文章建议

Agent Assist 文章建议功能会跟踪人工客服与最终用户之间的对话,并会为人工客服提供相关文档建议。人工客服可在对话进行过程中检查这些建议,并决定要阅读或与最终用户分享的文档。在人工客服与最终用户对话过程中,您可以使用文章建议来帮助人工客服了解和解决最终用户问题。

Agent Assist 提供了基准文章建议模型,可供您向客服人员建议文章。或者,您可以使用自己上传的对话数据训练自定义模型以提高性能。如果您想要训练自定义建议模型来用于文章建议,请与您的 Google 代表联系。

本文档将引导您完成使用 API 实现文章建议并在运行时通过此功能中获取建议的过程。您可以选择在设计时使用 Agent Assist 控制台来测试您的文章建议结果,但在运行时必须直接调用 API。如需详细了解如何使用 Agent Assist 控制台测试功能性能,请参阅教程部分

准备工作

在开始本指南之前,请先完成以下操作:

  1. 为您的 GCP 项目启用 Dialogflow API
  2. 为您的项目启用 Data Labeling API

配置对话配置文件

如需获取 Agent Assist 的建议,您必须创建一个包含您已上传的文档的知识库,并配置对话配置文件。如果您不希望直接调用 API,还可以使用 Agent Assist 控制台来执行这些操作。

创建知识库

在开始上传文档之前,您必须先创建一个知识库来放入文档。如需创建知识库,请对 KnowledgeBase 类型调用 create 方法。

REST 和命令行

在使用下面的请求数据之前,请先进行以下替换:

  • PROJECT_ID:您的 GCP 项目 ID
  • KNOWLEDGE_BASE_DISPLAY_NAME:所需的知识库名称

HTTP 方法和网址:

POST https://dialogflow.googleapis.com/v2/projects/PROJECT_ID/knowledgeBases

请求 JSON 正文:

{
  "displayName": "KNOWLEDGE_BASE_DISPLAY_NAME"
}

如需发送您的请求,请展开以下选项之一:

您应该收到类似以下内容的 JSON 响应:

{
  "name": "projects/PROJECT_ID/knowledgeBases/NDA4MTM4NzE2MjMwNDUxMjAwMA",
  "displayName": "KNOWLEDGE_BASE_DISPLAY_NAME"
}

knowledgeBases 后面的路径段包含新知识库 ID。

Python

def create_knowledge_base(project_id, display_name):
    """Creates a Knowledge base.

    Args:
        project_id: The GCP project linked with the agent.
        display_name: The display name of the Knowledge base."""
    from google.cloud import dialogflow_v2beta1 as dialogflow
    client = dialogflow.KnowledgeBasesClient()
    project_path = client.common_project_path(project_id)

    knowledge_base = dialogflow.KnowledgeBase(
        display_name=display_name)

    response = client.create_knowledge_base(
        parent=project_path,
        knowledge_base=knowledge_base
    )

    print('Knowledge Base created:\n')
    print('Display Name: {}\n'.format(response.display_name))
    print('Knowledge ID: {}\n'.format(response.name))

创建知识文档

您现在可以将文档添加到知识库。如需在知识库中创建文档,请Document 类型调用 create 方法。将 KnowledgeType 设置为 ARTICLE_SUGGESTION。此示例使用已上传到公开共享的 Cloud Storage 存储分区的包含退货订单信息的 HTML 文件。在您自己的系统中设置文章建议时,文档必须采用以下格式之一。如需详细了解文档最佳做法,请参阅“知识文档”文档

知识文档格式包括:

  • 存储在 GCS 存储分区中的文件。您可以在调用 API 时指定路径。
  • 文档中的文本内容,可以通过 API 请求发送。
  • 公开网址。

REST 和命令行

在使用下面的请求数据之前,请先进行以下替换:

  • PROJECT_ID:您的 GCP 项目 ID
  • KNOWLEDGE_BASE_ID:上一项请求返回的您的知识库 ID
  • DOCUMENT_DISPLAY_NAME:所需的知识文档名称

HTTP 方法和网址:

