# -*- coding: utf-8 -*- # Copyright 2024 Google LLC # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # from collections import OrderedDict import logging as std_logging import re from typing import ( Callable, Dict, Mapping, MutableMapping, MutableSequence, Optional, Sequence, Tuple, Type, Union, ) from google.api_core import exceptions as core_exceptions from google.api_core import gapic_v1 from google.api_core import retry_async as retries from google.api_core.client_options import ClientOptions from google.auth import credentials as ga_credentials # type: ignore from google.oauth2 import service_account # type: ignore from google.ai.generativelanguage_v1beta3 import gapic_version as package_version try: OptionalRetry = Union[retries.AsyncRetry, gapic_v1.method._MethodDefault, None] except AttributeError: # pragma: NO COVER OptionalRetry = Union[retries.AsyncRetry, object, None] # type: ignore from google.longrunning import operations_pb2 # type: ignore from google.ai.generativelanguage_v1beta3.types import safety, text_service from .client import TextServiceClient from .transports.base import DEFAULT_CLIENT_INFO, TextServiceTransport from .transports.grpc_asyncio import TextServiceGrpcAsyncIOTransport try: from google.api_core import client_logging # type: ignore CLIENT_LOGGING_SUPPORTED = True # pragma: NO COVER except ImportError: # pragma: NO COVER CLIENT_LOGGING_SUPPORTED = False _LOGGER = std_logging.getLogger(__name__) class TextServiceAsyncClient: """API for using Generative Language Models (GLMs) trained to generate text. Also known as Large Language Models (LLM)s, these generate text given an input prompt from the user. """ _client: TextServiceClient # Copy defaults from the synchronous client for use here. # Note: DEFAULT_ENDPOINT is deprecated. Use _DEFAULT_ENDPOINT_TEMPLATE instead. DEFAULT_ENDPOINT = TextServiceClient.DEFAULT_ENDPOINT DEFAULT_MTLS_ENDPOINT = TextServiceClient.DEFAULT_MTLS_ENDPOINT _DEFAULT_ENDPOINT_TEMPLATE = TextServiceClient._DEFAULT_ENDPOINT_TEMPLATE _DEFAULT_UNIVERSE = TextServiceClient._DEFAULT_UNIVERSE model_path = staticmethod(TextServiceClient.model_path) parse_model_path = staticmethod(TextServiceClient.parse_model_path) common_billing_account_path = staticmethod( TextServiceClient.common_billing_account_path ) parse_common_billing_account_path = staticmethod( TextServiceClient.parse_common_billing_account_path ) common_folder_path = staticmethod(TextServiceClient.common_folder_path) parse_common_folder_path = staticmethod(TextServiceClient.parse_common_folder_path) common_organization_path = staticmethod(TextServiceClient.common_organization_path) parse_common_organization_path = staticmethod( TextServiceClient.parse_common_organization_path ) common_project_path = staticmethod(TextServiceClient.common_project_path) parse_common_project_path = staticmethod( TextServiceClient.parse_common_project_path ) common_location_path = staticmethod(TextServiceClient.common_location_path) parse_common_location_path = staticmethod( TextServiceClient.parse_common_location_path ) @classmethod def from_service_account_info(cls, info: dict, *args, **kwargs): """Creates an instance of this client using the provided credentials info. Args: info (dict): The service account private key info. args: Additional arguments to pass to the constructor. kwargs: Additional arguments to pass to the constructor. Returns: TextServiceAsyncClient: The constructed client. """ return TextServiceClient.from_service_account_info.__func__(TextServiceAsyncClient, info, *args, **kwargs) # type: ignore @classmethod def from_service_account_file(cls, filename: str, *args, **kwargs): """Creates an instance of this client using the provided credentials file. Args: filename (str): The path to the service account private key json file. args: Additional arguments to pass to the constructor. kwargs: Additional arguments to pass to the constructor. Returns: TextServiceAsyncClient: The constructed client. """ return TextServiceClient.from_service_account_file.