Call Responses API

Gemini Enterprise Agent Platform 上的 XAI Grok 模型支持使用 Responses API 生成回答。

本页面介绍了如何使用 Responses API 调用 Grok 模型。

准备工作

如需将 Grok 模型与 Gemini Enterprise Agent Platform 搭配使用，您必须执行以下步骤。必须启用 Gemini Enterprise Agent Platform API (aiplatform.googleapis.com)。

  1. 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 role (roles/resourcemanager.projectCreator), which contains the resourcemanager.projects.create permission. Learn how to grant roles.

    Go to project selector

  2. Verify that billing is enabled for your Google Cloud project.

  3. Enable the Gemini Enterprise Agent Platform API, if it is not already enabled.

    Roles required to enable APIs

    To enable APIs, you need the serviceusage.services.enable permission. If you created the project, then you likely already have this permission through the Owner role (roles/owner). Otherwise, you can get this permission through the Service Usage Admin role (roles/serviceusage.serviceUsageAdmin). Learn how to grant roles.

    Enable the API

对 Responses API 进行无状态调用

store 的默认值为 false，因此除非您将 store 设置为 true，否则请求是无状态的。以下示例将 store 显式设置为 false（在 Python 中为 False）。

对 Responses API 进行非流式调用

以下示例展示了如何对 Responses API 进行非流式调用：

Python

在尝试此示例之前，请按照《Agent Platform 快速入门：使用客户端库》中的 Python 设置说明执行操作。

如需向 Agent Platform 进行身份验证，请设置应用默认凭据。如需了解详情，请参阅为本地开发环境设置身份验证。

在运行此示例之前，请务必设置 OPENAI_BASE_URL 环境变量或设置 OAuth 凭据。 如需了解详情，请参阅身份验证和凭证。

import google.auth
import google.auth.transport.requests
from openai import OpenAI
credentials, _ = google.auth.default(
    scopes=["https://www.googleapis.com/auth/cloud-platform"]
)
credentials.refresh(google.auth.transport.requests.Request())
client = OpenAI(
    base_url="https://aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/global/endpoints/openapi",
    api_key=credentials.token,
)
response = client.responses.create(
    model="MODEL",
    input="INPUT",
    max_output_tokens=MAX_OUTPUT_TOKENS,
    stream=False,
    store=False,
)
print(response)
  • PROJECT_ID：您的 Google Cloud 项目 ID。
  • MODEL：您要使用的模型名称，例如 xai/grok-4.7。
  • INPUT：模型的提示或输入。
  • MAX_OUTPUT_TOKENS：回答中可生成的词元数量上限。词元约为 4 个字符。100 个词元对应大约 60-80 个单词。

    指定较低的值可获得较短的回答，指定较高的值可获得可能较长的回答。

REST

设置您的环境后，您可以使用 REST 测试文本提示词。以下示例会向发布方模型端点发送请求。

在使用任何请求数据之前，请先进行以下替换：

  • PROJECT_ID：您的 Google Cloud 项目 ID。
  • MODEL：您要使用的模型名称，例如 xai/grok-4.7。
  • INPUT：模型的提示或输入。
  • MAX_OUTPUT_TOKENS：回答中可生成的词元数量上限。词元约为 4 个字符。100 个词元对应大约 60-80 个单词。

    指定较低的值可获得较短的回答，指定较高的值可获得可能较长的回答。

HTTP 方法和网址：

POST https://aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/global/endpoints/openapi/responses

请求 JSON 正文： 

{
  "model": "MODEL",
  "input": "INPUT",
  "max_output_tokens": MAX_OUTPUT_TOKENS,
  "stream": false,
  "store": false
}

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 application-default print-access-token)" \
     -H "Content-Type: application/json; charset=utf-8" \
     -d @request.json \
     "https://aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/global/endpoints/openapi/responses"

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 application-default print-access-token
$headers = @{ "Authorization" = "Bearer $cred" }

Invoke-WebRequest `
    -Method POST `
    -Headers $headers `
    -ContentType: "application/json; charset=utf-8" `
    -InFile request.json `
    -Uri "https://aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/global/endpoints/openapi/responses" | Select-Object -Expand Content