POST https://dialogflow.googleapis.com/v2/projects/PROJECT_ID/knowledgeBases/KNOWLEDGE_BASE_ID/documents

请求 JSON 正文:

{
  "displayName": "DOCUMENT_DISPLAY_NAME",
  "mimeType": "text/html",
  "knowledgeTypes": "ARTICLE_SUGGESTION",
  "contentUri": "gs://agent-assist-public-examples/public_article_suggestion_example_returns.html"
}

如需发送您的请求,请展开以下选项之一:

您应该收到类似以下内容的 JSON 响应:

{
  "name": "projects/PROJECT_ID/operations/ks-add_document-MzA5NTY2MTc5Mzg2Mzc5NDY4OA"
}

该响应是一个长时间运行的操作,您可以轮询该操作以检查完成情况。

Python

def create_document(project_id, knowledge_base_id, display_name, mime_type,
                    knowledge_type, content_uri):
    """Creates a Document.

    Args:
        project_id: The GCP project linked with the agent.
        knowledge_base_id: Id of the Knowledge base.
        display_name: The display name of the Document.
        mime_type: The mime_type of the Document. e.g. text/csv, text/html,
            text/plain, text/pdf etc.
        knowledge_type: The Knowledge type of the Document. e.g. FAQ,
            EXTRACTIVE_QA.
        content_uri: Uri of the document, e.g. gs://path/mydoc.csv,
            http://mypage.com/faq.html."""
    from google.cloud import dialogflow_v2beta1 as dialogflow
    client = dialogflow.DocumentsClient()
    knowledge_base_path = dialogflow.KnowledgeBasesClient.knowledge_base_path(
        project_id, knowledge_base_id)

    document = dialogflow.Document(
        display_name=display_name, mime_type=mime_type,
        content_uri=content_uri)

    document.knowledge_types.append(
        getattr(dialogflow.Document.KnowledgeType, knowledge_type)
    )

    response = client.create_document(parent=knowledge_base_path, document=document)
    print('Waiting for results...')
    document = response.result(timeout=120)
    print('Created Document:')
    print(' - Display Name: {}'.format(document.display_name))
    print(' - Knowledge ID: {}'.format(document.name))
    print(' - MIME Type: {}'.format(document.mime_type))
    print(' - Knowledge Types:')
    for knowledge_type in document.knowledge_types:
        print('    - {}'.format(KNOWLEDGE_TYPES[knowledge_type]))
    print(' - Source: {}\n'.format(document.content_uri))

创建对话配置文件

对话配置文件可配置一组参数,用于控制对话期间向客服人员提出的建议。以下步骤将使用 HumanAgentAssistantConfig 对象创建 ConversationProfile。如果您不希望直接调用 API,还可以使用 Agent Assist 控制台来执行这些操作。

我们建议您将初始置信度阈值设置为 0.1。如有必要,您可以增大阈值超出建议的范围。增大阈值会提高准确率并降低覆盖率(建议的数量更少);减小阈值会降低准确率并提高覆盖率(建议的数量更多)。

REST 和命令行

如需创建对话配置文件,请对 ConversationProfile 资源调用 create 方法。

noSmallTalk:如果为 true,则在简短对话消息(例如“您好”、“您好吗”等)后不会触发建议。如果为 false,则在简短对话消息后会触发建议。

onlyEndUser:如果为 true,则只有在最终用户消息后才会触发建议。如果为 false,则在最终用户和人工客服消息后都会触发建议。

在使用下面的请求数据之前,请先进行以下替换:

  • PROJECT_ID:您的 GCP 项目 ID
  • KNOWLEDGE_BASE_ID:您的知识库 ID

HTTP 方法和网址:

POST https://dialogflow.googleapis.com/v2/projects/PROJECT_ID/conversationProfiles

请求 JSON 正文:

