feat: [google-cloud-dialogflow] add sections field to HumanAgentAssistantConfig.SuggestionQueryConfig (#12129)

- [ ] Regenerate this pull request now. BEGIN_COMMIT_OVERRIDE feat: Add sections field to HumanAgentAssistantConfig.SuggestionQueryConfig feat: Add enable_conversation_augmented_query field to HumanAgentAssistantConfig.SuggestionFeatureConfig message feat: Add INTENT enum in SearchKnowledgeAnswer.AnswerType message feat: Add rewritten_query in field in SearchKnowledgeResponse message docs: Improved comments on audio_config proto END_COMMIT_OVERRIDE PiperOrigin-RevId: 591055870 Source-Link: googleapis/googleapis@2f93f44 Source-Link: googleapis/googleapis-gen@2154bfc Copy-Tag: eyJwIjoicGFja2FnZXMvZ29vZ2xlLWNsb3VkLWRpYWxvZ2Zsb3cvLk93bEJvdC55YW1sIiwiaCI6IjIxNTRiZmMwZDUwZGJhMGI4Yjg5ODBlODAyNDNkOTI4Yzk5NzcxMDgifQ== --------- Co-authored-by: Owl Bot <gcf-owl-bot[bot]@users.noreply.github.com>

Author: gcf-owl-bot[bot]

packages/google-cloud-dialogflow/google/cloud/dialogflow_v2/types/audio_config.py

@@ -409,23 +409,9 @@ class InputAudioConfig(proto.Message):
  documentation <https://cloud.google.com/speech-to-text/docs/basics#phrase-hints>`__
  for more details.
  model (str):
- Which Speech model to select for the given request. Select
- the model best suited to your domain to get best results. If
- a model is not explicitly specified, then we auto-select a
- model based on the parameters in the InputAudioConfig. If
- enhanced speech model is enabled for the agent and an
- enhanced version of the specified model for the language
- does not exist, then the speech is recognized using the
- standard version of the specified model. Refer to `Cloud
- Speech API
- documentation <https://cloud.google.com/speech-to-text/docs/basics#select-model>`__
- for more details. If you specify a model, the following
- models typically have the best performance:
-
- - phone_call (best for Agent Assist and telephony)
- - latest_short (best for Dialogflow non-telephony)
- - command_and_search (best for very short utterances and
- commands)
+ Optional. Which Speech model to select for the given
+ request. For more information, see `Speech
+ models <https://cloud.google.com/dialogflow/es/docs/speech-models>`__.
  model_variant (google.cloud.dialogflow_v2.types.SpeechModelVariant):
  Which variant of the [Speech
  model][google.cloud.dialogflow.v2.InputAudioConfig.model] to
@@ -665,10 +651,24 @@ class SpeechToTextConfig(proto.Message):
  model (str):
  Which Speech model to select. Select the model best suited
  to your domain to get best results. If a model is not
- explicitly specified, then a default model is used. Refer to
- `Cloud Speech API
+ explicitly specified, then Dialogflow auto-selects a model
+ based on other parameters in the SpeechToTextConfig and
+ Agent settings. If enhanced speech model is enabled for the
+ agent and an enhanced version of the specified model for the
+ language does not exist, then the speech is recognized using
+ the standard version of the specified model. Refer to `Cloud
+ Speech API
  documentation <https://cloud.google.com/speech-to-text/docs/basics#select-model>`__
- for more details.
+ for more details. If you specify a model, the following
+ models typically have the best performance:
+
+ - phone_call (best for Agent Assist and telephony)
+ - latest_short (best for Dialogflow non-telephony)
+ - command_and_search
+
+ Leave this field unspecified to use `Agent Speech
+ settings <https://cloud.google.com/dialogflow/cx/docs/concept/agent#settings-speech>`__
+ for model selection.
  use_timeout_based_endpointing (bool):
  Use timeout based endpointing, interpreting
  endpointer sensitivy as seconds of timeout

packages/google-cloud-dialogflow/google/cloud/dialogflow_v2/types/conversation.py

@@ -754,13 +754,19 @@ class SearchKnowledgeResponse(proto.Message):
  Most relevant snippets extracted from
  articles in the given knowledge base, ordered by
  confidence.
+ rewritten_query (str):
+ The rewritten query used to search knowledge.
  """
 
  answers: MutableSequence["SearchKnowledgeAnswer"] = proto.RepeatedField(
  proto.MESSAGE,
  number=2,
  message="SearchKnowledgeAnswer",
  )
+ rewritten_query: str = proto.Field(
+ proto.STRING,
+ number=3,
+ )
 
 
 class SearchKnowledgeAnswer(proto.Message):
@@ -786,13 +792,16 @@ class AnswerType(proto.Enum):
  ANSWER_TYPE_UNSPECIFIED (0):
  The answer has a unspecified type.
  FAQ (1):
- The answer is from FAQ doucments.
+ The answer is from FAQ documents.
  GENERATIVE (2):
  The answer is from generative model.
+ INTENT (3):
+ The answer is from intent matching.
  """
  ANSWER_TYPE_UNSPECIFIED = 0
  FAQ = 1
  GENERATIVE = 2
+ INTENT = 3
 
  class AnswerSource(proto.Message):
  r"""The sources of the answers.

