def sample_delete_conversation_profile(): # Create a client client = dialogflow_v2beta1.ConversationProfilesClient() # Initialize request argument(s) request = dialogflow_v2beta1.DeleteConversationProfileRequest( name="name_value", ) # Make the request client.delete_conversation_profile(request=request)
def list_conversation_profiles(project_id): """Lists the conversation profiles belonging to a project. Args: project_id: The GCP project linked with the conversation profile.""" client = dialogflow.ConversationProfilesClient() project_path = client.common_project_path(project_id) response = client.list_conversation_profiles(parent=project_path) for conversation_profile in response: print("Display Name: {}".format(conversation_profile.display_name)) print("Name: {}".format(conversation_profile.name)) return response
def create_conversation_profile_smart_reply(project_id, display_name, smart_reply_allowlist_name, smart_reply_model_name): """Creates a conversation profile with given values for smart reply Args: project_id: The GCP project linked with the conversation profile. display_name: The display name for the conversation profile to be created. smart_reply_allowlist_name: document name for smart reply allowlist. smart_reply_model_name: conversation model name for smart reply.""" 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", } feature_config = { "suggestion_feature": { "type_": "SMART_REPLY" }, "suggestion_trigger_settings": { "no_small_talk": True, "only_end_user": True, }, "query_config": { "document_query_source": { "documents": [smart_reply_allowlist_name] }, "max_results": 3, }, "conversation_model_config": { "model": smart_reply_model_name }, } 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_profile_smart_reply(project_id, display_name, smart_reply_allowlist_name, smart_reply_model_name): """Creates a conversation profile with given values for smart reply Args: project_id: The GCP project linked with the conversation profile. display_name: The display name for the conversation profile to be created. smart_reply_allowlist_name: document name for smart reply allowlist. smart_reply_model_name: conversation model name for smart reply.""" 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' } feature_config = { 'suggestion_feature': { 'type_': 'SMART_REPLY' }, 'suggestion_trigger_settings': { 'no_small_talk': True, 'only_end_user': True, }, 'query_config': { 'document_query_source': { 'documents': [smart_reply_allowlist_name] }, 'max_results': 3 }, 'conversation_model_config': { 'model': smart_reply_model_name } } 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 sample_get_conversation_profile(): # Create a client client = dialogflow_v2beta1.ConversationProfilesClient() # Initialize request argument(s) request = dialogflow_v2beta1.GetConversationProfileRequest( name="name_value", ) # Make the request response = client.get_conversation_profile(request=request) # Handle the response print(response)
def delete_conversation_profile(project_id, conversation_profile_id): """Deletes a specific conversation profile. Args: project_id: The GCP project linked with the conversation profile. conversation_profile_id: Id of the conversation profile.""" client = dialogflow.ConversationProfilesClient() conversation_profile_path = client.conversation_profile_path( project_id, conversation_profile_id) client.delete_conversation_profile(name=conversation_profile_path) print("Conversation Profile deleted.")
def sample_list_conversation_profiles(): # Create a client client = dialogflow_v2beta1.ConversationProfilesClient() # Initialize request argument(s) request = dialogflow_v2beta1.ListConversationProfilesRequest( parent="parent_value", ) # Make the request page_result = client.list_conversation_profiles(request=request) # Handle the response for response in page_result: print(response)
def sample_update_conversation_profile(): # Create a client client = dialogflow_v2beta1.ConversationProfilesClient() # Initialize request argument(s) conversation_profile = dialogflow_v2beta1.ConversationProfile() conversation_profile.display_name = "display_name_value" request = dialogflow_v2beta1.UpdateConversationProfileRequest( conversation_profile=conversation_profile, ) # Make the request response = client.update_conversation_profile(request=request) # Handle the response print(response)
def get_conversation_profile(project_id, conversation_profile_id): """Gets a specific conversation profile. Args: project_id: The GCP project linked with the conversation profile. conversation_profile_id: Id of the conversation profile.""" client = dialogflow.ConversationProfilesClient() conversation_profile_path = client.conversation_profile_path( project_id, conversation_profile_id) response = client.get_conversation_profile(name=conversation_profile_path) print("Got conversation profile:") print("Display Name: {}".format(response.display_name)) print("Name: {}".format(response.name)) return response
def sample_set_suggestion_feature_config(): # Create a client client = dialogflow_v2beta1.ConversationProfilesClient() # Initialize request argument(s) request = dialogflow_v2beta1.SetSuggestionFeatureConfigRequest( conversation_profile="conversation_profile_value", participant_role="END_USER", ) # Make the request operation = client.set_suggestion_feature_config(request=request) print("Waiting for operation to complete...") response = operation.result() # Handle the response print(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_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_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