{
  "name": "projects/PROJECT_ID/conversationProfiles/CONVERSATION_PROFILE_ID",
  "displayName": "my-conversation-profile-display-name",
  "humanAgentAssistantConfig": {
    "notificationConfig": {},
    "humanAgentSuggestionConfig": {
      "featureConfigs": [
        {
          "enableInlineSuggestion": true,
        "SuggestionTriggerSettings": {
             "noSmallTalk": true,
             "onlyEndUser": true,
           }
          "suggestionFeature": {
            "type": "ARTICLE_SUGGESTION"
          },
          "queryConfig": {
            "knowledgeBaseQuerySource": {
              "knowledgeBases": [
                "projects/PROJECT_ID/knowledgeBases/KNOWLEDGE_BASE_ID"
              ]
            }
          }
        }
      ]
    }
  },
  "sttConfig": {},
  "languageCode": "en-US"
}

如需发送您的请求,请展开以下选项之一:

您应该收到类似以下内容的 JSON 响应:

{
  "name": "projects/PROJECT_ID/conversationProfiles/CONVERSATION_PROFILE_ID",
  "displayName": "my-conversation-profile-display-name",
  "humanAgentAssistantConfig": {
    ...
  }
}

conversationProfiles 后面的路径段包含新对话配置文件 ID。

Python

def create_conversation_profile_article_faq(
        project_id,
        display_name,
        article_suggestion_knowledge_base_id=None,
        faq_knowledge_base_id=None):
    """Creates a conversation profile with given values

    Args: project_id:  The GCP project linked with the conversation profile.
        display_name: The display name for the conversation profile to be
        created.
        article_suggestion_knowledge_base_id: knowledge base id for article
        suggestion.
        faq_knowledge_base_id: knowledge base id for faq."""

    client = dialogflow.ConversationProfilesClient()
    project_path = client.common_project_path(project_id)

    conversation_profile = {
        'display_name': display_name,
        'human_agent_assistant_config': {
            'human_agent_suggestion_config': {
                'feature_configs': []
            }
        },
        'language_code': 'en-US'
    }

    if article_suggestion_knowledge_base_id is not None:
        as_kb_path = dialogflow.KnowledgeBasesClient.knowledge_base_path(
            project_id, article_suggestion_knowledge_base_id)
        feature_config = {
            'suggestion_feature': {
                'type_': 'ARTICLE_SUGGESTION'
            },
            'suggestion_trigger_settings': {
                'no_small_talk': True,
                'only_end_user': True,
            },
            'query_config': {
                'knowledge_base_query_source': {
                    'knowledge_bases': [as_kb_path]
                },
                'max_results': 3
            },
        }
        conversation_profile['human_agent_assistant_config'][
            'human_agent_suggestion_config']['feature_configs'].append(
                feature_config)
    if faq_knowledge_base_id is not None:
        faq_kb_path = dialogflow.KnowledgeBasesClient.knowledge_base_path(
            project_id, faq_knowledge_base_id)
        feature_config = {
            'suggestion_feature': {
                'type_': 'FAQ'
            },
            'suggestion_trigger_settings': {
                'no_small_talk': True,
                'only_end_user': True,
            },
            'query_config': {
                'knowledge_base_query_source': {
                    'knowledge_bases': [faq_kb_path]
                },
                'max_results': 3
            },
        }
        conversation_profile['human_agent_assistant_config'][
            'human_agent_suggestion_config']['feature_configs'].append(
                feature_config)

    response = client.create_conversation_profile(
        parent=project_path, conversation_profile=conversation_profile)

    print('Conversation Profile created:')
    print('Display Name: {}'.format(response.display_name))
    # Put Name is the last to make it easier to retrieve.
    print('Name: {}'.format(response.name))
    return response

在运行时处理对话

创建对话

当最终用户与人工或虚拟客服之间开始对话时,您需要创建对话如需查看建议,您还必须创建最终用户参与者和人工客服参与者,并将其添加到对话中。以下部分将引导您完成此过程。

首先,您必须创建一个对话:

REST 和命令行

如需创建对话,请对 Conversation 资源调用 create 方法。

在使用下面的请求数据之前,请先进行以下替换:

  • PROJECT_ID:您的 GCP 项目 ID
  • CONVERSATION_PROFILE_ID:您在创建对话配置文件时收到的 ID

HTTP 方法和网址:

POST https://dialogflow.googleapis.com/v2/projects/PROJECT_ID/conversations

请求 JSON 正文:

