# -*- 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. # import inspect import json import pickle import logging as std_logging import warnings from typing import Awaitable, Callable, Dict, Optional, Sequence, Tuple, Union from google.api_core import gapic_v1 from google.api_core import grpc_helpers_async from google.api_core import exceptions as core_exceptions from google.api_core import retry_async as retries from google.auth import credentials as ga_credentials # type: ignore from google.auth.transport.grpc import SslCredentials # type: ignore from cloudsdk.google.protobuf.json_format import MessageToJson import cloudsdk.google.protobuf.message import grpc # type: ignore import proto # type: ignore from grpc.experimental import aio # type: ignore from google.api import httpbody_pb2 # type: ignore from google.longrunning import operations_pb2 # type: ignore from googlecloudsdk.generated_clients.gapic_clients.aiplatform_v1beta1.types import prediction_service from .base import PredictionServiceTransport, DEFAULT_CLIENT_INFO from .grpc import PredictionServiceGrpcTransport 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__) class _LoggingClientAIOInterceptor(grpc.aio.UnaryUnaryClientInterceptor): # pragma: NO COVER async def intercept_unary_unary(self, continuation, client_call_details, request): logging_enabled = CLIENT_LOGGING_SUPPORTED and _LOGGER.isEnabledFor(std_logging.DEBUG) if logging_enabled: # pragma: NO COVER request_metadata = client_call_details.metadata if isinstance(request, proto.Message): request_payload = type(request).to_json(request) elif isinstance(request, cloudsdk.google.protobuf.message.Message): request_payload = MessageToJson(request) else: request_payload = f"{type(request).__name__}: {pickle.dumps(request)}" request_metadata = { key: value.decode("utf-8") if isinstance(value, bytes) else value for key, value in request_metadata } grpc_request = { "payload": request_payload, "requestMethod": "grpc", "metadata": dict(request_metadata), } _LOGGER.debug( f"Sending request for {client_call_details.method}", extra = { "serviceName": "google.cloud.aiplatform.v1beta1.PredictionService", "rpcName": str(client_call_details.method), "request": grpc_request, "metadata": grpc_request["metadata"], }, ) response = await continuation(client_call_details, request) if logging_enabled: # pragma: NO COVER response_metadata = await response.trailing_metadata() # Convert gRPC metadata `` to list of tuples metadata = dict([(k, str(v)) for k, v in response_metadata]) if response_metadata else None result = await response if isinstance(result, proto.Message): response_payload = type(result).to_json(result) elif isinstance(result, cloudsdk.google.protobuf.message.Message): response_payload = MessageToJson(result) else: response_payload = f"{type(result).__name__}: {pickle.dumps(result)}" grpc_response = { "payload": response_payload, "metadata": metadata, "status": "OK", } _LOGGER.debug( f"Received response to rpc {client_call_details.method}.", extra = { "serviceName": "google.cloud.aiplatform.v1beta1.PredictionService", "rpcName": str(client_call_details.method), "response": grpc_response, "metadata": grpc_response["metadata"], }, ) return response class PredictionServiceGrpcAsyncIOTransport(PredictionServiceTransport): """gRPC AsyncIO backend transport for PredictionService. A service for online predictions and explanations. This class defines the same methods as the primary client, so the primary client can load the underlying transport implementation and call it. It sends protocol buffers over the wire using gRPC (which is built on top of HTTP/2); the ``grpcio`` package must be installed. """ _grpc_channel: aio.Channel _stubs: Dict[str, Callable] = {} @classmethod def create_channel(cls, host: str = 'aiplatform.googleapis.com', credentials: Optional[ga_credentials.Credentials] = None, credentials_file: Optional[str] = None, scopes: Optional[Sequence[str]] = None, quota_project_id: Optional[str] = None, **kwargs) -> aio.Channel: """Create and return a gRPC AsyncIO channel object. Args: host (Optional[str]): The host for the channel to use. credentials (Optional[~.Credentials]): The