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