__func__(TextServiceAsyncClient, filename, *args, **kwargs) # type: ignore from_service_account_json = from_service_account_file @classmethod def get_mtls_endpoint_and_cert_source( cls, client_options: Optional[ClientOptions] = None ): """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. Args: client_options (google.api_core.client_options.ClientOptions): Custom options for the client. Only the `api_endpoint` and `client_cert_source` properties may be used in this method. Returns: Tuple[str, Callable[[], Tuple[bytes, bytes]]]: returns the API endpoint and the client cert source to use. Raises: google.auth.exceptions.MutualTLSChannelError: If any errors happen. """ return TextServiceClient.get_mtls_endpoint_and_cert_source(client_options) # type: ignore @property def transport(self) -> TextServiceTransport: """Returns the transport used by the client instance. Returns: TextServiceTransport: The transport used by the client instance. """ return self._client.transport @property def api_endpoint(self): """Return the API endpoint used by the client instance. Returns: str: The API endpoint used by the client instance. """ return self._client._api_endpoint @property def universe_domain(self) -> str: """Return the universe domain used by the client instance. Returns: str: The universe domain used by the client instance. """ return self._client._universe_domain get_transport_class = TextServiceClient.get_transport_class def __init__( self, *, credentials: Optional[ga_credentials.Credentials] = None, transport: Optional[ Union[str, TextServiceTransport, Callable[..., TextServiceTransport]] ] = "grpc_asyncio", client_options: Optional[ClientOptions] = None, client_info: gapic_v1.client_info.ClientInfo = DEFAULT_CLIENT_INFO, ) -> None: """Instantiates the text service async client. Args: credentials (Optional[google.auth.credentials.Credentials]): The authorization credentials to attach to requests. These credentials identify the application to the service; if none are specified, the client will attempt to ascertain the credentials from the environment. transport (Optional[Union[str,TextServiceTransport,Callable[..., TextServiceTransport]]]): The transport to use, or a Callable that constructs and returns a new transport to use. If a Callable is given, it will be called with the same set of initialization arguments as used in the TextServiceTransport constructor. If set to None, a transport is chosen automatically. client_options (Optional[Union[google.api_core.client_options.ClientOptions, dict]]): Custom options for the client. 1. The ``api_endpoint`` property can be used to override the default endpoint provided by the client when ``transport`` is not explicitly provided. Only if this property is not set and ``transport`` was not explicitly provided, the endpoint is determined by the GOOGLE_API_USE_MTLS_ENDPOINT environment variable, which have one of the following values: "always" (always use the default mTLS endpoint), "never" (always use the default regular endpoint) and "auto" (auto-switch to the default mTLS endpoint if client certificate is present; this is the default value). 2. If the GOOGLE_API_USE_CLIENT_CERTIFICATE environment variable is "true", then the ``client_cert_source`` property can be used to provide a client certificate for mTLS transport. If not provided, the default SSL client certificate will be used if present. If GOOGLE_API_USE_CLIENT_CERTIFICATE is "false" or not set, no client certificate will be used. 3. The ``universe_domain`` property can be used to override the default "googleapis.com" universe. Note that ``api_endpoint`` property still takes precedence; and ``universe_domain`` is currently not supported for mTLS. client_info (google.api_core.gapic_v1.client_info.ClientInfo): The client info used to send a user-agent string along with API requests. If ``None``, then default info will be used. Generally, you only need to set this if you're developing your own client library. Raises: google.auth.exceptions.MutualTlsChannelError: If mutual TLS transport creation failed for any reason. """ self._client = TextServiceClient( credentials=credentials, transport=transport, client_options=client_options, client_info=client_info, ) if CLIENT_LOGGING_SUPPORTED and _LOGGER.isEnabledFor( std_logging.DEBUG ): # pragma: NO COVER _LOGGER.debug( "Created client `google.ai.generativelanguage_v1beta3.TextServiceAsyncClient`.", extra={ "serviceName": "google.ai.generativelanguage.v1beta3.TextService", "universeDomain": getattr( self._client._transport._credentials, "universe_domain", "" ), "credentialsType": f"{type(self._client._transport._credentials).__module__}.{type(self._client._transport._credentials).