{
  "conversationProfile": "projects/PROJECT_ID/conversationProfiles/CONVERSATION_PROFILE_ID",
}

如需发送您的请求,请展开以下选项之一:

您应该收到类似以下内容的 JSON 响应:

{
  "name": "projects/PROJECT_ID/conversations/CONVERSATION_ID",
  "lifecycleState": "IN_PROGRESS",
  "conversationProfile": "projects/PROJECT_ID/conversationProfiles/CONVERSATION_PROFILE_ID",
  "startTime": "2018-11-05T21:05:45.622Z"
}

conversations 后面的路径段包含新对话 ID。

Python

def create_conversation(project_id, conversation_profile_id):
    """Creates a conversation with given values

    Args:
        project_id:  The GCP project linked with the conversation.
        conversation_profile_id: The conversation profile id used to create
        conversation."""

    client = dialogflow.ConversationsClient()
    conversation_profile_client = dialogflow.ConversationProfilesClient()
    project_path = client.common_project_path(project_id)
    conversation_profile_path = (
        conversation_profile_client.conversation_profile_path(
            project_id, conversation_profile_id))
    conversation = {'conversation_profile': conversation_profile_path}
    response = client.create_conversation(parent=project_path,
                                          conversation=conversation)

    print('Life Cycle State: {}'.format(response.lifecycle_state))
    print('Conversation Profile Name: {}'.format(
        response.conversation_profile))
    print('Name: {}'.format(response.name))
    return response

创建最终用户参与者

您必须将最终用户参与者和人工客服参与者添加到对话中才能查看建议。首先,将最终用户参与者添加到对话中:

REST 和命令行

如需创建最终用户参与者,请对 Participant 资源调用 create 方法。

在使用下面的请求数据之前,请先进行以下替换:

  • PROJECT_ID:您的 GCP 项目 ID
  • CONVERSATION_ID:您的对话 ID

HTTP 方法和网址:

POST https://dialogflow.googleapis.com/v2/projects/PROJECT_ID/conversations/CONVERSATION_ID/participants

请求 JSON 正文:

{
  "role": "END_USER",
}

如需发送您的请求,请展开以下选项之一:

您应该收到类似以下内容的 JSON 响应:

{
  "name": "projects/PROJECT_ID/conversations/CONVERSATION_ID/participants/PARTICIPANT_ID",
  "role": "END_USER"
}

participants 后面的路径段包含新的最终用户参与者 ID。

Python

def create_participant(project_id, conversation_id, role):
    """Creates a participant in a given conversation.

    Args:
        project_id: The GCP project linked with the conversation profile.
        conversation_id: Id of the conversation.
        participant: participant to be created."""

    client = dialogflow.ParticipantsClient()
    conversation_path = dialogflow.ConversationsClient.conversation_path(
        project_id, conversation_id)
    if role in ROLES:
        response = client.create_participant(parent=conversation_path,
                                             participant={'role': role})
        print('Participant Created.')
        print('Role: {}'.format(response.role))
        print('Name: {}'.format(response.name))

        return response

创建人工客服参与者

将人工客服参与者添加到对话中:

REST 和命令行

如需创建人工客服参与者,请对 Participant 资源调用 create 方法。

在使用下面的请求数据之前,请先进行以下替换:

  • PROJECT_ID:您的 GCP 项目 ID
  • CONVERSATION_ID:您的对话 ID

HTTP 方法和网址:

POST https://dialogflow.googleapis.com/v2/projects/PROJECT_ID/conversations/CONVERSATION_ID/participants

请求 JSON 正文:

{
  "role": "HUMAN_AGENT",
}

如需发送您的请求,请展开以下选项之一:

您应该收到类似以下内容的 JSON 响应:

{
  "name": "projects/PROJECT_ID/conversations/CONVERSATION_ID/participants/PARTICIPANT_ID",
  "role": "HUMAN_AGENT"
}

participants 后面的路径段包含新的人工客服参与者 ID。

Python

def create_participant(project_id, conversation_id, role):
    """Creates a participant in a given conversation.

