从工作流中访问 Vertex AI 模型

借助 Vertex AI 上的生成式 AI（也称为生成式 AI 或生成式 AI），您可以访问 适用于多种模态（文本、代码、图片、 语音）。您可以测试和调优这些大型语言模型 (LLM)，然后将其部署到依托 AI 技术的应用中使用。如需了解详情，请参阅 Vertex AI 上的生成式 AI 概览。

Vertex AI 具有可通过 API 访问的各种生成式 AI 基础模型，包括以下示例中使用的模型：

  • Gemini Pro 旨在处理自然语言任务、多轮文本和代码聊天以及代码生成。
  • Gemini Pro Vision 支持多模态提示。您可以在提示请求中包含文本、图片和视频，并获取文本或代码回答。
  • 适用于文本的 Pathways Language Model 2 (PaLM 2) 针对分类、汇总和实体提取等语言任务进行了微调。

每个模型都通过特定于您的 Google Cloud 项目的发布端点公开，因此您无需部署基础模型，除非您需要针对特定应用场景进行调优。您可以向发布商端点发送提示。提示是发送给 LLM 的自然语言请求，用于 。

本教程演示了通过使用 Workflows 连接器或 HTTP POST 请求向发布商端点发送文本提示，从 Vertex AI 模型生成回答的工作流。如需了解详情，请参阅 Vertex AI API 连接器概览和发出 HTTP 请求。

请注意，您可以单独部署和运行每个工作流。

目标

在本教程中，您将执行以下操作：

  1. 启用 Vertex AI 和 Workflows API，并向您的服务账号授予 Vertex AI User (roles/aiplatform.user) 角色。此角色可访问大多数 Vertex AI 功能。如需详细了解如何设置 Vertex AI，请参阅在 Google Cloud 上进行设置。
  2. 部署并运行提示 Vertex AI 模型的工作流 (Gemini Pro Vision) 来描述公开的图片 Cloud Storage如需了解详情，请参阅 公开数据。
  3. 部署并运行一个工作流，以并行遍历一系列国家/地区 并提示 Vertex AI 模型 (Gemini Pro) 来生成并返回国家/地区的历史。通过使用并行分支，您可以同时启动对 LLM 的调用，并等待所有调用完成，然后再合并结果，从而缩短总执行时间。如需了解详情，请参阅并行执行工作流步骤。
  4. 部署与上一个工作流类似的工作流；不过，请提示 Vertex AI 模型（适用于文本的 PaLM 2）生成并返回各个国家/地区的历史记录。如需详细了解如何选择模型，请参阅模型信息。
  5. 部署一个可以为大型文档生成摘要的工作流。由于上下文窗口（用于设置模型在训练期间的回溯时间）存在限制，因此该工作流会将文档拆分成较小的部分，然后提示 Vertex AI 模型 (Gemini Pro) 并行总结每个部分。如需了解详情，请参阅总结提示和预测范围、上下文窗口和预测窗口。

费用

在本文档中，您将使用 Google Cloud 的以下收费组件：

您可使用价格计算器根据您的预计使用情况来估算费用。 Google Cloud 新用户可能有资格申请免费试用。

完成本文档中描述的任务后，您可以通过删除所创建的资源来避免继续计费。如需了解详情，请参阅清理。

准备工作

在试用本教程中的示例之前，请确保您已完成以下操作。

控制台

  1. 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.

  2. In the Google Cloud console, on the project selector page, select or create a Google Cloud project.

    Go to project selector

  3. Make sure that billing is enabled for your Google Cloud project.

  4. Enable the Vertex AI and Workflows APIs.

    Enable the APIs

  5. Create a service account:

    1. In the Google Cloud console, go to the Create service account page.

      Go to Create service account
    2. Select your project.

    3. In the Service account name field, enter a name. The Google Cloud console fills in the Service account ID field based on this name.

      In the Service account description field, enter a description. For example, Service account for quickstart.

