智能回复

智能回复会跟踪人工客服与最终用户之间的对话，并会将建议的回复呈现给人工客服。建议的回复数由使用您自己的对话转录数据训练的自定义模型计算得出。

本文档将引导您完成使用 API 实现智能回复并在运行时通过此功能获取建议的过程。您必须使用 Agent Assist 控制台上传数据、训练模型，并在设计过程中测试智能回复结果。要在运行时查看智能回复建议，您必须直接调用 API。如需详细了解如何使用 Agent Assist 控制台训练模型并测试其性能，请参阅智能回复教程。

准备工作

在开始本指南之前，请先完成以下操作：

使用您自己的转录数据创建对话数据集。
使用对话数据集训练智能回复模型。
创建对话配置文件。
使用 Agent Assist 模拟器测试智能回复结果。

处理 PII 和子级数据

在您向 API 发送数据时，API 会尝试隐去所有个人身份信息 (PII)。如果您需要保证模型不包含 PII，则应在向 API 发送数据之前先进行清理。

如果数据包含从子级收集的信息，则应在向 API 发送子级的数据之前先进行移除。

在运行时处理对话

当最终用户与人工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。

创建对话

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

REST 和命令行

在使用任何请求数据之前，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：

curl（Linux、macOS 或 Cloud Shell）

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://dialogflow.googleapis.com/v2/projects/PROJECT_ID/conversations"

PowerShell (Windows)

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://dialogflow.googleapis.com/v2/projects/PROJECT_ID/conversations" | Select-Object -Expand Content

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

在 GitHub 上查看反馈

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。如需创建最终用户参与者，请对 Participant 资源调用 create 方法。为 role 字段提供对话 ID 和 END_USER。

REST 和命令行

在使用任何请求数据之前，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：

curl（Linux、macOS 或 Cloud Shell）

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

PowerShell (Windows)

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://dialogflow.googleapis.com/v2/projects/PROJECT_ID/conversations/CONVERSATION_ID/participants" | Select-Object -Expand Content

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

在 GitHub 上查看反馈

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}, timeout=600
        )
        print("Participant Created.")
        print("Role: {}".format(response.role))
        print("Name: {}".format(response.name))

        return response

创建人工客服参与者

如需创建人工客服参与者，请对 Participant 资源调用 create 方法。为 role 字段提供对话 ID 和 HUMAN_AGENT。

REST 和命令行

在使用任何请求数据之前，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：

curl（Linux、macOS 或 Cloud Shell）

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

PowerShell (Windows)

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://dialogflow.googleapis.com/v2/projects/PROJECT_ID/conversations/CONVERSATION_ID/participants" | Select-Object -Expand Content

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

在 GitHub 上查看反馈

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}, timeout=600
        )
        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。如需为对话添加和分析人工客服消息，请对 Participant 资源调用 analyzeContent 方法。提供对话 ID 和人工客服参与者 ID。

REST 和命令行

在使用任何请求数据之前，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：

curl（Linux、macOS 或 Cloud Shell）

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

PowerShell (Windows)

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://dialogflow.googleapis.com/v2/projects/PROJECT_ID/conversations/CONVERSATION_ID/participants/PARTICIPANT_ID:analyzeContent" | Select-Object -Expand Content

您应该收到类似以下内容的 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": [
    {
      "suggestSmartRepliesResponse": {
      "smartReplyAnswers": [
          {
            "reply": "I am here to help you.",
            "confidence": 0.5,
            "answerRecord": "projects/PROJECT_ID/answerRecords/ANSWER_RECORD_ID_1"
          },
          {
            "reply": "Sorry for the wait, we have a high volume of chats right now.",
            "confidence": 0.3,
            "answerRecord": "projects/PROJECT_ID/answerRecords/ANSWER_RECORD_ID_2"
          },
          {
            "reply": "Thank you for contacting us!",
            "confidence": 0.1,
            "answerRecord": "projects/PROJECT_ID/answerRecords/ANSWER_RECORD_ID_3"
          }
        ]
      }
    }
  ]
}

Python

在 GitHub 上查看反馈

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

添加和分析最终用户的消息

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。提供对话 ID 和最终用户参与者 ID。

响应包含消息 ID。如果您已在对话配置文件中将 SuggestionFeatureConfig.enable_inline_suggestion 设置为 true，则响应将包含智能回复建议列表。

