT_ID"

vertexai.init(project=project_id, location="us-central1")

reasoning_engine_list = reasoning_engines.ReasoningEngine.list()
print(reasoning_engine_list)

获取 Reasoning Engine

获取推理引擎的详细信息。

REST

在使用任何请求数据之前，请先进行以下替换：

  • PROJECT_ID：您的项目 ID。
  • PROJECT_ID：您的项目 ID。
  • LOCATION：处理请求的区域。必须为 us-central1。
  • REASONING_ENGINE_ID：推理引擎的 ID。

HTTP 方法和网址： 

GET https://LOCATION-aiplatform.googleapis.com/v1beta1/projects/PROJECT_ID/locations/LOCATION/reasoningEngines/REASONING_ENGINE_ID

如需发送请求，请选择以下方式之一：

curl

执行以下命令：

curl -X GET \
     -H "Authorization: Bearer $(gcloud auth print-access-token)" \
     "https://LOCATION-aiplatform.googleapis.com/v1beta1/projects/PROJECT_ID/locations/LOCATION/reasoningEngines/REASONING_ENGINE_ID"

PowerShell

执行以下命令： 

$cred = gcloud auth print-access-token
$headers = @{ "Authorization" = "Bearer $cred" }

Invoke-WebRequest `
    -Method GET `
    -Headers $headers `
    -Uri "https://LOCATION-aiplatform.googleapis.com/v1beta1/projects/PROJECT_ID/locations/LOCATION/reasoningEngines/REASONING_ENGINE_ID" | Select-Object -Expand Content

Python

如需了解如何安装或更新 Vertex AI SDK for Python，请参阅安装 Vertex AI SDK for Python。 如需了解详情，请参阅 Python API 参考文档。 

import vertexai
from vertexai.preview import reasoning_engines

# TODO(developer): Update and un-comment below lines
# project_id = "PROJECT_ID"
# reasoning_engine_id = "REASONING_ENGINE_ID"

vertexai.init(project=project_id, location="us-central1")

reasoning_engine = reasoning_engines.ReasoningEngine(reasoning_engine_id)
print(reasoning_engine)

删除 Reasoning Engine

删除推理引擎。

REST

在使用任何请求数据之前，请先进行以下替换：

  • PROJECT_ID：您的项目 ID。
  • LOCATION：处理请求的区域。必须为 us-central1。
  • REASONING_ENGINE_ID：推理引擎的 ID。

HTTP 方法和网址： 

DELETE https://LOCATION-aiplatform.googleapis.com/v1beta1/projects/PROJECT_ID/locations/LOCATION/reasoningEngines/REASONING_ENGINE_ID

如需发送请求，请选择以下方式之一：

curl

执行以下命令：

curl -X DELETE \
     -H "Authorization: Bearer $(gcloud auth print-access-token)" \
     "https://LOCATION-aiplatform.googleapis.com/v1beta1/projects/PROJECT_ID/locations/LOCATION/reasoningEngines/REASONING_ENGINE_ID"

PowerShell

执行以下命令： 

$cred = gcloud auth print-access-token
$headers = @{ "Authorization" = "Bearer $cred" }

Invoke-WebRequest `
    -Method DELETE `
    -Headers $headers `
    -Uri "https://LOCATION-aiplatform.googleapis.com/v1beta1/projects/PROJECT_ID/locations/LOCATION/reasoningEngines/REASONING_ENGINE_ID" | Select-Object -Expand Content

Python

如需了解如何安装或更新 Vertex AI SDK for Python，请参阅安装 Vertex AI SDK for Python。 如需了解详情，请参阅 Python API 参考文档。 

import vertexai
from vertexai.preview import reasoning_engines

# TODO(developer): Update and un-comment below lines
# project_id = "PROJECT_ID"
# reasoning_engine_id = "REASONING_ENGINE_ID"

vertexai.init(project=project_id, location="us-central1")

reasoning_engine = reasoning_engines.ReasoningEngine(reasoning_engine_id)
reasoning_engine.delete()

后续步骤