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_v1beta.services.model_service.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 os
import re
from typing import (
Callable,
Dict,
Mapping,
MutableMapping,
MutableSequence,
Optional,
Sequence,
Tuple,
Type,
Union,
cast,
)
import warnings
from google.api_core import client_options as client_options_lib
from google.api_core import exceptions as core_exceptions
from google.api_core import gapic_v1
from google.api_core import retry as retries
from google.auth import credentials as ga_credentials # type: ignore
from google.auth.exceptions import MutualTLSChannelError # type: ignore
from google.auth.transport import mtls # type: ignore
from google.auth.transport.grpc import SslCredentials # type: ignore
from google.oauth2 import service_account # type: ignore
from google.ai.generativelanguage_v1beta import gapic_version as package_version
try:
OptionalRetry = Union[retries.Retry, gapic_v1.method._MethodDefault, None]
except AttributeError: # pragma: NO COVER
OptionalRetry = Union[retries.Retry, 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_v1beta.services.model_service import pagers
from google.ai.generativelanguage_v1beta.types import tuned_model as gag_tuned_model
from google.ai.generativelanguage_v1beta.types import model, model_service
from google.ai.generativelanguage_v1beta.types import tuned_model
from .transports.base import DEFAULT_CLIENT_INFO, ModelServiceTransport
from .transports.grpc import ModelServiceGrpcTransport
from .transports.grpc_asyncio import ModelServiceGrpcAsyncIOTransport
from .transports.rest import ModelServiceRestTransport
class ModelServiceClientMeta(type):
"""Metaclass for the ModelService client.
This provides class-level methods for building and retrieving
support objects (e.g. transport) without polluting the client instance
objects.
"""
_transport_registry = OrderedDict() # type: Dict[str, Type[ModelServiceTransport]]
_transport_registry["grpc"] = ModelServiceGrpcTransport
_transport_registry["grpc_asyncio"] = ModelServiceGrpcAsyncIOTransport
_transport_registry["rest"] = ModelServiceRestTransport
def get_transport_class(
cls,
label: Optional[str] = None,
) -> Type[ModelServiceTransport]:
"""Returns an appropriate transport class.
Args:
label: The name of the desired transport. If none is
provided, then the first transport in the registry is used.
Returns:
The transport class to use.
"""
# If a specific transport is requested, return that one.
if label:
return cls._transport_registry[label]
# No transport is requested; return the default (that is, the first one
# in the dictionary).
return next(iter(cls._transport_registry.values()))
[docs]class ModelServiceClient(metaclass=ModelServiceClientMeta):
"""Provides methods for getting metadata information about
Generative Models.
"""
@staticmethod
def _get_default_mtls_endpoint(api_endpoint):
"""Converts api endpoint to mTLS endpoint.
Convert "*.sandbox.googleapis.com" and "*.googleapis.com" to
"*.mtls.sandbox.googleapis.com" and "*.mtls.googleapis.com" respectively.
Args:
api_endpoint (Optional[str]): the api endpoint to convert.
Returns:
str: converted mTLS api endpoint.
"""
if not api_endpoint:
return api_endpoint
mtls_endpoint_re = re.compile(
r"(?P<name>[^.]+)(?P<mtls>\.mtls)?(?P<sandbox>\.sandbox)?(?P<googledomain>\.googleapis\.com)?"
)
m = mtls_endpoint_re.match(api_endpoint)
name, mtls, sandbox, googledomain = m.groups()
if mtls or not googledomain:
return api_endpoint
if sandbox:
return api_endpoint.replace(
"sandbox.googleapis.com", "mtls.sandbox.googleapis.com"
)
return api_endpoint.replace(".googleapis.com", ".mtls.googleapis.com")
# Note: DEFAULT_ENDPOINT is deprecated. Use _DEFAULT_ENDPOINT_TEMPLATE instead.
DEFAULT_ENDPOINT = "generativelanguage.googleapis.com"
DEFAULT_MTLS_ENDPOINT = _get_default_mtls_endpoint.__func__( # type: ignore
DEFAULT_ENDPOINT
)
_DEFAULT_ENDPOINT_TEMPLATE = "generativelanguage.{UNIVERSE_DOMAIN}"
_DEFAULT_UNIVERSE = "googleapis.com"
[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:
ModelServiceClient: The constructed client.
