# -*- coding: utf-8 -*- # Copyright 2022 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 functools import re from typing import ( 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 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_v1beta2 import gapic_version as package_version try: OptionalRetry = Union[retries.Retry, gapic_v1.method._MethodDefault] except AttributeError: # pragma: NO COVER OptionalRetry = Union[retries.Retry, object] # type: ignore from google.ai.generativelanguage_v1beta2.types import discuss_service from .client import DiscussServiceClient from .transports.base import DEFAULT_CLIENT_INFO, DiscussServiceTransport from .transports.grpc_asyncio import DiscussServiceGrpcAsyncIOTransport class DiscussServiceAsyncClient: """An API for using Generative Language Models (GLMs) in dialog applications. Also known as large language models (LLMs), this API provides models that are trained for multi-turn dialog. """ _client: DiscussServiceClient DEFAULT_ENDPOINT = DiscussServiceClient.DEFAULT_ENDPOINT DEFAULT_MTLS_ENDPOINT = DiscussServiceClient.DEFAULT_MTLS_ENDPOINT model_path = staticmethod(DiscussServiceClient.model_path) parse_model_path = staticmethod(DiscussServiceClient.parse_model_path) common_billing_account_path = staticmethod( DiscussServiceClient.common_billing_account_path ) parse_common_billing_account_path = staticmethod( DiscussServiceClient.parse_common_billing_account_path ) common_folder_path = staticmethod(DiscussServiceClient.common_folder_path) parse_common_folder_path = staticmethod( DiscussServiceClient.parse_common_folder_path ) common_organization_path = staticmethod( DiscussServiceClient.common_organization_path ) parse_common_organization_path = staticmethod( DiscussServiceClient.parse_common_organization_path ) common_project_path = staticmethod(DiscussServiceClient.common_project_path) parse_common_project_path = staticmethod( DiscussServiceClient.parse_common_project_path ) common_location_path = staticmethod(DiscussServiceClient.common_location_path) parse_common_location_path = staticmethod( DiscussServiceClient.parse_common_location_path ) @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: DiscussServiceAsyncClient: The constructed client. """ return DiscussServiceClient.from_service_account_info.__func__(DiscussServiceAsyncClient, info, *args, **kwargs) # type: ignore @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: DiscussServiceAsyncClient: The constructed client. """ return DiscussServiceClient.from_service_account_file.__func__(DiscussServiceAsyncClient, filename, *args, **kwargs) # type: ignore from_service_account_json = from_service_account_file @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 DiscussServiceClient.get_mtls_endpoint_and_cert_source(client_options) # type: ignore @property def transport(self) -> DiscussServiceTransport: """Returns the transport used by the client instance. Returns: DiscussServiceTransport: The transport used by the client instance. """ return self._client.transport get_transport_class = functools.partial( type(DiscussServiceClient).get_transport_class, type(DiscussServiceClient) ) def __init__( self, *, credentials: Optional[ga_credentials.Credentials] = None, transport: Union[str, DiscussServiceTransport] = "grpc_asyncio", client_options: Optional[ClientOptions] = None, client_info: gapic_v1.client_info.ClientInfo = DEFAULT_CLIENT_INFO, ) -> None: """Instantiates the discuss 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 (Union[str, ~.DiscussServiceTransport]): The transport to use. If set to None, a transport is chosen automatically. client_options (ClientOptions): Custom options for the client. It won't take effect if a ``transport`` instance is provided. (1) The ``api_endpoint`` property can be used to override the default endpoint provided by the client. GOOGLE_API_USE_MTLS_ENDPOINT environment variable can also be used to override the endpoint: "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). However, the ``api_endpoint`` property takes precedence if provided. (2) If GOOGLE_API_USE_CLIENT_CERTIFICATE environment variable is "true", then the ``client_cert_source`` property can be used to provide client certificate for mutual TLS 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. Raises: google.auth.exceptions.MutualTlsChannelError: If mutual TLS transport creation failed for any reason. """ self._client = DiscussServiceClient( credentials=credentials, transport=transport, client_options=client_options, client_info=client_info, ) async def generate_message( self, request: Optional[Union[discuss_service.GenerateMessageRequest, dict]] = None, *, model: Optional[str] = None, prompt: Optional[discuss_service.MessagePrompt] = None, temperature: Optional[float] = None, candidate_count: Optional[int] = None, top_p: Optional[float] = None, top_k: Optional[int] = None, retry: OptionalRetry = gapic_v1.method.DEFAULT, timeout: Union[float, object] = gapic_v1.method.DEFAULT, metadata: Sequence[Tuple[str, str]] = (), ) -> discuss_service.GenerateMessageResponse: r"""Generates a response from the model given an input ``MessagePrompt``. .. 