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.model_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.api_core import operation # type: ignore
from google.api_core import operation_async # type: ignore
from google.longrunning import operations_pb2 # type: ignore
from google.protobuf import field_mask_pb2 # type: ignore
from google.protobuf import timestamp_pb2 # type: ignore
from google.ai.generativelanguage_v1beta3.services.model_service import pagers
from google.ai.generativelanguage_v1beta3.types import tuned_model as gag_tuned_model
from google.ai.generativelanguage_v1beta3.types import model, model_service
from google.ai.generativelanguage_v1beta3.types import tuned_model
from .client import ModelServiceClient
from .transports.base import DEFAULT_CLIENT_INFO, ModelServiceTransport
from .transports.grpc_asyncio import ModelServiceGrpcAsyncIOTransport
[docs]class ModelServiceAsyncClient:
"""Provides methods for getting metadata information about
Generative Models.
"""
_client: ModelServiceClient
# Copy defaults from the synchronous client for use here.
# Note: DEFAULT_ENDPOINT is deprecated. Use _DEFAULT_ENDPOINT_TEMPLATE instead.
DEFAULT_ENDPOINT = ModelServiceClient.DEFAULT_ENDPOINT
DEFAULT_MTLS_ENDPOINT = ModelServiceClient.DEFAULT_MTLS_ENDPOINT
_DEFAULT_ENDPOINT_TEMPLATE = ModelServiceClient._DEFAULT_ENDPOINT_TEMPLATE
_DEFAULT_UNIVERSE = ModelServiceClient._DEFAULT_UNIVERSE
model_path = staticmethod(ModelServiceClient.model_path)
parse_model_path = staticmethod(ModelServiceClient.parse_model_path)
tuned_model_path = staticmethod(ModelServiceClient.tuned_model_path)
parse_tuned_model_path = staticmethod(ModelServiceClient.parse_tuned_model_path)
common_billing_account_path = staticmethod(
ModelServiceClient.common_billing_account_path
)
parse_common_billing_account_path = staticmethod(
ModelServiceClient.parse_common_billing_account_path
)
common_folder_path = staticmethod(ModelServiceClient.common_folder_path)
parse_common_folder_path = staticmethod(ModelServiceClient.parse_common_folder_path)
common_organization_path = staticmethod(ModelServiceClient.common_organization_path)
parse_common_organization_path = staticmethod(
ModelServiceClient.parse_common_organization_path
)
common_project_path = staticmethod(ModelServiceClient.common_project_path)
parse_common_project_path = staticmethod(
ModelServiceClient.parse_common_project_path
)
common_location_path = staticmethod(ModelServiceClient.common_location_path)
parse_common_location_path = staticmethod(
ModelServiceClient.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:
ModelServiceAsyncClient: The constructed client.
"""
return ModelServiceClient.from_service_account_info.__func__(ModelServiceAsyncClient, 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:
ModelServiceAsyncClient: The constructed client.
"""
return ModelServiceClient.from_service_account_file.__func__(ModelServiceAsyncClient, 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 ModelServiceClient.get_mtls_endpoint_and_cert_source(client_options) # type: ignore
@property
def transport(self) -> ModelServiceTransport:
"""Returns the transport used by the client instance.
Returns:
ModelServiceTransport: 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 = ModelServiceClient.get_transport_class
def __init__(
self,
*,
credentials: Optional[ga_credentials.Credentials] = None,
transport: Optional[
Union[str, ModelServiceTransport, Callable[..., ModelServiceTransport]]
] = "grpc_asyncio",
client_options: Optional[ClientOptions] = None,
client_info: gapic_v1.client_info.ClientInfo = DEFAULT_CLIENT_INFO,
) -> None:
"""Instantiates the model 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,ModelServiceTransport,Callable[..., ModelServiceTransport]]]):
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 ModelServiceTransport 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 = ModelServiceClient(
credentials=credentials,
transport=transport,
client_options=client_options,
client_info=client_info,
)
[docs] async def get_model(
self,
request: Optional[Union[model_service.GetModelRequest, dict]] = None,
*,
name: Optional[str] = None,
retry: OptionalRetry = gapic_v1.method.DEFAULT,
timeout: Union[float, object] = gapic_v1.method.DEFAULT,
metadata: Sequence[Tuple[str, str]] = (),
) -> model.Model:
r"""Gets information about a specific Model.