"""
credentials = service_account.Credentials.from_service_account_info(info)
kwargs["credentials"] = credentials
return cls(*args, **kwargs)
[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:
ModelServiceClient: The constructed client.
"""
credentials = service_account.Credentials.from_service_account_file(filename)
kwargs["credentials"] = credentials
return cls(*args, **kwargs)
from_service_account_json = from_service_account_file
@property
def transport(self) -> ModelServiceTransport:
"""Returns the transport used by the client instance.
Returns:
ModelServiceTransport: The transport used by the client
instance.
"""
return self._transport
[docs] @staticmethod
def model_path(
model: str,
) -> str:
"""Returns a fully-qualified model string."""
return "models/{model}".format(
model=model,
)
[docs] @staticmethod
def parse_model_path(path: str) -> Dict[str, str]:
"""Parses a model path into its component segments."""
m = re.match(r"^models/(?P<model>.+?)$", path)
return m.groupdict() if m else {}
[docs] @staticmethod
def tuned_model_path(
tuned_model: str,
) -> str:
"""Returns a fully-qualified tuned_model string."""
return "tunedModels/{tuned_model}".format(
tuned_model=tuned_model,
)
[docs] @staticmethod
def parse_tuned_model_path(path: str) -> Dict[str, str]:
"""Parses a tuned_model path into its component segments."""
m = re.match(r"^tunedModels/(?P<tuned_model>.+?)$", path)
return m.groupdict() if m else {}
[docs] @staticmethod
def common_billing_account_path(
billing_account: str,
) -> str:
"""Returns a fully-qualified billing_account string."""
return "billingAccounts/{billing_account}".format(
billing_account=billing_account,
)
[docs] @staticmethod
def parse_common_billing_account_path(path: str) -> Dict[str, str]:
"""Parse a billing_account path into its component segments."""
m = re.match(r"^billingAccounts/(?P<billing_account>.+?)$", path)
return m.groupdict() if m else {}
[docs] @staticmethod
def common_folder_path(
folder: str,
) -> str:
"""Returns a fully-qualified folder string."""
return "folders/{folder}".format(
folder=folder,
)
[docs] @staticmethod
def parse_common_folder_path(path: str) -> Dict[str, str]:
"""Parse a folder path into its component segments."""
m = re.match(r"^folders/(?P<folder>.+?)$", path)
return m.groupdict() if m else {}
[docs] @staticmethod
def common_organization_path(
organization: str,
) -> str:
"""Returns a fully-qualified organization string."""
return "organizations/{organization}".format(
organization=organization,
)
[docs] @staticmethod
def parse_common_organization_path(path: str) -> Dict[str, str]:
"""Parse a organization path into its component segments."""
m = re.match(r"^organizations/(?P<organization>.+?)$", path)
return m.groupdict() if m else {}
[docs] @staticmethod
def common_project_path(
project: str,
) -> str:
"""Returns a fully-qualified project string."""
return "projects/{project}".format(
project=project,
)
[docs] @staticmethod
def parse_common_project_path(path: str) -> Dict[str, str]:
"""Parse a project path into its component segments."""
m = re.match(r"^projects/(?P<project>.+?)$", path)
return m.groupdict() if m else {}
[docs] @staticmethod
def common_location_path(
project: str,
location: str,
) -> str:
"""Returns a fully-qualified location string."""
return "projects/{project}/locations/{location}".format(
project=project,
location=location,
)
[docs] @staticmethod
def parse_common_location_path(path: str) -> Dict[str, str]:
"""Parse a location path into its component segments."""
m = re.match(r"^projects/(?P<project>.+?)/locations/(?P<location>.+?)$", path)
return m.groupdict() if m else {}
[docs] @classmethod
def get_mtls_endpoint_and_cert_source(
cls, client_options: Optional[client_options_lib.ClientOptions] = None
):
"""Deprecated. 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.