authorization credentials to attach to requests. These credentials identify this application to the service. If none are specified, the client will attempt to ascertain the credentials from the environment. credentials_file (Optional[str]): Deprecated. A file with credentials that can be loaded with :func:`google.auth.load_credentials_from_file`. This argument will be removed in the next major version of this library. scopes (Optional[Sequence[str]]): A optional list of scopes needed for this service. These are only used when credentials are not specified and are passed to :func:`google.auth.default`. quota_project_id (Optional[str]): An optional project to use for billing and quota. kwargs (Optional[dict]): Keyword arguments, which are passed to the channel creation. Returns: aio.Channel: A gRPC AsyncIO channel object. """ return grpc_helpers_async.create_channel( host, credentials=credentials, credentials_file=credentials_file, quota_project_id=quota_project_id, default_scopes=cls.AUTH_SCOPES, scopes=scopes, default_host=cls.DEFAULT_HOST, **kwargs ) def __init__(self, *, host: str = 'aiplatform.googleapis.com', credentials: Optional[ga_credentials.Credentials] = None, credentials_file: Optional[str] = None, scopes: Optional[Sequence[str]] = None, channel: Optional[Union[aio.Channel, Callable[..., aio.Channel]]] = None, api_mtls_endpoint: Optional[str] = None, client_cert_source: Optional[Callable[[], Tuple[bytes, bytes]]] = None, ssl_channel_credentials: Optional[grpc.ChannelCredentials] = None, client_cert_source_for_mtls: Optional[Callable[[], Tuple[bytes, bytes]]] = None, quota_project_id: Optional[str] = None, client_info: gapic_v1.client_info.ClientInfo = DEFAULT_CLIENT_INFO, always_use_jwt_access: Optional[bool] = False, api_audience: Optional[str] = None, ) -> None: """Instantiate the transport. Args: host (Optional[str]): The hostname to connect to (default: 'aiplatform.googleapis.com'). 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. This argument is ignored if a ``channel`` instance is provided. credentials_file (Optional[str]): Deprecated. A file with credentials that can be loaded with :func:`google.auth.load_credentials_from_file`. This argument is ignored if a ``channel`` instance is provided. This argument will be removed in the next major version of this library. scopes (Optional[Sequence[str]]): A optional list of scopes needed for this service. These are only used when credentials are not specified and are passed to :func:`google.auth.default`. channel (Optional[Union[aio.Channel, Callable[..., aio.Channel]]]): A ``Channel`` instance through which to make calls, or a Callable that constructs and returns one. If set to None, ``self.create_channel`` is used to create the channel. If a Callable is given, it will be called with the same arguments as used in ``self.create_channel``. api_mtls_endpoint (Optional[str]): Deprecated. The mutual TLS endpoint. If provided, it overrides the ``host`` argument and tries to create a mutual TLS channel with client SSL credentials from ``client_cert_source`` or application default SSL credentials. client_cert_source (Optional[Callable[[], Tuple[bytes, bytes]]]): Deprecated. A callback to provide client SSL certificate bytes and private key bytes, both in PEM format. It is ignored if ``api_mtls_endpoint`` is None. ssl_channel_credentials (grpc.ChannelCredentials): SSL credentials for the grpc channel. It is ignored if a ``channel`` instance is provided. client_cert_source_for_mtls (Optional[Callable[[], Tuple[bytes, bytes]]]): A callback to provide client certificate bytes and private key bytes, both in PEM format. It is used to configure a mutual TLS channel. It is ignored if a ``channel`` instance or ``ssl_channel_credentials`` is provided. quota_project_id (Optional[str]): An optional project to use for billing and quota. 