.. 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_get_model():
# Create a client
client = generativelanguage_v1beta3.ModelServiceAsyncClient()
# Initialize request argument(s)
request = generativelanguage_v1beta3.GetModelRequest(
name="name_value",
)
# Make the request
response = await client.get_model(request=request)
# Handle the response
print(response)
Args:
request (Optional[Union[google.ai.generativelanguage_v1beta3.types.GetModelRequest, dict]]):
The request object. Request for getting information about
a specific Model.
name (:class:`str`):
Required. The resource name of the model.
This name should match a model name returned by the
``ListModels`` method.
Format: ``models/{model}``
This corresponds to the ``name`` 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.Model:
Information about a Generative
Language Model.
"""
# 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([name])
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, model_service.GetModelRequest):
request = model_service.GetModelRequest(request)
# If we have keyword arguments corresponding to fields on the
# request, apply these.
if name is not None:
request.name = name
# Wrap the RPC method; this adds retry and timeout information,
# and friendly error handling.
rpc = self._client._transport._wrapped_methods[
self._client._transport.get_model
]
# Certain fields should be provided within the metadata header;
# add these here.
metadata = tuple(metadata) + (
gapic_v1.routing_header.to_grpc_metadata((("name", request.name),)),
)
# 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 list_models(
self,
request: Optional[Union[model_service.ListModelsRequest, dict]] = None,
*,
page_size: Optional[int] = None,
page_token: Optional[str] = None,
retry: OptionalRetry = gapic_v1.method.DEFAULT,
timeout: Union[float, object] = gapic_v1.method.DEFAULT,
metadata: Sequence[Tuple[str, str]] = (),
) -> pagers.ListModelsAsyncPager:
r"""Lists models available through the API.
.. 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_list_models():
# Create a client
client = generativelanguage_v1beta3.ModelServiceAsyncClient()
# Initialize request argument(s)
request = generativelanguage_v1beta3.ListModelsRequest(
)
# Make the request
page_result = client.list_models(request=request)
# Handle the response
async for response in page_result:
print(response)
Args:
request (Optional[Union[google.ai.generativelanguage_v1beta3.types.ListModelsRequest, dict]]):
The request object. Request for listing all Models.
page_size (:class:`int`):
The maximum number of ``Models`` to return (per page).
The service may return fewer models. If unspecified, at
most 50 models will be returned per page. This method
returns at most 1000 models per page, even if you pass a
larger page_size.
This corresponds to the ``page_size`` field
on the ``request`` instance; if ``request`` is provided, this
should not be set.
page_token (:class:`str`):
A page token, received from a previous ``ListModels``
call.
Provide the ``page_token`` returned by one request as an
argument to the next request to retrieve the next page.
When paginating, all other parameters provided to
``ListModels`` must match the call that provided the
page token.
This corresponds to the ``page_token`` 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.services.model_service.pagers.ListModelsAsyncPager:
Response from ListModel containing a paginated list of
Models.
Iterating over this object will yield results and
resolve additional pages automatically.
"""
# 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([page_size, page_token])
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, model_service.ListModelsRequest):
request = model_service.ListModelsRequest(request)
# If we have keyword arguments corresponding to fields on the
# request, apply these.
if page_size is not None:
request.page_size = page_size
if page_token is not None:
request.page_token = page_token
# Wrap the RPC method; this adds retry and timeout information,
# and friendly error handling.
rpc = self._client._transport._wrapped_methods[
self._client._transport.list_models
]
# Validate the universe domain.
self._client._validate_universe_domain()
# Send the request.
response = await rpc(
request,
retry=retry,
timeout=timeout,
metadata=metadata,
)
# This method is paged; wrap the response in a pager, which provides
# an `__aiter__` convenience method.
response = pagers.ListModelsAsyncPager(
method=rpc,
request=request,
response=response,
retry=retry,
timeout=timeout,
metadata=metadata,
)
# Done; return the response.
return response
[docs] async def get_tuned_model(
self,
request: Optional[Union[model_service.GetTunedModelRequest, dict]] = None,
*,
name: Optional[str] = None,
retry: OptionalRetry = gapic_v1.method.DEFAULT,
timeout: Union[float, object] = gapic_v1.method.DEFAULT,
metadata: Sequence[Tuple[str, str]] = (),
) -> tuned_model.TunedModel:
r"""Gets information about a specific TunedModel.
