Article Suggestion

The Agent Assist Article Suggestion feature follows a conversation between a human agent and an end-user and provides the human agent with relevant document suggestions. A human agent can examine these suggestions while the conversation proceeds and make a decision about which documents to read or to share with the end-user. You can use Article Suggestion to help a human agent understand and resolve end-user issues while the human agent and end-user are in a conversation.

Agent Assist provides baseline Article Suggestion models that you can use to suggest articles to your agents. Optionally, you can train a custom model using your own uploaded conversation data to improve performance. If you want to train a custom suggestion model for use with Article Suggestion, please contact your Google representative.

This document walks you through the process of using the API to implement Article Suggestion and get suggestions from this feature during runtime. You have the option of using the Agent Assist Console to test your Article Suggestion results during design-time, but you must call the API directly during runtime. See the tutorials section for details about testing feature performance using the Agent Assist Console.

Before you begin

Complete the following before starting this guide:

  1. Enable the Dialogflow API for your GCP project.
  2. Enable the Data Labeling API for your project.

Configure a conversation profile

In order to get suggestions from Agent Assist you must create a knowledge base containing your uploaded documents and configure a conversation profile. You can also perform these actions using the Agent Assist Console if you would prefer not to call the API directly.

Create a knowledge base

Before you can begin uploading documents you must first create a knowledge base to put them in. To create a knowledge base, call the create method on the KnowledgeBase type.

REST & CMD LINE

Before using any of the request data, make the following replacements:

  • PROJECT_ID: your GCP project ID
  • KNOWLEDGE_BASE_DISPLAY_NAME: desired knowledge base name

HTTP method and URL:

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

Request JSON body:

{
  "displayName": "KNOWLEDGE_BASE_DISPLAY_NAME"
}

To send your request, expand one of these options:

You should receive a JSON response similar to the following:

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

The path segment after knowledgeBases contains your new knowledge base 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))

Create a knowledge document

You can now add documents to the knowledge base. To create a document in the knowledge base, call the create method on the Document type. Set KnowledgeType to ARTICLE_SUGGESTION. This example uses an HTML file with return order information that was uploaded to a publicly shared Cloud Storage bucket. When you set up Article Suggestion in your own system, documents must be in one of the following formats. See the knowledge documents documentation for more information about document best practices.

Knowledge document formats:

  • A file stored in a Cloud Storage bucket. You can specify the path when you call the API.
  • The text contents of a document, which you can send in an API request.
  • A public URL.

REST & CMD LINE

Before using any of the request data, make the following replacements:

  • PROJECT_ID: your GCP project ID
  • KNOWLEDGE_BASE_ID: your knowledge base ID returned from previous request
  • DOCUMENT_DISPLAY_NAME: desired knowledge document name

HTTP method and URL:

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

Request JSON body:

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

To send your request, expand one of these options:

You should receive a JSON response similar to the following:

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

The response is a long-running operation, which you can poll to check for completion.

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))

Create a conversation profile

A conversation profile configures a set of parameters that control the suggestions made to an agent during a conversation. The following steps create a ConversationProfile with a HumanAgentAssistantConfig object. You can also perform these actions using the Agent Assist Console if you would prefer not to call the API directly.

We recommend that you set an initial confidence threshold of 0.44 (0.1 if you are using the legacy baseline model). You can increase the threshold beyond the recommend range if necessary. Increasing the threshold results in higher accuracy and lower coverage results (fewer suggestions); decreasing the threshold results in lower accuracy and higher coverage (more suggestions).

REST & CMD LINE

To create a conversation profile, call the create method on the ConversationProfile resource.

noSmallTalk: If true, suggestions will not be triggered after small talk messages (such as "hi", "how are you", and so on). If false, suggestions will be triggered after small talk messages.

onlyEndUser: If true, suggestions will be triggered only after end-user messages. If false, suggestions will be triggered after both end-user and human agent messages.

Before using any of the request data, make the following replacements:

  • PROJECT_ID: your GCP project ID
  • KNOWLEDGE_BASE_ID: your knowledge base ID

HTTP method and URL:

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

Request JSON body:

{
  "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"
}

To send your request, expand one of these options:

You should receive a JSON response similar to the following:

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

The path segment after conversationProfiles contains your new conversation profile 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

Handling conversations at runtime

Create a conversation

When a dialog begins between an end-user and a human or virtual agent, you create a conversation. In order to see suggestions, you must also create both an end-user participant and a human agent participant and add them to the conversation. The following sections walk you through this process.