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. always_use_jwt_access (Optional[bool]): Whether self signed JWT should be used for service account credentials. Raises: google.auth.exceptions.MutualTlsChannelError: If mutual TLS transport creation failed for any reason. google.api_core.exceptions.DuplicateCredentialArgs: If both ``credentials`` and ``credentials_file`` are passed. """ self._grpc_channel = None self._ssl_channel_credentials = ssl_channel_credentials self._stubs: Dict[str, Callable] = {} if api_mtls_endpoint: warnings.warn("api_mtls_endpoint is deprecated", DeprecationWarning) if client_cert_source: warnings.warn("client_cert_source is deprecated", DeprecationWarning) if isinstance(channel, aio.Channel): # Ignore credentials if a channel was passed. credentials = None self._ignore_credentials = True # If a channel was explicitly provided, set it. self._grpc_channel = channel self._ssl_channel_credentials = None else: if api_mtls_endpoint: host = api_mtls_endpoint # Create SSL credentials with client_cert_source or application # default SSL credentials. if client_cert_source: cert, key = client_cert_source() self._ssl_channel_credentials = grpc.ssl_channel_credentials( certificate_chain=cert, private_key=key ) else: self._ssl_channel_credentials = SslCredentials().ssl_credentials else: if client_cert_source_for_mtls and not ssl_channel_credentials: cert, key = client_cert_source_for_mtls() self._ssl_channel_credentials = grpc.ssl_channel_credentials( certificate_chain=cert, private_key=key ) # The base transport sets the host, credentials and scopes super().__init__( host=host, credentials=credentials, credentials_file=credentials_file, scopes=scopes, quota_project_id=quota_project_id, client_info=client_info, always_use_jwt_access=always_use_jwt_access, api_audience=api_audience, ) if not self._grpc_channel: # initialize with the provided callable or the default channel channel_init = channel or type(self).create_channel self._grpc_channel = channel_init( self._host, # use the credentials which are saved credentials=self._credentials, # Set ``credentials_file`` to ``None`` here as # the credentials that we saved earlier should be used. credentials_file=None, scopes=self._scopes, ssl_credentials=self._ssl_channel_credentials, quota_project_id=quota_project_id, options=[ ("grpc.max_send_message_length", -1), ("grpc.max_receive_message_length", -1), ], ) self._interceptor = _LoggingClientAIOInterceptor() self._grpc_channel._unary_unary_interceptors.append(self._interceptor) self._logged_channel = self._grpc_channel self._wrap_with_kind = "kind" in inspect.signature(gapic_v1.method_async.wrap_method).parameters # Wrap messages. This must be done after self._logged_channel exists self._prep_wrapped_messages(client_info) @property def grpc_channel(self) -> aio.Channel: """Create the channel designed to connect to this service. This property caches on the instance; repeated calls return the same channel. """ # Return the channel from cache. return self._grpc_channel @property def predict(self) -> Callable[ [prediction_service.PredictRequest], Awaitable[prediction_service.PredictResponse]]: r"""Return a callable for the predict method over gRPC. Perform an online prediction. Returns: Callable[[~.PredictRequest], Awaitable[~.PredictResponse]]: A function that, when called, will call the underlying RPC on the server. """ # Generate a "stub function" on-the-fly which will actually make # the request. # gRPC handles serialization and deserialization, so we just need # to pass in the functions for each. if 'predict' not in self._stubs: self._stubs['predict'] = self._logged_channel.unary_unary( '/google.cloud.aiplatform.v1beta1.PredictionService/Predict', request_serializer=prediction_service.PredictRequest.serialize, response_deserializer=prediction_service.PredictResponse.deserialize, ) return self._stubs['predict'] @property def raw_predict(self) -> Callable[ [prediction_service.RawPredictRequest], Awaitable[httpbody_pb2.HttpBody]]: r"""Return a callable for the raw predict method over gRPC. Perform an online prediction with an arbitrary HTTP payload. The response includes the following HTTP headers: - ``X-Vertex-AI-Endpoint-Id``: ID of the [Endpoint][google.cloud.aiplatform.v1beta1.Endpoint] that served this prediction. - ``X-Vertex-AI-Deployed-Model-Id``: ID of the Endpoint's [DeployedModel][google.cloud.aiplatform.v1beta1.DeployedModel] that served this prediction. Returns: Callable[[~.RawPredictRequest], Awaitable[~.HttpBody]]: A function that, when called, will call the underlying RPC on the server. """ # Generate a "stub function" on-the-fly which will actually make # the request. # gRPC handles serialization and deserialization, so we just need # to pass in the functions for each. if 'raw_predict' not in self._stubs: self._stubs['raw_predict'] = self._logged_channel.unary_unary( '/google.cloud.aiplatform.v1beta1.PredictionService/RawPredict', request_serializer=prediction_service.RawPredictRequest.serialize, response_deserializer=httpbody_pb2.HttpBody.FromString, ) return self._stubs['raw_predict'] @property def stream_raw_predict(self) -> Callable[ [prediction_service.StreamRawPredictRequest], Awaitable[httpbody_pb2.HttpBody]]: r"""Return a callable for the stream raw predict method over gRPC. Perform a streaming online prediction with an arbitrary HTTP payload. Returns: Callable[[~.StreamRawPredictRequest], Awaitable[~.HttpBody]]: A function that, when called, will call the underlying RPC on the server. """ # Generate a "stub function" on-the-fly which will actually make # the request. # gRPC handles serialization and deserialization, so we just need # to pass in the functions for each. if 'stream_raw_predict' not in self._stubs: self._stubs['stream_raw_predict'] = self._logged_channel.unary_stream( '/google.cloud.aiplatform.v1beta1.PredictionService/StreamRawPredict', request_serializer=prediction_service.StreamRawPredictRequest.serialize, response_deserializer=httpbody_pb2.HttpBody.FromString, ) return self._stubs['stream_raw_predict'] @property def direct_predict(self) -> Callable[ [prediction_service.DirectPredictRequest], Awaitable[prediction_service.DirectPredictResponse]]: r"""Return a callable for the direct predict method over gRPC. Perform an unary online prediction request to a gRPC model server for Vertex first-party products and frameworks. Returns: Callable[[~.DirectPredictRequest], Awaitable[~.DirectPredictResponse]]: A function that, when called, will call the underlying RPC on the server. """ # Generate a "stub function" on-the-fly which will actually make # the request. # gRPC handles serialization and deserialization, so we just need # to pass in the functions for each. if 'direct_predict' not in self._stubs: self._stubs['direct_predict'] = self._logged_channel.unary_unary( '/google.cloud.aiplatform.v1beta1.PredictionService/DirectPredict', request_serializer=prediction_service.DirectPredictRequest.serialize, response_deserializer=prediction_service.DirectPredictResponse.deserialize, ) return self._stubs['direct_predict'] @property def direct_raw_predict(self) -> Callable[ [prediction_service.DirectRawPredictRequest], Awaitable[prediction_service.DirectRawPredictResponse]]: r"""Return a callable for the direct raw predict method over gRPC. Perform an unary online prediction request to a gRPC model server for custom containers. Returns: Callable[[~.DirectRawPredictRequest], Awaitable[~.DirectRawPredictResponse]]: A function that, when called, will call the underlying RPC on the server. """ # Generate a "stub function" on-the-fly which will actually make # the request. # gRPC handles serialization and deserialization, so we just need # to pass in the functions for each. if 'direct_raw_predict' not in self._stubs: self._stubs['direct_raw_predict'] = self._logged_channel.unary_unary( '/google.cloud.aiplatform.v1beta1.PredictionService/DirectRawPredict', request_serializer=prediction_service.DirectRawPredictRequest.serialize, response_deserializer=prediction_service.DirectRawPredictResponse.deserialize, ) return self._stubs['direct_raw_predict'] @property def stream_direct_predict(self) -> Callable[ [prediction_service.StreamDirectPredictRequest], Awaitable[prediction_service.StreamDirectPredictResponse]]: r"""Return a callable for the stream direct predict method over gRPC. Perform a streaming online prediction request to a gRPC model server for Vertex first-party products and frameworks. Returns: Callable[[~.StreamDirectPredictRequest], Awaitable[~.StreamDirectPredictResponse]]: A function that, when called, will call the