__qualname__}", "credentialsInfo": getattr( self.transport._credentials, "get_cred_info", lambda: None )(), } if hasattr(self._client._transport, "_credentials") else { "serviceName": "google.ai.generativelanguage.v1beta3.TextService", "credentialsType": None, }, ) async def generate_text( self, request: Optional[Union[text_service.GenerateTextRequest, dict]] = None, *, model: Optional[str] = None, prompt: Optional[text_service.TextPrompt] = None, temperature: Optional[float] = None, candidate_count: Optional[int] = None, max_output_tokens: Optional[int] = None, top_p: Optional[float] = None, top_k: Optional[int] = None, retry: OptionalRetry = gapic_v1.method.DEFAULT, timeout: Union[float, object] = gapic_v1.method.DEFAULT, metadata: Sequence[Tuple[str, Union[str, bytes]]] = (), ) -> text_service.GenerateTextResponse: r"""Generates a response from the model given an input message. .. code-block:: python # 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.ai import generativelanguage_v1beta3 async def sample_generate_text(): # Create a client client = generativelanguage_v1beta3.TextServiceAsyncClient() # Initialize request argument(s) prompt = generativelanguage_v1beta3.TextPrompt() prompt.text = "text_value" request = generativelanguage_v1beta3.GenerateTextRequest( model="model_value", prompt=prompt, ) # Make the request response = await client.generate_text(request=request) # Handle the response print(response) Args: request (Optional[Union[google.ai.generativelanguage_v1beta3.types.GenerateTextRequest, dict]]): The request object. Request to generate a text completion response from the model. model (:class:`str`): Required. The name of the ``Model`` or ``TunedModel`` to use for generating the completion. Examples: models/text-bison-001 tunedModels/sentence-translator-u3b7m This corresponds to the ``model`` field on the ``request`` instance; if ``request`` is provided, this should not be set. prompt (:class:`google.ai.generativelanguage_v1beta3.types.TextPrompt`): Required. The free-form input text given to the model as a prompt. Given a prompt, the model will generate a TextCompletion response it predicts as the completion of the input text. This corresponds to the ``prompt`` field on the ``request`` instance; if ``request`` is provided, this should not be set. temperature (:class:`float`): Optional. Controls the randomness of the output. Note: The default value varies by model, see the ``Model.temperature`` attribute of the ``Model`` returned the ``getModel`` function. Values can range from [0.0,1.0], inclusive. A value closer to 1.0 will produce responses that are more varied and creative, while a value closer to 0.0 will typically result in more straightforward responses from the model. This corresponds to the ``temperature`` field on the ``request`` instance; if ``request`` is provided, this should not be set. candidate_count (:class:`int`): Optional. Number of generated responses to return. This value must be between [1, 8], inclusive. If unset, this will default to 1. This corresponds to the ``candidate_count`` field on the ``request`` instance; if ``request`` is provided, this should not be set. max_output_tokens (:class:`int`): Optional. The maximum number of tokens to include in a candidate. If unset, this will default to output_token_limit specified in the ``Model`` specification. This corresponds to the ``max_output_tokens`` field on the ``request`` instance; if ``request`` is provided, this should not be set. top_p (:class:`float`): Optional. The maximum cumulative probability of tokens to consider when sampling. The model uses combined Top-k and nucleus sampling. Tokens are sorted based on their assigned probabilities so that only the most likely tokens are considered. Top-k sampling directly limits the maximum number of tokens to consider, while Nucleus sampling limits number of tokens based on the cumulative probability. Note: The default value varies by model, see the ``Model.top_p`` attribute of the ``Model`` returned the ``getModel`` function. This corresponds to the ``top_p`` field on the ``request`` instance; if ``request`` is provided, this should not be set. top_k (:class:`int`): Optional. The maximum number of tokens to consider when sampling. The model uses combined Top-k and nucleus sampling. Top-k sampling considers the set of ``top_k`` most probable tokens. Defaults to 40. Note: The default value varies by model, see the ``Model.top_k`` attribute of the ``Model`` returned the ``getModel`` function. This corresponds to the ``top_k`` field on the ``request`` instance; if ``request`` is provided, this should not be set. retry (google.api_core.retry_async.AsyncRetry): Designation of what errors, if