    4. Click Create and continue.

    5. Grant the Vertex AI > Vertex AI User role to the service account.

      To grant the role, find the Select a role list, then select Vertex AI > Vertex AI User.

    6. Click Continue.

    7. Click Done to finish creating the service account.

  6. In the Google Cloud console, on the project selector page, select or create a Google Cloud project.

    Go to project selector

  7. Make sure that billing is enabled for your Google Cloud project.

  8. Enable the Vertex AI and Workflows APIs.

    Enable the APIs

  9. Create a service account:

    1. In the Google Cloud console, go to the Create service account page.

      Go to Create service account
    2. Select your project.

    3. In the Service account name field, enter a name. The Google Cloud console fills in the Service account ID field based on this name.

      In the Service account description field, enter a description. For example, Service account for quickstart.

    4. Click Create and continue.

    5. Grant the Vertex AI > Vertex AI User role to the service account.

      To grant the role, find the Select a role list, then select Vertex AI > Vertex AI User.

    6. Click Continue.

    7. Click Done to finish creating the service account.

gcloud

  1. 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.
  2. Install the Google Cloud CLI.

  3. To initialize the gcloud CLI, run the following command: 

    gcloud init

  4. Create or select a Google Cloud project.

    • Create a Google Cloud project:

      gcloud projects create PROJECT_ID

      Replace PROJECT_ID with a name for the Google Cloud project you are creating.

    • Select the Google Cloud project that you created:

      gcloud config set project PROJECT_ID

      Replace PROJECT_ID with your Google Cloud project name.

  5. Make sure that billing is enabled for your Google Cloud project.

  6. Enable the Vertex AI and Workflows APIs: 

    gcloud services enable aiplatform.googleapis.com workflows.googleapis.com

  7. Set up authentication:

    1. Create the service account: 

      gcloud iam service-accounts create SERVICE_ACCOUNT_NAME

      Replace SERVICE_ACCOUNT_NAME with a name for the service account.

    2. Grant the roles/aiplatform.user IAM role to the service account: 

      gcloud projects add-iam-policy-binding PROJECT_ID --member="serviceAccount:SERVICE_ACCOUNT_NAME@PROJECT_ID.iam.gserviceaccount.com" --role=roles/aiplatform.user

      Replace the following:

      • SERVICE_ACCOUNT_NAME: the name of the service account
      • PROJECT_ID: the project ID where you created the service account
    8.
  8. Install the Google Cloud CLI.

  9. To initialize the gcloud CLI, run the following command: 

    gcloud init

  10. Create or select a Google Cloud project.

    • Create a Google Cloud project:

      gcloud projects create PROJECT_ID

      Replace PROJECT_ID with a name for the Google Cloud project you are creating.

    • Select the Google Cloud project that you created:

      gcloud config set project PROJECT_ID

      Replace PROJECT_ID with your Google Cloud project name.

  11. Make sure that billing is enabled for your Google Cloud project.

  12. Enable the Vertex AI and Workflows APIs: 

    gcloud services enable aiplatform.googleapis.com workflows.googleapis.com

  13. Set up authentication:

    1. Create the service account: 

      gcloud iam service-accounts create SERVICE_ACCOUNT_NAME

      Replace SERVICE_ACCOUNT_NAME with a name for the service account.

    2. Grant the roles/aiplatform.user IAM role to the service account: 

      gcloud projects add-iam-policy-binding PROJECT_ID --member="serviceAccount:SERVICE_ACCOUNT_NAME@PROJECT_ID.iam.gserviceaccount.com" --role=roles/aiplatform.user

      Replace the following:

      • SERVICE_ACCOUNT_NAME: the name of the service account
      • PROJECT_ID: the project ID where you created the service account
    14.

