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.generative_service.client
# -*- coding: utf-8 -*-
# Copyright 2025 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
from http import HTTPStatus
import json
import logging as std_logging
import os
import re
from typing import (
Callable,
Dict,
Iterable,
Iterator,
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
import google.protobuf
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
try:
from google.api_core import client_logging # type: ignore
CLIENT_LOGGING_SUPPORTED = True # pragma: NO COVER
except ImportError: # pragma: NO COVER
CLIENT_LOGGING_SUPPORTED = False
_LOGGER = std_logging.getLogger(__name__)
from google.longrunning import operations_pb2 # type: ignore
from google.ai.generativelanguage_v1beta.types import generative_service, safety
from google.ai.generativelanguage_v1beta.types import content
from google.ai.generativelanguage_v1beta.types import content as gag_content
from .transports.base import DEFAULT_CLIENT_INFO, GenerativeServiceTransport
from .transports.grpc import GenerativeServiceGrpcTransport
from .transports.grpc_asyncio import GenerativeServiceGrpcAsyncIOTransport
from .transports.rest import GenerativeServiceRestTransport
class GenerativeServiceClientMeta(type):
"""Metaclass for the GenerativeService 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[GenerativeServiceTransport]]
_transport_registry["grpc"] = GenerativeServiceGrpcTransport
_transport_registry["grpc_asyncio"] = GenerativeServiceGrpcAsyncIOTransport
_transport_registry["rest"] = GenerativeServiceRestTransport
def get_transport_class(
cls,
label: Optional[str] = None,
) -> Type[GenerativeServiceTransport]:
"""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 GenerativeServiceClient(metaclass=GenerativeServiceClientMeta):
"""API for using Large Models that generate multimodal content
and have additional capabilities beyond text generation.
"""
@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:
GenerativeServiceClient: 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:
GenerativeServiceClient: 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) -> GenerativeServiceTransport:
"""Returns the transport used by the client instance.
Returns:
GenerativeServiceTransport: The transport used by the client
instance.
"""
return self._transport
[docs] @staticmethod
def cached_content_path(
id: str,
) -> str:
"""Returns a fully-qualified cached_content string."""
return "cachedContents/{id}".format(
id=id,
)
[docs] @staticmethod
def parse_cached_content_path(path: str) -> Dict[str, str]:
"""Parses a cached_content path into its component segments."""
m = re.match(r"^cachedContents/(?P<id>.+?)$", path)
return m.groupdict() if m else {}
[docs] @staticmethod
def model_path(
model: str,
) -> str:
"""Returns a fully-qualified model string."""
return "models/{model}".format(
model=model,
)
[docs] @staticmethod
def parse_model_path(path: str) -> Dict[str, str]:
"""Parses a model path into its component segments."""
m = re.match(r"^models/(?P<model>.+?)$", path)
return m.groupdict() if m else {}
[docs] @staticmethod
def 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 = GenerativeServiceClient._DEFAULT_UNIVERSE
if universe_domain != _default_universe:
raise MutualTLSChannelError(
f"mTLS is not supported in any universe other than {_default_universe}."
)
api_endpoint = GenerativeServiceClient.DEFAULT_MTLS_ENDPOINT
else:
api_endpoint = GenerativeServiceClient._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 = GenerativeServiceClient._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
def _add_cred_info_for_auth_errors(
self, error: core_exceptions.GoogleAPICallError
) -> None:
"""Adds credential info string to error details for 401/403/404 errors.
Args:
error (google.api_core.exceptions.GoogleAPICallError): The error to add the cred info.