any, should be retried. timeout (float): The timeout for this request. metadata (Sequence[Tuple[str, Union[str, bytes]]]): Key/value pairs which should be sent along with the request as metadata. Normally, each value must be of type `str`, but for metadata keys ending with the suffix `-bin`, the corresponding values must be of type `bytes`. Returns: google.ai.generativelanguage_v1beta3.types.GenerateTextResponse: The response from the model, including candidate completions. """ # Create or coerce a protobuf request object. # - Quick check: If we got a request object, we should *not* have # gotten any keyword arguments that map to the request. has_flattened_params = any( [ model, prompt, temperature, candidate_count, max_output_tokens, top_p, top_k, ] ) if request is not None and has_flattened_params: raise ValueError( "If the `request` argument is set, then none of " "the individual field arguments should be set." ) # - Use the request object if provided (there's no risk of modifying the input as # there are no flattened fields), or create one. if not isinstance(request, text_service.GenerateTextRequest): request = text_service.GenerateTextRequest(request) # If we have keyword arguments corresponding to fields on the # request, apply these. if model is not None: request.model = model if prompt is not None: request.prompt = prompt if temperature is not None: request.temperature = temperature if candidate_count is not None: request.candidate_count = candidate_count if max_output_tokens is not None: request.max_output_tokens = max_output_tokens if top_p is not None: request.top_p = top_p if top_k is not None: request.top_k = top_k # Wrap the RPC method; this adds retry and timeout information, # and friendly error handling. rpc = self._client._transport._wrapped_methods[ self._client._transport.generate_text ] # Certain fields should be provided within the metadata header; # add these here. metadata = tuple(metadata) + ( gapic_v1.routing_header.to_grpc_metadata((("model", request.model),)), ) # Validate the universe domain. self._client._validate_universe_domain() # Send the request. response = await rpc( request, retry=retry, timeout=timeout, metadata=metadata, ) # Done; return the response. return response async def embed_text( self, request: Optional[Union[text_service.EmbedTextRequest, dict]] = None, *, model: Optional[str] = None, text: Optional[str] = None, retry: OptionalRetry = gapic_v1.method.DEFAULT, timeout: Union[float, object] = gapic_v1.method.DEFAULT, metadata: Sequence[Tuple[str, Union[str, bytes]]] = (), ) -> text_service.EmbedTextResponse: r"""Generates an embedding from the model given an input message. .. code-block:: python # 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.ai import generativelanguage_v1beta3 async def sample_embed_text(): # Create a client client = generativelanguage_v1beta3.TextServiceAsyncClient() # Initialize request argument(s) request = generativelanguage_v1beta3.EmbedTextRequest( model="model_value", text="text_value", ) # Make the request response = await client.embed_text(request=request) # Handle the response print(response) Args: request (Optional[Union[google.ai.generativelanguage_v1beta3.types.EmbedTextRequest, dict]]): The request object. Request to get a text embedding from the model. model (:class:`str`): Required. The model name to use with the format model=models/{model}. This corresponds to the ``model`` field on the ``request`` instance; if ``request`` is provided, this should not be set. text (:class:`str`): Required. The free-form input text that the model will turn into an embedding. This corresponds to the ``text`` field on the ``request`` instance; if ``request`` is provided, this should not be set. retry (google.api_core.retry_async.AsyncRetry): Designation of what errors, if any, should be retried. timeout (float): The timeout for this request. metadata (Sequence[Tuple[str, Union[str, bytes]]]): Key/value pairs which should be sent along with the request as metadata. Normally, each value must be of type `str`, but for metadata keys ending with the suffix `-bin`, the corresponding values must be of type `bytes`. Returns: google.ai.generativelanguage_v1beta3.types.EmbedTextResponse: The response to a EmbedTextRequest. """ # Create or coerce a protobuf request object. # - Quick check: If we got a request object, we should *not* have # gotten any keyword arguments that map to the request. has_flattened_params = any([model, text]) if