部署描述图像的工作流 (Gemini Pro Vision)

部署使用连接器方法的工作流 (generateContent) 向 Gemini Pro Vision 发出请求 发布方端点。该方法支持使用多模态输入生成内容。

该工作流会提供文本提示和 Cloud Storage 存储桶中公开图片的 URI。您可以查看图片，还可以在 Google Cloud 控制台中查看对象详情。

工作流会根据模型生成的回答返回图片的说明。

如需详细了解提示 LLM 时使用的 HTTP 请求正文参数和响应正文元素，请参阅 Gemini API 参考文档。

控制台

  1. 在 Google Cloud 控制台中，前往工作流页面。

    进入 Workflows

  2. 点击 创建。

  3. 输入新工作流的名称：describe-image。

  4. 在区域列表中，选择 us-central1（爱荷华）。

  5. 对于服务账号，选择您之前使用的服务账号 创建。

  6. 点击下一步。

  7. 在工作流编辑器中，输入工作流的定义：

    main:
    params: [args]
    steps:
    - init:
        assign:
            - project: ${sys.get_env("GOOGLE_CLOUD_PROJECT_ID")}
            - location: "us-central1"
            - model: "gemini-1.0-pro-vision"
            - text_combined: ""
    - ask_llm:
        call: googleapis.aiplatform.v1.projects.locations.endpoints.generateContent
        args:
            model: ${"projects/" + project + "/locations/" + location + "/publishers/google/models/" + model}
            region: ${location}
            body:
                contents:
                    role: user
                    parts:
                    - fileData:
                        mimeType: image/jpeg
                        fileUri: ${args.image_url}
                    - text: Describe this picture in detail
                generation_config:
                    temperature: 0.4
                    max_output_tokens: 2048
                    top_p: 1
                    top_k: 32
        result: llm_response
    - return_result:
        return:
            image_url: ${args.image_url}
            image_description: ${llm_response.candidates[0].content.parts[0].text}

  8. 点击部署。

gcloud

  1. 为您的工作流创建源代码文件：

    touch describe-image.yaml

  2. 在文本编辑器中，将以下工作流复制到源代码文件中：

    main:
    params: [args]
    steps:
    - init:
        assign:
            - project: ${sys.get_env("GOOGLE_CLOUD_PROJECT_ID")}
            - location: "us-central1"
            - model: "gemini-1.0-pro-vision"
            - text_combined: ""
    - ask_llm:
        call: googleapis.aiplatform.v1.projects.locations.endpoints.generateContent
        args:
            model: ${"projects/" + project + "/locations/" + location + "/publishers/google/models/" + model}
            region: ${location}
            body:
                contents:
                    role: user
                    parts:
                    - fileData:
                        mimeType: image/jpeg
                        fileUri: ${args.image_url}
                    - text: Describe this picture in detail
                generation_config:
                    temperature: 0.4
                    max_output_tokens: 2048
                    top_p: 1
                    top_k: 32
        result: llm_response
    - return_result:
        return:
            image_url: ${args.image_url}
            image_description: ${llm_response.candidates[0].content.parts[0].text}

  3. 输入以下命令以部署工作流：

    gcloud workflows deploy describe-image \
    --source=describe-image.yaml \
    --location=us-central1 \
    --service-account=SERVICE_ACCOUNT_NAME@PROJECT_ID.iam.gserviceaccount.com

执行工作流

执行某个工作流会运行与该工作流关联的当前工作流定义。

控制台

  1. 在 Google Cloud 控制台中，前往工作流页面。

    进入 Workflows

  2. 在 Workflows 页面上，选择 describe-image 工作流以转到其详情页面。

  3. 在工作流详情页面上，点击 执行。

  4. 对于输入，请输入以下内容：

    {"image_url":"gs://generativeai-downloads/images/scones.jpg"}

  5. 再次点击执行。

  6. 在输出窗格中查看工作流的结果。

    输出应类似如下所示：

    {
  "image_description": "There are three pink peony flowers on the right side of the picture[]...]There is a white napkin on the table.",
  "image_url": "gs://generativeai-downloads/images/scones.jpg"
}

gcloud

  1. 打开终端。

  2. 执行工作流：

    gcloud workflows run describe-image \
    --data='{"image_url":"gs://generativeai-downloads/images/scones.jpg"}'