    Args:
        project_id: The GCP project linked with the conversation profile.
        conversation_id: Id of the conversation.
        participant: participant to be created."""

    client = dialogflow.ParticipantsClient()
    conversation_path = dialogflow.ConversationsClient.conversation_path(
        project_id, conversation_id)
    if role in ROLES:
        response = client.create_participant(parent=conversation_path,
                                             participant={'role': role})
        print('Participant Created.')
        print('Role: {}'.format(response.role))
        print('Name: {}'.format(response.name))

        return response

添加和分析人工客服的消息

每次任一参与者在对话中输入消息时,您都需要向 API 发送该消息以进行处理。Agent Assist 的建议基于对人工客服和最终用户消息的分析。在以下示例中,人工客服通过询问“您需要什么帮助?”来开始对话。响应中尚未返回任何建议。

REST 和命令行

如需在对话中添加和分析人工客服消息,请对 Participant 资源调用 analyzeContent 方法。

在使用下面的请求数据之前,请先进行以下替换:

  • PROJECT_ID:您的 GCP 项目 ID
  • CONVERSATION_ID:您的对话 ID
  • PARTICIPANT_ID:您的人工客服参与者 ID

HTTP 方法和网址:

POST https://dialogflow.googleapis.com/v2/projects/PROJECT_ID/conversations/CONVERSATION_ID/participants/PARTICIPANT_ID:analyzeContent

请求 JSON 正文:

{
  "textInput": {
    "text": "How may I help you?",
    "languageCode": "en-US"
  }
}

如需发送您的请求,请展开以下选项之一:

您应会收到如下所示的 JSON 响应:

      {
        "message": {
          "name": "projects/PROJECT_ID/conversations/CONVERSATION_ID/messages/MESSAGE_ID",
          "content": "How may I help you?",
          "languageCode": "en-US",
          "participant": "PARTICIPANT_ID",
          "participantRole": "HUMAN_AGENT",
          "createTime": "2020-02-13T00:01:30.683Z"
        },
        "humanAgentSuggestionResults": [
          {
            "suggestArticlesResponse": {
              "latestMessage": "projects/PROJECT_ID/conversations/CONVERSATION_ID/messages/MESSAGE_ID",
              "contextSize": 1
            }
          }
        ]
      }
    }
  ]
}

Python

def analyze_content_text(project_id, conversation_id, participant_id, text):
    """Analyze text message content from a participant.

    Args:
        project_id: The GCP project linked with the conversation profile.
        conversation_id: Id of the conversation.
        participant_id: Id of the participant.
        text: the text message that participant typed."""

    client = dialogflow.ParticipantsClient()
    participant_path = client.participant_path(project_id, conversation_id,
                                               participant_id)
    text_input = {'text': text, 'language_code': 'en-US'}
    response = client.analyze_content(participant=participant_path,
                                      text_input=text_input)
    print('AnalyzeContent Response:')
    print('Reply Text: {}'.format(response.reply_text))

    for suggestion_result in response.human_agent_suggestion_results:
        if suggestion_result.error is not None:
            print('Error: {}'.format(suggestion_result.error.message))
        if suggestion_result.suggest_articles_response:
            for answer in suggestion_result.suggest_articles_response.article_answers:
                print('Article Suggestion Answer: {}'.format(answer.title))
                print('Answer Record: {}'.format(answer.answer_record))
        if suggestion_result.suggest_faq_answers_response:
            for answer in suggestion_result.suggest_faq_answers_response.faq_answers:
                print('Faq Answer: {}'.format(answer.answer))
                print('Answer Record: {}'.format(answer.answer_record))
        if suggestion_result.suggest_smart_replies_response:
            for answer in suggestion_result.suggest_smart_replies_response.smart_reply_answers:
                print('Smart Reply: {}'.format(answer.reply))
                print('Answer Record: {}'.format(answer.answer_record))

