FAQ Assist

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))

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))

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

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

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

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

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

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

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