    执行结果应类似如下所示：

      Waiting for execution [258b530e-a093-46d7-a4ff-cbf5392273c0] to complete...done.
  argument: '{"image_url":"gs://generativeai-downloads/images/scones.jpg"}'
  createTime: '2024-02-09T13:59:32.166409938Z'
  duration: 4.174708484s
  endTime: '2024-02-09T13:59:36.341118422Z'
  name: projects/1051295516635/locations/us-central1/workflows/describe-image/executions/258b530e-a093-46d7-a4ff-cbf5392273c0
  result: "{\"image_description\":\"The picture shows a rustic table with a white surface,\
    \ on which there are several scones with blueberries, as well as two cups of coffee\
    [...]
    \ on the table. The background of the table is a dark blue color.\",\"image_url\"\
    :\"gs://generativeai-downloads/images/scones.jpg\"}"
  startTime: '2024-02-09T13:59:32.166409938Z'
  state: SUCCEEDED

部署可生成国家/地区历史记录的工作流 (Gemini Pro)

部署一个工作流，以并行方式循环遍历国家/地区输入列表，并使用连接器方法 (generateContent) 向 Gemini Pro 发布商端点发出请求。该方法支持使用多模态输入生成内容。

该工作流会返回模型生成的国家/地区历史记录， 以地图形式呈现它们。

如需详细了解在发送提示时使用的 HTTP 请求正文参数 LLM 和响应正文元素，请参阅 Gemini API 参考文档。

控制台

  1. 在 Google Cloud 控制台中，前往工作流页面。

    进入 Workflows

  2. 点击 创建。

  3. 输入新工作流的名称：gemini-pro-country-histories。

  4. 在区域列表中，选择 us-central1（爱荷华）。

  5. 对于服务账号，选择您之前使用的服务账号 创建。

  6. 点击下一步。

  7. 在工作流编辑器中，输入工作流的定义：

    main:
    params: [args]
    steps:
    - init:
        assign:
            - project: ${sys.get_env("GOOGLE_CLOUD_PROJECT_ID")}
            - location: "us-central1"
            - model: "gemini-1.0-pro"
            - histories: {}
    - loop_over_countries:
        parallel:
            shared: [histories]
            for:
                value: country
                in: ${args.countries}
                steps:
                    - ask_llm:
                        call: googleapis.aiplatform.v1.projects.locations.endpoints.generateContent
                        args:
                            model: ${"projects/" + project + "/locations/" + location + "/publishers/google/models/" + model}
                            region: ${location}
                            body:
                                contents:
                                    role: "USER"
                                    parts:
                                        text: ${"Can you tell me about the history of " + country}
                                generation_config:
                                    temperature: 0.5
                                    max_output_tokens: 2048
                                    top_p: 0.8
                                    top_k: 40
                        result: llm_response
                    - add_to_histories:
                        assign:
                            - histories[country]: ${llm_response.candidates[0].content.parts[0].text}
    - return_result:
        return: ${histories}

  8. 点击部署。

gcloud

  1. 为您的工作流创建源代码文件：

    touch gemini-pro-country-histories.yaml

  2. 在文本编辑器中，将以下工作流复制到源代码文件中：

    main:
    params: [args]
    steps:
    - init:
        assign:
            - project: ${sys.get_env("GOOGLE_CLOUD_PROJECT_ID")}
            - location: "us-central1"
            - model: "gemini-1.0-pro"
            - histories: {}
    - loop_over_countries:
        parallel:
            shared: [histories]
            for:
                value: country
                in: ${args.countries}
                steps:
                    - ask_llm:
                        call: googleapis.aiplatform.v1.projects.locations.endpoints.generateContent
                        args:
                            model: ${"projects/" + project + "/locations/" + location + "/publishers/google/models/" + model}
                            region: ${location}
                            body:
                                contents:
                                    role: "USER"
                                    parts:
                                        text: ${"Can you tell me about the history of " + country}
                                generation_config:
                                    temperature: 0.5
                                    max_output_tokens: 2048
                                    top_p: 0.8
                                    top_k: 40
                        result: llm_response
                    - add_to_histories:
                        assign:
                            - histories[country]: ${llm_response.candidates[0].content.parts[0].text}
    - return_result:
        return: ${histories}