以下示例展示了完整的 curl 请求：

curl -s -X POST \
  -H "Authorization: Bearer $(gcloud auth application-default print-access-token)" \
  -H "Content-Type: application/json" \
  "https://aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/global/endpoints/openapi/responses" \
  -d '{
    "model": "xai/grok-4.7",
    "input": "Explain black holes in one short sentence.",
    "max_output_tokens": 100,
    "stream": false,
    "store": false
  }'

根据 Responses API 定义，非流式回答将包含唯一 ID、模型元数据、使用情况统计信息，以及包含所生成文本的输出数组。

{
  "background": false,
  "completed_at": 1778892918,
  "created_at": 1778892916,
  "error": null,
  "frequency_penalty": 0,
  "id": "c8AHavnIMP6UifEPgIfcgAg",
  "incomplete_details": null,
  "instructions": null,
  "max_output_tokens": null,
  "max_tool_calls": null,
  "metadata": {
    "system_fingerprint": "fp_39c5j0a3e9"
  },
  "model": "MODEL",
  "object": "response",
  "output": [
    {
      "content": [
        {
          "annotations": [],
          "logprobs": [],
          "text": "OUTPUT_TEXT",
          "type": "output_text"
        }
      ],
      "id": "msg_c8AHavnIMP6UifEPgIfcgAg",
      "role": "assistant",
      "status": "completed",
      "type": "message"
    }
  ],
  "parallel_tool_calls": true,
  "presence_penalty": 0,
  "previous_response_id": null,
  "prompt_cache_key": null,
  "reasoning": {
    "effort": "medium",
    "summary": "detailed"
  },
  "safety_identifier": null,
  "service_tier": "default",
  "status": "completed",
  "store": false,
  "temperature": 0.7,
  "text": {
    "format": {
      "type": "text"
    }
  },
  "tool_choice": "auto",
  "tools": [],
  "top_logprobs": 0,
  "top_p": 0.95,
  "truncation": "disabled",
  "usage": {
    "extra_properties": {
      "google": {
        "traffic_type": "ON_DEMAND"
      }
    },
    "input_tokens": 335,
    "input_tokens_details": {
      "cached_tokens": 320
    },
    "num_server_side_tools_used": 0,
    "num_sources_used": 0,
    "output_tokens": 305,
    "output_tokens_details": {
      "reasoning_tokens": 284
    },
    "total_tokens": 640
  },
  "user": null
}

对 Responses API 进行流式调用

以下示例展示了如何对 Responses API 进行流式调用：

Python

在尝试此示例之前，请按照《Agent Platform 快速入门：使用客户端库》中的 Python 设置说明执行操作。

如需向 Agent Platform 进行身份验证，请设置应用默认凭据。如需了解详情，请参阅为本地开发环境设置身份验证。

在运行此示例之前，请务必设置 OPENAI_BASE_URL 环境变量或设置 OAuth 凭据。 如需了解详情，请参阅身份验证和凭证。

import google.auth
import google.auth.transport.requests
from openai import OpenAI
credentials, _ = google.auth.default(
    scopes=["https://www.googleapis.com/auth/cloud-platform"]
)
credentials.refresh(google.auth.transport.requests.Request())
client = OpenAI(
    base_url="https://aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/global/endpoints/openapi",
    api_key=credentials.token,
)
stream = client.responses.create(
    model="MODEL",
    input="INPUT",
    max_output_tokens=MAX_OUTPUT_TOKENS,
    stream=True,
    store=False,
)
for event in stream:
    if event.type == "response.output_text.delta":
        print(event.delta, end="")
  • PROJECT_ID：您的 Google Cloud 项目 ID。
  • MODEL：您要使用的模型名称，例如 xai/grok-4.7。
  • INPUT：模型的提示或输入。
  • MAX_OUTPUT_TOKENS：回答中可生成的词元数量上限。词元约为 4 个字符。100 个词元对应大约 60-80 个单词。