"""
warnings.warn(
"get_mtls_endpoint_and_cert_source is deprecated. Use the api_endpoint property instead.",
DeprecationWarning,
)
if client_options is None:
client_options = client_options_lib.ClientOptions()
use_client_cert = os.getenv("GOOGLE_API_USE_CLIENT_CERTIFICATE", "false")
use_mtls_endpoint = os.getenv("GOOGLE_API_USE_MTLS_ENDPOINT", "auto")
if use_client_cert not in ("true", "false"):
raise ValueError(
"Environment variable `GOOGLE_API_USE_CLIENT_CERTIFICATE` must be either `true` or `false`"
)
if use_mtls_endpoint not in ("auto", "never", "always"):
raise MutualTLSChannelError(
"Environment variable `GOOGLE_API_USE_MTLS_ENDPOINT` must be `never`, `auto` or `always`"
)
# Figure out the client cert source to use.
client_cert_source = None
if use_client_cert == "true":
if client_options.client_cert_source:
client_cert_source = client_options.client_cert_source
elif mtls.has_default_client_cert_source():
client_cert_source = mtls.default_client_cert_source()
# Figure out which api endpoint to use.
if client_options.api_endpoint is not None:
api_endpoint = client_options.api_endpoint
elif use_mtls_endpoint == "always" or (
use_mtls_endpoint == "auto" and client_cert_source
):
api_endpoint = cls.DEFAULT_MTLS_ENDPOINT
else:
api_endpoint = cls.DEFAULT_ENDPOINT
return api_endpoint, client_cert_source
@staticmethod
def _read_environment_variables():
"""Returns the environment variables used by the client.
Returns:
Tuple[bool, str, str]: returns the GOOGLE_API_USE_CLIENT_CERTIFICATE,
GOOGLE_API_USE_MTLS_ENDPOINT, and GOOGLE_CLOUD_UNIVERSE_DOMAIN environment variables.
Raises:
ValueError: If GOOGLE_API_USE_CLIENT_CERTIFICATE is not
any of ["true", "false"].
google.auth.exceptions.MutualTLSChannelError: If GOOGLE_API_USE_MTLS_ENDPOINT
is not any of ["auto", "never", "always"].
"""
use_client_cert = os.getenv(
"GOOGLE_API_USE_CLIENT_CERTIFICATE", "false"
).lower()
use_mtls_endpoint = os.getenv("GOOGLE_API_USE_MTLS_ENDPOINT", "auto").lower()
universe_domain_env = os.getenv("GOOGLE_CLOUD_UNIVERSE_DOMAIN")
if use_client_cert not in ("true", "false"):
raise ValueError(
"Environment variable `GOOGLE_API_USE_CLIENT_CERTIFICATE` must be either `true` or `false`"
)
if use_mtls_endpoint not in ("auto", "never", "always"):
raise MutualTLSChannelError(
"Environment variable `GOOGLE_API_USE_MTLS_ENDPOINT` must be `never`, `auto` or `always`"
)
return use_client_cert == "true", use_mtls_endpoint, universe_domain_env
@staticmethod
def _get_client_cert_source(provided_cert_source, use_cert_flag):
"""Return the client cert source to be used by the client.
Args:
provided_cert_source (bytes): The client certificate source provided.
use_cert_flag (bool): A flag indicating whether to use the client certificate.
Returns:
bytes or None: The client cert source to be used by the client.
"""
client_cert_source = None
if use_cert_flag:
if provided_cert_source:
client_cert_source = provided_cert_source
elif mtls.has_default_client_cert_source():
client_cert_source = mtls.default_client_cert_source()
return client_cert_source
@staticmethod
def _get_api_endpoint(
api_override, client_cert_source, universe_domain, use_mtls_endpoint
):
"""Return the API endpoint used by the client.
Args:
api_override (str): The API endpoint override. If specified, this is always
the return value of this function and the other arguments are not used.
client_cert_source (bytes): The client certificate source used by the client.
universe_domain (str): The universe domain used by the client.
use_mtls_endpoint (str): How to use the mTLS endpoint, which depends also on the other parameters.
Possible values are "always", "auto", or "never".
Returns:
str: The API endpoint to be used by the client.
"""
if api_override is not None:
api_endpoint = api_override
elif use_mtls_endpoint == "always" or (
use_mtls_endpoint == "auto" and client_cert_source
):
_default_universe = ModelServiceClient._DEFAULT_UNIVERSE
if universe_domain != _default_universe:
raise MutualTLSChannelError(
f"mTLS is not supported in any universe other than {_default_universe}."