  3. 输入以下命令以部署工作流：

    gcloud workflows deploy gemini-pro-country-histories \
    --source=gemini-pro-country-histories.yaml \
    --location=us-central1 \
    --service-account=SERVICE_ACCOUNT_NAME@PROJECT_ID.iam.gserviceaccount.com

执行工作流

执行某个工作流会运行与该工作流关联的当前工作流定义。

控制台

  1. 在 Google Cloud 控制台中，前往工作流页面。

    进入 Workflows

  2. 在 Workflows 页面上，选择 gemini-pro-country-histories 工作流以转到其详情页面。

  3. 在工作流详情页面上，选择 执行。

  4. 对于输入，请输入以下内容：

    {"countries":["Argentina", "Bhutan", "Cyprus", "Denmark", "Ethiopia"]}

  5. 再次点击执行。

  6. 在输出窗格中查看工作流的结果。

    输出应类似如下所示：

    {
  "Argentina": "The history of Argentina is a complex and fascinating one, marked by periods of prosperity and decline, political [...]
  "Bhutan": "The history of Bhutan is a rich and fascinating one, dating back to the 7th century AD. Here is a brief overview: [...]
  "Cyprus": "The history of Cyprus is a long and complex one, spanning over 10,000 years. The island has been ruled by a succession [...]
  "Denmark": "1. **Prehistory and Early History (c. 12,000 BC - 800 AD)**\\n   - The earliest evidence of human habitation in Denmark [...]
  "Ethiopia": "The history of Ethiopia is a long and complex one, stretching back to the earliest human civilizations. The country is [...]
}

gcloud

  1. 打开终端。

  2. 执行工作流：

    gcloud workflows run gemini-pro-country-histories \
    --data='{"countries":["Argentina", "Bhutan", "Cyprus", "Denmark", "Ethiopia"]}' \
    --location=us-central1

    执行结果应类似如下所示：

      Waiting for execution [7ae1ccf1-29b7-4c2c-99ec-7a12ae289391] to complete...done.
  argument: '{"countries":["Argentina","Bhutan","Cyprus","Denmark","Ethiopia"]}'
  createTime: '2024-02-09T16:25:16.742349156Z'
  duration: 12.075968673s
  endTime: '2024-02-09T16:25:28.818317829Z'
  name: projects/1051295516635/locations/us-central1/workflows/gemini-pro-country-histories/executions/7ae1ccf1-29b7-4c2c-99ec-7a12ae289391
  result: "{\"Argentina\":\"The history of Argentina can be traced back to the arrival\
    [...]
    n* 2015: Argentina elects Mauricio Macri as president.\",\"Bhutan\":\"The history\
    [...]
    \ natural beauty, ancient monasteries, and friendly people.\",\"Cyprus\":\"The history\
    [...]
    ,\"Denmark\":\"The history of Denmark can be traced back to the Stone Age, with\
    [...]
    \ a high standard of living.\",\"Ethiopia\":\"The history of Ethiopia is long and\
    [...]
  startTime: '2024-02-09T16:25:16.742349156Z'
  state: SUCCEEDED

部署用于生成国家/地区历史记录的工作流 (PaLM 2 for Text)

您可能不想将 Gemini Pro 用作模型。以下示例使用与上一个示例类似的工作流；不过，它使用连接器方法 (predict) 向 PaLM 2 发出文本发布商端点请求。该方法会执行在线预测。