"""
if error.code not in [
HTTPStatus.UNAUTHORIZED,
HTTPStatus.FORBIDDEN,
HTTPStatus.NOT_FOUND,
]:
return
cred = self._transport._credentials
# get_cred_info is only available in google-auth>=2.35.0
if not hasattr(cred, "get_cred_info"):
return
# ignore the type check since pypy test fails when get_cred_info
# is not available
cred_info = cred.get_cred_info() # type: ignore
if cred_info and hasattr(error._details, "append"):
error._details.append(json.dumps(cred_info))
@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,
GenerativeServiceTransport,
Callable[..., GenerativeServiceTransport],
]
] = None,
client_options: Optional[Union[client_options_lib.ClientOptions, dict]] = None,
client_info: gapic_v1.client_info.ClientInfo = DEFAULT_CLIENT_INFO,
) -> None:
"""Instantiates the generative 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,GenerativeServiceTransport,Callable[..., GenerativeServiceTransport]]]):
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 GenerativeServiceTransport 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,
) = GenerativeServiceClient._read_environment_variables()
self._client_cert_source = GenerativeServiceClient._get_client_cert_source(
self._client_options.client_cert_source, self._use_client_cert
)
self._universe_domain = GenerativeServiceClient._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
if CLIENT_LOGGING_SUPPORTED: # pragma: NO COVER
# Setup logging.
client_logging.initialize_logging()
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, GenerativeServiceTransport)
if transport_provided:
# transport is a GenerativeServiceTransport 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(GenerativeServiceTransport, transport)
self._api_endpoint = self._transport.host
self._api_endpoint = (
self._api_endpoint
or GenerativeServiceClient._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[GenerativeServiceTransport],
Callable[..., GenerativeServiceTransport],
] = (
GenerativeServiceClient.get_transport_class(transport)
if isinstance(transport, str) or transport is None
else cast(Callable[..., GenerativeServiceTransport], 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,
)
if "async" not in str(self._transport):
if CLIENT_LOGGING_SUPPORTED and _LOGGER.isEnabledFor(
std_logging.DEBUG
): # pragma: NO COVER
_LOGGER.debug(
"Created client `google.ai.generativelanguage_v1beta.GenerativeServiceClient`.",
extra={
"serviceName": "google.ai.generativelanguage.v1beta.GenerativeService",
"universeDomain": getattr(
self._transport._credentials, "universe_domain", ""
),
"credentialsType": f"{type(self._transport._credentials).__module__}.{type(self._transport._credentials).__qualname__}",
"credentialsInfo": getattr(
self.transport._credentials, "get_cred_info", lambda: None
)(),
}
if hasattr(self._transport, "_credentials")
else {
"serviceName": "google.ai.generativelanguage.v1beta.GenerativeService",
"credentialsType": None,
},
)
[docs] def generate_content(
self,
request: Optional[
Union[generative_service.GenerateContentRequest, dict]
] = None,
*,
model: Optional[str] = None,
contents: Optional[MutableSequence[content.Content]] = None,
retry: OptionalRetry = gapic_v1.method.DEFAULT,
timeout: Union[float, object] = gapic_v1.method.DEFAULT,
metadata: Sequence[Tuple[str, Union[str, bytes]]] = (),
) -> generative_service.GenerateContentResponse:
r"""Generates a model response given an input
``GenerateContentRequest``. Refer to the `text generation
guide <https://ai.google.dev/gemini-api/docs/text-generation>`__
for detailed usage information. Input capabilities differ
between models, including tuned models. Refer to the `model
guide <https://ai.google.dev/gemini-api/docs/models/gemini>`__
and `tuning
guide <https://ai.google.dev/gemini-api/docs/model-tuning>`__
for details.
.. 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_generate_content():
# Create a client
client = generativelanguage_v1beta.GenerativeServiceClient()
# Initialize request argument(s)
request = generativelanguage_v1beta.GenerateContentRequest(
model="model_value",
)
# Make the request
response = client.generate_content(request=request)
# Handle the response
print(response)
Args:
request (Union[google.ai.generativelanguage_v1beta.types.GenerateContentRequest, dict]):
The request object. Request to generate a completion from
the model.
model (str):
Required. The name of the ``Model`` to use for
generating the completion.
Format: ``models/{model}``.
This corresponds to the ``model`` field
on the ``request`` instance; if ``request`` is provided, this
should not be set.
contents (MutableSequence[google.ai.generativelanguage_v1beta.types.Content]):
Required. The content of the current conversation with
the model.
For single-turn queries, this is a single instance. For
multi-turn queries like
`chat <https://ai.google.dev/gemini-api/docs/text-generation#chat>`__,
this is a repeated field that contains the conversation
history and the latest request.