First, you must create a conversation:

REST & CMD LINE

To create a conversation, call the create method on the Conversation resource.

Before using any of the request data, make the following replacements:

  • PROJECT_ID: your GCP project ID
  • CONVERSATION_PROFILE_ID: the ID you received when creating the conversation profile

HTTP method and URL:

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

Request JSON body:

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

To send your request, expand one of these options:

You should receive a JSON response similar to the following:

{
  "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"
}

The path segment after conversations contains your new conversation 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

Create an end-user participant

You must add both end-user and human agent participants to the conversation in order to see suggestions. First, add the end-user participant to the conversation:

REST & CMD LINE

To create an end-user participant, call the create method on the Participant resource.

Before using any of the request data, make the following replacements:

  • PROJECT_ID: your GCP project ID
  • CONVERSATION_ID: your conversation ID

HTTP method and URL:

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

Request JSON body:

{
  "role": "END_USER",
}

To send your request, expand one of these options:

You should receive a JSON response similar to the following:

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

The path segment after participants contains your new end-user participant 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}, timeout=600
        )
        print("Participant Created.")
        print("Role: {}".format(response.role))
        print("Name: {}".format(response.name))

        return response

Create a human agent participant

Add a human agent participant to the conversation:

REST & CMD LINE

To create a human agent participant, call the create method on the Participant resource.

Before using any of the request data, make the following replacements:

  • PROJECT_ID: your GCP project ID
  • CONVERSATION_ID: your conversation ID

HTTP method and URL:

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

Request JSON body:

{
  "role": "HUMAN_AGENT",
}

To send your request, expand one of these options:

You should receive a JSON response similar to the following:

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

The path segment after participants contains your new human agent participant 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}, timeout=600
        )
        print("Participant Created.")
        print("Role: {}".format(response.role))
        print("Name: {}".format(response.name))

        return response

Add and analyze a message from the human agent

Each time either participant types a message in the conversation, you need to send that message to the API for processing. Agent Assist bases its suggestions on analysis of human agent and end-user messages. In the following example, the human agent starts the conversation by asking "How may I help you?". No suggestions are returned yet in the response.

REST & CMD LINE

To add and analyze a human agent message in the conversation, call the analyzeContent method on the Participant resource.

Before using any of the request data, make the following replacements:

  • PROJECT_ID: your GCP project ID
  • CONVERSATION_ID: your conversation ID
  • PARTICIPANT_ID: your human agent participant ID

HTTP method and URL:

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

Request JSON body:

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

To send your request, expand one of these options:

You should receive a JSON response similar to the following:

      {
        "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

Add a message from the end-user and get suggestions

In response to the human agent, the end-user says "I want to return my order." This time, the API response contains a suggested document with its associated confidence score. Earlier in this tutorial we added one knowledge document to the knowledge base, and that document was returned. Confidence scores range from 0 to 1; higher values indicate a higher likelihood that the document is relevant to the conversation. A snippet containing the first 100 characters of the document is also returned. The snippet can help a human agent quickly determine whether the document is useful. We recommend that you provide this information to your human agent, who might choose to share the recommended document with the end-user.

REST & CMD LINE

To add and analyze an end-user message for the conversation, call the analyzeContent method on the Participant resource.

Before using any of the request data, make the following replacements:

  • PROJECT_ID: your GCP project ID
  • CONVERSATION_ID: your conversation ID
  • PARTICIPANT_ID: your end-user participant ID

HTTP method and URL:

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

Request JSON body:

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

To send your request, expand one of these options:

You should receive a JSON response similar to the following:

{
  "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

Complete the conversation

When the conversation ends, use the API to complete the conversation.

REST & CMD LINE

To complete the conversation, call the complete method on the conversations resource.

Before using any of the request data, make the following replacements:

  • PROJECT_ID: your GCP project ID
  • CONVERSATION_ID: the ID you received when creating the conversation

HTTP method and URL:

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

To send your request, expand one of these options:

You should receive a JSON response similar to the following:

{
  "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 request options

The sections above show how to create a simple ConversationProfile in order to receive suggestions. The following sections outline some optional functionalities that you can implement during a conversation.

Pub/Sub suggestion notifications

In the sections above, the ConversationProfile was created with only a human agent assistant. During the conversation you needed to call the API to receive suggestions after each message was added to the conversation. If you prefer to receive notification events for suggestions, you can set the notificationConfig field when creating the conversation profile. This option uses Cloud Pub/Sub to send suggestion notifications to your application as the conversation proceeds and new suggestions are available.