Class ParticipantsAsyncClient (2.36.0)

Service for managing Participants.

Return the API endpoint used by the client instance.

Returns the transport used by the client instance.

Return the universe domain used by the client instance.

Instantiates the participants async client.

Adds a text (chat, for example), or audio (phone recording, for example) message from a participant into the conversation.

Note: Always use agent versions for production traffic sent to virtual agents. See Versions and environments <https://cloud.google.com/dialogflow/es/docs/agents-versions>__.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import dialogflow_v2beta1

async def sample_analyze_content():
    # Create a client
    client = dialogflow_v2beta1.ParticipantsAsyncClient()

    # Initialize request argument(s)
    request = dialogflow_v2beta1.AnalyzeContentRequest(
        participant="participant_value",
    )

    # Make the request
    response = await client.analyze_content(request=request)

    # Handle the response
    print(response)

Returns a fully-qualified answer_record string.

Starts asynchronous cancellation on a long-running operation.

The server makes a best effort to cancel the operation, but success is not guaranteed. If the server doesn't support this method, it returns google.rpc.Code.UNIMPLEMENTED.

Returns a fully-qualified billing_account string.

Returns a fully-qualified folder string.

Returns a fully-qualified location string.

Returns a fully-qualified organization string.

Returns a fully-qualified project string.

Deprecated. use xref_SuggestArticles and xref_SuggestFaqAnswers instead.

Gets suggestions for a participant based on specific historical messages.

Note that xref_ListSuggestions will only list the auto-generated suggestions, while xref_CompileSuggestion will try to compile suggestion based on the provided conversation context in the real time.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import dialogflow_v2beta1

async def sample_compile_suggestion():
    # Create a client
    client = dialogflow_v2beta1.ParticipantsAsyncClient()

    # Initialize request argument(s)
    request = dialogflow_v2beta1.CompileSuggestionRequest(
    )

    # Make the request
    response = await client.compile_suggestion(request=request)

    # Handle the response
    print(response)

Returns a fully-qualified context string.

Creates a new participant in a conversation.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import dialogflow_v2beta1

async def sample_create_participant():
    # Create a client
    client = dialogflow_v2beta1.ParticipantsAsyncClient()

    # Initialize request argument(s)
    request = dialogflow_v2beta1.CreateParticipantRequest(
        parent="parent_value",
    )

    # Make the request
    response = await client.create_participant(request=request)

    # Handle the response
    print(response)

Returns a fully-qualified document string.

Creates an instance of this client using the provided credentials file.

Creates an instance of this client using the provided credentials info.

Creates an instance of this client using the provided credentials file.

Gets information about a location.

Return the API endpoint and client cert source for mutual TLS.

The client cert source is determined in the following order: (1) if GOOGLE_API_USE_CLIENT_CERTIFICATE environment variable is not "true", the client cert source is None. (2) if client_options.client_cert_source is provided, use the provided one; if the default client cert source exists, use the default one; otherwise the client cert source is None.

The API endpoint is determined in the following order: (1) if client_options.api_endpoint if provided, use the provided one. (2) if GOOGLE_API_USE_CLIENT_CERTIFICATE environment variable is "always", use the default mTLS endpoint; if the environment variable is "never", use the default API endpoint; otherwise if client cert source exists, use the default mTLS endpoint, otherwise use the default API endpoint.

More details can be found at https://google.aip.dev/auth/4114.

Gets the latest state of a long-running operation.

Retrieves a conversation participant.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import dialogflow_v2beta1

async def sample_get_participant():
    # Create a client
    client = dialogflow_v2beta1.ParticipantsAsyncClient()

    # Initialize request argument(s)
    request = dialogflow_v2beta1.GetParticipantRequest(
        name="name_value",
    )

    # Make the request
    response = await client.get_participant(request=request)

    # Handle the response
    print(response)

Returns an appropriate transport class.

Returns a fully-qualified intent string.

Lists information about the supported locations for this service.

Lists operations that match the specified filter in the request.

Returns the list of all participants in the specified conversation.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import dialogflow_v2beta1

async def sample_list_participants():
    # Create a client
    client = dialogflow_v2beta1.ParticipantsAsyncClient()

    # Initialize request argument(s)
    request = dialogflow_v2beta1.ListParticipantsRequest(
        parent="parent_value",
    )

    # Make the request
    page_result = client.list_participants(request=request)

    # Handle the response
    async for response in page_result:
        print(response)

Deprecated: Use inline suggestion, event based suggestion or Suggestion* API instead. See xref_HumanAgentAssistantConfig.name for more details. Removal Date: 2020-09-01.

Retrieves suggestions for live agents.

This method should be used by human agent client software to fetch auto generated suggestions in real-time, while the conversation with an end user is in progress. The functionality is implemented in terms of the list pagination <https://cloud.google.com/apis/design/design_patterns#list_pagination>__ design pattern. The client app should use the next_page_token field to fetch the next batch of suggestions. suggestions are sorted by create_time in descending order. To fetch latest suggestion, just set page_size to 1. To fetch new suggestions without duplication, send request with filter create_time_epoch_microseconds > [first item's create_time of previous request] and empty page_token.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import dialogflow_v2beta1

async def sample_list_suggestions():
    # Create a client
    client = dialogflow_v2beta1.ParticipantsAsyncClient()

    # Initialize request argument(s)
    request = dialogflow_v2beta1.ListSuggestionsRequest(
    )

    # Make the request
    page_result = client.list_suggestions(request=request)

    # Handle the response
    async for response in page_result:
        print(response)

Returns a fully-qualified message string.