This corresponds to the ``contents`` 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, Union[str, bytes]]]): Key/value pairs which should be
sent along with the request as metadata. Normally, each value must be of type `str`,
but for metadata keys ending with the suffix `-bin`, the corresponding values must
be of type `bytes`.
Returns:
google.ai.generativelanguage_v1beta.types.GenerateContentResponse:
Response from the model supporting multiple candidate
responses.
Safety ratings and content filtering are reported for
both prompt in
GenerateContentResponse.prompt_feedback and for each
candidate in finish_reason and in safety_ratings. The
API: - Returns either all requested candidates or
none of them - Returns no candidates at all only if
there was something wrong with the prompt (check
prompt_feedback) - Reports feedback on each candidate
in finish_reason and safety_ratings.
"""
# 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.
flattened_params = [model, contents]
has_flattened_params = (
len([param for param in flattened_params if param is not None]) > 0
)
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, generative_service.GenerateContentRequest):
request = generative_service.GenerateContentRequest(request)
# If we have keyword arguments corresponding to fields on the
# request, apply these.
if model is not None:
request.model = model
if contents is not None:
request.contents = contents
# Wrap the RPC method; this adds retry and timeout information,
# and friendly error handling.
rpc = self._transport._wrapped_methods[self._transport.generate_content]
# 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._validate_universe_domain()
# Send the request.
response = rpc(
request,
retry=retry,
timeout=timeout,
metadata=metadata,
)
# Done; return the response.
return response
[docs] def generate_answer(
self,
request: Optional[Union[generative_service.GenerateAnswerRequest, dict]] = None,
*,
model: Optional[str] = None,
contents: Optional[MutableSequence[content.Content]] = None,
safety_settings: Optional[MutableSequence[safety.SafetySetting]] = None,
answer_style: Optional[
generative_service.GenerateAnswerRequest.AnswerStyle
] = None,
retry: OptionalRetry = gapic_v1.method.DEFAULT,
timeout: Union[float, object] = gapic_v1.method.DEFAULT,
metadata: Sequence[Tuple[str, Union[str, bytes]]] = (),
) -> generative_service.GenerateAnswerResponse:
r"""Generates a grounded answer from the model given an input
``GenerateAnswerRequest``.
.. 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_generate_answer():
# Create a client
client = generativelanguage_v1beta.GenerativeServiceClient()
# Initialize request argument(s)
request = generativelanguage_v1beta.GenerateAnswerRequest(
model="model_value",
answer_style="VERBOSE",
)
# Make the request
response = client.generate_answer(request=request)
# Handle the response
print(response)
Args:
request (Union[google.ai.generativelanguage_v1beta.types.GenerateAnswerRequest, dict]):
The request object. Request to generate a grounded answer from the
``Model``.
model (str):
Required. The name of the ``Model`` to use for
generating the grounded response.
Format: ``model=models/{model}``.
This corresponds to the ``model`` field
on the ``request`` instance; if ``request`` is provided, this
should not be set.
contents (MutableSequence[google.ai.generativelanguage_v1beta.types.Content]):
Required. The content of the current conversation with
the ``Model``. For single-turn queries, this is a single
question to answer. For multi-turn queries, this is a
repeated field that contains conversation history and
the last ``Content`` in the list containing the
question.
Note: ``GenerateAnswer`` only supports queries in
English.
This corresponds to the ``contents`` field
on the ``request`` instance; if ``request`` is provided, this
should not be set.
safety_settings (MutableSequence[google.ai.generativelanguage_v1beta.types.SafetySetting]):
Optional. A list of unique ``SafetySetting`` instances
for blocking unsafe content.
This will be enforced on the
``GenerateAnswerRequest.contents`` and
``GenerateAnswerResponse.candidate``. There should not
be more than one setting for each ``SafetyCategory``
type. The API will block any contents and responses that
fail to meet the thresholds set by these settings. This
list overrides the default settings for each
``SafetyCategory`` specified in the safety_settings. If
there is no ``SafetySetting`` for a given
``SafetyCategory`` provided in the list, the API will
use the default safety setting for that category. Harm
categories HARM_CATEGORY_HATE_SPEECH,
HARM_CATEGORY_SEXUALLY_EXPLICIT,
HARM_CATEGORY_DANGEROUS_CONTENT,
HARM_CATEGORY_HARASSMENT are supported. Refer to the
`guide <https://ai.google.dev/gemini-api/docs/safety-settings>`__
for detailed information on available safety settings.