    for suggestion_result in response.end_user_suggestion_results:
        if suggestion_result.error:
            print('Error: {}'.format(suggestion_result.error.message))
        if suggestion_result.suggest_articles_response:
            for answer in suggestion_result.suggest_articles_response.article_answers:
                print('Article Suggestion Answer: {}'.format(answer.title))
                print('Answer Record: {}'.format(answer.answer_record))
        if suggestion_result.suggest_faq_answers_response:
            for answer in suggestion_result.suggest_faq_answers_response.faq_answers:
                print('Faq Answer: {}'.format(answer.answer))
                print('Answer Record: {}'.format(answer.answer_record))
        if suggestion_result.suggest_smart_replies_response:
            for answer in suggestion_result.suggest_smart_replies_response.smart_reply_answers:
                print('Smart Reply: {}'.format(answer.reply))
                print('Answer Record: {}'.format(answer.answer_record))

    return response

添加最终用户的消息并获取建议

响应人工客服时,最终用户表示“我想退回订单”。此时,API 响应包含建议的文档及其关联的置信度分数。在本教程的前面部分中,我们向知识库添加了一个知识文档,并且系统返回了该文档。置信度分数范围为 0 到 1。值越高表示文档与对话相关的可能性越高。此外,系统还会返回包含文档前 100 个字符的片段。该片段可以帮助人工客服快速确定文档是否有用。我们建议您将此信息提供给人工客服,他们可能会选择与最终用户分享推荐的文档。

REST 和命令行

如需为对话添加和分析最终用户消息,请对 Participant 资源调用 analyzeContent 方法。

在使用下面的请求数据之前,请先进行以下替换:

  • PROJECT_ID:您的 GCP 项目 ID
  • CONVERSATION_ID:您的对话 ID
  • PARTICIPANT_ID:最终用户参与者 ID

HTTP 方法和网址:

POST https://dialogflow.googleapis.com/v2/projects/PROJECT_ID/conversations/CONVERSATION_ID/participants/PARTICIPANT_ID:analyzeContent

请求 JSON 正文:

{
  "textInput": {
    "text": "I want to return my order.",
    "languageCode": "en-US"
  }
}

如需发送您的请求,请展开以下选项之一:

您应会收到如下所示的 JSON 响应:

{
  "message": {
    "name": "projects/PROJECT_ID/conversations/CONVERSATION_ID/messages/MESSAGE_ID",
    "content": "I want to return my order.",
    "languageCode": "en-US",
    "participant": "PARTICIPANT_ID",
    "participantRole": "END_USER",
    "createTime": "2020-02-13T00:07:35.925Z"
  },
  "humanAgentSuggestionResults": [
    {
      "suggestArticlesResponse": {
        "articleAnswers": [
          {
            "title": "Return an order",
            "uri": "gs://agent-assist-public-examples/public_article_suggestion_example_returns.html",
            "snippets": [
              "\u003cb\u003eReturn\u003c/b\u003e an \u003cb\u003eorder\u003c/b\u003e. Follow the steps below for Made-up Store \u003cb\u003ereturns\u003c/b\u003e. At this time, \nwe don't offer exchanges. In most cases, you can drop off \u003cb\u003ereturns\u003c/b\u003e at any Made-up\n ..."
            ],
            "metadata": {
              "title": "Return an order",
              "snippet": "\n  \n\n\u003ch1\u003eReturn an order\u003c/h1\u003e \nFollow the steps below for Made-up Store returns. At this time, we do...",
              "document_display_name": "my-kdoc"
            },
            "answerRecord": "projects/PROJECT_ID/answerRecords/ANSWER_RECORD_ID"
          }
        ],
        "latestMessage": "projects/PROJECT_ID/conversations/CONVERSATION_ID/messages/MESSAGE_ID",
        "contextSize": 2
      }
    }
  ]
}

Python

def analyze_content_text(project_id, conversation_id, participant_id, text):
    """Analyze text message content from a participant.

