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_v1.services.generative_service.async_client
# -*- coding: utf-8 -*-
# Copyright 2024 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
from collections import OrderedDict
import re
from typing import (
AsyncIterable,
Awaitable,
Callable,
Dict,
Mapping,
MutableMapping,
MutableSequence,
Optional,
Sequence,
Tuple,
Type,
Union,
)
from google.api_core import exceptions as core_exceptions
from google.api_core import gapic_v1
from google.api_core import retry_async as retries
from google.api_core.client_options import ClientOptions
from google.auth import credentials as ga_credentials # type: ignore
from google.oauth2 import service_account # type: ignore
from google.ai.generativelanguage_v1 import gapic_version as package_version
try:
OptionalRetry = Union[retries.AsyncRetry, gapic_v1.method._MethodDefault, None]
except AttributeError: # pragma: NO COVER
OptionalRetry = Union[retries.AsyncRetry, object, None] # type: ignore
from google.longrunning import operations_pb2 # type: ignore
from google.ai.generativelanguage_v1.types import content
from google.ai.generativelanguage_v1.types import content as gag_content
from google.ai.generativelanguage_v1.types import generative_service
from .client import GenerativeServiceClient
from .transports.base import DEFAULT_CLIENT_INFO, GenerativeServiceTransport
from .transports.grpc_asyncio import GenerativeServiceGrpcAsyncIOTransport
[docs]class GenerativeServiceAsyncClient:
"""API for using Large Models that generate multimodal content
and have additional capabilities beyond text generation.
"""
_client: GenerativeServiceClient
# Copy defaults from the synchronous client for use here.
# Note: DEFAULT_ENDPOINT is deprecated. Use _DEFAULT_ENDPOINT_TEMPLATE instead.
DEFAULT_ENDPOINT = GenerativeServiceClient.DEFAULT_ENDPOINT
DEFAULT_MTLS_ENDPOINT = GenerativeServiceClient.DEFAULT_MTLS_ENDPOINT
_DEFAULT_ENDPOINT_TEMPLATE = GenerativeServiceClient._DEFAULT_ENDPOINT_TEMPLATE
_DEFAULT_UNIVERSE = GenerativeServiceClient._DEFAULT_UNIVERSE
model_path = staticmethod(GenerativeServiceClient.model_path)
parse_model_path = staticmethod(GenerativeServiceClient.parse_model_path)
common_billing_account_path = staticmethod(
GenerativeServiceClient.common_billing_account_path
)
parse_common_billing_account_path = staticmethod(
GenerativeServiceClient.parse_common_billing_account_path
)
common_folder_path = staticmethod(GenerativeServiceClient.common_folder_path)
parse_common_folder_path = staticmethod(
GenerativeServiceClient.parse_common_folder_path
)
common_organization_path = staticmethod(
GenerativeServiceClient.common_organization_path
)
parse_common_organization_path = staticmethod(
GenerativeServiceClient.parse_common_organization_path
)
common_project_path = staticmethod(GenerativeServiceClient.common_project_path)
parse_common_project_path = staticmethod(
GenerativeServiceClient.parse_common_project_path
)
common_location_path = staticmethod(GenerativeServiceClient.common_location_path)
parse_common_location_path = staticmethod(
GenerativeServiceClient.parse_common_location_path
)
[docs] @classmethod
def from_service_account_info(cls, info: dict, *args, **kwargs):
"""Creates an instance of this client using the provided credentials
info.
Args:
info (dict): The service account private key info.
args: Additional arguments to pass to the constructor.
kwargs: Additional arguments to pass to the constructor.
Returns:
GenerativeServiceAsyncClient: The constructed client.
"""
return GenerativeServiceClient.from_service_account_info.__func__(GenerativeServiceAsyncClient, info, *args, **kwargs) # type: ignore
[docs] @classmethod
def from_service_account_file(cls, filename: str, *args, **kwargs):
"""Creates an instance of this client using the provided credentials
file.
Args:
filename (str): The path to the service account private key json
file.
args: Additional arguments to pass to the constructor.
kwargs: Additional arguments to pass to the constructor.
Returns:
GenerativeServiceAsyncClient: The constructed client.