Also refer to the `Safety
guidance <https://ai.google.dev/gemini-api/docs/safety-guidance>`__
to learn how to incorporate safety considerations in
your AI applications.
This corresponds to the ``safety_settings`` field
on the ``request`` instance; if ``request`` is provided, this
should not be set.
answer_style (google.ai.generativelanguage_v1beta.types.GenerateAnswerRequest.AnswerStyle):
Required. Style in which answers
should be returned.
This corresponds to the ``answer_style`` 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, Union[str, bytes]]]): Key/value pairs which should be
sent along with the request as metadata. Normally, each value must be of type `str`,
but for metadata keys ending with the suffix `-bin`, the corresponding values must
be of type `bytes`.
Returns:
google.ai.generativelanguage_v1beta.types.GenerateAnswerResponse:
Response from the model for a
grounded answer.
"""
# 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.
flattened_params = [model, contents, safety_settings, answer_style]
has_flattened_params = (
len([param for param in flattened_params if param is not None]) > 0
)
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, generative_service.GenerateAnswerRequest):
request = generative_service.GenerateAnswerRequest(request)
# If we have keyword arguments corresponding to fields on the
# request, apply these.
if model is not None:
request.model = model
if contents is not None:
request.contents = contents
if safety_settings is not None:
request.safety_settings = safety_settings
if answer_style is not None:
request.answer_style = answer_style
# Wrap the RPC method; this adds retry and timeout information,
# and friendly error handling.
rpc = self._transport._wrapped_methods[self._transport.generate_answer]
# 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._validate_universe_domain()
# Send the request.
response = rpc(
request,
retry=retry,
timeout=timeout,
metadata=metadata,
)
# Done; return the response.
return response
[docs] def stream_generate_content(
self,
request: Optional[
Union[generative_service.GenerateContentRequest, dict]
] = None,
*,
model: Optional[str] = None,
contents: Optional[MutableSequence[content.Content]] = None,
retry: OptionalRetry = gapic_v1.method.DEFAULT,
timeout: Union[float, object] = gapic_v1.method.DEFAULT,
metadata: Sequence[Tuple[str, Union[str, bytes]]] = (),
) -> Iterable[generative_service.GenerateContentResponse]:
r"""Generates a `streamed
response <https://ai.google.dev/gemini-api/docs/text-generation?lang=python#generate-a-text-stream>`__
from the model given an input ``GenerateContentRequest``.
.. 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_stream_generate_content():
# Create a client
client = generativelanguage_v1beta.GenerativeServiceClient()
# Initialize request argument(s)
request = generativelanguage_v1beta.GenerateContentRequest(
model="model_value",
)
# Make the request
stream = client.stream_generate_content(request=request)
# Handle the response
for response in stream:
print(response)
Args:
request (Union[google.ai.generativelanguage_v1beta.types.GenerateContentRequest, dict]):
The request object. Request to generate a completion from
the model.
model (str):
Required. The name of the ``Model`` to use for
generating the completion.
Format: ``models/{model}``.
This corresponds to the ``model`` field
on the ``request`` instance; if ``request`` is provided, this
should not be set.
contents (MutableSequence[google.ai.generativelanguage_v1beta.types.Content]):
Required. The content of the current conversation with
the model.
For single-turn queries, this is a single instance. For
multi-turn queries like
`chat <https://ai.google.dev/gemini-api/docs/text-generation#chat>`__,
this is a repeated field that contains the conversation
history and the latest request.
This corresponds to the ``contents`` 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, Union[str, bytes]]]): Key/value pairs which should be
sent along with the request as metadata. Normally, each value must be of type `str`,
but for metadata keys ending with the suffix `-bin`, the corresponding values must
be of type `bytes`.
Returns:
Iterable[google.ai.generativelanguage_v1beta.types.GenerateContentResponse]:
Response from the model supporting multiple candidate
responses.