如需详细了解在发送提示时使用的 HTTP 请求正文参数 LLM 和响应正文元素，请参阅 PaLM 2 for Text API 参考文档。

控制台

  1. 在 Google Cloud 控制台中，前往工作流页面。

    进入 Workflows

  2. 点击 创建。

  3. 输入新工作流的名称：text-bison-country-histories。

  4. 在区域列表中，选择 us-central1（爱荷华）。

  5. 对于服务账号，选择您之前使用的服务账号 创建。

  6. 点击下一步。

  7. 在工作流编辑器中，为工作流输入以下定义：

    main:
    params: [args]
    steps:
    - init:
        assign:
            - project: ${sys.get_env("GOOGLE_CLOUD_PROJECT_ID")}
            - location: "us-central1"
            - model: "text-bison"
            - histories: {}
    - loop_over_countries:
        parallel:
            shared: [histories]
            for:
                value: country
                in: ${args.countries}
                steps:
                    - ask_llm:
                        call: googleapis.aiplatform.v1.projects.locations.endpoints.predict
                        args:
                            endpoint: ${"projects/" + project + "/locations/" + location + "/publishers/google/models/" + model }
                            region: ${location}
                            body:
                                instances:
                                    - prompt: '${"Can you  tell me about the history of " + country}'
                                parameters:
                                    temperature: 0.5
                                    maxOutputTokens: 2048
                                    topP: 0.8
                                    topK: 40
                        result: llm_response
                    - add_to_histories:
                        assign:
                            - history: ${llm_response.predictions[0].content}
                            # Remove leading whitespace from start of text
                            - history: ${text.substring(history, 1, len(history))}
                            - histories[country]: ${history}
    - return_result:
        return: ${histories}

    请注意，根据所使用的模型，您可能需要从响应中移除所有不必要的空格。

  8. 点击部署。

gcloud

  1. 为您的工作流创建源代码文件：

    touch text-bison-country-histories.yaml

  2. 在文本编辑器中，将以下工作流复制到源代码文件中：

    main:
    params: [args]
    steps:
    - init:
        assign:
            - project: ${sys.get_env("GOOGLE_CLOUD_PROJECT_ID")}
            - location: "us-central1"
            - model: "text-bison"
            - histories: {}
    - loop_over_countries:
        parallel:
            shared: [histories]
            for:
                value: country
                in: ${args.countries}
                steps:
                    - ask_llm:
                        call: googleapis.aiplatform.v1.projects.locations.endpoints.predict
                        args:
                            endpoint: ${"projects/" + project + "/locations/" + location + "/publishers/google/models/" + model }
                            region: ${location}
                            body:
                                instances:
                                    - prompt: '${"Can you  tell me about the history of " + country}'
                                parameters:
                                    temperature: 0.5
                                    maxOutputTokens: 2048
                                    topP: 0.8
                                    topK: 40
                        result: llm_response
                    - add_to_histories:
                        assign:
                            - history: ${llm_response.predictions[0].content}
                            # Remove leading whitespace from start of text
                            - history: ${text.substring(history, 1, len(history))}
                            - histories[country]: ${history}
    - return_result:
        return: ${histories}

    请注意，根据所使用的模型，您可能需要从响应中移除所有不必要的空格。

  3. 输入以下命令以部署工作流：

    gcloud workflows deploy text-bison-country-histories \
    --source=text-bison-country-histories.yaml \
    --location=us-central1 \
    --service-account=SERVICE_ACCOUNT_NAME@PROJECT_ID.iam.gserviceaccount.com

部署汇总大型文档的工作流 (Gemini Pro)

部署一个工作流，将大型文档划分为较小的部分，并并行向 Gemini Pro 发布商端点发出 http.post 请求，以便模型可以同时对每个部分进行总结。工作流终于 将所有部分摘要合并成一个完整的摘要。

如需详细了解提示 LLM 时使用的 HTTP 请求正文参数和响应正文元素，请参阅 Gemini API 参考文档。

工作流定义假定您已创建 Cloud Storage 您可以向其上传文本文件更多信息 Workflows 连接器简介 (googleapis.storage.v1.objects.get) 用于从 Cloud Storage 存储桶检索对象，请参阅 连接器参考信息。