)
api_endpoint = ModelServiceClient.DEFAULT_MTLS_ENDPOINT
else:
api_endpoint = ModelServiceClient._DEFAULT_ENDPOINT_TEMPLATE.format(
UNIVERSE_DOMAIN=universe_domain
)
return api_endpoint
@staticmethod
def _get_universe_domain(
client_universe_domain: Optional[str], universe_domain_env: Optional[str]
) -> str:
"""Return the universe domain used by the client.
Args:
client_universe_domain (Optional[str]): The universe domain configured via the client options.
universe_domain_env (Optional[str]): The universe domain configured via the "GOOGLE_CLOUD_UNIVERSE_DOMAIN" environment variable.
Returns:
str: The universe domain to be used by the client.
Raises:
ValueError: If the universe domain is an empty string.
"""
universe_domain = ModelServiceClient._DEFAULT_UNIVERSE
if client_universe_domain is not None:
universe_domain = client_universe_domain
elif universe_domain_env is not None:
universe_domain = universe_domain_env
if len(universe_domain.strip()) == 0:
raise ValueError("Universe Domain cannot be an empty string.")
return universe_domain
def _validate_universe_domain(self):
"""Validates client's and credentials' universe domains are consistent.
Returns:
bool: True iff the configured universe domain is valid.
Raises:
ValueError: If the configured universe domain is not valid.
"""
# NOTE (b/349488459): universe validation is disabled until further notice.
return True
@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._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._universe_domain
def __init__(
self,
*,
credentials: Optional[ga_credentials.Credentials] = None,
transport: Optional[
Union[str, ModelServiceTransport, Callable[..., ModelServiceTransport]]
] = None,
client_options: Optional[Union[client_options_lib.ClientOptions, dict]] = None,
client_info: gapic_v1.client_info.ClientInfo = DEFAULT_CLIENT_INFO,
) -> None:
"""Instantiates the model service 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.
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 the ``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_options = client_options
if isinstance(self._client_options, dict):
self._client_options = client_options_lib.from_dict(self._client_options)
if self._client_options is None:
self._client_options = client_options_lib.ClientOptions()
self._client_options = cast(
client_options_lib.ClientOptions, self._client_options
)
universe_domain_opt = getattr(self._client_options, "universe_domain", None)
(
self._use_client_cert,
self._use_mtls_endpoint,
self._universe_domain_env,
) = ModelServiceClient._read_environment_variables()
self._client_cert_source = ModelServiceClient._get_client_cert_source(
self._client_options.client_cert_source, self._use_client_cert
)
self._universe_domain = ModelServiceClient._get_universe_domain(
universe_domain_opt, self._universe_domain_env
)
self._api_endpoint = None # updated below, depending on `transport`
# Initialize the universe domain validation.
self._is_universe_domain_valid = False
api_key_value = getattr(self._client_options, "api_key", None)
if api_key_value and credentials:
raise ValueError(
"client_options.api_key and credentials are mutually exclusive"
)
# Save or instantiate the transport.
# Ordinarily, we provide the transport, but allowing a custom transport
# instance provides an extensibility point for unusual situations.
transport_provided = isinstance(transport, ModelServiceTransport)
if transport_provided:
# transport is a ModelServiceTransport instance.
if credentials or self._client_options.credentials_file or api_key_value:
raise ValueError(
"When providing a transport instance, "
"provide its credentials directly."
)
if self._client_options.scopes:
raise ValueError(
"When providing a transport instance, provide its scopes "
"directly."