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_v1beta2 async def sample_generate_message(): # Create a client client = generativelanguage_v1beta2.DiscussServiceAsyncClient() # Initialize request argument(s) prompt = generativelanguage_v1beta2.MessagePrompt() prompt.messages.content = "content_value" request = generativelanguage_v1beta2.GenerateMessageRequest( model="model_value", prompt=prompt, ) # Make the request response = await client.generate_message(request=request) # Handle the response print(response) Args: request (Optional[Union[google.ai.generativelanguage_v1beta2.types.GenerateMessageRequest, dict]]): The request object. Request to generate a message response from the model. model (:class:`str`): Required. The name of the model to use. Format: ``name=models/{model}``. This corresponds to the ``model`` field on the ``request`` instance; if ``request`` is provided, this should not be set. prompt (:class:`google.ai.generativelanguage_v1beta2.types.MessagePrompt`): Required. The structured textual input given to the model as a prompt. Given a prompt, the model will return what it predicts is the next message in the discussion. This corresponds to the ``prompt`` field on the ``request`` instance; if ``request`` is provided, this should not be set. temperature (:class:`float`): Optional. Controls the randomness of the output. Values can range over ``[0.0,1.0]``, inclusive. A value closer to ``1.0`` will produce responses that are more varied, while a value closer to ``0.0`` will typically result in less surprising responses from the model. This corresponds to the ``temperature`` field on the ``request`` instance; if ``request`` is provided, this should not be set. candidate_count (:class:`int`): Optional. The number of generated response messages to return. This value must be between ``[1, 8]``, inclusive. If unset, this will default to ``1``. This corresponds to the ``candidate_count`` field on the ``request`` instance; if ``request`` is provided, this should not be set. top_p (:class:`float`): Optional. The maximum cumulative probability of tokens to consider when sampling. The model uses combined Top-k and nucleus sampling. Nucleus sampling considers the smallest set of tokens whose probability sum is at least ``top_p``. This corresponds to the ``top_p`` field on the ``request`` instance; if ``request`` is provided, this should not be set. top_k (:class:`int`): Optional. The maximum number of tokens to consider when sampling. The model uses combined Top-k and nucleus sampling. Top-k sampling considers the set of ``top_k`` most probable tokens. This corresponds to the ``top_k`` field on the ``request`` instance; if ``request`` is provided, this should not be set. retry (google.api_core.retry.Retry): Designation of what errors, if any, should be retried. timeout (float): The timeout for this request. metadata (Sequence[Tuple[str, str]]): Strings which should be sent along with the request as metadata. Returns: google.ai.generativelanguage_v1beta2.types.GenerateMessageResponse: The response from the model. This includes candidate messages and conversation history in the form of chronologically-ordered messages. """ # Create or coerce a protobuf request object. # Quick check: If we got a request object, we should *not* have # gotten any keyword arguments that map to the request. has_flattened_params = any( [model, prompt, temperature, candidate_count, top_p, top_k] ) if request is not None and has_flattened_params: raise ValueError( "If the `request` argument is set, then none of " "the individual field arguments should be set." ) request = discuss_service.GenerateMessageRequest(request) # If we have keyword arguments corresponding to fields on the # request, apply these. if model is not None: request.model = model if prompt is not None: request.prompt = prompt if temperature is