.. 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_get_tuned_model():
# Create a client
client = generativelanguage_v1beta3.ModelServiceAsyncClient()
# Initialize request argument(s)
request = generativelanguage_v1beta3.GetTunedModelRequest(
name="name_value",
)
# Make the request
response = await client.get_tuned_model(request=request)
# Handle the response
print(response)
Args:
request (Optional[Union[google.ai.generativelanguage_v1beta3.types.GetTunedModelRequest, dict]]):
The request object. Request for getting information about
a specific Model.
name (:class:`str`):
Required. The resource name of the model.
Format: ``tunedModels/my-model-id``
This corresponds to the ``name`` 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.TunedModel:
A fine-tuned model created using
ModelService.CreateTunedModel.
"""
# 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([name])
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, model_service.GetTunedModelRequest):
request = model_service.GetTunedModelRequest(request)
# If we have keyword arguments corresponding to fields on the
# request, apply these.
if name is not None:
request.name = name
# Wrap the RPC method; this adds retry and timeout information,
# and friendly error handling.
rpc = self._client._transport._wrapped_methods[
self._client._transport.get_tuned_model
]
# Certain fields should be provided within the metadata header;
# add these here.
metadata = tuple(metadata) + (
gapic_v1.routing_header.to_grpc_metadata((("name", request.name),)),
)
# 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 list_tuned_models(
self,
request: Optional[Union[model_service.ListTunedModelsRequest, dict]] = None,
*,
page_size: Optional[int] = None,
page_token: Optional[str] = None,
retry: OptionalRetry = gapic_v1.method.DEFAULT,
timeout: Union[float, object] = gapic_v1.method.DEFAULT,
metadata: Sequence[Tuple[str, str]] = (),
) -> pagers.ListTunedModelsAsyncPager:
r"""Lists tuned models owned by the user.
.. 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_list_tuned_models():
# Create a client
client = generativelanguage_v1beta3.ModelServiceAsyncClient()
# Initialize request argument(s)
request = generativelanguage_v1beta3.ListTunedModelsRequest(
)
# Make the request
page_result = client.list_tuned_models(request=request)
# Handle the response
async for response in page_result:
print(response)
Args:
request (Optional[Union[google.ai.generativelanguage_v1beta3.types.ListTunedModelsRequest, dict]]):
The request object. Request for listing TunedModels.
page_size (:class:`int`):
Optional. The maximum number of ``TunedModels`` to
return (per page). The service may return fewer tuned
models.
If unspecified, at most 10 tuned models will be
returned. This method returns at most 1000 models per
page, even if you pass a larger page_size.
This corresponds to the ``page_size`` field
on the ``request`` instance; if ``request`` is provided, this
should not be set.
page_token (:class:`str`):
Optional. A page token, received from a previous
``ListTunedModels`` call.
Provide the ``page_token`` returned by one request as an
argument to the next request to retrieve the next page.
When paginating, all other parameters provided to
``ListTunedModels`` must match the call that provided
the page token.
This corresponds to the ``page_token`` 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.services.model_service.pagers.ListTunedModelsAsyncPager:
Response from ListTunedModels containing a paginated
list of Models.
Iterating over this object will yield results and
resolve additional pages automatically.