    指定较低的值可获得较短的回答，指定较高的值可获得可能较长的回答。

REST

设置您的环境后，您可以使用 REST 测试文本提示词。以下示例会向发布方模型端点发送请求。

在使用任何请求数据之前，请先进行以下替换：

  • PROJECT_ID：您的 Google Cloud 项目 ID。
  • MODEL：您要使用的模型名称，例如 xai/grok-4.7。
  • INPUT：模型的提示或输入。
  • MAX_OUTPUT_TOKENS：回答中可生成的词元数量上限。词元约为 4 个字符。100 个词元对应大约 60-80 个单词。

    指定较低的值可获得较短的回答，指定较高的值可获得可能较长的回答。

HTTP 方法和网址：

POST https://aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/global/endpoints/openapi/responses

请求 JSON 正文： 

{
  "model": "MODEL",
  "input": "INPUT",
  "max_output_tokens": MAX_OUTPUT_TOKENS,
  "stream": true,
  "store": false
}

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 application-default print-access-token)" \
     -H "Content-Type: application/json; charset=utf-8" \
     -d @request.json \
     "https://aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/global/endpoints/openapi/responses"

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 application-default print-access-token
$headers = @{ "Authorization" = "Bearer $cred" }

Invoke-WebRequest `
    -Method POST `
    -Headers $headers `
    -ContentType: "application/json; charset=utf-8" `
    -InFile request.json `
    -Uri "https://aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/global/endpoints/openapi/responses" | Select-Object -Expand Content
 

对 Responses API 进行有状态调用

如需发出有状态请求，您必须配置Organization Policy Service以允许这些请求。具体而言，请更新限制条件 constraints/vertexai.allowedPartnerModelFeatures，方法是将 publishers/xai/models/MODEL_NAME:stateful_responses_api 添加到允许的值（例如 publishers/xai/models/grok-4.7:stateful_responses_api）。如需了解详情，请参阅控制模型访问权限。

Responses API 通过有状态请求支持多轮对话。store 的默认值为 false，因此，如需发出有状态请求，您必须将 store 明确设置为 true。在有状态对话中，您可以通过在 previous_response_id 字段中提供先前回答的 id 来引用该回答。

创建回复

以下示例展示了使用 Responses API 进行的两轮对话：

第 1 轮：初始请求

在第一轮对话中，发出请求并将 store 设置为 true。如果您省略 store 参数，则该参数默认为 false，并且响应不会存储。

Python

在尝试此示例之前，请按照《Agent Platform 快速入门：使用客户端库》中的 Python 设置说明执行操作。

如需向 Agent Platform 进行身份验证，请设置应用默认凭据。如需了解详情，请参阅为本地开发环境设置身份验证。

在运行此示例之前，请务必设置 OPENAI_BASE_URL 环境变量或设置 OAuth 凭据。 如需了解详情，请参阅身份验证和凭证。

import google.auth
import google.auth.transport.requests
from openai import OpenAI
credentials, _ = google.auth.default(
    scopes=["https://www.googleapis.com/auth/cloud-platform"]
)
credentials.refresh(google.auth.transport.requests.Request())
client = OpenAI(
    base_url="https://aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/global/endpoints/openapi",
    api_key=credentials.token,
)
response = client.responses.create(
    model="MODEL",
    input="randomly pick 3 colors, just return the words of the colors",
    store=True,
)
print(response)
  • PROJECT_ID：您的 Google Cloud 项目 ID。
  • MODEL：您要使用的模型名称，例如 xai/grok-4.7。