packages/google-cloud-dialogflow/google/cloud/dialogflow_v2/types/conversation_profile.py

@@ -354,12 +354,12 @@ class AutomatedAgentConfig(proto.Message):
  If environment is not specified, the default ``draft``
  environment is used.
  session_ttl (google.protobuf.duration_pb2.Duration):
- Optional. Sets Dialogflow CX session life
- time. By default, a Dialogflow CX session
- remains active and its data is stored for 30
- minutes after the last request is sent for the
- session. This value should be no longer than 1
- day.
+ Optional. Configure lifetime of the
+ Dialogflow session. By default, a Dialogflow CX
+ session remains active and its data is stored
+ for 30 minutes after the last request is sent
+ for the session. This value should be no longer
+ than 1 day.
  """
 
  agent: str = proto.Field(
@@ -431,6 +431,9 @@ class SuggestionFeatureConfig(proto.Message):
  at answer records.
 
  Supported features: KNOWLEDGE_SEARCH.
+ enable_conversation_augmented_query (bool):
+ Optional. Enable including conversation context during query
+ answer generation. Supported features: KNOWLEDGE_SEARCH.
  suggestion_trigger_settings (google.cloud.dialogflow_v2.types.HumanAgentAssistantConfig.SuggestionTriggerSettings):
  Settings of suggestion trigger.
 
@@ -457,6 +460,10 @@ class SuggestionFeatureConfig(proto.Message):
  proto.BOOL,
  number=14,
  )
+ enable_conversation_augmented_query: bool = proto.Field(
+ proto.BOOL,
+ number=16,
+ )
  suggestion_trigger_settings: "HumanAgentAssistantConfig.SuggestionTriggerSettings" = proto.Field(
  proto.MESSAGE,
  number=10,
@@ -569,6 +576,10 @@ class SuggestionQueryConfig(proto.Message):
  Determines how recent conversation context is
  filtered when generating suggestions. If
  unspecified, no messages will be dropped.
+ sections (google.cloud.dialogflow_v2.types.HumanAgentAssistantConfig.SuggestionQueryConfig.Sections):
+ Optional. The customized sections chosen to
+ return when requesting a summary of a
+ conversation.
  """
 
  class KnowledgeBaseQuerySource(proto.Message):
@@ -679,6 +690,72 @@ class ContextFilterSettings(proto.Message):
  number=3,
  )
 
+ class Sections(proto.Message):
+ r"""Custom sections to return when requesting a summary of a
+ conversation. This is only supported when ``baseline_model_version``
+ == '2.0'.
+
+ Supported features: CONVERSATION_SUMMARIZATION,
+ CONVERSATION_SUMMARIZATION_VOICE.
+
+ Attributes:
+ section_types (MutableSequence[google.cloud.dialogflow_v2.types.HumanAgentAssistantConfig.SuggestionQueryConfig.Sections.SectionType]):
+ The selected sections chosen to return when
+ requesting a summary of a conversation. A
+ duplicate selected section will be treated as a
+ single selected section. If section types are
+ not provided, the default will be {SITUATION,
+ ACTION, RESULT}.
+ """
+
+ class SectionType(proto.Enum):
+ r"""Selectable sections to return when requesting a summary of a
+ conversation.
+
+ Values:
+ SECTION_TYPE_UNSPECIFIED (0):
+ Undefined section type, does not return
+ anything.
+ SITUATION (1):
+ What the customer needs help with or has
+ question about. Section name: "situation".
+ ACTION (2):
+ What the agent does to help the customer.
+ Section name: "action".
+ RESOLUTION (3):
+ Result of the customer service. A single word
+ describing the result of the conversation.
+ Section name: "resolution".
+ REASON_FOR_CANCELLATION (4):
+ Reason for cancellation if the customer requests for a
+ cancellation. "N/A" otherwise. Section name:
+ "reason_for_cancellation".
+ CUSTOMER_SATISFACTION (5):
+ "Unsatisfied" or "Satisfied" depending on the customer's
+ feelings at the end of the conversation. Section name:
+ "customer_satisfaction".
+ ENTITIES (6):
+ Key entities extracted from the conversation,
+ such as ticket number, order number, dollar
+ amount, etc. Section names are prefixed by
+ "entities/".
+ """
+ SECTION_TYPE_UNSPECIFIED = 0
+ SITUATION = 1
+ ACTION = 2
+ RESOLUTION = 3
+ REASON_FOR_CANCELLATION = 4
+ CUSTOMER_SATISFACTION = 5
+ ENTITIES = 6
+
+ section_types: MutableSequence[
+ "HumanAgentAssistantConfig.SuggestionQueryConfig.Sections.SectionType"
+ ] = proto.RepeatedField(
+ proto.ENUM,
+ number=1,
+ enum="HumanAgentAssistantConfig.SuggestionQueryConfig.Sections.SectionType",
+ )
+
  knowledge_base_query_source: "HumanAgentAssistantConfig.SuggestionQueryConfig.KnowledgeBaseQuerySource" = proto.Field(
  proto.MESSAGE,
  number=1,
@@ -710,6 +787,13 @@ class ContextFilterSettings(proto.Message):
  number=7,
  message="HumanAgentAssistantConfig.SuggestionQueryConfig.ContextFilterSettings",
  )
+ sections: "HumanAgentAssistantConfig.SuggestionQueryConfig.Sections" = (
+ proto.Field(
+ proto.MESSAGE,
+ number=8,
+ message="HumanAgentAssistantConfig.SuggestionQueryConfig.Sections",
+ )
+ )
 