request is not None and has_flattened_params: raise ValueError( "If the `request` argument is set, then none of " "the individual field arguments should be set." ) # - Use the request object if provided (there's no risk of modifying the input as # there are no flattened fields), or create one. if not isinstance(request, text_service.EmbedTextRequest): request = text_service.EmbedTextRequest(request) # If we have keyword arguments corresponding to fields on the # request, apply these. if model is not None: request.model = model if text is not None: request.text = text # Wrap the RPC method; this adds retry and timeout information, # and friendly error handling. rpc = self._client._transport._wrapped_methods[ self._client._transport.embed_text ] # Certain fields should be provided within the metadata header; # add these here. metadata = tuple(metadata) + ( gapic_v1.routing_header.to_grpc_metadata((("model", request.model),)), ) # Validate the universe domain. self._client._validate_universe_domain() # Send the request. response = await rpc( request, retry=retry, timeout=timeout, metadata=metadata, ) # Done; return the response. return response async def batch_embed_text( self, request: Optional[Union[text_service.BatchEmbedTextRequest, dict]] = None, *, model: Optional[str] = None, texts: Optional[MutableSequence[str]] = None, retry: OptionalRetry = gapic_v1.method.DEFAULT, timeout: Union[float, object] = gapic_v1.method.DEFAULT, metadata: Sequence[Tuple[str, Union[str, bytes]]] = (), ) -> text_service.BatchEmbedTextResponse: r"""Generates multiple embeddings from the model given input text in a synchronous call. .. code-block:: python # 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.ai import generativelanguage_v1beta3 async def sample_batch_embed_text(): # Create a client client = generativelanguage_v1beta3.TextServiceAsyncClient() # Initialize request argument(s) request = generativelanguage_v1beta3.BatchEmbedTextRequest( model="model_value", texts=['texts_value1', 'texts_value2'], ) # Make the request response = await client.batch_embed_text(request=request) # Handle the response print(response) Args: request (Optional[Union[google.ai.generativelanguage_v1beta3.types.BatchEmbedTextRequest, dict]]): The request object. Batch request to get a text embedding from the model. model (:class:`str`): Required. The name of the ``Model`` to use for generating the embedding. Examples: models/embedding-gecko-001 This corresponds to the ``model`` field on the ``request`` instance; if ``request`` is provided, this should not be set. texts (:class:`MutableSequence[str]`): Required. The free-form input texts that the model will turn into an embedding. The current limit is 100 texts, over which an error will be thrown. This corresponds to the ``texts`` field on the ``request`` instance; if ``request`` is provided, this should not be set. retry (google.api_core.retry_async.AsyncRetry): Designation of what errors, if any, should be retried. timeout (float): The timeout for this request. metadata (Sequence[Tuple[str, Union[str, bytes]]]): Key/value pairs which should be sent along with the request as metadata. Normally, each value must be of type `str`, but for metadata keys ending with the suffix `-bin`, the corresponding values must be of type `bytes`. Returns: google.ai.generativelanguage_v1beta3.types.BatchEmbedTextResponse: The response to a EmbedTextRequest. """ # Create or coerce a protobuf request object. # - Quick check: If we got a request object, we should *not* have # gotten any keyword arguments that map to the request. has_flattened_params = any([model, texts]) if request is not None and has_flattened_params: raise ValueError( "If the `request` argument is set, then none of " "the individual field arguments should be set." ) # - Use the request object if provided (there's no risk of modifying the input as # there are no flattened fields), or create one. if not isinstance(request, text_service.BatchEmbedTextRequest): request = text_service.BatchEmbedTextRequest(request) # If we have keyword arguments corresponding to fields on the # request, apply these. if model is not None: request.model = model if texts: request.texts.extend(texts) # Wrap the RPC method; this adds retry and timeout information, # and friendly error handling. rpc = self._client._transport._wrapped_methods[ self._client._transport.batch_embed_text ] # Certain fields should be provided within the metadata header; # add these