第 1 轮的示例回答：

{
  "background": false,
  "completed_at": 1793834418,
  "created_at": 1793834418,
  "error": null,
  "frequency_penalty": 0,
  "id": "5f76a045-71d9-45af-8b07-1f2feae0675a",
  "incomplete_details": null,
  "instructions": null,
  "max_output_tokens": null,
  "max_tool_calls": null,
  "metadata": {
    "system_fingerprint": "fp_grok47_0"
  },
  "model": "xai/grok-4.7",
  "object": "response",
  "output": [
    {
      "content": [
        {
          "annotations": [],
          "logprobs": [],
          "text": "cerulean amber violet",
          "type": "output_text"
        }
      ],
      "id": "msg_5f76a045-71d9-45af-8b07-1f2feae0675a",
      "role": "assistant",
      "status": "completed",
      "type": "message"
    }
  ],
  "parallel_tool_calls": true,
  "presence_penalty": 0,
  "previous_response_id": null,
  "prompt_cache_key": null,
  "reasoning": {
    "effort": "high",
    "summary": "detailed"
  },
  "safety_identifier": null,
  "service_tier": "default",
  "status": "completed",
  "store": true,
  "temperature": 0.7,
  "text": {
    "format": {
      "type": "text"
    }
  },
  "tool_choice": "auto",
  "tools": [],
  "top_logprobs": 0,
  "top_p": 0.95,
  "truncation": "disabled",
  "usage": {
    "context_details": {
      "input_tokens": 220,
      "output_tokens": 144
    },
    "extra_properties": {
      "google": {
        "traffic_type": "ON_DEMAND"
      }
    },
    "input_tokens": 220,
    "input_tokens_details": {
      "cached_tokens": 0
    },
    "num_server_side_tools_used": 0,
    "num_sources_used": 0,
    "output_tokens": 144,
    "output_tokens_details": {
      "reasoning_tokens": 139
    },
    "total_tokens": 364
  },
  "user": null
}

REST

设置您的环境后，您可以使用 REST 测试文本提示词。以下示例会向发布方模型端点发送请求。

在使用任何请求数据之前，请先进行以下替换：

  • PROJECT_ID：您的 Google Cloud 项目 ID。
  • MODEL：您要使用的模型名称，例如 xai/grok-4.7。

HTTP 方法和网址：

POST https://aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/global/endpoints/openapi/responses

请求 JSON 正文： 

{
  "model": "MODEL",
  "input": "randomly pick 3 colors, just return the words of the colors",
  "store": true
}

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 application-default print-access-token)" \
     -H "Content-Type: application/json; charset=utf-8" \
     -d @request.json \
     "https://aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/global/endpoints/openapi/responses"

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 application-default print-access-token
$headers = @{ "Authorization" = "Bearer $cred" }

Invoke-WebRequest `
    -Method POST `
    -Headers $headers `
    -ContentType: "application/json; charset=utf-8" `
    -InFile request.json `
    -Uri "https://aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/global/endpoints/openapi/responses" | Select-Object -Expand Content

以下示例展示了完整的 curl 请求：

curl -X POST \
  -H "Authorization: Bearer $(gcloud auth application-default print-access-token)" \
  -H "Content-Type: application/json" \
  "https://aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/global/endpoints/openapi/responses" \
  -d '{
    "model": "xai/grok-4.7",
    "input": "randomly pick 3 colors, just return the words of the colors",
    "store": true
  }'

第 1 轮的示例回答：

{
  "background": false,
  "completed_at": 1793834418,
  "created_at": 1793834418,
  "error": null,
  "frequency_penalty": 0,
  "id": "5f76a045-71d9-45af-8b07-1f2feae0675a",
  "incomplete_details": null,
  "instructions": null,
  "max_output_tokens": null,
  "max_tool_calls": null,
  "metadata": {
    "system_fingerprint": "fp_grok47_0"
  },
  "model": "xai/grok-4.7",
  "object": "response",
  "output": [
    {
      "content": [
        {
          "annotations": [],
          "logprobs": [],
          "text": "cerulean amber violet",
          "type": "output_text"
        }
      ],
      "id": "msg_5f76a045-71d9-45af-8b07-1f2feae0675a",
      "role": "assistant",
      "status": "completed",
      "type": "message"
    }
  ],
  "parallel_tool_calls": true,
  "presence_penalty": 0,
  "previous_response_id": null,
  "prompt_cache_key": null,
  "reasoning": {
    "effort": "high",
    "summary": "detailed"
  },
  "safety_identifier": null,
  "service_tier": "default",
  "status": "completed",
  "store": true,
  "temperature": 0.7,
  "text": {
    "format": {
      "type": "text"
    }
  },
  "tool_choice": "auto",
  "tools": [],
  "top_logprobs": 0,
  "top_p": 0.95,
  "truncation": "disabled",
  "usage": {
    "context_details": {
      "input_tokens": 220,
      "output_tokens": 144
    },
    "extra_properties": {
      "google": {
        "traffic_type": "ON_DEMAND"
      }
    },
    "input_tokens": 220,
    "input_tokens_details": {
      "cached_tokens": 0
    },
    "num_server_side_tools_used": 0,
    "num_sources_used": 0,
    "output_tokens": 144,
    "output_tokens_details": {
      "reasoning_tokens": 139
    },
    "total_tokens": 364
  },
  "user": null
}