"""
return GenerativeServiceClient.from_service_account_file.__func__(GenerativeServiceAsyncClient, filename, *args, **kwargs) # type: ignore
from_service_account_json = from_service_account_file
[docs] @classmethod
def get_mtls_endpoint_and_cert_source(
cls, client_options: Optional[ClientOptions] = None
):
"""Return the API endpoint and client cert source for mutual TLS.
The client cert source is determined in the following order:
(1) if `GOOGLE_API_USE_CLIENT_CERTIFICATE` environment variable is not "true", the
client cert source is None.
(2) if `client_options.client_cert_source` is provided, use the provided one; if the
default client cert source exists, use the default one; otherwise the client cert
source is None.
The API endpoint is determined in the following order:
(1) if `client_options.api_endpoint` if provided, use the provided one.
(2) if `GOOGLE_API_USE_CLIENT_CERTIFICATE` environment variable is "always", use the
default mTLS endpoint; if the environment variable is "never", use the default API
endpoint; otherwise if client cert source exists, use the default mTLS endpoint, otherwise
use the default API endpoint.
More details can be found at https://google.aip.dev/auth/4114.
Args:
client_options (google.api_core.client_options.ClientOptions): Custom options for the
client. Only the `api_endpoint` and `client_cert_source` properties may be used
in this method.
Returns:
Tuple[str, Callable[[], Tuple[bytes, bytes]]]: returns the API endpoint and the
client cert source to use.
Raises:
google.auth.exceptions.MutualTLSChannelError: If any errors happen.
"""
return GenerativeServiceClient.get_mtls_endpoint_and_cert_source(client_options) # type: ignore
@property
def transport(self) -> GenerativeServiceTransport:
"""Returns the transport used by the client instance.
Returns:
GenerativeServiceTransport: The transport used by the client instance.
"""
return self._client.transport
@property
def api_endpoint(self):
"""Return the API endpoint used by the client instance.
Returns:
str: The API endpoint used by the client instance.
"""
return self._client._api_endpoint
@property
def universe_domain(self) -> str:
"""Return the universe domain used by the client instance.
Returns:
str: The universe domain used
by the client instance.
"""
return self._client._universe_domain
get_transport_class = GenerativeServiceClient.get_transport_class
def __init__(
self,
*,
credentials: Optional[ga_credentials.Credentials] = None,
transport: Optional[
Union[
str,
GenerativeServiceTransport,
Callable[..., GenerativeServiceTransport],
]
] = "grpc_asyncio",
client_options: Optional[ClientOptions] = None,
client_info: gapic_v1.client_info.ClientInfo = DEFAULT_CLIENT_INFO,
) -> None:
"""Instantiates the generative service async client.
Args:
credentials (Optional[google.auth.credentials.Credentials]): The
authorization credentials to attach to requests. These
credentials identify the application to the service; if none
are specified, the client will attempt to ascertain the
credentials from the environment.
transport (Optional[Union[str,GenerativeServiceTransport,Callable[..., GenerativeServiceTransport]]]):
The transport to use, or a Callable that constructs and returns a new transport to use.
If a Callable is given, it will be called with the same set of initialization
arguments as used in the 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 ``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 = GenerativeServiceClient(
credentials=credentials,
transport=transport,
client_options=client_options,
client_info=client_info,
)
[docs] async 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, str]] = (),
) -> 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_v1
async def sample_generate_content():
# Create a client
client = generativelanguage_v1.GenerativeServiceAsyncClient()
# Initialize request argument(s)
request = generativelanguage_v1.GenerateContentRequest(
model="model_value",
)
# Make the request
response = await client.generate_content(request=request)
# Handle the response
print(response)
Args:
request (Optional[Union[google.ai.generativelanguage_v1.types.GenerateContentRequest, dict]]):
The request object. Request to generate a completion from
the model.
model (:class:`str`):
Required. The name of the ``Model`` to use for
generating the completion.
Format: ``name=models/{model}``.
This corresponds to the ``model`` field
on the ``request`` instance; if ``request`` is provided, this
should not be set.
contents (:class:`MutableSequence[google.ai.generativelanguage_v1.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_async.AsyncRetry): Designation of what errors, if any,
should be retried.
timeout (float): The timeout for this request.
metadata (Sequence[Tuple[str, str]]): Strings which should be
sent along with the request as metadata.