Safety ratings and content filtering are reported for
both prompt in
GenerateContentResponse.prompt_feedback and for each
candidate in finish_reason and in safety_ratings. The
API: - Returns either all requested candidates or
none of them - Returns no candidates at all only if
there was something wrong with the prompt (check
prompt_feedback) - Reports feedback on each candidate
in finish_reason and safety_ratings.
"""
# 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.
flattened_params = [model, contents]
has_flattened_params = (
len([param for param in flattened_params if param is not None]) > 0
)
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, generative_service.GenerateContentRequest):
request = generative_service.GenerateContentRequest(request)
# If we have keyword arguments corresponding to fields on the
# request, apply these.
if model is not None:
request.model = model
if contents is not None:
request.contents = contents
# Wrap the RPC method; this adds retry and timeout information,
# and friendly error handling.
rpc = self._transport._wrapped_methods[self._transport.stream_generate_content]
# 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._validate_universe_domain()
# Send the request.
response = rpc(
request,
retry=retry,
timeout=timeout,
metadata=metadata,
)
# Done; return the response.
return response
[docs] def embed_content(
self,
request: Optional[Union[generative_service.EmbedContentRequest, dict]] = None,
*,
model: Optional[str] = None,
content: Optional[gag_content.Content] = None,
retry: OptionalRetry = gapic_v1.method.DEFAULT,
timeout: Union[float, object] = gapic_v1.method.DEFAULT,
metadata: Sequence[Tuple[str, Union[str, bytes]]] = (),
) -> generative_service.EmbedContentResponse:
r"""Generates a text embedding vector from the input ``Content``
using the specified `Gemini Embedding
model <https://ai.google.dev/gemini-api/docs/models/gemini#text-embedding>`__.
.. 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_embed_content():
# Create a client
client = generativelanguage_v1beta.GenerativeServiceClient()
# Initialize request argument(s)
request = generativelanguage_v1beta.EmbedContentRequest(
model="model_value",
)
# Make the request
response = client.embed_content(request=request)
# Handle the response
print(response)
Args:
request (Union[google.ai.generativelanguage_v1beta.types.EmbedContentRequest, dict]):
The request object. Request containing the ``Content`` for the model to
embed.
model (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.
content (google.ai.generativelanguage_v1beta.types.Content):
Required. The content to embed. Only the ``parts.text``
fields will be counted.
This corresponds to the ``content`` 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, Union[str, bytes]]]): Key/value pairs which should be
sent along with the request as metadata. Normally, each value must be of type `str`,
but for metadata keys ending with the suffix `-bin`, the corresponding values must
be of type `bytes`.
Returns:
google.ai.generativelanguage_v1beta.types.EmbedContentResponse:
The response to an EmbedContentRequest.
"""
# 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.
flattened_params = [model, content]
has_flattened_params = (
len([param for param in flattened_params if param is not None]) > 0
)
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, generative_service.EmbedContentRequest):
request = generative_service.EmbedContentRequest(request)
# If we have keyword arguments corresponding to fields on the
# request, apply these.
if model is not None:
request.model = model
if content is not None:
request.content = content
# Wrap the RPC method; this adds retry and timeout information,
# and friendly error handling.
rpc = self._transport._wrapped_methods[self._transport.embed_content]
# 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._validate_universe_domain()
# Send the request.
response = rpc(
request,
retry=retry,
timeout=timeout,
metadata=metadata,
)
# Done; return the response.
return response
[docs] def batch_embed_contents(
self,
request: Optional[
Union[generative_service.BatchEmbedContentsRequest, dict]
] = None,
*,
model: Optional[str] = None,
requests: Optional[
MutableSequence[generative_service.EmbedContentRequest]
] = None,
retry: OptionalRetry = gapic_v1.method.DEFAULT,
timeout: Union[float, object] = gapic_v1.method.DEFAULT,
metadata: Sequence[Tuple[str, Union[str, bytes]]] = (),
) -> generative_service.BatchEmbedContentsResponse:
r"""Generates multiple embedding vectors from the input ``Content``
which consists of a batch of strings represented as
``EmbedContentRequest`` objects.
