As of January 1, 2020 this library no longer supports Python 2 on the latest released version.
Library versions released prior to that date will continue to be available. For more information please
visit Python 2 support on Google Cloud.
Source code for google.ai.generativelanguage_v1beta3.services.text_service.async_client
# -*- 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 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
[docs]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
)
[docs] @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
[docs] @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
[docs] @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,
)
[docs] 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, str]] = (),
) -> 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, str]]): Strings which should be
sent along with the request as metadata.
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
[docs] 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, str]] = (),
) -> 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, str]]): Strings which should be
sent along with the request as metadata.
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
[docs] 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, str]] = (),
) -> 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, str]]): Strings which should be
sent along with the request as metadata.
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
[docs] 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, str]] = (),
) -> 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, str]]): Strings which should be
sent along with the request as metadata.
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",)