Returns:
google.ai.generativelanguage_v1.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.
has_flattened_params = any([model, contents])
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:
request.contents.extend(contents)
# Wrap the RPC method; this adds retry and timeout information,
# and friendly error handling.
rpc = self._client._transport._wrapped_methods[
self._client._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._client._validate_universe_domain()
# Send the request.
response = await 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, str]] = (),
) -> Awaitable[AsyncIterable[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_v1
async def sample_stream_generate_content():
# Create a client
client = generativelanguage_v1.GenerativeServiceAsyncClient()
# Initialize request argument(s)
request = generativelanguage_v1.GenerateContentRequest(
model="model_value",
)
# Make the request
stream = await client.stream_generate_content(request=request)
# Handle the response
async for response in stream:
print(response)
Args:
request (Optional[Union[google.ai.generativelanguage_v1.types.GenerateContentRequest, dict]]):
The request object. Request to generate a completion from
the model.
model (:class:`str`):
Required. The name of the ``Model`` to use for
generating the completion.
Format: ``name=models/{model}``.
This corresponds to the ``model`` field
on the ``request`` instance; if ``request`` is provided, this
should not be set.
contents (:class:`MutableSequence[google.ai.generativelanguage_v1.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_async.AsyncRetry): Designation of what errors, if any,
should be retried.
timeout (float): The timeout for this request.
metadata (Sequence[Tuple[str, str]]): Strings which should be
sent along with the request as metadata.
Returns:
AsyncIterable[google.ai.generativelanguage_v1.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.
has_flattened_params = any([model, contents])
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:
request.contents.extend(contents)
# Wrap the RPC method; this adds retry and timeout information,
# and friendly error handling.
rpc = self._client._transport._wrapped_methods[
self._client._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._client._validate_universe_domain()
# Send the request.
response = rpc(
request,
retry=retry,
timeout=timeout,
metadata=metadata,
)
# Done; return the response.
return response
[docs] async 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, str]] = (),
) -> 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_v1
async def sample_embed_content():
# Create a client
client = generativelanguage_v1.GenerativeServiceAsyncClient()
# Initialize request argument(s)
request = generativelanguage_v1.EmbedContentRequest(
model="model_value",
)
# Make the request
response = await client.embed_content(request=request)
# Handle the response
print(response)
Args:
request (Optional[Union[google.ai.generativelanguage_v1.types.EmbedContentRequest, dict]]):
The request object. Request containing the ``Content`` for the model to
embed.
model (:class:`str`):
Required. The model's resource name. This serves as an
ID for the Model to use.
This name should match a model name returned by the
``ListModels`` method.
Format: ``models/{model}``
This corresponds to the ``model`` field
on the ``request`` instance; if ``request`` is provided, this
should not be set.
content (:class:`google.ai.generativelanguage_v1.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_async.AsyncRetry): Designation of what errors, if any,
should be retried.
timeout (float): The timeout for this request.
metadata (Sequence[Tuple[str, str]]): Strings which should be
sent along with the request as metadata.
Returns:
google.ai.generativelanguage_v1.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.
has_flattened_params = any([model, content])
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._client._transport._wrapped_methods[
self._client._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._client._validate_universe_domain()
# Send the request.
response = await rpc(
request,
retry=retry,
timeout=timeout,
metadata=metadata,
)
# Done; return the response.
return response
[docs] async def batch_embed_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, str]] = (),
) -> 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_v1
async def sample_batch_embed_contents():
# Create a client
client = generativelanguage_v1.GenerativeServiceAsyncClient()
# Initialize request argument(s)
requests = generativelanguage_v1.EmbedContentRequest()
requests.model = "model_value"
request = generativelanguage_v1.BatchEmbedContentsRequest(
model="model_value",
requests=requests,
)
# Make the request
response = await client.batch_embed_contents(request=request)
# Handle the response
print(response)
Args:
request (Optional[Union[google.ai.generativelanguage_v1.types.BatchEmbedContentsRequest, dict]]):
The request object. Batch request to get embeddings from
the model for a list of prompts.
model (:class:`str`):
Required. The model's resource name. This serves as an
ID for the Model to use.