not None: request.temperature = temperature if candidate_count is not None: request.candidate_count = candidate_count if top_p is not None: request.top_p = top_p if top_k is not None: request.top_k = top_k # Wrap the RPC method; this adds retry and timeout information, # and friendly error handling. rpc = gapic_v1.method_async.wrap_method( self._client._transport.generate_message, default_timeout=None, client_info=DEFAULT_CLIENT_INFO, ) # 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),)), ) # Send the request. response = await rpc( request, retry=retry, timeout=timeout, metadata=metadata, ) # Done; return the response. return response async def count_message_tokens( self, request: Optional[ Union[discuss_service.CountMessageTokensRequest, dict] ] = None, *, model: Optional[str] = None, prompt: Optional[discuss_service.MessagePrompt] = None, retry: OptionalRetry = gapic_v1.method.DEFAULT, timeout: Union[float, object] = gapic_v1.method.DEFAULT, metadata: Sequence[Tuple[str, str]] = (), ) -> discuss_service.CountMessageTokensResponse: r"""Runs a model's tokenizer on a string and returns the token count. .. code-block:: python # This snippet has been automatically generated and should be regarded as a # code template only. # It will require modifications to work: # - It may require correct/in-range values for request initialization. # - It may require specifying regional endpoints when creating the service # client as shown in: # https://googleapis.dev/python/google-api-core/latest/client_options.html from google.ai import generativelanguage_v1beta2 async def sample_count_message_tokens(): # Create a client client = generativelanguage_v1beta2.DiscussServiceAsyncClient() # Initialize request argument(s) prompt = generativelanguage_v1beta2.MessagePrompt() prompt.messages.content = "content_value" request = generativelanguage_v1beta2.CountMessageTokensRequest( model="model_value", prompt=prompt, ) # Make the request response = await client.count_message_tokens(request=request) # Handle the response print(response) Args: request (Optional[Union[google.ai.generativelanguage_v1beta2.types.CountMessageTokensRequest, dict]]): The request object. Counts the number of tokens in the ``prompt`` sent to a model. Models may tokenize text differently, so each model may return a different ``token_count``. model (:class:`str`): Required. The model's resource name. This serves as an ID for the Model to use. This name should match a model name returned by the ``ListModels`` method. Format: ``models/{model}`` This corresponds to the ``model`` field on the ``request`` instance; if ``request`` is provided, this should not be set. prompt (:class:`google.ai.generativelanguage_v1beta2.types.MessagePrompt`): Required. The prompt, whose token count is to be returned. This corresponds to the ``prompt`` field on the ``request`` instance; if ``request`` is provided, this should not be set. retry (google.api_core.retry.Retry): Designation of what errors, if any, should be retried. timeout (float): The timeout for this request. metadata (Sequence[Tuple[str, str]]): Strings which should be sent along with the request as metadata. Returns: google.ai.generativelanguage_v1beta2.types.CountMessageTokensResponse: A response from CountMessageTokens. It returns the model's token_count for the prompt. """ # Create or coerce a protobuf request object. # Quick check: If we got a request object, we should *not* have # gotten any keyword arguments that map to the request. has_flattened_params = any([model, prompt]) if request is not None and has_flattened_params: raise ValueError( "If the `request` argument is set, then none of " "the individual field arguments should be set." ) request = discuss_service.CountMessageTokensRequest(request) # If we have keyword arguments corresponding to fields on the # request, apply these. if model is not None: request.model = model if prompt is not None: request.prompt = prompt # Wrap the RPC method; this adds retry and timeout information, # and friendly error handling. rpc = gapic_v1.method_async.wrap_method( self._client._transport.count_message_tokens, default_timeout=None, client_info=DEFAULT_CLIENT_INFO, ) # 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),)), ) # Send the request. response = await rpc( request, retry=retry, timeout=timeout, metadata=metadata, ) # Done; return the response. return response async def __aenter__(self): 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__ = ("DiscussServiceAsyncClient",)