here. metadata = tuple(metadata) + ( gapic_v1.routing_header.to_grpc_metadata((("model", request.model),)), ) # Validate the universe domain. self._client._validate_universe_domain() # Send the request. response = await rpc( request, retry=retry, timeout=timeout, metadata=metadata, ) # Done; return the response. return response async def count_text_tokens( self, request: Optional[Union[text_service.CountTextTokensRequest, dict]] = None, *, model: Optional[str] = None, prompt: Optional[text_service.TextPrompt] = None, retry: OptionalRetry = gapic_v1.method.DEFAULT, timeout: Union[float, object] = gapic_v1.method.DEFAULT, metadata: Sequence[Tuple[str, Union[str, bytes]]] = (), ) -> text_service.CountTextTokensResponse: r"""Runs a model's tokenizer on a text and returns the token count. .. code-block:: python # 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.ai import generativelanguage_v1beta3 async def sample_count_text_tokens(): # Create a client client = generativelanguage_v1beta3.TextServiceAsyncClient() # Initialize request argument(s) prompt = generativelanguage_v1beta3.TextPrompt() prompt.text = "text_value" request = generativelanguage_v1beta3.CountTextTokensRequest( model="model_value", prompt=prompt, ) # Make the request response = await client.count_text_tokens(request=request) # Handle the response print(response) Args: request (Optional[Union[google.ai.generativelanguage_v1beta3.types.CountTextTokensRequest, dict]]): The request object. Counts the number of tokens in the ``prompt`` sent to a model. Models may tokenize text differently, so each model may return a different ``token_count``. model (:class:`str`): Required. The model's resource name. This serves as an ID for the Model to use. This name should match a model name returned by the ``ListModels`` method. Format: ``models/{model}`` This corresponds to the ``model`` field on the ``request`` instance; if ``request`` is provided, this should not be set. prompt (:class:`google.ai.generativelanguage_v1beta3.types.TextPrompt`): Required. The free-form input text given to the model as a prompt. This corresponds to the ``prompt`` field on the ``request`` instance; if ``request`` is provided, this should not be set. retry (google.api_core.retry_async.AsyncRetry): Designation of what errors, if any, should be retried. timeout (float): The timeout for this request. metadata (Sequence[Tuple[str, Union[str, bytes]]]): Key/value pairs which should be sent along with the request as metadata. Normally, each value must be of type `str`, but for metadata keys ending with the suffix `-bin`, the corresponding values must be of type `bytes`. Returns: google.ai.generativelanguage_v1beta3.types.CountTextTokensResponse: A response from CountTextTokens. It returns the model's token_count for the prompt. """ # Create or coerce a protobuf request object. # - Quick check: If we got a request object, we should *not* have # gotten any keyword arguments that map to the request. has_flattened_params = any([model, prompt]) if request is not None and has_flattened_params: raise ValueError( "If the `request` argument is set, then none of " "the individual field arguments should be set." ) # - Use the request object if provided (there's no risk of modifying the input as # there are no flattened fields), or create one. if not isinstance(request, text_service.CountTextTokensRequest): request = text_service.CountTextTokensRequest(request) # If we have keyword arguments corresponding to fields on the # request, apply these. if model is not None: request.model = model if prompt is not None: request.prompt = prompt # Wrap the RPC method; this adds retry and timeout information, # and friendly error handling. rpc = self._client._transport._wrapped_methods[ self._client._transport.count_text_tokens ] # Certain fields should be provided within the metadata header; # add these here. metadata = tuple(metadata) + ( gapic_v1.routing_header.to_grpc_metadata((("model", request.model),)), ) # Validate the universe domain. self._client._validate_universe_domain() # Send the request. response = await rpc( request, retry=retry, timeout=timeout, metadata=metadata, ) # Done; return the response. return response async def __aenter__(self) -> "TextServiceAsyncClient": return self async def __aexit__(self, exc_type, exc, tb): await self.transport.close() DEFAULT_CLIENT_INFO = gapic_v1.client_info.ClientInfo( gapic_version=package_version.__version__ ) __all__ = ("TextServiceAsyncClient",)