第 2 轮：跟进请求

在第二轮对话中，在 previous_response_id 参数中引用上一条回答中的 ID。如果您还想存储此回答（例如，为了继续对话或稍后检索），请将 store 设置为 true。

Python

在尝试此示例之前，请按照《Agent Platform 快速入门：使用客户端库》中的 Python 设置说明执行操作。

如需向 Agent Platform 进行身份验证，请设置应用默认凭据。如需了解详情，请参阅为本地开发环境设置身份验证。

在运行此示例之前，请务必设置 OPENAI_BASE_URL 环境变量或设置 OAuth 凭据。 如需了解详情，请参阅身份验证和凭证。

import google.auth
import google.auth.transport.requests
from openai import OpenAI
credentials, _ = google.auth.default(
    scopes=["https://www.googleapis.com/auth/cloud-platform"]
)
credentials.refresh(google.auth.transport.requests.Request())
client = OpenAI(
    base_url="https://aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/global/endpoints/openapi",
    api_key=credentials.token,
)
response = client.responses.create(
    model="MODEL",
    input="What is the second color?",
    previous_response_id="PREVIOUS_RESPONSE_ID",
    store=True,
)
print(response)
  • PROJECT_ID：您的 Google Cloud 项目 ID。
  • MODEL：您要使用的模型名称，例如 xai/grok-4.7。
  • PREVIOUS_RESPONSE_ID：上一条回答的 ID，用于继续对话。

第 2 轮回答示例：

{
  "background": false,
  "completed_at": 1793835029,
  "created_at": 1793835029,
  "error": null,
  "frequency_penalty": 0,
  "id": "ee8b6aa6-777e-4aa2-8db4-46bbfe84491b",
  "incomplete_details": null,
  "instructions": null,
  "max_output_tokens": null,
  "max_tool_calls": null,
  "metadata": {
    "system_fingerprint": "fp_grok47_0"
  },
  "model": "xai/grok-4.7",
  "object": "response",
  "output": [
    {
      "content": [
        {
          "annotations": [],
          "logprobs": [],
          "text": "amber",
          "type": "output_text"
        }
      ],
      "id": "msg_ee8b6aa6-777e-4aa2-8db4-46bbfe84491b",
      "role": "assistant",
      "status": "completed",
      "type": "message"
    }
  ],
  "parallel_tool_calls": true,
  "presence_penalty": 0,
  "previous_response_id": "5f76a045-71d9-45af-8b07-1f2feae0675a",
  "prompt_cache_key": null,
  "reasoning": {
    "effort": "high",
    "summary": "detailed"
  },
  "safety_identifier": null,
  "service_tier": "default",
  "status": "completed",
  "store": true,
  "temperature": 0.7,
  "text": {
    "format": {
      "type": "text"
    }
  },
  "tool_choice": "auto",
  "tools": [],
  "top_logprobs": 0,
  "top_p": 0.95,
  "truncation": "disabled",
  "usage": {
    "context_details": {
      "input_tokens": 239,
      "output_tokens": 41
    },
    "extra_properties": {
      "google": {
        "traffic_type": "ON_DEMAND"
      }
    },
    "input_tokens": 239,
    "input_tokens_details": {
      "cached_tokens": 0
    },
    "num_server_side_tools_used": 0,
    "num_sources_used": 0,
    "output_tokens": 41,
    "output_tokens_details": {
      "reasoning_tokens": 40
    },
    "total_tokens": 280
  },
  "user": null
}