    Args:
        project_id: The GCP project linked with the conversation profile.
        conversation_id: Id of the conversation.
        participant_id: Id of the participant.
        text: the text message that participant typed."""

    client = dialogflow.ParticipantsClient()
    participant_path = client.participant_path(project_id, conversation_id,
                                               participant_id)
    text_input = {'text': text, 'language_code': 'en-US'}
    response = client.analyze_content(participant=participant_path,
                                      text_input=text_input)
    print('AnalyzeContent Response:')
    print('Reply Text: {}'.format(response.reply_text))

    for suggestion_result in response.human_agent_suggestion_results:
        if suggestion_result.error is not None:
            print('Error: {}'.format(suggestion_result.error.message))
        if suggestion_result.suggest_articles_response:
            for answer in suggestion_result.suggest_articles_response.article_answers:
                print('Article Suggestion Answer: {}'.format(answer.title))
                print('Answer Record: {}'.format(answer.answer_record))
        if suggestion_result.suggest_faq_answers_response:
            for answer in suggestion_result.suggest_faq_answers_response.faq_answers:
                print('Faq Answer: {}'.format(answer.answer))
                print('Answer Record: {}'.format(answer.answer_record))
        if suggestion_result.suggest_smart_replies_response:
            for answer in suggestion_result.suggest_smart_replies_response.smart_reply_answers:
                print('Smart Reply: {}'.format(answer.reply))
                print('Answer Record: {}'.format(answer.answer_record))

    for suggestion_result in response.end_user_suggestion_results:
        if suggestion_result.error:
            print('Error: {}'.format(suggestion_result.error.message))
        if suggestion_result.suggest_articles_response:
            for answer in suggestion_result.suggest_articles_response.article_answers:
                print('Article Suggestion Answer: {}'.format(answer.title))
                print('Answer Record: {}'.format(answer.answer_record))
        if suggestion_result.suggest_faq_answers_response:
            for answer in suggestion_result.suggest_faq_answers_response.faq_answers:
                print('Faq Answer: {}'.format(answer.answer))
                print('Answer Record: {}'.format(answer.answer_record))
        if suggestion_result.suggest_smart_replies_response:
            for answer in suggestion_result.suggest_smart_replies_response.smart_reply_answers:
                print('Smart Reply: {}'.format(answer.reply))
                print('Answer Record: {}'.format(answer.answer_record))

    return response

完成对话

对话结束后,请使用 API 完成对话。

REST 和命令行

如需完成对话,请对 conversations 资源调用 complete 方法。

在使用下面的请求数据之前,请先进行以下替换:

  • PROJECT_ID:您的 GCP 项目 ID
  • CONVERSATION_ID:您在创建对话时收到的 ID

HTTP 方法和网址:

POST https://dialogflow.googleapis.com/v2/projects/PROJECT_ID/conversations/CONVERSATION_ID:complete

如需发送您的请求,请展开以下选项之一:

您应会收到如下所示的 JSON 响应:

{
  "name": "projects/PROJECT_ID/conversations/CONVERSATION_ID",
  "lifecycleState": "COMPLETED",
  "conversationProfile": "projects/PROJECT_ID/conversationProfiles/CONVERSATION_PROFILE_ID",
  "startTime": "2018-11-05T21:05:45.622Z",
  "endTime": "2018-11-06T03:50:26.930Z"
}

Python

def complete_conversation(project_id, conversation_id):
    """Completes the specified conversation. Finished conversations are purged from the database after 30 days.

    Args:
        project_id: The GCP project linked with the conversation.
        conversation_id: Id of the conversation."""

    client = dialogflow.ConversationsClient()
    conversation_path = client.conversation_path(project_id, conversation_id)
    conversation = client.complete_conversation(name=conversation_path)
    print('Completed Conversation.')
    print('Life Cycle State: {}'.format(conversation.lifecycle_state))
    print('Conversation Profile Name: {}'.format(
        conversation.conversation_profile))
    print('Name: {}'.format(conversation.name))
    return conversation

API 请求选项

以上部分展示了如何创建简单的 ConversationProfile,以便接收建议。以下部分概述了您可以在对话期间实现的一些可选功能。

Pub/Sub 建议通知

在上述部分中,ConversationProfile 是仅通过人工客服助理创建的。在对话期间,您需要调用 API,以便在每个消息添加到对话后接收建议。如果您希望收到建议的通知事件,则可以在创建对话配置文件时设置 notificationConfig 字段。在对话进行且有新建议可用时,此选项会使用 Cloud Pub/Sub 向应用发送建议通知。