)
self._transport = cast(ModelServiceTransport, transport)
self._api_endpoint = self._transport.host
self._api_endpoint = self._api_endpoint or ModelServiceClient._get_api_endpoint(
self._client_options.api_endpoint,
self._client_cert_source,
self._universe_domain,
self._use_mtls_endpoint,
)
if not transport_provided:
import google.auth._default # type: ignore
if api_key_value and hasattr(
google.auth._default, "get_api_key_credentials"
):
credentials = google.auth._default.get_api_key_credentials(
api_key_value
)
transport_init: Union[
Type[ModelServiceTransport], Callable[..., ModelServiceTransport]
] = (
ModelServiceClient.get_transport_class(transport)
if isinstance(transport, str) or transport is None
else cast(Callable[..., ModelServiceTransport], transport)
)
# initialize with the provided callable or the passed in class
self._transport = transport_init(
credentials=credentials,
credentials_file=self._client_options.credentials_file,
host=self._api_endpoint,
scopes=self._client_options.scopes,
client_cert_source_for_mtls=self._client_cert_source,
quota_project_id=self._client_options.quota_project_id,
client_info=client_info,
always_use_jwt_access=True,
api_audience=self._client_options.api_audience,
)
[docs] 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`` such as its version
number, token limits,
`parameters <https://ai.google.dev/gemini-api/docs/models/generative-models#model-parameters>`__
and other metadata. Refer to the `Gemini models
guide <https://ai.google.dev/gemini-api/docs/models/gemini>`__
for detailed model information.
.. 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_v1beta
def sample_get_model():
# Create a client
client = generativelanguage_v1beta.ModelServiceClient()
# Initialize request argument(s)
request = generativelanguage_v1beta.GetModelRequest(
name="name_value",
)
# Make the request
response = client.get_model(request=request)
# Handle the response
print(response)
Args:
request (Union[google.ai.generativelanguage_v1beta.types.GetModelRequest, dict]):
The request object. Request for getting information about
a specific Model.
name (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.Retry): 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_v1beta.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._transport._wrapped_methods[self._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._validate_universe_domain()
# Send the request.
response = rpc(
request,
retry=retry,
timeout=timeout,
metadata=metadata,
)
# Done; return the response.
return response
[docs] 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.ListModelsPager:
r"""Lists the
```Model``\ s <https://ai.google.dev/gemini-api/docs/models/gemini>`__
available through the Gemini 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_v1beta
def sample_list_models():
# Create a client
client = generativelanguage_v1beta.ModelServiceClient()
# Initialize request argument(s)
request = generativelanguage_v1beta.ListModelsRequest(
)
# Make the request
page_result = client.list_models(request=request)
# Handle the response
for response in page_result:
print(response)
Args:
request (Union[google.ai.generativelanguage_v1beta.types.ListModelsRequest, dict]):
The request object. Request for listing all Models.
page_size (int):
The maximum number of ``Models`` to return (per page).
If unspecified, 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 (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.Retry): 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_v1beta.services.model_service.pagers.ListModelsPager:
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._transport._wrapped_methods[self._transport.list_models]
# Validate the universe domain.
self._validate_universe_domain()
# Send the request.
response = rpc(
request,
retry=retry,
timeout=timeout,
metadata=metadata,
)
# This method is paged; wrap the response in a pager, which provides
# an `__iter__` convenience method.
response = pagers.ListModelsPager(
method=rpc,
request=request,
response=response,
retry=retry,
timeout=timeout,
metadata=metadata,
)
# Done; return the response.
return response
[docs] 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_v1beta
def sample_get_tuned_model():
# Create a client
client = generativelanguage_v1beta.ModelServiceClient()
# Initialize request argument(s)
request = generativelanguage_v1beta.GetTunedModelRequest(
name="name_value",
)
# Make the request
response = client.get_tuned_model(request=request)
# Handle the response
print(response)
Args:
request (Union[google.ai.generativelanguage_v1beta.types.GetTunedModelRequest, dict]):
The request object. Request for getting information about
a specific Model.
name (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.Retry): 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_v1beta.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._transport._wrapped_methods[self._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._validate_universe_domain()
# Send the request.
response = rpc(
request,
retry=retry,
timeout=timeout,
metadata=metadata,
)
# Done; return the response.
return response
[docs] 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.ListTunedModelsPager:
r"""Lists created tuned models.