.. 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_batch_embed_contents():
# Create a client
client = generativelanguage_v1beta.GenerativeServiceClient()
# Initialize request argument(s)
requests = generativelanguage_v1beta.EmbedContentRequest()
requests.model = "model_value"
request = generativelanguage_v1beta.BatchEmbedContentsRequest(
model="model_value",
requests=requests,
)
# Make the request
response = client.batch_embed_contents(request=request)
# Handle the response
print(response)
Args:
request (Union[google.ai.generativelanguage_v1beta.types.BatchEmbedContentsRequest, dict]):
The request object. Batch request to get embeddings from
the model for a list of prompts.
model (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.
requests (MutableSequence[google.ai.generativelanguage_v1beta.types.EmbedContentRequest]):
Required. Embed requests for the batch. The model in
each of these requests must match the model specified
``BatchEmbedContentsRequest.model``.
This corresponds to the ``requests`` 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, Union[str, bytes]]]): Key/value pairs which should be
sent along with the request as metadata. Normally, each value must be of type `str`,
but for metadata keys ending with the suffix `-bin`, the corresponding values must
be of type `bytes`.
Returns:
google.ai.generativelanguage_v1beta.types.BatchEmbedContentsResponse:
The response to a BatchEmbedContentsRequest.
"""
# 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.
flattened_params = [model, requests]
has_flattened_params = (
len([param for param in flattened_params if param is not None]) > 0
)
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, generative_service.BatchEmbedContentsRequest):
request = generative_service.BatchEmbedContentsRequest(request)
# If we have keyword arguments corresponding to fields on the
# request, apply these.
if model is not None:
request.model = model
if requests is not None:
request.requests = requests
# Wrap the RPC method; this adds retry and timeout information,
# and friendly error handling.
rpc = self._transport._wrapped_methods[self._transport.batch_embed_contents]
# 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._validate_universe_domain()
# Send the request.
response = rpc(
request,
retry=retry,
timeout=timeout,
metadata=metadata,
)
# Done; return the response.
return response
[docs] def count_tokens(
self,
request: Optional[Union[generative_service.CountTokensRequest, dict]] = None,
*,
model: Optional[str] = None,
contents: Optional[MutableSequence[content.Content]] = None,
retry: OptionalRetry = gapic_v1.method.DEFAULT,
timeout: Union[float, object] = gapic_v1.method.DEFAULT,
metadata: Sequence[Tuple[str, Union[str, bytes]]] = (),
) -> generative_service.CountTokensResponse:
r"""Runs a model's tokenizer on input ``Content`` and returns the
token count. Refer to the `tokens
guide <https://ai.google.dev/gemini-api/docs/tokens>`__ to learn
more about tokens.
.. 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_count_tokens():
# Create a client
client = generativelanguage_v1beta.GenerativeServiceClient()
# Initialize request argument(s)
request = generativelanguage_v1beta.CountTokensRequest(
model="model_value",
)
# Make the request
response = client.count_tokens(request=request)
# Handle the response
print(response)
Args:
request (Union[google.ai.generativelanguage_v1beta.types.CountTokensRequest, 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 (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.
contents (MutableSequence[google.ai.generativelanguage_v1beta.types.Content]):
Optional. The input given to the model as a prompt. This
field is ignored when ``generate_content_request`` is
set.
This corresponds to the ``contents`` 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, Union[str, bytes]]]): Key/value pairs which should be
sent along with the request as metadata. Normally, each value must be of type `str`,
but for metadata keys ending with the suffix `-bin`, the corresponding values must
be of type `bytes`.
Returns:
google.ai.generativelanguage_v1beta.types.CountTokensResponse:
A response from CountTokens.
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.
flattened_params = [model, contents]
has_flattened_params = (
len([param for param in flattened_params if param is not None]) > 0
)
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, generative_service.CountTokensRequest):
request = generative_service.CountTokensRequest(request)
# If we have keyword arguments corresponding to fields on the
# request, apply these.
if model is not None:
request.model = model
if contents is not None:
request.contents = contents
# Wrap the RPC method; this adds retry and timeout information,
# and friendly error handling.
rpc = self._transport._wrapped_methods[self._transport.count_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._validate_universe_domain()
# Send the request.
response = rpc(
request,
retry=retry,
timeout=timeout,
metadata=metadata,
)
# Done; return the response.
return response
[docs] def bidi_generate_content(
self,
requests: Optional[
Iterator[generative_service.BidiGenerateContentClientMessage]
] = None,
*,
retry: OptionalRetry = gapic_v1.method.DEFAULT,
timeout: Union[float, object] = gapic_v1.method.DEFAULT,
metadata: Sequence[Tuple[str, Union[str, bytes]]] = (),
) -> Iterable[generative_service.BidiGenerateContentServerMessage]:
r"""Low-Latency bidirectional streaming API that supports
audio and video streaming inputs can produce multimodal
output streams (audio and text).