REST

设置您的环境后，您可以使用 REST 测试文本提示词。以下示例会向发布方模型端点发送请求。

在使用任何请求数据之前，请先进行以下替换：

  • PROJECT_ID：您的 Google Cloud 项目 ID。
  • MODEL：您要使用的模型名称，例如 xai/grok-4.7。
  • PREVIOUS_RESPONSE_ID：上一个回答的 ID。

HTTP 方法和网址：

POST https://aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/global/endpoints/openapi/responses

请求 JSON 正文： 

{
  "model": "MODEL",
  "input": "What is the second color?",
  "previous_response_id": "PREVIOUS_RESPONSE_ID",
  "store": true
}

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 application-default print-access-token)" \
     -H "Content-Type: application/json; charset=utf-8" \
     -d @request.json \
     "https://aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/global/endpoints/openapi/responses"

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 application-default print-access-token
$headers = @{ "Authorization" = "Bearer $cred" }

Invoke-WebRequest `
    -Method POST `
    -Headers $headers `
    -ContentType: "application/json; charset=utf-8" `
    -InFile request.json `
    -Uri "https://aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/global/endpoints/openapi/responses" | Select-Object -Expand Content

以下示例展示了完整的 curl 请求：

curl -X POST \
  -H "Authorization: Bearer $(gcloud auth application-default print-access-token)" \
  -H "Content-Type: application/json" \
  "https://aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/global/endpoints/openapi/responses" \
  -d '{
    "model": "xai/grok-4.7",
    "input": "What is the second color?",
    "previous_response_id": "5f76a045-71d9-45af-8b07-1f2feae0675a",
    "store": true
  }'

第 2 轮回答示例：

{
  "background": false,
  "completed_at": 1793835029,
  "created_at": 1793835029,
  "error": null,
  "frequency_penalty": 0,
  "id": "ee8b6aa6-777e-4aa2-8db4-46bbfe84491b",
  "incomplete_details": null,
  "instructions": null,
  "max_output_tokens": null,
  "max_tool_calls": null,
  "metadata": {
    "system_fingerprint": "fp_grok47_0"
  },
  "model": "xai/grok-4.7",
  "object": "response",
  "output": [
    {
      "content": [
        {
          "annotations": [],
          "logprobs": [],
          "text": "amber",
          "type": "output_text"
        }
      ],
      "id": "msg_ee8b6aa6-777e-4aa2-8db4-46bbfe84491b",
      "role": "assistant",
      "status": "completed",
      "type": "message"
    }
  ],
  "parallel_tool_calls": true,
  "presence_penalty": 0,
  "previous_response_id": "5f76a045-71d9-45af-8b07-1f2feae0675a",
  "prompt_cache_key": null,
  "reasoning": {
    "effort": "high",
    "summary": "detailed"
  },
  "safety_identifier": null,
  "service_tier": "default",
  "status": "completed",
  "store": true,
  "temperature": 0.7,
  "text": {
    "format": {
      "type": "text"
    }
  },
  "tool_choice": "auto",
  "tools": [],
  "top_logprobs": 0,
  "top_p": 0.95,
  "truncation": "disabled",
  "usage": {
    "context_details": {
      "input_tokens": 239,
      "output_tokens": 41
    },
    "extra_properties": {
      "google": {
        "traffic_type": "ON_DEMAND"
      }
    },
    "input_tokens": 239,
    "input_tokens_details": {
      "cached_tokens": 0
    },
    "num_server_side_tools_used": 0,
    "num_sources_used": 0,
    "output_tokens": 41,
    "output_tokens_details": {
      "reasoning_tokens": 40
    },
    "total_tokens": 280
  },
  "user": null
}

获取回复

您可以通过 ID 检索之前生成的回答。

Python

在尝试此示例之前，请按照《Agent Platform 快速入门：使用客户端库》中的 Python 设置说明执行操作。

如需向 Agent Platform 进行身份验证，请设置应用默认凭据。如需了解详情，请参阅为本地开发环境设置身份验证。

在运行此示例之前，请务必设置 OPENAI_BASE_URL 环境变量或设置 OAuth 凭据。 如需了解详情，请参阅身份验证和凭证。

import json
import google.auth
import google.auth.transport.requests
from openai import OpenAI
credentials, _ = google.auth.default(
    scopes=["https://www.googleapis.com/auth/cloud-platform"]
)
credentials.refresh(google.auth.transport.requests.Request())
client = OpenAI(
    base_url="https://aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/global/endpoints/openapi",
    api_key=credentials.token,
)
response_id = "RESPONSE_ID"
retrieved_response = client.responses.retrieve(response_id)
print(json.dumps(retrieved_response.model_dump(), indent=2))
  • PROJECT_ID：您的 Google Cloud 项目 ID。
  • RESPONSE_ID：要检索的回答的 ID。