underlying RPC on the server. """ # Generate a "stub function" on-the-fly which will actually make # the request. # gRPC handles serialization and deserialization, so we just need # to pass in the functions for each. if 'stream_direct_predict' not in self._stubs: self._stubs['stream_direct_predict'] = self._logged_channel.stream_stream( '/google.cloud.aiplatform.v1beta1.PredictionService/StreamDirectPredict', request_serializer=prediction_service.StreamDirectPredictRequest.serialize, response_deserializer=prediction_service.StreamDirectPredictResponse.deserialize, ) return self._stubs['stream_direct_predict'] @property def stream_direct_raw_predict(self) -> Callable[ [prediction_service.StreamDirectRawPredictRequest], Awaitable[prediction_service.StreamDirectRawPredictResponse]]: r"""Return a callable for the stream direct raw predict method over gRPC. Perform a streaming online prediction request to a gRPC model server for custom containers. Returns: Callable[[~.StreamDirectRawPredictRequest], Awaitable[~.StreamDirectRawPredictResponse]]: A function that, when called, will call the underlying RPC on the server. """ # Generate a "stub function" on-the-fly which will actually make # the request. # gRPC handles serialization and deserialization, so we just need # to pass in the functions for each. if 'stream_direct_raw_predict' not in self._stubs: self._stubs['stream_direct_raw_predict'] = self._logged_channel.stream_stream( '/google.cloud.aiplatform.v1beta1.PredictionService/StreamDirectRawPredict', request_serializer=prediction_service.StreamDirectRawPredictRequest.serialize, response_deserializer=prediction_service.StreamDirectRawPredictResponse.deserialize, ) return self._stubs['stream_direct_raw_predict'] @property def streaming_predict(self) -> Callable[ [prediction_service.StreamingPredictRequest], Awaitable[prediction_service.StreamingPredictResponse]]: r"""Return a callable for the streaming predict method over gRPC. Perform a streaming online prediction request for Vertex first-party products and frameworks. Returns: Callable[[~.StreamingPredictRequest], Awaitable[~.StreamingPredictResponse]]: A function that, when called, will call the underlying RPC on the server. """ # Generate a "stub function" on-the-fly which will actually make # the request. # gRPC handles serialization and deserialization, so we just need # to pass in the functions for each. if 'streaming_predict' not in self._stubs: self._stubs['streaming_predict'] = self._logged_channel.stream_stream( '/google.cloud.aiplatform.v1beta1.PredictionService/StreamingPredict', request_serializer=prediction_service.StreamingPredictRequest.serialize, response_deserializer=prediction_service.StreamingPredictResponse.deserialize, ) return self._stubs['streaming_predict'] @property def server_streaming_predict(self) -> Callable[ [prediction_service.StreamingPredictRequest], Awaitable[prediction_service.StreamingPredictResponse]]: r"""Return a callable for the server streaming predict method over gRPC. Perform a server-side streaming online prediction request for Vertex LLM streaming. Returns: Callable[[~.StreamingPredictRequest], Awaitable[~.StreamingPredictResponse]]: A function that, when called, will call the underlying RPC on the server. """ # Generate a "stub function" on-the-fly which will actually make # the request. # gRPC handles serialization and deserialization, so we just need # to pass in the functions for each. if 'server_streaming_predict' not in self._stubs: self._stubs['server_streaming_predict'] = self._logged_channel.unary_stream( '/google.cloud.aiplatform.v1beta1.PredictionService/ServerStreamingPredict', request_serializer=prediction_service.StreamingPredictRequest.serialize, response_deserializer=prediction_service.StreamingPredictResponse.deserialize, ) return self._stubs['server_streaming_predict'] @property def streaming_raw_predict(self) -> Callable[ [prediction_service.StreamingRawPredictRequest], Awaitable[prediction_service.StreamingRawPredictResponse]]: r"""Return a callable for the streaming raw predict method over gRPC. Perform a streaming online prediction request through gRPC. Returns: Callable[[~.StreamingRawPredictRequest], Awaitable[~.StreamingRawPredictResponse]]: A function that, when called, will call the underlying RPC on the server. """ # Generate a "stub function" on-the-fly which will actually make # the request. # gRPC handles serialization and deserialization, so we just need # to pass in the functions for each. if 'streaming_raw_predict' not in self._stubs: self._stubs['streaming_raw_predict'] = self._logged_channel.stream_stream( '/google.cloud.aiplatform.v1beta1.PredictionService/StreamingRawPredict', request_serializer=prediction_service.StreamingRawPredictRequest.serialize, response_deserializer=prediction_service.StreamingRawPredictResponse.deserialize, ) return self._stubs['streaming_raw_predict'] @property def predict_long_running(self) -> Callable[ [prediction_service.PredictLongRunningRequest], Awaitable[operations_pb2.Operation]]: r"""Return a callable for the predict long running method over gRPC. Returns: Callable[[~.PredictLongRunningRequest], Awaitable[~.Operation]]: A function that, when called, will call the underlying RPC on the server. """ # Generate a "stub function" on-the-fly which will actually make # the request. # gRPC handles serialization and deserialization, so we just need # to pass in the functions for each. if 'predict_long_running' not in self._stubs: self._stubs['predict_long_running'] = self._logged_channel.unary_unary( '/google.cloud.aiplatform.v1beta1.PredictionService/PredictLongRunning', request_serializer=prediction_service.PredictLongRunningRequest.serialize, response_deserializer=operations_pb2.Operation.FromString, ) return self._stubs['predict_long_running'] @property def fetch_predict_operation(self) -> Callable[ [prediction_service.FetchPredictOperationRequest], Awaitable[operations_pb2.Operation]]: r"""Return a callable for the fetch predict operation method over gRPC. Fetch an asynchronous online prediction operation. Returns: Callable[[~.FetchPredictOperationRequest], Awaitable[~.Operation]]: A function that, when called, will call the underlying RPC on the server. """ # Generate a "stub function" on-the-fly which will actually make # the request. # gRPC handles serialization and deserialization, so we just need # to pass in the functions for each. if 'fetch_predict_operation' not in self._stubs: self._stubs['fetch_predict_operation'] = self._logged_channel.unary_unary( '/google.cloud.aiplatform.v1beta1.PredictionService/FetchPredictOperation', request_serializer=prediction_service.FetchPredictOperationRequest.serialize, response_deserializer=operations_pb2.Operation.FromString, ) return self._stubs['fetch_predict_operation'] @property def invoke(self) -> Callable[ [prediction_service.InvokeRequest], Awaitable[httpbody_pb2.HttpBody]]: r"""Return a callable for the invoke method over gRPC. Forwards arbitrary HTTP requests for both streaming and non-streaming cases. To use this method, [invoke_route_prefix][Model.container_spec.invoke_route_prefix] must be set to allow the paths that will be specified in the request. Returns: Callable[[~.InvokeRequest], Awaitable[~.HttpBody]]: A function that, when called, will call the underlying RPC on the server. """ # Generate a "stub function" on-the-fly which will actually make # the request. # gRPC handles serialization and deserialization, so we just need # to pass in the functions for each. if 'invoke' not in self._stubs: self._stubs['invoke'] = self._logged_channel.unary_stream( '/google.cloud.aiplatform.v1beta1.PredictionService/Invoke', request_serializer=prediction_service.InvokeRequest.serialize, response_deserializer=httpbody_pb2.HttpBody.FromString, ) return self._stubs['invoke'] @property def explain(self) -> Callable[ [prediction_service.ExplainRequest], Awaitable[prediction_service.ExplainResponse]]: r"""Return a callable for the explain method over gRPC. Perform an online explanation. If [deployed_model_id][google.cloud.aiplatform.v1beta1.ExplainRequest.deployed_model_id] is specified, the corresponding DeployModel must have [explanation_spec][google.cloud.aiplatform.v1beta1.DeployedModel.explanation_spec] populated. If [deployed_model_id][google.cloud.aiplatform.v1beta1.ExplainRequest.deployed_model_id] is