.. 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_bidi_generate_content():
# Create a client
client = generativelanguage_v1beta.GenerativeServiceClient()
# Initialize request argument(s)
setup = generativelanguage_v1beta.BidiGenerateContentSetup()
setup.model = "model_value"
request = generativelanguage_v1beta.BidiGenerateContentClientMessage(
setup=setup,
)
# This method expects an iterator which contains
# 'generativelanguage_v1beta.BidiGenerateContentClientMessage' objects
# Here we create a generator that yields a single `request` for
# demonstrative purposes.
requests = [request]
def request_generator():
for request in requests:
yield request
# Make the request
stream = client.bidi_generate_content(requests=request_generator())
# Handle the response
for response in stream:
print(response)
Args:
requests (Iterator[google.ai.generativelanguage_v1beta.types.BidiGenerateContentClientMessage]):
The request object iterator. Messages sent by the client in the
BidiGenerateContent call.
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, Union[str, bytes]]]): Key/value pairs which should be
sent along with the request as metadata. Normally, each value must be of type `str`,
but for metadata keys ending with the suffix `-bin`, the corresponding values must
be of type `bytes`.
Returns:
Iterable[google.ai.generativelanguage_v1beta.types.BidiGenerateContentServerMessage]:
Response message for the
BidiGenerateContent call.
"""
# Wrap the RPC method; this adds retry and timeout information,
# and friendly error handling.
rpc = self._transport._wrapped_methods[self._transport.bidi_generate_content]
# Validate the universe domain.
self._validate_universe_domain()
# Send the request.
response = rpc(
requests,
retry=retry,
timeout=timeout,
metadata=metadata,
)
# Done; return the response.
return response
def __enter__(self) -> "GenerativeServiceClient":
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()
[docs] def list_operations(
self,
request: Optional[operations_pb2.ListOperationsRequest] = None,
*,
retry: OptionalRetry = gapic_v1.method.DEFAULT,
timeout: Union[float, object] = gapic_v1.method.DEFAULT,
metadata: Sequence[Tuple[str, Union[str, bytes]]] = (),
) -> operations_pb2.ListOperationsResponse:
r"""Lists operations that match the specified filter in the request.
Args:
request (:class:`~.operations_pb2.ListOperationsRequest`):
The request object. Request message for
`ListOperations` method.
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, Union[str, bytes]]]): Key/value pairs which should be
sent along with the request as metadata. Normally, each value must be of type `str`,
but for metadata keys ending with the suffix `-bin`, the corresponding values must
be of type `bytes`.
Returns:
~.operations_pb2.ListOperationsResponse:
Response message for ``ListOperations`` method.
"""
# Create or coerce a protobuf request object.
# The request isn't a proto-plus wrapped type,
# so it must be constructed via keyword expansion.
if isinstance(request, dict):
request = operations_pb2.ListOperationsRequest(**request)
# Wrap the RPC method; this adds retry and timeout information,
# and friendly error handling.
rpc = self._transport._wrapped_methods[self._transport.list_operations]
# 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()
try:
# Send the request.
response = rpc(
request,
retry=retry,
timeout=timeout,
metadata=metadata,
)
# Done; return the response.
return response
except core_exceptions.GoogleAPICallError as e:
self._add_cred_info_for_auth_errors(e)
raise e
[docs] def get_operation(
self,
request: Optional[operations_pb2.GetOperationRequest] = None,
*,
retry: OptionalRetry = gapic_v1.method.DEFAULT,
timeout: Union[float, object] = gapic_v1.method.DEFAULT,
metadata: Sequence[Tuple[str, Union[str, bytes]]] = (),
) -> operations_pb2.Operation:
r"""Gets the latest state of a long-running operation.