  class ConversationModelConfig(proto.Message):
  r"""Custom conversation models used in agent assist feature.

packages/google-cloud-dialogflow/tests/unit/gapic/dialogflow_v2/test_conversation_profiles.py

@@ -3491,6 +3491,7 @@ def test_create_conversation_profile_rest(request_type):
  "suggestion_feature": {"type_": 1},
  "enable_event_based_suggestion": True,
  "disable_agent_query_logging": True,
+ "enable_conversation_augmented_query": True,
  "suggestion_trigger_settings": {
  "no_smalltalk": True,
  "only_end_user": True,
@@ -3516,6 +3517,7 @@ def test_create_conversation_profile_rest(request_type):
  "drop_virtual_agent_messages": True,
  "drop_ivr_messages": True,
  },
+ "sections": {"section_types": [1]},
  },
  "conversation_model_config": {
  "model": "model_value",
@@ -3953,6 +3955,7 @@ def test_update_conversation_profile_rest(request_type):
  "suggestion_feature": {"type_": 1},
  "enable_event_based_suggestion": True,
  "disable_agent_query_logging": True,
+ "enable_conversation_augmented_query": True,
  "suggestion_trigger_settings": {
  "no_smalltalk": True,
  "only_end_user": True,
@@ -3978,6 +3981,7 @@ def test_update_conversation_profile_rest(request_type):
  "drop_virtual_agent_messages": True,
  "drop_ivr_messages": True,
  },
+ "sections": {"section_types": [1]},
  },
  "conversation_model_config": {
  "model": "model_value",

packages/google-cloud-dialogflow/tests/unit/gapic/dialogflow_v2/test_conversations.py

@@ -2819,7 +2819,9 @@ def test_search_knowledge(request_type, transport: str = "grpc"):
  # Mock the actual call within the gRPC stub, and fake the request.
  with mock.patch.object(type(client.transport.search_knowledge), "__call__") as call:
  # Designate an appropriate return value for the call.
- call.return_value = conversation.SearchKnowledgeResponse()
+ call.return_value = conversation.SearchKnowledgeResponse(
+ rewritten_query="rewritten_query_value",
+ )
  response = client.search_knowledge(request)
 
  # Establish that the underlying gRPC stub method was called.
@@ -2829,6 +2831,7 @@ def test_search_knowledge(request_type, transport: str = "grpc"):
 
  # Establish that the response is the type that we expect.
  assert isinstance(response, conversation.SearchKnowledgeResponse)
+ assert response.rewritten_query == "rewritten_query_value"
 
 
 def test_search_knowledge_empty_call():
@@ -2864,7 +2867,9 @@ async def test_search_knowledge_async(
  with mock.patch.object(type(client.transport.search_knowledge), "__call__") as call:
  # Designate an appropriate return value for the call.
  call.return_value = grpc_helpers_async.FakeUnaryUnaryCall(
- conversation.SearchKnowledgeResponse()
+ conversation.SearchKnowledgeResponse(
+ rewritten_query="rewritten_query_value",
+ )
  )
  response = await client.search_knowledge(request)
 
@@ -2875,6 +2880,7 @@ async def test_search_knowledge_async(
 
  # Establish that the response is the type that we expect.
  assert isinstance(response, conversation.SearchKnowledgeResponse)
+ assert response.rewritten_query == "rewritten_query_value"
 
 
 @pytest.mark.asyncio
@@ -5088,7 +5094,9 @@ def test_search_knowledge_rest(request_type):
  # Mock the http request call within the method and fake a response.
  with mock.patch.object(type(client.transport._session), "request") as req:
  # Designate an appropriate value for the returned response.
- return_value = conversation.SearchKnowledgeResponse()
+ return_value = conversation.SearchKnowledgeResponse(
+ rewritten_query="rewritten_query_value",
+ )
 
  # Wrap the value into a proper Response obj
  response_value = Response()
@@ -5103,6 +5111,7 @@ def test_search_knowledge_rest(request_type):
 
  # Establish that the response is the type that we expect.
  assert isinstance(response, conversation.SearchKnowledgeResponse)
+ assert response.rewritten_query == "rewritten_query_value"
 
 
 def test_search_knowledge_rest_required_fields(