not specified, all DeployedModels must have [explanation_spec][google.cloud.aiplatform.v1beta1.DeployedModel.explanation_spec] populated. Returns: Callable[[~.ExplainRequest], Awaitable[~.ExplainResponse]]: A function that, when called, will call the underlying RPC on the server. """ # Generate a "stub function" on-the-fly which will actually make # the request. # gRPC handles serialization and deserialization, so we just need # to pass in the functions for each. if 'explain' not in self._stubs: self._stubs['explain'] = self._logged_channel.unary_unary( '/google.cloud.aiplatform.v1beta1.PredictionService/Explain', request_serializer=prediction_service.ExplainRequest.serialize, response_deserializer=prediction_service.ExplainResponse.deserialize, ) return self._stubs['explain'] @property def count_tokens(self) -> Callable[ [prediction_service.CountTokensRequest], Awaitable[prediction_service.CountTokensResponse]]: r"""Return a callable for the count tokens method over gRPC. Perform a token counting. Returns: Callable[[~.CountTokensRequest], Awaitable[~.CountTokensResponse]]: A function that, when called, will call the underlying RPC on the server. """ # Generate a "stub function" on-the-fly which will actually make # the request. # gRPC handles serialization and deserialization, so we just need # to pass in the functions for each. if 'count_tokens' not in self._stubs: self._stubs['count_tokens'] = self._logged_channel.unary_unary( '/google.cloud.aiplatform.v1beta1.PredictionService/CountTokens', request_serializer=prediction_service.CountTokensRequest.serialize, response_deserializer=prediction_service.CountTokensResponse.deserialize, ) return self._stubs['count_tokens'] @property def generate_content(self) -> Callable[ [prediction_service.GenerateContentRequest], Awaitable[prediction_service.GenerateContentResponse]]: r"""Return a callable for the generate content method over gRPC. Generate content with multimodal inputs. Returns: Callable[[~.GenerateContentRequest], Awaitable[~.GenerateContentResponse]]: A function that, when called, will call the underlying RPC on the server. """ # Generate a "stub function" on-the-fly which will actually make # the request. # gRPC handles serialization and deserialization, so we just need # to pass in the functions for each. if 'generate_content' not in self._stubs: self._stubs['generate_content'] = self._logged_channel.unary_unary( '/google.cloud.aiplatform.v1beta1.PredictionService/GenerateContent', request_serializer=prediction_service.GenerateContentRequest.serialize, response_deserializer=prediction_service.GenerateContentResponse.deserialize, ) return self._stubs['generate_content'] @property def stream_generate_content(self) -> Callable[ [prediction_service.GenerateContentRequest], Awaitable[prediction_service.GenerateContentResponse]]: r"""Return a callable for the stream generate content method over gRPC. Generate content with multimodal inputs with streaming support. Returns: Callable[[~.GenerateContentRequest], Awaitable[~.GenerateContentResponse]]: A function that, when called, will call the underlying RPC on the server. """ # Generate a "stub function" on-the-fly which will actually make # the request. # gRPC handles serialization and deserialization, so we just need # to pass in the functions for each. if 'stream_generate_content' not in self._stubs: self._stubs['stream_generate_content'] = self._logged_channel.unary_stream( '/google.cloud.aiplatform.v1beta1.PredictionService/StreamGenerateContent', request_serializer=prediction_service.GenerateContentRequest.serialize, response_deserializer=prediction_service.GenerateContentResponse.deserialize, ) return self._stubs['stream_generate_content'] @property def chat_completions(self) -> Callable[ [prediction_service.ChatCompletionsRequest], Awaitable[httpbody_pb2.HttpBody]]: r"""Return a callable for the chat completions method over gRPC. Exposes an OpenAI-compatible endpoint for chat completions. Returns: Callable[[~.ChatCompletionsRequest], Awaitable[~.HttpBody]]: A function that, when called, will call the underlying RPC on the server. """ # Generate a "stub