.. 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_v1beta
def sample_list_tuned_models():
# Create a client
client = generativelanguage_v1beta.ModelServiceClient()
# Initialize request argument(s)
request = generativelanguage_v1beta.ListTunedModelsRequest(
)
# Make the request
page_result = client.list_tuned_models(request=request)
# Handle the response
for response in page_result:
print(response)
Args:
request (Union[google.ai.generativelanguage_v1beta.types.ListTunedModelsRequest, dict]):
The request object. Request for listing TunedModels.
page_size (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 (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.Retry): 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_v1beta.services.model_service.pagers.ListTunedModelsPager:
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._transport._wrapped_methods[self._transport.list_tuned_models]
# Validate the universe domain.
self._validate_universe_domain()
# Send the request.
response = rpc(
request,
retry=retry,
timeout=timeout,
metadata=metadata,
)
# This method is paged; wrap the response in a pager, which provides
# an `__iter__` convenience method.
response = pagers.ListTunedModelsPager(
method=rpc,
request=request,
response=response,
retry=retry,
timeout=timeout,
metadata=metadata,
)
# Done; return the response.
return response
[docs] 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.Operation:
r"""Creates a tuned model. Check intermediate tuning progress (if
any) through the [google.longrunning.Operations] service.
Access status and results 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_v1beta
def sample_create_tuned_model():
# Create a client
client = generativelanguage_v1beta.ModelServiceClient()
# Initialize request argument(s)
tuned_model = generativelanguage_v1beta.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_v1beta.CreateTunedModelRequest(
tuned_model=tuned_model,
)
# Make the request
operation = client.create_tuned_model(request=request)
print("Waiting for operation to complete...")
response = operation.result()
# Handle the response
print(response)
Args:
request (Union[google.ai.generativelanguage_v1beta.types.CreateTunedModelRequest, dict]):
The request object. Request to create a TunedModel.
tuned_model (google.ai.generativelanguage_v1beta.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 (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.Retry): 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.Operation:
An object representing a long-running operation.
The result type for the operation will be
:class:`google.ai.generativelanguage_v1beta.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._transport._wrapped_methods[self._transport.create_tuned_model]
# Validate the universe domain.
self._validate_universe_domain()
# Send the request.
response = rpc(
request,
retry=retry,
timeout=timeout,
metadata=metadata,
)
# Wrap the response in an operation future.
response = operation.from_gapic(
response,
self._transport.operations_client,
gag_tuned_model.TunedModel,
metadata_type=model_service.CreateTunedModelMetadata,
)
# Done; return the response.
return response
[docs] 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_v1beta
def sample_update_tuned_model():
# Create a client
client = generativelanguage_v1beta.ModelServiceClient()
# Initialize request argument(s)
tuned_model = generativelanguage_v1beta.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_v1beta.UpdateTunedModelRequest(
tuned_model=tuned_model,
)
# Make the request
response = client.update_tuned_model(request=request)
# Handle the response
print(response)
Args:
request (Union[google.ai.generativelanguage_v1beta.types.UpdateTunedModelRequest, dict]):
The request object. Request to update a TunedModel.
tuned_model (google.ai.generativelanguage_v1beta.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 (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.Retry): 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_v1beta.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._transport._wrapped_methods[self._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._validate_universe_domain()
# Send the request.
response = rpc(
request,
retry=retry,
timeout=timeout,
metadata=metadata,
)
# Done; return the response.
return response
[docs] 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_v1beta
def sample_delete_tuned_model():
# Create a client
client = generativelanguage_v1beta.ModelServiceClient()
# Initialize request argument(s)
request = generativelanguage_v1beta.DeleteTunedModelRequest(
name="name_value",
)
# Make the request
client.delete_tuned_model(request=request)
Args:
request (Union[google.ai.generativelanguage_v1beta.types.DeleteTunedModelRequest, dict]):
The request object. Request to delete a TunedModel.
name (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.Retry): 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._transport._wrapped_methods[self._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._validate_universe_domain()
# Send the request.
rpc(
request,
retry=retry,
timeout=timeout,
metadata=metadata,
)
def __enter__(self) -> "ModelServiceClient":
return self
[docs] def __exit__(self, type, value, traceback):
"""Releases underlying transport's resources.
.. warning::
ONLY use as a context manager if the transport is NOT shared
with other clients! Exiting the with block will CLOSE the transport
and may cause errors in other clients!
"""
self.transport.close()
DEFAULT_CLIENT_INFO = gapic_v1.client_info.ClientInfo(
gapic_version=package_version.__version__
)
__all__ = ("ModelServiceClient",)