"""
# 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([page_size, page_token])
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, model_service.ListTunedModelsRequest):
request = model_service.ListTunedModelsRequest(request)
# If we have keyword arguments corresponding to fields on the
# request, apply these.
if page_size is not None:
request.page_size = page_size
if page_token is not None:
request.page_token = page_token
# Wrap the RPC method; this adds retry and timeout information,
# and friendly error handling.
rpc = self._client._transport._wrapped_methods[
self._client._transport.list_tuned_models
]
# Validate the universe domain.
self._client._validate_universe_domain()
# Send the request.
response = await rpc(
request,
retry=retry,
timeout=timeout,
metadata=metadata,
)
# This method is paged; wrap the response in a pager, which provides
# an `__aiter__` convenience method.
response = pagers.ListTunedModelsAsyncPager(
method=rpc,
request=request,
response=response,
retry=retry,
timeout=timeout,
metadata=metadata,
)
# Done; return the response.
return response
[docs] async def create_tuned_model(
self,
request: Optional[Union[model_service.CreateTunedModelRequest, dict]] = None,
*,
tuned_model: Optional[gag_tuned_model.TunedModel] = None,
tuned_model_id: Optional[str] = None,
retry: OptionalRetry = gapic_v1.method.DEFAULT,
timeout: Union[float, object] = gapic_v1.method.DEFAULT,
metadata: Sequence[Tuple[str, str]] = (),
) -> operation_async.AsyncOperation:
r"""Creates a tuned model. Intermediate tuning progress (if any) is
accessed through the [google.longrunning.Operations] service.
Status and results can be accessed through the Operations
service. Example: GET
/v1/tunedModels/az2mb0bpw6i/operations/000-111-222
.. 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_create_tuned_model():
# Create a client
client = generativelanguage_v1beta3.ModelServiceAsyncClient()
# Initialize request argument(s)
tuned_model = generativelanguage_v1beta3.TunedModel()
tuned_model.tuning_task.training_data.examples.examples.text_input = "text_input_value"
tuned_model.tuning_task.training_data.examples.examples.output = "output_value"
request = generativelanguage_v1beta3.CreateTunedModelRequest(
tuned_model=tuned_model,
)
# Make the request
operation = client.create_tuned_model(request=request)
print("Waiting for operation to complete...")
response = (await operation).result()
# Handle the response
print(response)
Args:
request (Optional[Union[google.ai.generativelanguage_v1beta3.types.CreateTunedModelRequest, dict]]):
The request object. Request to create a TunedModel.
tuned_model (:class:`google.ai.generativelanguage_v1beta3.types.TunedModel`):
Required. The tuned model to create.
This corresponds to the ``tuned_model`` field
on the ``request`` instance; if ``request`` is provided, this
should not be set.
tuned_model_id (:class:`str`):
Optional. The unique id for the tuned model if
specified. This value should be up to 40 characters, the
first character must be a letter, the last could be a
letter or a number. The id must match the regular
expression: `a-z <[a-z0-9-]{0,38}[a-z0-9]>`__?.
This corresponds to the ``tuned_model_id`` 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.api_core.operation_async.AsyncOperation:
An object representing a long-running operation.
The result type for the operation will be
:class:`google.ai.generativelanguage_v1beta3.types.TunedModel`
A fine-tuned model created using
ModelService.CreateTunedModel.
"""
# 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([tuned_model, tuned_model_id])
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, model_service.CreateTunedModelRequest):
request = model_service.CreateTunedModelRequest(request)
# If we have keyword arguments corresponding to fields on the
# request, apply these.
if tuned_model is not None:
request.tuned_model = tuned_model
if tuned_model_id is not None:
request.tuned_model_id = tuned_model_id
# Wrap the RPC method; this adds retry and timeout information,
# and friendly error handling.
rpc = self._client._transport._wrapped_methods[
self._client._transport.create_tuned_model
]
# Validate the universe domain.
self._client._validate_universe_domain()
# Send the request.
response = await rpc(
request,
retry=retry,
timeout=timeout,
metadata=metadata,
)
# Wrap the response in an operation future.
response = operation_async.from_gapic(
response,
self._client._transport.operations_client,
gag_tuned_model.TunedModel,
metadata_type=model_service.CreateTunedModelMetadata,
)
# Done; return the response.
return response
[docs] async def update_tuned_model(
self,
request: Optional[Union[model_service.UpdateTunedModelRequest, dict]] = None,
*,
tuned_model: Optional[gag_tuned_model.TunedModel] = None,
update_mask: Optional[field_mask_pb2.FieldMask] = None,
retry: OptionalRetry = gapic_v1.method.DEFAULT,
timeout: Union[float, object] = gapic_v1.method.DEFAULT,
metadata: Sequence[Tuple[str, str]] = (),
) -> gag_tuned_model.TunedModel:
r"""Updates a tuned model.