function" on-the-fly which will actually make # the request. # gRPC handles serialization and deserialization, so we just need # to pass in the functions for each. if 'chat_completions' not in self._stubs: self._stubs['chat_completions'] = self._logged_channel.unary_stream( '/google.cloud.aiplatform.v1beta1.PredictionService/ChatCompletions', request_serializer=prediction_service.ChatCompletionsRequest.serialize, response_deserializer=httpbody_pb2.HttpBody.FromString, ) return self._stubs['chat_completions'] @property def embed_content(self) -> Callable[ [prediction_service.EmbedContentRequest], Awaitable[prediction_service.EmbedContentResponse]]: r"""Return a callable for the embed content method over gRPC. Embed content with multimodal inputs. Returns: Callable[[~.EmbedContentRequest], Awaitable[~.EmbedContentResponse]]: A function that, when called, will call the underlying RPC on the server. """ # Generate a "stub function" on-the-fly which will actually make # the request. # gRPC handles serialization and deserialization, so we just need # to pass in the functions for each. if 'embed_content' not in self._stubs: self._stubs['embed_content'] = self._logged_channel.unary_unary( '/google.cloud.aiplatform.v1beta1.PredictionService/EmbedContent', request_serializer=prediction_service.EmbedContentRequest.serialize, response_deserializer=prediction_service.EmbedContentResponse.deserialize, ) return self._stubs['embed_content'] def _prep_wrapped_messages(self, client_info): """ Precompute the wrapped methods, overriding the base class method to use async wrappers.""" self._wrapped_methods = { self.predict: self._wrap_method( self.predict, default_timeout=None, client_info=client_info, ), self.raw_predict: self._wrap_method( self.raw_predict, default_timeout=None, client_info=client_info, ), self.stream_raw_predict: self._wrap_method( self.stream_raw_predict, default_timeout=None, client_info=client_info, ), self.direct_predict: self._wrap_method( self.direct_predict, default_timeout=None, client_info=client_info, ), self.direct_raw_predict: self._wrap_method( self.direct_raw_predict, default_timeout=None, client_info=client_info, ), self.stream_direct_predict: self._wrap_method( self.stream_direct_predict, default_timeout=5.0, client_info=client_info, ), self.stream_direct_raw_predict: self._wrap_method( self.stream_direct_raw_predict, default_timeout=5.0, client_info=client_info, ), self.streaming_predict: self._wrap_method( self.streaming_predict, default_timeout=None, client_info=client_info, ), self.server_streaming_predict: self._wrap_method( self.server_streaming_predict, default_timeout=None, client_info=client_info, ), self.streaming_raw_predict: self._wrap_method( self.streaming_raw_predict, default_timeout=None, client_info=client_info, ), self.predict_long_running: self._wrap_method( self.predict_long_running, default_timeout=None, client_info=client_info, ), self.fetch_predict_operation: self._wrap_method( self.fetch_predict_operation, default_timeout=None, client_info=client_info, ), self.invoke: self._wrap_method( self.invoke, default_timeout=None, client_info=client_info, ), self.explain: self._wrap_method( self.explain, default_timeout=None, client_info=client_info, ), self.count_tokens: self._wrap_method( self.count_tokens, default_timeout=None, client_info=client_info, ), self.generate_content: self._wrap_method( self.generate_content, default_timeout=None, client_info=client_info, ), self.stream_generate_content: self._wrap_method( self.stream_generate_content, default_timeout=None, client_info=client_info, ), self.chat_completions: self._wrap_method( self.chat_completions, default_timeout=None, client_info=client_info, ), self.embed_content: self._wrap_method( self.embed_content, default_timeout=None, client_info=client_info, ), } def _wrap_method(self, func, *args, **kwargs): if self._wrap_with_kind: # pragma: NO COVER kwargs["kind"] = self.kind return gapic_v1.method_async.wrap_method(func, *args, **kwargs) def close(self): return self._logged_channel.close() @property def kind(self) -> str: return "grpc_asyncio" __all__ = ( 'PredictionServiceGrpcAsyncIOTransport', )