部署工作流后，您可以通过创建适当的 Eventarc 触发器，然后将文件上传到存储桶。对于 请参阅 将 Cloud Storage 事件路由到 Workflows。 请注意，必须启用其他 API，还必须启用其他角色 包括向服务账号授予 Storage Object User 角色 (roles/storage.objectUser) 角色，支持使用 Cloud Storage 对象的操作。有关详情，请参阅 准备创建触发器部分。

控制台

  1. 在 Google Cloud 控制台中，前往工作流页面。

    进入 Workflows

  2. 点击 创建。

  3. 输入新工作流的名称：gemini-pro-summaries。

  4. 在区域列表中，选择 us-central1（爱荷华）。

  5. 对于服务账号，选择您之前使用的服务账号 创建。

  6. 点击下一步。

  7. 在工作流编辑器中，输入工作流的定义：

    main:
    params: [input]
    steps:
    - assign_file_vars:
        assign:
            - file_size: ${int(input.data.size)}
            - chunk_size: 64000
            - n_chunks: ${int(file_size / chunk_size)}
            - summaries: []
            - all_summaries_concatenated: ""
    - loop_over_chunks:
        parallel:
            shared: [summaries]
            for:
                value: chunk_idx
                range: ${[0, n_chunks]}
                steps:
                    - assign_bounds:
                        assign:
                            - lower_bound: ${chunk_idx * chunk_size}
                            - upper_bound: ${(chunk_idx + 1) * chunk_size}
                            - summaries: ${list.concat(summaries, "")}
                    - dump_file_content:
                        call: http.get
                        args:
                            url: ${"https://storage.googleapis.com/storage/v1/b/" + input.data.bucket + "/o/" + input.data.name + "?alt=media"}
                            auth:
                                type: OAuth2
                            headers:
                                Range: ${"bytes=" + lower_bound + "-" + upper_bound}
                        result: file_content
                    - assign_chunk:
                        assign:
                            - chunk: ${file_content.body}
                    - generate_chunk_summary:
                        call: ask_gemini_for_summary
                        args:
                            textToSummarize: ${chunk}
                        result: summary
                    - assign_summary:
                        assign:
                            - summaries[chunk_idx]: ${summary}
    - concat_summaries:
        for:
            value: summary
            in: ${summaries}
            steps:
                - append_summaries:
                    assign:
                        - all_summaries_concatenated: ${all_summaries_concatenated + "\n" + summary}
    - reduce_summary:
        call: ask_gemini_for_summary
        args:
            textToSummarize: ${all_summaries_concatenated}
        result: final_summary
    - return_result:
        return:
            - summaries: ${summaries}
            - final_summary: ${final_summary}

ask_gemini_for_summary:
    params: [textToSummarize]
    steps:
        - init:
            assign:
                - project: ${sys.get_env("GOOGLE_CLOUD_PROJECT_ID")}
                - location: "us-central1"
                - model: "gemini-pro"
                - summary: ""
        - call_gemini:
            call: http.post
            args:
                url: ${"https://" + location + "-aiplatform.googleapis.com" + "/v1/projects/" + project + "/locations/" + location + "/publishers/google/models/" + model + ":generateContent"}
                auth:
                    type: OAuth2
                body:
                    contents:
                        role: user
                        parts:
                            - text: '${"Make a summary of the following text:\n\n" + textToSummarize}'
                    generation_config:
                        temperature: 0.2
                        maxOutputTokens: 2000
                        topK: 10
                        topP: 0.9
            result: gemini_response
        # Sometimes, there's no text, for example, due to safety settings
        - check_text_exists:
            switch:
            - condition: ${not("parts" in gemini_response.body.candidates[0].content)}
              next: return_summary
        - extract_text:
            assign:
                - summary: ${gemini_response.body.candidates[0].content.parts[0].text}
        - return_summary:
            return: ${summary}