.. 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_update_tuned_model():
# Create a client
client = generativelanguage_v1beta3.ModelServiceAsyncClient()
# Initialize request argument(s)
tuned_model = generativelanguage_v1beta3.TunedModel()
tuned_model.tuning_task.training_data.examples.examples.text_input = "text_input_value"
tuned_model.tuning_task.training_data.examples.examples.output = "output_value"
request = generativelanguage_v1beta3.UpdateTunedModelRequest(
tuned_model=tuned_model,
)
# Make the request
response = await client.update_tuned_model(request=request)
# Handle the response
print(response)
Args:
request (Optional[Union[google.ai.generativelanguage_v1beta3.types.UpdateTunedModelRequest, dict]]):
The request object. Request to update a TunedModel.
tuned_model (:class:`google.ai.generativelanguage_v1beta3.types.TunedModel`):
Required. The tuned model to update.
This corresponds to the ``tuned_model`` field
on the ``request`` instance; if ``request`` is provided, this
should not be set.
update_mask (:class:`google.protobuf.field_mask_pb2.FieldMask`):
Required. The list of fields to
update.
This corresponds to the ``update_mask`` 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.TunedModel:
A fine-tuned model created using
ModelService.CreateTunedModel.
"""
# 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([tuned_model, update_mask])
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, model_service.UpdateTunedModelRequest):
request = model_service.UpdateTunedModelRequest(request)
# If we have keyword arguments corresponding to fields on the
# request, apply these.
if tuned_model is not None:
request.tuned_model = tuned_model
if update_mask is not None:
request.update_mask = update_mask
# Wrap the RPC method; this adds retry and timeout information,
# and friendly error handling.
rpc = self._client._transport._wrapped_methods[
self._client._transport.update_tuned_model
]
# Certain fields should be provided within the metadata header;
# add these here.
metadata = tuple(metadata) + (
gapic_v1.routing_header.to_grpc_metadata(
(("tuned_model.name", request.tuned_model.name),)
),
)
# 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 delete_tuned_model(
self,
request: Optional[Union[model_service.DeleteTunedModelRequest, dict]] = None,
*,
name: Optional[str] = None,
retry: OptionalRetry = gapic_v1.method.DEFAULT,
timeout: Union[float, object] = gapic_v1.method.DEFAULT,
metadata: Sequence[Tuple[str, str]] = (),
) -> None:
r"""Deletes a tuned model.
.. 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_delete_tuned_model():
# Create a client
client = generativelanguage_v1beta3.ModelServiceAsyncClient()
# Initialize request argument(s)
request = generativelanguage_v1beta3.DeleteTunedModelRequest(
name="name_value",
)
# Make the request
await client.delete_tuned_model(request=request)
Args:
request (Optional[Union[google.ai.generativelanguage_v1beta3.types.DeleteTunedModelRequest, dict]]):
The request object. Request to delete a TunedModel.
name (:class:`str`):
Required. The resource name of the model. Format:
``tunedModels/my-model-id``
This corresponds to the ``name`` 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.
"""
# 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([name])
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, model_service.DeleteTunedModelRequest):
request = model_service.DeleteTunedModelRequest(request)
# If we have keyword arguments corresponding to fields on the
# request, apply these.
if name is not None:
request.name = name
# Wrap the RPC method; this adds retry and timeout information,
# and friendly error handling.
rpc = self._client._transport._wrapped_methods[
self._client._transport.delete_tuned_model
]
# Certain fields should be provided within the metadata header;
# add these here.
metadata = tuple(metadata) + (
gapic_v1.routing_header.to_grpc_metadata((("name", request.name),)),
)
# Validate the universe domain.
self._client._validate_universe_domain()
# Send the request.
await rpc(
request,
retry=retry,
timeout=timeout,
metadata=metadata,
)
async def __aenter__(self) -> "ModelServiceAsyncClient":
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__ = ("ModelServiceAsyncClient",)