This name should match a model name returned by the
``ListModels`` method.
Format: ``models/{model}``
This corresponds to the ``model`` field
on the ``request`` instance; if ``request`` is provided, this
should not be set.
requests (:class:`MutableSequence[google.ai.generativelanguage_v1.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_async.AsyncRetry): Designation of what errors, if any,
should be retried.
timeout (float): The timeout for this request.
metadata (Sequence[Tuple[str, str]]): Strings which should be
sent along with the request as metadata.
Returns:
google.ai.generativelanguage_v1.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.
has_flattened_params = any([model, requests])
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:
request.requests.extend(requests)
# Wrap the RPC method; this adds retry and timeout information,
# and friendly error handling.
rpc = self._client._transport._wrapped_methods[
self._client._transport.batch_embed_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._client._validate_universe_domain()
# Send the request.
response = await rpc(
request,
retry=retry,
timeout=timeout,
metadata=metadata,
)
# Done; return the response.
return response
[docs] async def count_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, str]] = (),
) -> 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_v1
async def sample_count_tokens():
# Create a client
client = generativelanguage_v1.GenerativeServiceAsyncClient()
# Initialize request argument(s)
request = generativelanguage_v1.CountTokensRequest(
model="model_value",
)
# Make the request
response = await client.count_tokens(request=request)
# Handle the response
print(response)
Args:
request (Optional[Union[google.ai.generativelanguage_v1.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 (:class:`str`):
Required. The model's resource name. This serves as an
ID for the Model to use.
This name should match a model name returned by the
``ListModels`` method.
Format: ``models/{model}``
This corresponds to the ``model`` field
on the ``request`` instance; if ``request`` is provided, this
should not be set.
contents (:class:`MutableSequence[google.ai.generativelanguage_v1.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_async.AsyncRetry): Designation of what errors, if any,
should be retried.
timeout (float): The timeout for this request.
metadata (Sequence[Tuple[str, str]]): Strings which should be
sent along with the request as metadata.
Returns:
google.ai.generativelanguage_v1.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.
has_flattened_params = any([model, contents])
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:
request.contents.extend(contents)
# Wrap the RPC method; this adds retry and timeout information,
# and friendly error handling.
rpc = self._client._transport._wrapped_methods[
self._client._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._client._validate_universe_domain()
# Send the request.
response = await rpc(
request,
retry=retry,
timeout=timeout,
metadata=metadata,
)
# Done; return the response.
return response
[docs] async def list_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, str]] = (),
) -> 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_async.AsyncRetry): Designation of what errors,
if any, should be retried.
timeout (float): The timeout for this request.
metadata (Sequence[Tuple[str, str]]): Strings which should be
sent along with the request as metadata.
Returns:
~.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._client._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._client._validate_universe_domain()
# Send the request.
response = await rpc(
request,
retry=retry,
timeout=timeout,
metadata=metadata,
)
# Done; return the response.
return response
[docs] async def 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, str]] = (),
) -> 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_async.AsyncRetry): Designation of what errors,
if any, should be retried.
timeout (float): The timeout for this request.
metadata (Sequence[Tuple[str, str]]): Strings which should be
sent along with the request as metadata.
Returns:
~.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._client._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._client._validate_universe_domain()
# Send the request.
response = await rpc(
request,
retry=retry,
timeout=timeout,
metadata=metadata,
)
# Done; return the response.
return response
[docs] async def 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, str]] = (),
) -> 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_async.AsyncRetry): Designation of what errors,
if any, should be retried.
timeout (float): The timeout for this request.
metadata (Sequence[Tuple[str, str]]): Strings which should be
sent along with the request as metadata.
Returns:
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._client._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._client._validate_universe_domain()
# Send the request.
await rpc(
request,
retry=retry,
timeout=timeout,
metadata=metadata,
)
async def __aenter__(self) -> "GenerativeServiceAsyncClient":
return self
async def __aexit__(self, exc_type, exc, tb):
await self.transport.close()
DEFAULT_CLIENT_INFO = gapic_v1.client_info.ClientInfo(
gapic_version=package_version.__version__
)
__all__ = ("GenerativeServiceAsyncClient",)