  8. 点击部署。

gcloud

  1. 为您的工作流创建源代码文件：

    touch gemini-pro-summaries.yaml

  2. 在文本编辑器中，将以下工作流复制到源代码文件中：

    main:
    params: [input]
    steps:
    - assign_file_vars:
        assign:
            - file_size: ${int(input.data.size)}
            - chunk_size: 64000
            - n_chunks: ${int(file_size / chunk_size)}
            - summaries: []
            - all_summaries_concatenated: ""
    - loop_over_chunks:
        parallel:
            shared: [summaries]
            for:
                value: chunk_idx
                range: ${[0, n_chunks]}
                steps:
                    - assign_bounds:
                        assign:
                            - lower_bound: ${chunk_idx * chunk_size}
                            - upper_bound: ${(chunk_idx + 1) * chunk_size}
                            - summaries: ${list.concat(summaries, "")}
                    - dump_file_content:
                        call: http.get
                        args:
                            url: ${"https://storage.googleapis.com/storage/v1/b/" + input.data.bucket + "/o/" + input.data.name + "?alt=media"}
                            auth:
                                type: OAuth2
                            headers:
                                Range: ${"bytes=" + lower_bound + "-" + upper_bound}
                        result: file_content
                    - assign_chunk:
                        assign:
                            - chunk: ${file_content.body}
                    - generate_chunk_summary:
                        call: ask_gemini_for_summary
                        args:
                            textToSummarize: ${chunk}
                        result: summary
                    - assign_summary:
                        assign:
                            - summaries[chunk_idx]: ${summary}
    - concat_summaries:
        for:
            value: summary
            in: ${summaries}
            steps:
                - append_summaries:
                    assign:
                        - all_summaries_concatenated: ${all_summaries_concatenated + "\n" + summary}
    - reduce_summary:
        call: ask_gemini_for_summary
        args:
            textToSummarize: ${all_summaries_concatenated}
        result: final_summary
    - return_result:
        return:
            - summaries: ${summaries}
            - final_summary: ${final_summary}

ask_gemini_for_summary:
    params: [textToSummarize]
    steps:
        - init:
            assign:
                - project: ${sys.get_env("GOOGLE_CLOUD_PROJECT_ID")}
                - location: "us-central1"
                - model: "gemini-pro"
                - summary: ""
        - call_gemini:
            call: http.post
            args:
                url: ${"https://" + location + "-aiplatform.googleapis.com" + "/v1/projects/" + project + "/locations/" + location + "/publishers/google/models/" + model + ":generateContent"}
                auth:
                    type: OAuth2
                body:
                    contents:
                        role: user
                        parts:
                            - text: '${"Make a summary of the following text:\n\n" + textToSummarize}'
                    generation_config:
                        temperature: 0.2
                        maxOutputTokens: 2000
                        topK: 10
                        topP: 0.9
            result: gemini_response
        # Sometimes, there's no text, for example, due to safety settings
        - check_text_exists:
            switch:
            - condition: ${not("parts" in gemini_response.body.candidates[0].content)}
              next: return_summary
        - extract_text:
            assign:
                - summary: ${gemini_response.body.candidates[0].content.parts[0].text}
        - return_summary:
            return: ${summary}

  3. 输入以下命令以部署工作流：

    gcloud workflows deploy gemini-pro-summaries \
    --source=gemini-pro-summaries.yaml \
    --location=us-central1 \
    --service-account=SERVICE_ACCOUNT_NAME@PROJECT_ID.iam.gserviceaccount.com

清理

为避免因本教程中使用的资源导致您的 Google Cloud 账号产生费用，请删除包含这些资源的项目，或者保留项目但删除各个资源。

删除项目

控制台

  1. In the Google Cloud console, go to the Manage resources page.

    Go to Manage resources

  2. In the project list, select the project that you want to delete, and then click Delete.
  3. In the dialog, type the project ID, and then click Shut down to delete the project.

gcloud

Delete a Google Cloud project:

gcloud projects delete PROJECT_ID

删除各个资源

删除工作流 创建 Deployment 清单

后续步骤