Parses a answer_record path into its component segments.

Parse a billing_account path into its component segments.

Parse a folder path into its component segments.

Parse a location path into its component segments.

Parse a organization path into its component segments.

Parse a project path into its component segments.

Parses a context path into its component segments.

Parses a document path into its component segments.

Parses a intent path into its component segments.

Parses a message path into its component segments.

Parses a participant path into its component segments.

Parses a phrase_set path into its component segments.

Parses a session_entity_type path into its component segments.

Returns a fully-qualified participant string.

Returns a fully-qualified phrase_set string.

Returns a fully-qualified session_entity_type string.

Adds a text (e.g., chat) or audio (e.g., phone recording) message from a participant into the conversation. Note: This method is only available through the gRPC API (not REST).

The top-level message sent to the client by the server is StreamingAnalyzeContentResponse. Multiple response messages can be returned in order. The first one or more messages contain the recognition_result field. Each result represents a more complete transcript of what the user said. The next message contains the reply_text field, and potentially the reply_audio and/or the automated_agent_reply fields.

Note: Always use agent versions for production traffic sent to virtual agents. See Versions and environments <https://cloud.google.com/dialogflow/es/docs/agents-versions>__.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import dialogflow_v2beta1

async def sample_streaming_analyze_content():
    # Create a client
    client = dialogflow_v2beta1.ParticipantsAsyncClient()

    # Initialize request argument(s)
    audio_config = dialogflow_v2beta1.InputAudioConfig()
    audio_config.audio_encoding = "AUDIO_ENCODING_ALAW"
    audio_config.sample_rate_hertz = 1817
    audio_config.language_code = "language_code_value"

    request = dialogflow_v2beta1.StreamingAnalyzeContentRequest(
        audio_config=audio_config,
        input_audio=b'input_audio_blob',
        participant="participant_value",
    )

    # This method expects an iterator which contains
    # 'dialogflow_v2beta1.StreamingAnalyzeContentRequest' objects
    # Here we create a generator that yields a single `request` for
    # demonstrative purposes.
    requests = [request]

    def request_generator():
        for request in requests:
            yield request

    # Make the request
    stream = await client.streaming_analyze_content(requests=request_generator())

    # Handle the response
    async for response in stream:
        print(response)

Gets suggested articles for a participant based on specific historical messages.

Note that xref_ListSuggestions will only list the auto-generated suggestions, while xref_CompileSuggestion will try to compile suggestion based on the provided conversation context in the real time.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import dialogflow_v2beta1

async def sample_suggest_articles():
    # Create a client
    client = dialogflow_v2beta1.ParticipantsAsyncClient()

    # Initialize request argument(s)
    request = dialogflow_v2beta1.SuggestArticlesRequest(
        parent="parent_value",
    )

    # Make the request
    response = await client.suggest_articles(request=request)

    # Handle the response
    print(response)

Gets suggested faq answers for a participant based on specific historical messages.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import dialogflow_v2beta1

async def sample_suggest_faq_answers():
    # Create a client
    client = dialogflow_v2beta1.ParticipantsAsyncClient()

    # Initialize request argument(s)
    request = dialogflow_v2beta1.SuggestFaqAnswersRequest(
        parent="parent_value",
    )

    # Make the request
    response = await client.suggest_faq_answers(request=request)

    # Handle the response
    print(response)

Gets knowledge assist suggestions based on historical messages.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import dialogflow_v2beta1

async def sample_suggest_knowledge_assist():
    # Create a client
    client = dialogflow_v2beta1.ParticipantsAsyncClient()

    # Initialize request argument(s)
    request = dialogflow_v2beta1.SuggestKnowledgeAssistRequest(
        parent="parent_value",
    )

    # Make the request
    response = await client.suggest_knowledge_assist(request=request)

    # Handle the response
    print(response)

Gets smart replies for a participant based on specific historical messages.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import dialogflow_v2beta1

async def sample_suggest_smart_replies():
    # Create a client
    client = dialogflow_v2beta1.ParticipantsAsyncClient()

    # Initialize request argument(s)
    request = dialogflow_v2beta1.SuggestSmartRepliesRequest(
        parent="parent_value",
    )

    # Make the request
    response = await client.suggest_smart_replies(request=request)

    # Handle the response
    print(response)

Updates the specified participant.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import dialogflow_v2beta1

async def sample_update_participant():
    # Create a client
    client = dialogflow_v2beta1.ParticipantsAsyncClient()

    # Initialize request argument(s)
    request = dialogflow_v2beta1.UpdateParticipantRequest(
    )

    # Make the request
    response = await client.update_participant(request=request)

    # Handle the response
    print(response)