示例响应：

{
  "background": false,
  "completed_at": 1793834418,
  "created_at": 1793834418,
  "error": null,
  "frequency_penalty": 0,
  "id": "5f76a045-71d9-45af-8b07-1f2feae0675a",
  "incomplete_details": null,
  "instructions": null,
  "max_output_tokens": null,
  "max_tool_calls": null,
  "metadata": {
    "system_fingerprint": "fp_grok47_0"
  },
  "model": "xai/grok-4.7",
  "object": "response",
  "output": [
    {
      "content": [
        {
          "annotations": [],
          "logprobs": [],
          "text": "cerulean amber violet",
          "type": "output_text"
        }
      ],
      "id": "msg_5f76a045-71d9-45af-8b07-1f2feae0675a",
      "role": "assistant",
      "status": "completed",
      "type": "message"
    }
  ],
  "parallel_tool_calls": true,
  "presence_penalty": 0,
  "previous_response_id": null,
  "prompt_cache_key": null,
  "reasoning": {
    "effort": "high",
    "summary": "detailed"
  },
  "safety_identifier": null,
  "service_tier": "default",
  "status": "completed",
  "store": true,
  "temperature": 0.7,
  "text": {
    "format": {
      "type": "text"
    }
  },
  "tool_choice": "auto",
  "tools": [],
  "top_logprobs": 0,
  "top_p": 0.95,
  "truncation": "disabled",
  "usage": {
    "context_details": {
      "input_tokens": 220,
      "output_tokens": 144
    },
    "extra_properties": {
      "google": {
        "traffic_type": "ON_DEMAND"
      }
    },
    "input_tokens": 220,
    "input_tokens_details": {
      "cached_tokens": 0
    },
    "num_server_side_tools_used": 0,
    "num_sources_used": 0,
    "output_tokens": 144,
    "output_tokens_details": {
      "reasoning_tokens": 139
    },
    "total_tokens": 364
  },
  "user": null
}

REST

设置您的环境后，您可以使用 REST 测试文本提示词。以下示例会向发布方模型端点发送请求。

在使用任何请求数据之前，请先进行以下替换：

  • PROJECT_ID：您的 Google Cloud 项目 ID。
  • RESPONSE_ID：要检索的回答的 ID。

HTTP 方法和网址：

GET https://aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/global/endpoints/openapi/responses/RESPONSE_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 application-default print-access-token)" \
     "https://aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/global/endpoints/openapi/responses/RESPONSE_ID"

PowerShell

执行以下命令：

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

Invoke-WebRequest `
    -Method GET `
    -Headers $headers `
    -Uri "https://aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/global/endpoints/openapi/responses/RESPONSE_ID" | Select-Object -Expand Content

以下示例展示了完整的 curl 请求：

curl -H "Authorization: Bearer $(gcloud auth application-default print-access-token)" \
  -H "Content-Type: application/json" \
  "https://aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/global/endpoints/openapi/responses/5f76a045-71d9-45af-8b07-1f2feae0675a"