REST 和命令行

在使用任何请求数据之前，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 reserve a room.",
    "languageCode": "en-US"
  }
}

如需发送您的请求，请展开以下选项之一：

curl（Linux、macOS 或 Cloud Shell）

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

PowerShell (Windows)

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://dialogflow.googleapis.com/v2/projects/PROJECT_ID/conversations/CONVERSATION_ID/participants/PARTICIPANT_ID:analyzeContent" | Select-Object -Expand Content

您应该收到类似以下内容的 JSON 响应：

{
  "message": {
    "name": "projects/PROJECT_ID/conversations/CONVERSATION_ID/messages/MESSAGE_ID",
    "content": "I want to reserve a room.",
    "languageCode": "en-US",
    "participant": "PARTICIPANT_ID",
    "participantRole": "END_USER",
    "createTime": "2020-02-13T00:07:35.925Z"
  },
  "humanAgentSuggestionResults": [
    {
      "suggestSmartRepliesResponse": {
      "smartReplyAnswers": [
          {
            "reply": "Where would you like to reserve a room?",
            "confidence": 0.5,
            "answerRecord": "projects/PROJECT_ID/answerRecords/ANSWER_RECORD_ID_1"
          },
          {
            "reply": "What type of rooms would you like to reserve?",
            "confidence": 0.3,
            "answerRecord": "projects/PROJECT_ID/answerRecords/ANSWER_RECORD_ID_2"
          },
          {
            "reply": "How long do you want to stay?",
            "confidence": 0.1,
            "answerRecord": "projects/PROJECT_ID/answerRecords/ANSWER_RECORD_ID_3"
          }
        ]
      }
    }
  ]
}

Python

在 GitHub 上查看反馈

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

获取建议（可选）

您可以随时获取建议。您可以选择为任何消息指定消息 ID，以便接收基于该消息的建议。如果未设置此字段，则默认情况下，建议基于任一参与者的最新消息。

如需获取建议，请对 Suggestion 资源调用 suggestSmartReplies 方法。提供对话 ID、人工客服参与者 ID 以及任一参与者的消息 ID（可选）。

响应包含智能回复建议。

REST 和命令行

在使用任何请求数据之前，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 reserve a room.",
    "languageCode": "en-US"
  }
}

如需发送您的请求，请展开以下选项之一：

curl（Linux、macOS 或 Cloud Shell）

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

PowerShell (Windows)

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://dialogflow.googleapis.com/v2/projects/PROJECT_ID/conversations/CONVERSATION_ID/participants/PARTICIPANT_ID:analyzeContent" | Select-Object -Expand Content

您应该收到类似以下内容的 JSON 响应：

{
  "message": {
    "name": "projects/PROJECT_ID/conversations/CONVERSATION_ID/messages/MESSAGE_ID",
    "content": "I want to reserve a room.",
    "languageCode": "en-US",
    "participant": "PARTICIPANT_ID",
    "participantRole": "END_USER",
    "createTime": "2020-02-13T00:07:35.925Z"
  },
  "humanAgentSuggestionResults": [
    {
      "suggestSmartRepliesResponse": {
      "smartReplyAnswers": [
          {
            "reply": "Where would you like to reserve a room?",
            "confidence": 0.5,
            "answerRecord": "projects/PROJECT_ID/answerRecords/ANSWER_RECORD_ID_1"
          },
          {
            "reply": "What type of rooms would you like to reserve?",
            "confidence": 0.3,
            "answerRecord": "projects/PROJECT_ID/answerRecords/ANSWER_RECORD_ID_2"
          },
          {
            "reply": "How long do you want to stay?",
            "confidence": 0.1,
            "answerRecord": "projects/PROJECT_ID/answerRecords/ANSWER_RECORD_ID_3"
          }
        ]
      }
    }
  ]
}

Python

在 GitHub 上查看反馈

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

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

REST 和命令行

在使用任何请求数据之前，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：

curl（Linux、macOS 或 Cloud Shell）

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

PowerShell (Windows)

执行以下命令：

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

Invoke-WebRequest `
  -Method POST `
  -Headers $headers `
  -Uri "https://dialogflow.googleapis.com/v2/projects/PROJECT_ID/conversations/CONVERSATION_ID:complete" | Select-Object -Expand Content

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

在 GitHub 上查看反馈

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