Args:
request (:class:`~.operations_pb2.GetOperationRequest`):
The request object. Request message for
`GetOperation` method.
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, Union[str, bytes]]]): Key/value pairs which should be
sent along with the request as metadata. Normally, each value must be of type `str`,
but for metadata keys ending with the suffix `-bin`, the corresponding values must
be of type `bytes`.
Returns:
~.operations_pb2.Operation:
An ``Operation`` object.
"""
# Create or coerce a protobuf request object.
# The request isn't a proto-plus wrapped type,
# so it must be constructed via keyword expansion.
if isinstance(request, dict):
request = operations_pb2.GetOperationRequest(**request)
# Wrap the RPC method; this adds retry and timeout information,
# and friendly error handling.
rpc = self._transport._wrapped_methods[self._transport.get_operation]
# 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()
try:
# Send the request.
response = rpc(
request,
retry=retry,
timeout=timeout,
metadata=metadata,
)
# Done; return the response.
return response
except core_exceptions.GoogleAPICallError as e:
self._add_cred_info_for_auth_errors(e)
raise e
[docs] def delete_operation(
self,
request: Optional[operations_pb2.DeleteOperationRequest] = None,
*,
retry: OptionalRetry = gapic_v1.method.DEFAULT,
timeout: Union[float, object] = gapic_v1.method.DEFAULT,
metadata: Sequence[Tuple[str, Union[str, bytes]]] = (),
) -> None:
r"""Deletes a long-running operation.
This method indicates that the client is no longer interested
in the operation result. It does not cancel the operation.
If the server doesn't support this method, it returns
`google.rpc.Code.UNIMPLEMENTED`.
Args:
request (:class:`~.operations_pb2.DeleteOperationRequest`):
The request object. Request message for
`DeleteOperation` method.
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, Union[str, bytes]]]): Key/value pairs which should be
sent along with the request as metadata. Normally, each value must be of type `str`,
but for metadata keys ending with the suffix `-bin`, the corresponding values must
be of type `bytes`.
Returns:
None
"""
# Create or coerce a protobuf request object.
# The request isn't a proto-plus wrapped type,
# so it must be constructed via keyword expansion.
if isinstance(request, dict):
request = operations_pb2.DeleteOperationRequest(**request)
# Wrap the RPC method; this adds retry and timeout information,
# and friendly error handling.
rpc = self._transport._wrapped_methods[self._transport.delete_operation]
# 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,
)
[docs] def cancel_operation(
self,
request: Optional[operations_pb2.CancelOperationRequest] = None,
*,
retry: OptionalRetry = gapic_v1.method.DEFAULT,
timeout: Union[float, object] = gapic_v1.method.DEFAULT,
metadata: Sequence[Tuple[str, Union[str, bytes]]] = (),
) -> None:
r"""Starts asynchronous cancellation on a long-running operation.
The server makes a best effort to cancel the operation, but success
is not guaranteed. If the server doesn't support this method, it returns
`google.rpc.Code.UNIMPLEMENTED`.
Args:
request (:class:`~.operations_pb2.CancelOperationRequest`):
The request object. Request message for
`CancelOperation` method.
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, Union[str, bytes]]]): Key/value pairs which should be
sent along with the request as metadata. Normally, each value must be of type `str`,
but for metadata keys ending with the suffix `-bin`, the corresponding values must
be of type `bytes`.
Returns:
None
"""
# Create or coerce a protobuf request object.
# The request isn't a proto-plus wrapped type,
# so it must be constructed via keyword expansion.
if isinstance(request, dict):
request = operations_pb2.CancelOperationRequest(**request)
# Wrap the RPC method; this adds retry and timeout information,
# and friendly error handling.
rpc = self._transport._wrapped_methods[self._transport.cancel_operation]
# 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,
)
DEFAULT_CLIENT_INFO = gapic_v1.client_info.ClientInfo(
gapic_version=package_version.__version__
)
if hasattr(DEFAULT_CLIENT_INFO, "protobuf_runtime_version"): # pragma: NO COVER
DEFAULT_CLIENT_INFO.protobuf_runtime_version = google.protobuf.__version__
__all__ = ("GenerativeServiceClient",)