示例响应：

{
  "background": false,
  "completed_at": 1793834418,
  "created_at": 1793834418,
  "error": null,
  "frequency_penalty": 0,
  "id": "5f76a045-71d9-45af-8b07-1f2feae0675a",
  "incomplete_details": null,
  "instructions": null,
  "max_output_tokens": null,
  "max_tool_calls": null,
  "metadata": {
    "system_fingerprint": "fp_grok47_0"
  },
  "model": "xai/grok-4.7",
  "object": "response",
  "output": [
    {
      "content": [
        {
          "annotations": [],
          "logprobs": [],
          "text": "cerulean amber violet",
          "type": "output_text"
        }
      ],
      "id": "msg_5f76a045-71d9-45af-8b07-1f2feae0675a",
      "role": "assistant",
      "status": "completed",
      "type": "message"
    }
  ],
  "parallel_tool_calls": true,
  "presence_penalty": 0,
  "previous_response_id": null,
  "prompt_cache_key": null,
  "reasoning": {
    "effort": "high",
    "summary": "detailed"
  },
  "safety_identifier": null,
  "service_tier": "default",
  "status": "completed",
  "store": true,
  "temperature": 0.7,
  "text": {
    "format": {
      "type": "text"
    }
  },
  "tool_choice": "auto",
  "tools": [],
  "top_logprobs": 0,
  "top_p": 0.95,
  "truncation": "disabled",
  "usage": {
    "context_details": {
      "input_tokens": 220,
      "output_tokens": 144
    },
    "extra_properties": {
      "google": {
        "traffic_type": "ON_DEMAND"
      }
    },
    "input_tokens": 220,
    "input_tokens_details": {
      "cached_tokens": 0
    },
    "num_server_side_tools_used": 0,
    "num_sources_used": 0,
    "output_tokens": 144,
    "output_tokens_details": {
      "reasoning_tokens": 139
    },
    "total_tokens": 364
  },
  "user": null
}

删除回复

您可以通过 ID 删除之前生成的回答。

删除响应后，您将无法检索该响应，也无法在新请求中将其 ID 用作 previous_response_id。删除某个回答并不会从同一对话中的后续回答中移除其内容：通过 previous_response_id 引用该回答的每个回答都会将该内容保留为上下文，直到您也删除该回答。如需移除整个对话，请删除其中存储的所有回答。

Python

在尝试此示例之前，请按照《Agent Platform 快速入门：使用客户端库》中的 Python 设置说明执行操作。

如需向 Agent Platform 进行身份验证，请设置应用默认凭据。如需了解详情，请参阅为本地开发环境设置身份验证。

在运行此示例之前，请务必设置 OPENAI_BASE_URL 环境变量或设置 OAuth 凭据。 如需了解详情，请参阅身份验证和凭证。

import google.auth
import google.auth.transport.requests
from openai import OpenAI
credentials, _ = google.auth.default(
    scopes=["https://www.googleapis.com/auth/cloud-platform"]
)
credentials.refresh(google.auth.transport.requests.Request())
client = OpenAI(
    base_url="https://aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/global/endpoints/openapi",
    api_key=credentials.token,
)
response_id = "RESPONSE_ID"
delete_response = client.responses.delete(response_id)
print(delete_response)
  • PROJECT_ID：您的 Google Cloud 项目 ID。
  • RESPONSE_ID：要删除的回答的 ID。

示例响应：

{
  "deleted": true,
  "id": "5f76a045-71d9-45af-8b07-1f2feae0675a",
  "object": "response"
}

REST

设置您的环境后，您可以使用 REST 测试文本提示词。以下示例会向发布方模型端点发送请求。

在使用任何请求数据之前，请先进行以下替换：

  • PROJECT_ID：您的 Google Cloud 项目 ID。
  • RESPONSE_ID：要删除的回答的 ID。

HTTP 方法和网址：

DELETE https://aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/global/endpoints/openapi/responses/RESPONSE_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 application-default print-access-token)" \
     "https://aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/global/endpoints/openapi/responses/RESPONSE_ID"

PowerShell

执行以下命令：

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

Invoke-WebRequest `
    -Method DELETE `
    -Headers $headers `
    -Uri "https://aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/global/endpoints/openapi/responses/RESPONSE_ID" | Select-Object -Expand Content

以下示例展示了完整的 curl 请求：

curl -X DELETE \
  -H "Authorization: Bearer $(gcloud auth application-default print-access-token)" \
  -H "Content-Type: application/json" \
  "https://aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/global/endpoints/openapi/responses/5f76a045-71d9-45af-8b07-1f2feae0675a"

示例响应：

{
  "deleted": true,
  "id": "5f76a045-71d9-45af-8b07-1f2feae0675a",
  "object": "response"
}

后续步骤